SUPPORTED BY International Journal of Advanced Research in Computer Science (E ISSN: )

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2 SUPPORTED BY International Journal of Advanced Research in Computer Science (E ISSN: ) DOI prefix: dx.doi.org/ /ijarcs

3 PROCEEDINGS OF THIRD NATIONAL CONFERENCE ON ADVANCES IN COMPUTING AND INFORMATION TECHNOLOGY (ACIT-2018) 11 th May 2018 Venue: Swami Vivekananda Block, REVA University, Rukmini Knowledge Park, Yelahanka, Bengaluru

4 Chief Patron Dr. P. Shyama Raju Chancellor, REVA University Patrons Dr. S. Y. Kulkarni Vice-Chancellor, REVA University Dr. M. Dhanamjaya Registrar, REVA University Dr. N. Ramesh Director Planning & Placement, REVA University Dr. V. G. Talwar Advisor, REVA University Convener Dr. Sunilkumar S. Manvi Principal, REVA ITM & Director, School of Computing and Information Technology, REVA University General Chair Dr. Ashwinkumar U.M. Dr. Mallikarjun M Kodabagi Organizing Chair Dr. Mallikarjuna Shastry P.M.

5 Advisory Committee Dr. Kirankumari Patil, Director, UIIC, REVA University Dr. Vishwanath R Hulipalled, Professor, School of C&IT, REVA University Dr. Chandrashekar S N, SJCIT, Chikkaballapura Prof. Sathish G. C, Associate Professor, School of C&IT, REVA University Dr. Sathish Babu, SIT, Tumkur Dr. Champa H N, Professor, UVCE Dr. B P Divakar, Director, R&D, REVA University Dr. Udaya Rani, School of C&IT, REVA University Dr. Shirshu Varma, IIIT, Allahabad. Technical Committee Dr. Gopal Krishna Shyam, Chair. Dr.Prabhakar M Prof. Venkatesh Prasad Prof. Kanaiya V K Prof. Nirmala S Guptha Prof. Manjunath P C Prof. Shantala Devi Patil Prof. Akram Pasha Prof. Ananda Shankar Prof. Sarvamangala D R Prof. Meenakshi Sundaram Prof. Mylar Reddy Prof. Laxmi Rananavare Prof. Mallikarjun M

6 Publication & Organizing Committe Prof. Naveen Chandra Gowda, Asst. Prof., School of C&IT, REVA University Prof. Supreeth S, Asst. Prof., School of C&IT, REVA University Prof. Manjunatha P C, Sr.Asst. Prof., School of C&IT, REVA University Prof. Nikhil S Tengli, Asst. Prof., School of C&IT, REVA University Prof. Kiran M, Asst. Prof., School of C&IT, REVA University Prof. Ranjitha U N, Asst. Prof., School of C&IT, REVA University Local Organizing Committe Prof.Sushma R.Y. Prof. Nimrita kaur Prof. Shilpa K.A. Prof. Raghavendra Reddy Prof. Raghavendra N Prof. Anil Ambore, Prof Bindushree D C Prof Sujatha K Prof. Vinay Kumar M Prof. Anooja Ali, Prof. Sheelavathy V Prof Pavithra P Prof. Vani K Prof. Ajil A Prof. Ila Chandrakar Prof. Bijay Kumar Joshi Prof. Lalitha L A Prof. Rashmi C Prof. Shilpa V Prof. Keerthi Hiremath Prof. Shilpa V Prof. Asharani V Prof. Chaitra Desai, Prof. Shivakumar Naik, Prof. Ravishankar Prof. Tanuja K Prof. Sailaja T. Prof. Priyanka Bharathi Prof.Archana B. Prof. Arunkumar Prof. Anitha K Prof. Shilpa N.R. Prof.Lithin Prof.Gopinath R Prof.Surendra Babu Prof.Thirumagal E

7 Prof. Chaitra B Prof. Chaitra N Prof. Sureka T Prof. Spoorthy R Prof.Geetha B. Chayadevi Amaresh Keshav Arunkumar Varalakshmi Ashwin Gangadhar Palanivel Sharath Gangadhar

8 THIRD NATIONAL CONFERENCE ON ADVANCES IN COMPUTING AND INFORMATION TECHNOLOGY Table of Contents ACIT-2018 Sl. Particulars Page No. No. 1 Messages... ii 2 Technical Session Schedule... vii 3 Detailed Shcedule vii 4 Papers Index... xiii 5 Papers... 1

9 MESSAGES Message from Chancellor: The foundation stones of REVA University are laid on the essence of academic pursuit and excellence. Excellence in any work can be achieved with utmost dedication, hard work, and perseverance. We, at REVA, have made this dictum our motto and our way of life in every single activity in the campus. Research and development forms the backbone of our curriculum at REVA. The staff and students are engaged in various path-breaking innovative research activities all throughout the year. Every school of our University organizes conferences and seminars frequently on contemporary and relevant topics in order to facilitate research in those areas which will lead to necessary metamorphosis in the academia as well. The School of Computing and Information Technology at REVA, right from its inception, has been active in research and innovation and has setup an ambient academic environment for its students and research scholars. With the commitment of highly qualified and efficient staff, the school endeavors vigorously to make a mark in the field of research and development. The National Conference organized by the school on Advances in Computing and Information Technology is another venture to provide a platform for academicians teachers, students, research scholars, and industry personnel all over the nation to discuss on contemporary trends and innovations in Computing and Information Technology. I wish the conference all the very best and urge all the participants to brainstorm the various thrust areas of the conference. I also wish all of you a happy stay in our campus and look forward to your participation in various events in the campus. Dr. P. Shyama Raju Chancellor, REVA University, Bengaluru, India.

10 Message from Vice-Chancellor: The conferences are necessary to bring in culture of information exchange and feedback on developing trends in technologies. I am delighted to note that the School of Computing and Information Technology is organizing the National Conference entitled Advances in ComputingandInformation Technology (ACIT). Certainly, this type of conference not only brings all the researchers, students at one platform, but it also inculcates the research culture among the entire fraternity of Education in the country, thereby, contributing to the development of nation. I hope that this conference would certainly induce innovative ideas among the participants paving way for new inventions and technologies in the Computing and Information Technology. I Congratulate, Dr. Sunilkumar S. Manvi, Director, School of Computing and Information Technology, and his team for initiating the conduction of such aconference in our esteemed University. I wish the conference a grand success. Dr. S. Y. Kulkarni Vice-Chancellor, REVA University, Bangalore, India

11 Message from Registrar: The science and engineering research conducted in academic institutions, industry, R&D Laboratories and elsewhere plays a critical role in raising our standard of living, creating jobs, improving health andproviding for national security and development. I am extremely happy to note that School of Computing and Information Technology, REVA University is organizing the National conference on advances in computing and information technology (ACIT). I am sure that the conference of this type will inculcate the much needed research culture among the students and teachers and trigger interactions among researchers to exchange the ideas of recent advances in the areas of Computing and Information Technology I wish the conference a grand success. Dr. M. Dhanamajya Registrar, Reva University, Bengaluru, India

12 Message from Director, R&D: Research and Innovations in REVA university is regarded as one of the most important activities besides teaching and learning. The university with dedicated & qualified faculty, dynamic research scholars, moderate research facilities, well drafted research quality assurance guidelines, and supportive management provides an excellent ambience to pursue research. Research circles, mentored by senior faculty members are active in all the schools and are primarily responsible for cultivating interdisciplinary research cultures in students and faculty. The university encourages dissemination of research outcomes to the society through conferences such as this one being organized by the school of Computing & IT, so that scientific debates trigger exchange of ideas among the research community resulting in solutions for the welfare of the society. I congratulate the school director, coordinators, reviewers, students and faculty for their contributions to this conference and I wish the researchers all the best for their future research endeavors. Dr. B. P.Divakar Director, R&D, REVA University, Bengaluru, India

13 Message from Director, School of C & IT: The conferences have to be organized at various levels to offer a platform to various level of researchers. This national conference provides a forum to all Indian researchers to exchange the information on research and innovations and enhance the quality of research.the Technical committee of the conference has reviewed articles to maintain the quality interactions and publications by using anti plagiarism software and feedback from reviewers. The conference provides a platform for researchers to get networked and exchange the ideas on various areas such as data analytics, wireless networks, app development, data mining, image processing, pattern recognition, and applications of IT. High quality deliberations that happen in conference will lead to high standard publications at international levels which feed into the industry's innovation pipeline. Industry expects such inputs to create innovation and the next big things. I wish all the participants a happy stay in campus and a fruitful interaction on their works. Dr. Sunilkumar S. Manvi Director, School of Computing and Information Technology, REVA University, Bengaluru, India.

14 Technical Session Schedule ACIT-2018 Saturday, 11-May :30-09:30 Registration 09:30-10:00 Inauguration 10:00-11:00 Keynote 11:00-11:15 Tea Break 11:30 1:00 Presentation Session A 1.1.B 1.1.C 1: 00-01:45 Lunch 02:00-03:30 Presentation Session A 1.2.B 1.2.C 04:00 High Tea

15 Session 1.1.A 11:30 am to 1:00 pm Venue 1 Room No: 206 Detailed Schedule Saturday, 11-May-2018 Paper ID NACIT18001 NACIT18002 NACIT18003 NACIT18004 NACIT18005 NACIT18006 NACIT18007 NACIT18008 NACIT18009 NACIT18010 NACIT18011 NACIT18012 NACIT18013 NACIT18014 NACIT18015 NACIT18016 NACIT18017 NACIT18018 NACIT18019 NACIT18020 NACIT18021 Data Mining Title of the Paper A Radicle Study Of Fire-Fly Algorithm Application To Predict Chronical Kidney Disease Better Healthcare Using Machine Learning Cognitive Way of Detecting Cyberbullying in Chatbots Natural Language Question Answering Sentiment Analysis for Product Reviews Spatial Movie Prediction using conglomeration of Online Data Using Sentiment Analysis for Website Evaluation Student Information Ai Chatbot The Effective Utility Of Attributes With Threshold Based Collaboration With Combinational Tuples In Data Mining Structural Balance Theory Based Recommendation Document Clustering Using Cosine Similarity Event Management Information System Voice Assistant- Heisenberg Machine Learning Techniques As A Service Based On Microservice Architecture On Cloud Platform Location Based Disease Outbreak Detection System Inferring Twitter Data A Rating Approach Based On Sentiment Analysis Football Match Outcome Prediction using Sentiment Analysis of Twitter data Predicting the Stock Price Using Linear Regression Prediction Accuracy Comparison of Predictive models using Machine Learning for Diabetes Data set. Secure Sensitive Data Sharing on Big Data Platform

16 Wireless Sensor Networks and Ad Hoc Networks Session 1.1.B 11:30 am to 1:00 pm Venue 2 Room: 221 Paper ID NACIT18022 NACIT18023 NACIT18024 NACIT18025 NACIT18026 NACIT18027 NACIT18028 NACIT18029 NACIT18030 NACIT18031 NACIT18032 NACIT18033 Title of the Paper A Mac Protocol Which Reduces the QoS Parameters like Energy Efficiency In WSN An Online Pet Store Cluster based data aggregation scheme in Underwater Acoustics Sensor Networks Data Storage and Management in Vehicular Cloud Computing Vhdl Model Of Smart Sensor Energy Efficient Path Selection (Eeps) Of Mobisink For Efficient Data Collection In Wsns Identifying Trust Nodes Based On Stay Time For Security In VANET Kase Plus Live Location Tracker Oculus_A Smart Wearable for the Visually Impaired Autonomous Vehicle With The Aid Of Computer Vision Drug Interaction With Ehr Framework And Safe Medical Tags For Reducing Medical Mistakes

17 Session 1.1.C 11:30 am to 1:00 pm Venue 3 Room No: 314 Embedded System and IoT Paper ID NACIT18034 NACIT18035 NACIT18036 NACIT18037 NACIT18038 NACIT18039 NACIT18040 NACIT18041 NACIT18042 NACIT18043 NACIT18044 NACIT18045 NACIT18046 NACIT18047 NACIT18048 NACIT18049 NACIT18050 NACIT18051 NACIT18052 NACIT18053 NACIT18054 NACIT18055 Title of the Paper A Smart Parking System for Metrocities Accident Prevention System for Vehicles using V2V Communication Cloud Based Web Application Supporting Vehicle Toll Payment Conversion of static piece of paper into Smart pieces of Technology Using RFID Crowd sourcing-based Disaster Management Using Cloud Computing in Internet of Things Paradigm Smart Notes - An Innovative Way Of Sharing And Obtaining Notes Smart Bins Using Dry And Wet Waste Detectors Design Of Smart Retail Shopping Guide Using Iot And Cloud Drive Mode Application For Road Safety Homecare And Assistance Of Activities For The Elderly Theft Prevention By Using Finger-Print And Vehicle Tracking Iot Based Oil Spill Detection System Real Time Information Dissemination Using Gps On Vehicle Accident Detection Rfid Based Smart Library Management System Iot Based Anti-Poaching Alarm System For Trees In Forest Using Wireless Sensor Networks Iot Application On Secure Smart Shopping System An Iot-Based Water Supply Monitoring And Controlling System Automated Food Wastage Management IOT Based System To Prevent Illegal Logging Of Trees Mindwave Based Robot Control And Home Automation Patient Healthcare Monitoring System Using Li-Fi Programmed Recognition And Notification Of Potholes And Mounds On Roads To Assist Drivers

18 Cloud Computing and Parallel Programming Session 1.2.A 2:00 pm to 3:30 pm Venue 1 Room No: 206 Paper ID Title of the Paper NACIT18056 A Cloudlet Based Multi-Objective Virtual Machine Allocation NACIT18057 A Cuckoo Hashing Scheme for Collision Reduction in Cloud Storage Systems NACIT18058 A Framework of Improving Cloud Security NACIT18059 A Recent Seclusion-Apprised Of Popular Analyses Plan For Cloud Data Allocate With Category Users NACIT18060 Traffic Management Using Mapreduce For Big Data Analytics NACIT18061 OpenRAP A Distributed, Scalable, Offline CDN NACIT18062 Cloud Task Scheduling Based on Organizational Authorization NACIT18063 FiDoop_An Interactive GUI to Identify Frequent Items Using MapReduce NACIT18064 Frameworkfor data security from SQL Injection in cloud computing NACIT18065 A Queuing Method for Adaptive Censoring in Big Data Processing NACIT18066 cloud computing Feature Scurity and Expected Solution NACIT18067 Security Issues in Cloud Environment

19 Session 1.2.B 2:00 pm to 3:30 pm Venue 2 Room No: 221 Session 1.2.C 2:00 pm to 3:30 pm Venue 3 Room No: 314 Network and Cyber Security Paper ID NACIT18068 NACIT18069 NACIT18070 NACIT18071 NACIT18072 NACIT18073 NACIT18074 NACIT18075 NACIT18076 NACIT18077 NACIT18078 NACIT18079 NACIT18080 NACIT18081 NACIT18082 NACIT18083 Paper ID Title of the Paper A Framework Encryption Method On Cloud Storage For Data Security A Systematic And Composed Big Data Entry Restriction Scheme With Isolation-Preserving Policy Advanced ATM Multilevel Authentication Using Fingerprint Verification And OTP Validation Nfc Featured Three Level Atm Security SPADES Scalable And Privacy Assured Detection Of Spams Privacy Preserving In Big Data Clusters With C-Means Algorithm Privacy Preserving For Big Data In Mobile Cloud- Computing Using Encryption Strategy Attribute Based Storage Supporting Security Netspam_A Fake Review Detector Security Techniques To Prevent Threats And Attacks In Cloud Environment Text Based Cyberbullying Detection Security System for Exquisite Trees File Synchronization Between Digital Safes Security Concerns in Cloud Computing Multifaceted Authentication for Secure User Access to Cloud 3-Dimension Hand Motion Driven Security Image Processing Title of the Paper NACIT18084 A Critical Appraisal of Bio-Inspired HDR Image from Low-light Image Enhancement NACIT18085 A Lossless Data Hiding Technique Using Secure LSB In Images NACIT18086 Classification Of Brain Tumors In Mri Images NACIT18087 Emotion Identification and Classification using Convolutional Neural Networks NACIT18088 Whole Exome Sequence Analysis to Predict Functional Biomarkers for Pancreatic Cancer NACIT18089 Approach to Text Extraction from Image NACIT18090 Face Recognition with 2D-Convolutional Neural Network NACIT18091 Public To Parliament NACIT18092 ROXY-Doctor s Digital Assistant NACIT18093 LIVE DRAWBOT

20 Papers Index Session 1.1.A: Data Mining Paper ID Title Page No NACIT18001 NACIT18002 NACIT18003 NACIT18004 NACIT18005 NACIT18006 NACIT18007 NACIT18008 NACIT18009 NACIT18010 NACIT18011 NACIT18012 NACIT18013 NACIT18014 NACIT18015 NACIT18016 NACIT18017 NACIT18018 NACIT18019 NACIT18020 NACIT18021 A Radicle Study Of Fire-Fly Algorithm Sanjeet Mandal, Spoorthi Rakesh Application To Predict Chronical Kidney Disease Ankitha, Architha, Chandana, Gulshan, Surekha Thota Better Healthcare Using Machine Learning Abhishek Yadav, Arpitha.M.S, Amit singh, Mithun.K.A, Spoorthi Rakesh Cognitive Way of Detecting Cyberbullying in Chatbots H.M. Chandana, Karnik Pooja Jagdish, Anna Mary, Bhawana Dorbi, Naveen Chandra Gowda Natural Language Question Answering Umma Khatuna Jannat, Nirmala S Guptha, Supreeth S, Anitha K, Shashikala N Sentiment Analysis for Product Reviews Anil B, Aman Kumar Singh, Aditya Kumar Singh, Aman, Surekha Thota Spatial Movie Prediction using conglomeration of Online Data Karan B Yajaman, Vemuri Venkata Lithin Kumar, Madhura K.V, Mary Sheetal A.N, Raghavendra Nayaka Using Sentiment Analysis for Website Evaluation Adarsh M Revadi, Apoorva D A, Ashish N Mehta, B Vamsi, Sowmya Sundari L K Student Information Ai Chatbot Shubhanshu Jha, C Lakshmi Karthikey, Shashwat Bagaria, Utkarsh Satsangi, Surekha Thota The Effective Utility Of Attributes With Threshold Based Collaboration With Combinational Tuples In Data Mining Deepa.V.Patil, Sheelavathy.V Structural Balance Theory Based Recommendation Monika.N.S, Lavanya.G.P, K.Vaishnavi, Kavya.M, Kanaiya.V.K Document Clustering Using Cosine Similarity Ranjith Kumar N S, Prekshitha N, Keerthi K P, Prema S, Naveen Chandra Gowda Event Management Information System Sharadha.N.L, Sushmitha.H, Sangeetha.S, Sharan Patil, Rashmi.C Voice Assistant- Heisenberg P. Sai Venkata Srivastav, P. Uttareshwar Vikasrao, Rohan Vijay Wargia, Rohit Kumar Singh, Naveen Chandra Gowda Machine Learning Techniques As A Service Based On Microservice Architecture On Cloud Platform Nithin M A, Nitin Raj L, Prashant S Indimath, Praveen Kumar G A, Shilpa K A Location Based Disease Outbreak Detection System Inferring Twitter Data Pooja, M Megha, Poojarani, Priyanka, Raghavendra Nayak.P A Rating Approach Based On Sentiment Analysis Rathan M, Anjum Shirol, Deeksha R N, Deepika C, Divya V Shiggavi Football Match Outcome Prediction using Sentiment Analysis of Twitter data Rathan M, Anupriya S, Deepthi Raj N, Sanketh Predicting the Stock Price Using Linear Regression Sasidhar Reddy Bommareddy, Kaushik P, K Sai Smaran Reddy, K V Vinay Kumar, Vishwanath R Hulipalled Prediction Accuracy Comparison of Predictive models using Machine Learning for Diabetes Data set. Doreswamy G S, Santosh Kumar J, Nandish Secure Sensitive Data Sharing on Big Data Platform Shivani Kumari, Shashi V, Vidya Harika, Shruti, Sujata K

21 Session 1.1.B: Wireless Sensor Networks and Ad Hoc Networks Paper ID Title Page No NACIT18022 NACIT18023 NACIT18024 NACIT18025 NACIT18026 NACIT18027 NACIT18028 NACIT18029 NACIT18030 NACIT18031 NACIT18032 NACIT18033 A Mac Protocol Which Reduces the QoS Parameters like Energy Efficiency In WSN Geetha B, Thirumagal E, Archana N B, Shalini Tiwari An Online Pet Store Priyanka S Gowda, Harshitha K B, Vamsi Krishna M, Parikshit Sarode, Surekha Thota Cluster based data aggregation scheme in Underwater Acoustics Sensor Networks Vani Krishnaswamy, Sunilkumar. S. Manvi Data Storage and Management in Vehicular Cloud Computing Sunilkumar S. Manvi, Nayana Hegde Vhdl Model Of Smart Sensor Poojitha V, Ravikiran R Energy Efficient Path Selection (Eeps) Of Mobisink For Efficient Data Collection In Wsns Kumar Swamy B.V, Gowramma Y.P, Ananda Babu J Identifying Trust Nodes Based On Stay Time For Security In VANET Bonish Koirala, Shrikant S. Tangade, Sunilkumar S Manvi Kase Plus Manjunath, A Ananda Shankar Live Location Tracker Debojyoti Das, Ankur Thakur, Mallangouda, Nikhil S. Tengli Oculus_A Smart Wearable for the Visually Impaired Akash James, Ashish Raman Nayak, Sai Somanath Komanduri, Ujwal P, Ashwin Kumar U.M. Autonomous Vehicle With The Aid Of Computer Vision Karthik K, Syed Matheen Pasha, Veeresh M P, Mahesh Lingappa, Srivinay Drug Interaction With Ehr Framework And Safe Medical Tags For Reducing Medical Mistakes V Sharath, Yashodhara M N, Geetha D V, Amulya P, Raghavendra Reddy

22 Session 1.1.C: Embedded System and IoT Paper ID Title Page No NACIT18034 A Smart Parking System for Metrocities Aishwarya Babu, Anandita Kushwaha, Akshitha D, Anu Rawat, Nimrita Koul NACIT18035 Accident Prevention System for Vehicles using V2V Communication Adithya S, Mahesh Kumar P, Priyanka L, Harshini C M, Mamatha E NACIT18036 Cloud Based Web Application Supporting Vehicle Toll Payment Ashwini T N, Brunda P Hiremath, P N Rachana, Savitha D G, Mamatha A NACIT18037 Conversion of static piece of paper into Smart pieces of Technology Using RFID Jai Prakash Sah, Shilpa NR NACIT18038 Crowd sourcing-based Disaster Management Using Cloud Computing in Internet of Things Paradigm Ambika R, Meghashree R M, Pragathi S N, Yeshaswini P V, Meghashri E M NACIT18039 Smart Notes - An Innovative Way Of Sharing And Obtaining Notes Nandakishore G, Niranjan V Dhooli, Reshma R, RudrarajuRamya, Chaithra M H NACIT18040 Smart Bins Using Dry And Wet Waste Detectors Ramya R, Pooja G, Ranjitha D, Tabassum Taj B, Nirmala S Guptha NACIT18041 Design Of Smart Retail Shopping Guide Using Iot And Cloud V. Pavithra, Vanitha.H, Vidyashree.M.Channalli, Yashaswini.K, Prof. Nikhil S Tengli NACIT18042 Drive Mode Application For Road Safety Amogh M B, Harshitha, Amrutha shetty, Amogh P K NACIT18043 Homecare And Assistance Of Activities For The Elderly Supriya.R, Sowmya.S, Yashaswini.Y.N, Syeda Umme Hani, Ambika.B.J NACIT18044 Theft Prevention By Using Finger-Print And Vehicle Tracking M.Sankeerthana, M.Naga Yeshwanth, P.Chaitanya Kumar, S Rishitha, Supreeth S NACIT18045 Iot Based Oil Spill Detection System Akanksha D Hegde, Disha Prakash Achari, K.N.Nithya Sree, Nisha Sudhakar Chantar, Abhijith H V NACIT18046 Real Time Information Dissemination Using Gps On Vehicle Accident Detection Harish Kumar M M, K Nirosh, Manikandan R, M Vijay Kumar Reddy, M Prabhakar NACIT18047 Rfid Based Smart Library Management System Tanuja.K, Tanushree.M, Vijay Krishna.D.L, Vindhya.R, Gopinath R NACIT18048 Iot Based Anti-Poaching Alarm System For Trees In Forest Using Wireless Sensor Networks Ghousia Sultana B, Jagadish R, Nadiya Noor Syed, Nagashree C NACIT18049 Iot Application On Secure Smart Shopping System Vishwas B, Swathi V Raidurg, Apoorva S, Anand Rao Pawar H, Laxmi Rananavare NACIT18050 An Iot-Based Water Supply Monitoring And Controlling System Maruthi H V, Lakshmi Priya, Lavanya A R, Meda Manideep, Laxmi Jayannavar NACIT18051 Automated Food Wastage Management Megha Priya N, Pratiksha N K, Keerthana R, Supriya, Shruthi G NACIT18052 IOT Based System To Prevent Illegal Logging Of Trees Shreya J Kumar, Jayashree M, Abhijeet Suman, Ashwin R, Abhijith H V NACIT18053 Mindwave Based Robot Control And Home Automation Rasheeda Banu Y, Rashmi K V, Syed Moin, Syed Zeeshan, Chetan S NACIT18054 Patient Healthcare Monitoring System Using Li-Fi Vyshnavi Rani P, Tanadar Rahul, Eswar Reddy M, Manjunatha G S, Nagamahesh B S NACIT18055 Programmed Recognition And Notification Of Potholes And Mounds On Roads To Assist Drivers Karan K Sanghvi, Nithin Nayak, Saurabh Gang, Sreelatha P K

23 Session 1.2.A: Cloud Computing and Parallel Programming Paper ID Title Page No NACIT18056 NACIT18057 NACIT18058 NACIT18059 NACIT18060 NACIT18061 NACIT18062 NACIT18063 NACIT18064 NACIT18065 NACIT18066 NACIT18067 A Cloudlet Based Multi-Objective Virtual Machine Allocation Priyanka Bharti,Rajeev Ranjan A Cuckoo Hashing Scheme for Collision Reduction in Cloud Storage Systems Rabia Basri, Sanjay S V, Shilpa Bhasker, Shruthi J, Vani Krishnaswamy A Framework of Improving Cloud Security Sanjay Kumar, Saurabh Subham, Shah Lucky, Syed Abdul Rehman, Jyoti Kiran Mirji A recent seclusion-apprised of Popular analyses plan for Cloud Data allocate with category Users Mahendrareddy, AnandShankar Traffic Management Using Mapreduce For Big Data Analytics Naveen Kumar, Vinay Yadav, Upendra Singh Tomar, Ruksa Sethi, Anil Kumar Ambore, OpenRAP: A Distributed, Scalable, Offline CDN Sriram V Ramaswamy, Sumukha K V, Ravish Ahmad, Shah Abdul Ghani, Kiran M Cloud Task Scheduling Based on Organizational Authorization Chiranjeevi B, Sundus Hasan, Dhanush K V, Dona Mercy B, A Ajil FiDoop: An Interactive GUI to Identify Frequent Items Using MapReduce Raksha D, P Hari Prasad Reddy, Rohith M Nayak, Mukesh P U, Raghavendra Reddy Framework for data security from SQL Injection in cloud computing P Sarika, Shweta Kumari, Seema M, Sona Singh, Supreeth S A Queuing Method for Adaptive Censoring in Big Data Processing Ayesha Banu R, Kamalashree M, Mahamat Mahmoud Salim Breck, Pavithra Rayasam, Aruna Kumara.B Cloud Computing Feature Scurity And Expected Solution- Survey Paper ArunKumar, HardikKumar, Gourish Malage, GopalKrishna Shyam Security Issues in Cloud Environment Menakarani R, Malathi Kulkarni, Rekha R Suryawanshi, Mir Abdul Samim Ansari, Gopal K.Shyam

24 Session 1.2.B: Network and Cyber Security Paper ID Title Page No NACIT18068 NACIT18069 NACIT18070 NACIT18071 NACIT18072 NACIT18073 NACIT18074 NACIT18075 NACIT18076 NACIT18077 NACIT18078 NACIT18079 NACIT18080 NACIT18081 NACIT18082 NACIT18083 A Framework: Encryption Method On Cloud Storage For Data Security Vaibhav Agarwal, Jai Prakash Sah, Satish Chand, Shilpa N.R A Systematic and Composed Big Data Entry Restriction Scheme With Isolation- Preserving Policy Abraham Rajan, Venkatesh Prasad Advanced ATM Multilevel Authentication Using Fingerprint Verification and OTP Validation Hari Narayanan, Uttham K, I Mohammed Junaid, Mohammed Ibrahim NFC Featured Three Level ATM Security Aatiqa Sharief, Arpitha Patil, Anushree G K, Jyoti Kumari, Vani Krishnaswamy SPADES: Scalable and Privacy Assured Detection of Spams Amogh Datt, Dilip Kumar, Abdul Suhail, Chanakya Madasi, Laxmi Jayannavar Privacy Preserving in Big Data Clusters with C-Means Algorithm Abhishek, Ambuj Shekhar Singh, Akash, Shashikala N Privacy Preserving for Big Data in Mobile Cloud-Computing using Encryption Strategy Divya L, Udaya Rani Attribute Based Storage Supporting Security De-Duplication For Encrypted Data In Cloud Nayana V, Pallavi A R, Prajwal D R, Priyanka M, Asha K NETSPAM: A Fake Review Detector Adarsh P V, Allam Kuladeep, Neha G, B Harshitha Reddy, Geetha B Security Techniques To Prevent Threats and Attacks in Cloud Environment M. Naina Bharathi Rao, Rashmi G O, Farheen Sheik, Gopal Krishna Shyam Text Based Cyberbullying Detection Sowparnika shree N, Shalini N, Sushma Reddy A, Rohini R T, Shiva Kumar Naik Security System for Exquisite Trees Pragya Verma, Rishika S G, Rajitha M, Rumaan Shaik Sheriff, Priyadarshini R File Synchronization between Digital Safes Shah Vishal, Shweta Hiremath, Sushma V, Vijayalaxmi, Manjunath P C Security Concerns in Cloud Computing Mir Abdul Samim Ansari, Pooja Mahaling, Shewtha S Patil, Menakarani R, Gopal K.Shyam Multifaceted Authentication for Secure User Access to Cloud Naveen Chandra Gowda, Sunilkumar S. Manvi 3-Dimension Hand Motion Driven Security Archana S Kashyap, Deeksha P Shetty, Poojitha P, Soundarya A N, Deeksha Hegde B

25 Session 1.2.C: Image Processing Paper ID Title Page No NACIT18084 NACIT18085 NACIT18086 NACIT18087 NACIT18088 NACIT18089 NACIT18090 NACIT18091 NACIT18092 NACIT18093 A Critical Appraisal of Bio-Inspired HDR Image from Low-light Image Enhancement Syed Arif Islam, Akram Pasha A Lossless Data Hiding Technique using Secure LSB in Images Sandeep Sharma, Kshitj Yadav, Saurabh Singh, Rafsan Ali, Sunilkumar S Manvi, Nimrita Koul Classification Of Brain Tumors In Mri Images Uday R, Yamini K.D, Vishu B.V, Vijay Kumar C, Sarvamangala D.R Emotion Identification and Classification using Convolutional Neural Networks Nishchal Poornadithya.C, P.Chimanna Chengappa, Thangaraj Raman, Shantanu Pandey, Gopal Krishna Shyam Whole Exome Sequence Analysis to Predict Functional Biomarkers for Pancreatic Cancer Maheswari L Patil, Shivakumar B Madagi, C.N.Prashantha Approach to Text Extraction from Image Disha Bhat, Dimple M K, Charitha D, Amruthashree R V, Shruthi G Face Recognition with 2D-Convolutional Neural Network Vaibhavkrishna Bhosle, Sowmya M S,Supriya S, Varun Subramani,Shruthi G Public To Parliament Karthik K B, Shashikumar D K, Shreyas M S, Sriram C R, Sukruthgowda M A ROXY-Doctor s Digital Assistant Vikas Madhava, Nishanth B, Mohammed Mazhar, Chaithra N, Spoorthi Rakesh Live Drawbot Sarita S Pol, Syed Wasif Raza Quadri, Deepak Kumar Pandey, Harsha Bandi,, Nikhil S Tengli

26 Papers

27 Volume 9, Special Issue No. 3, May 2018 International Journal of Advanced Research in Computer Science (ISSN: ) CONFERENCE PAPER Available Online at A RADICLE STUDY OF FIRE-FLY ALGORITHM Sanjeet Mandal School of C & IT REVA UNIVERSITY Bengaluru, India sanjeetmndl96@gmail.com Prof. Spoorthi Rakesh School of C & IT REVA UNIVERSITY Bengaluru, India spoorthirakesh@reva.edu.in Abstract - Firefly algorithm (FA) is a metaheuristic algorithm which was proposed by Dr. Xin-She Yang in 2008 at Cambridge University [1-2], inspired by the flashing behaviour of firefly insects. In this paper, we will see how firefly algorithm is being used in different engineering process applications and also how it is more efficient than others. Firefly algorithm is better than many other methods as it gives optimal solution with minimum time complexity. Our paper show how firefly algorithm is modified and used with other methods to implement and obtain solution for different problems. Firefly algorithm has advantages over other algorithm such as automatical subdivision and the ability of dealing with multimodality. Also the parameters in firefly algorithm can be tuned to control the randomness as iterations proceed, so that convergence can also be sped up by tuning these parameters. These above advantages makes it flexible to deal with continuous problems, clustering and classifications, and combinational as well. keywords: Firefly algorithm, metaheuristic, hybridization, particle swarn optimization, image segmentation, thresholding, knapsack problem, travelling salesman problem, vector quantization, improved differential evolution, optimization. I. INTRODUCTION Firefly algorithm is a metaheuristic algorithm inspired by firefly insects [1-2]. FA is a population based algorithm. The fireflies insect use their flashing behaviour to attract other fireflies, usually of opposite sex but firefly objects used in mathematical models are unisexual and can attract any other firefly. For any two firefly objects, the attractiveness is proportional to their brightness, the brighter one attracts the lesser one and their intensity is inversely proportional to their distance. Modified firefly algorithm [4] gives best performance as compared to firefly algorithm and genetic algorithm. Firefly algorithm struct in local optimization. Firefly algorithm uses for classification, clustering, optimization and several engineering application. Firefly algorithm mainly categories in two type: improved firefly algorithm and hybrid firefly algorithm. ALGORITHM In pseudocode[3] the algorithm can be stated as: 1) 2) Objective Generate an function: initial population f(x), X=(x1, of fireflies x2,.... Xi.,xd); (i=1, 2... n) 3) Formulate light intensity I so that it is associated with f (X) (for example, for maximization problems, I α f(x) or simply I =f(x) ;) 4) Define absorption coefficient γ While (t < MaxGeneration) for i = 1: n (all n fireflies for j= 1 : n (n fireflies) if I > I i) Vary attractiveness with distance r via (- γ ) move firefly i towards j; Evaluate new solutions and update light intensity; end if end for j end for i Rank fireflies and find the current best; end while Begin Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 1

28 Sanjeet Mandal et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, 1-5 Post-processing the results and visualization; Table 2: Minimum fuel cost and emission for various load demands using the firefly algorithm. end Note that the number of objective function evaluations per loop is one evaluation per firefly, even though the above pseudocode suggests it is n*n. (Based on Yang's matlab code.) Thus the total number of objective function evaluations is (number of generations) * (number of fireflies). The main update formula for any pair of two fireflies Xi and Xj is Where α t is a parameter controlling the step size, while is iis a vector drawn from a Gaussian or other distribution. It can be shown that the limiting case corresponds to the standard Particle Swarm Optimization (PSO) [5]. In fact, if the inner loop (for j) is removed and the brightness is Ij replaced by the current global best, then FA essentially becomes the standard PSO. II. RADICLE STUDY OF FF ALGORITHM 1. Solving the Economic Emissions Load Dispatch problem (Apostolopoulos and Vlachos, 2011).[6] Electricity generation must meet the increasing demand with time. The power plant generating electricity must be efficient and reliable and at the same time the cost of generation of per unit electricity must be minimum to maintain the profit. The amount of emission produced by the burning fossil fuel must be minimized. The firefly algorithm is used to solve this economic emission load dispatch problem to maintain both the quality and quantity with minimum pollution. Firefly algorithm used to reduce a single pollutant nitrogen oxide, NOx while maintaining the quality. The results obtained are comparably better than the results obtained by other stochastic alternative optimization algorithms such as the goal attainment SQP method, the particle swart optimization, and the Genetic algorithms, in terms of efficiency and success rates. The execution time was less than 3 seconds and the optimal solution very quickly (probably from 10 th iteration). 2. Multilevel Image Thresholding Selection (Horng and Jiang2010).[8] Thresholding is the simplest method of image segmentation. From a grayscale image, thresholding can be used to create binary images. The simplest thresholding methods replace each pixel in an image with either a black or white pixel depending on its image pixel Iij whether it is less or greater than some fixed Constant T. In this paper, maximum entropy based firefly thresholding method algorithm which is a new multilevel MET (Multilevel Entropy Thresholding) algorithm is used which is based on firefly also for multilevel thresholds selection using the maximum entropy criterion. The achieved segmentation results through this maximum entropy based firefly thresholding method algorithm is highly/significantly improved and has the shortest execution time. This maximum entropy based firefly thresholding method can be used further for real-time image analysis problem like target recognition, document analysis & much more. For testing five images and corresponding histograms are:(a)lena(girl),(b)pepper,(c)bird, (d)camera and (e)goldhill. Table 3. The computation times and the corresponding PSNR of the five different multilevel thresholding methods. Table 1: Minimum fuel cost and emission for various load demands using new particle swarm optimization [7]. Table 4: The value of the objective function with regard with the corresponding thresholds listed in above table. Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 2

29 Sanjeet Mandal et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, 1-5 proper/appropriate conversion of the continuous function as are attractiveness, distance and movement into a new discrete function. The results obtained are better than many other methods. Table 6: Comparison between FA, ACO, GA and SA for Solving Standard TSP Instances [13]. 3. Finding Optimal Test Sequence Generation (Srivastava, Mallikarjun and Yang, 2013).[9] In a software development life cycle [10], the most important but complex is the software testing [11]. To analyse the software testing and to optimize the process is a challenging task. In this paper, firefly algorithm is implemented to generate an optimal test paths. Modified firefly algorithm [3] is implemented to optimize the test cases by defining appropriate objective functions and introducing a guidance matrix in transversing the graph. The obtained simulations through this paper show that the test path generated are critical and optimal paths. The implemented approach is the first of kind in software testing field, which minimizes the test cases by optimizing the test paths for the case. Since the simulations are optimal it will minimize the test efforts. The approaching method can be further used for other expensive testing procedure. Table 5: Test cases for comparing FA and ACO. 5. Vector Quantization for Image Compression (Horng, 2012).[14] Vector Quantization(VQ)[15,16] is a classical quantization technique from signal processing that allows the modeling of probability density functions by the distribution of prototype vectors. It has a simple decoding architecture and high compression ratio. Codebook designing is the most essential part in vector quantization. An appropriate codebook must be used as it affects the quantity of image compression like global codebook generation. Linde Buzo Gray (LBG) is a traditional method of generation of VQ Codebook which results in lower Peak signal to noise ratio (PSNR) value. In this study, an Improved Differential Evolution (IDE) Algorithm based codebook training has been presented for Image Compression.. The Peak signal to noise ratio (PSNR) of vector quantization is optimized by using IDE-LBG Algorithm where PSNR is considered as the fitness function for the optimization problem. The algorithm parameters have been tuned suitably for efficient codebook design. It is observed that using the proposed IDE-LBG Algorithm the PSNR values and the quality of reconstructed image obtained are much better than that obtained from the other algorithms in comparison for six different Codebook sizes. Table 7. PSNR values of image compression for 5 different 512 x 512 images with 6 different Codebook sizes. 4. Solving Travelling Salesman Problem (Kumbharana and Pandey, 2013).[12] We all are familiar with Knapsack Problem, vehicle routing, travelling salesman problem etc. which are some complex problems. Inspite of all the advanced technology still there are so many complex problems that elude scientists. In this paper, the firefly algorithm is used to solve the travelling salesman problem. Given a list of cities with their distances between each one, it gives the shortest possible route that visits each city and returns to its origin city. A few modifications have been made to construct a 6. Job Shop Scheduling Problem.[17] Job shop scheduling problem is a non-deterministic polynomial (NP) hard problem. The objective of this paper is Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 3

30 Sanjeet Mandal et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, 1-5 to show how recently developed FA is used to solve JSS and investigate the parameter setting of the proposed algorithm. The experiment was implemented using five benchmarking JSSP datasets from a classical OR library. The analysis of the experiment results shows the best -so-far schedule better with appropriate parameter setting than without adapting parameter setting. In this paper FA was implemented to final the lowest makespan for the five benchmark JSSP datasets adopted from the OR-Library. The results is obtained in one-third number of experimental run compared with the conventional designs. The obtained results through both with optimized parameter setting and without using one compared and found that the performance is better when the optimized parameter setting are being used. Fig. 1. Comparison of Makespan with other Algorithm Fig. 2. Flowchart of the Firefly Colony Optimization 7. A novel firefly algorithm based Ant colony optimization for solving combinatorial optimization problems.[17] A new firefly optimization algorithm have been presented which is inspired by ant colony optimization algorithm which is called Firefly Colony Optimization algorithm (FCO). The implemented algorithm in this paper is a distributed and constructive greedy metaheuristic which produces greedily good solutions based on the positive feedback and avoid the low quality solutions. The performance of the proposed methodology have been assessed on the bin packing problem. In this paper, a new constructive and distributed version of the firefly algorithm have been proposed which is called the Firefly Colony Optimization (FCO). FCO have the common properties of the bioluminescent communication of fireflies and foraging behavior of ants. FCO obtains near optimal results with significant faster convergence ability. Application Methods used Results obtained Solving the Firefly algorithm, Minimum Economic hybridization, emission of Emissions SQP method nitrogen oxide Load Dispatch problem Multilevel Image firefly algorithm, Image Improved segmentation Thresholding Segmentation, and shortest Selection particle swarn execution time optimization Finding Optimal Test Modified firefly algorithm Optimal simulation and Sequence minimized Generation number of test Solving Travelling Salesman Problem Vector Quantization for Image Compression firefly algorithm, computational intelligence, optimization Linde-Buzo-Gray (LBG) Algorithm, Improved cases Obtained results are better than ACO, GA and SA in most of the instances. PSNR values and the quality of reconstructed Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 4

31 Sanjeet Mandal et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, 1-5 Job Shop Scheduling Problem A novel firefly algorithm based Ant colony optimization for solving combinatorial optimization problems Particle Swarm Optimization (IPSO) Algorithm, Bat Algorithm (BA), Firefly Algorithm (FA). firefly algorithm, job shop, scheduling Firefly Algorithm, Firefly Colony Optimization, Greedy Algorithm, Bin packing problem. image obtained are much better lowest makespan and the number of experimental runs is reduced by 66.7% FCO obtains near optimal results with significant faster convergence ability Table 8: list of applications with methods used and results obtained. III. CONCLUSION We have observed through this paper that how firefly algorithm is used to solve and obtain results for various problems. We have observed that for discrete problems and combinatorial optimisation, discrete versions of firefly algorithm have been developed with superior performance, which can be used for travelling-salesman problems, graph colouring and other applications. In addition, extension of firefly algorithm to multi-objective optimisation has also been investigated. There is no doubt that firefly algorithm will be applied in solving more challenging problems in the near future, and its literature will continue to expand. IV. REFERENCES 1. X. S. Yang, Nature-Inspired Meta-Heuristic Algorithms, Luniver Press, Beckington, UK, Firefly Algorithm: Recent Advances and Application Xin-She Yang Solving the Economic Emissions Load Dispatch problem (Apostolopoulos and Vlachos, 2011). 7. K. S. Kumar, V. Tamilselvan, N. Murali, R. Rajaram, N. S. Sundaram, and T. Jayabarathi, Economic load dispatch with emission constraints using various PSO algorithms, WSEAS Transactions on Power Systems, vol. 3, no. 9, pp , Multilevel Image Thresholding Selection (Horng and Jiang2010). 9. Finding Optimal Test Sequence Generation (Srivastava, Mallikarjun and Yang, 2013). 10. Ian Sommerville, Software Engineering, eighth edition, Pearson Edition, Boston, Aditya P. Mathur, Foundation of Software Testing, Pearson education, India, Solving Travelling Salesman Problem (Kumbharana and Pandey, 2013). 13. David Bookstaber, Simulated Annealing for Traveling Salesman Problem, Spring, Vector Quantization for Image Compression (Horng, 2012). 15. R.M. Gray, Vector quantization, IEEE Signal Process. Mag. 1 (2) (1984) D. Ailing, C. Guo, An adaptive vector quantization approach for image segmentation based on SOM network, Neurocomputing 149 (2015) Application of Firefly Algorithm in Job Shop Scheduling Problem for Minimization of Makespan K.C.Udaiyakumara*, M.Chandrasekaran b. 18. A Novel Firefly algorithm Based Ant Colony Optimization for Solving Combinatorial Optimization Problems. Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 5

32 Volume 9, Special Issue No. 3, May 2018 International Journal of Advanced Research in Computer Science (ISSN: ) CONFERENCE PAPER Available Online at APPLICATION TO PREDICT CHRONICAL KIDNEY DISEASE Ankitha, Architha, Chandana, Gulshan and Surekha Thota School of C & IT, REVA University Bangalore, India Abstract: With the promises of predictive analytics on big data, and the use of machine learning algorithms, predicting future is no longer a difficult task, especially for health sector, that has witnessed a great evolution following the development of new computer technologies that gave birth to multiple fields of research. Many efforts are done to cope with medical data explosion on one hand, and to obtain useful knowledge from it, predict diseases and anticipate the cure on the other hand. This prompted researchers to apply all the technical innovations like big data analytics, predictive analytics, and machine learning algorithms in order to extract useful knowledge and help in making decisions. In this paper, we will present an overview on the evolution of big data in healthcare system, and we will apply few learning algorithms on a set of medical data. The objective of this research work is to predict kidney disease by using machine learning algorithms that is random forest and build an application for professionals in the medical field and doctors.this application will help patients who are likely to suffer CKD by saving enormous cost of bills produced for different treatments like dialysis and kidney transplantation. If a person knows beforehand that he is showing symptoms of CKD he can prevent it from occurring by changing his lifestyle and stay healthy. Keywords: MACHINE LEARNING MODELS, WEB APPLICATION, RANDOM FOREST ALGORITHM, PREDICTING KIDNEY DISEASES =en I. INTRODUCTION The aim was to develop an application (web/mobile) that can predict whether a person would be diagnosed with chronic kidney disease in future or not, given certain health parameters of the person. This involved collecting large datasets of medical records pertaining to various individuals who were diagnosed with either having a chronic kidney disease or not having [1]. With this dataset, a Machine learning model was trained and developed, that could predict the probability of a person likely to be diagnosed with chronic kidney disease in future, given some current parameters regarding the person's health. This ML model was then embedded in a web server application that exposed certain API end points. A web application or a mobile application was developed that could consume the exposed API end points, POST certain health parameters/details about the person to the web server, which predicts the likeliness of the said person being diagnosed with a chronic kidney disease in future. II. RELATED WORKS There are a lot of pre-existing apps for professionals and patients that are used to predict CKD in different countries. Design and evaluation of a mobile application to assist the selfmonitoring of the chronic kidney disease in developing countries was given in 2016 whose model was built on 80 data records of American patients. This app is specifically for patient s self-evaluation. One of the famous app built for professional users is available on (The model was built by using records of American patients and is rated 2/5). The links of other apps available worldwide are provided in [9] to [16] There are other advanced apps that uses artificial intelligence and neural networks which are still under development. In our app health predictor ( WzcMyi4sQbKNN6L/view?usp=drive_web) We work with simple and basic algorithms which makes it less complex and we use the details of Indian patients which consist of 400 records. Therefore the prediction is going to be more accurate with respect to the Indian patients. Chronic diseases (such as diabetes, asthma, heart disease, lung disease, cancer, depression, stroke, hypertension, and Alzheimer s) are responsible for 7/10 deaths each year, and treating people with chronic diseases accounts for 86 percent of health care costs with 68 percent of Medicare beneficiaries. Suffering from two or more chronic diseases, readmission rates associated with chronic diseases have recently grabbed attention from policy makers and healthcare providers due to their high cost burden on the healthcare system. As healthcare experts, care providers and policy makers try to identify new ways to lower healthcare costs while improving care process and delivery, information systems and analytics can play a pivotal role in the effective prevention and proactive management of chronic diseases while lowering costs and improving patient outcomes [2]. This became our motivation to build an application for chronical kidney failure. Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 6

33 Ankitha et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, 6-9 III. RESEARCH This is the step by step explanation of the work flow and procedure used to build the model, website and application. are 25 different attributes/parameters (health parameters) for every record/person. IV. METHODOLOGY 1-Built a model using Tools used: Pycharm CE IDE, Anaconda distribution of the Python, Jupyter notebook Libraries used: scikit-learn, pandas, pickle [6] Algorithms used: Random forest classifier Language: Python 3.6 We used the random forest classifier rather than decision tree even though it is easier because of the following reasons [8]: Random forest is an ensemble method in which a classifier is constructed by combining several different Independent base classifiers. The independence is theoretically enforced by training each base classifier on a training set sampled with replacement from the original training set. This technique is known as bagging, or bootstrap aggregation. In Random Forest, further randomness is introduced by identifying the best split feature from a random subset of available features. The ensemble classifier then aggregates the individual predictions to combine into a final prediction, based on a majority voting on the individual predictions. It can be shown that an ensemble of independent classifiers, each with an error rate e, when combined significantly reduces the error rate. Suppose we have 10 independent classifiers, each with error rate of 0.3 (ϵ=0.3) In this setting, the error rate of the ensemble can be computed as below (we are taking a majority vote on the predictions. An ensemble makes a wrong prediction only when more than half of the base classifiers are wrong) ϵensemble= i=10i=6(10i)ϵi(1 ϵ)10 i 0.05ϵensemble= i=6i =10(10i)ϵi(1 ϵ)10 i 0.05 It can be seen that with the theoretical guarantees stated above an ensemble model performs significantly well. However in practice it is not possible to assure such classifier independence as they are trained from the same data, but still introduction of randomness helps achieve independence to a certain degree and it has been empirically observed that ensembles perform significantly well over individual base classifiers. Random forests overcome several problems with decision trees, including: Reduction in over fitting: by averaging several trees, there is a significantly lower risk of over fitting. Less variance: By using multiple trees, you reduce the chance of stumbling across a classifier that doesn t perform well because of the relationship between the train and test data. As a consequence, in almost all cases, random forests are more accurate than decision trees. [5] We collected the dataset using url: ase This dataset consists of 400 real records/instances, collected from Apollo Hospitals, TN over a period of two months. There Figure 1: work flow 1-Attribute Information Numerical Attributes 1. Age - age in years, 2. Blood Pressure - bp in mm/hg, 3. Blood Glucose Random - bgr in mgs/dl, 4. Blood Urea - bu in mgs/dl, 5. Serum Creatinine - sc in mgs/dl, 6. Sodium - sod in meq/l, 7. Potassium - pot in meq/ 8. Haemoglobin - hemo in gms, 9. Packed Cell Volume, 10. White Blood Cell Count - wc in cells/cumm, 11. Red Blood Cell Count - rc in millions/cmm, Nominal Attributes 1. Specific Gravity - sg ,1.010,1.015,1.020,1.02 5), 2. Albumin - al - (0,1,2,3,4,5), 3. Sugar - su - (0,1,2,3,4,5), 4. Red Blood Cells - rbc - (normal, abnormal), 5. Pus Cell pc - (normal, abnormal), 6. Pus Cell clumps - pcc - (present, not present), 7. Bacteria - ba - (present, not present), 8. Hypertension -htn - (yes, no), 9. Diabetes Mellitus-dm - (yes,no), 10. Coronary Artery Disease -cad - (yes, no), 11. Appetite - appet - (good, poor), 12. Pedal Edema - pe - (yes, no), 13. Anemia - ane - (yes, no), 14. Class -class - (ckd, notckd) Table 1 : Attribute Information Attributes considered are specified in Table 1. Altogether we have taken 25 attributes under consideration. Out of which 11 are numeric and 14 are nominal. 2-Build the Machine learning model After successfully building the model, we use ```pickle``` library in python to save the ML model. The ML model file is saved. We will load this saved/pickled ML model inside the Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 7

34 Ankitha et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, 6-9 web server application to make predictions [3]. which processes the inputs and returns a response. The response is whether the person (with the health parameters given as inputs) will be diagnosed with CKD or not. V. RESULT After building the model using random forest algorithm we can predict the failure rate by 88.8% accuracy [7]. Figure 2: Machine Learning modelling This is the best model to use though it is complex its accuracy is 88.8% which holds good for our database consist of 400 records of Indian patients which were collected over 2 months from Apollo hospital in Tamil Nadu. The results may vary depending upon the patient s location and its climatic conditions. 3-Develop a Tornado Web server Tools used: Pycharm CE IDE, Anaconda distribution of the Python Libraries used: python tornado web framework, scikit-learn, pickle Language: Python 3.6 Tornado is a scalable, non-blocking web server and web application framework written in Python. We create and expose certain API end points in the web server, which external applications can consume (call a HTTP GET/POST request), the web server processes the request, uses the machine learning model, predicts the outcome and returns the response to the client (web/mobile application) 4-Run the web server To make the actual prediction, make a POST request. 5-Android Application Tools used: Android Studio IDE Language: Java The Android app has a simple layout with 24 input fields and a button. Each of the input fields corresponds to a particular health parameter (the attributes we used while training the machine learning model). The user can input the health parameters and hit the button. 6-Web application Tools used: Visual studio code Framework: Angular 2 for front end, HTML, CSS, bootstrap Language: typescript The web app has a simple layout with 24 input fields and a button. Each of the input fields corresponds to a particular health parameter (the attributes we used while training the machine learning model). The user can input the health parameters and hit the predict button. A HTTP POST is made to the python tornado web server, Figure 3: ROC curve VI. CONCLUSION In this study, machine learning algorithms was applied on chronic kidney disease dataset for prediction of patients who have chronic kidney disease or those who are not sick, based on the data of respective attribute for each patient. As conclusion, the application of data mining methods for predictive analysis is utmost important in the field of health care because it gives us the power to face diseases earlier and therefore save people s lives through the anticipation of cure. In this research, we used several learning algorithm to predict patients with chronic kidney failure disease (CKD), and patients who are not suffering from this disease (not CKD) [4]. Simulation results showed that Random forest classifier proved its performance in predicting with best results in terms of accuracy and minimum execution time. Hence we used random forest classifier available in sciket learn to build an application which is coded on pycharm. This application will be of a major use in medical field. It will reduce the time, cost and effort comparatively and the patient can take counter measures when he knows these details beforehand. It fits best for prevention is Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 8

35 Ankitha et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, 6-9 better than cure. Anticipating diseases still remains a major challenge in medical field and pushes us to increase our efforts in developing more machine learning algorithms to exploit information intelligently and extract the best knowledge from it. REFERENCES: [1] A review on predictive analysis- kavya arumugam [2] Australasian Conference on Information Systems Al Khatib et al 2015, Sydney [3] (Senior Consultant Nephrologist), Apollo Hospitals, Managiri, Madurai Main Road, Karaikudi, Tamilnadu, India. [4] The Role of Information Systems and Analytics in Chronic Disease Prevention and Management-Indranil Bardhan [5] sease [6] Dataset: [7] [8] Preventive Healthcare: A Neural Network Analysis of Behavioral Habits and Chronic Diseases Viju Raghupathi and Wullianallur Raghupathi [9] [10] [11] [12] [13] [14] [15] [16] Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 9

36 Volume 9, Special Issue No. 3, May 2018 International Journal of Advanced Research in Computer Science (ISSN: ) CONFERENCE PAPER Available Online at BETTER HEALTHCARE USING MACHINE LEARNING Abhishek Yadav School of Computing and Information Technology REVA University, Bangalore, India Arpitha.M.S School of Computing and Information Technology REVA University, Bangalore, India Amit singh School of Computing and Information Technology REVA University, Bangalore, India Mithun.K.A School of Computing and Information Technology REVA University, Bangalore, India Prof. Spoorthi Rakesh School of Computing and Information Technology REVA University, Bangalore, India Abstract: Today s world is more conscious about the health. Even though the medical field is evolving with the improved facilities, the death rate is increasing day by day. Heart disease is one such cause for increasing death rate. Medical industry contains large amount of hidden information of many patients. This hidden information can be used to take important decisions by applying data mining techniques. Data mining acts as a solution for many healthcare problems and it is helpful in predicting heart disease in early stages. Naïve Bayes algorithm is one such data mining technique which helps in the prediction of heart disease in patients. Cleveland dataset is used to analyze few parameters of the patients from the previous records such as age, chest pain, sex, blood pressure, ECG reading etc. A machine learning model which helps in predicting the heart disease is built. Keywords:Data mining, Naïve Bayes, Gaussian, Bernoulli 1.INTRODUCTION Heart disease is recorded as one of the deadly disease in today s world. It has been increasing in the developing countries of the world, including China and India. In today's world heart diseases is the major cause of deaths. The top five countries with the highest death rates due to heart disease are Russia, Bulgaria, Romania Hungary, and Argentina. The major causes for heart diseases are fatty food habits, obesity, high blood pressure, mental stress, diabetes etc. Records of the affected patients are anyways of no use, but we can use them to train our model and help in predicting the heart disease. Using this model we can predict the disease in early stages and diagnose the patient. Considering some of the major parameters we have designed a model using the famous Naive Bayes algorithm. Naive Bayes technique is the basis for several machinelearning and data processing ways. The algorithm is used to develop models with predictive capabilities. Naïve Bayes classification technique is more efficient than the decision tree or K-nearest neighbor classifiers. It gives the result faster with higher efficiency and it can be trained with the input data containing minimum records. This algorithm learns from the existing data and predicts the future. Naïve Bayes algorithms are of three types namely Gaussian (used when the data follows normal distribution), Bernoulli (used when data has only 2 classes) and Multinomial (used when data has more than 2 classes).this paper discusses about the Cleveland dataset which is used for training and testing the model. 2. METHODOLOGY This paper gives the idea of how to predict the heart disease in early stages. Using Naïve Bayes algorithm (Machine learning algorithm which predicts the faster and by using the limited dataset) the system is developed which predicts the heart disease. The work carried out involves applying 3 models on the input dataset which includes pure Naïve Bayes, Gaussian and Bernoulli. The model is trained using the input dataset. After training, the testing is performed using the testing data and the system shows the final result. The system is designed to be user friendly by developing an interactive GUI (Graphical User Interface). 2.1 INPUT DATA SOURCE The system uses a Cleveland dataset collected from Cleveland hospital. This dataset holds data of 76 attributes, but all published experiments refer to a subset of 14 attributes. This dataset contains record of 303 patients which is split into training and testing data. The goal field refers to Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 10

37 Abhishek Yadav et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, the presence or absence of heart disease. It is an integer value numbered from 0(absence) to 4(presence). The experiments performed using Cleveland dataset is concentrated on predicting the presence (values 1, 2, 3, 4) or absence (value 0) of heart disease. P (C/A) = P (A/C) * P (A) / P (C) P (C/A) = P (A1/C)*P(A2/C)*P(A3/C)...P(An/C)* P(C) Where: P(C/A) = posterior probability of class C, given the predictor A. P(A/C) = probability of the predictor A, given that the class C was true. P(C) = prior probability of class C. P(A) = prior probability of predictor A. Working of Naive Bayes algorithm: Analyze the input dataset given and convert that into frequency table. Next step is to find the likelihood table by finding the probabilities. And the last step is to apply the Naive Bayesian equation and calculating the posterior probability of each class in the dataset. The classes with the maximum posterior probability will be the final result. 2.2 ALGORITHMS USED A. Naïve Bayes classifier Fig 1: flowchart of the system Naive Bayes is a machine learning algorithm used for prediction. It is a supervised learning method used for classification which helps in classifying the record to a particular class. The word supervised in this context means that, the dataset will be holding the records of input and corresponding output values and the model will learn from this dataset and predict the class of unknown record. It is recorded as one of the simple and easiest algorithm to create models which can predict the results. Naïve Bayes algorithm is based on Bayes theorem which states that, The presence of any particular feature in a class is completely independent of the presence of any other feature. The word Naive means the assumption of strong independence among the features. Bayesian equation: Bayes theorem is proposed by the British mathematician named Thomas Bayes. He introduced a formula which helps in determining the conditional probability, using which we can predict the class of unknown record. Bayesian equation is as follows; B. Gaussian Naïve Bayes classifier Naive Bayes can be extended to real-valued attributes; most commonly by assuming a Gaussian distribution. This extension of naive Bayes is called Gaussian Naive Bayes. Gaussian technique is used when the data follows normal distribution. Other techniques can be used to estimate the distribution of the data, but the Gaussian (or Normal distribution) is the easiest to work with because we only need to estimate the mean and the standard deviation from the training data. C. Bernoulli Naïve Bayes classifier This classifier is suitable only for discrete values unlike the Gaussian Model. Bernoulli s Classifier is built only for Boolean or Binary values. For the Bernoulli naive Bayes classifier, we let X={0,1}X={0,1}. Then, we let p(x Y)p(X Y) be modeled as Bernoulli distribution: p(x Y)=θX(1 θ)1 X We have to model a Bernoulli distribution for each class and each feature, so our terms look like: p(xj Y=yk)=θXjkj(1 θkj)1 Xjp(Xj Y=yk)=θkjXj(1 θkj) 1 Xj 2.3 PROCESSING DATA The input data is divided into training and testing data. Training data is used for training and testing data is used to test the outcome. During processing many mathematical operations are performed on the input data. These operations include finding frequency table, finding probabilities and Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 11

38 Abhishek Yadav et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, applying Naïve Bayes equation. The class with the highest probability is the outcome of the prediction. 2.4 RESULT OF THE ANALYSIS The system works in the way as shown in fig 1.The developed system efficiency predicts the presence or absence of the disease. The system is trained and tested using the Cleveland dataset. The efficient testing is performed by giving the input of new patient. The system also have and interactive GUI(Graphical User Interface) which shows the output along with the efficiency. Final result for different cases and for each model is shown in fig 2. Cases Results of Naïve Bayes Results of Gaussian Results of Bernoulli Case Case Case Case Case Fig 2: Final result 3. CONCLUSION Heart disease is the major cause for increasing death rates in today s world. Even the medical field is evolved with the advanced equipment s and facilities, the death rate is not decreasing. The major cause for increasing death rates are modern lifestyle, food habits, stress level and evolving of new diseases. Most of the diseases cannot be predicted in the early stages and heart disease is one such kind. Heart disease cannot be predicted until the patient is affected with the stroke. Most of the developing countries like India and China are facing problems in health sectors. This paper discusses about the work carried out to predict the deadliest heart disease. Using data mining technique called Naïve Bayes; the prediction can be carried out. Hospitalswhich treat patients with heart disease will have the records of their reports and symptoms. This data can be collected from the hospitals and can be used anonymously (without revealing patient s name) to train the machine learning models to predict the heart disease in early stages. Cleveland dataset which is collected from the Cleveland hospital is used to train and test the model. This dataset contains records of 303 patients and information about the age, gender, blood pressure, ECG report, etc. All these collected information can be used to train the model which is developed using Naïve Bayes algorithm. This model predicts the result with higher efficiency and in faster rate. The result is displayed using an interactive GUI (Graphical User Interface). The final outcome is displayed in binary format (1- presence of heart disease, 2-absence of heart disease) and along with the accuracy is also displayed. 4. FUTURE SCOPE AND ENHANCEMENT The system which is developed using Naive Bayes algorithm has an efficiency of 85%. This system can be further expanded to detect other deadly diseases other than heart disease. This system can be further improvised in terms of efficiency and speed by incorporating other data mining techniques. It can be enhanced further. For an instance, the number of attributes that are considered in Cleveland dataset are only 14, this number can be increased. We can increase the number of records used to train the model. Larger the size of training dataset higher the efficiency. We can also apply other processing techniques such as clustering, association and time series. 5. REFERENCES [1] Dhanashree S. Medhekar, Mayur P. Bote, Shruti D. Deshmukh, Heart Disease Prediction System using Naive Bayes cea161d7445fe8b.pdf [2] Shadab Adam Pattekari and AsmaParveen, PREDICTION SYSTEM FOR HEART DISEASE USING NAIVE BAYES e0409cb pdf [3] Garima Singh, Kiran Bagwe, ShivaniShanbhag, Shraddha Singh, Sulochana Devi, Heart disease prediction using Naïve Bayes -- [4] K.Vembandasamy, R.Sasipriya and E.Deepa, Heart Diseases Detection Using Naive Bayes Algorithm -- [5] Vincy Cherian, Bindu M.S, Heart Disease Prediction Using Naïve Bayes Algorithm and Laplace Smoothing Technique V5I2P13.pdf [6] Monika Gandhi, Dr. ShailendraNaranyan Singh, Predictions in Heart Disease Using Techniques of Data Mining-- [7] Jagdeep Singh, Amit Kamra, Harbagh Singh, Prediction of Heart Diseases Using Associative Classification-- [8] Prediction of heart disease using a hybrid technique in data mining classification-- [9] Theresa Princy. R, J. Thomas, Human Heart Disease Prediction System using Data Mining Techniques-- Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 12

39 Abhishek Yadav et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, [10] S.Indhumathi, Mr.G.Vijaybaskar, WEB BASED HEALTH CARE DETECTION USING NAIVE BAYES ALGORITHM ISSUE pdf [11] Mrs.G.Subbalakshmi, Mr. K. Ramesh, Mr. M. Chinna Rao, Decision Support in Heart Disease Prediction System using Naive Bayes Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 13

40 Volume 9, Special Issue No. 3, May 2018 International Journal of Advanced Research in Computer Science (ISSN: ) CONFERENCE PAPER Available Online at COGNITIVE WAY OF DETECTING CYBERBULLYING IN CHATBOTS H.M. Chandana School of Computing and Information Technology Reva University Bangalore, India Karnik Pooja Jagdish School of Computing and Information Technology Reva University Bangalore, India Anna Mary School of Computing and Information Technology Reva University Bangalore, India Bhawana Dorbi School of Computing and Information Technology Reva University Bangalore, India Naveen Chandra Gowda School of Computing and Information Technology Reva University Bangalore, India Abstract - The increase in application of information and communication technology is transforming the society into an unknown ground of the continuous evolving cyber world. We are dealing with cyberbullying which is one of the major issues in the cyber security. This paper focuses on prevention/detection of cyberbullying in online communication venues particularly, in the chat rooms where, even let alone chatbots in huge numbers with anonymous ids can communicate, at times creating havoc on such platforms by doing it with unworthy messages. Keywords chatbot, chat room, client agent, cyberbullying I. INTRODUCTION The cyber world is considered as a complex adaptive system. We are dealing with cyberbullying which is one of the major issues in cyber security. Cyberbullying is a crime in which the attacker harasses a victim using electronic communication such as or instant messaging or messages posted to a website or a discussion group. Cyberbullying can take place via human being or chatbots. Chatbots are intelligent programs which are designed in such a way as to simulate human conversation. The chatbots which are cyber attackers, rely upon the anonymity afforded by the internet to allow them to stalk their victim without being detected. There are some chatbots which can cause harm to the society and some of the large organizations can be threatened by their attacks. In chat rooms, a chatbot can send unworthy messages to the other user just to create network traffic. A chat room is a designated area or forum on the World Wide Web that allows users to communicate with each other through instant messaging. The main purpose of a chat room is to provide entertainment for many people so that they can make new friends and share different things. Most of the companies made the chat rooms on their website just to get the knowledge about their products when the people communicate with each other about the company s products, good and bad of the company, they watch the communications between the users so they judge the comments of their products. According to the security requirement, we need a system against cyberbullying in chatbots. Previous approaches were focusing on detection of cyberbullying after the impact. We have used a cognitive approach on the proposed client agent which resides on the client system, examines bullying in chats and mails, and blocks delivery of unworthy content beforehand unlike detection later. II. LITERATURE SURVEY Cyberbullying and social networking harassment are the two areas where textual patterns have been used to detect and filter unwanted messages. One of the early approaches was proposed by Spertus, E. [1] where the authors try to detect stalking messages based on selecting 47 syntax and semantic features, achieving 64% detection rate. Another approach discussed by Dinakar, K. in his paper [2] was to classify cyber bullying based on binary and multiclass text classification, and reportedly binary class classification outperforms multi-class classifiers. However, using attackers characteristics, and their stalking behaviors can improve the performance of cyber bullying detection [3], while in [4] it was shown that performance improves when combining content, sentiment and contextual features to detect harassment on the Web 2.0. A semi supervised algorithm was proposed in [5] utilizing lexical association of profane language to detect offensive tweets. In [6] Maple et al. have defined cyberstalking as a course of actions that involves more than one incident perpetrated Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 14

41 H.M. Chandana et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, through or utilizing electronic means that cause distress, fear or alarm. The review paper by Alok Mishra et.al [7] illustrates cyber stalking, its approaches, impacts, provision of legal acts, and measures to be taken to prevent it. There is an immediate need for research in the various dimensions of cyber stalking to assess this social problem. The survey paper by Anurag Pandey et.al [8] tried to address this issue by reviewing the steps that can be undertaken to detect cyber bullying on online social networks. This paper aims to review the different methods and algorithms used for detection in cyber bullying and provide a comparative study amongst them so as to decide which method is the most effective approach and provides the best accuracy. The review paper done by Paul Bocij [9] describes the first study to focus exclusively on the prevalence and impact of cyberstalking. A Web-based questionnaire was used to collect data from a group of respondents who were recruited by snowball sampling via . A total of 169 respondents completed the questionnaire. The results of the study found that approximately a third of respondents might be considered victims of cyberstalking. Furthermore, when asked to indicate the level of distress felt as a result of their experiences, almost a quarter of respondents chose a value of ten on a ten-point scale. The review paper by Per Carlbring et.al [10] discusses about the factors which are involved in cyberstalking. He has also mentioned the problems faced by the victims of cyberstalking such as loneliness, anxiety, self-harming behavior, sleeping problems etc. He has also mentioned some therapies for the victims that will help them to deal with the consequences of cyber bullying like REBT (Relational Emotive Therapy), CBT (Cognitive Behavioral Therapy), IPT (Interpersonal Psychological Therapy) etc. The study done by Qing Li [11] examines the nature and extent of adolescents' cyberbullying experiences, and explores the extent to which various factors, including bullying, culture, and gender, contribute to cyberbullying and cyber victimization in junior high schools. In this study, one in three adolescents was a cyber victim, one in five was a cyberbully, and over half of the students had either experienced or heard about cyberbullying incidents. Close to half of the cyber victims had no idea who the predators were. Culture and engagement in traditional bullying were strong predictors not only for cyberbullying, but also for cyber victimization. Gender also played a significant role, as males, compared to their female counterparts, were more likely to be cyberbullies. To prevent chatbots (cyber stalkers) from posting defamatory or derogatory statements about their stalking on social media or other web pages. IV. PROPOSED SYSTEM A client agent, residing at the user end will block all the unworthy messages before sending it to the other user who is involved in the chat session and hence building a safer cyber space. The client agent will be present at each user end and it will be running at the backend without being realized by the user. At the same time, it does not compromise on the privacy of the user. The system retrieves the messages, preprocess it and classifies if the bullying word is present or not in the conversation and then sends the message to the other user. System Architecture Fig: System Architecture Modules Identified 1) Authentication Check 2) Capture 3) Preprocess 4) Classify 5) Decision V. METHODOLOGY Activity Diagram Fig depicts the activity diagram of chat room and the client agent. Initially, we have the authentication process which allows the valid user to enter the chat room and initiate a chat request to all the users to begin the chat with the users who accepted the request. During the chat session, all the messages are processed and classified to check if the message is cyberbullying or not and depending on this, the system allows the user to continue chatting or block the user. III. PROBLEM STATEMENT Problem Definition Cyberbullying is done by chatbots to harass an individual or a group of individuals by sending threatening messages and the victims are unaware about the fact that they are being stalked and even if it is detected, it is identified after the impact. Objectives To provide client side checking of the messages to avoid any third party to interfere and have access to users messages and cause privacy threat. To provide authentication and data integrity for the chat room users. Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 15

42 H.M. Chandana et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, You Now each look token like is compared a disgusting to stopwords and punctuations. If it is a stopword such as, a, an, the, to etc or a punctuation, alcoholic such as bastard :,! etc.! the! word. is of less weightage and is ignored, otherwise yield for lemmatization. The yield result would include: You look like disgusting alcoholic bastard The meaningful words in their different grammatical forms are brought down to their original form. The result of this step would be: You look like disgust Finally the meaningful lemmas are returned as keys of a dictionary which is termed as featurization here. The corresponding result for the above example would be: { You : True, look : True, like : True, disgust : True, alcoholic : True, bastard : True Step by analysis Authentication Authentication refers to verifying user s Identity. The very first step in any online interaction is authentication. In our system the user is prompted to login before using the chat room. Any new user has to register himself / herself. The registered user then needs to confirm registration through a confirmation link sent to his id upon which he can use the chat room. Retrieving user messages Once the user starts inviting others for a chat session and starts conversing, it is when our client agent comes to picture. User types his message and clicks on send. Instead of directly sending the message, the message is sent to a module residing in the client system. In this step the message is retrieved or in other words captured before release. The retrieved message is sent for preprocessing. Consider the sample retrieved message: You look like a disgusting, alcoholic bastard!!. Preprocessing retrieved message The next step is to break the message into tokens with literal meanings. Irrespective of the meanings the message is first broken into words. The process is referred as tokenization. In the example above, the results would be: Classifying the message The next step is to classify the message as a worthy or unworthy message. Each word present as the keys of dictionary passed on from the previous step has to be compared with the dataset of bad words which itself is a dictionary having alphabets as keys in order, so that traversal and searching time is reduced. If a match is found the result should be reflected in some way possibly by maintaining a temporary variable and incrementing it whenever there is a match. For passing a result to another module the best way is to pass it in the form of a boolean value. You do not increment look do not increment like disgust do not increment do not increment Boolean result must be returned which determines the unworthiness/worthiness of the message as a whole. In the example: Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 16

43 H.M. Chandana et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, Two bad words found. The temporary variable would have a value of 2. Thus return True Decision- the corresponding response The final step is sending the message. Not all users should be able to read a sent message if it is unworthy. From the Boolean response received in the previous step a decision is made. VI. RESULTS Integration Testing Table: Integration testing VII. CONCLUSION An attempt is made to detect cyberbullying at the early stages without using any third party applications. Cyberbullying is at rise. Both positive and negative experiences are abundant on the chatrooms. Many children and youngsters are becoming victims of the harmful online communication. The ultimate goal of the proposed system is to use a client agent system at the user end and classify the messages and detect cyberbullying activities before the message could jump into the network. This system is more efficient than other systems where a third party is involved for detection after the attack. Development of this project has given a good exposure to Python and NLP techniques and thus extending and contributing to our knowledge base. However the same techniques can be used in other social networking sites to detect cyberbullying activities and build a safer cyber space. VIII. FUTURE ENHANCEMENTS This project remains a simple and easy prototype to detect the text based cyberbullying activities in chat rooms. However, this can be made more efficient using various other fields in machine learning such as NLP and optimization techniques and can also be used in various other social networking sites to detect cyberbullying activities. We can enhance our system by including images for detecting cyberbullying. A threshold can be set so that an alert message is sent after every bullying word used by the chatbot in the messages and finally block it if it exceeds the limit. IX. REFERENCES [1] Spertus, E. (1997). Smokey: Automatic Recognition of Hostile Messages, In: Proceedings of the Innovative Applications of Artificial Intelligence, [2] Dinakar, K. (2011). Modeling the Detection of Textual Cyberbullying, in The Social Mobile Web, [3] Dadvar, M., Ordelman, R., Jong, F. D. (2012). Trieschnigg. D. Towards User Modelling in the Combat against Cyberbullying, in Natural Language Processing and Information Systems. Springer-Verlag Berlin Heidelberg, [4] Yin, D., Xue, Z., Hong, L., Davison, B. D., Kontostathis, A., Ed- wards, L. (2009). Detection of Harassment on Web 2.0, In: Proceedings of the Content Analysis in the WEB 2.0 (CAW2.0) Workshop at WWW2009. [5] Xiang.G., Fan.B.,Wang, L., Hong, J., Rose, C. (2012). Detecting offensive tweets via topical feature discovery over a large scale twitter corpus, In: Proceedings of the 21st ACM International Conference on Information and knowledge management - CIKM 12. New York, New York, USA: ACM Press, [6] C. Maple, E. Short, A. Brwon, C. Bryden, and M. Salter. Cyberstalking in the UK: Analysis and Recommendations. International Journal of Distributed Systems and Technologies, 3(4):34-51, [7] Alok Mishra, Deepti Mishra; Cyber Stalking : A Challenge for Web Security,2008. [8] Rekha Sugandhi, Anurag Pande, Siddhant Chawla, Abhishek Agrawal, Husen Bhagat; Methods for detection of cyberbullying : A survey, In: Intelligent Systems Design and Applications (ISDA), [9] Paul Bocij, Victims of Cyberstalking: An Exploratory Study of Harassment Perpetrated via the Internet, vol 8, number 10-6, [10] Agate, J., Ledward, J., Social media: how the net is closing in on cyber bullies. Entertain. Law Rev. 24 (8), [11] Qing Li; Bullying in the new playground: Research into cyberbullying and cyber victimization; In: Australian Journal of Education Technology, 2007, 23(4), Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 17

44 Volume 9, Special Issue No. 3, May 2018 International Journal of Advanced Research in Computer Science (ISSN: ) CONFERENCE PAPER Available Online at NATURAL LANGUAGE QUESTION ANSWERING Umma Khatuna Jannat REVA University, Bengaluru, India Nirmala S Guptha REVA University, Bengaluru, India nirmalaguptha@reva.edu.in Supreeth S REVA University, Bengaluru, India supreeth.s@reva.edu.in Anitha K REVA University, Bengaluru anitha.k@reva.edu.in Shashikala N REVA University, Bengaluru, India shashikalan@reva.edu.in Abstract: As the amount of information available online is growing, the need to access it becomes increasingly important and the value of natural language processing applications becomes clear. Machine translation helps us conquer language barriers that we often encounter by translating technical manuals, support content or catalog at a significantly reduced cost. The challenge with machine translation technologies is not in translating words, but in understanding the meaning of sentences to provide a true translation. In this textual analysis can be one of the important areas where future behaviours can be predicted, Extraction of the information from any user-given passage or body of text plays a major role in Data Analysis. The user is not restricted by any general genre or category of data.the system can pose the questions based on the given textual contents. For the predicted answer system can assess the correct answer. A simple reading comprehension, while there are a lot of systems that run on machine learning algorithms, most if not all of them are closed domain with training from existing wiki articles or corpora. This work is one step closer to tackling this problem and provides a true understanding between man and machine. Keywords:Natural Language Processing, Machine Translation, Extraction, Comprehension. I. INTRODUCTION The sheer measure of data in our regular day to day existence is totally stunning in the present day and age. To comprehend everything, manual extraction is administered simply in view of its sheer size, so it falls on us PC architects to figure out how to computerize the extraction of data in a way that is significant, simple and generally secure. That was the motivation to begin this undertaking, to gadget a framework that will read through a group of content and give you the best and most important data in a sorted out and justifiable to make it straightforward huge assortments of content without reading through the entire thing. Obviously, this is less demanding said than done and we encountered various difficulties attributable to the very idea of dialect. Be that as it may, we continued on and the outcome is a task we are glad to have made. Have a data extraction framework that is lightweight, versatile and simple to utilize. As said previously, the common of dialect is a sufficient test for this sort of attempted. Past this, the approach we took to accomplish the objective was additionally something of an issue. Our first break was attempting to actualize a profound learning model however that would restrict our client contribution to specific fields and that isn't the vision we had. Next time, we attempted straight up string coordinating with fluffy and likelihood, while this strategy certainly worked, it was not tried and true in its exactness. At long last, we settled on regas said previously, the very idea of dialect is a sufficient test for this sort of attempted. Past this, the approach we took to accomplish the objective was likewise something of an issue. Our first split was endeavouring to actualize a profound learning model yet that would restrict our client contribution to specific fields and that isn't the vision we had. Next time, we attempted straight up string coordinating with fuzzy and likelihood, while this strategy unquestionably worked, it was not tried and true in its exactness. At last, we settled on consistent articulations to acclimatize data into something we can answers from. The contention could be improved that CFG was however this was only a less demanding idea for us to wrap our heads around and it works. The following test was to compose the syntax to get the greater part of the normally utilized sentences in our dialect which took a considerable measure longer than we anticipated. In general, we have made the most ideal rendition with our opportunity and restricted know-what about the area yet there is positively space for a considerable measure of changes. Normal articulations to acclimatize data into something we can answer from. The contention could be improved that CFG was yet this was only a less demanding idea for us to wrap our heads around and it works. The following test was to compose the syntax Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 18

45 Umma Khatuna Jannat et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, to get the vast majority of the regularly utilized sentences in our dialect which took a great deal longer than we anticipated. By and large, we have made the most ideal variant with our chance and restricted know-what about the space yet there is unquestionably space for a ton of enhancements. II. LITERATURE SURVEY In a system developed Athira P. M, Et.al [5], presented an architecture of ontology-based domain-specific natural language question answering that applies semantics and domain knowledge to improve both query construction and answer extraction. The web as a broad scope, auto-updating knowledge store, answer is mined automatically with a wide range of queries with much less work than is required by modern search engines. The system is able to filter semantically matching sentences and their relations effectively, it ranked the correct answers higher in the result list. Another system developed by Pragisha K. Et.al [6], described about the. It receives Malayalam natural language questions from the user and extracts most appropriate response by analyzing a collection of Malayalam documents. The system handles four each question. The main answer extraction module is NER in Malayalam. The proposed system design and implementation of a QA system in Malayalam also covered the implementation of some linguistic resources classes of factual questions what, which, Where and which, it extracts precise answer and short answer for user queries in Malayalam. Research and reviews in question answering system developed by Sanjay K Dwivedi Et.al[7] propose taxonomy for characterizing Question Answer (QA) systems, survey of major QA systems described in literature and provide a qualitative analysis of them. It includes the QA system like Linguistic Approach, Statistical approach, pattern matching approach, Surface Pattern based, Template based etc, They observed that the choice of a technique is highly problem specific. Often a hybrid approach, blending evidently different techniques, provides improved results in the form of high speed, increased relevancy, and higher accuracy and recall measures. QA techniques based on linguistic approach, statistical approach and pattern based approach will continue to remain in sharp focus. In a System developed by Poonam Gupta Et.al [8] A Survey of Text Question Answering Techniques. Question answering is a difficult form of information retrieval characterized by information needs that are at least somewhat expressed as natural language statements or questions, and was used as one of the most natural type of human computer communication. In comparison with classical IR, where complete documents are considered similar to the information request, in question answering, and specific pieces of information are come back as an answer. The user is interested in a precise, understandable and correct answer, which may consult to a word, sentence, paragraph, image, audio fragment, or an entire document [9]. The main purpose of a QA system is to find out HOW, WHY, WHEN, WHERE, HOW, WHAT, WHOM and WHO? [10].QA systems combines the concepts of information retrieval (IR) with information extraction (IE) methods to identify a set of likely set of candidates and then to produce the final answers using some ranking scheme [11].Types of QA systems are Web Based Question Answering Systems.IR / IE Based Question Answering Systems. Restricted Domain Question Answering systems. Rule Based Question Answering Systems. Template Matching Automatic Answering System For natural languages questions proposed by Pachpind Priyanka Et.al [12], Frequently Asked QA System that replies with prestored answers to user questions asked in regular English, rather than keyword or sentence structure based retrieval mechanisms. Techniques: pattern matching technique Types of QA Systems are, closed-domain QA that deals with questions under a specific domain. Open domain QA that deals with questions about almost everything, and can rely only on general ontology and world knowledge. Main modules are: Pre-processing: (a) converting SMS abbreviations into common English words (b) removing stop words, and (c)removing vowels. Question template matching: The pre-processed text is coordinated against each and every pre stored template awaiting it finds the best template. Answering the matching answer will be returned to the end user. III. QUESTION ANSWERING APPROACHES 1. Linguistic approach An inquiry noting rationale contains AI based strategies that incorporate Natural Language handling (NLP) strategy and learning base. The learning data is composed as generation administer, rationale outlines, layouts, metaphysics and semantic systems; it is utilized amid the examination of QA match. Parsing, Tokenization, and POS labeling are semantic strategies, it actualized to clients address for detailing it into an exact inquiry that predetermined concentrate the particular reaction from basic database. In late work the restriction of learning base is acknowledged as the capacity to give a circumstance particular are Clark et al [1] introduced methodologies for increasing on the web content with information base inquiry noting capacity. Existing inquiry noting START [2], QA framework by chang Et.al [3] and mishra Et.al [4] have obtained web as their insight asset. 2. Statistical Approach Significance of measurable approach is expanded by the sudden development of accessible online content archives. Factual methodologies are autonomous of SQL and can plan questions in common dialect shape. One burden of factual approach is it treats each term freely and neglects to recognize etymological highlights for a blend words or expression. Measurable methods effectively connected to the diverse phases of the QA framework. The system utilized for characterization reason for existing is Maximum entropy models, bolster vector machine (SVM) classifiers, Bayesian classifiers. The vital work in light of the measurable technique was s IBMfactual QA [9] framework. It utilized most extreme entropy display for question/answer based different N-gram highlights. 3. Pattern Matching Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 19

46 Umma Khatuna Jannat et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, Approach The example coordinating methodology utilizes the expressive energy of content examples. It replaces the complex handling engaged with other contending approaches. World Cup 2014 held?" takes after the example "Where was held?" and its answer example will be "was held at". There are two methodologies: Surface Pattern based and Template based. A large portion of the examples coordinating QA frameworks utilize the surface content examples while some of them likewise depend on formats for reaction age. Surface Pattern: based It is either human made or naturally learned examples through illustrations. Answer sentences for instance, the inquiry "Where was Football" is extricated utilizing factual procedures or information mining measures. Example learned by in self-loader and the most good application region is little and medium size site. Template based: This approach makes utilization of preformatted designs for questions. The fundamental focal point of this approach is more on exhibit as opposed to clarification of inquiries and answers. The formats set is worked keeping in mind the end goal to contain the ideal number of layouts secure that it adequately cover the space of issue, and every one of its individuals speaks to an extensive variety of inquiries of their own sort. The substance spaces of Templates, which are missing components bound to the idea of the inquiry that must be filled to create the question layout to recover the relating reaction from the database. The reaction returned by inquiry will be crude information; it is back pedalling to the client. IV SYSTEM ANALYSIS AND DESIGN The system is completely based in Python with no extra dependencies except the modules included in the code itself. As of this version of the project, nothing else is needed or used to execute it. An overview of the working of the system :-Take the input in the form of input, Spell-check and prepare it for the grammar, Parse it through the custom made grammar with the regexpparser from nltk,parsed tree is traversed to check labels against required context, Labels are either made into keys or values of a dictionary that serves as the knowledge base, Take the input of question from the user, Depending on the type of question ( who, what, where and when only), the respective modules are executed, Each module is tailor made to answer that specific question word, If the system is not able to answer the question or the data is insufficient to determine a probable outcome, the system will ask for choices, User can put up to 4 options, one must be correct then the Answer is displayed. V IMPLEMENTATION After having moderate success with string matching, we now use regular expressions to extract relevant information. Each sentence is parsed through the RegexpParser() from the nltklibrary. Ideally, we would be able to predict every possible pattern but that is unlikely and a majority will have to do.we use the parts of speech tags of each word to associate them. A few code snippets pattern = """ P3: {<NNP>+ <CC>* <VB.*>*} P8: {<P3><TO IN DT R.*>* <VB.*>* <JJ.* NN NNS>* <VB.*>* <TO IN DT R.*>* <P3>* <NN NNS>* } P2: {<P8><TO>* <NNP>? } C1: {<P8><P7>} P1: {<NNP><VB.*>* <NNP>? <TO IN DT R.*>* <VB.*>* <NNS NN>* <NNP>?} P6: {<CD>* <NN.*>* <VB.*>* <NN.*>?} P7: {<P6><R.*>* <CC>* <TO>* <VB.*>* <NN.*>?} C1: {<P8 P2><P7>} P5: {<DT>* <NN.*>* <VB.*>* <IN>* <VB.*>* <JJ>* <NN>* <NNS>* <NNP>? } P4: {<NNP>+ <VB.*>* <TO>* <VB.*>* <NN.*>?} P7: {<P6><R.*>* <CC>* <VB.*>* <J1><R.*>* <J1><VB.*>* <J1><NN.*>?} """ #This is the custom made grammar to catch patterns tags=nltk.pos_tag(nltk.regexp_tokenize(text,"[\w//,]+")) chunker=nltk.regexpparser(pattern) tree=chunker.parse(tags) #Parsing with the regexpparser from nltk for sub in tree.subtrees(): words=[] ifsub.label() in label: fori in sub.leaves(): words.append(i[0]) count=-1 fori in words: count=count+1 ifi in ent: phrase[' '.join(words)]=words[count] nnpc=1 break #Assimilation of information using dictionaries ifnnpc==0: count=-1 fori,j in nltk.pos_tag(words): count=count+1 if j=='nn' or j=='nns': phrase[' '.join(words)]=words[count] break VI RESULTS Following is an excerpt from our system taken as is Enter text the college was beautiful, Sam was running home, Sham was in class, Tom was after Jerry, the wind was blowing Enter question Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 20

47 Umma Khatuna Jannat et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, What was beautiful? College Enter question Who was running? Sham Where was Sham running? home Who was after Jerry? Tom What was the wind doing? Blowing Figure 3:Another example for who type question. Figure 1: Entering the text VII CONCLUSION Our project is a start towards something truly special where natural language is more than just a byte stream for a machine. As the amount of information available online is growing, the need to access it becomes increasingly important and the value of natural language processing applications becomes clear. Machine translation helps us conquer language barriers that we often encounter by translating technical manuals, support content or catalogs at a significantly reduced cost. The challenge with machine translation technologies is not in translating words, but in understanding the meaning of sentences to provide a true translation. We hope our program can take us one step closer to tackling this problem and provide a true understanding between man and machine. VIII REFERENCES [1] Clark P, Thompson J, and Porter B. "A knowledgebased approach to question answering". In Proceedings of AAAI 99 Fall Symposium on Question-Answering Systems, 1999, pp [2] Katz B. "Annotating the World Wide Web using natural language". In Proceedings of the 5th RIAO conference on Computer Assisted Information Searching on the Internet, 1997, pp Figure 2: One example for who type question [3] Chung H, Song YI, Han KS, Yoon DS, Lee JY, and Rim HC. "A practical QA System in Restricted Domains. In Workshop on Question Answering in Restricted Domains". 42nd Annual Meeting of the Association for Computational Linguistics (ACL), 2004, pp [4] Cai D, Dong Y, Lv D, Zhang G, Miao X."A Web-based Chinese question answering with answer validation". In Proceedings of IEEE International Conference on Natural Language Processing and Knowledge Engineering, pp , Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 21

48 Umma Khatuna Jannat et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, [5] Athira P. M., Sreeja M. and P. C. Reghuraj"Architecture of an Ontology-Based Domain-Specific Natural Language Question Answering System." Department of Computer Science and Engineering, Government Engineering College, Sreekrishnapuram, Palakkad, Kerala, India, [6] Pragisha K. design and implementation of a QA system in Malayalam. [7] Sanjay K Dwivedi, Vaishali Singh. "Research and reviews in question answering system" Department of Computer Science, B. B. A. University (A Central University) Luck now, Uttar Pradesh, , India. [8] Poonam Gupta, Vishal Gupta Assistant Professor, Computer Science & Engineering Department University Institute of Engineering & Technology Panjab University, Chandigarh. [9] Kolomiyets, Oleksander. And Moens, Marie-Francine. A survey on question answering technology from an information retrieval perspective. Journal of Information Sciences 181, DOI: /j.ins Elsevier. [10] Moreda, Paloma.,Llorens Hector., Saquete, Estela. And Palomar, Manuel. Combining semantic information in question answering systems Journal of Information Processing and Management 47, DOI: /j.ipm Elsevier. [11] Ko, Jeongwoo., Si, Luo., and Nyberg Eric. Combining evidence with a probabilistic framework for answer ranking and answer merging in question answering Journal: Information Processing and Management 46, DOI: /j.ipm Elsevier. [12]Pachpind Priyanka P, BornareHarshita N, KshirsagarRutumbhara B, Malve Ashish An Automatic Answering System Using Template Matching For Natural Language Questions.D BE Comp S.N.D COE & RC, YEOLA, Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 22

49 Volume 9, Special Issue No. 3, May 2018 International Journal of Advanced Research in Computer Science (ISSN: ) CONFERENCE PAPER Available Online at SENTIMENT ANALYSIS FOR PRODUCT REVIEWS Anil B, School of Computing and Information Technology, REVA University, Bangalore. India Aman Kumar Singh, School of Computing and Information Technology, REVA University, Bangalore. India Aditya Kumar Singh, School of Computing and Information Technology, REVA University, Bangalore. India Aman, School of Computing and Information Technology, REVA University, Bangalore. India SurekhaThota School of Computing and Information Technology, India REVA University, Bangalore. India ABSTRACT: Sentiment analysis or opinion mining is the study of extracting opinions and providing polarity to the pieces of text using DMT and NLP techniques respectively. Nowadays internet is used as a source of learning, getting reviews for various products or services, getting ideas. Millions of reviews are generated on the internet every day for a product. Because of the huge number of reviews, it is very difficult to handle and understand the reviews. Sentiment analysis is the research area which is used to extract the opinion from given review and classifying the polarity of the opinion using the process of NLP, computational linguistics, text analytics. There are many algorithms which are used to tackle NLP problems. In this paper, we have discussed different methods and we will concentrate on Vader for classifying and analyzing the reviews. We have collected the data for product reviews from Amazon, Flipkart, and Snapdeal. Perform the sentiment analyse on the reviews related to a particular product, compute the polarity score and finally provide a single line review of the product that helps the customer to easily decide whether to purchase the product. KEYWORDS: sentiment, analysis, DMT, NLP I. INTRODUCTION Sentiment is a thought, attitude or judgment that is expressed by feeling. Sentiment analysis is also known as opinion mining which studies people sentiment towards an entity. Sentiment analysis is the process of understanding and classifying the sentiments defined. The Internet is the best place for getting the data for sentiment analysis. From a user point of view, people express their views on various social media through blogs, forums or social networking sites. As we are concentrating on product reviews the user post their views about a certain product on Amazon, Flipkart, Snapdeal in the form of reviews. Per day millions of reviews are posted on social media or online shopping website, classifying and analyzing these reviews is a very difficult task to perform. We are using Vader lexicon which is a part of Natural language processing which is used to determine whether the sentiment if the review is positive, negative or neutral. Sentiment analysis does not depend on any platform or domain. Sentiment analysis is not only restricted to the product review or twitter tweets and can be used in social media network, healthcare, management and can be used by various organizations for their growth. Opinion mining has two methodologies namely sentiment analysis and sentiment classification. There are several flaws that hinder the process of sentiment analysis, some general flaws are people freely post their content so the quality of opinion cannot be guaranteed, online spammer post spam on websites some spams are meaningless at all while others are irrelevant, known as the fake opinion, the Ground truth is also not available. Ground truth refers to a tag of a certain opinion indicating whether it is positive, negative or neutral. Vader is a lexicon and sentiment analysis tool, vader.txt is a text file which is a data set used to give both polarity and intensity of sentiments which will solve the problem of ground truth. Manually creating sentiment lexicon is very intensive and error-prone process so it is no wonder the researcher and practitioner rely heavily on existing lexicon as a primary resource. Vader.lexicon.txt contains words and emoticons, each of which has been assigned with a polarity score, which will decide it is positive, negative or neutral. We have designed a web-based application which works on this sentiment analysis process. Our aim is to provide the genuine review of the product from certified buyers. we have provided the users with two options, first the user can select the product from our database and if the product is not present in our database the user has the option to copy paste the URL of product, using the URL we will do web scrapping and fetch Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 23

50 Anil B et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, the reviews of that particular product, and we will analyze and prompt the result with our generic review and the percentage of acceptance. If at all the polarity of the service review is in negative we will voluntarily inform by sending an to the respective seller as well as the website hosting that seller about the negative performance. II. LITERATURE SURVEY Sentiment analysis is done in two ways, one is machine learning and other is lexicon based approach. In lexicon based approach, includes dictionary based approach, in this there's a predefined dictionary which contains the polarity for each word. Here in the analysis every word is taken into account and polarity is calculated by using dictionary included. Biggest disadvantage of dictionary based approach is sentiment polarity is calculated based on words but not on the context level [1], which doesn't completely satisfy for the analysis. In Corpus based approach, which comes under lexicon based approach, this approach solves the problem of finding opinion words with context orientation. This method depends on the syntactic patterns which occur along side of opinion words [3]. Syntactic patterns are defined with the constraints like AND, OR, EITHER, HOWEVER, BUT and other such words. Statistical approach, as it uses statistical techniques, used to find co-occurrence pattern or opinion words. Klenner and Fahrni[4] proposed of extracting posterior polarity using the adjectives occurrence. Bose and Hue [6] considered as a fact that the reviews will be in completely random style as the customers were from different backgrounds. Semantic approach, values are computed on the principles of similarly between the words. This method is presented by Vossen and Marks [2], which analyses the relationship between the actors and subjects by the description of verbs, nouns and adjectives. Natural language processing techniques with lexicon approach, this is used to find the syntactical structures and help in finding semantic relationships. Park and Min [5] proposed on their paper that they used these NLP techniques to recognize time and tense expressions using few other mining techniques. They captured time expressions related to use of products and with time period in which they purchased. Romeo and Moreo[7] used lexicon based approach sentiment analysis algorithms after using Natural Language Processing techniques. They used specific modules of taxonomy lexicon which are specifically used for news sentiment analysis for the analysis of customer opinions on the topics of news items. III. METHODOLOGY The proposed methodology has below important steps. 1. Database of products reviews 2. Sentiment Sentences extraction and POS tag: 3. Polarity Score computation: 4. Web scrapping 1. Database of products reviews : Our aim was to provide flexibility to the customers, so that they can choose the reviews specified either on Amazon, Flipkart or Snapdeal websites. Hence gathering of reviews from these websites plays a crucial role. Using web scrapping, we have gathered the product reviews that belong to 5 major categories like books, laptop, mobile, wristwatch, and TV.These reviews are maintainedin a database for faster access. Number of product reviews that are stored in the database is shown infigure 1. FIGURE 1 NUMBER OF REVIEWS IN DATABASE Each review we extracted contains the following information: 1. Reviewer name 2. Product id, product name, and product images 3. Date of the review posted 4. Rating 5. Review text For the particular product, each of the retrieved review undergoes sentiment extraction and POS tagging. 2. Sentiment Sentences extraction and POS tag: We consider a sentence as sentiment sentence if it contains at least one positive or negative word. All the word present in the sentence is first tokenized into separate English words. Every word in the sentence have some role which describes how the word is used, these roles are known as part of speech i.e., verb, noun, pronoun, adjectives, adverbs, preposition, conjunction and interjection. POS Tagger plays a very important role in sentiment analysis because of the following reason: 1. Noun and pronoun do not have any sentiment usually 2. It is also used to distinguish the word that can be used in different POS 3. Polarity Score computation: Using Vardar lexicon, we will assign a polarity to each of the tokenized English words. Then it will calculate the polarity for the whole review text, which we consider as sentence polarity. The sentence polarity can either be positive or negative. The range of positive polarity is between 0 to +1 and range of negative is between 0 to -1. Now we will calculate the compound polarity using the mathematical formula: CP= SSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSS NNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNN This compound polarity is used to depict the bar graph present in our result page.figure 2depicts how bar graph varies based on CP. Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 24

51 Anil B et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, FIGURE 2 CP AS BAR GRAPH Our resultant rating will rate the product between 0 to 100 percent, which is graphically represented by a bar graph containing one generic review. 4. Web scraping: If the product is not found in our database the user will get a prompt, where the user has to provide the URL of the product from any of the three websites namely Amazon, Flipkart, and Snapdeal. In the back end, an algorithm will scrap the reviews of the product from the provided URL. The flow chart of web scrapping is shown in Figure 3. FIGURE 4 CGI SCRIPT The overall flow of sentiment analysis is depicted in Figure 5 FIGURE 3 WEB SCRAPPING PROCESS IV. System design: We have designed our system using platforms such as: 1. Python 2. HTML, CSS 3. JavaScript, Bootstrap For data collection, we have used webharvy tool to extract the reviews in CSVformat. These CSV files are then read by panda module which will, in turn, provide dataset, which is a collection of reviews. These datasets are the processed by NLTK Module to give a compound polarity. We have used NLTK module of python for sentiment analysis and CGI module for web development whose flow is depicted in Figure 4 FIGURE 5SYSTEM FLOW OF PRODUCT REVIEW V. RESULT The main aim to develop this system is to provide the user with genuine review and to reduce the time for the user to decide whether the product is worth or not. Advantages of using sentiment analysis for reviews: If at all the user is going to check the reviews from many websites, it will consume more time for them to understand Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 25

52 Anil B et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, and decide whether the product is worth enough for them to buy or not. It is also not possible to read and analyze each and every review from different sites, reading few of the reviews to decide whether the product is worth or not may end up in taking a wrong decision. By using sentiment analysis the user will get the promising result without investing much time in searching for the reviews which is depicted in Figure 6. It will save time and effort and can decide just by looking at the compound polarity and generic one line review. FIGURE 6 REVIEW ANALYSIS OF PROPOSED METHODOLGY VI. CONCLUSION In this paper, we have conducted a schematic study on sentiment analysis on product reviews. The task of sentiment analysis, especially in the field of product reviewing is in nurture state and would take some time to become fully fledged into its domain. Right now, we have worked with only the very simplest of algorithms among many like Naive Bayes, SVM etc..., we can enhance our model by adding extra information like the closeness of the word to all its superlative degrees to make our result more precise and effective. After analyzing some of the research domain in SA, it is believed that SA algorithms and SA techniques are still in an open field of research. The later model could be considered as reference model which can be used to compare with the proposed model. Sentiment analysis uses information from micro-blogs, e- commerce, newsgroup etc. for market research to build a business intelligence to understand the subjective reason why the consumer is or not responding to something (ex. why the particular group of people buying only certain company product? What companies should do to tackle this situation?). Collected reviews from the different site could be the solution to this and can help companies to think in right directions. Customers often use sentiment analysis to automatically sort their opinion, to identify the best product for their use. As customer becomes more or more automated through Machine Learning understanding the sentiment of given case to its root level becomes very important. As per developers and Business perspective view it creates a boom in the tech market, where developers create an understanding of public interest and business leaders try to produce 360 views of their brand products. Our work relies on all these parameters; its aim is to give one line segregated review of the user-selected product. As with advancement in many other fields, it is believed that introduction of deep learning in SENTIMENT ANALYSIS could produce a cutting-edge application to ease the task. REFERENCES [1] QiuGuang, He Xiaofei, Zhang Feng, Shi Yuan, Bu Jiajun, Chen Chun. DASA: dissatisfaction-oriented advertising based on sentiment analysis. Expert SystAppl 2010 [2] Maks Isa, VossenPiek. A lexicon model for deep sentiment analysis and opinion mining applications. Decis Support Syst [3] Hatzivassiloglou V, McKeown K. Predicting the semantic orientation of adjectives. In: Proceedings of annual meeting of the Association for Computational Linguistics (ACL 97); [4]Fahrni A, Klenner M. Old wine or warm beer: targetspecific sentiment analysis of adjectives. In: Proceedings of the symposium on affective language in human and machine, AISB; 2008 [5] Min Hye-Jin, Park Jong C. Identifying helpful reviews based on customer s mentions about experiences. Expert SystAppl [6] Hu Nan, Bose Indranil, KohNoi Sian, Liu Ling. Manipulation of online reviews: an analysis of ratings, readability, and sentiments. Decis Support System [7] Moreo A, Romero M, Castro JL, Zurita JM. Lexicon-based comments-oriented news sentiment analyzer system. Expert SystAppl 2012 Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 26

53 Volume 9, Special Issue No. 3, May 2018 International Journal of Advanced Research in Computer Science (ISSN: ) CONFERENCE PAPER Available Online at SPATIAL MOVIE PREDICTION USING CONGLOMERATION OF ONLINE DATA Karan B Yajaman, School of Computing and Information Technology Reva University, Bangalore Vemuri Venkata Lithin Kumar, School of Computing and Information Technology Reva University, Bangalore Raghavendra Nayaka P School of Computing and Information Technology Reva University, Bangalore Madhura K.V, School of Computing and Information Technology Reva University, Bangalore Mary Sheetal A.N School of Computing and Information Technology Reva University, Bangalore Abstract: The entire focus of this project is a use big data and datamining techniques a gather information of a movie from YouTube, Twitter and IMDB a predict its success rates. It also focuses on which part of the world the movie is more likely a not get a good response and hence enable advertising in such areas a better the performance of the movie. The above goals are met but using text analysis and text mining, lexicon methods, use of bi-grams and tri grams and Geo-spatial mining of global tweets. Keywords: Twitter, YouTube, IMDB, location-analysis, lexicon, bi-grams, tri-grams, sentiment-analysis. 1.1 INTRODUCTION Demand for opinions and sentiments What people think about a product or a state of affairs has continuously been a significant piece of material for most of us throughout the choice-making procedure. Long before consciousness of the World Wide Web became prevalent; many of us requested our friends a recommend a vehicle mechanic or a clarify who they were preparing a vote for in their local elections, asked for reference letters concerning job candidates from coworkers, or referred Consumer Information a choose what mobile phone a purchase. But the Internet and the Web have now (amongst other things) made it probable a discover out about the sentiments and familiarities of those in the large amount of people that are neither our private associates nor well-known specialized critics that is, individuals we have never heard of. And contrariwise, more and more individuals are creating their feelings accessible a strangers via the Internet. 1.2 Twitter Twitter was started up on July 13, Twitter is an enormously widespread micro blogging provision. It has a very huge set of users, consisting of numerous millions of users (305 million unique Twitter users as of January 2016). It can be well-thought-out a focused social network, where every user has a list of subscribers which is popularly known as followers. Every user posts their statuses often also known as tweets, these statuses consist of small messages of full size of 140 characters. These statuses typically contain private information about the users or they can even consist of news or links a article contents such as pictures and videos. rovoking posts and links all around Twitter. Twitter has fascinated lots of attention from companies for the enormous potential it offers for viral advertising. Due a its enormous spread, Twitter is progressively used by news establishments a filter news obtained by the public. 1.3 Social Media in predicting Movie success Social media has burst ina a group of wired discourse where persons create content such as textual, images or videos, share it and save it at an extraordinary rate. Examples comprise of sites such as Myspace, Facebook, Twitter and Digg. Because of its comfort of usage and spread, social media is quickly altering the community discourse in culture and setting fashions in themes that range from the atmosphere and government policies a science and technology and the show business industry. Since social media can also be interpreted as a form of communal wisdom, we opted a examine its influence at forecasting real world consequences. Asanishingly, we revealed that the conversation of a community can be used a create measurable forecasts that outperform the results of simulated markets. These usually include the interchange of state depending securities, and if big enough and correctly designed, they are extra accurate than other methods for mining diffused information, such as reviews and opinion surveys. In the circumstance of social media, the huge data availability and high difference of the information that spreads through large user societies offers an exciting chance for connecting that data that permits for exact forecasts about specific consequences, without having a establish market mechanisms. The real-world consequences can be effortlessly detected from box-office income for movies. Our objectives are as follows. Movie creaars spend a lot of energy and cash in propagation of their movies and have also incorporated the Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 27

54 Karan B Yajaman et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, Twitter service for this resolution. We focus on the methodology of viral promotion and prerelease publicity on Twitter, and the part that attention plays in predicting box office revenues. Our theory is that movies that are more and often well-spoken about will have a higher chance of being watched. Next, we examine how opinions are created, the positive and negative opinions propagation and how they impact people. For an underperforming movie, the initial evaluations could be sufficient a dishearten others from viewing it; on the other hand, it is likely for interest a be created by optimistic reviews and sentiments over time. For this purpose, we execute sentiment analysis on the information, using text classifiers a differentiate positive leaning tweets from the negative. Our chief conclusions are as follows: We demonstrate that social media data can be operational pointers of real world performance. Our examination of the emotion content in the tweets displays that they can help progress box-office revenue forecasts based on tweet degrees even before the movie is released 2 EXCISTING WORKS 2.1 Prediction Markets Forecast markets relate payo ffs with the consequences of upcoming events. The objective is a have the payoffs a be related with the probability of the consequences based on the information and visions of the members. As Wolfers and Zitzewitz say, there are three chief types of forecast markets. First up is a winner-take-all market where an agreement reimbursement a fixed sum only if some happening occurs. The value for the agreement varies with how probable people think the occasion will happen. Thus, it exposes the market anticipation that the event will happen. Index market is the second type of the market. Here, the agreement pays in some constant style based on a metric, such as the fraction vote that an applicant will obtain. In this circumstance, the market is forecasting the probable value of the metric. Lastly, the third type of prediction markets is spread betting, where contribuars bid on a threshold that specifies whether or not an occasion occurs. 2.2 Prediction Movie Success Cinemas provide a motivating and more measured sandbox for forecast algorithms. Unlike many other areas, cinemas can be more effortlessly associated a each other because they have certain characteristic standardization, i.e. they can be associated by achievement in the nth week even if the release times of these movies are different. Though they ao have numerous recognized and concealed issues, many significant issues affecting their accomplishment are more community and judgment related than for instance facars affecting the standard business. Unlike sack forecast, there are strong goals and a strong time line for picture success predictions. Also contrasting sacks, the pictures success is much more unswervingly affected by the overall belief and opinions of the public, as these are the same persons that go watch the cinemas and hence donate straight a their achievement or absence thereof. The sentiments of the chance movie goer can be communal with others and may essentially ffect a whether other individuals will or will not go see the same picture founded on the evaluation Using Blogs Cinemas have an identified release date, blogs hypothetically provide a great medium a debate and size the hype surroundinga picture before and after it is released. They filtered the highest 300 pictures from 2008 in terms of income and filtered out pictures with mutual word designations that were probable a activate untrue positives when calculating reference atals in blogs. They produced 120 structures with a foundation in the subsequent groups: picture reference amounts in blogs, reference counts bearing in mind the position and in-degree of the blogs, features restricted by a time range, structures considering only optimistic posts, structures addressing spam, and mixtures of all of these. The reference sums considered issues such as the reference appearing in the heading of the post. Their blog standings biased highly ranked blog mentions higher as well as mentions in blogs with greater in-degrees, a metric comparable a page rank. Their time range analysis detached structures by week from 5 weeks previously a release a 5 weeks afterwards a try a apprehend the buzz every week. In respects a sentiment analysis, they used Ling Pipe a achieve hierarchical classification on the five sentences around the movie reference a determine optimistic posts in one method and including rejection posts in another. In order a avoid spam posts, they filtered the posts based on the length of the post specifically a avoid extremely small posts. They assessed their structures using Pearson s association and Kullback-Leibler deviation against a few different consequence variables Using the News Having observed the foretelling power of the television, Zhang and Skiena pursued a examine the effect of news on film performance. They sought a display that by means of news statistics which is informative, commercially fruitful movies, performers, and executives all enjoy momenaus media publicity. The structure Lydia was used a examine the text of articles obtained in news. Likened a the Sadikov et al. study, they used pictures traversing from 1960 a 2008 and functioned with 498 pictures in whole. They also assed out cinemas with picture titles comprising very common words that lead a untrue positives after using Lydia. The structure allowed them a combine not only news mention counts but also optimistic and undesirable sentiment data on the cinemas from the articles in the news. They looked at news surrounding picture titles, executives, the apmost 3 acars and apmost 15 acars 1 week, a month, and 4 months before each picture s release time. They constructed both regression simulations as well as k-nearest-neighbor (KNN) simulations. For the regression representations, they tested for multilinear associations between financial plan, MPAA rating, opening screens, and categories and the picture s atal revenue. They also attempted construction of the model while parting financial plan out. In Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 28

55 Karan B Yajaman et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, accumulation a this, they attempted using KNN under the hypothesis that comparable pictures will have alike earns. Thus, they planned the spaces between cinemas based on their standards in feature space (the structures are the similar variables listed for progression) and regulate which cinemas clump collected ina crowds with comparable characters. Cinemas in the testing set with comparable characters as cinemas in the training set should hypothetically achieve similarly. While KNN with only a single neighbor completed poorly, they developed decent consequences by means of around seven of the closest neighbors. Zhang and Skiena specified that picture news mentions were extremely connected with movie earns. Sentiment measures also connected fine. They also specified that models founded purely on news might accomplish on parity with representations founded on IMDb information, chiefly for extraordinarily earning cinemas. Conjoining the news information with the IMDb information paved way a the finest results. Generally, regression functioned improved for short grossing cinemas and KNN functioned improved for extraordinarily earning cinemas. Lastly, they specified that article counts functioned well generally, but news emotion only accomplished well in the KNN modeling methods. Zhang and Skiena also state their methods deliver a big boost over preceding work since they can make forecasts before the picture release. Some models require up a a few months postrelease a make precise forecasts. Being able a foresee prerelease positively makes the forecaster more eye-catching. 2.4 Using Social Network and Sentiment Analyses Portion of the theory of the development is that social network position helps a forecast movie accomplishment as discussed by Gloor et al.. They produced social networks for the cinemas in three ways: using web hunts, using blog explorations, and using advertisements on movie forums. The network is constructed for example by Googling an applicable expression such as The Revenant Movie 2016, and then Googling for the sheets that connect a the highest 10 hits on the initial search. This can be done repeatedly a few intervals a produce a large chart in which the nodes are websites and edges are the relations among websites as given from repeated explorations for pages that connect a the apmost ten results of the preceding search. In accumulation a computing the network position of the picture title itself, Gloor et al. achieved opinion analysis on IMDb forums a collect the overall mood in the direction of a picture. They used tags a classify references a the picture title or condensed references thereof inside a post. Lists of optimistic and destructive words were then used a govern the general emotion of the post awards the picture using typical information recovering algorithms such as term frequencyinverse document frequency. They also built a network by means of the post writers so that their between-ness importance, i.e. social network position, could be considered. The positivity and pessimism of a post were biased using the between-ness of its writer, thus weighting a more significant poster s assistance more seriously in the complete sentiment score computation. Gloor et al. postulated that uniting the sentiment in the direction of a picture with its between-ness must in theory give a forecast about not only the overall sentiment about the movie, but also the greatness of the feeling. Krauss et al. delivered a justification of the foretelling value of between-ness and emotion in regard a cinema properties in their Oscar forecast paper. They used the Oscar craze and buzz forum on IMDb and web as well as blog explorations a forecast which cinemas would successfully win Oscars and which would achieve well in the box office. Five of the seven cinemas ranked extremely by their procedure received Oscars, while another movie obtained a nomination and the final movie in their list received nothing. They also stated that cinemas with a high level of optimistic discussion achieved well in the box office. Yafeng Lu et al [23] have noted that a key logical task across many areas is model building and examination for predictive analysis. Information is collected, analyzed for relationships and structures are selected and charted a estimate the reaction of a classification under examination. As social media information has developed a be more copious, information can be apprehended that may possibly represent social patterns in people. In turn, this amorphous social media data can be analyzed and combined as a key feature for predictive intelligence. They present an outline for the expansion of predictive analytics exploiting social media information. They associate feature selection mechanisms, resemblance evaluations and model cross authentication through a diversity of interactive imaginings a provision analysts in model construction and prediction. A discover how predictions could be accomplished in such a context, they present outcomes from a user study concentrating on social media information as a forecaster for movie box-office accomplishment. Their outline focuses on assimilating multiple source information from social media for exploration and forecast. They combine trend examination, sentiment analysis, resemblance metrics and feature selection for the creation of the model, evaluation and forecast. A assess this framework, they arranged their implementation a the difficulty of weekend box-office forecast. They combined information from IMDB, Twitter and YouTube and discover this information across a diversity of visual analytics modules. The structure was built using D3, JSON, R and WEKA. The usage of R and WEKA permitted for straight addition of multivariate regression as well as support vecar machines, whereas D3 was used a generate diagrams and visuals for the communicative visualization. Client-server style was selected in order a permit easy convenience and testing of the structure across stages, and they also explored the usage of Amazon cloud services. They used the Jersey RESTful web service a permit the interface among the backend server and the web interface. Preprocessing was completed for the processes of sentiment analysis as well as word occurrence sums and nearly collaborative rates are found for visualizing the information. By nearly collaborative, they mean that if the information is accumulated, the visualizations can be efficient at a value higher than 10 frames per second (FPS), if the information is not accumulated then the user will see a delay symbol and characteristically understand a 5 second lag on the initial query, after which the examination of that picture s structures will be at collaborative frame rates. Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 29

56 Karan B Yajaman et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, Drawbacks Though there are many advances in the field of movie success prediction with more data being fed a predict the movie success based on variety of facars including the status of the acar, the direcar, the investment in the movie and so on. But there has been very little advancement in the field of the movie success prediction based on comparative geo location success. For example, the success of the movie in India compared a UK or the movie success in Akyo compared a London. These facars are very beneficial for the geo location-based marketing as well as the hype creation of the movie. We aim a provide such a framework which can predict the success of a picture based on the location in comparison a another location. There are not many current approaches that effectively perform this kind of comparison with respect a different locations because it had not been effective a obtain the location of a person precisely when he or she posts some information. The location of the user can be obtained but if the used had moved from one place a another place, say for instance Delhi a Bombay, the result would show that the tweet was from Delhi instead of Bombay. These features limited geo location-based access of such online data. 3.PROPOSED METHOD With the usage of geo location tagging which is a feature enabled in every smartphone released of late, the user can easily present their location along with their posts. We use this feature a perform sentiment analysis only on the data obtained from particular locations. With the feature enabled, the user s exact location i.e. the latitude and the longitude at the time of posting the online data will be recorded. This can be obtained and hence we only query the particular locations in this way which provides us with an improved data set mined specifically from a location in order a perform sentiment analysis over the data. A add more accuracy, we even consider the YouTube data such as the view count, the likes as well as dislikes count and the comments themselves along with IMDB data such as the IMDB and MetaScore rating a predict the success of the movie. 3.1 Advantages Project accurately predicts the success rates of movies from location a location. It compares the different expectations of the movie from one place a another. It counts the most used words for expressing the outlook of the movie. 4.METHADOLOGY THE MODULES THAT WILL BE DESCRIBED IN THIS CHAPTER ARE AS FOLLOWS: Accessing the Data Sentiment Analysis Final Results 4.1 Accessing the Data This section of the chapter consists of the details about how data has been accessed by queries awards YouTube, IMDB and Twitter YouTube Data Analytics URL youtstats=new URL(" outubelink+"&key= +key+ &part=statistics,snippet"); The above code specifies the URL used a perform the YouTube Data Analytics. The URL consists of the YouTube API V3 in its web form which provides results in a JSON array. The URL consists of a section named youtubelink which consists of the YouTube link ID. For example, with the YouTube link of a trailer of a movie being: means that the youtubelink section of the URL only consists of the final part after v= i.e. gttfd6tisfw. The next important section of the URL is key which is the secret key provided for each user separately who registers for the YouTube developers access after enabling each kind of Data Analytics within YouTube required. The final section of the URL is the snippets and statistics which specify what sorts of data are needed in the JSON response. The snippets and statistics specify only the view count, the dislike count, the like count and the comments in the video along with the date on which the video was uploaded on. The above code was specified in try and catch exceptions a specify errors such as internet connection lost or website cannot be accessed is used a obtain YouTube Data regarding a video. The YouTube Reporting as well as the APIs for YouTube Analytics lets the user retrieve data related a a particular video, playlist, channel using YouTube Analytics information a mechanize multifaceted recording tasks, construct cusam control panel, and much more. The Reporting API provisions applications that can provide with information and accumulate majority reports, then deliver implements a filter, sort, and source the data. The Analytics API provisions directed and real-time enquiries a create cusam intelligences in reply a user communication. The following code explains in brief the JSON values obtained from YouTube and hence convert it ina their respective string as well as numerical formats. String jsonstring=sy.astring(); The string result obtained from the YouTube API response is first converted ina a JSON string in order a be parsed ina a JSON array which can be iterated through. Since they have key value pairs, it is essential that they can be obtained in the easiest way possible. JsonParser jparser= Json.createParser(new ByteArrayInputStream(jsonString.getBytes())) ; We have used JSON Parser function of the external library provided by JSON for Java and parsed a complete JSON array ina individual bits which are in the form of key and value pairs and can be iterated throughout until there are keys within the JSON Parsed array. The values that we require are particularly searched throughout the iteration from the first value of the Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 30

57 Karan B Yajaman et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, JSON parsed array a the last value which is done as follows: while(jparser.hasnext()) if(jparser.getstring().equals("viewcount")) {jparser.next(); String temp=(jparser.getstring()); viewcount=integer.parseint(newstr); } The above code iterates throughout the entire JSON array and obtains only the value which is related a the key viewcount. This is similarly performed for other keys such as likecount, dislikecount and productiondate. This provides all the data needed for the YouTube Data analytics and the comments from YouTube Trailer video is extracted in a similar way. long hitval=viewcount/days*30; if(hitval> ) youtres.append("the movie is predicted a be a Box Office Super Hit"); The hitval represents the YouTube Hit Ratio i.e. the view counts for 30 days. The viewcount value i.e. the number of times the video is viewed is divided by the days between aday and the day the video was actually published on and multiplied with thirty. This gives the view count per month. If this particular value is greater than 3.5 million, the movie is predicted a be a box office hit based solely on the view count whereas the sentiment analysis and further analysis comes up later on in the application. Similarly, the hit value would represent average hit and flop as well IMDB Data Analytics URL imdbdata = new URL(" The above URL whose results are obtained in JSON array as well provide the IMDB details such as the Title of the movie, it s poster, it s plot, acar list as well as the details such as the MetaScore rating, IMDB rating as well. The results provided by OMDB api are user donated which are used in our application a obtain IMDB details since IMDB itself does not allow such data extraction. A make the user s work the easiest as possible, we have not used many inputs a be entered by the user. Hence the OMDB api provides the best way a provide all sorts of details from IMDB including the ratings as well as plots and the poster itself Twitter Data Analytics TwitterFacary tf = new TwitterFacary(cb.build()); Twitter twitter = tf.getinstance(); Query query = new Query(movName); query.setcount(2); The configuration builder has been used by utilizing the Twitter4J which is a purely Java based Twitter Analysis library. Once the configuration has been built with the appropriate authentication key, code and the consumer secret key and code, the application connects a twitter through Twitter4J which provides an easy implementation a perform Twitter analysis. The Twitter4J has a simple query system which initially sets the query length that is limited a a hundred tweets at a time. Hence we have performed the analysis a obtain twitter data from time a time at regular intervals of time. query.setgeocode(new GeoLocation(40.71,-74.00), 150, Query.KILOMETERS); The above line is a part of the setting the parameters of the query for Twitter Data Analytics. It sets the radius for the location from which the Tweets are a be obtained. There are three parameters in the setgeocode query for obtaining Tweets from a specific location only. The first parameter is the GeoLocation which specifies the latitude followed by the longitude of the location. For instance, the above example is set for New York. The second parameter specifies the radius of the location in kilometers which has been set a 150 in the above instance and the third parameter specifies the unit of the second parameter which can either be set in kilometers or meters. QueryResult result = twitter.search(query); The above line of code performs the actual query regarding the search for a hashtag in a particular location in Twitter. After setting the query parameters such as the query term, the location and the amount of results a be obtained, twitter.search(query) where twitter is an instance of TwitterFacary queries Twitter for the particular keyword obtained only in the specified location as long as the user of the phone has allowed geo tagging within his or her application. for (Status status : result.gettweets()) { a=status.getcreatedat(); tweets[i]=status.gettext(); } The for loop presented above is for each and every tweet obtained. For every such tweet obtained, we can also obtain details such as the created time of the tweet, the number of retweets, the number of favorites and even information such as the text itself of the tweet, the user profile, the location of the user and so on. This provides a better approach a perform such analysis with the wide array of data provided by Twitter a analyze. 4.2 Sentiment Analysis The sentences obtained from Twitter are separated and each one is analyzed independent of each other. This analysis is performed by utilizing a Naïve Bayes Sentiment Analysis algorithm with a word comparison unit which consists of over 5000 positive and negative words specifically classified for the purpose of the success prediction of the movies. Along with the single word comparison for each of the word present in a tweet which are accumulated agether a provide the result, we have also utilized bi-grams and tri-grams set of words which are commonly used a perform sentiment analysis. An example of such a bi-gram is shown below which specifies the bi-gram of get ready which for instance can be in a sentence such as Get Ready for the release of the movie! if(splitted[j].equals("get")&&splitted[j+1].equals("ready")) The sentences obtained from Twitter are separated and each one is analyzed independently. 4.3 Final Results The final visual results are shown by utilizing the programming of JavaFX which provides a smooth and fluid way a represent text and images and even graphical items on the screen. Along with just the UI being made by utilizing JavaFX, we have also utilized the combination of the results obtained from YouTube, IMDB as well as Twitter a show the results i.e. the comparative success prediction of movies between different locations. The YouTube data such as the created date of video, views per month as well as the created username in order a distinguish between overlapped areas of circles which may occur when two circles specified in geolocation for a country such as United States of America Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 31

58 Karan B Yajaman et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, overlaps within itself where many such circles are present. We have also performed sentiment analysis on the YouTube comments as well as the Twitter data providing their results. The IMDB analysis focuses on obtaining the title, the plot of the movie along with the metascore as well as IMDB ratings which are often termed as the major rating of a movie overall as every movie is rated in IMDB based on various facars by critics and demographics. Utilizing these results provided by the fluid and clear UI of JavaFX provides a great way of providing the output as well as input screen using Eclipse as the IDE. 4.3 Results In this part we have effectively been able a predict the movie s success rate at UK and US and compare their expectation We also compared the results for the movies between London and New York and have made graphs depicting the positive, negative and neutral comments from twitter. This helps movie producers a find out where the movie might not do so well and advertise more for its success in those areas. REFERENCES 1. comscore/the Kelsey group. Online consumer-generated reviews have significant impact on offline purchase behavior. Press Release, November John A. Horrigan. Online shopping. Pew Internet & American Life Project Report, Paul Hitlin and Lee Rainie. The use of online reputation and rating systems. Pew Internet & AmericanLife Project Memo, Ocaber Lee Rainie and John Horrigan. Election 2006 online. Pew Internet & American Life Project Report, January Thomas Hoffman. Online reputation management is hot but is it ethical? Computerworld, February D. M. Pennock, S. Lawrence, C. L. Giles, and A. F. Nielsen. The real power of artificial markets. Science, 291(5506): , Jan Kay-Yut Chen, Leslie R. Fine and Bernardo A. Huberman. Predicting the Future. Information Systems Frontiers, 5(1):47 61, Jure Leskovec, Lada A. Adamic and Bernardo A. Huberman. The dynamics of viral marketing. In Proceedings of the 7th ACM Conference on Electronic Commerce, B. Jansen, M. Zhang, K. Sobel, and A. Chowdury. Twitter power: Tweets as electronic word of mouth. Journal of the American Society for Information Science and Technology, D. M. Pennock, S. Lawrence, C. L. Giles, and F. A. Nielsen. The real power of artificial markets. Science, 291(5506): , Jan Thomas Malone. What is collective intelligence?, October is collective intelligence. 12. Eric Bonabeau. Decisions 2.0: The power of collective intelligence. MIT Sloan Management Review, January J. Wolfers and E. Zitzewitz. Prediction markets. Journal of Economic Perspectives, 18(2): , Joyce Berg, Robert Forsythe, Forrest Nelson, and Thomas Rietz. Results from a dozen years of election futures markets research. In Charles Plott and Vernon Smith, ediars, Handbook of Experimental Economic Results. Elsvier, Amsterdam, 2001 Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 32

59 Volume 9, Special Issue No. 3, May 2018 International Journal of Advanced Research in Computer Science (ISSN: ) CONFERENCE PAPER Available Online at USING SENTIMENT ANALYSIS FOR WEBSITE EVALUATION Adarsh M Revadi School of Computing and Information Technology REVA University Bengaluru, India adarshmr.05@gmail.com Ashish N Mehta School of Computing and Information Technology REVA University Bengaluru, India ashishmehta731@gmail.com Apoorva DA School of Computing and Information Technology REVA University Bengaluru, India apoorvaanandgowda@gmail.com B Vamsi School of Computing and Information Technology REVA University Bengaluru, India vamsibandaru1997@gmail.com Sowmya Sundari LK School of Computing and Information Technology REVA University Bengaluru, India sowmyakkr@gmail.com Abstract: In thepresent era of technological advancements, evaluating the authenticity of a website is the need of the hour. Thispaper discusses our solution to design an efficient Website Evaluation system that rates websites on the basis of user comments. We use HTML, CSS, ASP.net as front end, SQL Management Studio as backend and use Natural Language Processing to implement the solution. Parameters for evaluation include authenticity of the website, efficiency in delivering the services as advertised and providinggood customer support. A database of keywords constantly updated by the admin will be used to evaluate the website and users will be able to see the rating of every website added by the admin. Keywords: Natural Language Processing, Data Mining, Sentiment Analysis I. INTRODUCTION The evaluation of a website is very important to know which websites are fraudulent and which ones are trustworthy. As many of the internet users use online transactions, their data is at risk if they registerand divulge their personal details on fraudulent websites. By using our solution, a user can easily share their review about the website. The algorithm will rate the website based on comments of various users. It matches the keywords in thedatabase and will rate the website based on Sentiment Analysis. Since the algorithm ranks the website based on weightage of keywords in database, the result is accurate. II. RISKSPOSEDBYFRAUDULENTWEBSITES It is no secret that while the advancement of technology has largely impacted our lives in a positive manner, even negative forces have come into play resulting in serious issues like online fraud, hacking, phishing, cyber bullying etc. An overview of the risks posed by such fraudulent websites is presented here. [10] A. Internet Fraud Any fraudulent activity taking place over the internet is an example of Internet Fraud. One of the most common occurrences of this type of fraud is fake advertising. Scammers post lucrative ads for high end vehicles or gadgets at extremely tempting prices on online ad portals such as OLX, Quikr, ebay etc. Unsuspecting buyers agree to purchase the product for which they are told to wire a certain amount to a third party as shipping or processing fees. Once, the payment has been made, the seller changes their correspondence details and erases their online presence much to the dismay of the buyers. Other types of Internet Fraud include Charity frauds, Lottery frauds, Gambling frauds etc. [6] B. Hacking Hacking is the process of gaining illegal and unauthorized access to an individual s online accounts such as Facebook, Twitter, Gmail etc. People generally fall prey to such hacking episodes because they make the mistake of revealing sensitive information such as their online account passwords on other fraudulent websites which are on the lookout to indulge in nefarious activities based on this sensitive information. The fallout of such incidents is Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 33

60 Adarsh M Revadi et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, maligning an individual s reputation by posting unwarranted posts, sending inflammatory s posing as the victim or even changing the password and providing the new password to the victim in exchange for a sum of money. [8] C. Spamming Another issue that needs immediate attention is the act of spamming. This process involves sending a barrage of unrelated or unwanted advertisements to a person s account by advertisers seeking to increase their customer base. They get access to an individual s id mainly as a result of the individual providing their id on spamming websites or while filling out an online form. While this is not as serious as other issues, it still needs to be resolved at the earliest to ensure the user is not being harassed online. [9] 6) Rating Calculation: The algorithm looks into the reviews and tries to find words that match with the keyword in the database on the basis of which the ratings are generated. The following use-case diagram gives a diagrammatic representation of the Modules. D. Cyber Bullying Cyber Bullying may be defined as onlinebullying and harassment taking place over digital devices like smartphones, personal computers and tablets. Cyber Bullying is rampant on Social media sites like Facebook, Twitter, Instagram, Snapchat etc. It involves sending hateful and derogatory messages on these online platforms which include, but not limited to body shaming and gender stereotyping. It can also include sharing personal details of a person and online blackmailing. [7] III. METHODOLOGY FOR WESBITE EVALUATION The basis of the methodology used in our project is making use of two modules, namely the User Module and the Admin Module. The Admin is tasked with adding websites for the users to rate and adding relevant keywords to the database on the basis of which the websites are rated. The users can review the websites based on parameters such as authenticity, timely delivery of the products, customer support etc. and view the ratings of other websites reviewed by other users. The module concept is explained in detail below. [1][4][5] A. Modules The various Modules along with their description are provided below. 1) Admin Login: The Admin logs in using an ID and password. He is responsible for adding websites for users to rate. 2) Add Keyword:Admin adds relevant keywords to the database. 3) User Login/Register:A user must first register and then login to review different websites. 4) Comment:The users comments are used for ranking the website by assigning a particular score and giving the rating of the websites based on this score. 5) Viewing the website rating: The user can view website ratings as well as other users comments. Fig. 1. Use-Case Diagram of the Modules B. Analysis of Data The whole essence of our project is captured in the fact that we need to obtain reviews from users. Fundamentally, there are two kinds of reviews: Single word reviews and multi-word reviews. Therefore, we needed to create two separates tables in the database; one for storing single word reviews and the other for storing multi-word reviews. The admin can access and add more words to the two respective tables in the database to capture all kinds of reviews, both positive and negative. Hence, we have created two tables, mkey and skey in the database; skey stores single word reviews and mkey stores multi-word reviews. The two tables contain three columns each; the first column to store the word or a phrase in case of multi-word reviews; the second column which assigns a score to the review and the third column which is a flag and is set to 0 by default and changes to 1 whenever it comes across a single word review and changes to 2 when it comes across a multi-word review.for positive reviews, we add a positive score in the score column of either the skey/mkey table depending on the nature of the review (single word or multi-word reviews) and we add a negative score for negative reviews. This is clearly illustrated in the two figures below. [3] Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 34

61 Adarsh M Revadi et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, reputation of a website by its competitors. Other aspects of improvements are to restrict the number of reviews a particular user can provide to a particular website and to also link the person s account with any one of his Social- Networking accounts for having a more secure level of identity of that person. Fig. 2. mkey table showing mutli-word reviews C. Results of the Analysis The reviews have now been successfully segregated into single word and multi-word tables. The challenge is to now use the keywords to generate a rating. This is achieved by calculating the cumulative sum of the positive and negative scores. The cumulative sums are given a rating by using 9- point increments starting from 0 up to a sum of 39. Anything less than or equal to a score of 0 is placed in the category No Rating. For instance, a website with a cumulative sum (based on positive and negative reviews) of 25 will be rated 3 stars since it falls in the third 9-point increment which is between 20 and 29. Every website with a sum greater than 39 is given a 5-star rating. [2] IV. FUTURE SCOPE AND ENHANCEMENT As technology keeps progressing rapidly in leaps and bounds, the world we know today would have completely changed and pretty much everything would be available at the click of a button. As technology keeps playing a bigger role in our lives with each passing second and continues to impact us in a positive manner, even the negative elements such as fraudsters and online scammers would develop more comprehensive and fail-proof systems that would lie undetected while scamming innocent customers and clients who had put full faith in the system while divulging their personal details. Hence, going into the future, the need of the hour would be to design more secure systems that can detect fraudulent websites with increasing levels of accuracy. The most obvious method to detect such fraudulent websites would be to obtain the reviews of people who have had negative experiences and develop a rating system that can warn potential clients of a particular website in case the website turns out to be fraudulent. Like with all solutions, our solution is not perfect. The main aspects where we can improve on is to detect fake reviews that could be used to potentially malign the V. CONCLUSION In a technologically advancing world, it is of utmost importance to design a system that can accurately rate websites and keep a tab of authentic websites and separate it from inauthentic and fraudulent websites.these fraudulent websites have a singular aim of trying to extract customer sensitive information and use it for indulging in nefarious activities that could potentially cause a person to lose money or valuable information without his/her knowledge. This is especially prevalent across people who are not very tech savvy and are relatively technically illiterate. Hence, our solution aims to overcome these issues by providing a platform for people to verify the authenticity of various websites and they can easily make a choice whether to trust certain websites or not. Our solution has the potential to limit the rampant instances of fraud, spamming, phishing etc. The essence of our solution is captured in the following points: This project aims to better the existing solutions and offer a more reliable and efficient solution. The main goal is to develop a user-friendly system where users can easily review a website and identify its rating.. Large volumes of reviews can easily be handled by the admin by adding keywords into the sky and make sky table showing single-word reviews tables. The aim is to design a website that requires minimum maintenance while ensuring maximum efficiency. REFERENCES [1] Pravesh Kumar Singh and Mohd Shahid Husain, MethodologicalStudyofOpinionMiningand SentimentAnalysis Techniques, IJSC 2014 [Online]. Available: [2] Rushabh Shah and Bhoomit Patel, Procedure of Opinion Mining and Sentiment Analysis: A Study, IJCET, 2014 [Online]. Available: [3] Nidhi R. Sharma, Prof. Vidya D. Chitre, Mining, Identifying and Summarizing Features from Web Opinion Sources in Customer Reviews, IJIACS, 2014 [Online]. Available: ct/ [4] paper/f pdf Ravendra Ratan Singh Jandail, A proposed Novel Approach for Sentiment Analysis and Opinion Mining,IJU 2014 [Online]. Available: Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 35

62 Adarsh M Revadi et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, [5] Hesham Ahmed Hassan, Opinion Mining and Sentimental Analysis Approaches: A Survey, Cairo, 2014 [6] Internet fraud, Wikipedia.org, [Online]. Available: [7] What is Cyberbullying, stopbullying.gov, 2018 [Online]. Available: it/index.html [8] Definition of Hacking, economictimes.indiatimes.com, 2018 [Online]. Available: ck ing [9] Types of Spam, securelist.com, 2018 [Online]. Available: [10] How to deal with fake websites, blog.trendmicro.com, 2018 [Online]. Available: with-fake-websites/ Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 36

63 Volume 9, Special Issue No. 3, May 2018 International Journal of Advanced Research in Computer Science (ISSN: ) CONFERENCE PAPER Available Online at STUDENT INFORMATION AI CHATBOT Shubhanshu Jha, Shashwat Bagaria, C Lakshmi Karthikey, Utkarsh Satsangi, Surekha Thota School of C & IT, REVA University, Bangalore, India Abstract-Automatic conversation system is an intelligent human machine interaction using natural language. Main goal of it is to allow the user and machine to make a natural harmonious conversation. Thus enabling the machine to recognize human motivation and to respond accurately, is not only an important manifestation of advanced intelligence, but also a very challenging work in harmonious human interaction field [1]. A conversation system consists of speech recognition, speech synthesis, and dialogue management and conversation generation. In this research, we focus on automatic generation of conversation between a computer and a human being with little knowledge of the computer.in this paper, we influenced a PC to end up a preparation to accomplice of a man who isn't great at discussion, to wind up a band together with a man. Therefore, in this research, we are focusing specifically on chat" by developing an interactive AI which converse mainly by using machine learning. We perform a word unit prediction by using Hill Climbing algorithm and based on relevancy ranking, the relevant conversation is made.our main focus, is to build a Student chat bot which helps the colleges to have 24*7 automated query resolution. This helps the students to have the right information from the trusted source. Also the administration of information is made easy for the institutions. keywords: CHATBOT, AI, KNOWLEDGE BASE, HILL CLIMBING ALGORITHM 1.1 INTRODUCTION With the fast development of deep learning techniques on various AI domains, many researches have explored the use of deep learning techniques to build conversational agents in recent years. The current state of art sentence generation model is the model of sequence-to-sequence, AKA., seq2seq [2]. In the generation of a conversation using the seq2seq model, it is said that a conversation is established to a certain extent in a one-time conversation, but a conversation sentence that causes discomfort in consecutive conversation is often generated. We need to find the solution for creating a more responsive system which does not create a sense of discomfort to the user. We also need to keep the simplicity of program in mind while designing the system to enhance its efficiency. Objective of this project is to develop a system capable of conversing with the human counterpart and present the details passed down from a trustworthy source of any institution.here in this project we tried to create a system where a student (in this case) can access the details or information regarding any subject related to the institution (in this case a college). We have tried to create an interactive platform for the users and students to receive appropriate and accurate information regarding their subject of interest. The main task in this project would be to create a system with an interactive graphical user interface where the student can get every information regarding the institution, a knowledge base to store every information provided by the admin and required to the student, and a well-developed machine learning algorithm to process the input provided by the user. 2. EXISTING SYSTEM The seq2seq display was initially proposed in the field of machine interpretation, yet it can be connected in creating the discussion also. The seq2seq display has an encoder unit and a decoder unit. The framework inputs the former arrangement to the encoder unit word by word. The seq2seq display permits the consecutive and recursive mapping from encoded words to its assigned decoded portrayal till the terminal flag, i.e., the symbol<eos>. Associate this gathering of words as a reaction of the framework [3]. In original seq2seq model, the conversation is generated in English, but it has also been showed that conversation can be generated in Japanese [4]. In the generation of a conversation using the seq2seq model, it is said that a conversation is established to a certain extent in a one-time conversation, but a conversation sentence that causes discomfort in consecutive conversation is often generated. While generative models generate new responses from scratch. The main disadvantage of retrieval-based models is that they are unable to handle unseen cases for which no appropriate predefined response exists. But generative models can refer Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 37

64 Shubhanshu Jha, et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, back to entities in the input and give the impression that you re talking to a human. Basically, the deep learning based conversation generation includes two kinds of models: retrieval-based models [5] [6] and generative models [7] [8] [9]. Retrieval-based models use a repository of predefined responses and some kind of heuristic to pick an appropriate response based on the input and context. 2.1 DRAWBACKS 1. Retrieval-based models are that they are unable to handle unseen cases for which no appropriate predefined response exists. 2. It is said that a conversation is established to a certain extent in a one-time conversation, but a conversation sentence that causes discomfort in consecutive conversation is often generated. 3. PROPOSED SYSTEM Hill Climbing Algorithm is a technique to solve certain optimization problems. In this technique, we start with a sub-optimal solution and the solution is improved repeatedly until some condition is maximized. The idea of starting with a sub-optimal solution is compared to starting from the base of the hill, improving the solution is compared to walking up the hill, and finally maximizing some condition is compared to reaching the top of the hill. Hence, the hill climbing technique can be considered as the following phases Constructing a sub-optimal solution obeying the constraints of the problem Improving the solution step-by-step Improving the solution until no more improvement is possible Hill Climbing strategy is for the most part utilized for taking care of computationally difficult issues. It takes a gander at the present state and quick future state. Thus, this system is memory proficient as it doesn't keep up a pursuit tree. Hill climbing search calculation is basically a circle that ceaselessly moves toward expanding confidence of an occurrence. It stops when it comes to a "pinnacle" where no neighbour has higher esteem. This calculation is thought to be one of the easiest strategies for executing heuristic hunt. The hill climbing originates from that thought in the event that you are endeavouring to locate the highest point of the hill and you go up course from any place you are. This heuristic consolidates the benefits of both profundity first and breadth first searches into a solitary strategy. The name hill climbing is derived from simulating the situation of a person climbing the hill. The person will try to move forward in the direction of at the top of the hill. His movement stops when it reaches at the peak of hill and no peak has higher value of heuristic function than this. Hill climbing uses knowledge about the local terrain, providing a very useful and effective heuristic for eliminating much of the unproductive search space. It is a branch by a local evaluation function. The hill climbing is a variant of generate and test in which direction the search should proceed. At each point in the search path, a successor node that appears to reach for exploration. The steps thus involved in the Hill Climbing Algorithm are: Step 1: Evaluate the underlying state. In the event that it is an objective state at that point stop and return achievement. Something else, influence introductory state as present to state. Step 2: Loop until the point when the arrangement state is found or there are no new administrators introduce which can be connected to current state. a) Select an state that has not been yet commenced to the present state and apply it to deliver another state b) Perform these to assess new state I. In the event that the present state is an objective state, at that point stop and return achievement. II. In the event that it is superior to the present state, at that point make it current state and continue further. III. On the off chance that it isn't superior to the present state, at that point proceed on the up and up until the point when an answer is found. Step 3: Exit. 3.1 ADVANTAGES Hill climbing technique is useful in job shop scheduling, automatic programming, circuit designing, and vehicle routing and portfolio management. It is also helpful to solve pure optimization problems where the objective is to find the best state according to the objective function. It requires fewer conditions than other search techniques. 4. METHODOLOGY Hill Climbing algorithm basically uses the information provided and compares it to the pre described data. It creates Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 38

65 Shubhanshu Jha, et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, a search sequence which can be understood using a graph full of ridges, plateaus and troughs. Hill climbing is a case of an educated hunt technique since it utilizes data about the inquiry space to seek in a sensibly productive way. The Hill climbing look dependably moves towards the objective. Utilizing heuristics it discovers which heading will take it nearest to the objective. The name hill climbing is gotten from reproducing the circumstance of a man climbing the hill. A climber is lost most of the way up/down a mountain around evening time. His camp is at the highest point of the mountain. Despite the fact that it is dim, the climber the explorer realizes that each progression he takes up the mountain is a stage towards his objective. There are more complex and task oriented versions of hill climbing algorithm are present. We are avoiding the use of such complex versions to maintain simplicity and efficiency of the project in hand. 4.1 DATA FLOW DIAGRAM 4.2 MODULES Figure b: Various Modules ADMIN MODULE: The admin is the de facto provider of the knowledge to the knowledge base or the relevant details towards various information as and when required. All the data which is being passed down to the system goes in purely through the user discretion. The admin has the rights to change the detail where and when necessary and provide the missing information if required USER REGISTRATION MODULE: This is nothing but a page where the user be it an admin or any student or staff can register their presence in the system which allows them to visit various information or rights to access various information present in the system CUSTOMER OR STUDENT MODULE: Figure: A Data Flow Diagram As shown in the Figure a, the system receives the question which is parsed and forwarded to the AI. The AI runs it through the algorithm and generates a response in case the relevant detail is encountered or else forwards it to the staff or admin for appropriate response. It is the work of the administrator or the online staff to update the knowledge base with the relevant answer to these types of questions. In any institution, every stakeholder of the institution deserves the right to be informed about various matter of interest based upon the designation of the stakeholder.in this scenario, the student can log in the system and ask the question in the interactive platform of the Chabot. This is the platform where the user can ask the question and receive relevant answer based upon the accuracy of the answer COMPLAINT MODULE: There are cases where a user may not find the relevant answer of the question or may encounter any problem in the system itself, the problems can be conveyed to the admin of the system for further scrutiny. Using the complaint module, the user can pose a complaint to the admin using his registration id which needs to be resolved as soon as possible purely upon admins discretion. Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 39

66 Shubhanshu Jha, et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, ARCHITECTURE We have effectively made a website page which is available to each partner of the foundation with an intelligent Chabot stage. The clients can make inquiries on this stage and get the significant answer or demand the administrator of the framework to give the information base applicable response for additionally utilize. This framework can be utilized as a part of different establishments as we have utilized here to exhibit its pertinence regarding the instructive foundations. 6. CONCLUSION Figure c: Architecture Diagram As shown in the Figure c, the user enters the question which is sent down to the parser of the system. The parser will parse the input given by the user and split the input and create key words or tokens to be used further down the architecture. The parser eliminates the articles which are nothing but the un-necessary words or stop words. This reduces the complexity of the algorithm. The tokens are used for the purpose of matching of input given by the user and the knowledge base of the created system. If the set of parsed tokens match the data present in the knowledge based to a particular accuracy as decided by the administrator, then the data is forwarded to the member or the user using the present graphical user interface. The data which is not present in the knowledge base is forwarded to the staff for relevant answer updating in the knowledge base. 5. RESULTS In this venture we have effectively finished the objective to make an intuitive stage for the clients of the framework. Accordingly we made an insightful Chabot framework which can consequently answer to our inquiries in much productive and propelled way. The framework works with the arrangement of slope climbing calculation for speedier and precise outcomes. As future upgrade we will have the capacity to include the SMS framework with the venture so that if another client expects to do visit SMS ready will be sent to administrator and if the administrator what's to see he/she can see Chabot answering continuously. In the event that administrator needs to take control over the talk he/she would have the capacity to do continuously. In facilitate improvement we can include unimportant answer catch additionally in that on the off chance that we get any unessential inquiry we can click so it will consequently come in grumbling and later administrator can swap another response for same inquiry. REFERENCES [1] I. A. I. Lopatovska, "Theories, methods and current research on emotions in library and information science, information retrieval and human computer interaction," Information Processing and Management, pp , 2011, vol. 47. [2] I. S. O. V. Q. V. Le:, "Sequence to sequence learning with neural networks," NIPS, [3] O. V. Q. V. Le:, "A neural conversational model," ACL, [4] Kaoru Nasuno Yutaka Matsuo, "Generating a reply on Twitter using a neural conversational model," in The 30th Annual Conference of the Japanese Society for Artificial Intelligence, [5] N. P. I. S. J. P. Ryan Lowe, " Dialogue Corpus: A Large Dataset for Research in Unstructured Multi- Turn Dialogue Systems," The Ubuntu, SIGDIAL, [6] M. S. J. K. Rudolf Kadlec, " Improved Deep Learning Baselines for Ubuntu Corpus Dialogs,," Machine Learning for SLU & Interaction NIPS 2015 Workshop,, [7] Z. L. H. L. Lifeng Shang, " Neural Responding Machine for Short-Text Conversation,," ACL, [8] M. G. M. A. C. B. 2. Alessandro Sordoni, " A Neural Network Approach to Context-Sensitive Generation of Conversational Responses,," NAACL-HLT, [9] G. Z. B. P. Kaisheng Yao, " Attention with Intention for a Neural Network Conversation Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 40

67 Volume 9, Special Issue No. 3, May 2018 International Journal of Advanced Research in Computer Science (ISSN: ) CONFERENCE PAPER Available Online at THE EFFECTIVE UTILITY OF ATTRIBUTES WITH THRESHOLD BASED COLLABORATION WITH COMBINATIONAL TUPLES IN DATA MINING Deepa.V.Patil REVA ITM, Bangalore, India Sheelavathy.V REVA University, BangaloreIndia I Abstract: In data mining utilization of attributes with based on the threshold with respect to the generated attributed weightages and producing the pattern of utilities is a huge challenge. Generally the patterns here are the seed and also results, So by considering the existing data pattern mining techniques cannot estimate which weightage belongs to which item and also its not possible to get the combinational attributes with the given threshold[4]. also. So to overcome this issue this work proposed an efficient framework called Threshold Based collaboration with Combinational tuples(tbct). This approach reduces the complex pattern making with critical dataset which is in the form of structured pattern with proper assigned weightages in the linear passion. The utility of the pattern is with respect to novel approach[9] and non-random tuple formation but with considering the threshold as a seed segment[12].the novel model is used for efficient and effective fetching of the dataset. The flow of this approach when tuples are framing parellally the weightages will be collaborated to reach the threshold segment. Once the tuple combination is fully qualified with respect to segment the TBCT will utilize this to increase with additional tuple combinations till it reaches the maximum utilization and fully qualified patterns without ignoring any single or multiple combinational tuples or pattern with novel approach. (Index Terms: pattern, threshold, tuple, collaboration, weightage) IIIntroduction:In the data mining lot of techniques and approaches to make the effective patterns based on their weightages even if the seed data or dataset or data pattern with pre assigned weightages. In this work our pattern is made up with the attributes, each and every attribute[3] is collaborated with proper weightage and with pattern capacity and the capacity is the total sum of the weightages of individual pattern. So all the pattern[11] with their own individual weight and respectively sum of the weights. This work is mainly concentrating on the seed pattern which shown in the fig1. utilization of the attributes as tuples with respect to their weightages[15][2] using novel method which is the flavor of the TBCT(Threshold Based collaboration with Combinational tuples). The generation of the initial tuple pattern is random from any seed pattern (data) and will be extended to utilized to reach to that segmentation. sthe novel approach is irrespective of the padded pattern and this padded pattern[7][9] is generated with incremental collobation of tuple growth mechanism. The tuple growth is combinational tuples and one criteria and other one is mainly on the threshold. The general flow of the TBCT approach shown in below figure. fig1 As the tuples with the pattern shown for the first tuple or pattern right side is are the weightages and left side are the attributes and middle is the sum of the weightages. So the each and every tupled pattern is having its own threshold and this threshold belongs to its own relevant pattern only. The utilization of the pattern with respect to threshold (this threshold is to discover the pattern to reach the segmentation with non-repetitive mode, with proper extension to frame the new pattern recursively till it finds the maximum fig2 Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 41

68 Deepa V Patil et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, Stages of the TBCT: First fetching of the dataset to memory as datastrucure and prioritize the attributed tuple which is having minimal sum of the weights and maximum sum of the weights. The TBCT will use minimal sum[17] [7] tuple to create initial pattern with respect to threshold by omitting the other attributes till it just reaches to threshold and this pattern can be reused to adding tuples with novel approach without ignoring any neural combination of the attributes IIIRelated Work:The full version of the utility of the attributes is the improvised version to frequent item set mining. This frequent pattern mining is one of the oldest approach and popular issue in data mining. Let us take the following dataset. This is transactional model dataset made by the customers. The transaction is a set of items made with respect to customers. By seeing the following fig customers some items [a b c d e] and second one is associated with [a b e]. interestingness, valuable of profit to the user with respect to item. There are 2 aspects in the transaction database with utility of items. External Utiltiy: The valuable information about the unique items, which is called as external utility. Internal Utility: The valuable information of distinct items in transactions called as internal utility. Utilizing the item set is treated as a product of external utility and respectively its internal utility. The item set is treated as high utility item set. If its utility frequency is less than user defined threshold then it will be treated as low-level utility pattern. Mining high utility itemsets:here is the basic discussion about the definitions of the utility frequency of an item, utility frequency of item set with respect to transaction, database utility of item set and related works and definition of the problem of mining utility and then after that strategies. The frequent item set is combination of items which appears at least pre assigned and specified transactions. Generally let I = {i1, i2, i3, im} be a set of items and DBS = {T1, T2, T3 Tn} is a set of transactions where each and every transaction is also a set of items(itemset). Mension a minimum support threshold minsup as itemset S is frequent iff. Transaction Items T1 {a, b,c,d,e} T2 {a, b, e} T3 {c, d, e} T4 {a, b, d, e} fig3 Here is the goal is to find the item set mining to discover the frequent item set mining. Many popular algorithms are discovered the most combinational and frequent by using minimum support threshold. So these types of algorithms discovered set of items ie itemsets which appear in every transaction. So if we take 2 as minimum threshold the following is the result fig. Item Set Support {e} 4 {d, e} 3 {b,d,e} 2 {a} 3 fig4 So these types of the algorithms will be having some limitations and not all the combinational itemsets will not be considered so this work is the extension to these kind of approaches. fig5 As frequent item set is most time consuming process during the search process. When searching in this criteria antimonotype property is used. VI Algorithm: TBCT(Threshold Based collaboration with Combinational tuples):once the dataset fig1 loaded to memory The threshold will be considered as segment. λ>=21 is the minimum segmented threshold so below is the limitation to TBCT. Once the threshold is validated and the data structure is loaded in novel based model. Novel based means checks the nearest patterns which matches will be put up in nearest pools for sake fast processing. Once this sequence is framed and TBCT takes first novel index which is with lowest weight and it will be started with seed pattern and later that will be extended with padded pattern. This is clearly mensioned in fig2. IV Literature survey: Utility Mining: In perspective of utility mining which emerges a valuable model of topic in data mining field. High utility mining dataset from refers to discover the itemsets with high gains. The meaning of utility of item set is Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 42

69 Algorithm: Deepa V Patil et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, fig6 fig7 VIIPractical Results: The following is the practical result from the dataset which is there in fig1 with λ=34 which is the threshold. VII Future work:this work can be extended with attributes with properties and with weights. In this work attributes are assigned with weights but not with properties. So these properties can be prioritized and can be allocated with user definedand dynamic allocation of properties at testing phase. So practically by extending this work we can come to know the maximum work load boundaries of this algorithm. Instead of novel model we can extend this with dependency way which isproperties sharing among the attributes. So this reflects the result as high utility with properties even. IX. References: [1] C. Creighton and S. Hanash, Mining Gene Expression Databases for Association Rules, Bioinformatics, vol. 19,no. 1, pp , [2] M.Y. Eltabakh, M. Ouzzani, M.A. Khalil, W.G. Aref, Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 43

70 Deepa V Patil et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, and A.K. Elmagarmid, Incremental Mining for Frequent Patterns in Evolving Time Series Databases, Technical Report CSD TR#0802, Purdue Univ., [3] Erwin, R.P. Gopalan, and N.R. Achuthan, Efficient Mining of High Utility Itemsets from Large Data Sets, Proc.12th Pacific-Asia Conf. Advances in Knowledge Discovery and Data Mining (PAKDD), pp , [4]Y. Liu, W. Liao and A. Choudhary, A fast high utility itemsets mining algorithm, in Proc. of the Utility- BasedData Mining Workshop, [5] A. Erwin, R.P. Gopalan, and N.R. Achuthan, Efficient Mining of High Utility Itemsets from Large Data Sets, Proc. 12th Pacific-Asia Conf. Advances in Knowledge Discovery and Data Mining (PAKDD), pp , [6] E. Georgii, L. Richter, U. Ru ckert, and S. Kramer, Analyzing Microarray Data Using Quantitative Association Rules, Bioinformatics,vol. 21, pp , [7] J. Han, G. Dong, and Y. Yin, Efficient Mining of Partial Periodic Patterns in Time Series Database, Proc. Int l Conf. on Data Eng.,pp , [8] J. Han and Y. Fu, Discovery of Multiple-Level Association Rules from Large Databases, Proc. 21th Int l Conf. Very Large Data Bases, pp , Sept [9] J. Han, J. Pei, and Y. Yin, Mining Frequent Patterns without Candidate Generation, Proc. ACM-SIGMOD Int l Conf. Management of Data, pp. 1-12, [10] S.C. Lee, J. Paik, J. Ok, I. Song, and U.M. Kim, Efficient Mining of User Behaviors by Temporal Mobile Access Patterns, Int l J. Computer Science Security, vol. 7, no. 2, pp , [11] H.F. Li, H.Y. Huang, Y.C. Chen, Y.J. Liu, and S.Y. Lee, Fast andmemory Efficient Mining of High Utility Itemsets in Data Streams, Proc. IEEE Eighth Int l Conf. on Data Mining, pp , [12] Y.-C. Li, J.-S. Yeh, and C.-C. Chang, Isolated Items Discarding Strategy for Discovering High Utility Itemsets, Data and Knowledge Eng., vol. 64, no. 1, pp , Jan [13] Y.-C. Li, J.-S. Yeh, and C.-C. Chang, Isolated Items Discarding Strategy for Discovering High Utility Itemsets, Data and Knowledge Eng., vol. 64, no. 1, pp , Jan [14] C.H. Lin, D.Y. Chiu, Y.H. Wu, and A.L.P. Chen, Mining Frequent Itemsets from Data Streams with a Time- Sensitive Sliding Window, Proc. SIAM Int l Conf. Data Mining (SDM 05), [15] Y. Liu, W. Liao, and A. Choudhary, A Fast High Utility Itemsets Mining Algorithm, Proc. Utility-Based Data Mining Workshop,2005. [16] R. Martinez, N. Pasquier, and C. Pasquier, GenMiner: Mining nonredundant Association Rules from Integrated Gene Expression Data and Annotations, Bioinformatics,vol. 24, pp ,2008. [17] J. Pei, J. Han, H. Lu, S. Nishio, S. Tang, and D. Yang, HMine:Fast and Space-Preserving Frequent Pattern Mining in Large Databases, IIE Trans. Inst. of IndustrialEngineers, vol. 39, no. 6, pp , June [18] J. Pei, J. Han, B. Mortazavi-Asl, H. Pinto, Q. Chen, U. Moal, and M.C. Hsu, Mining Sequential Patterns by Pattern-Growth: The Prefixspan Approach, IEEE Trans.Knowledge and Data Eng.,vol.16, no.10, pp ,Oct Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 44

71 Volume 9, Special Issue No. 3, May 2018 International Journal of Advanced Research in Computer Science (ISSN: ) CONFERENCE PAPER Available Online at STRUCTURAL BALANCE THEORY BASED RECOMMENDATION Monika.N.S Department of C&IT, REVA University, Bangalore K.Vaishnavi Department of C&IT, REVA University, Bangalore Kanaiya.V.K Department of C&IT, REVA University, Bangalore Lavanya.G.P Department of C&IT, REVA University, Bangalore Kavya.M Department of C&IT, REVA University, Bangalore Abstract The Internet is growing rapidly and huge amount of data is collected in it. So data mining is very necessary. Recommending appropriate product items to the target user is challenging for continuous success of E-commerce. Most existing E-commerce recommender system aims to recommend the right product to the consumer, assuming the properties of each product are fixed. Through E-commerce, user can browse, compare and select the product items that they like in a most convenient manner, which brings great facility to the E-commerce user. There are varieties of products in each E-commerce company which are ready to be selected, compared and purchased by target user. Therefore, for the continuous success of E-commerce companies, the product should be recommended appropriately to the target user. Nowadays, many of the E-commerce system has adopted various techniques for recommendations e.g. collaborative filtering(cf) based technique, this help to realize which product has to be recommended. The reason why we put forward a Structural Balance Theory (SBT) based recommendation is that, due to the sparsity of big rating data in E-commerce, similar friends and similar product items may be absent from the user-product purchase network, which lead for big challenge to recommend appropriate product items to the target user. Our system provides user specific recommendation based on enemy of an enemy is a friend concept. Keywords Structural Balanced Theory (SBT), Collaborative filtering (CF). I. INTRODUCTION With the growth of internet, E-commerce has gained fast development and accumulated a huge number of online users all over the world. Today, many E-commerce websites have provided various product items to their massive online users Predicting the users preference and recommending items is the major factor for success of E-commerce websites. The most common type of recommendation is Collaborative filtering [5]. Collaborative filtering is product specific. If any person search for a particular product he gets the same set of recommendations whereas SBT provides user specific recommendation based on enemy of an enemy is a friend concept. The aim of the project is to enhance the recommendation of products in E-commerce websites through possible friends, according to enemy s enemy is a friend concept of SBT, and recommend the product items preferred by possible friends to the user. II. LITERATURE SURVEY [1] Develops a predicting model according to some special characteristics of the C2C E-commerce. This model only uses the information about frequencies and timings of transactions. Due to collection of a large amount of data, the growth of cost would be faster to make enterprise unprepared. Risk is high. As the large amount of data would inevitably involve the personal privacy. In [2] an approach that creates the Poisson Lognormal Distribution (PLN) for modeling purchase frequency counts and predicting future purchases based on past performance. The PLN model does not have direct calculation from its parameters.hence we apply numerical estimation based on random draws and average the results to estimate the frequency distribution. [3] A new similarity method is proposed by improving the traditional similarity function with the weight of items. When putting the similarity of items and the factor of time as the weight of the target item. Method is not accurate, because the weight about item is not simply given. Need to find out the Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 45

72 Monika N S et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, more valuable information in a flood of information [5] Collaborative Filtering (CF) is a popular technology for recommender systems. CF methods suffer from such problems as data sparsity and big-error in predictions. Computation is more as we have to estimate the sensitivity of different number of user groups. The cost of these preprocessing procedures depends on the particular clustering method used and can be higher [4] The tremendous growth in the amount of available information and the number of visitors to Web sites in recent years poses some key challenges for recommender systems. In this paper different item-based recommendation generation algorithms are analyzed. Different techniques for computing item-item similarities are analyzed. III. SYSTEM DESIGN The user logs in to the web app through a unique id. The target user information is extracted from the service database. Recommendations are provided based on the previous storage of ratings in HDFS. Which is being processed using spark with the concept of enemy of an enemy is a friend. Fig 2: Use case diagram IV. PROPOSED SYSTEM E-commerce is known for its modernized approach and convenience in online business transaction. E-commerce allows users to browse, compare and select the product items with ease. For instance, Amazon, ebay, Best buy and many E- commerce companies have provided various items to their massive users online. The success of E-commerce companies lies in accurately predicting items of the target users and further recommending appropriate product items. One of those recommendation approach that aids the above is CF-based recommendation. There is one factor which leads to the failure of traditional CF-based recommendation approach -"the absence of items similar to the ones purchased by target user and his/her friends, from the user-product purchase network ". This creates a huge challenge for accurate product-item recommendation, which could possibly be solved by E- commerce. The big rating data observation allows us to determine users with similar product purchase, with the help of CF-recommendation. V. SOFTWARE REQUIREMENTS Fig 1: System design Use case diagram (Fig 2) is a behaviour diagram used to describe some actions that the system can perform in addition with one or more external users of the system. I. HADOOP: Hadoop is used in our project to handle large data sets. It is an open source framework written in java to handle huge data sets. II. MySQL: A database is a separate application that stores collection of data. Each database has one or more Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 46

73 Monika N S et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, distinct APIs for creating, accessing, managing, searching and replicating the data it holds. III. SPARK: It is used for faster computation and code optimization. Scala is the language that is used in spark. IV. TOMCAT SERVER: Tomcat server is used for client server interaction. V. HTTP: It used for UI support. VI. EXPERIMENTAL RESULTS Following are the steps for the process of predicting the recommendations of products: Fig 4: Starting HTTP Server by fixing the port. Step 1: At first we configure Hadoop later we have established secure password less communication and started all nodes. Step 2: Tomcat server which is used for client server interaction is started as shown in Fig 3. Step 3: HTTP server is started which is shown in Fig 4, used for UI support. Fig 5: Project Code Step 4: (Fig 5) In the code we first check the users who have given ratings for the previous product purchased by the target user and also rated. Then according to the threshold, we check if the difference between the ratings of the users and the target user is equal to the threshold. Now we consider the matching users and group them as enemies. In this group according to another threshold we classify enemies and consider to be the friend of the target user. The products purchased by these friends will be recommended to the target user. Step 5: After running the code, we can see that those input data set files are extracted and are stored in HDFS as shown in Fig 7, which is further used for processing by spark. After processing we can see user specific recommendation by using their User-ID for predicting the recommendation in UI as shown in Fig 10. Fig 6: Running Stages of the files. Fig 3: Starting Tomcat server. Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 47

74 Monika N S et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, Fig 7: Files stored in HDFS. Fig 9: Front page of User interface. Fig 10: User specific recommendation are shown. VII. CONCLUSION Fig 8: Data set Stored. Our project would benefit users by recommending user specific product items. A product recommendation approach named Structural Balanced Theory-Recommendation is brought in this paper, which is very useful for dealing with specific recommendation situations when the product item preferred by user have no specific friends and when the user has no specific friends. Structural Balanced Theory makes use of Structural Balance Information which is present in user-product purchase network for recommendation by considering enemy of an enemy is friend concept. Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 48

75 Monika N S et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, VIII. REFERENCES [1] Y. Tian, Z. Ye, Y. Yan, and M. Sun, A Practical Model to Predict The Repeat Purchasing Pattern of Consumers in The C2C E-commerce, Electronic Commerce Research, vol. 15, no. 4, pp , [2] G. Trinh, C. Rungie, M. Wright, C. Driesener, and J. Dawes, Predicting Future Purchases with The Poisson Lognormal Model, Marketing Letters, vol. 25, no. 2, pp , [3] K. Choi and Y. Suh, A New Similarity Function for Selecting Neighbors for Each Target Item in Collaborative Filtering, Knowledge-Based Systems, vol. 37, pp , [4] B. Sarwar, G. Karypis, J. Konstan, et al, Item-based Collaborative Filtering Recommendation Algorithms,Proc. 10 th International Conference on World Wide Web(WWW 01), pp ,may 2001 [5] Y. Cai, H. Leung, Q. Li, et al, Typicality-based Collaborative Filtering Recommendation IEEE transactions on Knowledge and Data Engineering,vol.26,pp ,2014. Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 49

76 Volume 9, Special Issue No. 3, May 2018 International Journal of Advanced Research in Computer Science (ISSN: ) CONFERENCE PAPER Available Online at DOCUMENT CLUSTERING USING COSINE SIMILARITY Ranjith Kumar N S School of Computing and Information Technology Reva University Bangalore, India ranjith.ns47@gmail.com Prekshitha N School of Computing and Information Technology Reva University Bangalore, India prekshitha.gowda.n@gmail.com Keerthi K P School of Computing and Information Technology Reva University Bangalore, India: keerthikiccha626@gmail.com Prema S School of Computing and Information Technology Reva University Bangalore, India prema.s2608@gmail.com Naveen Chandra Gowda School of Computing and Information Technology Reva University Bangalore, India ncgowdru@gmail.com Abstract: Clustering or Cluster Analysis is a process of grouping similar objects in such a way that the objects in the group (cluster) are similar to each other than the objects in other groups (clusters). Clustering is an unsupervised machine learning technique where only the input data is served 3 (unlike as in supervised, a set of sample input and output pair is provided) to the system corresponding to which it recognizes a pattern and predicts the output automatically, Hence complete automation is achieved here. In specific to our work that is Document clustering is organizing the text files into clusters containing similar files (File Content). High precise clustering algorithms like K-means play an important role in data storage, data manipulation and information retrieval systems. Search engines like Google, Yahoo, Bing etc. uses Document clustering in addition to high-end processors and servers to retrieve the information in response to the various search queries. The most commonly used clustering technique is K-means, where the objects are divided into k number of clusters with similar objects in it. The present work is focused on Document clustering using Cosine Similarity where the pre-processing work is carried out by a readymade Java library known as Apache Lucene. The texts in the documents are broken down into strings, and the extracted strings is fed to the Apache Lucene which pre-processes the data, the number of repetitions of each word and gives the output as JSON objects. Then the cosine similarity is calculated with these indexed words. The final result of this work outputs the documents that are similar to each other, that are exactly similar to each other (copy documents) and the ones which are unique (outlier). The applications of document clustering include mining useful data in large datasets, web page clustering, search engines, anti-plagiarism checkers etc. Keywords: Cluster, Manipulation, JSON, Lucene, Index. I. INTRODUCTION Data which can be defined as set of information has its own importance in this digital world. Typically the Data is stored in devices ranging from small Pen-drive (range: 1GB to 1TB) to huge data centers like facebook s data centre having the capacity hundreds of peta bytes. Coming to data processing, it can be defined as anything that you do with the data other than storing. The data is stored efficiently using high tech devices, tools like Big Data hadoop etc. Data retrieval or information retrieval is process of fetching the stored data whenever it is needed. This is carried out by high end processors, various machine learning algorithms predominantly the Clustering techniques like K-means, Cosine similarity etc. In this era of Information technology, with the exponential growth of data day by day, we need advance tools to handle the gathering data. Tools like SQL which process only the structured data is becoming useless these days. With enormous amount of Un-structured data, we need such a tools which can process the data in Giga bytes per second. As a result of several researches the data scientists came up with the tool know as Hadoop for processing and storing the huge data, which they named as big data. Clustering is a process of grouping the similar data in clusters. Since the clustering is an unsupervised learning, it doesn t need any human to intervention. There are many types of clustering such as Exclusive clustering, Overlapping clustering, Hierarchical clustering, and Probabilistic clustering. In Exclusive clustering the data are grouped in exclusive way, Overlapping clustering uses fuzzy sets to Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 50

77 Ranjith Kumanr N. S. et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, cluster data, the probabilistic clustering uses a probabilistic approach to cluster the data. Document clustering is the process of automatically grouping related documents into clusters, which is gaining significant importance and is one of the most important task in artificial intelligence and machine learning. Document clustering has many applications in the area of Information retrieval and data mining. Example of document clustering is web document clustering. There are two common algorithms in document clustering, they are hierarchical based algorithm and other algorithm is developed using k means algorithm. These algorithms are classified as hard or soft clustering algorithms. Hard clustering computes a hard assignment where each document isa member of exactly one cluster. Soft clustering computes soft assignment and it is distributed over all clusters. Dimensionality reduction method is the subtype of soft clustering, which includes latent semantic indexing and topic models. II. LITERATURE SURVEY There are many works carried out in this field which includes [1] A News recommending app: In this work they have built an application that recommend news for the users based on the previous clicks. After investigating many algorithms they have proposed two new algorithms namely: Triplet based graph partitioning and feature based clustering. [2] Text Classification: In this thesis, they have used mainly three algorithms like the Information Bottleneck Algorithm, The Agglomerative Information Bottleneck Algorithm and the Naive Bayes Classifier. The Information Bottleneck Algorithm is used to compute the distance between the data points of the documents. However, this algorithm is restricted to hard clustering only. To overcome this limitation, the agglomerative information bottleneck algorithm is used. The input served for this algorithm is Joint probability distribution of words that belong to particular category. [3] A Comparison of Document Cluster Techniques:: In this thesis Michael Steinbach from the University of the New Yorkpresents the results of an experimental study of some common document clustering techniques. Majorly in particular, they have compared the two main algorithms namely: the agglomerative hierarchical clustering and the K-means are done. [4] An Introduction to Information Retrieval: They have tried to implement a information retrieval system or simply a search engine which returns the similar files or data pertaining to the query which the user has searched for. The concepts that they have considered in this thesis are Flat Clustering, Hierarchical clustering, Matrix decompositions and latent semantic indexing, Web search basis, Web crawling and indexes, Link Analysis etc. [5] A Comparison between the Algorithms: SOM and K-means: Yieng Chen and Bing Qin in their papers compared SOM and K means algorithm. K means is easy to realize and it usually has low computation cost, so it has become a well-known text clustering method. [6] Document Clustering using Confidence Measurement: Csorba and Vajk (2006) presented a topic which is based on document clustering technique where there is no need to assign all the documents to the clusters. Under such conditions the clustering system can provide a much cleaner result by rejecting the classification of documents with ambiguous topics. This is achieved by applying a confidence measurement for every classification result and by discarding documents with a confidence value less than a predefined lower limit. This means that the system returns the classification for a document only if it feels sure about it. [7] An hierarchal algorithm for document clustering: Sunetal developed anhierarchal algorithm for document clustering. They used cluster overlapping phenomenon to design cluster merging criteria. The system computes in order to improve time efficiency and the veracity and the line passed through the two cluster s center instead of the ridge curve. Based on the hierarchical clustering method it used the Expectation- Maximization (EM) algorithm in the Gaussian mixture model to count the parameters and make the two subclusters combined then their overlap is the largest. [8] Funand Chen introduced an evolutionary PSO learningbased method for optimally clustering N data points into K- clusters. The hybrid PSO and K-Means, with an alternative metric algorithm. This is developed to automatically detect the cluster centers of geometrical structure datasets. In this algorithm, the special alternative metric is considered to improve the traditional K-Means clustering algorithm to deal with various sets of data. III. PROBLEM STATEMENT Since all the previous works had concentrated more on the precision of the clusters that have to be formed, and didn t concentrate on data pre-processing. This work mainly focuses on trying to improve the performance of document clustering and reduce the computing time with the use of Cosine Similarity in addition with the Ready-made library developed by apache known as Apache Lucene. IV. PROPOSED SYSTEM Traditionally, Document Clustering uses K-Means algorithm which lowers the performance of clustering significantly. By making use of Cosine similarity, we can achieve better results with significant increment of performance. Cosine Similarity with Apache Lucene significantly reduces the computational time and generates the results in significantly lesser time. A. System Architecture Figure 1: System Architecture Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 51

78 Ranjith Kumanr N. S. et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, Raw Data: Set of data which is to be sent for clustering process. Extracted Strings: In this step all the words in documents are separated and passed to lucene for indexing. Lucene Library: Lucene is a featured text search engine library, which is used to generate the indexed files from the extracted strings. Indexed File: File with an index which makes the clustering process easy. Cosine Similarity: Cosine similarity is measure of similarity between the files that measures the cosine of the angle between them. The cosine of 0 degree is 1, and it is less than 1 for any other angle in the interval [0,2pi), where the outcome is bound between [0,1]. Cosine Similarity Algorithm: Cosine similarity algorithm which computes the cosine similarity between the documents and generates the similarity matrix. Similarity Matrix: Similarity matrix are used for sequence alignment. Higher scores are given to more similar documents and lower scores for dissimilar documents. After analysis documents are divided into 3types: Similar Documents: Documents which are similar but not exactly copied. Copy Documents: Documents which are the exact copy of other documents. Outliers: Documents which is completely different from all other documents. V. SYSTEM ANALYSIS VI. IMPLEMENTATION A. Database Module In this module we are trying to implement and store the file entities, file names, file contents, then the term vectors, similarity matrix, similar documents, copy documents, outliers and document clusters. All these files are stored in the created array lists. The methods used in this modules are Database getinstance() for creating an object, getfilecontents() for extracting the contents of the file, setfilecontents() for saving the contents of the file, getfilenames() for acquiring the file name which have been uploaded, setfilename() for storing the file name, getfileentities() for acquiring the entities of the file, setfileentities() for saving the entities of the file, gettermvectors() for extracting the entities of the file, getsimilaritymatrix(), getsimilardocuments(), setsimilardocuments() for displaying the similar documents, getcopydocuments(), setcopydocuments() for displaying the copied documents, getoutlierdocuments(), setoutlierdocuments() for displaying the outier documents, getdocumentclusters() for storing the document clusters, setdocument clusters() for displaying the document clusters, cleardatabase() for clearing all the data which has been stored in the respective arraylists. B. Cosine Similarity Module In this module, we compute the similarity matrix using the indexed data which have been acquired from the Apache Lucene. Cosine similarity is computed by comparing each document with all other documents which has been uploaded to the system. How to compute cosine similarity? Each word in the document is compared with the each word in the other document. First, the word-count is computed, then using the formulae of cosine, the cosine similarity is computed as shown in the below example. Figure 2: Package Diagram There are six modules in this project work namely Database module for storing the information of the documents like file name, file entity and contents of the file, Cosine Similarity module for computing the cosine similarity between any files, Lucene Class module for preprocessing the data, Processor module for controlling the other modules, Result class module for computing the results, and Clustering Resource module for storing the files which are similar, which are exact of some other file and the files which are unique. The detailed functionality of each module will be explained in the later sections. Figure 3: Cosine Similarity Computation Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 52

79 Ranjith Kumanr N. S. et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, C. Lucene Class Module In this module we are implementing apache lucene library. The extracted strings from the text or pdf files will be fed to apache lucene library which computes the indexed files of the uploaded data to the system. The methods used in this module are getindexwriter() which is used to write the index, getindexreader() which is used to read the index files, getdirectory() which is used to store the indexed files in the array list, gettermvectors() which is used to get the term vectors of any two documents, getdocument() which is used to extract the documents uploaded to the system. After computing the word counts of uploaded documents in the form of indexed documents it is sent to compute the cosine similarity. D. Processor Module This is the main module which controls all the other modules in the system. Intially it calls the clustering resource module which takes the input from the user and then extract the strings and then which is fed to the lucene module, which inturn calls the cosine similarity module for computing the similarity matrix and then which is analysed and following set of output is displayed namely : similar documents, copy documents, outliers.the methods used in this module are processor() which accepts the index path of the strings. Then it is used to display the results in the proper format. E. Clustering Resource Module Similar Files: Figure 4: Similarity Matrix Here the files that are similar to each other are displayed. Figure 5: Similar Documents Copy and Outlier Documents: Here the documents that are exactly copy to each other and the documents that are unique are displayed. This is the module which manages the documents that have been uploaded. Methods used in clustering resource modules are getfiles() which provides the data path index path, jsonobjectprepare which contains the details about the copy documents outlier documents, similar documents, and then the document clusters. F. Result Class Module This is the module in which we are used to display our result. In this module we use getdocument1(), setdocument1(), getdocument2() and setdocument2() functions. getdocument1() is used to fetch the result of the 1st document. setdocument1() is used to display the result of 1st document. getdocument2() is used to fetch the result of 2nd document. setdocument2() will display the result of 2nd document. Document Clusters: Figure 6: Copy and Outlier Documents Finally, the document clusters, in which a cluster consists of similar files are displayed here. VII. RESULTS After computing the cosine similarity, the users can view the results as shown below. Similarity Matrix: Here the cosine value of each document with all other documents that have been clustered is displayed in the form of matrix, later by which is analyzed and clusters are formed accordingly. Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 53

80 Ranjith Kumanr N. S. et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, Search Engine: Then we can develop a search engine by using text analysis with lemmatization and stemmer. WordNet Library: In Addition with WordNet Library we can increase the precision of the clusters formed. Anti-Plagiarism Checker: We can develop an antiplagiarism checker by formatting the raw data and then by checking the content of the given document with the other data all over the world. Figure 7: Document Clusters IX. REFERENCES VIII. CONCLUSION AND FUTURE SCOPE In this, we can conclude that we can possibly develop a precise system to get a general algorithm by continues machine learning process, which can work the best in clustering all types of datasets. Thus we tried to implement the algorithms which can work well in different types of datasets. The main contribution of this project is to preprocess the data, generation of index files using Apache Lucene, and then clustering by cosine similarity. Document clustering is very useful to retrieve information application in order to reduce the time consuming time and get high precision. So, for future work, the mentioned clustering algorithms can be used for clustering and compare them to find out the best result. In future, we can add many functionalities to this project which are mentioned below: Text Format: Automatically detecting the format of the text document and then extracting the strings for the Apache Lucene in order to generate the indexed files for computing the cosine similarity. [1] Pankaj Jajoo, News Recommending Application based on the previous clicks IIT kharagpur. [2] Noam Slonim, Naftali Tishby, The power of Word Clusters for Text Classification, Hebrew University, Jerusalem, Israel. [3] Michael Steinbach, A comparison of Document Clustering Techniques, University of Israel. [4] Christopher D.Manning, Prabhakar Raghavan, and Hinrich Schutze, An Introduction to Information Retrieval Cambridge University, England. [5] Yieng Chen and Bing Quin, A Comparison between the Algorithms: SOM and K-Means, City university of Hong Kong. [6] Csorba and Vajk, Document Clustering using Confidence Measurement, Peking university, Beijing. [7] Sunetal, An hierarchical algorithm for document clustering, M.Tech, Indian Institute of Technology Madras. [8] Funand Chen, PSO method for document clustering M.Sc, University of Beijing. [9] Cuiand Potok, PSO based hybrid algorithm for Document clustering, University of Alaska. [10] Charu C. Agarwal and Cheng Xiang Zhai, A Survey on the probllems of text clustering, University of Oklahoma, USA. Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 54

81 Volume 9, Special Issue No. 3, May 2018 International Journal of Advanced Research in Computer Science (ISSN: ) CONFERENCE PAPER Available Online at EVENT MANAGEMENT INFORMATION SYSTEM Sharadha.N.L, Students of Reva University, Bangalore Sangeetha.S, Students of Reva University, Bangalore Sushmitha.H, Students of Reva University, Bangalore Sharan Patil, Students of Reva University, Bangalore Rashmi. C Asst. Professor of Reva University, Bangalore Abstract-When we talk about event management first thing that comes into our mind is how do we organize the event. Organizing an event is a tedious and time-consuming job for event management committee. Scheduling of the eventplays an important role may it be any event from the scale of small to larger.the use of the web (www) has had many positive effects on education it overcomes time and space limitation in traditional schools. The purpose of making this application is to provide an easiness of finding the event schedule at one place. The user will find the technical event and cultural event schedules in one application rather to visit different web pages. The invention satisfies the foregoing needs and avoids the drawbacks and limitations and frustrations of the prior art, and provides a better, more timely and effective process of communication to schedule and coordinate events by utilizing Internet-based application. The people fail to answer the call or would be unable to check the so this application will lead a better communication for the people about the event. This application makes it easy for the people to know the venue for the particular event held at. The invention satisfies the foregoing needs and avoids the drawbacks and limitations and frustrations of the prior art, and provides a better, more timely and effective process of communication to schedule and coordinate events by utilizing Internet-based application. This Application contains three major Users. First the admin will allow the user for accessing this application by giving the permission rights and access control. Second The college login will have a right to see the events listing and they can post their new schedule of a technical event as well. And third The End User I.e. Student, they can see the list of the events and schedule of event. Keywords-www(world wide web),internet-based Application, Organizing, Admin,User. I. INTRODUCTION In the present-day scenario involving digital approach for the real-time problems, here is our first step to ease the way to carry out the Event Management Information System(EMIS) for our college fest. The primary objective of this project is to develop an application for Online Event Handling. This application has been implemented using Web Technologies and Integrated with an Admin Controlled System which enhances the complete Event Management tasks. The following are the constructive goals set to achieve through this web application minimize the manual workload by providing a digital user-friendly interface, system admin is provided with all access permission to database and system. Through a simple onestep registration, user is entitled to a wide array of information management and services at fingertips and they can view/search the list of events with their respective venues and schedule at a particular location. The Event Management Information system is a system which will be used to implement in the institutes, where it becomes easy for the users to view, register, and vote for events conducted during the fest. This web application helps the users to register, vote for events from anywhere. The users should be first approved by the admin only then they can login to their accounts. It provides a regular flow of information for managerial decision-making and control. This project deals with registering to the events online, where students as well as the staff can view the events list and register. It tracks all the events one user has registered displays those events only in that users account in a sub module called booked events. The users complete profile as in their student id (which the college has provided), phone number can be viewed. The fields such as id and phone number can be edited. The design and implementation of EMIS system is to replace the current paper records. The admin has the rights to block and Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 55

82 Sharadha N. L et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, unblock the users. The system utilizes user authentication, displaying only information necessary for an individual. All data is stored securely on MYSQL server managed by the administrator and ensures highest possible level of security. The system features a complex logging system to track all users access and ensure conformity to data access. Previously, the college relied heavily on paper records for this initiative. While paper records are a traditional way of managing data, there are several drawbacks to this method. Paper records are difficult to manage and track. The physical exertion required to retrieve, alter, and re-file the paper records are all nonvalue-added activities. This system provides a simple interface for the maintenance of user s information. It can be used by educational institutes or colleges to manage their college fest easily. All these problems are solved using online Event Management System. Overall this project of ours is being developed to help the students as well as the staff to reduce the human efforts by making the information available to everyone by making use of the on-the-go approach. II. LITERATURE SURVEY When we talk about college fest the key point of discussion is how the fest is managed, how the events are managed, how the events are divided, who collects the event fee, etc. The proposed system is a solution for this kind of problem which makes administration task much easy. The basic idea behind this project is to build an application that provides a single colorful window that provides all information displayed with a creative userinterface. An on-the-go feature where the services are available 24*7. A. EXISTING SYSTEM A great no of people working clueless under different unorganized teams for the events. Everything manual and lacks a digital approach. No single point of contact for any information needed at times. Hectic use of paper and other materials for the events. Lack of effective campaigning and promotion activities. Unexpected last-minute changes tending to hamper the schedule. Verification and validation mechanism is absent all through the event. Event performance judgement being monopolized and fails to take audience poll into account. B. PROPOSED SYSTEM Judiciously chosen teams working towards smooth functioning of the events with a common goal in clear perception. One step ahead with digital workhouse performing the tasks flawlessly controlled by administrator. The most effective and innovative digital campaign that leads to more active participants and keeps the enthusiasm high all through. Instant notifications being sent to any unexpected changes done at the last minute. A stepby-step verification and validation performed just to ensure smooth functioning. One step registration which leads to privileges of booking/reserving services. Once logged into the portal, user can avail a host of facilities as in Detailed report about the events as in vectors pie charts, bar graphs, etc. Voting facility, previous winner, overall ranking or standing, popular votes. Real time instant updated, constant updated and summary. Survey about the participation and feedback. Computer generated schedules and are displayed for user convenience. III. IMPLEMENTATION Implementation is also the important phase where the developing of the proposed system is based on the decisions made previously in the design and system requirement phase. Selecting the platform to implement the system also the guidelines to develop the code are also discussed in this section. The decisions made on selection of the language, code and other aspects are based on the environment the system works on. usually, the implementation consists of following steps: Planning Investigating the present system and the requirements on implementation Providing training for the user about the newly developed system This is the phase of SDLC, where the theoretical System designed is turned into the actual working System. Thus, this phase is considered as the trivial phase that yields. The required results making the user confident enough about the system to use effectively. As mentioned in the about section, the steps of the implementation phase are designed with care to achieve the expected results. There are three major decisions made on the project before implementation, they are as follows: platform selection. Selecting programming language Coding guidelines. There are two modules Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 56

83 Sharadha N. L et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, Admin module User module A. ADMIN MODULE The admin is the authorized person to perform many function. the primary function of admin is to approve the user by checking if the registered user is valid or not. the admin can also block or permanently delete any user when required the admin is liable for all his action. admin is also authorized to create an event based upon the categories and can also add respective information for the same. he is also capable of scheduling the events and to start or to stop the registration for the same. admin is permitted to view the number of participants in events by option provided. the events for which the user has booked and willing to participate is also shown in a separate option as booked events. The status of whether the fees of events has been paid or due will also be reflected in the same category. Admin is capable of pushing necessary announcements at any given instant. the announcement contains a descriptive plain text and will also be able to view the same or even delete the announcement if given as situation as such. Admin is the final authority to announce the winner list or the results of the various events and the two winners are respectively allocating points for the same. B. USER MODULE The user module involves varies features user would be able to use in the application. the user firstly has to register by providing al necessary credential. once the registration is done, there will be a request sent to admin in order to approve the user by cross verifying his credential details. once it is verified and approved, the user will get the one-time password for the same and would be able to login to the web application using the same.as the user signs in, there would be plethora of options presented in an impressive user-friendly layout. amongst the options are changing his password and updating the id and contact number. the user can now view the events scheduled by getting into the respective categories and can be able to view the complete information for the same. users are permitted to participate in events by booking with options provided. the events for which a user has booked and willing to participate his also shown in a separate option as booked events. the status of weather the fees has been paid or due will also be reflected in the boked events category. the updated of the fees status once paid is also reflected showing the same paid. The user also views the rules and regulation of all events through the rules section. the user can see the contestants participating in the voting and can select hos choice and successfully cast one vote. he can as well see the contestants to who has to cast his vote. User can constantly check the live updating of score of each and every branch reflecting on the home page. apart from all these, the user will also get the announcement done by the admin. IV. CONCLUSION The web application assists in automating the existing manual system. This is a paperless work. It can be monitored and controlled remotely. It reduces the manpower required and provide accurate information. Malpractice can be reduced. All years together gathered information can be saved and can be accessed any time. Therefore, the data stored in though repository helps in taking decisions by management. So, it is better to have a web-based system. All the stakeholders, faculty and management can get the required information without delay. This system is essential in the colleges and universities. The system is developed using web design fully met meets the objective of the system which it has been developed. The system has reached a steady state that all bugs have been eliminated. The system is operating at a high-level of efficiency and all the teachers and user associated with the system understands it s advantage. The system solves the problem. It was intended to solve as requirements specification. It is always prudent to opt for a student information system that is designed using Modern system architecture to cope with changing requirements. This system should encompass very solid information coding and distinctly outlined business application, separating the presentation of Details and my thoughts of support. The availability of a computerized student information system offers a perceptivity that provides For practical education involvement and new levels of attainment. V. FUTURE WORKS In future the work of instant messaging and further module development can be done which will make our project fully automated and also more reliable software for the event should be provided. Users would be able to book the ticket and pay for it through internet-banking facility. Also, students from other college will be given the access to participate and book the event they want to participate without manually registering for the event. Near future, Admin will upload the videos of the fest and students will be given permission to access, like and comment on it for better conduction of fest next year. ACKNOWLEDGEMENT The authors sincerely thank Assistant Professor Rashmi.C for his Expert guidance and support throughout the work. The Authors also thank Reva University for the infrastructure and Support provided. Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 57

84 Sharadha N. L et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, REFERENCES [1] Zhibing Liu, huixia wang, Hui Design and implementation of student information management system international symposium on intelligence information processing and trusted computing /10 IEEE. [2]Zhi-gang Yue, you-wei Jin, The development and design of the student management system based on the network environment,2010 International conference on multimedia communications, / IEEE. [3]Tang yu-fang,zhang young-sheng, Design and implementation of college student information management system based on the web services, national science foundation of Shandong [4] Province (Y2008G22), / IEEE. [5] M.A. Norasiah and ANorhayati. Intelligent student information system. 4 th International conference on telecommunication technology preceding, Shah Alam, Malaysia, / IEEE. [6] Jin Mei-shan 1 Qiu Chang-li 2 Jing 3. The Designment of student information management system based on B/S architecture / IEEE. [7] Y.T. Yu, M.F. Lau, A comparison of MC/DC, MUMCUT and several other coverage criteria for logical decision, Journal of systems and Software, 2005, in press. Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 58

85 Volume 9, Special Issue No. 3, May 2018 International Journal of Advanced Research in Computer Science (ISSN: ) CONFERENCE PAPER Available Online at VOICE ASSISTANT- HEISENBERG P. SAI VENKATA SRIVASTAV School of Computing and Information Technology Reva University Bangalore, India ROHAN VIJAY WARGIA School of Computing and Information Technology Reva University Bangalore, India P. UTTARESHWAR VIKASRAO School of Computing and Information Technology Reva University Bangalore, India ROHIT KUMAR SINGH School of Computing and Information Technology Reva University Bangalore, India Naveen Chandra Gowda School of Computing and Information Technology Reva University Bangalore, India Abstract: When we want to get any work done we always search for different options that are available at our disposal. We cannot expect accurate help from humans all the time, but what if a automated device with its own intelligence could solve our problems. A device which can work as our personal assistant. Many companies have put huge investments in these ideas and created voice assistants for example Google assistant, Cortana, Alexa echo and so on. But the main drawback in here is, every assistant is different from other, it has its own perspective of answering a question. To overcome this problem the methodology used is, fusion of two assistant s together in a single device and activate both of them separately by there specified key word. With help of this the owner can always have a second opinion for his problem and choose the best amongst the solutions provided. Keywords: google assistant; cortana; alexa; fusion; I. INTRODAUCTION Isn t it fun when someone else does our work or help us in doing the work? Well we cannot expect a human being to do our work all the time, but what if a we had a digital assistant with us that could do all the work, a human can do, wouldn t it be cool? For example, if we want to know the weather forecast all we have to do is ask the device what s today weather going to be like or if we want to stream music we can ask the device to help us out or if our phone is connected and we can t find its location, no problem just ask the device find my phone and gives you the location. If you are sad then our device will cheer up by some funny jokes or help you with some positive thoughts. But every assistant out in the market can do this job but what make HEISENBERG special amongst them is he can assist you in two or more solutions for the same questions. A. Motivation It s always been in the human mind, that how could god create something that has its own intelligence and ability to think and humans have always wanted to replicate their work like god did, hence they started their own ways and methods to provide a brain, to the machines they develop. That is what made us fascinated, as a group to take up this project. It s always nice to have some help before doing any work and developing an assistant device who is ever ready to assist you also played as a motivation for us. One of the main reason was, that it helps in reducing human effort and provide an easy solution and help in organizing the problems and solving them, for humans. B. Objectives The To provide support to the user in his activities, assist him with suitable solution for the question. To obtain relevant answers from the internet using the LAN connection given to it. To able to recognise the voice and with help of machine leaning understand it. Ability to give the output through speakers. To house all the hardware in a single section with complete code. To clone both the API in a single device using shell scripting. Activate both the assistants using their own keywords i.e. for Google by saying Ok Google and for amazon Alexa. II. LITERATURE SURVEY Speech recognition has in years has become a practical concept, which is now being implemented in Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 59

86 P. Sai Venkata Srivastav et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, different languages around the world. Speech recognition has been used in real-world human language applications, such as information recovery. Speech in human can be said as the most common means of the communication because the information maintains the basic role in conversation. The conversation or speech that is captured by a microphone or a telephone is converted from acoustic signal to a set of words in speech recognition. A set of word can either be the final result or it can then apply the synthesis to pronounce into sounds, which means speech-to-speech. Its means that, speech recognition can serve as the input to further linguistic processing in order to achieve speech understanding. Speech recognition systems can be characterized by environment, vocabulary, acoustic model, speaking style, speaking mode, language model, perplexity, Signal to Noise Ratio (SNR) and transducer. A. Developments and Research Challenges There are abundant complications when trying to create an intelligent system. Much of the old or simple AI is a list of conditions for what reaction to have based on expected stimuli. But this is arguably not intelligence, and imitating true intelligence requires an understanding of how the input relates to the output, as well as a large interdisciplinary effort among most AI subfields along with psychology and linguistics. Many complications involve Human Machine Interaction because of the complexity of human interaction. A lot of the communication that happens between humans cannot be coded facts a machine could simply recite. There are hundreds of subtle ways that humans interact with each other that affect communication. Intonations in voices, body language, responses to various stimuli, emotions, popular culture facts, and slang all affect how two people might communicate. This is hard to model in a machine that does not have a basic common-sense model already in place that can learn or make inferences. Fuzzy Logic, which is modelled after human s excellent ability of making approximations without any real values, poses many complications. Computation, by definition, require numbers and not words or concepts. Complications arise when trying to imitate human intuition or common sense. The amount of background information that is taken for granted by humans is immense and hard to replicate in machines. Finally, using all of the subfields of AI to develop Strong AI (Artificial Intelligence equal or better than human intelligence) is incredibly complicated. Developing a system that has sentient thought would require us to fully understand how the brain and consciousness work, which we do not. There are a multitude of difficult complications within AI research. AI is a complex field, but much progress has been made in the last few years. There is endless exciting new research in the field of AI; this review is far from a global summary of the progress made in the last ten years. There are also scores of subfields within AI, but the only topics covered here will be Machine Learning, Natural Language Processing, Knowledge Management and Fuzzy Logic, Human Machine Interaction, and Image Processing and Computer Vision. These fields overlap and research in one field is not possible without research in another channel of AI. Much of the research covered in this review could be applicable to developing Strong AI but is not direct research for Strong AI. Some of the research indirectly attempts to solve Searle s Chinese Room problem. Creating a machine capable of understanding the concepts behind words is important because it allows for more humanlike conversations as well as improved translation between human languages. There is also fascinating research into rediscovering basic formulas by simply observing physical models, research into detecting human emotion through audio and visual cues, and research into reliably removing unwanted background data from the subject in video surveillance using Machine Learning. III. PROBLEM STATEMENT To clone both the assistants into a single device and activate both of them separately with its designated keyword. And to provide appropriate answers for the user posed question. IV. PROPOSED SYSTEM The voice assistant is a fusion of two assistant s together in a single device and activate both of them separately by their specified key word. With help of this the owner can always have a second opinion for his problem and choose the best amongst the solutions provided. V. SYSTEM ANALYSIS During the system study phase, requirements of Voice Assistant were categorized into system and hardware requirements. We use Raspberry PI 3 Model B as the main core hardware component. It works as a central system that holds the code which includes google and Alexa API, shell script. Several generations of Raspberry Pi have been released. All models feature a Broadcom system on a chip (SoC) with an integrated ARM compatible central processing unit (CPU) and on-chip graphics processing unit (GPU). Processor speed ranges from 700 MHz to 1.4 GHz for the Pi 3 Model B+; on-board memory ranges from 256 MB to 1 GB RAM. Secure Digital (SD) cards are used to store the operating system and program memory in either SDHC or MicroSD sizes. The boards have one to four USB ports. For video output, HDMI and composite video are supported, with a standard 3.5 mm phono jack for audio output. Lower-level output is provided by a number of GPIO pins which support common protocols like I²C. The B-models have an 8P8C Ethernet port and the Pi 3 and Pi Zero W have on-board Wi-Fi and Bluetooth. We use Raspberry Pi Model 3 for the project and Raspberry Pi 3 Model B was released in February 2016 with a 64-bit quad core processor, and has on-board WIFI, Bluetooth and USB boot capabilities. On Pi Day 2018 model 3B+ appeared with a faster 1.4 GHz processor and a 3 times faster network based on gigabit ethernet (300 Mbit / s) or 2.4 / 5 GHz dual-band Wi-Fi (100 Mbit / s). Other options are: Power over Ethernet (PoE), USB boot and network boot (an SD card is no longer required). This allows the use of the Pi in hard-toreach places (possibly without electricity. Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 60

87 P. Sai Venkata Srivastav et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, C. Noobs package Now the system is ready and has all the software s installed and cloned, run the following command to start the installer: sudo /home/pi/assistants-pi/installer.sh VII. CONCLUSION AND FUTURE ENHANCEMENT Figure 1: System Design Diagram VI. IMPLEMENTATION A. Noobs package To start off, first need to set up Raspberry Pi. To do so, simply download the NOOBS package, extract it to microsd card and plug it into Raspberry Pi. Hook up the USB Keyboard, Mouse, Mic and the Speaker to the Pi and turn it on. You will now get a first-time setup. Select Debian and install to start the installation of Debian on Raspberry Pi. B. API Downloads Create our own Amazon Developer Account and create a security profile and Next up, create a Google Developer Account and enable the Google Assistant API. Once you re through with that, download the credentials. json file of your Google Product to /home/pi. To provide the user his own personal assistant with help of artificial intelligence. Not only give user one assistant, but two assistants in the same device for the same cost. End of the day the user can use which ever assistant he wants be it Google or Alexa. Raspberry pi allows us to store the API and used for assembling all the extra hardware like speaker s, mic etc into one place. As Google or Amazon keep upgrading their API we can update and install the new version of it into our own device. If wanted we can add our own AI and integrate it with the device. Thus, provide the user an assistant at cheap price compared to a human assistant using AI. We name it HISENBERG. Develop our own assistant API which can analyse the images and get the count of students present. To able to analyse the time table and mention free period present during a combined class. To able to call out SRN of students and record the attendance based on their reply. VIII. REFERENCES [1]: [2]: [3]: (Sam Olds - WRTG 3014) [4]: ants-vocaux-ang-.pdf [5]: media=project.pdf [6]: specs-benchmarks/ Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 61

88 Volume 9, Special Issue No. 3, May 2018 International Journal of Advanced Research in Computer Science (ISSN: ) CONFERENCE PAPER Available Online at MACHINE LEARNING TECHNIQUES AS A SERVICE BASED ON MICROSERVICE ARCHITECTURE ON CLOUD PLATFORM Nithin M A 1, Prashant S Indimath 2, Nitin Raj L 3, Praveen Kumar G A 4 and Shilpa K A 5 REVA University,Bengaluru nithin.akr@gmail.com, lalanitinpadmavj@gmail.com prashantimath@gmail.com, pravi8205@gmail.com shilpaka@reva.edu.in Abstract: Machine Learning has been saturating each circle of our lives from self-governing driving, motion picture suggestions and internet shopping to focused publicizing efforts, identifying peculiar masses in the mind and securities exchange investigation. With the effect machine learning has had on our lives, there have been advocates for democratizing the science behind it to make it more open to individuals who may require it. In our undertaking, we intend to make a commitment to this drive by uncovering the energy of machine learning as an administration on a cloud based dispersed stage. We have endeavored to extract the complexities of preparing and anticipating on information however much as could reasonably be expected to enable the end client to center around the utilization of the machine adapting as opposed to the science behind it. We have planned a conveyed structure fit for both programmed scale-up and scale-out to guarantee that the client gets the execution they look for from the administration with no hiccups. I. INTRODUCTION Machine Learning has blasted over the most recent couple of years from being utilized by only a modest bunch of PC engineers investigating whether PCs could figure out how to play recreations and copy the human mind, to an expansive teach that has delivered principal factual and computational hypotheses of learning patterns and examples in any types of information. As of late, machine learning has been joined into all applications, from driving autos to observing your heartrate with a specific end goal to distinguish irregularities. However, even now, machine learning is as yet distant outside a specialty group pushing forward it improvement. Nonetheless, that pattern is changing and as of late, we have seen the group working towards making machine learning available to all [2]. Our task means to give anyone access to the learning capacities of machine learning without the stuff of understanding the internal workings of its different calculations. Also, the energy of machine learning is opened up by the advantages of distributed computing. Clients can offload the computational and administration hard work to the cloud wherein the execution ensure is guaranteed by the natural programmed scale-out and scale-up plan standards of the same. Our system is essentially focused towards IoT designers needing constant preparing and expectation on crude information, which can be proficient through our Programming interface endpoints. Be that as it may, we have outlined our system to likewise take into account a more involved and guided setting up of a machine learning pipeline through an instinctive UI. II. LITERATURE SURVEY The interest for information extraction has been expanding. With the developing measure of information being created by worldwide information sources (e.g., online networking and portable applications) and the advancement of setting particular information (e.g., the Web of Things), organizations and analysts need to interface every one of these information and concentrate profitable data. Machine learning has been increasing much consideration in information mining, utilizing the introduction of new arrangements. This paper proposes an engineering to make an adaptable and versatile machine learning as an administration. An open source arrangement was actualized and introduced. As a contextual investigation, a conjecture of power request was produced utilizing true sensor and climate information by running diverse calculations in the meantime. III. ARCHITECTURAL DESIGN This area portrays the proposed MLaaS engineering, which is intended to help machine taking in by social occasion information from numerous sources and building various models utilizing distinctive calculations. The approach centers around prescient displaying, however it is versatile to different applications. The extent of this engineering manages the machine learning itself, overlooking the front-end angles, for example, the UI. In a Model-View- Controller (MVC) viewpoint, this engineering center around the model layer while the controller and view layers are just actualized as a major aspect of the contextual investigation. The SCA outline in Figure 2 portrays an abnormal state diagram of the design. The Modeler composite is in charge of building new prescient models. A prescient model is an occasion of Modelµ composite, running a particular calculation. The cardinality 0..N demonstrates that MLaaS can run products examples of Model-µ composite in the meantime, through the Assemble, Prepare, Test and Anticipate administrations. The model property demonstrates that each occasion can keep running with various settings. Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 62

89 Nithin M. A. et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, The engineering functions as takes after: the Machine Learning as an Administration composite gets crude information from information sources through its Send Preparing Set administration. To start with, information are gotten and arranged by the Information Gatherer composite. The Modeler composite at that point gets the readied information to prepare a Modelµ case. While accepting an indicator set from the Send Indicator Set administration, the Model-µ occasion figures the forecast and serves it to outside modules through the Get Expectation benefit. The predetermined administrations give very much characterized interfaces that expansion the design's adaptability to new information sources and yields: the Send Preparing Set and Send Indicator Set administrations empower the incorporation of different information sources that will be converged by Information Gatherer; the Construct, Prepare, Test and Anticipate shoppers empower the engineering to be pluggable with various Modelµ cases; and the Get Report, Get Test and Get Forecast administrations empower distinctive UIs and outside frameworks to devour the information. The accompanying subsections portray every one of the composites appeared in Figures : 1. Information Gatherer Composite The Information Gatherer composite is in charge of getting information, pre-handling it, and sustaining it to the model. One case is made for each Send Preparing Set, Send Test Set or Send Indicator Set administrations, with the goal that they can keep running in parallel and autonomously. The Information Gatherer composite is comprised of three parts orchestrated in a pipeline as delineated in Figure 3; they can be depicted as takes after: The Merger segment blends every got datum (single information focuses or bunches) from various information sources (e.g., sensors or databases). Informational collections with various construction are joined into a solitary multicolumn blueprint by related qualities (e.g., time-stamp for time-arrangement information, classes, identifiers, and so forth). Whenever completed, it advances the information to the Anomalies Remover part. The Exceptions Remover part evacuates anomalies (e.g., missing qualities, zeros, greatly high qualities, and so on.). Once completed, it advances the cleaned information to the Pre-Processor segment. The Pre-Processor part adjusts the informational collection by re-inspecting, making segments, getting the most extreme, least, or normal qualities, and so forth. When it is done, it sends the pre-handled information to the goal segment in the Modeler composite. 2.Modeler Composite This is the center composite in the design, since it is in charge of building, preparing, testing, and running the Model-µ examples. It is comprised of five parts as delineated in Figure 2, which can be depicted as takes after: The Developer segment gets from Assemble Demonstrate the parameters (e.g., calculation and property estimations) to manufacture and send another model (a Model-µ case) for the Construct customer. At the point when the case is made, Developer sends the model identifier back to the purchaser and advances it to the Student and Indicator segments. The Student segment gets the pre-handled information from the Prepare administration and advances them to the ordained Model-µ occurrence. When it gets the preparation report from the Model-µ example through the Prepare shopper callback, it advances it to Reports Stockpiling. The Reports Stockpiling part gets the report from the Student segment through the Store Report administration and serves it to outer shoppers through the Get Report benefit. The Indicator part gets the indicator set from the Foresee administration and advances it to the Model through the Anticipate buyer, which will restore the forecast through a callback. The forecast will be come back to the Foresee requester and furthermore sent to Expectations Stockpiling. Indicator is additionally in charge of sending the testing set The Forecasts Stockpiling part gets and stores the expectations and tests from the Store Forecast and Store Test benefits and gives them to outside buyers through the Get Expectation and Get Test administrations. C. Demonstrate µ Composite The Model-µ composite is an engineering for building distinctive models. It holds all the actualized calculations source codes (e.g., Multilayer Perceptron), however just a single must be stacked. The calculation to be stacked and its parameters ought to be determined when calling the Manufacture benefit. At the end of the day, for each Form Display benefit ask for, another case of a Model-µ composite is made. The model property portrays how the model should be fabricated and executed. It is made out of four sub-properties: modelid: is the model one of a kind identifier, calculation: indicates which calculation will be utilized by the model, parameters: change the calculation conduct, and k: the quantity of folds to use in the K-Overlap Cross-Approval. The Prepare, Test, and Foresee benefit details empower the Modeler composite to collaborate with any Model-µ case. The Model-µ composite is comprised of four parts, which can be portrayed as takes after: Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 63

90 Nithin M. A. et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, The Constructor segment is in charge of stacking the correct calculation and setting the properties of the model in-position utilizing the Fabricate benefit ask for parameters. At the point when the case is set up and running, it is prepared to give Prepare, Test and Foresee administrations. The Mentor part gets the preparation set from the Prepare administration and advances it to Validator and Indicator segments through the Approve and Prepare benefits individually. At the point when approval is done, the Coach segment gets the approval report from the Approve benefit callback and returns it to the purchaser through Prepare benefit callback. The Validator segment gets the preparation set from the Approve benefit, bolsters it to the model and approves the model (e.g., K-Overlap Cross-Approval), restoring a report. The Indicator part gets the preparation set from the Prepare administration to nourish the model for future forecast demands. While getting indicator sets through the Foresee benefit, it computes and restores the forecasts. The actualized calculations source code must be capable just to train and anticipating. Testing and approving don't rely upon the calculation itself, yet on the outcomes, which can be found by utilizing the calculation's preparation and foreseeing capacities. Subsequently, testing and approving capacities are obligations of Validator and Indicator segments, expanding institutionalization and decreasing the exertion while including another calculation. IV. CASE STUDY The framework was assembled utilizing Node.js as a result of its simplicity and readiness for coding and sending Web administrations and taking care of JSON. Since there are at present no SCA structures for Node.js, one must be executed. JSON was utilized for Web benefit correspondence, information stockpiling and the SCA ancient rarity descriptor file. A basic UI was created to produce powerful delineations of the outcomes got. The source code is accessible in an open archive. A. Calculations: To assess the structural flexibility of running distinctive machine learning models in the meantime, Display composite was executed to help the accompanying calculations: The J48 Decision Tree Algorithm J48 is a decision tree learner based on C4.5 [Quinlan, 1993]. C4.5 is an update of the ID3 temp_b t >40 <=40 temp_c t =40-45 temp_c t =40-45 temp_a t-2 >32 <=32 temp_a t-1 temp_c t-1 >30 <=30 >25 <=25 temp_c t =40-45 Figure 2.4: A Decision Tree to predict the current temperature at site C based on temperature readings taken from a set of nearby sites. algorithm [Quinlan, 1986]. We describe here the ID3 algorithm. A choice tree orders a given occurrence by going it through the tree beginning at the best and moving down until the point that a leaf hub is come to. The incentive at that leaf hub gives the anticipated yield for the occurrence. At every hub a trait is tried and the branches from the hub relate to the qualities that characteristic can take. At the point when the occurrence achieves a hub, the branch taken relies upon the esteem it has for the property being tried at the hub. A choice tree that can be utilized to foresee the present hour temperature for site C in our climate illustration is given Figure 2.4. So if we somehow happened to characterize an occasion in our climate case with this tree we would begin at the root hub that tests the trait temp_at-2 and in view of the esteem taken by this characteristic in the given occurrence we will take the left or right branch. When we achieve a hub in the wake of taking a branch, the quality related with it is tried and the comparing branch taken until the point that we achieve a leaf hub, which gives the esteem taken for the yield trait temp_ct. The ID3 calculation fabricates a choice tree in light of the arrangement of preparing occurrences given to it. It adopts a voracious best down strategy for the development of the tree, beginning with the formation of the root hub. At every hub the property that best groups all the preparation occurrences that have achieved that hub is chosen as the test trait. At a hub just those properties are considered which were not utilized for arrangement at different hubs above it in the tree. To choose the best trait at a hub, the data pick up for each characteristic is computed and the quality with the most noteworthy data pick up is chosen. Data pick up for a quality is characterized as the decrease in entropy caused by part the examples in view of esteems taken by the trait. The data pick up for a property An at a hub is ascertained utilizing Sv Informatio ngain( S, A) = Entropy( S) Entropy( S) v Values( A) S where S is the arrangement of occurrences at that hub and S is its cardinality, Sv is the subset of S for which quality A has esteem v, and entropy of the set S is figured as numclasses Entropy( S) = p i log 2 p i, i= 1 temp_c t =40-45 temp_c t =40-45 Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 64

91 Nithin M. A. et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, where pi is the extent of occurrences in S that have the ith class an incentive as yield quality. Another branch is included beneath the hub for each esteem taken by the test quality. The preparation occurrences that have the test property estimation related with the branch brought are passed down the branch, and this subset of preparing cases is utilized for the formation of further hubs. On the off chance that this subset of preparing occasions has a similar yield class esteem then a leaf is created at the branch end, and the yield trait is alloted that class esteem. For the situation where no occasions are passed down a branch then a leaf hub is included at the branch end that doles out the most widely recognized class an incentive in the preparation cases to the yield property. This procedure of producing hubs is proceeded until the point that every one of the occasions are effectively ordered or every one of the properties have been utilized or when its unrealistic to isolate the cases. Augmentations were added to the fundamental ID3 calculation to (1) manage consistent esteemed characteristics, (2) manage cases that have missing ascribe esteems and to (3) avoid overfitting the information (clarified beneath). At the point when a discrete esteemed characteristic is chosen at a hub the quantity of branches shaped is equivalent to the quantity of conceivable qualities taken by the property. On account of a persistent esteemed characteristic two branches are shaped in view of an edge esteem that best parts the occasions into two. For instance, in Figure 2.4 the characteristic at the root hub, temp_at-2, has a limit estimation of 32. The edge is the chosen as the estimation of the quality that expands the data pick up of the given preparing cases. Fayyad and Irani [1993] stretched out this way to deal with split a nonstop esteemed quality into in excess of two interims. There may emerge situations where an example has no an incentive for a quality (i.e., missing qualities) or has an obscure property estimation. The missing quality can be supplanted by the most widely recognized an incentive for that characteristic among the preparation occasions that achieve the hub where this trait is tried. In C4.5, the likelihood for every conceivable esteem taken by the trait with missing quality is ascertained, in light of the circumstances it is found in the preparation occurrences at a hub. The likelihood esteems are then utilized for count of data pick up at the hub. In the ID3 calculation, some of the time because of too little of a preparation set being utilized, the tree fabricated effectively groups the preparation occurrences yet fizzles when connected on the whole conveyance of information since it centers around the false relationship in the information when the rest of the measure of information is little; this is know as overfitting. To abstain from overfitting, C4.5 utilizes a system called control post pruning. In govern post-pruning, after the tree is assembled, it is changed over into an arrangement of tenets. For instance, the administer created for furthest left way of the tree in Figure 2.4 is In the event that (temp_at-2 > 32 AND temp_at> 30 AND temp_bt> 40) At that point temp_ct= From each administer produced for the tree, those precursors are pruned (i.e., evacuated) which don't diminish the precision of the model. Precision is estimated in view of the occurrences exhibit in the approval set, which is a subset of the preparation set not utilized for building the model. The K-Means algorithm The K-Means calculation was proposed in 1967 by MacQueen. This calculation has two fundamental parameters: (1) a database, (2) a positive whole number K speaking to the quantity of groups to be removed from the database. The K-Means calculation comprises of the accompanying advances: (1) The calculation peruses the database in memory. The database contains a few cases. (2) The calculation instate K discharge groups. Each group has a model, which is a haphazardly created case. (3) Each case in the database is doled out to the group having the nearest model. (4) At that point, the model of each bunch is recomputed as the normal of the considerable number of examples in that group. (5) At that point, Step3 and Stage 4 are rehashed a few times, until the point that the groups end up stable. Presently let me delineate these means with an illustration so it turns out to be clear. By applying K-Means on the database of 2D focuses, (1) K-Means will first load the database of 31 focuses in memory. At that point, accept that the client has set K = 3 to create 3 bunches. (2) K-Means will at first make three exhaust groups which will have some irregular focuses as models. Let say that these three focuses (models) are (2,2), (16, 4) and (11, 0). We can speak to this outwardly as takes after (where the models are spoken to by the X image): At that point (3) K-means will allocate each of the 31 focuses to the bunch having the nearest model. The outcome will be as per the following: Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 65

92 Nithin M. A. et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, At that point step (3) is connected once more. K-Means allocates each point to the bunch having the nearest model. Since the bunch did not change after this progression, the K- Means calculations stop and the last outcome is the accompanying three groups, here showed with hues: At that point (4) the model of each group is recomputed as the normal of the focuses that it contains. The new models are around (1.75, 2.75), (9.1, 5.2) and (12.9, 10.7) for the three bunches of focuses. B. Results: At that point (3), each point is doled out to the bunch having the nearest model. The outcome is the groups appeared in the photo beneath. As should be obvious, a few focuses have moved starting with one bunch then onto the next. At that point, step (4) is connected again to recompute the model of each group as the normal of its focuses. The new models are (0.7, 0.7), (12.4,10.8) and (8.7,4.1). Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 66

93 Nithin M. A. et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, V. CONCLUSION With the developing measure of information accessible, organizations and specialists are requesting attainable and reasonable approaches to extricate learning from this information. This paper has displayed a novel engineering for a versatile, adaptable, and non-blocking machine learning as an administration in view of SCA and concentrating on prescient demonstrating. The proposed engineering can bolster numerous information sources and make different models with various calculations, parameters, and preparing sets. To demonstrate the idea, the framework was worked to anticipate power request utilizing certifiable information. Once the primary engineering is working and no less than one calculation coded, it is easy to execute different calculations. It is conceivable to execute different models simultaneously. For future research, MLaaS can be adjusted to machine learning applications other than prescient displaying, for instance, design acknowledgment, anomaly identification, positioning and grouping. VI. REFERENCES: [1] E. Alpaydin, Introduction to machine learning. MIT press, [2] a. S. Ahmad, M. Y. Hassan, M. P. Abdullah, H. a. Rahman, F. Hussin, H. Abdullah, and R. Saidur, A review on applications of ANN and SVM for building electrical energy consumption forecasting, Renewable and Sustainable Energy Reviews, vol. 33, pp , [Online]. Available: [3] M. Yesilbudak, S. Sagiroglu, and I. Colak, A new approach to very short term wind speed prediction using k- nearest neighbor classi fication, Energy Conversion and Management, vol. 69, pp , [4] Service Component Architecture Assembly Model Specification Version 1.1. Accessed: [Online]. Available: [5] T. Erl, Service-Oriented Architecture: Concepts, Technology, and De- sign. Pearson Education India, [6] F.-C. Lin, L.-K. Chung, W.-Y. Ku, L.-R. Chu, and T.-Y. Chou, Service component architecture for geographic information system in cloud computing infrastructure, in Advanced Information Networking and Applications (AINA), 2013 IEEE 27th International Conference on. IEEE, 2013, pp [7] T. Calmant, J. C. Am erico, D. Donsez, and O. Gattaz, A dynamic sca-based system for smart homes and fices, of in Service-Oriented Computing-ICSOC 2012 Workshops. Springer, 2013, pp [8] C.-C. Lo, D.-Y. Chen, and K.-M. Chao, Dynamic data driven smart home system based on a service component architecture, in Computer Supported Cooperative Work in Design (CSCWD), th Interna- tional Conference on. IEEE, 2010, pp [9] S. Chan, T. Stone, K. P. Szeto, and K. H. Chan, PredictionIO: a distributed machine learning server for practical software development, in Proceedings of the 22nd ACM international conference on Conference on information & knowledge management. ACM, 2013, pp [10] A. Baldominos, E. Albacete, Y. Saez, and P. Isasi, A scalable machine learning online service for big data real-time analysis, in Computational Intelligence in Big Data (CIBD), 2014 IEEE Symposium on. IEEE, 2014, pp Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 67

94 Volume 9, Special Issue No. 3, May 2018 International Journal of Advanced Research in Computer Science (ISSN: ) CONFERENCE PAPER Available Online at LOCATION BASED DISEASE OUTBREAK DETECTION SYSTEM INFERRING TWITTER DATA Pooja School of Computing and Information Technology REVA UNIVERSITY Bangalore, India M Megha School of Computing and Information Technology REVA UNIVERSITY Bangalore, India Poojarani School of Computing and Information Technology REVA UNIVERSITY Bangalore, India Priyanka School of Computing and Information Technology REVA UNIVERSITY Bangalore, India Raghavendra Nayak.P School of Computing and Information Technology REVA UNIVERSITY Bangalore, India Abstract: Irresistible ailments slaughter in excess of 17 million individuals consistently. Huge flare-ups, known as scourges, are winding up more continuous. What's more, more genuine diseases have risen in the previous decade than whenever beforehand. We require better reconnaissance frameworks to identify pestilences early. Yet, while there is the possibility to anticipate pestilences by mining information of bits of gossip and news reports (talk observation), or groups of malady indications (syndrome reconnaissance) depicted by online networking clients, we're not exactly there yet. Conventional infection observation depends on information acquired from specialists, healing centers or research facilities through formal revealing frameworks. This yields substantial and precise information about developing flare-ups and the effect of control procedures, for example, immunizations. Be that as it may, it's frequently not convenient. Advanced information are currently openly accessible from numerous sources. Individuals discuss pandemics via web-based networking media utilizing watchwords, for example, "fever" and "disease" before they are authoritatively distinguished. Keywords: Disease,Information,Regional,Flu,Influenza,worldwide,location. 1 INTRODUCTION Rising and re-developing irresistible maladies are a huge general wellbeing concern, and early recognition and quick reaction is urgent for ailment control. These difficulties have prompted the requirement for new methodologies and advances to fortify the limit of conventional reconnaissance frameworks for distinguishing rising irresistible ailments. Over the most recent couple of years, the accessibility of novel online information sources has contributed significantly to irresistible ailment reconnaissance. This examination investigates the prospering field of online irresistible ailment reconnaissance frameworks by inspecting their present status, significance, and potential difficulties. Web-based social networking presents a chance to improve scourge recognition and control. In any case, informal data is unstructured and not made for general wellbeing purposes. Calculations intended to get "fever", for example, may recognize false positives, for example, "Bieber fever". So we require all around built calculations for information mining. The tremendous amount of information accessible requires super-figuring force, and techniques to sift through foundation "clamor" dependably. Strategies, for example, time arrangement investigation can be utilized to contrast quite a while of information with test if a pandemic flag is higher than anticipated contrasted with earlier years. We utilize these techniques to enhance customary observation information, so they can be connected to online networking information. Machine learning holds guarantee for the future, however we require astute human examination and master understanding of the information. Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 68

95 Pooja et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, A reconnaissance framework for recognizing episodes of Ebola utilizing Twitter, for instance, could set geospatial labels for particular areas, for example, the African landmass. It could look for a bunch of terms on the Twitter sphere, for example, "discharge", "fever", "infection", "Ebola", "Lassa" (an ailment that can be mistaken for Ebola). The proposed approach investigates Twitter to distinguish episodes of infections in view of particular areas given certain parameters. It will enable the administration to track the flare-up and avert to promote infection spreading inside specific areas. It will likewise help the people going by a specific city be arranged well ahead of time to be safe if there is an across the board assault of an illness. A. Related work 2 EXISTING SYSYTEM Web-based social networking correspondence is an inexorably used outlet for individuals to uninhibitedly make and post data that is scattered and devoured worldwide through the Internet. News media, conventional logical outlets, and online networking make a stage for minority perspectives and individual data, which isn't being caught by different sources. Web-based social networking can make a feeling of namelessness, taking into account unadulterated individual appearance when contrasted with conventional up close and personal gatherings, particularly among youngsters and about cozy issues [1]. In this regard, online networking give an extra casual wellspring of information that can be utilized to recognize wellbeing data not answered to restorative authorities or wellbeing offices and to uncover perspectives on wellbeing related themes, particularly of a delicate sort. In the previous 10 years, look into articles associating sickness reconnaissance with Internet utilize have expanded in number, no doubt because of the expansion in accessibility of wellbeing related data from different Internet destinations. For instance, Wikipedia article hits [2], Google look terms (Google Flu Trends) [3], and online eatery reservation accessibility (OpenTable) [4] were demonstrated against the quantity of patients with flu like ailment (ILI) announced by the Centers for Disease Control and Prevention (CDC). A few writing surveys have taken a gander at the capability of this sort of research to profit human wellbeing. Moorhead et al. directed a survey of research concentrates to recognize potential uses, advantages, and constraints of online networking to draw in the overall population, patients, and wellbeing experts in wellbeing correspondence [5]. In spite of the fact that articles distinguished advantage from utilizing online networking in wellbeing correspondences, the writers take note of an absence of research concentrated on the assessment of short-and long haul impacts on wellbeing correspondence rehearses. Bernardo et al. given a perusing audit of the utilization of pursuit questions and online networking in ailment observation [6]. To start with detailed in 2006, the surveyed writing featured exactness, speed, and cost execution that was practically identical to existing sickness observation frameworks and suggested the utilization of online networking projects to help those frameworks. Velasco et al. characterized their writing survey to contain just companion checked on articles on occasion based infection observation [7] in which they distinguished and depicted 12 existing frameworks. Walters et al. depicted various frameworks executed and devoted to biosurveillance, characterized as "the train in which differing information streams, for example, these are portrayed in genuine or close constant to give early cautioning and situational familiarity with occasions influencing human, plant, and creature wellbeing," a large number of which base on human ailment episodes [8]. The paper calls attention to that including rising media, for example, web journals and Short Message Service (SMS), into these frameworks alongside institutionalized measurements to assess the execution of various observation frameworks is essential to the progression of these early cautioning frameworks. B. problem statement As opposed to convention, web-based social networking, which has so far not been broadly received in reconnaissance frameworks, can possibly give the necessaries to a constant scourge discovery framework in light of its prompt responsiveness and crowdsourcing power. The issue of identifying and following pandemic episodes through online networking can be characterized as takes after. Given a sickness or plague of intrigue, a period window, and a flood of literary or mixed media information from online networking, the errand is the means by which to extricate pertinent spatial and transient learning about the pandemic in reality. For instance, Twitter clients may post around a sickness, and the social connections in the system, which may compare to realworld connections, can give us hints about who these evil individuals will in all likelihood interact with. Furthermore, there are in excess of a thousand tweets every day that incorporate the watchwords like "flu" and "influenza". Moreover, for social administrations like Twitter and Instagram, client exercises recovered from open API regularly accompany precise GPS-based area labels, which can be precisely and promptly devoured by forecast and discovery components based upon web-based social networking information. By utilizing online networking observation framework strategy creators can know the basic point for plagues control, the viability of the arrangement, regardless of whether the genuine status is not the same as the detailed situations, and so forth; the medicinal experts can take a shot at the treatment technique prior; and general wellbeing officers could all the more successfully designate social insurance laborers, assets and medications. In spite of late advances in mining methods, there is a hole to connect between cutting edge and an attractive realtime observation framework. Not just the monstrous size of the datasets challenge current information mining approaches, Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 69

96 Pooja et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, structurelessness, heterogeneity, and spam clients likewise posture genuine difficulties. Essentially applying existing information mining calculations to the information can't connect this hole. Outlining a framework that can gather gigantic unstructured web-scale huge information in insignificant deferral and process it productively is at the center of this ongoing test. Our commitment is to openly give the proper datasets, on which imminent strategies can be tried. 3. SCOPE OF PROJECT It will help the government to track the outbreak and prevent further disease spreading within certain location. It helps the individuals to be prepared before visiting the city. 4. OBJECTIVES Recovery of Tweets with a specific end goal to perform information examination and anticipate sickness flare-ups/perform ailment reconnaissance. Perform Sentiment examination of tweets to perform extremity of the general population and the check of discusses a sickness. Gather the consequences of the assumption investigation and term recurrence based examination to perform syndromic reconnaissance 5. METHODOLOGY EXTERNAL DEPENDENCIES grounds that the multinomial guileless bayes classifier doesn't deal with strings straightforwardly. lowercasetokens just changes over all capitalized letters to bring down case ones. For this first endeavour no other preprocessing was finished. The multinomial credulous bayes classifier was then constructed utilizing Wekas worked in techniques. For sting the classifier I utilized 10-overlay cross approval. The subsequent classifier utilizes 1150 distinctive ascribes to make the characterization. Until further notice we are just utilizing diverse words to make the grouping so our properties are simply words. The classifier grouped 75.5% of the examples effectively, with a weighted normal exactness of 77.5% and review of 75.5%. Words that lone repeat once are generally not great to use since they skew the results. By definition a word happening just once needs to have a place with only one class, however there are no single words, or if nothing else not very many, that can't mean the inverse on the off chance that there is a refuting word some place before it. For instance the sentence "I have flu" may by and large demonstrate that somebody is wiped out, however by embeddings "don't" we get "I don't have flu" which most likely implies that somebody isn't debilitated. Since of this we make the following stride in the pre-processing and evacuate all words that don't happen no less than twice in the dataset. In this dataset we just have 200 occasions implying that a parcel of the words just happen once, this separating really evacuates a large portion of the characteristics, leaving 236 words that happen twice or more. As anyone might expect the subsequent classifier performs superior to the first classifier, grouping 78% of the occasions accurately with a weighted exactness of 80.8% and review of 78%. We can give the execution a huge lift by applying yet another hardly any pre-processing steps. is the library that I use so as to streamline the connection with the Twitter servers. It contains the greater part of the usefulness that I need with a specific end goal to gather tweets from the Twitter servers. Like Weka and R it is open source, it enables us to utilize it for any reason, even financially. Since Twitter4J as of now had all the usefulness I required when it came to taking care of tweets, it was a bit much for me to construct my own part for communication with the Twitter servers. PROJECT APPROACH In an underlying methodology two hundred examples where utilized, 100 positive examples and 100 negative. Since the multinomial guileless bayes classifier generally have been one of the standard classifier to use for content arrangement this appears like a decent place to begin. The starting analysis was directed utilizing Weka (see segment 4.1.2). Initial an arff-record was made comprising of two fields, content, for this situation the content in the tweet, and in addition the classification that the particular example (tweet) have a place with, this is a twofold decision of genuine or false, valid on the off chance that the case shows that somebody is debilitated, generally false Wekas worked in preprocessor was then utilized, in the principal endeavor just the channel StringToWordVector with lowecasetokens set to genuine was connected. The StringToWordVector takes a string, and changes over into a vector comprising of the individual words from that string. This is essential on the Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 70

97 Pooja et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, IMPLEMENTATION By presenting a rundown of stop words to expel before classification, the rundown being referred to is the stop word list that is utilized as a part of the PunBB discussion programming.program usageas can be found in figure 2, the whole usage is extremely revolved around the class TwitterMain. This class is in charge of the kept running of the program, and thus calls alternate classes who performs particular capacities. In figure 2 coordinate associations between classes are shown with a thick line and conditions to third party bundles are shown with specked lines. TwitterMain This is the fundamental piece of the program, basically it is an endless circle. It begins by getting a rundown of tweets from the class TwitterWrapper, it at that point calls DBWrapper and changes over this rundown of tweets to a rundown of ExtractedTweets. The rundown of changed over tweets is at that point go off to Preprocessor, which play out all the preprocessing of the content, for example, evacuating retweets. We at that point send the rundown of ExtractedTweets to the Weka class where each example is ordered. The rundown of preprocessed and arranged tweets are then passed of to DBWrapper again for inclusion into the database. Ultimately we have another call to the DBWrapper class and have it refresh the TweetsByDate and Tdatum tables. This procedure is then rehashed three times, one for every one of the parts of Sweden. Once this procedure is done we call REvaluator and it does the forecasts for the day. 6. APPLICATIONS This proposed system helps in giving access to the disease related information through twitter application. The disease outbreaks can be predicted easily with the location based data access. It gives a very fast and quick data access to disease data when compared to government based data retrieval system. The proposed system is cost-effective and adaptable. 7. CONCLUSION We introduce an approach to twitter data processing aiming at extracting health related information in a given area during an assigned period; this is achieved by and also exploiting the SNOMED-CT medical terminology and sentiment analysis technique. The final goal is to get the data for studying the spatial-temporal evolution of a selected disease in the area being considered, and first results are encouraging. 8. FUTURE ENHANCEMENT The application can be enhanced further by using a finer grained sentimental analysis to accurately identify symptom based tweets. For example, I have flu and I had flu provide opposite sentiments towards having flu symptoms. REFERENCES [1]. Iafusco D, Ingenito N, Prisco F. The chatline as a communication and educational tool in adolescents with insulin-dependent diabetes: preliminary observations. Diabetes Care. 2000;23: [2]. McIver D, Brownstein J. Wikipedia Usage Estimates Prevalence of Influenza-Like Illness in the United States in Near Real-Time. PLoS Comput Biol. 2014;10: 1 8. [3]. Ginsberg J, Mohebbi MH, Patel RS, Brammer L, Smolinski MS, Brilliant L. Detecting influenza epidemics using search engine query data. Nature. 2009;457: pmid: [4]. Nsoesie E, Buckeridge D, Brownstein J. Guess Who s Not Coming to Dinner? Evaluating Online Restaurant Reservations for Disease Surveillance. J Med Internet Res. 2014;16. [5]. Moorhead S, Hazlett D, Harrison L, Carroll J, Irwin A, Hoving C. A New Dimension of Health Care: Systematic Review of the Uses, Benefits, and Limitations of Social Media for Health Communication. J Med Internet Res. 2013;15. [6]. Bernardo TM, Rajic A, Young I, Robiadek K, Pham MT, Funk JA. Scoping Review on Search Queries and Social Media for Disease Surveillance: A Chronology of Innovation. J Med Internet Res. 2013;15: e147. pmid: [7]. Acera E, Agheneza T, Denecke K, Kirchner G, Eckmanns T. Social Media and Internet-Based Data in Global Systems for Public Health Surveillance: A Systematic Review. Milbank Q. 2014;92: pmid: [8]. Walters R, Harlan P, Nelson N, Hartley D. Data Sources for Biosurveillance. Wiley Handbook of Science and Technology for Homeland Security. John Wiley & Sons, Inc.; pp Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 71

98 Volume 9, Special Issue No. 3, May 2018 International Journal of Advanced Research in Computer Science (ISSN: ) CONFERENCE PAPER Available Online at A RATING APPROACH BASED ON SENTIMENT ANALYSIS Rathan M School of C & IT, REVA UNIVERSITY, Bangalore, India rathan.m@reva.edu.in Anjum Shirol School of C & IT, REVA UNIVERSITY, Bangalore, India anjumshirol.as@gmail.com Deeksha R N School of C & IT, REVA UNIVERSITY, Bangalore, India deeksharn4@gmail.com Deepika C School of C & IT, REVA UNIVERSITY, Bangalore, India deepikacsr@gmail.com Divya V Shiggavi School of C & IT, REVA UNIVERSITY, Bangalore, India divyavs1996@gmail.com Abstract: Due to the expansion sought after for web based business with individuals leaning toward internet buying of merchandise and items, there is tremendous sum data being shared. The online business sites are stacked with huge volume of information. Likewise, web-based social networking helps an incredible arrangement in sharing of this data. This has enormously impacted customer propensities everywhere throughout the world. Because of the distinctive audits gave by the clients, there is a criticism situation being created for helping clients purchase the correct item and controlling organizations to upgrade the highlights of item suiting shopper's request. The main drawback of accessibility of this immense volume of information is its assorted variety and its basic non-consistency. The client thinks that its hard to definitely discover the survey for a specific component of an item that s/he plans to purchase. Likewise, there is a blend of positive and negative surveys consequently making it troublesome for client to locate an apt reaction. Likewise these audits experience the ill effects of spammed surveys from unauthenticated clients. So to dodge this perplexity and make this survey framework more straightforward and easy to understand we propose a system to extricate include based feeling from an assorted pool of audits and preparing it further to isolate it concerning the parts of the item and further characterizing it into positive and negative audits utilizing machine learning based approach. Keywords: sentiment, analysis, data, online I. INTRODUCTION The world wide web can be seen as a store of sentiments from clients spread crosswise over different sites and systems, and the present citizens look into surveys and suppositions to judge items, visit gatherings to discuss about occasions and strategies. With this blast in the volume of and dependence on client surveys and assessments, producers and retailers confront the test of computerizing the examination of such huge measures of information (client audits, suppositions, opinions). Furnished with these outcomes, venders can upgrade their item and tailor involvement for the client. Thus, approach creators can break down these presents on get moment and complete criticism. Or on the other hand utilize it for new thoughts that democratize the arrangement making process. This paper is the result of our examination in gettogether assessment and audit information from famous entryways, web based business sites, discussions or interpersonal organizations; and handling the information utilizing the tenets of regular dialect and punctuation to discover what precisely was being discussed in the client's survey and the suppositions that individuals are communicating. Our approach perseveringly examines each line of information, and creates a pertinent synopsis of each audit (arranged by viewpoints) alongside different graphical perceptions. A novel use of this approach is assisting item producers or the legislature in measuring reaction. II. RELATED WORK Sentiment analysis techniques are basically isolated into two classes: Lexicon based systems and machine learning based procedures. Vocabulary based methodologies use lexicons which comprise of a colossal arrangement of words with extremity esteems related with them. For instance, "glad" is related with an esteem +1 demonstrating that it is a positive term though "miserable" is related with an esteem 1 showing that it is a negative term. There are a few word references accessible, for example, SentiWordNet [4] to perform dictionary based notion examination. In a sentence whose extremity is to be resolved, the extremity esteem related with each word is gotten and the entirety of these qualities for a sentence decides the extremity of the sentence. Think about the case of the sentence, " This motion picture made me cheerful." The supposition esteem related with "glad" is +1 while with the other four expressions of the sentence, it is zero. Consequently, the subsequent entirety of the notion estimations of the considerable number of expressions of the sentence is +1 which regards it as a positive sentence. Despite Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 72

99 Rathan M et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, the fact that it appears to be straightforward and successful, there are a couple of confinements to this approach. One of the obvious confinement is that it views each word as free of words nearby it in a sentence. For instance, the extremity for the sentence "This was not a decent motion picture." is registered to be +1 on the grounds that "not" is neither positive, nor negative and isn't related with any assessment esteem. To conquer this confinement, nullification taking care of can be utilized. Invalidation taking care of searches for words, for example, "not" which triggers the inversion of the assumption estimations of the words inside the sentence. If there should be an occurrence of the above sentence, the extremity of the word " glad" is turned around and it is related with a conclusion estimation 1 of rather than +1. Consequently, considering it as a negative sentence. There have been approaches created to enhance refutation taking care of with the expansion of linguistic relations inside dictionary based ways to deal with enhance the productivity. Machine learning construct approaches in light of the other hand use prepared informational index to perform slant investigation. Prepared information se there alludes to sentences whose polarities are arranged already frequently by human exertion. Any new sentences that are to be characterized are broke down as for the prepared informational collection utilizing different approaches keeping in mind the end goal to decide the supposition of the sentence. There are a few learning calculations which use the preparation informational index to decide the extremity of the sentences tried against them, for example, Support Vector Machines (SVM) and Naive Bayes classifier. Further, there have been approaches advanced to per-frame opinion investigation utilizing outfit of such machine learning approaches. It winds up hard to order numerous sentences by human exertion in restricted time and subsequently there have been numerous methodologies which investigate the blend of vocabulary based and also machine learning based methodologies. A typical issue with utilizing preparing information created by a figuring approach is that it may not be ordered precisely. There might be misclassifications inside the preparation information. Dynamic learning based approach takes a gander at the preparation information and gives conceivable misclassifications to the client for explanation. This approach enhances the precision of the model by diminishing the misclassifications inside the preparation dataset and utilize the negligible measure of human exertion required for order. Rathan M[1] in their research paper title Aspect based twitter opinion mining of mobile phone reviews miniaturized scale blogging locales, for example, Twitter are frequently considered as rich wellspring of feelings of the majority towards items. The character length confine in tweets urges individuals to utilize emoticons, emojis and out of vocabulary words. Because of the colossal volume of tweets being produced, it is hard to physically name tweets and make a regulated learning model for notion examination. Investigating these difficulties, there look paper intends to make an element level supposition examination display for Twitter information mining including highlights, for example, emoticon recognition, spelling remedy and emoji location. The proposed display comprises of computerized preparing information marking by utilizing vocabulary based approach. It is a philosophy based framework with the space of "Cell phone". Notwithstanding the general dictionary utilized, an arrangement of vocabularies particular for each property of the space "Cell phone" are utilized to enhance characterization exactness for preparing information age. This is utilized to arrange tweets acquired about a specific cell phone utilizing SVM classifier. Trial comes about demonstrate that the classifier in view of computerized preparing information gives great exactness. It likewise exhibits the significance of emoticon location and the trait particular vocabularies which help enhance the characterization exactness. A. Muhammad[2] have presented a vocabulary based slant investigation display named Smart SA which processes the extremity of each word inside a sentence in light of nearby setting and worldwide con-content. Neighborhood setting here alludes to change in extremity because of the words adjoining the word whose extremity is to be processed. For example, "great" would have distinctive feeling esteems in the accompanying two cases: "It was a decent film" and "It was not a decent motion picture". Worldwide setting alludes to the extremity of the terms that may have diverse slants under various situations. For instance, "since quite a while ago" utilized with "battery" and "stacking time" gives distinctive slant esteems. Shrewd SA uses an area particular vocabulary alongside a summed up dictionary to perform conclusion examination. The summed up dictionary utilized as a part of this case is SentiWordNet. On the off chance that just a single of the two vocabularies group a specific word, the conclusion esteem is considered from that specific vocabulary alone. In any case, if the two vocabularies arrange a specific word, weighted scores utilizing the consequence of both the dictionaries is utilized to connect the word with an opinion esteem. In the event that the sentence comprises of an emoji, the sentence isn't grouped and the score of the emoji itself is considered as the extremity of the sentence. The creators have likewise utilized the idea of particular words arranged as intensifiers and diminishers. Intensifier words, for example", "significantly" increment the feeling esteem related with the sentence and diminisher words like "somewhat" diminish the slant esteem related with the sentence. D V Nagarjuna Devi et al. [3] proposes a framework that uses a managed characterization approach called as help vector machine. This paper guarantees that the proposed classifier approach gives out the best outcome. It additionally distinguishes different difficulties in assessment investigation like mockery and contingent sentences, linguistic blunders, spam location and anaphora determination. Sentence level arrangement is done on input information which is additionally grouped by the subjectivity/objectivity. Facilitate perspective extraction is finished utilizing SentiWordNet. This is then additionally bolstered to SVM classifier to locate the general feeling. Vidal et al. [4] have distinguished the use of emojis and emoticons inside Twitter encourages. They demonstrate that almost one out of each four tweets investigated comprised of emoticons or emojis are an awesome method to distinguish opinions of tweets. It gives a chance to perform opinion examination on subjective explanations which utilize non printed terms, for example, emoticons to show the extremity. They recovered tweets which comprised of watchwords breakfast, tidbit, lunch and supper. They haphazardly chose 4000 tweets out of the ones recovered toper frame manual examination on the tweets. It was demonstrated that individuals utilized emoticons progressively when contrasted Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 73

100 Rathan M et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, with emojis. It is indicated that24 percent of tweets considered had emoticons or emojis included.68.1% of these tweets considered utilized emoticons while 30.9% of the tweets considered utilized emojis and furthermore demonstrates the significance of considering emoticon and emoji location while breaking down web-based social networking information for suppositions. Hu and Liu [5] outlined a rundown of positive words and a rundown of negative words, individually, in view of client audits. The positive rundown contains 2006 words and the negative rundown has 4783 words. The two records additionally incorporate some incorrectly spelled words that are much of the time display in online networking content. Assessment arrangement is basically a characterization issue, where includes that contain conclusions or supposition data ought to be distinguished before the grouping. Sandeep Kadam [6] now daily's quick improvement of web based business sites inspire individuals to express their audits about item or administration according to their advantage. Online audits are exceptionally useful for acquiring any item. Be that as it may, numerous surveys are long, which portrays their supposition in regards to item with few sentences. This makes it difficult for other individuals to judge the nature of the item at a bargain and choose whether it ought to be purchased or not. Another issue is that if there are vast quantities of online audits then it winds up troublesome for makers to keep up a record of client feelings with respect to their items. Accordingly framework proposed a component based synopsis technique for rundown of motion picture audits into positive and negative survey classes. The majority of the current work is centered around item audits. Yet, here framework concentrated on particular area that is motion picture survey. The film audit mining is not quite the same as item survey mining, purpose for that when a man composes a motion picture audit, he/she remarks not just on motion picture components, for example, music, discourse yet in addition on the related individuals who added to its creation, for example, the chief of the motion pictures, the on-screen characters and the performing artists. Then again, there are particular remarked highlights identified with item surveys in light of the fact that individuals may like a few highlights and abhorrence others. Because of this, it ends up hard to arrange feeling introduction of surveys as positive or negative. Likewise, there are numerous similar sentences in item audits. In this way, motion picture survey mining is nearly, a more difficult and intriguing space than item audit mining. Machine learning systems for slant grouping of audits including Naive Bayes, most extreme entropy and bolster vector machine(svm), to give some examples. String and Lee thought about the execution of these three machine learning strategies as far as highlights. Throb and Lee found that help vector machine (SVM) classifier performs preferred with include nearness over other machine learning procedures. Creators found that element nearness is more imperative than highlight recurrence. Framework considered opinion arrangement precision as well as framework reaction time to plan an application in versatile condition. In the event that framework will utilize SVM with highlight nearness, at that point it will require greater investment to stack SVM demonstrate on a framework. Liu, Lu and Jou found that execution of SVM classifier with highlight recurrence criteria performs superior to include nearness. In the paper titled "Element disclosure and task for sentiment mining applications"[7] the creators recommend that Opinion mining turned into an essential point of concentrate lately because of its extensive variety of uses. There are likewise numerous organizations offering sentiment mining administrations. One issue that has not been contemplated so far is the task of substances that have been discussed in each sentence. Give us a chance to utilize gathering exchanges about items for instance to make the issue concrete. In a run of the mill dialog post, the creator may give conclusions on numerous items and furthermore think about them. The issue is the way to identify what items have been discussed in each sentence. On the off chance that the sentence contains the item names, they should be distinguished. We call this issue substance revelation. In the event that the item names are not expressly said in the sentence but rather are suggested because of the utilization of pronouns and dialect traditions, we have to deduce the items. We call this issue substance task. These issues are vital on the grounds that without realizing what items each sentence discusses the feeling mined from the sentence is of little utilize. In the paper titled "Thumbs up or thumbs down? Semantic introduction connected to unsupervised order of audits"[8] the creators suggest that This paper shows a basic unsupervised learning calculation for characterizing surveys as prescribed (thumbs up) or not suggested (thumbs down). The characterization of an audit is anticipated by the normal semantic introduction of the expressions in the survey that contain descriptors or qualifiers. An expression has a positive semantic introduction when it has great affiliations (e.g., "unpretentious subtleties") and a negative semantic introduction when it has awful affiliations (e.g., "exceptionally arrogant"). In this paper, the semantic introduction of an expression is computed as the common data between the given expression and "superb" short the shared data between the given expression and "poor". A survey is named prescribed if the normal semantic introduction of its expressions is sure. The calculation accomplishes a normal exactness of 74% when assessed on 410 audits from Epinions, inspected from four unique areas (surveys of autos, banks, motion pictures, and travel goals). The precision ranges from 84% for car audits to 66% for motion picture surveys. In the paper titled "Mining and condensing client reviews"[9] the creators recommend that Merchants offering items on the Web regularly request that their clients audit the items that they have acquired and the related administrations. As web based business is ending up increasingly famous, the quantity of client surveys that an item gets develops quickly. For a famous item, the quantity of surveys can be in hundreds or even thousands. This makes it troublesome for a potential client to peruse them to settle on an educated choice on whether to buy the item. It additionally makes it troublesome for the maker of the item to follow along and to oversee client feelings. For the maker, there are extra troubles in light of the fact that numerous dealer destinations may offer a similar item and the producer regularly delivers numerous sorts of items. In this exploration, we expect to mine and to condense all the client audits of an item. This synopsis assignment is not the same as conventional content outline since we just mine the highlights of the item on which the clients have communicated their assessments and whether the sentiments are sure or negative. We don't abridge the surveys by choosing a subset or Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 74

101 Rathan M et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, modify a portion of the first sentences from the audits to catch the primary focuses as in the great content rundown. Our errand is performed in three stages: (1) mining item includes that have been remarked on by clients; (2) distinguishing assessment sentences in each audit and choosing whether every feeling sentence is sure or negative; (3) compressing the outcomes. III. PROPOSED WORK In the proposed approach the calculation will initially get the surveys of items from the given URL and after that parse the audits clean them. Locate the positive and negative extremity for each audit against the item. The item is again evaluated on the different traits to be specific Screen, Phone, Price, Speaker, Battery, Camera and Quality and afterward give the general slant conveyance of item. Today the sheer volume of information being that is produced ever is gigantic and seeming well and good out of that information is a dull undertaking. Be that as it may, consistent endeavors and research around there have driven the mechanization of the procedure to some degree. With this undertaking, we mean to advance this computerization procedure. Utilizing a mix of information total procedures, NLP, phonetic investigation and prominent representation methods we produce outwardly engaging and straightforward diagrams which give condensed criticism. This is finished by performing definite assumption investigation on the information. The fields of supposition mining and assessment examination are particular however profoundly related. Conclusion mining centers around extremity identification [positive, negative or neutral] while estimation examination includes feeling acknowledgment. Since distinguishing the extremity of content is frequently a stage in feeling examination. B. Data Cleanimg In the wake of gathering reviews of a specific item from the site, we will delimit the information. In delimiting process, we will recover the prevent words from the store and contrast these stop words and the words in the reviews(we can likewise include stop words in the vault). On the off chance that we discover stop words in the reviews, we will evacuate it. This is the way toward cleaning information. A. Review Collection Initially, we will gather audits from any of the diffrent sites which contains particular item name. At that point we will approve the surveys which we have gathered. we can store separated surveys in an exceed expectations sheet. we will give these put away reviews or surveys replicated specifically from the site to our application. C. Tokenization Subsequent to cleaning the information, we will have the surveys with no of the stopwords. In tokenization process, we will again delimit the information utilizing space as delimiter. At that point rundown of words in the surveys are gotten. Each word is separated and put away in the application as token id, token name, product id and product name. D. Frequency Computation In recurrence calculation,the rundown of tokens are perused for an application. At that point we will get the special arrangement of tokens and process the Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 75

102 Rathan M et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, circumstances word shows up in the audit. Presently it will create a recurrence id and we will make a set like (freq id, review id, token name, product id, recurrence). E. Feature Based Frequency From the recurrence set, we will locate the arrangement of one of a kind Products and after that discover the rundown of extraordinary highlights. we will acquire recurrence by doing summation per include crosswise over items. This gives Feature based recurrence set. F. FEM Matrix Computation From the recurrence set, we will locate the arrangement of extraordinary items, discover the rundown of interesting highlights. we will again discover the rundown of positive tokens and acquire positive recurrence per include. We will rehash this over each item. This gives POS based Frequency set. We will get FEM per include and per item. This procedure is called FEM Computation. G. Rank Products After the previously mentioned forms, we will get the chart for the highlights of individual items, with the goal that it is less demanding to know which item is useful for a specific element. IV. RESULT We utilized the dataset from various site. Examinations was done on this named datasets utilizing different component extraction procedure. We utilized the structure where the preprocessor is connected to the crude sentences which make it more fitting to get it. Further, the diverse machine learning systems prepares the dataset with include vectors which gives the extremity of the substance. Our application procedure the crude information in various advances like Data Cleaning i.e., evacuation of stopwords, Tokenization i.e., delimiting the surveys utilizing space as delimiter that gives rundown of words, Frequency Computation i.e., how often a specific word has rehashed. We will register the element vector which tells positive, negative or impartial rating of the item alongside its rating highlights. We have likewise spoken to this outcome graphically. Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 76

103 Rathan M et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, For instance, in the above diagram we have considered three mobile phones to be specific Product 1, Product 2, Product 3 and we have thought about battery as an element. The diagram is demonstrating that survey about battery for every one of the items is neutral. V. CONCLUSION AND FUTURE WORK As size of data show on the web has taken a state of the goliath it has turned into a need to build the productivity of the web search tools. Web mining is pointing toward this path. In this work we have done both component construct and situated in light of negative, unbiased and positive extremity. We examine the surveys, the framework produces a mainstream conclusion about the mobile phone, which thusly can be valuable for the assembling organizations. The framework likewise gives mobile phone suggestion usefulness, where in a client can enter his normal mobile phone detail, and the framework furnishes the client with a rundown of options, joined by the general population's opinion rating for the mobile phone. In this way a beginner client gets the prominent supposition from the client surveys and can settle on an educated choice while acquiring the mobile phone. Further, more Number of items can be mulled over. With the goal that more items can be suggested. More Number of highlights can be mulled over to recommend more interesting and great items. Detecting reviews that contain sarcasm and furthermore which are fake. REFERENCES [1]. Consumer insight mining: Aspect based Twitter opinion mining of mobile phone reviews Rathan M,,Vishwanath R. Hulipalled, K.R. Venugopal, L.M. Patnaik [2]. A. Muhammad, N. Wiratunga, R. Lothian, Contextual sentiment analysis forsocial media genres, Knowl. Based Syst. 108 (2016) [3]. D V Nagarjuna Devi, Chinta Kishore Kumar,Siriki Prasad, A Feature Based Approach for Sentiment Analysis by Using Support Vector Machine, 2016 IEEE 6th International Conference on Advanced Computing [4]. L. Vidal, G. Ares, S.R. Jaeger, Use of emoticon and emoji in tweets forfood-related emotional expression, Food Qual. Pref. 49 (2016) [5]. Hu M, Liu B (2004) Mining and summarizing customer reviews. In:Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and datamining. ACM, NewYork, NY, USA. PP [6]. Sandeep Kadam, Savita Harer Sentiment Classification and Feature based Summarization of Movie Reviews in Mobile Environment [7]. X. Ding, B. Liu, and L. Zhang, Entity discovery and assignment for opinion mining applications, in Proceedings of ACM SIGKDDInternational Conference on Knowledge Discovery and Data Mining (KDD 09), [8]. P. Turney, Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews, in Proceedings of theassociation for Computational Linguistics, pp , July [9]. M. Hu and B. Liu, Mining and summarizing customer reviews, in Proceedings of ACM SIGKDD International Conference on KnowledgeDiscovery and Data Mining (KDD 04), Aug Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 77

104 Volume 9, Special Issue No. 3, May 2018 International Journal of Advanced Research in Computer Science (ISSN: ) CONFERENCE PAPER Available Online at FOOTBALL MATCH OUTCOME PREDICTION USING SENTIMENT ANALYSIS OF TWITTER DATA Rathan M School of C & IT, REVA UNIVERSITY Bangalore Deepthi Raj N School of C & IT, REVA UNIVERSITY Bangalore Anupriya S School of C & IT, REVA UNIVERSITY Bangalore Sanketh School of C & IT, REVA UNIVERSITY Bangalore Vishnu V School of C & IT, REVA UNIVERSITY Bangalore Abstract: Twitter provides us with APIs for developers to engage with the Twitter platform.the biggest advantage in this is the Large Scale Machine Learning on twitter data. We use this Twitter API as a platform to extract tweets that can be used to predict football match outcomes. The extracted tweets are cleaned and structured.sentiment analysis is performed on the these tweets by implementing SVM algorithm. Our main objective is to create a predictive model which also considers the odds-on favorite, players and teams current form to predict the outcome more efficiently. Taking all of these into consideration, we implement Text Mining, Sentiment Analysis and Machine Learning to predict the windraw-lose ratio of the teams and represent it graphically in the form of a pie chart. Keywords: Predictive Analysis, Text Mining, Sentiment Analysis, Twitter API, SVM, Naïve Bayes, odds-on favorite, forms, team- A,B, players. I. INTRODUCTION The primary launch of Twitter was in July Online networking has caught the consideration of the whole world as it is thundering quickly in sending their thoughts over the globe, easy to understand and free of cost, requiring just a working web connection. Individuals are broadly utilizing this platform to share their sentiments, opinions and contemplations loud and clear. People post around 500 million tweets per day. Twitter, a web-based social networking site, is obviously an information-rich asset that has extraordinary potential in Data Analytics. One route in which the contents and the information available from Twitter (via a tweet), that can be utilized to satisfy its potential, and one that this paper expects to additionally explore, is in the field of text mining and sentiment analysis, i.e. aspire to decide if a tweet can be named either positive, neutral or negative. This paper talks about performing Sentiment analysis on the tweets to predict based on peoples opinion.traditionally sentiment analysis comes under text analysis. People tweet about almost everything nowadays. Hence the tweets usually contains a lot of information about any topic or news about almost everything in the world.this also includes information about football matches and players. Twitter has turned out to be an important wellspring of data, basically, on the grounds that it can give, now and again, more useful information than the ones found in other measurable or recorded sources, or at a few cases, the information that doesn't exist by any stretch of the imagination. A couple of illustrations are players' wounds, sacked mentors especially in real time during the matches, and the normal notion among the fan base of each group. This analysis endeavors to distinguish to what degree the data mined from Twitter can be valuable for foreseeing football results. Performing sentiment analysis on tweets can be hideous, because the tweets are unstructured, and can have images, videos, URLs, emoticons, hashtags, internet slangs, abbreviations, non-conventional spelling and grammar, and much more with the text; these are some of the complexities that need to be addressed. That is why cleaning the tweets and making it structured is of utmost importance. This is done through two distinct models. In the first place, we evaluate the execution of model that constructs exclusively with respect to Twitter highlights. At that point, we can extend the research in this paper by constructing a model that considers historical data and other statistics about the teams. At last, we consolidate both methodologies with a specific end goal to comprehend whether Twitter can give any extra data over the already existing data through the historical statistics II. LITERATURE SURVEY Football match outcome prediction is not as easy as it seems to be. There are many ways to predict an outcome, that is, through Decision trees, Neural Networks, Naïve Bayes, Bayesian networks or Random forest algorithms. There are different ways to clean and prepare the dataset, but considering these factors Players performance, team s performance, Home team features, away team features, Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 78

105 Rathan M et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, response variables etc. will give more efficiency and probability to our outcome. III. PROBLEM STATEMENT This paper focuses on building an iterative training model to train the dataset and get the predicted outcome in the form of a pie chart. A. Objectives: To implement an algorithm for automatic classification of text into positive, negative or neutral. Sentiment analysis to determine the altitude of the mass (i.e., positive, negative, or neutral) towards the subject of interest. Graphical representation of the sentiment in the form of pie chart. B. Motivation An aspect of social media data such as the Tweets is that it includes rich structured information about the individuals. It can lead to more accurate tools for extracting semantic information. It provides means for empirically studying properties of social interactions. An aspect of social media data, such as the tweets, is that it includes rich information about the individuals. Since the majority of people decide to express their views on social media, it s interesting to analyze people's emotion and attitude. It can lead to more accurate tools for extracting semantic information. Football, being one of the most watched sports in the world, and considering its popularity among people of all ages and all parts of the world, we expect to get a lot of information from the tweets in the form of opinions, predictions, forms etc that are very useful for us. D. Sentiment Analysis Sentiment analysis is the process of categorizing the tweets into: Positive, Negative, and Neutral. This way of segregating these tweets will help us to identify the sentiments and attitude of a fan and provides more information for the training of the data. E. Key factors to be Identified Odds on favorite: It is the ratio of the probable outcomes by total number of outcomes. This helps us to recognize us the win, draw or lose probability of a particular team in that match. Player s form: This is deduced with the help of a player s statistics, his historical data, on how well he has performed in the past matches, on how his performance is affected on his team. Real time form of player can also be considered b\from the tweets. F. Machine learning Algorithm SVM (Support Vector Machine): It is a supervised learning algorithm which does both classification and regression problems (but the approach is different for both). SVM is used because it is fast, reliable, and yields results for many learning outcomes. SVM is a non-probabilistic binary linear classifier algorithm which takes inputs and predicts for each given input. Naïve Bayes: Naïve Bayes classification algorithm uses probability classifiers to predict outcomes based on the maximum likelihood of that data. C. System Design This paper focuses on few important features that is unique apart from other models, those are: 1. Calculating Odds-on favorite for the teams 2. We classify teams into TEAM A and TEAM B, those players in respective teams are termed as TEAM A PLAYERS and TEAM B PLAYERS. 3. The important feature which is taken into account and plays an important role in predicting is Player s form - i.e. an individual s form is considered while predicting that team s winning probability. 4. Machine learning algorithms- here, SVM and Naïve Bayes algorithm are used. Machine learning has two phases in it i.e. the learning phase and the prediction phase. IV. IMPLEMENTATION With the help of Twitter API we retrieve tweets about a particular match which is yet to start, with the help of specific hashtags as search keywords. With these tweets a dataset is created. Data cleaning and data preparation for the further classification and prediction algorithms. Fig.1 Flow chart. Fig 1. Shows us the flow of the project. 1. Input: The tweets are extracted from the twitter API through our personal access keys, tokens and secret key. 2. Then the retrieved tweets are cleaned and processed according to our needs. 3. The pre-processed data is given as input to the classification algorithm (i.e. is SVM and Naïve bayes) Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 79

106 Rathan M et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, These tweets can be classified into positive, negative and neutral. 5. The classified tweets from the algorithm are processed to predict the outcome of the match (it can be either win, or lose or can be drawn). 6. The Output is represented as a pie chart. RESULTS AND ANALYSIS The implementation of this paper leads us to the prediction of the match and it is represented in the form of a pie chart. Output: Results from the data set: ACKNOWLEDGMENT This research was supported by Reva University. We thank our professor Rathan M from the School of Computing and Information Technology, REVA University, who provided insight and expertise that greatly assisted us in this research. REFERENCES [1] Predicting football results using Bayesian nets and other machine learning techniques- A. Joseph, N.E. Fenton, M. Neil (2017). [2] Exploring polynomial classifier to predict match results in football championships- Rodrigo G. Martins, Alessandro S. Martins, Leandro A. Neves, Luciano V. Lima, Edna L. Flores, Marcelo Z. do Nascimento(revised on April 2017). [3] Psychology of Sport and Exercise- Adam Gledhill, Chris Harwood, Dale Forsdyke (revised on March 2017). [4] A Hybrid Approach for Sentiment Analysis using Classification Algorithm- Ruchika Aggarwal, Latika Gupta (June 2017). [5] Football Match Winner Prediction- Saurabh Vaidya Harshal Sanghavi, India Kushal Gevaria. [6] Support Vector Machine Based Prediction System for a Football Match Result- Chinwe Peace Igiri. [7] Predicting Soccer Match Results in the English Premier League- Ben Ulmer, Matthew Fernandez School of Computer Science Stanford University. [8] Twitter passed 500M users in June 2012,140M of them in US; Jakarta Biggest Tweeting City. [9] SENTIMENT ANALYSIS OF TWITTER DATA- V.Lakshmi, K.Harika, H.Bavishya, Ch.Sri Harsha,feb [10] Sentiment Analysis on Twitter Data- Anika Rehman,Ahmad Ali retrieved /061 [11] Twitter Sentiment Analysis : The Good the Bad and the OMG!- Efthymios Kouloumpi(2014) [12] Using Twitter to predict football outcomes -Stylianos Kampakis, University College London, Andreas Adamides, University College London [13] Sentiment Analysis on Twitter Data- Aanshi Desai,Haranshvir Gujral, Sindhu Nair,Oct 2014 [14] A novel way to Soccer Match Prediction- Jongho Shin Robert Gasparyan by Stanford University. [15] Dutch football prediction using machine learning classifiers- Research paper business analytics Abel Hijmans. Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 80

107 Volume 9, Special Issue No. 3, May 2018 International Journal of Advanced Research in Computer Science (ISSN: ) CONFERENCE PAPER Available Online at PREDICTING THE STOCK PRICE USING LINEAR REGRESSION Sasidhar Reddy Bommareddy Department of Computer Science, Reva Institute of Technology and Management, Bangalore. Kaushik P Department of Computer Science, Reva Institute of Technology and Management, Bangalore. K Sai Smaran Reddy Department of Computer Science, Reva Institute of Technology and Management, Bangalore. K V Vinay Kumar Department of Computer Science, Reva Institute of Technology and Management, Bangalore. Dr.Vishwanath R Hulipalled School of Computing and Information Technology REVA University. Bangalore Abstract: Stock price prediction is always a predominant goal for every investor which helps them to knowing the future prices considering the previous records. We made our effort in predicting the same using statistical modeling approach, the Linear Regression Technique for finding Open, Close, High and Low values of TCS from National Stock Exchange Of India (NSEI). Keywords: Linear Regression, National Stock Exchange of India, Prediction, Stock Market. I. INTRODUCTION Stock Market is a public market for trading of company stock and derivatives at an agreed price. It is generally a dynamic market where the prices vary and it becomes difficult for an investor for predicting the prices considering external factors like factors like political situations, public image on the company according to efficient market hypothesis [16]. Stock Market became one of the integral parts of global economy to the extent that any fluctuation in the market influences personal and corporate financial lives and the economic health [2]. For the past decades, predicting the stock prices has been a trendy topic in financial applications. If the accuracy of prediction is more, decisions can be taken easily for the future [1].The risk of falling of stock prices is very rare due to market fluctuations, but there is a risk again [2]. Successful prediction of stock prices can yield significant profits. There are two basic methodologies investors rely on when the objective of analysis is to determine what stock to buy and at what price: Fundamental Analysis, Technical Analysis. Fundamental Analysis is the examination of the underlying forces that affect the well being of the economic, industry groups and companies. To forecast the future stock fundamental analysis combines economic, industry and company analysis along with political climate to derive a stock current fair value and forecast future value. On the other hand technical analysis is a methodology that is used to predict the direction of price movements based on current and past movements represented in a chart. This analysis basically uses charts to understand the pattern and trend of market. In this paper we use linear regression, which is statistical learning technique where we predict the value depending on the criterion variable. The main motivation behind this work is that it is very crucial for stock market investors to estimate the behavior or trend of stock market prices in order to invest in a company/category which is and is going to trend in profits in coming future. The unpredictable volatile market index makes it a highly challenging task to accurately forecast its path of movement. In this context we are comparing two predictive techniques to know the technique with best efficiency in terms of predicted values vs. the actual market values. In the following sections, in section 2 we look at the literature review of some reference papers where we understand their work, later in section 3 we will go through the Linear regression. In further sections, we will look at the results we observed with conclusion and future scope of the work. II. LITERATURE SURVEY Muhammad Waquar et al [1] study, underlined the utilization of principal component analysis (PCA) to improve the performance of machine learning model in classification of high dimensional data. But as it was investigated they concluded that PCA does not always guarantee the improve of accuracy. Dinesh Bhuriya et al [2] used linear regression, polynomial and RBF regression to predict the stock prices using 5 variables and compared the above models and concluded that linear regression is best among all other used. P ASamarak et al [3] in their research work stated that abnormality detection is key step for achieving the self-healing concept. With their study, they explored applications of supervised machine learning techniques to detect abnormal behavior in systems. When used different techniques author Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 81

108 Sasidhar Reddy Bommareddy et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, found Random forest algorithm provides high level accuracy with oversampling techniques.eswara Reddy et al [4] in their research paper for highly volatile financial TSD, they proposed ARIMA-GARCH model which is suitable for multi-step ahead forecasting, involves MA filter based decomposition as a preprocessing step on given TSD. Harun Ercan [5] in his experimental results found that the forecast values are close to actual values on using NARX model on Baltic Stock Market. Qiubin Liang et al [6] in their research on stock market trend prediction proposed a model to forecast the stock prices considering the importance of future representation for machine learning based model. They investigated Restricted Bolztmann Machine for feature extraction. On experimental results, they found that features extracted by Bernoulli Restricted Bolztmann Machine gave a higher direction accuracy. In addition, it is found that this method is only effective for trend prediction. Kai Chen et al [7] work yielded good accuracy of results when normalization was considered. Also, SSE index accuracy was also improved. But this result was only in case of Shanghai Securities ETF180.Poonam Somani et al [8] in their research on stock market prediction used Hidden Markov Model. They surveyed on various techniques like neural networks, support vector machine and concluded that Markov Model is more efficient in extracting the information from the dataset. Eslam Nader Desokey et al [9] in their research paper on Enhancing stock prediction clustering using K-Means with genetic algorithm (GA) has observed accuracy of 89.31% by selection of new model using new centroid selection optimization for K-Means with GA.Samarawickrama et al [10] found that when considering the forecast error or test error MLP models produce the highest and the lowest errors. The forecasting accuracy of the best feed forward networks is approximately 99%. SRNN and LSTM networks generally produce lower errors compared with feed forward networks but in some occasions, the error is higher than feed forward networks. Compared to other two networks, GRU networks are producing comparatively higher forecasting errors. RohitVerma et al [11] study used a neural network for predicting the stock market but the accuracy was found to be accurate until when large data with sudden variations is not considered.bihui Luo et al [12] used an algorithm in based on the derivation of the theory of calculus, which has strong versatility, and it is widely used in the field of application.the main disadvantage here is, the structure of Hidden layer is difficult to determine. R.M. Kapila Taranga Raatnayanka et al [13] in their study focused mainly on identifying the suitable hybrid forecasting approach based on ANN with traditional ARIMA approach under the high volatility. R.M.C.D.K. Rajasinghe et al [14] work which was based on Random Walk Hypothesis, suggests the unpredictable nature of prices in financial market. Closing prices were predicted and it was found that actual and predicted are closely moving. Felix Ming Fai Wong et al [15] proposed a unified latent factor model to model the joint correlation between stock price and newspaper content, which allows us to make predictions on individual stocks, even those that do not appear in the news, they used ADMM algorithm to formulate the sparse matrix factorization problem. Bing Yang et al [16] in their research on Stock market index prediction using deep neural network ensemble (DNN) after experiment on Shanghai stock market using DNN algorithm they concluded that the accuracy of prediction for close values is not satisfactory. III. DATA AND PREPROCESSING Data Collection: We have collected the history data of TCS from the National Stock Exchange of India (NSEI) from Quandl consisting of Open, Close, High, Low and Volume as attributes. Each attribute has its own value and time at which they are recorded. Total data of 100 months is collected in which 99 months is used for training and the rest 1 month for testing the performance of the model. Pre-Processing: Data pre-processing is a collective name for all methods that aim to ensure the quality of the data. In this stage we basically perform pre-processing on the data by selecting the best features which are extracted from the data collected. The data collected is then modified for just selecting only the required type of the data and removing the unwanted data. The various pre-processing stages include Feature generation which is used for selecting only the required type of the data. The data is then trained and a model is built based on this for later prediction process. The data model built is then scaled and only the required data is selected. This is the preprocessing technique. Prediction Process:In this process, Linear Regression algorithm is applied on trained data where we predict the prices of Open, Close, High, Low values. This is the stage where the actual algorithm/technique is applied and the prediction process takes place. Linear Regression Mathematical Calculation: Linear regression model tries to produce the best possible straight line for the dataset. For determining the best fit we attempt to minimize the distance between all points and their distance to our line. In this regression technique we predict the value of one variable of Y from the other values of X, where Y is the Criterion Variable and X is called the Predictor Variable that we are basing our Prediction. The model calculates the prediction with the following formulae yy = aa + bbbb Here a represents the index or the intercept and b represents the slope or the coefficient of the variable x. aa = yy bbxx The value of b is calculated by the formulae: bb = nn xxxx ( xx yy) nn xx 2 ( xx) 2 (1) (2) (3) Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 82

109 Sasidhar Reddy Bommareddy et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, The values of the above terms are calculated as follows: Where, yy = yy nn y = y 1 + y 2 + y y n (4) nn MMMMMM = 1 nn ( ii=1 YY ii YY ) 2 ii MMMMMM = nn ii=1 yy ii xx ii nn xx = xx nn (5) IV. RESULT ANALYSIS Where, x = x1 + x 2 + x x n After calculating the values of (4) and (5) substitute those in equation (2). Now calculate equation (3) and finally substitute (2) and (3) in equation (1). The following is the Algorithm which we have used for prediction using Linear Regression: AlgorithmLinear Regression Input: x_train: Open values of a company y_train: Close values of a company x_test: Open value for which close value is predicted n:- Size of training dataset Output: predict_value:- Predicted value. 1: deflinear_regression (x_train, y_train, x_test, n) 2: begin 3: fori = 0 to n 4: begin 5: x_sum = 0,y_sum=0 6: x_sum + = x [ i ] 7: y_sum + = y [ i ] 8: end 9: x_mean = x_sum / n 10: y_mean = y_sum / n 11: for i = 0 to n 12: begin 13: xy_sum = 0 14: xy_sum + = x[ i ] * y[ i ] 15: x2_sum + = x[ i ] * x[ i ] 16: end 17: x_sum_sq = x_sum * x_sum 18: l= n * xy_sum - (x_sum * y_sum) 19: m = n * x2_sum - x_sum_sq 20: b = l / m 21: a = y_mean - ( b * x_mean) 22: predict_value = a + b * x_test 23: return predict_value 24: end Model Evaluation: This is the final stage in the model. In this stage we are evaluating the performance of the algorithm in terms of Mean Squared Error (MSE), Mean Absolute Error (MAE) and R2-Score. Fig 1: Comparison of actual open values with predicted open values for company TCS between to Fig 2: Comparison of actual high values with predictedhigh values for company TCS between to Fig 3: Comparison of actual low values with predicted low values for company TCS between to Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 83

110 Sasidhar Reddy Bommareddy et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, Fig 4: Comparison of actual close values with predicted close values for company TCS between to Table I Error Analyses of Linear Regression Technique. Models Open High Low Close MSE MAE R 2 Sco re CONCLUSION We performed study on TCS stock prices in National Stock Exchange of India (NSEI) using Linear Regression Technique by predicting the values of Open, Close, High, and Low. Our main goal for this study is to assist stock market investors understand the future prices of TCS as predicting the stock prices is always a challenging task as the market is dynamic in nature. Future scope of this study involves considering more multiple companies from any stock exchange of different countries and also performing comparison of any different techniques of prediction so that one can understand which technique has less MSE, MAE and R 2 Score. REFERENCES [1] Muhammad Waqar, Hassan Dawood,Muhammad Bilal Shahnawaz, Mustansar Ali Ghazanfar, Ping Guo. Prediction of Stock Market by Principal Component Analysis, 13th International Conference on Computational Intelligence and Security, [2] Dinesh Bhuriya, Ashish Sharma, Upendra Singh. Stock Market Prediction using Linear Regression, International Conference on Electronics, Communication and Aerospace Technology ICECA [3] P. A. Samarakoon, D. A. S. Athukorala, System abnormality detection in stock market complex trading systems using machine learning techniques, Information Technology Conference (NITC), 2017 National. [4] C. Narendra Babu, B. Eswara Reddy, Selected Indian stock predictions using a hybrid ARIMA-GARCH model, Advances in Electronics, Computers and Communications (ICAECC), 2014 International Conference. [5] Harun Ercan. Baltic Stock Market Prediction by Using NARX, Computer Sciences and Information Technologies (CSIT), th International Scientific and Technical Conference. [6] Qiubin Liang, WengeRong, Jiayi Zhang, Jingshuang Liu, Zhang Xiong. Restricted Boltzmann Machine Based Stock Market Trend Prediction, Neural Networks (IJCNN), 2017 International Joint Conference. [7] Kai Chen, Yi Zhou, Fangyan Dai, A LSTM-based method for stock returns prediction: A case study of China stock market, Big Data (Big Data), 2015 IEEE International Conference. [8] Poonam Somani, ShreyasTalele, SurajSawant, Stock market prediction using Hidden Markov Model, Information Technology and Artificial Intelligence Conference (ITAIC), 2014 IEEE 7th Joint International. [9] Eslam Nader Desokey, Amr Badr, Abdel Fatah Hegazy. Enhancing Stock Prediction Clustering Using K-Means with Genetic Algorithm, Computer Engineering Conference (ICENCO), th International. [10] A.J.P. Samarawickrama, T.G.I. Fernando. A Recurrent Neural Network Approach in Predicting Daily Stock Prices, Industrial and Information Systems (ICIIS), 2017 IEEE International Conference. [11] RohitVerma, PkumarChoure, Upendra Singh, Neural networks through stock market data prediction, Electronics, Communication and Aerospace Technology (ICECA), 2017 International conference. [12] Bihui Luo, Yuan Chen, Weichen Jiang, Stock Market Forecasting Algorithm Based on Improved Neural Network, Measuring Technology and Mechatronics Automation (ICMTMA), 2016 Eighth International Conference. [13] R. M. KapilaTharangaRathnayaka, D.M.K.N Seneviratna, Wei Jianguo, HasithaIndikaArumawadu, A hybrid statistical approach for stock market forecasting based on Artificial Neural Network and ARIMA time series models, Behavioral, Economic and Socio-cultural Computing (BESC), 2015 International Conference. [14] R.M.C.D.K. Rajasinghe, W.D.N.M. Weerapperuma, W.U.N.N. Wijesinghe, K.K.K.P. Rathnayake, L. Seneviratne, A Hybrid System for Forecasting Stock Price Variations in the Stock Market, Software, Knowledge, Information Management and Applications (SKIMA), th International Conference. [15] Felix Ming, Fai Wong, Zhenming Liu, Mung Chiang, Stock Market Prediction from WSJ: Text Mining via Sparse Matrix Factorization, Data Mining (ICDM), 2014 IEEE International Conference. [16] Bing Yang, Zi-Jia Gong, Wenqi Yang, Stock Market Index Prediction Using Deep Neural Network Ensemble, 36th Chinese Control Conference, July 26-28, Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 84

111 Volume 9, Special Issue No. 3, May 2018 International Journal of Advanced Research in Computer Science (ISSN: ) CONFERENCE PAPER Available Online at PREDICTION ACCURACY COMPARISON OF PREDICTIVE MODELS USING MACHINE LEARNING FOR DIABETES DATA SET Doreswamy G S M. Tech (CSE), CEC Bengaluru, India Nandish Asst. Professor, CEC Bengaluru, India Santosh Kumar J Assoc. Professor, KSSEM Bengaluru, India Abstract- Diagnosis of Diabetes disease at beginning stage is important for healthier treatment. In today s scenario equipments like sensors are used for discovery of infections. Accurate classification techniques are necessary for automatic detection of disease samples. this study utilizes data mining techniques for classification of Diabetes patients. Five algorithms (Logistic Regression and Artificial Neural Network, SVM, Random forest) were implemented for classification using R platform. Classification and prediction of medical datasets poses real challenges in Data Mining. To deal with these challenges Logistic Regression (LR) and Artificial Neural Network (ANN) SVM, Random Forest are commonly used. LR enables us to examine the relationship between a categorical outcome and a set of descriptive variables. LR explains that there can be one or more self-governing variables that can establish the problem outcome. ANN resembles the human brain and here the information is processed by simple elements called neurons and signals are transmitted between the neuron From the experimental results it is identified that for Diabetes dataset NN with 10 fold using percentage split prediction correctness of 84.52% is achieved. Keywords - Diabetes disease, Logistic Regression, Neural network, R. 1. Introduction In recent years, electronic health records in modern hospitals and medical institutions larger to get better the value of patient care and increase the output and efficiency of health care Diabetes. So methods for efficient processor based analysis are needed due to the inadequacy of traditional manual data analysis. Machine learning methods has been a wonderful support for making prediction of a particular system by training. In recent year's machine learning has been the developing, reliable and supporting tool in medical area. Due to recent advances in machine learning, medical analysis improves diagnostic accuracy, reduces cost and reduces human resource. Medical analysis is a difficult and complete task, and it should be carried out well and precisely. Most of the medical decisions are made based on doctor s recommendation and skill rather than the knowledge concealed in the database. This practice may lead to errors and surplus medical cost which can affect quality of medical service provided to the patients. Data mining techniques can improve the quality of medical decisions considerably. We propose a new model for medical predictions based on LR.SVM.RF and ANN. LR is one of the data mining methods used for analyzing problems where the outcome is determined based on one or more independent variables.it is used to predict the binary outcome. In LR, the non-independent variable is binary i.e., it consists of data represented as 0 (FALSE, failure, etc.) or as 1 (TRUE, success, etc.).lr is used in various biomedical fields such as cancer analysis, survival forecast, kidney transplant etc. LR is implemented on the health care databases for detecting the patterns which are useful for either forecasting or determining the diseases. ANN is measured as an vital field of Artificial Intelligence. The ANN model development was motivated by the neural design of human brain.anns is successfully used in various fields such as environmental science, study of numbers, study of medicine, study of computers etc. ANNs are also being used in many business areas like accounts and audits, funding, managing and decision making, promotion and manufacture etc. ANNs have turned out to be a well-liked model and recently they are used to identify diseases and to forecast the patients survival proportion [2]. The planned research work is mainly paying attention to obtain better classification accuracy with less number of attributes by which we reduce the amount of time required for prediction and also improve the classification accuracy. This will result in reducing the number of tests that is to be done while predicting the presence or absence of disease. 2. LITERATURE SURVEY Multilayer Feed-forward Network: This network is made of input, hidden and output layers.supervised learning is an approach to find the input-output association from the training using a set of data. Learning system is fed with the input data and generate output, which is then compare with the target to calculate the error signal. The error is sent to the learning system for more training until the least value of error is generated [3][4]. The learning procedure takes place by inputting the data to be train by the network. The information from the input layer is spread to the hidden layer for information process. Then the output layer will Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 85

112 Doreswamy G S et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, extra process the information to obtain the results. The outputs are then compared with the preferred values for error computations. For prediction and diagnosis of various diseases with good accuracy Data Mining techniques are widely used. The two most successful data mining tools, Neural Networks and Genetic Algorithms are used for prediction ofdiabetesdisease. To initialize the Neural Network weights global optimization advantage of Genetic Algorithms is used. Using this technique the learning is faster, more stable and accuratewhile diagnosing a disease the patient has to undergo various tests which are costly and sometimes all the tests are not required. For automated detection of Diabetesis an intelligent and effective methodology is designed based on Neural Network. There exists many methods to diagnose Diabetesbut the main drawback is that the patient has to undergo various tests. Using this method user can check whether he/she is suffering from Diabetes or not. Pattern Recognition and Data Mining techniques are used in hazard prediction of cardiovascular medicine. The data to be modeled is classified using Data Mining technique. A large amount of varied data is usually generated from the current medicine. The change of this huge quantity of data to constructive information and knowledge is the major challenge. The Data Mining techniques permit the discovery of medicine and support the predictions on the individual[5]. 3. METHODOLOGY 3.1. Data Collection: The Diabetes was selected from UCI Machine learning repository for this study. It is a trial of the entire Indian population gathered. The dataset comprised of 345 rows and seven different Colums. The class value was reported based on these parameters as either 1 or 0 to represent the Diabetes Pre-processing: Was applied to standardize the missing values. The missing parameter along with their instances were replaced by 0 value Randomization and splitting of dataset:the features chosen in the earlier step were approved to develop classification model. The Dataset was randomized to obtain an random permutated sample. It was followed by dividing of the dataset into training and test sets Classification algorithms: Different data mining algorithms like of Logistic Regression, SVM, RF and NN were implemented in R platform for classification. R is a admired statistical computing structure for performing data mining experiments. A 10-fold cross validation is applied. The algorithms are briefly discussed below: Logistic Regression: Logistic Regression, also known as Logit Regression or Logit Model, is a mathematical model used in statistics to estimation the probability of an event happening can be by given earlier data. Logistic Regression work with binary data, where either the occurrence happens (1) or the occurrence does not happen (0). So given feature x it tries to find whether some occurrence y happens or not. So y can either be 0 or 1. In the case where the event happens, y is given the value 1. If the occurrence does not happen, then y is given the value of Neural Network: Neural Networks are a machine learning structure that attempts to mimic the learning pattern of natural neural networks. Biological neural structure have interconnected neurons with dendrites that get inputs, and then based on these inputs they create an output signal through an axon to another neuron. To create a neural network, we simply start to add layers of perceptrons together, creating a multi-layer perceptron model of a neural network. We have an input layer which directly take in your feature inputs and an output layer which will create the outputs. Any layers in between are known as hidden layers because they don't directly "see" the feature inputs or outputs 4. RESULTS AND DISCUSSION To compare the Accuracy of LR SVM RF and ANN model for diabetes data set using cross validation sample and percentage as test options. The stipulation of the datasets is as shown in table 1. Table 1. Specification of medical datasets Sl. No Dataset Instances Total attributes 1 Diabetes classes For full attribute set of Diabetes dataset the classification accuracy attained is 84.52% by NN with K fold as shown in Table 2accuracy attained for full set of attributes. Percentage Split Technique Used for Finding Classification Accuracy 50% 66% 70% 75% 80% LR NN NN with 10 Fold SVM RF CONCLUSION The research work compare the different machine learning methods like LR,NN,SV,RF and NN with 10 Fold for Diabetes dataset. From the experimental results it is recognized that for Diabetes dataset with NN along 10 Fold using split ratio of 66%, prediction accuracy 84.52% is achieved. REFERENCES [1]. Raghavendra B.K., Jay B. Simha, Performance Evaluation of Logistic Regression and Neural Network Model with Feature Selection Methods and Sensitivity Analysis on Medical Data Mining, International Journal of Advanced Engineering Technology (Vol. II, Issue: I, January-March 2011), pp [2]. Raghavendra B.K., S.K. Srivatsa, Raghavendra S, Shivashankar S.K., Comparison of Logistic Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 86

113 Doreswamy G S et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, Regression and Neural Network Model with and without hidden Layers, Universal Journal of Applied Computer Science and Technology, Vol.1, 2011, pp [3]. Raghavendra S, and Indiramma M., Performance Evaluation of Logistic Regression and Artificial Neural Network Model with Feature Selection Methods using Cross Validation Sample and Percentage Split on Medical Datasets, Second International Conference on Emerging Research in Computing, Information, Communication and Applications (ERCICA- 2014), August [4]. AnkitaDewan and Meghna Sharma, Prediction of Heart Disease Using a Hybrid Technique in Data Mining Classification, 2015, page(s): [5]. SunitaSoni, Ujma Ansari, Dipesh Sharma and JyotiSoni, Predictive Data Mining for Medical Diagnosis: An Overview of Heart Disease Prediction, International Journal of Computer application ( ), vol. 17, no.8, March (2011). Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 87

114 Volume 9, Special Issue No. 3, May 2018 International Journal of Advanced Research in Computer Science (ISSN: ) CONFERENCE PAPER Available Online at SECURE SENSITIVE DATA SHARING ON BIG DATA PLATFORM Shivani Kumari Vidya Harika Computing and Information Technology, REVA University Computing and Information Technology, REVA University Shashi V Shruti Computing and Information Technology, REVA University Computing and Information Technology, REVA University shashivg09@gfmail.com shrutimona21@gmail.com Sujata K Computing and Information Technology, REVA University sujathak@reva.edu.in Abstract Utilizers store immense measures of delicate information on a major information stage. Through sharing sensitive information, we can bring down the cost of giving utilizers customized information benefits in a less demanding way. Albeit, secure information sharing is troublesome. This paper proposes a substructure for secure information sharing on a major information stage, including secure information conveyance, stockpiling, use, and decimation on a semi-trusted enormous information sharing stage which incorporates idea of intermediary reencryption calculation in light of heterogeneous figure content change. The substructure shields the security of clients' sensitive information successfully and shares this information securely. In the meantime, information proprietors hold finish control of their own information in contemporary technologies. I. INTRODUCTION Secure data which implies ensuring computerized data, for example, those in database, from damaging powers and from the undesirable activities of approved client, for example, cyberattack or data breach. Secure data which implies ensuring advanced data, for example, those in database, from ruinous powers and from the undesirable activities of approved client, for example, cyberattack or data breach. Huge data has a gigantic potential to reshape our lives with its prescient power. As more individual data is gathered up by more capable PCs, gigantic arrangements of data-huge data-have accessible for admissible uses as well as dangers. However, because of probability of antagonistic use, there are both security and protection dangers of enormous data we are worried about. Beforehand encryption, get to, control, trusted figuring and data security demolition innovation in a distributed storage was polished. With respect to innovation, property-based encryption, decryption calculation and figure content access control instrument was utilized. Later trusted stage module was presented which incorporated a thought of building a trusted terminal stage in light of a security chip and after that setting up trust between stages through remote system.. And eventually data destruction scheme was introduced to present hoping attacks of extending the lengths of network and how to solve the problem. The main purpose of data protection is this can be explained in three procedures,they are regulatory compliance within unstructured data must begin with an understanding of the data to print itself. And second is for used reports, clear directness and the knowledge that only data owner have. Finally, regular view of stored data, transparency of data should be maintained. Implementation and continuous monitoring of suitable work flows to ensure that audit demands can be efficiently fulfilled. II. PROBLEM STATEMENT Security issues in data sharing: In this phase we can know what and all happens if we don t protect or secure our personalized data. The following procedure will help us understand the issue which we are facing: Tampering can happen which means where someone controlling a router between you and the server can alter the contents of data as it... HTTPS is protection. The web has changed our lives in incalculable positive ways, however it has a dim side. Individual protection has been lost, abandoning you in danger from shady people, organizations and security offices. There are many snoopers who tries to access our accounts without our permission which leads to further problems. If data is not secured loose of data is very easy. Web browsing is also a platform where there is no security for one s personal data. If your data is not secured whichever sites ou have visited can be public, where users face problem with it. This leads to unsecure data which creates troublesome to the users. III. RELATED WORK Here, we center around past work on applicable points, for example, encryption, get to control, confided in figuring, and data security annihilation innovation in a cloud storage condition. What's more, our framework endures the trade-off assault towards traits specialists, which isn't shrouded in numerous current works. Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 88

115 Shivani Kumari et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, The main contributions are: 1) The plot can secure client's protection against each single expert. (N 2) specialists does not bring the entire framework down. 2) This plot is tolerant against expert trade off, and compromising of up to 3) We give itemized analysis on security and performance to demonstrate possibility of our plan. 4) We first implement the genuine toolbox of multiauthority. Many accessible encryption plans have been created. Song et.al. proposed an accessible encryption in which each expression of a document is encrypted independently. Goh proposed a plan to construct an index of catchphrases for each document with pseudo randomizing functions utilized as hash functions. This security demonstrate is insufficient as it cannot avoid certain assaults. IV. Algorithms: IMPLEMENTATION 1. Ontology Management System: a. Location Ontology: The predefined location ontology is used to connect location information with the query items. The majority of the keywords and key-phrases from the documents returned for inquiry q are extracted. On the off chance that a keyword or key-state in a retrieved document d coordinates a location name in our predefined location ontology, it will be treated as a location concept of d. b. Content Ontology: Like the substance metaphysics, the area cosmology together with explore information are utilized to make feature vectors containing the customer area inclinations. They will then be changed into an area weight vector to rank the ordered records as per the customer's area inclinations. 2. Hash Tag Generation for Query Terms and Privacy Data Client area and questions are put away into server which is hash coded by MD5 (One way encryption). 3. Individual Behavior Collections utilizing SPY NB Calculation Spy NB acquires customer lead models from inclinations extricated from explore information. Accepting that customers just tap on reports that are important to them, Spy NB views the clicked records as positive cases, and foresee tried and true negative archives from the unlabelled (i.e., un clicked) archives. To do the expectation, the "spy" strategy fuses a novel voting methodology into Naive Bayes classifier to anticipate a negative course of action of reports from the unlabelled record set. 4. Personalized Searching using Adaptive Ranking Technique Positioning SVM is utilized to take in a customized positioning capacity for rank adjustment of the inquiry things as indicated by the customer substance and area inclinations. For a given request, a plan of substance ideas and a course of action of area ideas are separated from the rundown things as the report features. Since each report can be spoken to by a segment vector, it can be dealt with as a point in the part space. Utilizing the inclination joins as the info, RSVM goes for finding a straight positioning capacity, which holds for whatever number archive inclination coordinates as could be permitted. A versatile execution is utilized as a part of our tests. SECURITY ANALYSIS With the help of MD5 algorithm (one-way Encryption) we are going to secure our data (query keyword) so that nobody can hack that data. V. FUTURE WORK A. SECURE END TO END COMMUNICATION AND ACCESS CONTROL To guarantee that the information are just open by approved customers and for end to end secure exchange of information, get the chance to control strategies and distinctive encryption methods like IBE, ABE, and PRE, are utilized. The principle issue of scrambling considerable datasets utilizing existing methods is that we have to recuperate or unscramble the whole dataset before propel activities could be performed. These methods does not empower information proprietors to easily perform fine grained activities, for instance, sharing records for information investigation. Systems, for instance, PRE have Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 89

116 Shivani Kumari et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, solved this issue up to some expand. In any case, to get the characteristics from the information, once in a while the information should be imparted different conditions to various organizations. As various organizations have diverse cryptographic keys, the information should be unscrambled and after that reencrypted again which not just has a computational overhead yet what's more has a likelihood of information spillage. To deal with these sort of issues, we require encryption strategies which licenses information sharing between various social occasions without decoded and reencrypting process. B. DATA ANONYMIZATION Information is anonymized by expelling the individual points of interest to pre-serve the security of customers. It shows that it would not be possible to recognize an individual just from the anonymized information. Nevertheless, because of the availability of colossal volumes of information and extraordinary information explanatory instruments, the current anonymization methods are ending up progressively inadequate. In gigantic information situations, anonymization should be an option that is other than masking or summing up specific fields. One needs to carefully investigate if the anonymized information are helpless against any ambushes. For that, we have to think about various ambush models and data adversity metric for enormous information anonymization. In addition, most of the current anonymization systems are for static information, while much useful information is dynamic. Subsequently, we have to propose Secure multiparty calculation procedures, for instance, homomorphic encryption can be conveyed to comprehend such issues. new assurance and utility estimations. Additionally, information anonymization is an awkward strategy and it should be robotized to adjust to the growing 3 V's. C. DECENTRALIZED STORAGE As our own information are step by step gathered and put away on centralized cloud server over the time, we have to comprehend the related shot with respect to security. The idea of centralized gathering and storage of individual information ought to be tested. In centralized storage, a solitary purpose of disillusionment would show the lost of the whole information. One flaw or information break in assurance can prompt a staggering results, which is going on more as often as possible with modern strategies for ambushes. Rather than incorporating all the calculation, we can bring the calculation to astute specialists running individually individual gadgets. Utilizing such plans, plans of action can at show be beneficial and we can recover our assurance by facilitating our information in individual encoded mists. There are researchers who are emphatically proposing to embrace decentralized storage. A couple of works have been finished with wanders like OwnCloud and the IndieWeb. To receive the point of view of information dissemination, we require counts that are skilled to work over unbelievable information conveyance and assemble models that learn in a noteworthy information setting. D. EFFECTIVE MACHINE LEARNING TECHNIQUES AND DISTRIBUTED DATA ANALYTICS Machine learning and information mining ought to be adjusted to release the most extreme limit of gathered information. These days, machine learning systems, together with the change of computational power (e.g., cloud computing), have come to expect a basic part in huge information investigation. They are utilized broadly to utilize the prescient vitality of colossal information. For example, the prescient vitality of gigantic information is widely utilized as a part of therapeutic science and cosmology. Most of these calculations are finished by third assembling resources on private information, which can speak to a hazard to the assurance of customers. To guarantee security, machine learning figurings, for instance, grouping, bunching and affiliation administer mining should be conveyed in an assurance safeguarding way. Now and again the information possessed by an association (e.g., mending offices) does not have adequate data to find important learning in that space and procuring that information may be extravagant or troublesome because of honest to goodness limitations and fear of security infringement. To handle such issues, we have to outline security saving conveyed systematic structures which can process distinctive datasets from various associations while saving the insurance of each dataset. VI.ACKNOWLEDGEMENT This work was supported by the National Natural Science Foundation of China (Nos , , , and U ), and Independent Innovation Fund of Huazhong University of Science and Technology (Nos. 2012TS052, 2012TS053, 2013QN120, and CXY13Q019). We sincerely thank the anonymous reviewers for their very comprehensive and constructive. VI. CONCLUSIONS The amount of data increasing day by day, it s difficult to think the applications without producing and executing data driven calculations or algorithms. We have conducted study on the protection issues when dealing with big data. We have investigated privacy issues when dealing with big data. Challenges in each period of big data life cycle and we seen advantages and disadvantages of existing protection preserving technologies in case of big data applications. A great deal of work have been done to protect data of clients from data generation to data processing, yet there still exist a few open issues and challenges. Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 90

117 Shivani Kumari et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, Journal Papers: VII. REFERENCES J. Bethencourt, A. Sahai, and B. Waters, Ciphertextpolicy attribute-based encryption, in IEEE Symposium on Security and Privacy, 2007, pp [5] M. Chase, Multi-authority attribute-based encryption, Theory of Cryp [1] S. Ananthi, M.S. Sendil, and S. Karthik, Privacy preserving keyword search over encrypted cloud data, in Proc. 1st Advances in Computing and Communications, Kochi,India,2011. [2] Ciphertext-policy attribute-based encryption, in IEEE Symposium on Security and Privacy, 2007, pp [5] M. Chase, Multi-authority attribute-based encryption, Theory of Cryp [3] A. M. Azab, P. Ning, Z. Wang, X. Jiang, X. Zhang, and N. C. Skalsky, HyperSentry: Enabling stealthy incontext measurement of hypervisor integrity, in Proc. 17th ACM Conference on Computer and Communications Security, Chicago, USA, 2010, pp [4] Trusted Computing Group, TNC architecture for interoperability, resources/tnc architecture for interoperability specification, [5] H. Zhang, L. Chen, and L. Zhang, Research on trusted network connection, (in Chinese), Chinese Journal of Computers, vol. 33, no. 4, pp , [6] D. Feng, Y. Qin, D. Wang, and X. Chu, Research on trusted computing technology, (in Chinese), Journal of Computer Research and Development, vol. 48, no. 8, pp. Books: [1] R.E. Moore, Interval analysis (Englewood Cliffs, NJ: Prentice-Hall, 1966). Theses: [1] D.S. Chan, Theory and implementation of multidimensional discrete systems for signal processing, doctoral diss., Massachusetts Institute of Technology, Cambridge, MA, Proceeding Paper: [1] W.J. Book, Modeling design and control of flexible manipulator arms: A tutorial review, Proc. 29th IEEE Conf. on Decision and Control, San Francisco, CA, 1990, Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 91

118 Volume 9, Special Issue No. 3, May 2018 International Journal of Advanced Research in Computer Science (ISSN: ) CONFERENCE PAPER Available Online at A MAC PROTOCOL WHICH REDUCES THE QOS PARAMETERS LIKE ENERGY EFFICIENCYIN WSN Prof. Geetha B Assistant Professor School of C&IT Bangalore, Karnataka, India Prof. Thirumagal E Assistant Professor School of C&IT Bangalore, Karnataka, India Prof. Archana N B Assistant Professor School of C&IT Bangalore, Karnataka, India Prof. Shalini Tiwari Assistant Professor School of C&IT Bangalore, Karnataka, India Abstract: Energy efficiency and delay are the two important parameters to be considered in Wireless Sensor Networks. Wireless Sensor Networks need to have less delay and consume less energy. In this paper we compare the MAC protocol which is based on CSMA/CA with the S_MAC protocol which is based on event driven.later we come to the conclusion that S_MAC protocol which is based on event driven consumes less energy than that of MAC protocol based on CSMA/CA. Keywords: WSN, Mac, QoS I. INTRODUCTION Today Wireless Sensor Networks have gained the attention of research community because of the new concepts introduced day by day and the sum of challenges ahead [4]. Wireless Sensor Networks have huge range of applications in military and other areas[5].wireless Sensor Networks are equipped with sensors and these sensors consist of a battery and it is assumed that the battery is irreplaceable. Research has been made in WSN to reduce the energy consumed by the nodes that is to provide energy-efficient operation of every node[3]. Mac protocol is divided into two groups: contention based and schedule based. Contention based MAC protocols are subjected to collision. In Schedule based MAC Protocol, packets are allotted with fixed time slots during which the nodes can transmit the packets. This schedule based MAC protocol is not subjected to collision.keeping in mind the energy efficiency and delay as two important parameters it is necessary to design a MAC protocol which is energy efficient and less delay. It is necessary that the data is delivered to the correct destination and need a assurance that there is guaranteed delivery of data in many real time applications. It is also necessary there is timely deliver of packets so that there is no delay in the delivery of packets and also for sensor networks other factors like reliability and latency are also considered. In this paper, wepresent a novel Medium Access Control (MAC) protocol thatensures reliability and low latency in contention-basedchannel conditions while it focuses on minimizing energyconsumption. II. LITERATURE SURVEY The Energy-Aware QoS Provisioning Protocol (EAQPP) is a development of CSMA/CA protocols and RMAC in MAC layer and is similar with TinyOS MAC protocol. In this protocol[1] they have made few assumptions:each and every node is given with one ID and all the nodes know their neighboring IDsThe topology of the network is static, i.e., the sensor nodes are static and the only possible change in the network topology is due to discharge of sensor nodes. If some duplicated packets are transmitted, they are dropped.every node has a Data Radio(DR) and Wakeup Radio(WR). Initially all the nodes are in sleep mode that is Data Radio is in sleep mode but Wakeup Radio is in listen mode. When a node wants to send a packet, it sends a wakeup tone after a random back-off duration, and thus after detection of wakeup tone by all neighbor nodes, the neighboring nodes switch on their Data Radios. The sender node which wishes to send its packet turns on its data radio and initially sends a filter packet. This filter packet contains the information of the recipient node like receiver ID and other information. When the recipient node receives the filter packet which contains its ID and other information, the recipient node turns on its Data Radio and other remaining neighboring nodes turn off their Data Radios and go back to sleep mode.the sender transmits its packet to next node via Data Radio and then goes to sleep mode. The receiver keeps its DR turn on until it receives its complete packet. The receiver node sends ACK packet if the Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 92

119 Geetha B et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, packet is received successfully. The intermediate nodes do not send ACK packet. The intermediate nodes only use implicit acknowledgement mechanism. The receiver sends NACK packet if the packet is not received successfully. The S_MAC protocol [2] mainly concentrates on reducing energy consumption. That is the S_MAC protocol mainly concentrates on energy efficiency. Reducing Energy consumption and maximizing network life time is one of the key factor of S_MAC protocol. The MAC protocol needs to have good scalability in order to adapt the node distribution and topology changes. This S_MAC protocol keeps throughput and bandwidth utilization and other performance indicators asa secondary goal. Currently there are number of MAC protocols which are proposed and are specific to some applications. But one of the main aim of S_MAC protocol is to reduce energy consumption. In order to reduce the energy consumptionof idle listening, the protocol uses periodic work/sleepmechanism. This protocol will send nodes which are not involved in sending or receiving to sleep mode therebyreducing energy consumption. S_MACprotocol divides time period into multiple frames. This frame consists of two parts active time and sleep time. Active time is further divided into, the synchronizationtime and data time. Synchronization time maintainsynchronization scheduling by sending SYNC packets.inorder to reduce the energy consumption caused by thedata conflicts, protocol adopts backoff mechanism tocompete for the channel to complete to receive or senddata. The protocol adopts the adaptive circular listenermechanism and RTS/CTS mechanism to reduce energy consumption. But the MAC protocol based on CSMA/CA does not use the RTS/CTS mechanism. protocol based on CSMA/CA was more when compared to S_MAC protocol. The simulation snapshots are as follows: Figure 2: The nodes switching on their Data Radio as soon as Wakeup tone is heard. The nodes which needs to send packets sends a wakeup tone.when the wakeup tone is heard all the intermediate nodes including the recipient node switch on their Data Radios. III. PROPOSED WORK The MAC protocol based on CSMA/CA was compared with S_MAC protocol. The parameter considered was the energy efficiency. Since in MAC protocol based on CSMA/CA uses concepts like Data Radio and Wakeup Radio, the energy consumed by this protocol is more when compared to S_MAC protocol since in S_MAC protocol the intermediate nodes goes to sleep mode when there is no data transmission Active mode sleep Active mode Figure 3: The nodes sending the packets through intermediate nodes. The nodes sending data to recipient node by turning on their data radio. RTS/CTS/DATA/ ACK RTS/CTS/DA TA/ACK Figure1: Data transfer by S_MAC protocol IV. IMPLEMENTATION The two protocols discussed above were implemented using NS2 simulator tool. The number of packets were considered to be 33 and at the deliver of each and every packet energy consumed was calculated. It was found that energy consumed by MAC Figure 4: Nodes divided into clusters and data sent from one cluster to another Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 93

120 Geetha B et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, Figure 7: Simulation results of S_MAC protocol Figure 5: Nodes sending packets from sink to destination V. SIMULATION RESULTS When MAC protocol which is based on CSMA/CA was compared with S_MAC protocol the energy consumed by MAC protocol based on CSMA_CA was more than that of S_MAC protocol. The results are as follows: VI. CONCLUSION It is found that S_MAC protocol consumes less energy when compared to MAC protocol based on CSMA/CA.The MAC protocol consumes more energy in sending filter packet and switching on/off the data radios and wakeup radios. But the S_MAC protocol will the send the packets which are not sending or receiving data to sleep mode thereby reducing energy consumption. VII. REFERENCES [1] Zahra Zarei, S. Mostafa Safavi, and Akbar Abbasi A MAC Protocol for Provisioning Qos and Energy Efficiency in WSN International Journal of Computer Theory and Engineering, Vol. 4, No. 4,pp August [2] Xin Hou, Xingfeng Wei, Ertian Hua and YujingKong A self adaptive and Energy Efficient MAC protocol based on event driven Journal of Networks, v0l. 8, no. 1,pp January 2013 Figure 6: Simulation results of MAC protocol based on CSMA/CA [3] SU Jun, HU Fang-yu. Energy-saving Improvement forsmac Protocol in WSN. Computer Engineering, Vol.35no.5,pp , [4] SUN Li-min, LI Jian-zhong, CHEN Yu, etc. The wireless sensor network. Beijing: Tsinghua university press, [5] Akyildiz I F, Weilian S, et al. A survey on sensor networks. IEEE Communications Magazine, vol.40, pp , Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 94

121 Volume 9, Special Issue No. 3, May 2018 International Journal of Advanced Research in Computer Science (ISSN: ) CONFERENCE PAPER Available Online at AN ONLINE PET STORE Priyanka S Gowda School of Computing and Information Technology, REVA University, Bangalore. Harshitha K B School of Computing and Information Technology, REVA University, Bangalore. Vamsi Krishna M School of Computing and Information Technology, REVA University, Bangalore. Parikshit Sarode School of Computing and Information Technology, REVA University, Bangalore. Surekha Thota School of Computing and Information Technology, REVA University, Bangalore. Abstract: The current difficulties in purchasing pets on any pet store for customers includes a dire task to hunt for pet stores in real life which may or may not satisfy user needs. To handle this problem online pet store is developed with the help off web technologies. We propose an online method of purchasing pets by using web technologies by creating an interactive website. Online pet store is developed using scripting languages for front end and server-side programming. It makes use of a relational database to keep a well-defined record of all the pets available on the store for sale. The online transactions are done by using a payment gateway which accepts debit or credit cards for payment. Any user can register to the website and the details are then entered in the website database, which are used to verify the user and maintaining proper logs. I. INTRODUCTION The innovative design and simplicity allows the users and potential customers to navigate through the webpage with relative ease. The options are clear and boldly presented. The website accommodates a variety of creatures on sale. The user can browse freely and decide on the item he would like to purchase. The goal of such a website is to facilitate shopping the comfort of your own house. It is a dire task to hunt for pet stores in real life, which may or may not satisfy your needs. Online shopping eliminates unnecessary stress and cost associated with transport, navigation and unsatisfactory results. The online Pet Store has a couple of breeds/species of four animals available for sale. A brief description of each breed is mentioned, along with a reasonable price and the item code. The store has the option of payment via cash and credit card. For a successful purchase, the store aims to deliver the product to the customer directly within 3 days after complete health checkup. II. LITERATURE SURVEY The current system of purchasing pets is a dire task of locating pet stores near by the customer which may or may not satisfy the needs and satisfactions of the customer [1]. The pet store owners also get inconvenient as they have to display the products or the pets for a limited amount of time. They cannot properly market the pets as other products can be advertised due to obvious reasons (shortage of time) [2]. By using online Pet store both customer and employees can conveniently buy pets without these problems. The traditional Pet Stores sells pets to any customers without collecting proper information about the customer [2]. This may sometimes lead to the mistreatment or harm to the animals. The online pet store verifies each user or customer and the responsibility of the pets is given [3]. This system thus includes the Annual Maintenance Cost (AMC) in which an annual checkup of the pets is done by the veteran. The traditional pet store may make use of unregistered sources to get the pets on their store [2][3]. Where as in online pet store the entries of the sources from where the pet has been brought up is maintained and verified. This assures the customer about the background in which the pet has been brought up [4]. The online system verifies the current condition of the pet according to the standards specified by the Animal and Plant Health Inspection Service [4]. Thus, any pet to be sold on the online pet store is assured to be in good condition as per the guidelines. Therefore, the online pet store adds security and assurance to both the customers and the seller regarding the quality of services [4]. III. SYSTEM DESIGN A. Different Modules The various modules used in this project are elaborated as follows Login Module: Existing user can login into the pet store by providing user name and password. If the user is a new user, he gets registered himself by providing his personal information. Login module is shown in Figure 1 Admin Module: The pet store manager enters the details of the new pets available for sale. If the pet is already sold out, it has to be removed from the catalogue. Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 95

122 Priyanka S Gowda et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, Catalogue Module: The Catalog module displays all the pets and pet products to be sold out. Each of which is uniquely identified by a product id. The selected pets/products will be added to cart. If the customer purchase pet, and option related to its annual maintenance will be given. If the customer chooses AMC, then the AMC cost will be added to the cart. Payment Module: Online payment or cash on delivery option is provided to the customer. After successful payment, the pet will be delivered after complete medical tests. Veteran Module: It not only contains the details of nearest veterans but also blocks the veteran calendar if the customer chooses AMC scheme, So that the veteran can go for monthly check up of the pet. After the visit, the veteran can update the details regarding the pet and blocks the next visit date in the calendar. Customer feedback Module: The customer can provide his feedback related to pet or service in this module Order History Module: Proper logs of each and every transaction is maintained in the database C. Algorithm Used The Online Pet Store makes use of an algorithm to carry out all processes from the display of pets to the proper delivery of pets with medical checkup to the users mentioned address is depicted in Figure 3 The algorithm is as follows. Step 1 Login Form: The login form includes the user to give proper credentials for the registration or login of the user. Step2 Admin: The admin refers to the store manager to include the pets or other products which are to be sold online are to be displayed to the user. Step3 Veteran Form: The veteran form includes the details of the veteran and comments of the veteran for the previous checkups. It also includes the various Annual Maintenance schemes for the pets for proper health maintenance done by various AMC schemes. Step4 Catalog: This includes the Add to Cart option. This further lets the user to select the Annual Maintenance Cost and the selection of the veteran visit date. If the date is not available for visit then it is incremented. Step5 Payment Method: The payment options are selected according the user from online payment or cash on delivery. The delivery information is entered in this step and the payment of pets and the AMC is done. Step6 Order History and Feedback: This step displays the user the order history and the current progress of the pending deliveries so that the user can track the delivery process. It also includes the feedback from and the reviews or comments for the user. In case of any query, the user can use the helpline available in this field. Figure 1displays the sequence diagram for the browse catalog top-level use case B. Complete Flow of the project As mentioned before, the top-level use cases are here more appropriate to describe the user communication with the system. This is because they provide information not only about the system behavior, but also about the sequence of interactions that the customer usually performs in order to achieve a goal. Once the customer has some line items in his shopping cart, the next step is to navigate to the cart page. Here the user can remove or modify his line items until he is ready to start the checkout process. Customer can look at the same pet species multiple number of time. There, after entering all shipping and billing information, the customer will confirm the purchase and the system will request the payment platform to process the payment, displaying the order details. The customer can choose an option between cash on delivery or online transaction through our payment gateway based on convenience. Figure 2 Flow Chart of Online Pet Store Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 96

123 Priyanka S Gowda et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, IV. RESULTS V. CONCLUSION The results of the development of the online pet store over the traditional pet store are convenience to use, interactivity, large volume in trading. As shown in the Figure 4, the number of pets is given on x-axis and the time requirement to purchase is mentioned on the y-axis. The Figure 3 displays the data of the online pet store for the same values as used in traditional pet store. The difference can be clearly observed. As sown in Figure3, the first blue line represents the data of traditional pet store and the second red line represents online pet store data. Thus, the online pet store is efficient and more convenient than the traditional Pet Store. By this project, we can assure with managing all sorts of specialized services such as animal training course, grooming services. It reduces the staff training costs. The main aim of this project was to design a web application which contains many links assures that the customer receives a healthy pet. Also keeps track of healthy pet, so that resale s of pets can be provisioned in future VI. REFERENCES 1. Païvi Sievänen, Paper: The pet sector and pet stores in Finland. 2. Business Opportunity Profile. Pet Shop 3. How You Can Profit from E-Business. For more information on those publications, visit the Innovation PEI website at 4. Animal Welfare Inspection Guide, by The U.S. Department of Agriculture (USDA). 5. Ronald W. Coon, Sr., CPA, DABFA, Owens Community College Perrysburg, Ohio, Online Shopping FORENSIC ACCOUNTING CLASS. Below10 Below20 Below30 Below40 Below50 Figure 4: pet store data graph. Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 97

124 Volume 9, Special Issue No. 3, May 2018 International Journal of Advanced Research in Computer Science (ISSN: ) CONFERENCE PAPER Available Online at CLUSTER BASED DATA AGGREGATION SCHEME IN UNDERWATER ACOUSTICS SENSOR NETWORKS Vani Krishnaswamy School of Computing & Information Technology REVA University Bangalore, India Sunilkumar. S. Manvi School of Computing & Information Technology REVA University Bangalore, India Abstract: The main objectives of Underwater Acoustic Sensor Network (UWASN) are to reduce the energy consumption and increase the life span of the network. This can be achieved using appropriate clustering and data aggregation scheme. In this paper the classification of various data aggregation schemes are discussed. The data aggregation scheme based on similarity function is simulated in MATLAB. The experimental result shows that proposed scheme performs better in terms of energy consumption. Keywords: UWASN; similarity function; clustering; data aggregation. I. INTRODUCTION The rapid growth of Underwater Wireless Acoustic Sensor Networks (UWASN) in its numerous applications such as mine reconnaissance, oceanographic data collection, environmental pollution monitoring, undersea explorations etc has created immense awareness among researches in UWASN [1]. The acoustic signals are used for UWASN for communication compared to Radio Frequency (RF) waves used in terrestrial wireless networks. Hence the numerous challenges posed by UWASN include limitation of bandwidth, battery data capacity, multipath fading and propagation delay [2]. The sensor nodes are distributed and placed randomly inside the water. UWASN consists of numerous sensor nodes and one or two sink node. The UW sensor nodes are used to acquire the data, collect and process the data and transmit the gathered data to the sink node. To perform all these tasks by the sensor nodes requires more energy. Hence researches are focusing on various clustering schemes to reduce the consumption of energy and to increase the lifetime of the network [3]. Due to inadequate memory of UW sensor nodes, they are not able to store large amount of data. Hence a suitable data aggregation scheme is essential to reduce the heavy traffic, packet loss, energy loss congestion and to increase the communication towards the sink node [4,5]. The study of the research papers on the various schemes of data aggregation exhibited that the energy of the sensor node network can be reduced. In [6] the authors have proposed that implying the data aggregation scheme at special nodes will reduce the redundancy at the sink node and increase the efficiency of the energy. In [7] the authors have proposed a data aggregation scheme which explains that during data transmission there is a possibility of collision among the cluster heads. In [8] a scheme has been proposed for reducing the data aggregation time based on shortest hop tree algorithm. We propose a cluster based data aggregation scheme by considering the parameters like energy and distance. The scheme works as follows. (1) Clustering and selection of Cluster Head(CH) is performed based on fuzzy algorithm considering the parameters such as energy level and the distance to sink. (2) Cluster heads act as aggregators which use similarity function to aggregate data from the cluster members by using Euclidean distance. (3) The aggregated data will be transmitted to the sink. The rest of the paper is organized as follows. Section 2 explains the various data techniques. Section 3 explains the network environment for cluster formation and selecting the cluster head using hierarchical topology control scheme and data aggregation scheme using similarity function. Section 4 deals with simulation and its parameters with result analysis. Section 5 deals with summary of the proposed work and future works. II. DATA AGGREGATION TECHNIQUES The classification of various data aggregation techniques in UWASN is shown in the Fig. 1. Based on the earlier survey papers [10] the data aggregation techniques are classified into three types. A. Cluster based techniques: In this, the entire network is divided into group of nodes as clusters that connects to each other. A crisp and steady network is created using cluster based arrangement which reduces the consumption of energy in the network. Further based on various parameters such as similarity function, Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 98

125 Vani Krishnaswamy et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, distance and mobility the cluster based techniques are classified. B. Non Cluster Based Techniques: In this, the nodes which are randomly deployed are either stationary or dynamic in the network. The cluster heads which act as aggregators transmit the aggregated data to the base station directly or indirectly. Further based on different parameters such as relay and mobile sink the non cluster techniques are classified. C. Other Various Techniques: In this, with and without aggregation techniques based on various QoS parameters such as packet drop, delay and consumption of energy with respect to time are compared. Fig. 1. Classification of Data aggregation Techniques. III. PROPOSED SCHEME In this paper a data aggregation scheme is designed for Cluster based UAWSNs to lessen the redundancy of the data and consumption of energy A. Network environment The network model explained here is similar to that presented in paper [9] with the following features. The sensor nodes are placed randomly in an underwater environment to form a 3-D static network where the communications between the sensor nodes are full duplex. The 3-D position information of each sensor node is achieved by positioning algorithms or by the use of hardware units, which are detected by acoustic waves. The sensor nodes in the network are homogeneous and transmit the required information with different ranges of communication radius. The position of the Base Station is usually on the surface of the sea. The energy possessed by the BS is unlimited and it can communicate using underwater acoustic waves and radio waves. The processing and aggregation of data at the base station is carried out by each sensor node. During this process, the energy of the sensor node is depleted gradually, this in turn results in a dead node. When the number of dead nodes in the network exceeds beyond the threshold limit, then the network is considered to be dead. Therefore, the aim of the topology control is to increase the life span of the network to the maximum feasible extent. B. Data Aggregation scheme: The cluster formation is performed by taking into account the given 3-D network environment. Cluster structure is characterized by two types of nodes called Member cluster nodes and Cluster Head nodes which are considered as the backbone of the network. The Member cluster nodes that are connected to its own Cluster Head node lie dormant to save the energy consumption of the network. Whereas the Cluster Head nodes that are connected to the closest neighbor node of other clusters is usually in a shallower location. The process of selecting the Cluster Head nodes and the reconstruction of the network is called as around. This process is achieved periodically to balance the energy consumption of the network and increase the lifespan of the network. The clusters are formed based on fuzzy clustering scheme[11]. The CH periodically receives the information from the member sensor nodes in the network. The collective data received by the CH will be transmitted to the base station. In order to avoid the data redundancy which results in duplication of data and reduce the energy consumption in transmissions, a data aggregation scheme is proposed taking the idea from the work given in [12]. We have implemented a data aggregation scheme with a similarity function called Euclidean distance in the CHs. A CH collects and gathers all the data transmitted from its member nodes and stores them as a set of data called vector. Whenever a new vector is formed, both the vectors are compared using a similarity function. After comparison if the two vectors are found to be similar then the CH will transmit only one data instead of both to the BS, which avoids data redundancy in the network. IV. SIMULATION & RESULT ANALYSIS This section presents simulation model, simulation arameter inputs and the results. A. Simulation model A node is considered to be dead or alive depending on the available energy. If the energy of the node reduces to 0, then it is considered as a dead node. Simultaneously in the network, if the count of dead nodes exceeds a cutoff value, then the entire network is said to be deceased. Network environment discussed in Section 2 is simulated for analyzing the performance of clustering scheme. The simulations were carried out using MATLAB and the performances of the proposed data aggregation scheme were analyzed in terms of the energy consumed with and without data. The sensor nodes were randomly deployed in the region S and were assumed to be fixed and able to communicate with each other. B. Performance parameters We assume that the base station is built at position (50m X50m X100m). For simulations, the number of sensor nodes N set were 100 and with data packet size of 400 bits for every transmission time. The initial energy of each node and electronic energy set are 0.05J and 50nJ/bit respectively. In Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 99

126 Vani Krishnaswamy et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, addition, the energy of the base station is assumed to be unlimited as it is solar powered. TABLE I. SIMULATION PARAMETERS Variable Parameter Value S Distribution area 100 X 100 X 100m3 n Number of nodes 100 l length of every data package 400 bit E energy cost of data aggregation 5mJ/bit DA Eelec Electronics energy 10mJ/bit Tr Transmitting power 2.0W Pr Receiving power 0.75W C. Results Figure 2. depicts the consumption of the energy in the network, considering both the schemes of with and without data aggregation. The overall consumption of the energy in the network with and without data aggregation is indicated by red line and blue line respectively. The cluster head nodes aggregate the data and transmit to the BS. Also, the energy is saved in the data aggregation scheme as it uses similarity function. Also, the energy is saved in the data aggregation scheme as it uses similarity function through which it reduces the number of duplicate data transmissions from cluster-heads to the BS/sink. As a result, a clustered network with data aggregation consumes less energy compared to a clustered network without data aggregation. Fig. 2. Energy consumption Vs Offered Load V. CONCLUSION In this study, a process of clustering of nodes is carried out using fuzzy clustering algorithms based on the energy and distance of the sensor nodes. The cluster head nodes act as data aggregators which transmit the aggregated data to the base station based on the factors such as the energy, distance between the nodes within the cluster and to the cluster head node, the distance between the cluster head node to the BS. Further, using similarity function the aggregated data at CH is transmitted to the BS. An experimental result shows that the energy consumed using the proposed data aggregation scheme is less when compared to scheme without data aggregation. As a future enhancement, various data aggregation schemes can be compared and analyzed based on various QoS parameters. REFERENCES [1] Akyildiz, T.F, Pompili, D. and Melodia, T. Underwater acoustic sensor networks: research challenges, Ad Hoc Networks, vol. 3, No. 3, pp , [2] I. F. Akyildiz, D. Pompili, and T. Melodia. State-of-theart in protocol research for underwater acoustic sensor networks, WUWNet 06, pp.7, [3] Wang, F., Wang, L., Han, Y., Liu, B., Wang, J. and Su, X. A Study on the Clustering Technology of Underwater Isomorphic Sensor Networks Based on Energy Balance, Sensors 2014, pp [4] Halder S and Bit SD. Enhancement of wireless sensor network lifetime by deploying heterogeneous nodes, J Netw Comp Appl 38: pp , [5] Alam KM, Kamruzzaman J, Karmakar G and Murshed M. Dynamic adjustment of sensing range for event coverage in wireless sensor networks, J Netw Comput Appl 46, pp , [6] Liu H, Liu Z, Du H, Li D and Lu X. Minimum latency dataaggregation in wireless sensor network with directional antenna, In: Wang W, Zhu X, Du DZ (eds) Combinatorial optimization and applications. COCOA Lecture Notes in Computer Science, vol Springer, Heidelberg, pp [7] Minming T, Jieru N, Hu W and Xiaowen L. A data aggregation model for underground wireless sensor network,. In: IEEE World Congress on Computer Science and Information Engineering,vol 1. IEEE, USA, pp , [8] Wu Z, Tian C, Jiang H and Liu W. Minimum-latency aggregation scheduling in underwater wireless sensor networks,. In IEEE International Conference on Communications (ICC), Japan.pp. 1 5, 2011.doi: /icc [9] Nitin Goyal, MayankDave and Anil Kumar Verma. Fuzzy Based Clustering and Aggregation Technique for Under Water Wireless Sensor Networks. International Conference on Electronics and Communication System (lcecs-2014), Feb ,2014. [10] Goyal, N., et al. Data aggregation in underwater wireless sensor network: Recent approaches and issues,. Journal of King Saud University Computer and Information Sciences (2017), [11] Q. Ni, Q. Pan, H. Du, C. Cao and Y. Zhai. A Novel Cluster Head Selection Algorithm Based on Fuzzy Clustering and Particle Swarm OpTimization, IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 14, no. 1, pp , Jan/Feb [12] Tran, K. T. M. Oh, S. H. and Byun, J. H. Well-suited similarity functions for data aggregation in cluster-based underwater wireless sensor networks International Journal of Distributed Sensor Networks,Vol. 2013, Article ID ,2013. Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 100

127 Volume 9, Special Issue No. 3, May 2018 International Journal of Advanced Research in Computer Science (ISSN: ) CONFERENCE PAPER Available Online at DATA STORAGE AND MANAGEMENT IN VEHICULAR CLOUD COMPUTING Sunil Kumar S. Manvi Dept. of CIT REVA University Bengaluru,India Nayana Hegde Dept.of Electronics and Communication Engg Sri Krishna Institute of Technology Bengaluru,India Abstract: Vehicular cloud computing (VCC) is an emerging technology providing real time data. VCC is providing a new experience to travellers. VCC enablesusers to store the data on the move. It helps them to enjoy the cloud applications without investment. Uploading data into VCC offersmaintenance free and easy access from anywhere. It provides complete set of resources for storing data for various applications. Huge amount of data is transferred, stored and processed every day. In vehicular cloud, data is distributed among many nodes. It is challenge to maintain, manipulate process and analyse very large amount of data. Due to multiple service providers, the privacy of the data needs to be provided. The goal of this paper is to give a broad overview of data storage and management in vehicular cloud environment and demonstrate the cloud setup using SimITSframework. Keywords: Wireless Sensor Networks (WSNs), Trust-Aware Routing Protocols security and software s. Storage space in VCC, will be limited as it depends on the movable nodes for resources. I.INTRODUCTION The objectives of paper are as follows. The rapid development of connected vehicles offers great opportunities to intelligent transportation system (ITS). Vehicular Cloud Computing (VCC) has created a whole new helpful functionality to travellers.vcc provides technology to connectivity to the cloud while vehicles are on move without compromising on security. Information collected by vehicles is and sent to VCC.In VCC all uploaded data are normalized, stored, aggregated and combined with information from other sources.this data is distributed (in part or in full) to the VCC applications and users who have been granted the relevant List the issues and challenges of data processing in VCC. Management of data storage in VCC. Allocation and sharing of resources. To discuss the online query in VCC and measure performance. Ability to operate on encrypted data. Ability to run in a heterogeneous environment. The paper is organized as follows. Section II presents the access rights. The fine grained access control makes it possible Related Work. Section III discusses the VCC architecture. to only expose the precise subset of information needed by the Section IV presents results and discussion. Conclusions are subscribing service, for instance an analytics system [1]. provided in section V. The data that is being created and collected are going to be of huge volume. VCC shouldmanage both structured and unstructured data.in the digital era, security is an on-going concern. Users need to trust that the data they provide to VCC remain in a secure place.vcc features dynamic data management allowing users to interact with their vehicle and access services from any type of device, such as tablets, computers,smartphones and the vehicle head unit [2]..All communication is encrypted and authenticated using certificates distributed by acertificate Authority. It provides a Public KeyInfrastructure (PKI) with certificate based mutual authentication between vehicle and cloud. Mutual authentication ensures that both vehicle and cloud (i.e. server side) can verify authenticity of each other. Access can be revoked or suspended if any anomaly is detected [3]. Storage service in VCC is not identical to traditional cloud storage service.in commercial cloud system, storage facility available to users will be unlimited and at low cost as well. Commercial cloud storage is powered and managed with II. RELATED WORK Rasheed Hussain [6]. put forth the scheme of classification of Vehicular cloud computing technology and communication pattern of Vehicular cloud stack. VANET based cloud architecture is divided into three categories. Vehicular clouds (VC), vehicles using clouds (VuC), and hybrid vehicular clouds (HVC). Each framework is particular designed for performing specific task by itself. The nature of dynamic changing topology helps to provide long range traffic information and also provide network as a service to other nearby vehicles. Cooperation as a service helps other vehicles to know the traffic and safety related information. KAZI MASUDUL [7]. Proposed work on Social IoT. They proposed how vehicles can be used for application of IoT. How the underutilized physical resources in vehicular cloud environment and create a model of well guiding and messing service for travellers. The proposed model will be used for both safety related and entertainment applications. Proposed system uses the SAE J2735 message set. Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 101

128 Sunil Kumar S Manvi et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, Eltoweissy[8].proposed and promoted a most adaptable and comprehensive model of Vehicular Cloud computing. They explained how advancement of embedded system, wireless sensor network and vehicular ad hoc network can form a cooperative cloud service for betterment of society. Distinguishing usual cloud computing from mobile cloud environment, they showed that they can be used for applications like mobile analytics laboratory, sharing services on terrestrial, aerial, or Aquatic pathways or theatres of operations in addition to general purpose applications. SoffieneJelassi [9]. Explained technique to transform data to a form where it can be processed as a steady and continuous stream using cloud based VANET architecture. This model composed of vehicular cloud, central cloud and roadside units. This can be done in moving vehicles, static vehicles or passengers present in moving vehicles. The Virtual Machines present in cloud service acts as servers to the road side video streaming. Torzo [10].poposed a new architecture for vehicular cloud environment, to improve travelling experience. It enables optimal usage of RAM memory of each node. They explained collection of vehicular information, traffic routing decision, continuous traffic monitoring with the help of IoT to benefit the users and help them to access service at low cost and less time. The underutilized computing power, memory, sensing and internet connectivity, of large number of autonomous vehicles on roads, parking lots and streets can be coordinated and allocated to other authorized users. Internet access, computing power and storage capabilities can be rented to drivers and other customers exactly as similar to usual cloud computing service[14]. III ARCHITECTURE OF VEHICULAR CLOUD. Fig. 1.VCC service architecture Fig.2. Static data storage model in VCC In VANET cloud service architecture which is popular as Network as a Service (NaaS), Storage as a Service (STaaS), and Cooperation as a Service (CaaS). Platform as a Service (PaaS) is not very popular in Vehicular clouds. Figure 1 shows the different layers of service architecture of VCC.Network as a Service or NaaS provides internet access to other vehicles which need that facility. Storage as a Service or SaaS is similar to having virtual network hard-disk. Some users prefer to have backup of their data on an external harddisk for safety. Data as a service or DaaS works as a virtual data provider to other vehicles. So vehicular cloud acts as data provider for travel related things like, nearest fuel stations, hotels, etc for requesting drivers [11].Applications mainly deal with various announcements, information upload/down load, mp3 download or sharing between V2V and V2I. Information about nearest fuel stations, hotels their services and prices, tollgates etc. are shred[13]. As the usage of vehicular cloud service increases the security requirements increases. Major thearts for vehicular cloud services are: denial of services, identity spoofing, modification repudiation, repudiation, Sybil attack, and information disclosure [4]. VCC. Fig.3.Dynamic data storage model in VCC IV DATA MANAGEMNT In this section we give details of data management in VCC provides distributed storage service to its users. This is useful for many applications which require data storage. Since VCC is adhoc in nature and uses wireless communication, storage facility is not always reliable [12]. Service is better when large number of vehicles is in proximity of each other. A factory parking area shown in figure 2 is one such example. It is possible to host a static VCC in this case. Figure 3 shows dynamic cloud computing example. If vehicles are moving, then the data stored in the vehicle, which is part of Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 102

129 Sunil Kumar S Manvi et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, VCC need to be copied and saved back, before it is moves. This process is quite costly and time consuming. Also updating data base frequently and providing security and trust to the users is also challenging. Following are the data management tasks: Challenge of data processing in VCC. In VCC systems, the data is generated by mobile vehicles and RSUs, using sensors. In real time data processing VCC system requires data synchronization which brings tremendous challenges to the wireless network transmission. Indexing and filtering of enormous data is difficult. 1) Management of data storage in VCC Huge Data on daily basis is proceed, stored, and transferred over the VCC. There are some issues related with security of data and how to manage the data. Data security and data residency are the key concern of vehicular cloud. 2) Allocation and sharing of resources. Size of the data centre and services involved in it will decide the resource sharing in VCC. 3) Ability to run in a heterogeneous environment In VCC different vehicles have various make and different software s. So to run cloud virtual machine in such heterogeneous environment is a challenge. 4) DataSegregation It is the process of grouping the data available on cloud on the basis of similar properties. This process helps in accessing the data easily whenever they are needed. 5) Data back up It is the process of duplicating data. It helps to recover data when it is lost or corrupted by some problems. In VCC duplication of data is very important. 6) Data security To provide secure environment for vehicular cloud services following requirements should be considered: o Confidentiality o o 7) Data Recovery Integrity Availability o Authentication [11] It is the process of retrieving the unavailable data. Data may be corrupted, lost or damaged during storage or segregation or encryption process. Data is recovered from duplicate storage device. Secure data erasing or deletion is also a task of storage system. V.RESULTS AND DISCUSSION This section depicts the results which are obtained byrunning the simits, an open-source simulatorfor dynamic vehicular cloud.it performs vehicle to infrastructure communicationusing IEEE p standard. The simulation includesurban and highway scenario and vehicles moving in single one way road. Distance between each vehicle is less than 30 meters.simulation parameters are as follows: time slot duration 160 Micro second, number of vehicles 10/ 20, information size 20/30 bytes and data rate of 6 Mbps.Figure 4 shows the graph obtained throughput per channel for 20 users in urban scenario. Simulation is carried for 65secs of time slot. Fig.4. Throughput per channel for 20 users Figure 5 describes no of data transmitted from nodes to cloud. No of transmission Throughput Fig.5. No transmissions for 20 users Figure 6 describes the throughput per channel for urban scenario for 10 users. Throughput Throughput per channel time slot in sec No trasmissions Time slot in sec Fig.6. Throughput per channel for 10 users Throughput per channel No trasmissions Throughput per channel Time slot in sec Throughput per channel Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 103

130 Sunil Kumar S Manvi et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, Figure 7 describes no of data transmitted from nodes to vehicular cloud in urban scenario for 10 users. Throughput Fig.7. No transmissions for 10 users Figure 8 describes the throughput per channel for highway scenario for 20 users. No of transmission Fig.8. Throughput per channel for 20 users Figure 9 describes no of data transmitted from nodes to vehicular cloud in highway scenario for 20 users. Throughput Throughput per channel Time slot in sec No trasmissions time slot in sec Throughput per channel No trasmissions Throughput per channel Time slot in sec Throughput per channel VI. CONCLUSION Paper first briefs about SaaS for VCC. Then VCC architecture details are discussed. The paper focused on data segregation, access management, availability and backup, data security, secure data deletion, and data recovery. We have simulated storage system of VCCusing SimITS simulator and analysed dynamic cloud. REFERENCES [1] N.Vijayaraj,D.Rajalakshmi,Mr.C.S.Sanoj"Issues andchallenges Of Scheduling and Protection Algorithms for Proficient Parallel Data Processing in Cloud" 2013 [2] Amir H. Basirat,Asad I. Khan,"Introducing an Intelligent MapReduce Framework fordistributed Data Processing in Clouds",International Symposium on Network Computing and Applications,2013 [3] Vamsi Krishna Myalapalli, Thirumala Padmakumar Totakura, "Optimizing Big Data Processing in Cloud by Integrating Versatile Front End to Database Systems",International Conference on Energy Systems and Applications,2015 [4] XinJin,Yaming Wu,"PPViBe: Privacy Preserving Background Extractor via Secret Sharing in Multiple Cloud Servers"IEEEInternation Conference on Networking,2016 [5] PallaviKulkarni,Dr.RajashriKhanai, "Security Frameworks for Mobile Cloud Computing:ASurvey",International Conference on Electrical, Electronics, and Optimization Techniques,2016 [6] RasheedHussain,ZeinabRezaeifar,HeekuckOh,"A Paradigm Shift from Vehicular Ad Hoc Networks to VANET-Based Clouds",WirelessPersCommun 2015,pp [7] K. M. ALAM, M. SAINI, and A. E. SADDIK, Toward social internet of vehicles: Concept, architecture, and applications," Digital Object Identifier, pp , April [8] M. E. S. Olariu and M. Younis, Towards autonomous vehicular clouds," Journal, pp [9] S. Jelassi, A. Bouzid, and H. Youssef, Qoe-driven video streaming system over cloud-based vanet," Springer International Publishing, pp , [10] O. Terzo, G. C. K. Goga, and L. Mossucca, Hybrid cloud architecture for vanet simulations," Internet of Things Intercooperative ComputTechnol, pp , [11] SunilkumarManvi, Nayana Hegde, Vehicular Cloud Computing Challenges and Security in Book Recent Developments in Intelligent Communication Applications IGI Global 2017 [12] SunilkumarManvi, Nayana Hegde, Key Management Authentication and Non Repudiation for Information Transaction in Vehicular Cloud Environment, IEEE International Conference on Cloud Computing in Emerging Markets (CCEM), 2016 [13] SunilkumarManvi, Nayana Hegde Emerging Applications in Vehicular Cloud Computing, 2017 International Conference on Computer Communication and Informatics, Sri Shakti Engineering College Coimbatore, Tamil Nadu Fig.9. Throughput per channel for 20 users Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 104

131 Volume 9, Special Issue No. 3, May 2018 International Journal of Advanced Research in Computer Science (ISSN: ) CONFERENCE PAPER Available Online at VHDL MODEL OF SMART SENSOR POOJITHA V Bachelor of Engineering, Electronics and Communication SJC Institute of Technology Chickaballapur, Karnataka poojithav.09@gmail.com RAVIKIRAN R Assistant Professor, Electronics and Communication SJC Institute of Technology Chickaballapur, Karnataka rksravikiran@gmail.com Abstract The paper focus on VHDL model of smart sensor is proposed to obtain solution to the challenge of designers. It is an optimal platform for implementing algorithm for smart sensor unit for noise reduction (noise cancellation of voice signal) using IEEE 1451 standard. There are lots of researches on noise cancellation but using smart sensor for sensing real time voice with the interfered noise will be more efficient. To achieve good result the signal is first sensed using signal sensing process then it is conditioned & processed using VHDL. The VHDL program we have developed acts as the smart sensor as above mentioned step. The VHDL allows the complete simulation of entire system & hence it is possible to simulate together parameter of different domain. Key Words: IEEE1451, smart sensor, VHDL Introduction I. INTRODUCTION An evolving semiconductor technology has resulted in the low cost microprocessor, hence the design & overall cost of the control system can be minimized as it is built on single chip. The idea of incorporating the electronics & the sensor is to develop the intelligent sensor. Smart sensors are extremely growing & are used across all over the industries. Growing demand of smart sensor & field network technologies have led the networking of smart sensor a very economical & attractive solution for broad range of measurement & control application. The main aim of smart sensor in an integrating electronics is to perform logic function, two way communications& making the decision. Smart sensors are capable to provide desired output & interpretive power which constantly improves smart sensor performance & capabilities. Srecently the on growing adoption & importance of smart sensor for real time application has increased rapidly & manufactures are constantly taking efforts in developing the efficient & effective smart sensor. VHDL is commonly used to write text models that describe a logic circuit. Such a model is processed by a synthesis program, only if it is part of the logic design. A simulation program is used to test the logic design using simulation models to represent the logic circuits that interface to the design. This collection of simulation models is commonly called a testbench. VHDL has file input and output capabilities, and can be used as a general- purpose language for text processing, but files are more commonly used by a simulation testbench for stimulus or verification data. There are some VHDL compilers which build executable binaries. In this case, it might be possible to use VHDL to write a testbench to verify the functionality of the design using files on the host computer to define stimuli, to interact with the user, and to compare results with those expected. However, most designers leave this job to the simulator. The key advantage of VHDL, when used for systems design, is that it allows the behavior of the required system to be described (modeled) and verified (simulated) before synthesis tools translate the design into real hardware (gates and wires). Another benefit is that VHDL allows the description of a concurrent system. VHDL is a dataflow language, unlike procedural computing languages such as BASIC, C, and assembly code, which all run sequentially, one instruction at a time. A VHDL project is multipurpose. Being created once, a calculation block can be used in many other projects. However, many formational and functional block parameters can be tuned (capacity parameters, memory size, element base, block composition and interconnection structure). II. SMART SENSORS Smart sensors are designed to collect information from one physical quantity to electrical signal. The traditional sensors which were designed consist of three major parts: sensing element which is used to sense the physical quantity, signal conditioning & processing elements is used to for amplification, filtering & to provide the linearization & compensation, sensor interface is used to interface with the external world which has been illustrated in figure II.I. Figure II.I: Traditional sensor The major difference between the traditional sensor & smart sensor is its intelligence, storing data capability, decision making ability which is onboard. A Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 105

132 Poojitha V et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, microprocessor. microprocessor is build with the digital signal processing analog to digital conversion or frequency to code conversion, calculation & interfacing function which provides self diagnostics, identification &self adaption function. The microprocessor base system provides the data storage & controls the power consumption. It is featured with on board CPU small size, wireless capability & low cost which is shown in figure II.II. Continuous improvement in upcoming technologies eventually brought other important parameter in picture such as memory & batteries which will allow more capable & reliable devices. Most of the smart sensors are wirelesses that are based on RF communication. These sensors use low radiated power to avoid the heavy costs. The ever growing market of smart sensor is capable of providing services such as configuration, remote diagnosis & real time application. The undesirable characteristics of smart sensors have been reduced due to presence of controllers & processors in building it. Smart sensor improves the cost of by minimizing the set up & by repetitive testing time. Smart sensors are able enhance the self calibration, computation, communication, multi sensing. It is able to increase the system reliability as it is capable of finding its flaws & error & produce the consequence. The smart sensors developers are constantly taking efforts to improvise the characteristics of smart sensors such as non linearity, cross sensitivity & offset. Sensing Element Figure II.II: smart sensor using Microprocessor A. IEEE 1451 STANDARD Signal Conditioning Micropr ocessor The sensors to the digital world of processors, controllers & networks the IEEE 1451 is approved standard. An effort to overcome the problem that arise while interfacing smart sensor to microprocessor field bus & network has led IEEE 1451 & National Institute Standard Technology to define new set of standards. The aim of this standard is to provide common functionality, network & vendor independent, define transducer electronics data sheet. It does not specify a specific field bus protocol but provide specific data sheet of IEEE1451. It supports the introduction of self contained nodes that keep the configuration data physically associated with nodes. The memory requirement is high at IEEE 1451.As it is very difficult to establish communication with digital world hence the major purpose of this standard is to minimize the complexities at designers face. IEEE 1451 enables system design with plug & play modules by stating th bus architecture, addressing protocols, wiring & error correction & calibration. The plug & play concept of IEEE1451 allows liberty to select among sensor, networks & interface modules. These standard is able to produce information related to software model which is object oriented, network independent with standard digital interface & communication protocols for accessing sensor. III. METHODOLGY The block diagram of the proposed design is shown in the figure III.I. The VHDL model for smart sensor consists of the sensing element, signal conditioning & signal processing blocks. The input to the model is voice signal through the microphone which is interfered with noise signal. This input signal is feed to smart sensor mode using VHDL model. Figure III.I: Block Diagram of the smart sensor using VHDL The sensing element sense the analog signal converts into the digital signal using analog to digital converter. The signal conditioning block consist of amplification, filtering, converting, range matching, isolation and any other processes required making sensor output suitable for processing after conditioning. Signal Processing is usually done to measure, filter and is used to compress continuous real time analog signals. The first step is usually to convert the signal from an analog to a digital form, by sampling and then digitizing it using an analogto-digital converter (ADC). The required output signal is fed to another analog output signal, which requires a digital to analog converter (DAC). A Signal Processing block consist of Program& data memory, compute engine & input & output. IV. FILTER STRUCTURE IMPLEMENTATION In many signal processing application, FIR filters are preferred over the IIR. The main advantages of the FIR filter over their IIE equivalent are the following: 1. Fir filter with exactly liner phase can be easily be deigned. 2. There exists computationally efficient realization for implementing FIR filters. These include both non recursive realizations. 3. FIR filters realizations 4. FIR filters realized non recursively are inherently stable & free of limit cycle oscillations when implemented on finite world length digital system. 5. Excellent design methods are available for various kinds of FIR filters within the arbitrary specifications. 6. The output noise to multiplication round off errors in an FIR filter is usually very low & the sensitivity to variations in the filter coefficients is also low. A. REALIZATION OF FIR FILTER A digital FIR filter can be designed by convoluting N coefficients which are also called as the length of filter. Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 106

133 Poojitha V et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, Signal x is the input to the filter & y is the output from the filter as described by Eqn. 1 B. FILTER STRUCTURE The direct form of an FIR filter is shown in Fig. 3 which mainly consists of shift registers, adders and multipliers. The signal samples are multiplied by filter coefficients and are gathered together in the adder block. Figure IV.I: The direct form of FIR filter C. CHARACTERISTICS OF LINEAR PHASE FIR FILTERS Some properties of linear-phase FIR filters are reviewed in this section, such as the conditions for linear phase and the zero locations of these filters as well as different representation forms for the frequency response. CONDITIONS FOR LINEAR PHASE Let {h[n]} be the impulse response of a causal FIR filter of length N+ 1. The transfer function of this filter is be defined by constant. 2. Absolute values are required for the transducer that s why to 0 values are discarded & only absolute values are kept. 3. Variable defined for 128 bit input. 4. FIR filter of Butterworth type design elements are filter length 129,structure of filter is Direct Form,Stable, Type -1 linear phase 5. Conversion digital values to analog for further analog display like DSO. D. PARALLEL VHDL IMPLEMENTATION The VLSI chip designs are becoming more complex, it is unrealistic for designers to build and test a prototype of a microelectronic circuit. Therefore, today's chip designers use VHDL. These designers are discovering that sequential execution limits the speed at which a VHDL simulation can be run. By distributing VHDL throughout a parallel processor, designers complete a simulation run more quickly. If each individual simulation is faster, designers are able to perform more tests on different configurations of the circuit. These easily reconfigured C circuits and tests result in a more robust chip design. Increasing the reliability of each chip increases the reliability and accuracy of the weapon systems which use these chips. More reliable weapon systems increase the ability to deliver the payload to the intended target and avoid collateral damage. To develop parallel simulations for VHDL requires familiarity in two distinct areas. The first is simulations in general. One must understand simulation approaches to choose which would be best for modeling the behavior of an electronic circuit. The second area is VHDL itself. One cannot parallelize VHDL without comprehending the interdependencies of sequential VHDL's separate stages. V. SIMULATION RESULT The code is written in VHDL & it is stimulated on Xilinx 9.2 version which in depicted in figure V.III which shows the input to smart sensor & figure V.IV shows the variation in output. This language is used to describe hardware for the purpose of simulation, modeling, design, testing and documentation. The languages are used to represent the functional and wiring details of digital systems in a compact form. It must uniquely and unambiguously define the hardware at various levels of abstraction. The IEEE standard VHDL is such a language. The figure V.I & V.II below shows the RTL schematic of Smart Senor. Figure IV.I: Zero Phase frequency response for Type I linear filter The corresponding frequency response is given by in the above, N is the order of the filter. ALGORITHM 1. Analog values for 28=256 i.e is ranged -127 to 128 to Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 107

134 Poojitha V et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, Figure V.III: input to smart senor Figure V.IV: output of smart sensor VI. CONCLUSIONS Figure V.I: shows the RTL schematic of smart sensor The paper focused on the concept of smart sensor, filter implementation, methodology & algorithm to implement the VHDL model of Smart sensor. The standard VHDL is presented which is event driving modeling technique for high simulation for smart sensor implementation, to develop the software model that process the voice signal & reduce the noise signal& thus can be implemented on FPGA kit. This modeling scheme provides fast simulation test and it is useful tool for generating an integrated smart sensor. REFERENCES Figure V.II: shows the view schematic of smart senor module [1] Alessandro Depari, Member, IEEE, Paolo Ferrari, Alessandra Flammini, Daniele Marioli, and Andrea Taroni, A VHDL Model of a IEEE Smart Sensor: Characterization and Applications, ieee sensors journal, vol. 7, no. 5, May 2007 [2] Travis, Bill. Sensors Smarten Up, EDN, pp , March 4, 1999, Cahners Publishing. [3] Lee, Kang. The Proposed Smart Transducer Interface Standard, Proceedings of the Instrumentation Measurement Conference (IMTC) 98, St. Paul, MN, May 18 21, 1998, vol. 1,pp A. [4] Travis, Bill. Smart Sensor Standard Will Ease Networking Woes, EDN, pp , June 22, 1995, Cahners Publishing. [5]Sanjit K. Mitra & James F., Digital Signal Processing. ISBN , 1993 Pg [6]. Prasit Kumar Bandyopadhyay; Arindam Biswas; Pramit Kumar Bandyopadhyay, Durbadal Mandal, Rajib Kar., FPGA Based High Frequency Noise Elimination System From Speech Signal Using Xilinx System Generator, Volume , Issue 2, pg Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 108

135 Volume 9, Special Issue No. 3, May 2018 International Journal of Advanced Research in Computer Science (ISSN: ) CONFERENCE PAPER Available Online at ENERGY EFFICIENT PATH SELECTION (EEPS) OF MOBISINK FOR EFFICIENT DATA COLLECTION IN WSN Kumar Swamy B.V Robert Bosch Engineering and Business Solutions, Bangalore, India. Kalpataru Institute of Technology, Karnataka, India. Dr. Gowramma Y.P Robert Bosch Engineering and Business Solutions, Bangalore, India. Kalpataru Institute of Technology, Karnataka, India. Ananda Babu J Robert Bosch Engineering and Business Solutions, Bangalore, India. Kalpataru Institute of Technology, Karnataka, India. Abstract: WSNs are considered as the most promising technologies of this decade. The major objective of sensor nodes is to gather information from distinct places where other mechanism cannot be applied. However, the huge challenge is in collecting of data which is scattered across the network. Energy also plays a significant role in deciding an optimized route to collect this data spread across the network. This problem is analyzed and provided an efficient solution in the proposed algorithm. Energy Efficient Path Selection (EEPS) is introduced that will help in forming a efficient path selection for MobiSink to traverse through the network. An intelligent layer is defined which will find data collection points (DCP) in advance in the network. Current research works focus on how to reduce the energy of sensor nodes to increase the lifetime of the network. But, advance identification mobile sink's moving trajectory not only optimizes the energy consumption, but also reduces the latency. The proposed algorithms and protocols are validated through simulation experiments using Network Simulator (NS2). Keywords: Energy Aware Secured, Secure routing protocol, Lifetime Optimization, Energy Consumption, Uniform Energy, 1. Introduction Wireless Sensor Networks (WSNs) comprise of each sensor hubs which gather the most valuable information from condition and transfer them to a sink where they are hence prepared. WSNs are utilized in extensive variety of utilizations, for example, security observation, front line, interruption discovery, target following purposes and so forth. Organization of expansive arrangement of sensor hubs particular to application makes them difficult to hand put where battery substitution is generally lumbering (particularly in cruel conditions like war zones) [1]. Being practical for a drawn out period is its basic target. As a rule in many-to-one multi-jump WSNs, the sink's one-bounce region hubs regularly 'pipe' (forward) information for the benefit of every other hub. Plainly, organize in which information accumulation rate overwhelms information sending rate (in a rush hour gridlock extraordinary application, e.g. Video-based target following), regularly clog develops at the bottleneck (hubs at the sink's one-jump neighborhood). Therefore, bundle dropping occurrence and additionally retransmission turns out to be more regular, prompting progressively corrupted system execution. Moreover, problem area (sink's one-bounce neighbor) hubs pass on prior (they deplete their vitality as they forward higher volume of activity contrasted with different hubs) in respect to different hubs in the system. Vitality consumption to problem area hubs causes arrange dividing, prompting contend disengagement of the sink hub bringing about whole system disappointment. Subsequently this problem area issue must be satisfactorily managed, through measures that keep the disappointment of the sink's one jump neighbors by diminishing burden caused to these hubs. Applying a productive versatile sink procedure into the multi-bounce steering conventions is a compelling arrangement that has a tendency to avert organizes apportioning in WSNs [2]. Rather than supplanting these problem area hubs, the key thought is to move the sink occasionally to different parts of the system with adequate vitality for information gathering. Amid the sink' direction, as the sink's one bounce neighbors continues changing in time, the vitality utilization and the movement stack (for sink's neighbor hubs) could be adjusted everywhere throughout the system. This instrument thus expands the system lifetime [3]. In this manner, improvement for vitality utilization is predicted as an essential issue, particularly to drag out system lifetime in WSNs [4]. The utilization of sink versatility in WSN is regularly perceived as a standout amongst the best methods Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 109

136 Kumar Swamy B. V. et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, for stack adjusting, eventually prompting less fizzled sensor hubs and longer system lifetime. This likewise improves unwavering quality, precision, adaptability, cost adequacy and simplicity of arrangement [5]. Late research on information accumulation uncovers that social event sensor information from blunder inclined courses (long and multi jump courses) to a static sink, utilizing sink portability for information gathering is all the more encouraging for vitality effective information gathering [6]. The primary test of this method is the trouble is dynamic course revelation of the portable sink to traverse the system, to such an extent that the information gathered from sensor hubs are inside particular time. II. Related Work This section describes the related work refers to carry out the work One of the major disadvantage in using mobile sink is the increased latency that is caused during collection of data which is due to the speed of the mobile sink typically ranging between m/swhich is referred in [7], [8]. Increase of latency leads to more time consumption and performance of the network will be dropped drastically. In a single hop approach, distance between the sink and the source is only one hop. The mobile sink visits each sensor node and gathers its data, apparently, in such networks the energy consumption of sensor nodes are minimized (as communication is done using only one hop), The wireless Sensor Networks (WSNs) that comprise of each sensor hubs which gather information from condition and transfer all the mobile sink where they are hence prepared. WSNs are utilized in extensive variety of utilizations, for example, security observation, front line, interruption discovery, target following purposes and so forth. Organization of expansive arrangement of sensor hubs particular to application makes them difficult to hand put where battery substitution is generally lumbering (particularly in cruel conditions like war zones) [1]. Being practical for a drawn out period is its basic target. As a rule in many-to-one multi-jump WSNs, the sink's one-bounce region hubs regularly 'pipe' (forward) information for the benefit of every other hub. Plainly, organize in which information accumulation rate overwhelms information sending rate (in a rush hour gridlock extraordinary application, e.g. Video-based target following), regularly clog develops at the bottleneck (hubs at the sink's one-jump neighborhood). Therefore, bundle dropping occurrence and additionally retransmission turns out to be more regular, prompting progressively corrupted system execution. Moreover, problem area (sink's one-bounce neighbor) hubs pass on prior (they deplete their vitality as they forward higher volume of activity contrasted with different hubs) in respect to different hubs in the system. Vitality consumption to problem area hubs causes arrange dividing, prompting contend disengagement of the sink hub bringing about whole system disappointment. Subsequently this problem area issue must be satisfactorily managed, through measures that keep the disappointment of the sink's one jump neighbors by diminishing burden caused to these hubs. Applying a productive versatile sink procedure into the multi-bounce steering conventions is a compelling arrangement that has a tendency to avert organize apportioning in WSNs [2]. Rather than supplanting these problem area hubs, the key thought is to move the sink occasionally to different parts of the system with adequate vitality for information gathering. Amid the sink' direction, as the sink's one bounce neighbors continues changing in time, the vitality utilization and the movement stack (for sink's neighbor hubs) could be adjusted everywhere throughout the system. This instrument thus expands the system lifetime [3]. In this manner, improvement for vitality utilization is predicted as an essential issue, particularly to drag out system lifetime in WSNs [4]. The utilization of sink versatility in WSN is regularly perceived as a standout amongst the best methods for stack adjusting, eventually prompting less fizzled sensor hubs and longer system lifetime. This likewise improves unwavering quality, precision, adaptability, cost adequacy and simplicity of arrangement [5]. Late research on information accumulation uncovers that social event sensor information from blunder inclined courses (long and multi jump courses) to a static sink, utilizing sink portability for information gathering is all the more encouraging for vitality effective information gathering [6]. The primary test of this method is the trouble is dynamic course revelation of the portable sink to traverse the system, to such an extent that the information gathered from sensor hubs are inside particular time. With direct communication between MobiSink and sensor node has gained more importance, but route formation with all nearest possible points in the network increases the travel distance of MobiSink leads to loss of energy[13]. Additionally, problem concerning mobile sink to avoid data loss due to sensor buffer overflow causing high latency in existing approach was further evaluated and thus the proposed work was designed to enhance effectiveness in mobile sink data gathering methodology with reduced latency and prolonged network lifetime. 3. PROPOSED WORK This paper presents anenergy Efficient Path Selection (EEPS) Mechanism of MobiSink(MS) for Efficient Data Collection in WSNs. Nodes within the network along with Mobisink together play the role of identifying efficient data collection points (DCP) which help in identifying travel path of the MobiSink in well advance. Mean point finder (MPF) algorithm is used to locate the DCPs in the network. After identification, all the DCPs will be updated to MobiSink via recursive data update approach. 3.1 Mean Point Finder Source nodes along with MobiSink within the network will play this algorithm. The network will be uniformly distributed throughout the network area. It is also considered Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 110

137 Kumar Swamy B. V. et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, that, network will have more number of source nodes distributed randomly in the network. Random distribution is used to select the source nodes in the network. Assuming, there are N number of nodes in the network and Snumber of source nodes within the network. Then, all nodes within a distance of dd cc will form a group, where dd cc is a constant value called convergent distance. Group formation is given using (1). Source nodes will be notified once MobiSink reaches to the DCP. Upon receiving the notifiermessage from MobiSink, source nodes will activate and start sending the data to the MobiSink. 4. System Architecture SSSS ii = dddddddd SS ii, SS jj < dd cc, wwheeeeeeee, jj NN (1) Once the groups are formed, each group will find the mean position from the all points identified in the SSSS ii group using (2). DDDDDD ii = mmmmmmmmmmmmssss ii (2) 3.2 Recursive Location Update All data collection points will be found in mean point finder algorithm. Every source node group will have one data collection point. In this approach, a node is selected as Informer Node (IN) for every source node group. Selection of the node is based on their distance with respect to MobiSink node and is defined in (3). IIII = sshoooooooooooooooooooo(ssss ii, MMMM) (3) Once all INs are identified in each source node group, they will update their DCP to the MobiSink node by sending a message via shortest tree algorithm using (4). NN ii = dddddddd(nn ii, MMMM) < dddddddd(ss RR, MMMM) (4) Where,SS RR = RRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRR 3.3Path Formation NN ii = NNNNNNNNhoooooooooooo Figure 4.1 EEPSArchitecture Figure 4.1 above shows the architecture design of the proposed algorithm. Nodes are distributed within the network uniformly. Encircled area forms the source node groups at various locations. MobiSink is considered to away from the network at beginning of the protocol.the number of source nodes will be defined before, but the selection of the source nodes will be random. 5. RTIM Protocol Proposed protocol is divided into two phases. 1. Identification of the Route Phase 2. Data Gathering Phase Figure 5.1 below shows the flow diagram of the proposed protocol. Previous process will update all DCP to MobiSink node. MobiSink node will wait till a reset timer so that all DCPs will be reached to MobiSink. MobiSink will calculate distance of every point from its current location using (5). DD dddddddddd ii = dddddddddddddddd (dddddd ii, dd mmmm ) (5) All DD dddddddddd ii distances are sorted in ascending order. Mobisink will then start traversing from the nearest distance point till the farthest distance point. 3.4 Data Collection by MobiSink MobiSink will frame the travelling route path (TRP) list after sorting the distance values obtained from the DCPs. MobiSink with traverse through each of the points defined in the TRP. After reaching every TRP, MobiSink will set a timer. It will wait in that position till the timer expires and will wait to receive data from source nodes. Figure 5.1 RTIM Flow Diagrams 5.1 Route Identification Phase In Route Identification Phase, all the source nodes start broadcasting. Neighbor source nodes will be notified with this and hence, all those nearby source nodes will form a group. Once the groups are formed, all the source nodes Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 111

138 Kumar Swamy B. V. et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, within that group will find the DCP by calculating the mean position from the collected location points of source nodes within the group set. An informer node will be identified within the group which is the closest node to the MobiSink node from the group. This informer node will notify DCP to the MobiSink node. 5.2 Data Gathering Phase In data gathering phase, MobiSink node will collect DCP from all the source node groups in the network. After finding all the DCP, MobiSink will form TRP and apply sort algorithm to find an optimized route. By using this route information, MobiSink will start traversing in the network by moving each DCP in the TRP. After reaching every DCP, MobiSink will set a timer and will wait in the DCP to receive data. All source nodes surrounding that DCP will be notified once MobiSink reaches the position. And, source nodes will start sending data to the MobiSink node. Data transfer will be one hop where source node where data originates will directly transfer data to the MobiSink node. Hence, data loss will be very minimal. 6.Performance Analysis 6.1 Energy Consumption: In our proposed approach, the main objective is to reduce the travelling distance of the MobiSink node. Intelligence is incorporated into the network which will identify the possible travel path and will be optimized by applying the proposed algorithm. Figure 6.1 Energy Consumption As show in above results, convergent points calculated in IMPR will be more than the DCP in the proposed RTIM protocol. Hence, MobiSink node will take more distance to traverse in the network. Hence, the energy consumption will be higher. 6.2 Packet Delivery Ratio Packet redundancy is not required in this protocol since the communication of data transfer is one hop level. The data to be sent from source node to MobiSink is direct. Hence, the chances of packet loss are very less. Only DCPs are updated through multi-hop and this mechanism takes place once in each iteration. This will not create overhead to the actual data transfer. Figure 6.2 shown below shows the results of packet delivery ratio. Energy consumption will be higher when node is travelling. Lesser the distance travelled will save the energy of the node for long time. By considering it, the source nodes and MobiSink will take participation in the route identification. MobiSink node after reaching every DCP, will halt for a defined amount of time and will spend energy to receive packets sent by source nodes. DCPs are further sorted which will ensure the travelling path covers less amount of distance covered by MobiSink. Figure 6.1 shows the result of energy consumption by the network. Results obtained from the proposed algorithm are compared with IMPR. It is to be observedthat; proposed algorithm improves in the energy consumption due to optimized path selection. Figure 6.2 Packet Delivery Ratio As shown in the above figure 6.2, packet delivery ratio decreases with increase in the number of source nodes since more number of nodes will participate in data transfer. The results are compared with IMPR protocol; where MobiSink node will travel all convergent point where mean point algorithm is not applied. This would cause time synchronization problem between sender node and MobiSink and hence, data loss will be higher. Whereas in RTIM, the DCPs are calculated through mean point, and hence, MobiSink will be equidistant from all source nodes within the source node group and so, is reachable to all. Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 112

139 Kumar Swamy B. V. et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, Conclusion Despite the fact that there are examines works that arrangement with versatile sink information gathering component, the greater part of these works are restricted to accumulation of information by coming to at each source focuses. For extensive scale wireless sensors systems, challenge are to gather information in WSNs through upgraded arrange execution. This paper presents Energy Efficient Path Selection (EEPS) of MobiSink for Efficient Data Collection protocol suitable for large scale network also MobiSink initiates path formation algorithmic which creates Travelling Route Path(TRP) list and optimizing TRP helps in creation of route in advance. During its trajectory, MobiSink performs efficient data gathering using RTIM process. Data is gathered from sensor nodes without incurring excessive delay thereby optimizing traffic flow thereby prolong the lifetime of the network. RTIM protocol is scalable for large size networks. Multi-sink approaches could benefit the advantages of both static and mobile sink approaches. Therefore, the hybrid multiple mobile sinks is an open problem for future trends. REFERENCES [1] De Morais Cordeiro, C., Agrawal, D.P.: Ad Hoc and Sensor Networks: Theory and Applications. World Scientific, Singapore (2011) [2] Xing, G., Li M., Wang, T., Jia, W. And Huang, J. (2012) Efficient Rendezvous Algorithms for Mobility-Enabled Wireless Sensor Networks, IEEE Transactions on Mobile Computing. [3] Gao, S., Zhang, H., Song, T. And Wang, Y.(2011) Efficient Data Collection in Wireless Sensor Networks with Path-Constrained Mobile Sinks. IEEE Transactions on Mobile Computing. [4] C. Chou, K. Ssu, H. Jiau, W. Wang, and C. Wang, A Dead-End Free Topology Maintenance Protocol for Geographic Forwarding in Wireless Sensor Networks, IEEE Trans. Computers, vol. 60, no. 11, pp , Nov [5] K. Dantu, M. Rahimi, H. Shah, S. Babel, A. Dhariwal, and G. Sukhatme, Robomote: enabling mobility in sensor networks, in Proceedings of the 4th international symposium on Informationprocessing in sensor networks (IPSN).IEEE Press, 2005, pp [6] R. Pon, M. Batalin, J. Gordon, A. Kansal, D. Liu, M. Rahimi, L. Shirachi, Y. Yu, M. Hansen, W. Kaiser, and Others, Networked info mechanical systems: a mobile embedded networked sensor platform, in Proceedings of the 4th international symposium on Information processing in sensor networks. IEEE Press, 2005, pp Mobile Sinks in Massive Sensor Networks, Proc. IEEE Int l Symp. Parallel and Distributed Processing (IPDPS), pp. 1-8, May [8] R. Sudarmani and K. R. S. Kumar, Energy-efficient clustering algorithm for heterogeneous sensor networks with mobile sink, European Journal of Scientific Research, vol. 68, no. 1, pp , [9] N. M. Khan, I. Ali, Z. Khalid, G. Ahmed, A. A. Kavokin, and R. Ramer, Quasi centralized clustering approach for an energy efficient 67 72, ACM, May [10] M. Ma and Y. Yang, Data Gathering in Wireless Sensor Networks with Mobile Collectors, Proc. IEEE Int l Symp. Parallel and Distributed Processing (IPDPS), pp. 1-9, Apr [11] Suraj Sharma and Sanjay Kumar Jena, Data Dissemination Protocol for Mobile Sink in Wireless Sensor Networks,Journal of Computational Engineering, Volume 2014 (2014), Article ID [12] Jin Wang, Xiaoqin Yang, Zhongqi Zhang, Bin Li and Jeong Uk Kim, "A Survey about Routing Protocols with Mobile Sink for Wireless Sensor Network", International Journal of Future Generation Communication and Networking,Vol.7, No.5 (2014), pp , ISSN: , IJFGCN, [13] K.Vijayalakshmi and Dr.J.Martin Leo Manickam, Mobisink- Intelligent Mobility Pattern based Routing Protocol for Efficient Data Gathering in Large Scale Wireless Sensor Networks, International Conference on Control, Instrumentation, Communication and Computational Technologies, ICCICCT, 2016 Authors: Kumar Swamy B.V received the B.E. and M.Tech. Degree from Visvesvaraya Technological University (V.T.U), Karnataka State, India in 2007 and 2009, respectively and a Ph.D research scholar at V.T.U. He is currently a research scholar at the Kalpataru Institute of Technology, Karnataka, India. His current research interests include intelligent networking, big data analytics and deep learning. He has filed for 5 patents in big data and application modernization areas. [7] T. Park, D. Kim, S. Jang, S. Sun Yoo, and Y. Lee, Energy Efficient and Seamless Data Collection with Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 113

140 Kumar Swamy B. V. et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, Dr. Gowramma Y.P received a B.E. degree from A.I.T, Chikmagalore, India in 1995 and M.Tech. Degree from N.I.T.K, Surathkkal, India in 2000 and Ph.D degree from Visvesvaraya Technological University, Karnataka State, India in She is currently working as Professor at the Kalpataru Institute of Technology, Karnataka, India. Her current research interests include image processing, computer networks and system analysis. Ananda Babu J received the B.E. and M.Tech. Degree from Visvesvaraya Technological University, Karnataka State, India. in 2007 and 2009, respectively and a Ph.D research scholar at V.T.U. He is currently an Assistant Professor at the Kalpataru Institute of Technology, Karnataka, India. His current research interests include computer networks and wireless sensor networks. Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 114

141 Volume 9, Special Issue No. 3, May 2018 International Journal of Advanced Research in Computer Science (ISSN: ) CONFERENCE PAPER Available Online at IDENTIFYING TRUST NODES BASED ON STAY TIME FOR SECURITY IN VANET Bonish Koirala School of Electronics and Communication Engineering REVA University Bengaluru, India bonkoirala@gmail.com Shrikant S. Tangade School of Electronics and CommunicationEngineering REVA University Bengaluru, India shrikantstangade@reva.edu.in Sunilkumar S Manvi School of Computing and Information Technology REVA University Bengaluru, India ssmanvi@reva.edu.in Abstract: Many published works for security in VANET con- siders that Road Side Units are stable nodes and On board Units are always mobile. In general scenario, there are also an abundant number of vehicles in a network which arenot always mobile. Since each vehicle has high computation, power andstoragecapability,theycanbeusedtoprovidebetternetwork stability and compute trust for other nodes. Mostly those nodes which belong to a network or stays in it for a longer period of time can be used for this purpose. In this paper, we consider this natureofthevehiclestomaintainsecurityinthenetwork. Keywords: Trust Management, VANET, Evaluators, Se- curity, Stay Time I. INTRODUCTION As a sub class of Mobile Ad hoc Network, the Vehicular Ad hoc Network has vehicles as nodes of the network. But since the vehicles have high speed and their movementis unpredictable, network stability and security is a major issue in VANET. In VANET, the nodes of the network are RoadSide Units and On Board Units. Road Side Units are stable i.e. they are confined to their position. On Board Units are embedded within the vehicles so they are the main mobile units of the network. These units communicate with each other within 300 meters of range using Dedicated Short Range Communication (DSRC) as defined in IEEE p. It was later redefined as Wireless Access for Vehicular Environment in the IEEE 1609 standardforuseinintelligenttransportationsystem. Vehicular networks are dynamic by nature so movement of the nodes in the network is unpredictable. This is mainly because movement of nodes is focused on maintaining the network but to take the vehicle from source to destination. Due to this factor, the vehicular network is not stable and susceptible to many different vulnerabilities. Therefore, securing the nodes of the network is very important as the nodes itself carries human beings. In this paper we provide a proposal for trust management which contributes to better network stability and ultimately towards bettersecurity. Many of the recent works focused on cryptography to manage security of the network. Cryptography includes digital signature and verification, encryption and decryption through theuseofcryptographykeys.itisanultimatemethodtosecure any network. But cryptographic entities increases theoverheadin messages which increases the computation andtransmission delay. Since delay is not tolerable in a dynamic and sensitive network of VANET, trust management is considered more ef- fective than proposals fully based on cryptographic measures. Trust between vehicles is based on reputation at a local or a global scale, opinions generated from neighborhood vehicles and the history of interactions. It is a belief on a node or a data that it does not provide fake or malicious alerts and can be trusted. If a vehicle has maintained a good level of trust within the network in a local or a global scale, then the alerts sent by it can be trusted with a minimum amount of computation [1]. The rest of the paper is organized as following: Section II briefly explains previously proposed models and methods for trust management. Section III explores our proposed method- ology for trust management that gives better network stability and security. Section IV shows the result of the proposedwork andsectionvgivestheconclusiontoourpaper. II. RELATEDWORKS Previously published proposals for Trust Management in VANET are primarily based on either node trust or data trust orboth.manyofthoseproposalstakeexampleofhowthetrust is maintained in the real world and how it can be implemented in the Vehicular Ad hoc Network. Some earlier works in Trust Management according to the element to be trusted is given below: Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 115

142 Bonish Koirala et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, A. Entity Oriented TrustModels Yang et. al [2] has proposed similarity mining technique based on Euclidean distance and evaluation of reputation. Two similarities are considered: similarity of message and vehicles. Reputation are used as weightage to compute node trust and is updated frequently. Khan et. al [3] proposed to compute thetrustofnodesusinganumberofverifierswithinacluster.the verifiers are chosen using 3 decision parameters i.e. load, distrust value and distance. Once the distrust value exceeds a threshold, it is detected asamaliciousnode. Hada ddou et. al [4] proposed a credit based system for trust management. A nodegetsacertaincreditvalueduringregistrationinanetwork which is appended along an alert while broadcasting. Thenodeis rewarded with some more credit value if the alert is found to be true. When the credit value reaches zero, it is regarded as an untrustworthynode. B. Data Oriented TrustModels Golleet.al[5]proposed a sensor driven method which helps a vehicle to build its own model of VANET. This model consist of all the possible information it has on the VANET. A node will gather explanations for the data messages it receives. The received information is compared with its own observedmodel of the VANET. The explanation which ismoreconsistent with its model is accepted. Gurunget. al [6] has proposed a message and route similarity based method. Vehicles can compute message trust themselves without need of third party. The message trust is computed based on its contents and the route it took to reach to the receiver. The message with highest trust value is accepted if two messages have same origin but different content. Shaikh and Alzahrani [7] have proposed trust evaluation based on V2V communication. Confidence is calculated for messages received by a vehicle and trust is evaluated for the message. The method consist of 3 phases of message trust evaluation. Confidence is measured for unique sender, the messages related to an event and the message havinghighesttrustvalueisaccepted. C. Hybrid TrustModels Marmol and Perez [8] has proposed a technique of trustinga message based on peer reputation. Direct experience, peer and central authority recommendation is considered for evaluating reputation of a node. The reputation will categorize the node in 3 fuzzy sets based on which decision to whether discard the message or accept but not forward or accept and forward is taken. Importance of message depends on reputation of senders. Sedjelmaciet. al [9] proposed a cluster basedin- trusion detection mechanism to detect and prevent security attacks from malicious nodes. The leader of the cluster is chosenbasedonthenodemobilityanditstrustlevel.detection of malicious nodes is done at a local cluster level which is rule based,atagloballevelwhereclusterheadsdohybriddetection anddecisionismadeatagloballevel. III. PROPOSEDPROTOCOL In our proposed protocol, we focus on those vehicles which mostly remain within a network for a very long period of time. Such vehicles rarely move within the network i.e. only once or twice a day. These vehicles could be public service vehicles such as a police van or an ambulance. It can also be personal vehicles which remain in the garage or a parking lot when the owner is in home or office respectively. On a daily scale, vehicles of this nature are found in an abundant quantity in any area. So these types of vehicles can be used for faster communication, better network stability and evaluation oftrust of nodes within the network. Other nodes that pass through the network frequently but does not belong to that network are treated as normal nodes. Their trust values are computed by thenodesthatbelongtothenetworki.e.thosenodeswhichstay in the network for longer time as explained earlier. The list of notations used in this paper is given in Table I. LIST OF NOTATIONS AND THEIR DESCRIPTIONS Notation GTA EvN NrN UrN RSU STi ATi DR PsID Description Government Trusted Authority Evaluator Node Normal Node Unregistered Node Road Side Unit Stay Time of a vehicle i (in hours) Away Time of a vehicle i (in hours) Decay Rate Pseudonym of a vehicle A. NetworkArchitecture The network architecture for the proposed methodology is given in Fig. 1. The list of entities in the proposed protocol are givenbelow: Fig. 1. Network Architecture a) GTA: A central Government Trusted Authority (GTA) manages the RSUs of the network. Since, a Government Trusted Authority is the supreme trusted entity of the network, theyarefitformanagingthersus. b) RSU: The RSUs are also most trusted entities of the network second only to the GTA itself. A number of RSUs exchange road information with each other within a Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 116

143 Bonish Koirala et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, networkto keep their database updated about the network and its nodes. TheRSUsalsochoosenewevaluatorsforthenetwork. c) EvN: EvNs are the nodes that stay for the longest in the network. But they are chosen on the basis of both their trust level and their stay time in the network. The EvNs are distributed in the network and they have the responsibility of monitoring the other nodes of the network. This includes keeping track of their stay time in the network, monitoring the alerts sent by those nodes to identify valid against invalid ones and using this information to evaluate trust level of each nodes.theevnssharetheseinformationwitheachothersodecision s can be made through general agreement. Due to this, no EvN can alter the database with malicious intent since each EvN has the same copy. d) NrN: NrN are just normal nodes that dont belong to the network but frequently pass through the network on a daily basis. Their trust level are computed by the EvN on the basis of the alerts that they have broadcasted. The number of NrNs in a network can be in hundreds as many vehicles passthrough the same network everyday. B. ProcessOfChoosingANewEvN Those nodes which belong to a network or stay for the longest time within a network and having a high trust level are eligible to be the evaluator nodes (EvN). Any alert message broadcasted by any nodes have timestamp attached with it that represents the time that the alert was sensed. The EvNs make use of this timestamp to compute the stay time (ST) of the node. Since many of the normal nodes just pass by a network, they have less probability of being an EvN. Butsome malicious nodes may also stay within a network for a longer time with intent of attack. This is why the trust level of a node alsocarriesequalweightagetobechosenasaevn. For our proposed protocol, we have set maximum stay time of a node to be recorded to be 8 hours which is the number of business hours in most areas. To avoid the increment of stay time of each node, we apply a decay factor to the stay time. This decay factor is applied if the presence of a node through alert or trust level update request is not sensed for a longer period of time. The Eq. (1) gives the equation for calculation of the stay time of any node i. Both Stay Time (ST) and Away Time (AT) can be computed through the use oftimestampsalongwiththealertsentbythenodes. ST i =min(st i (1 DR) AT i,8) (1) The election to choose a new EvN is held by the RSU. Any NrN that has stayed in the network for a longer time with a high trust level can provide its pseudonym (PsID) to the RSU. The RSU keeps a queue of these pseudonyms to refer whenever election is held. It takes the pseudonym that is first in the queue and provides it to the group of EvNs. The EvNs in return provide the stay time and the trust level of the node associated with the pseudonym. Using this information, the RSU first computes the time factor as given in Eq. (2) and then the eligibility of the node to be aevn is calculated as given in Eq.(3). Simulation Parameters Value Area of Simulation 1km 2 Number of nodes 100 Vehicle Speed 80 km/hr to 100 km/hr Communication Range 300m Number of RSU 3 Channel Link Bandwidth 10MHz Time of simulation 200s Propagation Speed 3 x 10 8 m/s Fig. 2. EvN Selection Procedure IV. RESULTS ANDDISCUSSION Here, we have listed the trust requirements met by our proposal, simulation environment and its performance analysis. A. SecurityAnalysis The following security requirements are met by our proposed scheme: 1) Scalable: The proposed scheme focuses on the basic characteristics of the vehicle that they are not always mobile. So EvN nodes can be found anywhere. Also RSUs and GTA are also found in most of the network. Therefore, the proposed schemeisscalabletoanynetworksize. 2) Justifiable: EvNs exchange information with themselves. The RSUs also share node information with eachother. So, each nodes in the network is monitored and treated as same. Whether it is a NrN or an EvN, each nodes can be rewarded and punished for their valid and invalid activities respectively 3) Location/Time Specific: The EvNs make use of the timestamps associated with the alerts. It is one of theimportant factortochooseanewevn.therefore,ourproposalisspecific with time andlocation. 4) Integrated Confidence: Since the databases is managed and maintained by each of the evaluators, they can reach a consensus whenever conflict arises. This way confidence will be from a number of nodes rather than a single node. And when the confidence comes from a number of nodes, trust is established in thenetwork. B. SimulationEnvironment The proposed scheme is simulated in NS3. The simulation parameters are given in Table IV. Timefactor= ST (2) Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 117

144 Bonish Koirala et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, Eligibility=0.5 TL i +0.5 Timefactor (3) Trust Level of a node ranges between [0, 1]. If the result of Eq.(3)ishighenough,theRSUnowelectsthenodeassociated with Fig. 3. Time required to choose EvN vs number of nodes C. Performance Analysis For performance analysis, we have considered the delay in choosing the evaluators in the presence of malicious nodes. The graph is plotted of total time required to choose evaluator against the number of nodes in the network. There are 3 different End to End delay between the entities whilechoosing the evaluators, i.e. from NrN to RSU for placing PsIDin queue, RSU to EvN for providing PsID and EvN back to RSU for sending Trust level and Stay Time of the node associated with the PsID. The formula for End to End to End Delay is given in Eq.(4) EndtoEndDelay=TransmissionDelay+PropagationDela y+queuingdelay+processingdelay (4) The transmission delay and propagation delay occurs in all 3 interactions whereas queueing delay happens only in the first interaction. The processing delay is considered negligible. Fig. 3 depicts the total time required to choose evaluators against the number of nodes available in the network considering 50% of them are eligible for selection as evaluators. V. CONCLUSION AND FUTUREWORK In our paper, we have considered choosing the most trusted the pseudonym as a new EvN. If not, RSU considers the next pseudonym until this condition is fulfilled. A graphical representationoftheprocessisgiveninfig.2. nodes that stay in a network for the longest time as evaluators. This provides better network stability as most of theevaluating node stay in a fixed position. Other nodes can use these evaluatorsformessagepropagationandtrustevaluation.since, evaluators are most trusted nodes themselves, they canprovide strong security in the network for the passerby nodes. A number of security requirements can be fulfilled usingthe evaluator nodes. The need to consult the government trusted authorityforsecurityandencryptionisalsoeliminated. Researchers can focus on the proposed methodologyfor better network stability and security. For future, we plan to use improvised equations and calculations for formatting the alert messages, the mode of registration, and procedure of trust evaluation of the nodes that will be based on the methodology given above. REFERENCES [1] C Kerrache, C T Calafate, J Cano, N Lagraa& Pietro, Trust Management for Vehicular Networks: An Adversary-Oriented Overview, IEEE Access 2016,pp , vol 4, Dec., [2] N. Yang, A similarity based trust and reputation management framework for VANETs, Int. J. Future Generat. Commun. Netw., vol. 6, no. 2, pp ,2013 [3] U. Khan, S. Agrawal,& S. Silakari, Detection of malicious nodes (DMN) in vehicular ad-hoc networks, Procedia Comput. Sci., vol. 46, pp , Apr [4] N. Haddadou, A. Rachedi, & Y. Ghamri- Doudane, Trustand exclusion in vehicular adhoc networks: An economicincentivemodelbasedapproach,comput.,commun. IT Appl. Conf.(ComComAp), pp , Apr.2013 [5] P.Golle,D.Greene,&J.Staddon,Detectingandcorrectingmalicious data in VANETs, 1st ACM Int. Workshop Veh. Ad Hoc Netw., Oct. 2004, pp [6] S. Gurung, D. Lin, A. C. Squicciarini,& E. Bertino, Information-oriented trustworthiness evaluation in vehicular ad-hoc networks, NSS, pp ,2013. [7] R.A.Shaikh&A.S.Alzahrani,Intrusionawaretrustmodelforvehicular ad hoc networks, Secur. Commun. Netw., vol. 7, no. 11, pp , Nov [8] F. G. Mrmol& G. M. Prez, TRIP, a trust and reputation infrastructure- based proposal for vehicular ad hoc networks, J. Netw. Comput. Appl., vol.35,no.3,pp ,2012. [9] H. Sedjelmaci& S. M. Senouci, An accurate and efficient collaborative intrusion detection framework to secure vehicular networks,comput. Elect. Eng., vol. 43, pp ,Apr Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 118

145 Volume 9, Special Issue No. 3, May 2018 International Journal of Advanced Research in Computer Science (ISSN: ) CONFERENCE PAPER Available Online at KASE PLUS Manjunath REVA University, Bangalore, India A Ananda Shankar REVA University, Bangalore, India Abstract: As days are passing the use of cloud services like saving user data be it personal, official or any other data is becoming part of our daily routine. Though the companies which provide these cloud based services ensure that they provide enough safety and security to the user data these companies have failed time and again to create enough doubt on the safety of user data when stored in cloud. So Kase Plus enables the users to create the encryption keys for their data so that they can share the keys with the person to whom they would like to provide the access to, and authenticate the user before he lays his eyes on the data. Keywords: authentication, encryption, safety, security 1. Introduction: As the days passed from the kilobytes to mega, giga, tera, peta and zeta have emerged to the need of cloud. Storage is so needful now a days that even while we are buying telephones we think of memory and storage. Memory space is something that everybody looks for and if memory space is free a person feels lots of comfortable to load the data he requires. The image quality and pictures, txt files, audio and video formats have been so improved that it takes lots of space in all devices. It is not at all possible, to carry our hard drives wherever we go and work and thus cloud gives opportunity to preserve and store our official and non-official data online. If we are out of our workplace and want to access the important s on the road Cloud enables us to do this. But it has its own risks such as how secure our data is when we are storing it in the cloud. In this paper we try to address the security of the user data that is stored on the cloud by ways of enabling the user to encrypt the data which is stored on the cloud and share the keys to decrypt the data with the targeted reader/known reader. 2. Related Work: Imagine a situation where a highly reputed multinational company has saved there business mined data in the cloud storage offered by another highly reputed company. What if this cloud storage gets hacked?? Leakage of info. Communication b/w to entities in action is not clear. Countable large amount of keys are being used for both encryption and searching. No facilities are made available for a search on cipher texts. No passing of info and data via groups. Concerns over inadequate information being viewed. Statistically complexity is seen in the existing boundaries of system. 3. Literature Survey: The paper proposed by tzeng et.al [1], the concept has become appealing with introduction of mathematical formulas and tools. ID S should be efficient so when delegated keys carried over mobile media is crypt and system leakage resilient. Flexible process is in delegation of key. combination of keys forms a cluster of security and user who has that combinatory keys allowed to download or view the contents. The writings and work proposed by Ren et.al[2], proposes that Confidential distribution on basis of attributes arbitrary members to represent parties of hybrid cloud, Control model access consisting of two level helps in achievable fined-grained access. It also illustrates the access control of information of users. The IEEE paper proposed by chen et.al [3], says that keywords should be managed eminently and searching of protected files should be done with access control. In this theory basic ideology is storing of data in third or another parties. PKG allow client with sources limited and huge amount of system protected facts are distributed for experts at low cost. To improve accuracy, intrusion detection is introduced,. Searchable area is identified by a new technical implementation. In this hybrid cloud are given importance because many times we have to deal not only with public and private cloud but also hybrid cloud which are widely used now a days for all cloud works and computations. Thus network security place crucial role. 4. Proposed Solution: To avoid the issues that are explained above in this paper the author proposes to enable the user to encrypt the data that he chooses to store in the cloud and hide the keywords from being available for searching. So when the owner of the data like to show his data which is stored in the cloud with another person all that he/she has to do is share the decryption keys to read the data and the make the keywords to be made available for searching. This way Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 119

146 Manjunath et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, the data owner who chooses to use the cloud storage to save his data will have an additional layer of protection for his data. So in this case the data is still protected even when the cloud storage gets hacked. The fig1 shows the architecture of the proposed solution ADVANTAGES: Search keys on cipher-texts are introduced. Any device can be made used to retrieve, including mobiles. Minimal number key used for both encryption and decryption operations. Concerns over communication gap is eliminated. Possessor gives only one key for entire list of his works to receiver. The seeker can crack that one-key to download all the works uploaded to his quota. Mined-data can be passed on via flocks. //cloud login: <%@page import="databaseconnection.*,java.sql.*"%> <% String uname = request.getparameter("uid"); //session.setattribute("uname",uname); String password = request.getparameter("pwd"); con = databasecon.getconnection(); stmt = con.createstatement(); if(uname.equals("cloud")&&password.equals("cloud" )){ //session.setattribute("uname",uname); response.sendredirect("cloudhome.jsp?msg1=succ"); }else{ response.sendredirect("admin.jsp?msg1=unsucc"); } %> //Registration Page <% key1.keygeneration(); String PK = key1.getpk(); String MK = key1.getmk(); con = databasecon.getconnection(); stmt = con.createstatemen(); inti = stmt.executeupdate("insert into user-register (name,uname,password, ,contact,pk,mk,sk,statu s,groupname)values('"+name+"','"+uname+"','"+pass word+"','"+ +"','"+contact+"','"+pk+"','"+mk+ "','active','normal','"+gnm+"')"); if(i>0){ response.sendredirect("user.jsp?msg=succ"); }else{ response.sendredirect("registration.jsp?msg=unsucc "); }%> Fig1 Algorithm: //user-page code <%! Connection con; Statement stmt,stmt1,stmt2; ResultSet rs,rs1,rs2; String fileid,uploadfile, ,pk; int count; byteencdataa[]=null; %> <% String msg = request.getparameter("msg"); if(msg!= null){ out.println("<script>alert('params Generated Successfully for User')</script>"); %> <h2>user's Details</h1></font> <%con = databasecon.getconnection(); stmt = con.createstatement(); stmt2=con.createstatement(); %><div class="form_settings"> <table bgcolor="red" cellspacing="20"> <tr><th><font size="+2">name</th><th><font size="+2">username</th><th align="center"><font size="+2"> </th><th><font size="+2">params</th></tr> <% ResultSet r=stmt.executequery("select *from userregister where status='normal' ");while(r.next()));; 5. Practical Results:In terms of practical results there in enhanced security interms of the data owner can generate his own password or authentication for the data pack which the owner is saving in the cloud. In the below fig2 to fig5 we will see step by step how the data stored in the cloud can be encrypted to generate the aggregate keys and hide the keywords from being available for search results for the fraudsters. Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 120

147 Manjunath et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, Fig2 Fig3 6. Future Work This work can be extended to further enhance the safe and secure sharing on the data stored on cloud storage by enabling the data owner and the data user to communicate in real time to generate the aggregate keys for getting access to data. In this paper I only propose to implement the encryption at the owner side and share it among the potential users but this encryption keys can be stolen from the data viewer and can still be misused. 7. Conclusion: Files can be downloaded, if the user enters proper combination of agg-key that is received to make him a authorized person. Searching of keywords and creating trapdoors to send them to cloud using aggregate-key is done efficiently. The paper proposes to have a second layer of protection for the user data which is stored on the cloud storage for which only the data owner know the decryption keys which he could choose whom to share with in order to share his data with another person and also can avoid unintended person from accessing the data even when the cloud storage is hacked. 8. References: Fig4 [1] C. Chu, S. Chow,W. Tzeng, et al. Key-Aggregate Cryptosystem for Scalable Data Sharing in Cloud Storage, IEEE Transactions on Parallel and Distributed Systems, [2] S. Yu, C. Wang, K. Ren, and W. Lou, Achieving Secure, Scalable, and Fine-Grained Data Access Control in Cloud Computing, Proc. IEEE INFOCOM, pp , [3] J. W. Li, J. Li, X. F. Chen, et al. Efficient Keyword Search over Encrypted Data with Fine-Grained Access Control in Hybrid Cloud, In: Network and System Security 2012, LNCS, pp , Fig5 Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 121

148 Volume 9, Special Issue No. 3, May 2018 International Journal of Advanced Research in Computer Science (ISSN: ) CONFERENCE PAPER Available Online at LIVE LOCATION TRACKER Debojyoti Das School of Computing and Information Technology REVA UNIVERSITY, Bangalore, India Mallangouda School of Computing and Information Technology REVA UNIVERSITY, Bangalore, India Ankur Thakur School of Computing and Information Technology REVA UNIVERSITY, Bangalore, India Prof. Nikhil S. Tengli School of Computing and Information Technology REVA UNIVERSITY, Bangalore, India Abstract: This paper includes Android Application Development of a GPS based Live Location partaking in which with the assistance of any Android gadget. Some other GPS empowered handset could be found. Despite the fact that objective client might be found anyplace on the planet, he should have organize availability and be GPS empowered. At first, the application is created for Android stage just, yet can be extended to cross-stage use with gadget particular help as far as Google Maps and The test confirmed the possibility of the cell phone situating framework, which would real be able to time show the route arrangement. In this paper, we show the live area tracker of clients in the distributed computing condition. With this, we have the likelihood of the area availability of clients both through sharing the connection. 1. INTRODUCTION A live area following framework utilizing a GPS module for various cell phones and different clients. GIS are data administrations available by means of Android gadgets through the versatile system that use the area of the cell phone. The proposed GPS and remote system based ongoing area following framework might be stretched out to different application areas that identify client area data and use this whenever, anyplace. HyperTrack is a stage that gives data administrations in light of the current or a known area, bolstered by the electronic guide stage. HyperTrack API bolsters the coordination of area following highlights in applications. The API's calls are adequate to save engineers the strenuous methodology of creating complex following framework starting with no outside help. The HyperTrack API requires API key validation, sends HTTP-organized demands, and returns JSON-arranged reaction. The HyperTrack portable SDK produces this development information as area, movement and gadget wellbeing utilizing different sensors and OS APIs on the gadget. Android as an Operating System: a start-up, Android, Inc. As Google entered mobile market, it purchased Android and in a bid to encourage independent development works, it released the designer apparatuses under the open source Apache License. The lenient permitting enables the OS and related programming to be altered andse. The tolerant permitting enables the OS and related programming to be adjusted and Being a flexible working system, android OS is a balanced version of Linux, at first made by Memory Management: The OS underpins multi-threading however relying upon the moment memory accessibility, it can murder application in order to decrease over-burdening. The RAM administration is such so control utilization is at least. To the extent outsider applications are viewed as, the SDK gives sufficient library substances, for example, Services, Background Tasks and Foreground Tasks for working with application lifetime. Network Connectivity: The OS underpins a full scope of network arrangements extending from 2G to LTE bolster. It underpins information parcel transmissions through GPRS/EDGE bolster. Web can likewise be gotten tothrough Wi-Fi, WiMAX and shared among different gadgets through tying (both over Wi-Fi and USB) bolster. PC correspondence is set up through gadget administration programming. HTTP benefit is upheld and through utilization of Google APIs, the telephone is a successful GPS empowered gadget. 2. SCOPE OF PAPER The App "Live Location Sharing" is a GPS benefit based application which would help us in finding the correct position of individuals relying on their present area. Area would be shown on the guide see on our android set and show working can to the present use of Map Service. Some Key focuses about the App: All users locations would be retrieved from an online database so as to centrally control the permissions for viewing. For restricting user access, user authentication would be supported. Periodic refreshing has to be present so that each time the location changes or after a fixed interval of time the values in database should be updated.all devices would be having a unique ID (UID) and this would be used for searching Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 122

149 Debojyoti Das et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, for the user. The app would have additional support in terms of: o Street View o Pin Points on the Map o Getting Address from the Map o Locating Multiple users o Zooming In / Zooming Out o Application User Data Manipulation (password) o Change of UID support 2. Time 3. Co-ordinates 4. Altitude Track a user in real time mode on the Internet: Any person having the right to watch a user s track must log in the device under the user s ID and OTP. Then he/she can choose the track to be drawn. A list of the user s tracks is available.. Delete Track: The user can delete tracks from a list of tracks. If the track deleted is the current one. User Function: Smartphone user using this application has two basic advantages. First one is whenever a person is on a trip; he/she is able to record his/her footprints on a website that is monitored by the administrator. The User object represents the user who is beingtracked. Every SDK instance needs to be initialized with a user to identify the mobile device. Fig 2.1: System Architecture 3. METHODOLOGY The application will permit to do: Client creation: Any client who needs to be followed should be enroll. The application wills ffer o a web interface to enter the accompanying information: 1. Client Id (Mobile No.) 2. One Time Password. At that point the client gets a client Id (versatile No.) and is recorded in the database. Adjustment of profile: Once signed in, the client can adjust its pro file like name and profile picture. Creation of a track for a user: The user, once registered, can log in the system and create a location sharing link. There is two possibilities to do it: 1. From Mobile App. 2. From Web App. This will represent the first point of the location. The location track has a unique identifier within the system. The time and date when the track is created is taken from the server s clock. Sharing GPS coordinates to a server using mobile data: The user get the current position from its GPS device and send them to the server with device. The position must contain: 1. User Id Admin Function: The administrator function embedded in the GPS-Based Location Sharing System is to provide the functionality for monitoring the recorded footprints of each user individually on a computer screen. The application provides the administrator with the latest area refresh utilizing HyperTrack Admin Id. Versatile SDK: Developers utilize HyperTrack to purchaser client's development information into different items. The HyperTrack portable SDKcreates this developmentinformation as area, action and gadget wellbeing utilizing different sensors and OS APIs on the gadget. The SDK at that point transmits them to our server with near-zero battery affect. Area refresh recurrence: The SDK accomplishes lowdormancy with almost zero battery utilization through variable area refresh recurrence. The recurrence of information transmission can change from 2 seconds to 30 mins, contingent upon your utilization designs. In the case of following live, the conclusion to-end inactivity will be sub-5 seconds to guarantee an ongoing background. In the event that nobody is following, at that point the SDK preserves battery by not sending any information. The SDK has arrangements that oversee frequencies at which this information is gathered and transmitted to the server. The SDK accompanies a default set of designs to adjust precision, realtime-ness and battery proficiency while producing development information. These arrangements comprise of constants and gadget level principles. Different Device Support: The SDK is intended to help just a single dynamic gadget for each client at once. In the event that there are different gadgets for a client, the second gadget won't track. Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 123

150 Debojyoti Das et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, CONCLUSION Toward the end, we find that, the applications were extremely valuable. The tracker dissimilar to others is free of cost. The system association module created would be useful in N number of situations where synchronization or information trade between gadgets is wanted. The stick point module causes us in finding clients and in the meantime separating between custom areas, home area and companions' areas. REFERENCES [1] Wei-Meng Lee, 2011, Beginning Android Application Development, Wiley India Pvt. Ltd. [2] RetoMeier, Professional Android 2 Applications Development,2010 [3] Marko Gargenta, Learning Android, O'Reily, 2011 [4] Travis Cornelius, New Boston Series : Android Application Development Tutorials,2017 [5] Web Resource: [6] Web Resource: [7]WebResource: [8]WebResource: [8]Web Resource: Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 124

151 Volume 9, Special Issue No. 3, May 2018 International Journal of Advanced Research in Computer Science (ISSN: ) CONFERENCE PAPER Available Online at OCULUS: A SMART WEARABLE FOR THE VISUALLY IMPAIRED Akash James Department of Computer Science & Engineering REVA Institute of Technology and Management Bangalore, India. Sai Somanath Komanduri Department of Computer Science & Engineering REVA Institute of Technology and Management Bangalore, India Ashish Raman Nayak Department of Computer Science & Engineering REVA Institute of Technology and Management Bangalore, India. Ujwal P. Department of Computer Science & Engineering REVA Institute of Technology and Management Bangalore, India Prof. Ashwin Kumar U.M. Department of Computer Science & Engineering REVA Institute of Technology and Management Bangalore,India. Abstract: Oculus is a smart wearable that focuses on assisting the visually impaired. It provides features such as Object detection, face classification, classification of Indian currency, environment summarization, and Optical character recognition. All these features are made available to the user through the use of a myriad of technologies such as TensorFlow, MQTT, Tesseract, and OpenCV. The wearable device utilizes a Raspberry Pi interfaced with a Pi camera as the computational unit, which is used to record the surroundings and this video is streamed to server. The results are relayed back to the user through voice using Google Speech Engine. Due to this amalgamation, Oculus proves to be a reliable augmentation to the user. Keywords: Deep Learning; FaceNet; Google Speech Engine; Image Classification; Inception; Natural Language Processing; Object Detection; Optical Character Recognition; Raspberry Pi 3; Smart Wearable; TensorFlow; Visually Impaired; I. INTRODUCTION According to the WHO, there are 39 million people that are blind and another 245 million who are visually impaired. Visual perception is an ability that a human heavily relies upon. We perceive up to 80 percent of all impressions by the means of our vision. Without vision, life as we know it, cannot suffice. Oculus aims at assist the lifestyle of the visually impaired. It is an experimental smart wearable device which makes it possible for people who are destitute of vision to hear the world around them. Oculus uses powerful technologies such as Deep Learning and Embedded systems amalgamated together into a smart wearable device that facilitate rehabilitative features to a visually destitute person. to a local IP address. This is done through the Motion daemon process. It is a program that monitors a signal from a video camera, in this case a Pi camera, and is able to detect if there is a significant change, thus detect if there is motion. Apart from this, the Pi board is also programmed to handle the communication with the server. II. METHODOLOGY A. Smart Wearable Device Oculus is in the form of a glove that a user can put on as an assistive device. It uses a Raspberry Pi 3 which is interfaced with a Pi camera and a keypad membrane. The Raspberry Pi is programmed to stream the video recorded from the Pi camera Figure 1 Oculus Smart Glove Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 125

152 Akash James et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, It uses Message Queue Telemetry Transport (MQTT) as the communication protocol. MQTT is a lightweight publish/subscribe messaging protocol. It is useful in low power devices, but can also be applied to many different scenarios. The individual keys are linked to a specific module. When the user presses a key button, a specific command is published through MQTT to the server on a particular topic. The server has already subscribed itself to the topic that the Raspberry pi publishes to. B. Server The server handles all computational tasks of the modules housed by Oculus. Every object is a class that comprises of special features that helps in classification. Oculus uses TensorFlow framework for object detection. It uses a pre-trained model, the ssd_mobilenet_v1_coco, which is available from the TensorFlow detection model zoo. The model was trained on the coco dataset. The ssd_mobilenet_v1_coo is the fastest available model and requires least computational power. We sacrifice accuracy for the speed because we are using an embedded system and all computation needs to have real-time results. A comparison of different model sis given below The recommended specifications are: Processor: Intel Core i7 7700HQ GPU: NVidia GTX 1080Ti VRAM: 8GB GDDR5 RAM: 16GB DDR4 Storage: 512GB SSD The software and support libraries required are: TensorFlow OpenCV 3.2 CUDA Toolkit 9.1 cudnn Python Tesseract Google Speech Bazel Mosquitto MQTT Motion The Server hosts five modules, they are: 1. Object Detection 2. Face Recognition 3. Indian Currency classification 4. Environment summarisation 5. Optical Character Recognition Figure 2 Comparison of different models F. Face Recognition Model Face recognition is a technology focused on identifying and recognizing people based of facial features. Oculus uses FaceNet and Triplet Loss algorithm to learn new faces and identify them. FaceNet has been trained on Labelled Faces in the wild (LFW) and YouTube Faces Database in which it has 95.63% and 95.12% accuracy respectively. C. TensorFlow TensorFlow is an open-source software library that capitalizes high performance numerical computation. It is highly flexible that allows it s framework to be deployed on a number of systems (CPUs, GPUs, TPUs) making it highly compatible across platforms. It was originally developed by engineers and researches from the Google Brain team which is a part of Google s AI organization. TensorFlow is used in creation of several features such as image classification, text summarization, object detection, frame prediction, voice recognition and lastly, the highlight of this study, face recognition. D. Google Speech Engine Google Speech Engine packs a powerful Natural Language toolkit that can help in both Text-to-Speech (TTS) and Speech-to-Text (STT). E. Object Detection Model Object detection is a computer vision technology that is able to localise the objects in a frame of a video or an image. Figure 3 Architecture of Facial Recognition in Oculus FaceNet uses a principle called One-Shot learning, where the neural network does not require a large data set for training. To achieve this, it utilizes something called Triplet Loss. Triplet Loss algorithm uses three images, namely the anchor, the positive and the negative. It focuses on reducing the Euclidian distance between the anchor and the positive, and increasing the distance between the anchor and the negative. The Triplet loss equation is as follows - Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 126

153 Akash James et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, f(a) refers to the output encoding of the anchor f(p) refers to the output encoding of the positive f(n) refers to the output encoding of the negative alpha is a constant used to make sure that the network does not try to optimise towards f(a) - f(p) = f(a) - f(n) = 0. [ ]+ is equal to max(0, sum) Once a new model is created, incoming frames can be searched for faces and recognition can be performed. G. Image Classification Image classification involves labelling the image after some form of feature extraction has been performed on it. Feature extraction is usually performed by comparing nearby pixels. The neural network used in Oculus for Image classification is Inception-V3. It has been trained at google on various dataset predominantly for ImageNet Large Visual Recognition Challenge using the data from describing all the objects around with the help of Natural Language Processing Optical Character Recognition, that extracts textual information from images, making it easy for the user to hear the information around them A. Object Detection Module When the user initiates an Object Detection request from the glove, an initiation command is sent from the glove to the server. A video stream begins from the Raspberry Pi and the Object Detection Model on the server is initialized. The incoming video stream is broken down into frames. Each frame is fed to the model to extract features and combine them to find possible matches of objects. The inferred objects are relayed back to the user using voice feedback. This is done through the Google speech engine. B. Face Recognition Module The user, again, can initiate the Face Recognition module from the glove. An initiation command is sent from the glove to the server over MQTT. A video stream is initiated and streamed from the glove. The frames that are captured by OpenCV are passed through the pipeline. If the person is recognized by the system and the accuracy is above a certain threshold, the details of the person in the frame are relayed back to the user through voice feedback. If not, an unrecognized face message can be given to the user. To re-train the image classification models for our own datasets, we use transfer learning. Transfer learning refers to retraining the penultimate and ultimate layers in order to group extracted features to result in classification of our own data set. H. Optical Character Recognition Optical Character Recognition is a technology through which characters are extracted out of an image. Optical character recognition initially performs edge detection on an image. Once that s done, the edges that are similar to the edges of characters are extracted and grouped together to replicate the textual information embedded within the image. Tesseract is a package that helps to perform OCR on images. III. IMPLEMENTATION The above technologies are combined together to construct the experimental smart glove which has 5 predominant modules as follows Object Detection, that identifies and notifies the user about the objects around him Face Recognition, which constantly looks for faces and recognizes faces of known individuals within the vicinity of the user Indian Currency Classification, which identifies the newly introduced currency notes in India Environment Summarization, that describes the surrounding of the user by extracting and C. Indian Currecny Classification After demonetization in India in India, new bank notes were rolled out by the Reserve Bank of India. These new currency notes are not visually impaired friendly. They cannot be easily differentiated by them. We therefore, incorporate another module in the Oculus system that helps in currency determination. We use a custom trained Inception-v3 model. Using transfer learning technique to fine tune the model to work with the Indian currency notes. Inception-v3 boasts an error rate of 3.46%, a human who attempted the classification had an error rate of approximately 5% When the user initiates currency classification, an image of the currency note at hand is taken and transferred to the Server using the scp (Secure copy) service. This requires that both the server and Raspberry Pi are connected over ssh. The scp service uses the port 22 by default and is connected via an encrypted connection or a secure shell connection. The results are relayed back to the user through audio as always. D. Environmental Summarization This module of Oculus uses a TensorFlow implementation of the image-to-text model. A Show and tell is a deep neural network that learns how to describe the contents of an image. This feature is useful when the user just wants an update on his surroundings. The way this module is triggered is exactly the same as the Indian Currency Detection module. The server waits for the corresponding code which is sent through the MQTT messaging service by the Raspberry Pi. An image is also captured and sent through the scp service. This image is Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 127

154 Akash James et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, passed through the pipeline and the results are relayed back to the user through audio feedback. E. Optical Character Recognition The last module is the Optical Character Recognition. This can extract text from images of documents and read it to the user. We preprocess the image in OpenCV to remove any noise. We then use the Tesseract software to recognize text from images. As a final step we pass the extracted test through a spell-check module to further reduce the errors. This can be useful to the visually impaired, as not many documents are available in braille, which incidentally also happens to be hard to learn. A. Facial Recognition Our facial recognition is trained to detect four faces and the results are as follows - IV. RESULTS Oculus has proven to be effective in rehabilitation of the visually impaired. The results of each module are as follows Object Detection Object Detection module analyses all incoming frames and labels the objects it recognizes and iterates the labels over audio. Figure 6 Confidence levels of models for trained faces From the above image, it is evident that the model is very accurate and has high probability for test images. B. Indian Currency Detection Our Indian Currency Detection shows decent confidence levels in classification. Figure 7 Classfying a Rs. 500 note. C. Environment Summarization We used an image of a person surfing. The results were as follows Figure 4 Object Detection detecting chairs in a video stream Figure 8 Environment Summarization using a test image Figure 5 Object Detection in the on outdoors FROM THE ABOVE IMAGE, THE COMBINATION OF OBJECT EXTRACTION, ESTABLISHING A RELATIONSHIP AMONGST THEM AND FORMING AN ENGLISH SENTENCE USING NLP WORKS SMOOTHLY. Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 128

155 Akash James et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, D. Optical Character Recognition Figure 9 OCR using a test image Optical character recognition removes noise from an image and extracts characters. The sentences are relayed to the user over audio. Acknowledgment We thank our institution for all the help provided. We re glad to the open source community around the world that gladly share their knowledge with us. References [1] [2] TensorFlow by Google [3] Towards Data Science [4] [5] BUILDING A FACIAL RECOGNITION PIPELINE WITH DEEP LEARNING IN TENSORFLOW, COLE MURRAY PIPELINE-WITH-DEEP-LEARNING-IN-TENSORFLOW-66E B8 [6 ]J. SCHMIDHUBER, "DEEP LEARNING IN NEURAL NETWORKS: AN OVERVIEW", NEURAL NETWORKS, VOL. 61, PP , JAN [7] KRIZHEVSKY, I. SUTSKEVER, G. E. HINTON, "IMAGENET CLASSIFICATION WITH DEEP CONVOLUTIONAL NEURAL NETWORKS", ADV. NEURAL INF. PROCESS. SYST., PP. 1-9, [8] M. Abadi et al., "TensorFlow: A System for Large-Scale Machine Learning TensorFlow: A system for large-scale machine learning", 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI'16), pp , [9] Y. Lecun, C. Cortes, C. J. C. Burges, The MNIST Database Courant Institute NYU, 2014, [online] Available: [10] Liming Wang, Jianbo Shi, Gang Song, and I-fan Shen Object Detection Combining Recognition and Segmentation Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 129

156 Volume 9, Special Issue No. 3, May 2018 International Journal of Advanced Research in Computer Science (ISSN: ) CONFERENCE PAPER Available Online at AUTONOMOUS VEHICLE WITH THE AID OF COMPUTER VISION Karthik K, Department of Computer Science & Engineering, Sai Vidya Institute Of technology, Rajanukunte, Bangalore- India Syed Matheen Pasha, Department of Computer Science & Engineering, Sai Vidya Institute Of technology, Rajanukunte, Bangalore- India Veeresh M P, Department of Computer Science & Engineering, Sai Vidya Institute Of technology, Rajanukunte, Bangalore- India Mahesh Lingappa Department of Computer Science & Engineering, Sai Vidya Institute Of technology, Rajanukunte, Bangalore- India Prof.Srivinay Department of Computer Science & Engineering, Sai Vidya Institute Of technology, Rajanukunte, Bangalore-India Abstract- A Web controlled and partially autonomous vehicle system is presented in this paper. It highlights the idea to develop a remote controlled car which can be driven from anywhere using Internet over a secured server. This car will also have limited automation features like traffic light detection, obstacle avoidance system and lane detection system so that it can drive itself safely in case of connectivity failure. The main goal here is to minimize the risk of human life and ensure highest safety during driving. At the same time the car will assure comfort and convenience to the controller. A miniature car including the above features has been developed which showed optimum performance in a simulated environment. The system mainly consists of a Raspberry Pi, an Arduino, a Picamera, a sonar module, a Web interface and Internet modem. The Raspberry Pi was mainly used for the Computer Vision algorithms and for streaming video through Internet. The proposed system is very cheap and very efficient in terms of automation. Keywords: web vehicle, computer, vision I.INTRODUCTION With the ever-growing technological advancement, human civilization is looking for automation in every sphere of life. Automated car is one of the latest trends which has been massively recognized by people all around the world as they want maximum security and comfort during driving. Nowadays, road accident is one of the prime concerns for the people. It became very frequent and uncertain. Most of the road accidents occur due to lack of abidance of the traffic rules. Most of the time, the drivers become drowsy or distracted during driving and eventually hit objects ahead of them. If the driving process can be handled with the aid of Computer Vision and efficient sensors then the risk of human mistakes can be highly reduced. Besides, sometimes it gets necessary to access the car from a remote location in order to reduce hassles. In this case, it would be a lot more convenient if the car could be viewed from a remote computer and driven by interaction through the computer keyboard. This could be as easy as playing a computer game. Our work is based on Internet of Things technology and Computer Vision to remotely control our vehicle and automation features. Since 1920 the research for vehicle automation has been conducted on, although first promising took place around 1950s. During 1980 with Carnegie Mellon University s Navlab and ALV[1], [2] the first ever autonomous car has been seen. This has paved the way for the companies to work on autonomous vehicle research. In July 2013, Vislab demonstrated BRAIVE a vehicle that moved autonomously on a mixed traffic route. Cities like Belgium, France, Italy and the UK are planning to operate transport systems for driver less cars. Germany, Netherlands and Spain have allowed testing robotic cars in traffic. Google self-driving car is a recent trend. Sebastian Thrun, professor of Standford University and his team have developed the algorithm and led the development of the Google self-driving car [3]. Google self-driving cars are designed to navigate safely through city streets. They have sensors to detect objects. It will be marketed in The driving becomes a lot boring during traffic jam. In this situation traffic light detection system and obstacle detection comes in handy. Researches have been done regarding traffic light detection using heuristic models Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 130

157 Karthik K et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, and color segmentation. However in this paper a Haar Cascade Classifier is developed with respect to the working environment which can easily be extended to work in real life environment by collecting a lot more frames from the environment and by using powerful computer. Various lane detection techniques have been observed. Lane detection techniques using OpenCV based on Receiver Operating Characteristic curve and Detection Error Trade-Off curve and using perspective image have already been worked on. In this paper lane detection is done using canny edge algorithm and Hough line transformation which has shown good rate of success in the working condition. II.PROPOSED METHOD For the connectivity failure the car needs to work on its own. It needs to keep itself safe from collision and abide by the traffic rules. The Arduino controls the motor driver circuit. It is connected with sonar, an ultrasonic sensor which evaluates the attributes of a target by interpreting the echoes from radio waves. It is used to detect the distance of obstacles from the car. If an obstacle is detected then the Arduino stops the motor from running operations. Meanwhile Raspberry Pi uses computer vision algorithm to detect the lane and traffic light signals. Python Open Source Computer Vision (OpenCV) is a library of programming functions mainly aimed at real-time computer vision. It has over 2500 optimized algorithms which can be used for image processing, detection, object identification, classification of actions, traces and other functions. The Raspberry Pi is interfaced with the Arduino with serial communication. It controls the Arduino to run the car accordingly. Streaming Video and Remote Access The Raspberry Pi works on Linux operating system. It can host web pages through Apache server. It responds to requests to serve up web pages, which can be simple HTML or sophisticated web-based apps. To make the Pi capable of hosting websites Apache is installed on it. Apache is a free, open-source HTTP (Hypertext Transfer Protocol) web server application. A website was built for hosting the streamed video and for controlling the car remotely. Fig.1. Working procedure Firstly the car can be remotely controlled through Internet using a web browser. In case of connectivity failure it can act autonomously in a good weather condition. The proposal consists of complex Computer Vision algorithms and video transmission with Internet. Raspberry pi and Arduino are the main devices to implement the prototype. The Raspberry Pi streams the video to the internet. A user can access the streaming using a web browser. It takes a lot of processing power for simultaneously working on video streaming and running Computer Vision. The Raspberry Pi 2 model B is a single-board computer with a powerful processing unit and serial and camera interface (CSI). The Raspberry Pi camera module can be used to take high-definition video. It can be accessed through the V4L (Video for Linux) APIs, and there are numerous third-party libraries built for it, including the Picamera Python library which will be beneficial to the live streaming purpose. Apache is a popular web server application that was installed on the Raspberry Pi to allow it to server web pages. Apache can serve HTML files over HTTP, and with additional modules can serve dynamic web pages using scripting languages such as python. A web pages was hosted that shows the video streaming sent from the Picamera. To access into the web page one only need to know the IP address of the Raspberry Pi and a user name and password to log in. From the web page the car can be fully driven. MJPG streamer was used to stream video from Raspberry Pi. The easiest was to install it is by using subversion. There is a facility in Linux operating system named daemon which runs the selected programs automatically during system boot up. The scripts for MJPG streamer, traffic light detector and lane detector are all run through daemon. So whenever the Raspberry Pi is powered up it automatically keeps streaming the video from its camera to its web server. Now if type Pi s IP address):port number then the streaming data can be viewed from any web browser. The web page hosting the video streaming was developed using script is used to handle keyboard interruption from the user. This keyboard interruption can be processed and sent through the internet to the remote Raspberry Pi which is located inside the car. The Pi in turn sends signal to Arduino to control the motor through serial communication. Obstacle Avoidance: A sonar sensor (HC-SR04) has been used for this purpose. It emits very high frequency (40 KHz) of sound. It has two transducers - a transmitter and a receiver. The Transmit transducer sends out a short burst of (8 cycles) of pulse train. The sonar module timing diagram is shown in Fig. 2. The Receive transducer in turns waits for an echo. If an object exists on its perimeter an echo bounces back to the Receive transducer. Distance of the object is calculated the following equation. Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 131

158 Karthik K et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, d=v*(t/2) (1) d distance from an object, t total time from transmission to reception, v velocity of sound which is typically 340m/s in room temperature. The sonar is connected with an Arduino which calculates the distance and controls the motor rotation accordingly. The sonar is placed in front of the vehicle and is mounted on the servo motor which can rotate up to 180 degrees. This way if an obstacle comes ahead, then it rotates the sonar and check if the road is clear around. If no obstacle is found then it turns the car and picks an alternative way. Traffic Light Detection The traffic light detection procedure can be briefly Preprocessing: The image frames captured from the video were converted into gray scale images. Haar Feature Based Cascade Classifier: This was chosen for traffic light detection. This has two parts mainly training and detection. We can generate both using OpenCV. To build a Haar Cascade it was needed to generate some positive and negative images. The result will be generated as sample images. Positive Samples images of different traffic light signals in different angles Negative Samples collected from the related environment where no traffic signals were present. Gaussian Filter:To reduce the image noises Gaussian Filter was used. Removing non Edge: Pixels that were not part of an edge were removed. Thus an image with thin edge is observed. Hysteresis: Canny uses two thresholds. If a pixel gradient is higher than the upper threshold, it is accepted as an edge. Otherwise it is rejected. If a pixel gradient is between the thresholds then it is only accepted if it is connected with pixel of upper threshold. Hough Line Transformation:After canny edge detection Hough line transformation is applied. Hough line transformation is applied. Hough transformation is very efficient for detecting any shape if it can be mathematically expressed even if it is a little distorted. To determine the Hough line two parameters are needed- Ƿ, the perpendicular distance from origin to the line and θ, the angel formed by this perpendicular line and horizontal axis measured in counter-clockwise. These can form the parametric line equation. Ƿ=χcosθ+ʮsinθ (2) There exists an OpenCV function for doing this named cv2. HoughLines(). This function takes the Ƿ, θ as argument. It also takes an extra argument which determines the threshold for allowing a pixel as a line. From the perspective of Bangladesh the drivers always drives through the left side of the road. So it needs to detect the left lane marker. III.RESULT The miniature car including the above features has been developed which showed optimum performance in a simulated environment.the sonar sensor is a setup in front of the car. The camera is fixed on the window and the processing units are fixed within as shown in fig below. Color Detection: The BGR image was converted to HSV, because it s get converted to represent the color in HSV. Then the threshold value for green and red colors were selected individually for the image and finally green or red part was extracted. Lane Detection For the lane detection technique the popular canny edge detection and Hough line transform was used. This algorithm is highly efficient for a road with clearly visible lane marker. In edge detection algorithm the boundaries of an image are generally detected. Canny algorithm is selected for its very low error rate, good localization and minimal response that is only one detector response per edge. For good efficiency several steps are needed to be maintained.fig.4 shows the process in a nutshell. Gaussian Filter: Suitable masking was done to filter out the noise from the original image using Gaussian filter. Finding Intensity Gradient: After smoothing was done Sobel kernel was used in both horizontal and vertical direction to get the first directive in both directions. Gradient is the change of brightness in a series of pixels. Fig.implemented model IV. CONCLUSION In this paper a method to implement some automation feature in a regular vehicle is described. Utilizing this is a small prototype is designed. This is successfully done. However raspberry pi maybe powerful but yet we need a much powerful computing machine if we need to implement a much more. Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 132

159 Karthik K et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, REFRENCES [1] Johann Bornstein& Yoram Koren, Obstacle Avoidance with Ultrasonic Sensors, IEEE Journal on Robotics and Automation, vol. 4, no. 2, pp , [2] Yue Wanga, Eam Khwanga Teoha & Dinggang Shenb, Lane detection and tracking using B-Snake, Image and Vision Computing 22 (2004, available at: pp [3] H.Dahlkamp, A.Kaehler, D.Stavens, S.Thrun, and G.Bradski. Self-supervised monocular road detection in desert terrain. G.Sukhatme, S.Schaal, W.Burgard, and D.Fox, editors & Proceedings of the Robotics Science and Systems Conference, Philadelphia, PA, [4] Hanspeter A. Mallot, Heinrich H. Blthoff, J.J. Little, Stefan Bohrer, "Inverse perspective mapping simplifies optical flow computation and obstacle detection", Biological cybernetics, vol. 64.3, pp , [5] Joel C. McCall & Mohan M.Trivedi, Video-Based Lane Estimation and Tracking for Driver Assistance: Survey, System, and Evaluation, IEEE Transactions on Intelligent Transportation Systems, vol. 7, no. 1, March 2006, pp [6] Tushar Wankhade & Pranav Shriwas, Design of Lane Detecting and Following Autonomous Robot, IOSR Journal of Computer Engineering (IOSRJCE) ISSN:2278,pp Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 133

160 Volume 9, Special Issue No. 3, May 2018 International Journal of Advanced Research in Computer Science (ISSN: ) CONFERENCE PAPER Available Online at DRUG INTERACTION WITH EHR FRAMEWORK AND SAFE MEDICAL TAGS FOR REDUCING MEDICAL MISTAKES V Sharath, REVA Institute of Technology and Management Yashodhara M N, REVA Institute of Technology and Management Prof. Raghavendra Reddy School of C and IT, REVA University Geetha D V, REVA Institute of Technology and Management Amulya P, REVA Institute of Technology and Management Abstract: Today cell phones are used for different applications. Like smart guide, learning various software tools. Online learning classes etc. so it can also be used for medical applications. In this venture, we have proposed novel engineering for enhancing social insurance framework with the help of Android-based cell phones with NFC and Bluetooth interfaces, smartcard innovation on alter safe secure component (SE) for putting away qualifications and secure information, and a wellbeing secure administration on a crossbreed cloud for security and wellbeing record administration. Keywords: Medical Tags, MobileDoc, mobile based secure healthcare, NFC in healthcare. I Introduction The main moto of this paper is to propose the use of medical tags decreasing therapeutic blunders, and Secure Health card for putting away Electronic Health Record (EHR) in light of Secure NFC Tags, by using Card Emulation Mode or NFC P2P Mode or. We present an advanced mobile phone application intended to help patients maintaining a strategic distance from these errors. Secure Healthcare administrations is a need for creating countries, where the cost of human administrations is high and security and insurance are essential issues and making countries like India, where there is a mass populace to manage in facilities and healthy human administrations strategies are required. A capable, trustworthy, generous and secure prosperity stream is imperative to supervise patients, their prosperity records effectively and for the right care to reach to the patient at the perfect time. Recognizing verification of things for secure helpful procedure is particularly principal for an ensured work process. For example, secure identifiers on the medications can push human services capable to direct redress medication to a patient to diminish bumbles. Close by this issue, the Patient Health Record organization is fundamental both for patients and furthermore recuperating focus organization. In making countries like India, there is no united organization of prosperity records and records are generally held by patients in a paper mastermind OPD (Out Patient Department) card, which is both unbalanced to keep up close by the paper-based reports and besides hazardous. With the present movements in mobile phones including secure accreditation storing, greater limit capacity, remote correspondence interfaces, and computational power, they can be used as a piece of restorative administrations for not simply gathering basic prosperity parameters, as in the Body Area Networks, yet what's more for therapeutic administrations organization. Assurance and security is a particularly fundamental piece of social protection [5]. We propose that the patient should hold all or genuine patient's EHR electronically, on a Healthcard that is either on an outside Smartcard accessible by a PDA or on the PDA held by a patient. A Healthcard hung on a mobile phone can hold the entire EHR including reports and tests. The permitted bit can be gotten to securely by an endorsed helpful provider by a clear tap of the phone. Because of the computational capacities the records can be condensed and sorted out for a quick move to be made. We have moreover rapidly said an essential security framework essential for the applications. Since NFC NDEF compose is slanted to security strikes, we have utilized lowlevel APIs on Android-based phones, to securely get to NFC marks, for instance, MIF ARE Classic names with NFC-An (ISO I443). III Literature Survey Paper Methodology Ref. [1] NFC Performance/Result It gives great correspondence between two electronic gadgets. Gadgets can be a cell phone or some other electronic gear's.. [2] Emergency NFC tags Emergency NFC tags help us to store the medical data about the patient. However, this tag does not give the compose insurance and it can't be reused. [3] Smart poster Since "NFC labels are Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 134

161 V. Sharath et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, proposes using secure NFC tags [4] Wireless Medical Card with NFC and high speed interface with bulk memory card. IV Proposed Work defenseless against spoofing and additionally cloning", so information is put away in the server with high security. Proposed a comparable bigger card utilizing an equipment alter safe SE in view of a microsd card on a cell phone with NFC and Bluetooth interface. We have proposed the design for NFC based secure medicinal services as outlined in Fig. 1 for i) Secure restorative identifiers ii) Health card holding EHR utilizing Android cell phones. We have proposed a protected social insurance benefit like Health Secure on a half and half cloud to which all clinics can buy in. The Health Secure half and half cloud gives administration to keeping up Cryptographic servers for a secure system and. up up Cryptographic servers for a protected framework and Storage server to give fortification and in extra space for widened EHR. Adaptable ADMIN is a mobile phone of an endorsed therapeutic head. Android application is the patient's cell phone with the Health card and MobileDoc is the specialist's cell phone. Figure1: Architecture Since a greater screen would be more met all requirements to see and invigorate the prosperity records, MobileDoc could either be a NFC engaged tablet, for minimization or a PC with outside sharp card per client. For NFC P2P based and card replicating based Health cards, we use patient's and master's game plan of open and private keys. The unevenly shared key is utilized for encoding information. Doctor's facility organization has an application for safely perusing/composition with a cell phone, Mobile ADMIN; to oversee keen card based labels and patient Health cards. Portable ADMIN can enroll with the proposed Health Secure cloud benefit on a half and half cloud, which can issue security keys for our engineering. The mobiles utilize straightforward interfaces of NFC and Bluetooth for accreditation stockpiling and correspondence. With the assistance of Android application and with tolerant related information put away in the database utilizing as a part of nearby server it comprehends the patients better. a) Technology Used NFC is a best in class remote advancement which gives essential interfaces to the device to contraption correspondence and likewise access to NFC, RFID and smartcard labels. NFC engaged mobile phone can work in three modes: Reader mode: in which gadget can read and keep in touch with NFC based inactive labels. Peer to Peer (P2P) mode: in which NFC gadgets can associate and trade data with each other. Card copying mode: in which NFC gadget can work as a contactless card. NFC names are of different sorts and use NDEF (NFC Data Exchange Format) for securing and sending data. NFC marks must have an ensured examined and create access for fundamental applications, for instance, those related to social protection. NDEF gives no insurance against information control, overwrite securities and computerized signature records can't maintain a strategic distance from malevolent alteration of labels. NFC enabled phones have an ensured part (SE)which is a sheltered microchip (a clever card chip) that consolidates a cryptographic processor to encourage exchange with confirmation and security and gives secure memory to securing applications and accreditations. It comes in different edge factors, for instance, embedded, microsd card or a UICC (SIM) card. As a result of ease of accessibility, we have used SWP empowered smaller scale SD card (by GO-Trust) as an SE to administer cryptographic keys and in addition tolerant restorative records. SWP is a contract based convention between Contactless frontend (CLF) and UICC. It is Java Card consistent. Java Card is an innovation which empowers Java to construct applets to keep running in light of keen cards with exceptionally restricted memory and handling capacities and gives information epitome, firewall, and cryptography. The keen card determination models, ISO/IEC 7816 for contact and ISO/IEC 14443for contactless, indicate that correspondence between a host application and a shrewd card is done through Application Protocol Data Units (APDUs). b) Applications Useful in emergency and tumultuous conditions like mass populated healing centres. NFC can give essential control of conferring singular records to any approved specialist by the basic tap of portable devices.. Bluetooth can be used close by NFC to provide speedier access to bulk data from the mobile phone. Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 135

162 V. Sharath et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, c) Advantages An efficient, reliable, powerful and secure wellbeing stream is imperative to oversee patients. Health card on a cell phone can be useful for a patient who can impart to specialist or medical attendant by sitting at home. V Results Temp Pulse Tilt Pid Table 1: result the above table 1 shows the result of our proposed method. IV Conclusion and Future Enhancement In this work, we have proposed NFC enabled Android cell phones for enhancing Healthcare process for secure medical object ID and patient Health card on an outside tag, another way Health cards can be endorsed by a Health Cards Secure organization on a half and half cloud, to offer administration to redesigned security and broadened stockpiling for wellbeing records. We intend to deal with the engineering of the Wellbeing secure administration later on. The applications are easy to use with an essential touch of NFC for secure correspondence. This will upgrade the prosperity stream in swarmed clinics of creating nations like India and in addition to created countries. The plan of action will profit the patients and in addition therapeutic expert since they can utilize the ordinarily held cell phones advantageously. The cost of the engineering can be lessened by utilizing smaller scale SD cards or UICC cards with inbuilt NFC radio wire. These types of SEs can be issued as Wellbeing cards on a mass scale to lessen the cost and to be operational on non- NFC cell phones. The proposed configuration can be used for applications other than social protection with secure identifiers and secure exchange of extensive information between gadgets. In spite of the fact that MIFARE Exemplary security calculation Cryptal has been broken, some other secure smartcard-based labels like MIF ARE Want could be utilized as a part of future in light of the accessibility of the APIs. We have recommended an essential security system necessity. A point by point design, execution, testing, and field association of the security structure is advanced and will be tended to in our future work We furthermore plan to upgrade the security structure using personality encryption using zero learning affirmations and property based encryption for shared key organization, get the chance to control of prosperity data, and designation of rights by the patient to other confided face to face for a gathering of wellbeing information. The present security framework relies upon Public Key Infrastructure. Character-based Encryption and Attribute- Based Encryption methodology can likewise be looked at in future. References [1] VedatCoskun, BusraOzdenizci and Kerem Ok, "A Survey on Near Field Communication (NFC) Technology", J. Wireless Personal Communications: An International Journal, vol. 71, pp , [2] Sebastian Dunnebeil, Felix Kobler, Philip Koene,.Ian Marco Leimeister, and Helmut Krcmar, "Encrypted NFC Emergency Tags Based on the German Telematics Infrastructure", IEEE proceedings on Near Field Communication (NFC), rd International Workshop, pp IEEE Press, [3] Jason Wu, Lin Qi, Ram Shankar Siva Kumar, Nishant Kumar, and Patrick Tague, "S-SPAN: Secure Smart Posters in Android using NFC", IEEE International Symposium on World of Wireless, Mobile and Multimedia Networks, pp. 1-3, IEEE Press, 2012 [4] Stefan Krone, Bjoern Almeroth, Falko Guderian and Gerhard Fettweis, "Towards A Wireless Medical Smart Card", IEEE Design, Automation & Test in Europe Conference & Exhibition,pp ,2012 [5] M. Roland and.i. Langer, "Digital Signature Records for the NFC Data Exchange Fonnat", IEEE Proceedings of the Second International Workshop on Near Field Communication (NFC), pp, 71-76, [6] SasikanthAvancha, Amit Baxi, and David Kotz, "Privacy in mobile technology for personal healthcare", ACM Computing Surveys (CSUR),vol. 45 Issue I, article 3, [7] F. Baldwin. Believing in Biometrics: Biometric technologies not only exist they work and are now affordable. Healthcare Informatics, August [8]. R. Blair. Like it, Yes. Need it, Yes. Buy it, Nah. Health Management Technology Journal, September [9] N. Borisov, I. Goldberg, and D. Wagner. Intercepting Mobile Communications: The Insecurity of CiteSeer.IST Scientific Literature Digital Library, [10] J. Brown. Trust installs wireless at eight London hospitals: 7,000-user network supports UCLH project to replace paper processes. Computing Magazine, UK, September [11] T. Castle. Online Authentication using Combined SmartCard and Fingerprint Recognition. Centre for Applied Research into Education Technology, University of Cambridge, August [12] D. Chadwick. Smart Cards aren t always the smart choice. IEEE Computer Magazine, December Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 136

163 Volume 9, Special Issue No. 3, May 2018 International Journal of Advanced Research in Computer Science (ISSN: ) CONFERENCE PAPER Available Online at A SMART PARKING SYSTEM FOR METROCITIES Aishwarya Babu School of CIT REVA University, Bangalore- India Anandita Kushwaha School of CIT REVA University Bangalore- India Akshitha D School of CIT REVA University, Bangalore - India Anu Rawat School of CIT REVA University Bangalore - India Nimrita Koul School of CIT, REVA University, Bangalore, India Abstract The objective of this work is to implement an automated parking system to avoid the congestion in car parking lots in metro cities, especially in places like multiplexes, function halls, hospitals, shopping malls, office complexes etc. The problem with current parking arrangements is that the driver has to go looking for an available parking slot, or a security person has to lead the driver to it, this becomes cumber some particularly during peak traffic hours, and this manual system is prone to suboptimal parking arrangements which leads to problems while checking out. To overcome the discomfort experienced in current parking systems, we have proposed a computerized system for quick, reliable, error free management of parking bay as well as the process of checking in and out of vehicle. Keywords computer assisted parking, error free parking, automated parking, sensors I. INTRODUCTION During past few decades, the number of personal vehicles especially cars have increased exponentially in developing countries like India. The main reason has been development of metro- cities and an increase in urban employment opportunities along with growth of buying power of citizens. The number of cars on the roads in India has increased 10 fold in last 10 years as shown in Fig. 1. Fig.2 shows the huge number of human employees working in parking systems. The problem of parking such a huge number of cars in public spaces is becoming a major research area for vehicular research since this affects our daily experience. In a conventional, busily crowded parking bay, a driver has to keep driving and looking for the available slot in parking area, this search is a time consuming and cumbersome process, which has a negative impact on our productivity and social interactions. In these systems, the possibility of errors due to human negligence can also lead to accidents. Billing has to be done by a human who has to keep track of the in time and out time of a vehicle, if there are meters which operate on coins, they still need a human operator to manage them. These difficulties make it necessary to develop intelligent parking systems which can provide following facilities: The driver should have information about the availability of a free slot in the parking bay, this can be provided through GPS. A driver should be able to reserve a parking slot even while he is nearing the parking bay. The bills should be calculated automatically and error free. The amount should be collected through various e- payment options. The security of vehicles should be ensured. Staff at the parking bay should be off leaded in their repetitive work, that can easily be delegated to computers Fig. 1-Number of vehicles in thousands registered in prominent cities in India in 2012, Image Courtesy Barclays Research Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 137

164 Aishwarya Babu et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, development of computer assisted parking system which can tell a driver the availability of vacant parking slots in any lane right in the entrance. A lot of time is wasted in searching for vacant slots and many a times create jams. Use of automatic car parking system will reduce human efforts and time with additional comfort. OBJECTIVES Objective of the Parking system is to manage the details of duration, vehicles, parking slots, parking fees. The project is totally built at administrative end thus only administrator is guaranteed the access. The purpose of the project is to build an application program to reduce the manual work for managing the duration, vehicles, parking slots. It tracks all details about parking slots, parking fee. Fig.2 Number of human employees (in thousands) employed in Parking Systems in India from 1960 to Image Courtesy data.gov.in II. ITERATURE SURVEY Implementation of automated parking dates to 1905 in Paris [11]. In 1920, Ferrari designed automated parking systems for big scale cities. [10]. in late 1950s, parking bays were fitted with systems to operate like Pigeon holes these remain in operation till date. In 1990s, the development of more advanced parking systems took a big leap as the industrial revolution had a major boom. Around 2010 and onwards, an estimated 1.6 million automated parking bays were set up in Japan alone. However, there continue to be challenges and it is for this reason current work was taken up. MOTIVATION III. SMART PARKING SYSTEM The main motivation for this project is to manage the parking space in large buildings, apartments, malls. The increase in city traffic is one of the major effects of population growth especially in urban areas. Due to this searching for vacant parking area during peak hours is not only time consuming but also results in wastage of fuel. The drivers keep searching for suitable parking lot which leads to increase in congestion. So this project aims at reducing the above problem All over the world in general and in metro cities in particular, the public places like shopping malls, multiplex system, hospitals, offices, market areas see a large rush of customer vehicles, particularly cars. Our conventional parking areas have many lanes\slots for car parking. To park a car the driver has to look for an available slot in all the lanes. This involves lots of manual labor and time investment, this problem is sufficient to call for the 1. Monitoring of parking space and updated indication of vacant parking slot 2. Automated payment of parking charges 3. Provides searching facility based on car number and slot number 4. Add, remove and view cars. And entire information is stored in database 5. Admin has the ability to add the number of slots, addition or removal of cars. This software also helps the manager to keep a track of all cars location of each car, time a car was parked and unparked, along with maintaining the billing or accounting information for the car. There is a provision of providing a discount of 6% on weekends and festivals. The software has a visual representation of all parking slots, available and occupied slots are indicated in different colors. Once the slot is booked the slot turns red (red implies slot is booked). Using this software we can track total amount collected each day. This is calculated based on car in time and out time. Existing system provides us with a receipt whenever we park our vehicle which has information regarding our in time and while exiting the parking we get to know our parking charges based on it. Our system aims at providing the clients directly with the slot number and stores there in time and other details along with car number in system so while going out their car number is entered. Automatically car is searched based on car number and total amount is calculated. We can also search the car, delete the car and add the car. Every information regarding the parking is stored in database. Admin makes the system more secure and reliable as administrator have control over addition of slots and removal or addition of cars. Admin is password protected. Thus only administrator can access. Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 138

165 Aishwarya Babu et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, SCHEMAS SCREEN SHOTS Fig. 3 Schema for entering a new car to parking bay Fig. 5 - Administrator s Interface Fig. 6 Visualization of Available and Unavailable Slots Fig. 4 Schema for removal of a car from parking bay CONCLUSIONS This system provides a convenient, safe, quick access to an available parking slot to a driver without searching for it while driving. In today s fast paced and over populated world this is quite important a service. Real time monitoring and status reporting of parking slots, automated billing are the features much appreciated by customers. Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 139

166 Aishwarya Babu et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, FUTURE ENHANCEMENT The capacity of current system is restricted, in future this can be increased, the system can be extended to work for bigger vehicles as well. Use of sensors and high definition cameras can be used to automate it further and improve efficiency. There can be a provision to send a message to driver nearing a parking bay to inform him of parking slot availability in the nearest parking bay well in advance. REFERENCES [1] Faheem, S.A. Mahmud, G.M. Khan, M. Rahman, H. Zafar, A Survey of Intelligent Car Parking System, Elsevier, Journal of Applied Research and Technology Volume 11, Issue 5, October 2013, Pages [2] M.A.R. Sarkar, A.A. Rokoni, M.O. Reza, M.F. Ismail, "Smart Parking system with image processing facility", I.J. Intelligent Systems and Applications, 2012, vol. 3, pp [3]. [4] Z. L. Wang, C. H. Yang, and T. Y. Guo, "The design of an autonomous parallel parking neuro -fuzzy controller for a car like like mobile robot," in Proceedings of the SICE Annual Conference, Taipei, 2010, pp [4]. J. Dongjiu Geng, Yue Suo, Yu Chen, Jun Wen, Yongqing Lu, Remote Access and Control System Based on Android Mobil Phone, vol.2. Journal of Computer Applications, 2011, pp [5]. Hamada R.H.AI - Absi,Patrick Sebastian, Vision - Based Automated Parking System in 10th International Conference on Information science,2010 [6]. Sarfraz nawaz, Christos Efstratiou, Celia Mascolo, Parksense: A smartphone based sensing system foron street parking in Cambridge university press., 2014 [7]. B. K. Konstantinos Domdouzis and C. Anuba., An experimental study of the effects of different medium on the performance of rfid system, vol. 21. Advanced Engineering Informatics, 2011 [8]. K. Finkenzeller, Fundamentals and Applications in C ontactless Smart Cards and Identification. John Wiley and Sons Ltd, 2003 [9]. K. M. R. Sudeep Dogra, Radio frequency identification (RFID) applications: A brief introduction, advanced engineering informatics. The IUP journal of Electrical and Electronics Engineering, 2011, p [10]. Sanders McDonald Shannon car parking sustainability, The Transportation Research Forum. Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 140

167 Volume 9, Special Issue No. 3, May 2018 International Journal of Advanced Research in Computer Science (ISSN: ) CONFERENCE PAPER Available Online at ACCIDENT PREVENTION SYSTEM FOR VEHICLES USING V2V COMMUNICATION Adithya S Department of ISE, Sai Vidya Institute of Technology,Bangalore Priyanka L Department of ISE, Sai Vidya Institute of Technology,Bangalore Mahesh Kumar P Department of ISE, Sai Vidya Institute of Technology,Bangalore Mamatha E Department of ISE, Sai Vidya Institute of Technology,Bangalore Harshini C M Department of ISE, Sai Vidya Institute of Technology,Bangalore Abstract For the realization of a safe road environment, a wireless communication system is needed. The proposed system is used to provide the passenger safety and to prevent accident on roads/highways and pass the message to the nearest vehicle in case of emergency. A wireless communication is used for vehicles to prevent the accidents; this is obtained using the ZigBee technology. The proposed system aims to improve the vehicle safety and the passenger safety, in this system we are using the different sensors which helps the driver to be aware of the faults inside the vehicle such as fire and smoke that had occurred. The implementation of forward collision system, do-not pass warning, intersection assist helps to avoid accidents. Keywords vehicle-to-vehicle communication, ZigBee technology, GPS, object sensor, fire sensor, smoke sensor, Blynk application. 1. INTRODUCTION The vehicle to vehicle communication (V2V) technique has been proposed to meet the road safety. Accidents have become one of the leading causes for death.this led to the motivation of reducing the number of accidends and provide safety to passengers in the vehicle. As number of vehicle is increasing the accident rate also increased because of less safety in these vehicles. Suppose if a vehicle passing on a road with 40kmph and if break is applied and the rear vehicle had no idea about the decrease of speed, in that case collision of vehicle may occur. The proposed system involves many features which help in preventing accidents such as the forward collision [1] system which indicates if there is obstacle nearby. ZigBee is used for wireless communication between vehicles. Status of the vehicle is stored in cloud throughblynk Related work andcontribution Because of more vehicles on road, traffic and transportation delay on urban areas are increasing day by day. By using V2V communication it is possible to detect the movement of another vehicle. Now a days almost all vehicle are attached a Global Positioning System (GPS)Technology, with the help of this we can able to know where the other vehicle is and a blind spot detection is possible through this technology, if there is an internal accidents in the vehicle like smoke and fire the sensors used to detect these can help the driver and passenger about the emergency, if the accident has occurred to the vehicle the immediate message is passed to the nearest vehicle through ZigBee technology and blynk application is using for controlling the arduino and other vehicles can find the longitude and latitude of the vehicle in trouble Outline The introduction is explained in section1.the proposed system is explained in the section 2. The Architecture is described in the section 3. Section4 explains Advantages. Final section explains conclusion and references. Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 141

168 Adithya S et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, PROPOSEDSYSTEM some of the features that can help in avoiding accidents Intersection assist: When intersection is approached, an alert message will be displayed if any other vehicle is travelling in a cross street, at high speed that it could stop or hit the car in the side. This feature helps in avoiding fatal T-bone accidents Do-not-pass warning: Often driving on a two lane road, a warning message will be displayed when a vehicle passing in the opposite direction makes it unsafe to cross a slower moving vehicle. The system sends an alert message when vehicle that is ahead of two or more vehicles in the same lane which is probably out of sight hits the brake unexpectedly. This feature can help prevent rear-endcollision. Fig 1: System Design Fig 1 is the proposed system design which involves following 2.1. Internal accidentprevention This part of the system includes different sensors like fire, smoke and accident sensors which are communicating with arduino. If smoke or fire accident occurs in the vehicle these sensors senses and sends the signal to the arduino, then the arduino do certain operations like displaying alert messages onlcd[3] Forward collision warning: When there are chances of a vehicle which is moving at high speed hits a slow moving vehicle ahead, an alert message will be displayed. This feature also alerts in advance of a stopped vehicle in the same lane, which cannot be seen because of vehicles in front or if it s around the bend in theroad Blind-spot warning: This feature is very useful while driving during night times. When the driver is unable to see outside of a vehicle, a warning message or a beep sound will be generated. 3. ARCHITECTURE 2.2. WirelessCommunication A wireless communication[2] is possible through ZigBee technology which consists of transmitter and receiver. A message can pass through this from one vehicle to another provided both vehicles have ZigBee GPS Today almost every vehicle has a GPS module. Global positioning system (GPS), which is used to locate the vehicle by knowing the longitude and latitude of thevehicle BlynkApplication Blynk application is an open source, specially designed for hardware interface. Blynk app allows us to create interfaces. Blynk server is responsible for all the communication between the smartphone and hardware. V2V communication that is vehicle to vehicle communication is constructed in order to prevent crashes in a very large number of cases and situations. These are Fig 2: System Architecture The fig 2 is the architecture of the vehicle to vehicle communication module of one vehicle. Different modules are connected to the Arduino controller according to their pin configuration. When the power Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 142

169 Adithya S et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, supply is given to Arduino, communication takes place between the modules simultaneously. The different sensors that are included are fire sensor, smoke sensor, object sensor, accident sensor. The fire sensor senses or detects any sign of fire within the vehicle, if any detection of fire is noticed an alert message is displayed on LCD and a buzzer beeps to alert the driver about the fire and the vehicle stops immediately. The smoke sensor works similar to the fire sensor. The object sensor detects any objects in front within the specified range, if the object is detected then the message is displayed on LCD, buzzer beeps and the vehicle is stopped, using this we can implement forwardcollision. The accident sensor is used to sense any hit or jerk or accident to the vehicle. GPS[5] is fixed in the vehicle to track the location. Using this we can implement do-not pass warning, intersection assist and blind spot warning. Using blynk application we can control the arduino FLOW CHART Firstly, initialize all the sensors, LCD and switches, when the vehicle starts all the sensors gets activated all starts sensing for internal accident such as fire and smoke and obstacles is detected using ultrasonic(object) sensor, if any of these sensors are detected then the vehicle stops and the alert message will be displayed on LCD. By using wireless technology ZigBee, the status of vehicle can be read by other vehicles within the specified range. The information of thevehiclecan be stored on cloud platform through Blynk. The ZigBee continuously provide communication between vehicles. 4. ADVANTAGES This system helps in reducing the accident rate, and provide safety for passengers, vehicle damage can be avoided. If it is implemented in large scale it could be cost effective since almost every vehicles comes with GPS. Monitoring the vehicles is easy. 5. CONCLUSION In the view of road safety, there is a much need for the system which at most prevents accidents. This can be achieved by implementing some of the features which are mentioned above. This helps in reducing the number of accidents in real time scenario. Since wireless communication is implemented, there will be better interaction of messages between vehicles. As blynk application is used, the vehicle location and the current activity of the vehicle can be stored and accessed by the authenticated users of blynk. The main purpose is to provide safety for the passengers who are in the vehicle and prevent accidents as much as possible. REFERENCES [1] Real time crash avoidance system on cross roads based on IEEE p devices and GPS receivers by S.Ammoun andf.nashashibi. [2] V2V wireless communication protocol for enhancing highway traffic safety by F.Dion and S.Biswas in2006. [3] Efficient vehicle to vehicle communication protocol for VANETs IEEE 2014 by Rajivmisra. [4] V2V wireless communication protocol for rear end collision avoidance on highway by M.Adams and S.Roy in IEEE Internationalconference. [5] Design and implementation of real time wireless system for vehicle safety and vehicle to vehicle communication by Mallikarjuna gowda.c.p, Rakshith.K.R, Raju hajare in IEEE international conference Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 143

170 Adithya S et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, Appendix: Next Page (Flow Chart) Start Initialize LCD,switches,sensors Start Vehicle GPS vibrati on Ultrasonic no yes yes Smoke yes no no Fire yes Stopvehicle Processing signals ZigBee cloud Vehicle2 stop Fig3. Flow chart Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 144

171 Volume 9, Special Issue No. 3, May 2018 International Journal of Advanced Research in Computer Science (ISSN: ) CONFERENCE PAPER Available Online at CLOUD BASED WEB APPLICATION SUPPORTING VEHICLE TOLL PAYMENT Ashwini T N Department of ISE, Sai Vidya Institute of Technology, Bangalore Brunda P Hiremath Department of ISE, Sai Vidya Institute of Technology, Bangalore Mamatha A Department of ISE, Sai Vidya Institute of Technology, Bangalore P N Rachana Department of ISE, Sai Vidya Institute of Technology, Bangalore Savitha D G Department of ISE, Sai Vidya Institute of Technology, Bangalore Abstract- The requirements for new web applications that supports different types of devices and the purposes are growing continuously. In the paper, main advantages of web application development features covering integration with different technologies are considered. Integration and possibilities of application of cloud based web applications in real scenarios with NFC card are considered and described in the paper. The implementation and design of a cloud based web application which supporting vehicle toll payment system using NFC device is presented and described. The development framework and popular technologies used to realize a vehicle toll payment by using NFC card are described. The paper describes concept of vehicle toll payment over an online payment system. Processing, monitoring and control in the cloud based web application of such payments using NFC card are described and presented. Keywords: Vehicle Toll Payment System, Cloud, Web Server, Near Field Communication, Android Enabled NFC Device I. INTRODUCTION The use of web applications in business environment has become standard. Main reasons are ease of maintenance as well as version release as it's made only on the central server without need for access to a user's computer. In addition, web oriented applications are available over an Internet for use in any location and on any kind of small constrained device. Such web applications can be integrated with different applications, control, management services, and other applications as well, to realize management, monitoring reporting, etc. The cloud systems can be divided into private, public and hybrid. Public cloud systems are provided by other organizations in a manner of renting distinct volumes of system resources, services and storage. All three types of cloud can be considered for web application development and hosting, depending on security requirements of web application domain. In case of a web application, security is on a high level because of access to a single central server, rather than using large amounts of workstations. NFC is set of communication protocols which connects two devices, i.e., android devices over 4cm of distances. Web server application acts as a interface between android application and cloud for request and response purpose. Both web applications and cloud plays a vital role in the vehicle toll payment system and also gives more reliability and scalability to whole system. According to present system, the toll payment is manual collection methodology where driver has to stop at toll and pay the required fee directly to collector. Amount to be paid by each vehicle is determined by its characteristics. Present system is time consuming. Whereas, proposed system is tapping NFC card at tollgate. II. LITERATURE SURVEY Drazen Pasalicet.al.Proposed a work Vehicle toll payment system based on Internet of Things [1]describes it is completely works only with the use of internet. To enable different physical environment, IOT is used. And also IOT has ability to bring other devices together to form a big network. Branimir Cvijic et.al., proposed a paper Web application supporting vehicle toll payment system [2] presents Main advantages of web application development are becoming popular by its development features which consists of integration of different technologies. Processing, monitoring, and control in cloud based web application of payment process are explained using IOT devices. Satyasrikanth Pet.al, worked on Automated Toll Collection System Using RFID [3] comprises Electronic toll payment system is mainly implemented for eliminating a need for motorists and toll admins to manually perform toll payment process. ETC significantly improves an efficiency of tolls and traffic needs of the toll road. Limitations of related work is Cost of collecting cash from hundreds of toll locations in a city is huge. Toll payment system requires more security and manpower in Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 145

172 Aishwini T N et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, collecting amount, counting cash. Time consumption is more for collecting tax. There are many chances of escaping without doing payment. Then all vehicles which are behind will be queued up. III. PROPOSED SYSTEM Cloud based web application supporting vehicle toll payment system mainly comprises of design and implementation of using a NFC card, which calls vehicle toll payment requests for web server in web application. Further, web application realizes user (driver) desired toll payment over an electronic payment system. Cloud based web application itself is developed to be hosted on a server directly or on a virtualmachine. MODULE DESCRIPTION The proposed system has four modules Web Server Application Admin Web Application Android Admin Application Authority Android Application insufficientand stop process. To be continued, after successful payment. Then update B in vehicle record. Send Transaction Success Message to Android App. Display Success Message in Android. TECHNOLOGIES USED Following technologies are used to implement proposed system: JAVA (JDK 1.6) System Pentium 4 JSP Servlets MySQL Android Studio ARCHITECTURE& FLOWCHART & DFD Following fig 1 shows System Architecture, fig 2 shows Flow chart of system and fig 3 shows Data flow diagram. Description of four modules is as follows Web Server Application In web server application, all the toll details and vehicle details will be maintained. Admin Web Application Module consists of user information s like NFC Card no, Vehicle No, Date of Registration, Vehicle Type, Vehicle Model, Card Expiry Date etc.will be stored and dumped into the NFC card. Web server calls an android applications only when NFC is detected. Fig 1. System Architecture Admin Android Application User details like NFC Card no, Vehicle No, Date of Registration, Vehicle Type, Vehicle Model and Card Expiry Date will be dumped into the NFC tag. Authority Android Application When a vehicle owner taps a card to android toll application, first data is converted into original data.all card details will be displayed. Card number,vehicle detail and tollgate details will be send to web server. Web Server receives Card No and fetches Vehicle Record and its Balance (Y). If Card number is miss matched then stop. Fetch the fare details from Toll Gate table based on Vehicle type, fetch the amount (X) from toll fare details, if balance is Fig 2. Flow Chart of the System When a person registers,an NFC card with unique ID is given. Authority has to install android application. When user taps a NFC card on android mobile. Request will be sent to webserver through an android app and it will check Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 146

173 Aishwini T N et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, whether user have balance or not.if not, person has to go to admin and recharge and amount will be deducted after recharge. If person already has a balance then amount will be deducted automatically & acknowledgement will be sent to user. Fig 5. Snapshot of Admin login Fig 6. Snapshot of Toll Gate Details Fig 3. DFD NFC Tapping Tollgate Once user tap a NFC card on an android mobile, it will display details about the user, after toll ID & vehicle details will be sent to web server which will interact with database once amount fixed for that particular toll gate & vehicle will be fetched the fare, if user has sufficient balance or not, if not error message will be sent to user &person has to recharge. If aperson has balance then amount will be sent. Fig 7. Snapshot of Vehicle Details IV. RESULTS Facilities available are Fig 8. Snapshot of Recharge Amount to Card Fig 9. Snapshot of Toll payment android application Fig 4.Snapshot ofoutlook of the project Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 147

174 Aishwini T N et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, Fig 10. Snapshot of options in toll payment application Fig 15. Snapshot of Toll Transactions Fig 11. Snapshot of writing vehicle details in card Fig 16. Snapshot of list of vehicles passed by tollgate V. APPLICATIONS Fig 12. Snapshot of vehicle details on card Standardizations and transparency at Toll fair collection and its utilization.security is enhanced as both the centralized server and tolldevice knows who is crossing the toll. Need for manual toll based system is completely reduced. Vehicle documents can be checked once. Fig 13. Snapshot of login of Tollgate session application VI. CONCLUSION Web application is developed to support integration into the cloud. As in vehicle toll payment application, number of devices can be enormous, the scalability of an application is supported and represents a vital role. System is easy to use for travellers as well as for companies engaged in highway management. The information stored in database can be used to create reports about number of vehicles and category of vehicles go through system of payment per day. In addition, it can be easy at any time to retrieve the data on the amount of money which is on daily or any other period paid at tollgate. In some cases discounts are added on tolls, free refreshments at rest stops along the road, integrate with other products in order to use specific routes with such kind of payment. Fig 14. Snapshot of read data in toll gate session application VII. REFERENCES [1] Drazen Pasalic; Branimir Cvijic; Dusunka Bundalo; Zlatko Bundalo; Radovan Stojanovic. (2016, August). Vehicle toll payment system based on Internet of Things. Published in Embedded Computing (MECO), th Mediterranean Conference. Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 148

175 Aishwini T N et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, [2] Branimir Cvijic; Drazen Pasalic; Dusunka Bundalo; Zlatko Bundalo. (2016 June). Web application supporting vehicle toll payment system. Published in Embedded computation (MECO), th Mediterranean Conference. [3] Satyasrikanth P, Mahaveer Penna, Dileep Reddy Bolla (August 2016). Automatic Toll Collection System Using RFID. Published in IJCSMC, Vol. 5, and Issue. 8, August 2016, pg [4] M. Armbrust, A. Fox, R. Griffith, A. D. Joseph, R. Katz, A. Konwinski,G. Lee, D. Patterson, A. Rabkin, I. Stoica, M. Zaharia. (2010, April). A View of Cloud Computing. Communications of the ACM. [Online].53(4).pp available: 1 0/4/81493-a-view-of: cloudcomputing/ full text [5] J. Gubbi, R. Buyya, S. Marusic, M. Palaniswami, "Internet of Things(loT): A vision, architectural elements,and future directions", Future Generation Computer Systems, Vol. 29, No 7, September, 2013., pp , [doi> I /j.future ] [6] S. Bandyopadhyay, M. Sengupta, S. Maiti, S. Dutta. (2011, August). Role ofthe Middleware for Internet of Things: A Study. International Journal of Computer Science & Engineering Survey (IJCSES). [Online]. 2(3). Available: cses/papers/0811 cses07. PDF [7] ASP.NET Overview, [Online]. Available: [8] H.P.Halvorsen, "ASP.NET Web programming", Hogskolen&Telemark, Telemark University College. Department of Electrical Engineering, Information Technology and Cybernetics, March Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 149

176 Volume 9, Special Issue No. 3, May 2018 International Journal of Advanced Research in Computer Science (ISSN: ) CONFERENCE PAPER Available Online at CONVERSION OF STATIC PIECE OF PAPER INTO SMART PIECES OF TECHNOLOGY USING RFID Jai Prakash Sah Computer Science and Engineering, RITM, Bangalore, INDIA Shilpa NR School of Computing and Information Technology REVA University, Bangalore, INDIA Abstract: RFID Technology is a part of the idea of getting wireless systems which are much needed in present times. Its roots go back to 1940s but back then it was not as developed as it is today. It has various applications (Document Management, Library Management & Traffic management are a few of them) which has made our daily life very easier. It has still not been used to its full potential and there are a lot of fields where it can be used. We will discuss a few of the applications which are being implemented in today s world but not on a large scale due to the cost of RFID tags. We will also be discussing new ways in which the RFID technology can be used in Future. Keyword: RFID,RFID Tag, RFID Reader, Antennaes, Applications, Systems I. INTRODUCTION RFID stands for Radio Frequency Identification. It generally means transferring information and reading information using radio waves. These way of transfer has been developing since 1940s but was first implemented in 1973 by Charles Walton during WWII when he used RFID technology to open doors without key. It was used to differentiate between enemy jets from their own. These aided the British army a lot during WWII. In 1990s IBM developed and patented a system that offers longer read range and was called Ultrahigh Frequency RFID system. In mid 1990s IBM sold its patent to Intermec. Intermec RFID were used for different applications but the technology was not popular. In 1999 UHF RFID was boosted by the establishment of Auto-ID Center. Again in between two air interface protocols were developed. They were Electronic Product Code numbering Scheme and Network architecture for looking up data associated on RFID tag on the internet. The two protocols were licensed to the uniform code council. In Present day we have many applications to RFID Technology. Some of these applications include Document Tracking, Asset Tracking, Health Monitoring, Locating people and much more. It can also be used for security threats and issues. It can also be used for transportations of logistics. out of a business establishment by scanning this code and get the information related to the product. Barcodes have around for decades and they are very versatile with large variety of uses especially in retail and manufacturing settings, and in transport and shipping. We can see barcode printed on the packaging at the stores. When these code is passed over or under a barcode reader, we get the information related to the product as well as updates the database regarding the sale of the product. Apart from sale Barcodes are valuable also for managing inventory and raw materials internally, so that we know what we have in stock. Organization of the paper The residual part of the paper is organized as follows. Section 2 discusses architecture of RFID and pros and cons of RFID. Section 3 discusses the working principle of RFID. Section 4 discusses few existing applications of RFID in present day. Section 5 contains the proposed applications of RFID in Future. II. ARCHITECTURE OF RFID AND THEIR PROS AND CONS Like RFID Technology there are other technologies like QR Codes and Bar Codes which like RFID are all systems for conveying large amount of data in a small format. They offer speed and labor savings among other benefits. But between all three are differences based on the purposes they are best suited for. QR codes can be scanned and read by a camera-equipped smartphone along with the scanner app such as i-nigma for the iphone. An average can now decode this QR code without any special equipment and walk in and Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 150

177 Jai Prakash Sah et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, III. WORKING PRINCIPLE OF RFID Working Principle of RFID Technology depends on the method by which tags are powered.in this section we will take Passive tag and discuss the methods to power them. Basically there are two ways to power tags. They are: 1. Induction Coupling method Fig.1 Architecture of RFID Our architecture is composed of two main parts RFID Tag: It is a microchip combined with an antenna system in a compact package which contains memory and logic circuits to receive and send data back to the reader. A larger antenna usually means a larger range. There are two types of tags available known as active and passive tags. Active tags have internal batteries that allows a longer reading range, while passive tags are powered by the signal from its reader and thus have shorter range. Tags can also be classified based on type of memory it uses. It can Read-only, readwrite or write-once read many (WORM). RFID Reader: In order for an RFID system to function, it needs a reader, or scanning device, that is capable of reliably reading the tags and communicating results to a database. When a reader broadcasts radio waves, all tags designed to respond to that frequency and within range will respond to it. PROs AND CONs of RFID Technology Pros 1. RFID tags are very simple to install/inject inside the body of animals/products, thus helping to keep a track on them 2. RFID tags can store data up to 2kb. 3. Unlike Barcodes it doesn t require a line of sight to work. 4. RFID technology cannot be easily replicated and therefore, it increases the security of the product. Cons 1. It is difficult for an RFID reader to read the information in case of RFID tags installed in liquids and metal products. 2. RFID technology has been referred to as invasive technology. 4. Still Costly at large. Fig.2 Passive RFID using inductive coupling In this approach the RFID tag gets power from the reader through inductive coupling method. The reader consists of a coil connected to an AC supply such that a magnetic field is formed around it. The tag coil is placed in the vicinity of the reader coil and an electromotive force is induced it by the virtue of Faraday s law of induction. The EMF causes a flow of current in the coil, thus producing a magnetic field around it. By the virtue of Lenz law, the magnetic field of the tag coil opposes the reader s magnetic field and there will be a subsequent increase in the current through the reader coil. The reader intercepts this as the load information. This system is suitable for very short distance communication. The AC voltage appearing across the tag coil is converted to DC using rectifier and filter arrangement. 2. Electromagnetic Wave Propagation Method Fig.1 Passive RFID using EM-Wave Transmission The antenna present in the reader transmits EM waves which are received by the antenna present in the tag as potential difference across the dipole. This voltage is rectified and filtered to get the DC power. The receiver antenna is kept at different impedance which causes it to reflect a part of the received signal. The reflected signal by the reader and Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 151

178 Jai Prakash Sah et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, monitored accordingly. IV. EXISTING APPLICATIONS OF RFID In this section we discuss few applications of RFID technology in different fields and how it contributes to making the life a little easier for us. 1. Review of RFID Technology in Smart Parking: In this study, a solution has been provided for the problems encountered in parking-lot management systems via RFID technology. RFID readers, RFID labels, computers, barriers and software are used as for the main components of the RFID technology. The software has been handled for the management, controlling, transaction reporting and operation tasks for parking lots located on various parts of the city. Check-ins and check-outs of the parking-lots will be under control with RFID readers, labels and barriers. Personnel costs will be reduced considerably using this technology. Advantages of this system Check-ins and check-outs will be handled in a fast manner without having to stop the cars so that traffic jam problem will be avoided during these processes. Vehicle owners will not have to make any payments at each check-out thus a faster traffic flow will be possible. Ticket jamming problems for the ticket processing machines will be avoided as well. 2. Review of Shopping By Blind People: Detection of Interactions in Ambient assisted living environments using RFID: In this solution Ambient Assisted Living can be implemented using RFID tag and RFID reader. RFID tag contains all the information of the object and stores in store server. Impaired person will be holding an RFID reader, as soon as he or she reaches near the products, RFID detector detects the RFID tag and sends the tag number to the server. The server will send the information stored in the RFID tag to the user s smart phone through voice command. An audio message is played to assist the user in navigating and identifying the items. This system will work without any human help. No need to carry any costly hardware for visually impaired people, only they need smart phone and RFID reader. Advantages of this system It performs navigation inside the building. It guides the user through calculated routes. 3. Review of Application of RFID Technology in Libraries: The RFID technology works through flexible, paper thin RFID tags, which can be placed inside the cover of each and every document/book. Complete information about each document is entered into the library Management Software. Whenever a user brings a document for issue-return purpose, the RFID reader from the tag reads the information pertaining to that book and transmits the data into the software and document is issued in a few seconds without the assistance of the library staff. As the user takes the document outside the library, the antenna placed at the exit gate automatically reads the information contained on the RFID tag to verify whether the document is properly issued or not. In case, it is not issued to the user as per library norms or it is being stolen from the library, the antenna senses it and gives an instant alert. Thus, it results in successful theft reduction of documents. RFID technology is not only being used for circulation purpose in the libraries, it is also used for stock taking purpose. Advantages of this scheme Reduction in queue at circulating desk. Saving time of the library staff while issue/return of document Security of library collection. 4. Review of Android based Traffic Rules Violation Detection System Vechitrack : In this system there are automatic traffic rules violation detection devices, message sending and automatic fine receiving from owner s bank account. RFID reader will read the RFID tag number which is given to the individual vehicle while purchasing or passing the vehicle for number plate received by RTO which will be mandatory. The camera will also be there for capturing the image of the vehicle that has broken the rule. RFID reader will read the RFID tag number and send this information to server database. Server database will send the information from its stored database related to RFID tag number and image on traffic police android application. Later, the application will deduct the fine automatically from the owner s bank account according to the rule broken by vehicle driver. It also sends the information about the violation and fine deducted through message to the violator of traffic. Advantages of this scheme Reduces all issues related to traffic rule on the road. Low Crime rate due to installation of cameras V. THE PROPOSED APPLICATION OF RFID TECHNOLOGY The Paper document, one of the most critical asset to many organizations, vital to the flow of information, but sometimes tracking and searching them is like searching for a needle in a haystack, wasting critical time and energy in the process. We can avoid this dilemma by the use of RFID Technology to manage the documents. By just pasting the RFID tag on the paper document we can convert them from static pieces of paper to smart pieces of technology. We can paste the tag while receiving a paper application and enter its tag number in the computer system and that s it. We will not only have the location of each paper document but also the information as to how long it has been there and we can also design an alarm system for documents which are to be forwarded within a week or month or certain time limit. When searching for a certain document we type its tag number in the system and all RFID readers in the office building start scanning and searching for the document and Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 152

179 Jai Prakash Sah et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, when the document is found, its information is sent back to you and all this happens in a matter of seconds. We can also use a handheld reader to locate the exact location of the document. Advantages of these systems: Tracks current location of the document. Very Fast. Gives information regarding how long it has been at the current location. Has an alert system We can also use the alert system in store to alert the store owners about the products which are going to be expired within a month/week. VI. CONCLUSION RFID technology is taking off in many places like in malls, libraries etc. at an increasingly rapid pace. Though there are many firms employing this technology today, but due to its customizable feature and continuing improvement the communities are beginning to get involved in its development. It is easy to envision that, the RFID tags contents will increase in power, prices are expected to decline and tag will dramatically improve its efficiency, security and accuracy. Also major concerns need to be addressed for successfully implementing this technology. So that it will change our personal and work lives and adorns the conventional management with a new idea and usher for a bright future. VII. REFERENCES [1] MR. Neeraj Kumar Singh, Prof. Preeti Mahajan, Application Of RFID Technological in Libraries, In International Journal of Library and Information Studies, Vol4(2) Apr-Jun, 2014 [2] Anil Pingale, Pooja Bhoite, Vijaylaxmi Mankar, Pradip yadav, Prof A.N Kaulage, A Survey on Bus Tracking and Bus Arrival Time, Location Prediction System, In Imperial Jounal of Interdisciplinary Research, vol-2,issue- 12,2016 [3]Kasliwal komal S., Gandhi Labhesh R., Deore Punam D., Saitwal Pooja D, Android based Traffic Rules violation Detection system vehitrack, In International Jounal of Computer Applications( ), volume 163-No 8,April 2017 [4] Sahil Bhosale, Rohit Chavan, Sunil Bhadvan,Prajakta Mohite, Automatic Vehicle Identification and toll Collection using RFID, In International Research Journal of Engineering Technology, Volume: 03 Issue: 02, Feb [5] Zeydin Pala, Nihat Inanc, Smart Parking applications using RFID Technology, In RFID Eurasa,2007 1st Annual, 4-5 sept [6] Snigdha kesh, S.Vasanti Venkateshwar, Ananthanagu, Shopping By Blind People: Detection of interactions in ambient Assisted Living Enviroments using RFID,International Journal of Wireless Communications and Networking Technologies, Volume 6,No. 2, Feb-Mar 2017 Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 153

180 Volume 9, Special Issue No. 3, May 2018 International Journal of Advanced Research in Computer Science (ISSN: ) CONFERENCE PAPER Available Online at CROWD SOURCING-BASED DISASTER MANAGEMENT USING CLOUD COMPUTING IN INTERNET OF THINGS PARADIGM Ambika R Computer Science & Engg., SVIT ambikar.14cs@saividya.ac.in Pragathi S N Computer Science & Engg., SVIT pragathisn.14cs@saividya.ac.in Meghashree R M Computer Science & Engg., SVIT meghashreerm.14cs@saividya.ac.in Yeshaswini P V Computer Science & Engg., SVIT yeshaswinipv.14cs@saividya.ac.in Meghashri E M Asst Prof Computer Science & Engg., SVIT meghashree.em@saividya.ac.in Abstract: Natural calamities like Earthquakes, Tsunami and man-made disasters bomb explosion, building collapse often occurs and they cannot be stopped. Early detection of such disasters can help save many lives and natural properties. Disaster Management refers to manage disaster response in the country. In India it is recorded that 21.8% people die annually due to natural disasters. In this paper, early detection of fire, flood, rain, wind is done which helps in early rescue and proper planning by the rescue team. Sensors which sense such disasters and when the sensed value crosses the limit, the information would be sent to cloud. Through cloud computing we can use the benefits of storing data on cloud and data offloading. When the data is sent to cloud, from the cloud the data is sent to user. An app would be developed which would provide information of the disaster through voice output and also a normal message would be sent to user. For the disaster management to be handled efficiently, we make use of Iot and cloud computing technologies Keywords: Disaster Management, Crowd sourcing, Voice Output, and Data Offloading 1. Introduction Everyone admits that early prediction of disasters in better than later repair and damage caused due to disaster. In last few decades, serious man made or natural disasters like Fire, Flood, Twister and Tsunami occurs, and causes loss to huge number of people, resources and properties. Natural disaster like Fire caused accidently or due to manmade disaster causes loss to huge no people. Flood is caused due to overflow of water and heavy rain. Early detection also helps to save livelihoods and the properties created by natural development of forest. This paper helps in early detection of disasters and provides a more efficient way to help the rescue team to plan out efficiently. 2. Literature Survey Prabodh Sakhardande and his team from Maharashtra Institute, Pune made a study on Disaster Management System using IoT for Smart City Management. This paper discusses how communication and sensing technology of Internet of Things can be used for management of smart city and disaster management. It hardware is used for Iot management and Smart City Management [1]. Partha and this team discussed on how Internet of Things can be used for disaster management. This paper discusses issues related to disasters such as early detection, notifying, analysis of data, monitoring of remote application, analyzing real time system, and victim localization were discussed [2]. In this paper it discusses how the technology of cloud computing can be used with Internet of things. Various data about disaster is collected through Iot and data is stored is cloud. The interaction of private and public cloud is predicted through effective server utilization using cloud computing. The technology of Iot was used in USA to connect objects and things which are in a particular range using either wired or wireless technologies to connect the things which are in the range. Around 2005, when the technology of Iot emerged, it helped the sensible objects to be connected through a network and was used for communication. It was used for different types of applications, to observe objects in war sensitive area. The technology emerged for emergency purposes and was used in defense team [4]. This paper discusses starting with process of with identifying the different types disaster or risk, causes for disaster, responses for emergency purpose, allocation of various resources, effective planning of the resource Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 154

181 Ambika R et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, allocated and recovery from disaster. Iot emerged and was used emergency purposes and it helped in every stage of disaster management [5]. 2. Component Description The following hardware components/software modules have been used in this paper 2.1 Renesas Microcontroller- The microcontroller used is RL-78. It has a General-purpose register with 8 bits and 32 registers (8 bits 8 registers 4 banks). It has ROM of 512 KB, RAM of 32 KB and Data flash memory of 8 KB. Above the chip it has high speed oscillator, flash memory with single power supply and debugger function used for debugging function. It has total 64 Pins ports, out of which 58 Input/output Pins and 11 ports used for Input/output and has various registers. The control registers is used to control the stack memory, program sequence. The program counter contains the address of the next program to be executed and it is 20-bit register. The program status consists of various flags set/reset by instruction execution. The interrupt enabled flag controls the interrupt related acknowledgement and handles the interrupt. Zero flag, is set to 1 when the operation is zero and is set to 0 for all other operations. There are also Register bank select flags,rbs0, and RBS1 which is used to one of the four register which is 2-bit flag. Fig 1:- Renesas microcontroller 2.2 Accelerometers sensor- It uses the second law of motion given by Newton. Accelerometer sensors use Newton s second law of motion, F=ma, where we can measure the acceleration by the force exerted by the object, mass of the object given. Accelerometer sensor is used to measure the speed of wind, its tilt, angle of tilt and also the gravity. An accelerometer measures various parameters like Tilt and till angle, acceleration, Incline, Rotation Vibration, Collision, Gravity. Fig 2:- Accelerometer Sensor 2.3 Vibration Sensor- This sensor is used as piezoelectric transducer. They convert mechanical components like force, acceleration into electrical component and vice-versa. This sensor is placed at the level of ground and if there is the sensor vibrates more, those moments is converted into electrical component. It is also used for security purposes. It sends a signal to control panel about the sensed data caused due to wind. It operates on voltage of 12volt Direct current, frequency ranging from 0.5Hz to 20Hz and sensitivity greater than or equal to 0.2g. 2.4 Water sensor-a water sensor is used in various types of applications to detect the level of water. It will detect the amount of water and it will send alert when the water gets leaked. There are several types of water sensors, some of them include used for different applications like for pressure, bubblers. Water sensors are used to detect the level of water and send data if there is any excess of water. 2.4 Temperature Sensor- A temperature sensor is used to detect variation in amount of temperature. It is used to detect fire in our paper. If the sensor senses it crosses a limit, it will send the sensed data to cloud. Fires caused accidently or due to manmade disaster can be sensed by using temperature sensor. It senses temperature varying from low to high range of temperature. 2.5 GPRS (General Packet Radio service)- It transmits data at speed of 55 up to 114 Kbps and a packet-based used for wireless communication service. Mobile phone, laptop, computer users get the advantage of Internet continuously supplied. A GSM-GPRS board, this board can be interfaced with RS-232 DB9 connector. The rest of the required items assume that the boards you have also interface using a RS- 232 connector and a laptop with spare USB port. A power supply should be given to GPRS board. Generally, the GPRS boards require 2 Amp supply. So we should read the board specifications and get the power supply accordingly. Fig 3:-GPRS Module 2.6 GSM Module- GSM module is used to give the location of the object. In this project we are using GPS-634R which is integrated circuit and contains ceramic patched antenna. Through Local Area Network (LAN) we connect the antenna. It contains engines with 51 channel acquisition engines and 14 channel track engines. These engines have capacity of receiving signals up to 66 GPS satellite.it transfers into the precise timing information and also position that can be read from either UART port or RS232 serial port. It is designed to have small size and low power consumption; operating over supply voltage of 3.5V~6.0V is Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 155

182 Ambika R et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, supported. The GSM antenna is tested to its 100% and it works more reliable. The smart antenna works flexible, robust and can be switched off in final stage of operation. 2.7 Cubesuite Cubesuite was developed in integrated development environment. It offers various functionalities like security, easy to use and debug, simple in usage and also used as the best debugger. It is used for editing, changing and debugging functions. It is easy to install and operate. This software is highly user friendly and has it is open software, anyone can download and user the software. It is also used for graphical debugging functions. This software is used for Renesas microcontroller and also for programming the renesas microcontroller. It is also compatible with Renesas hardware tools where we can edit, change and debug with advanced facilities. 2.8 Renesas Flash Programmer- It is a programming Graphical User Interface. It provides usable and functional support for programming the on-chip flash memory of Renesas microcontrollers in each phase of development and mass production. 2.9 Programming languages- The programming languages used in this project is Embedded C and Java. Embedded C is used for hardware component and Java used for software component. Embedded C is used for programming the Renesas microcontroller and Java used for the web page. Visual Studio is used for developing the app which would send the voice output about the disaster. 3.1 Working Principle The system we are implementing involves detecting fire, wind, rain and earthquakes and if the sensed data croses a limit and transmitting the processed data to cloud, and user would recieve a message and voice output,about the sensed data.for processing the data we implement Renesas microprocessor. Below figure shows the block diagram of the system to be implemented temperature. It is used to sense the fire and if it crosses a limit, the sensed data would be send to cloud. The Vibration Sensor is used to detect variation in the level of earthquake. If the sensor detects the vibration crosses the limit,the sensed data would be send to cloud. The level sensor is used to detect variation in level of rain. If the sensor detects that water level crosses the limt,the sensed data would be send to cloud. The accelerometer sensor is used to detect variation in the level of wind. If accelerometer sensor detects that wind,crosses the limit the sensed data would be send to cloud. In the project four different diasters like Fire is detected by Temperature sensor,earthquake detected by Vibration Sensor,Flood detected by Level Sensor and Twister is detected by sensor. The sensor will keep sensing if any variation in any one of the sensor, the data would be send to renesas microcontroller.the renesas microcontroller will collect the data and send to cloud.amazon cloud is used, where it will have tables containing information about the date,time,location,type of diaster,value of the diaster. The type of cloud used is public cloud,where it can be accesible to all. GPRS module is used which sends the location of the diaster prone area. Once the data is received to cloud it would automatically notify the user about the diaster.the GSM Module is used sent a message to user about the disaster and is also used for providing internet connection. An app would be developed through which the user would get voice output about the disaster. If the user does not notices the message, voice output would inform the user about the diaster. Through this project we can detect the disaters early, and it would save million lives of people and resources.early detection is always better than latter rescuing of lives of people. 4. Conclusion This paper proposes a more practical and cost-effective model for diaster management using cloud computing in Iot paradism. The proposed diaster detection system is used to detect diaster and further early detection prevents loss due to human life and natural resources. This system can be used in diaster prone area where it will be active all the time and the when the sensed data crosses the limit,it would be notified to the user.it uses the technology of cloud computing where data would be stored in cloud.it offers the mechanism of data-offloading mechanism, where we can access the data offline.early detection helps save million lives of people and many natural resources.it also helps the rescue operation to plan out effeciently and rescue the lives of people.this model reduces the cost by early predicting, and reduces the cost which would be invested in latter to save resourses. Fig 4:- Block Diagram of the system The components used in this system are Tempeature sensor, Vibration Sensor, Level Sensor, Accelerometer, GSM Module, LCD, Amazon Cloud and Renesas Microcontroller. The temperature sensor is used to detect variation in 5. REFERENCES [1]. Prabodh Sakhardande, Sumeet Hanagal and Savita Kulkarni (2016), Design of disaster management system using IoT based interconnected network with smart city monitoring Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 156

183 Ambika R et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, [2]. Partha Pratim Ray, Mithun Mukherjee,and Lei Shu (2017), Internet of Things for Disaster Management: Stateof-the-art and Prospects [3]. JayavardhanaGubbi, RajkumarBuyya, SlavenMarusic,MarimuthuPalaniswami (2013), Internet of Things (iot): A vision, architectural elements, and future directions [4]. L.Yang, S.H.Yang, L.Plotnick (2012), How the internet of things technology enhances emergency response operations [5]. Himadri Nath Saha, Supratim Auddy, Subrata Pal, Shubham Kumar (2017), Disaster Management using Internet of Things Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 157

184 Volume 9, Special Issue No. 3, May 2018 International Journal of Advanced Research in Computer Science (ISSN: ) CONFERENCE PAPER Available Online at SMART NOTES - AN INNOVATIVE WAY OF SHARING AND OBTAINING NOTES Mr. Nandakishore G Professor, School of C&IT - REVA University, Mr. Niranjan V Dhooli Professor, School of C&IT, B.Tech, REVA University Ms. Chaithra M H Professor, School of C&IT - REVA University Ms. Reshma R Professor, School of C&IT - REVA University, Ms. RudrarajuRamya Professor, School of C&IT - REVA University, Abstract - Smart Notes is a platform where students and teachers can share the notes, text books, study materials and assignments online. Usually the notes are shared through or Whatsapp and are unorganized. We propose on designing an Android application and a Web Application that can address this issue since the teachers can upload the notes and assignments through the app or the website. This helps the students to access the notes they require from anywhere, anytime. In addition to this we are also implementing a feature where in a user can order the photocopy of the notes they require through the app or the website. The orders made will be forwarded to the vendors. Once their photocopy is ready, the students will be notified regarding the same and the time when they can go collect their orders. Keywords : Android, Web Applicaton I. INTRODUCTION One of the major issues that the students face nowadays is obtaining all the notes, study materials and text books as and when they require from a single source. As most of the notes and study materials are shared either through Whatsapp, Gmail and many such social media platforms, these notes are unorganized. This creates a difficulty of searching the study materials everywhere when it is required. In addition to that students also have to wait in long queues and crowds in front of photocopy shops when they need to obtain the hard copy of these study materials for their examinations. Through this project called "Smart Notes", we are implementing an innovative solution to these problems by creating a Web application and an Android application where in the lecturers or the authorized person can upload their study materials, notes, text books, assignments etc. online and the students can view, download a soft copy or even order a hard copy of the required study materials. If a student orders for a hard copy of the notes, the respective vendors get the requests and they can process it. Once the ordered hard copy is ready, the students are notified through an as to when they can go collect their orders. All the study materials required are available for the students at a single platform called "Smart Notes" anywhere, anytime. The project "Smart Notes" is implemented both as a Web application and an Android application so that the students or lecturers can use it through their smart phones or their laptops and personal computers. This way the access to notes is always available if there is internet facility from anywhere, anytime. A. Existing System A web application is a collection on web-pages with some specific functions which let you perform a certain activity, over the web. A server-side dynamic web page is a web page whose construction is controlled by an application server processing server-side scripts. In server-side scripting, parameters determine how the assembly of every new web page proceeds, including the setting up of more client-side processing. A dynamic web application generates the pages/data in real time, as per the request, a respective response will trigger from the server end and will reach the client end (your end). Depending upon the response the client side code will take action as it's supposed to. Most of the web applications are static and it is not as proactive as dynamic web pages. The existing study material sharing sources are majorly the social media applications like Whatsapp, Hike messenger etc. or sources like Gmail or Google Drive etc. where the study materials are shared randomly in an unorganized way. This creates difficulties for students in finding these study materials exactly from a source as and when they require as they will have to search through multiple sources to obtain their study materials. It creates a lot of time loss and confusions. Moreover even to obtain the hardcopy of these study materials the students will have to wait in long queues in front of the photocopy shops. B. Proposed System Through this project, we are proposing in building a dynamic and reactive web application using the MeteorJS framework Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 158

185 Nandakishore G et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, and also building an Android application that can be used handy from anywhere using a smart phone. This makes the process of obtaining the study materials Meteor js is used as it supports any templating language and templates automatically update with changes in database. Libraries can be downloaded and apps can be compiled by one command line. Meteor data source has feature of reactivity. That means that with system data changes, all other components used in the app update in real time. This is an example of reactive programming concept, where code written in imperative style will automatically influence data that depends on it. Requests from users are managed in similar way. Latency compensation feature enables immediate screen update after user made a change. In this case Meteor- based app doesn t need to wait for the database update and users won t need to reload the page in order to see their changes.hence using the Meteor js framework makes the web application more feasible and userfriendly. II. IMPLEMENTATION "Smart Notes" is implemented separately as a web based application and an Android application. This helps the users to access the "Smart Notes" application from anywhere and anytime since it can even be accessed using a smart phone easily with the help of the Android application. The flowchart is as shown below : All the server side logic and the client-side logic of the web application is written in JavaScript. The templating used is the Blaze template along with BS4, BS3, HTML, CSS and Jquery for the front-end development. The back- end logic is completely written using JavaScript. We use MongoDB as the database to store all our information. MongoDB is a NoSQL database. It is chosen because it is scalable and flexible. It does not require any rigid or pre- defined schema. The entire webpages of the "Smart Notes" application is designed to be reactive. Any changes that is made either in the database or in the frontend is dynamically pushed into DOM making it dynamic. Hence it makes our application to be operating in real-time without the user having to refresh the web pages to see the changes that are made. Each and every changes made on the web pages are reflected immediately. I "Smart Notes" Web Application The "Smart Notes" web application has five different modules with each of these modules having different functionalities. The various modules are as follows : 1. Login module 2. Homepage module 3. Teachers / Faculty homepage 4. Vendor homepage 5. Server module A. Login module The login module contains the register page and login page. This module is mainly used for signing up and logging in into the "Smart Notes" application. The Sign up can be done by a student, a faculty / professor or even a vendor. Once the user opens the "smart notes" application and signs up by entering all the information like his or her -id and password along with the details like he or she is a student or a vendor or a professor, that user will be registered on the application and all his details are saved in the database. That is, the sign up form submits the request to server, after the validation of the user is done, the user will be successfully registered. Once the user is successfully registered, the user can log in into the "Smart Notes" application by entering his or her id and password. The process of user validation is done by the UserAccounts module. If the details entered are appropriate, then the user is logged in into the application. After a successful register or login user is redirected to the home page. If the user is a student then the student homepage is displayed, or else if the user is a faculty then the faculty homepage is displayed or if the user is a vendor, then the vendor homepage is displayed. Hence a customised home page is displayed based on the user's profile. Figure 1 : Flowchart Representation A. Implementation details onference Paper: Third National Conference on Advances in Computing and Information Technology Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 159

186 Nandakishore G et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, figure 2 :Login Module B. Homepage module Once the user has logged in into the application, he or she is directed to a customized home pageaccording to theircorresponding user profile. The home page module consists of the following pages : 1. View Notes 2. Order Notes 3. View profile 4. Logout The "ViewNotes" page consists of select menus where the user can view and select the notes that he or she wants. The "OrderNotes" page can pull up when the user clicks check out on a particular notes that they want to order a hard copy. The order is then updated to the data base and the vendor is notified regarding the order. The profile page shows the user profile and their details. The logout page's functionality is to log out the particular user from the application. figure 3&4:Student Homepage C. Teacher / Faculty Homepage If the user that has logged in to the application is a teacher / a faculty then he or she is directed to the teacher homepage. The faculty home page consists of "AddNotes" through which the respective teacher / faculty can add the notes, text books and study materials so that the students can access it. The data which is entered by the faculty is directly sent to the server and added on to the data base that has logged in, then the teacher homepage is displayed, if it is a student then the student home page is displayed, and if it is a vendor, then a vendor homepage is displayed. D. Student Homepage If the user that has logged in into the application is a student, then he or she is directed to this page once they log in into the application. Here the student can select the academic year, their respective department, their current semester and the subject of the study materials that they would require. After entering all the above details, once the user clicks on fetch, they get a pop-up option in order to add the notes to cart. If the student requires a hard- copy of the notes, then he can order it by clicking on the "add to cart" button. E. Vendor Homepage onference Paper: Third National Conference on Advances in Computing and Information Technology Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 160

187 Nandakishore G et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, The vendor page has the menu called "vieworders" that displays all the orders made by the respective user. The vendor can use the features in this page to print, cancel and send the notification to user once the orders made by the students are printed. After successful fulfilling of order or cancellation, the corresponding orders are removed from the orders list. 5. Review order page G. Login page Once a user has the "Smart Notes" application in his or her smart phone, the user can open the application and start using it. At first, the user is taken to the login page when he or she has opened the application. If the user is an already existing user registered with the "Smart Notes" application, then he or she can enter the details like the registered -id and password and log in into the application through the login page H. Register page If the user is not registered, then the user can click on the "no account? create one!" option on the login page to go to the registration or sign up page. This page allows a new user to create an account in the "Smart Notes" application. The user can create an account by entering the details like his or her name, -id along with a password. The user will have to confirm their password by entering it twice. Once the registration is done, the user's information will be added to the database immediately and saved. If the -id already exists, then it will not be accepted into the database. I. Home Page Figure 5&6:Vendor Homepage F. Server module This module handles all the requests made to the web and android application by different users and processes these requests. The Android application also connects with the same server that is connected by the web application to handle and process various user requests. II "Smart Notes" Android application The "Smart Notes" Android application is similar to that of the web application that is mentioned above. The implementation is done completely using java and Android using tools like Android Studio. Since everyone uses a smart phone these days, having an android application can help the users to access the "Smart Notes" application handy wherever they are. The "Smart Notes" android application has the following pages in it namely : 1. Login Page 2. Register or Sign up page 3. Home page 4. Order page Once a user has registered he or she can login into the application. After logging in by entering the necessary details, the user is redirected to a home page where there is a dropdown option with "login", "register", "fetch notes", "communicate" and "about" where in the user can select any of these option according to their requirements. J. Fetch Notes In order to obtain the notes, the students can click on the "fetch notes" option in the home page. Once the user clicks on the "fetch notes" option, he or she will be redirected to the "fetch notes" page. In this page, the user can enter the details like their department, the semester in which he or she is in, their current academic year and the subject details of the notes or study materials that they would like to fetch. After entering all these necessary details, the user can click on the "search" button to obtain the notes if it is available. K. Order Notes Page After searching and fetching the required notes, the student can order the notes through this page by selecting the required notes and clicking on the "Add to cart" button. The user also has an option to view the selected notes. L. Review Orders feature onference Paper: Third National Conference on Advances in Computing and Information Technology Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 161

188 Nandakishore G et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, Once the user wants to order a particular notes, they can select the notes and click on "add to cart". Having added to cart, the user is redirected to the "review orders" page where in the user can review the orders that they have made and also view the billing amount for each of their orders and even cancel an order by clicking on the "delete" button. M. Logout feature At the right side of the home page, there is a menu-bar that has the following options namely : 1. Logout 2. View Cart 3. Place Order The user can select the "logout" option, if he or she wants to logout or exit from the "Smart Notes" application. The user can also view the added notes by clicking on the "view cart" option. There is also an option to order the notes required by selecting the "place order" option. III. RESULT We have a full stack webpage along with the an Android application developed by using the above techniques. The following result is achieved by hosting the webapp in local at port 3000.The Android app uses this address for communication with server. The vendor page is opened in a different browser connecting the same server. IV. CONCLUSION AND FUTURE ENHANCEMENTS The "Smart Notes" application is a student-friendly dynamic application that helps the students to obtain all the notes, study materials and text books at one place anywhere and at anytime. It also facilitates the faculties to share all the notes to the students related to each subjects by uploading it into the application. This ensures that every student has the access to all the study materials that are required for their examinations. Since the "Smart Notes" application is available as both a web application and an android application, it makes its accessibility more convenient to the users. The students can view the study materials that they require and also order them online if they need a hard copy of these study materials. This helps the students in saving the time of going to the photocopy shops and also prevents them from having to wait in long queues and crowds in-front of the photocopy shops especially during the examination times. As the students are notified regarding their orders after they are processed, they can directly go and collect their orders. This Smart Notes application can be expanded to be used by all the colleges in the state or as an free student-friendly software by making it capable enough to process large volumes of data processing. This helps the students and faculties to have a common platform for sharing study materials, accessing and obtaining it anywhere and anytime. V. REFERENCES [1]. The Design And Implementation Of Cloud Notes System Web-Based Y Song, X Dong, J L I In Medicine And Education (Itme), 2016 (Ieeexplore.Ieee.Org) [2]. Web-Based Collaborative Construction of Curriculum Texts in Higher Education.. Wenxin Deng1and Fang Lu(ieeeexplore.ieee.org) [3]. [4]. [5]. and onference Paper: Third National Conference on Advances in Computing and Information Technology Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 162

189 Volume 9, Special Issue No. 3, May 2018 International Journal of Advanced Research in Computer Science (ISSN: ) CONFERENCE PAPER Available Online at SMART BINS USING DRY AND WET WASTE DETECTORS Ramya R School of C&IT REVA University, Bengaluru Ranjitha D School of C&IT REVA University, Bengaluru Pooja G School of C&IT REVA University, Bengaluru Tabassum Taj B School of C&IT REVA University, Bengaluru Nirmala S Guptha School of C&IT REVA University, Bengaluru nirmalaguptha@reva.edu.in Abstract: The idea is to develop a smart bin system which is capable of segregating the dry and wet waste automatically without any human intervention. Our idea basically comes in the domain of sewage and waste management. As the technology is rapidly growing no one is interested in manually managing the sewage and waste system. We have selected this domain, as this domain deals with cleanliness. Cleanliness of surroundings leads to a healthy environment which in turn leads to a healthy family and healthy individual. Keywords Sewage and waste management, Dry and wet waste, Sensors. I. INTRODUCTION This domain is of concerned and important in today's society because many diseases are likely to come due to the mismanagement of waste. Not only to individuals has it also harmed the nature, thereby harming all the other living species on the earth. Squander isn't something that ought to be disposed of or discarded with no respect for some time later. It can be a significant asset if tended to accurately, through approach and practice. With discerning and predictable waste administration hones there is a chance to receive a scope of rewards. Those benefits include: 1. Economic - Enhancing financial proficiency through the methods for asset utilize, treatment and transfer and making markets for reuses can prompt effective practices in the creation and utilization of items and materials bringing about important materials being recuperated for reuse and the potential for new 2. Social -Enhancing financial proficiency through the methods for asset utilize, treatment and transfer and making markets for reuses can prompt effective practices in the creation and utilization of items and materials bringing about important materials being recuperated for reuse and the potential for new employments and new business openings. 3. Environmental - Decreasing or disposing of unfriendly effects on the earth through diminishing, reusing and reusing, and limiting asset extraction can give enhanced air and water quality and help in the lessening of green gas outflows. 4. Inter-generational Equity - Following powerful waste administration practices can give resulting ages a more vigorous economy, a more pleasant and more comprehensive society and a cleaner situation. II. LITERATURE SURVEY We have referred many papers related to our domain and have analyzed importance of waste management. Some of the papers which we used as reference to our project are as follows: 1. Smart bin: Smart waste management system In our work, shrewd container framework that recognizes totality of litter receptacle. The framework is intended to gather information and to convey the information through remote work organize. The framework additionally utilizes obligation cycle procedure to diminish control utilization and to amplify operational time. The Smart canister framework was tried in an outside domain. Through the proving ground, we gathered information and connected sense-production techniques to acquire litter container use and litter receptacle every day regularity data. With such data, litter canister suppliers and cleaning temporary workers can settle on better choice to expand profitability. 2. IOT based smart garbage alert system using Arduino UNO Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 163

190 Ramya R et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, The fundamental topic of the work is to build up a keen clever junk ready framework for an appropriate waste administration. This paper proposes a savvy ready framework for junk leeway by giving an alarm flag to the civil web server for moment cleaning of dustbin with appropriate confirmation in light of level of refuse filling. This procedure is helped by the ultrasonic sensor which is interfaced with Arduino UNO to check the level of trash filled in the dustbin and sends the alarm to the city web server once if waste is filled. In the wake of cleaning the dustbin, the driver affirms the errand of exhausting the waste with the guide of RFID Tag. RFID is a registering innovation that is utilized for check process and what's more, it additionally upgrades the shrewd waste ready framework by giving programmed ID of refuse filled in the dustbin and sends the status of tidy up to the server insisting that the work is finished. The entire procedure is maintained by an implanted module incorporated with RF ID and IOT Facilitation. The ongoing status of how squander gathering is being done could be observed and lined up by the region expert with the guide of this framework. Notwithstanding this the vital medicinal/exchange measures could be adjusted. 3. Smart Garbage Monitoring System for Waste Management This venture introduces the advancement of a keen junk observing framework with a specific end goal to gauge squander level in the rubbish canister progressively and to alarm the region, specifically cases, by means of SMS. The proposed framework is comprised by the ultrasonic sensor to gauge the waste level, the GSM module to send the SMS, and an Arduino Uno which controls the framework activity. It assumes to produce and send the notice messages to the region by means of SMS when the waste receptacle is full or full, so the junk can be gathered promptly. Besides, it is required to add to enhancing the effectiveness of the strong waste transfer administration. 4. Smart waste management system using IOT In the proposed framework, open dustbins will be furnished with inserted gadget which helps continuously observing of level of refuse in trash canisters. The information in regards to the junk levels will be utilized to give advanced course to trash gathering vans, which will lessen cost related with fuel. The heap sensors will build effectiveness of information identified with trash level and dampness sensors will be utilized to give information of waste isolation in a clean canister. The investigation of constant information assembled will assist region and government specialists with improving plans identified with keen waste administration with the assistance of different framework created reports. III. Motivation In our country, currently our beloved prime minister is moving on with a motto of Swachh Bharat and in accordance to which he has requested people to separate dry and wet waste and put them in different bins. But I don't think many of us are following it and people still dump all the waste into a garbage bag and they just dump it somewhere or give it to garbage vehicles People are not doing it intentionally but they are doing it due to lack of time. If this waste is not separated it causes severe problems to the people, environment and to our country also. Looking at all the problems faced by people and also by garbage collectors for segregation of this waste, we thought why we can t automate the segregation so that it would help people to save their time and also increases cleanliness in society. The thought of why was later transformed into our idea. IV. IOT Based Waste Management Using Smart Dustbin In this project is to design and build a prototype for an automatic open dustbin that can consequently open the cover when it identifies the general population who need to toss out their waste. It additionally can identify the level of the junk that inside the dustbin. If the dustbin is full of trash at the certain level, the lid will not open even when there are people who want to throw out their trash. Dustbins are furnished with a sensor which helps in following the level and weight of the rubbish canisters and a special ID will be accommodated each dustbin in the city so it is anything but difficult to recognize which trash container is full. In order to avoid the decaying smell around the bin harm-less chemical sprinkler is used which will sprinkle the chemical as soon as the smell sensors detect the decaying smell. Waste Management is all the activities and actions required to manage waste from inception to its final disposal. So this can be done by implementing IoT based waste management using smart dustbin. Figure 1. Waste Management system Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 164

191 Ramya R et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, Figure 2. Waste Management system-an Example statistics V. System Design: VIII. Figure 5. Flow chart of the working Model of Smart Bin Working Model Screen Shots and Results Figure 6.Working Model Figure 3. Design Model of Smart Bin VI. System Design: Figure 7. Connection Setup Figure 4. Block diagram of the proposed syste VII.Work Flow of the proposed system: Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 165

192 Ramya R et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, With the assistance of appropriate innovation (GPS and SOFTWARE APPLICATIONS) we can direct the trucks to pick the most limited way. 4. It additionally supports the "Brilliant CITY" venture and "Computerized INDIA". 5. This can be utilized as equipment in future that reuse squander. 6. This can serve for residential use, in businesses, clinics for the waste administration. 7. Automated conveyance of fertilizer to plants. 5. CONCLUSION By developing such a hardware we can solve many problems and we can automate waste management system. We can use this hardware in different applications like, the wet waste such as food, peels of fruits collected in the compartment as manure to the plants in our agricultural farms. From the dry waste again we can separate biodegradable and non-biodegradable and we can send biodegradable waste for recycling. Figure 8. Complete prototype dry and wet waste Management Model 6. FUTURE ENHANCEMEN Instead of using moisture sensor we can make use of capacitive plates. This project can also be extended for detecting of plastic, metallic waste, e-waste. We can use Wi-Fi module and alert the user when the dry or wet waste compartments are filled completely by sending mail or SMS to the authorized person. REFERENCES Figure 9. Serial port output IX. APPLICATIONS 1. Smart Street Bin depicts the extent of work of "Shrewd Bins" in dealing with the waste accumulation framework for a whole city. 2. This can be best utilized by Municipal Corporation for their advancement of administration with respect to gathering of squanders _BE_ Waste_Management Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 166

193 Volume 9, Special Issue No. 3, May 2018 International Journal of Advanced Research in Computer Science (ISSN: ) CONFERENCE PAPER Available Online at DESIGN OF SMART RETAIL SHOPPING GUIDE USING IOT AND CLOUD V. Pavithra School of Computing and Information Technology, REVA UNIVERSITY, Bangalore, India. Vidyashree.M.Channalli School of Computing and Information Technology, REVA UNIVERSITY, Bangalore, India. Vanitha.H School of Computing and Information Technology, REVA UNIVERSITY, Bangalore, India. Yashaswini.K School of Computing and Information Technology, REVA UNIVERSITY, Bangalore, India. Prof. Nikhil S Tengli School of Computing and Information Technology, REVA UNIVERSITY, Bangalore, India. Abstract There are no avenues for point of sale marketing in retail chains. Shopping in stores lack the intuitive recommendations that ecommerce can offer. There does not exist a platform that can provide an efficient and easy shopping experience. The paper is aimed to provide a flawless shopping experience. The solution is to provide a platform for retail chains to make the process of shopping seamless and non-tiring activity. This paper provides the unique shopping experience to the customers when compared to conventional shopping. This method/concept is quite unique and new. Recently a variation of this concept can be seen, which was implemented by Amazon as Amazon Go. This paper aims at building a proof of concept for the platform. Aim is to fulfill the pain points which are mentioned in the problem statement. This will include working on certain hardware for hassle free billing, a central system which has the main intelligence, a mobile application to add on to flawless shopping experience and point of sale marketing. Keywords ARDUINO IDE, ANDROID, NFC CARD, SIM900 MFRC522 SCANNER; 1. INTRODUCTION In modern times, the platform of E- commerce is flourishing. This is mainly due to the ease of shopping online and plethora of choices that the user can get just at button click. Previously, in conventional model of shopping customers had to shop around in different places. But this pain was addressed by the introduction of mall concept. Later to bring the prices of the commodities into stream line MRP (maximum retail price) was fixed on it, thus avoiding customer vendor bargain. There are many solutions seemed to solve the pin in shopping experience of the customer. Once the IT revolution kicked in, the era of trade began and the retail shopping was left to dust. There were no major improvements on the side of retail shopping, except fine polishing the same shopping experience with neater, glamorous shops. This paper aims at energizing the retail shopping experience by accumulating and implementing certain benefits of the E- commerce industry and inculcating the digital platform to enhance the shopping experience. Few examples of the benefits of the E-commerce include: Instant comparison of related products with respect to its price and quality Purchase of products within few clicks Information on relevant products and discounts on them are revealed in real time Information related to the product across a range of platforms such as social, other vendor websites are available We can see such points firmly hold the customer base to that platform. But there are certain cases where such platform would fail to provide complete customer satisfaction. Those are: In case of certain commodities such as Furniture, the customer needs to engage with the commodity to get the feel of it. When customer is buying vegetables and fruits, there can t be any guarantee made by the websites that Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 167

194 V. Pavithra et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, they would be delivered fresh. Hence even now the customer would prefer to go to grocery shops. For daily essential items, customer would want to have it immediately and cannot wait for it to be delivered. These are few instances where in the customer would still want to go to a retail store. 2. EXISTING SYSTEM There are certain aspects to shopping which an E-commerce platform cannot fulfill. Such needs are to be fulfilled by the retail stores. But during shopping customer faces many issues which make the experience unpleasant. There are certain problems which can be pointed out during the process of shopping. These problems include: The customer needs to stand in long queues at the entrance of the shops after finishing shopping at the store. This can be frustrating at times, when he needs to move out due to some reasons but the person at the counter needs to scan all the items of every person standing in the queue and generate the bill. Marketing is an important factor to bring back customers to brick and mortar stores and that is done after checkout. Usually when the customer shops and gets the bill, after sometime he is offered with promotion offers and great discounts of the similar items that he purchased. But this could have been more effective, if this information could be given at the time of shopping. There are no avenues for point of sale marketing in retail chains. There are hardly any kiosks or monitors which provide real time info on what are the great discounts happening. The choices are left to the customer to buy a variant of a product without prior knowledge on its prices. Shopping in stores lack the intuitive recommendations that ecommerce can offer. At a retail shop the customer won t be able to make an effective choice. What is there was an option for the customer to know at the point which variant of the product has a better price and would go for it There does not exist a platform that can provide an efficient and easy shopping experience. As mentioned in the above following points, these are some of the main issues in retail shopping experience that is being targeted at. This paper aims to find solutions to these points and in the process create a unified platform for the betterment of retail shopping. 3. SCOPE OF PAPER A. Objectives Scope of work includes in implementing a minimum viable platform which can help in realizing this solution. Scope of work mainly constitutes addressing the pain points which were mentioned in the problem statement. First level of work would be to have a solution which would be working end to end. This would include implementing data collection nodes at the shop floor and having it sent to a central system. Next level of work would be to have a complete flow of this data from the data collection points back to the customer/ shop floor. For example, data collection points can be the smart carts containing NFC card readers or smart phones. Thus, priority would be to implement them to collect data and send them to the system. Secondly once the data is in the system, next task would be to complete the data flow. This data which was collected from carts must then be sent to the customer s smart phone. Once this is done we can concentrate on adding value to the data collected and proving some service with the help of this data. For example, we can provide the service of recommendations on the products bought by customer in real time. Finally, if it is feasible within the timeframe of this paper, we can consider certain transactions data sets and add it to our system. From this we can perform certain mining activities and extract certain buying patterns. This can be mimicked in the system to show the true potential of having such data resting in the system. This can also add to the scope of future work on this paper. Advantages There are many advantages of proposed system. They are as following: Internet of Things in Retail. RFID or NFC for products. ibeacons Inventory Stock Reordering Real Time Smart Cart Smart-Location-aware, recommendations Promotions Digital participation and Data Exchange in the store Point-of-sale Marketing Higher Conversions Extreme Demand in Market Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 168

195 V. Pavithra et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, Motivation for Retailers 4. METHODOLOGY Fig 3.1: Architecture Diagram Let us look at the problems faced and then jump into how the methodology is inculcated to solve these Problems As discussed in the previous section, there are certain aspects to shopping which an E-commerce platform cannot fulfill. Such needs are to be fulfilled by the retail stores. But during shopping customer faces many issues which makes the experience unpleasant. There are certain problems which can be pointed out during the process of shopping. These problems include: The customer needs to stand in long queues at the entrance of the shops after finishing shopping at the store. This can be frustrating at times, when he needs to move out due to some reasons but the person at the counter needs to scan all the items of every person standing in the queue and generate the bill. Marketing is an important factor to bring back customers to brick and mortar stores and that is done after checkout. Usually when the customer shops and gets the bill, after sometime he is offered with promotion offers and great discounts of the similar items that he purchased. But this could have been more effective, if this information could be given at the time of shopping. There are no avenues for point of sale marketing in retail chains. There are hardly any kiosks or monitors which provide real time info on what are the great discounts happening. The choices are left to the customer to buy a variant of a product without prior knowledge on its prices. Shopping in stores lack the intuitive recommendations that ecommerce can offer. At a retail shop the customer won t be able to make an effective choice. What is there was an option for the customer to know at the point which variant of the product has a better price and would go for it There does not exist a platform that can provide an efficient and easy shopping experience. As mentioned in the above following points, these are some of the main issues in retail shopping experience that is being targeted at. This paper aims to find solutions to these points and in the process, create a unified platform for the betterment of retail shopping. The objectives of this paper are clear. The paper is aimed at addressing the pain points mentioned in the problem statement and to provide a seamless shopping experience. The solution is to provide a platform for retail chains to make the process of shopping seamless and non-tiring activity. In a generalized way, every problem statement or the pain points mentioned can provide a clear objective to this paper. Below gives the detail list of the expected outcomes from this paper. First objective would be to solve the problem of long queues in retail stores. This can be addressed by making the shopping real time experience rather than picking up the things and finally waiting at the queue. The baskets or the customer in the store can be made smart so that shopping happens right at the floor when the product is picked up and the customer just needs to go to the counter to simply pay the bill. If customer is made smart. That is, if the customer has a mobile he can simply walk out of the store by simply paying in the application. Second objective of this paper is to facilitate point of sale marketing. This could be enabled for a smart customer having a mobile phone. When a customer puts a product in the smart basket present in the store, automatically he would be presented with products with better discounts on his mobile application. Alternatively, if there are monitors present on the aisle this information could be displayed in real time based on the analysis of the product data which is put in the basket. Final objective would be to try to provide all these solutions as a platform for the retail shops to benefit and ease the customers from all the pain points mentioned. This paper provides the unique shopping experience to the customers when compared to conventional shopping. This method/ concept are quite unique and new. Previously there have been attempts made to enhance shopping experience. 5. CONCLUSION In this paper, our main goal was to prove the concept of smart retail shopping. Since with the help of our demonstration we are successfully able to show how using various technologies Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 169

196 V. Pavithra et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, such as Android, NFC and Server we are consistently showing the date in the application as well as solving most of the problems in Retail. If we need to re iterate over the problems faces by the customers, we are solving their problem one by one. When customer is not knowing about the item data, we are showing those in the application and thus removing the confusion from his mind. When customer is lost in the shop as to where to pick up the items from the shop, We are providing the location in the application and thus the customer will have clear idea as to where will he get the item. Finally, when the customer puts in the item in the basket, the basket will be a smart basket which will identify the item immediately and send the data to the server so that the item is added to the bill immediately. Now the customer can go and check out at the counter without having to wait for the scanning process or even better he can pay online and walk out of the store. Thus, as we can see we have successfully eradicated the pain points in retail shopping by introducing smart techniques. 6. REFERENCES [1] Smart Shopping: conceptualization and measurement, Kelly Green Atkins (Department of Management and Marketing, East Tennessee State University, Johnson City, Tennessee, USA), Young Kyung Kim (University of Tennessee, Knoxville, Tennessee, USA), [2] Smart Home Mobile RFID-Based Internet-of-Things Systems and Services, Advanced Computer Theory and Engineering, ICACTE '08. International Conference. [3] Home automation using Ardunio Wi-Fi module ESP8266, AIKTC,May-2016 [4] El Mahboul Abdelaziz SMART SHOPPING CART SYSTEM, [5] Ardunio and GSM based smart energy meter for advanced metering and billing system, Electrical Engineering and Information Communication Technology (ICEEICT), 2015 International Conference [6] IoT applications on Secure Smart Shopping System,Ruinian Li, Tianyi Song, Nicholas Capurso,19 May 2017 [7] i-shop: A Model for Smart Shopping,Anal Kumar, A.B.M. Shawkat,Date of Conference: 5-6 Dec Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 170

197 Volume 9, Special Issue No. 3, May 2018 International Journal of Advanced Research in Computer Science (ISSN: ) CONFERENCE PAPER Available Online at DRIVE MODE APPLICATION FOR ROAD SAFETY Amogh M B Dept of ISE Sai Vidya Institute of Technology. Amrutha Shetty Dept of ISE Sai Vidya Institute of Technology. Harshitha Dept of ISE Sai Vidya Institute of Technology. Amogh P K Asst prof Dept of ISE Sai Vidya Institute of Technology. Abstract: Smartphones are becoming an important belonging due to their multifunction use and sophisticated features. It is one device that can take care of all of your handheld computing and communication needs in a single, small package. It is one palm sized device catering to all computing and communication needs. Unlike many traditional cell phones, smartphones allow individual users to install, configure, and run applications of their choice. A smartphone offers the ability to confirm the device to your particular way of doing things. The most standard cell phone software offers only limited choices for re-configuration, forcing you to adapt to the way it is set up. On a standard phone, whether or not you like the built-in calendar application, you are stuck with it except for a few minor tweaks. Today's smart phones are one of the assistant to do and solve things. Smart phone provides a variety of entertainment in the form of music and video. They allow access to the internet to get latest news feeds about current events occurring across the globe.however, not all the right places to hear the ringing of smartphones like the prayers, lectures, and meetings. In this situation, people may forget to set their smartphone into the silent mode. As a lifestyle that not only brings discomfort to others but to our life. I. Introduction Today people are so attached to smartphone that even a minute sound can alter the behaviour of the person he feels the urge to check his phone which cannot be avoided. This may lead to serious accidents when the person is driving. We often see large hoarding on National highways, state highways and inter-city roads displaying caution messages such as don't drink and drive, Don't use cell phones while driving when a person is driving at high speed he needs his full concentration on the road and minute distraction can lead to accidents. In this paper we propose a drive mode application it disconnects the incoming call and sends a SMS to the caller as well as the receiver. For example if Amogh is driving the car and gets a call from his friend akash then the call will be cancelled and SMS will be sent to the Akash saying that Amogh is driving and please call later and another SMS is also sent to the driver after he exits the drive mode application the message will be displayed saying that Akash had called at 2:00 when you were driving. II. Existing system In this era of new smartphone technology there are new emerging innovations the Motorola Cell phone came up with Moto gestures which has some particular physical movements which can start up an application. The same Motorola came up with an idea that if the cell phone is kept upside down then the call gets hung up and sometimes it fails to hang up. People sometime tend to keep the phone upside down which may initiate this response which is blunder. To eliminate this we have developed the drive mode application for road safety. For a better and safer driving experience use the drive mode application that eliminates the interference of the smartphone with the human when he is driving. The different reasons to use Cell Phones in Car: 1. Texting: Users can send SMS or use internet chatting apps like (Whatsapp,Facebook Messenger) while driving which is dangerous. 2. Internet Surfing: By navigating through the World Wide Web or Internet, 3. Calling: Users may use the cell phone to make/receive a call while driving. 4. Scrolling down news feeds: Users may use Cell phones to check the news feed on some social media applications. 5. Inline usage: Users may use the phone to send pictures or share a document with a co-passenger which may lead to serious fatal Accidents. Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 171

198 Amogh M B et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, III. Proposed system Call Manager: The should be cancelled based on the Threshold speed given (in this its 20km/h). This system would reduce the number of fatal injuries. This system would route the Incoming Calls to ensure the driver will not be distracted with the incoming calls. The caller who has called the driver shall be notified via SMS the Person is currently driving please call later or leave a message.the Driver also shall be Notified via SMS that the particular person has called at this time.eg Arush tried calling you at 1:00, Call was diverted because the drive mode was enabled The proposed system would take into consideration the person GPS to calculate the acceleration if the Acceleration is below 20km/h then the calls won't be diverted.if the acceleration of the car is above 20km/h then the calls will be diverted. SMS Manager: The SMS should be sent to the caller. An SMS should be sent to the Receiver.(Eg: If x calls y while Driving then X received an SMS saying Hey you reached Y I am driving call you later.) IV. Implementation The below figure shows that how exactly the implementation of the entire process takes place. System Architecture Fig 2 Fig 1 THRESHOLD COMPUTATION: The IR sensor will monitor the wheel rotation. It will give data to the Arduino Microcontroller. Arduino Microcontroller: It converts RPM to km/h. Based on this speed will be calculated. That speed will be displayed in the LCD. The speed will sent to Bluetooth on the Request of Android. When the user is driving the car the wheel rotation will be monitored and the threshold will be computed. To calculate the wheel rotation we are using the IR sensor which gives the RPM which will be converted to a Km/h.The IR sensor will give the particular data to the Arduino Microcontroller where the actual computation takes place. We have the Bluetooth which is used for transmitting for the android application. In the embedded part we are also using the LCD for displaying the speed (which is for our reference). The android application then receives the speed if the speed is above 20km/h then the call hang up takes place. Complete working flow chart for the system is given below Bluetooth: It transmits the data to the Android Unit. On request of the Android Device. GPS:Its Used to Monitor the User location. Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 172

199 Amogh M B et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, VI. Conclusion Fig 3 V. Advantages 1)Reducing the Number of Accidents. 2)Drivers won t be distracted. 3)Call routing enables Hazzel free Driving. 4)High Security. Drive mode application in Android Mobiles is a next level of Intelligent Software which reduces human intervention for simple task such as sound profile switching. Android Smartphone becomes much smarter by this application. We are also strongly concerned of the in app notifications and pop-ups in smartphones may be distracting we are working to completely isolate this device and provide a good non technological interference with the human. VII. References [1] Auto silent system using location services in android mobiles. [2] Smart India Hackathon. Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 173

200 Volume 9, Special Issue No. 3, May 2018 International Journal of Advanced Research in Computer Science (ISSN: ) CONFERENCE PAPER Available Online at HOMECARE AND ASSISTANCE OF ACTIVITIES FOR THE ELDERLY Supriya. R School of C&IT REVA University, Bengaluru, India Sowmya. S School of C&IT REVA University, Bengaluru Yashaswini. Y. N School of C&IT REVA University, Bengaluru, India Syeda Umme Hani School of C&IT REVA University, Bengaluru, India Ambika. B. J School of C&IT REVA University, Bengaluru, India Abstract: The existing demographic transform towards an ageing people is introducing radical changes into our society. A human being who lives in nursing homes or care units, becomes depressed because of be deficient in of independence. Our aging population demands a reliable resolution to stay active for a long time, avoid social inclusion and assistance for performing daily life activities independently in their own homes. In this project, we aim to develop a system that help and direct elderly people for independent living in their own homes. It decreases the health expenses as well as overstress of health care professional since their medical aspect, like reminding timely consumption of medicines as per the prescription through a voice output.this module guides the elderly person to complete their day by day life activities. It provides the care giver assistants by keeping track of the surrounding environmental conditions around elderly persons in their own homes and alert their family away from them in case of an emergency through a phone call. Keywords: I.INTRODUCTION Smart house is viewed like an autonomous sound living for elderly individuals. A progress within telephone innovation and new style of figuring worldview allows continuous procurement, preparing, and following of exercises in brilliant home. It decreases the wellbeing consumptions and burden of social insurance experts in care office units. Brilliant home is a canny specialist to see the earth and process the tactile information. Cell phone application imparts through web administrations and helps the elderly individual to finish their day by day life exercises. It encourages the guardian collaborator by following the elderly people in their own particular homes and maintains a strategic distance from specific mishaps. Moreover, it additionally assists the relatives with tracking the exercises, when they are outside from homes. Smart homes are measured as one system to give a level of autonomy at homes and develop their personal satisfaction [1]. It provide a chance to lessen the wellbeing consumption and weight of medicinal services experts. In both developed and developing nations, quantities of smart phone consumers are expanding step by step. Smart phone runs an entire working framework and provides a stage to application designers and consumers Google Android is a standout among the most focused markets due to its open source stage. Many applications have been shaped extending from the intelligent recreations to social insurance area. Mainly the restorative area applications empower the clients to combine with the framework to give continuous client help and improve the normal population way of life [2]. Our proposed arrangement is to exploit assistive advances in smart home and cell phone, and build up an everyday life movement related application for the health of elderly population and guardians. It helps the elderly individual to terminate the exercises autonomously in their own particular houses, leftover portion for taking medications and in the meantime encourages relatives and parental figures to track them when the elderly people remain in the home. II.LITERATURE SURVEY Our project is to analyze the surrounding and report any changes by using real time data monitored by sensor (temperature and heart beat). The sensor detects and collects data and sends it to the NodeMCU. The NodeMCU has code for converting the Analog signals collected by the sensors to digital signals communicating it to the GSM for further communication to the Android application through Bluetooth and WIFI. Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 174

201 Supriya R et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, The University of Harvard research project, Code Blue [3], implement low-power wireless devices to organize ad hoc sensor network communications for existing medical care. They execute patient triage application in.net compact framework, running on an ipaq PDA with Windows CE. The application runs as active tags to store the information of a patient s identity, status, and history. Oresko. [4] Implemented a prototype system for wearable Cardiovascular Disease (CVD) detection on Windows Smartphone. It performs real-time ECG acquisition and display, feature extraction, and beat categorization. They implemented two Smartphone-based platforms for continuous monitoring and recording of a patient s ECG signals. John K. [5] implemented a Smartphone application to avoid intake medicine mistakes. It can remind its users to take the correct medicines on time and keep an intake record for future review by healthcare professionals. It was developed on a Windows Mobile 6.0 with the help of built in calendar of.net framework. III.FLOW DIAGRAM Fig 2: System Architecture First step requires the tool kit to be connected to the android smart phone through wifi. When the heartbeat sensor senses the pulse of the elder person when he places his index finger on the sensor, the data will be sent to Node MCU where the analog data will be converted to digital data and will be displayed on the OLED. Similarly the data collected by the DHT sensor will be sent to Node MCU and if the temperature rises above the maximum temperature specified, a call will be made to the caretaker through the GSM, which will help them to take the necessary actions. is based on the ESP-12 module. The firmware uses the Lua scripting language. It is based on the elua project, and built on the Espresso. It uses many open source projects, such as lua-cjson, and spiffs. HEARTBEAT SENSOR: Heart beat sensor is developed to provide digital output of heart beat when a finger is placed on it. When the heartbeat detector is working, the LED flashes in unison with each heartbeat. This effect can be connected to microcontroller directly to compute the Beats per Minute (BPM) rate. It works on the principle of light modulation by blood flow through finger at each pulse. BLUETOOTH MODULE: Fig 1: Working flow chart IV.SYSTEM DESIGN HC05 module is very easy to use Bluetooth SPP (Serial Port Protocol) module, designed for transparent wireless serial connection setup. The HC05 Bluetooth Module can be used in a Master or Slave configuration, making it a great result for wireless communication. This serial port Bluetooth module is fully qualified Bluetooth V2.0+EDR (Enhanced Data Rate) 3Mbps Modulation with complete 2.4GHz radio transceiver and baseband. It uses CSR Blue core 04 External single chip Bluetooth systems with CMOS technology and with AFH (Adaptive Frequency Hopping Feature). When the heart beat detector is on and working, the beat LED Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 175

202 Supriya R et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, flashes in unison among each heartbeat. This digital result can be connected to microcontroller directly to measure the Beats per Minute. It works on the principle of light modulation by blood flow through finger at each pulse. V.EXPERIMENTAL RESULT exercises for elderly people. Rundown of subscribed elderly people, their last finished exercises and alarms for basic circumstances are produced for guardians and relatives. It reduces the wellbeing consumptions and weight of human services experts. Our application is very much coordinated with brilliant home condition and clinic framework. Most of the embedded systems have significantly different designs according to their functionalities and utilities. The microcontroller located at the centre of the system forms the control unit of the whole project. This project consists of Temperature and Humidity sensor(dht), Heart Beat sensor, OLED, GSM and an android device. The android Application also provides alert for timely tablet intake in the form of voice output. For demo purpose OLED is used to display the changes in output values of sensors and any event occurring VI.REFERENCES Fig 3: Hardware connection Fig 4: Output display on OLED V.CONCLUSION To give versatility to following the day by day life exercises, cell phone is a helpful and reasonable gadget because of its enriched functionalities. In this DHT SENSOR:venture, we have proposed the mobile phone that may lessen the requests on senior's considerations and exertion while performing everyday life exercises. It produces isolate cautions for inadequate basic, steady, planned and ignores [1] P. Crilly and V. Muthukkumarasamy, "Using smart phones and body sensors to deliver pervasive mobile personal healthcare," in proceeding of 6th International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP), Brisbane, pp , Dec [2] N. Armstrong, C.D. Nugent, G. Moore, and D.D. Finlay, "Developing Smartphone applications for people with Alzheimer's disease," in Proceeding of 10th IEEE International Conference on Information Technology and Applications in Biomedicine (ITAB), pp. 1-5, Corfu, Greece, [3] Malan, T. Fulford-jones, M. Welsh, and S. Moulton, "Code Blue: An ad hoc sensor network infrastructure for emergency medical care," in Proceeding of International Workshop on Wearable and Implantable Body Sensor Networks, London, [4] J.J. Oresko et al., "A Wearable Smartphone-Based Platform for Real- Time Cardiovascular Disease Detection Via Electrocardiogram Processing," in proceeding of IEEE connections on Information Technology in Biomedicine, vol. 14, no.3, pp , [5] Mei-Ying Wang, J.K. Zao, P.H. Tsai, and J.W.S. Liu, "Wedjat: A Mobile Phone Based Medicine In-take Reminder and Monitor," in Proceeding of 9th IEEE International Conference on Bioinformatics and Bioengineering (BIBE '09), Hsinchu, Taiwan, pp Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 176

203 Volume 9, Special Issue No. 3, May 2018 International Journal of Advanced Research in Computer Science (ISSN: ) CONFERENCE PAPER Available Online at THEFT PREVENTION BY USING FINGER-PRINT AND VEHICLE TRACKING M. Sankeerthana School of C&IT, Reva University, Bangalore , India P. Chaitanya Kumar School of C&IT, Reva University, Bangalore , India M. Naga Yeshwanth School of C&IT, Reva University, Bangalore , India S Rishitha School of C&IT, Reva University, Bangalore , India Prof. Supreeth S School of C&IT, Reva University, Bangalore , India Abstract Vehicle tracking system is a device that uses GPS (Global Positioning System) in order to know the exact location of the vehicle. This paper represents how the vehicle is been tracked and how to reduce the vehicle theft. The device is designed and developed for theft prevention where a lot of vehicles have been stolen in the daily life. Usually everybody have a doubt that their vehicles will be safe or not, especially when they park their vehicles in areas having more crowd. We want to make people, that wherever they go their vehicle should be safe and to make them tension free. In order to overcome that problem we came with an idea, where we can track the vehicle using GPS. But here we got a doubt that only tracking the vehicle and showing its location to the owner will not be up to the required levels, to safeguard our vehicle. So further what we can do to protect our vehicle from being stolen? Further enhancement that we made is using finger-print module, to start the ignition of the vehicle. The whole module is placed inside a vehicle whose location is to be known and tracked in real-time. When we request to track the location of the vehicle, the GSM module in the tracking system sends a message to the owner mentioning the exact place of the vehicle through Latitude and Longitude. If some unauthorized person tries to start the vehicle, we will get a message regarding it, then we will send a message STOP to switch-off the vehicle engine. A microcontroller will be used to control the GPS and GSM/GPRS modules. Actually here the GSM module communicates with the micro controller for switching off vehicle s engine with a single alert message. By this the users will be able to continuously monitor a moving vehicle on demand. This vehicle tracking system has been a backbone in solving crime, collecting statistical data and many more. Keywords- Gps, Gsm & Microcontroller, Finger-print I. INTRODUCTION Vehicle tracking is a need of the society in general for comfortable and safe movements of vehicles in cities. Now-adays almost of the public having an own vehicle, theft is happening on parking. The safety of the vehicle is essential for public vehicles. The location of the vehicle is identified by using Global Positioning System (GPS) and Global System Mobile Communication (GSM). These systems constantly watch the movement of the vehicle and reply the status on demand to the owner. If user wants to track the vehicle, user need to send a message to GSM device like requesting the position of the vehicle, when the device gets activated it takes the received latitude and longitude positions values from the GPS and sends a message to the requested number which is Already defined in the program. Now-a-days GPS are used in cars, cabs, ambulances and police vehicles, are common sites on road of development. A Program has been written which is used to locate the exact position of the vehicle, so that we can track the moving vehicle on Google Map. The system is suitable for monitoring owner s vehicle and will inform to owner whenever the vehicle is taken out from parking place and starts tracking. This system improves the average accuracy of GPS signal reception. This system consists of GPS module installed on the vehicles will send the location of vehicle to receiver boards installed on it. The objective of tracking system is to manage and control the transport using GPS transceiver to know the current and exact locationvehicle. II. LITERATURE SURVEY * In this project the proposed GPS/GSM based System has two parts, one is a mobile unit and another is controlling station. And the interfaces, connections, data transmission of the vehicle tracking device and received data from the mobile Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 177

204 M. Sankeerthana et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, unit and the control stations are working successfully and these results are suitable with GPS technologies. * A vehicle tracking system is an electronic device, which should be installed in a vehicle so that it allows the owner or some other third party to track the vehicle's location. The paper is written to design a vehicle tracking system that works by using GPS and GSM technology. This system built is based on embedded system, mainly used for tracking and positioning of any vehicle by using Global Positioning System (GPS) and Global system for mobile communication (GSM). The device will continuously watch a moving Vehicle and reports the status of the Vehicle on demand. Since we are making the system theft proof we did a little research on tracking. Here all major tracking processes what all people heard or seen will come into either of these two categories: 1. Active Tracking 2. Passive Tracking 1. Active Tracking: These devices collect information regarding vehicle such as GPS location, speed, and heading and sometimes a trigger event. Usually they transmit the data in near-real-time via cellular networks to a computer or datacentre for evaluation. 2. Passive Tracking: These devices also collect the same information but once the vehicle returns to a predetermined point the device is removed and the data downloaded to a computer for evaluation. This tracking of any vehicle or any electronic gadget started in the year 1957 of date 4th October by Soviet Union who launched the first satellite by name Sputnik. In the year 1957 of October month dated 4th, MIT scientists have noticed that the frequency of the radio signals transmitted by the small Russian satellite increased as it approached and decreased as it moved away. This lead to attain more interest on tracking of that Russian satellite signal. This tracking lead to so many technological advancements as of now. We come to know some of the milestones under increment in technology of GPS. Milestones are as follows: Sputnik (1957): - It is a small interest by MIT scientists whose curiosity lead to creation of this tracking satellite called Sputnik which is used to detect radio signals of any electronic gadget. Transit (1959): - This is the first real satellite navigation system designed by navy to locate the submarines. Space Satellites (1963): - This technology started to control by Aerospace Corporation and they used it for military to send signals continuously to the receivers on the ground. GPS enabled Mobile Phone (1999): - The first commercially available GPS phone manufactured by Benefon and they named as Benefon Esc. Block II: - Cape Carnival launched this new generation of satellite. Block III: - The Air force has launched this with an additional civilian GPS signal that will enhance the performance of existing GPS service. III. MOTIVATION The motivation for the Tracking System is the desire for advanced features in an inexpensive receiver. At present, all the Original Equipment Manufacturer(OEM) GPS receivers i.e., the single GPS receiver boards with no display, case, etc, the proprietary firmware results which makes certain assumptions on the system application which will be not to the expectation level. Now there is no single system so that integrates all tracking and tracing of any movable objects. The device is impossible to replace if lost (produced in low numbers, antiques, and unique works of art). The equipment we used in our project is for various reasons, which involves the type of the item and its purpose: 1. The device is very easy to steal when used in areas like retail/supermarket products, office stationery. 2. The item may be left unattended in an unsafe environment for a certain amount of time (vehicle may not be in parking area). 3. Even when the improper use of the device it may cause damage and some other unauthorized actions as like theft of car keys, identity theft. 4. The device is desirable to others (rare parts of our vehicle, auto parts, and industrial designs). IV. PROBLEM DOMAIN This project concentrates on security which can be done using domain called IoT. Because most of the times vehicles will be stolen at any point of time. Since we need to provide security for those we came up with this project based on IoT. Our problem lies with so many situations and so many places also where owner vehicle is kept in the parking lot or in his office premises or crowded areas. To solve all these problems we took IoT domain for our project. V. PROBLEM DEFINITION As like we told above places, we will be having surveillance cameras which can observe vehicles only up to their premises. But that is not sufficient to protect a vehicle. So to provide better security we did research on present technologies and came up with this project. When it comes to IOT based project we need to consider life of hardware equipment also. In that perspective it became a big deal for us to select efficient hardware where its life can be more. So we are very much concerned about securing owner s vehicle as well as efficiency of equipment also. As I told above there are certain CCTV cameras which are placed at certain locations in order to monitor flow of events at that place. The visuals from each of these CCTV cameras are projected onto a main system which requires to be monitored upon continuously, hence increasing human interference. The current system is not to be trusted in places like without proper lighting and places where severe power shortage occurs. So at that time whole system need to get restarted and it can be a huge loss also. 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205 M. Sankeerthana et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, BLOCKDIAGRAM: VI. PROBLEM STATEMENT Nowadays most of people facing a problem that when their vehicle is being stolen they cannot find where their vehicle is. For this we are using hardware which we want to make it cost efficient also. Here we are considering safety of people also that is while travelling in cabs and some other transport. Here is the place where life of an individual matters a lot (during accidents). As well as we need to navigate the stolen vehicle, we are using Google maps API to track the vehicle. We can tell this project is an enhancement of present security alarm system in vehicles. Because the surveillance system of present vehicles is only up to smaller distance and we can't track if it s stolen. This problem is solved by our project using sms alert. VII. PROBLEM SOLVING The designed project is an embedded IOT based device which is used to for tracking and to know the position of the vehicle, by using GPS and GSM modem. * In this we have used an Arduino microcontroller to interface with various hardware parts. The designed system will continuously monitor a moving Vehicle and report the status of the Vehicle on demand. * In order to do that, an 8052 microcontroller is interfaced serially to a GSM Modem and GPS Receiver. Here a GSM modem is used to send the location in the form of Latitude and Longitude of the vehicle from the unknown place. The GPS modem will continuously gives the data i.e. the latitude and longitude regarding the position of the vehicle. * The GPS modem provides many parameters in the form of output, but here only the NMEA data coming out from the GPS modem is read and displayed on to the LCD. Then the data is sent to the mobile at the other end from where the position of the vehicle is requested by the user. Further an EEPROM is used to save the data received by GPS receiver. * Here the hardware interfaces to microcontroller are GPS modem, GSM module, LCD display. Then a MUX is used to interface both the GSM and GPS modem to the controller. * Further we used RS-232 protocol for serial communication between the modems and the microcontroller. Then a serial driver IC is used for converting TTL voltage levels to RS-232 voltage levels. * Sensors like fire detector and infrared sensor are used for identifying different types of problems faced in the vehicle such as fire, accident, theft etc. In these situations the device automatically sends the messages to the intended receiver. * When the owner sends a request to the number placed in the modem, then the system automatically sends a return message to that particular mobile about the position of the vehicle in terms of latitude and longitude. A Program has been coded which is used to locate the exact position of the vehicle and then it s used on Google Map to know the location of the vehicle. Fig: Block diagram of vehicle tracking system and use of finger-print module. VIII. CONCEPT & OVERVIEW These automobiles tracking system gathers input from GPS and send it through the GSM module to the desired mobile using mobile communication. Automobile Tracking System is one of the biggest technological advancements to track the activities of the vehicle. The security system uses the GPS (Global Positioning System), to find the exact location of the monitored vehicle and then uses satellite to send the coordinates and the location data to the monitoring center. At monitoring area various software s are used to plot the Vehicle location on the map. By this the Vehicle owners are able to track their exact vehicle location on a real-time basis. Due to the real-time tracking facility, vehicle tracking systems are becoming more popular among owners of expensive vehicles. IX. RESULTS & SENSTIVITY ANALYSIS The IOT based tracking system is made by a few communication technologies. The framework comprises of vehicle following applications and even a focal server framework. By the use of this framework, clients will have the chance of observing the area graphically and also helps in Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 179

206 M. Sankeerthana et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, getting some other important data of the vehicle. The device allows the owner to scan the received vehicle details on the applications like Google Map and it interface with database server for vehicles track subtle elements. Utilizing the IOT based framework empowers user with diverse working framework stages to effectively achieve the requested subtle elements by the presence of web access. It demonstrates an outline of a common IOT based vehicle tracking framework. The area is acquired from satellite by using GPS receiver that coordinates and sent through GPRS, and then the GSM system will send the data to the objective server in the form of HTTP packets. The client can receive the signal from satellites and calculates the position of vehicle and converts them into latitude, longitude information. Then this information is sent to the user by using GSM modem or mobile phone connected to the circuit board. In case of accident, the GSM modem or mobile will send an alert message to our family member whose number is registered. The Experiment was conducted to check the sensitivity of GPS tracking system. X. ENHANCEMENTS In this project the enhancement we used is finger print module to start the ignition of the vehicle. So if anyone wants to use our vehicle mean they need to start the vehicle. So we analyzed that part and we came up with this enhancement. When the vehicle get to be started the person need to give their finger print to the module. The person s finger print should be installed in the module before. Since the person s finger print is already installed in the module it will compare it with the previously installed one. When these matches the vehicle will automatically starts. In case, when our friend or colleague or relatives want to take our vehicle their finger prints should be installed. If any unknown person takes our vehicle they need to give finger print. When it doesn t match, it gives an alert message. If somehow the person starts the vehicle by manually connecting the ignition wires then we will get a message that our vehicle ignition is started. Then we can send the message as STOP in order to stop the ignition of the vehicle. XIII. CONCLUSION The Tracking system is getting to be progressively large in urban areas and it is more secure than the other frameworks. It has the continuous ability and rises with a specific final goal to fortify the relations among individuals, vehicle by presenting present day data advances or technologies and ready to structures real time accurate and compelling exhaustive transportation framework. Developing this device is simple which makes it available to future a prerequisite which likewise makes it more efficient. The proposed work is of low cost, reliable and has the function of theft prevention and providing accurate vehicle location. We will conclude our project as follows: Vehicle theft and accidents in the transportation system had a huge significant loss of lives and loss in productivity. * To improve the security, safety, efficiency of the transportation systems and enables new mobile services and gadgets for the travelling public. * It is one of the most critical and challenging issues for the industries. * It s one of the cheaper, official, securable and reliable systems for security. * Automobile tracking both in case of personal as well as business purpose increases in safety and security, communication medium, performance monitoring and increases productivity measures. * Main idea of this project is to give more security to the vehicles in case of vehicle theft. * In this project tracking of vehicle plays an important role where owner can easily track his vehicle using this device. XI. FUTURE SCOPE EEPROM can be used to store the previously tracked positions up to 256 locations. And you can navigate up to N locations by increasing its memory. The size of the kit can be reduced by using a single GPS+GSM. The accuracy can be extended up to 3m by increasing the cost of the GPS receivers. The kit can be used for bomb detection by connecting to the bomb detector. By using high sensitivity vibration sensors we can detect the accident. When the vehicle unexpectedly met with an accident on the road then with help of vibration sensor we can detect that vehicle Is collided with something, so that it can send the location to the owner, hospital and police and also to other persons. XII. ACKNOWLEDGEMENT This is a project report on GPS & GSM BASED VEHICLE TRACKING SYSTEM FOR ANTI- THEFT we are very thankful to our project guide Prof. SUPREETH S, Department of C&IT, REVA UNIVERSITY, for his invaluable guidance and assistance, without which the accomplishment of the task would have never been possible. We also thank him for giving this opportunity to explore into the real world and realize the interrelation. REFERENCES 1. Krishna Kant, Microprocessor and microcontroller, Eastern, Company Edition, New Delhi Kunal Maurya, Mandeep Singh, Neelu Jain, Real Time Vehicle Tracking System using GSM and GPS Technology- An Anti-theft Tracking System, International Journal of Electronics and Computer Science Engineering, ISSN /V1N William Stalling, Wireless Communication and Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 180

207 M. Sankeerthana et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, Networks, 2 nd edition, prentice hall of India, st 4. Temex Times Galileo, GPS World, Feb E.D.Kalpan, Understanding GPS: Principles and Applications, Artech House Publisher, Feb Muhammed Ali Mazidi, Janice Gillispie, Mckinlay, Rolin D., The Microcontroller in Embedded System: UsingAssembly and C, 2 nd edition published by Pearson Education. Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 181

208 Volume 9, Special Issue No. 3, May 2018 International Journal of Advanced Research in Computer Science (ISSN: ) CONFERENCE PAPER Available Online at IOT BASED OIL SPILL DETECTION SYSTEM Akanksha D Hegde Department of Information Science and Engineering Sai Vidya Institute Of Technology Bangalore, India K.N.Nithya Sree Department of Information Science and Engineering Sai Vidya Institute Of Technology Bangalore, India Disha Prakash Achari Department of Information Science and Engineering Sai Vidya Institute Of Technology Bangalore, India Nisha Sudhakar Chantar Department of Information Science and Engineering Sai Vidya Institute Of Technology Bangalore, India Abhijith H V Department of Information Science and Engineering Sai Vidya Institute Of Technology Bangalore, India Abstract - About two-third of the earth covered with water. Oceanographic research is one of the leading area of research. Major area of oceanographic research is the detection of oil spills. Ocean is home for several aquatic creatures. Every year many aquatic creatures loose life due to pollution that occurs through the leakage of oil. Oil leakage occurs due to several reasons like the breakage of oil pipes, leakage of oil from the ships and through industrial wastes. Oil spill detection is a very important challenge faced by the researchers in oceanographic realm. In this project we present a new scheme detection of oil spill using the Internet of things. We propose a method of applying the Wireless Sensor Networks (WSNs) to detect oil spills in ocean. In addition, we propose inclusion of intelligence at multiple aggregation levels to improve the efficiency of deployed network. As additional intelligence is granted to sensor nodes, instead of being passive detectors, they work as intelligent observers, thereby making the detection process, an inter-network of intelligent nodes. Keywords Oil spill, IOT, WSN, Sensors I. INTRODUCTION As 70% of the earth is covered with water. This percentage in volume is about 332,519,000 cubic miles of water. Of this vast volume of water, NOAA's National Geophysical Data Centre estimates that, 321,003,271 cubic miles is in ocean of the tiny percentage that's not saline water of the oceans and about two percent is frozen as glacier and ice caps. Less than one percent of all the water on Earth is potable and fresh. A tiny amount of water exists as water vapour in earth s atmosphere. Climatic activity is determined by interaction of oceans, winds and also by the land masses. Oceans are most necessary for the running of gaseous cycles, bio-chemical cycles, for the formation of clouds and the most vital water cycle. Human activities in and around the world s oceans obstruct the delicate balance maintained by the presence of such huge water body. Most noticeable and well researched effect of human activity on oceans is depletion of coral reefs. Oceans act as sink of many gases and help create a life supporting environment on earth. Any pollution caused in the ocean has both the direct and indirect effects.that a WSN framework can be effectively applied to problem of monitoring water for extended periods of time. There are many Challenges, like the detection of spill is event-triggered observation. By nature Oil does not dissolve in water and also forms a film on the surface instead. The film formed is neither stationary, nor is it limited in its area. Gaining an insight to these specific matters is aim of our work. We intend to add intelligence to the application of WSN and Internet of Things (IOT) to oil spill monitoring in oceans. II.RELATED WORK As lot of work has been carried out in ocean for the detection of oil spills which are focused on determining the thickness of oil spill. The hypothesis is that thicker spill leads to the effects of the pollution. They determine the pixel intensity concerned with many competing parameters [7].Image Processing Communication and Automation has worked on image analysis in oil spill detection. Threshold method is used to detect oil spill. SAR image processing is applied based on minimum cross-entropy with gamma distribution. Major drawback of the work is its intrinsic time lapse involved in the data analysis. Oil spill like pollution must be detected as quickly as possible. In Real time detection it is most suitable for such problems. The method proposed [8] works with bimodal images that have two classes of pixels. A.Gasul et.al [9], have proposed and tested a new method for SAR image Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 182

209 Akanksha D Hegde et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, analysis to detect oil spills. Their method works well with low resolution and also with distorted images. In spite of multi stages analysis algorithm implemented, ensuring error free end result is still difficult. Kruti Vyas et.al in [10] have applied feature extraction of SAR images in oil spill detection. They have recognized three indepnedent features for this purpose. They have performed a battery of experiments while considering types of images. Mario Monteiro et.al, have made an extensive study of various aspects and challenges of detection of oil spills. Their work is particularly in connection with seagull the project. Their contribiution is specifically in application of camera fitted unmanned aerial vehicles in detecting water pollution. [11] documents various challenges involved in detection of spills, with emphasis on time lapse between actual spill and its detection. Unmanned surface vehicle is developed and analysed for performance in [12] by Deqing Liu et.al. Their work concentrates on frequent oil spills that happens at harbors, oil rigs and drilling platforms. They have designed a fluorosensor laser detector to achieve this aim. A feasibility analysis is also carried out by the authors. Md. Shafi.K.T, et.al have developed a simple resonance based application to detect oil spill using planar microwave[13]. Proposed sensor is developed, deployed and tested in their work. The sensor designed is capable of detecting pollution beyond 5%. Sicong Liu et.al have presented a solution to oil spill detection problem in a multi-temporal domain in [14]. Their basic architecture is based on a coarse to fine framework. Their framework requires minimum human intervention and is almost automatic. It is applicable on large scale detection of spills. III.PROPOSED SYSTEM The Arduino uno is used for the interfacing of the modules in the System. All the data stored in the cloud will be sent to the authority continuously through the android app if oil is detected necessary actions are taken to control the oil spill in oceans. Figure 1 shows the proposed architecture. A. PH Sensor IV IMPLEMENTATION PH is the measure of the increase of hydrogen ions in water. additional carbon dioxide in fresh water can decrease the ph making the water body more acidic.run-off including the addition on ions in water is also important i.e. phosphates, chlorides etc which are dissolved solids. B. Turbidity Sensor Turbidity can be measured using either an electronic turbidity meter or a turbidity tube.turbidity is the quantitative measure of suspended particles in a fluid it can be soil in a water.turbidity measurements are way to measure the amount of material suspended in water sample they are commonly used to monitor the effectiveness of filtration process. C. Conductivity Sensor Measuring the conductivity is an accurate way to determine salinity as the process solution Coates the electrode surfaces, sensor output signal begins to decrease producing an artificially low conductivity measurement. If we are using the conductivity probe for water quality analysis, the conductivity meter infact measures the conductance, and displays the reading converted into conductivity. G=1/R(S) D. BLYNK Server It is designed for an IOT, It can display sensor data, store and visualize. Blynk server allows to create an interface. Blynk server is responsible for all the communications between the smart phone and hardware.in Blynk libraries, all popular hardware platforms enable communication with the server and also the incoming and outgoing commands are processed. Figure 2 depicts the flow of the system. Fig 1: System Architecture The proposed system consists of following features: The PH sensor deployed at the ground level of ocean is used to collect the ph level of water. The ph level of the water varies as the oil spill occurs in the ocean and their it stores the data in the cloud. The Turbidity is a sensor deployed above the ph sensor, which is used to measure the suspended particles in the fluid and it stores its data in the cloud for later detection of particles. Conductivity sensor is deployed at the water level, which is used to determine the salinity the salinity of water changes as the oil spill occurs and then sends data to the cloud. Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 183

210 Akanksha D Hegde et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, REFERENCES Fig 2: Flowchart Firstly deploy all the sensor nodes i.e. Water PH sensor, turbidity sensor, conductivity sensors and check whether oil spill has occurred making the use of all the three above mentioned sensors.then transfer the above collected data through the sensors. Then process the data,later use all the data collected through above modules to verify whether oil spill is occurred if yes then send the collected data to the concerned authority through the android. V RESULT Sensor outputs with respect to Arduino: Ph sensor: normally ph range will be 0-14, we experimented that initially output was randomly displaying in serial monitor, values are like 35,36,38 etc, after that sensor dipped into good water it shows as neutral range like 7-8 this is normal drinking water, again with same sensor we put salt into water the ph value becomes acidic in range that is 8,9,10,12 Turbidity sensor: It is a measure of suspended particles in fluid, sil particles are totally undesired. We experimented that to check the rust particles and contents found in the water through voltage levels, we checked it for different water. Conductivity sensor: mainly used for measuring the electrical conductivity in a solution. We put two electrodes to check the salt water or pure water, and pure water does not conduct the electrical conductivity and salt water conduct the electrical conductivity as NaCl will be present. VI CONCLUSION Adding intelligence to oil spill detection makes detection realtime and provides insight in to combating the pollution in the future. Collecting raw data triggered by an event gives minimal insight into crucial facts like thickness of oil film, expanse of oil spill and also reduces number of redundant transmissions. Adding intelligence to sensor nodes to make decision on oil spill can be considered for future enhancement. [1]. Abhijith. H. V, S. Deepak Raj and Dr. H. S. Ramesh Babu, Intelligent Oil Spill Detection in Ocean using Internet of Underwater Things, Department of Information Science and Engineering, Sai Vidya Institute of Technology, Bangalore, India. [2] Roger Revelle & Hans Suess, Carbon dioxide exchange between the atmosphere and ocean and the question of an increase in atmospheric CO2 during the past decades, Tellus, 9, p 19-20, 1957 [3] Stocker, T. "The silent services of the world ocean." Science. 13 November 2015: 350 (6262), [4] Spalding, M. and B. Brown. "Warm-water coral reefs and climate change." Science. 13 November 2015: 350 (6262), Y. [5] Levin, L. and N. Le Bris. "The deep ocean under climate change." Science. 13 November 2015: 350 (6262), [6] F. Regan, A. Lawlor, B. Flynn, J. Torres, R. Martinez-Catala, C. O Mathuna, and J. Wallace, A demonstration of wireless sensing for long term monitoring of water quality, in IEEE 34th Conference on Local Computer Networks, Oct. 2009, pp [7] Pangilinan, Mark Nelson; 2Anacan, Rommel; and 1Garcia, Ramon, Design and Detection of an Oil Spill detection and Transmittion System using Artificial illumination system using LEDs,, 2016 [8] Reem Alattas, Oil Spill Detection in SAR Images Using Minimum Cross-Entropy Thresholding, 7th International Congress on Image and Signal Processing, IEEE 2017 [9] A. Gasul, X Fabregas, J Jimenez, F. Marques, V Moreno, MA. Herrero, OIL SPILLS DETECTION IN SAR IMAGES USING MATHEMATICAL MORPHOLOGY [10] Kruti Vyas, Pooja Shah, Usha Patel, Tanish Zaveri, Rajkumar, Oil spill detection from SAR image data for remote monitoring of marine pollution using light weight imagej implementation, 5th Nirma University International Conference on Engineering (NUiCONE), IEEE 2015 [11] Mario Monteiro Marques, Vitor Lobo, Ricardo Batista, J. Almeida, Maria de Fátima Nunes, Ricardo Ribeiro, Alexandre Bernardino, Oil Spills Detection: Challenges addressed in the scope of the SEAGULL project, UID/EEA/50009/2013 [12] Deqing Liu, Xiaoning Luan, Feng Zhang, Jiucai Jin, Jinjia Guo, Ronger Zheng, An USV-based Laser Fluorosensor for Oil Spill Detection, Tenth International Conference on Sensing Technology, IEEE 2016 [13] Muhammed Shafi K. T., Nilesh Kumar Tiwari, Abhishek Kumar Jha, and M. Jaleel Akhtar, Microwave Planar Resonant Sensor for Detection of Oil spills, IEEE, 2016 [14] Sicong Liu, Member, IEEE, Mingmin Chi, Member, IEEE, Yangxiu Zou, Alim Samat, Member, IEEE, J on Atli Benediktsson, Fellow, IEEE, and Antonio Plaza, Fellow, IEEE, Oil Spill Detection via Multitemporal Optical Remote Sensing Images: A Change Detection Perspective, IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, VOL. 14, NO. 3, MARCH 2017 Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 184

211 Volume 9, Special Issue No. 3, May 2018 International Journal of Advanced Research in Computer Science (ISSN: ) CONFERENCE PAPER Available Online at REAL TIME INFORMATION DISSEMINATION USING GPS ON VEHICLE ACCIDENT DETECTION Harish Kumar M M mmharishkumar@gmail.com, School of C&IT,REVA University, Bangalore, India Manikandan R manikandandinish@gmail.com, School Of C&IT, REVA University, Bangalore, India K Nirosh nnniri567@gmail.com, School of C&IT, REVA University, Bangalore, India M Vijay Kumar Reddy mademvijaykumarreddy407@gmail.com, School of C&IT, REVA University, Bangalore, India Dr M Prabhakar prabhakar.m@reva.edu.in, School of C&IT, REVA University, Bangalore, India Abstract: In Today s generation the use of vehicles by the population has become redundant with which the accidents happening has increased to a greater extent. This project is done for detecting the accidents happening and providing the location of the accident to the mobile number which is previously coded. This helps for a fast service from the concerned person. The GPS and GSM models are used to give the exact latitude and longitude values by notifying the concerned person through the Blynk application and by sending SMS. The Vibration, Gas and Tilt Sensors when reach a maximum threshold value the information is transferred to the Arduino Uno Atmega 328 controller(notifying accident detected). This in turn help in sending the information to the concerned person through through the Blynk application and through SMS. This system can help companies to track the vehicles which is usually the rental vehicles by sending message to the concerned numbers. Keywords:- GPS, SMS, GSM, Arduino Uno Atmega 328, Gas and Vibration sensors. I. INTRODUCTION According to Survey done London School of Economics reports that Air pollution can be one of the major reason for hundreds of accidents. So this project aims for detecting accidents and provides various safety feautures to the vehicle which is equipped with this model and gives the information such as latitude and longitude of the vehicle, gas detection in fog, vibration if any threat happens, and a tilt information when the vehicle moves upside down. This helps in a fast service to the concerned person whose number is coded previously. This model is multi way communication i.e incase if there is no network service the information s will be provided through the Blynk application which is third party application. If incase there is no wifi and if there is good signal strength in the network operator then the information will be sent through SMS. II. LITERATURE SURVEY In 2010,Rajesh kanan et al,[1]. Proposed a method to detect accident at any place and report to nearby 'service provider'. Service provider arranges the necessary help through ADRS system (accident detection report system) placed in vehicle detects accident via sensors and transmit the information to nearby emerging service provider(esp) via transreceiver module. The proposed system has some drawbacks in terms of range coverage and setting up of ESP adds on cost.in 2012,s.p Bhumkar et al[2], proposed system monitors the fatigue levels of driver using eyeblink sensors and alcholic sensor. These impacts are saved in microcontrollers internal memory and data is sent to base using gsm. The proposed system concentrates mainly on internal conditions of car and driver and gathers data doesn't take any responce action.in 2013,Roberto et al[3]. proposed system gathers information from sensors like accelerometer,gps and microphone at the time of accidents. sends remote notification by mobile phone and simulations. This system concentrates on early detection of accident and adds on costs. Doesn't sense data accurately.in 2016,K.R.Tharani et al. [4], Proposed a system to monitor acceleration of vehicle's via tilt sensor position and other sensor values sends a message via gsm to emergency contactsthe proposed system can be improved by using other sensors like gas,alcohal sensors. It's not portable system and there is delay in sending message if network is not there. In 2016, c.mohamed aslam et al. [5], Proposed a System that gathers data from speed sensor,seat belt sensor and eye blink sensor based on threshold values. It automatically sends messages to traffic police based on data collected. If Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 185

212 Harish Kumar M M et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, accident has already occured it send location via text messages to ambulance.the Proposed system is only Concerned about Internal conditions and does'nt use pressure,tilt or any sensors to accurately detect the accidents and it is not portable and adds on cost. III. Methodology In this proposed method we have used Arduino microcontroller which is the heart of the project. It control the all function of the device namely Smoke sensor, Tilt sensor, Vibration sensor, GSM, GPS. As soon as the smoke has occurred in the environment then smoke sensor will detect the smoke then comparator will compare actual voltage & threshold voltage if any difference is there then the sensor send signal to controller similarly Tilt sensor which is used to tilting of the object then send signal to controller similarly vibration has occurred then then the vibration sensor send signal to controller then corresponding alert message has been send to receiver side which is in blynk app through the wi-fi internet. Similarly send location to the android mobile through the blynk app. Smoke Tilt sensor Vibration sensor IV. Block Diagram ARDUIN O Power supply GSM GPS WI-FI ANDROID MOBILE BLYNK It also has a DC adapter which can hold up to 12V of power supply. It has a crystal oscillator and a reset button. B. Gas Sensor Gas sensor which is commonly known as the alcoholic sensor is used for detecting the smoke in the place where it is kept. This sensor has a faster response time in detecting the alcohol or smoke. Few gas which this sensor can detect are Carbon dioxide, Carbon Monoxide, Sulphur Dioxide etc. It is a 4 pin configuration used for input and output. C. Vibration Sensor In this Project the vibration sensor (Piezoelectric) is used in order to detect threats. Whenever any threat is attempted this piezoelectric can send information when vibration is detected. It is a 4 pin configuration. D. Tilt Sensor The Tilt Sensor is one of the sensor which is used for detecting accident this can be mainly used if incase a vehicle is turned upside down incase if anything goes wrong such as speed control etc. The tilt sensor is a 5 pin configuration in which we have used two pins for Inputs and a pin for output which is given to the controller. E. GPS Module The GPS used in this project is NEO6mv2. This module is used in this project for getting the latitude and longitude value through the Blynk application. This GPS module contains a antenna for receiving the latitude and longitude values from any one of the 24 satellites which revolves the earth and forms a triangular shape. The Global Positioning System is used in order to provide location of the proposed model which can share the real time tracking, which can alert the concerned person in case any emergency, it can also give a history as of where the model has moved from the starting point to the end point, you can access the it anywhere anytime needed etc. F. GSM Module Most of the models use the GSM model for communication purposes so that it will be easy to share information. In this project we are using SIM800 module for the communication purpose this module works with the frequency band of 800MHz. We make use of the GSM module in our project to send message to the concerned person in case of accident detection. Fig(1). Proposed Detection System V. Hardware Components And Design A. Arduino UNO Atmega 328 Arduino Uno is a micro-controller board to get started with the coding and electronics section with a easy understanding and with the cool features. This micro-controller has a 14 digital I/O pins. In which 6 pins can be used as pulse width modulation and 6 pins can be used as analog input pins. It has a USB cable for providing power supply through systems which can hold a maximum of 3.3V power supply. VI. ALGORITHM Step 1: Start Step 2: Vehicle starts. Step 3: The Tilt sensor gets triggered. Step 4: If the value is greater than 310 and lesser than 390 then end else goto step 5. Step 5: Vehicle continues if gas sensor triggered Step 6: If value is greater than 0 then end else goto step 7. Step 7: Vehicle continues if vibration detected. Step 8: If vibration value is greater than 0 then end else goto step 4. Step 9: GPS will detect the location of the vehicle Step 10: GSM will send the message to coded number. Step 11: Stop Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 186

213 Harish Kumar M M et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, VII. Flow Chart increases to the maximum value then the information will be sent to the controller board from which the information will be stored in the Blynk server which in turn sends the information to the Blynk application and through a SMS for the concerned person s number With the exact coordinating values of the latitude and longitude. IX. REFERENCES VIII. CONCLUSION This Proposed model is for the detection of the vehicle accident. The proposed model can be an support for building up a smart transportation system if we utilize this model in a proper way. In this proposed model we have placed various sensors which detects the accident such as Gas sensor, Tilt sensor, and a vibration sensor. If the range of the sensor [1] Sachin M S, Prasanna P, Automatic vehicle accident detection and traffic control system, International Journal on Recent and Innovation Trends in Computing and Communication, Vol. 3 Issue 6, [2] D. Kumar, S. Gupta et. al., Accident Detection and Reporting System Using GPS and GSM Module, Journal of Emerging Technologies and Innovative Research, Volume 2, Issue 5, [3] Sri Krishna C Varma, Poornesh et. al. Auto matic Vehicle Accident Detection and Messaging System Using GPS and GSM Modem, International Journal of Scientific & Engineering Research, Volume 4, Issue 8, [4] Pratiksha R., Shetgaonkar et. al., Proposed Model for the Smart Accident Detection System for Smart Vehicles using Arduino board, Smart Sensors, GPS and GSM International Journal of Emerging Trends & Technology in Computer Science vol. 4, issue 4, [5] The 8051 Microcontroller and Embedded Systems by Muhammad Ali Mazidi and Janice Gillispie Mazidi, Pearson Education. [6] 8051 Microcontroller Architecture, programming and application by KENNETH JAYALA. [7] Wang Wei, Fan Hanbo Traffic Accident Automatic Detection And Remote Alarm Device, IEEE, Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 187

214 Volume 9, Special Issue No. 3, May 2018 International Journal of Advanced Research in Computer Science (ISSN: ) CONFERENCE PAPER Available Online at RFID BASED SMART LIBRARY MANAGEMENT SYSTEM Tanuja.K B.Tech Student, School of C& IT REVA University Vijay Krishna.D.L B.Tech Student, School of C& IT REVA University Tanushree.M B.Tech Student, School of C& IT REVA University Vindhya.R B.Tech Student, School of C& IT REVA University Gopinath R Assistant Professor, School of C&IT REVA University Bengaluru, India gopinath.r@reva.edu.in Abstract: Radio Frequency Identification (RFID) means a system that transfers the information wirelessly, using radio frequency waves. It is automatic identification technology. This project is about RFID based Smart Library Management System (SLMS) that allows fast transaction flow and will make easy to handle the activities like issue and return of books from the library without much manual intervention. This system is based on RFID readers and passive RFID tags that are able to store the information electronically which can be read by the RFID readers. The books are kept in library attached with RFID tags/cards. The benefit of this project exceeds the development of a smart world in a smart way as inventory management becomes much easier, as well as all the books and magazines can be read by the RFID reader rather than scanned manually by the customer. Issuing and returning the books makes the information of the sameto get stored in the cloud. This system will make users to issue and return of books via RFID tags very easy and also calculate the corresponding fine associated with the period of time the absence of the book from the library. Keywords: RFID; RFID tags; RFID readers (MFRC522); Radio frequency; I. INTRODUCTION RFID BASED SMART LIBRARY MANAGEMENT SYSTEM is a software application to maintain the records related to book purchase, book issue, book returns, free and all necessary requirements for the Library to manage day to day operations of maintaining the record of customers. Internet of Things (IoT) is an emerging technology in today s industry, which has a greater impact on society. IoT is a network of physical devices, sensors, embedded software which enables the devices to exchange data between them. The main aim of any technology is to make human life as simple as it could be. In today s technology based life, maintaind a track on knowledge through books is very much necessary and to have a good self based learning and self tracking of the books is also very much important. This self work can actually save lot of time to customers as well it could reduce labour. Otherwise, customers have to spend a lot of time in the queue at the librarian s counter. Our major objective is to reduce the customer s waiting time, by generating a message on their mobile for security issues and as well for keeping a record of the customer s account at the cloud. This system is based on Radio Frequency Identification (RFID) technology. RFID technology makes use of radio waves to transfer the data between the reader and the movable RFID tag or card. RFID technology was invented during early 1940's but it entered the mainstream during 1990's and RFID tags were used for item tagging during 2007 and beyond. RFID technology consists of three parts 1. The antenna 2. The reader 3. The RFID tags which contains information. The antenna emits the radio signals to activate the tag, the reader encodes the data present in the RFID tags. In this system we are going to design a system in which user can get all information about name of the books he/she had issued. They will also get to know return date of the book. If user is not registered then there is option for new registration (sign up). The tag is attached to the each book in the library. These tags have the unique code and because of this uniqueness in code we are using it for different items. For this smart library management system we used RFID instead of Barcode due to more advantages over barcode. Radio Frequency Identification (RFID) technology is in use since the 1970s which is contact-less data captured using radio frequency electromagnetic waves. Semi-passive tags have internal batteries which are used only to power its internal circuit. Passive tags don t have internal batteries. An Active RFID tag contains its own transmitter along with its own power supply (which is usually a battery) for the transmission of data whereas a Passive RFID tag does not contain its own power supply, it waits for the reader to send Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 188

215 Tanuja K et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, the energy to the antenna which is converted to the radio waves to transfer the data whenever the tag is present within the read zone. II. LITERATURE REVIEW Since the beginning of 20th century, information technology that kept evolving has changed the method of traditional learning and work mode, greatly influencing human development in a vast range. As the main core of information technology, computer has fundamentally changed human s thinking capacity and ideologies over many aspects in the same field. The most important development is computer software that has penetrated into all aspects of humans in their daily life to become the pillars of new evolving technical industries in this century. With the further informationization, some traditional service like economy or the society of management modes in libraries cannot serve readers effectively and conveniently any longer, besides information management and search become more difficult since more resources and books are added in libraries. The existing technologies for library management systems include Manual Registering, bar code readers to match the frequency of the objects using radio waves. The library management systems with bar code readers will be very expensive and having more disadvantages such as it needs a human intervention to read the details from bar code and require reading the code in a line of sight communication. So, by overcoming the drawbacks with the existing technologies we are introducing the Smart card technology for library management systems. Since a long time, some management work in libraries is done manually which is not only inefficient but also a waste of labor and financial resources. Digital technology methods of storing information can avoid this phenomenon and even serve more readers. The application of computer technology in libraries has reduced library labor staffs routine work and people waiting for borrowing and returning of books has become less. Later, the new technology of barcode emerged and it has improved the library information management system like never before. Barcodes are basically corresponding to books that is each book consisting of unique barcodes, thus reducing error rates of library staff and preventing book loss.this in turn improves the work efficiency. With the constant development of information and era in the long run, consulting for the evokement of public attention. At the same time, the emergence of knowledge economy has made libraries the most important consulting service place. However, in some large-scale libraries structures, people usually had to wait for a long time in queue to borrow or return books and books are even stolen sometimes. It seems urgently necessary to apply some new techniques in library administration. Nowadays in libraries the combination of data mining, RFID and computer software has reduced manual labor and has improved financial resources too. This has enhanced library information at its highest level, thus improving library services. Besides cloud-based technology and data mining can enhance the work efficiency to a great extent in providing better services for readers in library. III. PROPOSED WORK We propose simple and yet powerful framework using sensor RFID,which is a wireless identification technology used to verify various objects simultaneously and it has a wide application in logistics and supply chain management. Although, initial motivation to implement RFID to boost efficiency is widely discussed in academic literature, many organizations are slow in warming up or even reluctant to implement RFID to support effective and efficient business processes. Some reasons for slow diffusion are attributed to the incorrect implementation and misalignment between strategy, practices and technology where mindful RFID innovations can bring superior benefits. The modules we have used are 1. User entry module 2. Book borrowing and returning module 3. Notification module 1. User Entry Module: This is one module which is used for entering the name of the user, identity, phone number for notification and OTP generation for security issues, affiliation, the maximum period of borrowing, the maximum number of books allowed to be borrowed and the fine that has to be paid incase of the book missing or if the book has not been returned within the given stipulated time. Figure 1: User entry module flow chart 2. Books borrowing and returning Module: This module comprising of borrowing books where the library user borrows books and its operation is being maintained. This module works in a way where the user presents his/her tag, the module identifies the user. The user Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 189

216 Tanuja K et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, is then supposed to present the books at the reader for scanning. Next, this module receives the signal from book tags to the reader. This module then issues a query to the database to determine whether or not there is a request for borrowing. Theuser selects the allowed books and changes their status like how many books left to be returned and also the fine that has to be if there is any due prior to that transaction. This module of returning books is used by the library userfor returning books and to administer the information. This module works in the way which identifies the user and ask him to present the books for returning. Next, the module receives the book tags from the reader. This module then issues a query to the database to determine whether or not there is a request for returning books within the allowed period. reference to data and information and also the fine to be paid if incase of any missing books is also being notified. IV. COMPONENTS The proposed system requires the following major components to accomplish the particular functionality. [1] Components [2] Description [3] NodeMCU [4] Is an open source IOT platform development environment that is built around a very inexpensive WIFISystem-on-a-Chip (SoC)called the ESP8266. [5] RFID Reader [6] Radio frequency and tags/card Identification(RFID)is the use of radio waves to read and capture information stored on a tag attached to an object. A RFID tag/card can be read from up to several feet away and does not need to be within direct line-of-sight of the reader to be tracked. A RFID reader is a device used to gather information from an RFID tag, which is used to track objects. [7] GSM (Global System for Mobile Communicatio ns) [8] It a standard developed to describe the protocols formobile devices such as tablets.it is a cellular network which connects to cell phones immediately. V. MERITS OF PROPOSED WORK Figure 2: Book borrowing and returning module flow chart 3. Notification Module: This module is used by the library users for security issues, the theft of RFID based library card can be avoided when that particular person is being notified at the time of use. The due date for submission can also be intimated for The main aspect of this work is to proficiently aid the customer or the user in the absence of laborstaff. This method provides facilities of registration for the users by giving their details, which as a result is time saving and does not require manual work of labor. Since this work is done online, there is no much paper work. By implementing this proposed work, the library management system becomes efficient. Moreover, the usage of RFID in our work mainly has more advantages over barcode system that was once used. RFID has a range of about 100feet but barcode can read data only upto few inches and read/write option is available for RFID unlike barcodes which can only read. VI. CONCLUSION AND FUTURE WORK In this paper the proposed system gives the capability of making our work in library more convenient. This work proposes comprises the integration of the passive RFID technology into a library management system. This integral Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 190

217 Tanuja K et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, integration part makes both the library users and labor staff s task easy, convenient, practical and smart. This design of library management system is open and modular that can be extended for additional upgrading of functionalities. Future work might correspond to locating the book more accurately. For example, the proposed work leads the user only to the rack where the book is present. Work can be carried out in locating the book within the rack. Also, there is scope for progress in the security aspects of the above proposed system. More complex and efficient security measures can be implemented to ensure safe transactions in the library. VII. REFERENCES [1] C. R. Medeiros, J. R. Costa, C. A. Fernandes, "RFID Reader Antennas for Tag Detection in Self-Confined Volumes at UHF", IEEE Antennas and Propag. Magazine, vol. 53, no. 2, pp , April [2] JebahJayKumar, Abishlin Blessy, Assistant Professor, BNM Institute of Technology, Bangalore, India. Chennai, India. Secure smart environment using IOT based on rfid - international journal of computer science and information technologies, vol.5 (2), 2014, ISSN: [3]. Int. J. Reasoning-based Intelligent Systems, Vol. 4, No. 4, 2012 Copyright 2012 Inderscience Enterprises Ltd. [4]. Parul Gupta* and Margam Madhusudan** Department of Library and Information Science, University of Delhi, Delhi *pgupta@libinfosci.du.ac.in; **mmadhusudhan@libinfosci.du.ac.in/rfid Technology in Libraries: A Review of Literature of Indian. [5] CunoPfister, Getting started with Internet of Things. [6] By Daniel M. Dobkin Newnes, The RF in RFID: Passive UHF RFID in Practice, 2007 [7] J. Markakis, T. Samaras, A. C. Polycarpou, J. N. Sahalos, "An RFID-Enabled Library Management System Using Low-SAR Smart Bookshelves", 2013 ICEAA Conference, Sept Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 191

218 Volume 9, Special Issue No. 3, May 2018 International Journal of Advanced Research in Computer Science (ISSN: ) CONFERENCE PAPER Available Online at IOT BASED ANTI-POACHING ALARM SYSTEM FOR TREES IN FOREST USING WIRELESS SENSOR NETWORKS Ghousia Sultana B Jagadish R Dept. of Computer Science, Dept. of Computer Science Sai Vidya Institute of Technology Sai Vidya Institute of Tech. ghousiasultanab.14cs@saividya.ac.in jagadishr.14cs@saividya.ac.in Nadiya Noor Syed Dept. of Computer Science Sai Vidya Institute of Technology nadiyanoorsyed.14cs@saividya.ac.in Prof Nagashree C Dept. of Computer Science Sai Vidya Institute of Technology nagashree.c@saividya.ac.in Abstract: Nowadays there are many incidents about smuggling of trees like Sandal, Sagwan etc. These trees are very costly and meagre. They are used in the medical sciences, cosmetics. To restrict their smuggling and to save forests around the globe some preventive measures needs to be deployed. We have developed a system which can be used to restrict smuggling. The design system uses three sensors tilt sensor(to detect the inclination of tree when its being cut),temperature sensor(to detect forest fires),sound sensor(for effective detection of illegal logging i.e. even the sounds generated while axing the tree are also sensed).data generated from these sensors is continuously monitored with the aid of Blynk App. With respect to the sensors, their output devices are activated through relay switch. For tilt sensor and sound sensor a buzzer is activated and for temperature sensor a water pump is activated. Generated data is stored in Blynk Server over the Wi-Fi module. Forest officials are notified when any event occurs so that appropriate action can be taken. Keywords: Tilt Sensor,Temperature Sensor,Arduino Uno, Wifi Module I. INTRODUCTION Poaching isn t related to India only, China, Australia and African countries are also struggling with same issue. Indian sandalwood costs to INR per kg [1] whereas in international market Red Sanders costs INR 10 crore per ton. The Indian sandalwood tree has become rare in recent years,in an attempt to control its possible loss the Indian government is trying to limit the exportation of sandalwood [2]. For an individual, maximum permissible purchase limit is not to exceed 3.8kg as per Govt. If the tree is already government controlled, then its removal is prohibited whether on private or temple grounds until the tree is thirty years old.smuggling of sandalwood has created socio economic and law and order problems in areas bordering in India.The main objective of this project is to develop a system which can be used to restrict smuggling of sandalwood trees. II. PROBLEM STATEMENT Currently there is no system or any medium to detect illegal logging and cutting of trees. A mean by which, the forest officials know what s happening with trees should be installed. Such system would help in detectingand alerting so that proper actions could be taken. Putting this problem in mind, we are designing a system which help us to achieve our goal i.e. TO PROTECT NATURE. III. LITERATURE SURVEY 1. Endangered red sandalwood seized from smugglers in Berhampur [3]. 2. The Times of India, Ahmadabad. Plan to curb interstate smuggling of forest woods teak trees cut, timber smuggled in Lucknow [4]. 4. Punjab News line Network (18 th December 2010)-The situation has gone quite worse as timber and lakhs or Rupees are criminally being sold right under the nose of department. IV. EXISTING SYSTEM According to a journal published in IJARCET [5] Anti- Smuggling of trees was designed using flex sensors and ZigBee. Disadvantages: Wireless Communication in this system used ZigBee Module which is very slow and has lesser range than Wi- Fi Module which is used in Proposed System. Flex Sensors are merely sensors but tilt sensors are inclinometers(which are used to measure slope or elevation and readouts apart from just signals). The existing system is not practically implemented. V. PROPOSED SYSTEM The main idea is to design a portable wireless sensor node which will be a part of a Wireless Sensor Network. This system will consist of two modules one involving sensors and controller module which will be at tree spot and another one is Android phone. The Blynk application will continuously receive sensor data. This is an IOT based project where the sensor data is continuously uploaded to cloud(blynk server) over a Wi-Fi module. In case of tilt sensor and the buzzer turns Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 192

219 Ghousia Sultana B et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, on when tree bends and for temperature sensor water pump is turned on in case of forest fire through relay switch. VI. A. Block Diagram Tilt Sensor P Temperature Sensor SYSTEM ARCHITECTURE Battery Arduino Uno Sound Sensor Relay Cloud Buzzer Water Pump Blynk Applic ation Fig 7.1 Architecture of Anti-Poaching Alarm System Module 1-Arduino:The Microcontroller Arduino Uno is a microcontroller board based on the ATmega328. It has 14 digitalinput/output, 6 analog inputs, a 16 MHz ceramic resonator, a USB connection, a power jack. A program written with the IDE for Arduino is called a "sketch". Sketches are saved on the development computer as files with the file extension.ino. A minimal Arduino C/C++ sketch, consist of only two functions: setup(): This function is called once when a sketch starts after power-up or reset. It is used to initialize variables, input and output pin modes, and other libraries needed in the sketch. loop(): After setup() is called, this function is called repeatedly by a program loop in the main program. It controls the board until it is powered off or is reset. Module 2-Tilt Sensor Tilt sensors are used to measure angle within a limited range of motion. Tilt sensors are called as inclinometers because the sensors just produce a signal but inclinometers produce both readout and a signal. These devices produce an electrical signal that varies with an angular movement. Module 3-Temperature Sensor It is a device which is designed specifically to measure the hotness or coldness of an object or in an environment. Temperature sensor used in our project is LM35.It s is a precision IC temperature sensor with its output proportional to the temperature (in C).With LM35,the temperature can be measured more accurately than with a thermistor. The operating temperature range is from -55 C to 150 C. Module 3-Sound sensor The Sound Sensor is a board that combines a microphone and some processing circuit. It not only provides an audio output but also a binary indication of the presence of sound and an analog representation of sound s amplitude. Module 4-Relay Switch High voltage electronic devices can be controlled using relays. A Relay is a switch which is electrically operated by an electromagnet. The electromagnet gets activated with a low voltage, for example 5 volts from a microcontroller and it pulls a contact to make or break a high voltage circuit. One of the most advantage is you can do with an Arduino is controling higher voltage ( V) devices like fans, lights, heaters, and other household appliances. Module 5-Blynk Application Blynk was designed for the Internet of Things. It can control hardware remotely, display sensor data and can store data.it has 3 components: Blynk App It allows us to create amazing interfaces for projects using various widgets provided. Blynk Server It is responsible for all the communications between the smartphone and hardware. We can use our Blynk Cloud or run our private Blynk server locally. Its open-source, could easily handle thousands of devices Blynk Libraries - for all the popular hardware platforms - enables communication with the server and process all the incoming and out coming commands. B. Flowchart VII. SYSTEM DESIGN Startd h hf Read data from sensors Condition for temperature or tilt No Yes Send data to cloud through Wi-Fi Module Blynk Server Stop Switch on Relay and run output devices Blynk App for status checking C. Interfacing Tilt Senor with Arduino Working: When the device gets powered and is in its normal upright position, then the rolling ball settles at the bottom of the sensor to form an electrical conduction between the two end terminals of the sensor.if the circuit gets tilted so that the rolling ball doesn t settles at the bottom of the sensor with the electrical conduction path, then the circuit becomes open.the circuit becomes short circuit and the LED gets sufficient current. Sequence Diagram: Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 193

220 Ghousia Sultana B et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, Fig 8.1 Sequence Diagram for Tilt Sensor D. Interfacing Temperature Sensor with Arduino Working: As long as the ions and electrons are moving current flows between the electrodes and the circuit, the circuit functions properly. If fire breaks out smoke particles get into the sensor and clog up the ionization chamber. Sequence Diagram: Fig 9.1:Tilt sensors output when tree is normal Fig 9.2: Tilt sensors output when a fall is detected Fig 8.2: Sequence Diagram for Temperature Sensor E. Interfacing Sound Sensor with Arduino This module allows you to know when sound has exceeded a set point you selected.sound is detected through a microphone and fed into an LM393 op amp. The sound level set point is adjusted through an on board potentiometer. When the sound level exceeds the set point, an LED on the module gets illuminated and the output is sent low. 2. In case of forest fires, when the temperature of the surroundings increases its sensed by the temperature sensor, through the relay switch the water pump is turned on.when the temperature goes down below the set value,the water pump stops functioning. VIII. IMPLEMENTATION 1. All the sensors and the controller will be set up at the tree. When tree logging occurs, the sound generated due to axing the tree is sensed by the sound sensor. Arduino through the relay switch activates the buzzer notifying the security personnel. Also if the tree bends beyond threshold angle, the buzzer is activated. Fig 9.3: Temperature sensors output 3. The data generated by all the sensors is continuously transmitted to the cloud which in our project is the Blynk Server.It inturn sends all of the data to Blynk Application, by which at the work place forest officials know the status of the trees and their environment. Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 194

221 Ghousia Sultana B et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, IX. REFERENCES [1] [2] [3] [4] mugglers-in-berhampur/ html. [5]International Journal of Advanced Research in Computer Engineering & Technology (IJARCET) Volume 3, Issue 9, Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 195

222 Volume 9, Special Issue No. 3, May 2018 International Journal of Advanced Research in Computer Science (ISSN: ) CONFERENCE PAPER Available Online at IOT APPLICATION ON SECURE SMART SHOPPING SYSTEM Vishwas B School of Computing and Information Technology REVA University Benguluru, India bvishwas21@gmail.com Apoorva S School of Computing and Information Technology REVA University Benguluru, India sappu1096@gmail.com Swathi V Raidurg School of Computing and Information Technology REVA University Benguluru, India swathivraidurg@gmail.com Anand Rao Pawar H School of Computing and Information Technology REVA University Benguluru, India anandraopawar08@gmail.com Laxmi B Rananavare School of Computing and Information Technology REVA University Benguluru, India laxmibrananavare@reva.edu.in Abstract Internet of Things (IoT) is relying on exchange of information and work progress through radio frequency identification (RFID), which is an emerging technology and one of the most important technology in the computing world. This kind of technologies has found its applications in various fields ranging from healthcare, construction, smart shopping, hospitality to transportation sector and many more. In this system, Billing can be generated from the shopping cart. The idea is to save customers time by providing digital billing system which you get through the registered mail of our website. A compartment is kept in which all the products are attached with RFID tags/cards. The benefit of this project exceeds the development of a smart world in a particular field as inventory management becomes much easier, as well as all the items can be read by the RFID reader rather than scanned manually by the laborer. Purchasing product information will get stored in the database. The billing will get generated at the mobile display as well as on the server. This system shows how RFID technology makes life easier and secure and thus helpful in the future. This system describes about IoT, concentrating its use in improving and securing the future shopping. Key Words IoT; RFID; Smart Shopping; RFID tags; Raspberry pi 3; RFID readers (MFRC522); Radio frequency; I. INTRODUCTION Internet of Things (IoT) is an emerging technology in today s industry, which has a greater impact on society. IoT is a network of physical devices, sensors, embedded software which enables the devices to exchange data between them. The main aim of any technology is to make human life as simple as possible. In today s modern life, shopping in a mall or a supermarket has become an everyday activity, where the customer has to spend a lot of time in the queue at the billing counter. Our major objective was to reduce the customer s waiting time, by generating an automatic bill. Our proposed Secure Smart Shopping System creates a better shopping experience for the customer. This system is based on Radio Frequency Identification (RFID) technology. RFID technology makes use of radio waves to transfer the data between the reader and the movable RFID tag or card. RFID technology was invented during early 1940's but it entered the mainstream during 1990's and RFID tags were used for item tagging during 2007 and beyond. RFID technology consists of three parts 1. The antenna 2. The reader 3. The RFID tags which contains information. Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 196

223 Vishwas B et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, The antenna emits the radio signals to activate the tag, the reader encodes the data present in the RFID tags. The tag contains the microchip attached to the antenna, where the chip can store up to 2 kilobytes of data. There are two types of RFID tags available 1. Active Tags 2. Passive Tags An Active RFID tag contains its own transmitter along with its own power supply (which is usually a battery) for the transmission of data whereas a Passive RFID tag does not contain its own power supply, it waits for the reader to send the energy to the antenna which is converted to the radio waves to transfer the data whenever the tag is present within the read zone. RFID systems can operate at three different frequencies 1. Low Frequency (LF): operates at the frequency of 30 KHz to 300 KHz, and can be read within a range of 10cm. 2. High Frequency (HF): operates at the frequency of 3MHz to 30MHz, and can be read within a range of 10cm to 1m. 3. Ultra-High Frequency (UHF): operates at the frequency of 300MHz to 3GHz, and can be read within a range of 12m. Our proposed system makes use of High-Frequency Passive tag. The data transmitted by the RFID tag is stored in the database and it is further processed by Raspberry pi3 to generate the automatic bill. The system makes use of Raspberry pi3 Model B. Raspberry pi3 is neither a microprocessor nor microcontroller, it is a small computer with the system on the chip( SoC), which contains a multi-core processor, I/O peripherals, USB port, ROM, RAM. Raspberry pi3 uses either free or open source software which helps in expanding the learning environment. It also provides processor pins as GPIOs which are directly accessible and helps to understand the hardware implementation from the basic level. II. EXISTING SYSTEM In the existing system and as of related work we have done many of the papers and authors have stated the system of smart trolley but not of the entire smart shopping system. During these days, shopping and purchasing items in malls and supermarkets has become a daily routine. In most of these malls and supermarkets after the customer purchases the items and goes to the billing counter for paying the bill, the cashier uses barcode system to scan the item and generate the bill which is long and time consuming process and this leads to long queues at the billing counters. To overcome the above mentioned problem we have used RFID instead of barcode in our Smart Shopping System. Barcodes are continuous black bars which contain some useful information and that information could be read by a scanner. Information in barcode can be recognized by measuring the width of bars and the distance between those bars. As we all know that RFID stands for radio frequency identification, it uses radio waves to interact between an item and a system. In our smart shopping system, we are replacing RFID instead of barcode because RFID has more advantages over barcode which are listed above. i) Barcodes needs sightline but RFID does not need sightline. In case of a system which has barcode and if an item has to be read/scanned, the item and the scanner has to be placed directly in front of each other or else the barcode won t scan the item properly. But in case of RFID it s not the same. Here the RFID reader and the item need not be in sightline because they use radio waves to communicate with each other. ii) Barcodes can only be read but RFID can be both read/write. Barcode system of shopping leads to lots of confusion because barcodes can only be read and they cannot be rewritten. For example, if in a store, and if there is a discount sale going on and if the store employee has forgotten to apply the discount for a particular item then he cannot apply the discount for the item once again because barcodes cannot be rewritten/modified as needed. But in case of RFID, the tags can we rewritten/modified as needed. iii) Barcodes are not durable but RFID are durable. If any information has to be written on a barcode, it has to be printed on paper labels which are not very strong and can be easily damaged due to harsh climatic conditions and won t give proper results or else they are unreadable. But in case of RFID they are usually protected by a hard case so that they can withstand heat and harsh climatic conditions. Because of this RFID s are more durable when compared to barcodes. iv) Barcode does not encrypt data but RFID has the ability to encrypt the data. Barcodes can easily be hacked and any third party user can easily read the data because the data is always readable in barcode. But in case of RFID, the information is very secure because the information is in a encrypted form and it cannot be easily read. v) Barcode can contain/store limited amount of information whereas RFID tags can contain and store data in huge amounts. vi) Barcode can scan/read only one tag at one time but RFID can scan/read up to 40 tags at once. vii) If we use RFID instead of barcode, customer time will be saved and manual work will also be reduced. III. COMPONENTS The proposed system requires the following major softwares to accomplish the proposed functionality. Software Description Required Raspbian It is Debian based operating system for (Operating Raspberry pi. This system uses Raspbian System) Stretch. It is a relational database management system. SQLite3 SQLite is not a client-server database engine as it supports easy way to access database and store information into the database through network It is free and open-source terminal emulator; PuTTY serial console and network file transfer application. This system uses the software to establish SSH connection between Microsoft windows and raspbian OS Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 197

224 Vishwas B et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, IV. DESIGN/METHODOLOGY: 1) REGISTRATION: When the user wishes to shop in our system, first he has to login in to our system. If he is a new user then he has to first register with the system and then he is allowed for shopping. The user has to register by giving details like name, mobile number, -id and a password where all the fields are validated and password is encrypted and all these data is stored in the database. 2) LOGIN: After the user registers for the system, he can log into the system by entering his/her -id and password. The system now will validate the -id and password and logs the user into the system. 3) POST LOGIN: After the user has registered and logged in to the system, the system requests the user to scan his user card which would be given to him at the time of check in.as soon as the user scans his card, the system will be navigated to the menu page, where the menu page contains options like thisi) Add item to the cart. ii) Remove item from the cart. iii) Display the bill. iv) Pay and Checkout. iv) Exit. i).add item to the cart: When user wants to shop and add items to the cart he can select this option. As soon as the user selects this option, the system tells the user to scan the item which he/she wants to shop and add it into the cart. After the user scans his item which is equipped with RFID tags/cards, the system tells the user that the item is added/updated to his cart. The system gives the user an option to wish to add more items to his cart or not. The user can choose any of the options and proceed further for billing. ii).remove item from the cart: The user after shopping his items can also wish to remove the items from the cart by selecting this option. The system tells the user to scan the item which he wants to remove. If proper item which he wants to remove is scanned and if that item is found in the cart then the system pops up with a message saying that the item is removed from your cart. If that item is not found in the cart, the system pops up with a message saying that the item is not found in your cart. Later the user can choose any of the options and proceed for billing. iii).display the bill: The user wishes to see his/her bill at any time of shopping by selecting this option. As soon as the user selects option 3, his/her bill will be displayed. The bill contains item id, item name, quantity, price, total number of items and the total amount that the user has to pay at the time of checkout. Fig 1: Overview of Smart Secure Shopping System iv) Pay and Checkout: The user after seeing the bill can select this option number 4 for paying the bill amount. The system asks user to enter the amount that he has to pay, if he pays the entire amount then he can check out. If he pays partial amount from the total amount, the remaining Amount will be displayed and the system requests the user to pay the remaining amount and checkout. v).exit: The user after seeing his/her bill and paying the entire amount can exit from the shopping system by choosing option number 5. V. CONCLUSION The Internet of Things (IoT), put just, is the following sensible advance for remote systems, by putting Radio Frequency Identifications (RFIDs, basically remote GPS beacons) in each possible question made by organizations and governments. Likewise, we have utilized IoT and RFIDs to make a framework which will gigantically affect lives of ordinary citizens who sit tight for quite a while in 'Q's in shopping centers for charging. Our framework, The Secure Smart Shopping System is a charging framework which is quicker and effective than the conventional charging Systems. In this task, we have effectively made a Secure Smart Shopping System where clients can without much of a stretch shop by enrolling once and signing in. This framework is easy to understand, dependable, productive and temperate as we have used light-weight servers and databases. The basic approach and predictable client encounter accomplished through the keen shopping arrangement will help become the digitalized showcase and, permit purchaser gadget makers to all the more effortlessly assemble items that can bolster worldwide sending. Thus, shoppers will have the capacity to Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 198

225 Vishwas B et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, encounter a scope of new portable associated gadgets more qualified for variable innovation applications. VI. FUTURE WORK As a future enhancement we will be able to send the generated bill to the registered address of the customer so that if the user wishes to view their bill in the future, they can view it in their . Further, we will be able to display the in a predesigned format so that it can be understood easily and make it available for download too for the customer. We will also be able host our website online so that any customer can register himself into our system through the internet. We need to increase endeavor in order to create a more realistic and reliable system which can handle more customers and items. Implementation of the same over a large network of institutions is possible with relative effort which could be taken up as a future perspective of the Development Phase. VII. REFERENCES [1]K.Lalitha, M.Ismail, Sasikumar Gurumurthy, A.TejaswiSree Vidyanikethan Engineering College, Tirupathi," Design of an intelligent shopping basket suing IOT" India International Journal of pure and applied mathematicsspecial Issue Volume 114 no , online version: [2]Dr.K.A.Shirsath-Nalavade, Aarti Jaiswal, Swati Nair, Gayatri Sonawane, Suchita, Head Of Department, Computer Engineering, Sandip Institute of Engineering & Management, G. Students,Computer Engineering, Sandip Institute of Engineering & Management,"IOT based smart shopping cart (SSC) with automated billing and customer Relationship management" in (international journal for research in applied science & Engineering Technology)Volume 5 Issue X, October 2017,ISSN: ; [3]Jebah Jay Kumar, Abishlin Blessy, Assistant Professor, BNM Institute of Technology, Bangalore, India. Chennai, India. Secure smart environment using IOT based on rfid -international journal of computer science and information technologies, vol.5 (2), 2014, ISSN: [4] Hsin-Han Chiang, Wan-Ting You, Shu-Hsuan Lin, Wei- ChihShih., Development of Smart Shopping Carts with Customer-Oriented Service. International Conference on System Science and Engineering (ICSSE) [2016]. [5]J. D. Jadhav, Shital Gaddime, Kiran Hiware, Neeta Khadtsarer., A Fast and Smart Shopping Experience Using Android and Cloud. International Journal of Innovative Research and Advanced Studies (IJIRAS) [2016]. [6]Zeeshan Ali, Reena Sonkusare, RFID based Smart Shopping: An Overview., International Conference on Advances in Communication and Computing Technologies [2014] [7]Mr.P. Chandrasekar, Ms.T. Sangeetha, Smart Shopping Cart with Automatic Billing System through RFID and Zigbee. IEEE. [2014]. [8]Varsha Jalkote, Alay Patel, Vijaya Gawande, Manish Bharadia., Futuristic Trolley for Intelligent Billing with Amalgamation of RFID and ZIGBEE. International Conference on Recent Trends in engineering Technology (ICRTET) [2013] [9]Dhavale Shraddha D, Dhokane Trupti J, Shinde Priyanka S, Department of Electronics and Telecommunication Engineering, AISSMS s, Institute Of Information Technology, Pune ,India IOT based Intelligent Trolley for Shopping Mall,2016 IJEDR, volume 4, Issue 2,ISSN: [10]Manikandan T, Mohammed Aejaz M.A, Nithin Krishna N.M, Mohan Kumar A.P, Manigandan R Rajalakshmi Engineering College, Chennai, RFID based Advanced shopping trolley for super market (Journal of chemical and pharmaceutical sciences), Aug 06, 2017,ISSN: [11]N.Gowtham, G.Ramachandra Kumar, K.Narasimha Department of Electronics and Communication Engineering, Sreyas Institute Engineering &Technology, Hyderabad, IOT APPLICATIONS ON SECURE SMART SHOPPING SYSTEM,(Indian Journal of Science and Research)17(2): , 2018,ISSN: (Print) [12]Komal Ambekar, Vinayak Dhole, Supriya Sharma, Tushar Wadekar, SMART SHOPPING TROLLEY USING RFID, International Journal of Advanced Research in Computer Engineering & Technology (IJARCET) Volume 4 Issue 10, October 2015,ISSN: [13]Yerlan Berdaliyev, Alex Pappachen James, 2016, RFID-Cloud smart cart system, Advances in Computing, communication and Informatics (ICACCI), Electronic ISBN: [14]By Rong Chen, Li Peng, Yi Qin, 2010, Supermarket Shopping Guide System based on Internet Of Things, IET International Conference on Wireless Sensor Network 2010 Electronic ISBN: [15]Shreyas Dighe et al, International Journal of Advanced Research in Computer Science, 8(9), Nov Dec, 2017, , ANALYSIS OF SMART STORE SOLUTIONS USING PROXIMITY TECHNOLOGIES,ISSN No [16]You-Chiun Wang and Chang-Chen Yang Department of Computer Science and Engineering National Sun Yat-sen University Kaohsiung, Taiwan, R.O.C. Intelligent Shopping Trolley (IST) System by WSN to Support Hypermarket IoT Service Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 199

226 Vishwas B et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, [17] Erik Bryn jolfsson and Andrew McAfee, The Second Machine Age: Work, Progress and Prosperity in a Time of Brilliant Technologies, [18]CunoPfister, Getting started with Internet of Things. [19] Simon Monk, Programming, October 5, 2015 [20] Timothy Short, Raspberry Pi 3: Beginner to Pro Step by Step Guide (Raspberry Pi ), October 4, 2016, [21] Pedro M. Reyes, RFID: A Guide to Radio Frequency Identification McGraw-Hill Education, 2011 [21] By Daniel M. Dobkin Newnes, The RF in RFID: Passive UHF RFID in Practice, 2007 [22]Gus, How to setup a raspberry Pi RFID RC522 Chip, Oct3, 2017 Updated Nov, 2017, [23]Bakul Sinha, Simple CRUD in php with SQLite Database, 27 December, 2016, Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 200

227 Volume 9, Special Issue No. 3, May 2018 International Journal of Advanced Research in Computer Science (ISSN: ) CONFERENCE PAPER Available Online at AN IOT-BASED WATER SUPPLY MONITORING AND CONTROLLING SYSTEM Maruthi H V Reva Institute Of Technology and Management, Bengaluru,India Lakshmi Priya Reva Institute Of Technology and Management, Bengaluru,India Lavanya A R Reva Institute Of Technology and Management, Bengaluru,India Meda Manideep Reva Institute Of Technology and Management, Bengaluru,India Laxmi Jayannavar Reva University, Bengaluru, India laxmijayannavar@reva.edu.in Abstract: This paper presents an IOT antithesis which help to evaluate and plan the nature of water. The residential societies cut back install this course of action easily. OPC UA(Object Linking and Embedding for Process Control Unified Architecture)[1] is a platform individualistic service-oriented hut for the lead of processes in the logistic and industry sectors. The time signature roles in raw material authority are solid metering and deciding having hassle with tariff, as with a free hand as billing system. The pattern to did what one is told and conclude the price of raw material in the swine pipeline on a internet server is approaching in this freebie. There are all systems to do the cognate, notwithstanding this is about via the internet by the whole of the corroborate of raspberry pi and arduino to control the affairs of the disbursement of mineral deposit. Raspberry pi a mini personal digital assistant accepts the word from arduino micro-controller which is accessible by computer to the go with the tide meter and it besides uploads the announcement onto outweigh infrastructure to what place database is configured. The Hall portion sensor based dance meter is hand me down to contrast the flow figure of the water. The day-to-day figure of raw material to its users and mineral deposit distributors is portrayed by web headquarters solutions. This freebie includes brought pressure to bear up on authority, resources ministry and leakage management aspects of raw material monitoring course of action and it besides bring to a meet to prognosticate the outlay of the water in age for its users using progress data analytics. Keywords: water supply monitoring and controlling, problem statement, Water Level sensor[2],iot, cloud(thing speak). 1. INTRODUCTION Water is a trans parent, unflavored, and as much as colorless chemical core that is troublesome for bodily known forms of life. mineral deposit plays an carrying a lot of weight role in the presence economy and it is a having to do with source for drinking, carte du jour preparation, irrigation and trading purposes. The antithetical uses of raw material include: Domestic use. Industrial use. Mining use. Use in power generation. Aqua cultural use. Recreational use. Despite the circumstance that mineral deposit is the practically copious resource on the globe, practically 3% is honest mineral deposit, and seldom 1% of that is at hand for drinking. The expanded family and scanty rainfalls are making the lag preferably worse. Due to require of monitoring, raw material is not as a result of supplied properly. sprinkling areas in cities gat what is coming to one costing an arm and a leg water to what place some that a way do not get sufficient water. to pick up this problem there is a require of, round-the-clock monitoring during water spend, factual water lend scheduling and pertinent distribution of water. the at variance problems augment wastage of water merit to illegitimate consumption, bustle of tanks, leakage in pipeline, alternate water spend etc. There is a epitome called ICT(Information and information technology technology)[3], which is a ponder qualifier in the by the number of evolving innovative solutions to devote the problems of mineral deposit scarcities. By facilitating the total and cut and try of environmental word, ICT enables researchers and climatologists to organize more fair models for bare the brunt forecasting. The dominating areas to what place ICT can romp a pivotal nature in water authority are Mapping of water staple and brave forecasting. Major roles for ICT in water management: Remote sensing from satellites. In-situ worldly sensing systems. Geographical impression system. Sensor networks and Internet. The surplus of the complimentary is as follows: Section II describes moratorium statement. Section III describes prove challenges in the explanation of raw material authority models. Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 201

228 Maruthi H V et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, Section IV defines the requirements taken facing accept the responsibility for our architectural proposal. Section V defines the hardware and software requirements for the system. Section VI defines the union of IOT into the water powers that be system. Section VII defines the sell diagram of water monitoring system. Section VIII defines crowning achievement and references. 2. PROBLEM STATEMENT To overcome the mineral deposit supplying problems by developing sensible mineral deposit management program, we have taken a survey in bengaluru to certainly understand the water distribution and integrated problems by the whole of the system. By the show of the survey we observed that for the most part the employment is manual and requires a sensible technology to give organized distribution. IOT proclamation helps to design and develop a reliable cost confined program antithesis for real predate monitoring of water distribution system, by concentrating on firm methods. IOT is a hand one is dealt where millions of entities boot sense, attain and interconnect over public or private Internet Protocol(IP) networks. These interconnected entities collects and analyzes data consistently by providing outstrip decision making strategies. 3. BACKGROUND&CHALLENGES Supplying rich water of appropriate standing and amount has been a well known of the practically important issues in human history. Most turbulent civilizations were started alongside water sources. Greater the nation, more the home of challenges to rival user demands. People began to supplant water from at variance locations to their employment areas. For instance, the Romans constructed watercourse to express water from firm sources to their communities. Now-a-days, raw material devote program comprises of the fundamental structure that accumulates, processes, stores, and dispenses raw material surrounded by raw material sources and consumers. Very few fresh innate sources, especially in the southwest point of the USA, and swiftly multiplicative nation has on the way to to the crave for operating methods to manage a water spend system. For instance, saved water has address oneself to an basic water resource for whiskey and nonwhiskey uses. Structural position increases including novel conveyance systems and service and recharge facilities and operations decisions, namely as apportioning stray and carrying on the wrong track conservation practices, are created by the whole of the disclose and infinity demands in minds. As additionally components and linkages between sources and users are firm, the difficult situation of the water provide system and load in understanding at which point the system will execute to modifications grows. Many efforts on the society of a raw material spend route have been made over for sustainable mineral deposit distribution. However, the complexity of system tentative the site flat application at the willingly era. As mineral deposit demands pressures gather progressively on the prompt mineral deposit devote system, profuse studies aimed to mushroom a commanding officer water devote system to bolster decision makers to raw material greater fair systems for a search for pot of gold range functioning period. These attempts further include the optimization of lock stock and barrel system interpretation and life cost. Under supposing situations one as pipeline maintaining pipeline, non-revenue water, futuristic metering super structure, the compulsory goal of this free ride is to the way one sees it sure water distribution system challenges are gat back on one feet and provide water sources to users reliably in a more sustainable and timely rule of thumb as a longterm plan. The final cause of distribution route is to put water to consumer mutually suitable how things stack up, charge and quantity. Distribution program is used spend water from its candy man to the connect of usage. Advanced metering multitude enables as much as lesser Commercial losses in publication than temporal losses, anyhow this does not act in place of that commercial departure slump is complete scanty important. Shortest accessible accruement anticipate is gained by profitable loss reduction, as any action beeline away get an revive in billed non fiction and an deficit finance in revenues. Commercial losses art an element of three dominating elements: customer meter under-registration. illegal acquaintance along with others other forms of water heist and problems and mistakes in metering, front page new controlling, and billing. Billing issues that can urge consumption volumes include meter drill practices manipulation of reversals of over-estimation. procedures hand me down for dealing by all of objections about steep bills. customer leaks estimation of consumption. meter change-outs. tracking underlying accounts, and the processes for the passport and deviation of perplexed meters Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 202

229 Maruthi H V et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, REQUIREMENTS FOR A REFERENCE ARCHITECTURE We impose the from that day forward required activities that am about to be achieved to materialize a human water powers that be model:[4] REQUIREMENT#1: The position should deal by all of these water administration functions: off the beaten track powers that be of elements actual by style and functioning of integral units; detailed list of staple in the water incorporate, style of operations and circumstances everywhere the network. REQUIREMENT#2: It should upboost interoperability by all of other applications one as geographic reference systems and besides databases containing information roughly soils, meteorology,surroundings, culture etc. REQUIREMENT#3: It should distribute a negotiable and protractible house for the mix of contrasting systems. To do that, it am about to determine bring to light interfaces between package and process gat a handle on something layers, and also answer IOT systems for a direct clear to companionless water authority devices. REQUIREMENT#4: It should sponsor consolidation by the whole of legacy systems, dominant futuristic equipment. water management substructure currently cut apart systematically in the sub urban regions art an element of many reticulate and falling hook line and sinker devices that am about to be managed per older or underlying systems Raspberry pi Water flow sensor figure 3: water flow sensor Flow of chilled to the bone material is measured at the member of the working class of linger sensor. It constitutes of the components: a automatic teller machine card valve advantage, a rotor and a Hall Effect sensor. When congenial flows at the common laborer of the valve, the pinwheel rotor rotates and its urge and flow price would be forthwith proportional individually other. With every scam of the pinwheel rotor, electrical pulse will be produced every Hall Effect sensor Solenoid valve figure 2: Raspberry pi Raspberry pi is a cost skilled, close to the ground and portable period of time of personal digital assistant board. It has a an arm and a leg performance ahead of the game processor. Its main ego language is raspbian OS which can further develop manuwriting or program by the agency of python language. Raspberry pi 3 has CPU 1.2 GHz BCM2836 quad-core ARM Cortex-A7 Memory, 1GB RAM. It has a 40 gape GPIO connector, micro SD. Main consider of raspberry pi is an IOT. Raspberry is compatible by the whole of IOT. All the front page new is collected by the whole of a raspberry pi and it behavior continuously and urge data receptive the cloud. figure 4: Solenoid valve A solenoid valve is a analogy which is operated electromechanically. The brisk current controls the valve on a solenoid. If the valve is two-port the linger is wary or over, if it is three-port valve, the product is switched during the two hits the bricks ports. we are via 2-channel 5V communicate interface, which is soft level am air module. each channel needs a 15-20mA city worker current. The academic work of this televise module is, it gave a pink slip approach the an arm and a leg current, which will be secondhand in brown goods an equipment. It has a human interface that can be controlled urgently by microcontroller. Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 203

230 Maruthi H V et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, Relay module them more practicable by minimizing energy costs, handling costs and human involvement. Cost savings: water powers that be organizations and users can gain from improved asset operation by reduction cost. figure 5: Relay Module Cabels (m-m, m-f, f-m) Asset utilization: companies can easily locate assets (machinery, equipments, tools) and run preventive assistance on troublesome pieces of common people and accessories by practicable tracking of assets. Productivity increase: IoT has the capacity to do process knowledge, resource stability, utility lead depletion globally and it further allows real-time approach and new business models. it balances executed vs. accessible skills and improving effort efficiency. Expansion of new and existing enrollment models: Figure 6: Cables A rush am all ears (also met with as jumper, jumper spy, jumper pay television, DuPont spy, or DuPont pay television specified for such manufacturer of them) is an electrical wire or accumulation of them in a cable mutually a connector or gape at each complete (or routinely without them practically "tinned"), which is normally hand me down to interconnect the components of a breadboard or distinctive prototype or explain circuit, internally or by all of other apparatus or components without soldering. 5.2 Software requirement The programming definition used in this duty is PYTHON. It is a commanding officer purpose programming language. There are diverse free servers for viewing story on to leave in the shade, thing describe is a well known of them. Mysql server as craft union database. Web application by the agency of Microsoft feast for the eye viewer. 6. IOT FOR WATER MANAGEMENT. By as a result of some considerations from the enrollment, urban and technical incorporate of examine, the equipping of IOT abilities in water management scenes bounce be attained. The enroll is as follows: [5] Efficiency increase: water powers that be organizations and associations can act with regard to real-time operational approach and account sensors and actuators to respond and enhance water management architecture, making IOT is convenient in whole of the three defined layers. In the subsystem enclose, IOT subsystems influence via standard package interface and accomplished to observe processes in the coordination enclose, it designs beautiful coordination applications by permitting SME'S. in the management and malfeasance layer, IOT identification capabilities roll over to issue altered wrinkle services for an at variance water distribution join community. As we explained once, IoT stake, Internet angle, capacity orientation, and development orientation pillars. We couple these pillars by all of the strength of objects to (i) be identifiable, (ii) communicate and (iii) to unite, in turn among themselves, fabricating networks of interlinked objects, or by all of end-users or various entities in the network. The exposed MEGA ideal includes these properties: Internet-oriented: The three layers are communicated by all of two World Wide Web interfaces allowing the definition of a flexible and scalable communication course of action for situated the larger subsystems that are incomplete for a heart and soul in to water management system The Coordination and Management - Exploitation layers are defined as Cloud services. With the nightmare of the machine-to-machine (M2M) computer network, IoT is experienced by contemporary Cloud computing infrastructures, which begin to suggest so called cloud-based IoT solutions. A sustained retrieve to subsystems bounce be attained by homogenization through a wealthy range of communication methods and higher granularity, to deal by the whole of multiple communication technologies. Subsystem has been proposed as practically granular principle to be reachable, by all of foregoing incidents and it is aspiring higher granularity in the sealed years, subsystems are discreet by many devices and interrelations, as described in the Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 204

231 Maruthi H V et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, temporal and regulation models. These elements bounce be redefined as Smart Objects,that are good to characterize its maintain possible interactions. it commit provide the consequently information: Object properties, process and interaction information. It is applicable to define indescribable system identification features subsystem abilities, as a enroll of tasks experienced by them. Knowledge-oriented: The characteristics and behaviour of subsystems that are in commonplace are modeled right to lavish heterogeneity. already stated we suggest a physical ideal, executing raw material management processes in a hierarchical manner, and a process ideal, organizing the capital punishment of distinct processes. Based on these, efficient execution is supported on collaboration during subsystems and the Coordination enclose, based on the dispute of statement among them. The subsystems afford idea based on sensors autonomously. These models bounce be enriched mutually semantics to narrate, imagine, and merge information, theorize beautiful knowledge devoted to water management. it furthermore helps to compose machine-interpretable and self-descriptive account in the IoT domain. 6. BLOCK DIAGRAM 8. CONCLUSIONS AND FUTURE WORK As mineral deposit is one of the consistent basic needs of career and with tremendous increase of population, water administration has acquire a key component on all matters of human lives and either scenarios such as cities, natural areas cultivation etc. To enable information reuse(goal of the PSI directive), easier accomplishment of procedure rules and resource monitoring. In our expected system, mutually the manage of android academic work, raw material freely bouncecel be monitored from anywhere. Motor gave a pink slip be subdued automatically, entire smart machinery is achieved. It is a fit as a fiddle system and close to the ground in size. This appliance gave a pink slip be am a source of into hast a weakness for at bi pedal level. It gave a pink slip be implemented as a choice in a bungalow or at techno logical level. In a bungalow it boot be secondhand as in the means described behind and at transaction it gave a pink slip be hand me down to violence water held a candle to of contrasting tanks consisting of march to a different drummer types of liquids. According to the level of liquids, notifications prospective sent to the statutory person. At capital and labor we boot evaluate ultrasonic sensors which give preferably undeniable and calibrated information. The one more application about is to act with regard to it in hail of a mind areas to astute the clan by transportation watch it to the tribe nearby. This can be achieved by implementing this apparatus at the banks of the banks of the rivers which are prone to floods. so, if water candidly rises after a determined freely, notification will be generated on app and astute can furthermore be secondhand in dams in evocative fashion. REFERENCES [1]. Smart water management using IoT. Wadekar, water level sensors to check water level, [2]. An Internet of Things-based model for Smart water management. [3]. An IOT based reference architecture for Smart water management process. Jowua, [4]. OPC UA (Object Linking and Embedding for Process Control [5]. ICT (Information and communications technology) [6]. integrating iot in wate management Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 205

232 Volume 9, Special Issue No. 3, May 2018 International Journal of Advanced Research in Computer Science (ISSN: ) CONFERENCE PAPER Available Online at AUTOMATED FOOD WASTAGE MANAGEMENT Megha Priya N REVA UNIVERSITY School of Computing and Information Technology Kattigenahalli, Yelahanka-64 Pratiksha N K REVA UNIVERSITY School of Computing and Information Technology Kattigenahalli, Yelahanka-64 Keerthana R REVA UNIVERSITY School of Computing and Information Technology Kattigenahalli, Yelahanka-64 Supriya REVA UNIVERSITY School of Computing and Information Technology Kattigenahalli, Yelahanka-64 Shruthi G REVA UNIVERSITY School of Computing and Information Technology Kattigenahalli, Yelahanka-64 Abstract: Smartphone's are increasingly integrated with everyday uses. It utilizes for various activities like e -commerce, social media, a messaging, and a chart and map location application. In current trends, usage of technology is important to avoid fraudulent activities. A mobile application is a computer program designed to run on mobile devices such as smart phones and tablet computers. Most such devices are sold with several applications included as pre-installed software. The traditional approach in hotel sales report management is keeping a register for daily sales report. This work aims to substitute the traditional pen and paper method by automating the food-ordering process in restaurant and thus improving the dining experience of the customer. Many owners hire managers for the cash counters to report the daily sales to them in their respective hotels. But, these managers will not give the exact count of sales done per day. They will not give the printed or generated bill to the customer. Instead they will give written bill and cheat both customers and owners. Real investors or owners of the hotel are not aware of theseactivities. Managers will be making money and owners will be cheated from these fraud managers. This mobile application will provide convenience for the owners and customers where in all the bills will be generated for the customers and sales will be recorded in the database which can be viewed timely by the owner. Without recording the bill to the database the bill manager cannot generate bill to the customer. That is the ordered details are directly sent to owner. By this application owner will be not cheated because he can view the daily sales and also they canimprove the food items according to the customer more choice. So to overcome by that issue, we introduce this mobile android application for all hotel owners. I. INTRODUCTION In current trends, usage of technology is important to avoid fraudulent activities. A mobile application is a computer program designed to run on mobile devices such as smart phones, tablets and computers. Most devices are sold with several applications which are included as pre-installed software. The topic which we are proposing is related to the frauds happening in hotels, restaurants, canteens etc., and the traditional approach in hotel sales report management is by keeping a register for daily sales report. Report in the sense the same pen and paper method used to take orders from the customers and later note it down in the register. These sales report which are in hands of hotel managers are the only record of transactions that an owner gets to cross check with. Where the manager can misuse it by involving himself in fraudulent activities by editing the report as to make profit out of it. Hence introducing an android application which can solve daily transaction issues which are usually faced by the owners of the hotels. The proposed application will provide convenience for both owners and the customers where in all the bills will be generated for the customers and sales will be recorded in the database which can be viewed timely by the owners. Here owners are benefitted with this as they get to view daily sales and keep a track on profit made every day. Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 206

233 Megha Priya N et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, II. LITERATURE SURVEY Many android applications have been proposed [1]. Here it is proposed that the design of touch based digital ordering systems for restaurants using Android, Bluetooth and GSM. Android has become extremely popular in our daily life. It has revolutionized the use of mobile technology to support automation of routine tasks in wireless environment. In this [1] an android application is designed which works through Bluetooth to order food through cell phone and the ordered details are directly sent to the chef and also the manager. So generation of bill isn t time consuming and also the order details will be recorded in the application. In this [2] also they have developed an android application for digital dining in restaurants using android. Here an android application is used to order food digitally and the details will be directly sent to the kitchen and also a copy sent to manager and cash counter desk. Here[3] an application is developed for automated food ordering system with real time customer feedback. Here an additional point is that it also takes feedback from the customers automatically after dining.an[4] application is developed to improve the performance of the work which includes ordering, generating bills, profit details etc. at the restaurant using PC touch screen.here[5] they have implemented customizable online food ordering system using web based application for restaurants, hotels etc. 2. Vendor application Here the food vendor can add different types of food and also delete every day. And the food list added will be visible to student so that student can order and vendor can supply right amount of food. So food won't be wasted. And important feature about vendor application is he doesn t get to edit the sales record so that he can make profit out of it. He can only record sales not delete it. 3. Owner application In this application the owner can actually view, record and also delete sales. He has the facility to add up vendors and students. By this feature he won't be cheated in any way. He can get accurate report about daily sales and he can run his canteen or hotels as per daily transactions. IV. RESULTS STUDENT LOGIN III. SYSTEM ANALYSIS AND DESIGN This digital ordering system uses two different android apps: 1) Student application 2) Restaurant app. The student s android app is made exclusively for students and is identified by the unique id through which a student has installed the app. The order sent from a particular student app is sent along with the ordered details for the entire day. The second type of app is the restaurant app. It has three different types of users vendor and the owner. The app has different functionality as per the user type. Owner has administrative access on the app and has complete control over the entire system and the centralized database. The vendor can view the ordered items and prepare right amount of food as per the order. Here the vendor cannot edit the sales details as his application type doesn t have access to do so. But the owner can edit the details that are he can record sales and also delete sales as per his needs. This system consists of 3 different areas:- 1. Student application Here student can be added or student can be given access to the application only through the owner where only he has the control over it. Once student gets his access he can order food on daily basis as per his requirement. Once ordered the details will be directly sent to vendor and the owner Figure 1: Student login Figure 2: Student login details Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 207

234 Megha Priya N et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, Figure 3: Ordered details ADMIN LOGIN Figure 5: View sales Figure 4: After logging in Figure 6: Adding new user / food Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 208

235 Megha Priya N et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, VENDOR LOGIN IV. CONCLUSION In this work an android application for lodgings, flasks and eateries. The framework is contrasted with before nourishment requesting customary technique which is conventional pen and paper strategies, record based exchange techniques and so forth. We have contrasted and already existed frameworks where the supervisor profits by swindling proprietors and furthermore the clients. This approach which we have ensured that the proprietor doesn't get deceived and clients are additionally fulfilled. The main component which makes this application way unique is that the proprietor application has altering highlights for deals choice which isn't accessible for merchant application. Furthermore, deals see choice is accommodated both seller and the proprietor. As the understudy arranges his nourishment specifically the measure of sustenance which must be readied will be exact and no nourishment will be squandered in flasks. V. REFERNCES Figure 7: Sales Option [1] Bhaskar Kumar Mishra, TanmayBakshi, Bhavani Singh Chowdhary, Touch Based Digital Ordering System on Android using GSM and Bluetooth for Restaurants, in International Journal of Advance Research in Computer Science and Management Studies, January [2] ReshamShinde, Priyanka Thakare, Neha Dhomne, Sushmita Sarkar, Design and Implementation of Digital dining in Restaurants using Android, in International Journal of Advance Research in Computer Science and Management Studies, Volume 2, Issue 1, January [3] Shweta ShashikantTanpure, Priyanka R. Shidankar, Madhura M. Joshi, Automated Food Ordering System with Real-Time Customer Feedback, in International Journal of Advanced Research in Computer Science and Software Engineering, Volume 3, Issue 2, February [4] Nibras Othman Abdul Wahid (2014). "Improve the Performance of the Work of the Restaurant Using PC Touch Screen", in Computer Science Systems Biology. [5] Sushmita Sarkar, ReshamShinde, Priyanka Thakare, Neha Dhomne, KetkiBhakare, Integration of Touch Technology in Restaurants using Android, in IJCSMC, Vol. 3, Issue. 2, February 2014, pg Figure 8: Add/ delete food [5] VarshaChavan, PriyaJadhav, SnehalKorade and Priyanka Teli, Implementing Customizable Online Food Ordering System Using Web Based Application, in International Journal of Innovative Science, Engineering & Technology, Vol. 2 Issue 4, April Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 209

236 Volume 9, Special Issue No. 3, May 2018 International Journal of Advanced Research in Computer Science (ISSN: ) CONFERENCE PAPER Available Online at IOT BASED SYSTEM TO PREVENT ILLEGAL LOGGING OF TREES Shreya J Kumar Department of Information Science and Engineering Sai Vidya Institute of Technology Bangalore, India. Jayashree M Department of Information Science and Engineering Sai Vidya Institute of Technology Bangalore, India. Abhijeet Suman Department of Information Science and Engineering Sai Vidya Institute of Technology Bangalore, India. Ashwin R Department of Information Science and Engineering Sai Vidya Institute of Technology Bangalore, India. Abhijith H V Department of Information Science and Engineering Sai Vidya Institute of Technology Bangalore, India, Abstract The purpose of this paper is to develop an anti-smuggling system that would be useful in the protected forest areas. Smuggling of the trees such as sandal, Sagwan, teak etc., is major national concern. To avoid such type of smuggling and to save the forests around the globe, we have come up with an idea, for which we have proposed a system based on Internet of things which can be used to detect the illegal cutting of tree and restrict the tree smuggling. Keywords- IOT; Sensor; Data Processing; Sensor Network. I. INTRODUCTION Ages ago, when earth was framing its inward center and condition, it clearly had a decent arrangement of every zone, some portion of nature should fit in cycle together so it would act and work like a well-kept machine.yet, from recent years we have been reading in the daily papers about cutting and exporting of the trees like shoe, Teak and so forth. These trees are expensive and less available in the market. We have been disturbed by illegal activities like smuggling of Precious and commercial trees such as Teakwood, Sandalwood, Sagwan etc., from the protected Forest areas. The trees are protected by marking them some tags manually in certain places. Logging is the cutting, skidding, on-site processing, and loading trees or logs onto trucks or such vehicles. It has been as of now government controlled however not legitimately kept up, and evacuation is not permitted whether on individual or open grounds until the tree is 30 years of age. This has not prevented numerous poachers from chopping trees down when experts are not viewing. To prevent such kind of carrying and to spare the backwoods around the world some preventive strategies of frameworks should be created. We are framing a framework which can be utilized to confine this pirating. In this paper we are proposing a Internet of things based system that can be used to avoid the Smuggling of the trees which would in turn stop the de-forestation and uphold the Environmental stability, which would help to solve one of the issues with the Global Warming. II. NEED FOR STUDY & RELATED WORK Indonesian organization Korindo transporting timber in March 2004, and it was being foreign made to France, UK, Belgium. Korindo was well known for unlawful cutting of timber from the rainforests of Indonesia. In May 2003, an Indonesian Government came to know from the examination that Korindo was doing illegal exporting of timber with the assistance of aristocrats. Joined Nation has announced it as a world biosphere save and it shapes a biggest secured territory of timberland in South East Asia.[2] In spite of the financial significance of exchange timber and woodland items, the vast majority of the worldwide nations have no legitimate intend to stop or end such exercises on the grounds that in fact it's difficult to recognize illegal exported timber and different trees. Logical strategies to pinpoint the geographic starting point of timber are right now being worked on. Conceivable activities should meet with WTO control of non-separation to limit imports. They should be organized in two-sided agreements.[2]. From Indonesia the greater part of the illicit wood creation is being completed in Malaysia. This is key travel nation. Prevention of Illegal logging of Trees using IOT Though in Brazil, Amazon range holds 80% illicit exchanging this abuses government controls. At the center of illicit logging is across the board defilement regularly called as 'Green Gold'.[2] Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 210

237 Shreya J Kumar et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, teak trees cut, timber snuck LUCKNOW [3]. Imperiled red sandalwood seized from runners in Berhampur [4]. The town of Suifenhe in China is home to a timber manufacturing plant that procedures more than 5 billion pounds of wood for every year, the majority of which originates from Russia By pirating [5]. Punjab News line Network on Saturday, 18 December2010. The circumstance has gone very more awful as timber, worth lakhs and lakhs of rupees is in effect unlawfully sold directly under the nose of the concerned office. The Times of India, Ahmedabad. Plan to check between state pirating of backwoods woods. However, the first true public and official statement on the subject was the one made during the G8 summit in Birmingham in1998 and subsequently at the 2000 G8 summit in Okinawa (EU, 2002). It is worth mentioning that, in 1998, the G8 adopted n action plan (G8 Action Program on Forests) that acknowledged the need for more information on the extent of the problem prior to proposing countermeasures. An Asian ministerial conference (FLEG Forest Law Enforcement Governance) was also organized by the World Bank in Bali, in September, This initiative brought Asian wood producers and wood importers countries together to lay the foundations for concerted efforts in combating illegal activities in the forestry sector. The meeting was also significant in that Ministers agreed to a very clearly worded declaration calling for clear action, arguably elevating illegal logging to the highest political levels. This new kind of cooperation has led to similar regional FLEG conferences being organized in Europe and Africa during 2002 and 2003 and contributes to raising awareness of the issue at the international level. In addition, bilateral cooperation agreements to curtail illegal logging and trade have been signed. Moreover, the FAO, the World Resource Institute (WRI) and the Royal Institute of International Affairs (RIIA) have also organized panels and stakeholder meetings on the subject in 2002 and Illegal logging and illicit trade in timber are subjects that are now being dealt with more openly by governments (Doherty,2002). This is also the case for corruption that has been brought to the forefront in international discussions surrounding forests This has prompted several governments, non-governmental organizations(ngos), private companies and international organizations to focus on the issue (FAO, 2002). There exist few methods based on RFID to detect the movement of trees [18]. There is a need for detecting at the time of cutting tree and taking necessary action at that time only. III. PROPOSED WORK The system consisting of 3 units: 1. Tree unit 2. Sink Node unit 3. Server unit 1. Tree Unit The Tree unit would be the essential unit for the execution of the framework. This unit would consists of three sensors to give the data of Cutting Down the trees, Damage with flame, and so forth. The tree unit comprises of three sensors: 1. Accelerometer Sensor 2. Flex Sensor 3. Temperature Sensor Fig 1: Proposed Model In the tree unit a microcontroller is the heart of the venture, situated at the inside and controls operations of the framework. Tree cutting will be detected by accelerometer sensor, bending of tree will be detected by flex sensor and in case of fire it will be detected by temperature sensor. 2. Sink Node Unit The Sink node acts as an interface between forest tree network and the internet. It gathers the information from various tree units and forwards the information to server using GSM module. 3. Server Unit The server receives the data from sink node through internet. It stores the data in the cloud based database. Server processes the data and detects the suspicious activity based on the threshold values of various sensors. If there is any suspicious activity regarding the tree cutting, the server will send the alert message to the concerned authority mobile phones. Server will be having GSM module. All the information is sent through GSM to mobile application. With the help of GSM modem authorized person will get the SMS on our registered mobile phone which contains information regarding temperature of the tree and movement of the trees by accelerometer sensor and even a voice alert over an android application. By this information we are able to alert and control the illegal logging of trees. IV. EXPERIMENT RESULT The test bed is created using Arduino microcontroller, sound sensor, tilt sensor and temperature sensor and these modules are assembled. We fixed the tree unit to the model. We are using an open source server,the blynk server. Android application is created using eclipse. Figure 2 shows the data gathered by the server. Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 211

238 Shreya J Kumar et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, REFERENCES Fig 2: Data in the data base Fig 3: Various alert message displayed on LCD Fig 4: Alert Received on mobile phone through the app We have also considered different cases like tree may fall due to natural events like rain or through fire. If someone is trying to cut the tree using weapons, someone may try to fire the tree base.. V. CONCLUSION Smuggling of trees is major challenge. Through this paper we are proposing a new idea to prevent the tree logging and smuggling. This is also helping the government or the authorized person concern where the Smuggling is happening and who owns that the forestry or tree and how it is happening like cutting of tree, fire or because of the high temperature around the surroundings of the forest. The proposed idea is tested through Arduino based prototype. [1] Yichang, China;Guangyu He Junli Wan Research on Zigbee wireless communication technology Wei Wang In Electr.Eng. &amp Renewable Energy Sch., China Three Gorges University. [2] logging [3] trees-cut-timber-smuggled/articleshow/ cms [4] [5] [6] Muhammad Ali Mazidi, RolnD.Mckenley, "The 8051 Microcontroller and embedded system using assembly [7] Chonggang Wang, Tao Jiang, Qian Zhang ZigBee Network Protocols and Applications. [8] ZigBee Alliance, ZigBee Specification. Version 1.0ZigBee Document r06. [9] Anil Kulkarni, Ajay Khandare, MandarMalve, "Wireless Sensor Network (WSN) for protection high cost trees in remote jungles from fire and poaching", International Seminar on Sandalwood: Current Trends and Future Prospects, pp , Feb [10] Digital Output MEMS Accelerometer-ADXL345 Analog Devices, [11] SrideviVeerasingam, SaurabhKarodi, SapnaShukla, "Design of Wireless Sensor Network node on Zigbee for Temperature Monitoring", 2009 International Conference on Advances in Computing Control and Telecommunication Technologies IEEE Journals, 2009.\ [12] Problem analysis and proposed solutions on illegal logging in WWF southern Russia report pg. no. 18, Southern Russia report. [13] The Peruvian Environ mental La w Society (2003) Case Study on the Development and Implementation of Guidelines for the Control of Illegal Logging with a view to Sustainable Forest Management in Peru http. [14] [15] m/2014/ 07/ 10/timbersmugglers-attack-forest-staff. [16] [17]blogwsj.com/indiarealtime/2013/12/30/Australia/Sm uggle s-sandalwood-to-feed-indian-demand. [18] C. Srinivasan and H. Ranganathan on RFID sensor network based automation system for monitoring and tracking of sandalwood trees. [19] [20] WWF Latvia (2003) </rev>the features of illegal logging and related trade in Baltic Sea region; WWF International (2002) The Timber Footprint of the G8. [21] Annual report 2003 from the Commission to the Council and the European Parliament on the EC Development Policy and the implementation of External Assistance in Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 212

239 Volume 9, Special Issue No. 3, May 2018 International Journal of Advanced Research in Computer Science (ISSN: ) CONFERENCE PAPER Available Online at MINDWAVE BASED ROBOT CONTROL AND HOME AUTOMATION Rasheeda Banu Y Department of ECE, Dr. Ambedkar Institute of Technology, Bengaluru, India Rashmi K V Department of ECE, Dr. Ambedkar Institute of Technology, Bengaluru, India Syed Moin Department of ECE,, Dr. Ambedkar Institute of Technology Bengaluru, India Syed Zeeshan Department of ECE, Dr. Ambedkar Institute of Technology, Bengaluru, India Dr Chetan S Department of ECE,, Dr. Ambedkar Institute of Technology Bengaluru, India Abstract- Brain Computer Interface (BCI) systems are the tools which are proposed to help the damaged people whom are impotent of making a motor response to interface with computer using brain signals. The aim of BCI is to translate brain activity into digital form which performs as a command for computer. The BCI application can be used in different areas such as education, industrial, gaming, robotics, home automation and medical areas. In our project EEG based brain controlled robot and home automation using Zigbee has been developed using BCI with the help of NeuroSky technology. The fetched brain signals are transmitted to microcontroller via Zigbee module. Robotic module designed consists of renesas microcontroller coupled with dc motor to perform the control. The attenuation level was used to monitor the direction of robotic and meditation level was used to monitor the home appliances. The wireless BCI system could allow the paralyzed people to control the robotic and home appliances without any difficulty, provided it is portable and wearable. Keywords- EEG, NeuroSky, ZigBee, BCI I. INTRODUCTION Nowadays, humans have fantized to communicate and interact with machines through the thoughts and also create devices that work with human mind and thoughts. Human mind imagination is captured In the form of modern science fiction stories and ancient myths. However, cognitive neuroscience and brain imaging technologies have recently started to provide people with ability to interface with human brain. Using sensors some of the physical activity that occurs within the brain corresponds with the forms of thought can be monitored. For the needs of people in growing society recognition on, researchers have used this technology to build brain computer interfaces (BCIs), communication systems, i.e., a computer system that does not depend on peripheral muscles and nerves of the brain. BCI is used to build a direct control channel between users brain, i.e., users intention and computer system. Such a system can help two kinds of people, firstly the people who have damage in their physical system to recover their activities with a wheelchair, or control over a neuroprosthesis or a robot and so on. Secondly for healthy people, it could be an additional man machine interface, which is able to increase the productivity and the efficiency in the high throughput tasks. Among different techniques for the non-invasive measurement of electro physiological signals of brain oscillations, electroencephalography is commercially used and has excellent results, which enables real time interaction through BCI. Electroencephalography refers to an electrophysiological monitoring method which will record the electrical activity which is occurring at the surface of the brain using electrodes or sensors placing on the scalp of the brain. EEG measures electrical signals from the brain in voltage fluctuations currently occurring in the neurons of the brain. This action, in-turn, will appear on the screen of the computer which in turn connected to electrodes implanted in the brain as waveforms of varying amplitude and frequency measured in voltage or as digital values. EEG waveforms are categorized in accordance to their amplitude, shape frequency as well as the site on the scalp at which electrical signals are recorded. The most intimate grouping uses EEG waveform frequencies like alpha, beta, theta, delta, spindal, etc. The most frequently used approach to diagnose epilepsy and stroke is EEG, which causes irregularity in EEG readings. It is also used to diagnose brain death, sleep disorders, comma, muscle injury and encephalopathy. EEG Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 213

240 Rasheeda Banu Y et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, used to be a primary method of diagnosis for tumors, epilepsy, stroke and other brain disorders. In this paper the 4 wheel robot is built using DC robot and L293. In this system we use renesas microcontroller for robot analysis and human automation. In this there are 2 parts, one is transmitter and other is receiver. In which transmitter part consists of mind wave neurosky, renesas microcontroller and Zigbee. Whereas in receiver part Zigbee, renesas microcontroller, l293, used. DC motor, relay, toggle switch and sockets are used. Transmitter part has mind wave neurosky using Zigbee it will be responsible for the actions of robot and home automation. Receiver part uses Zigbee for the movement of robot in forward, reverse, left and right directions. And it also uses relay for 2 purposes, one is to run the fan and other to glow the light. This has toggle switch which is used for robot movement and home automation. In this system, the mind wave mobile headset by Neurosky is utilized to read the EEG signals, shown in figure 1. The e- Sense algorithm by neurosky exercises the EEG signals and wirelessly transmits the calculated attention and meditation values through Bluetooth to a master device i.e., HC 05 Bluetooth at the rate of 1hertz. The frequencies of the electrical signals can be measured by placing a sensor on the scalp. Table 1 gives a general view of some of the commonly recognized frequencies that tend to be generated by different types of activities of brain. II. BLOCK DIAGRAM Figure 1: Neurosky wireless EEG-headset III. METHODOLOGY The proposed single trail EEG classification [2] in BCI Consists of two modules: Signal acquisition For the accession system, the most frequently used recording method is EEG. The EEG method uses electrodes or sensors which are an appeal on the scalp, there is the main advantage of using this approach that is the portability of the recording system. Further methods required bulky instrumentation which is costly or is very expensive and those are invasive. The different phases of this module are: Brainwaves Table 1: Frequency range of different brain waves Brain Frequency Mental States Waves Ranges and conditions Delta 0.1 3Hz Deep sleep, dreamless sleep, unconsciousness. Theta 4 7Hz Creative, recall, fantasy, imaginary, dream. Alpha 8 12Hz Relaxed(not drowsy), conscious. Low range 12 15Hz Relaxed yet beta focused. Medium 16 20Hz Thinking, aware range beta ofself and surroundings. High range 21 30Hz Alertness, beta agitation. Think Gear Think gear is the equipment which is contemporary interior of every neurosky product that allows the device to Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 214

241 Rasheeda Banu Y et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, integrate with the users brain waves. It consists of sensors which touch the forehead of the user, the reference and junction points presented in the ear clip, and the on-board chip that operates all of the data. Both the dominant brain waves and the e-sense meters (attention and meditation) are calculated on the think gear chip. e-sense e-sense is the neurosky with fix algorithm for distinguishing mental states. To compute esense, the neurosky think gear technology fleshes out the raw brain wave signal and removes the atmosphere noise and muscle moment. The esense algorithm is then appealed to the remaining signal, resulting in elucidated esense meter values. e-sense meter For each different types of e-sense,i.e., the attention and meditation meter value are described on e-sense respective scale of 1 to 100. The value across at any given moment in time is considered neutral and is similar in notion to base lines. The across is considered slightly elevated, may be interpreted as levels of e-sense values may be higher than normal for a given person. Values across are considered elevated, denoting they are strongly expressive of heightened levels of the e-sense. Similarly, on the other end the values from indicates reduced levels of the e-sense, while the value across 1-20 indicates strongly lowered levels of the e-sense. Attention esense The esense attention meter specifies the strength of the user s level of mental attention or focus, which occurs during extreme concentration and directed (but stable) mental activity. The meter value ranges from Lack of concentration, wandering thoughts, distraction, or anxiety may decrease the attention meter level. Meditation esense The esense meditation meter specifies the user s level of mental relaxation or calmness. The value ranges from The account that meditation is a meter of the person s mental states, not physical states so just relaxing of all body muscles may not quickly result in an enhanced meditation level. Nevertheless, for most people in most everyday situation, relaxing the body frequently helps the mind to relax as well. Meditation is associated to decrease the activity by the active mental process in the brain. It has been an observed that closing the eyes is often an effective method for increasing the meditation meter level. Distractions, anxiety, sensory stimuli and wandering thoughts may lower the meditation meter level. Zigbee Zigbee is the most popular industry wireless mesh networking standard for connecting sensors, instrumentation and control systems. Zigbee, a specification for communication in a wireless personal area network (WPAN), has been called Internet of Things [4]. Theoretically, your Zigbee-enabled coffee maker can communicate with your Zigbee-enabled toaster. Zigbee is an open, global, packet based protocol designed to provide an easy-to-use architecture for secure, reliable, low power wireless networks as in figure. 2. Zigbee and IEEE are low data rate wireless networking standards that can eliminate the costly and damaged prone wiring industrial control applications. Flow or process controls equipments can be placed anywhere and still communicate with rest other systems. It can also be moved, since the network doesn t care about the physical location of the sensor, pump or valve. Figure 2: Zigbee module Renesas The expanding family of renesas RL78 microcontroller (Figure 3) consists of both general purpose and application specific devices. This increasingly popular MCU s make possible ultralow power applications by giving system designers advanced power saving features and high performance operations. Because the devices offer important capabilities such as an innovative snooze mood that demonstrably superior solutions for a vast span of battery powered applications [6]. In this project, the renesas microcontroller is worned to gather and examine the electrical brain signals from the sensors to be used in actual-time or stored for future analysis. Figure 3: Renesas microcontroller-rl78 IV. CONCLUSION An EEG based brain controlled robotic and home appliance was proposed for disabled people and senior s people to lead their daily life without any arduousness. Two applications were developed. The electrical signals of attention used for making a robot to move in forwarding direction for 10 feet s and coming back to the starting position, and meditation signals where used for controlling home appliances. Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 215

242 Rasheeda Banu Y et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, Based on various metrics the performance was analyzed. The prototype model of EEG-based controlling robotic and home appliances has been developed with the help of neurosky technology. ACKNOWLEDGEMENTS I deemed to be my greatest pleasure to thank our Institute, Dr.Ambedkar Institute of Technology, Bengaluru. We would like to express our special thanks of gratitude to our mentor for their kind support and guidance, which helped us to improve this paper. REFERENCES [1] Hong Zeng, Member, IEEE, and Aiguo Song, Senior Member, IEEE, Optimizing Single-Trial, EEG Classification by Stationary Matrix Logistic Regression in Brain Computer Interface, Neural Networks and Learning Systems, [2] B. Bijitha, IEEE Transactions, Nandakumar Vol. Paramparambath 27, Nov [2] Brain Computer Interface Binary Classification Using Regularized CSP and Stacked Concept, International Journal of Engineering Trends and Technology (IJETT), V38 [3] Vehicle (5), Renji Driving V. Mathew, Safety August Jasmine System 2016 Using Basheer An EEG Based [3] Automotive CAN Protocol, International Journal of Engineering trends and Technology(IJETT), V26(4), August [4] W. Samek, Member, IEEE, Motoaki, Kawanabe, and Klaus Robert Muller, Member, IEEE, Divergence Based Framework for Common SpatialPatternsAlgorithms,Biomedical Engineering, IEEE, VOL. 7, April [5] Dwipjoy Sarkar, Atanu Chowdhury. A Real Time Embedded System Application For Driver drowsiness and Alcoholic Intoxication Detection, International Journal Of Engineering Trends and Technology (IJETT), VOl.10 (9), April [6] M. Arvaneh, Student Member, IEEE, C.Guan, Senior Member, IEEE, Kai Ken Ang, Member, IEEE, and ChaiQuek, Senior Member, IEEE, Optimizing Spatial Filters by minimizing Within Class dissimilarities in Electroencephalogram based Brain Computer Interface, Neural Networks and learning Systems, IEEE Transactions, VOL.24, April [7] NeuroSky Mindwave Headset, Available: [8] SiliveruRamesh, K.Harikrishna, J.Krishna Chaitanya, Brainwave Controlled Robot Using Bluetooth, International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, vol. 3, pp , August [9] Sridhar Raja.D, Application of BCI in Mind Controlled Robotic Movements in Intelligent Rehabilitation, International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, vol. 2, pp ,April [10] Luzheng Bi, Xin-An Fan, Yili Liu, EEG-Based Brain-Controlled Mobile Robots: A Survey, IEEE transaction on human machine systems, vol. 43, pp , March 2013 Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 216

243 Volume 9, Special Issue No. 3, May 2018 International Journal of Advanced Research in Computer Science (ISSN: ) CONFERENCE PAPER Available Online at PATIENT HEALTHCARE MONITORING SYSTEM USING LI-FI VYSHNAVI RANI P ESWAR REDDY M Department of Computer Science and Engineering SVIT, Department of Computer Science and Engineering SVIT, Bangalore, India Bangalore, India TANADAR RAHUL MANJUNATHA G S Department of Computer Science and Engineering SVIT, Department of Computer Science and Engineering SVIT, Bangalore, India Bangalore, India NAGAMAHESH B S Department of Computer Science and Engineering SVIT, Bangalore, India Abstract Steady observing of patient's wellbeing condition in healing center is either manual or remote devotion (Wi-Fi) based framework. Wi-Fi-based framework turns out to be ease back in speed because of exponentially expanded adaptability. In this situation, light devotion (Li-Fi) finds the spots wherever Wi-Fi is pertinent with extra highlights of fast information organize. Aside from the speed factor, Li-Fi is more reasonable in doctor's facility application for observing the patient's conditions without recurrence obstruction with human body. This task proposes a use of Li-Fi organize in the doctor's facility for checking the patient's conditions, for example, temperature, pulse, body developments, and eyeblink conditions utilizing individual sensors. The gathered information from the sensors is transmitted to the sink, and further these information are prepared utilizing microcontroller and sent to show unit. In view of the idea of unmistakable light correspondence, a model is worked with the PIC microcontroller and essential sensors as peripherals and tried it's working. Along these lines, the use of Li-Fi as a wellbeing observing framework exhibited tentatively. Keywords Wi-Fi, Li-Fi, Radio-spectrum, Sensors, LED, UART. I. INTRODUCTION Light fedility (Li-Fi) is a progressive answer for the fast information arrange, proposed by a German physicist Harold Haas. Li-Fi systems bolster the transmission of information through brightening of light radiating diode (LED) knob, in this manner it is likewise named as obvious light interchanges (VLC). In the age of web, there is a consistent desire for speedier, secure, and solid wire-remote availability in all fields, while remote systems are more best in all residential application as a rule and social insurance application specifically. The explanation behind relying upon remote system in clinic is the links which are running over the patient's body interconnecting the gadgets may cause sullying. Reliance on the remote web expands the weight on remote constancy (Wi-Fi) innovation which, thus, makes a gigantic interest for data transfer capacity and radio range. To lessen the heap on Wi-Fi, a substitute mean of remote web is Li-Fi discovers which discover its applications in relatively every field, even in vehicle. For quite a while, medicinal innovation has fallen behind the rest. The degree for remote correspondence in the medicinal field is determined to the ascent; there are numerous gadgets which take a shot at Wi- Fi, for example, imbuement pumps, defibrillators, lung ventilators, and anaesthesia machine. At the point when a specialist should utilize attractive reverberation imaging scanners alongside implantation pumps, which deal with Wi-Fi there comes about a frequency overlapping issue. With more number of remote therapeutic gadgets coming up, using the radio recurrence (RF) range expands which lead an electromagnetic impedance (EMI) that outcomes in possibly dangerous occasions identified with medicinal hardware tasks. Aside from the impedance with therapeutic hardware, an EMI influences human body likewise as illnesses, insusceptible brokenness, electromagnetic touchiness, and so on., and in most pessimistic scenario, it might prompt malignancy. Another impediment of Wi-Fi in healing center framework is its security issue. Tolerant data must be private and secure however stay open to approved people. Doctor's facilities are places where both EMI affectability and security of therapeutic subtle elements are issues with the employments of Wi-Fi. To battle the above constraints of Wi-Fi in wellbeing checkingframework, Li-Fi is utilized, which is a novel innovation for high density remote information scope alleviating radio obstructions in kept territories. VLC has clear extension in numerous zones, for example, brilliant stores, purchaser hardware, resistance and security, vehicle and transportation, flight, doctor's facility, submerged correspondence, and perilous condition and it has spread over the locales of America, Europe, and Asia-pacific. The VLC showcase is relied upon to develop from USD million out of 2015 to USD 8,502.1 million by 2020, at a Compound Annual Growth Rate of 91.8% in the vicinity of 2015 and The worldwide Li-Fi advertise is relied upon to show development at a vigorous pace in the vicinity of 2016 and Huge data transfer capacity inferable from the developing RF range crunch, together with a high level of security and vitality productivity are relied upon to support the Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 217

244 VYSHNAVI RANI P et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, worldwide Li-Fi advertise. Since the innovation includes unmistakable light wavelength and not radio waves, it is more averse to negatively affect human wellbeing. Specialists frequently contrast Li-Fi with free space optics as it additionally uses light to exchange information, yet it can't be utilized as a part of the spots where it is hard to lay the optical fiber like doctor's facilities. Parallel working with different EMI gadgets is attainable with Li-Fi and is additionally helpful for mechanical surgeries and mechanized techniques. Amid surgery, Li-Fi framework alongside different sensors is expected to get prompt direction from specialists in the treatment by sharing information, recordings/live insights about the patient for the best outcomes. Thus, a Li-Fi-based social insurance checking healing center framework secure patient's body from assault of numerous kinds of illness, as the protection energy of patients, is low. Improving the patient's wellbeing conditions as well as interchanges among the doctors and clinicians. Remote innovation with Li-Fi framework empowers clinicians to screen patients remotely and give them opportune wellbeing data, updates, and support. Li-Fi innovation improves restorative field to the following level and has a plenty of benefits when introduced and utilized valuably. Association of this paper is as per the following. The essential engineering of Li-Fi based checking framework is exhibited in LI-FI system. A concise talk about the proposed model is exhibited in the area model which is trailed by the portrayal of different sensors under the heading of "part of sensors". Broadened utilization of Li-Fi innovation identified with the proposed paper is featured in the accompanying area. Conclusion is determined towards the finish of the paper and references are recorded. canbe utilized as a source to transmit data. Transmitting data through Li-Fi makes it speedier and less demanding The idea of Li-Fi will make the correspondence quicker and more powerful in future in different circles over the world. It will be more proficient as it can go through regions where human mediation isn't conceivable. It pulls in a lot of enthusiasm for business in the correspondence divisions and will soon have the capacity to use this innovation at more prominent speeds in each field of correspondence and will along these lines empower straightforward entry of information in a flash. This at last diminishes the time utilization and the work result is adequately expanded. Along these lines this innovation will be a greener, more secure and cleaner method for correspondence. IV. PROTOTYPE MODEL The model comprising of transmitter, collector and different sensors is created. The equipment setup of interfacing biomedical sensors with Li-Fi board and the yield. II. EXISTING SYSTEM The explanation behind relying upon remote system in healing center is the links which are running over the patient's body interconnecting the gadgets may cause sullying. Reliance on the remote web expands the weight on remote constancy (Wi-Fi) innovation which, thusly, makes a gigantic interest for transmission capacity and radio range. With more number of remote medicinal gadgets coming up, using the radio recurrence (RF) range expands which lead an electromagnetic impedance (EMI) that outcomes in conceivably perilous occasions identified with therapeutic gear activities. Aside from the impedance with restorative gear, an EMI influences human body likewise as maladies, safe brokenness, electromagnetic excessive touchiness, and so on, and in most pessimistic scenario, it might prompt growth. Another impediment of Wi-Fi in doctor's facility framework is its security issue. Understanding data must be private and secure however stay available to approved people. Healing facilities are places where both EMI affectability and security of therapeutic subtle elements are issues with the employments of Wi-Fi. To battle the above restrictions of Wi-Fi in wellbeing observing framework. III. PROPOSED SYSTEM The change in the field of remote correspondence gives us adaptability to make our life simpler and secure. Here the proposed framework replaces the need of Wi-Fi and lights TRANSMITTER SECTION The transmitter section contains one direct present (DC) control supply to supply 5 V DC. DC control supply comprises of a stage down transformer for changing over 230 V-5V, an extension rectifier; a voltage controller LM7805, and a channel capacitor of 1000 mf. Every one of the sensors is associated with PIC 16F877A. The PIC16F877A is low power elite microcontroller with 8KB in-framework streak memory. The uncommon element of this microcontroller is the nearness of in-constructed widespread offbeat collector/transmitter, which is utilized for serial transmission. The flag is transmitted through the Li-Fi transmitter, and the wellspring of transmission is LED. The exchanging recurrence of the LED must be sufficiently high to keep away from any glimmering that may imperil the wellbeing of the human eyes. The regulation plan executed in this framework is the on-off keying (OOK) non-come back to-zero (NRZ) adjustment conspire. OOK NRZ is a piece of adequacy move scratching balance Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 218

245 VYSHNAVI RANI P et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, which speaks to the computerized data through the nearness and nonappearance of the bearer wave. RECEIVER SECTION A photodiode is utilized as a recipient in this area which fills in as a light to power converter. The subsequent electric flag would be powerless and uproarious, consequently, it goes through flag preparing and intensifications units. An envelope identifier and a low pass channel are additionally used to demodulate the flag from the bearer wave and to expel highrecurrence commotion separately. At last, a voltage comparator is utilized to change the flag into advanced arrangement, before passing it to the microcontroller for additionally handling. The transmitter and the beneficiary segment ought to be put in observable pathway position. The got data can be portrayed as a chart to examine the patient's wellbeing by interfacing the collector end to the PC. The wellbeing report of the patient can be transmitted to the concerned individual naturally with no human mediation through the web. V. CONCLUSION We are developing this device to ensure that the doctor is notified about patient condition, so that patient can be monitored. VI. REFERENCES 1. Shivaji K, Amogh D, Pavan J. A survey on Li-Fi technology. In: Proceeding of International Conference on Wireless Communication, Signal Processing and Networking (WiSPNET), March; Pooja B, Ratul M, Balaji S. Smart vehicular communication system using Li Fi technology. In: Proceeding of International Conference on Computation of Power, Energy Information and Commuincation (ICCPEIC), April; van der Togt R, van Lieshout EJ, Hensbroek R, Beinat E, Binnekade JM, Bakker PJ. Electromagnetic interference from radio frequency identification inducing potentially hazardous incidents in critical care medical equipment. JAMA 2008;299(24): Chang HJ. Framework for data communication in the hospital using Li-Fi technology. Int J SciEng Res2016;7(8): Available from: Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 219

246 Volume 9, Special Issue No. 3, May 2018 International Journal of Advanced Research in Computer Science (ISSN: ) CONFERENCE PAPER Available Online at PROGRAMMED RECOGNITION AND NOTIFICATION OF POTHOLES AND MOUNDS ON ROADS TO ASSIST DRIVERS Karan K Sanghvi Department of CSE SVIT Bangalore, India Nithin Nayak Department of CSE SVIT Bangalore, India Saurabh Gang Department of CSE SVIT Bangalore, India Sreelatha P K Department of CSE SVIT Bangalore, India Abstract The maintenance of road is plight or an issue in countries on verge of development. There is the significance of well-maintained street as; perpetuated streets contribute a noteworthy part to the nation's economy. Identification of potholes and humps is necessary, since identification of these pavement distresses not only aids driver to avoid traumatic accidents and damages to vehicles such as alignment of wheels in vehicles etc but also helps authorities to maintain roads. In this paper it has been described about the methods those were developed previously for detection of potholes. This paper proposes a financially savvy and feasible solution to distinguish potholes and protuberances on streets and give auspicious alarms to drivers to keep away from mishaps or vehicle harms. Echolocation technique is used to identify potholes and also to measure their height and depth respectively.ultransonic sensors are being deployed in order to implement this echolocation technique and hence, identify one of the pavement distress, pothole. RF module is being utilized for detection of hump since this module makes wireless communication possible. This serves as a valuable source of information to the Government authorities and to vehicle drivers. Keywords Echolocation, Ultrasonic Sensors, RF Transmitter, RF Receiver, Blynk I. INTRODUCTION India is known to be the second most populated nation in the world. It is a nation with swift growth in the economy and is known to have a substantial system of streets. Streets are overwhelming methods of transportation in India today. They carry, nearly 90 percent of nation's traveler movement and 65 percent of its cargo. However, most of the streets in, India are tapered and congested with poor surface quality and street support needs are not agreeably met. Roads which are constructed in India for transportation normally have speed breakers in order to control the velocity with which vehicles travel and avoid accidents. However, these speed breakers are unevenly distributed with uneven and unscientific heights. Statistics shows that over last few decades, there has been tremendous growth in the vehicle population. The multiplication in number of vehicles has not only resulted in Traffic congestion but has also resulted in increase in the number of traumatic accidents and pollution. Pothole, may seem one of the pavement distresses which is a deep natural underground cave formed by erosion of rock, especially by the action of water as shown in fig 1and Hump may seem just another pavement distress which is just a rounded raised mass of earth or land but these may have adverse affect if care is not taken. The road accident statics in India, of Survey in 2013 included information that over 1, 37,000 people were killed in road accidents which is more than the number of people those who get killed in all wars put together or either natural calamity street crash happens each day in India and furthermore around 20 kids younger than 14 pass on consistently in light of street crashes in the nation [3]. With the proposed system an attempt has been made to provide feasible solution to aid driver and especially aid government for maintaining the road. The remaining sections of the paper are as follows: Section II concentrates on technological problems faced in order to implement the proposed system effectively in India. Section III describes the related work or work that has been done or research that is going in this field of detection of pothole and hump. Section IV describes various components that are integrated to develop proposed system. Section V describes proposed system. Experimental results and conclusion and future enhancement that is possible in the proposed system has been mentioned in Section VI and Section VI I respectively. Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 220

247 Karan K Sanghvi et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, Fig 1.Pothole: The figure shows pothole formed on road due to rain. Pothole, one of the pavement distresses is a deep natural underground cave formed by erosion of rock, especially by the action of water and moving vehicles on this puddle of water may lead to traumatic accidents. II. RELATED WORKS He Youquan, Wang Jian, Qiu Hanxing, Zhang Wei, Xie Jianfang in their paper have described that nowadays frequent accidents are occurring due to rash driving of vehicles at crucial areas despite of knowing the fact that speed thrills but kills but in addition to this they have described another major reason for frequent accidents and that is related to issue of improper maintenance of roads [2]. This paper portrays an undertaking that gives a framework to caution the drivers when their vehicle approaches schools, doctor's facilities, and swarmed zones. It additionally gives the opportune alarm to the drivers via naturally identifying the nearness of potholes and protuberances on streets. The sign of swarmed zones is finished with the assistance of two units, for example, transmitter unit which gives zone based data to the drivers and a beneficiary unit that contains a LCD to show the zone based data [2]. The presence of gaps and protuberances are recognized by utilizing an ultrasonic sensor and is indicated to the drivers by utilizing a buzzer. This framework gives an approach to manage the speed of the vehicle at swarmed regions and furthermore to forestall vehicle harm [2]. Artis Mednis, Girts Strazdins, Reinholds Zviedris, Georgijs Kanonirs, Leo Selavo in their paper have proposed, The significance of the street framework for the general public could be contrasted with the significance of veins for people. To guarantee street surface quality it ought to be checked ceaselessly and repaired as necessary. The ideal circulation of assets for street repairs is conceivable giving the accessibility of complete and target ongoing information about the condition of the streets. Participatory detecting is a promising methodology for such information accumulation. The paper is portraying a versatile detecting framework for street inconsistency recognition utilizing Android OS based advanced mobile phones. Selected data processing algorithms is discussed and their assessment gave genuine positive rate as high as 90% utilizing certifiable information. The ideal parameters for the calculations are resolved and additionally proposals for their application. [5] Zhen Zhang, Xiao Ai, C. K. Chan and Naim Dahnoun in their paper proposed, a stereo vision based pothole identification framework is proposed. Utilizing the map generated by a proficient disparity computation algorithm, potholes can be distinguished by their separation from the fitted quadratic street surface. The framework delivers the size, volume, and position of the potholes which permits the pothole repair to be organized by its seriousness. The quadratic street surface model takes into account camera orientation variety, street seepage and up/downhill inclinations. [6] Mircea Strutu, Grigore Stamatescu, Dan Popescu in their paper have proposed a street surface imperfection distinguishing proof framework in light of 3D accelerometers, GPS and video modules deployed on vehicles in which they have likewise given clear depiction of the mobile platform architecture and the central data aggregation algorithm.[7] III. TECHNOLOGY PROBLEMS GPS, even though highly reliable, may fail at times, some equally reliable alternatives has to be provided instead of GPS. Internet connectivity becomes mandatory for using not all areas have good internet coverage. Absence of vigorous information encryption, i.e. gathering, conveying, preparing and checking information is at the core of the total IoT application process. Each IoT framework manages a humongous measure of information, which is close to home in every one of the cases. Subsequently, there is a flat out requirement for information encryption that would guarantee that no individual information is hacked or spilled into undesirable hands. The results of such information misusing can be desperate and would represent an extreme danger to the security of clients. Vulnerable to side channel attacks. IV. COMPONENTS USED Arduino Uno: It is a microcontroller board based on Atmega 328. It has 14 digital input/output,6 analog inputs, a 16 MHz ceramic resonator, a USB connection, a power jack, an ICSP header, and a reset button. A minimal Arduino C/C++ sketch consists of two functions namely: setup():this function is called once when a sketch starts after power up or reset. Loop():After this is a function which is called repeatedly to achieve specific task. Ultrasonic Sensor: An Ultrasonic sensor is a device that can measure the distance to an object by utilizing acoustic waves. By utilizing it one can measure distance by sending out a acoustic wave at a specific frequency and listening for that sound wave to bounce back. It works on principle of echolocation shown in figure 2. By recording the elapsed time between the sound wave being generated and the sound wave bouncing back, it is possible to calculate the distance between the sonar sensor and the object [1].The formula utilized to calculate the distance or depth of the pothole is : d=v x Δt d=344 x Δt Velocity of sound represented by v=344 as it is well known fact that the speed of the sound is 344 meters per second. Fig 2. Ultrasonic Sensor RF Module:. An RF module (radio recurrence module) is a (typically) small electronic gadget used to transmit or potentially receive radio signals between two gadgets. As the name recommends, RF module works with the assistance of Radio Frequency. The relating recurrence run that is frequency varies between 30 khz and 300 GHz. Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 221

248 Karan K Sanghvi et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, Fig 3. RF Module GPS: GPS, is an acronym utilized for Global Positioning System, it is the main framework in exhibit time which can demonstrate to you your correct position on the Earth, in any climate, anyplace.gps is based on satellite ranging, i.e. distance from satellites is precise reference points, and we determine our distance from them. Fig 5. System Workflow On Receiver Fig 4. GPS V. PROPOSED SYSTEM In this work, we have proposed a financially savvy and feasible solution for distinguishing proof of potholes and protuberances. Ultrasonic sensors are utilized to recognize potholes and furthermore to gauge their profundity and stature individually. RF transmitters are put out and about where there is a mound and RF recipients are set inside the car and at whatever point there is a protuberance the RF beneficiary will receive the signal and will alarm the driver through Buzzer and LCD display. In this prototype after detection of distresses such as hump information is sent to the cloud through WIFI and this data can be accessible on Blynk installed on the Android smart phone. The sensed-data includes pothole depth, height of hump which is sent to the server (Mobile) via WIFI. Then this data is stored in the database (Mobile). [4] This serves as an important wellspring of data to the Government specialists and to vehicle drivers. At a point, if any pothole or protuberance is present then the sensors will detect this and momentarily the signal will be actuated and will be shown on the LCD to caution the driver with the goal that prudent step can be taken to sidestep mischance s. The systems workflow has been represented by a block diagram as shown below in Figure 5 and Figure 6.The prototype for the detection of distresses is shown in Figure 7. Fig 6. System Workflow On Transmitter Fig 7. Prototype Of Distresses detection V. RESULT The location of pothole and hump is being detected effectively by using GPS module and suitable message is being displayed with the magnitude of latitude and longitude as shown in experimental results as shown in snapshots given below. The Snapshot in figure 8 shows hump detected in the yelehenka area of the Bangalore and the latitude and longitude magnitude related data that is detected through the proposed system is notified by utilizing Blynk application and it is illustrated in the figure 9. Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 222

249 Karan K Sanghvi et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, mounds and alarming individual who is driving the vehicle and subsequently helping him. It is likewise helping an individual who is driving vehicle to sidestep potential mishaps. The compact application created utilizing Blynk in Android Smartphone is an additionally preferred standpoint as it gives opportune caution of protuberances and potholes. REFERENCES Fig 8: Location Of Hump Fig 9: Blynk Application Notification VI. CONCLUSION The model proposed in this venture satisfies two basic necessities that is programmed recognition of pothole and [1] Rajeshwari S., Santhosh Hebbar, Varaprasad G., Implementing Intelligent Traffic Control System for Congestion Control, Ambulance Clearance and Stolen Vehicle Detection, IEEE Sensors Journal, Vol.15, No.2, pp , [2] He Youquan, Wang Jian, Qiu Hanxing, Zhang Wei, Xie Jianfang, A Research of Pavement Potholes Detection Based on Three-Dimensional Project Transformation, In Proceedings of International Congress on Image and Signal Processing, pp , [3] India Transport Sector. [Online]. Available: [4] Faith Orhan, P. Erhan Eren, Road Hazard Detection and Sharing with Multimodal Sensor Analysis on Smartphones, In Proceedings of International Conference on Next Generation Mobile Apps, Services and Technologies, pp , [5] Artis Mednis, Girts Strazdins, Reinholds Zviedris, Georgijs Kanonirs, Leo Selavo, Real Time Pothole Detection using Android Smartphones with Accelerometers, In Proceedings of Distributed Computing in Sensor Systems Workshop, pp.1-6, [6] Zhen Zhang, Xiao Ai, C. K. Chan and Naim Dahnoun, An Efficient Algorithm for Pothole Detection using Stereo Vision, In Proceedings of IEEE Conference on Acoustic, Speech and Signal Processing, pp , [7] Mircea Strutu, Grigore Stamatescu, Dan Popescu, A Mobile Sensor Network Based Road Surface Monitoring System, In Proceedings of IEEE Conference on System Theory, Control and Computing, pp , Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 223

250 Volume 9, Special Issue No. 3, May 2018 International Journal of Advanced Research in Computer Science (ISSN: ) CONFERENCE PAPER Available Online at ACloudletBasedMulti-ObjectiveVirtualMachineAllocation PriyankaBharti SchoolofComputingandInformationTechnologyREVAUnive rsity,bengaluru Rajeev Ranjan School of Computer Science and Applications. REVA University, Benaluru Abstract:Clouddatacentersonintegrationwithvirtualizationprovidesthedynamicandflexibleresourceprovisioningforth ecomputationorprocessingofthedataintensiveapplications.tocarryouttheoperationsefficientlyinvirtualizedenvironment,energyconsumptionhasbecomeoneo fmajorchallenges.thereforeoptimalvirtualmachinemappingtothephysicalmachinesarerequiredotherwisepowerconsumptioncandrasticallyhiket heoverallcost.thispaperproposesanovelmulti-objectiveap- proachforallocatingthevirtualmachines(vms)usingdynamicdatastructurer- Treewhichisanalogoustobinpackingproblem.CloudSimtoolkitisusedtoconductthesimulationandresultsareanalyzed,whic hshowsthereductioninenergyconsumptionandslaviolations.hence,providesenoughscopeforfutureresearch. CloudComputing;Datacenters;Virtualization;VirtualMachines;Virtualmachineallocation; I. INTRODUCTION Cloud Computing in terms of optimized execution,canbeexpressedasanondemanddistributionoffaulttolerant,reliable, secure and sustainable services overthe internet.this featurehas attracted manylargeand small businessorganizationsandthuscloudcomputinghasbecomeoneo fthemostfavorabletechnologiesinthetodaysitworld.withtheinc rementinthevolumeofenduserstherearesignificantchallengesco nfrontedbythecloudserviceproviders(csp)whilegrantingontheflyserviceswithintheirreliabilityandubiquity.thisledtothegrowt hofclouddatacenterswhichinturnincreasetheenergyconsumptio n.theglobalenergyconsumptionbydatacentershasascendedby5 6%from2015to2017,andin2017isfiguredouttobeinthevicinityof 1.1%and1.5%oftheaggregatepowerutilization(1).Asanticipated by(2;3;4),energyconsumptioninclouddataceneterswillkeepong rowingquicklyunlessprogressedenergyefficientassetadministrat ionarrangementsareproducedandconnected. Inspiredbytheliterature,itseemsthattheobjectiveofenergyconser vationcanbeaccomplishedbymeansofkeepingtheutilizationofre sourcesinanidealusagelevel.tolimittheresultantenergyconsump tion,thequantityofactivehostsshouldbediminishedandidlehostss houldbeturnedout.inspiteofthefactthattheconventionalheuristic algorithmmaydiscoverananswerforvmsallocationforenergyeff iciency, theyaresimpletofallintolocaloptimalsolution. Inthispaper,wecentralizeourresearchontheVMallocation,basedonamultidimensionalbinpackingproblemandsearc hingofthenearestneighbour(whichishostinourcase)toeffectivelya ccommodatethedynamicallyarrivingvmtoscaledowntheenergyc onsumptionusingthecloudsimtoolkit.theproblemiscategorizeda snphardinnature. II. RELATED WORKS VMallocationproblem,mainlyfocusesondevelopmentofalgor ithmsthatincludesseveralparameterswhicharecollectedtogethert oformagroup.further,theliteratureexaminesthesegroupsasenerg yconsumptionminimizationgroup,networktrafficminimizationg roup,costoptimizationgroup,performancemaximizationgroup,r esourceutilizationmaximizationgroup. Somefamousallocationpoliciesdefined bythesegroupsareroundrobin Enterprise Cloud and DatacenterVirtualization (5;6; 7),Striping PolicyEucalyptus( icy.thereisaconnectionamongenergyconsumptionandresource utilizationwhichfocusesonthepairofresources:diskandcpu,anda MBFDheuristic algorithmis used forallocation ofvms.keepinginviewtheexecutionnecessities,breakdowninus ages of Storage andcpu to limit the energy consumptioninheterogeneousdatacenterisproposed.therefore,t heidealharmonyamongenegymetricsandresourceutilizationresi dearound70%storageusageand50%onthecpu. Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 224

251 Priyanka Bharti et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, VMscanalsobeallocatedbyconsideringbothVMplacementanddataplacement.(8;9;10)havesuggestedanapproachtha treducescrossnetworktrafficandbandwidthusage.toaccomplis hthisaim,themindistvmdataplacementproblemisdefinedandsolvedbyusingmetaheuristicsappro achinvolvingantcolonyoptimization(aco)approach. (8)statesamultiobjectiveapproachwhichtriestominimizethevirtualmachinesc CDARAmodeliscomposedof,CloudProvider,User,Broker,an dcloudmarketplace.cloudmarketplacefurthersubsistofcisan dauctioneer.cisaidsuserstofindappropriateresourceandservice saccordingtotheirrequirements. III.PROPOSEDWORK MostlyVMallocationstrategiesinliteraturetargetonenergyeffi cientallocation,butarenotconsideringthedelayofschedulingwhil eallocationofvmstoanyhost.themotivebehindthisresearchistod esignamultiobjectivevmallocationapproachwhichmapvmstohostandisalso energyefficientwithminimumdelayscheduling.toachievetheaim,weformulateourallocationproblemaslinearprogrammingand solveitbyusing metaheuristics approachoffindingnearestneighbor. Theclient srequestisreceivedbydatacentercontrollerbrokerand itinstantlyexchangesthedemandtother-treebroker. TheR- TreeBrokerworkis twofolded.firstly, itconstructsthe2dr- TreeconsideringmemoryandCPUasdimensionsforaccessiblehos tinthedatacenterandrecoversthespecifiedmemoryandcpufromt heclientdemand.itfurtherplaces themas querypoint on theframed R- Tree.Besides,itkeepsuptwokindofListforallocationpurpose,(i)a DistanceVectorListwhichkeepsupthedistancecomputedfromthe querypointtoallaccessiblehostinr- Treeand,(ii)aDistanceAllocationVectorListwhichkeepsupthecur rentconsumptionofthehostintermsofprocessingelements.forallo cation.the R- TreeBrokertraversetheDVListandchoosethehosthavingleastdista nceamongallthedistances.next,r- TreeBrokerchecksthesuitabilityofhostusingthresholdvalueby traversing thedavlist. IntheeventthatDAVListisvacant,atthatpointVMisallocatedtohost havingminimumdistance. IV.RESULTS This section presents the results of proposed scheme obtained during the simulation. The proposed work has been assessed by simulation using CloudSim toolkit. It supports client specified approaches for distribution of hosts to virtual machine. CloudSim is a system for modelling and recreation of distributed computing frameworks. It has been fiercely used to assess algorithm, applications, and approaches before genuine improvement of cloud products. hedulingdelayandoperatinghostsbyswitchingofftheremainingh ostsusingamulti-dimensionaldatastructuredividedkdtree. CombinatorialDoubleAuctionResourceAllocation(CDARA) modelisproposedby(9),whichcombinestheadvantagesof(8;10) models,andefficientlyallocatesthe In CloudSim, we have Datacenters which consists of hosts, and each hosts has a number of Processing Elements(PE). On these hosts, numerous running VMs are present. Further, these VMs have number of Cloudlets which represents the dynamic requirements of the clients. Initially, VMs are created at the time of simulation, and jobs (cloudlets) can be submitted to these VMs. Both Hosts and Virtual machines which are used in CloudSim for simulation of proposed work, are Heterogeneous in nature. Algorithm SLA Violation Cost ($) Metrics Energy Consumption Proposed Anti-Affinity Round-Robin Balance Simple TABLE I: Simulation Result in terms of Cost, Energy Consumption and SLA Violation Itisobservedthat VMallocation strategiesonly targetontheenergyefficientallocation.thedelayofsche dulingwhileallocationofthesevmstoanyhosthasnotbe enexploredmuchintheliterature. Totestthe efficiencyofourapproach,wehavecompareditwithothe rstandardvmallocationpoliciesviz:antiaffinityvms chedulingproject ( er/readme.md),load Balance VM Scheduling Project( /blob/master/readme.md), Round RobinScheduling( eite/ )andvmallocation Policy Simple(Default AllocationPolicy incloudsim).thecomparativeresultsintable1showthe analysisofenergyconsumptionandslaviolationsofp roposedapproachwithotherstandardallocationpolicies. Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 225

252 Priyanka Bharti et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, V.CONCLUSION Thisstudypresentsamultiobjectivealgorithmtofindbetterallocationsolutionsbet weenvirtualmachinesandhosts.theproposedapproach booststheutilizationofthedatacenterresourcesina mannerthat the total energyconsumptionisreducedalongwithreductioninpe rcentageofenergyconsumption,slaviolations,datace ntercost,whilekeepingthecountofoperatinghostsatami nimallevel.initialvmallocationisbasedonr- Treestructurewherethehostsarearrangedinthedatacent erintheformofa2dr- Tree,makesearchingprocessfastsothatoverallscheduli ngdelaycanbereduced.theexperimentalresultsarecom paredwiththeexistingstandardallocationtechniqueson variousparametersandthesimulationresultsshowthatth eproposedalgorithmcannotonlyprovidebetterresultsth anthetraditionalrulebasedallocationtechniques,butalsooutperformstheinb uiltandotherstandardalgorithmsintermsofenergyconsu mption,slaviolationsandschedulingdelay. REFERENCES [1]"AmazonElasticComputeCloud",Availableonlineat: s.amazon.com/ec2/,accessedonjune24,2015 [2]"IBMCloudComputing: Infrastructure asa Service",Availableonlineat: [3]M.Wooldridge, "AnIntroduction tomultiagentsystems", SecondEdition,JohnWiley&Sons,2012 [4]X.Li,H.Zhang,Y.Zhang,"DeployingMobileComputationinCloudService",Proceedingsofthe1stInternationalConferen ceoncloudcomputing,springer-verlag,vol.1, No.4,2009,pp [5] Mahantesh N. Birje, Sunilkumar S. Manvi, Sajal K. Das, "Reliable resource brokering scheme in wireless grids based on non-cooperative bargaining game", Elsevier Journal of Network and Computer Applications, Vol. 39, No. 1, 2014, pp [6] Domenico Talia, "Cloud Computing and Software Agents: Towards Cloud Intelligent Services", Proceedings of 12th Workshop on Objects and Agents, Vol. 74, No. 2, 2011, pp. 2-6 [7] J. Octavio, Gutierrez-Garcia, Kwang-Mong, "Self- Organizing Agents for Service Composition in Cloud Computing", Proceedings of the 2010 IEEE Second International Conference on Cloud Computing Technology and Science, Vol.3, No.2, 2010, pp [8] Dinesh Kumar, R. Ashwin, "Multi-Agent based Cloud Services", International Journal of Computer Applications, Vol. 6, No. 1, 2013, pp [9] Sunilkumar S. Manvi, Gopal KirshnaShyam, "Resource Management for IaaS in Cloud Computing: A Survey", Elsevier Journal of Network and Computer Applications, Vol. 41, No.1, 2014, pp [10] Gopal KirshnaShyam, Sunilkumar S. Manvi, "Modeling Resource Virtualization Concept in Cloud Computing Using Finite State Machines", International Journal of Cloud Computing, Inderscience, 2015 (in press) Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 226

253 Volume 9, Special Issue No. 3, May 2018 International Journal of Advanced Research in Computer Science (ISSN: ) CONFERENCE PAPER Available Online at A CUCKOO HASHING SCHEME FOR COLLISION REDUCTION IN CLOUD STORAGE SYSTEMS Rabia Basri, School of C&IT, Bangalore, India, basri.rabia15@yahoo.com Shilpa Bhasker, School of C&IT, Bangalore, India, shilpabhasker096@gmail.com Sanjay S V, School of C&IT, Bangalore, India, sanjay.sv777@gmail.com Shruthi J, School of C&IT, Bangalore, India, shruthij1997@gmail.com Vani Krishnaswamy School of C&IT, Bangalore, India, vanikrishnas@reva.edu.in REVA University Abstract: Retrieving a specific data from any large scale storage systems have been the most complicated process in the world of technologies. With this high speed growth of the information, cloud servers are required to operate and examine the bulk of highdimensional and unstructured dataprecisely. Although, cloud computing systems provide services to the large storage devices, it is still challenging to obtain accurate results for query requests in a real-time manner. The cuckoo hashing methods have been used widely because of its simple flow and ease of work. But, due to the collisions between the data, the cuckoo hashing scheme suffers from endless loops and high insertion latency. In order to serve these problems, we present an efficient cuckoo hashing scheme called Mincounter. The purpose behind the Mincounter is to reduce the occurrence of endless loops during data insertion. The scheme has the main features of offering efficient insertion and query services and delivering high performance of cloud servers, as well as extending the services of cloud computing servers. The Mincounter is implemented in a large scale database in the real world. The results represent the efficiency of Mincounter scheme. Keywords: Cloud storage, cuckoo hashing, ranking process, data insertion and query. 1. INTRODUCTION Since there is a rise in the awareness of big data and other technologies, the digital world lives increasingly in the cloud computing servers, above the distinct land of huge hardware datacenters associated to infinite number of distributed devices, all ruled and described by increasingly advanced Software. As stated in the record of International Data Corporation in the year 2014, the world has been digitalized double of its size in every two years. So the bits in the data world and stars in the sky would look alike. Cloud computing devices serve big number of resources in the system, but the demanding part is to fetch the results from the cloud at exact time and keep it precise or to the point. To make better the whole system s performance and storage orderliness many schemes had been proposed, in particular- hierarchical bloom filter index (which increases the speed of searching process), optimization of queries, approximation of the membership of queries on the cloud, search of multiple keywords from the encrypted cloud s data, similar keys search in the files, reducing the retrieval latency for the information and so on. But all of these schemes have been unsuccessful to meet the requirements of the real world queries. To overcome such problems, data structures which are based on hashing method are used. It helps in the build of the indexes and high speed fetching process. Though they serve as a fast retrieval process, they are subjected to high space utilization and high-latency possibility due to collisions. To overcome such risks and to improve the performance, we are supposed to handle the following challenges. Intensive data migration while we are adding any new item into the server, if we encounter the collision, then the current data is migrated to the position it is asking for and the data which was existing in that place will be kicked out. The existing data has to wait until any empty position is found. This is a severe challenge. Space inefficiency whenever the existing item is dismissed out from the slot, we need to check whether there is another empty position where we can place this item. Due to this problem of finding the place for the kicked out item, it harms the storage space of the system. High insertion latency because of the above mentioned Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 227

254 Rabia Basri et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, challenges, the items that are waiting for the particular slot are places randomly in the tables of hashing. This exhibits the unpredictability f the addresses of the items and their positions they are settled. Thus the outcome of this particular problem leads to increase in the insertion latency. Therefore, to deal with such difficult problems, this paper has the following contributions. Searchable encryption Multi-level indexing and Key aggregation The rest of this paper is organized as follows. In Section 2, we will review some related work about different types of hashing techniques. In Section 3, we brief about the overview of the system design and it s working through simple flow diagrams. The results of the implementation for the same are presented in Section 4. We conclude this paper and discuss the future work in Section RELATED WORKS In today s world the information technology has been growing rapidly and it is required to process and analyse bulk amounts of high dimensional and disordered data efficiently and in a real time manner which calls for many query operations. On that note we bring one of the most useful cuckoo hashing scheme called the Min counter into picture due to its ease of use and clarity. Min counter has proved to be of high applicability supporting various user friendly features such as good query services and offers very efficient insertion thereby enhancing the performance of cloud servers and in turn improving the user experience for cloud users. Since cloud computing systems consume large amounts of resources we need an alternative process to better the system performance and efficiency due to which we have proposed schemes such as hierarchical bloom filter index to fasten up the searching process, Query optimization for parallel data processing, Open addressing, multi keyword search of encrypted cloud data, Similarity search in file system, Retrieval for ranked queries over cloud data etc. Various schemes including open addressing is taken from reference [3], parallel data processing from [4], multi keyword search of encrypted cloud data [1] etc. Given below is the summary of these references and the idea derived from them. Cao N [1] in his paper, he defines the solution for a secure ranked keyword search problem over encrypted cloud data. Ranked search amplifies system usability by enabling search result relevance ranking instead of returning separate results and thereby ensuring precise file retrieval. Q Liu [2] this paper describes how searchable encryption can be carried out for large storage data using multikeyword ranked search. And this is carried out by taking large amounts of outsourced documents into account from the cloud. And thus we make use of techniques such as n- nearest neighbour and relevance score to develop an efficient scheme that can return the ranked search results using multi- keyword search accurately. Q. Li [5]in this paperhe discusses how with the quick development of data, query performance is an affair in cloud storage applications. The cuckoo hashing consumes a large amount of system resources, since an item insertion may suffer from frequent kick out operations or even endless loops.to reduce the query response time, cuckoo hashing via d hash functions has been adopted to achieve O(1) query efficiency. Efficient queries in cloud computing applications are important to offer cost-effective services. In order to support real-time query performance, we propose a novel cuckoo hash based scheme, called Necklace. Necklace addresses the future problem of infinite loops when inserting and querying items. This paper presents, Necklace is to reduce data migration in cloud storage services via examine and determine the relationship between candidate storage locations to identify the shortest augmenting path. Compared with state-of-the-art work, extensive experiments, using a real-world trace, demonstrate the benefits of using Necklace. Y. Sun[6] discusses how with the quick development of data, cloud computing servers need to process and examine large amounts of data timely and correctly, requires many query operations. Due to clarity and ease of use, cuckoo hashing schemes have been used in real-world cloudrelated applications. The cuckoo hashing experiences endless loops and high insertion latency, even high risks of alteration of entire hash table. A cuckoo hashing scheme, called MIN COUNTER has been proposed. This paper presents, cuckoo hashing scheme, named Min Counter for large scale cloud computing systems. The Min Counter presents the three main challenges in hash-based data structures, i.e., fast data migration, low space utilization and high insertion latency. Advantage of MINCOUNTER is cold buckets to relieve hash collisions and decrease insertion latency. Min Counter enhances the quality of experience for cloud users. 3. OVERVIEW OF SYSTEM WORKING 3.1 The Mincounter Architecture The Mincounter is one of the most efficient cuckoo hashing scheme for the insertion of the data. Because of its simple Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 228

255 Rabia Basri et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, manner of flow and easier to use, it handles of the challenge regarding the space and storage, insertion and data migration. Fig 1 represents the architecture of the Mincounter in accordance with the storage. Fig 2: System Architecture 1.1 Uploading a file Fig 3 explains the flow of uploading the file, giving the category to which it belongs and the key generation. architecture of Mincounter storage system 3.2 Working Fig 2 demonstrates the working process of the experiment in brief. The admin logs in with appropriate username and password. He configures the storage sever and generates a key. He uploads the file with only the keywords and encrypts the file with a private key and gives the access authorities to the users, accepting or rejecting the users requests solely depends on the admin s definition of access authorities to the different users. He will be responsible to change the passwords or the private keys if necessary and no others can do it. Fig 3: Flow diagram for uploading a file 1.1 Keyword search Fig 4 represents the process of searching a keyword. Each of the keyword is encrypted and a specific hash key is generated. If the user has download any of the file using any particular keyword, then he must be having access authority to that particular file, if had then, he must give his private key to decrypt the file and download them. Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 229

256 Rabia Basri et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, Result for keyword search Fig 6: Result showing the file found with the keyword and ranked according to the weightage of the keyword present in the file Fig 4: Flow diagram for searching a keyword 4. RESULTS 4.1 Result for uploading a file 5. CONCLUSION AND FUTURE WORK This paper proposes min counter scheme to counter endless loop occurrences and other query related abnormalities in large-scale cloud computing systems. This scheme of min counter focuses primarily on parameters such as intensive data migration, high insertion latency and low space utilization and thereby improving cloud server performance and user experience in cloud applications. We can further extend it as future implementation to perform the same scheme not only for text documents as discussed but also for multimedia content which may offer a good amount of contribution for further cloud applications. 6. REFERENCES Fig 5: Result showing successful upload of a file Fig 5 shows the experimental result of uploading a file. There is a dialogue box that appears after we upload a file saying file uploaded successfully. [1] N. Cao, C. Wang, M. Li, K. Ren, and W. Lou, Privacy-preserving multi-keyword ranked search over encrypted cloud data, Proc. TPDS, vol. 25, no. 1, pp , [2] Q. Liu, C. C. Tan, J.Wu, and G.Wang, Efficient information retrieval for ranked queries in costeffective cloud environments, Proc. INFOCOM, pp , 2 [3] R. P. Brent, Reducing the retrieval time of scatter storage techniques, Communications of the ACM, vol. 16, no. 2, pp , 1973 [4] S. Wu, F. Li, S. Mehrotra, and B. C. Ooi, Query optimization for massively parallel data processing, Proc. SOCC, [5]Q. Li, Y. Hua, W. He, D. Feng, Z. Nie, Y. Sun,"Necklace: An efficient cuckoo hashing scheme for cloud storage services" [6]Y. Sun, Y. Hua, D. Feng, L. Yang, P. Zuo, S. Cao, "MinCounter: An efficient cuckoo hashing scheme for cloud storage systems. Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 230

257 Volume 9, Special Issue No. 3, May 2018 International Journal of Advanced Research in Computer Science (ISSN: ) CONFERENCE PAPER Available Online at A FRAMEWORK OF IMPROVING CLOUD SECURITY Sanjay Kumar, Compute and Information Technology, REVA University kqwerty50@gmail.com Saurabh Subham, Compute and Information Technology, REVA University saurabhsubham76@gmail.com Shah Lucky, Compute and Information Technology, REVA University shah.lucky24@gmail.com Syed Abdul Rehman, Compute and Information Technology, REVA University syedtouhid8@gmail.com Jyoti Kiran Mirji Compute and Information Technology, REVA University jyotimirji@reva.edu.in Abstract: Cloud computing is being the major technology which has been evolved in this era where developing the product and the providing the web services has been enhanced. There are various cloud-based services namely Azure, AWS, google drive and so on. Processing power is more efficient in the cloud environment. But with the launch of cloud services in the cloud environment with the less cost, the privacy of the people is being hindered. Challenges in the cloud computing which need to be rectified and resolved. Here we are trying to resolve one such issues of it s security by providing a powerful framework namely Homomorphic encryption method where the operation can take place on the cipher text or encrypted data. Keywords: Cloud Computing, Security, Framework INTRODUCTION Today cloud computing plays a vital role in storing our important data online. Whatever we do online say like playing music, watching online videos etc. is likely to be due to cloud computing. Cloud computing administrations are being utilized by a large number of the associations from minor new companies to worldwide enterprises, government organizations to non-benefits are utilizing the innovation for a wide range of reasons. Cloud computing is a big step from the traditional way businesses to advancement of IT resources. Personal devices connected to the remote devices to store the data hosted on the internet called as cloud computing. This is entirely different when compared to the storing of the data on the local storage devices for computations or operations. This model would allow people to use services as per their requirement like platform, infrastructure, software. Thus, avoiding people to invest huge amount in order to get their resources. I. CLOUD COMPUTING As per the customer need National Institute of Standards and Technology(NIST) has given five main characteristics which should be available as the cloud services. They are: 1) On-demand self-service: User can do any operation without any interface between them. And use his services. 2) Network access: data is accessible with any internet host connected devices. 3) Location independent resource pooling: assets ought to be shared to the clients independent of their area wherever they are. 4) Physical transparency: client can advance on the arrangement asset limit according to their necessity it is possible that it might be for co-agent utilize or it might be for individual utilize 5) Pay per use: Individuals should be charged in view of the assets utilized by them relying upon their utilization. NIST has also defined the cloud delivery models. They are: Infrastructure as a Service (IaaS): Infrastructure as a service (IaaS) is an instant processing infrastructure, provisioned and managed over the Internet. IT allows us to pay expense as per the use of their resources. It will give user to choose their requirement as they want and scale-up and down. It helps us in managing and avoiding the extra expenses in buying own servers and datacenters. Every asset is offered as a different service segment and you just need to lease a specific one for whatever length of time that you require it. The cloud computing service provider helps in dealing with the framework, when we buy, introduce, design and deal with our software's. Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 231

258 Sanjay Kumar et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, Platform as a Service (PaaS): Platform as a service (PaaS) is an entire advancement and organization condition in the cloud, with assets that enable us to convey everything from straightforward cloud-based apps to sophisticated, cloudenabled undertaking applications. We can purchase the assets of our need from a cloud service provider on a pay-as-you-go basis and access them over a safe Internet association. PaaS incorporates infrastructure servers, storage and systems administration yet additionally middleware, advancement devices, business insight (BI) services, database management frameworks and so forth. PaaS bolster the entire web application lifecycle: building, testing, sending, managing and updating. PaaS allows to avoid the cost and unpredictability of purchasing and managing software licenses, the fundamental application infrastructure and middleware or the advancement devices. We can manage the applications we create and the cloud service provider typically manages everything else. Software as a Services(SaaS): Software as a service (SaaS) allows clients to interface with and utilize cloud-based apps over the Internet, for example, , calendaring and office apparatuses (like Microsoft Office 365). SaaS provides a complete software solution which we purchase on a pay-as-you-go basis from a cloud service provider. The greater part of the hidden framework, middleware, application programming and application information are situated in the specialist co-op's server farm. The specialist organization deals with the equipment and programming and with the proper administration understanding, will guarantee the accessibility and the security of the application and our information. SaaS enables our association to get rapidly up and running with an application at negligible cost. Cloud model can be deployed in several other ways like: 1) Private cloud model 2) Community cloud model 3) Public cloud model 4) Hybrid cloud model III.ETHICAL ISSUE ANALYSIS Ethical issues in cloud computing is the major concern which include security and privacy. Cloud computing will confront continuous issues which is extremely elusive. Being over the top about the moral issues in cloud computing may take care of a portion of the issues without influencing the parameters and the advance. For every ethical issues computer is not meant to be responsible it s the cloud service provider which is responsible for it. It implies that association has not met the legitimate prerequisites to give the administrations. Associations giving administrations needs to comprehend the necessities and outcomes of slipups. III. SECURITY ISSUES IN CLOUD With the expansion utilization of cloud administrations and organization of the cloud benefit, security issues in the cloud are likewise expanding. The organizations providing these services should take care of it. Information in cloud environment: can influence the associations with respect to security Aspects in the cloud. Based on confidential Information put away and handled in the cloud may prompt different assaults. Capabilities of attacker: aggressors can be ordered into inside and outside assailants. The inner aggressor has inside information and gain admittance to numerous assets in the billow of a specific individual. An outside assailant won't not approach inside subtle elements but rather, they abuse the classified data, information s and vulnerabilities. Risk management in the cloud: associations need to comprehend the danger of moving to cloud condition in different cloud environment. Cloud service providers approach their information, in the event that it isn't scrambled. Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 232

259 Sanjay Kumar et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, The information which are never again required, they store information on a few servers. Erasing their information doesn't mean finish erasure of information on all servers and geographic area of these servers are obscure to the clients or associations utilizing the cloud service. New cloud security risks: Individuals should be set up for new kinds of attacks which won't not exist, for example, side channel assaults, person to person communication assaults, mobile assaults. Existing issues should be surely knowing, and legitimate protection systems should be dealt with. IV. ISSUES IN CLOUD COMPUTING There is a need to give security and protection to the clients utilizing cloud services, and just approved people have control over the information ought to be at the highest priority on the rundown. Security of information from unapproved get to is all the more difficult. Appropriate convention can help in the assurance of the information. Cloud service provider needs to guarantee the accessibility of cloud services and information. Logs help in distinguishing and separating the issues where the issue has occurred and client can experience the logs and correct it without obstructing the advance. integrity, and availability known as CIA model. 1) Confidentiality: It gives access to the data to only authorized people. 2) Integrity: The data modification is not possible. 3) Availability: Data is accessible whenever it is required by the person. Trust is the major issue that how a customer can give his data to the third party? On what basis he should trust that organization? Here we are proposing the powerful framework to improve the cloud security. different devices separately. It is 16 pass block encryption which cannot be broken. It is the better approach than DES. It is compact and secure as the key length is variable 3. AES: AES has the block cipher length of 128 bits. The encryption and decryption implementation are not identical. AES is the byte-oriented algorithm. It has symmetric key. This algorithm is used world-wide. The AES design is based on a substitution-permutation network (SPN) and does not use the Data Encryption Standard (DES) Feistel network. But these techniques don t perform operation on the encrypted data. VI. PROPOSED FRAMEWORK In this paper we propose simple and powerful framework using homomorphic encryption of data to store and perform secure operations in the cloud. Homomorphic Encryption enables us to convey calculations on encoded information (ciphertext), prompts scrambled outcomes. The outcomes are decoded will coordinate the activities performed on plain text. The proposed framework comprises straightforward strides for better security and unravel a considerable lot of the moral and security issues in a cloud situation. First the plain text is encrypted into cipher text using homomorphic encryption method and any kind of operation is carried on the encrypted data. And we can see the operation carried out in the plain text is same because of the use of homomorphic encryption method. To get the plain text from the cloud, we have to unscramble the information utilizing the homomorphic strategy with same parameters and download the decrypted data and if we want the encrypted data we can also download it. V. BACKGROUND STUDY Some of the encryption techniques are already there. Some of them are as follows: 1. RSA: RSA is one of the secure algorithm that is use for transfer the data, in 1978 Ron Rivets, Adi Shamir and Leonard Adelman, who first publicly describe the algorithm. RSA is relatively slow algorithm, and because of this, it is less commonly use to directly encrypt user data. It uses asymmetric encryption type of cryptography. It uses co-prime numbers theory. It uses public key to encrypt the message into cipher text and uses private key to decrypt the scramble text into original message. 2. Blow Fish: This algorithm achieve efficiency of data encryption up to 4bits per clock. It is used for the high data encryption for VI. HOMOMORPHIC ENCRYPTION SCHEME Homomorphic encryption scheme should allow two basic operations on encrypted data without using the private key. They are: - Additive Property: D (E(m1) *E(m2) mod n 2 ) = (m1 + m2) mod n Multiplicative Property: D (E(m1) m2 mod n 2 ) = (m1*m2) mod n Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 233

260 Sanjay Kumar et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, Key Generation cloud situations. Appropriation of the proposed system will understand a significant number of the issues in cloud condition identified with moral and security viewpoints. Future work may include To integrate our proposed system with the big data computing and to focus on mobile application for accessing the data in the finger tips. REFERENCES Encryption Decryption VII. APPLICABILITY It provides security to the data stored in the cloud. Confidentiality of data is achieved. Control on the authorization of data will be maintained. It allows customer to work on the encrypted data. IX. COLNCLUSION Cloud computing gives numerous points of interest to people and little associations; it can likewise make some genuine security issues with individual and secret information. Cloud specialist co-ops should take legitimate safety efforts to keep all security related issues. We have proposed a basic yet intense system utilizing homomorphic encryption for taking care of information security issues in [1] Q. Zhang, L. Cheng, and R. Boutaba," Cloud computing: state-of-theart and research challenges," Journal of internet services and applications, vol. I, no. I, pp. 7-18,2010 [2] B. P. Rimal, E. Choi, and I. Lumb, "A taxonomy and survey of cloud computing systems," lnc,i MS and ldc, pp ,2 009 [3] C. Rong, S. T. Nguyen, and M. G. Jaatun, "Beyond lightning: A survey on security challenges in cloud computing," Computers & Electrical Engineering, vol. 39,n o. I, pp ,2013 [4] J. W. Rittinghouse and J. F. Ransome, Cloud computing: implementation, management, and security. CRC press,2 016 [5] R. H. Weber and R. Weber, Internet of Things. Springer,2010,v ol. 12 [6] P. Mell and T. Grance, "Effectively and securely using the cloud computing paradigm," NIST, Information Technology Laboratory, pp ,2009 [7] M. Spinola, "An essential guide to possibilities and risks of cloud computing," Retrieved March,v ol. 24, p. 2011, [8] V. Ratten, "Entrepreneurial and ethical adoption behaviour of cloud computing," The Journal of High Technology Management Research, vol. 23,n o. 2,p p ,2012. [9] O. Freestone and V. Mitchell, "Generation y attitudes towards ethics and internet-related misbehaviours," Journal of Business Ethics, vol. 54, no. 2, pp ,2004. [10] A framework of improving cloud security by Jayachander Surbiryala, Chunlei Li, Chunming Rong Department of Electrical Engineering and Computer Science University of Stavanger, Norway Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 234

261 Volume 9, Special Issue No. 3, May 2018 International Journal of Advanced Research in Computer Science (ISSN: ) CONFERENCE PAPER Available Online at A RECENT SECLUSION-APPRISED OF POPULAR ANALYSES PLAN FOR CLOUD DATA ALLOCATE WITH CATEGORY USERS Mahendra Reddy Student, Mtech CSE dept Reva University, Bangalore, India Anand Shankar Associate Professor, CSE dept. Reva University, Bangalore, India Abstract In public auditing schemes based sharing schemes between group of users to cloud server. the graph is capricious to the group in that case user is only informed of his nearby resident, then integrity of shred data is inevitable and the authentication mechanism based every group and group users are access polices and security mechanism and shared data with some plan have been draw to allow collective evidence with storage data and allocate service in the mass, users can effortlessly change and divide data as a category, A classification key concurrence with these later is very acceptable for community-based web, here mainly communication focused on between the group of users and server, here we suggest a up to the isolation public allocate apparatus for divide cloud data by build a holomorphic confirmable category name, Then access control mechanism using data consumer has download data successfully allowed. Those who are signed in different blocks that users do modification will be perform. For security reasons, at any time is reverse from the category, the chunk which were before write by this repeal user need to be leave by a previous users. Keywords Data probity Homomorphic Confirmable, Non frame ability, Provable Safety. All components are interconnect to the cloud and we can easily perform operations. 1.INTRODUCTION Cloud computing is large storage device,we all are use to store the large data in cloud whenever we want to store more number of applications,files are stored in cloud. We all are using public cloud because of we can easily access anyone. Cloud computing interconnect to data centers are provide by various software,hardware, information resources. the company are easily connect to the cloud.deployment of cloud computing are must be use private cloud,public cloud,hybrid cloud.in single organization we are using private cloud, the private network is highly secure. All are using public cloud because of everyone we can easily connect to public cloud, public cloud is efficiency is very high. We want to store different types of data are stored in cloud computing.the public cloud is provide by cloud service providers. When users application,,documents are stored in cloud but in between cloud and user there is no more security in this we are providing security. The users are prepare data whatever data we have that data we are convert into encryption format then only we can store data in cloud after that whenever we want data go back and take permission to the data owner then only we can visible data otherwise we can t able to see The data owner send key to the user then only user decrypted the files and see the data. In between user and server there is no security. Whenever we are upload data that data will be change in different format but sometimes data will be deleted. Data owner will be also a user of the group, data owner are could prepare a data that has to be uploaded, data owner generate key and whatever data we have that data encrypts the data and stored the data in sever using encryption key, before receive data from server, The users data request to cloud service provider then replay back to send key to users then only we can easily download the file and visible the data. The cloud computing is we can store more data is efficient manner. Whenever we have more data trying to store mobile then my mobile storage is very less. We have to go for cloud computing, we can store more data in cloud. Figure 1.1Block Diagram of cloud computing service the components have less memory storage I want to store more data in that we are using cloud and store more data. The above figure is cloud computing service means cloud we can easily interact with pc, server, database, network,for example: we don t have database but not possible to store the data, all components have less memory storage I want to store more data in that we are using cloud and store more data. In our each of them can be initialized alone using. the cloud computing is a problem, there is no middle identify to initialize users. Using cloud computing can increase the reliability and scalability.it uses the remote servers and internet.it provides collection of resources applications.in this problem there is no central authority. 2. LITERATURE SURVEY In proposed we are providing security in between user and server when data owner prepare data and convert data in encryption format then only data owner send to cloud server,in existing system there is no security in between user and cloud server and there is no group Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 235

262 Mahendra Reddy et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, members here we create group whenever the user fill the register form at that time we must be select the group, the user upload the file in server when he want to download send request to data owner the data owner generate key send to user then only download the file otherwise not possible, we can store more data in cloud server there is no size limit, in previous case there using single group member they all are not using group member now we are using group member.it will consider Google Docs,Dropbox. Data owner will be also a user of the group, data owner are could prepare a data that has to be uploaded, data owner generate key and whatever data we have that data encrypts the data and stored the data in sever using encryption key, before receive data from server, users data request to cloud service provider then replay back to send key to users then only we can easily download the file and visible the data. Whatever data we have convert to encryption format send to cloud server whenever user wants to download decryption from data user. here we are using keys for encryption of data and decryption of data,we are using advance encryption algorithm for key agreement when user is cipher text to plaintext, there is no storage limit in cloud server we can choose multiple and upload the multiple file in cloud server. If the valuable data regarding marketing, industries and others are in faulty hands, it can be used to damage our market and industries. In our traditional file sharing applications even though the contents are encrypted the access policy which contains attributes such as file name are in plain text format. 3. SYSTEM ANALYSIS Level1 The data owner perform operations like login, data owner upload the files and view the files list and view profile and access permit and approve the files. The data consumer perform the operation such as user login and upload the file and view the list and access permit. Figure 3.2. Data flow diagram Level3 Those register user must be login but before login we must be register themselves by providing the valid login credential, the user can view the other group users and list of files, The group members registration on group manager, the GM(Group Managers) send request to the cloud whenever group members access data to the cloud but group manager send key to group members. Figure 3.1. Use case diagram Level2 The user process perform operation such as group key locality and user revocation, group key locality perform operation such as end user login and data owner login. Data owner perform operations like file upload and generate key and storing the file in server, end user perform operation such as organize user and client user In organize user perform operations like view profile, and upload file and access file permit, client user perform operation like view. View file contents of uploaded files in storage server, when files are upload into cloud computing whenever user wants to download the file take download and view permission to the data owner. Figure 3.3. Key distribution process Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 236

263 Mahendra Reddy et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, Level4 Data owner will be also a user of the group, data owner are could prepare a data that has to be uploaded, data owner generate key and whatever data we have that data encrypts the data and stored the data in sever using encryption key, before receive data from server, users data request to cloud service provider then replay back to send key to users then only we can easily download the file and visible the data. 4. SYSTEM DESIGN: Figure 4.1. Block Diagram of System Model of NPP The above figure the data owner prepare the data and whatever data we have that data send to encryption service provider the encryption service provider will be convert text to cipher text file using through key,the result is send to data owner the data user send file access request to data transmission then only data owner send data file cipher text to data user, The data user without key we can t convert data file cipher text to plaintext in this case data user communicate with trusted authority then only we can convert plaintext otherwise not possible. The data user privilege update request to trusted authority, trusted authority send public key to data owner then only data owner send request to those who have register and privilege in trusted authority.the trusted authority send attribute key to data user then only data user convert into cipher text to plain text. In any of the file sharing applications online or offline, the encryption of data is very important. If the valuable data regarding marketing, industries and others are in faulty hands, it can be used to damage our market and industries. In our traditional file sharing applications even though the contents are encrypted the access policy which contains attributes such as file name are in plain text format.. This can be used to understand what the content is about. So, it is important to encrypt the file name also and generate Tag number of each file. When the data consumer wants to access a file he has to provide right authentication and request for the file. 5. FUTURE ENHANCEMENT For project demo concern, we have a prototype module. In future, this can be taken to the product level. To make this project as user friendly and durable, we need to make it compact and less cost. In this project there is no the key problem and store the large data in cloud computing. Going further we store the multiple files at a time store the large data, there is no storage limit. 5.1 APPLICATION File Sharing: File transformation is one of most crucial part of cloud data storage management system. The File Uploaded to server with encryption data or raw data with checking and after the data is certain keys or here using group key based data is stored on cloud server. Data owner or Group owner having unique key to communicate with group users and other group users. The Data owner can upload data to its group members and store the files into storage server. View file contents of uploaded files in storage server. when files are upload into cloud computing whenever user wants to download the file take download and view permission to the data owner. The user take permission on data owner when data owner send key to user then only user see the data otherwise not possibility to see the data. In any of the file sharing applications online or offline, the encryption of data is very important. If the valuable data regarding marketing, industries and others are in faulty hands, it can be used to damage our market and industries. In our traditional file sharing applications even though the contents are encrypted the access policy which contains attributes such as file name are in plain text format. This can be used to understand what the content is about. So, it is important to encrypt the file name also and generate Tag number of each file. When the data consumer wants to access a file he has to provide right authentication and request for the file. Once Data owner acknowledges and sends the tag number. 6. CONCLUSION The Data owner can upload data to its group members and store the files into storage server. View file contents of uploaded files in storage server. In any of the file sharing applications online or offline, the encryption of data is very important. when files are upload into cloud computing whenever user wants to download the file take download and view permission to the data owner, When the data consumer wants to access a file he has to provide right authentication and request for the file. Once Data owner acknowledges and sends the tag number data owner send key to user that user apply the key then only view the files and download the file. 7. REFERENCES [1] D. Fernandez, L. Soars, J. Gomes, et al, Security issues in cloud environments: a survey, International Journal of Information Security, vol. 12, no. 2, pp , [2] W. Hsian, C. Yang, and M. Hwang, A survey of public auditing for secure data storage in cloud computing, International Journal of Network Security, vol.18, no.1, pp , [3] J. Yu, K. C. Wang, et al, Enabling Cloud Storage Auditing with Key- Exposure Resistance, IEEE Transactions on Information Forensics and Security, vol.10, no.6, pp , [4] Q. Wang, C. Wang, et al, Enabling public auditability and data dynamics for storage security in cloud computing IEEE Transactions on Parallel and Distributed Systems, [5] C. Wang, Q. Wang, et al, Privacy-preserving public auditing for data storage security in cloud computing, Proceedings of IEEE INFOCOM, pp. 1-9, 2010 Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 237

264 Volume 9, Special Issue No. 3, May 2018 International Journal of Advanced Research in Computer Science (ISSN: ) CONFERENCE PAPER Available Online at TRAFFIC MANAGEMENT USING MAPREDUCE FOR BIG DATA ANALYTICS NAVEEN KUMAR School of Computing and Information Technology REVA University, Bangalore, India UPENDRA SINGH TOMAR, School of Computing and Information Technology REVA University, Bangalore, India VINAY YADAV, School of Computing and Information Technology REVA University, Bangalore, India RUKSA SETHI, School of Computing and Information Technology REVA University, Bangalore, India Prof.ANIL KUMAR AMBORE, School of Computing and Information Technology REVA University, Bangalore, India ABSTRACT-The MapReduce programming model simplifies large-scale processing on trade goods cluster by exploiting parallel map tasks and cut back tasks. though several efforts are created to boost the performance of MapReduce jobs, they ignore the network traffic generated within the shuffle part, that plays a crucial role in performance improvement.historically, a hash perform is employed to partition intermediate knowledge among cut back tasks, which, however, isn't traffic-efficient as a result of configuration and knowledge size related to every key don't seem to be taken into thought. During this paper, we have a tendency to study to scale back network traffic value for a MapReduce job by coming up with a unique intermediate knowledge partition theme. what is more, we have a tendency to conjointly contemplate the aggregation placement downside, wherever every aggregation will cut back united traffic from multiple map tasks. A decomposition-based distributed algorithmic rule is projected to subsume the large-scale improvement downside for large knowledge application and a web algorithmic rule is additionally designed to regulate knowledge partition and aggregation in a very dynamic manner. Finally, in depth simulation results demonstrate that our proposals will considerably cut back network traffic value below each offline and on-line cases. KEYWORDS:Aggregation,Hadoop,MapReduce,Partitioning. 1.INTRODUCTION MapReduce has risen as the most well-known figuring system for huge information preparing because of its straightforward programming model and programmed administration of parallel execution. MapReduce and its open source usage Hadoop have been embraced by driving organizations, for example, Hurray!, Google and Facebook, for different enormous information applications, for example, machine learning bioinformatics and digital security MapReduce isolates a calculation into two primary stages, in particular guide and diminish, which thus are completed by a few guide assignments and decrease undertakings, separately. In the guide stage, delineate are propelled in parallel to change over the first info parts into middle of the road information in a type of key/esteem sets. These key/esteem sets are put away on nearby machine and sorted out into various information allotments, one for every lessen undertaking [1][2]. In the lessen stage, each decrease undertaking brings its own particular offer of information parcels from all guide assignments to produce the last outcome. There is a rearrange advance amongst delineate decrease stage. In this progression, the information created by the guide stage are requested, divided and exchanged to the proper machines executing the lessen stage. The subsequent system movement design from all guide undertakings to all lessen errands can cause an awesome volume of system activity, forcing a genuine imperative on the proficiency of information scientific applications. 2. PROBLEM STATEMENT The information delivered by the guide stage is arranged, divided and exchanged to the fitting machines running the diminished stage. The system movement design coming about because of all guide errands for all lessening undertakings can cause a substantial volume of system Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 238

265 Naveen Kumar et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, activity, forcing a genuine imperative on the proficiency of information examination applications[3]. 3. EXISTING SYSTEM The middle of the road data is dragged in a way steady with a hash that works in Hadoop, which can create enormous system activity because of overlooking the setup and the measure of the data identified with each key[8]. To manage this bother caused by the issue of non-activity parcel, I tend to consider the areas of each assignment and the extent of the data identified with each key amid this archive. Utilizing the dissemination keys with a bigger data size to decrease assignments nearer to the guide, organize activity can be impressively lessened[4]. To additionally decrease arrange activity at interims of a Guide Lessen work, I tend to respect data joined with consistent keys before making them remote diminishment errands. in spite of the fact that a similar execution, known as a combiner, has just been received by Hadoop, it works promptly when it is a guide assignment only for its produced data, without utilizing the data total open doors between numerous undertakings in totally unique machines. 3.1 DRAWBACKS Customarily, the utilization of hash An is utilized to partition middle of the road information between scale diminishment assignments, which, nonetheless, isn't productive in rush hour gridlock because of topology and the measure of learning identified with each key does not appear to be taken in thought.it brings about monster arrange activity because of overlooking the topology and the span of information identified with each key. 4.1 ADVANTAGES Every authority will lessen the implicit activity of different guide errands. It is intended to control the segment of learning and collection in a to great degree dynamic way. It will impressively decrease the estimation of system movement for each situation disconnected and on the web. 5. METHODOLOGY Most existing work centres around Guide Lessen execution change by enhancing the information exchange. Blancaetal have analyzed the inquiry in the case of improving system use can prompt a superior framework execution and found that high system use and low system blockage must be accomplished at the same time for an occupation with great execution. Be that as it may, little consideration has been paid to upgrading the system execution of the rearrange procedure that creates a lot of information movement in Guide Decrease undertakings[5]. A basic one factor for organize execution in the rearrange stage is the between time information parcel. Aside from information division, there have been numerous endeavours On neighbourhood total, blend of in-creators and In-System Total to Decrease System Activity Enhancing map employments Kondyetal has started one Authoritative capacities that lessen the measure of information Rearranged and converged to diminish work ARCHITECTURE System activity is regularly diminished significantly. 4. PROPOSED SYSTEM In this record, we together consider apportioning and accumulating information for a Guide Lessen undertaking with an objective that is to limit add up to arrange movement [6][7]. Specifically, we propose an appropriated calculation for huge information applications, decaying the first expansive scale issue into a few sub-issues that can be unravelled in parallel [9]. Likewise, an online calculation is intended to deal with to segment information and total powerfully. At last, broad recreation comes about show that our proposition can altogether lessen the cost of system movement in both disconnected and online cases. Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 239

266 Naveen Kumar et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, DATA FLOW DIAGRAM Admin can see all cloud account details Hash Details Admin can see the hash tag generated for all blocks which is uploaded by users Transactions Admin can see transaction details of any user by selecting the desired user account Sign Out After finishing the all task Admin should click on sign out option to come out from existing session User Module User Login The information stream outlines represent that learning is handled by a framework as far as data sources and yields. The procedure starts with a general picture of the organization and keeps on dissecting every one of the valuable zones of intrigue. This examination can be dispensed just in the measure of fundamental points of interest. A DFD can basically draw simple images of abuse. What's more, troublesome procedures can be basically programmed making the abuse of DFDs simple to utilize, free downloadable pattern devices. A DFD could be a model to manufacture and examine data forms. DFD represents the stream of information in a strategy that is unnecessarily reliant on data sources and yields. A DFD can even be named as a Model technique. A DFD shows a business or specialized technique bolstered by the learning of put away skin, and the data that streams from the strategy to an alternate outcome and furthermore toward the end. 7. MODULES 7.1. ADMIN MODULE: Admin Login Admin should provide admin id and password for login Show Profile Admin can see his profile details and he can edit profile details and password User Details Admin can see all user details. He can add new user, edit existing user details, able to delete any user account Cloud Details User should provide user id and password for login Show profile User can see his profile details and he can edit profile details and password Upload File The user must select the system file and click to load the option.the files will be divided into small blocks (500 bytes in each block).generate hash tag for the whole block.compare the generated hash block with the existing hash tag of the database if the hash tag matches, in that case we will not load that block in the cloud, we will increase the instance number of that block in the database table. If the hash tag does not match in that case, we will add the block hash details in the database and load that block into the cloud.lba - The technique of addressing logical blocks is used to identify which blocks are present in a file Download File The user must select a file to download. Request sending to the server, the server must search for the IP address of the client's system and use the search function to identify the IP Region address, let it be selected Region (SR). Each region has a storage space and a map area. In the storage space, all the data of the downloaded blocks are available and the details of the downloaded block are available in the map area. The use of the LBA server has to find the block numbers that are in the selected file. The server should check in the SR Map area if all blocks required for the file are available, if all blocks are available in the SR storage space, download and combine the blocks and deliver them to the user. If there are few blocks Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 240

267 Naveen Kumar et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, available and some are missing, get the missing Top Region blocks and place them in the SR storage space and update the SR Map details Transactions User can see all its transaction details Sign Out After finishing the all task, user should click on sign out option to come out from existing session. 8. SYSTEM SPECIFICATION Hardware Requirements System : Pentium IV 2.8 GHz. Hard Disk : 20 GB. Floppy Drive : 1.44 Mb. Monitor : 15 VGA Colour. Mouse : Logitech. Ram : 512 Mb. Software Requirements Operating system: Windows XP. Coding Language: Java Data Base : MYSQL Server TECHNICAL ASPECT The proposed system is developed with: Front End : JSP Back End : MYSQL Server 9. CONCLUSION In this article, I considered the joint improvement of halfway information dividing and collection in Guide Lessen to limit the system movement cost of huge information applications. I proposed a three-level model for this issue and depicted it as a blended number nonlinear issue, which was then changed into a straight shape and unravelled by a scientific instrument. For huge scale information preparing issues, this paper planned an appropriated calculation to take care of the issue on various machines. Also, I have stretched out our calculation to process Guide Lessen occupations online without certain framework parameters. At last, I directed countless to assess our calculation in the disconnected case and online case. The re-enactment comes about demonstrate that our proposition can successfully lessen the system movement cost under different system settings. REFERENCES [1]. International Digital Library For Education & Research Volume 1,Issue 3,Mar [2]. On Traffic-Aware Partition and Aggregation in Map Reduce for Big Data Applications. IEEE Transaction on Parallel and Distributed System (Volume: 27,Issue:3,March [3].An Efficient Network Traffic-Aware Partition for Big Data Application and Aggregation Techniques using Map Reduce IJATIR. All rights reserved. [4] J. Dean and S. Ghemawat, Mapreduce: simplified processing on massive clusters, Communications of the ACM, vol. 51, no. 1, pp , [5] W. Wang, K. Zhu, L. Ying, J. Tan, and L. Zhang, Map task planning in mapreduce with knowledge locality: outturn and heavy- traffic optimality, in INFOCOM, 2013 Proceedings IEEE.IEEE, 2013, pp [6] F. Chen, M. Kodialam, and T. Lakshman, Joint planning of process and shuffle phases in mapreduce systems, in INFOCOM,2012 Proceedings IEEE. IEEE, 2012, pp [7] Y. Wang, W. Wang, C. Ma, and D.Meng, Zput: A speedy knowledge uploading approach for the hadoop distributed filing system, in Cluster Computing (CLUSTER),2013 IEEE International Conference on. IEEE, 2013, pp [8] T. White, Hadoop: the definitive guide: the definitive guide. O ReillyMedia, Inc., 2009.[6] S. Chen and S. W. Schlosser, Map-reduce meets wider forms of applications, Intelanalysis Pittsburgh, Tech. Rep. IRP-TR-08-05, 2008.[7] J. Rosen, N. Polyzotis, V. Borkar, Y. Bu, M. J. Carey, M. Iimer, T. Condie, and R.Ramakrishnan, Iterative mapreduce for giant scale machine learning, arxiv preprintarxiv: , [9] S. Venkataraman, E. Bodzsar, I. Roy, A. AuYoung, and R. S. Schreiber, Presto:distributed machine learning and graph process with distributed matrices, in Proceedingsof the eighth ACM European Conference on pc Systems. ACM, 2013, pp Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 241

268 Volume 9, Special Issue No. 3, May 2018 International Journal of Advanced Research in Computer Science (ISSN: ) CONFERENCE PAPER Available Online at OPENRAP: A DISTRIBUTED, SCALABLE, OFFLINE CDN Sriram V Ramaswamy School of Computing and Information Technology REVA University Bengaluru, India sriram.rmswmy@gmail.com Ravish Ahmad School of Computing and Information Technology REVA University Bengaluru, India ravishahmad16@gmail.com Sumukha K V School of Computing and Information Technology REVA University Bengaluru, India kvsumukha@gmail.com Shah Abdul Ghani School of Computing and Information Technology REVA University Bengaluru, India gshahabdul@gmail.com Kiran M School of Computing and Information Technology REVA University Bengaluru, India kiranm@reva.edu.in Abstract This paper aims to implement a fully functional localized scalable Content Delivery Network (CDN) that allows for seamless content distribution without any need for internet access from a cheap, low-powered, headless IoT device. The software can be run out of the box and can be used with minimal technical knowledge on the side of the user. This state-of-the-art proposed system can be implemented in many fields that require streaming and/or media download services, especially those that are highly localized in nature and do not need continuous connectivity with the internet. Keywords Content Delivery Network (CDN), NodeJS Server, React App, SQLite3, Internet of Things (IoT) I. LITERATURE REVIEW IoT networks today are very prevalent, bringing in ubiquitous computing to the masses. With a projected amount of connected devices to reach 24 billion by 2020 [1], it becomes important for decentralized networks to have proper footing within themselves so as to reduce workload on a few centrally located servers. On the other side of the spectrum, the reach of IoT networks hasn't been very promising. People do not have access to a decent cellular network a few kilometers outside an urban agglomeration. Studies show that only 9% of India has access to mobile internet, and cloud-based IoT networks, coupled with expensive hardware, can make proper connectivity just a dream for many [3]. Therefore, it becomes important that any solution that decides to solve this problem needs to be cost-effective and fully functional. The rise of FOSS and Linux-based operating systems has enabled nearly seamless transitioning between IoT devices capable of running their own operating systems. What difference remains, is mainly about which mini-computer to use. The following table provides an easy comparison of commonly available boards, and their features. Raspberry Pi Boards Beaglebone Black TABLE I. ASUS Tinkerboard Qualcomm Dragonboard DIFFERENT IOT BOARDS Features 1GB RAM, 1.2GHz CPU 512MB RAM, 3D GPU 2 GB RAM, 1.8 GHz CPU 1GB RAM, 1.2GHz CPU These boards are available with most electronics deals and can easily be purchased online. The fact that these boards do not cost more than $100 implies a very good economic viability of these devices when compared to a full-sized server. Furthermore, all these boards can run a version of Debian/Ubuntu compiled specifically for them, wherein the operating system has an optional desktop interface and can remotely be connected via SSH. The common server frameworks are as follows [6][7][8][9][10][11]: NGINX Apache Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 242

269 Sriram V Ramaswamy et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, Django NodeJS Flask Drupal Of all these, NodeJS is an excellent choice due to its relative success and popularity among developers. While other frameworks like Drupal [a self-styled CDN network] and Django [a full-stack web framework] are popular, NodeJS is relatively easy to use and easy to deploy. II. ARCHITECTURE OVERVIEW The Database Service provides wrapper functions for common DBMS operations. III. TECHNOLOGY STACK The proposed system is a full-stack JavaScript project that can run on any platform and architecture. This state-of-the-art system can be divided into the following sections: A. Backend The backend is a NodeJS server that exposes a number of API routes to the user. These routes can be interfaced by the default front-end or any other application capable of making such HTTP requests and parsing the response data. This is a full-fledged API server; all routes return JSON objects which denote whether the proposed system was successful, and any corresponding data and messages. The backend also makes use of a database to record user activity. It uses a MYSQL database along with a wrapper that allows for seamless integration with the NodeJS backend. The wrapper is available in the form of an SDK which can be used by a developer to create plugins. Fig. 1. The Basic Architecture of Our State-of-the-Art Proposed System The architecture, as described above, forms the core functionality of OpenRAP. Plugins [described by the green vertical cuboids], can be implemented on top of these services as and when needed. Their functionalities are as follows: A. API Gateway The API Gateway contains all the routes that shall be implemented by the device in order to function appropriately. Those building plugins will need to define routes in the API Gateway so as to ensure accessibility of the newer functionalities. The APIs can be defined in a simple routes.js file. B. RAP Service The RAP Service contains the core functionality for the device, including file management, user management, etc. C. File Service The File Service provides interfacing for file operations and can be extended by means of plugins to, for example, serve files by means of a custom behavior. D. Search Engine The Search Engine is based on BleveSearch and can perform fuzzy searches in order to serve files and get data more easily. E. Auth Service The Auth Service provides authentication and authorization functions for the user. While it currently performs very less operations, it can be extended by means of plugins to perform authentication, authorization, token verification, etc. or even extend common tools like OAuth and Google Sign-In. F. Database Service B. Frontend The default frontend is available in the form a basic React App that allows for essential device management services such as file and user management. The frontend is completely independent of the backend and thus can be replaced with a custom frontend for any extensibility by the user. The advantages of using React include extensive documentation, a large amount of available third-party modules and JSX functionality. C. SDKs The proposed system comes with prebuilt SDKs that allow for easy, generic interfacing with the backend. The SDKs contain functions that in turn process the request and prepare an appropriate response for the user to use. These SDKs can be interfaced by the plugin writers as well in order to easily extend features of the device. The SDKs available are: a. DB SDK This is the database SDK that allows for the plugin writer to implement DBMS operations. This works best with a MySQL backend but can also be used for a lightweight DBMS such as SQLite3. The plugin writer need not write logic by themselves for a simple task; the SDK provides a simple promise-based interface for the plugin writer to use. b. File SDK The File SDK allows for easy file management operations, which run complex code implementing middleware and NodeJS's 'fs' module. As the underlying code is a mixture of promise-based and callback-based code, it can cause a lot of confusion and unnecessary effort for someone who needs to write a simple plugin and may not be very experienced with the JavaScript Environment. The File SDK provides basic functionalities that include, but are not limited to, copying and moving files, creating and deleting folders and uploading files. The File SDK forms the core part of the file management module of the device. Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 243

270 Sriram V Ramaswamy et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, c. Search SDK The Search SDK is a novelty addition to the module that is exclusively built for plugin use. The Search SDK acts as a wrapper around BleveSearch, which is an open-source golang library built on the ElasticSearch platform. The Search SDK performs internal API calls to the BleveSearch module, thereby acting as a middleware to the search functionality. The core module has no use case for the Search SDK, but this can be extended by means of plugins. d. Plugins Plugins are user-written code that allow for extensibility of the CDN's features. These plugins can be written in any language but work best when homogenous with the core modules, eliminating any unwanted need for trans-piling or interfacing. Plugins can use the SDKs available or use their own logic to get things working. IV. WORKING The state-of-the-art proposed system acts as a typical server with middleware and plugin support. Due to the heavily modular nature of the CDN, it can also be used in a distributed system with minimal alterations to the server. Those intending to scale the CDN up and host it in an AWS instance can also do it without much hassle. The proposed system is platformindependent and therefore can be easily hosted anywhere. The basic workflow of the code is as follows: A. The user performs an action. B. The React component registers that action and calls the appropriate function in <component>.actions.js while optionally using any data it might require from the state or variables present in <component>.reducer.js. C. The function present in <component>.actions.js calls the appropriate HTTP route using Axios. D. The request is caught by the backend server, where it verifies it against all routes.js files. Once a match is found, it calls the corresponding function in the appropriate controller.js file. E. The method present in the controller.js file calls the appropriate SDK/Helper functions and prepares the response object. V. PROMISES Promise-based functions have a unique structure when compared to other callback-based functions in that they allow for segregated logic in case of successful and unsuccessful executions by means of resolve and reject attributes. A method that typically executes perfectly "resolves" a value, while a method that fails to execute perfectly "rejects" the reason. This is handled separately by the function that calls it, at its thenattribute[5]. Therefore, a promise-based function can be expressed as follows: letfunc = (params) => { let defer = q.defer(); //q is an object of module Q dosomething(someotherparams, (err, data) => { if (err) { returndefer.reject(err); } else { returndefer.resolve(data); } } returndefer.promise; } The structure of the calling function is as follows: letfunc = (params) => { let defer = q.defer(); callapromisedfunction(someotherparam s).then(value => { //Process data returnanotherpromisedfunction(stillm oreparams); }).then(value => { returnyetanotherpromisedfunction(yet MoreParams); }).then(value => {... returndefer.resolve(somevalue); }).catch(e => { console.log(e); returndefer.reject(e); }); } Promises, especially in NodeJS, are designed to combat what is colloquially called "callback hell", i.e. asynchronous code that is a part of a synchronous execution chain. As the primary method of asynchronous code handling is to use callbacks, anyone using asynchronous code repeatedly can find the code very hard to maintain. By employing promises, the code's readability can considerably increase, by a means called "flattening the pyramid" or "promise chaining". This allows code to maintain the same hierarchical level across execution[5]. V. DETAILED SDK FUNCTIONALITY This section outlines the detailed functionality the SDKs offer. A. File SDK Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 244

271 Sriram V Ramaswamy et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, The functionalities of File SDK are as follows: a. readfile: Reads a file b. writefile: Writes data to a file c. deletefile: Deletes a file d. deletedir: Deletes a dir e. copy: Recursively copies a file/folder f. move: Recursively moves a file/folder g. readdir: Reads a directory h. extractzip: Extracts a ZIP i. extracttar: Extracts a TAR j. createtar: creates a tar.gz file B. DB SDK a. createdatabase: Creates a database b. deletedatabase: Deletes a database c. createtable: Creates a table in a database d. listtables: Lists all tables in the database e. deletetable: Deletes a table in a database f. addrecord: Adds a new record to the table g. readrecords: Reads available records in a table C. Search SDK a. init: Initializes the search and DB directories, while loading any existing data b. createindex: Creates an index c. deleteindex: Deletes an index d. getallindices: Gets a list of available indices e. adddocument: Adds a document to an existing index f. deletedocument: Deletes the document in the index g. getdocument: Retrieves a document from the index h. count: Gets the count of documents in an index i. search: Searches for a query in the index VI. RAP SERVICES The RAP services offered by the device are as follows: The RAP service allows a superuser to modify the permissions of, delete, or create a new user. These permissions limit the ability of the user to perform administrative tasks to a set of well-defined roles. The dashboard contains data pertaining to a device's health and statistics such as the number of users connected, the version number, etc. This is by default available for all users. The statistics include disk, RAM and processor usage. The upgrade screen allows the user to upgrade the device by uploading a.tgz file. The File Management window employs the File SDK and allows the user to upload, modify and delete files in the device through the browser. It also provides an interface for folder creation and updation. The SSID Management Component allows the user to manage the SSID the device broadcasts. The Captive Portal Management Component modifies the default captive portal that users see on connecting to the device. This allows for custom messages and download links to be displayed instead of the default Apache2 page. VII. USE CASES The proposed system has a diverse set of use cases. The proposed system can be used to bring education to rural schools in remote and tribal areas with very less internet connectivity, allowing students to have a more immersive learning experience. Lessons can be preloaded into the device which can then be used by the students in their respective smartphones/tablets. Digital classrooms in urban areas can be decentralized by means of using OpenRAP for every classroom. This completely avoids the problem of a centralized server crashing or breaking down due to heavy load. Examinations can easily be conducted as a unique login can be provided to the students by building a plugin over the Auth service which can provide more unique examinations and a higher degree of transparency. Buses can now contain a device that streams movies and other data on-demand to customers who require it, thereby allowing other passengers to enjoy their ride peacefully. Airplanes can have different devices for different sections of the aircraft, thereby allowing people to register their seats and book meals as and when they require them. Operas and stage plays can allow the audience to download and view the script/schedule of the play(s), leading to an even more immersive experience for theatre-goers. Banks can now have an automatic, phone-based token system instead of requiring customers to use the queue. Locally available services can now be automatically obtained and citizens can be directed to use them. VIII. FUTURE ENHANCEMENTS Possible improvements of this paper can include: Allowing for massively scaled application using software such as Docker and Kubernetes. As the proposed system is extremely modular, newer modules and plugins can be added and removed as and when needed. Integration with technologies such as Blockchain in order to locally store data securely, further increasing utility in sectors such as banking and data storage. By deploying the software into cloud-based instances such as Amazon EC2, one can allow for millions of similar clusters in areas with high connectivity. Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 245

272 Sriram V Ramaswamy et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, REFERENCES [1] Arxiv.org, [Online]. Available: [Accessed: 20-Apr- 2018]. [2] [Online]. Available: best-raspberry-pi- alternatives. [Accessed: 20- Apr- 2018]. [3] 018. [Online]. Available: access-to-mobile-internet- Report/articleshow/ cms. [Accessed: 20- Apr- 2018]. [4] Raspberry Pi 3 Model B - Raspberry Pi, Raspberry Pi, [Online].Available: model-b/. [Accessed: 20- Apr- 2018]. [5] Promise, MDN Web Docs, [Online]. Available: US/docs/Web/JavaScript/Reference/GlobalObjects/Promis e.[accessed:20 Apr 2018]. [6] The Web framework for perfectionists with deadlines Django, Djan- goproject.com, [Online]. Available: Apr- 2018]. [7] NGINX High Performance Load Balancer, Web Server, Reverse Proxy,NGINX, [Online]. Available: [Accessed:20- Apr- 2018]. [8] Node.js, Node.js, [Online]. Available: [Accessed: 20- Apr- 2018]. [9] Welcome Flask (A Python Microframework), Flask.pocoo.org, [Online]. Available: [Accessed: 20- Apr- 2018]. [10] Drupal - Open Source CMS, Drupal.org, [Online]. Available: [Accessed: 20- Apr- 2018]. [11] Welcome! - The Apache HTTP Server Project, httpd.apache.org, [Online]. Available: [Accessed: 20- Apr- 2018]. Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 246

273 Volume 9, Special Issue No. 3, May 2018 International Journal of Advanced Research in Computer Science (ISSN: ) CONFERENCE PAPER Available Online at CLOUD TASK SCHEDULING BASED ON ORGANIZATIONAL AUTHORIZATION Chiranjeevi B B.Tech Student, School of C& IT REVA University Sundus Hasan B.Tech Student, School of C& IT REVA University Dhanush K V B.Tech Student, School of C& IT REVA University Dona Mercy B B.Tech Student, School of C& IT REVA University A Ajil Assistant Professor, School of C&IT REVA University Bengaluru, India ajil.a@reva.edu.in Abstract: Change of imperativeness capability in distributed computing is an essential research subject nowadays. The reducing of operational costs made warmth and condition impact are a segment of the reasons behind this. A 2011 report by Greenpeace found that if worldwide cloud computing was a nation; it would utilize the fifth most power on the planet. It is possible to improve data viability in server cultivates by running diverse virtual machines on a single physical machine. At that point, task scheduling is expected to for better productiveness. Appropriate task scheduling can help in using the accessible resources ideally, subsequently limiting the resource usage and CPU utilization also. Additionally, present day cloud computing situations need to give high QoS to their customers (clients) bringing about the need to manage control execution exchange off. The objective of this wander is to develop a Cloud errand planning calculation using a subterranean insect settlement streamlining methodology to support QoS for clients in Heterogeneous Environment. The fundamental objective of this calculation is to limit the makespan of a given errands list. The proposed calculation considers the trade off between essentialness use and execution and most extreme usage of asset information and CPU restrict factor to achieve the objectives. The proposed calculation has been executed and evaluated by using JCloud test framework which has been used by most experts to related to asset planning for distributed computing. Keywords: Distributed Computing, Task Scheduling, Makespan, Insect Colony Optimization. I. INTRODUCTION In case of cloud computing, services can be used from multiple and widespread resources as opposed to remote servers or neighborhood machines. There is no standard meaning of cloud computing. By and large, it consists of a bundle of distributed servers known as masters, giving demanded services and resources to various clients known as clients (cloud consumer) in a system with scalability and unwavering quality of datacenter. The distributed computers give on-demand services to cloud consumers. Services might be of software resources (e.g. SaaS) or physical resources (e.g. PaaS) or equipment/infrastructure (e.g. IaaS). Amazon EC2 (Amazon Elastic Compute Cloud) is a case of cloud computing services. The National Institute of Standards and Technology's (NIST) characterize a Cloud computing as "cloud computing is a model for empowering all-over, helpful, ondemand arrange access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications and services) that can be quickly supplied and released with insignificant administration exertion or service supplier connection. " The cloud is a virtualization of resources that maintains and handle itself. It builds on an extensive variety of various computing technologies such as superior computing, distributed systems, virtualization, storage, organizing, security, administration and robotization, SLA, and QoS. II. EXISTING SYSTEM Cloud discovers its underlying foundations in IT situations (endeavor), the thought is bit by bit entering logical and scholastic ones. Distributed computing is encountering a quick improvement both in academe and industry; it is advanced by the business as opposed to scholarly which decides its emphasis on client applications. This innovation means to offer dispersed, virtualized, and versatile assets as utilities to end clients like cloud customers. It can possibly bolster full acknowledgment of 'registering as an advantage' sooner rather than later. With the help of virtualization innovation, cloud stages empower endeavors (IT condition) to rent processing power as virtual machines to clients. Since these end clients may utilize a huge number of virtual machines (VMs), it is troublesome for the cloud suppliers to physically allocate undertakings to processing assets in the cloud. In this way, we require an effective calculation for assignments planning for the cloud condition. III. PROPOSED SYSTEM Swarm Intelligence procedures are progressively used to take care of improvement issues and along these lines, they Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 247

274 Chiranjeevi B et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, result in incredible contrasting options to accomplish the objectives proposed in this work. SI is a discipline that deals with characteristic and simulated systems composed of numerous individuals that facilitate themselves using decentralized control and self-association. Cases of frameworks that concentrated by SI are ants' colonies. An leeway of these procedures gets from their ability to research arrangements in far reaching look spaces in an extraordinarily successful way close by negligible starting information. Utilizing SI procedures is an intriguing method to manage adjust for all intents and purposes to the NPfulfillment of work planning.in this part we will make clear quickly the methods used in this work; ACO algorithm we will apply to this technique to solve the VM distribution issue. Besides that, to ensure QoS for customers proposed versatile threshold using IQR strategy which will be shown later on in this section. At long last, the entire algorithm will be presented including pseudo code and information chart. 2 Entities in the cloud environment Login page for Organization (Enterprises) Ant Colony Optimization algorithm Insect Colony Optimization "ACO" is one of the instances of SI strategies. It is a meta-heuristic calculation with the gainful adjacent scan for combinatorial issues. It's a general framework that can be utilized to influence a particular calculation to tackle a particular outline approach to issue. ACO mimics the lead of bona fide subterranean insect states in nature to scan for sustenance and to interface with each other by pheromone laid on ways voyaged. Various kinds of research utilize ACO to take care of NP-troublesome issues, for example, voyaging salesperson issue, chart shading issue, vehicle coordinating issue, et cetera. Regardless of the way that ACO was proposed in a 1992 doctoral proposition by M. Dorigo, the primary point by point depiction of the calculation is all around credited to a 1996 follow-up paper by M. Dorigo, V. Maniezzo, and A. Colorni. Since by then, ACO has been comprehensively examined and built up. Ant in Nature Subterranean insect Colony Optimization (ACO) [29, 30] is a Metaheuristics roused by the perception of honest to goodness subterranean insect provinces and in view of their total searching behavior [26]. Ants are social creepy crawlies and lives in settlements. Their direct is spoken to by the target of province survival. While hunting down sustenance, ants as frequently as conceivable go between their home and food sources. In any case, ants examine the district encompassing their home haphazardly. While moving, ants expel unique substances from their body called pheromones along their ways. Ants can notice pheromones discharged by different ants. While picking their heading, they tend to pick, in probability, ways set apart by solid pheromone fixations. When an insect finds a sustenance source, it assesses the amount and the idea of the food and conveys some of it back to the home. In the midst of the entry trip, the amount of pheromones that a subterranean insect leaves on the ground may depend upon the amount and nature of the support. The pheromone trails will control distinctive ants to the support source. In any case, if after some time ants don't visit a particular way, pheromone trails begin to disperse, in this manner decreasing their appealing quality. The more the time a subterranean insect needs to move down the route and back yet again, the less the pheromone trails are fortified. The winding correspondence between the ants by methods for pheromone trails empowers them to find the most brief ways between their home and source. From an algorithmic point of view, the pheromone dispersal process is helpful for keeping up a key separation from the joining to an adjacent perfect arrangement. Fig.1 indicates two conceivable ways from the home to the food source, yet one of them is longer than the other one. Fig. 1(a) indicates how ants will begin pushing arbitrarily toward the begin to research the ground and after that pick one of two ways. The ants that take after the shorter way will regularly accomplish the sustenance source before the others ants, and in doing all things considered the past social event of ants will leave them a pheromone trail. In the wake of accomplishing the food, the ants will turn back and endeavor to find the home. Likewise, the ants that play out the round outing speedier fortify more quickly the amount of pheromone in the shorter path, as appeared in Fig. 1(b). The ants that accomplish the sustenance source through the slower way will find engaging return to the home utilizing the briefest way. Over the long haul, most ants will pick the left path as appeared in Fig.1(c). Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 248

275 Chiranjeevi B et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, Inherent Parallelism. 2- Used in a dynamic application. 3- Positive criticism prompts the fast disclosure of good arrangements. 4- Distributed calculation stays away from untimely union. 5-The insatiable heuristic helps locate the attractive arrangement in the early arrangement in the beginning times of the pursuit procedure. Figure 1 Adaptive behavior of ants 6- The aggregate communication of a populace of operators. 1) Ant Colony Optimization The above conduct of genuine ants has enlivened ACO. One of its principle thoughts is abusing the roundabout correspondence among the elements of a subterranean insect state. ACO utilizes pheromone trails as a sort of circulated data which is altered by ants to mirror their collected understanding while at the same time taking care of a specific issue. At every execution step, ants process an arrangement of attainable moves and select the best one (as per some probabilistic tenets) to complete all the visit. The change likelihood for moving from a place to another depends on the heuristic data and pheromone trail level of the move. The higher the estimation of the pheromone and the heuristic data, the more beneficial it is to choose this move and resume the hunt. ACO Disadvantages 1- Theoretical examination is troublesome. 2- Sequence of arbitrary choice (not free). 3-Probability appropriation changes by cycle. 4- Research is exploratory as opposed to hypothetical. 5- Time of meeting dubious. 2) Basic Steps for ACO Calculation: Basic Steps for ACO 1. Initialize the pheromone 2. While criteria not fulfilled, at that point rehash 3. Initially set areas of all ants on a section state 4. Selection of next state 5. While not coming to the last state at that point rehash from stage 4, if achieved then Step 6. 3 rd Entity of cloud environment (cloud user) 6. Pheromone phases (store, daemon and vanish pheromone) 7. Check whether criteria fulfilled or not, if fulfilled at that point end, if not then rehash from stage End. a) POINT OF INTEREST AND INCONVENIENCES OF ACO b) ACO Advantages Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 249

276 Chiranjeevi B et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, For performing tests physical machine has a 2.53 GHz Intel(R) Core(TM) i3 processor, 4GB RAM, 640 GB hard circle, and 64-bit Operating System Windows 7 Professional. In our examination most recent rendition of CloudSim tool stash, CloudSim was utilized for showing and recreation of distributed computing conditions and appraisal of proposed calculation. The tests are done in heterogeneous condition, where there are two kinds of hosts and four sorts of VMs with different attributes and dynamic load for errands. The earth comprises of a Datacenter with M Hosts and N number of VMs asks. Number of Hosts is settled to 100 Hosts and we will change the amount of VMs. Every examination has been reiterated ten times to take normally. IV. EXPERIMENTAL RESULTS As the concentrated on framework is an IaaS in distributed computing, it's more intelligent to evaluate the proposed calculation on a significant scale virtualized server cultivate foundation. Regardless, Real gear for test evaluation isn't generally open to specialists and it's to a great degree difficult to lead repeatable considerable scale investigates a real foundation, which is required to survey and take a gander at the proposed calculation. Thusly, to guarantee the repeatability of the investigations, reproductions have been picked as a reasonable technique to survey the execution of the proposed calculation. For testing the viability of a particular game plan that will be executed on a cloud we require a reproduction framework that can give a space that is near the bona fide cloud, and can make comes about that can help in the investigation of the arrangements with the goal that we can pass on them on genuine Clouds. Some essentialness careful reenactment bundles are GreenCloud, MDCSim, JCloud and GSSIM. Among the available bundles, JCloud has been the most extensively utilized by researchers. For our examinations, the JCloud tool stash has been picked as a reenactment structure. The tool stash has been made by the Cloud Computing and Distributed Systems (CLOUDS) Laboratory, University of Melbourne. By and by, it underpins showing and reenactment of distributed computing conditions comprising of both single and between masterminded mists (association of mists), and furthermore bolsters essentialness capable organization of server farm assets. Beside the essentialness utilization showing and accounting, the ability to reproduce benefit applications with dynamic workloads has been solidified. The genuine limitation of JCloud is the nonattendance of a graphical UI (GUI). In any case, notwithstanding this, JCloud is as yet utilized as a part of colleges and the business for the reenactment of cloud-based calculations. The motivation behind why we have not picked servers with more centers is that it is imperative to reproduce a broad number of servers to evaluate the effect of VM combination. In this manner, recreating less fit CPUs is invaluable, as less workload is required to over-trouble a server. By the by, twofold focus CPUs are adequate to survey asset organization calculations intended for multi-focus CPU designs. To make a recreation based evaluation correlated, it is imperative to lead tests utilizing workload follows from a honest to goodness framework. In CloudSim we utilize the data given as a bit of the CoMon wander, a checking foundation for PlanetLab. The data will comprise of the data on the CPU use by more than a thousand VMs from servers arranged at more than 500 places the world over. The between time of utilization estimations is 5 minutes and each took after record have 288 lines, thusly, everybody speaks to. VMs CPU use around 24 hours. Meeting QoS prerequisites are imperative for Cloud processing. QoS prerequisites are normally formalized as SLAs, which can be settled regarding attributes, for example, minimum throughput, most noteworthy reaction time or slightest data transmission et cetera. Limit resource usage and CPU utilization Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 250

277 Chiranjeevi B et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, V. CONCLUSION AND FUTURE WORK In this paper, ACO calculation for achieving distributed computing errands booking has been introduced. Initially, the best estimations of parameters for ACO calculation, probably chose. By then, the ACO calculation in applications with the amount of errands fluctuating from 100 to 1000 surveyed. Reproduction comes about exhibit that ACO calculation outflanks FCFS and RR calculations. In future work, the effect of need among errands and load changing will be considered. we have explored and contemplated the utilization of swarm insight strategy Ant Colony Optimization (ACO). We have taken motivation from subterranean insect state frameworks for outlining an approach of load changing in cloud frameworks. The approach displayed in this work depends on Ant settlement streamlining. We have reproduced proposed ACO calculation utilizing JCloud. We differentiated execution of the calculation and Basic ACO. The Results demonstrates that the proposed calculation can perform well for stack changing occupations in the cloud. To abridge, this paper presents swarm knowledge framework Ant province improvement for booking and load changing and demonstrates its advantages in circulated and dynamic load altering territory. VI. REFERENCES [1] Florence, A.P. and Shanthi, V., Intelligent Dynamic Load Balancing Approach for Computational Cloud. International Journal of Computer Applications: pp.15-18, (2013). [2] Sharma, T. and Banga, V.K., Efficient and Enhanced Algorithm in Cloud Computing. International Journal of Soft Computing and Engineering (IJSCE),(March 2013). [3] R. Brown et al., Report to congress on server and data center energy efficiency: Public law , Lawrence Berkeley National Laboratory, [4] Zhang, Q., Cheng, L., and Boutaba, R., Cloud computing: state-of-the-art and research challenges. Journal of Internet Services and Applications, pp. 7-18,(2010). [5] Singh, A., Gupta, S., and Bedi, R., Comparative Analysis of Proposed Algorithm With Existing Load Balancing Scheduling Algorithms In Cloud Computing. International Journal of Emerging Trends &Technology in Computer Science (IJETTCS), pp , (2014). [6] Tiwari, M., Gautam, K., and Katare, K., Analysis of Public Cloud Load Balancing using Partitioning Method and Game Theory. International Journal of Advanced Research in Computer Science and Software Engineering, pp , (2014). [7] Ratan, M. and Anant, J., Ant colony Optimization: A Solution of Load Balancing in Cloud. International Journal of Web & Semantic Technology(IJWesT), (2012). [8] Elina Pacini, Cristian Mateos, and Carlos García Garino. Balancing throughput and response time in online scientific Clouds via Ant Colony Optimization.Adv. Eng. Softw. 84, C (June 2015), [9] Reena Panwar, A Comparative Study of Various Load Balancing Techniques in Cloud Computing. International Journal of Engineering Research & Technology (ijert), Vol. 3 - Issue 9 (September ). [10] Martin Randles, David Lamb, A. Taleb- Bendiab, A Comparative Study into Distributed Load Balancing Algorithms for Cloud Computing, 2010 IEEE 24thInternational Conference on Advanced Information Networking and Applications Workshops. [11] Kaleeswari and Juliet, N., Dynamic Resource Allocation by Using Elastic Compute Cloud Service. International Journal of Innovative Research in Science, Engineering and Technology (IJIRSET), pp , Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 251

278 Volume 9, Special Issue No. 3, May 2018 International Journal of Advanced Research in Computer Science (ISSN: ) CONFERENCE PAPER Available Online at FIDOOP: AN INTERACTIVE GUI TO IDENTIFY FREQUENT ITEMS USING MAP REDUCE Raksha D Computing and Information Technology, REVA University P Hari Prasad Reddy Computing and Information Technology, REVA University Rohith M Nayak Computing and Information Technology, REVA University Mukesh P U Computing and Information Technology, REVA University Prof. Raghavendra Reddy Computing and Information Technology, REVA University Abstract: Due to an exponential increase of real-time data monitoring systems, the extraction of frequent itemset from the large database is a challenging task. Memory usage and excessive runtime for less amount of data, automatic parallelization are the limitations in existing algorithms of frequent itemsets. FiDoop based itemset algorithm is introduced by using MapReduce framework to overcome this problem. This system includes activities such as data uploading, preprocessing, threshold, find support and confidence, merge and result. We implement FiDoop on our in-house Hadoop cluster. To improve FiDoop s performance a workload balance matric is used to measure load balancing across the cluster's computing node is developed. Initially, data is selected from the dataset and uploaded to the server, then the preprocessing stage removes columns which contain unwanted entries. The information is analyzed and partitioned to compute threshold value. Finally, frequent itemsets are merged to acquire frequent pattern. This proposed system is mainly developed for improving accuracy and is evaluated based on the performance measures. Keywords - FiDoop, MapReduce, Frequent itemset mining. I. INTRODUCTION The process of extracting information from the huge data set is termed as data mining. Market analysis, fraud detection, customer retention, production control and science exploration are the applications used to extract the information or knowledge[1]. The FiDoop is used for the frequent itemsets mining algorithm in data mining. The FiDoop is implemented with the mechanism which enables in automatic parallelization, load balancing and data distribution for parallel mining of frequent itemsets on the large cluster [9]. FiDoop used the MapReduce programming model because to improve the performance of the frequent itemset mining Hadoop clusters in data mining, the FIM is considered as a critical part of data analysis and from the data sets the information is taken based on events occurred frequently[3]. Frequent Item Sets Mining (FIM) includes the main problem in association rule mining and sequence mining [10]. There are two types of algorithms in FIM, such as, i. Apriori. ii. FP growth. Apriori: This algorithm is most traditional and essential for mining the frequent itemsets. It is used to find the all frequent itemsets in given data set. The states "All nonempty itemsets of a frequent itemset must be frequent. This depends on the Apriori algorithm of their property. It follows the two stages, such as general phase and prune phase.the disadvantage of the algorithm is to generate a large number of candidate sets and it required to repeatedly analyzing the database and validates the huge set of candidates by pattern matching. It is costly for each transaction in the database to determine the support of the candidate item set[8]. FP-Growth Algorithm: FP-Growth is an important frequency pattern mining method that generates the frequent itemset without candidate generation. It utilizes the treebased structure so it creates the conditional frequent pattern tree and the conditional pattern base which satisfy the minimum support. The software is something that needs to be addressed and evaluated on an ongoing basis because it is really foundational to the entire operations. Security concerns, scalability, usability, marketing tools and other factors have to be taken into account when you're looking for the right software to rely on. It's no secret that the biggest shortcoming of e-commerce businesses is the inability to let their customers touch, feel, smell, and see firsthand products before taking a decision. While there is currently no solution for solving this problem, you can compensate for this deficiency in other areas of the business. Some of the best tips include offering appropriate pricing, giving free shipping and making checkout process easy with simplified Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 252

279 Raksha D et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, shopping carts. As the e-commerce economy experiences rapid growth, more businesses will be entering this increasingly crowded space. This means it will be more important than ever to stay on top of SEO in order to stand out from the competition. Connecting with a skilled SEO will help you stay competitive in the long run. This paper provides basic audit of existing problems and the techniques we implement to overcome it and the survey will be mentioned in section 2, details of techniques and the tools used will be mentioned in section 3, the results and the screenshots will be mentioned in section 4 and section 5 focuses on conclusion and future scope. II. LITERATURE SURVEY J. Tsay et al.[1], proposed work "FIU: A new methodfor mining frequent itemsets,. This paper gives a productive technique that is needed and the frequent item setultrametric trees (FIUT), which will be helpful for mining constant relentless item sets in a database which is required. FIUT uses a technique that continuous things FIU-tree structure to improve the skills that are used to get item sets timely and continuous. Looking at the related work, FIUT will be having 4 noteworthy advantages. The main advantages are no need of scanning again and again for mining and parallel mining and that requires less storage space D.chen et al.[2] proposed work he says the "Tree structured partition based, used for the parallel frequent pattern mining on the memory system which is shared ".From this paper, we will be observing an algorithm of tree-segment for parallel mining of frequent item sets examples.here we need to include FP-Growth algorithm, which consists the construction of tree and mining.the main aim is to build a single FP-tree in the memory, and segment it in the few parts and appropriate them to diverse strings.calculation of heuristic is to equalize the load of work over the clusters that belong to computers.streams are seen on the sorts of datasets and checks difference the outcomes of other parallel methodologies.the results of the outcome give the methodology that has an extraordinary point of reference over the effectiveness and will be having certain sorts of datasets.quality increases the proposed parallel algorithm gives great adaptability where it will be able to adjust for to the new conditions. K.-M. Yu et al.[3], works are as A load-balanced distributed parallel mining algorithm,. Because of the development caused exponential in overall data all the organizations need to manage the data that is required all over. Most essential difficulties for information mining is rapidly and effectively finding the relationship among information. For finding continuous examples the most wellknown strategy is Apriori calculation.coming to the advantages of these algorithms parallel mining on database workload balance, automated paralization and is its expensive data mining are its disadvantages E.-H. Han, G. Karypis, and V. Kumar[4] depicts"scalable parallel data mining for association rules,". One of the indispensable issues in information mining is finding or observing association rules from databases of exchanges where every arrangement of items is done here as comprise exchange.coming to the advantages of these are data over data nodes,parallel mining are caused by distribution,and the disadvantages of these are they require more time for mining,and not efficient L. Zhou et a.l[5] proposed a Balanced parallel FP-growth with MapReduce,. Without representing others it assumes a key section in mining amalgamation, connections, and countless other critical information mining errands. Regretted, as the volume of dataset gets bigger step by step, most of the FIM calculations in writing get to be ineffectual because of either excessively tremendous asset prerequisite or as well much correspondence cost.the advantages of these are using map reduce programming for the large databases and parallel FP.The disadvantages are like it is very expensive for mining and lacks in load balancing. III. METHODOLOGY As analyzing the hidden pattern in existing E-Commerce application is not possible, we have created our own E- commerce application which is also named as FiDoop. FiDoop is a graphical user interface which uses MapReduce programming model (which is the core concept in Bigdata) to analyze hidden pattern. FiDoop accepts transaction dynamically from the user and generates the frequent items based on transactions carried out by the user which are stored in MongoDB. MongoDB stores data in JSON file format and collections and documents are in XML format. Fidoop uses HTML, JSP, CSS,JavaScript as front-end technology and MongoDB as a back-end database. Figure 1: Flowchart for Admin Admin login: The code for Admin login accepts Admin username and password from a text box. When the user clicks on submit button, the form is submitted and valid() function is called to validate where the username and password are entered. When the user clicks on the submit button, admin1,jsp is called and the username and password are validated. Add Products: The code for adding products uses the insert() method of MongoDB to insert the product into MongoDB database. Before calling the insert() method, Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 253

280 Raksha D et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, connection to database is established using MongoClient(), DBCollection() and DB() classes in MongoDB. A query is written creating a BasicDBObject() class object.another code under add products accepts parameters passed by upload.jsp and stores them in String variables. These variables are used to insert the data into MongoDB database using the code and query explained in the previous page. The value stored in the variables is obtained from the form created in upload.jsp. is written to put the data accessed from the database on to the table. Tools Used Tomcat server 8.0 Eclipse IDE MongoDB Hence the proposed system uses the above codes and tools, which allows the admin/retailer to identify the frequent items and to analyze latest trends in the market. IV. RESULT From the above-proposed techniques, the following results are drawn. according to the purchased items, the FiDoop gives a tabular representation of the products frequently purchased and sub-products purchased basedon the main product. the following screenshots are the results drawn. Figure 2: Flowchart for User Search Products: The code for search product performs the task of searching products based on the search input from the user. The user has the privilege of search products based on category, product name, product model, and price. The above-mentioned fields are taking into different DBObjects and a BasicDBList is created. The object of BasicDBList is then passed as a parameter to the find() method. Find() method retrieves the documents that match the condition and assigns it to DBCursor to display.another code under search product displays the documents that were retrieved by the find() query. DBCursor object is used to get the specific field value from the document retrieved. The values retrieved from MongoDB are displayed in a table. MapReduce: The MapReduce code retrieves the document with the matching sub product name and the subcount associated with it using the object of DBOject class. The sub count is stored in sub product count. The Mapper and the Reducer functions are called through the MapReduceCommand object. Mapper performs the mapping function to map the sub-products that are purchased with the products to which they are associated. Reducer reduces the number of documents retrieved that are similar and increases the count of the subproduct. The incremented count of the subproduct is inserted into the subproduct field present in collection upload [11]. Figure 3: MapReduce main product Figure 3 shows the MapReduce done on the frequently purchased main product and we can observe that transaction counts are based on the products delivered to the customer. so that the ADMIN can analyze the trend in the market and invest based on that. Figure 4 shows the MapReduce done on the sub products based on the main product. i.e,after purchasing the main product which of the sub-products that a customer is willing to purchase. Here in the above figure after buying Sony laptop, 14 customers have purchased Wireless Mouse, 9 customers have purchased Hard Disk and 5 customers have purchased Speaker as a sub-product. So admin is clear about the trend and can invest more in Wireless Mouse and Hard Disk than Speaker. Displaying Products: The products uploaded in MongoDB by connecting to the database and accessing the collection in which the uploaded products are present. Find() method in the code gets all the documents present in the upload collection. hasnext() method points to the document one by one, till all documents are accessed. The document pointed by hasnext()method is displayed using the HTML code that Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 254

281 Raksha D et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, Provide an abstract view of purchased items to admin. Payment options can be implemented in a more realistic way. Introduce automatic mail delivery system upon successful transactions. To accept the product review about a particular product by the customer. To send the message to the customer number on successful placement of order. To track the order details of the customer. Figure 4: MapReduce subproduct V. CONCLUSION AND FUTURE SCOPE With FiDoop application, we can analyze the various patterns of business that happen over an e-commerce application. Data generated in the databases act as a potential source of information that can be pipelined to an analytics tool. FiDoop helps a user understand the ongoing trends in the market and help contribute the admin to work on prescriptive analysis. All these years most of the E- commerce applications ruled the market based on predictive analysis. FiDoop incorporates prescriptive analysis to help analyze the various trends happening in the market. It incorporates various technology like eclipse and mongodb[6] to give the more realistic performance of the system. Its also have some common features like purchasing the item, adding the items, deleting the items etc. By finding the purchase patterns it helps the marketing department to meet the demand of the consumer.these patterns will be driving force to analyze the market trends because this keeps changing across the time and it also depends on the mindset of the people. The opinion of the customer is very much important for the producer in order to develop the particular product.these opinions can be found out by this system by the number of product sold. We have a number of applications to purchase the items, but we don't have the system that would produce the transaction pattern to analyze the market trends, so this application would the driving force to meet the demand of the customer. Transactions filters can be improved. VI. REFERENCES [1] Y.-J. Tsay, T.-J. Hsu, and J.-R. Yu, FIUT: A new method for mining frequent itemsets, Inf. Sci., vol. 179, no. 11, pp , [2] D. Chen et al., Tree partition based parallel frequent pattern mining on shared memory systems, in. 20th IEEE Int. Parallel Distrib. Process. Symp. (IPDPS), Rhodes Island, Greece, 2006, pp [3] K.-M. Yu, J. Zhou, T.-P. Hong, and J.-L. Zhou, A loadbalanced Distributed parallel mining algorithm, Expert Syst. Appl., vol. 37, no. 3, pp , [4] E.-H. Han, G. Karypis, and V. Kumar, Scalable parallel data mining for association rules, IEEE Trans. Knowl. Data Eng., vol. 12, no. 3, pp , May/Jun [5] L. Zhou et al., Balanced parallel FP-growth with MapReduce, in Proc. IEEE Youth Conf. Inf. Comput. Telecommun. (YC- ICT), Beijing, China, 2010, pp [6] K. W. Lin, P.-L. Chen, and W.-L. Chang, A novel frequent pattern mining algorithm for very large databases in cloud computing. [7] S. Hong, Z. Huaxuan, C. Shiping, and H. Chunyan, The study of improved FP-growth algorithm in MapReduce, inproc. 1st Int. Workshop Cloud Comput.Inf. Security, Shanghai, China, 2013, pp [8] M.-Y. Lin, P.-Y. Lee, and S.-C. Hsueh, Apriori-based frequent itemset mining algorithms on MapReduce, in Proc. 6th Int. Conf. Ubiquit. Inf. Manage. Commun.(ICUIMC), Danang, Vietnam, 2012,pp.76:1 76:8. [9] L. Liu, E. Li, Y. Zhang, and Z. Tang, Optimization of frequent itemset mining on multiple-core processor, in Proc. 33rd Int. Conf. Very Large Data Bases, Vienna, Austria, 2007, pp [10] A. Javed and A. Khokhar, Frequent pattern mining onmessage passing multiprocessor systems, Distrib.Parallel Databases, vol. 16, no. 3, pp , [11] J. Dean and S. Ghemawat, MapReduce: A flexible data processing tool, Commun. ACM, vol. 53, no. 1, pp , Jan Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 255

282 Volume 9, Special Issue No. 3, May 2018 International Journal of Advanced Research in Computer Science (ISSN: ) CONFERENCE PAPER Available Online at FRAMEWORK FOR DATA SECURITY FROM SQL INJECTION IN CLOUD COMPUTING P Sarika Reva University Bengaluru, India Seema M Reva University Bengaluru, India Shweta Kumari Reva University Bengaluru, India Sona Singh Reva University Bengaluru, India Supreeth S Reva University, Bengaluru, India Abstract-SQL Injection is one of the top 10 web app vulnerabilities where any unauthorized person directly invokes the malicious SQL query in database that will raise the issue of integrity and privacy of data which will definitely fails the maintenance of database. Till now there is no proper provision of providing security in the Infrastructure layer of cloud. In this paperwe are trying to provide solution by Implementing it in cloud computing using as-a-service delivery model that means we are assuring security for databases from malicious SQL queries that will hamper the company s database. These services can be consumed by any IAA Sapplications.Problems occurring from SQL injection can be avoided with the correct validation. The validation of the query which is written in SQL form are validated by using the Grammar checking using Regular Expressions of the query and it also includes the use of hybrid techniques for detecting malfunctions by passing our query fromwhite List and Black List. White List test contains the syntax which should always show pass status if the query is not harmful. Black List contains some special symbols such as $, *, --, etc and conditions like a=a or 1=1. So, black list should always show fail status which means that the query is safe to run. Keywords: As-a-service, Black list, White list, Pay-asyou-go, PaaS, SaaS, IaaS, SQLIID 1. INTRODUCTION Cloud computing is a very trending technology now a days. Cloud computing is a technology that uses internet to provide services to their users in a virtualised way. This increases the availability, scalability,pay-per-use and back up support of the data.therefore,every one wants to keep their data in cloud. This will make sure that data will always be present whenever we need it.so,if once we will store the data in cloud it will be always be present there and we can easily fetch the data whenever and where ever we want. Cloud has it s so many characteristics like scalability, resiliency etc. We are mainly working on pay-per-use characteristics of cloud. Cloud services are in three forms i.e.saas(software as a service), IaaS(Infrastructure as a service) and PaaS(Platform as a service). SaaS is a process to rent the software online in cloud so that the people can use it and pay money according to their usage which is also called as pay-per-use.this basically deals with the applications.it is the most widely used form of cloud services.paas provides the services in the form of platform.it rents the OS,Linux,databases etc in cloud and get paid according to their usage. IaaS used to rent infrastructure such as networking,servers etc.in IaaS,the security related concern for the data is as same as the security related issues for the data centre.in IaaS,one can easily enter inside the others system and do the changes which may be harmful. At least there is security given in SaaS and PaaS level but there is no proper security measure taken for infrastructure as a service. In IaaS, providing a better form of security is a concern now a days. An SQL injection attack targets interactive web applications that employ database services[2]. SQL injection can affect any website that uses SQL based database.it is a process to run the malicious query in database so that even the unauthorized person can get person s credentials and data.no one can deny the importance of data in real life. This is the reason why company wants to protect their data from intruders. If someone manipulates the real data then it is very difficult to get back the original data. For preventing this to happen, company should provide an effective way of security. All the malicious SQL queries uses some of the special symbols and conditions which are always true. By writing such queries even an unauthorized person may get very sensitive information which harms the confidentiality and integrity of data. Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 256

283 P Sarika et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, For example, if a database contains user names and passwords,the application may contain code such as the following: query = "SELECT * FROM accounts WHERE name= "+ request.getparameter("name")+ " AND password= "+ request.getparameter("pass") + " "; This code generates a query intended to be used to authenticate auser who tries to login to a web site. However, if a malicious userenters badguy into the name field and OR a = a into the password field, the query string becomes: SELECT * FROM accounts WHEREname= badguy AND password= OR a = a ; whose condition always evaluates to true, and the user will bypassthe authentication logic[1]. Previously many works are done on SQL injection but they are very costly and not language independent as the solution was given on some specific language[2],[4],[5]. Our project acts as an intermediate between application and their database. It is language independent. It doen not require any modification of the source code. The user of our project can check the SQL query and should be the admin of some company so that he/she can check and analyse the type of query invoking their database. The best part is that we do not need the credentials of our client s user who are running query in database. So, in this way, we can retain their privacy. In our approach for finding the solution of this alarming issue, we are trying to protect the database from above conditions where we are validating the SQL query that is given to the database. Validating the SQL queries is done before getting run in the original database. Validation is done by use of grammar checking and regular expression. Grammar checking isa process where we are checking the grammar of the query like whether the query ends with semicolon or not etc. Regular expression is used to search for a definite pattern and in simpler words, we can say it is a combination of characters. We are also testing the query by white list and black list concept. White list contains the syntax, regular expression etc that will check whether it s safe to execute or not. If the query pass the white list test, then this means that the query is not malicious. Then, we are checking our query in black list. The normal and safe query should always fail this test then only it will make sure that query is not harmful. A non-malicious query should always pass the white list test and fails the black list test. After validation of the query, we are also giving the result whether the query is safe or not. If the query is not safe to run then the result will be displayed unsafe alongwith the reason why this query is harmful. This is also shown in Figure 1. QUERY GRAMMAR CHECK WHITE LIST BLACK LIST SAFE UNSAFE UNSAFE UNSAFE Figure 1: Flow Diagram of Query Status 2. Approach We have various divisions in our project:- User Account Operations This provides the users of our project with a user interface to get access to our project. A user can create an account after which he will be able to access his/her account. Other operations an user can perform on his account are Login, Logout, Edit profile, Delete profile and change password. Web Service Module The SQL intrusion injection detection process will be implemented as a web service. This module will be used by the admins of SQLiiD admins to get the endpoint of the SQLiiD service. The admins can then use this module for performing campaigning to various customers. The customers will be receiving an with the end point URL to access the SQLiiD web service along with their passcode. The will also contain a brief description of all the parameters the web service will be accepting in order to process the SQL query and return the result. Pay as you go This module is used by the SQLiiD admins for billing the customers based on their usage on the SQLiiD web service. The SQLiiD webservice, whenever invoked by the customer will be checking for the authorization. If it passes, it executes the core algorithm to analyze the inputted SQL string to determine if its safe to execute on the customer database or not. The entry will be made on the pay as you go module database indicating that the user have invoked the service. Results and Reports Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 257

284 P Sarika et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, This module will be used by the SQLiiD admins to view the results and reports of various webservice calls to the SQLiiD by the customers. The results and reports includes the client identifier who invoked the service, the timestamp at which the service was invoked, the SQL string against which the algorithm was been invoked, and the detailed report indicating whether or not its safe to execute the SQL sting on the customer database. Sample Application This application is developed only for demonstration purpose to show how the customers will be using our SQLiiD web services to analyze their SQL string for SQL injection detection. The entire architecture has been implemented in modules which includes data access layer, account operations, web service module, pay as you go, results and reports of this as shown in figure2. Implementation of hybrid technique for analyzing the SQL string for detecting whether its safe to run the SQL string on the database or not. Initially we are doing the Implementation of user account operations to provide authentication and authorization facilities for the admins of the SQLiiD. Then, the implementation of security layer for the customers to access the SQLiiD web service module is done. After that we are trying to implement for Pay as you go feature to enable the SQLiiD admins to price the customers based on their usage. The implementation of web service module to enable the SQLiiD admins to perform campaigning of the service to various customers is done. At last, we are going for the implementation of results and reports module to enable the SQLiiD admin to analyze the SQL string provided by the customers. 3.Assumptions and dependencies The main assumptions and dependencies identified are as follows: JDK has to be installed in the machine where all the three subcomponent will be executing. The application servers like either the JBOSS or the Apache Tomcat will have to be supported by the host machines Figure 2: Architecture diagram There shall not be any firewall or other engines that prevents the remote requests from the portal. There shouldn t be any permission related issues on any cluster. The host operating system should take of permitting all the requests to the cluster from the interface layer. 4. Implementation and Validation In figure 3, we have shown the data flow diagram of the project with respect to admin as well as user point of view. Figure 3: Data Flow Diagram Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 258

285 P Sarika et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, ARCHITECTURE MODELS 1.Account Access Layer Account operations module provides the following functionalities to the end users of our project. Register a new seller/ buyer account Login to an existing account Logout from the session Edit the existing Profile Change Password for security issues Forgot Password and receive the current password over an Delete an existing Account Account operations module will be re-using the DAO layer to provide the above functionalities. The DAO layer is the service layer which provides database CRUD (create, update, read, and delete) services to the other layers. It will contain the POJO classes to map the database tables into java object. It will also contain the Util classes to manage the database connections. Figure 4 2: Implementation of SQLiiD algorithm The service end point will be fixed that will read the cliend id, passcode and query string. Verification of the client id is done to make sure that client has been registered first before accessing the end point. If verification fails then an error message will pop up. If verification is successful then we will go for the grammar checking phase. Here, we will read the inputted SQL query and compare it against the defined regular expression for checking the grammar like check for parenthesis, slashes, semicolon etc. If it fails, then it will make an entry in the pay as you go report. Thus, the overall result for this particular process will be saved in the report of SQLID which will contain every details like day, query,date, query status etc. If the grammar checking result is passed then white list testing and blacklist testing is done. Then, finally whether the query is safe or unsafe to run in database is assured. The result of the query is maintained in pay as you report and also in the SQLID report that contains every information about the service usage. 3: Implementation of the web services module The SQL intrusion injection detection process will be implemented as a web service. This module will be used by the admins of SQLiiD admins to get the endpoint of the SQLiiD service. The admins can then use this module for performing campaigning to various customers. The customers will be receiving an with the end point URL to access the SQLiiD web service along with their passcode. The will also contain a brief description of all the parameters the web service will be accepting in order to process the SQL query and return the result. 4: Implementation of pay-as-you-go module and reporting module This module is used by the SQLiiD admins for billing the customers based on their usage on the SQLiiD web service. The SQLiiD webservice, whenever invoked by the customer will be checking for the authorization. If it passes, it executes the core algorithm to analyze the inputted SQL string to determine if its safe to execute on the customer database or not. The entry will be made on the pay as you go module database indicating that the user have invoked the service. This module will be used by the SQLiiD admins to view the results and reports of various webservice calls to the SQLiiD by the customers. The results and reports includes the client identifier who invoked the service, the timestamp at which the service was invoked, the SQL string against which the algorithm was been invoked, and the detailed report indicating whether or not its safe to execute the SQL sting on the customer database. 5: Implementation of SQLiD user application This application is developed only for demonstration purpose to show how the customers will be using our SQLiiD web services to analyze their SQL string for SQL injection detection. Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 259

286 P Sarika et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, Figure 5 Figure 6 Figure 7 Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 260

287 P Sarika et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, Conclusion and future work SQLIID framework as a service for SAAS providers has been proposed in this paper. It helps to detect all the SQL intrusion attack which is targeting directly to SAAS applications whichdoes not require any changes in source code. The deployment of framework in cloud makes this project more accessible and highly portable. The framework can be subscribed by SAAS providers so that it can be implemented on the different virtual machines which are associated with different SaaS applications. The future work of this project can be done by ensuring the pay-as-you-go characteristics. We can deploy this framework on live cloud environment so that it will increase the accessibility of using it s services. For preventing the security breaches, we can use various encryption techniques while generating one time password for new users while availing our services. REFERENCES 1.Z. Su. and G. Wassermann, SQLCheck:The Essence of Command Injection Attacks in Web Applications, Proceedings of the 5th international workshop on Software engineering and middleware, G. T. Buehrer, B. W. Weide, and P. a. G. Sivilotti, SQLGuard:Using parse tree validation to prevent SQL injection attacks, Proceedings of the 5th international workshop on Software engineering and middleware - SEM 205, no. September, pp , J. Williams and D. Wichers, Owasp: Top , owasp foundation, W. G. J. Halfond and A. Orso, Preventing SQL injection attacks using AMNESIA, Proceeding of the 28th international conference on Software engineering - ICSE 06, pp. 1 4, C. Gould and P. Devanbu, JDBC checker: a static analysis tool for SQL/JDBC applications, Proceedings. 26th International Conference on Software Engineering, pp , Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 261

288 Volume 9, Special Issue No. 3, May 2018 International Journal of Advanced Research in Computer Science (ISSN: ) CONFERENCE PAPER Available Online at A QUEUING METHOD FOR ADAPTIVE CENSORING IN BIG DATA PROCESSING Ayesha Banu R, Mahamat Mahmoud Salim Breck, School of Computing and Information Technology, School of Computing and Information Technology, REVA University REVA University. Kamalashree M, PavithraRayasam, School of Computing and Information Technology, School of Computing and Information Technology, REVA University. REVA University. ArunaKumara.B School of Computing and Information Technology, REVA University. Abstract:As per 2.5 quintillion bytes of information go on producing daily, the period of big data residues undeniably upon us.running scrutiny on widespread datasets is an experiment. Luckily, an important proportion of the information accumulated can be misplaced although preserving a confident value of numerical implication in various cases. Censoring delivers us an expected choice used for data decrease. But, the information carefully preferred through censoring is not at all consistently, which force the computational amount of necessity. In this article, we suggested a lively, queuing techniques to evenly available the data s dealing and surrendering the convergence act of censoring. The planned technique which involves simple and closed-form apprises, which does not take any kind of cost in rapports of accurateness relating towards the unique adaptive censoring technique. And we will be using the AES Algorithms to encrypting given Data form file systems and it will be uploading all files to data center using queuing model. I. INTRODUCTION Nowadays, mobile internet and terminal devices for internet of things produces massive volumes of dynamic data. Learning from massive amount of databases it is determined to bring advance stages in science and engineering. Extraction of most useful, yet low dimensional structure from the dimensions with high databases is necessary. The massive volume of data is usually acquired sequentially in time, motivating updating analysis rather than re-calculating new ones from scratch when each time a new result will be obtained. Redundancy is an impute of gigantic data encountered in various applications, and utilizing it judiciously offers an effective way of reducing data processing costs. In this estimate, the trials have been sponsored to lesser the data obligatory for inference tasks by optimal design, sequential optimization along with random big data samplings have been emphasized in recent works. Precisely, for linear regressions, random projection (RP)-based approaches have been promoted to reduce the size of largescale least-squares (LS) problems. Based on quantized data for decentralized spares LS solvers using frugal solvers of linear regressions which are obtainable by approximating regression coefficients. Selection on distributed estimation of dynamical processes and parameters using available required wireless sensor network Censoring has recently been appointed, thus determined performance for flexibility; see also for massive online regression. These works coordinates that estimation precision acquired with controlled estimations can be like that in light of uncensored information. Thus, editing offers the possibility to decrease information preparing to a small amount of the first expenses. Notwithstanding, editing still faces one issue: the information picked by controlling is non-uniform, which still requires an appeal for computational assets. At the end of the day, the information preparing unit still can't bargain every one of the information when the perceptions picked by editing lands in a brief span interim. In this article, we suggest a buffer structure; queuing method intended to get desirable selected data. In the wake of applying the lining technique, the enlightening information will turn out to be (nearly) uniform. Contrasted with traditional information versatile blue penciling strategy, the proposed technique can accomplish a similar execution while assuaging the computational asset necessity. The nonuniform issue of the uncensored information in settling largescale straight relapses is likewise presented here. II. LITERATURE SURVEY We summarized some of the following related works. K. Slavakis, G. B. Giannakis, and G. Mateos, proposed Modelling and optimization for big data analytics: Learningtools for our era of data deluge [1], with unavoidable sensors consistently gathering and putting away enormous measures of data, there is no uncertainty this is a Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 262

289 Ayesha Banu R et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, time of information storm. Gaining from these expansive volumes of information is relied upon to bring huge science and building progresses alongside enhancements in personal satisfaction. In any case, with such a major gift come huge difficulties. Running examination on voluminous informational indexes by focal processors and capacity units appears to be infeasible, and with the approach of gushing information sources, learning must frequently be performed progressively, commonly without an opportunity to return to past sections. Workhorse flag preparing (SP) and measurable learning devices must be reconsidered in the present high-dimensional information administrations. This article adds to the continuous cross-disciplinary endeavors in information science by advancing incorporating models catching an extensive variety of SP-pertinent information logical errands, for example, chief part investigation (PCA), word reference learning (DL), compressive examining (CS), and subspace grouping. It offers adaptable models and improvement calculations for decentralized and web based learning issues, while uncovering principal bits of knowledge into the different explanatory and usage exchange off included. Augmentations of the including models to convenient information drawing, tensor-and piece based learning undertakings are additionally given. At last, the nearby associations of the gave structure a few major information assignments, for example, organize perception, decentralized and dynamic estimation, expectation, and attribution of system interface stack movement, and ascription in tensor-based therapeutic imaging are featured. This lecture note presents a new method to capture and represent compressible signals [2], at a rate significantly below the Nyquist rate. This method, called compressive sensing, employs non-adaptive linear projections that preserve the structure of the signal; the signal is then reconstructed from these projections using an optimization process. In this paper authors presents and analyse a sampling algorithm for the basic linear-algebraic problem of l2 regression [3], the l2 relapse (or minimum squares fit) issue takes as info a lattice A Rn d (where we accept n d) and an objective vector b Rn, and it returns as yield Z = minx Rd b - Ax 2. Additionally, of intrigue is xopt = A+b, where A+ is the Moore-Penrose summed up converse, which is the base length vector accomplishing the base. Our calculation haphazardly tests r lines from the network An and vector b to build an incited l2 relapse issue with numerous less lines, yet with a similar number of segments. A significant element of the calculation is the nonuniform examining probabilities. The l2 relapse (or minimum squares fit) issue takes as information a network A Rn d (where we expect n d) and an objective vector b Rn, and it returns as yield Z = minx Rd b - Ax 2. These probabilities depend in a refined way on the lengths, Our calculation arbitrarily tests r lines from the grid An and vector b to build an actuated l2 relapse issue i.e., the Euclidean standards, of the lines of the left solitary vectors of An and the way in which b lies in the supplement of the section space of A. Under fitting suspicions, we indicate relative mistake approximations for both Z and xopt. Uses of this testing procedure are quickly talked about. This paper presents constrained least-squares regression problems, such as the Nonnegative Least Squares (NNLS) problem [4], where the variables are restricted to take only nonnegative values, often arise in applications. Motivated by the recent development of the fast Johnson-Linde-strauss transform, we present a fast-random projection type approximation algorithm for the NNLS problem. Our algorithm employs a randomized Hadamard transform to construct a much smaller NNLS problem and solves this smaller problem using a standard NNLS solver. We prove that our approach finds a nonnegative solution vector that, with high probability, is close to the optimum nonnegative solution in a relative error approximation sense. The Nonnegative Least Squares (NNLS) problem, where the variables are restricted to take only nonnegative values, often arise in applications. We experimentally evaluate our approach on a large collection of term-document data and verify that it does offer considerable speedups without a significant loss in accuracy. Our analysis is based on a novel random projection type result that might be of independent interest. In particular, given a tall and thin matrix Ö Rn d (n d) and a vector y Rd, we prove that the Euclidean length of Öy can be estimated very accurately by the Euclidean length of Ö~y, where Ö~ consists of a small subset of (appropriately rescaled) rows of Ö. In this paper authors proposed Randomized algorithms for very large matrix [5] problems have received a great deal of attention in recent years. Much of this work was motivated by problems in large-scale data analysis, largely since matrices are popular structures with which to model data drawn from a wide range of application domains, and this work was performed by individuals from many different research communities. While the most obvious benefit of randomization is that it can lead to faster algorithms, the use of randomization can lead to simpler algorithms that are easier to analyse or reason about when applied in countries either in worst-case asymptotic theory and/or numerical implementation, there are numerous other benefits that are at least as important. For example, the use of randomization can lead to simpler algorithms that are easier to analyse or reason about when applied in counterintuitive settings; it can lead to algorithms with more interpretable output, which is of interest in applications where analyst time rather than just computational time is of interest; it can lead implicitly to regularization and more robust output; and randomized algorithms can often be organized to exploit modern computational architectures better than classical numerical methods. Existing System As in excess of 2.5 quintillion bytes of information are created each day, the period of huge information is without a doubt upon us. Running examination on broad datasets is a test. Luckily, a noteworthy level of the information accumulated can be precluded while keeping up a specific nature of measurable deduction as a rule. Running investigation on broad datasets is a test. Running investigation on broad datasets is a test. Blue penciling gives us a characteristic alternative to information diminishment. Nonetheless, the information picked by editing happen nonuniformly, which may not mitigate the computational asset Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 263

290 Ayesha Banu R et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, prerequisite. Proposed System In this paper, we propose a dynamic, lining strategy to smooth out the information handling without giving up the meeting execution of controlling. The proposed technique involves basic, shut shape refreshes, and has no misfortune as far as precision contrasting with the first versatile editing strategy. Reproduction comes about approve its adequacy. III. ARCHITECTURAL REPRESENTATION The figure demonstrates the whole architecture of the queuing process. At this point first random selection of multiple files taking place. According to file sizes threshold value given by user is compared. Using that threshold values. We check file sizes based on min and max value and identify the priority of the users. Subsequent to applying the lining technique (joining standard), the useful information will turn out to be (nearly) uniform. Contrasted with customary information versatile controlling strategy, the proposed technique can accomplish a similar execution while calming the computational asset prerequisite. In transfer status technique we have to utilize the strategy for the diverse needs and record appeared by the client. Contrasted with ordinary information versatile blue penciling strategy, the proposed technique can accomplish a similar execution while diminishing. IV. IMPLEMENTATION As soon as the system planned, the step changes the planned one in to real code, so as to gratify the user requirements as needed. When the initial design was done for the system, the department was consulted for acceptance of the design so that further proceedings of the system development can be accepted. The goal of system design to recognize any fault in system. Implementation includes correct working to end-users. The implemented software is maintained for running the software. Initially the system was run parallel with manual system. The system has remained verified with data and has verified to be fault-free and user-friendly. Training was given to end-user about the software and its features. Pseudo Code for Queuing Model Module Description Queue Start Figure 1: System architecture In the initial process of the system the system here the basic requirements are we are considering the Ten users and for every user s some files and particular sizes will be selected and for every user randomly setting the priority. Every time randomly selecting. In the convergence rule for values will be generating for sensors and every time system will be generating the different priority value and Selecting the different file. Queuing iteration generation (Convergence rule) In the queuing iteration for every value s sensed by the sensors the convergence rule will be generated based upon the threshold value. If the value of the threshold is less than the size of the file user uploading it will check for the other files to iterate and the file name and priority will be changing. Upload status For i in 1 to 10 F(i)<=RAND(1-150) For each U in 1 to 10 SHUFFLE(U,i); and For maxvalsize=get(f, MAX ); minvalsize=get(f, MIN ); T=(maxvalsize+minvalsize)/2 S=ACT(SENSOR) If (S START ) CHECK(U,F) Q=prioritymarf(U) Qn=SENSOR(Q, START ); Sc(Q,F); Algorithm STEP1: Start //Files are selected from file system STEP2: File sizes are initialized to 0 STEP3: System will randomly select file for each user STEP4: Priority is assigned to each user accordingly STEP5: It senses the max size and min size of each file STEP6: It calculates the threshold value T=(maxvalsize+minvalsize)/2 STEP7: System will split data if it exceeds threshold value STEP8: It uploads to cloud server Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 264

291 Ayesha Banu R et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, STEP9: Data centre is updated STEP10: End V. RESULTS AND DISCUSSIONS The result in which report discoveries the study of methodology to gather info. The result of the report is organized in a logical order without bias or clarification Figure 2: Queuing started Figure 3: start Broadcast VI. CONCLUSION Versatile controlling decreases the information sizes in huge information. Notwithstanding, its information is nonuniform, which requests for extra computational asset. The arranged technique is straightforward, shut frame refreshes and without additional computational trouble. Its presents to be limited with an indistinguishable bound from that of AC- RLS calculation. Reproductions show arranged calculation to achieve blue penciling yield of huge information. REFERENCES [1] K. Slavakis, G. B. Giannakis, and G. Mateos, Modeling and optimization for big data analytics: Learning tools for our era of data deluge, IEEE Signal Processing Magazine, vol. 31, no. 5, pp.18 31, Sep [2] D. Donoho, Compressed sensing, IEEE Transactions on Information Theory, vol. 52, no. 4, pp , Apr [3] P. Drineas, M. W. Mahoney and S. Muthukrishnan, Sampling algorithms for l2regression and applications, in ACM-SIAM Symposium on Discrete Algorithms, pp , Jan [4] C. Boutsidis and P. Drineas, Random projections for the nonnegative least-squares problem, Linear Algebra and its Applications, vol. 431, no. 5, pp , Dec [5] M. Mahoney, Randomized algorithms for matrices and data, Foundations and Trends in Machine Learning, vol. 3, no. 2, pp , Nov [6] C. Rago, P. Willett, and Y. Bar-Shalom, Censoring sensors: A lowcommunicationrate scheme for distributed detection, IEEE Trans.Aerosp. Electron. Syst., vol. 32, no. 2,pp , Apr [7] D. P. Woodruff, Sketching as a tool for numerical linear algebra, Foundations and Trends in Machine Learning, vol. 10, no. 1-2, pp , Oct [8] Y. Plan and R. Vershynin, One-bit compressed sensing by linear programming, IEEE Transactions on Signal Processing, vol. 66, no. 8, pp , Aug [9] G. Mateos, J. A. Bazerque, and G. B. Giannakis, Distributed sparse linear regression, IEEE Transactions on Signal Processing, vol. 58, no. 10, pp , Oct [10] E. J. Msechu and G. B. Giannakis, Distributed measurement censoring for estimation with wireless sensor networks, in Proc. 12th Int. Workshop Signal Process. Adv. Wireless Commun., San Francisco, CA, Jun. 2011, pp Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 265

292 Volume 9, Special Issue No. 3, May 2018 International Journal of Advanced Research in Computer Science (ISSN: ) CONFERENCE PAPER Available Online at CLOUD COMPUTING FEATURE SCURITY AND EXPECTED SOLUTION- SURVEY PAPER ArunKumar Student, Data Engineering And Cloud Computing, REVA University. HardikKumar Student, Data Engineering And Cloud Computing, REVA University Gourish Malage Student, Data Engineering And Cloud Computing, REVA University GopalKrishna Shyam Associate Professor, REVA University Abstract Using a set of accessible services and resources through the web is termed Cloud Computing. information centers placed round the world give the cloud services. As this field of technology features a rise in recent times, in cloud model security it's still controversial that impacts the cloud model adoption. the protection downside becomes a lot of difficult below the cloud model as new dimensions have entered into the matter scope associated with the model design, multi-tenancy, elasticity, and layers dependency stack. This paper we have a tendency to square measure introduce an in depth analysis of the cloud security drawback. we've got determine the matter from the cloud offered characteristics prospect, the cloud design prospect, and therefore the cloud service delivery models prospect. supported our survey we have a tendency to researched an in depth specification of the cloud security drawback and key options that should be coated by any suggested security answer. I. INTRODUCTION Distributed computing gives progressive age of web based, to a great degree versatile circulated processing frameworks inside that technique assets unit of estimation offered 'as an administration'. The premier wide utilized meaning of the distributed computing model is presented by NIST[1] as "a model for empowering helpful, on-request arrange access to a mutual pool of configurable registering assets (e.g., systems, servers, stockpiling, applications, and administrations) that might be rapidly provisioned and free with negligible Management exertion or administration provider association." Multi habitation and snap ar 2 key qualities of the cloud display. Multi-Tenancy permits sharing a proportional administration case among entirely unexpected inhabitants. Scaling all over assets apportioned to an administration bolstered this administration requests. every trademark represent considerable authority in rising asset use, esteem and repair accommodation case to orchestrate your original copy. For accommodation, regardless of the potential edges and incomes that might be picked up from the distributed computing model, the model still highlights a huge amount of open issues that effect the model noteworthiness and sweeping statement. Businessman secure, multi-tenure and seclusion data administration benefit quality, snap motors, SLA administration, and cloud security territory unit acknowledged open examination issues inside the distributed computing model. From the cloud shoppers' viewpoint, security is that the most essential worry that hampers the appropriation of the distributed computing model [2] in light of the fact that: Enterprises supply security Management to an outsider that has their IT resources (loss of control). Co-presence of advantages of arranged inhabitants inside a proportionate Area and exploitation consistent occurrence of the administration while being ignorant of the quality of security controls Utilized. The lack of security ensures inside the SLAs between the cloud clients and conjointly the cloud providers. Hosting this arrangement of significant resources on out in the open advertised Foundation will build the possibility of assaults. From the cloud suppliers' point of view, security needs heaps of consumptions (security arrangements' licenses), assets (Security might be an asset serious undertaking), and might be a troublesome disadvantage to ace (as we have a tendency to examine later). However skipping security from the distributed computing model guide will abuse the normal incomes as clarified on prime of. Along these lines cloud providers must be constrained to see purchasers' issues and chase down new security arrangements that determination such issues. II. LITERATURE REVIEW Cloud computing security challenges and problems mentioned by various researchers. The Cloud Computing Use Cases cluster [3] discusses the various use case scenarios and connected needs which will exist within the cloud computing model. They take into account use cases from completely different views together with customers, developers and security engineers. ENISA [4] investigated the various security risks associated with adopting cloud computing together with the affected assets, the risks probability, impacts, and vulnerabilities in cloud computing Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 266

293 Arun Kumar et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, This will result in such risks. Similar efforts mentioned in Top Threats to Cloud Computing by CSA [5]. Balachandra et al [6] discuss the safety SLA s specifications and objectives associated with knowledge locations, segregation and knowledge recovery. Meiko et al [8] discuss the technical security problems arising from adopting the cloud computing similar to flooding attacks model, XMLattacks, and Browsers connected attacks. CSA [5] discusses essential areas of cloud computing. They deliver a collection of best practices for the cloud supplier, customers and security vendors to follow in every domain. CSA revealed a collection of elaborate reports discussing for a few of those domains. In our analysis we have a tendency to did a deep investigation within the cloud model to spot the foundation causes and key collaborating dimensions in such security issues/problems mentioned by the previous work. This can facilitate higher to grasp the problem and deliver solutions. I III. THE CLOUD COMPUTING ARCHITECTURE AND SECURITY IMPLICATIONS Cloud Computing model is been having 3 service delivery models and it additionally has main 3 preparation models. The preparation models are: (1) Private cloud: a cloud platform is study for specific organization. (2) Public cloud: The general public user features a cloud platform to register and to use the accessible infrastructure. (3) Hybrid cloud: a private cloud that may touch use resources publically clouds. Public clouds square measure the foremost vulnerable preparation model as a result of their accessible for public users to host their services United Nations agency is also malicious users. The cloud service delivery models, as in Figure1, include: - Infrastructure-as-a-benefit (IaaS): wherever cloud providers convey calculation assets, stockpiling Associate in Nursing system as a web based administrations. This administration demonstrate is predicated on the virtualization innovation. Amazon EC2 is that the most familiar IaaS provider. - Platform-as-a-benefit (PaaS): wherever cloud providers convey stages, instruments and diverse business benefits that change clients to create, send, and deal with their own Applications, while not putting in any of those stages or bolster apparatuses on their local machines. The PaaS display is likewise facilitated on high of IaaS show or on high of the cloud foundations straightforwardly. Google Apps and Microsoft Windows Azure square measure the first amazing PaaS. - Software-as-a-benefit (SaaS): wherever cloud providers convey applications facilitated on the cloud framework as web based administration for complete clients, while not requiring putting in the applications on the clients' PCs. This model is likewise facilitated on high of PaaS, IaaS or specifically facilitated on cloud framework. SalesForce CRM is Associate in nursing case of the SaaS provider. Figure 1: cloud service models IV. CLOUD COMPUTING CHARACTERSTICS AND SECURITY IMPLICATIONS To accomplish economical appliance of resources, billow suppliers accept to be accountable to access their adeptness appliance admitting abbreviating value. At identical time shoppers accept to be accountable to use assets as so abundant as appropriate admitting accepting the adeptness to extend or abatement assets burning accurate absolute demands. The billow accretion archetypal meets such desires via a win resolution by carrying 2 key characteristics: multi-tenancy and snap. Each characteristics end up to acquire austere implications on the billow archetypal security. Multi-tenancy implies administration of action resources, storage, services, and applications with another tenants. Multi-tenancy has absolutely altered ability approaches as apparent in Figure 2. In access one, every addressee has their own committed instance with their own customizations (customization ability embrace appropriate development to amuse applicant needs). In access two, every addressee uses an ardent instance, like access one, admitting all instances breadth assemblage identical about with absolutely altered configurations (adjustment of appliance ambit or interfaces). In access three, all tenants allotment identical instance with runtime agreement (the appliance is breach into amount appliance allotment and added locations that breadth assemblage loaded accurate this addressee requests like SalesForce.com). In access four tenants breadth assemblage directed to a amount aerialist that redirects tenants requests to an adequate instance accurate accepted instances load. Approaches three and four breadth assemblage the foremost chancy as tenant s breadth assemblage ancillary on identical adjustment in anamnesis and hardware. This administration of assets violates the acquaintance of tenants IT assets that ends up in the claim for defended multi residency. To bear defended multi-tenancy there care to be abreast a part of tenants ability (at rest, action and transition) and website accuracy wherever tenants haven't any abstracts or administration over the accurate area of their assets (may accept top akin administration on ability area like country or Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 267

294 Arun Kumar et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, arena level), to abstain planned attacks that accomplish to co-locate with the victim assets. In IaaS, abreast care to yield into annual VMs storage, processing, memory, accumulation reminiscences, and networks. In PaaS, abreast care to awning isolatation a part of active casework and APIs calls. In SaaS, abreast care to abstract a part of affairs administered on identical instance by absolutely altered tenants and tenants knowledge Figure 2: Multi-tenancy Furthermore, security necessities outlined by service consumers ought to be migrated with the service and initiates a method to enforce security necessities on the new surroundings, as outlined by cloud consumers, and updates the present cloud security model. A. VM Attacks Cloud computing layout is separated into 2 totally exceptional sections like Front and Back ends are connected thru the community. The the front facet side is that the consumer or user and therefore the cloud provider is at the rear cease. The the front ends include the client s laptop and required to get right of entry to the cloud design. On the rear end of the cloud computing consists of definitely different digital machines (VM s), laptop, know-how storage gadget and servers that generate the cloud computing services. Cloud computing is relied upon VM generation. The Hypervisor, Sphere, VMware are used for Cloud implementation. The cloud builders needed to require care of cloud attacks once the implementation is completed and additionally beware by way of utilising Intrusion prevention System (IPS) and Intrusion Detection Systems (IDS). The IPS and IDS troubles will remedy by victimization suitable firewall. B. Loss of Governance the Loss of Governance drawback relied upon to losing security and frame controls in cloud computing. It incorporates moving knowledge to the cloud, it refers to dropping management over vicinity, redundancy and submitting system. Service-degree agreement (SLA) might not have warranted on cloud supplier zone. There's no accurate SLA it is commonplace SLA s do not appear to be present inside the cloud. Thus, the loss of governance downside changed into determined. C. Lack -In Lack of security coverage approach might also lead to supplier lock-in downside. This technique could need to time period the wishes for the cloud providers to certify they're ready to assure that expertise migrated from the gift provider. But, the cloud user can not switch understanding from one service provider to a specific service provider. For that reason to overcome this Application Programming Interface (API s) must be utilized, this will be equal. Thus all of us will put it to use on the cloud. D) Data Loss or Leakage Data loss or break out, which means that an statistics loss that occur in any device. Information loss takes place as soon as data is likewise logically or bodily indifferent from the company or consumer both accidentally or designedly. As soon as the path, as an instance, patient or client facts, style specs or source code, property, tariffs, trade secrets and techniques, budgets and forecasts are leaky out. It s a bad effect on the cloud enterprise surroundings. By protective and encrypting the integrity of cloud data on the time transit is required. In addition, evaluation of expertise mystery writing and manufacturing at every runtime and style ought to be carried out. In added a completely particular Universal Serial Bus (USB) memory bus for moving data protection in a cloud environment. Table one indicates the diverse types of cloud security magnificence and consequently the desk display the various sorts of Cloud Security issues and Classifications V. DATA INTEGRITY AND DATA CONFIDENTIALITY Data integrity And information confidentiality denote to the belongings that cloud statistics have not been destroyed or altered in an unauthorized method. The information outsourced and keep on in the cloud putting as a result of the customers do not have the ok bodily garage for his or her facts. However corroborative the exactitude of the cloud storage statistics can be a promising problem for cloud storage protection. So that it will result in the cloud statistics Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 268

295 Arun Kumar et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, integrity and information confidentiality all through a cloud placing, at some stage in this survey intakes the idea of present statistics integrity and statistics confidentiality. Differing forms of records integrity and data confidentiality primarily based on the whole cloud security idea turned into enforced in the cloud storage as follows. Discuss absolutely one-of-a-kind demanding situations faced by means of encoding and get right of entry to management mechanisms, moreover to, recent upgrades to satisfy the ones problems of facts confidentiality protection in cloud computing. In the cloud there's no assurance that records keep on within the cloud square measure secured or by means ofvictimization Third Party Auditor (TPA). Thus on overcome this integrity of facts difficulty, the consumer need to be ready to make use of the aid of a TPA and it is tested the integrity of the data. This method is difficult to cloud house owners. Thus, in the integrity of facts and evidence that facts that securing the storage by employing a science key. safety assemble became enforced within the cloud storage as follows. Discuss completely distinct challenges round-faced by means of encoding and access control mechanisms supported know-how comfort and knowledge privateness, additionally to, latest enhancements to meet those problems of records comfort and records privacy defense in cloud computing. The computation global has been modified from centralized into allotted system and currently it adjustments lower back to the virtual centralization that is thought as cloud computing. The empire of the computation has been modified to the placement of knowledge and technique. A purchaser/consumer will hold and control the statistics and therefore the approach of his/her laptop in one hand. On the other hand, the shopper or the client is blind to anywhere the approach has been created and anywhere the records s ar store due to, the carrier and therefore the know-how maintenance have been supplied with the aid of a few supplier. From this we will be predisposed to understand the shopper has no control on that. The internet is employed due to the fact the communique media for the cloud computing. The dealer desires to offer a few assurance in a very provider stage agreement (SLA) concerning the protection of cloud. VII. CLOUD COMPUTING SERVICE DELIVERY MODELS AND SECURITY IMPLICATIONS VI. DATA AVAILABILITY AND DATA PRIVACY As a numerous security live, the facts privacy and comfort in cloud garage indicates to that the records are usable and on hand once accepted consumer s needs them from any safety device at any time most of the cloud. In companion earlier degree of cloud computing, cloud know-how comfort changed into additional concern as a result of the dearth of reliable infrastructure and mature. Differing sorts of statistics convenience and statistics privacy based cloud We summarize the important thing safety problems/vulnerabilities in each provider transport model. Some of these problems square degree the responsibility of cloud suppliers while others square measure the obligation of cloud consumers. A. IaaS Issues VM safety securing the VM working systems and workloads from not unusual security threats which have an effect on historical physical servers, like malware and viruses, victimization historic or cloudorientated protection answers. The VM s safety is that the duty of cloud clients. Every cloud customer will use their own security controls supported their wishes, predicted risk degree, and their own safety control approach. Securing VM pics repository - now not like physical servers VMs ar still beneath danger even when they ar offline. VM snap shots can be compromised via injecting malicious codes inside the VM document or perhaps headscarf the VM report itself. Secured VM pictures repository is that the duties of the cloud suppliers. Another issue related to VM templates is that such templates may hold the first owner records which may be utilized by a alternative purchaser. B. PaaS Security Issues SOA connected security troubles the PaaS model is predicated on the Service-orientated structure (SOA) version. This finally ends up in inheritable all security problems that exist within the SOA area like DOS assaults, Man-in-the-center assaults, XML-related attacks, Replay attacks, lexicon assaults, Injection attacks and input validation related attacks. Mutual authentication, authorization and WS-Security standards rectangular measure important to cozy the cloud provided services. This safety difficulty may be a shared responsibility amongst cloud providers, provider providers and clients. Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 269

296 Arun Kumar et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, API Security - PaaS should deliver genus Apis that supply control functions like enterprise features, protection features, application management, and many others. Such apis have to be provided with safety controls and requirements enforced, like OAuth [7], to put in force consistent authentication and authorization on calls to such genus Apis. Moreover, there may be a necessity for the isolation of genus Apis in reminiscence. This issue is below the obligation of the cloud service company. C. SaaS Security Issues In the SaaS version implementing and preserving security could be a shared responsibility most of the cloud suppliers and restore carriers (software companies). The SaaS model inherits the safety problems stated inside the preceding 2 models due to the fact it is built on high of each of them together with records security control[11] (statistics neighborhood, integrity, segregation, get entry to, confidentiality, backups) and network security. Internet software vulnerability scanning - internet packages to be hosted on the cloud infrastructure ought to be valid and scanned for vulnerabilities mistreatment net utility scanners. Such scanners must be up thus far with the these days discovered vulnerabilities and attack methods maintained in the National Vulnerability database (NVD) and consequently the Common Weaknesses Enumeration (CWE)[16]. Net application firewalls ought to be in situ to mitigate existing/discovered vulnerabilities (analyzing HTTP requests and responses for programs unique vulnerabilities). The 10 maximum crucial internet programs vulnerabilities in 2010 indexed with the aid of OWASP [17] location unit injection, go website scripting (Input validation) weaknesses. CONCLUSION The cloud computing model is one in all of the promising computing models for carrier carriers, cloud vendors and cloud customers. But to nice utilize the model we would like to dam the prevailing protection holes. Supported the primary factors defined on pinnacle of, we can summarize the cloud safety disadvantage as follows: some of the protection issues vicinity unit genetic from the used technologies like virtualization and SOA. Multi-tenancy and isolation can be a main measurement in the cloud protection downside that desires a vertical resolution from the SaaS layer proper all the way down to physical infrastructure (to expand physical alike boundaries amongst tenants rather than virtual limitations currently applied). Security control is extraordinarily crucial to manipulate and manipulate this range of needs and controls. The cloud version have to have a holistic protection wrapper, such any get admission to to any item of the cloud platform have to revel in safety parts 1st. Based in this dialogue we have a tendency to signify that cloud Computing security solutions need to: specialise inside the rely abstraction, victimization versionprimarily based procedures to seize absolutely one of a kind safety views and hyperlink such views in a really holistic cloud protection model. Inherent inside the cloud design. Anywhere added mechanisms (including physical assets engines) and Apis ought to offer flexible safety interfaces. Support for: multi-tenancy anyplace every person will see entirely his protection configurations, elasticity, to proportion and down supported the existing context. Support integration and coordination with distinct protection controls at completely one of a kind layers to supply included security. Be adaptive to meet continuous placing modifications and stakeholders desires. REFERENCES [1] Peter Mell, and Tim Grance, "The NIST Definition of Cloud Computing," 2009, Accessed April [2] Frank Gens, Robert P Mahowald and Richard L Villars. (2009, IDC Cloud Computing [3] Cloud Computing Use Case Discussion Group, "Cloud Computing Use CasesVersion 3.0," [4] ENISA, "Cloud computing: benefits, risks and recommendations for information security," 2009, omputing- risk-assessment, Accessed On July [5] Cloud Security Alliance (CSA). (2010). Available: [6] Balachandra Reddy Kandukuri, Ramakrishna Paturi and Atanu Rakshit, "Cloud Security Issues," in Proceedings of the 2009 IEEE International Conference on Services Computing, 2009, pp [7] B. Wang, Huang He, Yuan, Liu Xiao, Xi, Xu Jing, Min, "Open Identity Management Framework for SaaS Ecosystem," in ICEBE '09. pp [8] Meiko Jensen, Jörg Schwenk, Nils Gruschka and Luigi Lo Iacono, "On Technical Security Issues in Cloud Computing," in IEEE ICCC, Bangalore 2009, pp [9] Kresimir Popovic, Zeljko Hocenski, "Cloud computing security issues and challenges," in The Third International Conference on Advances in Humanoriented and Personalized Mechanisms, Technologies, and Services, 2010, pp [10] Meiko Jensen, Jörg Schwenk, Nils Gruschka and Luigi Lo Iacono, "On Technical Security Issues in Cloud Computing," in IEEE ICCC, Bangalore 2009, pp [11] S. Subashini, V. Kavitha, A Survey on Security Issues in Service Delivery Models of Cloud Computing, Journal of Network and Computer Applications, Vol. 34, No. 1, pp. 1-11, [12]WenjunLuo, GuojingBai, Ensuring the data integrity in cloud data storage,international Conference on Cloud Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 270

297 Arun Kumar et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, Computing and Intelligence Systems (CCIS), IEEE, ,15-17, [13]B. R. Kandukuri, V. R. Paturi, A. Rakshit, Cloud Security Issues, IEEE International Conference on Services Computing, pp , [14] Jansen, W.A., Cloud Hooks: Security and Privacy Issues in Cloud Computing, pp.1-10, 4-7, IEEE,2011. [15] D. Zissis and D. Lekkas, Addressing Cloud Computing Security Issues, Future Generation Computer Systems, Vol. 28, No. 3, pp , Issues in Service Delivery Models of Cloud Computing, Journal of Network and Computer Applications, Vol. 34, No. 1, pp. 1-11, [16] NIST. October, (2010). National Vulnerability Database (NVD). Available: Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 271

298 Volume 9, Special Issue No. 3, May 2018 International Journal of Advanced Research in Computer Science (ISSN: ) CONFERENCE PAPER Available Online at SECURITY ISSUES IN CLOUD ENVIRONMEN Menakarani R School of C&IT, REVA University, Bangalore Malathi Kulkarni School of C&IT, REVA University,Bangalore Rekha R Suryawanshi School of C&IT, REVA University,Bangalore Mir Abdul Samim Ansari, School of C&IT, REVA University,Bangalore. Gopal K.Shyam, Associate Professor, School of C&IT, REVA University, Bangalore Abstract Cloud computing has provided software support for various systems from server to service consumer.. Growth of cloud computing leads to various risks and challenges for system. Devices connected over internet lead to threats and security risks. Cloud provided different level of abstraction to ensure risk and privacy. Cloud Computing is the biggest buzzwords in the computer world these days. It lets in useful resource sharing that consists of software, platform and infrastructure by virtualization. Virtualization is the center era behind cloud resource sharing. Keywords : Cloud Computing, Distributed Computing, Virtualization, Security 1. INTRODUCTION Cloud Computing means computing model which originated from distributed computing, virtualization technology, utility computing and further computer technologies. It is main source for services on virtual machines which is distributed over large physical pool of resources. Scalability and availability for large level of enterprise application. These can be a program for development and deployment of cloud applications. Platform as a service-(paas) hardware infrastructure, infrastructure as a service (IaaS) virtualized infrastructure. Cloud Computing provides convenience, Ubiquitous and demand on network access to shared pool and definitions on computing resources which has efficiency to spread the provisionised and spreaded through fewer service provider interaction or management effort. Deployment Models Public Private Hybrid Community Essential Characteristics On Demand Service Broad Network Access Resource Pooling Rapid Elasticity Measured Services Service model SaaS PaaS IaaS Cloud computing can be distributed into four types: Distribution Computing- Utility and Grid Computing Hardware- Hardware Virtualization Multicore Chips Internet Technologies- SOA, Web 2.0, Web Services and Mashups. System Management-Automic computing data center Automation. Cloud computing applications are usually rated under subscription model. The cloud based services not only prohibited for Software applications. Table 1:Overview of cloud computing a) On Demand Self Service: On demand self Service helps the operators achieve and configure cloud services automatically; Consumer can provide provision computing capabilities to every service provider. b) Broad Network Access: Cloud resources and capabilities are provided over the network these are accessed through standard mechanism Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 272

299 Menakarani R et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, those are used by thick and thin client platforms, for example Laptops, mobiles, Workstations etc. c) Resource Pooling: The user can enter data into cloud from any location at any time. Computing Resources are pooled to help multiple consumers. d) Rapid Elasticity: Capabilities provided by cloud which can be elastically rapidly released or provisioned. Cloud Computing creates a dilemma of multiple computing resources. The resource are rated up and down. 2. CLOUD SERVICE MODELS Virtualized resources include storage, computation and communication. Cloud stack in the down layer such as Amazon EC2, Go Grid, Rack Space Cloud Servers. a) Platform as a Service: We can easily program cloud due to high level of abstraction. Platform as a service (PaaS).The users have such a place where they can deploy or develop any application. b) Software as a Service: On top of the cloud stack the application falls for the last users there they get the access which are provided by the SaaS from web portals. Ex: Facebook, YouTube. c) Infrastructure as a Service: It is a Service that provides APIs to low level network infrastructure like data partitioning, scaling, security, backup etc. 3. CLOUD DEPLOYMENT MODELS Private Cloud: It is an internal data which is not for public. Single Organization with many consumers (Ex; Business Units). Private cloud is taken by a third Party. Public cloud: The cloud Infrastructure is for public use. The public cloud is operated by any government or business or academic organizations. Community Cloud: Cloud Infrastructure is shared to few organizations and able to support a particular community where they have same goals. It may be operated and managed by any organization of a third party Community. Hybrid Cloud: It is the combination of one or more than two clouds.(private,public, Community). This cloud computing which increase the efficiency of control when it is from the public to enterprise cloud. They decreases the chance of errors. Private cloud maintain the security and their data privacy and compliance Quality of service (QoS). Private clouds have higher efficiency to maintain the network bandwidth and implement optimizations. 4. SECURITY CHALLENGES AND THREATS IN CLOUD COMPUTING Clouds are used in many Applications like collaboration services, business implementations, Online Presence and R & D projects, social networks and business tool. In all this sites they have become most essential due to analysis, estimation, control cloud services gives consumers a hope where they do not face loss of data or theft of data. Cloud Security: The Cloud Security deals with all the information related to Security of data. In the Deployment Model they face challenges like copying and sharing of the data and unencrypted data and the identity management and the threats will be believing the data. Service: In this Model they face challenge like storage and leakage of data and hacking and sharing the technology, the threats involved in service will be the misuse of the security. The network Model have few challenges like lack of security and more of unnecessary data, and hacking of data. Most of the threats are these networks are hacked by many users due to this there may be huge loss of data. The Application Model which has the challenges like hacking of many social applications such as Google here we lose the data privacy. 4.1 Information Security Principle a) Integrity: This refers to the quality of data. The data should not be remodeled by any user. The data should have its originality. b) Confidentiality: This word refers to protection of particular data. It confirms to maintain the privacy of their data. When there is increase in various application there will be higher risk to access also. c) Availability: The application Should be easy to use whenever it is necessary and for the proper authorized consumers to use at any moment. 5. CLOUD SECURITY REQUIREMENTS Before even adding the cloud we should check few requirements this needs many requirements except security but for this we can easily trust the robust security. Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 273

300 Menakarani R et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, Robust Security: The robust Security which helps in securing to data to an extend and also protects all the data which falls into this. Trust and assurance: It maintains the confidence of the entire data and protects the cloud infrastructure. It includes the hardware and software of all the data centers. It gives the consumers the trust and security about the data. Monitoring and Governance: It helps the people to check their security environment and their sustainability of how they perform. Cloud Security Controls: a) Front End Security b) Middle Layer c) Back End Security Front end security deals with security and process of certification. 6. SECURITY ARCHITECTURE This Section has the confidential of data and the users from securing the data. a) Isolation: It isolates the multiple data. b) Confidentiality: This particular word describes the security of data from unauthorized access. c) Access control and Identity Management: This can be accessed only by authorized users. 7. CONCLUSION From so many researches and technology cloud data protection and accessing the cloud data is more used in IT field and for Security. Cloud Computing security is considered as very Important and it should be added in cloud architecture to make sure security of information. REFERENCES [1] [2] Cisco White Paper, 525/ns537/white_paper_c html,published 2009, pp [3] John Viega, McAffee, Cloud Computing and the Common Man, published on the IEEE Journal ON Cloud Computing Security, pp , August [4] George Reese, Cloud Application Architectures, First edition, O Reilly Media, April 2009, ISBN , pp. 2-4, [5] [6] computing1.htm. [7] John Harauz, Lori M. Kaufman, Bruce Potter, Data Security in the World of Cloud Computing, published on the IEEE Journal on Cloud Computing Security, July/August 2009, Vol. 7, No.4, pp [8] John W. Rittinghouse, James F. Ransome, Cloud Computing Implementation, Management, and Security, CRC Press, August 17, 2009, ISBN , pp , [9] Cloud Security Issues and challenges [10] Marco Descher, Philip Masser, Thomas Feilhauer, A Min Tjoa, David Huemer, Retaining Data Control to the Client Infrastructure Clouds, published on the IEEE, 2009 International Conference on Availability, Reliability and Security, pp Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 274

301 Volume 9, Special Issue No. 3, May 2018 International Journal of Advanced Research in Computer Science (ISSN: ) CONFERENCE PAPER Available Online at A FRAMEWORK: ENCRYPTION METHOD ON CLOUD STORAGE FOR DATA SECURITY Vaibhav Agarwal Department of CSE, REVA ITM avaibhav51@gmail.com Jai Prakash Sah Department of CSE, REVA ITM jaisah12@gmail.com Satish Chand Department of CSE, REVA ITM satishchand921@gmail.com Shilpa N.R School of C&IT, REVA University. shilpanr.165@gmail.com Abstract In the recent years, Cloud Computing has turned into an important part of IT business. As a result of its monetary advantages, an ever increasing number of individuals are heading towards Cloud reception. As of now, there are various Cloud Service providers (CSP) enabling clients to have their applications and information uploaded onto Cloud. However Cloud Security keeps on being the greatest challenge in Cloud adoption and in this way keeps clients from using its services. Cloud service providers regularly try to implement different strategies to relieve clients of dangers relating to Cloud security. In this paper, we compare Hybrid Cryptographic Algorithm (HCA) that joins the advantages of both symmetric and asymmetric encryption therefore bringing about a protected Cloud Environment. The task centers on making a safe Cloud community wherein we make utilization of multifactor validation alongside numerous levels of hashing and encryption. Keyword- Cloud Computing, Information Security, Multifactor Authentication, Privacy, Cryptographic key establishment, Cloud services, Encryption techniques I. INTRODUCTION In today s time, Cloud computing is a very important and emerging area under Information technology domain which provide services to various network connections. Cloud computing can be described as distributed networking over a network such that the data stored onto cloud can be accessed by the authorized user from anywhere in a secure environment. This allows the users to access an application over many computers at a time. Cloud computing offers a pool of resources to be distributed among various users over internet. It provides flexible and dynamic access for virtualized computing. Resource pool of a cloud service provider is large but the main problem comes in organizing, provisioning, scheduling and optimizing of the resources present in it. Another significant challenge is the security concerns related to data stored in the cloud. A security rupture in the cloud record could prompt stolen information which would to be sure outcome in colossal misfortunes. With Cloud administrations taking care of basic information which can be gotten to from any place through the web makes security an unmistakable concern. The persevering idea of Cloud and its disbursal of information crosswise over different land areas adds up to high security dangers. While discussing Cloud Security there are numerous viewpoints which one needs to think about, for example, trusted authentication, appropriate authorization, and information security and protection. These are a portion of the fundamental security objectives which are to a great degree basic for each cloud supplier to incorporate. The vast majority of the present existing information insurance or secure cloud storage administrations center around record level encryption of the client's information, tailing one of two methodologies: either the information is transferred to the cloud supplier and is then encrypted, in which case the keys are overseen by the specialist organization, e.g. Dropbox or Google Drive; or the information is encoded at the client end and after that transferred to the safe stockpiling specialist co-op and the keys are overseen by the client, e.g. BoxCryptor or the Virtual Cloud Drive. But the algorithm proposed in the paper talks about combining both the approaches and applying a multilevel encryption standard to secure the files and data in the best possible way. We also incorporate features like multifactor authentication, and hashing techniques to prevent any kind of anticipated or unanticipated attacks. Organization of the paper The remaining part of the paper is classified as follows: Section 2 Architecture of proposed algorithm and advantages. Section 3 Security issues in cloud computing. Section 4 Existing Encryption standards and their drawbacks. Section 5 The proposed system model. Section 6 The modules in the proposed system. Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 275

302 Vaibhav Agarwal et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, II. ARCHITECTURE OF PROPOSED ALGORITHM AND ITS ADVANTAGES. Cons 1. More complex to configure compared to other algorithms. III. SECURITY ISSUES IN CLOUD COMPUTING Fig.1 Architecture of WSN The overall architecture of system comprises of different physical entities that constitute the entire ecosystem. The security services which our Secure Cloud Ecosystem can ensure are: Trusted Authentication: Just a genuine client will be permitted to get to administrations and information being facilitated on Cloud. Authorization: The framework guarantees appropriate approval by just permitting framework administrator to approach unscrambling keys. It is just the Cloud administrator who knows about the salted esteem added to each client secret key and Decryption Key before being spared in the database. Data Encryption: The framework makes utilization of Hybrid Encryption by enabling RSA and AES calculations to encode client information. The proposed framework use the advantages of both symmetric and asymmetric information encryption. We influence utilization of RSA2048 and AES256 for our encryption to process. Hashing: SHA512 and bcrypt encryption methods are used to secure user password. Key Management: The Private Key of AES is encoded and salted and securely put away into the database. The decrypting keys are additionally saved not long after the encryption gets over. The SHA512 key is secured utilizing keyed-hash message authentication code (HMAC). PROs and CONs of HCA Pros 1. HCA is a fast algorithm. 2. It is more secure as compared to any of the existing algorithms. 3. It is more reliable than other algorithms, as it is far better in all aspects. 4. Hashing is done while storing passwords. 5. It also uses Multi-factor Authentication. There are some major disturbing issues that should be referenced while enabling basic application and delicate information to open and shared cloud condition. So we should state that Security and protection are the key difficulties in the cloud computing. Some security issues, which we have introduced in this paper, are: Data confidentiality issue: Confidentiality is an arrangement of standards that limits access or area confinement on specific kinds of data. In cloud, information dwell publicly, so Confidentiality alludes to, client's information and calculation errand to be kept secret from both cloud supplier and different clients who is utilizing the administration. Plainly client's private information is revealed to specialist co-op on the accompanying circumstance as it were: i. When service provider knows where the user s private information resides in the cloud systems. ii. When service provider has the authority to access and gather user s private information in the cloud systems. iii. When service provider can figure out the meaning of user s information in the cloud systems. Data availability issue: When keeping information at remote area which is possessed by others, information proprietor may confront the issue of framework disappointment of the specialist organization. Also, if cloud quits working, the information won't be accessible as the information relies upon single specialist co-op. Dangers to information accessibility are flooding assaults causes Denial of Service(DOS) attack. Cloud processing is to give an onrequest administration of various levels. On the off chance that a specific administration is not any more accessible or the nature of administration can't meet the Service Level Agreement (SLA), clients may lose confidence in the cloud framework. Data integrity issue: It is the "completeness" and "wholeness" of the information which is the fundamental and focal needs of the data innovation. Integrity of information is imperative in the database similarly as respectability of information stockpiling is vital. The information trustworthiness proofs the legitimacy, consistency and normality of the information. It is the tireless information stockpiling which can be recovered in an indistinguishable design from what it was put away earlier. Thus, cloud storageis getting to be mainstream for the outsourcing of everyday administration of information. So integrity checking of the information in the cloud is Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 276

303 Vaibhav Agarwal et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, additionally essential to get away from all conceivable outcomes of information corruption and information crash. The cloud supplier ought to give surety to the client that integrity of their information is kept up in the cloud in its original form. process gets slow. V. THE PROPOSED SYSTEM MODEL IV. EXISTING ENCRYPTION STADARDS AND THEIR ALGORITHMS Based on the survey we see that there are 3 kinds of algorithm for encryption and decryption of data i.e. symmetric, asymmetric and hybrid techniques. The algorithms are as below: AES Algorithm (Symmetric) AES is an iterative cipher. It is based on substitution change arrange. It comprises of a series of connected activities. AES plays out every one of its calculations on bytes as opposed to bits. Convert the 128 bit block to be encrypted into 16 bytes block. AES operation is performed on a two-dimensional byte array of four rows and four columns. The variable number of rounds in AES depend on the length of key. It uses 10 rounds for 128-bit keys, 12 rounds for 192-bit keys and 14 rounds for 256-bit keys. Each round uses a different 128-bits round key, calculated from the original AES key. One advantage is, it is faster than Asymmetric Algorithm but it comes with the con that it is less secure and reliable. Hybrid Cryptographic Algorithm (HCA) Hybrid Cryptography is the thin line between safe, but slow encryption strategy over big data (Asymmetric Cryptography) and less secure but fast encryption standard (Symmetric Cryptography) Hybrid Cryptographic algorithm merges the speed benefits of One-Key encryption and decryption along with the security that the Public-Private Key pair provides and is thus considered a highly secure type of encryption. The idea is to encrypt the data using a Symmetric Key which can then be encrypted with the Private Key of the sender or the Public Key of the receiver. To decrypt, the receiver, will have to first decrypt the Symmetric key using the corresponding Asymmetric Key and use that Symmetric Key to decrypt the data they have received. VI. THE MODULES OF THE PROPOSED SYSTEM User Account Operations: Through this module a user can create an account after which he will be able to access his/her account. Other operations a user can perform on his account are Login, Logout, Edit profile, Delete profile and change password. RSA Algorithm (Asymmetric) RSA algorithm is asymmetric cryptography algorithm. It is based on two types of keys i.e. Public Key and Private Key. Public Key is given to everyone and Private Key is kept private. Data is encrypted using the public key and it can decrypted using private key. One advantage is, it is secure algorithm, but as the file size (to be encrypted) increases, the encryption and decryption Two factor Authentication: 2FA is also termed as two-step verification. It is a security process where the user provides two authentication factors to verify they are who they say they are. Direct authentication (SFA) is a security process where the user uses only one factor (password)to validate his/her account. So, 2FA provides an additional layer of security and makes it harder for hackers to access a person's devices and online accounts. Key Management: This module is used to manage the secret keys. The users can upload their secret keys which must be in multiples of 128 bits. They can view the list of all the keys uploaded by them and can perform other operations like downloading the keys and deleting the keys. It is mandatory for the users to upload at least one key before Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 277

304 Vaibhav Agarwal et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, they proceed further for data write operation. File Write: It allows the users to perform the file write operation on the cloud. He will be provided with an HTML interface where they can browse the file to be uploaded to the cloud. The users have to upload at least one of their secret keys before accessing this module. Then he can select any of the keys uploaded by them which has to be used for performing the hybrid cryptography on the file he has uploaded. File Read: It is used by the end users to download the files they had uploaded into the cloud. It performs the file read operation from the cloud and the decryption operation using the hybrid cryptographic system with the same key used for encryption. He will be able to see the decrypted file and will be downloaded into the client s system. Data Transmission: It is implemented for demonstration purpose only. When the end users send any confidential data from their devices to the cloud application, the confidential data must be encrypted from the client end before the data transmission begins. This module allows the users of our portal to experience how this kind of security has been implemented. Password Security: During the registration phase, the end users are going to select their passwords for their accounts. All the profile information including the password will be stored in some RDBMS like MySQL. However, there are chances that the attacker might compromise the RDBMS and acquire an illegal access to the user s profile data. So, for such situations, the user s password will not be stored as a plain text on MySQL, instead it will be stored as an encrypted text. VII. CONCLUSION As depicted in the paper, however there are outrageous benefits of utilizing a cloud-based framework, there are likewise numerous reasonable issues which must be unraveled. Cloud processing is an innovation with intense significance for Internet benefits as well as for the IT part all in all. All things considered, a few remarkable issues exist, especially identified with service-level agreements (SLA), security and protection. Until a proper security algorithm like this is implemented, its potential clients won't have the capacity to use the upsides of this innovation. This security module should take into account all the security issues emerging from all headings of the cloud. Each security dangers in the cloud ought to be examined at the large scale and small scale level and a coordinated arrangement (like HCA) must be planned and conveyed in the cloud, so that the potential consumers can feel secure, and be free from any kind of threats. The proposed hybrid cryptography implementation method solves all these problems and would be able to cater for the data security for any and all types of anticipated or unanticipated attacks pertaining to cloud storage. VIII. REFERENCES [1] Subashini and Veeraruna Kavitha. "A survey on security issues in service delivery models of cloud computing." Journal of network and computer applications 34.1 (2011): [2] Pawar, Pramod S., et al. "Security-as-a-service in multicloud and federated cloud environments." IFIP International Conference on Trust Management. Springer International Publishing, [3] Nair, Nikhitha K., K. S. Navin, and Soya Chandra. "Digital Signature and Advanced Encryption Standard for Enhancing Data Security and Authentication in Cloud Computing." (2015). [4] Wang, Cong, et al. "Privacy-preserving public auditing for data storage security in cloud computing." INFOCOM, 2010 Proceedings IEEE. IEEE, [5] Hendre, Amit, and Karuna Pande Joshi. "A semantic approach to cloud security and compliance." 2015 IEEE 8th International Conference on Cloud Computing. IEEE, [6] Khanna, Abhirup. "RAS: A novel approach for dynamic resource allocation." Next Generation Computing Technologies (NGCT), st International Conference on. IEEE, [7] Calheiros, Rodrigo N., et al. "CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms." Software: Practice and Experience 41.1 (2011): [8] Huang, Wei, et al. "The State of Public Infrastructureasa-Service Cloud Security." ACM Computing Surveys (CSUR) 47.4 (2015): 68. [9] Aich, Asish, Alo Sen, and Satya Ranjan Dash. "A Survey on Cloud Environment Security Risk and Remedy." Computational Intelligence and Networks (CINE), 2015 International Conference on. IEEE, [10] Singh, Aarti, and Manisha Malhotra. "Security Concerns at Various Levels of Cloud Computing Paradigm: A Review." International Journal of Computer Networks and Applications 2.2 (2015): Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 278

305 Volume 9, Special Issue No. 3, May 2018 International Journal of Advanced Research in Computer Science (ISSN: ) CONFERENCE PAPER Available Online at A SYSTEMATIC AND COMPOSED BIG DATA ENTRY RESTRICTION SCHEME WITH ISOLATION-PRESERVING POLICY Abraham Rajan, Department of CSE REVA ITM Bangalore, India Venkatesh Prasad Associate Professor Reva University, Bangalore, India Abstract: In modern world of technologies, each and every object like smart phones, computers connected to internet generates large amount of data. It becomes a very challenging issue for data to be stored in structured formats, specifically when it is stored to storages such as Cloud. It is also challenging that the privacy of data is maintained. We have an encryption technique known as Cipher text-policy attribute-based encryption (CP-ABE) which can be used by the users to encrypt their own data using attribute values which is defined over some access policies. If the attribute values of data consumers are matched with access policies of the data owners then such users are allowed to decrypt the data. In CP- ABE, we have access policies attached to the encrypted data in plain text formats which may contain some private information regarding the end-users. These methods will only partially conceal the private information while the attribute values are still exposed. In this paper, we propose data access control which also ensures privacy of the data owners and data consumers. And also we have bloom-filter which is used for attribute based decryption. It is used to assess whether the attribute is in the access policies defined by end-users. It can also find the exact location of the attribute if it is present in the access-policy. It has been assessed by many security analyst and execution performance evaluators that our proposed technique can prevent private information from any linear secret-sharing strategic access policies without engaging much overhead. In our project we provide two login constraints, one as data owner and the other as data consumer. The data owner uploads a file and generates a tag number for each file once the encryption is done. This will protect the attribute values such as file name from leaking. File name may contain some information regarding the attribute values which can be used by the intruders to decrypt the files. Therefore, by providing tag number it is able to protect privacy of the data owners. Keywords:Cipher text-policy attribute-based encryption (CP-ABE), data owners, data consumers, encryption, decryption. I. INTRODUCTION Big Data What is Big Data? Big Data is becoming the technology of the future with a lot of scope in Data world. In the modern world technology can be referred to as Data world. Each and every device such as mobile, telephone calls, internet browsing, bank transaction etc. and many more are referred to as sources of data. Each device produces data and the number of such devices which generate data are in billions as there are billions of data generating devices. It becomes a challenge how to store and process all these data. The data which is beyond storage capacity and which is beyond processing capacity can be mentioned as Big Data. There are many data generating factors such as sensors, cc camera, social networks such as Facebook, Whatsapp, Online shopping- Ecommerce, Airlines, Hospitality data. Assuming we have 100% of data in current world, 90% of it was generated from the last 6 years. In 1990 s the industry standard for a system is 1gb- 20gb harddisk, mb RAM, 10kbps data transfer speed. Right now, in 2018 the industry standard for a system is 1Tb-2Tb harddisk, 4gb-32gb RAM, 100mbps-1000mbps speed. In the span of about more than 25 years the harddisk capacity has increased by about 1000 times as it is same for RAM and data transfer speeds. It is clearly evident from the above scenario that data generation will keep on increasing. Every time the user wants more space to store his data, he cannot purchase a new hard disk. Fig.1.1. Keywords Describing Big Data This was taken as a challenge and IBM came up with IBM data servers. Users were given storage space on the servers and were charged accordingly. It was flexible, scalable and Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 279

306 Abraham Rajan et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, the data could be fetched from any remote place and could be processed. Three factors contribute to Big Data, they are Volume, Velocity, Variety. Volume may increase rapidly in gigabytes, terabytes or petabyte. Velocity matters as user needs to send, receive and process data. There are 3 different varieties of data- Structured, Unstructured and semi-structured data. RDBMS deals with Structured data which is dealing with only 20-25% of total data. Whereas Unstructured and Semi-structured data constitute to 70-80% of data. Videos, images, text which are generated through social media are Unstructured data. Log files generated through gmail, yahoo etc are semi structured data. Processing all these data s through a single resource will be time consuming, because of this reason Hadoop concept was introduced. Hadoop has been introduced as the best solution for Big Data. Doug Cutting is the founder of Hadoop. Core concepts of Hadoop are HDFS(Hadoop Distributed File System) and MapReduce. HDFS is technique for storing huge amount of data with cluster of commodity hardware whereas MapReduce is a technique for processing data stored in the HDFS. These all techniques help in analyzing the data which can used to predict the future of data which is a study based on Data Science. Big Data is becoming the technology of the future with a lot of scope in technology. We connect daily with technologies such as sensors, smart materials which connects to internet. which is typically equipped with sensors which sends and receives information through controllers and we use different communication control protocols to monitor and control the system. The information from the sensors can be stored onto a centralized location called cloud using Hadoop HDFS concept. 1.2 What are the abstract ideas in Big Data? Big Data as four major components that one as to consider before getting deep into Big Data. They are- Infrastructure security, Data Privacy, Data Management, Integrity and reactive security. In Infrastructure security the secure computations in distributed programming frameworks ensures that all computations are done without any mistakes. This becomes important when it involves bank transaction and other important procedures. We need to practice the best of security for non-relational data stores. While coming to Data Privacy we need to ensure a privacy preserving data mining, this is what we are concentrating in our project. The data privacy gives cryptographically enforced data-centric security and granular access control. To ensure privacy we encrypt the file name using some attribute and generate a tag number for a file which is 15-digit number. Figure 1.2.Abstract ideas in Big Data. In Data Management we need to take care that data storing and transaction logs are completely reliable. The Data integrity and reactive security make sure that there is Endpoint validation to check whether the data is accessed by valid user or not. The real time security monitoring system helps in prevented hackers from accessing the data by man in the middle attack and other techniques. 2. LITERATURE SURVEY Figure 2.1. Global Information Storage Capacity There are 2 types of storages as referred in the above figure, they are 1. Analog Storage 2. Digital Storage. Analog Storage refers to Paper, film, audiotape and vinyl which contributes to 6%. Whereas Analog video tapes (VHS) contribute to 94% of Analog Storage. Analog Storage as about 19 Exabyte s Total of storage. Digital Storage as Portable media, flash drives which contribute to 2%, portable hard disks 2.4%, CD s and minidisks 6.8%, computer servers and mainframes which includes 8.9%, Digital tape 11.8%, DVD/Bluray 22.8%, PC hard disks 44.5% which as 123 billion gigabytes of storage, and another 1% includes chip cards, memory cards, floppy disks, mobile phones, PDA s, camera/camcorders, video games). Digital Storage as 280 Exabyte s memory in total. Before 2002 it was only era of Analog Storage, but 2002 was believed to be the beginning of the digital age during which period it contributed to 50% of storage. In the year 2007, 94% of storage was digital which had 280 Exabyte s of memory capacity. 2.1 Energy-efficient data replication in big data based cloud datacenters Cloud computing is an emerging paradigm that provides computing, communication and storage resources as a service over a network. Communication resources often become a bottleneck in service provisioning for many cloud applications. Therefore, data replication which brings data (e.g., databases) closer to data consumers (e.g., cloud applications) is seen as a promising solution. This is in addition to the improved quality of service QoS obtained as a result of the reduced communication delays. The Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 280

307 Abraham Rajan et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, evaluation results, obtained from both mathematical model and extensive simulations, help to unveil performance and energy efficiency tradeoffs as well as guide the design of future data replication solutions. To address this gap, we propose a data replication technique for cloud computing data centers which optimizes energy consumption, network bandwidth and communication delay both between geographically distributed data centers as well as inside each datacenter. Advantages: It allows multiple virtual machines (VMs) to share the same physical server. Disadvantages: the popularity is not constant over time. 2.2 A Survey on Security Issues and Vulnerabilities on Big Data Cloud computing has gained significant traction for recent years. It is a form of distributed computing whereby resources and application platform are shared over the internet through on demand and pay on utilization basis. Several companies have already built Internet consumer services such as search engine, use of some websites to communicate with other user in websites, services, and services to purchase items online that use cloud computing infrastructure. However this technology suffers from threats and vulnerabilities that prevent the users from trusting it. The occurrence of these threats may result into damaging of confidential data in cloud environment. This survey paper aims to analyze the various unresolved security threats in cloud computing which are affecting the various stake-holders linked to it. It also describes the pros and cons of the existing security strategy and also introduces the existing issues in cloud computing such as data integrity, data segregation, and security and so on. Cloud computing is a general term for anything that involves delivering hosted services over the Internet. It is an emerging computing technology that uses the internet and central remote servers to maintain data. This system is very helpful for different users so that they can easily use the system without any external support to software and hardware. They can also access their personal files at any computer on internet. This technology allows for much more efficient computing by centralizing storage, memory, processing and bandwidth. Limitations of Big Data are There are hundreds of vendors in the Big Data space with each having its own limitations/ strengths. So it becomes very hard to learn multiple software s for each of the tasks. Also connecting these individual system using customized connectors becomes a big challenge. The main deterrent in the steep learning curve behind these technologies and hence no human resources can be found for implementation projects. 3. SYSTEM ANALYSIS Figure 3.1. Secure Data Sharing Scheme In any of the data sharing schemes we have Data owner and Data consumer/user. Data owner basically uploads the file and data user download the file. The question arises whether data user is a valid user or not. Because of this we need to provide some access restriction on files. If man in middle attack or any type of attack happened to steal the data, even though he was able to steal he should not be able to view the content. For this purpose we provide ESP (Encryption Server Provide) which encrypts the data and provides the intermediate result of encryption. The user calls privilege management request and gets a public key and sends the file to the cloud in cipher text format. The data user using privilege update request gains the attribute key and uses it to decrypt the file in DSP (Decryption Server Provide). Figure 3.1. Data flow Diagram Figure 3.2. Use case Diagram The Use case Diagram and data flow diagram indicate the same thing as below. First, the user needs to login through the cloud registration. If he is a new user, the user should register himself for the first time. The user can be Data owner or Data consumer. Data owner as the rights to encrypt and upload the files while the data consumer as rights to download and decrypt the files. For the data consumer to download the file, he has to send a request to the data Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 281

308 Abraham Rajan et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, owner, the data owners sends an acknowledgment by sending the 15-digit tag number of the file. Using this attribute key the data consumer can decrypt the downloaded files. The use case diagram also indicates the same thing as Data flow diagram. While login user can specify whether he is a data owner or data consumer. Once if he has login as Data owner he is allowed to upload the file content and encrypt it. He is even allowed to generate a tag number for file. The data consumer can download and decrypt the file. 4. SYSTEM DESIGN Fig.4.1. System Architecture The user can be Data Owner or data consumer. Different flows are determined for different type of users. The user needs to login through the cloud registration. If he is a new user, the user should register himself for the first time. The user can be Data owner or Data consumer. Data owner as the rights to encrypt and upload the files while the data consumer as rights to download and decrypt the files. The data owner encrypts data and sends to cloud Database which in our proposed system is our hard disk. For the data consumer to download the file, he has to send a request to the data owner, the data owners sends an acknowledgment by sending the 15-digit tag number of the file. Using this attribute key the data consumer can decrypt the downloaded files. 5. FUTURE ENHANCEMENT For understanding the real life industrial development in Big Data, we have developed a prototype which resembles the original model. In the future we could integrate all of the above mentioned components of the proposed system to form a simple unit reducing much overhead on the system. This project can be taken into product level which will be cost effective, durable and user friendly in terms of providing security and preserving privacy of the user. 5.1 APPLICATIONS File sharing Application:- In any of the file sharing applications online or offline, the encryption of data is very important. If the valuable data regarding marketing, industries and others are in faulty hands, it can be used to damage our market and industries. In our traditional file sharing applications even though the contents are encrypted the access policy which contains attributes such as file name are in plain text format. This can be used to understand what the content is about. So, it is important to encrypt the file name also and generate Tag number of each file. When the data consumer wants to access a file he has to provide right authentication and request for the file. Once Data owner acknowledges and sends the tag number. Using that tag number Data consumer can download and decrypt the file. 6. CONCLUSION We have created a GUI (Graphical User Interface) for the user to login to cloud. The user may be a Data owner or Data consumer. The Data Owner is allowed to upload files using Hadoop and HDFS process, encrypt it and generate tag numbers used to preserve the privacy policy of user. The data Consumer can request the file and get the tag number to download the file and decrypt it. In any of the file sharing applications online or offline, the encryption of data is very important. If the valuable data regarding marketing, industries and others are in faulty hands, it can be used to damage our market and industries. In our traditional file sharing applications even though the contents are encrypted the access policy which contains attributes such as file name are in plain text format. This can be used to understand what the content is about. So, it is important to encrypt the file name also and generate Tag number of each file. When the data consumer wants to access a file he has to provide right authentication and request for the file. Once Data owner acknowledges and sends the tag number. Using that tag number Data consumer can download and decrypt the file. References [1] K. Bilal, S. U. Khan, L. Zhang, H. Li, K. Hayat, S. A. Madani, N. Min-Allah, L. Wang, D. Chen, M. Iqbal, C. Z. Xu, and A. Y.Zomaya, Quantitative comparisons of the state of the art datacenter architectures, Concurrency and Computation: Practice and Experience, Vol. 25, No. 12, 2013, pp [2] K. Bilal, M. Manzano, S. U. Khan, E. Calle, K. Li, and A. Zomaya, On the characterization of the structural robustness of data center networks, IEEE Transactions on Cloud Computing, Vol. 1, No. 1, 2013, pp [3] D. Boru, D. Kliazovich, F. Granelli, P. Bouvry, and A. Y. Zomaya, Energy-efficient data replication in cloud computing datacenters, In IEEE Globecom Workshops, 2013, pp [4] Y. Deswarte, L. Blain, and J-C. Fabre, Intrusion tolerance in distributed computing systems, In Proceedings of IEEE Computer Society Symposium on Research in Security and Privacy, OaklandCA, pp , [5] B. Grobauer, T.Walloschek, and E. Stocker, Understanding cloud computing vulnerabilities, IEEE Security and Privacy, Vol.9, No. 2, 2011, pp Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 282

309 Volume 9, Special Issue No. 3, May 2018 International Journal of Advanced Research in Computer Science (ISSN: ) CONFERENCE PAPER Available Online at ADVANCED ATM MULTILEVEL AUTHENTICATION USING FINGERPRINT VERIFICATION AND OTP VALIDATION Hari Narayanan School of Computing and Information Technology REVA University Bengaluru, India Uttham K School of Computing and Information Technology REVA University Bengaluru, India uttham1@gmail.com Kiran M School of Computing and Information Technology REVA University Bengaluru, India kiranm@reva.edu.in I Mohammed Junaid School of Computing and Information Technology REVA University Bengaluru, India i.mohammedjunaid@gmail.com Mohammed Ibrahim School of Computing and Information Technology REVA University Bengaluru, India noumanibrahim786@gmail.com Abstract This paper outlines state-of-the-art solution for ATM authentication mechanismthat provides advanced ATM multilevel authentication using fingerprint verification and OTP (One-Time-Password) validation. The advanced components used to meet the state-of-theart solution are Arduino Uno Board, RFID Reader, Adafruit Fingerprint Sensor, Micro SD Card module, GSM module SIM800C and Keypad. The result shown from this proposed system provides better authentication, security and safety mechanisms for the actual ATM card holder during his banking transaction in ATM booth and completely avoids unauthorized users to get access with the illegal transactions. Keywords ATM Multilevel Authentication, Fingerprint Verification, OTP Validation, Arduino Uno I. INTRODUCTION ATMs (Automatic Teller Machine) have been a big help in the banking sector which helped user (account/card holder) to carry out financial transactions without being in a bank or without the help of banking staff. As convenient as these machines are they have a certain amount of drawback with respect to authentication, security and safety of user s financial assets. ATM authentication and security has been evolving domain. Some banks recently have incorporated fingerprint based authentication for ATMs, but even such solutions do not solve the major problem. Monitory transaction in ATM is one of the most vulnerable security threat in the banking process. Over last few years several instances of ATM misuses are being reported which includes card skimming, duplicating cards and shoulder surfing. Virtually users has no control over the amount being withdrawn from the ATM in their absence. With fingerprint scanner, a user is forced to be physically present in the ATM booth to obtain money. Therefore, in this paper we have developed a state-of-the-art solution for ATM authentication mechanism. Our proposed model integrates with biometric services (henceforth called as fingerprint sensors ) on top of the existing hardware and integrates them. It also deals with the case providing the solution where the actual card holder is not physically present in the ATM booth. II. EXISTING SYSTEM The existing ATM booths have ATMs that are fixed to wall which has a cash vault attached to it which is not accessible by the user. The existing system works on a single step PIN (Personal Identification Number) verification after which it lets the user continue with the banking transaction. The ATMs work on inter-bank gateways which allows all users to use any ATM machine of their convenience. III. STATE-OF-THE-ART PROPOSED SYSTEM The goal of this proposed model is to provide better authentication and security mechanisms for the ATM user during his transaction. The state-of-the-art proposed system has two important scenarios that has been discussed in the following points: 1. ATM card is scanned. RFID comes into action. When the card is scanned, data i.e., the respective 12-digit RFID tag is read serially via the serial pins 9 and 10 of Arduino. 2. After the 12-digit RFID tag is read serially, we extract the last 3 digits of the tag as a String and pass that as a parameter for opening the respective filename (which is the user database). 3. The entire data from the file is read which has the ATM PIN, fingerprint ID, Mobile Number and Name of the card holder. 4. Then, user is asked to enter his 4-digit PIN. 5. After the 4-digit PIN is entered, the control is transferred to the PIN validation function. Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 283

310 Hari Narayanan et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, If entered PIN is correct, then the next levels of authentication comes into picture. Otherwise, message is displayed saying that entered PIN in invalid. 7. The further levels of authentication are as follows: The user can either choose for Fingerprint verification or OTP validation. 8. If the actual user is physically present, then he can select either fingerprint or OTP. But, if the same user is not physically present, instead other genuine user (Friend/Relative/Security-Guard) is present, he has to select the OTP mechanism. 9. On selecting the OTP option, the system will generate a random 4-digit number and send it to the registered mobile number. Then the user can enter the received OTP number, and if it is matched, then user can proceed with the transaction to retrieve the cash. Otherwise, message is displayed saying that entered OTP is invalid. 10. If the actual user opt for fingerprint mechanism, then he can use fingerprint scanner available in the ATM machine. Once user s fingerprint is scanned, the control verifies with fingerprint that is stored in the database. If it is matched, then user can proceed with the transaction to retrieve the cash. Otherwise, message is displayed saying that Fingerprint mismatched. IV. SYSTEM DESIGN V. HARDWARE AND SOFTWARE REQUIREMENTS A. Arduino Uno Board[1] Fig. 2. Arduino Uno Board B. RFID Reader In our proposed system, RFID Tagholds all the bank account details of the user. Once the user enters the ATM, it scans the RFID card which hold the account details of an employee and allows user for further processing. Fig. 3. RFID Tag C. Adafruit Fingerprint Sensor Fingerprint scanners are the security and privacy systems of biometrics. They are used in police stations, in companies to check the employee login etc. In our proposed system, fingerprint is used for a security and validating user bank account credentials. If the fingerprint matches, it allows to withdraw the amount from ATM. The fingerprint sensor is connected to Arduino board using Adafruit library. Fig. 1. Data Flow Diagram of the proposed system Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 284

311 Hari Narayanan et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, G. Arduino IDE[4] Fig. 4. Adafruit Fingerprint Sensor D. Micro SD Card Module In our proposed system, SD card module is used to store all the RFID tags details, fingerprint template of a user and performs transferring of data. It requires input voltage of 3.3V/5V. It acts as a database in our proposed system. Through programming, we can perform read/write operation on SD Card using Arduino. Fig. 8. Arduino IDE VI. IMPLEMENTATION A. SD Card Module[3] Reading Data for Particular user from SD Card(Database): myfile = SD.open(filename, FILE_READ); if (myfile) { Fig. 5. SD Card Module // read from the file until there's nothing else in it: while (myfile.available()) { E. GSM Module for (int i = 0;; i++) { if(myfile.available()){ filetext[i] = myfile.read(); } else{ filetext[i] = '\0'; break; } } B. Fingerprint Scan Verification Module[2] finger.begin(57600); Fig. 6. GSM Module SIM800C F. Keypad In our proposed system, the Keypad allows the user to enter ATM PIN and OTP. uint8_t p = finger.getimage(); if (p!= FINGERPRINT_OK) return -1; p = finger.image2tz(); if (p!= FINGERPRINT_OK) return -1; p = finger.fingerfastsearch(); if (p!= FINGERPRINT_OK){ Serial.println("Fail"); return -1; } z = finger.fingerid; if ((fid - 48) == z) { Serial.println("Success"); } else { Serial.println("Fail"); } Fig. 7. 3x4 and 4x4 Keypad Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 285

312 Hari Narayanan et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, C. RFID Module[7] Reading RFID Tag: B. PIN Success if (myserial.available() > 0) { data_temp = myserial.read(); RFID_data[read_count] = data_temp; read_count++; } D. GSM Module Sending OTP using GSM module: Serial.println("AT+CMGF=1"); while(count <timestosend){ delay(1500); randomseed(analogread(0)); randnumber = random(1000, 10000); Serial.print("AT+CMGS=\""); Serial.print(phone_no); Serial.println("\""); while(serial.read()!='>');{ Serial.print(randNumber); } Fig Digit PIN Success In this case the PIN entered by the user matches with the PINavailable in the database. Then PIN is accepted and the user is given with the choice of selecting between OTP and fingerprint authentication. C. Fingerprint Scan Verification Failure E. Keypad Module[5] [6] Getting Character Entered via Keypad: keyentered = keypads.getkey(); while (keyentered == NO_KEY) { keyentered = keypads.getkey(); //UPDATE VALUE } A. PIN Failure VII. EXPERIMENTAL RESULTS Fig. 11. Fingerprint Scan Verification Failure In the given figure the PIN has been accepted and the user is given the options of picking the OTP and fingerprint and incase the actual user chooses to go ahead with the fingerprint scan and the scan fails as it is mismatched with the fingerprints present in the database. D. Fingerprint Scan Verification Success Fig Digit PIN Failure In this case the PIN entered by the user does not match with the PIN available in the database and the user has to start the process all over again. Fig. 12. Fingerprint Scan Verification Success In the given figure the PIN has been accepted and the user is given the options of picking the OTP and fingerprint and incase the actual user chooses to go ahead with the fingerprint scan and the scan succeeds and the user can continue with the transaction. Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 286

313 Hari Narayanan et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, E. OTP Validation Failure validation and in this case the OTP matches and the user can continue with the transactions. VIII. CONCLUSION AND FUTURE ENHANCEMENT The aim of this paper was to build an advanced ATM multilevel authentication mechanisms that provides more secured way of banking transactions to the ATM card holders in the ATM booth. The result shown from this proposed system provides better authentication, security and safety mechanisms for the actual ATM card holder during his banking transaction in ATM booth and completely avoids unauthorized users to get access with the illegal transactions. Fig. 13. OTP Validation Failure In the given case the PIN entered by the user matches the PIN in the database. The PIN is accepted and the user is given the choice of selecting between OTP and fingerprint authentication. The user chooses to go ahead with OTP validation and in this case it does not match and hence it does not let the user to continue. F. OTP Validation Success The extension of this proposed system can be implemented with the advanced biometric systems by having iris scan of the user. This can provide another level of authentication to the user. Also, future enhancement can be implemented by includingaadhaar number verification to validate whether Aadhaar number is linked to the respective bankof the account holder. REFERENCES [1] [2] Library [3] [4] [5] Fig. 14. OTP Validation Success In the given case the PIN entered by the user matches the PIN in the database. The PIN is accepted and the user is given the choice of selecting between OTP and fingerprint authentication. The user chooses to go ahead with OTP [6] [7] uno-tutorial Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 287

314 Volume 9, Special Issue No. 3, May 2018 International Journal of Advanced Research in Computer Science (ISSN: ) CONFERENCE PAPER Available Online at NFC FEATURED THREE LEVEL ATM SECURITY Aatiqa Sharief School of C&IT, Bangalore, India, Anushree G K School of C&IT, Bangalore, India, anushreegk1996@gmail.com Arpitha Patil School of C&IT, Bangalore, India, arpithavpatil@gmail.com Jyoti Kumari School of C&IT, Bangalore, India, jyotipoddar.35@gamil.com Vani Krishnaswamy School of C&IT, Bangalore, India, vanikrishnas@reva.edu.in Abstract: An ATM- (Automatic teller machine) has been operational since Since then this machine has been successfully installed in about 3.5 million cities worldwide. The flexibility of using credits and debits card anywhere has become an advantageous facility has become a common platform for cyber crimes. Another problem with this process is blocking of cards in case of lost or misuse, for which customer has to wait for long time throughout interaction with customer care services. This paper presents a possible solution to reduce the complexities caused in this process as stated. Using three level authentication with NFC (near field communication), and a dash matrix algorithm which generates OTP to overcome the transaction liabilities. The add on feature of this method is blocking of card with least time. By implementing QR code through quick response authentication scheme. NFC technology works with both NFC enabled or non NFC phones via transmitter, receiver and a Bluetooth. This proposed system is user friendly and also ensure security. Keywords: ATM transaction, Blocking of ATM Card, Dash. Matrix Algorithm, NFC, Negative PatternPassword. I. INTRODUCTION Due to intrinsic defenselessness of the ATM network and the system itself. It is important to beat this innate shortcoming, we portray a framework utilizing a moderately new Technology called NFC to implement security amid exchange and use. This paper's methodology aims at using both NFC-enabled and non-enabled cell phones for designing a dedicated application that can communicate with the ATM machine. Because of innately short range capacities of NFC, mobile phones can communicate with ATM machines within close proximity only. Our proposed system eliminates the need of PIN. [7] Since ordinary card blocking process have a tendency to be tedious and cumbersome, our system is designed to both overcome previously mentioned security issues. Thus using a three factor authentication scheme employing NFC an emerging technology evolved from a combination of contactless identification and inter connection providing data exchange, Dash Matrix Algorithm and One-time password, we describe and quantify the potential to overcome common transaction liabilities. It incorporates factors like : Secured ATM exchanges utilizing NFC NFC registration Blocking of ATM cards The main concern of the system is to provide secure usage of ATM cards and cost effectiveness by utility of novel and increasingly common technology, when also simultaneously proving to be user friendly. The remaining paper is composed as follows Section II discusses related works and significant subordinates utilized as a part of the proposed framework. Section III depicts the working of the system. Section IV examinations the proposed systems performance. Section V concludes the paper with future work and comments on the proposed systems advantages over contemporary counterparts. II. RELATED WORKS Various designs included in our system are derived from reference [7]. The novel usage of NFC is featured in reference [1] and [6], below are the summary of each of these sources. Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 288

315 Aatiqa Sharief et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, The OR code is used as a password validation method has been proposed by Young-Gon Kim etal [1], it just reads pictures and transmits them to the server. Thus showing viability against a few assaults, for example, brute-force attack, man-in the middle attack and keyboard hacking, are thus inspected and degrees of vulnerabilities have been compared. Hung-Min Sun etal [2], proposes a strategy by using short message service(sms) to thwart password reuse attacks and password stealing. Through opass, users just need to recollect a long-term secret key to login on any sites. Assessments of the opass model conclude the framework as being effective and reasonable in contrast to customary web validation mechanism. Blake Ross et al [3], depicts a program expansion, PwdHash that straightforwardly creates an alternate secret key for each website, enhancing web password security and along these lines guarding against password phishing and other different assaults. K. Sankaridevi [8] states that according to an analyst firm Berg Insight, global sales of handsets featuring Near Field Communication has increased ten-folds starting from the year 2011.As we see, NFC is a widely growing yet cheap and easy way to automate mundane tasks and make a better interaction between physical and digital worlds, putting it to use of security is one good opinion. Vedat Coskun, Busra Ozdenizci [5] explain that Near Field Communication is an innovation encouraging cell phone utilization by billions of individuals throughout the world that offers various services ranging from loyalty and payment applications to access keys for workplaces and houses.nfc is a methods for sending information over radio waves. Which in sense it is similar to Wi-Fi or Bluetooth, however NFC can be utilized to initiate electric currents unlike other protocols within passive components as well as just send data. It also in general is faster than Bluetooth. M. Roland and.i. Langer [6] proposed a novel engineering for enhancing health care system framework with the assistance of android based cell phones with Near Field Communication(NFC) and Bluetooth interfaces, smartcard innovation with tamper resistant Secure Element (SE) for storing secure information, and a safe wellbeing administration of a server for security and health record management. III. WORKING OF SYSTEM This section discusses the architecture and detailed explanation of essential modules used. A. System Design 1. ATM Machine 2. Prefixed NFC Card 3. NFC Enabled phone with internet facility B. System Overview 1) Increasing ATM Transaction Security Using NFC: This process ensures ease of use of ATM features with The proposed design consists of add on reliable and faster dealings with banks. At the client side (ATM Machine) a card is manually swiped or the ATM cards number is entered (in case of unavailability of card or card read failure).the client is then required to tap their NFC enabled Cell phone with internet access onto a prefixed NFC tag on the ATM. A web page opens in the Cell phone upon successful tagging requesting for the users id or pre-registered phone number as user input. After successful login the user is allowed to create their own password using pattern and dash matrix. An OTP is generated and sent to the users pre registered phone number, which is to be entered onto the ATM S screen before the session reaches time-out. The aforementioned sub-segment processes makes use of the following procedures which are elaborated below: a) NFC Tag reading The NFC-TAG holds a restricted URL, which is read by the user by tapping the cell phone over it, which leads to creating a password through the mobile device.[4] Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 289

316 Aatiqa Sharief et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, Balance Enquiry Mini Statement Fund Transfer Register Beneficiary Confirm/Reject Beneficiary Make Payment 2) Admin Web Application Module b) Dash Matrix algorithm Using a new algorithm called the Dash Matrix Algorithm (DMA), the Pattern Password is created, based on the location points of the registered pattern, thus generating dummy values.[2] It appears as a random set of 3*3 matrix values called the Dash pattern. Which dynamically every time the user tries to make a transaction [3]. In this module Admin can login, add user bank details, branch details and change his password. The admin in this case would be Bank, and will have access to users database. 3) Blocking of ATM cards During the registration of new users, every user is sent a mail with user id and a Negative Pattern Password based on the pattern they create during registration, that is drawn by holding down a touch click, during which the cursor is moved along the path in a single stroke without being lifted until completion. ( Fig 4) Only the values that appear on the matrix are to be entered as the input in the mobile device and it is called as the Pattern Value Password (PVP). The user has to, therefore, only temporarily remember the pattern and enter dummy values, in accordance to the pattern he had registered. This affirms the second step of the verification methodology. After which the user may access the ATM features. c) User Web Application Module Each time a new user is registered by the admin, the user receives a mail containing their user id and an URL which upon clicking leads to a page to generate Captcha Graphical Password, unique to every user. Thus upon every login on the web portal requires the user to enter the descriptions same as at the time of registration. Featuring the third level authentication (fig 3) Upon entering this NPP in the ATM Machine the ATM Card is blocked. In this way, card blocking is upgraded to feature greater ease of use and higher levels of safety. ( Fig 5) IV. RESULTS Ones the user has logged in, they may access the following features on the portal: User Profile The whole procedure from swiping the card to entering the OTP on the screen was tried under simulation, with Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 290

317 Aatiqa Sharief et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, machines possessing average home computer hardware specifications and running on Windows 7.[7] The cell phone used during the test was furnished with internet connectivity having a connection speed of 2.14 Mbps. by the negative pattern passwords is not only an advantage to the user, but also aids the Bank. Thus banks can provide immediate deactivation for the user s account by placing an additional phone call to owner of the card for confirmation. As a result, the NFC technology along with Dash matrix can provide security to relative fields and can be used further for any system. Thus, NFC usage is less time consuming, feasible, reliable and also cost efficient. We believe that making changes and improvements in the system will be easy. We look forward to complete this venture of ours and give our contribution to the windows. We look forward to people using this software and giving us the feedback on improvements and advancements. REFERENCES V. CONCLUSION AND FUTURE WORK The development of ATM system over the world has helped to withdraw cash at any bank that is a part of the system to which ATM card is linked. ATM card is protected by a PIN, keeping our money safe. But the future is still vulnerable to password attacks such as Peeping attacks, Brute-Force attack, Retrieving passwords from the systems, Skimmers, etc, fort his system we will use identical password as PVP which will increase the level of security and reduce the thefts chances. With the recent raise of NFC featuring phones and with this concept of an NFC three level authentication, it dismisses the process of using pin password and increase the security by three times and thus reducing thefts. In this method we generate an OTP with the mobile numbers that are previously registered to receive an OTP,which sometimes may delay. To overcome the issue of delaying OTP, we generate OTP on the webpage with a Timeout. This new method also adds on a feature that solves the problem for blocking the ATM cards. thus it cuts down the user's time as well as their anxiety. Due to generation of unique URL for each user's card the QR code is reliable. The scheme of confirming legitimate users [1] Young-Gon Kim and Moon-Seog Jun, A design of User Authentication system using QR code identifying method, 6th International Conference on Computer Science and Convergence,Information Technology, IEEE Transactions,pp ,Nov-Dec [2] Blake Ross, C. Jakson, N. Miyake, D. Boneh, and J.C. Mitchell, Stronger password authentication using browser extensions, in SSYM 05: Proc. 14th Conf. USENIX Security Symp., Berkeley, CA, pp. 2-2, USENIX Association,2005. [3] Zhengming Li, Lijie Xue and Fei Tan, Face detection in complex background based on skin color features and improved AdaBoost Algorithm Progress in Informatics and Computing (PIC ), 2010 IEEE International Conference,Vol. 2, pp , Dec [4] Dirk Volland,Kay Noyen,Onur Kayikci,Lukas Ackermann and Florian Michahelles," Switching the Role of NFC Tag and Reader for the Implementation of Smart Posters". [5] Vedat Coskun, Busra Ozdenizci and Kerem Ok, "A Survey on Near Field Communication (NFC) Technology" [6] M. Roland and.i. Langer, "Digital Signature Records for the NFC Data Exchange Format". [7] Anusha Mandalapu, Daffney Deepa, Laxman Deepak Raj, Anish Dev J, "An NFC featured three level authentication system for tenable transaction and abridgment of ATM card blocking intricacies". [8] K. Shankari Devi, V. Vennila and D. Jayakumar "Near Field Communication (NFC) Technology in Smart E- Transactions" Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 291

318 Volume 9, Special Issue No. 3, May 2018 International Journal of Advanced Research in Computer Science (ISSN: ) CONFERENCE PAPER Available Online at SPADES: SCALABLE AND PRIVACY ASSURED DETECTION OF SPAMS Amogh Datt Reva Institute of Technology and Management Bengaluru Visvesvaraya Technological University Dilip Kumar Reva Institute of Technology and Management Bengaluru Visvesvaraya Technological University Abdul Suhail Reva Institute of Technology and Management Bengaluru Visvesvaraya Technological University Chanakya Madasi Reva Institute of Technology and Management Bengaluru Visvesvaraya Technological University LaxmiJayannavar REVAUniversity, Bengaluru Abstract Spamisoneofthemostactivecyber- criminalactivities. Inthispaperweconsiderdevelopinganefficientspamdetectionplatformwhichdoesn trequiredisclosingof contentsandtoqualifythesystemwithbi gdatacomponents.collaborativespamdetectiontechniquescandealwithlargescale data;however,theydonotconsiderprivacyof content.distance-preservinghashingprovidesolutionsforprotectingtheprivacyof content.however,distancepreservinghashesarecomputationallyverydemanding.asasolution,weproposespades,abigdataspamdetectionplatformbuiltontopofhdfscenter edonprotectinguserprivacyandachievingscalability. INTRODUCTION Cyberactivitiespertainingtospamareoneofthemostactivecrim inalactivities.spam sarequitedangerousconsideringthemisleadingcontentitc ontainsinordertoluretherecipienttoclickonmaliciouslinkswhi chnotonlycandownloadvirusesbutcanleadtodisclosureofsens itiveinformationofrecipients. Thesesensitiveinformationcanevenbebankaccountinformati onorotherpersonalsensitiveinformation.somespam smayalsocontaintheideaofsellingstolenorillegalgoods.in theworstcase, spamactivitiescanevenbecomeasponsorofterro rism.spaminallitsformcanstillbeconsideredasadifficultprobl emtotacklewhichhasdrovemassiveamountofresearch. Thescaleatwhichspam saregeneratedonadailybasissetsspamdetectionasasuper iorexampleforanexemplarybig-dataproblem. Spam detectionplatformmustbesensitivetotimeconstraints andtry to detectataearlier stagebeforeitcanreachthe user. Apartfromallthereasonsmentionedabove,thesefactsab outspam smustprovideenoughmotivationtoconsiderfilteringspa mfromlegitimate sandshouldbeofprimeconcern. I. AveragePCuserreceives5-10spammessagesperday. II. Thenumberofspammessageshasgrowntoawhoppi ng190billionmessagesperday. III. Spammersmakeagrossworldw iderevenueofaroun d$500millionperyear.[2],[3] Therearemanyspamdetectionplatformsinthemarketforphishi ng detectionbasedonstructuralproperties, aspermacetia pproachtowebspamdetection, spamdetectionusingtextcluster ing. Manybulk deliverytoolkitssuchasBlack-hole,Whitehole,Coolpack,Zeushavebeenbuiltwithsophisticationtobypasssecurityofspamdetectionplatformsandensurethespam sareescapable. Spam detection platforms must beconstantlyupdatedtounderstanddifferentcompundedpatter nsandmultiplexformatofspamsinordertosubduetheirrapidlyf ar-reachingrampantproperty. Alongwithmakingspamdetectionavitalelementintacklingcybe rc rime,wemustalsomakesurethat, thepoliciesof spamdetectionabidebytheinevitableneedofprotectinguser spri vacy, byapplyingtothecontentsofthe ,theneedfultechnique sthathelpinachievingthesame. Hence, wecansafelydissolvethefactthattwoimportantadvantag ingcharacteristicsofanyspamdetectionplatformshouldbe, 1. Privacy 2. Scalability 2.RELATEDWORK Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 292

319 Amogh Datt et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, Duetovoluminouspresenceofspamdetectionknowledge, itisn otpossibletoprovideacompletescrutinizedversionoftheexistin gsystems. Ratherthecynosureherewouldbecomposedoftheonescloselyr elatedtoourproblemi.eapproachesthathasgivenusinsightstoe nsureprivacyandscalabilityoftheplatform. 2.1 Distance PreservingHashing Dataisvast,isgrowingexponentiallyandcannotbeexaminedi nareasonableamountoftime.thisisespeciallytrueforexamin erswhowishtoexaminerelevantdata.datareductionmethods mustbeincorporatedinordertodrawattentiontorelevantdataa ndignoretheunrelatedones s.hashingalgorithmssuchassh A-1,SHA- 2,Whirlpoolareinpracticetoachievedatareduction.Theideah ereistohashaknownsetofspam sandstorethosevalues. Wheneveranewspam arrives,hashvalueofthenew iscomparedwithknownhashvalue s.ifanynewvaluematchestheexistingstoredvalues,itisalmost certainthatthe isspam.however,acertaincharacteristic ofthistechniqueisthatsinglebitofchangeininputproducesaco mpletelydifferenthash.hence,spammerscanthwarttheeffect ofhashcomparisonsbymakingaslightchangeinthecontentwh ichensures goesundetected.theslightchangesmadeare termedas hashbusters andthewholeprocesscanbecalled hashbusting.[4],[5] 2.2 ContextTriggeredPiecewiseHashing(CTPH) CTPHisbasedongeneratingmany differenthashesforasinglefileforexample,ahashforfirst512b ytesandanotherhashfornext 512bytes,ratherthanjustone hashforentirefile.thismeansthatsmallchangesinanypartofth etextwillleaveoverallsignatureofthetextthesame.thiscanma sktheeffectshashbusterssince,asinglehashbustercannotstopfrommatchingtherestofhashes,unlessthee mailcontentisnotspam.thistechniquewasusedinspamsumd evelopedbydr.andrewstrigdell.itcomputesrollinghashove rachunkofdata.thedistancemeasurethatspamsumusesisbas edon stringeditdistance.stringeditdistanceisameasure ofhowmanyeditopearationsarerequiredtotakeoneofthesigna turesandturnitintotheother.[5] 2.3 CollaborativeSpamDetection Inthecollaborationtechniqueparticipantscometogetherinthedete ctionprocessratherthaninformingaboutthespam. Collaboratinginvolvesintroductionofsupplementaryresourcesin ordertoprocessdata. Theplatformsdevelopedtoworkonthistechniquearedesignedtob eshippedwithlargecomputationalcapabilitiestohandlesubstanial amountofdatainadiminutiveamoutoftime. MachineLearningalgorithmssuchasNaiveBayes,LinearDiscrim inantanalysis,anovacomestotherescueinthiscategory ofdetectionofspams.however, certainearlierimplementationsdi dnotprovidejurisdictionoverparallelizationoftheprocess. Efficiencyofmachinelearningalgorithmsprovideastrongreasont ousetheminspamdetection.hence,researchhasbeenconductedto deportthemonmapreduceplatformstoensureparallelizationand keepincheck, thetimeconstraints. Today, variouslibrariesaremadeavailabletorunspamdetectionchecks, keepinginmindscalability.however,theprincipleelementofpri vacyishinderedinthemeantimeandthereseemstobenotechnique tosupportthisparamountstep.[6],[7],[8],[9] 3.TheSPADESPlatform SPADESplatformisbuiltfordetectionofspamswithfocalpointb eingpreservingusersprivacyandachievingscalability.privacyo fthe contentispreservedbyusingaonewayhashtechnique.scalabilityisachievedbyusinghadoopdistr ibutedfilesystem(hdfs).here,wemaintainadigestofvariousp otentialsuspiciouswordsthatmightmakeupaspam scontent.everynew isfirstencodedandthenscannedforthesesuspiciouswords.f inally, / sisclassifiedasspamornotspam.[10],[11] SPADESplatformcanbedescribedbythefollowingintegrants: 1. AFront-Endcomponent. 2. Map-Reduce component for fileoperations. 3. Re-usable java component fordetectingspam s 3.1 Front -EndComponent The front end component acts as a source of all kinds of interactions.here, the platform connects with the end user. The end user will have to register and login before utilising the services of SPADES. Once logged in user will be allowed to upload as many s he wishes for the platform to classify. User can initiate the spam detection process. The results of the classification will be displayed to the user along with the reasons for classifying a particular as spam. Fig 1 gives an overview of how front end components communicate. 3.2 Map Reduce component for file operations: Our platform uses the famous Map-Reduce technique to govern the file input-output operations. Map Reduce technique serves as the primary source to sort vast amount of s parallely in a distributed environment. It manages all communications and data transfers between various parts of the system.this assures our platform completes the detection process ina challenging timeframe.[7] 3.3 Re-usable Java component for detecting spam s: This is the nucleus of the entire system. This java component performs all the required tasks in an efficient manner inorder to produce results, that forms the coreintention of the entire platform. The whole process is divided into two parts : 1. Obfuscation thread : is the process of encoding the entire using a encoding scheme. The Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 293

320 Amogh Datt et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, encoding process has beenthoroughly explained in the literature[1], our platform attempts to implement certain elements from the same literature. 2. Spam detection thread: Here parallel inspection of spam s is initiated where the process tries to find any potential suscpicious words that could make up a spam Obfuscation Thread: Obfuscation is the process of encoding e- mail content using a suitable hash algorithm. We use a hashing technique that is not reversible. This means once the content of the is encoded, the original content cannot be retrieved. This step encompasses the crucial need for non-disclosure of the e- mail content by embracing the right to privacy of the user. [1] Our platform uses MD5 scheme. MD5 algorithm produces a 128 bit-hash value.md5 is one in a series of message digest algorithms designed by Professor Ronald Rivest of MIT. Fig 2 represents the actual sample of the output of obfuscation/encoding process. Fig1OverviewoftheprocessesinvolvedontheFront-end [-3,72,119,6,63,37,10,88,-109,-41,-86,-78,43,83,5,-64,89,-19,-2,-115][37,90, -118,-111,-126,-98,91,71,-79,-83,-83,94,-33,15,117,65,-87,-75,108,-108][30,96, -23,-116,-61,101,-82,-26,-128,103,-81,13,58,115,-86,20,87,-62,115,73][-118, -31,-54,109,-56,10,-40,122,-13,64,57,-114,-71,21,109,36,-21,117,-116,0][21, 116,-67,-37,117,-57,-118,111,-46,37,29,97,-30,-103,59,81,70,32,19,25][91,-67, -41, 119, -51, 115, 50, -79, 124, 4, -74, 32, 67, -17, 89, 3, 118, -78, -100, -85] [-92, -84,-111 Fig 2 Sample output of Obfuscation Thread Spam DetectionThread: Spam detection thread performs the search of potential spamwords in the content of the . For this we use a stored set of potential spam words that can make up a spam .[7] The obfuscated file is received from Obfuscation thread. Every line intheobfuscated thread is scanned for potential spam words keeping the stored spamwords as a reference. We have to mention here that the stored spamwords are also encoded and string comparison between obfuscated file and the encoded stored files are performed. During the comparisons if spamwords are found, the process, flags the file as spam. Fig 3 shows the Flowchart of the process. Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 294

321 Amogh Datt et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, Fig 3 Flowchart of Spam Detection Process CONCLUSION SPADES platform aims to facilitate early detection of spam s in challenging time-frames. The platform successfully adopts encoding scheme to address the privacy issues of the user hence, no information of the e- mail content is leaked or stored. Successful implementation of Map Reduce framework ensures parallel processing of large database of s and produces results in peripheral time range.further, platform uses stored set of potential spamwords which reduces computational burden even more and allows platform to produce results in a further reduced time frame. For a single large batch of files we found out there was a 68% reduction in time. The platform aims to detect spams with high level accuracy. The rate of failure was found out to be 1 in a 100. Overall SPADES platform has successfully achieved its challenges by overcoming all the hinderancefaced. AKNOWLEDGEMENTS We would like to thank the reference paper[1] which we have used as a source of inspiration for implementing this project. REFERENCES: [1] AbdelrahmanAlMahmoud, Member, IEEE, Ernesto Damiani, Senior Member, IEEE, HadiOtrok, Senior Member, IEEE, YousofAl-Hammadi, Member, IEEE. Spamdoop: A privacy-preserving Big Data platform for collaborative spam detection pages 1-8, IEEE [2] Kaspersky Lab, Spam and Phishing Statistics ReportQ Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 295

322 Amogh Datt et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, [3] Kaspersky Lab, Spam and Phishing Statistics for2016. [4] J. Francois, S. Wang, W. Bronzi, R.tate, and T. Engel. Bot-cloud: Detecting botnets using mapreduce. In IEEE International Workshop on Information Forensics and Security (WIFS), pages1 6. IEEE,2011 [5] Jesse Kornblum, Digital Forensic Research Conference, Identifying almost identiical files using Context Triggered PiecewiseHashing [6] N. Spirin and J. Han. Survey on web spam detection: principles and algorithms. ACM SIGKDD explorations Newsletter, 1.l.13: pages 50 64,2012. [7] W. Shi and M. Xie. A reputation- based collaborative approach for spam filtering. Conference on Parallel and Distributed Computing Systems, AASRI rocedia, vol. 5: pages ,2013. [8] M. Sirivianos, K. Kim, and X. Yang. Socialfilter: Introducing social trust to collaborative spam mitigation. In INFOCOM,pages IEEE, 2011 [9] G. Caruana and M. Li. A survey of emerging approaches to spam filtering. ACM Computing Surveys, vol. 44: pages 1 27,2012. [10] C. Karlberger, G. Bayler, C. Kruegel, and E. Kirda. Exploiting redundancy in natural language to penetrate bayesian spam filters.pages1 7,2007. Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 296

323 Volume 9, Special Issue No. 3, May 2018 International Journal of Advanced Research in Computer Science (ISSN: ) CONFERENCE PAPER Available Online at PRIVACY PRESERVING IN BIG DATA CLUSTERS WITH C-MEANS ALGORITHM Abhishek Department of Information Science & Engineering Reva Institute Of technology and Management Bengaluru, India Akash Department of Information Science & Engineering Reva Institute of Technology and Management Bengaluru, India Ambuj Shekhar Singh Department of Information Science& Engineering Reva institute Of Technology and Management Bengaluru, India Shashikala N REVA University, Bengaluru shashikalan@reva.edu.in Abstract--- We are going to discuss the Privacy-preserving Possibilistic c-means Algorithm. This algorithm is used for clustering in a way that every data point is mapped to multiple clusters in the system. This mapping arrangement can have variable degree of membership. The data available is large in number and heterogenous in nature. The PCM algorithm uses the map reduce property to act on the data for clustering. BGV encryption scheme is applied to PCM algorithm to protect and preserve the data privacy particularly on cloud systems. Clustering is mainly used to differentiate and classify data items among various groups based on their attributes. By this process being undertaken the data items of similar attributes are places in a single group of data items. Many techniques of clustering are applied for data extraction and discovery of various aspects of data. Keywords--- Big Data Clustering, Privacy Preserving, Possibilistic c-means, HDFS, Map Reduce I. INTRODUCTION Big data its related mechanisms and techniques are growing exponentially, one main factor for this sudden growth in the trend of big data is the soaring popularity of social media platforms like twitter, YouTube and not to forget Facebook. Big data is nothing but an abnormally huge collection of heterogenous and complex data sets. The data sets are consisting of data items with many attributes and nodes. Particularly the data sets are composed of inter related data items which ranges from images, audios, texts etc. It leads to the heterogenous nature of data in terms of its structure, thus as a result we are left with structured and unstructured data. This is where PCM algorithm comes into play, it can be used effectively to depict the uniqueness of different data items spread across different clusters. It can easily check data corruption that occurs primarily due to noise or some other external factors. Although it can be used effectively for clustering it has a major drawback that it is designed with respect to small structured datasets, thus its not an easy job to apply it for the large and heterogenous data sets of big data. It can t be applied as it is to the big data sets especially because it fails to grasp the complex co-relations between the data sets observed. Hadoop file system was developed with an idea of a distributed system of storing and processing files. It was designed to be fault tolerant using cost efficient hardware. Its fault tolerance property is its USP apart from the fact that it is run on a commodity hardware. It can easily store large amounts of data and to make the access to the data convenient the data items are stored across several small machines. Multiple copies of the files are stored thus making the storage redundant so that its fault tolerance property can be fully satisfied in case of any accidental data loss. It also facilitates the parallel processing of applications. Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 297

324 Abhishek et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, DISADVANTAGES OF EXISTING SYSTEM: Existing systems fall short of producing accurate data set because they ignore the complex correlation hidden in heterogeneous dataset. Since Hadoop is more suitable for files of large capacity and existing system access only small datasets, thus they often have high time complexity. They are incapable of clustering large amount of data. In simple words, the short comings of existing systems are: a) Lack of scalability. b) File can t be transferred in proper way. c) Lots of hackers could hack the data. d) Improper security. e) Non-efficiency. f) Non-existence. III. PROPOSED SYSTEM FIG 1: SYSTEM FLOW CHART Cloud computing is a model for enabling services for users with universal, convenient and on demand network access to a shared pool of computing resources which are manually configurable. In the field of computer science, the cloud computing paradigm functions by outsourcing the computing services. This analogy can be more conveniently understood by considering how the electricity is outsourced. The consumers can simply make use of the electricity without thing about how it is made, transported or supplied. Most of the cloud computing infrastructure delivers the service by making use of the data centers on computer systems or by implementing storage virtualization techniques. The services can be accessed by anyone who is authorized for the access. The cloud appears to be a single point of access for all the computing services needed by the consumers. Open source software as well as open standards play an eminent role to the growth of cloud computing II. EXISTING SYSTEM For big data, an algorithm used for clustering based on graphical data structures was designed by Gao et al forming a general category of all the prior methods used for image text clustering. By grasping the complex relations between multiple attributes of data sets Chen et al. designed non-negative matrix it-factorization algorithm. Zhang et al. used a tensor vector space and as a result formed an algorithm for clustering of big data. This was achieved by him after modelling the correlations between various modalities of the heterogeneous data. This paper proposes a possibilistic c-means algorithm for big data clustering. Fuzzy clustering has one important scheme named as PCM. The complexity of each object to different cluster is reflected by PCM. Since PCM is initially designed for small structured datasets, PCM cannot be applied to Big Data clustering directly. PCM cannot even capture the complex correlation over multiple modalities. The paper proposes high-order PCM algorithm by extending the conventional PCM algorithm. A multidimensional array in mathematics is widely used to represent heterogeneous data is called as Tensor. The correlation over multiple modalities of the heterogeneous data object by using PCM algorithm is proposed in this paper. ADVANTAGES OF PROPOSED SYSTEM: a) Proper secure for entire process. b) The performance of communication is improved. c) Simple to store data in HDFS. d) Maintaining Feasibility. IV. INTRODUCTION TO HADOOP Hadoop is an open-source software framework that organizes data into an intensive distributed files or Applications. It provides the following benefits: Storage systems and processing are distributed across several small machines that communicate and work together. It provides the feature of fault tolerance and continues to work even in case of small malfunctions of hardware or software. Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 298

325 Abhishek et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, It is easily scalable as the cluster can be easily extended just by adding new machines. It has a high-level API such that it can easily provide programming abstraction and the users can easily focus on programming logic. unstructured data into structured data provides the accurate result of given dataset. Preprocessing is particularly applicable any application that involves huge datasets, e.g.: Data mining and IOT apps. Out-of-Range values are the result of loosely controlled Data-gathering method. Hadoop is a Map Reduce process in which the application is divided into many small input splits to access the data in parallel processing mode to reduce the time complexity of work each of which may be executed on the nodes of Hadoop Clusters. Each node can be configured either as a data node or a name node. Replication factor is made as 3 to enhance the high availability of data. The project includes these modules: Hadoop Common: The commodity hardware and common utilities that support the other Hadoop modules. Hadoop Distributed File System (HDFS): Hadoop distributed file system that stores the data in terms of blocks and provides high-throughput access to application data. Hadoop YARN: Yet another Resource negotiator. job scheduling and cluster resource management is the main functioning of yarn. Hadoop MapReduce: Mapper and reducers are the yarnbased system which uses parallel processing techniques to process the data. FIG 3: FLOW CHART OF PRE-PROCESSING 2. PCM CLUSTERING A cluster is a collection of objects with maximum intracluster similarity and minimum inert-cluster difference between objects. The term similarity is defined as the mathematical similarity and is measured by the norm distance. This paper proposes a fast PCM clustering algorithm. Initially, a base structure of fuzzy and possibilistic c-means (FCM and PCM) is presented. Algorithms are analyzed, and drawbacks and limitations are pointed out. Secondly, based on reformulation theorem, we keep on modifying c-means algorithm to optimize the cluster until we get the optimized output. FIG 2: HADOOP ARCHITECTURE V. MODULES This paper proposes 4 fundamental modules: 1. Preprocessing 2. PCM Clustering 3. Map Reduce 4. Fetch Cloud 1. PREPROCESSING. One of the main module for Data mining system is preprocessing of data. Preprocessing mainly focuses on cleaning of unwanted data or null values and unstructured data sets. Converting a raw data into a understandable format. Removing the inaccurate data and converting 3. MAP REDUCE FIG 4: C-MEANS ALGORITHM Map reduce is a computing paradigm and implementations associated with it for processing and generation of large data sets. This is done through an algorithm which functions parallelly and is distributed across machines. The strategy used for implementing the map reduce is a case of splitting, applying and combining the strategy for analysis of data. The most advantageous fact of Map Reduce technique is that it provides easy scaling of data processing particularly when multiple computing systems are used. The data Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 299

326 Abhishek et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, processing units are called the Mappers and Reducers correspondingly. The data processing application must be decomposed into mappers and reducers which is a complex task. Once this step is achieved making the application run on several machines is simply a task of configuration. BGV Map: Each of the worker nodes apply the function corresponding to the map part on the local data and the output is written to a local storage. Only one copy of any redundant data is processed this is ensured by the master node. Encryption Decryption 1. Shuffle: Master node redistribute the data in correspondence with the output keys. This is done in a fashion so that all the data corresponding to a single key remains with the same worker node. 2. Reduce: Each group of output data is processed in parallel per key by the worker node. Fetch Cloud FIG 6: FETCH CLOUD PROCESS Thus, map reduce gives the flexibility of processing map and reduce functions in a distributed fashion. FIG 5: MAP REDUCE FUNCTIONALITY 3. FETCH CLOUD Fetch cloud is the phenomenon involving extraction of data from the cloud server through some security mechanism. Many cloud service providers support Web architectures based on representational state transfer (REST) application programming interfaces (APIs). Feature selection and principal component analysis are the pre-processing techniques that were carried out to facilitate the clustering process. The choice of clustering technique is still a very subjective matter. FIG 7: USE CASE DIAGRAM The above figure represents the use case of paper that is being proposed. The interaction between 2 users can be depicted as the form of client-server process. Initial user acts as the server which operated the data and provides the result as demanded by the End user. Initial user can operate all the modules whereas end use can only derive the data which is fetched on the cloud by the initial user. DEMERITS System design is very complex. It is selectively secured. Reliability is less since it focusses only on security. It has high computational cost. It has low efficiency. MERITS High security Performance Its Ciphertext size is linear. It provides strong authority. It is highly portable. Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 300

327 Abhishek et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, It offers better security properties and well suited for client-server communication. It is well suited to produce structured data. VI. CONCLUSION This paper proposes the PCM algorithm for big data clustering particularly for highly complex and heterogenous data. The PCM procedure proposed also uses the BGV encryption technique for protecting and preserving the privacy of data especially on cloud computing systems. From the final observations it can be easily inferred that the PCM algorithm can be a highly effective tool to clusters large heterogenous data sets along with maintaining the privacy of the data sets. DHOPCM algorithm can be more suitable for clustering the data sets but as far as privacy protection is concerned the PCM technique stands out between the two. Since it is a highly scalable technique it can be improvised using more and more cloud servers. As the enhancement proposal of this algorithm we plan to implement and test it on much larger data sets in future. REFERENCES [1]. X. Wu, X. Zhu, G.-Q. Wu, and W. Ding, Data Mining with Big Data, IEEE Transactions on Knowledge and Data Engineering, vol.26, no.1, pp [2]. B. Ermis, Acar, and A.T. Cemgil, Link Prediction in Heterogenous Data via Generalized Coupled Tensor Factorization, Data Mining and Knowledge Discovery, vol.29, no.1, pp ,2015. [3]. Q. Zhang, L.T. Yang, and Z. Chen, Deep Computation Model for Unsupervised Feature Learning on Big Data, IEEE Transaction on services Computing, vol.9, no.1, pp , jan [4]. N. Soni and A. Ganatra, MOiD (multiple Objects Incremental DBSCAN)-A Paradigm Shift in Incremental DBSCAN, International Journal of Computer Science and Information Security, vol.14, no.4, pp ,2016. [5]. Z. Xie, S. Wang, and F.L. Chung, An Enhanced Possibilistic c-means Clustering Algorithm EPCM, Soft Computing, vol.12, no.6 pp ,2008. [6]. Q. Zhang, C. Zhu, L.T. Yang, Z. Chen, L. Zhao and P.Li, An Incremental CFS Algorithm for Clustering Large Data in Industrial Internet of Things, IEEE Transactions on Industrial Informatics, 2015.DOI: /TII [7]. X. Zhang, Convex Discriminative Multitasking Clustering, IEEE Transactions on Pattern Analysis and machine Intelligence, vol.37, no.1, pp.28-40, Jan [8]. B. Gao, T. Liu, T. Qin, X. Zheng, Q. Cheng, and W. Ma, Web Image Clustering by Consistent Utilization of Visual Features and Surrounding Texts, in Proceedings of the 13 th Annual ACM international conference on Multimedia,2005, [9]. Y. Chen, L. Wang, and M. Dong, Non-Negative Matrix Factorization for semi supervised Heterogeneous Data Coclustering, IEEE Transactions on Knowledge and Data Engineering, vol.22, no.10, pp , Oct [10]. L. Meng, A. Tan, and D. Xu, Semi-Supervised heterogeneous Fusion for Multimedia Data Co-Clustering, IEEE Transactions on Knowledge and Data Engineering, vol.26, no.9, pp , Aug Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 301

328 Volume 9, Special Issue No. 3, May 2018 International Journal of Advanced Research in Computer Science (ISSN: ) CONFERENCE PAPER Available Online at PRIVACY PRESERVING FOR BIG DATA IN MOBILE CLOUD-COMPUTING USING ENCRYPTION STRATEGY Divya L and Dr. Udaya Rani Computing and Information Technology School of Computing and Information Technology REVA University, Bengaluru Abstract - Big data applications are currently emerging technology in cloud computing. However working on big data applications brings its own challenge of privacy and security issues; larger data sizes leads to complex privacy issues. The implementation and adoption of these big data applications have redefined service models and enhanced application performances in several aspects. Data processing and transmissions reveal serious issues related to execution time of data encryption. Applications abstain from data encryptions to obtain an adoptive performance level conjunction with privacy concerns. This paper emphasizes on privacy and affirms atypical data encryption approach called Dynamic Data Encryption Strategy (D2ES). The performance of D2ES has been evaluated which confides the privacy enhancement. Our planned methodology is to selectively encrypt data and use privacy classification methods under timing constraints. This employs selective encryption strategy within the required execution time to capitalize the privacy protection scale. Keywords Big data, mobile cloud computing, Privacy-classification, encryption strategy. 1. INTRODUCTION Presently mobile cloud computing technology has privileged ennumber of applications. Big data applications are currently emerging technology in cloud computing. Big data applications contain large volume of data; retrieval of data across heterogeneous system is a tedious task. Therefore, big data applications require cloud computing technology to handle large volume of mobile data across distinct platforms. Further as an emerging technology cloud computing has lead into advanced serviceability to mankind such as mobile parallel computing and distributed scalable data storage. Privacy is the major concern in utilizing mobile cloud computing regardless of many services. With increased usage of mobile applications and social media communications, there is huge concern on privacy of user data. As privacy is concerned transferring the plain text over the network, mobile users are liable to different kinds of attacks such as Denial of Service attack, malware injection attack, monitoring which results in the loss of data integrity. When performance is considered transferring of fully encrypted data over the mobile users can maximize the performance of the system and transmission time increases. As result of these two scenarios there occurs a conflict between performance of the system and privacy issue. Our proposed design uses two essential terminologies such as paired data and pair matching hit and performs encryption under a given timing constraint, which secures privacy of user data which is transmitted over the heterogeneous platform. Securing users data is the vital factor in the proposed approach. When multiple mobile users communicate over the network, share the data within them, when the third party intruder tries to hack the data proposed approach performs encryption to secure the users data. Figure 1.Represents the high-level architecture of mobile cloud computing demonstrating equivalence between transmission efficiency and confidentiality of user s data. The paper focuses on the conflict between data transmission efficiency and data protection. To solve the problem, we propose a novel approach called D2ES model to secure the users data where the user s data is segregated according to its privacy standard and perform encryption under a recommended timing constraint. When multiple mobile users transmit data over the wireless network, securing the user data is a major concern. The dotted line represents that the data transmission between the mobile users and physical instruments needs to secure. The users data is stored in cloud physical servers which acts as a storage purpose for users. Hence the model in the proposed approach i.e. D2ES model has two principles Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 302

329 Divya L et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, (1) Segregate the user s data according to its privacy standard (2) Regulate encryption strategy to the data packets under the specified timing constraints. In our proposed approach, we propose 3 algorithms namely Weight Simulation Algorithm Assorting Production Algorithm Dynamically Regulate Encryption Algorithm Where each data package achieves its own weight via privacy level classification for this purpose it uses Weight Simulation Algorithm and sorting is achieved all data packets get its transformed weight by using an Assorting Production Algorithm. All sorted results are mapped into a table and we apply Dynamically Regulate Encryption Algorithm to all the data packets which achieved highest weight value. 2. LITERATURE SURVEY K. Gai, L. Qiu, M. Chen, H. Zhao, and M. Qiu. SA-EAST: security ware efficient data transmission for ITS in mobile heterogeneous cloud computing. ACM Transactions on Embedded Computing Systems, 16(2):60, The ordinary moved organize examinations and creating enthusiasm for adaptable learning to share and trading have driven different novel applications in advanced physical structures cpss for instance adroit transportation systems its in any case current its executions are limited by the conflicts among security and correspondence viability. Focusing on this issue this article proposes a security-careful capable data sharing and trading saeast show which is planned for securing cloud-based its utilization. In applying this approach we expect to obtain secure persistent sight and sound data sharing and trading. Our test appraisal has exhibited that our proposed show gives a convincing execution in securing exchanges for its. L. Wu, K. Wu, A. Sim, M. Churchill, J. Choi, A. Stathopoulos, C. Chang, and S. Klasky. Towards realtime detection and tracking of spatio-temporal features: Blob-filaments in fusion plasma. IEEE Transactions on Big Data, 2(3), A novel calculation and execution of ongoing recognizable proof and following of blobfibers in combination reactor information is exhibited. Comparative spatial-worldly highlights are critical in numerous applications, for instance, start parts in burning and tumor cells in a therapeutic picture. This work exhibits an approach for removing these highlights by partitioning the general assignment into three stages: nearby recognizable proof of highlight cells, gathering highlight cells into expanded element, and following development of highlight through covering in space. Through our broad work in parallelization, we exhibit that this approach can adequately make utilization of a substantial number of process hubs to recognize and track blob-fibers progressively in combination plasma. On an arrangement of 30 GB combination reenactment information, we watched straight speedup on 1,024 procedures and finished blob discovery in under three milliseconds utilizing Edison, a Cray XC30 framework at NERSC. S. Yu, W. Zhou, S. Guo, and M. Guo. A feasible IP trace back framework through dynamic deterministic packet marking. IEEE Transactions on Computers, 65(5): , DDoS assault source trace back is an open and testing issue. Deterministic bundle checking (DPM) is a basic and compelling trace back system, yet the current DPM based trace back plans are not down to earth because of their adaptability imperative. We saw a factor that exclusive a set number of PCs and switches are engaged with an assault session. In this manner, we just need to stamp these included hubs for trace back reason, as opposed to denoting each hub of the Web as the current plans doing. In light of this discovering, we propose a novel checking on request (MOD) trace back plot in view of the DPM system. With a specific end goal to trace back to included assault source, what we have to do is to check these included entrance switches utilizing the customary DPM technique. Like existing plans, we require took an interest switches to introduce an activity screen. At the point when a screen sees a surge of suspicious system streams, it will ask for an extraordinary check from an all around shared MOD server, and stamp the suspicious streams with the novel imprints. In the meantime, the MOD server records the data of the imprints and their related asking for IP Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 303

330 Divya L et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, addresses. Once a DDoS assault is affirmed, the casualty can get the assault sources by asking for the MOD server with the imprints removed from assault bundles. In addition, we utilize the checking space in a round-robin style, which basically addresses the adaptability issue of the current DPM based trace back plans. We set up a scientific model for the proposed trace back plot, and altogether investigate the framework. Hypothetical examination and broad certifiable information tests exhibit that the proposed trace back technique is achievable and successful. Y. Li, W. Dai, Z. Ming, and M. Qiu. Privacy Protection for Preventing Data Over-Collection in Smart City. IEEE Transactions on Computers, 65: , In smart city, a wide range of clients information is put away in electronic gadgets to make everything clever. A cell phone is the most generally utilized electronic gadget and it is the turn of every single keen framework. Be that as it may, current cell phones are not able to deal with clients' delicate information, and they are confronting the protection spillage caused by information overaccumulation. Information over-accumulation, which implies cell phones applications gather clients' information more than its unique capacity while inside the authorization scope, is quickly getting to be a standout amongst the most genuine potential security perils in brilliant city. In this paper, we contemplate the present condition of information over-gathering and concentrate some most regular information over-gathered cases. We show a versatile cloud system, which is a dynamic way to deal with destroy the information over-gathering. By putting every one of clients' information into a cloud, the security of clients' information can be significantly moved forward. We have done broad trials and the exploratory outcomes have shown the adequacy of our approach. S. Yu,W. Zhou,W. Jia, S. Guo, Y. Xiang, and F. Tang. Discriminating DDoS attacks from flash crowds using flow correlation coefficient. IEEE Transactions on Parallel and Distributed Systems, 23(6): , 2017 Distributed Denial Of Service (DDoS) assault is a basic risk to the Web, and botnets are typically the motors behind them. Complex bot-masters endeavor to impair identifiers by copying the movement examples of glimmer swarms. This represents a basic test to the individuals who guard against DDoS assaults. In our profound investigation of the size and association of current botnets, we found that the present assault streams are generally more like each other contrasted with the streams of glimmer swarms. In view of this, we proposed a segregation calculation utilizing the stream connection coefficient as comparability metric among suspicious streams. We figured the issue, and introduced hypothetical verifications for the practicality of the proposed segregation strategy in principle. Our broad investigations affirmed the hypothetical examination and showed the viability of the proposed technique practically speaking. 3. THE PROPOSED WORK Our proposed approach focuses on Dynamic Data Encryption strategy model. D2ES model is proposed to secure the users data. Where it selectively performs the encryption strategy to the sensitive data, calculate weights, and outputs the data in the encrypted form. It has three necessary steps Sorting by weights Data Alternatives Output Figure 2: Represents Phases of D2Es model Phase 1: Sorting by weights In this phase data package is taken as input where individual data package attains its own weight using privacy level classification. In this step, it uses Weight Simulation Algorithm to calculate weight of individual data package. Then they run privacy preserving sorting. When sorting is executed, individual data packets achieve transformed weight and S Table is used to map all the sorted results into a table. In this phase, we use Assorting Production Algorithm. All the sorted results are assigned in descending order to successfully classify the date packets according to its priority level. The data packets with the highest weight value are considered as sensitive data. The data packet with lowest weight value is considered as non-sensitive data. Phase 2: Data Alternatives In this phase the sorted results of S Table is accepted as input and we perform encryption strategy to the data packets with the highest weight value, where encryption should be performed under a specified timing constraint. We utilize Dynamically Regulate Encryption Algorithm to exercise encryption. Firstly we specify the timing constraint and the timing span to perform encryption then we apply Dynamically Regulate Encryption Algorithm. Where only the sensitive data is been Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 304

331 Divya L et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, encrypted, sensitive word is identified by which word has the highest weight value is considered as sensitive word to perform encryption. Phase 3: Output This phase, outlay the data which is been encrypted. The insensitive data is in unencrypted form and the sensitive data is encrypted there by securing the user data. The process aborts when all sensitive data is encrypted and when the data package doesn t contain any sensitive words to perform encryption. We view the sensitive data in the encrypted form. The output derived is in encrypted form which signifies that mobile users data is been secured which is the highest priority in our proposed work and it should be achieved in the specified timing constraint. Thereby securing the users data from hacked the attackers. 4. ALGORITHM 4.1 Weight Simulation Algorithm The main objective of this method is to calculate the weight of each data packet and to analyze whether encryption could be performed based on parity between data package. The Weight Simulation Algorithm is initiated for altering Scaling Table. Thus pairs matching collisions are used in this method in order to recognize the paired data. The Weight Simulation Algorithm is established to alter the Scaling Table utilizing weight values. Inputs are Scaling Table and Co-List Table. Output are modified Scaling Table. Where the Co-List Table is been pre-declared by the developers or the security policies, it is used to exercise pairs matching collisions. The main phases of algorithm include: (1) Input the initial Scaling Table and pre-declared Co-List Table (2) For all data DPi in Scaling Table, verify if the data DPi is included in Co-List Table. Identify the paired data DPj, when DPi is in Co-List Table and thus representing DPi is equivalent to DPj (DPi DPj). (3) The weight value needs to be modified if DPj is in Scaling Table. (4) Analyze the encryption time length between DPi and DPj. Prescribe infinity value to W en DPi, when execution time DPi is lesser than DPj else assign infinity value to DPj, which signifies we consider this data the highest encryption priority. (5) Finally when all data are operated and altered, output the modified Scaling Table. Weight Simulation Algorithm Input: Scaling Table, Co-List Table Output: modified Scaling Table 1. Input: Scaling Table, Co-List Table 2. For DPi in calculating table do 3. If DPi is in Co-List Table then 4. Get the pairs matching collision (DPi DPj) 5. If DPj is in calculating table then 6. If T en DPi < T en DPj then 7. en W DPi = 8. Else 9. W en DPj = 10. End If 11. End If 12. End If 13. End For 14. Output: Modified Scaling Table 4.2 Assorting Production Algorithm Assorting Production Algorithm is proposed to initiate Assort Table. Input is modified Scaling Table. Outputs are Assort Table and T exe m. T exe m refers to the sum of execution time of unencrypted data packets. The main phases of algorithm are: (1) Input Assort Table and initialize empty value to it and initialize empty value to it and initialize T exe m to zero. (2) For all data DPi in modified Scaling Table enter For loop. For each data DPi in the loop, calculate and alter the T exe m value. If the W en DPi value is infinity then the method is T exe m T exe m + N DPi * T en DPi (3) Else, calculate SDPi = W DPi / T en DPi. When W en DPi value is greater than zero. Add achieved SDPi to the Assort Table. (4) End the For Loop when all data of DPi are performed. (5) Sort all SDPi in the altered Assort Table in descending order. (6) Output is Assort Table Value and T exe m. Assorting Production Algorithm Input: Modified Scaling Table exe Output: Assort Table, T m. (1) Input: Assort Table (2) Initialize Assort Table ϕ (3) Initialize T exe m 0 (4) For DPi in modified Scaling Table do (5) If W en DPi = then (6) T exe m T exe m + N DPi * T en DPi (7) Else (8) If W en DPi > 0 then (9) Determine Assort, Assort DPi = W DPi / Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 305

332 Divya L et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, T en DPi. (10) Assign SDPi to Assort Table (11) End if (12) End if (13) End for (14) Classify Assort Table by SDPi in a descending order. (15) Return Assort Table, T exe m 4.3 Dynamically Regulate Encryption Algorithm Dynamically Regulate Encryption Algorithm is proposed to perform encryption strategy which formulates the absolute privacy protection for sensitive data inputs are Scaling Table, assorting table and timing constant where encryption strategy should be achieved under a prescribed timing constant. The main phases of algorithm are: (1) Input timing constant Tc, Assort Table and Scaling Table. Initialize Action Plan Dataset PDataset as empty set. (2) While loop is used to create an Action which depends on the available time. The data packets whose encryption should be performed are identified. (3) Update the execution Time Span Ts where each individual data packages non-encryption time mode is selected to update Ts. (4) Add the data package to the set PDataset when Ts is greater than zero and encryption time shouldn t exceed value of Ts which means highest priority weight goes first. (5) End While Loop, whenever If condition fails. (6) Output data set P Dataset consists a set of data package DPi encrypt all data package in PDataset. Dynamically Regulate Encryption Algorithm Input: Assort Table, Modified Scaling Table, Tc, T exe m. Output: Encrypted Data Packets (ENDP) (1) Input Assorting table, Modified Scaling Table, Tc, exe T m. (2) Initialize ENDP ϕ (3) Ts [ Tc (T exe m + N unen DPi * T unen DPi) (4) + T unen DPi))] (5) While Assort Table is not empty do (N unen DPi * (6) Get DPi having the maximum preference from Assort Table (7) For DPi i=1 to N DPi do (8) If Ts > T en DPi - T unen DPi then (9) Add one DPi to ENDP (10) TS TS ( T en DPi - T unen DPi) (11) Else (12) Break (13) End if (14) End for (15) End while (16) Output ENDP 5. CONCLUSION The outcome of proposed approach has an adaptive and superior performance. The proposed method maximises the privacy protection level under timing constraints in big data. The findings of this research provide big data solutions with an adaptive transmission approach focusing on protecting privacy by using selective encryption strategy within the required execution time requirements. 6. REFERENCES [1] K. Gai, L. Qiu, M. Chen, H. Zhao, and M. Qiu. SA- EAST: security ware efficient data transmission for ITS in mobile heterogeneous cloud computing. ACM Transactions on Embedded Computing Systems, 16(2):60, [2]S. Yu, W. Zhou, S. Guo, and M. Guo. A feasible IP trace back framework through dynamic deterministic packet marking. IEEE Transactions on Computers, 65(5): , [3] L. Wu, K. Wu, A. Sim, M. Churchill, J. Choi, A. Stathopoulos, C. Chang, and S. Klasky. Towards realtime detection and tracking of spatio-temporal features: Blob-filaments in fusion plasma. IEEE Transactions on Big Data, 2(3), [4] Y. Li, W. Dai, Z. Ming, and M. Qiu. Privacy Protection for Preventing Data Over-Collection in Smart City. IEEE Transactions on Computers, 65: , [5] S. Yu,W. Zhou,W. Jia, S. Guo, Y. Xiang, and F. Tang. Discriminating DDoS attacks from flash crowds using flow correlation coefficient. IEEE Transactions on Parallel and Distributed Systems, 23(6): , 2015 [6] Y. Wu, K. Wu, A. Sim, M. Churchill, J. Choi, A. Stathopoulos, C. Chang, and S. Klasky. Towards realtime detection and tracking of spatio-temporal features: Blob-filaments in fusion plasma. IEEE Transactions on Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 306

333 Divya L et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, Big Data, 2(3), [7] Yz. Yu,W. Zhou,W. Jia, S. Guo, Y. Xiang, and F. Tang. Discriminating DDoS attacks from flash crowds using flow correlation coefficient. IEEE Transactions on Parallel and Distributed Systems, 23(6): , 2014 [8] S. Yu,W. Zhou,W. Jia, S. Guo, Y. Xiang, and F. Tang. Discriminating DDoS attacks from flash crowds using flow correlation coefficient. IEEE Transactions on Parallel and Distributed Systems, 23(6): , 2014 Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 307

334 Volume 9, Special Issue No. 3, May 2018 International Journal of Advanced Research in Computer Science (ISSN: ) CONFERENCE PAPER Available Online at ATTRIBUTE BASED STORAGE SUPPORTING SECURITY DE-DUPLICATION FOR ENCRYPTED DATA IN CLOUD Nayana V, Pallavi A R, Prajwal D R, Priyanka M and Asha K Schoolof Computing & Information TechnologySchoolof Computing & Information Technology REVA University Bangalore, India nayanreddy85@gmail.com, palluammu833@gmail.com praju.prajwal25@gmail.com, priyankam.20156@gmail.com asha.k@reva.edu.in Abstract-Distributed computing gives an adaptable and helpful path for information sharing, which brings different advantages for both the general public and people. Be that as it may, there exists a characteristic protection for clients to specifically outsource the common information to the cloud server since the information regularly contain profitable data. In this way, it is important to put cryptographically improved access control on the common information. Trait based encryption is a promising crypto graphical crude to manufacture a reasonable information sharing framework. Be that as it may, get to control isn't static. To this end, we propose a thought called revocable-capacity Attribute-based encryption (RS-ABE), which can give the forward/in reverse security of figure message by presenting the functionalities of client renouncement and figure content refresh at the same time. The execution correlations demonstrate that the proposed RS-ABE plot has preferences as far as usefulness and effectiveness, and along these lines is possible for a viable and practical information sharing framework. At long last, we give execution consequences of the proposed plan to show its practicability. INTRODUCTION Distributed computing is a worldview that gives gigantic calculation limit and colossal memory space requiring little to no effort. It empowers clients to get planned administrations regardless of time and area over numerous stages (e.g., cell phones, PCs), and therefore conveys extraordinary accommodation to cloud clients. Among various administrations gave by distributed computing, distributed storage benefit, for example, Apple's icloud, Microsoft's Azure and Amazon's S3, can offer a more adaptable and simple approach to share information over the Internet, which gives different advantages to our general public. In any case, it likewise experiences a few security dangers, which are the essential worries of cloud clients. Initially, outsourcing information to cloud server suggests that information is out control of clients. This may cause clients' wavering since the outsourced information as a rule contain significant and touchy data. Besides, information sharing is frequently actualized in an open and unfriendly condition, and cloud server would turn into an objective of assaults. Surprisingly more dreadful, cloud server itself may uncover clients' information for unlawful benefit. Thirdly, information sharing isn't static. That is, the point at which a client's approval gets terminated, he/she should never again have the benefit of getting to the already and thusly shared information. In this manner, while outsourcing information to cloud server, clients additionally need to control access to these information with the end goal that lone those right now approved clients can share the outsourced information. LITERATURE SURVEY "Restrictive intermediary re-encryption (CPRE) empowers fine-grained appointment of unscrambling rights, and has some certifiable applications. In paper 1, the creator show a ciphertext-arrangement trait based CPRE plot, together with a formalization of the crude and its security examination. We show the utility of the plan in a cloud organization, which accomplishes fine-grained information sharing. This application executes cloud server-empowered client denial, offering an option yet more productive answer for the client renouncement issue with regards to finegrained encryption of cloud information. High client side effectiveness is another noticeable component of the application, which makes it workable for clients to utilize asset obliged gadgets, e.g., cell phones, to get to cloud information. Our assessments indicate promising outcomes on the execution of the proposed plot. [1] "Dodging the plate bottleneck in the information space deduplication document framework," in sixth USENIX Conference on File and Storage Technologies, FAST 2008, February 26-29, 2008, San Jose, CA, USA. USENIX, 2008, Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 308

335 Nayana V et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, pp Disk-based deduplication stockpiling has risen as the new-age stockpiling framework for big business information assurance to supplant tape libraries. Deduplication expels excess information fragments to pack information into an exceptionally conservative frame and makes it practical to store reinforcements on circle rather than tape. A critical necessity for big business information assurance is high throughput, regularly more than 100 MB/sec, which empowers reinforcements to finish rapidly. A noteworthy test is to recognize and kill copy information portions in light of present conditions on a minimal effort framework that can't bear the cost of enough RAM to store a record of the put away fragments and might be compelled to get to an on-circle list for each information segment.[2] "Dupless: Serveraided encryption for deduplicated capacity," in Proceedings of the 22th USENIX Security Symposium, Washington, DC, USA, August 14-16, USENIX Association, 2013, pp Cloud stockpiling specialist co-ops, for example, Drop box, Mozy, and others perform deduplication to spare space by just putting away one duplicate of each document transferred. Should customers routinely scramble their documents, in any case, investment funds are lost. Message-bolted encryption (the most noticeable appearance of which is focalized encryption) settle this pressure. In any case it is intrinsically subject to savage power assaults that can recoup records falling into a known set. We propose a design that gives secure deduplicated stockpiling opposing animal power assaults, and acknowledge it in a framework called DupLESS. In DupLESS, customers scramble under message-based keys got from a key-server by means of a careless PRF convention. It empowers customers to store scrambled information with a current administration, have the administration perform deduplication for their sake, but then accomplishes solid classification ensures. We demonstrate that encryption for deduplicated capacity can accomplish execution and space reserve funds near that of utilizing the capacity benefit with plaintext data.[3] "Intelligent message-bolted encryp-tion and secure deduplication," in Public-Key Cryptography - PKC eighteenth IACR International Conference on Practice and Theory in Public-Key Cryptography, Gaithersburg, MD, USA, March 30 - April 1, 2015, Proceedings, ser. Address Notes in Computer Science, vol Springer, 2015, pp This paper considers the issue of secure stockpiling of outsourced information in a way that grants deduplication. We are out of the blue ready to give protection to messages that are both associated and subject to general society framework parameters. The new fixing that makes this conceivable is collaboration. We expand the message-bolted encryption (MLE) crude of earlier work to intuitive message-bolted encryption (imle) where transfer and download are conventions. Our plan, giving security to messages that are related as well as permitted to rely upon general society framework parameters, is in the standard model. We clarify that association isn't an additional supposition by and by in light of the fact that full, existing deduplication frameworks are now interactive.[4] "Probabilistic encryption," J. Comput.Syst. Sci., vol. 28, no. 2, pp , 1984.A new probabilistic model of information encryption is presented. For this model, under appropriate many-sided quality suspicions, it is demonstrated that removing any data about the cleartext from the cyphertext is challenging for the normal for a foe with polynomially limited computational assets. The evidence holds for any message space with any likelihood dissemination. The primary usage of this model is exhibited. The security of this usage is demonstrated under the immovability suspicion of choosing Quadratic Residuosity modulo composite numbers whose factorization is unknown.[5] PROBLEM STATEMENT Proposed System We propose a revocable-stockpiling Attributebased encryption (RS-ABE), which can give the forward/in reverse security of ciphertext by presenting the functionalities of client renouncement and ciphertext refresh all the while. For giving security systems in bar/sub, we use the standards of Attribute-based encryption to help many-to-numerous associations amongst subscrabers and distributers. In spite of the fact that we along these lines show the usage of our security techniques regarding a covariant called property based encryption, comment that our approach additionally profits by other Attribute-based encryption plans. Advantages The office to create yields in a given time. Reaction time under specific conditions. Capacity to process a specific section of exchange at a specific speed. RNS crypto framework. Trait based Encryption. Advantages of the Proposed System Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 309

336 Nayana V et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, Proposed system gives authorization, confidentiality, and scalability. Extensions of the cryptographic methods to provide efficient routing of encrypted events by using the idea of searchable encryption. Multi credential routing a new event dissemination strategy which strengthens the weak subscription confidentiality. our approach, distributers and subscrabers associate with a key server. The certification winds up approved by the key server. A qualification comprises of two sections: 1) A parallel string which descrabes the capacity of a companion in distributing and getting occasions. 2) A proof of its Attribute. The last is utilized for confirmation against the key server and check whether the abilities coordinate the Attribute of the companion. Specifically, the Attribute-based encryption guarantees that a specific key can unscramble a specific figure message just if there is a match between the certifications of the figure content and the key. Distributers and subscrabers keep up partitioned private keys for each approved certification. Because of the free coupling amongst distributers and subscrabers, a distributer does not know the arrangement of applicable subscrabers in the framework. Along these lines, a distributed occasion is encoded with people in general key of every conceivable certification, which approves a subscraber to effectively decode the occasion. METHODOLOGY A framework is an efficient gathering of reliant segments connected together as indicated by an arrangement to accomplish a particular target. Its primary qualities are association, connection, relationship, reconciliation and a focal target. Analysis Examination is a definite investigation of the different tasks performed by a framework and their connections inside and outside of the framework. One part of investigation is characterizing the limits of the framework and deciding if a competitor framework ought to think about other related frameworks. Amid examination information are gathered on the accessible documents choice focuses and exchanges dealt with by the present framework. This includes gathering data and utilizing organized apparatuses for examination. CONCLUSION Distributed computing brings awesome comfort for individuals. Especially, it impeccably coordinates the expanded need of sharing information over the Internet. In this paper, to assemble a financially savvy and secure information sharing framework in distributed computing, we proposed a thought called RS-ABE, which underpins Attribute renouncement and ciphertext refresh at the same time with the end goal that a disavowed client is kept from getting to already shared information, and in addition in this way shared information. FUTURE SCOPE We propose a revocable-stockpiling Attributebased encryption (RS-ABE), which can give the forward/in reverse security of ciphertext by presenting the functionalities of client renouncement and ciphertext refresh at the same time. For giving security systems in bar/sub, we use the standards of Attribute-based encryption to help many-to-numerous cooperations amongst subscrabers and distributers. Despite the fact that we along these lines show the execution of our security techniques regarding a solid variation called trait based encryption, comment that our approach additionally profits by other Attribute-based encryption plans. In our approach, distributers and subscrabers interface with a key server. The certification winds up approved by the key server. A qualification comprises of two sections: System Analysis 1) A double string which descrabes the capacity of an associate in distributing and getting occasions. SystemAnalysis Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 310

337 Nayana V et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, ) A proof of its Attribute. The last is utilized for confirmation against the key server and check whether the capacities coordinate the Attribute of the associate. Specifically, the Attribute-based encryption guarantees that a specific key can unscramble a specific figure message just if there is a match between the qualifications of the figure content and the key. Distributers and subscrabers keep up isolated private keys for each approved accreditation. Because of the free coupling amongst distributers and subscrabers, a distributer does not know the arrangement of significant subscrabers in the framework. Along these lines, a distributed occasion is encoded with the general population key of every single conceivable accreditation, which approves a subscraber to effectively unscramble the occasion. REFERENCES 1.Y. Yang, H. Zhu, H. Lu, J. Weng, Y. Zhang, and K. R. Choo, Cloud based data sharing with fine-grained proxy reencryption, vol. 28, pp , B. Zhu, K. Li, and R. H. Patterson, Avoiding the disk bottleneck in the data domain deduplication file system, in 6th USENIX Conference on File and Storage Technologies, FAST S. Keelveedhi, M. Bellare, and T. Ristenpart, Dupless: Serveraided encryption for deduplicated storage, Washington, DC, USA, August 14-16, M. Bellare and S. Keelveedhi, Interactive messagelocked encryp-tion and secure deduplication, in Public-Key Cryptography - PKC S. Goldwasser and S. Micali, Probabilistic encryption, J. Comput.Syst. Sci., vol. 28, no. 2, pp , Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 311

338 Volume 9, Special Issue No. 3, May 2018 International Journal of Advanced Research in Computer Science (ISSN: ) CONFERENCE PAPER Available Online at NETSPAM: AFAKE REVIEW DETECTOR Adarsh P V, Allam Kuladeep, Neha G, B Harshitha Reddy and Prof. Geetha B School of C&IT, REVA University, Bangalore, Karnataka, India Abstract- Today, online reviews play a vital role in the marketing campaign. 90% of consumers read online reviews before buying any product. And 85% of them trust these reviews as much as personal advice. These reviews becomes an essential fact in a business s success where, genuine reviews can contribute to the welfare of the company and negative reviews can affect the reputation of the company and can lead to economic damage. Since anybody can write a review, it provides an opportunity for spammers to write fake reviews and this maybe done for the sake of money. In this paper, we introduce a novel framework. We are using three different methods to detect fake reviews which is using (i) IP address using meta path (ii)blocking negative reviews words and using sentiment analysis (iii)repetition of same words. This experiment s result outplay the existing approaches. Keywords metapath, sentiment analysis I. INTRODUCTION Now a days, a huge amount of people depends on the reviews posted on online portals so that they can wisely choose their products and services. These reviews provides important information, but some E-commerce websites does not provide quality control and anyone can write a fake review. This misleads the customers to buy a bad quality products and the manufacturers or producers with a great reputation in the market may be slandered by these spam reviews. Many approaches have been proposed to solve this issue but couldn t give the appropriate results completely. Overcoming these drawbacks of previous approaches, we propose a novel structure, named NetSpam, which uses spam highlights for demonstrating audit informational collections as heterogeneous information networks (HIN) to delineate identification strategy into a characterization issue in such networks. The outcomes demonstrate that this strategy beats the current techniques and among four classifications of highlights, including review-behavioral, user-behavioral, review-linguistic, and user-linguistic, the primary sort of highlights performs superior to alternate classifications. II. METHODOLOGY In the most recent decade, an incredible number of research ponders center on the issue of spotting spammers and spam audits.nonetheless, since the issue is non-trifling and testing,it stays a long way from completely settled. We can compress our discourse about past investigations in three after classifications. 1. Linguistic-based Methods This approach remove semantic based highlights to discover spam surveys. Feng et al. [1] utilize unigram, bigram and their structure. Different examinations [4], utilize different highlights like pairwise highlights (includes between two surveys; e.g. content closeness), level of CAPITAL words in a surveys for discovering spam audits. Lai et al. in utilize a probabilistic dialect displaying to spot spam. This investigation shows that 2% of surveys composed on business sites are really spam. 2. Behavior-basedMethods Methodologies in this gathering nearly utilize surveys metadata to extract includes; those which are ordinary example of a commentator behaviors. Feng et al. in [20] center around dispersion of spammers rating on various items and follows them. In [3], Jindal et. al separate 36 behavioral highlights and utilize a directed technique to discover spammers on Amazon and [14] demonstrates behavioral highlights demonstrate spammers' character superior to phonetic ones. Xue et al. in [9] utilize rate deviation of a particular client and utilize a trust-mindful model to discover the connection between clients for figuring last spamicity score. Minnich et al. in [8] utilize worldly and area highlights of clients to discover strange conduct of spammers. Li et al. in [10] utilize some essential highlights (e.g extremity of audits) and after that run a HNC (Heterogeneous Network Classifier) to discover last marks on Dianpings dataset. Mukherjee et al. in [16] nearly connect with behavioral highlights like rate deviation, furthest point and so on. Xie et al. in [17] additionally utilize a worldly example (time window) to discover singleton audits (surveys composed only once) on Amazon. Luca et al. in [18] utilize behavioral highlights to indicate expanding rivalry between organizations prompts huge development of spam surveys on items. Crawford et al. in [3] demonstrates Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 312

339 Adarsh PV et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, utilizing distinctive classifi-cation approach require diverse number of highlights to achieve wanted execution and propose approaches which utilize less highlights to accomplish that execution and thus prescribe to enhance their execution while they utilize less highlights which drives them to have better multifaceted nature. With this point of view our structure is questionable. This examination indicates utilizing diverse methodologies in grouping yield distinctive execution as far as various measurements. 3. Graph-based Methods For metapath creation, we characterize a broadened form of the metapath idea thinking about various levels of spam assurance. Specifically, two surveys are associated with each other on the off chance that they share same esteem. Hassanzadeh et al. [25] propose a fluffy based system and show for spam discovery, it is better to utilize fluffy rationale for deciding an audit's mark as a spam or nonspam. In reality, there are diverse levels of spam assurance. Negative Personal Pronouns Concentrates in this gathering mean to make a chart between clients, surveys and things and utilize associations in the diagram and additionally some system based calculations to rank or mark surveys (as spam or veritable) and clients (as spammer or genuine). Akoglu et al. in [11] utilize a system based calculation known as LBP (Loopy Belief Propagation) in directly adaptable emphasess identified with number of edges to discover last probabilities for diverse parts in organize. Fei et al. in [7] likewise utilize same calculation (LBP), and use burstiness of each survey to discover spammers and spam surveys on Amazon. Li et al. in [10] manufacture a diagram of clients, audits, clients IP and shows clientswith same IP have same names, for instance if a client with various diverse record and same IP keeps in touch with a few audits, they should have same mark. Wang et al. in [18] moreover make a system of clients, surveys and things and utilize fundamental suppositions (for instance an analyst is more reliable if he/she composes more genuine audits) and mark reviews.wahyuni in [14] proposes a half and half technique for spam location utilizing an calculation called ICF++ which is an expansion to ICF of [18] inwhich simply survey rating are utilized to discover spam discovery. This work utilize likewise feeling investigation to accomplish better exactness in specific. Avg Content Similarit y Fig. 1: An example for a network schema. IV. Screenshot 1 :Upload File User Review IMPLEMENTATION Pre Time III. PROPOSED WORK We proposed a novel system, named NetSpam, which uses spam highlights for demonstrating survey datasets as heterogeneous data systems to delineate discovery technique into an arrangement issue in such systems. A. System Schema Definition The following stage is characterizing system mapping in light of guaranteed rundown of spam highlights which decides the highlights occupied with spam location. This Schema are general meanings of metapaths furthermore, appear as a rule how extraordinary system segments are associated. For instance, if the rundown of highlights incorporates NR, ACS, PP1 and ETF, the yield mapping is as displayed in Fig. 1 B. Metapath Definition As specified in Area II-An, a metapath is characterized by a succession of relations in the system diagram. Table II demonstrates all the metapaths utilized as a part of the proposed system. As appeared, the length of client based metapaths is 4 and the length of reviewbased metapaths is 2. Screenshot 2 :Report Generation Report 1: Report 1 will be generated based on the reviews given by the specific user for the specific product. Report 2: Report 2 will be generated based on the reviews given by all the users for the specific product. Report 3: Report 3 will be generated based on the reviews given by the specific user for all the products. Report 4: Report 4 will be generated based on the fake reviews given by all the users for all the products. Report 5: Report 5 will be generated based on the reviews given by the Meta Fake users and Meta Fake IP Address for all the products. Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 313

340 Adarsh PV et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, gotten extensive consideration from different orders for over 10 years, data dissemination and substance partaking in multilayer systems is as yet a youthful research [12]. Tending to the issue of spam identification in such systems can be considered as another examination line in this field. REFERENCES Screenshot 3 :Pie Graph Screenshot 4 : Bar Graph V. CONCLUSION This examination presents a novel spam identification system to be specific NetSpam in view of a metapath idea and in addition another diagram based strategy to mark audits depending on a rank-based naming methodology. The execution of the star postured structure is assessed by utilizing two true named datasets of Howl and Amazon sites. Our perceptions demonstrate that ascertained weights by utilizing this metapath idea can be extremely powerful in distinguishing spam audits and prompts a superior execution. Likewise, we found that even without a prepare set, NetSpam can ascertain the significance of each component and it yields better execution in the highlights' expansion procedure, and performs superior to anything past works, with just few highlights. In addition, subsequent to characterizing four fundamental classifications for highlights our perceptions demonstrate that the audits behavioral classification performs superior to anything different classifications, as far as AP, AUC and additionally in the ascertained weights. The outcomes likewise affirm that utilizing distinctive supervisions, like the semiregulated strategy, have no recognizable impact on deciding the greater part of the weighted highlights, similarly as in various datasets. VI. FUTURE WORK For future work, metapath idea can be connected to other issues in this field. For instance, comparable system can be utilized to discover spammer groups. For discovering group, surveys can be associated through gathering spammer highlights, (for example, the proposed include in [8]) and audits with most noteworthy closeness in view of metapth idea are known as groups. Likewise, using the item includes is a fascinating future work on this investigation as we utilized highlights more identified with spotting spammers and spam audits. In addition, while single systems has [1] J. Donfro, A whopping 20 % of yelp reviews are fake. [2] M. Ott, C. Cardie, and J. T. Hancock. Estimating the prevalence of deception in online review communities. InACM WWW, [3] M. Ott, Y. Choi, C. Cardie, and J. T. Hancock. Finding deceptive opinion spambyanystretchoftheimagination.inacl,2011. [4] Ch. Xu and J. Zhang. Combating product review spam campaigns via multiple heterogeneous pairwise features. In SIAM International Confer- ence on Data Mining,2014. [5] N.JindalandB.Liu.Opinionspamandanalysis.InWSDM, [6] F.Li, M. Huang, Y. Yang, and X. Zhu. Learningtoidentifyreview spam.proceedingsofthe22ndinternationaljointconferen ceonartificial Intelligence; IJCAI,2011. [7] G. Fei, A. Mukherjee, B. Liu, M. Hsu, M. Castellanos, and R. Ghosh. Ex- ploiting burstiness in reviews for review spammer detection. In ICWSM, [8] A. j. Minnich, N. Chavoshi, A. Mueen, S. Luan, and M. Faloutsos. Trueview:Harnessingthepowerofmultiplereviewsites.In ACM WWW, [9] B. Viswanath, M. Ahmad Bashir, M. Crovella, S. Guah, K.P. Gummadi, B. Krishnamurthy, and A. Mislove. Towards detecting anomalous user behavior in online social networks. In USENIX, [10] H. Li, Z. Chen, B. Liu, X. Wei, and J. Shao. potting fake reviews via collectivepulearning.inicdm,2014.l. Akoglu, R. Chandy, and C. Faloutsos. Opinion fraud detection in onlinereviewsbynetworkeffects.inicwsm,2013. [11] R. Shebuti and L. Akoglu. Collective opinion spam detection: bridging review networksand metadata. In ACM KDD,2015. [12] S. Feng, R. Banerjee and Y. Choi. Syntactic stylometry for deception detection. Proceedings of the 50th Annual Meeting of the Association for ComputationalLinguistics:ShortPapers;ACL,2012. [13] N. Jindal, B. Liu, and E.-P. Lim. Finding unusual review patterns using unexpected rules. In ACM CIKM,2012. [14] E.-P. Lim, V.-A. Nguyen, N. Jindal, B. Liu, and H. W. Lauw. Detecting productreviewspammersusingratingbehaviors.inacmc IKM,2010. [15] A. Mukherjee, A. Kumar, B. Liu, J. Wang, M. Hsu, M. Castellanos, and R. Ghosh. Spotting opinion Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 314

341 Adarsh PV et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, spammers using behavioral footprints. In ACM KDD,2013. [16] S. Xie, G. Wang, S. Lin, and P. S. Yu. Review spam detection via temporal pattern discovery. In ACM KDD,2012. [17] G. Wang, S. Xie, B. Liu, and P. S. Yu. Review graph based online store reviewspammerdetection.ieeeicdm,2011. [18] Y. Sun and J. Han. Mining Heterogeneous Information Networks; PrinciplesandMethodologies,InICCCE,2012. [19] A. Mukerjee, V. Venkataraman, B. Liu, and N. Glance. What Yelp Fake ReviewFilterMightBeDoing?,InICWSM,2013. Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 315

342 Volume 9, Special Issue No. 3, May 2018 International Journal of Advanced Research in Computer Science (ISSN: ) CONFERENCE PAPER Available Online at SECURITY TECHNIQUES TO PREVENT THREATS AND ATTACKS IN CLOUD ENVIRONMENT M. Naina Bharathi Rao Reva University School of Computing & IT Bangalore, India Farheen Sheik Reva University School of Computing & IT Bangalore, India Rashmi G O, Reva University School of Computing & IT Bangalore, India Gopal Krishna Shyam, PhD Reva University School of Computing & IT Bangalore, India Abstract - Cloud computing is progressing at a very fast pace and more and more number of IT companies have either migrated to cloud or are in the process. So, it is very important to secure the data and gain trust of the Organizations. Off late, cloud computing is facing a lot of challenges in terms of security to data, network and other cloud resources due to Active and Passive attacks. For effectiveness of cloud computing, there is an urgent need to arrest these attacks by addressing the vulnerabilities and improve the security. This survey points out some of the security approaches used by prime companies in cloud computing, to avert attacks on data security. Keywords Cloud computing; Security; Attacks; Network; Cloud resources; I. INTRODUCTION The technology of cloud computing is the process of accessing IT services over the internet where the cloud service provider offers usage of shared resources such as network, storage, server, applications and services. Using these services over the internet is the main cause of concern to data security. In the current market, major share of cloud providers are Amazon, Google, IBM, Microsoft and Salesforce [1]. They provide ascendible computing platforms having great elasticity and available to customers for building a broad variety of applications. The different service models offered are SaaS (Software as a service), PaaS (Platform as a service), IaaS (Infrastructure as a service) and also some new additional services like SECaaS (Security as a service), Database as a service and Storage as a service. The various cloud deployment models are Public, Private, Community and Hybrid clouds. Some examples of the services provided by these leading cloud computing companies are: Google: Google App Engine This is a web framework and cloud computing platform that supports application programming interface for the storage of data, manipulation of images, accounts and services in Google. Microsoft: Microsoft Azure is a public cloud computing platform that provides a range of cloud services to develop and upgrade applications. Open source software: Eucalyptus is a software platform that can be used to implement IaaS in enterprises for creating private clouds. IBM Smart Cloud (Bluemix), Salesforce.com: SaaS, VMware: Provide Virtualization infrastructure [1]. Prior to adopting cloud computing in an organization, the security challenges in the areas like Confidentiality, Integrity and Availability need to be addressed. II. LITERATURE SURVEY Data loss or data leakage will make a destructive impact on the enterprise. From this they can lose the business. The data must be transferred with well protection. Therefore to ensure strong protection against the attacker a good encryption algorithm has to be implied. By applying one algorithm it can be made easy to decrypted by attacker. Hence by applying hybrid algorithm it will be better to protect data. As the data is stored outside the boundary of the organization, they need a strong encryption process during transmission for data protection. A proposed a model uses a hybrid process for encrypting and decrypting data, based on RSA and AES-128 algorithms [2]. In addition, HMAC algorithm is used to ensure data integrity and authentication. It explains the throughput, time taken and memory utilized to encrypt and decrypt data and is based on the size of the file. There are various security techniques that are used by the majority of cloud providers, to avert attacks when data transfer takes place between the cloud environment and local networks. Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 316

343 M. Naina Bharathi Rao et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, III. SECURITY ISSUES Security attacks are classified as Active and Passive. Passive attacks do not interrupt the network operations. Attackers pry on data that are being exchanged in the network without modifying it. Security requirement of confidentiality is violated in this kind of attacks. It is difficult to detect such attacks, as it does not affect the operation of the network. Passive attack includes Eavesdropping, Traffic Monitoring and Traffic Analysis. Active attack is an attempt by the attacker to make unauthorized changes to data or system that causes damage or destroys data in transit or storage. Active attacks include message modification, masquerading (impersonate or disguise), playback attack, and DoS (denial of service attack). These security challenges to data and system when using cloud computing needs to be resolved or minimized. IV. TOOLS AND TECHNIQUES FOR CLOUD SECURITY There are various tools and techniques for handling security related challenges [5]. (a) Firewall: Cloud firewall provides a security layer between the network and the cloud, which can be adequate for smaller threats. (b) Encryption: Sensitive data can be encrypted, to make it difficult for attackers to gain unauthorized access. There is a strategy proposed to secure the users data during transmission [2]. It uses a technology of double encryption and combines with the technology of hashbased message authentication code (HMAC) to ensure the data of users are transfer effective safely. The client generates a value of HMAC and attach it behind the encrypted message by AES algorithm. It ensures the integrity and message authentication of user data. Then the proposed work will use the RSA algorithm to encrypt the secret key of AES to make sure that the secret key of AES is sent safely. (c) Access control: Least privilege that is needed to do their job should be given to users. (d) CASBs: Cloud access security broker offloads processes of security monitoring and generate reports based on thresholds that are established (e) Platform providers: Cloud service providers can offer a fully integrated secure platform. A. Mitigation Techniques: Following threat-mitigation techniques are available for us to better secure our information [1] Identity-based authentication scheme, RSA encryption algorithm (asymmetric cryptographic algorithm), Dynamic network intrusion detection model, access control based on Multitenancy, Transport Layer Security Handshake, Homomorphic encryption based on public key or secret key, Third-party auditor performing independent audits, Probability sampling method, Exponential key exchange (Diffie Hellman key exchange algorithm), Facial recognition technology, Message Authentication Code, Masking of data, Assertion for Security markup language, Retrievability proof, Independent Net storages for Redundant array, HDFS, Symmetric encryption, Self-cleaning intrusion tolerance, Privacy manager, Services for Security Access Control, SLA, Intrusion detection system. V. SOLUTIONS AVAILABLE TO TACKLE SECURITY ISSUES Single password authentication which is an authenticating technique where users can use only one password to authenticate themselves for multiple services, for providing data protection in cloud. There are multiple benefits in this kind of authentication technique such as security enablement from dictionary attack and honeypot (decoy for trapping hackers). These techniques are used by most of the IT companies like Microsoft, Facebook and Google. Following are some of their solutions [1][4]: a) Amazon Macie has used automation to protect data using machine learning [3] Prior to automation Amazon Macie performed the tasks by using machine learning for understanding about their sensitive information, access and storage. Macie does analysis for accessing data and flagging anomalies, like downloading large-sized data, unique pattern of login, and data shown in unexpected locations. Macie gives alerts if someone makes sensitive data externally accessible by accident or stores credentials insecurely. b) Amazon s SSL Encryption and Hypervisor. AWS (Amazon Web Services) transfers information between their web servers, private clouds and browsers confidentially, using different schemes of authentication like Multi-factor authentication (MFA), Rights management and IAM (AWS Identity and Access Management). c) Dell Encrypts plaintext to ciphertext prior to storing data in cloud environment. This technique protects user s data and ensures that even the cloud service providers are unable to read or change the content stored in cloud. This protection method also ensures that data is fully protected when stored in an external drive or media. d) Online Tech provides enterprise cloud service confidentiality by using encryption methods such as FDE (Full Disk Encryption), that does data encryption on the hard disk, during the entire process of booting. e) McAfee has access control enabled in cloud computing. They offer various methods of access controls such as McAfee SSO (Single Sign On), McAfee Web Gateway which is a secure web gateway and McAfee one time password tokens. These security methods enable management of policies and ensures aversion of data loss. f) Fujitsu is one more vendor who provides access control with various authorization techniques like Virtual System Management and Central Management Authorization. These security techniques are effective in averting cross site scripting (XSS) and injection attacks such as SQL injection, command injection, Header Injection, Full Path Disclosure and Log Injection. Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 317

344 M. Naina Bharathi Rao et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, g) VMware offers Cloud Authorization that integrates policies of the service provider with the corporate policies. Soft tokens are provided for authorizing end users in a securely. h) OASIS Cloud authorization provides security methods based on the management of the authorization. With this method, they maintain user logs that give user location and information about the devices used. i) Microsoft uses FIM (Forefront Identity Manager), VPN (Virtual Private Network), and SSL (Secure Sockets Layer) Encryption. Salesforce.com uses IDS (Intrusion Detection System), TLS (Transport Layer Security) encryption, SAML (Security Assertion Markup Language) and MD5 hashing algorithm. IBM uses SLA (Service Level Agreements), SSL Encryption, third-party Auditor and VPN. VI. OPEN CHALLENGES AND FUTURE WORK Cloud Cyber Security Challenges fall under Privacy, Security & Trust issues. Privacy issues deal with protection of personal information of customers. Lack of User Control - Users do not own the infrastructure, and hence there is a threat of theft, authorized resale or misuse of user s data. Control over data life cycle - Cloud service provider may not comply with requests for data deletion. Changing cloud provider - Difficulty associated with moving out of cloud and avoiding vendor lock-in. Notifications and redress - Notification about privacy breaches and determining who is at fault etc. Unauthorized Secondary Usage - Service provider may sell users data for financial gain, advertisement reasons etc. Legally binding agreements are required with providers. Regulatory Compliance Complexity - Different laws may apply depending on where the data is stored, when the data is stored in multiple data centers, replicated across legal jurisdictions. Trans-border Data Flow Restrictions - EU and many countries restrict transfer and access to personal information across national borders. Many countries do not have adequate privacy regulations. Legal Uncertainty: Users can refuse personally identifiable information (PII), such as addresses, to be used for analysis, advertising and online marketing. Many legal uncertainties about privacy rights exist and appropriate legislations are to be created/updated. VII. CONCLUSION Cloud computing has brought both positive and negative factors for information security. The overall impact of cloud is dependent on how we enhance its strengths and minimize its disadvantages. This can lead to secure platform along with noticeable cost savings, improved efficiency and productivity improvements. Some of the popular security techniques identified are Access control based on roles, Identity-based Authentication, Thirdparty auditor, SLA, MAC (Message authentication code), Ticket based authentication that are time bound and POR (Proof of retrievability). These security methods have impact on the confidentiality, integrity and availability, also known as the CIA triad. Cloud computing considerations for security should be highlighted and included in every cloud architecture and infrastructure, to ensure data security. REFERENCES [1]Security Techniques for Protecting Data in Cloud Computing by Venkata Sravan Kumar Maddineni and Shivashanker Ragi [2] f223513c7f1d11cf39.pdf by Mohammad Ubaidullah Bokhari and Qahtan Makki Shallal,Dept. of Computer Science, Aligarh Muslim University Aligarh, India. [3] [4]Security Techniques for Data Protection in Cloud Computing by KireJakimoski [5] Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 318

345 Volume 9, Special Issue No. 3, May 2018 International Journal of Advanced Research in Computer Science (ISSN: ) CONFERENCE PAPER Available Online at TEXT BASED CYBERBULLYING DETECTION Sowparnika shree N, Computer Science and Information Technology REVA UNIVERSITY Bangalore, India Shalini N Computer Science and Information Technology REVA UNIVERSITY Bangalore, India shalinin141@gmail.com Sushma Reddy A, Computer Science and Information Technology REVA UNIVERSITY Bangalore, India sushmateddy28@gmail.com Rohini R T, Computer Science and Information Technology REVA UNIVERSITY Bangalore, India rohinirt1997@gmail.com Shiva Kumar Naik, Computer Science and Information Technology REVA UNIVERSITY Bangalore, India shivakumarnaik@reva.edu.in Abstract The aim of this concept is to achieve a good understanding of influence of cyberbullying on students and the actual possible need of prevention messages targeting students, children s, teenagers and adults. This study explores adolescents trusts and behaviors combined with cyberbullying. As the cyberbullying is rapidly increasing in our society and most of teenagers get affected by its devastating effects.cyberbullying is the abusement accurse in online to the adolescents, in mean side of media and its affecting all ages. Cyberbullying is when adult, teen or preteen is harassed abused or targeted by other child, teen, or preteen using internet, digital communication or cell phones. The disadvantages are increasingly common in social media. Cyberbullying has become a serious harmfulness which is affecting children s adolescents and young adults. The text based cyberbullying detection make automatic detection of bullying massages in social media possible and it would help to build and give a healthy and safe social media environment.in this meaningful research area, the method has implemented a web based application which avoids cyberbullying in various websites and applications messengers. Keywords Cyberbullying, abused words, harrassment, mental torture, filters,social media. 1 INTRODUCTION Social media is the bunch of internet based application that has been constructed on the ideological and technological support of web and which permits the creation and exchange of user generated content via social media, public can have and enjoy huge amount of information convenient communication, experience and so on. However social media may have some disadvantages such as cyberbullying which gives the bad opinion on the life of people especially to children s and teenagers. Cyberbullying can be defined as confront done on purpose, deliberate actions performed by a single person or a group of people via digital communication method such as messages sending and comments posting against a victim. Different from traditional bullying that usually happens at schools during face-to-face communication. Cyberbullying on social media can take place anywhere at any time, rapid increase in the usage of social media by the teenagers, pre-teenager s they get in to depression very easily because the age them have very sensitive thinking capacity, they can t able to come out from the harassment and commit suicide. Hence deliberate group of persons target children s, teenagers to abuse and harass them. The kind of sadist people will do such things abusement and harassment had become most commonly in social media day by day. Let us know what the cyberbullying. It is the thing one deliberately the persons or group of person like commenting the other post with the abuse words or can say bully words. Bully word is nothing but bad or vulgar words which have many categories like abuse words, sexual words, vulgar words, violence words, hate words and so on. These people will comment using these kinds of words and post it on the Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 319

346 Sowparnika shree N, et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, social media. It will effect on the person who had posted the picture or something else. This has become a business or can say work to the people. Now-a-days trolling is common in social media this is also a best example to understand the cyberbullying concept, trolling others with bully words is also abusement and harassment. 2 EXISTING SYSYTEM A. Problem statement The critical step is the numerical representation learning for text messages. Cyberbullying is hard to describe and judge from a third view due to its intrinsic ambiguities. To protection of internet users and privacy issues,only a small portion of messages are left on the internet,and most bullying posts are deleted. The major modifications include semantic dropout noise and sparse mapping constraints. In addition, they need to construct a bully space knowledge base to boost the performance of natural language processing methods. This is the modification need to be done in this paper to overcome the problem in the previous papers. In addition, they need to construct a bully space knowledge base to boost the performance of natural language processing methods. The major fications modi include semantic dropout noise and sparse mapping constraints. However, a direct use of these bullying features may not achieve good performance because these words only account for a small portion of the whole vocabulary and these vulgar words are only one kind of discriminative features for bullying. All the modules have input screens in this system. Whenever an user commits a mistake, there are error messages and alert messages sent to guide him or her in the right way so that same mistakes are not repeated. When it comes to module design, the process of converting the input created by the user into a computer-based format is called input design. The main aim of input design is to make the entered data very logical and error free. The application developed is very user-friendly, there are options provided for the user to select an appropriate input from various fields in different cases. OUTPUT DESIGN Output design is defined as the design which is mainly required to create an efficient method to communicate within the company among the project leaders ad the group members,i.e.. the administrator and the clients. The project leader manages his clients while creating new clients and by assigning new project works to them and to maintain a record of the project validity and by providing folder level access to each client on the user side, this is all about output of the VPN in the system allowing it. When the application is executed for the first time, the application starts running. The internet explorer is used as the browser once the server is connected and started. The project rns on local area network i.e,lan,so the host or the machine with server will act as administrator whereas the other devices connected to the same local area network act as clients. 3. SCOPE OF PROJECT Text based Cyberbullying detection is able to learn robust features from representation in an efficient and effective way.these robust features are learnt by reconstructing the original input from corrupted ones.the new feature space can improve the performance of cyberbullying detection even with a small labelled training corpus.semantic information is incorporated into the reconstruction process via the designing of web application which avoids cyberbullying. 4. METHODOLOGY SYSTEM DESIGN AND DEVELOPMENT INPUT DESIGN In the life cycle of software development, the input design is very important where the developers have to pay a little more attention. The data is fed into the application very accurately by the input design. The designing of the inputs must be so effective so that errors would be reduced. The front end or the input design is designed in order to have a validation control over input limit. Admin IMPLEMENTATION Admin module is one among the modules involved in the project, where the admin himself has to register first as an admin and them login wit valid username and password. If the login is successful, he can perform various operation like adding filters, viewig all users and authorizing them, viewing all the friend requests and responses,viewing all posts and comments and also to send an alert message if any cyberbullying action is initiated by any user. Viewing and Authorizing Users Here, the admin can view all the user details and if wanted can authorize the registered users and can give them permission to login and use the application where the user has to provide his details like User Name, Address, Id and mobile number. Viewing all Friends Request and Response Here, the admin has all rights to see the friend request and response history of any user. The details such as the requested user name and image, and the username and the image of the user to whom the request was sent, the date and status of the reqest if it is accepted or rejected. Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 320

347 Sowparnika shree N, et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, Add and View Filters This is the major module which is important, where the admin can add filters, i,e bag of words. The filters can be of various types like violence, vulgar, offensive, hate and sexual as various categories. View all posts Here, the admin can view all the posts added by the users with the details of the post and details of the user who had posted the post. The user can view his or her friend s post and comment on the post. Here, the user must make sure of not using any cyberbullying words in the comment section. If the user uses cyberbullying words to comment, then the message doesn t get delivered to the other end, instead the user gets an alert message why the comment is not posted. The alert message also consists of to which category and the count of cyberbullying words used by the user in the comment. Flow Chart 1: User Detect Cyber Bullying Users Here we come to the main part of the admin s operations, where admin can see all the cyberbullying users and the cyberbullying words used by those users. The admin makes a list of the users who had commented on a post using cyberbullying words to detect and filter. The admin makes a count of how many bullying words are used by an user and what are the categories used. Find Cyber Bullying Reviews Chart The admin can view all the posts with number of cyber bullying comments posted by users for a particular post. User This is a module where n numbers of users are present. User as usual has to register to the application before using the application by entering his details and after the registration, the user can login only if he is authorized by the admin. The user after successful login can perform various operations such as searching for friends, sending friend requests, posting images, adding comments to their own post and even to others posts, replying for other s comments for her post etc.. Flow Chart 2 : Admin Viewing Profile Details, Search and Request Friends The user can view his or her own profile, can set a profile image for their accout, the user can find friends by searching for them by their username and can send a friend request and can accept if any request is sent to them. Add Posts The user can post anything like images of their own or any product for promotion by giving few details of the post such as title of the post, purpose and description. View and Comment on Your Friends Post Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 321

348 Sowparnika shree N, et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, program. As shown by unit testing, the integration testing demonstrate the components of individual satisfaction that combined the components of testing is correct and consistent. The main aim of integration testing is to expose the problems that arise from the combination of components. Functional testing Functional testing are accessible as described by the business and technical requirements, system documentation, and user manuals. Functional testing will produce the systematic demonstrations. Functional testing focus on the following objects: Valid Input : determined classes of valid input must be accepted. Invalid Input : determined classes of invalid input must be rejected. Functions : determined functions must be exercised. Output : determined classes of application outputs must be exercised. Systems/Procedures: interfacing systems or procedures must be requested. Functional testing will focus on requirements, key functions, or any other special test cases. In addition, functional testing has determined Business process flows from systematic coverage pertaining along data field, predefined processes, and positive result processes must be included for testing. Before functional testing is complete, additional tests are determined and powerful value of present tests are identified. TYPES OF TESTS Unit testing TESTING Unit testing involves the internal program logic to validate the design of the test cases functioning properly, and the program will give accurate result or outputs. The individual software units of the application is the testing process as it is in don't after the completion of an individual unit testing before integration and the decision branches and internal code flow must be validated. This relies on knowledge of its construction and is invasive so, it is a structural testing. The basic tests at component level and test a specific business process, application, and/or system configuration is performed by unit testing. Unit testing make sure that each unique path of a business process performs correctly for the documented specifications and contains clearly defined inputs and excepted results. Integration testing Integration testing involves the design process of integrated software components to determine if they actually run as one System Testing System testing tests a configuration to make sure the known and predictable results and it ensure the entire integrated software system meets requirements. Configuration oriented system integration test is one of the example of system testing. System testing is basically based on the descriptions process and flows, emphasizing pre-driven process links and integration points. White Box Testing White Box Testing is a testing in which the tester has the complete knowledge of the internal workings, structure and language of the software, or minimum to it's purpose. White box testing is used where the black box testing cannot reach its level. TESTING METHODOLOGIES The following are the Testing Methodologies: o Unit Testing. o Integration Testing. o User Acceptance Testing. o Output Testing. o Validation Testing. Unit Testing Unit testing looks up verification effort on the minor unit of Software design which is the module. Unit testing follows particular paths in a module s control Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 322

349 Sowparnika shree N, et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, structure to make sure the total coverage and highest error detection. unit testing will look after for each module independently, making sure that it functions correctly as a unit. So, the name given as Unit Testing. All important processing path are tested for the expected results. All error handling paths are also tested. Integration Testing Integration testing directs the circumstance associated with the two difficulties of verification and program constructions. Once the software has integrated than a set of high order tests are conducted. One if the main aim of this type of testing is to take all the modules of unit tests and build a structured program which is dictated by design. at the present time of developing and can make any modification wherever required. The developed system will provide a user friendly interface that can easily be understood even by a person who is new to the system. Output Testing After the validation testing is performed, the next procedure for testing is output testing of the proposed system, since no system could be useful it does not produce the required output in the specified format. Asking the users about the format required by them they will test the outputs generated or displayed by the system under consideration. So the output format is considered in 2 ways one is on screen and another in printed format. The following are the types of Integration Testing: 1)Top Down Integration In this method the modules are integrated from top down and the method is an incremental approach to build the construction of structured program which is dictated by design. Beginning with the main program module, modules are integrated by moving downward by the control hierarchy. The main program module is lesser than the modules which are comprised into the design in either a depth first or breadth first manner. The software has been tested from main module and individual stubs are replaced when the test proceeds downwards. 2. Bottom-up Integration In this method the modules are integrated from bottom up and the method starts from the construction and testing with the modules at the lowest level in the structured program. Modules are subordinate to a specified level which always available and the requirements for stubs are eliminated. The bottom up integration approach may be executed with the following steps: The low-level modules are combined into clusters and perform a specific Software sub-function. A driver (i.e.) the control program for testing is written to coordinate test case input and output. The cluster is tested. Drivers are removed and grouped as rising in the working program. The bottom up approaches will test each module individually and then each module is integrated with a main module and tested for functionality. OTHER TESTING METHODOLOGIES User Acceptance Testing User Acceptance testing is a testing where it is a key factor for the success of any system. User consider the system s to test for the user acceptance by making all the time keeping in touch with the intended system for the users VALIDATION CHECKING Validation checks are carried out on the following disciplines: Text Field: The text field which included in the validation checking can contain only the number of characters lesser than or equal to its size. The text fields are alphanumeric in some tables and alphabetic in other tables and it cannot extend its specified size. If any changes in entry or incorrect entry then flashes an error message due to it's intrinsic nature. Numeric Field: The numeric field can contain only numbers from 0 to 9 as the name numeric. An entry of any character flashes an error messages due to it's intrinsic nature. The individual modules in the numeric field are checked for accuracy and what it has to perform in the validation checking. Each module in the numeric field are subjected to test and run along with sample data. The modules which are tested separately are integrated into a single system. Testing involves the execution of the real data information which is used in the program. The survival of any program s fault is deduced from the output. The testing should be planned before itself so that all the requirements are individually tested. A successive test gives out the fault for the wrong data and build output and remove the errors in the system. Preparation of Test Data Preparation of test data plays a critical role in the system testing field. It takes all the various kinds of test data to test. Once the preparation of test data is completed the system under study is tested using that test data. During testing process for the system by using test data, errors are again uncovered and corrected by using above testing steps and corrections are also noted for future use. Using Live Test Data: Live test data are actually extracted from organization files. After a system is partially constructed, programmers or analysts will usually ask users to set data Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 323

350 Sowparnika shree N, et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, from their common activities. Then, the programmers or analysts uses this data as a way to partially test the system. In other occurrence, programmers or analysts bring out a set of existing data from the files and undertake themselves. It is difficult to obtain live data in an ordered way to conduct exact testing. It is realistic data in which it will show how the system will perform for the typical processing requirement, assuming that the live data entered are in fact typical. Such data usually will not test all combinations or formats that will enter the system. This bias towards the typical values that does not provide a true systems test and also ignores the system failure. Using Artificial Test Data: Artificial test data are created mainly for testing purpose, since all combination of formats and values can be generated to test. In other words, the artificial data, it can be prepared quickly by generating a data utility program in the information systems department, make possible the testing of all login and control paths through the program. The artificial test data is used by the most effective test programs which generated by common users other than who wrote the programs. An independent team of testers formulates a testing plan, using the systems specification. The package Virtual Private Network has satisfied all the requirements specified as per software requirement specification and was accepted. USER TRAINING User training is required to educate whenever a new system is being developed. It is required to educate them to understand about the working of the system for those who have designed the primary working of the system. For this purpose the working of the project has been demonstrated by the users. It s working is easily understandable for those who have good knowledge about computers. Dataflow diagram: Fig:Class diagram: CONCLUSION This paper addresses to text based cyberbullying detection problems, here the web application using various soft wears as the specialized representation learning model for cyberbullying detection. In addition to this word embedding have been used to automatically expand and refine bullying word lists that is initialized by domain knowledge. Cyberbullying is the huge problem in the societies that are advanced enough to have the technologies to connect with other people online and it not easily fixable. REFERENCES 1) M. Kaplan and M. Haenlein, Users of the world, unite! the challenges and opportunities of social media, Business horizons, vol. 53, no. 1, pp , ) R. M. Kowalski, G. W. Giumetti, A. N. Schroeder, and M. R. Lattanner, Bullying in the digital age: A critical review and metaanalysis of cyberbullying research among youth ) M. Ybarra, Trends in technology-based sexual and non-sexual aggression over time and linkages to nontechnology aggression, National Summit on Interpersonal Violence and Abuse Across the Lifespan: Forging a Shared Agenda, ) B. K. Biggs, J. M. Nelson, and M. L. Sampilo, Peer relations in the anxiety depression link: Test of a mediation model, Anxiety, Stress, & Coping, vol. 23, no. 4, pp , ) G. Gini and T. Pozzoli, Association between bullying and psychosomatic problems: A meta- Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 324

351 Sowparnika shree N, et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, analysis, Pediatrics, vol. 123, no. 3, pp , ) S. R. Jimerson, S. M. Swearer, and D. L. Espelage, Handbook of bullying in schools: An international perspective. Routledge/Taylor & Francis Group, ) A. Kontostathis, L. Edwards, and A. Leatherman, Text mining and cybercrime, Text Mining: Applications and Theory. John Wiley & Sons, Ltd, Chichester, UK, ) J.-M. Xu, K.-S. Jun, X. Zhu, and A. Bellmore, Learning from bullying traces in social media, in Proceedings of the 2012 conference of the North American chapter of the association for computational linguistics: Human language technologies. Association for Computational Linguistics, 2012, pp ) Q. Huang, V. K. Singh, and P. K. Atrey, Cyber bullying detection using social and textual 10) analysis, in Proceedings of the 3rd International Workshop on Socially-Aware Multimedia. ACM, 2014, pp ) D. Yin, Z. Xue, L. Hong, B. D. Davison, A. Kontostathis, and L. Edwards, Detection of harassment on web 2.0, Proceedings of the Content Analysis in the WEB, vol. 2, pp. 1 7, Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 325

352 Volume 9, Special Issue No. 3, May 2018 International Journal of Advanced Research in Computer Science (ISSN: ) CONFERENCE PAPER Available Online at SECURITY SYSTEM FOR EXQUISITE TREES Pragya Verma School of C&IT, REVA University Bengaluru Rajitha M School of C&IT, REVA University Bengaluru Rishika SG School of C&IT, REVA University Bengaluru Rumaan Shaik Sheriff School of C&IT, REVA University Bengaluru Priyadarshini R School of C&IT, REVA University Bengaluru Abstract- We read in newspapers about smuggling of high cost trees like teak wood, sandal wood etc. These trees are used in the field of medical as well as in the field of cosmetics. Since huge amount is involved in selling and buying of these tree woods, a lot of incidents of cutting the trees and smuggling takes place. The importance of these exquisite trees is formidable compared to the vast species of fauna in existence, and threats of forest fires have also been rising with reports of wild fires rising beyond control and destroying precious wildlife. The main purpose of this project is to save trees and maintain the forest environment. Keywords- Arduino Nano, IR sensor, Temperature sensor, Moisture sensor, WiFi. I. INTRODUCTION The thieft/smuggling of sandal wood trees which are mostly carried out in borders of Tamil Nadu and some others regions in India. The problem which is mostly observed is that, there is no system or medium to detect these illegal cutting of trees or to notify authorities about the rise in temperatures or drops in water level below sustainable amounts, threats of forest fires have also been rising due to the fact that these fires spread to extents beyond control before anyone has been aware of, Considering this problem a system is designed to protect the nature. II. OVERVIEW OF PROPOSED DESIGN The main idea in this project is to design a sensor network system which consist of the hardware components such as arduino nano, WiFi, Temperature sensor, IR sensor, Moisture sensor, jump wires. The code is programmed in Embedded C and is compiled in Arduino IDE which is easy to upload the code to the board. The cloud used here is the Thinkspeak which is an open source cloud. 1. Arduino nano- It is a small panel of 8bit ATmega328 microcontroller with 32 pins. It is also a breadboard friendly controller. 2. WiFi- WiFi is commonly used for connecting devices in wireless mode. 3. Temperature sensor- This sensor is used to measure hotness and coldness of an object. This senses -55 C to 150 C temperature. 4. IR sensor- It is the tilt sensor which is used to measure slope or tilt within a limited range of motion. 5. Moisture sensor- A moisture sensor, senses and measures the moisture content and reports the relative humidity in the air. 6. Jump wires- These are an electric wires with two connecting pins on each node. 7. Embedded C- This is an extension of C programming language. The programs are embedded as a part of complete device. 8. Arduino IDE- The open-source Arduino Software (IDE) makes it easy to write code and upload it to the board. 9. Thinkspeak- "ThingSpeak is an open source Internet of Things (IoT) Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 326

353 Pragya Verma, et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, application and API to store and retrieve data from things using the HTTP protocol over the Internet or via a Local Area Network. 10. Solar Panel it is a small solar power supply for the components to work as individual unit with an independent source of power III. RESULTS AND DISCUSSION. In this section experimental results are observed and Discussed. Fig 3: graph of tilt sensor Fig 1: hardware connections Fig 4:graph of temperature sensor Fig 2: testing the model The graphs obtained by all the three sensors are as follows: Fig 5: graph of moisture sensor The fig 1 shows the hardware components used in the present work. In fig 2 the working of the model is explained where the sensors sense and gives the output to the cloud. The output is obtained in the form of graphs which is shown in fig 3, fig 4, fig 5, these graphs indicate change in angle of a tree, raise in temperature and humidity in the environment. The cloud ie thinkspeak is where the graphs are analysed by the user. IV. CONCLUSIONS & FUTURE SCOPE Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 327

354 Pragya Verma, et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, BE THE STRENGTH, PROTECT WILDLIFE. In this way we are developing this system to restrict poaching of trees in the forest and provide instant notification of rise in tempretures to take immeditate actions against forest fires. This system is implemented only for the high cost trees and environmental balance is maintained. The future scope of work is implemented of multi node network and include validating of ADXL sensed data by a microphone. ACKNOWLEDGMENT We are grateful to the Members of our institution in Bangalore for providing their technical encouragement and supporting our efforts in the case study for providing a optimized solution for anti-poaching of trees in forest by using sensor networks. REFERENCES 1. Research Article Akshay Sonwane, Design and development of wireless sensor node for anti poaching IJPRET, 2016; [1]. 2. M Gnana Seelan and Ch A S Murty An Integrated Solution For Both Monitoring and controlling for Automation using wireless sensor Networks: A case study International Journal of Network Security & Its Applications (IJNSA), Vol.5, No.1, January 2013[2]. 3. Lu De Yang Louisiana State University and Agricultural and Mechanical College Implementation of a wireless sensor network with Z430-RF2500 development tools and MSP430FG4618/F2013 experimenter boards from Texas Instruments. 4. F.G. Nakamura, F.P. Quintao, G.C. Menezes, and G.R. Mateus. An Optimal Node Scheduling for flat Wireless Sensor Networks. 5. Lozano, C., & Rodriguez, O. (2010). Design of Forest Fire Early Detection and Electrical Engineering (OJEEE). 6. Awang, A., &Suhaimi, M. H.(2007). RIMBAMONC A Forest Monitoring System Using Wireless Sensor Networks. 7. M. Tubaishat, S. Madria, Sensor Network An Overview [J]. IEEE Potentials, May 07, Malik Tubaishat and Sanjay Madria Sensor networks: an overview 2003.[4] Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 328

355 Volume 9, Special Issue No. 3, May 2018 International Journal of Advanced Research in Computer Science (ISSN: ) CONFERENCE PAPER Available Online at FILE SYNCHRONIZATION BETWEEN DIGITAL SAFES Shah Vishal, REVA University, Bengaluru, India Shweta Hiremath, REVA University, Bengaluru, India Sushma V, REVA University, Bengaluru, India Vijayalaxmi REVA University, Bengaluru, India Manjunath P C REVA University, Bangalore, India Abstract-The main concern of Cloud storage solutions is to provide the availability of mobility needs and different devices to end users. To provide such services to end user which resolve to a challenge for cloud services. This challenge is solved by developing a secure framework which ensures that the end user data is continuously and automatically synchronized when user moves from one device to another device. Additionally the framework provides with property of secured sharing the data of end user with specific user. To provide this framework we propose a protocol known as synchronization protocol through which we can detect the changes of two different file versions which are located in a system in variety of devices. To detect the changes between two different file versions in a system we adopt Hierarchical Hash Tree and to provide the sharing of data for user we adopt Web Socket protocol which increases the efficiency of synchronization protocol. Keywords: File synchronization, Web socket, Hierarchical hash tree, cloud storage service, synchronization protocol. 1. INTRODUCTION Nowadays each and every individual having multiple devices like Smartphones, desktop computers, laptops, iphones etc which are associated with different operating system even the file version will differ for variety of devices. To maintain the data consistency of end user and even to keep changes between two different file versions used by end user in same device or in different device. So we adopt Hierarchical hash tree which detects the changes between two different file versions in a system or different device. Synchronization protocol is adopted to maintain the data consistency of user when the user moves from one device to another additionally probative value is added to cloud storage which helps to maintain the data secured from malfunctioning. Many cloud storage solutions lack in maintaining the data transparency between client side and cloud side. The process and management of data and synchronizing is maintains transparency with client and cloud side by adding probative value to cloud storage solutions. By adding probative value to cloud storage solutions introduced by Safe box as a service[1]. The probative value which mainly helps us to keep transparency between cloud side and client side, even provides security, integrity and quality to store the user data in trusted third party like drop box.for file synchronization automatically many services provided different parties like drop box, drives etc.. Networking protocol are used to ensure data synchronization and the data transfer. The infrastructure sends messages to the concerned connected clients. The architecture notifies to user through a message that come changes have occurred and the synchronization must start. For example, for data transfer Dropbox uses HTTP and for notification long HTTP. Even Google chrome synchronization uses HTTP for data transfer and the notification exploits an existing XMPPbased Google Talk server. To ensure data replication in multiple solutions REST API is used such as CouchDB. In HTTP protocols mainly REST API s are used. To maintain the efficiency of synchronization protocol we must choose best protocol for data exchange. We adopt Web Socket API and protocol to reduce the size of date exchange between both end points. 2. OVERVIEW OF ARCHITECTURE DIAGRAM. The main goal of our paper is to develop a framework which is secured and even ensures the synchronization of data between client side and cloud side by adding probative value to cloud storage solutions. Further in our architecture diagram Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 329

356 Shah Vishal et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, we can describe the interchange of data between two different end users. The architecture diagram can be illustrated in different layers such as:- System Architecture diagram: Storage layer of client: In this architecture the main characteristic is non-proprietary were the client data is stored securely in client digital safe. Using HTML5 local storage API the client is securely stored which is considered as additional security by using HTML5 API.In fact, this gives an additional confidential security to client data by encryption method, integrity of data and even integrity of metadata which is added into existent APIs. Layer of Application: In this layer mainly the interaction of client side and with layer of application takes place for requests to perform which is mainly used by different interfaces of application. The first module, Digital Safe, implements the AFNOR specifications using system file of application interface the data which is stored locally can be managed[1,2]. The second module, is synchronization job which mainly detects the operations of users applied to client digital safe and to keep record of all user s operation. Synchronization job even maintains the interchange of messages between client digital safe and cloud digital safe by using synchronization digital secured protocol. Control Layer for Synchronization : To perform best efficient application we choose WebSocket protocol, were this protocol communicates in an bidirectional way between cloud and remote side with securely. The requests and responses of synchronization job is handheld by management server of synchronization the server even handles all conflict resolution. The WebSocket protocol sends a notification to digital safes about all changes whichever are propagated. This paper is astonished by using Hierarchical Hash tree.storage Layer for Server: The Digital Safe s of cloud is a astonished with architecture of standard which ensures a secured framework for documents associated with sensitive storage. Such framework is best outfit for both local digital safe and even security of cloud by handling all challenges of cloud storage. This Digital Safe of cloud is associated with three components: Server with metadata, storage servers and Manager with proof. 3. ABBREVIATIONS USED Account Operations: Account operations module provides the following functionalities to the end users of our project. Register a new account Login to an existing account Logout from the session Edit the existing Profile Change Password for security issues Forgot Password and receive the current password over an Delete an existing Account Synchronization Job: Firstly the user will be allowed to mention the local data path then we can start with synchronization job which will be running in background simultaneously watch job take place and checkin, checkout operations takes place, the cloud storage solutions maintains the efficient performance of watch job. New File: Here the end users will be enable to write a new file Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 330

357 Shah Vishal et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, on to the data storage layer. There are two different data storage layers. The first one is the local data path and the second one is the cloud data storage. The end user upon writing a new file, the data goes and sits in the local data path. The synch job then captures the event and uploads the file to the cloud data storage as well with the help of the watch job which was listening to all these types of events. The user must make sure he/she have started the synchronization job before writing a new file. The new file written by the user will be stored as a plain text in the local data path. However, the entire file will be encrypted using Advanced encryption standard (AES) algorithm and then will be written into the cloud data storage. We will be making use of Dropbox cloud data storage for demonstration this project research. My Files: Here, the end users will be able to perform the data read operations on the data he/she had uploaded to the cloud in the previous module. The end users will be provided with an interface where they can see the list of all the files they had written on to the local data path and hence to the cloud data storage. The files uploaded by the users will first be decrypted using the Advanced Encryption Standard (AES) algorithm and then will be allowing the users to download it into the local file system. The users will also be able to update the contents of the files and thus triggering the update event which was been listening to by the watch job and hence the cloud data storage also will be updated with the same changes. The user will also be able to perform the file delete operation on the existing file. Sharing: This module allows the end user to share the file(s) he/she have owned with any of the other registered users. The user will be provided with an interface where he/she can see the list of all other users registered in our portal. The client can then select any one of the user and share his/her file with that specific user. Upon sharing the files, the user with whom the file has been shared will be able to see that file(s) under the My Files section upon logging in. The user with whom the file has been shared, will also be able to perform the file read operation, file update operation, and the file delete operation. 4. Account Access Layer Account operations module provides the functionalities to the end users of our project are register new account, login to an existing account, logout, edit profile, change password for security issues, forgot password, delete an existing account. 5. Synchronization Module. Here, the user will be able to specify the local data path on to which the data stored on cloud should be synchronized. After specifying the local data path, the end user will be able to start the synchronization job. The synchronization job is a daemon process which will keep on running in the background and initiates another job called watch job. The watch job is the one which keeps listening to any events occurring on the local data path specified by the user. The watch job in specific will be listening to files. Delete event path to the cloud data storage is called as the checkin process. The watch job upon encountering any such event will make sure the cloud data storage also been updated with the same changes happening in the local data path. The process of downloading the files from the cloud storage into the local data path is called checkout process, and the process of uploading the changes from the local data 6. Cryptographic Layer In this layer the adoption of AES(Advanced Encryption Standard) algorithm is applied. Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 331

358 Shah Vishal et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, The end users are allowed to write a new file on to the data storage layer, The cloud storage solutions helps to perform synchronize job. The entire file will be encrypted using Advanced encryption standard (AES) algorithm and then will be written into the cloud data storage. Dropbox storage is chosen to perform these jobs on cloud storage. The end users will be able to perform the data read operations on the data he/she had uploaded to the cloud in the previous module and even with specified user the end user can share the file. about conflict resolution in architecture diagram. References [1] M. Msahli and A. Serhrouchni, Sbaas: Safe box as a service, in 9th IEEE International Conference on Collaborative. Computing: Networking, Applications and Worksharing, Nov [2] Afnor groups. [Online]. Available: [3] M. D. N. Jain and R. Tewari., Taper: Tiered approach for eliminating redundancy in replica synchronization, in. In Proc. of the USENIX Conference on File And Storage Systems, [4] Dropbox Conclusion and Future Work Irrespective of various devices used by end user the maintenance of user data securely is more important, resolved in this paper by developing a secure framework which maintains the user data securely and even allows the end user to share the data with specific users. By using synchronization protocol the end user is safely stored in cloud storage were as the ID associated with particular is stored in user local data path. This protocol which even helps to perform all file input and output operations additionally allows to share the file and even if there is any modification then the cloud storage solutions notifies through an . This framework mainly consumes less storage space overcome by existing problem, uses efficient bandwidth for synchronization of files. The results of this framework is mainly reduces the time used to detect the changes between two different file versions in a system or in a different devices, proportional reduces the time taken to perform file synchronization. In our framework the conflict resolution problem is caused in our architecture which can be further resolved in future work [5]J. H. Howard. Using reconciliation to share files between occasionally connected computers. Proceedings IEEE Workshop on Workstation Operating Systems, Napa,California, October 1993, pages [6] SyncML : The new era in data synchronization. [7] H. Yan, U. Irmak, and T. Suel, Algorithms for lowlatency remote file synchronization, in The 27th Conference on Computer Communications. IEEE INFOCOM 2008, April [8] A. Tridgell and P. Mackerras, The rsync algorithm. technical report trcs , department of computer science, in The Australian National University, Canberra, Australia, [9] Benjamin, C.Pierce, and J. Vouillon, What s in unison? a formal specification and reference implementation of a file synchronizer, in Tech. rep. MS-CIS , Department of Computer and Information Science, University of Pennsylvania, [10] C. Liang, L. Hu, Z. Lei, and J. Wang, Synccs: A cloud storage based file synchronization Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 332

359 Shah Vishal et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, approach, Jul [11] I. Drago, M. Mellia, M. M. Munafo, A. Sperotto, R. Sadre, and A. Pras, Inside dropbox: Understanding personal cloud storage services, in Proceedings of the 2012 ACM Conference on Internet Measurement Conference, ser. IMC 12, [12] R. Al-Ekram, A. Adma, and O. Baysal, diffx: An algorithm to detect changes in multi-version xml documents, in Proceedings of the 2005 Conference of the Centre for Advanced Studies on Collaborative Research, ser. CASCON 05, [13]. S. Chawathe, A. Rajaraman, H. Garcia-Molina, and J. Widom, Change detection in hierarchically structured information, in Proceedings of the 1996 ACM SIGMOD International Conference on Management of Data, ser. SIGMOD 96, [14] S. S. Chawathe and H. Garcia-Molina, Meaningful change detection in structured data, in Proceedings of the 1997 ACM SIGMOD International Conference on Management of Data, ser. SIGMOD 97, [15] J. C. Anderson, J. Lehnardt, and N. Slater, Couchdb the definitive guide. [Online]. Available: [16] M. jemel and A. serhrouchni, Security assurance of local data stored [17] N. Jain, M. Dahlin, and R. Tewari, Taper: Tiered approach for eliminating redundancy in replica synchronization, in In Proc. of the USENIX Conference on File And Storage Systems, 2005 Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 333

360 Volume 9, Special Issue No. 3, May 2018 International Journal of Advanced Research in Computer Science (ISSN: ) CONFERENCE PAPER Available Online at SECURITY CONCERNS IN CLOUD COMPUTING Mir Abdul Samim Ansari School of C&IT, Reva University, Bangalore India Pooja Mahaling School of C&IT, Reva University,Bangalore India Gopal K.Shyam Associate Professor, School of C&IT, Reva University, Bangalore Shewtha S Patil School of C&IT, Reva University,Bangalore India Menakarani R School of C&IT, Reva University,Bangalore, India Abstract: Cloud computing is a revolutionary way of storing and accessing data with five essential characteristics, three service models, and four deployment models. Businesses have realized the tremendous potentiality and benefits of cloud computing and have accepted the technology, but still a small amount of scepticism hovers around. In defiance of its potential characteristics, the organizations risk their sensitive data by storing it in the cloud. In this paper, we have identified various privacy and security challenges associated with the novelty of cloud computing. The security and privacy challenges listed in this paper perceives demand for implementation of sophisticated technologies to deal with them. Keywords: Cloud Computing Security, Network security, and Distributed Networks Security I. INTRODUCTION In the evolution of distributed systems, clouds have become a new trend and grid computing being the forerunner. Cloud computing was introduced in the industry by the companies like Microsoft, Sales force, Amazon, Google and Yahoo. Cloud computing has centralized server resources in a distributed architecture such that it can provide on-demand service or resources on a scalable platform. Cloud computing is based on pay-on-usage model for allowing convenient and provide access to a shared pool of configurable resources [1][4][5]. The pay-on-usage characteristic of cloud enables the cloud service providers offer services to the customers to utilize and create their own web services. Generally the cloud purveys three services, i.e., to lease a business application (Software as a service or SaaS), to lease computing and storage (Infrastructure as a service or IaaS), and to build a remote platform and customize according to business processes (Platform as a service or PaaS) [1][2][3][4]. The organizations prefer an IT solution that comprises cloud computing for various reasons as they just have to pay with respect to resource consumption. The management and control of the cloud infrastructure is encapsulated from the users, therefore, the burden of organization s infrastructure management is diminished. Cloud has four deployment models through which it can offer services to the costumers: Public cloud, Private cloud, Hybrid cloud, and Community cloud. A deployment models that allows the public to use the resources dynamically and on self-service basis over the internet hosted by a third party is called the public cloud. The data is stored in the cloud provider s data center and the provider is accountable for maintaining it. The public clouds are comparatively less secure as they are prone to malicious attacks. Private cloud provides a discrete and reliable cloud environment that can be operated only by an authorized client. The authorized client has the privilege to configure and manage according to their requirements. An additional advantage of this model is that the availability of resources to all the departments is enhanced. The private cloud is secure as it is confined only to an authorized organization. A hybrid cloud provides IT resources through a combination of public and private cloud. Hybrid clouds are private clouds that are managed centrally, provisioned as a single unit through a restricted secure network, and linked to other cloud services [1]. The community cloud infrastructure purveys the resources to be shared among a group of organizations for a purpose. In this, the cloud can be managed by the organizations themselves or given to the third party to manage it. These clouds have an agreement between the related organizations. 1. CLOUD SECURITY COMPOSITION The confidentiality and integrity of the users data within the cloud is a huge responsibility and a major concern. The users might store valuable information in cloud are not aware of what is happening to it in the cloud, therefore, the information has to be safeguarded without the user s privacy Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 334

361 Mir Abdul Samim Ansari et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, being compromised. The security of data in cloud is becoming extensively demanding with the growing technology as the loopholes in it is targeted by the attackers to gather user s information in cloud [1][6][9]. The cloud service provider should have the important security components such as the SLA monitor, load balancer, Resource monitor, Pay-per-use monitor, etc. The protection of data, attack on interfaces, attack on software and hardware virtualization, etc., are a few security issues in cloud. The virtual machines in cloud are created using the hypervisor, also known as Virtual Machine Monitor (VMM). The VMM creates virtual resources depending on the capacity of the underlying physical resources. A few security issues at the virtual machine layer are: virtual machine sprawl, identity management, access management, hypervisor protection, visibility lack of virtual network, etc. Cloud Security Alliance and Open Security Architecture are organization that work on the security of cloud computing. The focus on the security issues and foster practicing the best vulnerability mitigation procedures. The other standards that focus on cloud security are Internet Engineering Task Force and Storage Networking Industry Association [8]. 2. CLOUD COMPUTING CHALLENGES Protection and privateness square measure the two primaries worries concerning cloud computing. Within the cloud computing international, the virtual surroundings cloud clients get admission to computing energy that exceeds that contained inside their physical international. to travel into this virtual environment a user is to transfer records within the course of the cloud. There for many issues of safety arises [4] [7] [8] [16]. Figure 1: Challenges of Cloud Computing 3.1 Information Security It is attached shielding the confidentiality, integrity and accessibility of statistics irrespective of the shape the data may to boot take [9]. Losing control over data: These can be extensively arranged as dread of losing control The cloud world is altogether different from onprem corporate processing. The cloud specialist organization claims the physical premises and controls access to the offices. The supplier possesses the equipment, programming, and system get to, none of which are committed to any single client. Multiple clients could be utilizing the assets (multioccupancy) in the meantime. Common specialized and activities principles apply over all clients. SaaS applications are pre-characterized and might be configurable yet have restricted customization. Data Uprightness: Information uprightness is ensure that realities alterations least complex in response to approved exchanges. For example, if the supporter is chargeable for building and approving database questions and the server executes them aimlessly, the interloper will for the most part be fit for control the client viewpoint code to accomplish something he has authorization to do with the back-end database. Typically, which means the gatecrasher can read, change, or erase records voluntarily [3]. The basic stylish to ensure certainties honesty does not yet exists [8]. On this new universe of processing clients are all around required to just acknowledge the basic commence of acknowledge as valid with. In truth, a couple have guessed that concur with is the greatest concern managing distributed computing [7]. Danger of Seizure: In an open cloud, you are offering figuring assets to different organizations.. Uncovering your information in a domain imparted to different organizations could give the administration "sensible reason" to grab your advantages on the grounds that another organization has disregarded the law. Basically in light of the fact that you share the earth in the cloud, may put information in danger of seizure [4][8]. The main security against the danger of seizure for client is to encode their information. The subpoena will urge the cloud supplier to turn over client's information and any entrance it may have to that information, however cloud supplier won't have client's entrance or unscrambling keys. To get at the information, the court should come to client and subpoena client. Subsequently, client will wind up with a similar level of control client have in his private server farm [4][16]. Disappointment in Supplier's Security: Disappointment of cloud supplier to appropriately secure segments of its foundation particularly in the upkeep of physical access control brings about the bargain of supporter frameworks. Cloud can contain various substances, and in such a setup, no cloud can be more secure than its weakest connection [3][7]. It is normal that client must put stock in supplier's security. For little and medium size organizations supplier security may surpass client security. It is by and large troublesome for the points of interest that assistance guarantee that the correct things are being done [3][7]. Cloud Supplier Goes Down: This situation has various variations: chapter 11, choosing to take the business toward Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 335

362 Mir Abdul Samim Ansari et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, another path, or an across the board and expanded blackout. Whatever is going on, endorser chance losing access to their generation framework because of the activities of another organization. Supporter likewise hazard that the association controlling endorser information won't not ensure it as per the administration levels to which they may have been beforehand dedicated [4]. The main choice client have is to picked a moment supplier and utilize mechanized, consistent reinforcements, for which numerous open source and business arrangements exist, to ensure any present and recorded information can be recouped regardless of whether client loud supplier were to vanish from the substance of the earth [4]. 3.2 NETWORK PROTECTION The important network security categories are: Disseminated Refusal of Administration assaults: are specific kinds of Foreswearing of Administration assault. Disseminated Refusal of Administration assaults have turned into an instrument of decision for noxious associations around the world. In a DOS assault, the expectation is to a web application inaccessible to its proposed clients, more often than not by flooding the objective application with counterfeit activity or solicitations, which can over-burden frameworks and keep authentic movement from achieving the application server. In a Disseminated Refusal of Administration assaults assault, the assailant utilizes various sources to dispatch the phony activity normally tens or a huge number of traded off frameworks (referred to all in all as a botnet). This makes it hard to stop the assault by distinguishing and hindering a rundown of particular sources. Hence, Disseminated Refusal of Administration assaults can accomplish more harm than common Disseminated Refusal of Administration assaults assaults, by making your business-basic applications inaccessible to authentic clients for a more drawn out timeframe [14]. Man in the Center Assault: A technique for recognizing a man-in-the-center assault against correspondences between a customer gadget and a particular remote end point over a system, the strategy utilizing test programming introduced on the customer gadget, the technique including the test programming sending an association start ask for from the customer gadget over the system, coordinated to the remote end point, to at any rate halfway start a safe system association between the remote end point and the customer gadget, accepting at the customer gadget encryption accreditations sent to the customer gadget in light of the association start ask for, the test programming contrasting the got encryption qualifications and expected encryption certifications for the remote end point, and the test programming confirming that a man-in-the-center assault is available if the gotten encryption certifications do no match the normal encryption qualifications [13]. IP Satirizing: The gatecrashers imitate the IP address of a trusted host to get unapproved get to and send messages. To accomplish IP satirizing, the interloper should first execute different procedures to recognize the IP address of a put stock in have. Once the programmer distinguishes the IP address, the bundle headers can be altered and seems like it was sent by a confided in have. The gatecrashers imitate the IP address of a trusted host to acquire unapproved get to and send messages. To accomplish IP caricaturing, the gatecrasher should first execute different systems to distinguish the IP address of a put stock in have. Once the programmer recognizes the IP address, the parcel headers can be changed and seems like it was sent by a put stock in have. IP mocking is normally used to dispatch web assaults or to get unapproved access to PCs [15]. Port Filtering: Sending inquiries to servers on the Web with a specific end goal to acquire data about their administrations and level of security. On Web has (TCP/IP has), there are standard port numbers for each kind of administration. Port checking is additionally generally used to see whether a system can be traded off. In referencing the system this could be a neighborhood in your home or office or it could be the Web. A system is traded off of frameworks with addresses and on those frameworks you have administrations. The address is called an "IP Address" and the Administration could be numerous things however is essentially programming that is running on the framework and open over the system on a port number. It could be a web server, server or gaming server [8]. Packet Sniffing: Packet sniffing by other Tenants: Packet sniffing is listening (with software program) to the uncooked network device for packets that hobby you [4]. When that software sees a packet that fits certain standards, it logs it to a record. The maximum commonplace criteria for an interesting packet is one that consists of phrases like login or password [12][17]. It isn't possible for a digital example walking in promiscuous mode to receive or sniff site visitors that is meant for a one of a kind virtual example. At the same time as clients can place their interfaces into promiscuous mode, the hypervisor will not supply any visitors to them that aren t addressed to them [9]. Even two virtual instances which might be owned through the identical purchaser, located on the same physical host, cannot listen to each different site visitors. Assaults which include ARP cache poisoning do not paintings inside Amazon EC2. While Amazon EC2 does provide enough safety towards one patron inadvertently or maliciously attempting to view some other ought statistics, as a general practice client to encrypt sensitive visitors [9]. 3.3 General Security Issues They are more confused in a virtualized surroundings since you by and by must keep up music of security on levels: the physical host wellbeing and the virtual gadget security. On the off chance that the substantial host server's insurance will move toward becoming traded off, the greater part of the virtual machines living on that one of a kind host server are affected [20]. Example Separation: Seclusion ensuring that unique kind occurrences walking around the equivalent real device are Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 336

363 Mir Abdul Samim Ansari et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, remote from each extraordinary. Virtualization efficiencies in the cloud require virtual machines from different organizations to be co-situated on the indistinguishable substantial resources. Despite the fact that regular records focus security still applies inside the cloud condition, physical isolation and equipment fundamentally based security can't monitor contrary to assaults between virtual machines at a similar server [18]. Authoritative motivate section to is through the web as opposed to the over saw and compelled coordinate or onpremises association that is clung to inside the ordinary insights focus demonstrate. This development threat of presentation will require stringent following for changes in machine control and inspire passage to oversee confine [8]. Particular examples going for strolls at the indistinguishable contraption are segregated from each other by means of Xen hypervisor. Amazon is enthusiastic in the Xen People group, which guarantees insight of the stylish patterns. So also, the AWS firewalls live inside the hypervisor layer, among the real group interface and the example's virtual interface. All parcels need to by go through this buildup, therefore an illustration's buddies don't have any additional entrance to that occasion than some other host inside the net and can be dealt with just as they are on isolated physical hosts. The real Slam is isolated the utilization of comparable instruments [9][15]. Host running device: chiefs with a business venture need to get passage to the administration designs are required to us multi-segment validation to access reason manufactured organization has and those regulatory hosts are structures which are extraordinarily outlined, built, arranged, and solidified to shield the control flying machine of the cloud. All such inspire admission to logged and reviewed [12][11]. At the point when a worker never again has a business need to get to the control flying machine, the benefits and get right of section to those hosts and significant structures are disavowed [18]. Visitor working contraption: virtual occurrences are totally dealt with the guide of the client. Customers have finish root get to or regulatory control over cash owed, offerings, and projects. AWS does now not have any entrance rights to buyer times and can't sign into the guest OS [10][17]. AWS prescribes a base arrangement of assurance top notch hones alongside: shopper must impair secret word basically based access to based access to their hosts, and make utilization of some state of multi-segment verification to advantage get passage to their circumstances, or at a negligible endorsements principally based SSH show 2 get section to [9][13][15]. Furthermore, clients should utilize a benefit heightening component with running surfing a steady with-individual establishment. For example, if the visitor OS is Linux, in the wake of solidifying their illustration, they should use endorsement based SSHv2 to get right of passage to the advanced case, cripple far away root login, utilize order line logging, and utilize 'sodu' for benefit heightening. Clients should produce their own one of a kind key combines a decent method to ensure that they are exceptional, and never again imparted to different clients or with AWS [9]. AWS Multi-issue Validation (AWS MFA) is an extra layer of security that offers more grounded control over AWS account settings. It requires a substantial six-digit, unmarried-utilize code from a confirmation gadget in your real ownership promote on your in vogue AWS account qualifications sooner than get to is allowed to an AWS account settings. This is called Multi-issue Validation because of the reality components are checked before get right of passage to is conceded for you: supporter need to offer both their Amazon electronic mail-id and secret word (the primary "angle": something you perceive) AND the proper code from customer confirmation apparatus (the second "perspective": something you have) [13][9]. 3. CONCLUSION In conclusion, cloud computing is recently new technological development that has the potential to have a great impact on the world. It has many benefits that it provides to it users and businesses. For example, some of the benefits that it provides to businesses are that it reduces operating cost by spending less on maintenance and software upgrades and focus more on the businesses itself. But there are other challenges the cloud computing must overcome. People are very skeptical about whether their data is secure and private. There are no standards or regulations worldwide provided data through cloud computing. Europe has data protection laws but the US, being one of the most technological advance nation, does not have any data protection laws. Users also worry about who can disclose their data and have ownership of their data. But once, there are standards and regulation worldwide, cloud computing will revolutionize the future. REFERENCES [1] [2] Cisco White Paper, ns525/n s537/white_paper_c html, published 2009, pp [3] John Viega, McAffee, Cloud Computing and the Common Man, published on the IEEE Journal ON Cloud Computing Security, pp , August [4] George Reese, Cloud Application Architectures, First edition, O Reilly Media, April 2009, ISBN , pp. 2-4, [5] [6] computing1.htm. [7] John Harauz, Lori M. Kaufman, Bruce Potter, Data Security in the World of Cloud Computing, published on the IEEE Journal on Cloud Computing Security, July/August 2009, Vol. 7, No.4, pp Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 337

364 Mir Abdul Samim Ansari et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, [8] John W. Rittinghouse, James F. Ransome, Cloud Computing Implementation, Management, and Security, CRC Press, August 17, 2009, ISBN , pp , [9] Amazon White Paper, published June [10] Marco Descher, Philip Masser, Thomas Feilhauer, A Min Tjoa, David Huemer, Retaining Data Control to the Client Infrastructure Clouds, published on the IEEE, 2009 International Conference on Availability, Reliability and Security, pp [11] David Bernstein, Erik Ludvigson, Krishna Sankar, Steve Diamond, Monique Morrow, Blueprint for the Intercloud Protocols and Formats for Cloud Computing Interoperability, submitted to IEEE, 2009 Fourth International Conference on Internet and Web Applications and Services, pp [12] Liang-Jie Zhang, Qun Zhou, CCOA: Cloud Computing Open Architecture, published on IEEE, 2009 IEEE International Conference on Web Services, pp [13] Amazon White Paper, Introduction to Amazon Virtual Private Cloud, Available: published Aug 26, 2009, pp [14] Rajkumar Buyya, Chee Shin Yeo, Srikumar Venugopal, Market-Oriented Cloud Computing: Vision, Hype, and Reality for Delivering IT Services as Computing Utilities, grid Computing and Distributed Systems and Software Engineering, The University of Melbourne, Australia. [15] Jinesh Varia, Amazon Web Services, Building GrepTheWeb in the Cloud, Part 1: Cloud Architectures, Available: July 2008, pp [16] Jon Brodkin, Gartner: Seven Cloud-Computing Security Risks, Available: published July 2008, pp [17] IBM CIO White Paper, Staying aloft in tough times, April 2009, pp [18] Steve Hanna, Juniper Networks, Cloud Computing: Finding the Silver Lining, published 2009, pp [19] Manifesto, Open Cloud Manifesto, Dedicated to the belief that the cloud should be open, Available: published Spring 2009, pp-1-7. [20] Peter Fingar, Dot.Cloud: the 21 st century business platform built on cloud computing, First edition, Meghan-Kiffer Press, February 18, 2009, ISBN , pp Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 338

365 Volume 9, Special Issue No. 3, May 2018 International Journal of Advanced Research in Computer Science (ISSN: ) CONFERENCE PAPER Available Online at MULTIFACETED AUTHENTICATION FOR SECURE USER ACCESS TO CLOUD Naveen Chandra Gowda Research Scholar School of C&IT, REVA University Sunilkumar S. Manvi Director, School of C&IT, REVA University Abstract Distributed cloud computing is a developing, on-request web based innovation. It gives assortment of administration services over web, for example, programming, hardware equipment, information stockpiling and computing frameworks. The paper proposes a security checking framework which utilizes the multifaceted validation system and produces the secret word in numerous levels to get access to the cloud services. This framework is capable for frustrating Shoulder attack, Tempest attack, and Brute-force attack and many more which are available at customer side, with the utilization of robust strategies in the Graphical passcode. Keywords Cloud Computing Authentication, Graphical passcode, multifaceted authentication I. INTRODUCTION Cloud Computing is a web based innovation which gives assortment of administration services over web, for example, programming services, hardware equipment availability, information stockpiling and framework. Inside the cloud computing frameworks, the virtual environment allows the clients with high accessing capability that contained inside their own physical environment [2]. In such scenarios, too many security issues emerge as it contains numerous advancements including network systems, virtualization, working frameworks, resource reservation, process exchange administration, balancing of system or network load, concurrency control and memory administration [4]. It likewise incorporates data backup reinforcement and safe stockpiling of the backup media. Security is certain inside these capacities, but also rudimentary concerns exists that need consideration. Distributed cloud computing is turning into an enticing focus for cybercrime. If not all cloud suppliers supply satisfactory safety efforts, at that point these mists will turn out to be high- need focuses for cybercriminals [7]. As cloud frameworks are acquired design, so a solitary cyber assault offers chance to the aggressor to impact an extensive number of destinations through a solitary malignant malicious movement. There are numerous security issues that are emerging for accessing administration services in cloud. To expel these issues, a strong security framework is proposed with capable and more secure verification strategies. This framework is mindful to classification of the documents or secret information on cloud. II. SECURITY ISSUES The cloud security and protection is a major concern now a day. Security, protection and secure stockpiling of information [3] are two boundaries which are keeping the associations and clients from receiving the distributed cloud computing services. Accentuation must be given on security, protection and strength on the cloud based advancements and figuring to make them excellent among the corporate multitenant condition. Malicious and Abusive attacks are multiplying cloud security. The information leakage and security attacks can be caused by deficient validation, authorization, and better review controls [4]. A portion of the risks in distributed cloud computing are understood in conventional computing models. These dangers incorporate, for instance, malicious insiders, unreliable client validation, (for example, use of weak passwords), malicious code executing on the cloud or user, vulnerabilities of the mutual assets prompting data leakage, or hijacking the accounts by phishing strategies, obscure hazard profile[5]. Distributed computing is set as a perfect world to understand huge numbers of difficulties of the present time. Be that as it may, practically speaking security is a noteworthy detour to its boundless selection. A significant number of these dangers can be dealt with utilizing conventional security rehearses. Alphanumeric Passwords are the normal and regular method of verification for a scope of online login. Human proclivities in making Password draw programmers and excited Password Crackers to break down secret word effectively utilizing different systems, with open registering power and accessible substantial number of instruments. Regular attacks on passwords are Brute force attack, Dictionary attack and Hybrid attack [6]. The security issues of the distributed cloud computing are not countable. Here are the few security answers for the virtualization and web administrations, two noteworthy empowering advancements of distributed computing. It additionally clarifies the novel idea of coordinating the multifaceted security in the majority of the cloud offerings rather than the security-as-a-benefit idea [8]. The utilization of saving money applications are increasingly and prominent on Android Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 339

366 Naveen Chandra Gowda et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, gadgets however it isn't secure. To make secure on those private application, for example, keeping money, business application and individual information, secret key is given to improve protection. These days those passwords are effortlessly usurped by programmers through shoulder side attacks or watching recordings. To overcome from this burdens of getting to the record out in the open places, the proposed thought is to influence shrewd approach to validate the client to ledger through the pictorial secret key and by infusing the backhanded stick to the framework. To foresee the first secret word, transitory login pointer is utilized while bookkeeping to login. The Existing paper does not protect the client's record from programmers, when the secret key is abused. The overlook secret word module and keeping money benefit module is included which could be creative and a compelling plan to confirm the proposed framework [9]. III. PROPOSED SECURITY SYSTEM The proposed security framework is furnished with effective and more secure validation methods. This framework is mindful to classify the documents or private information. It is a multifaceted verification framework. It expels the time unpredictability issue. Figure 1: Architecture diagram (Persuasive educated snap point) secret key is utilized for group1. At the season of downloading, this secret word ought to be coordinate. On the off chance that it is coordinated, at that point the verification is effective. The client can get to the cloud administration services. Figure 2: Persuasive educated snap point scenario In the proposed security framework, user gets to the cloud administrations services. Client will transfer a record on the cloud. There are 3 Assurance groups. The inward most group is generally secure. The record classification is finished by utilizing Revised-CIA calculation. The R-CIA isolates the records into group1, group2 and group3 based on the priority of the file being uploaded to the cloud. Usually the priority for the file can be set by the user who uploads the file on to cloud. Based up on the priority of the file different authentication techniques will be applied for verification before it is placed. A set of TIB secret key is utilized in group3. GIP (Graphical Icon Password) secret word is utilized for group2. PESP Users must select a tick point inside the view port. On the off chance that they can't or then again unwilling to choose a point in the present view port, they may press the Shuffle catch to haphazardly reposition the view port. The view port aides clients to choose more random passwords that are more averse to incorporate hotspots. A client who is resolved to achieve certain snap point may still rearrange until the view port moves to the particular area, however this is a tedious and more dull process. Group 2: GIP (Graphical Icon Password) is the main graphical passcode conspire we propose here. In GIP, to moderate the problem area issue clients may tap on a subset of showed symbols as their passwords as opposed to choosing particular areas on a foundation picture. Test comes about demonstrate that the utilization of symbols in GPI makes conceivable to equitably circulate conceivable snap focuses to a specific degree. Group 1: PESP (Persuasive educated snap point) was proposed to address the issue of hot spots. Likewise with educated snap point, a secret key comprises of five snap focuses, one on each of five pictures. While a secret word creation, the greater part of the picture is darkened with the exception of a little view port zone that is haphazardly situated on the picture as appeared in Figure 2. Figure 3: GIP Interface Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 340

367 Naveen Chandra Gowda et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, Group 3: The TIB secret word is a multifaceted validation conspire. For the verification, it is require to present a multifactor virtual condition where the client explores and collaborates with different items. The succession of activities and associations at the items inside the condition builds the client's secret word. The TIB secret word can consolidate most existing verification plans, for example, textual passwords, Iris passwords, and different sorts of biometrics. already elaborated. The user must specify the level of authentication at the time of registration and also while availing the services of cloud. The user privileges in different levels will be gathered and stored in cloud while registration. Same can be used for verification while accessing. Figure 4: TIB Implementation Usually the user who uploads has a front camera / web cam with his device from which the pic of him can be captured and iris recognition can be made. At the same time an extra device need to be used for biometric recognition in user PC, if the user device supports a touch screen then it be converged in it. The final secret key space is a combination of all three. IV. GENERAL FRMEWORK As indicated by our proposed hypothesis, access to the cloud is confirmed utilizing a multi-dimensional passcode. The multidimensional passcode is produced by considering the numerous parameters of cloud worldview, for example, merchant subtle elements, purchaser points of interest, administrations, and benefits and so on. Such kind of parameters considered for input measurement. All those input measurements are joined together and generates a multidimensional passcode. Figure 5 delineates the engineering graph of multi-dimensional validation (authentication) framework. It has two substances i) Cloud Service Provider which gives assortment of cloud administrations services to end users and ii) Authenticated customer association to utilize cloud administration services (Before utilizing cloud administration services, organization/association validates and agrees with the cloud vendors). Figure 6: General Framework The general comparison among the three methods is outlined in the following table. Table 1: Comparison chart System / Group 1: GROUP 2: GROUP 3: Parameter PESP GIP TIB Time Less Average More Complexity Performance Moderate Moderate High Of System Securing Medium Medium High From Attacks V. CONCLUSION The proposed method helps in creating the secret word in many levels of association with the goal that the strict validation and approval is conceivable. The security level of cloud condition is considerably more grounded by utilizing multifaceted security framework. Contingent upon groups, levels of multifaceted security framework increments for secure access of cloud administration services. The framework is capable for obstructing Shoulder attack, Tempest attack, and Brute force attack and more which are available at customer side, with the utilization of solid strategies in the Graphical secret word. Figure 5: Authentication system The user who tries to access the cloud either for upload or download the cloud services, must be an authenticated user. The level of authentication is categorized in to three groups as Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 341

368 Naveen Chandra Gowda et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, REFERENCES [1] [2] Ao Zhou, Shangguang Wang, Bo Cheng, Cloud Service Reliability Enhancement via Virtual Machine Placement Optimization IEEE Transactions on Services Computing, Volume: 10, Issue: 6, Nov.-Dec. 1, 2017 [3] Abutalha Danish, Labhya Sharma, Harshit Varshney, Asad Mohammed Khan,, Alignment based graphical password authentication system, rd International Conference on, 31 October [4] Kuyoro S. O. Ibikunle F. and Awodele O. Cloud Computing Security Issues and Challenges,International Journal of Computer Networks (IJCN), Volume (3):Issue (5):2011. [5] Dinesha H A Agrawal V K, " Cloud-centric multifaceted authentication as a service for secure public safety device networks ", IEEE Communications Magazine, Volume: 54, Issue: 4, April [6] S. Vaithyasubramanian, A. Christy, An Analysis of CFG Password Against Brute Force Attack for Web Applications, Contemporary Engineering Sciences, Vol. 8, [7] Daniel Fraunholz; Daniel Krohmer; Simon Duque Anton; Hans Dieter Schotten, InvesTIBation of cyber crime conducted by abusing weak or default passwords with a medium interaction honeypot, 2017 International Conference on Cyber Security And Protection Of Digital Services (Cyber Security) [8] Naim Ahmad, Cloud computing: Technology, security issues and solutions, Anti-Cyber Crimes (ICACC), nd International Conference, 24 April [9] R. Sudha; M. Shanmuganathan, An Improved Graphical Authentication System to Resist the ShoulderSurfing Attack, 2017 International Conference on Technical Advancements in Computers and Communications (ICTACC). Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 342

369 Volume 9, Special Issue No. 3, May 2018 International Journal of Advanced Research in Computer Science (ISSN: ) CONFERENCE PAPER Available Online at 3-DIMENSION HAND MOTION DRIVEN SECURITY Archana S Kashyap, Deeksha P, Shetty, Poojitha P, Soundarya A N, DeekshaHegde B Department of CSE, Sai Vidya Institute of Technology, Bangalore, Indore Abstract: This work is based on developing application and demonstrate the use of gesture in air, work is started with design of gesture sensor i.e. hand motion sensor using 3 axis accelerometers and 12C protocol based ADC, this also uses a wireless sensor so user is free to make gesture in air. To demonstrate the application of the sensor we will design a security system, patterns will be made in 3D air space instead of touch screen, hence in our system one has to enter password not with keypad but by his hand gesture/motion in air. This requires special algorithm which converts raw accelerations measured by accelerometers in to the hand motions. I. INTRODUCTION With the advancements in technology various new sensor are introduced, one such sensor is accelerometers, these sensors have tremendous application in various aspects of life and engineering, they are also used in designing a wireless hand motion/gesture sensor. With this sensor it is used to develop pattern unlock security application as we have seen in android phone. The important and crucial difference is the pattern will be drawn in air. Security being an important issue now a days and everybody want to be stay safe and secured. To make a novel security system in which keypads are not used to get entry to house/office or to open a locker, one can get access or authenticate himself just by making some unique gesture using his/her hand motions in air. Once technology is mature one can make his signature in air to get access. The aim is not only to design a security application of gesture in air but to learn various use of accelerometer, new technologies, new protocols and different new algorithm approaches and all to be implements in low cost microcontroller with limited code memory and RAM. II. EXISTING SYSTEM The only development that is found in field of gesture detection is detection of tilt motions. Currently all existing methods uses camera, with color markers in fingers or without that. It is also the lock security that is given either using touch screen or digital locks. Consider the existing system that uses camera as the input device which captures the color image and the depth map at 640*480 resolution. Using the hand tracking function hand position is located. Then by threshold holding from the hand position with a certain depth interval, a rough hand region can be obtained. Second requirement is that the user should put on a black belt on the gesturing hands wrist, in order to segment the hand shape more accurately. Once the black color pixels are detected RANSAC is used to a line to locate the black belt. 100* 100 is generally a pixel resolution of hand shape, with severe distortions. After the hand shape is detected, it is represented as a time series curve. Such a shape representation has been successfully used for classification as well as clustering of shapes. The time service curve is used to relate distance between each contour vertex and a center point. The center point is defined as the point with the maximal distance after Distance Transform on the shape. And the initial point is according to the RANSAC line detected from the black belt. In the time series representation, the horizontal axis denotes the angle between each contour vertex and the initial point which is relative to the center point, which is normalized by 360. The vertical axis denotes the Euclidean distance between the contour vertices and the center point, which is normalized by the radius of the maximal inscribed circle. The time series curve captures nice topological properties of the hand, such as the finger parts. The another existing system is the pattern unlocking from the existing android phones. But if similar technology is used for opening doors or lockers it is good to see but it can be hacked easily. Even key based system can be hacked easily using chemical methods, thermal methods and keen observations. So there was a need to make a security such that it is easy to use, and multiple users can use that facility at the same time. Some basic attempts have been made by many researchers and engineers to use gesture as password but they have made tilt based sequence of tilts were password, these require careful entry and not easy to remember for example if password is S it is easy to remember compare to 2 right tilt then forward tilt then left tilt. Similar tilt based application were tried in home automation too. Disadvantages of existing system The existing system using camera has became the costly systems in terms of video/image processing. Keypad based access or security systems are prone to password leaks in various ways and this is improved to great extent in the future security systems. Not very user friendly. Advantages of Proposed System Being compact it can be used in small places like electronic safe, bank lockers, cars etc. for security purposes. Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 343

370 Archana S Kashyap et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, Unique gesture motions are having more combinations and it has more randomness which makes it more reliable Does not require expensive and sophisticated devices. User friendly and time saving. V. SYSTEM DESIGN The block diagram of the system is: Figl : Block diagram of Transmitter End Transmitter description: Accelerometer sensors are used to convert either linear or angular acceleration to an output signal. By using Newton's second law of motion, F = ma, by measuring the force from acceleration on an object whose mass is known. Using Accelerometers Acceleration,Tilt and tilt angle,incline, Rotation Vibration,Collision,Gravity etc. are measured. accelerometers in to the hand motions. Fig 3: Accelerometer The data from accelerometer is transmitted to microcontroller through the bus where further processing of the data takes place. The main gesture system uses P89V51RD2 advanced version of 8051 with bootloader feature which helps in fast and hassle free prototyping and code development.i2c is special communication protocol and using that protocol it will be interfaced to I2C based ADC PCF8591 which used only two wires for control and data acquisition compared to 14 line interfaces to ADC0808 so it will save space, cost and weight.adc makes use of successive approximation conversion technique. At thetrailing edge of acknowledged clock pulse and is executed while transmitting the result of the previous step. The processed data is transmitted to the receiver using RF transmitter. IV. PROPOSED SYSTEM An application is developed to demonstrate the use of gesture in air. The system is implemented using hand motion sensor i.e, is tri-axial accelerometer and I2C protocol based ADC. To demonstrate the application of the sensor, security system is designed in such a way that patterns will be made in 3D air space instead of touch screen, hence in the system one has to enter password not with keypad but by his hand gesture/motion in air. This requires special algorithm which converts raw accelerations measured by The small strokes. It compares theses strokes to strokes stored in EEPROM memory using specially pattern matching algorithms. At the end it concludes whether the gesture made is same as stored in EEPROM, then only access is granted and required action of unlocking the access mechanism is initiated and after some time interval locking mechanism is activated. There is provision to update the password for that main system is to be configured in password update mode and the new password can be updated in the system serially or wirelessly. It is I2C based Serial EEPROM which is used to store password gestures in form of strokes and time stamps, which are used by main system to compare with gestures made using hand movements or motions in air. Few switches are provided for user interface and for selecting different modes of operations, status output is used for audio visual indications of gesture registration, identification or rejection. Finally, for access control Stepper motors, magnetic locks, solenoids, relays can be used. We can use Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 344

371 Archana S Kashyap et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, ULN2003 high current drivers to operate above locking mechanism. A PC interface is provided to update configurations, monitoring, debugging and to view statistic of entered gestures. VI. ADVANTAGES: Keypad based access or security systems are prone to password leaks in various ways and this is improved to great extent in our security system. Being compact it can used in small places like electronic safe, bank lockers, cars etc. Unique gesture motions are having more combinations and it has more randomness which makes it more reliable. VII. DISADVANTAGES: This system is not suitable who are having shaky hands or who cannot make gestures like elderly people or children's. Complex processing algorithms are require for pattern matching to verify a gesture while in keypad based system its very simple to verify the password Cost of the system is more than keypad based system and touch screen based system. It is more complex and slower in operation. VIII. APPLICATIONS Security systems are playing a important role in present world and it will keep on growing in US almost every house has some sort of Security or Alarm system, our system has straight forward use wherever security is required. First use is in Access control system, in many places, companies, or organizations some places are restricted and its access is limited to authorized personals only in such places our system is very useful only one or two gestures you make on screen and you are allowed to go in secured area. In banks locker, electronic safe and similar our system is can be used because of its compact size and easy interface. In defense so many weapons rooms are there, missiles, tanks, air craft for all of them code locks are there which can be replaced by our security system. Even in ATMS, mobile phones gesture can be used as password instead of numbers. Your car or PC can have this system and they will turn on when right gesture is made on the screen. Various applications of Gesture in Air technology are possible in field of security, appliance control entertainment, education, medical and high end gamming. Gesture in air technology can be used for text entry and menu selection at large kiosks. The components are assembled and tested successfully. The circuit is designed in such a way that the pattern is recorded by press holding the switchin the transmitter part and stored in the EPROM in the receiver part. This data is transmitted by RF transmitter module. The pattern is made and recorded to match with the earlier stored pattern. The access is given only if there is a pattern match and if pattern is not matched for more than twice then buzzer is activated. X. CONCLUSION This paper includes the current works done on security system developed using gesture in air with the purpose of enhancement of security. This method provides another level of security when compared to other methods as the pattern is drawn in air it is difficult to track and provides more combinations when compared to numeric password. Hence no touch screen is used tracing of the password will be difficult. XI. REFERENCES [1]. The 8051 microcontroller by Kenneth J. Ayala. [2]. The 8051 microcontroller by I. Scott MacKenzie, Raphael C.-W. Phan. [3]. C and the 8051 by Thomas W. Schultz. [4]. Paper: User Evaluation of Lightweight User Authentication with a Single Tri-Axis Accelerometer by Jiayang Liu & Lin Zhong. [5]. Paper: uwave- Accelerometer-based Personalized Gesture Recognition and Its Applications by Jiayang Liu & Lin Zhong. [6]. Paper: Yet Another User Input Method: Accelerometer Assisted Single Key Input by Chunming Gao & Robert Pastel. [7]. I2C Serial EEPROM Interfacing: Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 345

372 Volume 9, Special Issue No. 3, May 2018 International Journal of Advanced Research in Computer Science (ISSN: ) CONFERENCE PAPER Available Online at A CRITICAL APPRAISAL OF BIO-INSPIRED HDR IMAGE FROM LOW-LIGHT IMAGE ENHANCEMENT Syed Arif Islam School of Computing & Information Technology Reva University Bangalore, India. antor003@gmail.com Prof.Akram Pasha School of Computing & Information Technology Reva University Bangalore, India. akrampasha@reva.edu.in Abstract: Capturing an image in a proper way is a difficult task to fulfil the observer s expectations. This is ever more finding and recognized that applications of bio-inspired algorithms addressed high solutions on time. To explore more and more intelligent algorithms are there to solve but the fast growth of bio-inspired are likely neural networks, genetic algorithms, particle swarm and ant colony optimization to explored by the researcher. This is due to the fact that bio-inspired based high dynamic range (HDR) is more robust, accurate and efficient in solving low light image enhancement processing problems. This paper reviews 30 out of 100 bio-inspired Algorithm kinds of research published in Google Scholar, Springer, ACM Digital Library and IEEExplore between the periods of 2010 to 2018 used to solve low light image processing problems. This paper covers the low light image enhancement for HDR using the bio-inspired algorithm. Keywords: Bio-inspired Algorithm., Image Processing, High Dynamic Range Compression, Image enhancement and Swarm intelligence. I. INTRODUCTION Digital cameras increasingly important for capturing second moments of the images now it s widely used. Conditions of the images if not respect to this sensitivity and coming to the color error of the images it might be conducted to lighting uniformity, illumination and depends on the camera technology. Digital cameras fulfill the customer expectation simply but due to the fact that difficult task for physical values of the light, what observe remembers perceives and human visual system processing mechanism. The human have a complex mechanism in visual system. Human eye and brain have the capability to adapt and perceived color and across a scene signals of the color. The individual observes high dynamic range scenes and deals with low down luminance the same as elevated luminance. As To date dynamic range capabilities to find most accurate and robust algorithm in a dynamic way in such way images enhancement, segmentation, and feature extraction. This is plays a big role in face detection [1] [2] [3], biometrics [4] [5], medical [6][7][8][9][10], remote sensing[11] [12] and numerous way. Finding the best solutions using extent of very large and dynamic scope for the bio-inspired algorithm. In Bio-inspired, has been proposed in some several fields Firefly algorithm, Artificial Bee Colony, Particle Swarm, Bat Algorithm. In this paper, we present a bio-inspired algorithm solving low light image enhancement. In this research we follow research question: RQ: How bio-inspired algorithm solving low light image enhancement for HDR? Motivation: To find out bio-inspired algorithm solving low light image enhancement using HDR phase. Bio- Inspired Figure: 1 Development of the bio-inspired algorithm II. Firefly Algorithm Artificial Bee Colony Particle Swarm Bat Algorithm LITERATURE REVIEW P Zhao and C Yang [13] High Dynamic Range images build a representative display, it is required to be the larger dynamic variety of radiance levels and mapped via compressive transform to the unique color display. Information for images quality tone reproduction is more important. Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 346

373 Sayed Arif Islam et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, Zheng et al. [14] the significance discussed of the medical images constructing of the low light images process for the purpose of cancer detection and the author implemented a various optimized techniques e.g.: noise removal, quantization, preprocessing which is training based. This paper proposed a new enhancement implementation called bio-inspired algorithms which are optimized. The algorithm performs segmentation process in an automatic process and the outcomes will be defined visually with a success factor. Xiao Wu et al. [15] presents Wireless capsule endoscopy (WCE) images for automatic detection on law images enhancement and it is focused on enhancing the capsule endoscopy images.wce images are dark and quality of images are poor and its resolution only pixels. Contrast Limited Adaptive Histogram Equalization (CLAHE) is the new images enhancement techniques for analyzing. It s a better contrast improvement for human visualization. P. Kale et al. [16] according to the author image enhancement have the variations in the atmospheric condition and its degrading the old historic images. Digital format can be converted for images enhancement with compare two algorithms e.g: Histogram Equalization method and Hybrid Binarisation Broilo et. al. [17] the paper author solved optimized iterative learning using some retrieval techniques particle swarm optimization and relevance feedback. This method investigated dynamic modify feature with a solution space towards the clusters description. III. METHODOLOGY In this research, there are different types of literature review techniques to help to identify current state [18] and we follow the guidelines from Kitchenham and Charters [19]. three issues we follow for this paper: the first issue is that we collected high dynamic range related empirical studies in and the second issue is that although global studying bioinspired work in low light enhancement papers only one hundred papers between the period 2010 to 2018 by using and/or/not. In this paper we search the databases such as IEEE, ACM, Springer, Elsevier, Google Scholar, and Scopus using some keywords e.g.: "bio-inspired Algorithms "," swarm intelligence", "high dynamic range(hdr)","low light images", "enhancement". IV. RESULT AND DISCUSSION The results of the research are discussed in two main sections: First section, find out the literature review of the current problem in low light enhancement. The second section; find out the literature review of the current solutions objectives for selecting algorithms in low light enhancement. In the literature review, conducted into the major cause s issues relating to technical issues, procedure, detection, physical properties of the images, visual quality, transform colors [28]. Data received from the literature review are showing in Table I and Table II. From the LR we identified 8 problem and 12 solutions. Table 1: Identification Limitations Year Author Title Approach Limitations 2012 Leslie N. Smith [20] 2010 qbal, K.; Odetayo, M.; James, A.; Salam, R.A.; Talib, A.Z.H [21] 2013 Koik, B. T., & Ibrahim, H[22] Estimating an Image s Blur Kernel from Edge Intensity Profiles Enhancing the low-quality images using Unsupervised Color Correction Method A Literature Survey on Blur Detection Algorithms for Digital Imaging Image intensity and Edge sharpness in blur extent measurement Images color corrections methods Low depth of field (DOF) image segmentation Not effective for the complex in a low light image. Illumination for high dynamic range images. Only work effective for low DoF (Depth of field) image. Year Author Title Approach Limitations 2011 inbo Chen; Zhenbang Gong; Hengyu Li; Shaorong Xie [23] 2013 Hitam, M.S.; Yussof, W.N.J.H.W.; Awalludin, E.A.; Bachok, Z.[24] 2010 Xiaogang Chen1, Jie Yang1, Qiang Wu2 and Jiajia Zhao1[25] 2013 Boon Tatt Koik and Haidi Ibrahim 26] 2011 V. Kanchev, K. Tonchev, O. Boumbarov [27] A. SUMMARY Low Complexity Underwater Image Enhancement Based on Dark Channel Prior Mixture contrast limited adaptive histogram equalization for underwater image enhancement MOTION BLUR DETECTION BASED ON LOWEST DIRECTIONAL HIGH- FREQUENCY ENERGY A Literature Survey on Blur Detection Algorithms for Digital Imaging SVM and wavelet-based histograms detections a blurred images Dark Channel method Adaptive Histogram Equalization The lowest direction for blur motion groundwork step for deblurring process Detect a blur images with a low light Implementati on time is decreased for low light images Only underwater images Only effective blur image. User interface needed Only outer image to the is for focus This table summarized from the literature review: In this literature review, we found that most of method and approaches used to find efficient images but those are not fulfilled with the complex features. Therefore, still some work in a variety of techniques such as high level and lowlevel images, blur images, images enhancements, content- Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 347

374 Sayed Arif Islam et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, based image, object extraction [28]. Moreover, images color contrast method is used for color improvement. This paper examines and our from the literature review of the applications of the Bio-Inspired algorithms in a various fields inspired the behaviors of Artificial Bee Colony and Clustering, Enhanced Artificial Bee Colony Optimization (EABCO), Ant Colony, Optimization, Fuzzy, Least-Squares Support Vector Machine(LSSVM), Particle Swarm and firefly algorithms A review of the solutions objectives for application of algorithms inspired by bio-inspired. Table 2: Solutions Objectives SN Algorithm Description 1 Artificial Bee Colony Effective to find the clusters of an and Clustering [29] HDR images 2 Artificial Bee Colony and Particle Swarm Optimization [30] 3 Enhanced Artificial Bee Colony Optimization (EABCO) [31] To get better performance of the algorithm using hybrid modified EABCPSO and OABC-PSO. A method proposed to identify suspicious medical images and automatically detects the breast cancer using EABCO algorithm. 4 Ant Colony Medical images identify using fuzzy Optimization, Fuzzy and ant colony optimization [32] 5 Least-Squares Support Vector Using PSO and LSSVM classified of the clustered micro classifications and Machine(LSSVM) and reduce the input feature Particle Swarm [33] 6 Firefly Algorithm [34] Can deal with the high dynamic range problems, Digital image, Feature, Selection, fault detection and also for Low light images enhancement. 7 Bat algorithm [35] Can solve a wide range of low light problems, highly nonlinear problems efficiently and scheduling, data mining and others. 8 Particle swarm optimization [36] 9 Contrast Limited Adaptive Histogram Equalization(CLAHE ) and Particle swarm optimization [37] 10 Particle swarm optimization and Genetic Algorithm [38] 11 Hierarchical evolutionary algorithm (HEA) and Particle swarm optimization [39] 12 Particle swarm optimization Clustering [40] Low light images segmentations stable and easy to coverage optimal solutions and high segmentations speed. Image Enhancement Contrast Enhancement Quality of image contrast for HDR Fast coverage of the Image segmentation V. CONCLUSION In this review paper, we have studied several different papers with HDR techniques of low light image enhancement using bio-inspired algorithm. Come to the conclusion that the field of the bio-inspired algorithm has several growing field in every area. When we combined the bio-inspired with others low light image enhancement techniques such as fuzzy sets, Particle Swarm Optimization (PSO), Artificial Bee Colony and Clustering, Enhanced Artificial Bee Colony Optimization (EABCO), Least- Squares Support Vector Machine (LSSVM), Firefly Algorithm. In a summary, we can say that bio-inspired algorithm is a very powerful technique which can be utilized efficiently in the field of low light images enhancement using HDR image processing. VI. REFERENCES [1] Banachowicz, B. P., Borthakur, S., Mlinar, M., Boettiger, U., & Perkins, A. E. (2018). U.S. Patent No. 9,883,128. Washington, DC: U.S. Patent and Trademark Office. [2] Çiftçi, S., Akyüz, A. O., & Ebrahimi, T. (2018). A reliable and reversible image privacy protection based on false colors. IEEE Transactions on Multimedia, 20(1), [3] Ming, W., & Zhan, X. (2017). U.S. Patent No. 9,852,499. Washington, DC: U.S. Patent and Trademark Office. [4] Piciucco, E., Maiorana, E., & Campisi, P. (2018). Palm Vein Recognition Using a High Dynamic Range Approach. IET Biometrics. [5] Guo, J. (2018). U.S. Patent Application No. 15/213,314. [6] Miller J.F., Smith S.L., Zhang Y.: Detection of microcalcifications in mammograms using multichromosome Cartesian genetic programming. GECCO '10: Proceedings of the 12th annual conference on Genetic and evolutionary computation, pages: , Portland, Oregon, USA (2010) [7] Choi, M., Wang, J., Cheng, W. C., Ramponi, G., Albani, L., & Badano, A. (2014). Effect of veiling glare on detectability in high-dynamic-range medical images. Journal of Display Technology, 10(5), [8] Colonero, C. B., Kelly, M. W., Blackwell, M. H., & White, L. L. (2018). U.S. Patent No. 9,866,770. Washington, DC: U.S. Patent and Trademark Office. [9] Chandra, S. S., Engstrom, C., Fripp, J., Neubert, A., Jin, J., Walker, D.,... & Crozier, S. (2018). Local contrast enhanced MR images via high dynamic range processing. Magnetic resonance in medicine. [10] Degirmenci, A., Perrin, D. P., & Howe, R. D. (2018). High dynamic range ultrasound imaging. International journal of computer assisted radiology and surgery, 1-9. [11] Clevenson, H., Pham, L. M., Teale, C., Johnson, K., Englund, D., & Braje, D. (2018). Robust High- Dynamic-Range Vector Magnetometry via Nitrogen- Vacancy Centers in Diamond. arxiv preprint arxiv: [12] Stillwell, R. A., Shupe, M. D., Thayer, J. P., Neely, R. R., & Turner, D. D. (2018). Multi-sensor measurements of mixed-phase clouds above Greenland. In EPJ Web of Conferences(Vol. 176, p ). EDP Sciences. 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375 Sayed Arif Islam et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, [13] Zhao, P., Xiong, Z., Liu, D., Wang, H., Yang, C., Ding, L.,... & Wu, F. (2017, November). Progressive tone mapping of brain images at single-neuron resolution. In Signal and Information Processing (GlobalSIP), 2017 IEEE Global Conference on(pp ). IEEE. [14] Y. Zheng, "Breast cancer detection with Gabor features from digital mammograms", algorithms Vol.3, No. 1, pp , 2010 [15] He, J. Y., Wu, X., Jiang, Y. G., Peng, Q., & Jain, R. (2018). Hookworm Detection in Wireless Capsule Endoscopy Images With Deep Learning. IEEE Transactions on Image Processing, 27(5), [16] Kale, P., & Gandhe, S. T. (2015, December). Hybrid binarization, histo-equalization: Comparison of old image enhancement techniques. In Information Processing (ICIP), 2015 International Conference on (pp ). IEEE. [17] Broilo, M., & De Natale, F. G. (2010). 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377 Volume 9, Special Issue No. 3, May 2018 International Journal of Advanced Research in Computer Science (ISSN: ) CONFERENCE PAPER Available Online at A LOSSLESS DATA HIDING TECHNIQUE USING SECURE LSB IN IMAGES Sandeep Sharma School of CIT, REVA University Bangalore-64 Kshitj Yadav School of CIT, REVA University, Bangalore -64 Saurabh Singh School of CIT, REVA University Bangalore -64 Rafsan Ali School of CIT, REVA University Bangalore-64 Dr. Sunilkumar S Manvi School of CIT, REVA University, Bangalore-64 Nimrita Koul School of CIT, REVA University, Bangalore-64 Abstract In this work we have implemented a lossless data hiding technique based on Least Significant Bit Embedding algorithm. Any image based data hiding method using LSB embedding sacrifices at least 12.5% accuracy in the image that is used as carrier. In this work we have come out with a better approach which results in better image quality and increases the amount of hidden data. Keywords Data Hiding, Image Steganography, Least Significant Bit Embedding I. INTRODUCTION The method of embedding hidden information in media like images, audio, video is known as Steganography. The aim is to thwart the theft of sensitive information by unintended recipients which may me anti nationals, terrorists. Using appropriate methods of steganography, sufficient amount of information can be hidden in an image or audio file without the unintended user coming to know about it. The difference between cryptography and steganography is that while cryptography makes the message incomprehensible for unintended user, steganography hides its existence. Least Significant Bit Embedding [1] technique uses the least significant bit of each unit of image to contain the bit hidden information instead of the bit of original image data. This is a very simple and effective technique for data hiding. However, the problem with existing schemes is that it make create blurriness to the vision of a sensitive observer or to an observer who is not interested in hidden message. Another issue with this simple technique is that experienced, malintentioned people can break these quite easily. In an electronic or digital media like image, audio or video the basic feature is color, represented by R, G, B values of each pixel in the media. Value of each of these three Primary colors is between 0 and maximum number of colors present in that media. E.g. we need 8 bits to show 256 colors. Each digital media is composed of pixels as primary units, it is the smallest controllable point in an image. A pixel on computer screen emits a color of light based on its. intensity. In the RGB framework, each of R, G and B are represented by a number which is represented by a number of bits. All the bits at a single bit position across an image form the biplane. Least significant bit steganography uses the bit plane of least significant bits to embed hidden message To hide information in a media object like image, audio or video[2] we need two things the carrier object or the cover and the data to be hidden in the carrier object. Steganography requires two pieces of data: the cover, and the data to be hidden. Not every cover is suitable to hide a message and should be chosen carefully, to make information hiding effective. A good candidate image for being a cover is one with a large number of colors otherwise the embedded information amy distort the original image too much such that the detection of hidden message becomes easier. The colors in a cover image should be distributed variously. Hackers can use the fact that in an embedded image, there is less correlation between the LSB bit plane and other bit planes in the image containing an embedding. Other thing required is data. To be embeddable data has to be serializable [1,2,3] such that it can be embedded bit by bit into the carrier object, it has to be smaller than the size of cover. Least Significant Bit Embedding Least Significant Bit (LSB)[4] embedding replaces information in a given pixel in least significant bit plane of Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 351

378 Sandeep Sharma et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, the carrier with data to be hidden. The benefit is that, this has least effect on color imbalance in image as color change by only 1 as compared by 2 on embedding at bit plane 2 [5] and so on. This s definitely a lossy method but the loss is not generally detectable to normal eye if the image is colorful. II. LITERATURE SURVEY There has been a sufficient amount of research work dedicated to steganography since last two decades, [6] presents a comprehensive survey of the works in this field, [8] presents a complex embedding technique in the area of image steganography, Authors in [1],[2],[3] have implemented different techniques for hiding information in images. Fig.2 Composing a secret message III. SECURE LSB EMBEDDING IN THIS WORK This work has implemented encryption [7] of the message before it is embedded using LSB and decryption after it is extracted from the carrier image. The aims is to overcome the loss in information in the carrier object by making the image to be embedded smaller in size. The loss causes lack of quality for the user who is not interested in hidden message and rather wants to use the carrier object for itself. This system is implemented in platform independent Java language and has a user friendly interface. SYSTEM DESIGN Fig. 3- Applying encryption on hidden message Fig.1. Embedding and retrieving hidden information. Fig.4 A Sample Image with hidden information which is encrypted Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 352

379 Sandeep Sharma et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, Fig. 5. Decrypting an embedded message Fig. 8. Left Image shows original image pixel intensity levels, right image shows intensity of same pixels after embedding encrypted message CONCLUSIONS Fig. 6 Extracting the hidden message after decryption Steganography has turned out to be very useful in communication of secret information. Least Significant Bit Embedding method is an easy, simple and intuitive method to add hidden information to a media without creating a major information loss in original media. Though steganography by itself can be cracked or hacked using statistical methods, when combined with cryptographic techniques, it can lead to very strong information hiding and secret communication systems which are difficult to break into. FUTURE ENHANCEMENT There are a number of ways to embed hidden information in media using LSB Embedding, current work can be extended to enhance to use an embedding strategy where the information is spread out to randomly generated bits, also a combination of cryptographic techniques can be applied to enhance the security of hidden message. REFERENCES Fig. 7 Steganography with Encryption [1] Fridrich, J., Goljan, M., & Du, R. (2001). Reliable detection of LSB steganography in color and grayscale images. Proceedings of the 2001 workshop on Multimedia and security new challenges - MM&Sec 01, 27. New York, New York, USA: ACM Press. doi: / [2] Gonzalez, Rafael C., and Paul A. Wintz. "Image Compression Standards." Digital Image Processing. 2nd ed. Upper Saddle River, NJ: Prentice-Hall, Print. [3] Lyu, S., & Farid, H. (2006). Steganalysis using higherorder image statistics. Forensics and Security, IEEE Transactions on, 1(1), [4] Muñoz, A. (2007). XStegSecret beta v0.1. Retrieved from Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 353

380 Sandeep Sharma et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, [5] Provos, N. (2001). Detecting steganographic content on the internet. Ann Arbor. Retrieved from ntitle:detecting+steganographic+content+on+the+internet# 0 [6] Rocha, A., Scheirer, W., & Boult, T. (2011). Vision of the unseen: Current trends and challenges in digital image and video forensics. ACM Computing Surveys. Retrieved from [7] Węgrzyn, M. Virtual Steganographic Laboratory for Digital Images (VSL). Retrieved from [8] Westfeld, A. (2001). F5 A Steganographic Algorithm High Capacity Despite Better Steganalysis, [9] Westfeld, A., & Pfitzmann, A. (n.d.). Attacks on Steganographic Systems: Breaking the Steganographic Utilities EzStego, Jsteg, Steganos, and S-Tools and Some Lessons Learned, Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 354

381 Volume 9, Special Issue No. 3, May 2018 International Journal of Advanced Research in Computer Science (ISSN: ) CONFERENCE PAPER Available Online at CLASSIFICATION OF BRAIN TUMORS IN MRI IMAGES Uday R, School of Computing and Information Technology, REVA University, Bengaluru, India Vishu B.V, School of Computing and Information Technology, REVA University, Bengaluru, India Yamini K.D, School of Computing and Information Technology, REVA University, Bengaluru, India Vijay Kumar C, School of Computing and Information Technology, REVA University, Bengaluru, India Sarvamangala D.R School of Computing and Information Technology, REVA University, Bengaluru, India Abstract This paper aims to detect presence of brain tumor given a MRI image. The problem of detecting the brain tumor is considered as classification problem and has been solved using one of the latest technique convolutional neural network (CNN). CNN is the latest approach used for solving many challenging applications in the field f image processing, signal processing. Keywords CNN, Classification, Preprocessing, Feature Extraction I. INTRODUCTION Brain tumor is a disease that can lead to disability and in severe condition can be fatal. If not detected and treated at an early stage, the patient might die [1, 2]. To diagnose brain tumor, MRI, CT, PET scan is done and the images obtained from these scanners is analyzed by radiologists and presence or absence of tumor is as per their analysis. Here we propose to use deep learning module called CNN [1] for classifying a given MRI image for presence or absence of tumor. Deep Learning modules refers to the class of computing machines that can learn a hierarchy of features by building high level features from low level ones. There by automating process of feature extraction for classification. CNN comprises several layers of processing involving learnable operators and hence has the ability to learn a hierarchy of information by building high level from low level. II. METHODOLOGY A. Data pre-processing In total, datasets from 215 subjects are collected and studied in this investigation, including 29 without tumor images (refer fig 1) and 187 with tumor images (refer fig 2). The images are augmented to increase the data size by rotation and shearing. The images are also normalized. A. Preprocessing Removing the unwanted noise from the image, data augmentation [3], normalization. B. Extraction The feature extraction [5] is done using deep learning, instead of traditional methods of SIFT, HOG. Figure 1. With tumor brain image C. Classification After the features are extracted and selected the classification [6] step using deep learning is performed on the resulted feature vector. Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 355

382 Uday R et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, Figure 2. Without tumor brain image In this process the feature extraction s through CNN. The preprocesses augmented image is fed into CNN architecture made of 2 layers of Convolution, 2 layers of maxpooling with ReLU the activation function and adam as the optimizer. The 64*64 image is first fed into 32 convolutional filters of size 3*3, the output of the convolutional layer is fed into ReLU and then sent to a maxpooling layer for reducing the output size. The reduced output is fed into 32 convolutional filters of size 3*3 and the resultant output to ReLU and further to maxpool layer. The CNN architecture used is shown in fig 3 C. Classification The features extracted are fed into 2 layers of fully connected neural network with activation function ReLU at the first layer and Sigmoid at the second layer. B. Extraction Figure 3. Architecture of proposed CNN Framework III. IMPLEMENTATION The code was written using python and used keras and tensorflow. The datset was taken from kaggle website. The training set consisted of 217 images with 187 images containing tumor and remaining without tumor. The CNN was trained for 10,000 epochs. And then tested using 3 fold cross validation and tested for accuracy. We obtained an accuracy of 92%. IV. CONCLUSION In this paper, we developed the CNN classifier, to identify the presence of brain tumor given a MRI image of the brain. The results denote good accuracy for prediction of the brain tumor. V. FUTURE WORK As a future enhancement we can identify the different types of brain tumors present in the human brain. There are two types of brain tumor s namely Malignant and Benign, in future we can easily identity the exact location and dimensions of the brain tumor. In severe conditions brain tumor leads to death, by using deep learning we can predict the life span of tumor. Generalize the solution further to carry out relevant clinical tasks such as predicting the overall survival of patents and predicting whether the tumor is shrinking, expanding or remain stable. Deeper CNN architectures are generally more promising in increasing the accuracy of segmentation output. Upon the presence of large MRI tumor datasets, it s highly recommended to try deeper CNN architectures. REFERENCES [1] B. Menze, A. Jakab, S. Bauer, J. Kalpathy-Cramer, K. Farahani, et al., The Brain Tumor of Multimodal Segmentation image Benchmark(BRATS), IEEE Transactions on Medical Imaging, Institute of Electrical and Electronics Engineers (IEEE), 2014, [2] BBC. The dementia timebomb. Retrieved: 31 March [3] ] I. Arel, D. C. Rose, and T. P. Karnowski, Deep learning-a new frontier in artificial intelligence research, Computational Intelligence Magazine, IEEE, vol. 5, no. 4, pp ,2010. [4] S. Sarraf, E. Marzbanrad, and H. Mobedi, Mathematical modeling used in Deep Learning, in Electrical and Computer Engineering(CCECE), 2014 IEEE 27th Canadian Conference on, IEEE, [5] I. Guyon, S. Gunn, M. Nikravesh, and L. A. Zadeh, Feature Extraction: Foundations and Applications (Studies in Fuzziness and Soft Computing). Secaucus, NJ, USA: Springer-Verlag New York, Inc., [6] A. Ghaheri, S. Shoar, M. Naderan, S.S. Hoseini, "The applications of genetic algorithms in medicine", Oman medical journal, vol. 30, no. 6, pp. 406, Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 356

383 Volume 9, Special Issue No. 3, May 2018 International Journal of Advanced Research in Computer Science (ISSN: ) CONFERENCE PAPER Available Online at EMOTION IDENTIFICATION AND CLASSIFICATION USING CONVOLUTIONAL NEURAL NETWORKS Nishchal Poornadithya C. Computer Science and Engineering, Reva Institute of Technology and Management, Bangalore, Karnataka, India Thangaraj Raman Computer Science and Engineering, Reva Institute of Technology and Management, Bangalore, Karnataka, India P.Chimanna Chengappa Computer Science and Engineering, Reva Institute of Technology and Management, Bangalore, Karnataka, India Shantanu Pandey Computer Science and Engineering, Reva Institute of Technology and Management, Bangalore, Karnataka, India Gopal Krishna Shyam Computer Science and Engineering, Reva Institute of Technology and Management, Bangalore, Karnataka, India Abstract In this paper we demonstrate the process of emotion detection using convolutional neural network (CNN). Creation of a real-time visual system helps us validate our model. This system achieves the tasks of emotion detection and classification simultaneously in one combined step using the CNN architecture. The training procedural setup is discussed in this paper after which we evaluate specific standard data sets. The evaluation has resulted in accuracies of around 66% in the FER-2013 emotion data set. The implementation of a new real-time guided back propagation technique is also used here. This explains the dynamics of weight changes and evaluates learned features. The gap between slow performances and real-time architectures can be reduced through the careful implementation of modern CNNs, use of ongoing regularization methods and visualization of previously hidden features. model achieves human level precision when classifying emotions. I. INTRODUCTION Service robotics have been successful mainly due to the smooth interaction between robot and user. Just from the seeing the face of its user, a robot should be able to extract relevant information, for example identify the gender or user s emotional condition. Accomplishing this using machine learning(ml) techniques has turned out to be very complicated due to the high variability of samples in each task [4]. Thus we end up with models containing a large number of parameters trained under thousands of samples [3]. On an average the accuracy achieved for classifying an image of a face in one of 7 different emotions is 65% +/- 5% [4]. The complications that arise can be observed by attempting to manually classify the FER-2013 dataset images in Fig. 1 that we are using for our model. The various emotions that we are trying to cover are: angry, disgust, fear, happy, surprised, neutral, sad.robot platforms that perform house hold tasks require robust and efficient facial expression systems. The technologies used in images related tasks are all based on Convolutional Neural Networks. The CNN architectures required for such tasks contain millions of parameters. Hence they cannot be used in robot platforms and real time systems. The implementations proposed in this paper have been validated in a real time facial-expression system that provides face-detection and gender classification. This Fig. 1: Images in FER-2013 dataset [4]. The learned features of CNNs remain hidden, hence making it complicated to establish a balance between their classification accuracy unnecessary parameters. Thus, in order to validate the features learned by CNN, we implemented a real-time visualization of the guided gradient of the back propagation proposed by Springenberg [11]. II. RELATED WORK Commonly used CNNS for feature extraction utilize a group of completely connected layers at the end. Most of the Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 357

384 Nishchal Poornadithya C. et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, parameters in the CNN are contained in the fully connected layers. In VGG16, approximately 90% of all its parameters are in their last fully connected layers [10]. The Inception V3, which is a recent architecture, has reduced the amount of parameters in the last layers by including a Global Average Pooling operation [12]. Using this technique, each feature map is reduced into a scalar value by taking the average over all elements in the feature map. This operation forces the network to extract global features from the input image. A modern day architecture like Xception [1] is developed from the combination of two of the most successful experimental assumptions in CNNs: use of residual modules [6] and depth-wise separable convolutions [2]. The latter further reduces the number of parameters separating the processes of feature extraction and combination within a layer. The model for the FER-2013 dataset is based on CNN trained with square hinged loss [13] achieving an accuracy of 71% [4] by using approximately 5 million parameters. 98% of all parameters were located in the last fully connected layers. The model with the second best accuracy [4] achieved 66% using an ensemble of CNNS. III. MODEL Two different models have been evaluated in this paper in accordance to their accuracy and number of parameters. Both of them aim at achieving the best accuracy over number of parameters ratio. Two major problems faced in image detection can be overcome by reducing the number of parameters. Firstly, using small CNNs improve slow performances in hardware constrained systems such as robot platforms. Secondly, when we reduce the parameters, we get a better generalization under an Occam s razor framework. The first model designed relies on the complete elimination of the fully connected layers. On the other hand, the second architecture involves the deletion of the fully connected layers and the inclusion of the combined depth-wise separable convolutions and residual modules. The above two models have been trained using the ADAM optimizer [8]. Our initial architecture makes use of Global Average Pooling to completely remove the fully connected layers. The last convolutional layer will have the same number of feature maps as number of classes, and a softmax activation function is applied to each reduced feature map. The initial architecture proposed is a standard fullyconvolutional neural network which has 9 convolutional layers, ReLUs [5], Batch Normalization [7] and Global Average Pooling. It contains on an average 600,000 parameters. The FER-2013 dataset which contains 35,887 grayscale images where each image belongs to one of the following classes: angry, disgust, happy, sad, fear, surprise, neutral also helps in validating gender classification algorithms and models. Using this dataset, our initial model reached an accuracy of 66%. Here we will call this model as sequential fully-cnn.the second model is driven by the Xception [1] architecture. The architecture is the combination of residual modules [6] and depth-wise separable convolutions [2]. Residual models are responsible for the modification of desired mapping between two consecutive layers. Thus, the learned features become the difference of the original feature maps and the desired features. As a result of this, the desired features H(x) are modified in oreder to solve an easier learning problem F(X) such that : H(x) = F(X) + x (1) (a) (b) Fig. 2: Difference between (a)standard convolutions and (b)depth-wise separable convolutions. Fig. 3: Proposed model for real-time classification. Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 358

385 Nishchal Poornadithya C. et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, Based on the initial architecture, we reduced the amount of parameters by eliminating the last fully connected layer. This was achieved using the depth wise separable convolutions. These are composed of two different layers: depth wise convolutions and point wise convolutions. These layers are used mainly to separate the spatial crosscorrelations from the channel cross-correlations [1]. Firstly, this is done by applying a D x D filter on every M input channels and then applying N 1 x 1 x M convolution filters to combine the M input channels into N output channels. Applying 1 x 1 x M convolutions combines each value in the feature map without considering their spatial relation within the channel. Depth wise separable convolutions reduces the computation with respect to the standard convolutions by a factor of 1/N + 1/D 2. Refer Fig. 2 to bring out the difference between a normal convolution layer and a depth wise separable convolution. Our final architecture represents a fullyconvolutional neural network that contains 4 residual depthwise separable convolutions where each convolution is followed by a batch normalization operation and a ReLU activation function. The final layer makes use of a global average pooling and a softmax activation function to produce a prediction. This model has around 60,000 parameters; which results in a reduction of 10x whem compared to our initial naive implementation, and 80x when compared to the original CNN. Fig. 3 displays our complete final architecture which we refer to as mini-xception. This architecture has reached a precision of 95% in gender classification task. There is a reduction of one percent with respect to our initial implementation. However, when tested on the FER-2013 dataset, the model obtained the same accuracy of 66% for the emotion classification task. The file size of our final architecture is a 855 kilobytes. On reduction of the architecture s computational cost we are able to join both models and use them consecutively in the same image without any serious reduction in time. The entire pipeline including the opencv face detection module, the gender classification and the emotion classification takes / ms on an i5-4210m CPU. This results in a speedup of 1.5x when compared to the original architecture of Tang. We also added to our implementation a real-time guided back-propagation visualization to observe which pixels in the image activate an element of a higher-level feature map. Given a CNN with only ReLUs as activation functions for the intermediate layers, guided-back propagation takes the derivative of every element (x, y) of the input image I with respect to an element (i, j) of the feature map f L in layer L. The reconstructed image R filters all the negative gradients; consequently, the remaining gradients are chosen such that they only increase the value of the chosen element of the feature map. IV. RESULTS The real-time emotion classification task in unseen faces can be observed in Fig. 4. Although we did not gather enough from the statistics, on a general basis, we found the application to be useful. The CNN model was able to perform well and predict good results in the faces that it had not witnessed before. Therefore it is safe to assume that its accuracy Fig. 4:Results of the provided real-time emotion classification could be increased by provinding more varied dataset and improving the quality of data.the result were not state of the art but they could be appreciated, given the constraints like our limited dataset and simple architecture.moreover, we can also observe that the features learned in our mini- Xception model are more interpretable than the ones learned from our sequential fully-cnn. Consequently the use of more parameters in our naive implementations leads to less robust features. V. CONCLUSIONS CNN is very effective when in image processing. Most of the times if there is a problem in emotion detection, then there may be problems like poor quality of image, low contrast images etc.thus, the basic model proposed here may not turn out to be the best. Although we only obtained a test accuracy of 66%, the result is stunning given our dataset and architecture. With more time to fine tune the many parameters of our network, we are highly confident that we could find a more tailored CNN architecture that suits our particular goal of emotion detection. Our project serves as an example as to how resilient evem a simple convolutional network could be under cicumstances. In practice, we can be sure that neural networks have a greater predictive power if we were to place our faces in a specified location of the camera.hence, this leads us to believe that we could use the better cameras of high resolution to get good images and train the model adequately. Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 359

386 Nishchal Poornadithya C. et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, REFERENCES [1] François Chollet. Xception: Deep learning with depthwise separable convolutions. CoRR, abs/ , [2] Andrew G. Howard et al. Mobilenets: Efficient convolutional neural networks for mobile vision applications. CoRR, abs/ , [3] Dario Amodei et al. Deep speech 2: End-to-end speech recognition in english and mandarin. CoRR, abs/ , [4] Ian Goodfellow et al. Challenges in Representation Learning: A report on three machine learning contests, [5] Xavier Glorot, Antoine Bordes, and Yoshua Bengio. Deep sparse rectifier neural networks. In Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, [6] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, [7] Sergey Ioffe and Christian Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. In International Conference on Machine Learning, [8] Diederik Kingma and Jimmy Ba. Adam: A method for stochastic optimization. arxiv preprint arxiv: , [9] Rasmus Rothe, Radu Timofte, and Luc Van Gool. Deep expectation of real and apparent age from a single image without facial landmarks. International Journal of Computer Vision (IJCV), July [10] Karen Simonyan and Andrew Zisserman. Very deep convolutional networks for large-scale image recognition. arxiv preprint arxiv: , [11] Jost Tobias Springenberg, Alexey Dosovitskiy, Thomas Brox, and Martin Riedmiller. Striving for simplicity: The all convolutional net. ArXiv preprint arxiv: , [12] Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna. Rethinking the inception architecture for computer vision. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, [13] Yichuan Tang. Deep learning using linear support vector machines.arxiv preprint arxiv: , Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 360

387 Volume 9, Special Issue No. 3, May 2018 International Journal of Advanced Research in Computer Science (ISSN: ) CONFERENCE PAPER Available Online at WHOLE EXOME SEQUENCE ANALYSIS TO PREDICT FUNCTIONAL BIOMARKERS FOR PANCREATIC CANCER Maheswari L Patil Research Scholar, Akkamahadevi Women s University Vijayapura Karnataka India navi.maheswari@gmail.com Prof. Shivakumar B Madagi Professor and Head Akkamahadevi Women s University Vijayapura Karnataka India madagisb@gmail.com Dr. C. N. Prashantha Assistant Professor, Department of Biotechnology REVA University, Bangalore prashanthacn@gmail.com Abstract: NGS is commonly called as massive-parallel sequencing, a technology that enabled genome to be sequenced efficiently. One of the applications of NGS is Whole exome sequencing that proves to be an effective method to to detect disease-causing variants and discover gene targets. Using whole exome sequencing data it is possible to identify clinical interventions of differentially expressed genes, somatic variants and targeted pathways to develop and assess the efficacy of novel therapies. The current work used pancreatic cancer genome sequencing data to predict significant gene variants by using exome sequencing data analysis. Here predicted 934 gene mutations identified by Exome-seq were validated by mrna-seq. Further focused on gene expression and predicted 914 genes are differentially expressed that covers mrna-sequences. There are 20 mutations such as HRNR, MERTK, RPL14, GLT6D1, NEO1, ZNF208, ZNF226, DNAH9 and SEZ6L is significantly involved in different pathways that integrated insulin secretion with in islet cells of pancreas and also involved in diabetes mellitus. Based on gene mutations, it is possible to screen the drugs based on pharmacogenomic characters that are functionally involved in pancreatic cancer pathways. These observations provide genetic predictors of outcome in pancreatic cancer and have implications for new avenues of therapeutic intervention. Keywords: Pancreatic cancer, whole Exome Sequencing, Genomics, cancer, NGS, Sequencing 1. INTRODUCTION Pancreatic cancer is the fourth leading cause for deaths due to cancer in the world wide [1-4].Some of the risk factors may be the reason for developing pancreatic cancer. Such as Age, Gender, Race and Ethnicity, Smoking, Diabetes, Intake of alcohol, Rare inherited conditions, Family history, gene alterations, reproductive factors, vitamin D, diabetes, chronic pancreatitis, and so on [5]. Three categories of Genes are reason for the pancreatic cancer tumor-suppressor genes [6] [7]; oncogenes, and DNA mismatch-repair genes [8]. Some of the diagnostic imaging techniques have been developed such as multi-detector-row computed tomography (MDCT), magnetic resonance imaging (MRI) and endoscopic ultrasound (EUS) ability to diagnose pancreatic carcinoma [9]. Surgical resection, radiotherapy, and chemotherapy are normally used for patients with pancreatic cancer [10]. NGS technology is being used to know the mutations in the genes responsible for the cancer. Sequence variants are identified using NGS technology which further helps in developing personalized medicine. Next Generation sequencing (NGS) technologies also called High Throughput Sequencing perform massive parallel sequencing, where millions of fragments of DNA from a single sample are accurately sequenced with cheaper in cost[11]. Next-generation sequencing (NGS) represents an effective method to capture genomic information about a cancer [12]. NGS is used in many fields related to biological sciences. NGS is helpful in gene expression profiling, studying protein binding sites in genomic DNA, study of entire genome of particular organism and many more [13]. Cancer is commonly called diseases of Genes which is caused by the genetic predisposition. Cancer genome sequencing is sequencing single, homogeneous or heterogeneous group of cancer cells. In recent year high- throughput sequencing technologies, provided means to study the cancer genome [14] and all somatic alterations in the cancer genomes [15]. Analyses of cancer genome sequences and structures provide insights for understanding cancer biology, diagnosis and therapy. Analysis of the cancer data revealed that tumour carries ~ genes with somatic alterations to single nucleotides or short base insertions and deletions [16]. NGS sequencing has Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 361

388 Maheswari L Patil et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, application include DNA-seq, RNA-seq, ChIP-seq, and methyl-seq, DNA-seq applied to whole genome sequencing (WGS), WES, or a specific targeted region of the genome [17]. The whole exome sequencing (WES) is a transcriptomics technique for sequencing all of the protein-coding genes in a genome. Using WES, it is possible to check germline and somatic mutations in cancer [18]. MATERIAL AND METHODS With the development of next-generation sequencing (NGS) technology, there is a tremendous progress in terms of speed, read length, and throughput, along with reduction in per-base cost. With these advances of NGS provided a way for the development of a large number of NGS applications. NGS applications commonly available are RNA-seq, ChIP-seq, and methyl-seq, whole genome sequencing (WGS), WES. The main goal of WGS or WES is to discover genomic variations in the form of single nucleotide variants (SNVs), DNA insertions or deletions (indels), copy number variations (CNVs), or other structural variants (Svs) [17]. Furthermore is to know the association of these variations to human disease and to develop new therapeutic strategies, to develop medicines. In the current work whole exome sequencing data of pancreatic cancer datasets is selected from ENA database. The pancreatic cancer has 3 samples that were sequenced using Illumine sequencer. The data selected used for quality analysis, preprocessing, alignment and variant analysis. The FastQC [19] tool performs quality control checks on sequencing data to predict quality of the raw reads to identify possible sequencing errors and filtering so that low quality scores were discarded. This step aims to remove low quality read. If necessary this is followed by the preprocessing steps such as base trimming, read filtering, or adaptor clipping which are performed prior to the alignment using Cutadapt[20] and Trimmomatic [21] tools. Further for filtering of high quality score sequences, alignment is done using the Bowtie2 [22] ( tool. The alignment of sequence reads is done by mapping reads to the reference genome. The reference whole genome sequence dataset used is hg19.fa and the sequence that is resulted is stored in.sam format. To convert aligned files of SAM to BAM, samtool program is executed which is followed by using samtool [23] sorting program to sort the sequences based on chromosomes. This sorted result is converted to mpileup and vcf file formats for prediction of the variants. Next is the post-alignment processing consists of duplicate removal, indel realignment, and base quality score recalibration. Picard [24] ( mark duplicate program is used to read the duplicates and removed by using remove duplicate algorithm. For identification of the genomic regions that contain indels and for improvement of the alignment quality in the target region indelrealigner from the Genome Analysis Toolkit (GATK) [25] [26] is used. Then is base quality score recalibration, commonly BQSR programs ie, BaseRecalibrator from the GATK is performed on data. Further for variant analysis, many programs are available for germline variant calling. In the current work GATK Tool kit programs are performed to predict somatic and germline variants that involved in pancreatic cancer. GATK algorithms such as RealignerTargetCreator are used for realignment to determine insertion and deletion (InDels) in the individual genome with respect to the reference genome. SIFT database is used to predict the physical properties of amino acids and amino acid substitution that affects protein function based on sequence homology. SIFT also predicts naturally occurring non-synonymous polymorphisms and missense mutations. In the present work DAVID functional annotation tool is selected for studying the functional annotation and enrichment analysis to predict gene functions that involved in different pathways and to predict the functional pathways PANTHER database is selected. The classification system is designed to classify proteins and their genes in order to facilitate highthroughput analysis. Proteins have been classified according to family and subfamily, molecular function, biological process and pathways similar to biological process. Proteins have been classified according to family and subfamily, molecular function, biological process and pathways similar to biological process, but a pathway also explicitly specifies the relationships between the interacting molecules with nssnp. The non-synonymous SNPs of novel genes are used to predict the pathways and pharmacogenomic characters to specify the drugs used for the treatment of specific gene mutations. PharmaGKB database is used to predict pharmacogenetic studies that influence genetic variations on drug response and disease predisposition. 2. RESULTS AND DISCUSSION The sequence reads of pancreatic cancer were initially tested the quality using FastQC and the results shows all the sequences reads and parameters shows high quality with a minimum length of 99 nucleotide bases of all sequence length. The high quality paired end sequence reads is used for reference based sequence mapping using Bowtie2. The reference genome of Homo sapiens with 3.5 GB file used for analysis was Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 362

389 Maheswari L Patil et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, downloaded from Ensemble genome browser. Bowtie2 tool with default parameters is used for mapping and the mapping statistics were listed in Table I. Table I: Mapping of exome sequences with reference genome Sample_data_sets Mapping ERR % ERR % ERR % Aligned reads and sorted bam files are used for variant analysis. Genomic variants predicted using GATK tool kit is in Table II. By using Exome-seq data, identified a total of 61.6% (934 out of 1517) of the mutations, the data identified by Exome-seq were validated by mrnaseq. If focused on the expressed genes, 94.3% (914 out of 969) of the mutations at those loci covered by five or more cdna sequence reads were successfully validated by mrna-seq. Additionally, 20 mutations at the loci with a lower coverage (less than five reads, but three reads or more) were also confirmed by mrna-seq. The percentages of mutations validated by mrna-seq varied across mutation types. Generally, the validation ratio of truncating mutations is lower than that of non-truncating mutations. The genomic variants for the 3 samples of pancreatic cancer are predicted using the GATK using the methods explained in Methodology. Table II: Genomic variants predicted using GATK tool kit Variants ERR232253_1_2 ERR232254_1_2 ERR232255_1_2 non-intronic variants Coding variants 99% (380 out of 381) 99% (386 out of 387) 99% (382 out of 383) Coding variants predicted 44% (170 out of 380) 44% (170 out of 386) 43% (166 out of 382) Tolerated 82% (140 out of 170) 82% (140 out of 170) 79% (132 out of 166) Damaging 18% (30 out of 170) 18% (30 out of 170) 21% (34 out of 166) Non-synonymous 50% (192 out of 380) 48% (189 out of 386) 47% (183 out of 382) Synonymous 50% (188 out of 380) 52% (197 out of 386) 53% (199 out of 382) Novel 2% (10 out of 381) 2% (8 out of 387) 2% (9 out of 383) The GATK analysis tool kit helps to analyze structural variants and copy number variants (CNVs). Also Genetic mutations were predicted from Genome Analysis Tool Kit (GATK) which is listed in Table III. Table III: Genetic mutations were predicted from Genome Analysis Tool Kit (GATK) Numb Chromoso Gene Re Supporti Mutant er of me ad ng allele somatic indels de pt reads ratio Mutation e Typ # SIFT prediction chr19 ABCA % AA_DELETION - 2 chr6 AKD % AA_DELETION - 1 chr19 CYP4F % AA_DELETION - 4 chr1 UBE2Q % FRAMESHIFT STOP chr19 SPTBN % FRAMESHIFT STOP chr22 SFI % FRAMESHIFT STOP chr3 PBRM % FRAMESHIFT STOP 4 chr1 TTF % FRAMESHIFT STOP chr11 TECTA % FRAMESHIFT STOP chr4 ARHGAP % FRAMESHIFT STOP chr7 AUTS % AA_INSERTION - 5 chr1 RERE % FRAMESHIFT STOP chr15 FMN % FRAMESHIFT STOP chr19 ELAVL % FRAMESHIFT - chr22 SERPIND % FRAMESHIFT STOP chr3 1 ATP11B % FRAMESHIFT STOP 9 chr1 MIA % FRAMESHIFT STOP chr10 CHUK % FRAMESHIFT STOP Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 363

390 Maheswari L Patil et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, chr11 DCDC % FRAMESHIFT STOP chr14 C14orf % FRAMESHIFT N/A chr15 ADAMTSL % FRAMESHIFT STOP chr16 3 CACNA % FRAMESHIFT STOP chr19 H ZNF % FRAMESHIFT - chr3 VHL % FRAMESHIFT - chr9 SVEP % FRAMESHIFT STOP 3 chr10 EIF3S % FRAMESHIFT STOP chr12 SBNO % FRAMESHIFT STOP chr3 PBRM % FRAMESHIFT STOP 6 chr1 DNALI % FRAMESHIFT STOP chr11 ADRBK % FRAMESHIFT STOP chr13 KPNA % FRAMESHIFT STOP chr3 VHL % FRAMESHIFT - chr5 FBXO % FRAMESHIFT STOP chr9 ZNF % FRAMESHIFT - 5 chr14 ZFP36L % FRAMESHIFT - chr18 KIAA % FRAMESHIFT STOP chr2 2 TBC1D % FRAMESHIFT STOP chrx chrx JARID1 C JARID1 C % FRAMESHIFT STOP % FRAMESHIFT STOP Functional Analysis Here 20 functional gene mutations were observed in pancreatic cancer and these gene mutations were evaluated using functional enrichment analysis. The most significant biological functions with 12 novel genes are functionally involved in cell-differentiation, cell development and disease progression with in pancreatic cells. The dysregulation of HRNR, MERTK, RPL14, GLT6D1, NEO1, ZNF208, ZNF226, DNAH9 and SEZ6L are significantly involved in different pathways that integrated insulin secretion with in islet cells of pancreas and also involved in diabetes mellitus. Non synonymous genetic mutations listed in Table IV and functional analyses of mutated genes were listed in Table V. Table IV: Overall lists of non synonymous genetic mutations CHROM POS REF_ALLELE ALT_ALLELE GENE_NAME dbsnp chr T C NOC2L rs chr G A NOC2L rs chr G C PLEKHN1 rs chr G A ISG15 rs1921 chr T C TAS1R3 rs chr T C MIB2 rs chr C T SLC35E2B rs chr G A SLC35E2 rs chr A G TMEM52 rs chr G A C1orf86 rs chr T C PANK4 rs chr A G TNFRSF14 rs4870 chr A G MMEL1 rs chr G A ACTRT2 rs chr T C PRDM16 rs chr C T ARHGEF16 rs chr C G MEGF6 rs chr G C WRAP73 rs chr C G CCDC27 rs chr T C CCDC27 rs Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 364

391 Maheswari L Patil et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, chr G C C1orf174 rs chr G A AJAP1 rs chr G C RNF207 rs chr T C TNFRSF25 rs chr G A NOL9 rs chr A G TAS1R1 rs chr G C DNAJC11 rs chr T C PER3 rs chr C T UTS2 rs chr G A UTS2 rs chr G T SLC45A1 rs chr C T CA6 rs chr A C CA6 rs chr T G CA6 rs chr G A GPR157 rs chr G A H6PD rs chr T C TARDBP rs chr C A MASP2 rs chr G A PTCHD2 rs chr G A FBXO44 rs Table V: Functional Analysis of mutated genes involved in pancreatic cancer Description P-value FDR q- Enrich Genes value ment response to monosaccharide 2.46E E HRNR, MERTK endocrine pancreas development 2.46E E RPL14, GLT6D1, NEO1 response to hexose 2.46E E ZNF208 response to glucose 2.46E E ZNF226, DNAH9, SEZ6L regulation of neural precursor 3.63E E SALL1, INSM1, PAX6, PTBP2, ASCL1, cell proliferation response to carbohydrate 3.75E E NEUROD1, NPTX1, NKX2-2 cell fate determination 3.75E E NKX2-2, PAX6, ISL1, ASCL1 type B pancreatic cell 4.22E E NKX2-2, INSM1 development glandular epithelial cell 5.09E E NEUROD1, INSM1, PAX6, ASCL1 differentiation pancreatic A cell fate 8.43E E NEUROD1, NKX2-2 commitment pancreatic PP cell fate 8.43E E NEUROD1, NKX2-2 commitment epithelial cell fate commitment 8.43E E NEUROD1, NKX2-2 positive regulation of neuron 1.12E E SALL1, NEUROD1, NEUROG3, differentiation NEUROD2, NKX2-2, ASCL1 columnar/cuboidal epithelial cell development 1.22E E NKX2-2, INSM1 glandular epithelial cell 1.22E E NKX2-2, INSM1 development cellular response to glucose 1.31E E NEUROD1, NPTX1, UCP2 stimulus cellular response to hexose 1.31E E NEUROD1, NPTX1, UCP2 - stimulus cellular response to 1.31E E NEUROD1, NPTX1, UCP2 monosaccharide stimulus glial cell fate specification 1.63E E NKX2-2, PAX6, ASCL1 oligodendrocyte cell fate 1.63E E NKX2-2, PAX6, ASCL1 specification Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 365

392 Maheswari L Patil et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, enteroendocrine cell differentiation regulation of neuron differentiation 1.63E E NEUROD1, INSM1, PAX6 1.72E E SALL1, NEUROG1, NEUROD1, NEUROD2, NKX2-2, PAX6, ISL1, ASCL1, STMN2 epithelial cell development 3.33E E NKX2-2, INSM1 neurogenesis 4.15E E NEUROD1, ANXA1, ASCL1 carbohydrate homeostasis 5.68E E NEUROD1, NPTX1, PAX6, UCP2 glucose homeostasis 5.68E E NEUROD1, NPTX1, PAX6, UCP2 cellular response to carbohydrate stimulus 5.74E E NEUROD1, NPTX1 regulation of neurogenesis 6.86E E SALL1, NEUROG3, NEUROD1, NEUROD2, NKX2-2, PAX6, ISL1, ASCL1, STMN2, positive regulation of neural precursor cell proliferation 9.27E E INSM1, PAX6, ASCL1 Pharmacogenomic analysis of genetic variants linked with drug molecules is carried out, resulted with prediction of Cetuximab, Erlotinib, Exemestane, Gefitinib, Imatinib, and other molecules are more effective with selected pancreatic cancer genomic variants, which listed in Table VI. If the associations of genotypes with drug-induced phenotypes are reproducible and have large effect sizes, clinical use of such information can be implemented for patient benefit. Table VI: Drugs are more sensitive to pancreatic cancer and other related cancer types Drug Drug target Cancer type (or types) Somatic markers Cetuximab EGFR Colorectal, head and neck EGFR and KRAS Erlotinib EGFR Lung, pancreatic EGFR Exemestane Aromatase Breast ESR1, ESR2 and PGR Gefitinib EGFR Lung EGFR Imatinib BCR ABL, KIT and PDGFRa Chronic myeloid leukaemia, Philadelphia chromosome, tyrosine kinases gastrointestinal KIT and PDGFRA Lapatinib ERBB2 receptor Breast ERBB2 Letrozole Aromatase Breast ESR1, ESR2 and PGR Panitumumab EGFR Colorectal EGFR and KRAS Tamoxifen Oestrogen receptor Breast ESR1, ESR2 and PGR Trastuzumab ERBB2 receptor Breast, stomach ERBB2 3. CONCLUSION There are many computational challenges that arise when developing translational cancer genomics applications, particularly those with the goal of personalized oncology. Here used personalized exome-seq pipelines to analyze sequence data from pancreatic cancer and have predicted 1567 gene variants of which 20 genes, involved in pancreatic cancer. Further have done functional annotation and enrichment analysis shows 12 genes have involved in many biological, cellular and functional characters. The results have listed KRAS associated gene mutations along with HRNR, MERTK, RPL14, GLT6D1, NEO1, ZNF208, ZNF226, DNAH9 and SEZ6L genes, these genes were used for functional biomarkers. We have also identified pharmacogenomic properties indicate the need to re-evaluate preclinical models of therapeutic response in the context of genomic medicine. 6. REFERENCES [1] Li J., Wientjes, M. G., and Au, J. L.-S Pancreatic Cancer: Pathobiology, Treatment Options, and Drug Delivery. The AAPS Journal, 12(2): [2] Khanal N., Upadhyay, S., Dahal, S., Bhatt, V. R., & Silberstein, P. T Systemic therapy in stage IV pancreatic cancer: a population-based analysis using the National Cancer Data Base. Therapeutic Advances in Medical Oncology, 7(4): [3] Michel M. Murr, Michael G. Sarr, Andrew J. Oishi, Jon A. van Heerden PancreaticCancer. CA: A Cancer Journal for Clinicians, 44(5): [4] Fang, Y., Yao, Q., Chen, Z., Xiang, J., William, F. E., Gibbs, R. A., & Chen, C Genetic and molecular alterations in pancreatic cancer: Implications for personalized medicine. Medical Science Monitor : International Medical Journal of Experimental and Clinical Research, 19: [5] Qiubo Zhang, Linjuan Zeng, Yinting Chen, et al Pancreatic Cancer Epidemiology, Detection, and Management. Gastroenterology Research and Practice, [6] Winter, J. M., Maitra, A., & Yeo, C. J Genetics and pathology of pancreatic cancer. HPB : The Official Journal of the International Hepato Pancreato Biliary Association, 8(5): [7] Maitra, A., & Hruban, R. H Pancreatic Cancer. Annual Review of Pathology, 3: Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 366

393 Maheswari L Patil et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, [8] Dong, X., Li, Y., Hess, K. R., Abbruzzese, J. L., & Li, D DNA Mismatch Repair Gene Polymorphisms Affect Survival in Pancreatic Cancer. The Oncologist, 16(1): [9] Miura, F., Takada, T., Amano, H., Yoshida, M., Furui, S., & Takeshita, K Diagnosis of pancreatic cancer. HPB : The Official Journal of the International Hepato Pancreato Biliary Association, 8(5): [10] Reynolds, R. B., & Folloder, J Clinical Management of Pancreatic Cancer. Journal of the Advanced Practitioner in Oncology, 5(5): [11] Raza, K., & Ahmad, S Principle, analysis, application and challenges of next-generation sequencing: a review. arxiv preprint, arxiv: [12] Gagan and Van Allen Next-generation sequencing to guide cancer therapy. Genome medicine, 7:80. [13] Buermans H.P.J., J.T. den Dunnen Next generation sequencing technology: Advances and applications, Biochimica et Biophysica Acta (BBA) - Molecular Basis of Disease; [14] Yadong Yang, Xunong Dong, Bingbing Xie, Nan Ding, Juan Chen, Yong jun Li, et al. 2015, Databases and Web Tools for Cancer Genomics Study Genomics, Proteomics & Bioinformatics 13(1): [15] Lynda Chin, William C, Hahn, Gad Getz and Matthew Meyerson Making sense of cancer genomic data. Genes and Development,Cold Spring Harbor Laboratory Press, 5: [16] Matan Hofree, Hannah Carter, Jason F. Kreisberg, Sourav Bandyopadhyay, Paul S. Mischel,Stephen Friend Trey. Ideker Challenges in identifying cancer genes by analysis of exome sequencing data. Nature Communications, 7: [17] Bao et al Review of Current Methods, Applications, and Data Management for the Bioinformatics Analysis of Whole Exome Sequencing. Cancer Informatics, 13(S2): [18] Bahareh Rabbani, Mustafa Tekin and Nejat Mahdieh The promise of whole-exome sequencing in medical genetics. Journal of Human Genetics 59(5):15. [19] Andrews S. FastQC. Babraham Bioinformatics; Cambridge, UK Available from ast qc. [accessed 11 December 2017] [20] Martin M Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J, 17:10 2. [21] Bolger AM, Lohse M, Usadel B. 2014, Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics, 30(15): [22] Langmead B. and S. L. Salzberg Fast gapped- read alignment with Bowtie 2. Nature Methods, 9(4):57 359, [23] Li H., B. Handsaker, A. Wysoker et al The Sequence Alignment/Map format and SAMtools. Bioinformatics, 25(16): [24] Takeshi Ogasawara, Yinhe Cheng, Tzy-Hwa Kathy Tzeng. Sam2bam: High-Performance Framework for NGS Data Preprocessing Tools. arxiv: [q- bio.gn] [25] McKenna. A, M. Hanna, E. Banks et al The genome analysis toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Research, 20(9): , [26] Huang D. W., B. T. Sherman, and R. A. Lempicki Bioinformatics enrichment tools: paths towards the comprehensive functional analysis of large gene lists. Nucleic Acids Research, 37(1): Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 367

394 Volume 9, Special Issue No. 3, May 2018 International Journal of Advanced Research in Computer Science (ISSN: ) CONFERENCE PAPER Available Online at APPROACH TO TEXT EXTRACTION FROM IMAGE Disha Bhat School of Computing and Information Technology REVA UNIVERSITY, Bangalore, India Dimple M K School of Computing and Information Technology REVA UNIVERSITY, Bangalore, India Charitha D School of Computing and Information Technology REVA UNIVERSITY, Bangalore, India Amruthashree R V School of Computing and Information Technology REVA UNIVERSITY, Bangalore, India Shruthi G School of Computing and Information Technology REVA UNIVERSITY, Bangalore, India Abstract: The multimedia resources in a database and on the web are increasing. The multimedia resources can be images and videos. It has become a very difficult task to develop the effective methods to manage as well as to retrieve these resources by their content. The Text which is an important object which carries high-level semantic information which is useful for this task. The current technologyis optical character recognition (OCR) is used to convert machine generated text which is printed against clean background to computer readable form (ASCII). But, text isoften printed against shaded or textured backgrounds or is embedded in images.examples include maps, photographs, advertisements, videos etc. Current document segmentation and recognition technologies cannot handle these situations well. Our system takes advantages of the distinctive characteristics of text that make it stand out from other image material that is, text possesses certain frequency and orientation information. We will first clean the image by changing the contrast and gradient of the image. Now the objects in the images are identified and numbered. Further in the text recognition process, these numbered objects are segregated into text and non-text. Later the recognised text is reconstructed to form a meaningful text present in the image. Also we are focusing on extracting the text such that certain portion of the images such as logos etc is retained. This is done by calculating the pixels of the required portion of the image to be retained and then training the system in such a way that it extracts all the text except the portion of the image to be retained. Keywords: image, text, extraction I. INTRODUCTION Today, the vast majority of the data is accessible on paper or as photos or recordings. Expansive data is put away in pictures. The present innovation is confined to removing content against clean foundations. Subsequently, there is a requirement for a framework to extricate content from general foundations. These are various applications in which text extraction is useful. These applications include digital libraries, multimedia systems, information retrieval system and geographical information system. The role of text that can be directly highlighted to user or fed into an optical character reader module for recognition. In this a new system is proposed which extracts text in images and also retains certain portion of the image such as a logo. The system takes coloured images as input. It detects text on the basis of certain text features: text possesses certain frequency and orientation information. Text shows spatial cohesion characters of the same text string are of similar heights, orientation and spacing. The image is then cleaned up so that the text stands out, to retain the logo or a certain part of the image. First calculate the pixels of the part of the image to be retained and then instruct the system in such a way that it extracts all other text from the image except that certain part. The optical character recognition technology has three types. The OCR, which targets on typewritten text, one glyph or character at a time. The ICR (Intelligent character recognition) which targets handwritten print script or cursive text one glyph or character at a time. The IWR (Intelligent Word Recognition) which targets one word at a time. All these technologies are unable to properly reconstruct the text after text recognition. So this motivated us to build such a system which can reconstruct the text properly. To improve the current and existing technology of OCR. To make the text reconstruction process more reliable and accurate. To retain certain part of the image such as logo this need not be extracted. Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 368

395 Disha Bhat et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, To make a cost effective software and make it useful for people. Image Acquisition II. LITERATURE SURVEY The overwhelming volume of paper based data in corporation and offices challenges their ability to manage documents and records.computers work efficiently and faster than human operators. Computers understand alpha numeric characters therefore the English letter should be into computer recognisable form.ocr allows us to convert a document into electronic text, it effectively separates individual characters.ocr has become one of the most successful application in pattern recognition and AI. Pre-Processing Segmentation Feature Extraction Classification Text Processing Figure 1: OCR Generation 1 st Generation OCR System Generation was IBM 1418 Designed to read a special IBM font 407 Recognition method was template matching 2 nd generation OCR System We were able to recognise regular machine printed and hand printed characters Characters and numbers were limited 3 rd Generation OCR System Large documents of handwritten were recognised easily Low cost and high performance Generation OCR System (OCR s today) Documents can be intermediated with text, graphics, tables and mathematical symbols, colour documents and noisy documents. 4 th Figure 2: Tasks involved in OCR Image acquisition: Input image for OCR system might be acquired by scanning document or by capturing photograph of document. This is also known as digitization process. Pre-processing:Pre-processing comprise arrangement of tasks and it used to upgrade a picture and make it reasonable for division. Commotion gets presented amid archive age. So Proper channel like mean channel, min-max channel, Gaussian channel and so forth might be connected to expel clamor from archive. Binarization process changes over dark scale or hued picture to high contrast picture. To upgrade perceivability and basic data of character Binary morphological activities like opening, shutting, diminishing, gap filling and so forth might be connected on picture. In the event that examined picture isn't be flawlessly adjusted, so we have to adjust it by performing incline edge amendment. Info report might be resized in the event that it is too extensive in size to lessen measurements to enhance speed of preparing. Segmentation: Character segmentation performs an operation of decomposition of an image into Sub images of individual symbols. It is one of the decision processes in a system for optical character recognition (OCR). Its decision that a pattern isolated from the image is that of a character or some other identifiable unit. Generally document is processed in hierarchical way. At first level lines are segmented using row histogram. From each row, words are extracted using column histogram and finally characters are extracted from words. Accuracy of final result is highly depends on accuracy of segmentation. Feature extraction: Feature extraction is the important part of any pattern recognition application. Feature extraction techniques like Linear Discriminant Analysis (LDA), Principle Component Analysis (PCA),Independent Component Analysis (ICA), Chain Code (CC), Scale Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 369

396 Disha Bhat et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, Invariant Feature Extraction (SIFT),Gradient based features, Histogram might be applied to extract the features of individual characters. These features are used to train the system. Classification: When image is provided as input to OCR system, its features are extracted and given as an input to the trained classifier like artificial neural network or support vector machine. Classifiers compare the input feature with stored pattern and find out the best matching class for input. Post processing:this progression isn't necessary; it enhances the precision of acknowledgment. Sentence structure examination, semantic investigation sort of more elevated amount ideas may be connected to check the setting of perceived character. OCR Classification based on Fonts Based on the OCR systems capability to recognize different character sets, a classification [Line, 1993], by the order of difficulty is as follows. Fixed font OCRs OCR machines of this category deal with the recognition of characters in only one specific typewritten font. Examples of such fonts are OCR-A, OCR-B, Pica, Elite, etc. These fonts are characterized by fixed spacing between each character. The OCR-A and OCR-B are the American and European standard fonts specially designed for optical character recognition, where each character has a unique shape to avoid ambiguity with other characters similar in shape. Using these character sets, it is quite common for commercial OCR machines to achieve a recognition rate as high as 99.99% with a high reading speed. The first generation OCRs were fixed font machines, and the methods applied were usually based on template matching and correlation. Multi-font OCRs Multi-text style OCR machines perceive characters from in excess of one textual style, rather than a settled text style framework, which could just perceive images of one particular textual style. For the prior age OCRs, the breaking point in the quantity of perceived text styles was because of the example acknowledgment calculation utilized: format coordinating, which required that a library of bit outline of each character from every textual style was put away (prerequisite of a tremendous database). The exactness is very great, even on corrupted pictures, as long as the text styles in the library are chosen with mind. Omni font OCRs An omni text style OCR machine can perceive images of most non-adapted textual styles without maintaining gigantic databases of particular textual style data. Generally omni text style innovation is described by the utilization of highlight extraction. The database of an omni text style framework will contain a depiction of every image class rather than the images themselves. This gives adaptability in programmed acknowledgment of characters from an assortment of text styles. Various current OCR-frameworks for English claim to be omni text style. In spite of the fact that omni text style is the normal term utilized for these OCRs, this does not imply that they perceive characters from every single existing textual style. Textual style characterization can diminish the quantity of elective shapes for each class, driving basically to singletext style character acknowledgment [Zhu, Tan and Wang, 2001]. Following is the review of methodologies utilized as a part of the writing for perceiving text styles in English. III.SYSTEM ANALYSIS AND DESIGN Optical character acknowledgment (OCR) strategy has been utilized as a part of changing over printed content into editable content. OCR is exceptionally valuable and prominent strategy in different applications. Exactness of OCR can be subject to content pre-preparing and division calculations. Here and there it is hard to recover content from the picture due to various size, style, introduction, complex foundation of picture and so forth. We start this paper with a presentation of Optical Character Recognition (OCR) strategy, History of Open Source OCR device Tesseract, design of it and test consequence of OCR performed by Tesseract on various types pictures are talked about. We finish up this paper by similar investigation of this device with other business OCR apparatus Transym OCR by considering vehicle number plate as info. From vehicle number plate we endeavoured to extricate vehicle number by utilizing Tesseract and Transym and thought about these devices in light of different parameters. Optical Character Recognition (OCR), Open Source, DLL, Tesseract, Transym Optical character Recognition (OCR) is a change of checked or printed content pictures, written by hand message into editable content for additionally handling. This innovation enables machine to perceive the content consequently. It resembles mix of eye and psyche of human body. APPLICATION Optical character recognition has been applied to a number of applications. Some of them have been explained below. An eye can see the content from the pictures however really the mind forms and in addition deciphers that separated content read by eye. Being developed of automated OCR framework, couple of issues can happen. In the first place: there is almost no unmistakable distinction between a few letters and digits for PCs to get it. For instance it may be troublesome for the PC to separate between digit "0" and letter "o". Second: It may be exceptionally hard to separate content, which is inserted in extremely dull foundation or imprinted on different words or illustrations. In 1955, the principal business framework was introduced at the per user's process, which utilized OCR to include deals report into a PC and after that after OCR strategy has turned out to be exceptionally useful in modernizing the physical office archives. Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 370

397 Disha Bhat et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, Table 1:Comparison of Optical Character Recognition Software's Name Founded year Online Compatibility Programmin g Language SDK? Fonts Output formats Langua ges Pytesser act 1985 No Windows MAC OSX Linux BSD C++, C Yes Any printing font Text h OCR, PDF, others with different user interface or the API 100+ Screenw orm 2013 No MAC OSX Objective C++ No? TXT 57 Healthcare Medicinal services experts dependably need to manage extensive volumes of structures for every patient, including protection frames and also broad wellbeing shapes. To stay aware of the majority of this data, it is helpful to include significant information into an electronic database that can be gotten to as vital. Frame handling apparatuses, fueled by OCR, can remove data from structures and place it into databases, with the goal that each patient's information is quickly recorded. IV. RESULTS Optical Music Recognition Initially it was aimed towards recognizing printed sheets which can be edited into playable form with the help of electronic methods. It has many applications like processing of different classes of music, large scale digitization of musical data and also it can be used for diversity in musical notation. Figure 3: input image Legal Industry OCR is utilized as a part of Legal industry for digitize records, and specifically entered to PC database. Lawful experts can additionally look reports required from enormous databases by just composing a couple of catchphrases. Automatic Number Recognition Automatic number plate recognition is used as a technique making use of optical character recognition on images to identify vehicle registration plates. They are used by various police forces and as a method of electronic toll collection on pay-per-use roads and cataloging the movements of traffic or individuals Handwriting Recognition It is the ability of a computer system which scans the image of handwritten text by scanner and extracts only handwritten character from that image. Figure 4: Output image of Figure 3 Figure 5: Input image Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 371

398 Disha Bhat et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, V. REFERNCES: Figure 6: Output image of Figure 5 Figure 7: Input Image Figure 8: Output Image of Figure 7 IV. CONCLUSION This work goes for takes points of interest of the particular attributes of content that influence it to emerge from other picture material that is, content has certain recurrence and introduction data. We will first clean the image by changing the contrast and gradient of the image. Now the objects in the images are identified and numbered. Further in the text recognition process, these numbered objects are segregated into text and non-text. Later the recognised text is reconstructed to form a meaningful text present in the image. Also we are focusing on extracting the text such that certain portion of the images such as logos etc is retained. This is done by calculating the pixels of the required portion of the image to be retained and then training the system in such a way that it extracts all the text except the portion of the image to be retained obtain average accuracy in the work. Although the results of OCR System are not good, they are not bad either, indicating that the OCR Technique is not awed. More training data may improve robustness and accuracy. [1] Deepayan Sarkar "Optical Character Recognition using Neural Networks" University of Wisconsin MadisonECE 539 Project, Fall [2] Evaluation of OCR Algorithms for Images with Different Spatial Resolutions and Noises School of Information Technology and Engineering Faculty of Engineering University of Ottawa Qing Chen, Ottawa, Canada, [3] A Neural Network Implementation of Optical Character Recognition Technical Report Number CSSE10-05 COMP 6600 Artificial Intelligence Spring [4] Sukhpreet Singh M.tech Student Optical Character Recognition Techniques: A Survey, Dept. of Computer Engineering, YCOE Talwandi Sabo BP. India. [5] "OCR System: A Literature Survey" [6] Amarjot Singh, ketanbacchuwar, Akshaybhasin Survey of OCR Applications. [7] M.D. Ganis, C.L. Wilson, J.L. Blue, Neural network-based systems for handprint OCR applications in IEEE Transactions on Image Processing, 1998, Vol: 7, Issue: 8, p.p [8] Sadagopan Srinivasan, Li Zhao, Lin Sun, Zhen Fang, Peng Li, Tao Wang,RavishankarIyer, Ramesh Illikkal, Performance Characterization and Acceleration of Optical Character Recognition on Handheld Platforms, Dong LiuIntel Corporation. [9] Sonia Bhaskar, Nicholas Lavassar, Scott GreenEE Implementing Optical Character Recognition on the Android Operating System for Business Cards 368 Digital Image Processing. [10] Rajbala Tokas1, Aruna Bhadu2 M.Tech*(CS), A Comparative analysis of feature extraction techniques for handwritten character recognition Swami Keshwanand Institute of Technology, Jaipur, Rajasthan, India, M.Tech*(SE) Govt. Engineering College. [11] Mansi shah &Gordhan B Jethava A Literature Review on Hand Written Character Recognition Department of Computer Science & Engineering Parul Institute of Technology, Gujarat, India. Information Technology Department Parul Institute of Engg. & Technology, Gujarat, India. Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 372

399 Volume 9, Special Issue No. 3, May 2018 International Journal of Advanced Research in Computer Science (ISSN: ) CONFERENCE PAPER Available Online at FACE RECOGNITION WITH 2D-CONVOLUTIONAL NEURAL NETWORK Vaibhav Krishna Bhosle School of Computing and Information Technology REVA UNIVERSITY, Bangalore, India Sowmya M S School of Computing and Information Technology REVA UNIVERSITY, Bangalore, India Prof Shruthi G School of Computing and Information Technology REVA UNIVERSITY, Bangalore, India SupriyaS School of Computing and Information Technology REVA UNIVERSITY, Bangalore, India Varun Subramani School of Computing and Information Technology REVA UNIVERSITY, Bangalore, India Abstract: An open source build algorithm to identify any miscellaneous activity in the area where some important or precious items like cash, gold, costly items or important documents are kept in the room. This is the most efficient and innovative algorithm that has the capacity and ability to replace existing security appliances like CCTV cameras and night vision cameras.in this 21st century the technology demands a lot of storage and computational power and hence the major parts of the IT industries are moving towards Sensor Networks. With Image Recognition becoming more and more efficient, there are a lot of new innovative applications coming in the market. This algorithm helps in the security industry where there is a huge problem of storage and efficiency. Keywords- Image Analysis, Sensor networks, VGG16, VGG19, Inception V3. I. INTRODUCTION Face recognition algorithm, this enables a user to be confident about that nothing miscellaneous is happening when they are not around. Mostly when adding a device enabled with the face recognition software must establish new devices. But this algorithm can be integrated with any of the pre-existing security measures. The user must just enable the service and can use the full potential of the algorithm. In this way the user never has to worry about any new cost for hardware devices. They can just upgrade the pre-existing devices and software with a simple installation procedure. Also, most of the face recognition application devices ask for the training data set for accuracy enhancement. This algorithm will create its own data sets and use for the training into a 2D-convolutional neural networks layer. Or, can also use popular deep learning architecture like, VGG16 or Inception v3. Which will give them freedom to choose any neural network architecture with the help of finetuning method. II. LITERATURE SURVEY There are numerous projects that are associated with this innovative device. To make things better and easier there will be n number of projects and technologies to come up in the market. We have already got some good appliances like: GPS for real time, Vision for real time control, Path planning in structured and semi-structured environment, Real time system identification. On-board laser mapping system [1] Deployed in Houghton Crater, Devon Island NWT Canada. As mentioned before, the objective is to enable the data from the camera to be sent back to the PC based station via RF signal. Like this project has many features that this project currently does not possess. The major concept for the project was to have a camera embedded into a flying copter that is to be further controlled with a wireless remote device. This was an ideal in the time of the invention where the reach of the humans was difficult with bare foot. Hence this took a major turn in the technology where the camera embedded devices became more and more popular in the upcoming decade. There further came the concept for the aerial robots which has not only the camera functions as well as were capable to do tasks for the user. Being only normal or simple tasks to be carried out, but still it was a critical invention. Low resolution Camera Driver for 8031-SDK [2], a CMOSbased low-resolution ( pixel) color camera's RS- 232 serial interface is connected to the SDK's internal serial port like we have most of the IOT devices in the market. This connection is used to allow the SDK to control the camera and to download raw image data from the camera into the SDK's SRAM. The SDK's external serial port is connected to a PC/Terminal, providing a basic user interface, which allows for user-controlled processing of the image and the copter also. Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 373

400 Vaibhav Krishna Bhosle et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, Wolf Paulus s project [3] covered until the image processing part while for this project, its objective is to send the image data back to the PC based station; it is still worth mentioning that Bayer Pattern concept is used in the image processing part. The Bayer Pattern is based on the premise that the human eye derives most of the luminance data from the green content of a scene and it is the resolution of this luminance data that is perceived as a the "resolution" of an image. Therefore, by ensuring that more of the pixels are "green", a higher resolution image can be created - compared with an alternating R-G-B color filter array with equal numbers of Red, Green and Blue pixels. Figure 1: Working Model The low-cost digital camera does not provide any image processing and therefore, after a photo has been taken, about 20 Kbytes uncompressed, raw image sensor data, is downloaded into the SDK's SRAM memory. Since the camera's serial interface only support a single transfer speed of 57,600 baud, optimized assembler routines had to be implemented to allow the SDK to receive data and immediately transfer it into external memory at Figure 3: Camera mounts on board Webcam Application Expansion Board [4] there are one worthy product in the market that will be mentioned here, as this product [4] has a lot of similarity with this project Evaluation Board is a Webcam application expansion board embedded with i2chip W3100A, a hardwired TCP/IP chip ( W3100A ), integrated with CMOS-type camera to transfer video data over the Internet without any PC. Its main components are the following: Camera Sensor, M-JPEG CODEC, Memory and an interface with 8051 EVB Evaluation Board is comprised of 8051 MCU, Memory, W3100A (TCP/IP), RTL8201 (Ethernet PHY) and an interface with Web Camera Module. Figure 3 shows the product, webcam application expansion board. that speed. About 20 Kbytes are needed to store the raw image data for one image. Figure 2: Typical Bayer Patterns For the lack of availability of a better protocol, the receiving terminal must log the receiving data, to eventually being able to display the bitmap. That was until we have new technologies like Bluetooth, LAN and NFC s. That however requires the SDK to send the data encoded. The Information is quoted () states the flow of the working model for the above explanation. III. OVERVIEW OF PROPOSED DESIGN The main idea for this kind of algorithm approach is to have a self-learning smart recognition. The can create its own data sets for learning/training and save a JASON formatted file in turn to have a saved model that could be used in the recognition process. This device uses Deep Neural Networks for image analysis. This algorithm is based on 2 (two) layers of Convolution Neural Networks. That can be trained to separate the images from the Human or any other object. Then we can also enhance the algorithm to also send an emergency notification or an alarm in the detection of human presence. This neural network has the efficiency of 87% (can be increased over time when a Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 374

401 Vaibhav Krishna Bhosle et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, huge dataset library is present, though the training time will still be a challenging task. Which will be intern gives a option to get a cloud service that will get the job done in no time). If there are more images of humans that in form any sources, the efficiency could be increased up to 98%. The modern security cameras are becoming more and better in capturing high quality videos that in turn has increased the demand for huge storage capacity. This Algorithm does is capture images only when there is any face present. We can say the camera is observing rather than recording. Hence the storage will no more be a problem as the algorithm will only use storage capacity to save the trained model and the current image in front of it for recognition. The user can forget the risk of having a huge data storing devices. Or, they can just integrate this algorithm in order to have a recognition feature to give a upgrade the pre-existing security measures. As, most of the security surveillance are also stored for future references in case of any crime reports. The algorithm can also use pre-existing libraries like VGG16, VGG19 and Inception V3 for image recognition. These are the popular build neural network structures that are capable of having a deep learning approach in a all different level. The networks are open source and can be implemented on any platform I.e. they are cross platform. The device has an efficiency on the image recognition, where it gives a plus point on the performance in real time scenario. The created dataset from the models are nothing but junk pictures in the storage which can be deleted once the trained model from the 2Dconvolutional neural network is securely generated i.e. error free or has a backup in a external component. Taking the automated deletion of the dataset is critical and hence not suggested to integrate with the present algorithm. Figure 4: Image Now we have the data set of 10 images. Fig 4, But we need to build a huge dataset and hence this will hardly give the accuracy of 0.87% for face recognition. But over time the algorithm will gain ability to capture a huge number of images with will give the buff in the accuracy. The best practice is to keep the accuracy level over 85% to get a satisfactory result. IV. RESULTS AND DISCUSSIONS In this section experimental results are observed and discussed. Due to absence of human images, Kaggle s cats and dogs data set is used to train and finetune the models. To check the working and stability of the algorithm. Once the dataset for the human faces is available i.e. around 1 million different pictures for a optimal accuracy and get a satisfactory result. Testing with poor dataset can lead into failure of the algorithm. That will yield junk output. Which will have an ill effect on the recognition accuracy. Figure5: Training with 2D-convolutional neural network The following algorithm shows to train a 2D-convolutional neural network for image recognition in Figure 5. The following algorithm recognises the new image provided (trained images will give 100% accuracy). Using the trained model that has been saved as in.jason file, Figure 6. The following below is a algorithmic program to get image from the default camera connected with most of the standardised laptop in the market. This gives the output as follows: Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 375

402 Vaibhav Krishna Bhosle et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, Figure 7: Training with VGG16 The below algorithm uses VGG16 neural network to Recognise the image in Figure 8. Figure 6: Recognition with 2D- convolution neural network The below algorithm uses VGG16 neural network to train the model in figure 7. Figure 8: Recognition with VGG16 The below algorithm uses Inception V3 neural network to train the model in figure 9. Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 376

403 Vaibhav Krishna Bhosle et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, Figure 10: Training with Inception V3 V. CONCLUSION Figure 9: Training with inception V3 The below algorithm uses Inception V3 neural network to Recognise the image in figure 10. As the application module is having a picture-based approach rather than video based, hence the storage utility is optimized. There is no requirement for complete change of the pre-established wirings and components, as the project is built based on the software enhancement i.e. the algorithm can be integrated with any of the present security technologies. The dataset for the training module is required is auto generated. The same dataset is updated periodically manually with a single click of a button. The algorithm is divided into 3 parts as, dataset generator, training model and recognition. Each part is incomplete without other and the flow of the algorithm goes in linear i.e. the dataset generator generates the dataset that will be in turn used in by the training model which saves a recognition structure in.jason file. This is now used for the recognition. VI. REFERENCES [1] AndreaZanella, "Internet of things for Smart Cities", IEEE 2014 [2] Wenbin Wu, "A data mining approach combining k- means clustering with bagging neural network for shortterm wind power forecasting", IEEE [3] Joseph Siryani, "A machine learning Decision-support system improves the internet of things smart meter operations", IEEE 2017 [4] Guanghan Ning, "Knowledge-Guided Deep Fractal Neural Networks for humans pose estimation", IEEE Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 377

404 Volume 9, Special Issue No. 3, May 2018 International Journal of Advanced Research in Computer Science (ISSN: ) CONFERENCE PAPER Available Online at PUBLIC TO PARLIAMENT KARTHIK K B Department of Computer Science and Engineering SVIT, Bangalore, India SHASHIKUMAR D K Department of Computer Science and Engineering SVIT, Bangalore, India SHREYAS M S Department of Computer Science and Engineering SVIT, Bangalore, India SRIRAM C R Department of Computer Science and Engineering SVIT, Bangalore, India SUKRUTHGOWDA M A Department of Computer Science and Engineering SVIT, Bangalore, India Abstract: Public To Parliament is a citizen engagement platform and is our endeavor to promote the active participation of Indian citizens in their country's governance and development. It is also aimed at creating a common platform for citizens (say for a specific constituency) to crowd source governance ideas from citizens also.using this application user shall be allowed to discuss and to contribute on various government projects and plans.if the people encounter some public issues it also allows users to upload photos of the same and give a brief description about it and thus creating awareness. Keywords: Public To Parliament, crowd source governance ideas, upload photos, brief description, creating awareness I. INTRODUCTION India is the largest democratic country in the world. Democracy is defined as the government of the people, by the people and for the people. And we all know that, isn t it? But it is not possible for all the citizens to participate in the government and that is why we select our representatives at regular intervals. But today if we look at it is it really happening? Are we able to communicate with the government? Are all our problems reaching the representatives we select? If no what are the real problems we are facing? Not all our problems are reaching the government. Even if they do, the results are null or not satisfactory. And most of the times they are ignored. And the process to file a complaint is quite old and is time consuming. So we have solution. Our application helps to overcome these problems. II. PROBLEM STATEMENT Not all our problems are reaching the government. Even if they do, the results are null or not satisfactory. And most of the times they are ignored. And the process to file a complaint is quite old and is time consuming. Citizens have disengaged and stopped holding elected officials accountable to representing their interests. Most people in this country feel like their voice and their vote don t count, so why should they bother? The local representatives don t come to know what the real problem is. The main objective of our application is to connect the citizens to Parliamentarians. III. LITERATURE SURVEY Let us consider a constituency. If there is a public issue there is no common platform which is effective in acknowledging it to the representative. And we cannot expect the representatives all the times to make time to listen to each of the problems. Once the problem is addressed, its progression should be intimidated to the public which in many cases could not happen. The process to file a complaint is too sluggish. There are many other citizen engagement apps. One such app is MODI app and PMO app is also very similar. These apps are another step from the PM to make him more accessible to citizens. And other similar app is MyGov, a Government funded app is other endeavor. But these apps somewhere fails to address the minute issues of minute constituencies and also fails to involve the local representatives. IV. PROPOSED SYSTEM The main idea is to build an android application which will provide a common platform for the people belonging to a constituency to create awareness about the different public issues. So our application facilitates the user to achieve the same. P2P s main features are: 1. News Feed: Gives user the updates on their constituencies. 2. Assembly: User can get all the information of their constituency. 3. Complaints: If any public issue, complaint can be registered and same can be seen by other users. 4. Polling: Registered complaints can be upvoted by Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 378

405 KARTHIK K B et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, other users if they are facing the same problem. 5. RTI: Users get all the information about the RTI and thus awareness is created. V. SYSTEM DESIGN The design of the system aims to detect the procedures which must be present in the system, the considerations and specifications of these procedures and to connect with one another to deliver the craved results. Toward the final of the framework plan all the real information structures, document organizations, yield organizes and also significant modules in the framework and their particulars are chosen. Commonpeople (citizens), PublicRepresentatives (MLA, MP, and Corporators) are the end users. To avail the services of P2P, the public other than the Public Representatives and Admin have to register themselves first. Id and password will be given to the Public Representatives and they don t have to register. With respect to the constituency they belong to, the users will get the news updates of their constituencies. Admin adds the news and it will be unique from one constituency to another. The user is going to get all the information about his constituency. For this purpose, the section Assembly has been included. If a user has a public concerned issue, he can go to Complaints section where he will be provided many sectors to choose. He can choose one and can post the complaint with a photo if needed and giving brief description about it. This helps to create awareness. All the complaints registered can be seen by all other users in Polling section. In the Polling section all the complaints registered can be seen and if any user feels that even he has gone through or facing the same problem, he can up vote it. Finally all the complaints will be acknowledged to the Public Representatives. Based on the number of up votes the seriousness of the problem can be understood. This helps the Public Representatives to serve the need of the people. Politicians get benefitted through this since it helps to maintain the accountability. The Admin has the authority to add news, view and delete the complaints if found it is irrelevant. Admin can also block the user if it is notified that he is creating chaos. The last service the users can avail is RTI section. Here awareness is created about the RTI like its importance and how it can be used. Fig 5.1: System Architecture VI. IMPLEMENTATION The implementation phase, the project plan is put into motion and the work of the project is performed. It is important to maintain control and communicate as needed during implementation. Progress is continuously monitored and appropriate adjustments are made and recorded as variances from the original plan. In any project, a project manager spends most of the time in this step. During project implementation, people are carrying out the tasks, and progress information is being reported through regular team meetings. The project manager uses this information to maintain control over the direction of the project by comparing the progress reports with the project plan to measure the performance of the project activities and take corrective action as needed. The first course of action should always be to bring the project back on course (i.e., to return it to the original plan). If that cannot happen, the team should record variations from the original plan and record and publish modifications to the plan. Throughout this step, project sponsors and other key stakeholders should be kept informed of the project s status according to the agreed-on frequency and format of communication. The plan should be updated and published on a regular basis. P2P app mainly has User Module. And Representative module is almost similar to User module. User module has the following sections. A. Home Section In this section the users can view the current news about their constituency. This helps users to keep themselves updated with the progression of their respective constituencies. The news is unique with respect to the constituencies they belong to. Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 379

406 KARTHIK K B et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, through or faced the same problem he can just up vote it. And based on the number of votes a problem get the seriousness of the problem can be realized. B. Assembly Section Fig 6.1: Home section For every citizen it is very significant to have the knowledge about his constituency he is dwelling in. Here the users get all the information about constituency. Fig 6.4: Polling section E. RTI Section Right to information which is one of the powerful tool in the hands of public against the government. RTI act provides method to acquire information from public authorities regarding the functioning of the govt. The importance of RTI lies in its welfare aspect. It empowers citizen against govt. thus making govt. and its officials more accountable towards general public. And in this section users get all the information about RTI and also how to make the best out of it.. Fig 6.2: Assembly section C. Complaint Section The users are provided with many sectors to choose. If someone has an issue which is of public concern any one of the appropriate sectors can be chosen and the problem can described and can be posted. Appropriate picture can be attached thus creating awareness about the public issues. Fig 6.5: RTI section VII. TEST CASES The below mentions are examples of some of test cases which are tested on the application. A. Signup Test The common man who wants to avail the services of P2P needs to give his details. Details will be checked and user thus creates his account. Fig 6.3: Complaint section D. Polling Section All the complaints registered by all the users can be viewed in Polling section. If a person feels that even he is going SL No. 1 Table 7.1: Signup Test Case Expected Test Case Test Result Result Enter valid details by user Registration successful Successful Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 380

407 KARTHIK K B et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, Enter invalid details by user B. Login Test Registration not successful Successful The login test has 3 cases. The login test is same for all modules such as admin, public representatives and public. If the id and password is valid, then respective module s home will be displayed or else it displays invalid credentials. If only id or password is entered, then invalid credentials are displayed. SL No Table 7.2: Signup Test Case Test Case Expected Result Test Result Enter valid Id login and successful Successful password Enter invalid Id and password Enter only Id or password C. Admin Module Test Invalid Credentials Login unsuccessful Successful Successful The admin maintains the records of users and also has the authority to add or remove users, add or delete the news posted and discard the complaints registered. Basically it has 3 test cases. SL No Table 7.3: Admin Test Case Test Case Expected Test Result Result News will Successful display in home page. 1 Click Add News 2 Click Block User 3 Click Discard Complaint User not allowed posting complaint. Complaint should be discarded from Polling Section. Successful Successful VIII. CONCLUSION We thought of taking a step ahead to solve the problem through a mobile app where the citizens could indulge and could experience the transparent governance. Our application mainly focuses on the citizen indulgence in creating awareness about the problems they are facing. In addition to this the application provides many other services. So one may think, That all sounds good, but how will you get the politicians to use it? Because Public To Parliament is specifically designed for political communications, it is just as valuable to the politicians as it is the constituents. To maintain good relation and to take the constituents into confidence there should be accountability between the politician and the citizens. The accountability linkage breaks down when politicians cannot deliver what voters demand or when politicians cannot convince voters that they should demand what politicians are prepared to deliver to them. The collapse of citizen-politician linkages of political accountability is an ever present danger in democracies with potentially grave consequences for politicians. That is why we say this app will surely help politicians also to maintain accountability by knowing the problems which they couldn t come to know due to the communication gap. IX. REFERENCES [1]HOW MOBILE APPS ARE HELPING THE PUBLIC SECTOR,CISCO'S TECHNOLOGY NEWS SITE. [2] BRIDGING THE GAP BETWEEN EU POLITICS AND CITIZENS? THE EUROPEAN COMMISSION, NATIONAL MEDIA AND EU AFFAIRS IN THE PUBLIC SPHERE, TAYLOR FRANCIS ONLINE. Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 381

408 Volume 9, Special Issue No. 3, May 2018 International Journal of Advanced Research in Computer Science (ISSN: ) CONFERENCE PAPER Available Online at ROXY-DOCTOR S DIGITAL ASSISTANT Vikas Madhava School of Computing and Information Technology, REVA University, Bengaluru. India Nishanth B School of Computing and Information Technology, REVA University, Bengaluru. India Mohammed Mazhar School of Computing and Information Technology, REVA University, Bengaluru. India Chaithra N School of Computing and Information Technology, REVA University, Bengaluru. India Spoorthi Rakesh School of Computing and Information Technology, REVA University, Bengaluru. India Abstract: Managing ever growing data in health sector is quite a big problem. Existing system consists of a person who manages the data and managing in ledgers, even though in many hospitals which use high-end technologies the data is managed in databases. Here this is managed by an IT person who is not a medical professional as a result there is a possibility of error while data passes from doctor to the person; even a bit of error can have fatal results. Our solution to this problem is developing the technology to reduce all the above stated problems, simplifying data entry procedure for medical professionals and retrieval of the particular data which is saved previously. The Impact done by developing this technology is optimizing the working pattern and increasing the efficiency in work and optimizing the time and reducing the probability of error, leading to a platform which is fast and reliable. Keywords Data Management, Data Security, Digitization, Medication. 1. INTRODUCTION Medical data management is the process of storing, securing, and analyzing data taken from diverse sources. Managing the wealth of available Medical data allows health systems to create clear and comprehensive views of patients; help in giving personalize treatments to the patient, help in improved communication, and also in enhancing health outcomes.[1] How can an physician truly improve the outcomes by and provide care at the lowest cost? But just simply showing the rate of patients with a condition who have become better doesn t really mean it shows a measurable difference. Therefore, Medical organizations need to use data-extraction tools and technology to demonstrate quality improvement. Such data will only become more important in future and bring growth in the usage of technology as pay-for-performance contracting and then the Medicare Physician Quality Reporting System (PQRS) initiatives. [2] Measuring data including patient-reported symptoms, complications, and improvements over time may seem like a daunting task to practices, but there are plenty of ways practices can establish benchmarks by which to measure quality improvement.[3] Looking at the benchmarks (such as the average total cost of data-management) as one of the major agencies that generates benchmark measures similar as the National Quality Forum. After the benchmarks used to identify the area which is needed to be improved, your practice needs goals to be settled for improving quality on those benchmarks. Finally, implement a process to analyze the raw data. [4] Physicians typically lack three use of quality improvement projects is not having understandable or specific technology (such as interventions that the certain benchmarks are not achieved by the patients) fortunately, there are a many technologies in the market that can help practices with analyzing data.[5] 2. ROXY-Doctor s Digital Assistant ROXY is a digital assistant which helps the doctors to store all details of patient, analyze it. This improves the communication and improves the performance in work. As the present scenario the Doctor s lack the use of technology and this leads them to loss in time and increase work load if in these places, technology is applied Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 382

409 Vikas Madhava et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, then it will be a great helping hand and also improve the use of technology in the country. Tech which we have developed covers three major domains: iii. Data Security: The data and the database is protected from destructive forces and from unauthorized access from other users.[8] i. Data Management ii. Data Analytics iii. Data Security Figure 1: System Architecture Figure.1 shows the system architecture of the application where there is module of doctors login. If the user is not authenticated for less than three times. The application gets locked and it terminates. Once the user logs in succesfully, the user can also register the other patients data and retrive the details of the already registered users. The data retirvied will have the details like the issues, medications given to the patients. The application also gves the statistics based on monthly basis and daily basis, number of patients on health issues and visitors. The application also gives the deatils regarding the number of inpatients, outpatients and the beds available. i. Data Management: It is a procedure was it is the development and execution of data architectures, practices, policies and procedures that manage the full data lifecycle properly of data. [6] 3. APPLICATIONS i. Paperless and completely digital ii. Minimalistic design iii. Very friendly user interface iv. Patient details are safe and secure v. More functionalities (statistics, count...etc) vi. Efficient management of data 4. RESULT The developed software efficiently manages the data. It takes all the parameter s as input from the doctor and stores it in the relational database and whenever there is a necessity the patient data will be fetched without any hassle. It takes details of variety of patients every day and analyzes the data and the analyzed data is projected in the form of pie chart. To make doctors life easy it keeps the count of inpatients and the limit of number of admissions available. It keeps the count of out- patients of that particular day. Figure.2 shows the login page where it takes the username and password for the authentication. If the patients profile is not registered then it takes the details as shown in the Figure 3.After storing the patient profile it can be fetched back as shown in Figure 4. This application keeps the count of number of in-patients and number out-patients and number of empty beds This application records the variety category of patients who are coming to the doctor the iput is given as shown in figure 6 these statiscs are stored in database and this data is being analyzed and depicted through graph as shown in figure 7 Figure 2: Login page ii. Data Analytics: Analysis of data is a process of inspecting, transforming and modeling data with the goal of discovering useful information. [7] Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 383

410 Vikas Madhava et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, Figure 3: Input of Data Figure 7: Graphical representation of statistics 5. CONCLUSION Figure 4: output after retrieving This application will make doctors life easier and reduces his work aswell as reduces the hassle of patients by making his record stored it with doctor, patient doesnt need to carry any documents with him/her from this we can expect significant reduce in mortality rate. So by developing tech which could simplify their job could help the industry greatly. Therefore this project will decreases the work load of the doctor in turn increasing the liability of the doctors on the technology for a better future. Health sector is 200 billion USD industry only in India, if we can reduce their risk and increase their efficiency and reduce their management costs this field in the market is untapped if we can do it it ll be great and it benefits us as well. For a doctor his time is very precious, more time is equal to more lives saved or more health issues solved and we try to give him more time. Figure 5: count of in- patients,out-patients and beds available 6. FUTURE ENHANCEMENT Future work includes making it a voice based virtual assistant by using natural language processing tool kit this will make the application more interactive and user friendly. The storage will be done in Cloud, so that the application can be accessed from anywhere by the user making the application more accessible. For further developments the Android, IOS and Windows Tablet version application will be developed.more features will be added using Artificial Intelligence based diagnosing system and will predict the patients further cases. Figure 6: input for statistics Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 384