Editor-In-Chief Chair Dr. Shiv Kumar

Ph.D. (CSE), M.Tech. (IT, Honors), B.Tech. (IT), Senior Member of IEEE

Blue Eyes Intelligence Engineering & Sciences Publication, Bhopal (M.P.), India.

Associated Editor-In-Chief Chair Prof. MPS Chawla

Member of IEEE, Professor-Incharge (head)-Library, Associate Professor in Electrical Engineering, G.S. Institute of Technology &

Science Indore, Madhya Pradesh, India, Chairman, IEEE MP Sub-Section, India

Dr. Vinod Kumar Singh

Associate Professor and Head, Department of Electrical Engineering, S.R.Group of Institutions, Jhansi (U.P.), India

Dr. Rachana Dubey

Ph.D.(CSE), MTech(CSE), B.E(CSE)

Professor & Head, Department of Computer Science & Engineering, Lakshmi Narain College of Technology Excellence (LNCTE),

Bhopal (M.P.), India

Associated Editor-In-Chief Members

Dr. Hai Shanker Hota

Ph.D. (CSE), MCA, MSc (Mathematics)

Professor & Head, Department of CS, Bilaspur University, Bilaspur (C.G.), India

Dr. Gamal Abd El-Nasser Ahmed Mohamed Said

Ph.D(CSE), MS(CSE), BSc(EE)

Department of Computer and Information Technology , Port Training Institute, Arab Academy for Science ,Technology and Maritime

Transport, Egypt

Dr. Mayank Singh

PDF (Purs), Ph.D(CSE), ME(Software Engineering), BE(CSE), SMACM, MIEEE, LMCSI, SMIACSIT

Department of Electrical, Electronic and Computer Engineering, School of Engineering, Howard College, University of KwaZulu-

Natal, Durban, South Africa.

Scientific Editors

Prof. (Dr.) Hamid Saremi

Vice Chancellor of Islamic Azad University of Iran, Quchan Branch, Quchan-Iran

Dr. Moinuddin Sarker

Vice President of Research & Development, Head of Science Team, Natural State Research, Inc., 37 Brown House Road (2nd Floor)

Stamford, USA.

Prof. (Dr.) Nishakant Ojha

Principal Advisor (Information &Technology) His Excellency Ambassador Republic of Sudan& Head of Mission in New Delhi, India

Dr. Shanmugha Priya. Pon

Principal, Department of Commerce and Management, St. Joseph College of Management and Finance, Makambako, Tanzania, East Africa, Tanzania

Dr. Veronica Mc Gowan

Associate Professor, Department of Computer and Business Information Systems,Delaware Valley College, Doylestown, PA, Allman,

China.

Dr. Fadiya Samson Oluwaseun

Assistant Professor, Girne American University, as a Lecturer & International Admission Officer (African Region) Girne, Northern

Cyprus, Turkey.

Dr. Robert Brian Smith

International Development Assistance Consultant, Department of AEC Consultants Pty Ltd, AEC Consultants Pty Ltd, Macquarie

Centre, North Ryde, New South Wales, Australia

Dr. Durgesh Mishra

Professor & Dean (R&D), Acropolis Institute of Technology, Indore (M.P.), India

Special Issue Section Editor

Mr. Siddth Kumar

Founder and Managing Director, IFERP, Technoarete Groups, India

Mr. Rudra Bhanu Satpathy

Founder and Managing Director, IFERP, Technoarete Groups, India

Dr. Mahdi Esmaeilzadeh

Founder & Chairman, of Scientific Research Publishing House (SRPH), Mashhad, Iran

Executive Editor Chair

Dr. Deepak Garg

Professor & Head, Department Of Computer Science And Engineering, Bennett University, Times Group, Greater Noida (UP), India

Executive Editor Members

Dr. Vahid Nourani

Professor, Faculty of Civil Engineering, University of Tabriz, Iran.

Dr. Saber Mohamed Abd-Allah

Associate Professor, Department of Biochemistry, Shanghai Institute of Biochemistry and Cell Biology, Shanghai, China.

Dr. Xiaoguang Yue

Associate Professor, Department of Computer and Information, Southwest Forestry University, Kunming (Yunnan), China.

Dr. Labib Francis Gergis Rofaiel

Associate Professor, Department of Digital Communications and Electronics, Misr Academy for Engineering and Technology,

Mansoura, Egypt.

Dr. Hugo A.F.A. Santos

ICES, Institute for Computational Engineering and Sciences, The University of Texas, Austin, USA.

Dr. Sunandan Bhunia

Associate Professor & Head, Department of Electronics & Communication Engineering, Haldia Institute of Technology, Haldia

(Bengal), India.

Dr. Awatif Mohammed Ali Elsiddieg

Assistant Professor, Department of Mathematics, Faculty of Science and Humatarian Studies, Elnielain University, Khartoum Sudan,

Saudi Arabia.

Technical Program Committee Chair

Dr. Mohd. Nazri Ismail

Associate Professor, Department of System and Networking, University of Kuala (UniKL), Kuala Lumpur, Malaysia.

Technical Program Committee Members

Dr. Haw Su Cheng

Faculty of Information Technology, Multimedia University (MMU), Jalan Multimedia (Cyberjaya), Malaysia.

Dr. Hasan. A. M Al Dabbas

Chairperson, Vice Dean Faculty of Engineering, Department of Mechanical Engineering, Philadelphia University, Amman, Jordan.

Dr. Gabil Adilov

Professor, Department of Mathematics, Akdeniz University, Konyaaltı/Antalya, Turkey.

Dr.Ch.V. Raghavendran

Professor, Department of Computer Science & Engineering, Ideal College of Arts and Sciences Kakinada (Andhra Pradesh), India.

Dr. Thanhtrung Dang

Associate Professor & Vice-Dean, Department of Vehicle and Energy Engineering, HCMC University of Technology and Education,

Hochiminh, Vietnam.

Dr. Wilson Udo Udofia

Associate Professor, Department of Technical Education, State College of Education, Afaha Nsit, Akwa Ibom, Nigeria.

Manager Chair

Mr. Jitendra Kumar Sen

Blue Eyes Intelligence Engineering & Sciences Publication, Bhopal (M.P.), India

Editorial Chair

Dr. Arun Murlidhar Ingle

Director, Padmashree Dr. Vithalrao Vikhe Patil Foundation’s Institute of Business Management and Rural Development, Ahmednagar

(Maharashtra) India.

Editorial Members

Dr. Wameedh Riyadh Abdul-Adheem

Academic Lecturer, Almamoon University College/Engineering of Electrical Power Techniques, Baghdad, Iraq

Dr. T. Sheela

Associate Professor, Department of Electronics and Communication Engineering, Vinayaka Mission’s Kirupananda Variyar

Engineering College, Periyaseeragapadi (Tamil Nadu), India

Dr. Manavalan Ilakkuvan

Veteran in Engineering Industry & Academics, Influence & Educator, Tamil University, Thanjavur, India

Dr. Shivanna S.

Associate Professor, Department of Civil Engineering, Sir M.Visvesvaraya Institute of Technology, Bengaluru (Karnataka), India

Dr. H. Ravi Kumar

Associate Professor, Department of Civil Engineering, Sir M.Visvesvaraya Institute of Technology, Bengaluru (Karnataka), India

Dr. Pratik Gite

Assistant Professor, Department of Computer Science and Engineering, Institute of Engineering and Science (IES-IPS), Indore (M.P),

India

Dr. S. Murugan

Professor, Department of Computer Science and Engineering, Alagappa University, Karaikudi (Tamil Nadu), India

Dr. S. Brilly Sangeetha

Associate Professor & Principal, Department of Computer Science and Engineering, IES College of Engineering, Thrissur (Kerala),

India

Dr. P. Malyadri

Professor, ICSSR Senior Fellow Centre for Economic and Social Studies (CESS) Begumpet, Hyderabad (Telangana), India

Dr. K. Prabha

Assistant Professor, Department of English, Kongu Arts and Science College, Coimbatore (Tamil Nadu), India

Dr. Liladhar R. Rewatkar

Assistant Professor, Department of Computer Science, Prerna College of Commerce, Nagpur (Maharashtra), India

Dr. Raja Praveen.N

Assistant Professor, Department of Computer Science and Engineering, Jain University, Bengaluru (Karnataka), India

Dr. Issa Atoum

Assistant Professor, Chairman of Software Engineering, Faculty of Information Technology, The World Islamic Sciences & Education

University, Amman- Jordan

Dr. Balachander K

Assistant Professor, Department of Electrical and Electronics Engineering, Karpagam Academy of Higher Education, Pollachi

(Coimbatore), India

Dr. Sudhan M.B

Associate Professor & HOD, Department of Electronics and Communication Engineering, Vins Christian College of Engineering,

Anna University, (Tamilnadu), India

Dr. T. Velumani

Assistant Professor, Department of Computer Science, Kongu Arts and Science College, Erode (Tamilnadu), India

Dr. Subramanya.G.Bhagwath

Professor and Coordinator, Department of Computer Science & Engineering, Anjuman Institute of Technology & Management

Bhatkal (Karnataka), India

Dr. Mohan P. Thakre

Assistant Professor, Department of Electrical Engineering, K. K. Wagh Institute of Engineering Education & Research Hirabai

Haridas Vidyanagari, Amrutdham, Panchavati, Nashik (Maharashtra), India

Dr. Umar Lawal Aliyu

Lecturer, Department of Management, Texila American University Guyana USA.

Dr. K. Kannan

Professor & Head, Department of IT, Adhiparasakthi College of Engineering, Kalavai, Vellore, (Tamilnadu), India

S. NoVolume-9 Issue-1S, October 2019, ISSN: 2278-3075 (Online)

Published By: Blue Eyes Intelligence Engineering & Sciences Publication Page No.

1.

Authors: Sellam V, SVivek Reddy, K Praveen, G C Krishna, J Abhinay Rao

Paper Title: Secure Data-Encryption Strategy for Big-Data in Mobile Cloud

Abstract: Now-a-days data plays a key role in Information Technology and while coming to privacy ofthat data it has become a considerable issue to maintain data security at high level. Large amounts of datagenerated through devices are considered as a major obstacle and also tough to handle in real time scenarios. Tomeetwith consistent performance applications at present abandon encryptions techniquesbecausethe time for theexecution and the completion of encryption techniques plays a key role during processing and transmissions ofdata. In this paper our moto is to secure data and proposed a new technique called Dynamic Data EncryptionStrategy (DDES)which selectively encrypts data and uses some algorithms which provides a perfect encryptionstrategy for the data packages under some timing constraints. By this method we can achieve data privacy andsecurity for big-data in mobile cloud-computing by using an encryption strategy respective to their requirementsduring execution time.

Keyword: Big-data, Mobile Cloud Computing, Security, Encryption Strategy, Privacy.

References:1. S. Yu, G. Gu, A. Barnawi, S. Guo, and I. Stojmenovic. “Malware propagation in large-scale networks”-2015. IEEE Transactions on

Knowl-edge and Data Engineering.2. H. Liu, H. Ning, Y. Zhang, Q. Xiong, and L.“Yang.Role-dependent privacy preservation for secure V2G networks in the smart grid”-

2104.IEEE Transactions on Information Forensics and Security.3. Privacy-Preserving Public Auditing for Secure Cloud Storage Cong Wang, Student Member, IEEE, Sherman S.-M. Chow, Qian Wang,

Student Member, IEEE, KuiRen, Member, IEEE, and Wenjing Lou, Member,IEEE.4. Y. Li, W. Dai, Z. Ming, and M. Qiu. Privacy Protection for Preventing Data Over-Collection in Smart City. IEEE Transactions on

Computers, 65:1339–1350, 2015.5. S. Yu, W. Zhou, S. Guo, and M. Guo. A feasible IP traceback framework through dynamic deterministic packet marking. IEEE

Transactions on Computers, 65(5):1418–1427, 2016.6. S. Rho, A. Vasilakos, and W. Chen. Cyber physical systems technologies and applications. Future Generation Computer Systems,

56:436–437, 2016.7. Sundaraguru R, and M.Banupriya, “Acknowledged Secure Data Dissemination” International Journal of Management, IT and

Engineering, Vol.3, No.8, pp. 40-54, 2013.8. Secure Certificateless Public Verification for Cloud-Based Cyber-Physical-Social Systems Against Malicious Auditors Yuan Zhang,

Student Member, IEEE, ChunxiangXu, Member, IEEE, Shui Yu, Senior Member, IEEE, Hongwei Li, Member, IEEE, and XiaojunZhang.

9. G. Wu, H. Zhang, M. Qiu, Z. Ming, J. Li, and X. Qin.A decentralized approach for mining event correlations in distributed systemmonitoring Journal.

10. F. Tao, Y. Cheng, D. Xu, L. Zhang, and B. Li, “Cloud computing and internet of things- based cloud manufacturing servicesystem.”,IEEE Transactions on Industrial Informatics, Vol no 10(2),pp 1435–1442.

1-4

2. Authors: Sellam.V, Siva Surya Narayanan.V.Y, Karthikeyan Kumar, Anurag, Sudipta Kumar Das

Paper Title: Spamex: A Multi Array Methodology to Detect Computerized Spammers in Twitter Social Media

Abstract: The Twitter interpersonal organizations give an approach to clients to keep up contact withothers. Expanding of the informal organization's quality allows every one of them to accumulate monstrousmeasures of nonpublic information with respect to their clients. Tragically, this data riches just as itsstraightforwardness to get to clients' data could draw in malevolent gathering's consideration. That is the reasonthese systems have been attacked by spammers while, there have been a great deal of work to analyze and fixthem. With reference to this issue spammers look for new ways that to concentrate on these systems every day,there have being nonstop activities to distinguish spammers and malignant email. The point of this paper is totake a gander at past works inside the field of spam identification in informal communities. In late time, on-lineinterpersonal organizations are loaded with differed undesirable dangers. Despite the fact that they gave us anopen stage to impart our contemplations to other people, notwithstanding, because of abuse of this incredibleasset, general clients are in jeopardized condition. For instance, YouTube has been utilized as a limited timeground by different craftsman to transfer their music recordings, film trailers, and so on and watchers can posttheir supposition on them. Tragically, regularly malevolent clients use to post phishing site connections,promotions, and deceitful data in the remarks segment, which may transmit infections or malwares.

Keyword: Spamming In Social Media, Hop To N Hop, Os Securities

References:1. Michael J Mlodzianoski, John M Schreiner, Steven P Callahan, Katarina Smolková, Andrea Dlasková, Jitka Šantorová, Petr Ježek,

and Joerg Bewersdorf. Sample drift correction in 3d fluorescence photoactivation localization microscopy. Optics express,19(16):15009–15019, 2011.

2. T. Anwar and M. Abulaish, “Ranking radically influential web forum users,” IEEE Transactions on Information Forensics andSecurity,vol. 10, no. 6, pp. 1289–1298, 2015.

3. Y. Boshmaf, I. Muslukhov, K. Beznosov, and M. Ripeanu, “Design and analysis of social botnet,” Computer Networks, vol. 57, no.2, pp. 556–578, 2013.

4. D. Fletcher, “A brief history of spam,” TIME, Tech. Rep., 2009.5. Y. Boshmaf, M. Ripeanu, K. Beznosov, and E. Santos-Neto, “Thwarting fake osn accounts by predicting their victims,” in Proc.

5-12

AISec., Denver,2015, pp. 81–89.6. N. R. Amit A Amleshwaram, S. Yadav, G. Gu, and C. Yang, “Cats:Characterizing automation of twitter spammers,” in Proc.

COMSNETS, Banglore, 2013, pp. 1–10.7. K. Lee, J. Caverlee, and S. Webb, “Uncovering social spammers: Social honeypots + machine learning,” in Proc. SIGIR, Geneva,

2010, pp. 435–442.8. G. Stringhini, C. Kruegel, and G. Vigna, “Detecting spammers on social networks,” in Proc. ACSAC, Austin, Texas, 2010, pp. 1–9.9. K. Vijayakumar, S. Suchitra and P. Swathi Shri, "A secured cloud storage auditing with empirical10. outsourcing of key updates", International Journal Reasoning-based Intelligent Systems, Vol. 11, No. 2, 2019. 11. G. Indrajith and K.Vijayakumar, “Automatic Mathematical and Chronological Prediction in Smartphone Keyboard” International

Journal of Engineering and Computer Science ISSN: 2319-7242Volume 5 Issue 5 May 2016, Page No. 16714-16718. 12. K. Vijayakumar and C. Arun, “A Survey on Assessment of Risks in Cloud Migration”, International Journal of Applied Engineering

Research, ISSN 0973-4562 Vol. 10 No.66 May 2015.

3.

Authors: Gowtham Sethupathi M, Akshay U, Charan Surya M, Gohul Nath M, Sai Venkat

Paper Title: Integrating Social Media Marketing With AI through Predictive Examination

Abstract: Social Media activities have a major shift in marketing of products and have become a universaltool for advertising products. Nowadays billions of people use social media for recreational purposes and thesocial media activities leave a digital footprint which makes it easy to analyze each online activity. But simplyhaving a enormous amount of data is of no use unless one can read the past and predict the future. Ai can runthrough data, observe patterns, give individual high quality content to each customer. The project isimplemented through U Methodology. This mode determines the prospect of a customer preferring a product inan online interaction through big data and predictive analysis. Involvement of artificial intelligence can reducehuman error and inclination towards a certain product. The best product a customer needs is instantaneouslydisplayed to the customer without any time lapse.

Keyword: Lapse, Methodology, Mode, Prospect

References:1. L. Schwartz, “Memory for people: Integration of face, voice, name,2. And biographical information,” in The SAGE Handbook of Applied Memory, vol. 1. London, U.K.: SAGE, 2013, pp. 3–3. Herrmann, J. Call, M. V.Hernandez-Lloreda,Hare, and4. M. Tomasello, “Humans have evolved specialized skills of social cognition: The cultural intelligence hypothesis,” Science, vol. 317,

no.5843, pp. 1360–1366, 2007.5. D. Goleman, Social Intelligence: The New Science of Human Relationships. New York, NY, USA: Bantam Dell, 2006.6. M. Burton and V. Bruce, “I recognize your face but I can't remember your name: A simple explanation?” Brit. J. Psychol., vol.

83, no. 1, pp. 45–60, 1992.Koleand A. F. Healy, “Memory for details about people: Familiarity, relatedness, and gendercongruency,” Memory Cogn., vol. 39, no. 4, pp. 637–648, 2011.

7. M. L. Meyer, “Social working memory:8. Neuro cognitive networks and plasticity,” Ph.D. dissertation, Dept. Psychol., Univ. California, at Los Angeles, Los Angeles, CA, USA,

2014.9. D. Baddeley, “Working memory: Looking back and looking forward,” Nat. Rev. Neurosci., vol. 4, no. 10, pp. 829–839,2003.10. Thornton and A. R. A. Conway, “Working memory for social information: Chunking or domain-specific buffer,” NeuroImage, vol. 70,

pp. 233–239, Apr. 2013.11. T. L. Griffiths, C. Kemp, and J. B. Tenenbaum, “Bayesian models of cognition,” in The Cambridge Handbook of Computational

Psychology, R. Sun, Ed. Cambridge, U.K.: Cambridge Univ.Press,2008.12. J. Surowiecki, The Wisdom of Crowds: Why the Many Are Smarter Than the Few and How Collective Wisdom Shapes Business,

Economies, Societies, and Nations. New York, NY, USA: Random House, 2004.13. M. D. Lee, M. Steyvers, M. De Young, and B. Miller, “Inferring expertise in knowledge and prediction ranking tasks,” Topics Cogn.Sci.,

vol. 4, no. 1, pp. 151–163, 2012.14. M. L. Meyer and M. D. Lieberman, “Social working memory: Neurocognitive networks and directions for future research,” Front.

Psychol., vol. 3, no. 12, pp. 1–11, 2012.15. J. S. Beer and K. N. Ochsner, “Social cognition: A multi level analysis,” Brain Res., vol. 1079, no. 1, pp. 98–105, 2006.16. Xu et al., “Exploring users’ attitudes towards social interaction assistance on Google Glass,” in Proc. 6th ACM Int. Conf. Augmented

Human, Singapore, 2015, pp. 9–12.17. J. J. Skowronski, R. J. McCarthy, and B. M. Wells, “Person memory: Past, perspectives, and prospects,” in The Oxford Handbook of

Social Cognition, D. E. Carlston, Ed. New York, NY, USA: Oxford Univ.Press, 2013.18. Social computing from social informatics to social intelligence Fei Yue Wang, Daniel Zeng)19. The Information ecology of Social Media and Online Commodities20. Narrative Intelligence [Micheal Meteas, Phoebe Sengers]21. Artificial Intelligence and soft computing- computing 22. Cognitive Modelling of the Human Brain [ Amit Komar]23. Steps Toward parallel Intelligence [Fei Yue Wang, Xio Wang Li, Li]

13-19

4. Authors: Amit Kaushal, Sarvpal Singh

Paper Title: Performance Optimization by Task Scheduling in Cloud Computing

Abstract: Cloud computing is associate on demand access to a shared pool of resources. Vendor’s ofcloud computing offer application and change technology, infrastructure, hardware, software, and integration forconsumer. The most aspect of cloud computing is accessibility and performance of their network association. Itrefers to the method of allocating users’ tasks to virtual machines (VMs) with a goal of minimizing the worktime and rising the resource utilization. Tasks programming is taken into account NP onerous drawback with O(m, n) run time complexness to schedule n tasks on m resources. A computer hardware adapts its programmingstrategy consistent with the ever-changing setting and therefore the variety of task. We provide comparison withMax–min scheduling formula and Genetic formula. Our hybrid algorithm provides higher performancecompared to different programming algorithm.

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Keyword: Task scheduling, Cloud computing, Auction based task scheduling algorithm, Genetic algorithm,Max–min scheduling Algorithm.

References:1. Niloofar Khanghahi and Reza Ravanmehr “Cloud computing performance evaluation: Issues and challenges” International Journal on

Cloud Computing: Services and Architecture (IJCCSA).Cloud Computing: Services and Architecture (IJCCSA), Vol.3, No.5, October2013 DOI 10.5121/ijccsa.2013.3503.

2. Van den Bossche, R., Vanmechelen, K., & Broeckhove, J. (2010, July). Cost-optimal scheduling in hybrid Iaas clouds for deadlineconstrained workloads. In Cloud Computing (CLOUD), 2010 IEEE 3rd International Conference on (pp. 228-235). IEEE.

3. Sindhu, S., & Mukherjee, S. (2011). Efficient task scheduling algorithms for cloud computing environment. In High PerformanceArchitecture and Grid Computing (pp. 79-83). Springer, Berlin, Heidelberg.

4. Ravichandran, S. and E. R. Naganathan, "Dynamic Scheduling of Data Using Genetic Algorithm in Cloud Computing", IntegratedIntelligent Research (IIR), International Journal of Computing Algorithm Vol 02, Issue 01, June 2013, Page No.11-15 ISSN: 2278-2397.

5. Gogulan, R., Kavitha, A., & Kumar, U. K. (2012). “A multiple pheromone algorithm for cloud scheduling with various QOSrequirements”. Int. J. Comput. Sci, 9, 3.

6. Kaleeswaran, A., Ramasamy, V., & Vivekanandan, P. (2013). “Dynamic scheduling of data using genetic algorithm in cloudcomputing”. Park College of Engineering and Technology, Coimbatore, India.

7. Agarwal, D., & Jain, S. (2014). Efficient optimal algorithm of task scheduling in cloud computing environment. arXiv preprintarXiv:1404.2076.

8. Lakra, A. V., & Yadav, D. K. (2015). Multiobjective tasks scheduling algorithm for cloud computing throughput optimization.Procedia Computer Science, 48, 107-113.

9. Branch, K. (2015). A novel task scheduling method in cloud environment using cuckoo optimization algorithm. International Journalof Cloud-Computing and Super-Computing, 2(2), 7-20.

10. Hamad, S. A., & Omara, F. A. (2016). Genetic based task scheduling algorithm in cloud computing environment. International Journalof Advanced computer Science and Applications, 7(4), 550-556.

11. Dandhwani, Vanita, and Vipul Vekariya."Evolutionary Algorithm Using K-mean For Task Scheduling in Cloud Computing."12. Madni, S. H. H., Latiff, M. S. A., Abdullahi, M., & Usman, M. J. (2017). Performance comparison of heuristic algorithms for task

scheduling in IaaS cloud computing environment. PloS one, 12(5), e0176321.13. Alia Teresa T M, Niyas Ibrahim and K R Ramesh Babu “Latency aware scheduling policy for tasks in IaaS cloud” 2013 International

Conference on Recent Trends in Information Technology (ICRTIT) on 25-27 July 2013 pp.725-731 ISBN:978-1-4799-1024-3/13/$31.00 ©2013 IEEE.

14. Tani, Hicham Gibet, and Chaker El Amrani. "Smarter round robin scheduling algorithm for cloud computing and big data." Journal ofData Mining and Digital Humanities (2018)

15. Jang, S. H., Kim, T. Y., Kim, J. K., & Lee, J. S. (2012). The study of genetic algorithm based task scheduling for cloud computing.International Journal of Control and Automation, 5(4), 157-162.

16. Ashutosh Ingole, Sumit Chavan, Utkarsh Pawde. “An optimized algorithm for task scheduling based on activity based costing in cloudcomputing” (NCICT) 2011, Proceedings published in International Journal of Computer Applications® (IJCA).

17. Zhong, H., Tao, K. and Zhang, X. 2010 “An Approach to Optimize Resource Scheduling Algorithm for Open-Source Cloud Systems”The Fifth Annual China Grid Conference.IEEE Computer Society, 978-0-7695-4106-8.

18. Tawfeek, M., El-Sisi, A., Keshk, A. and Torkey, F.2015. Cloud Task Scheduling Based on Ant Colony Optimization. InternationalArab Journal of Information Technology, Vol. 12, No. 2, 2015.

19. Gomathi, B. and Karthikeyan, K. 2013. Task scheduling algorithm based on hybrid Particle swarm optimization in Cloud Computingenvironment. Journal of Theoretical and Applied Information Technology, September 2013. Vol. 55 N° 1.

20. Goyal, T., Singh, A., & Agrawal, A. (2012). Cloudsim: simulator for cloud computing infrastructure and modeling. ProcediaEngineering, 38, 3566-3572.

21. Bappaditya Jana and Jayanta poray “ A Hybrid Task Scheduling Approach Based on Genetic Algorithm and Particle SwarmOptimization Technique in Cloud Environment” Advances in Intelligent systems and computing 695, Springer Nature Singapore PteLtd. 2018, https://doi.org/10.1007/978-981-10-7566-7_61.

22. Mohit Agarwal and Dr. Gur Mauj saran Srivastava.”A Genetic Algorithm inspired task scheduling in Cloud Computing” InternationalConference on Computing, Communication and Automation (ICCCA 2016 IEEE), ISBN: 978-1-5090-1666-2/16.

23. Ruba Abu Khurma, et.al ”Task Scheduling Algorithm In Cloud Computing Based On Modified Round Robin Algorithm” Journal ofTheoretical and Applied Information Technology 15th sep.2018.Vol.96. No 17. ISSN: 1992-8645.

24. Michael M. Zavlanos, Leonid Spesivtsev and George J. Pappas,"A Distributed Auction Algorithm for the Assignment Problem"Proceedings of the 47th IEEE Conference on Decision and Control Cancun, Mexico, Dec. 9-11, IEEE 2008, 978-1-4244-3124-3/08.

25. Buyya, R., Ranjan, R., Calheiros, N.: Modeling and Simulation of scalable cloud computing environments and the cloudsim toolkit:challenges and opportunities. In: Proceedings of the 7th High Performance Computing and Simulation Conference, Leipzig, Germany,pp. 1–11 (2009).

26. Mao, Y., Chen, X., Li, X.: Max-Min task scheduling algorithm for load balancing in cloud computing. J. Springer (2014).27. K. Vijayakumar and C. Arun, “Continuous Security Assessment of Applications in Cloud Environment”, International Journal of

Control Theory and Applications, ISSN: 0974-5645 volume No. 9(36), Sep 2016, Page No. 533-541.28. R.Joseph Manoj, M.D.Anto Praveena, K.Vijayakumar, “An ACO–ANN based feature selection algorithm for big data”, Cluster

Computing The Journal of Networks, Software Tools and Applications, ISSN: 1386-7857 (Print), 1573-7543 (Online) DOI:10.1007/s10586-018-2550-z, 2018.

29. K. Vijayakumar and V. Govindaraj, “An Efficient Communication Technique for Extrication and Cloning of packets on cloud”,International Journal of Applied Engineering Research, ISSN 0973-4562 Vol. 10 No.66 May 2015

5. Authors: P.Suganya, Sowmya Ramanathan, Prachi, CHVN Anirudh, A.Yogendra Reddy

Paper Title:Prediction of Breast Cancer by Segmenting the Image from Mammography using Neural NetworkClassifier

Abstract: An automated identification system to enable early identification of breast cancer which is oneof the most familiartypes of cancer amidst females which is identified using a diagnostic technique calledmammography. This identification ideologybanks on multiple instance learning (MIL) paradigms whichdemonstrate an aid in therapeutical assistance. Within the projected framework, breasts area unit is first dividedadaptively into regions. The GLCM options are extracted from wavelet sub bands. A classification of diagnosticexamination procedure as normal or abnormal is revealed from lesions which are masses or small calcificationsand the textural options. To arrive at the final results the abovefactors are interpreted from all parts andanalysed.In the event of an anomaly found in thereport, the parts that are detected by the machine drivenidentification will be displayed. Dual evaluation methodology is undertaken to outline this deviation. A neuralnetwork has to be trained before utilization; it is done by segmenting the lesions and feeding it to NN. The NNassigns an anomaly index to them and then the combination of local and global anomaly index takes place.

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Keyword: Multiple instance learning (MIL); diagnostic procedure; mammography; GLCM; computer aideddiagnosis (CAD); gray scale image.

References:1. Computer Aided TheragnosisBased On Tumour Volumetric Information In Breast Cancer, Mehrdad J. Gangeh, Senior Member, Ieee,

Simon Liu, Hadi Tadayyon, And Gregory J. Czarnota,Ieee 20182. S. M. Astley, “Computer-Based Detection And Prompting Of Mammographic Abnormalities,” Brit. J. Radiol., Vol. 77, No. Suppl 2,

Pp. S194– S200, 2004.3. J. Grim, P. Somol, M. Haindl, And J. Daneˇs, “Computer-Aided Evaluation Of Screening Mammograms Based On Local Texture

Models,” Trans. Img. Proc., Vol. 18, Pp. 765–773, Apr. 2009.4. X. Gao, Y. Wang, X. Li, And D. Tao, “On Combining Morphological Component Analysis And Concentric Morphology Model For

Mammographic Mass Detection,” Ieee Trans. Inf. Technol. Biomed., Vol. 14, No. 2, Pp. 266– 273, Mar. 2010.5. M. Sameti, R. Ward, J. Morgan-Parkes, And B. Palcic, “Image Feature Extraction In The Last Screening Mammograms Prior To

Detection Of Breast Cancer,” Ieee J. Sel. Topics Signal Process., Vol. 3, No. 1, Pp. 46 –52, Feb. 2009.6. N. Eltonsy, G. Tourassi, And A. Elmaghraby, “A Concentric Morphology Model For The Detection Of Masses In Mammography,”

Ieee Trans. Med. Imag., Vol. 26, Pp. 880–889, Jun. 20077. M. Heath, K. Bowyer, D. Kopans, R. Moore, And W. P. Kegelmeyer, “The Digital Database For Screening Mammography,” In Proc.

5th Int. WorkshopDigital Mammography, 2001, Pp. 212–218.8. M. Fraschini, “Mammographic Masses Classification: Novel And Simple Signal Analysis Method,” Electron. Lett., Vol. 47, Pp. 14–15,

2011.9. M. Hussain, S. Wajid, A. Elzaart, And M. Berbar, “A Comparison Of Svm Kernel Functions For Breast Cancer Detection,” In Proc.

8th Int. Conf. Comput. Graph., Imag. Vis., 2011, Pp. 145–150.

6.

Authors: S.Ananthi Arun, Ananthanarayanan ,S.Sivakannan ,G.Prashanth

Paper Title: Wavelet Transforms Applications and Interpretation Based on the Signal Genesis

Abstract: A signal from any measurement system provides insight into its genesis, thereby enabling anunderstanding of a certain activity or phenomenon. Seismic signals, radar echo signals, physiological signals,signals from specially fabricated instruments such as MRI, CT scanner all provide information by using ananalysis that resolves the signal into its frequency components. While the Fourier transform and its fast –evaluating algorithm known as FFT are standard for such analysis, there are presently additional signaltransforms in use, of which “ Wavelets” or Wavelet transform or wavelet decomposition are becoming veryimportant. If the Fourier transform resolved the signal into its spectral components of Sine and Cosine waves,the Wavelets do the same in terms of non- sinusoidal oscillatory wave-shapes of burst – like appearance. Thispaper deals with the choice of wavelet transforms based on signal genesis and the interpretation required fromthe analysis of the signal, that one is expected to infer.

Keyword: Fourier Transform, Haar, Morlet. Daubechies Wavelet transform, Denoising, Data Compression.

References:1. P.M. Bentley and J.T.E. McDonnell, ”Wavelet transforms: an Introduction,” Electronics and Communication Engineering Journal,

August 1994.2. Howard.L, Resnikoff, Raymond O.Wells Jr., ‘ Wavelet Analysis, - The scalable structure of information’, Springer publications, New

York, 2000.3. D.Gopikrishna, Compression of EEG signals using Novel Wavelet ased techniques with application to Telemedicine, Jl. of Inst.Sci.,

Bangalore, 2004.4. A Petrosian and F.G. Meyer, ‘Computer aided Radiographic Images’ Chapter IV in wavelets in signal and image analysis.-., K luver

Publishers,USA, Vol.19, 2000.5. AA Petrosian and F.G. Meyer, ‘ An application of DWT in Geismic analysis’ , Chapter-16, Kluver Publishers, USA, Vol.19, 2000.6. S.Ananthi and K.Padmanabhan, “Application of Gabor Spectrum for NMR signals ” , Oriental Journal of Chemistry, pp-1-12, 2000.7. K.Padmanabhan ,S.Ananthi, R.Vijayarajeswaran, A practical Approach to Digital Signal Processing, New Age International

Publishers, New Delhi, revised edition 2002. 8. Handbook of Mathematical Functions with Formulas, Graphs, and Mathematical Tables.-Chapter 8: Applied Mathematics Series 55,

Dover Publications. p. 332. ISBN 0-486-61272-4.9. Speigel, Math. Handbook of Formulas and Tables, Schaum Series Publications, 1990.10. Attewell. P.B. and Sandford M.R, On the Creiterion of failure for Geological Rock Crack Distribution, Symposium on Anisotropic

Rocks, 1974.11. M. Do and W. Vetterli. The contourlet transform: an efficient directional multiresolution image representation. IEEE Trans. Im. Proc.,

14(12):2091–2106, 2005.12. D. Donoho and X. Huo. Beamlets and multiscale image analysis. In T. J. Barth, T. Chan, and R. Haimes, editors, Multiscale and

multiresolution methods, volume 20 of Lecture Notes in Computational Science and Engineering, pages 149–196. Springer, 2002.

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7. Authors: V.Kavitha , P.Shanmugapriya

Paper Title: A Framework for Estimating Dependency based on Eccentricity

Abstract: Version control systems are employed for change management on the existing code base duringdevelopment phase and maintenance phase. The changes may or may not affect the existing ones. If the changesaffect the existing code they may lead to errors (compile time and run time) or instability of the system. Versioncontrol systems employ various strategies to cater the needs of various scenarios but it leads to delays. Theagility of the system depends on the updating mechanism and the strategy it deploys while handling multipleusers and dependent modules at the similar time. The capability to handle changes by version control systemsdepends upon the strategy to organize the current modules, update them and serve them to no of users parallely.The objective is to propose a system that is found to be an enhanced version of the current system.

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Keyword: Version control systems, change management, instability, multiple users, dependent modules,enhanced version.

References:1. Rey, S. and Sarrazin, H. How telecoms can get more from Internet Protocol. McKinsey on IT, 2006. 2. N. Crespi. How telecoms can get more from Internet Protocol. Sciences Technology Information projects, 2009. 3. Park, K.H. and Favrel, J. Virtual enterprise-Information system and networking solution. Computers & industrial Engineering 37 (1-2)

(1999) 441-444. 4. Madhavji, N.H. Environment evolution: The prism model of changes. IEEE Trans. Software Engineering 18 (5) (1992) 380-392. 5. Cobena, G., Abiteboul, S. and Marian, A. Detecting changes in xml documents. Proceedings of the 18th International Conference on

Data Engineering, 2002, 41-52. 6. Aalst, W.M.P.V.D The application of Petri Nets to workflow management. Journal of Circuits, Systems, and Computers 8 (1) (1998) 21-

66. 7. Hinz, S., Schmidt, K. and Stahl, C. Transforming BPEL to Petri Nets. Proceedings of the Third International Conference on Business

Process Management, 2005. 8. Badouel, E., Llorens, M. and Olivier, J. Modeling Concurrent Systems: Reconfigurable Nets. Proc. Int. Conf. on Parallel and

Distributed Processing Techniques and Applications, 2003, 1568-1574. 9. Brambilla, M., Ceri, S., Comai, S. and Tziviskou, C. Exception handling in workflow driven web applications. Proceedings of the 14th

international conference on World Wide Web, 2005, 170-179. 10. K. Vijayakumar, Arun C, “Integrated cloud-based risk assessment model for continuous integration”, International Journal Reasoning-

based Intelligent Systems, Vol. 10, Nos. 3/4, 2018.11. K. Vijayakumar, S. Suchitra and P. Swathi Shri, "A secured cloud storage auditing with empiricaloutsourcing of key updates",

International Journal Reasoning-based Intelligent Systems, Vol. 11, No. 2, 2019. 12. G. Indrajith and K.Vijayakumar, “Automatic Mathematical and Chronological Prediction in Smartphone Keyboard” International

Journal of Engineering and Computer Science ISSN: 2319-7242Volume 5 Issue 5 May 2016, Page No. 16714-16718.

8.

Authors: A. Sangeetha, M. Jamuna , N. Manoj

Paper Title: Treatment of Grey Water using Natural Zeolites

Abstract: Water is the inevitable source for all living things. Due to rapid development in industrializationand urbanization leads to contamination of surface water as well as groundwater which results in water scarcity.Thus grey water which is easily available can be considered as an alternate source of water. Grey water is thewaste water generated from office buildings or household appliances like bathrooms, kitchens, washingmachines, etc. without fecal contamination. The usage of the soap solution in bathrooms, kitchen dish washingareas and detergents used in washing areas leads to the increasing carbonates and bicarbonates of calcium andmagnesium ions in grey water. The present study deals with the treatment of grey water by boiling and zeoliteprocess for the removal of hardness. Natural zeolites are environmentally and economically acceptable hydratedalumina-silicate materials for grey water and wastewater treatment. The treated water can be used for laundrypurposes, toilet flushing and gardening thereby reducing the demand for fresh water.

Keyword: Calcium ion, Grey water, Magnesium ion and Natural Zeolite

References:1. Nurul Widiastuti, Hongwi Wu, Ha Ming Ang. “The potential application of natural zeolite for grey water”, January 2008, 218 (2008)

271–280, DOI: 10.1016/j.desal.2007.02.022.2. Karmen Margeta, Nataša Zabukovec Logar, Mario Šiljeg and Anamarija Farkaš. “Natural Zeolites in Water Treatment – How Effective

is Their Use”, January 2013, http://dx.doi.org/10.5772/507383. Karnapa Ajit, “A Review on Grey Water Treatment and Reuse”, International Research Journal of Engineering and Technology

(IRJET), Volume: 03 Issue: 05 | May-2016.4. Parameshwara Murthy P.M, Sadashiva Murthy B.M, Kavya S, “Grey water Treatment and Reuse : ATechnological Review”, Global

Journal for Research Analysis, Volume – 5, Issue – 3, March – 2016.5. Barlokova D, “Natural Zeolites in the Water Treatment Process”, Slovak Journal of Civil Engineering, 2008.6. Dariusz Andraka, Lech Dzienis, Menlibai Myrzakhmetov, Kairat Ospanov, “Application of Natural Zeolite for Intensification of

Municipal Wastewater Treatment”, Journal of Ecological Engineering, Volume 18, Issue 2, March 2017.7. Denes Kallo, “Applications of Natural Zeolites in Water and Wastewater Treatment”, DOI: 10.1238/rmg.2001.45.15. 8. Ciambelli P, Corbo P, Porcelli c, Rimoli A, “Ammonia Removal from Wastewater by Natural Zeolites”, Zeolites 5:184-187.9. Misaelides P, “Application of natural zeolites in environmental remediation: A short review”, Micropor.Mesopor.Mater. 144:15-18,

2011.10. Fu F, Wang Q, “Removal of heavy metal ions from wastewaters: A review”. Journal of Environmental Management.92:407-418, 2011.11. Wang S, Peng Y, “Natural zeolites as effective adsorbents in water and wastewater treatment”, Journal of Chemical Engineering.

156:11-24, 2010.

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9. Authors: D.Karthikeyan

Paper Title:Examination of Metal Doped Zeolite as Catalyst to Reduce NOX Emission from Lean Burn GasolineEngines

Abstract: Lean burn gasoline direct injection (GDI) engines are the most preferred gasoline enginesbecause of their low fuel consumption and high thermal efficiency. However, these engines produce exhaustgases that are particularly rich in oxygen and therefore the present three-way catalytic converter (TWC) is notsuitable for converting the generated NOX emission into Nitrogen gases. In this present work, a new method ofreducing Nitrogen Oxides emission in a gasoline engine is attempted by using an ordinary oxidation catalysttogether with a deNOX(zeolite-based) catalyst. In this work, Na-form of ZSM-5 zeolite was used as a catalystand cupric chloride (CuCl2) and ferric chloride (FeCl3) where used as transition metals. Cu-ZSM5 and Fe-ZSM5 catalyst were prepared separately in our laboratory. Na+ ion exchange method is used to prepare thecatalyst. After that Cu-ZSM% and Fe- ZSM5 catalyst were washcoated separately onto the blank monoliths.Oxidation monoliths ( for oxidation of CO and HC into CO2 and H2O) were purchased directly from market.One oxidation monolith and one zeolite coated monolith were placed in a stainless steel container and canned

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with inlet and outlet cones ( forming catalytic convertor ). Experiments were conducted on a 2 cylinder MultiPoint Port Fuel Injection engine along with a dynamometer. Exhaust emissions such as NOX, CO, HC, O2, CO2were measured with AVL Di-gas-444 Analyzer. Exhaust gas temperature is measured with the use of athermocouple. Firstly load tests (4, 7, 10, 13, and 16KW) were conducted on the engine without catalyticconvertor was fixed close to the outlet pipe and the test were conducted again with same loading condition asmentioned above. Then by the same above procedure is followed to conduct test with Cu-ZSM5 and Fe-ZSM5catalytic convertors. From the results it is observed that both Cu and Fe zeolite catalyst minimize emissions thanthe commercial catalytic converter.

Keyword: catalytic converter, NOX reduction, oxidation catalyst, ZSM5 zeolite.

References:1. Venso Tomasic , “Application of the monoliths in DeNOX catalysis”, Catalysis Today 119, 2007, 106-113 .2. Venso Tomasic and Franjo Jovic. “State-of-the-art in the monolith catalyst/rectors.” Elsevier, Applied Catalysis A: General 311,

(2006): 112-121.3. Rajakrishnamoorthy.P, Elavarasan.G, Karthikeyan.D, Saravanan.C.G, (2019), “Emission reduction in SI engine by using metal doped

Cu-ZSM5 and Ce.Cu-ZSM5 zeolite as Catalyst”, International Journal of Innovative Technology and Exploring Engineering, vol.(8),Issue(9), 1423-1427.

4. Pratyush nag, B.B.Ghosh, Randip K.Das and Maya Dutta Gupta- NOx Reduction in SI Engine Exhaust Using Selective CatalyticReduction technique- SAE Paper980935.

5. Randip K. Das, Souvik Bhattacharyya, B.B. Ghosh and Maya Dutta Gupta- “Development and Performances Studies for SI EngineEmission Control”- SAE Paper 971652, 1997.

6. Elavarasan, G., Rajakrishnamoorthy, P., Karthikeyan, D. and Saravanan, C.G. (2019).Utilization of Coal Fly Ash as a Raw Material forthe Synthesis of Zeolite like Substance. International Journal on Emerging Technologies, 10(1): 176-182.

7. Elavarasan, G., Kannan.M, Karthikeyan, D. (2019). “History of emisison standards in India – A critical review”. International Journalof Research and Analytical Reviews, 6(2): 28-35.

8. D.Karthikeyan, C.G.Saravanan-Experimental analysis of flyash based, ion-exchanged zeolite as catalyst for S.I engine emissionControl:Journal of KONES Powertrain and Transport, vol-20,no.3 2013.229-235.E

9. D. Karthikeyan, C.G. Saravanan1 T.Jeyakumar2- Catalytic Reduction of S.I. Engine Emissions Using Zeolite as Catalyst SynthesizedFrom Coal Fly Ash- International Journal of Engineering and Technology Volume 6 No.2, February, 2016.

10. Bankim B Ghosh, Prokash chandra Roy, Mita Ghosh, Paritosh Bhattacharya, Rajasekhar Panua, Prasanta K Santra- Control of SIEngine Exhaust emissions using non-precious catalyst (ZSM-5) supported bimetals and noble metals as catalyst – ICE20051025.

11. BB ghosh, PC Roy, PP Ghosh, MN Gupta, PK Santra, Control of SI Engine Exhaust emission using ZSM-5 supported Cu-Pt bimetalsas catalyst,2002 – 01-2147.

12. Ghosh B.B., Santra P.K., Ghosh P.P., Banerjee T., Ghosh A and Ramanujam.S – “ Catalytic Reduction of SI Engine emissions usingCu- ION Exchange ZSM-5” – SAE PaperNo.2001-26-0013.

13. Joseph R. Theis – “Selective Catalytic Reduction for Treating the NOx Emissions from Lean-Burn Gasoline Engines: PerformanceAssessment” – SAE Paper No.2008- 01-0810.

14. Joseph R. Theis – “Selective Catalytic Reduction for Treating the NOx Emissions from Lean-Burn Gasoline Engines: DurabilityAssessment” – SAE Paper No.2008-01- 0811.

15. Elavarasan. G, Thiagarajan. L, Kannan. M, Karthikeyan. D, “Finding the Lubrication oil properties of an Internal Combustion Engineusing a capacitive sensor”, International Journal of Engineering and Advanced Technology (2019) vol.8(5).2368-2374.

16. Juan M. Zumaro, Maria A.Ulla, Eduardo E. Miro – “Zeolite washcoating onto Cordierite honeycomb reactors for environmentalapplications” – Chemical Engineering Journal 106(2005)25-33.

17. Meille V (2006) Review on methods to deposit catalysts on structured surfaces Appl Catal A-Gen 315:1-17.18. Lisi L, R. Pirone, G. Russo, and V. Stanzione. “Cu-ZSM5 Based Monolith Reactors for NO Decomposition.” Elsevier, Chemical

Engineering Journal 154 (2009): 341-347.19. Qi Gongshin, Yuhe Wang, and Ralph T. Yang. “Selective Catalytic Reduction of Nitric Oxide with Ammonia over ZSM-5 Based

Catalyst for Diesel Engine Applications.” Springer 121 (2008):111-117.20. Metkar P.S, Nelson Salazar, Rachel Muncrief, Vemuri Balakotaiah, Michael P. Harold. “Selective Catalytic Reduction of NO with NH3

on Iron Zeolite Monolithic Catalysts: Steady-state and Transient Kinetics.” Elsevier, Applied in Catalysis.21. Ulla M.A, R. Mallada, J. Coronas, L. Gutierrez, E. Miro, J. Santamaria (2003), Synthesis and characterization of ZSM-5 coatings on to

cordierite honey comb supports, International Journal of Applied Catalysis A: General, Vol.253, pp.257-269.22. Liu Zhiming, HAO Jiming, FU Lixin, LI Junhua and CUI Xiangyu (2004), Advances in catalytic removal of NO x under lean-burn

conditions, Chinese Science Bulletin, Vol.49(21), pp.2231-2241.

10. Authors: S.Gurusubramani., M.SureshAnand, J.JeganAmarnath, D.Sathishkumar, A.Sheela

Paper Title: Augmented Reality in Military Applications

Abstract: The Art of Modern Warfare has seen a significant shift with the onset of the 21st Century asMilitary Operations have become more diverse and intense in nature. The rise of insurgency and terrorism withthe onset of the “War on Terror” led by the USA and its allies had led to a need where soldiers are morespecifically trained in Military Operations in Urban Terrain ( MOUT), Counter-Insurgency and Counter-Terrorism duties. This has prompted the enlistment of late headways done in the field of Computers andinnovation like Augmented Reality (AR) into military applications to improve a soldier’s battle-spaceknowledge as real-world objects can then be augmented onto the real-world environment to create better trainingfacilities for the soldiers and help the soldier to adapt up to complex conditions and increment communitariancircumstance mindfulness among warriors occupied with high-hazard tasks. AR can limit inadvertent blow-back,increment risk markings like the likelihood of Improvised Explosive Devices (IEDs) and increment war zoneobservation. AR in coming time would be viewed as a venturing stone to future military applications as anarranged correspondence framework would mean a minimal loss of life and maximum impact during MilitaryOperations.

Keyword: Military Operations, Augmented Reality, Battle-Space, Battlefield Augmented Reality System

References:1. "The Lengthy History of Augmented Reality". Huffington Post. May 15, 2016.

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2. How Augmented Reality Works. (2001, February 19). Retrieved from https://com puter.howstuffworks.com/augmented-reality3.htm3. Milzarski, E. (2018, November 01). How AR technology could change the way future troops train. Retrieved from

https://www.wearethemighty.com/gear-tech/military- training-augmented-reality4. Mbwpadmin. (2017, November 15). Cost Of Military Spending | MilitaryBases.com Blog. Retrieved from

https://militarybases.com/military-articles/cost-of-military-spendin g/5. Global Military Augmented Reality Market Estimated At $1.79 Billion By 2025. (n.d.). Retrieved from

https://www.techristic.com/global-military-augmented-reality-mar ket-estimated-at-1-79-billion-by-2025/6. Morozov, M. (2018, September 28). Augmented reality in Military: AR Can Enhance Warfare and Training. Retrieved from

https://jasoren.com/augmented-reality-mi litary/7. Lynch, G. (2017, September 16). AR warfare: How the military is using augmented reality. Retrieved from

https://www.techradar.com/news/death-becomes-ar-how-the-mili tary-is-using-augmented-reality..

11.

Authors: J.JeganAmarnath, M.SureshAnand, D.Sathishkumar, S.Gurusubramani, A.Sheela

Paper Title: Military Database Security via Justapose Database Shuffling

Abstract: This paper aims for Database security which expresses the need for preventing data leak frommilitary databases which often leads to sensitive data leaks which bring the defence machinery of the nation atperil. Cyber-Attacks and Large-Scale Phishing from neighbouring nations have brought the immediate need ofsecuring the database which concerns the confidentiality of the defence proceedings and hence the paperproposes some techniques and a unique project proposal to overcome the drawbacks of the existing technologieswhich can henceforth be secured to find a secure environment for the data. Lack of awareness regarding thegravity of the confidential data and what harm it might bring upon the defence set-up can lead to essential databeing compromised and hence our paper proposes a novel solution in Database security by employing RSMmethod which invokes data dredging and mining.

Keyword: Data dredging, Slicing and Dicing, Shuffling, Data mining, Data Security, Database Management

References:1. R. Agrawal, J. Kiernan, R. Srikant, and Y. Xu. Hippocratic databases. In The 28th International Conference on Very Large Databases

(VLDB), 2002.2. M. M. et al. The Platform for Privacy Preferences 1.0 (P3P1.0) Specification, Apr. 2002. W3C Recommendation3. G. Hogben, T. Jackson, and M. Wilikens. A fully compliant research implementation of the P3P standard for privacy protection:

Experiences and recommendations. In Proceedings of the 7th European Symposium on Research in Computer Security (ESORICS2002), volume 2502 of LNCS, pages 104–125. Springer, Oct. 2002

4. R. Wenning. Minutes of the P3P 2.0 workshop, July 2003. Available at http://www.w3.org/2003/p3p-ws/minutes.html5. K.LeFevre, Agrawal.R, V.Ercegovac, R. Ramakrishnan, Xu.Y, and D. DeWitt. Limiting disclosure in hippocratic databases, Aug.

2004. In 30th International Conference on Very Large Data Bases (VLDB), Toronto, Canada. 206 6. Li.N, J.Mitchell.J.C, and W. H. Winsborough. Design of a role-based trust management framework. In Proceedings of the 2002 IEEE

Symposium on Security and Privacy, pages 114–130. IEEE Computer Society Press, May 2002. 7. Li.N, Yu.T, & Ant.A.I ´on. A semantics-based approach to privacy languages. Technical Report TR 2003-28, CERIAS, Nov. 2003. 8. McDonald.N,Stonbraker.M, & Wong.E. Preliminary specification of ingres. Technical Report 435-436, University of California,

Berkeley, May 1974.9. Motro.A. An access authorization model for relational databases based on algebraic manipulation of view definitions. In The Fifth

International Conference on Data Engineering (ICDE), pages 339–347, Feb. 1989.10. Oracle Corperation-Oracle Database: Security Guide, December 2003. Available at www.oracle.com. 11. Rizvi.S, Mendelzon.A, Sudarshan.S & Roy.P. Extending query rewriting techniques for fine-grained access control. In Proceedings of

the 2004 ACM SIGMOD International Conference on Management of Data, pages 551–562, Paris, France, 2004. ACM Press.12. Schunter.M, Herreweghen.E.V, and Waidner.M., Expressive privacy promises — how to improve the platform for privacy preferences

(P3P). Position paper for W3C Workshop on the Future of P3P. Available at http://www.w3.org/2002/p3pws/pp/ibm-zuerich.pdf.

13. Schutzer.D.M. Citigroup P3P position paper. Position paper for W3C Workshop on the Future of P3P. Available at http://www.w3.org/2002/p3p-ws/pp/ibmzuerich.pdf.

14. K. Vijayakumar,C.Arun,Automated risk identification using NLP in cloud based development environments,J Ambient Intell Human Computing,DOI 10.1007/s12652-017-0503-7,Springer May 2017.

15. K. Vijayakumar, Arun C, “Integrated cloud-based risk assessment model for continuous integration”, International Journal Reasoning-based Intelligent Systems, Vol. 10, Nos. 3/4, 2018.

16. K. Vijayakumar, S. Suchitra and P. Swathi Shri, "A secured cloud storage auditing with empirical outsourcing of key updates",International Journal Reasoning-based Intelligent Systems, Vol. 11, No. 2, 2019.

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12. Authors: D.Sathishkumar, M.SureshAnand, J.JeganAmarnath, A.SangeeraniDevi, S.Gurusubramani

Paper Title: I-Voting on Cloud Framework

Abstract: With large scale automation gathering pace among various branches and services, the paperproposes an Online Voting System built on a Cloud Framework which combines the fairness and privacy of theexisting system which uses Electronic Voting Machines, with the transparency and pliability of the new-agetechnology by using Cloud Computing Framework wherein a community cloud can secure the requiredbiometrics and couple it with the web framework and mobile application to ensure a free-and-fair elections withthe voting right at the voters fingertip. This paper proposes a Cloud Framework which can implement thisInternet-Voting (I-Voting) by employing Aadhar UIDAI (Unique Identification Authority of India) secured byencryption techniques which can be deployed by a software or an application where the user can validate hisbiometrics and vote in quick-time, which can then be encrypted and stored in a database from where the resultscan be accessed quickly saving time and resources. The proposed application combines Cloud Framework, DataSecurity and Biometric Verification along with what advantages it offers against the existing systems.

Keyword: Internet Voting, Voting, Cloud Computing, Elections in India, Election

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References:1. Ramesh, S. and Muralibhaskaran, V. (2019). Internet voting using cloud computing - IET Conference Publication. [online]

Ieeexplore.ieee.org. Available at: https://ieeexplore.ieee.org/document/6143423 [Accessed 6 Jul. 2019].2. The Economic Times. (2019). 3 lakh paramilitary, 20 lakh state police personnel deployed in 7-phase LS polls. [online] Available at:

https://economictimes.indiatimes.com/news/elections/lok-sabha/india/3-lakh-paramilitary-20-lakh-state-police-personnel-deployed-in-7-phase-ls-polls/articleshow/69412844.cms [Accessed 6 Jul. 2019].

3. Ieeexplore.ieee.org. (2019). Voting from the Home or Office? Don't Hold Your Breath - IEEE Journals & Magazine. [online] Availableat: https://ieeexplore.ieee.org/document/4270552/ [Accessed 6 Jul. 2019].

4. Ieeexplore.ieee.org. (2019). The patchwork of internet voting in Canada - IEEE Conference Publication. [online] Available at:https://ieeexplore.ieee.org/document/7001134/ [Accessed 6 Jul. 2019].

5. Ieeexplore.ieee.org. (2019). Trustworthy Electronic Voting Using Adjusted Blockchain Technology - IEEE Journals & Magazine.[online] Available at: https://ieeexplore.ieee.org/document/8651451/ [Accessed 6 Jul. 2019].

6. K. Vijayakumar and C. Arun, “A Survey on Assessment of Risks in Cloud Migration”, International Journal of Applied EngineeringResearch, ISSN 0973-4562 Vol. 10 No.66 May 2015.

7. K. Vijayakumar and C. Arun, “Continuous Security Assessment of Applications in Cloud Environment”, International Journal ofControl Theory and Applications, ISSN: 0974-5645 volume No. 9(36), Sep 2016, Page No. 533-541.

8. R.Joseph Manoj, M.D.Anto Praveena, K.Vijayakumar, “An ACO–ANN based feature selection algorithm for big data”, ClusterComputing The Journal of Networks, Software Tools and Applications, ISSN: 1386-7857 (Print), 1573-7543 (Online) DOI:10.1007/s10586-018-2550-z, 2018.

13. Authors: A.Sheela, M.SureshAnand, J.JeganAmarnath, S.Gurusubramani., D.Sathishkumar

Paper Title: Blockchain in India

Abstract: Blockchain has been the recent talk of the town in the sphere of digital technology whichattempts to bring transparency in the wake of an Indian Digital Economy by inducing transparency and crackingon the rampant corruption in Public and Private Sectors by making use of Cryptographic Techniques to makethe system hack-proof which is aimed at bringing an upheaval not only in the field of Finance but also otherindustries like Agriculture, Telecom, Mass Media and Public Sector Undertakings where coupled withcontemporary technologies like Internet of Things, Blockchain can do wonders. This paper aims to give a briefview of about Blockchain, the basic ideas that revolve around it and how it can be implemented in a nation likeIndia with over a billion population.

Keyword: Blockchain, Blockchain in India, Technology in India, Cryptography

References:1. S. Khatwani, "11 Blockchain Startups From India - CoinSutra Picks", CoinSutra - Bitcoin Community, 2019. [Online]. Available:

https://coinsutra.com/indian-blockchain-s tartups/. [Accessed: 14- Mar- 2019].

2. "Public Sector & BFSI Driving Blockchain Adoption in India: NASSCOM Report", Crypto-News India, 2019. [Online]. Available:https://www.crypto-news.in/news/public- sector-bfsi-driving-blockchain-adoption-india-nasscom-report/. [Accessed: 14- Mar- 2019].

3. N. Tanvi Ratna, "Opinion | The importance of blockchain for India", https://www.livemint.com, 2019. [Online]. Avail- able:https://www.livemint.com/Opinion/wNYS0WfLeUIDPc FddJL84K/The-importance-of-blockchain-for-India.html. [Ac- cessed: 14-Mar- 2019].

4. "Here’s how blockchain can return confidence to the Indian food industry", The Economic Times, 2019. [Online]. Available:https://m.economictimes.com/small-biz/security -tech/technology/heres-how-blockchain-can-return-confide nce-to-the-indian-food-industry/articleshow/68020591.cms. [Accessed: 14- Mar- 2019].

5. "The great chain of being sure about things", The Economist, 2019. [Online].Available:https://www.economist.com/briefing/2015/10/31/the-great-chain-of-being-sure-about- things. [Accessed: 14- Mar- 2019].

6. "Blockchain: Future of financial and cyber security - IEEE Conference Publication", Ieeexplore.ieee.org, 2019. [On- line]. Available:https://ieeexplore.ieee.org/document/7918009 /. [Accessed: 14- Mar- 2019].

7. "Blockchain: Challenges and applications - IEEE Con- ference Publication", Ieeexplore.ieee.org, 2019. [Online]. Available:https://ieeexplore.ieee.org/document/8343163. [Ac- cessed: 14- Mar- 2019].

8. "A Vehement ’No’ to Cryptocurrencies but Blockchain is ’Welcome’, Arun Jaitley Says", News18, 2019. [Online]. Available: https://www.news18.com/news/business/a-veheme nt-no-to-cryptocurrencies-but-blockchain-is-welcome-arun-jai tley-says-1648003.html.[Accessed: 14- Mar- 2019].

9. Sinha, "How Andhra Pradesh Is Emerging As India’s Blockchain Hub", Analytics India Magazine, 2019. [Online]. Available: https://www.analyticsindiamag.com/how-andhra-pr adesh-is-emerging-as-indias-blockchain-hub/. [Accessed: 14-Mar- 2019].

10. S. Nakamoto, "Bitcoin: A Peer-to-Peer Electronic Cash System", Bitcoin.org, 2019. [Online]. Available: https://bitcoin.org/bitcoin.pdf. [Accessed: 14- Mar- 2019].

11. "Blockchain Explained: Ultimate Guide on How Blockchain Works", BitDegree Tutorials, 2019. [Online]. Available:https://www.bitdegree.org/tutorials/blockchain-expl ained/. [Accessed: 14- Mar- 2019].

12. P. Mathew, Manorama Year Book 2019 (English). Malayala Manorama (5 December 2018), 2019, pp. 73-78.

13. "Moving From Bleeding Edge To Cutting Edge: How The Blockchain Will Reshape Finance", Forbes.com, 2019. [Online]. Available:https://www.forbes.com/sites/mraneri/201 6/04/14/moving-from-bleeding-edge-to-cutting-edge-how-the -blockchain-will-reshape-finance/#3f7600a62370. [Accessed: 14- Mar- 2019].

14. T. Research, "Blockchain Technology Market to Gain Revenue Worth US$20 bn by 2024 - TMR", Prnewswire.com, 2019. [Online].Available: https://www.prnewswire.com/new s-releases/blockchain-technology-market-to-gain-revenue-wo rth-us20-bn-by-2024---tmr-686428221.html. [Accessed: 14-Mar- 2019].

15. "Using Blockchain Technology to Boost Cyber Security", Steelkiwi.com, 2019. [Online]. Available: https://st eelkiwi.com/blog/using-blockchain-technology-to-boost-cyber security/. [Accessed: 14- Mar- 2019].

16. "The Meaning of Decentralization", Medium, 2019. [Online]. Available: https://medium.com/@VitalikButerin/the-meaning-of-decentralization-a0c92b76a274. [Accessed: 14-Mar- 2019].

17. "One Bitcoin Transaction Consumes As Much Energy As Your House Uses in a Week", Motherboard, 2019. [Online]. Available:https://motherboard.vice.com/en_us/articl e/ywbbpm/bitcoin-mining-electricity-consumption-ethereum-e nergy-climate-change.[Accessed: 14- Mar- 2019].

18. C. Blenkinsop, "Blockchain’s Scaling Problem, Explained", Cointelegraph, 2019. [Online]. Available: https://

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cointelegraph.com/explained/blockchains-scaling-problem-ex plained. [Accessed: 14- Mar- 2019].

14.

Authors: Latha.K, Sheela.T

Paper Title: Revocable IBE combined with CRA to overcome the inadaptability problem

Abstract: Identity based encryption (IBE) is an open key cryptographic system and takes out therequesting of the Public key infrastructure(PKI) and confirmation relationship by and large key settings. Due tothe nonappearance in PKI, the cancelation problem has become a primary issue in the IBE settings. Two or threecancellable IBE plans have been already proposed concerning this point. As of late, by embeddings anoutsourcing figuring framework into the IBE, Li et al. presented a cancellable IBE scheme with the feature ofkey-update cloud authority association (KU-CSP). Regardless, their arrangement faces two disadvantages. Onedemerit is that the costs of figuring, correspondence are more than past cancellable IBE designs. Alternatelimitation is nonattendance of adaptability as in KU-CSP should maintain secret regard for individual customer.Here another cancellable IBE plot with cloud cancellation authority (CRA) to understand the two disadvantagesin which the execution is by and large upgraded and the CRA has only a system puzzle for each one of theircustomers.

Keyword: Encryption technique, Cloud computing, outsourcing computation, Authentication, cancellationauthority.

References:1. W. Aiello, S. Lodha, and R. Ostrovsky, “Fast digital identity revocation,” in Advances in Cryptology (CRYPTO’98). New York, NY,

USA: Springer, 1998, pp. 137–152.2. V. Goyal, “Certificate revocation using fine grained certificate space partitioning,” in Financial Cryptography and Data Security, S.

Dietrich and R. Dhamija, Eds. Berlin, Germany: Springer, 2007, vol. 4886, pp. 247–259.3. F. Elwailly, C. Gentry, and Z. Ramzan, “Quasimodo: Efficient certificate validation and revocation,” in Public Key Cryptography

(PKC’04), F. Bao, R. Deng, and J. Zhou, Eds. Berlin, Germany: Springer, 2004, vol. 2947, pp. 375–388.4. D. Boneh and M. Franklin, “Identity-based encryption from the Weil pairing,” in Advances in Cryptology (CRYPTO ‘01), J. Kilian,

Ed. Berlin, Germany: Springer, 2001, vol. 2139, pp. 213–229.5. A. Boldyreva, V. Goyal, and V. Kumar, “Identity-based encryption with efficient revocation,” in Proc. 15thACMConf. Comput.

Commun. Security (CCS’08), 2008, pp. 417–426.6. A. Sahai and B. Waters, “Fuzzy identity-based encryption,” in Advances in Cryptology (EUROCRYPT’05), R. Cramer, Ed. Berlin,

Germany: Springer, 2005, vol. 3494, pp. 557–557.7. R. Canetti, B. Riva, and G. N. Rothblum, “Two 1-round protocols for delegation of computation,” Cryptology ePrint Archive, Rep.

2011/518, 2011 [online]. Available: http://eprint.iacr.org/2011/518.8. U. Feige and J. Kilian, “Making games short (extended abstract),” in Proc. 29th Annu. ACM Symp. Theory Comput. (STOC’97),

1997, pp. 506–516.9. S. Hohenberger and A. Lysyanskaya, “How to securely outsource cryptographic computations,” in Proc. 2nd Int. Conf. Theory

Cryptography (TCC’05), 2005, pp. 264–282.10. R. Canetti, B. Riva, and G. Rothblum, “Two protocols for delegation of computation,” in Information Theoretic Security, A. Smith, Ed.

Berlin, Germany: Springer, 2012, vol. 7412, pp. 37–61.11. X. Chen, J. Li, J. Ma, Q. Tang, and W. Lou, “New and secure outsourcing algorithms of modular exponentiations,” in Proc. 17 th Eur.

Symp. Res. Comput. Security (ESORICS), 2012, pp. 541–556.12. M. J. Atallah and K. B. Frikken, “Securely outsourcing linear algebra computations,” in Proc. 5th ACM Symp. Inf. Comput. Commun.

Security (ASIACCS’10), 2010, pp. 48–59.13. A. Shamir, “Identity-based cryptosystems and signature schemes,” in Advances in Cryptology (CRYPTO), G. Blakley and D. Chaum,

Eds. Berlin, Germany: Springer, 1985, vol. 196, pp. 47–53.14. C. Cocks, “An identity based encryption scheme based on quadratic residues,” in Cryptography and Coding, B. Honary, Ed.

Berlin/Heidelberg: Springer, 2001, vol. 2260, pp. 360–363.15. R. Canetti, S. Halevi, and J. Katz, “A forward-secure public-key encryption scheme,” in Advances in Cryptology (EUROCRYPT’03),

E. Biham, Ed. Berlin, Germany: Springer, 2003, vol. 2656, pp. 646–646.16. D. Boneh and X. Boyen, “Efficient selective-id secure identity-based encryption without random oracles,” in Advances in Cryptology

(EUROCRYPT’04), C. Cachin and J. Camenisch, Eds. Berlin, Germany: Springer, 2004, vol. 3027, pp. 223–238.17. D. Boneh and X. Boyen, “Secure identity based encryption without random oracles,” in Advances in Cryptology (CRYPTO’04), M.

Franklin, Ed. Berlin, Germany: Springer, 2004, vol. 3152, pp. 197–206.

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15. Authors: Kamatchi Sankar, Latha Parthiban

Paper Title: Privacy Preserving Data Mining for Pharmacovigilance

Abstract: In this paper, visual cryptography concept is used for preserving the privacy of sensitive dataused for pharmacovigilance. Overall analysis of adverse events of a specific drug helps in finding the potentialdanger of using the a specific drug. Preserving data owners privacy is done by using visual cryptographytechnique. Tetracycline drugs adverse effect present in FAERS dataset is extracted, encrypted and decrypted bythe novel methodology proposed.

Keyword: Visual Cryptography, Pharmacovigilance, Adverse event.

References:1. Ching-Nung Yang, Li-Zhe Sun, Song-Ruei Cai (2016) Extended color visual cryptography for black and white secret image. Theor.

Comput. Sci.609:143-161.2. Ching-Nung Yang, Che-Yu Lin (2015) Almost-aspect-ratio-invariant visual cryptography without adding extra subpixels. Information

Sciences.312: 131-1513. Benton A, Ungar L, Hill S, Hennessy S, Mao J, Chung A, et al. Identifying potential adverse effects using the web: a new approach to

medical hypothesis generation. J Biomed Inform 2011;44:989–96.4. Hadzi-Puric J, Grmusa J. Automatic drug adverse reaction discovery from parenting websites using disproportionality methods. In:

IEEE/ACM conference on advances in social networks analysis and mining; 2012. p. 792–7.5. Yang CC, Yang H, Jiang L, Zhang M. Social media mining for drug safety signal detection. In: Proceedings of the 2012 international

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workshop on smart health and wellbeing; 2012. p. 33–44.6. Sriramakrishnan and Latha Parthiban “ Data mining techniques for finding serious Adverse Events Journal of Chemical and

Pharmaceutical Sciences, Volume 9 Issue 4, 2016 ISSN: 0974-21157. Shetty KD, Dalal SR. Using information mining of the medical literature to improve drug safety. J Am Med Inform Assoc

2011;18:668–74.8. Wang, X., Chase, H., Markatou, M., Hripcsak, G. and Friedman, C. Selecting information in electronic health records for knowledge

acquisition. J Biomed Inform, 43, 4, 595-601.9. Dr. K V Prasad et al , “Patient Self Controllable and Multi Level Privacy Preserving Co-Operative Authentication in Distributed m –

Healthcare Computing System”, IJMETMR(2017), Vol.4, P.P.824-829.10. Harshit Kumar, “Review paper on Big Data in healthcare informatics”, International Research Journal of Engineering and Technology

(IRJET), IEEE (2017), Vol.04, P.P.197-201.

16. Authors: Kamatchi Sankar, Latha Parthiban

Paper Title: Data Mining Algorithms for Pharmacovigilance

Abstract: In this paper, various data mining algorithms for pharmacovigilance is analyzed and a decisionsupport system for hospital is proposed.. Overall analysis of adverse events of a specific drug helps in findingthe potential danger of using the specific drug. Decision support system with good classification accuracy toimprove its use in hospital for computer aided diagnosis by doctors is also analyzed,

Keyword: Pharmacovigilance, Adverse event, classification accuracy, computer aided diagnosis.

References:1. Liu V, Kipnis P, Rizk NW, Escobar GJ. Adverse outcomes associated with delayed intensive care unit transfers in an integrated

healthcare system. J Hosp Med 2012;7: 224–30. 2. Escobar GJ, Greene JD, Gardner MN, et al. Intra-hospital transfers to a higher level of care: contribution to total hospital and

intensive care unit (ICU) mortality and length of stay (LOS). J Hosp Med 2011;6:74–80. 3. Delgado MK, Liu V, Pines JM, et al. Risk factors for unplanned transfer to intensive care within 24 hours of admission from the

emergency department in an integrated healthcare system. J Hosp Med 2013;8:13–9. 4. Zegers M, de Bruijne MC, Wagner C, et al. Adverse events and potentially preventable deaths in Dutch hospitals: results of a

retrospective patient record review study. Qual Saf Health Care 2009;18:297–302. 5. Vlayen A, Verelst S, Bekkering GE, et al. Incidence and preventability of adverse events requiring intensive care admission: a

systematic review. J Eval Clin Pract 2012;18:485–97. 6. Boulain T, Runge I, Delorme N, et al. Patients hospitalized in general wards via the emergency department: early identification of

predisposing factors for death or unexpected intensive care unit admission-A historical prospective. Emerg Med Int2014;2014:203747.

7. Bapoje SR, Gaudiani JL, Narayanan V, et al. Unplanned transfers to a medical intensive care unit: causes and relationship topreventable errors in care. J Hosp Med 2011;6:68–72.

8. Frost SA, Alexandrou E, Bogdanovski T, et al. Unplanned admission to intensive care after emergency hospitalisation: risk factorsand development of a nomogram for individualising risk. Resuscitation 2009;80(2):224–30.

9. Smith T, Den Hartog D, Moerman T, et al. Accuracy of an expanded early warning score for patients in general and trauma surgerywards. Brit J Surg 2012;99: 192–7.

10. Ludikhuize J, Smorenbueg SM, de Rooij SE, et al. Identification of deterioration patients on general wards; measurement of vitalparameters an potential effectiveness of the modified early warning score. J Crit Care 2012;27: 424.e7–424.e13.

11. Petersen JA, Mackel R, Antonsen K, et al. Serious adverse events in a hospital using early warning score -what went wrong?Resuscitation 2014;85:1699–703.

12. Gold CA, Mayer SA, Lennihan L, et al. Unplanned transfers fromhospitalwards to the neurological intensive care unit. NeurocritCare 2015;23:159–65.

13. Calder L, Pozgay A, Riff S, et al. Adverse events in patients with return emergency department visits. BMJ Qual Saf 2015;24:142–8. 14. Dunn KL, Reddy P, Moulden A, et al.Medical record review of deaths, unexpected intensive care unit admissions, and clinician

referrals: detection of adverse events and insight into the system. Arch Dis Child 2006;91:169–72. 15. Naessens JM, O'Byrne TJ, Johnson MG, et al. Measuring hospital adverse events: assessing inter-rater reliability and trigger

performance of the Global Trigger Tool. Int J Qual Health Care 2010;22:266–74.16. Lindquist M, Ståhl, M., Bate, A., Edwards, I. R., Meyboom, R. H.B. A retrospective evaluation of a data mining approach to aid

finding new adverse drug reaction signals in the WHO international database. Drug Safety 2000;23(6):533-542.17. K. Zarkogianni et al., ‘‘A review of emerging technologies for the management of diabetes mellitus,’’ IEEE Trans. Biomed. Eng.,

vol. 62, no. 12, pp. 2735–2749, Dec. 2015. 18. F. Mansourypoor and S. Asadi, ‘‘Development of a reinforcement learningbased evolutionary fuzzy rule-based system for diabetes

diagnosis,’’ Comput. Biol. Med., vol. 91, pp. 337–352, Dec. 2017. 19. L. Bickley, Bates’ Guide to Physical Examination and History-Taking, 11th ed. Philadelphia, PA, USA: LWW, 2013. 20. A. Minutolo, M. Esposito, and G. De Pietro, ‘‘A fuzzy framework for encoding uncertainty in clinical decision-making,’’ Knowl.-

Based Syst., vol. 98, pp. 95–116, Apr. 2015. 21. M. Maniruzzaman et al., ‘‘Comparative approaches for classification of diabetes mellitus data: Machine learning paradigm,’’

Comput. Methods Programs Biomed., vol. 152, pp. 23–34, Dec. 2017. 22. O. Biran and C. Cotton, ‘‘Explanation and justification in machine learning: A survey,’’ in Proc. Int. Justification Conf. Artif. Intell.

Workshop Explainable Artif. Intell., 2017, pp. 8–13. 23. J. M. Alonso, C. Castiello, M. Lucarelli, and C. Mencar, ‘‘Modeling interpretable fuzzy rule-based classifiers for medical decision

support,’’ in Medical Applications of Intelligent Data Analysis: Research Advancements. Hershey, PA, USA: IGI Global, 2013, pp.1064–1081.

24. S. Guillaume and B. Charnomoridc, ‘‘Learning interpretable fuzzy inference systems with FisPro,’’ Inf. Sci., vol. 181, no. 20, pp.4409–4427, 2011. [9] L. A. Zadeh, ‘‘From computing with numbers to computing with words. From manipulation of measurementsto manipulation of perceptions,’’ IEEE Trans. Circuits Syst. I. Fundam. Theory Appl., vol. 46, no. 1, pp. 105–119, Jan. 1999.

25. J. M. Alonso, A. Ramos-Soto, E. Reiter, and K. van Deemter, ‘‘An exploratory study on the benefits of using natural language forexplaining fuzzy rule-based systems,’’ in Proc. IEEE Int. Conf. Fuzzy Syst., Jul. 2017, pp. 1–6.

26. J. M. Alonso, C. Castiello, and C. Mencar, ‘‘Interpretability of fuzzy systems: Current research trends and prospects,’’ in SpringerHandbook of Computational Intelligence. Berlin, Germany: Springer, 2015, pp. 219–237.

27. R. Meza-Palacios, A. A. Aguilar-Lasserre, E. L. Ureña-Bogarín, C. F. Vázquez-RodrÃguez, R. Posada-GÃşmez, and A. Trujillo-Mata, ‘‘Development of a fuzzy expert system for the nephropathy control assessment in patients with type 2 diabetes mellitus,’’Expert Syst. Appl., vol. 72, no. 1, pp. 335–343, Apr. 2017.

28. A. D. Torshizi, M. H. F. Zarandi, G. D. Torshizi, and K. Eghbali, ‘‘A hybrid fuzzy-ontology based intelligent system to determinelevel of severity and treatment recommendation for benign prostatic hyperplasia,’’ Comput. Methods Programs Biomed., vol. 113, no.1, pp. 301–313, 2014.

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29. S. El-Sappagh, M. Elmogy, and A. M. Riad, ‘‘A fuzzy-ontology-oriented case-based reasoning framework for semantic diabetesdiagnosis,’’ Artif. Intell. Med., vol. 65, no. 3, pp. 179–208, 2015.

30. K. Vijayakumar,C.Arun,Automated risk identification using NLP in cloud based development environments,J Ambient Intell Human Computing,DOI 10.1007/s12652-017-0503-7,Springer May 2017.

31. K. Vijayakumar, Arun C, “Integrated cloud-based risk assessment model for continuous integration”, International JournalReasoning-based Intelligent Systems, Vol. 10, Nos. 3/4, 2018.

32. K. Vijayakumar, S. Suchitra and P. Swathi Shri, "A secured cloud storage auditing with empiricaloutsourcing of key updates",International Journal Reasoning-based Intelligent Systems, Vol. 11, No. 2, 2019.

17.

Authors: Manoj K. Demde, Prashant Sharma

Paper Title: Design of Vision based Edge Detection under Different Conditions

Abstract: This work gives a detailed account of putting into effect of a Sobel algorithm to detect an Edge. Inthis model the camera is put on the vehicle(car) by recorded the real time video. The video is taken by camerawill go to the different image processing steps which includes Intensity image, changing size, Interested regionand Edge detection algorithm used to detect edges. Edge detection techniques various mathematical toolswhich are used to making outpoints in an image at which the image contrast changes sharply.So many Edgedetection algorithms used there such as search-based and zero-crossing based.. The most commonly searchbased algorithms used are Sobel and canny's edge detection algorithm. In this work of making observations wegive out with MATLAB SIMULINK model for Sobel algorithm which are used to detect edge.In this work of making observations work as first started to make a constructive video using Prescan softwareand a real time video of Lane taken by camera in dissimilar light, weather and road conditions is processed byusing image processing algorithms , and Edge detection to detect edges. In this work of making observations wegive out with MATLAB SIMULINK design to be for image processing steps and Sobel edge detectionalgorithm.

Keyword: Resize, Black and white Image,, Intensity, BI method, Sobel algorithm

References:1. Faiz Habeeb. K1, N Kunan, N Tuturaja " A study on Lane Departure Warning System and Object Detection on Roads forForward

Collision Avoidance "International Journal of Innovative Research in Electrical, Electronics, Instrumentation and ControlEngineering, Vol. 6, Issue 5, ISSN (Online) 2321-2004, May 2018.

2. Rashmi , Mukesh Kumar, and Rohini Saxena, “Algorithm and techniques on various Edge detection :A Survey”, Signal & ImageProcessing : An International Journal (SIPIJ) Vol.4, No.3, pp65-75,June 2013.

3. Jan Botsch, Biao Hu, “Real-time lane detection and tracking on high performance computing devices”, Abgabedatum: 20. M arz̈2015.

4. Alibi Rakhmatulin, “Multiple regions of interest and performance evaluation for the lane detection and tracking in a heterogeneousplatform”, May 15, 2016

5. Wenhong Zhu,Fuqiang Liu,"A vision based Lane detection and Tracking Algorithm in Autoatic Drive",2008 IEEE Pacific asiaworkshop on Computational Intelligence and industrial application, pp799-803,2008.

6. Yi Wan,Qisong Xie,"A Novel Framework for Optimal RGB to Gray scale conversion ," 2016 8th International Conference onIntelligent Human-Machine Systems and Cybernetics (IHMSC) ,Vol 2, pp 345-348,IEEE Explore,2016.

7. R Roy., M Pal. and T Gulati., Zooming Digital Images using Interpolation Techniques, International Journal of Application orInnovation in Engineering & Management (IJAIEM), Volume 2, Issue 4, 34-45, ISSN 2319 –4847, 2013

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18. Authors: P. G. Akila, K. Batri, G. Sasi, R. Ambika

Paper Title: Denoising of MRI Brain Images using Adaptive Clahe Filtering Method

Abstract: Image processing is a method of making the quality of an image better after removing unwantedinformation from image in various applications and domains to process computer effectively. Enhancement is,used to improve the quality effects of an image for further analysis. Enhancement of image can be done byfiltering, de noising and contrast enhancement. Even though contrast enhancement of images is applied indifferent fields it is used effectively in the medical field. Medical Imaging is now recently used in most of theapplications like Radiography, MRI, Nuclear medicine, Ultrasound Imaging, Tomography, Cardiograph, andFundus Imagery and so on. The main problem in analysis of medical images is the poor contras .in medicalimage analysis the detection of tumor, cancerous cells, malignant or benign has to be classified effectively. Inthis paper various spatial domain techniques and their effectiveness in terms of quality improvement arediscussed. The measuring metrics used for comparing different methods are parameters Peak Signal to NoiseRatio (PSNR) and Mean Square Error (MSE), DICE coefficient, etc,.

Keyword: filter, PSNR, dice co efficient, transformation

References:1. Rafael C. Gonzalez, and Richard E. Woods, “Digital Image Processing”, 2nd edition, Prentice Hall, 2002.2. Arici,”A Histogram modification framework and its application for image contrast enhancement”,IEEE transcation on image

processing ,20093. Nymlkhagva Sengee, and Heung Kook Choi, “Brightness preserving weight clustering histogram equalization”, IEEE Trans.

Consumer Electronics, vol. 54, no. 3, pp. 1329 - 1337, August 20084. K.Jain ,”Fundamentals of digital image processing”, Englewood’s cliffs: Prentice –hall 1997.5. P.G.Akila,Dr.K.Batri,”survey on different preprocessing methods of enhancement and segmentation in medical image

analysis”Journal of advanced research in dynamical and control systems,vol 10,2018.6. Beilei Xu, Yiqi Zhuang Hualian Tang,Li Zhang, “Object-Based Multilevel Contrast Stretching Method for Image Enhancement”

IEEE Transactions on Consumer Electronics, Vol. 56, No. 3, Aug 20107. Andrea Polesel, Giovanni Ramponi, and V. John Mathews, “Image Enhancement via Adaptive Unsharp Masking” IEEE transactions

on image processing, vol. 9, no. 3, march 2000

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8. Showkat Hassan Malik ,Mewar S.M.K. Quadri ”Contrast Enhancement and Smoothing of CT Images for Diagnosis” InternationalConference on Computing for Sustainable Global Development ISSN-3805-4416-8/15 2015 IEEE

9. Sudhir Kumar Suman, B.Ganesan and G. Yamuna, “Hybrid Contrast Enhancement Approach for Medical Images”,InternationalJournal of Computer Applications (IJCA), pp. 9-12.

10. Elena S. Yelmanova , M. Romanyshyn“Medical Image Contrast Enhancement Based on Histogram “2017 IEEE InternationalConference on Electronics and Nanotechnology ISSN--5386-1701vol-4.

11. Faisel G.Mohammed, Sega G. Mohammed, “Contrast & Brightness Enhancement for Low Medical X-Ray Images”, InternationalJournal of Scientific & Engineering Research,Volume 4, Issue 5, May-2013.

12. G.N.Sarage,SagarJambhorkar, “Enhancement of Chest X-Ray images Using Filtering Techniques”,Volume 2,Issue 5 May 20122277 128X.

13. Ying Shen , WeihuaZhu,“Medical Image Processing using A Machine Vision based Approach”, International Journal of SignalProcessing, Image Processing and Pattern Recognition Vol. 6, No. 3, June, 2013.

14. Mandip Kaur and Richa Sharma,“Restoration of Medical Images Using Denoising”, International Journal for Science and EmergingTechnologies with Latest Trends “5(1): 35-38 (2013) ISSN No. 2277-8136

15. Md. Moniruzzaman , Md. Shafuzzaman and Md. Foisal Hossain, “Detailed Regions Based Medical Image Contrast Enhancement “International Conference on Advances in Electrical Engineering ISSN -4799-2465-3/13 IEEE

19.

Authors: N.Gopinath, A.Chinnasamy, M.SureshAnand, V. Pandimurugan

Paper Title: Enhancement of MRI Prostate Image Using Histogram Equalization Techniques

Abstract: In this cutting edge world, Medical image processing in computerized field needs a compellingMRI image modality with less commotion and improved contrast of image. This is conceivable by utilizingimage enhancement methodology. Image enhancement is referenced as a system of changing or altering imageso as to make it progressively sensible for explicit applications and is utilized to enhance or improve contrastproportion, splendor of image, expel clamor from image and make it less hard to perceive. The purpose behindinclining toward Medical Resonance Imaging (MRI) is that it is a mind boggling medical technology whichgives more useful information regarding malignancy, stroke and various another ailments. It helps the doctors todistinguish the diseases more adequately. MRI has exceptionally low difference proportion. To improve thecontrast of MRI image, we utilized Histogram equalization technique. In which, Histogram Equalization, LocalHistogram Equalization, Adaptive Histogram Equalization and Contrast Limited Adaptive HistogramEqualization techniques were used and it is pondered.

Keyword: Medical image processing, MRI Prostate image, Image enhancement, Histogram Equalization,Local Histogram Equalization, Adaptive Histogram Equalization and Contrast Limited Adaptive Histogram

References:1. Sajid Ullah Khan, Najeeb Ullah, Imran Ahmed, Irshad Ahmad, Wang Yin Chai, “MRI Imaging, Comparison of MRI with other

Modalities, Noise in MRI Images and Machine Learning Techniques for Noise Removal: A Review”,Article in Current MedicalImaging Reviews, DOI: 10.2174/1573405614666180726124952, July 2018

2. Hongmei Zhu, “Medical Image Processing Overview”, https://pdfs.semanticscholar.org/4594/3c7f9170778ada18fe0726fe9f90ff211dcc.pdf

3. Hardeep kaur, Jyoti Rani, “MRI brain image enhancement using Histogram equalization Techniques”, IEEE WiSPNET 2016conference, 978-1-4673-9338-6/16/$31.00 ©2016 IEEE

4. Sargun and Shashi B. Rana, “A Review of Medical Image Enhancement techniques for Image Processing”, International Journal ofCurrent Engineering and Technology, E-ISSN 2277 – 4106, P-ISSN 2347 – 5161, Vol.5, No.2 ,April 2015.

5. Sumaya, Zena Vatsa, “MRI Image Enhancement”, International Journal of Science and Research (IJSR) ISSN (Online): 2319-7064,Volume 4 Issue 5, May 2015.

6. Ravindra Pal Singh and Manish Dixit, “Histogram Equalization: A Strong Technique for Image Enhancement”, International Journal ofSignal Processing, Image Processing and Pattern Recognition Vol.8, No.8 (2015), pp.345-352

7. Shefali Gupta, Yadwinder Kaur, “ Review of Different Local and Global Contast Enhancment Techniques for Digital Image”,International Journal of Computer Applications (0975 – 8887) Volume 100– No.18, August 2014

8. Julang Jiang, Yousheng Zhang, Feng Xue, Min Hu, “Local Histogram Equalization with Brightness Preservation”, Acta ElectronicaSinica, no. 5, pp. 861-866, 2006.

9. D. J. Ketchum. Real-time image enhancement techniques. Proc. SPIE/OSA, 74:120–125, 1976.10. A. Rosenfeld and A. C. Kak. Digital Picture Processing. Computer Science and Applied Mathematics. Academic Press, 1982.11. Rajesh Garg, Bhawna Mittal, Sheetal Garg, “Histogram Equalization Techniques For Image Enhancement”, IJECT Vol. 2, Issue 1,

March 201112. Brij Bhan Singh, Shailendra Patel, “Efficient Medical Image Enhancement using CLAHE Enhancement and Wavelet Fusion”,

International Journal of Computer Applications (0975 – 8887) Volume 167 – No.5, Jun

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20. Authors: Kavitha D, Anusha R, Deepa Lakshmi V, Mythili N, Shali A

Paper Title: Smart System for Efficient Monitoring of Food Grains

Abstract: Agriculture is the main source of Indian economy. 16 % of total GDP is depends onagriculture. So, maintaining a proper environment for storing the food grains is important. The food grains arestored in the silos. It is required a automated system which is used for monitoring the quality attributes ofagricultural and food products. For increasing the productivity the food industry uses pests which affects thehealth of the consumers. Here a sensor based monitoring system which maintains the temperature, humidity anda co2 sensor is used to detect the presence of the insects and ultrasonic sensor keeps away the insects from thefood grains and also avoids the usage of pests. The information is stored in the cloud and is passed to the mobiledevice of the silos incharge.

Keyword: Agriculture,FoodGrain Storage, Wireless network,Sensors,IOT,Cloud.

References:1. Bin Liu Maryam Rezaei “Food loss and waste in the food supply chain,” Food and Agriculture Organisation of the United Nations.

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July 2017.2. Nagarajan V and Rajasekaran A International Journal of Applied Engineering Research, “Improved cluster head selection for energy

efficient data aggregation in sensor networks,” vol. 11, 2016.3. Mythili Thirugnanam, Amol Bandal “Quality measurements offruits and vegetables using sensor network,” Big Data and Cloud

Computing Challenges(ISBCC–16Š). Springer, 2016, pp. 121–130.4. M.S .Anap, S.G Galande, “A Parameter Monitoring and Control of Grain Storage by Embedded System”. International Journal of

Informative and Futuristic Research, 2(11), 4172-4179.5. Ahmed Toman ,Mabrouk, and, Aya Abdelmonsef, “Smart grain storage monitor and control,” American Scientific Research Journal

for Engineering, Technology, and Sciences, vol. 31, no. 1, pp. 156–162, 2017. 6. Jie Zhang, Xiaodong Zhang,and Xiujuan Li, “Design and implementation of embedded monitoring system for grain storage,” in

Information Management and Engineering (ICIME), 2010, pp. 197–200.

21.

Authors: V.Lalitha , Divya Dharshini A , Divya B,Desu Sravya

Paper Title: Anti-Spoofing Authentication

Abstract: Biometrics,One Among the existing security authentication schemes, involves parts of body foridentification, hence used in various fields. Due to the level of its simplicity fingerprint recognition remainspopular. But the introduction of many technologies has made to lose its uniqueness by just taking a finger printscan, print it on special paper and then swipe. This project is mainly designed to new optical method, which isbased on liveness detection on fingers. Infrared pulse sensor which is a new method is employed to enhanceimage contrast of the finger vein and to know the pattern of blood flow for liveness detection.

Keyword: Biometrics, Infrared pulse sensor, Android Application.

References:1. Yuan, J.; Jiang, H.; Yan, W.; Cintron-Colón, H.R.; V.L.; Debauch, D.C. W.J.; Wang, J. Vessel sampling and blood flow velocity

distribution with vessel diameter for characterizing the human bulbar conjunctival microvasculature. Eye Contact Lens Sci. Clin. Pract.2016

2. Conlon, S.P , Goldman, R.; Hunt, D.; Mock, M.;.; Roth, B. Micro vein enhancer. U.S. Patent 8,255,040 B2, 28 August 20123. Czerniecki, B.J Choe, R.; Konecky, S.D.; Corlu, A.; Lee, K.; Durduran, T.; Busch, D.R.; Pathak, S.;.; Tchou, J.; Fraker, D.L.; et al.

Differentiation of benign and malignant breast tumors by In Vivo three-dimensional parallel-plate diffuse optical tomography.4. M. Suresh Anand, N.Mohankumar,A.Kumaresan,"An Efficient Framework for Indian Sign Language Recognition Using Wavelet

Transform",Circuits and Systems, Scientific Research Publishing, Volume 7, Issue no. 8, Jun 20165. Kim, K.S.,Moon, S.; Lee, J.; Yun, J.; Lim, J.; Gwak, M.-J.;; Lee, J.-H. Two-Axis electrostatic gimbaled mirror scanner with self-

aligned tilted stationary combs. IEEE Photonics Technol. Lett. 2016 6. Park, K.R. Pham, T.D.; Park, Y.H.; Nguyen, Kwon, S.Y.; Nonintrusive finger vein recognition system using nir image sensor and

accuracy analyses according to various factors. Sensors 2015

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22.

Authors: Shalia ,Kavitha D, Sangeerani Devi A

Paper Title:Smart Driver Assistance System for Accident Forestalling

Abstract: Traffic accidents that happenedaround the worldare increasing a lot. If modern technology isincorporated within vehicle to find the status of the driving person at regular intervals and assist driver aboutsign boards so that the driver would not lose focal point. The sign board is monitored by using webcam and thetext from the image is converted into audio and it directs the driving person. This system observes the heartbeat,sense drowsiness of driver and checks whether the driver has consumed alcohol. If any disaster is noticed,system sends an alert message including the location to the service, sickbay and the person’s family membersand if there is no serious risk, then the aware message can be ended by the driver in order to avoid wasting thevaluable time.

Keyword: Alertness, sign board, Heart Rate Monitor, optical character recognition, alcohol consumptiondetection, accident detection.

References:1. T. H. Yee and P. Y. Lau, "Mobile vehicle crash detection system," 2018 International Workshop on Advanced Image Technology

(IWAIT), Chiang Mai.2. Lichen Cheng, Lun Li, Jiqiang Liu, ShuoQiu, Wei Wang, Zonghua Zhang,Xiangliang Zhang, "CreditCoin: A Privacy-Preserving

Blockchain-Based Incentive Announcement Network for Communications of Smart Vehicles", IEEE Transactions on IntelligentTransportation Systems, 2018.

3. C. H. Mohammed Koya, "An Android Application for Accident Alert", International Journal of Innovative Research in ScienceEngineering and Technology, vol. 6, no. 5, March 2017.

4. V. LokeshManikanta Kumar, , B. Siva Nagaraju, D. Chandra Sekharand J. Yaswanth Kumar, "Accident Alert using IOT andAndroid Application", International Journal for Research in Applied Science & Engineering Technology, vol. 6, no. IV, April 2018.

5. M.H.Awadalla, andT.Jamil,I.Mohammed "Design and implementation of an eye blinking detector system for automobile accidentprevention," SoutheastCon 2016, Norfolk, VA.

6. K. Kalyan, V. K. Chugh and C. S. Anoop, "Non-invasive heart rate monitoring system using giant magneto resistance sensor," 201638th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Orlando, FL.

7. Amit Meena, SrikrishnaIyer, Monika Nimje, SaketJogJekar, SachinJagtap, Mujeeb Rahman, "Automatic Accident Detection andReporting Framework for Two Wheelers", IEEE International Conference on Advanced Communication Control and ComputingTechnologies (ICACCCT), pp. 962-967, May 2014.

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23. Authors: Elakya.R, Badri Narayan Mohan, Vijay Kishore.V, Keerthivasan R, Naresh Kumar Solanki

Paper Title: Phishing Scam Detection using Machine Learning

Abstract: As a wrongdoing of utilizing specialized intends to take sensitive data of clients and users in the 114-118

internet, phishing is as of now an advanced risk confronting the Internet, and misfortunes due to phishing aredeveloping consistently. Recognition of these phishing scams is a very testing issue on the grounds that phishingis predominantly a semantics based assault, which particularly manhandles human vulnerabilities, anyway notsystem or framework vulnerabilities. Phishing costs. As a product discovery plot, two primary methodologiesare generally utilized: blacklists/whitelists and machine learning approaches. Every phishing technique hasdifferent parameters and type of attack. Using decision tree algorithm we find out whether the attack islegitimate or a scam. We measure this by grouping them with diverse parameters and features, thereby assistingthe machine learning algorithm to edify.

Keyword: Decision Tree Algorithm, Machine Learning, Phishing scam, Client sensitive information.

References:1. Ebubekir Buber, Önder Demir and Ozgur Koray Sahingoz, Istanbul, Turkey; Feature Selections for the Machine Learning based

Detection of Phishing Websites. (2017)2. Peng Yang, Guangzhen Zhao and Peng Zeng; Phishing Website Detection based on Multidimensional Features driven by Deep

Learning, Southeast University, Nanjing 211189, China. (2019)3. Joby James, Sandhya L and Ciza Thomas, Detection of Phishing URLs Using Machine LearningTechniques, SCT College of

Engineering. (2013)4. F.C. Dalgic, A.S. Bozkir and M. Aydos; Phish-IRIS: A New Approach for Vision Based Brand Prediction ofPhishing Web Pages via

Compact Visual Descriptors, Hacettepe University, Turkey. (2018)5. Sreyasee Das Bhattacharjee, Ashit Talukder, Ehab Al-Shaer, Pratik Doshi; Prioritized Active Learning for Malicious URL Detection

using Weighted Text-Based Features, University of North Carolina, Charlotte. (2017)6. Ram Basnet, Srinivas Mukkamala and Andrew H. Sung; Detection of Phishing Attacks: A Machine Learning Approach. (2008)7. Murat Karabatak and Twana Mustafa; Performance Comparison of Classifiers on ReducedPhishing Website Dataset, Firat University

Elazig, Turkey. (2018)8. Wesam Fadheel, Mohamed Abusharkh and Ikhlas Abdel-Qader On Feature Selection for the Prediction of Phishing Websites. (2017)9. Yasin Sönmez, Türker Tuncer, Hüseyin Gökal, Engin Avc; Phishing Web Sites Features Classification Based on Extreme Learning

Machine. (2018)10. Anu Vazhayil, Vinayakumar R and Soman KP; Comparative Study of The DetectionOf Malicious URLS Using Shallow and Deep

Networks. (2018)

24. Authors: Vinita Malik, Sukhdip Singh

Paper Title:Assessing Software Risks by Implementing Cloud Readiness and Benchmarking in the PervasiveEnvironments

Abstract: The evolution of revolutionary computational paradigms has been witnessed in terms of thepervasive systems which not only offer evolving service portfolio but also integrated sensor data fromvariable sources but surfaces various challenges of data interaction, integration and adaptation. Thisresearch has been provisioned with the comprehensive focus on pervasive devices user interaction, accesscontrol modeling, identity management, trust and service discovery modeling. In addition, we also proposed adeep insight into Pervasive computing characteristics, risks, and risks models, environmental, ethical and socialimpacts. We have explored pervasive computing environment risk models by considering social, human andenvironmental risks. This state of art will create a case for developing new models for access control, identity,trust, risk management in ubiquitous or pervasive computing environments. The paper has implemented thebenchmarking of smart applications by a smart tool by gathering various application and business metrices toimprove software performance. This research has also implemented the cloud readiness of a pervasive powerreader application for PaaS environment using a smart vendor solution. The cloud readiness score is evaluatedby questionnaire qualitative information and source code scan. Various boosters and roadblocks are found out togive score to cloud readiness for any application.

Keyword: Pervasive/Ubiquitous Computing, Risks, Trust, Access Control, Privacy, Identity Management,Cloud Readiness, Benchmarking

References:1. A. H. Mousa, “Ubiquitous/Pervasive Computing” , International Journal of Innovative research & development , vol 2 ,pp:276-

282,20132. M.Weiser, “The computer for the 21st century, Scientific American”, 265(No. 3), pp : 94 –104,19913. Y.Ren, A.Boukerche., “ Modelling and managing trust for wireless and mobile adhoc networks”, In: proceedings of IEEE

conference on Communications (ICC), 20084. J.M.Seigneur, “ Trust, Security and Privacy in global computing” , PhD theses , Trinity College Dublin , 20055. S.A.Weis, “ Security parallels between people and pervasive devices” , in : Proceedings of the 3rd IEEE International conference

on pervasive computing and communications workshop ,pp. 105-109,20056. Z. Hayat, J.Reeve., “Ubiquitous security for ubiquitous computing” ,Information Security technical report ,12, pp. 172-178,20077. B. Abdulrazak., Y. Malik, “Review of challenges, Requirements and approaches of pervasive computing system

Evaluation”, IETE Technical Review, 29,6, pp.506-522,20128. L.M.Hilty, C.Som , “ Assessing the Human, Social and Environmental risks of pervasive computing” ,Human and Ecological

Risk Assessment ,10, PP. 853-874,20049. J.Sen, “Ubiquitous Computing: Applications, Challenges and future trends”, PP. 1-41,201210. C.A.D Costa., “Towards a General software Infrastructure for ubiquitous Computing, Journal of Pervasive Computing” ,

IEEE CS , pp.64-73,200811. R. Richardson., “CSI Computer Crime and Security Survey”, Computer security institute, 200912. Carnegie Mellon University, http://www.cert.org/octave/download/intro.html

13. Clusif,http://www.clusif.asso.fr/fr/production/ouvrages/pdf/MEHARI-2010-Overview.pdf, 2010 14. Insight Consulting, http://dtps.unipi.gr/files/notes/ 2009-2010/eksamino_5/politikes_kai_diaxeirish_asfaleias/

egxeiridio_cramm.pdf, 200915. T. Ledermuller, N.L.Clarke, “Risk assessment for mobile devices”, Lecture notes in computer science ,210-221,2011

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16. F.Liu,Y.Chen., K.Dai.,Z.Wang, “ Research on risk probability estimating using fuzzy clustering for dynamic securityassessment” ,LNAI,3642,pp. 539-547,2005

17. R.Sandhu, P. Samarati, “Access control: principles and practice. IEEE Communications Magazine”, 32(9),pp. 40–48.,199418. R.Sandhu, E.Coyne, H. Feinstein, C. Youman, , “Rolebased access control models”. IEEE Computer, 29(2), 38–47,199619. S.Park,Y. Han, & T. Chung, , “Context-role based access control for context-aware application”. In Lecture notes in computer

science: Vol. 4208. High performance computing and communications (pp. 572–580). Berlin/Heidelberg: Springer, 200620. M.J.Covington, P.Fogla, Z.Zhan, M.Ahamad, “A context-aware security architecture for emerging applications”. In Proc. 18th

annual computer security applications conference (ACSAC ’02), Washington, 2002 (p. 249). Los Alamitos: IEEE ComputerSociety, 2002

21. J.Joshi, E.Bertino, A.Ghafoor,. “Hybrid role hierarchy for generalized temporal role based access control model.” In Proc. 26thinternational computer software and applications conference on prolonging software life: development and redevelopment(COMPSAC ’02), Washington, DC (pp. 951–956). Los Alamitos: IEEE Computer Society, 2002

22. H.Zhang, Y.He, Z., “Spatial context in role- based access control .In Lecture notes in computer science”, vol. 4296. InformationSecurity and Cryptology—ICISC2006, November 2006 (pp. 166–178), 2006

23. Z.Guangsen, P.Manish, “Context-aware dynamic access control for pervasive applications”, In Proc. communication networksand distributed systems modeling and simulation conference, San Diego, California (pp.219–225) ,January 2004

24. Y.Kim, C.Mon, D. Jeong ,”Context-aware access control mechanism for ubiquitous applications”, In Lecture notes in computerscience: vol. 3528. Advances in web intelligence (pp. 236–242). Berlin/Heidelberg: Springer, 2005

25. Ahmed, N. Zhang. “Towards the realization of context risk aware access control in pervasive computing, TelecommunicationSystems”, 45:127-137, 2010

26. M.Langheinrich., “A Privacy awareness system for ubiquitous computing environments”, In: Proceedings of UbiComp, LNCS,237-245, 2002

27. U. Jendriche, M. Kreutzer, “Pervasive privacy with identity management”, In : Proceedings of the workshop on Security inubiquitous computing , 2002

28. J.A.Muhtadi, R. Campbell., “Routing through the mist Privacy preserving communication in ubiquitous computingenvironment”, In: Proceedings of the international conference on distributed computing systems , 2002

29. A.Beressford, F.Stajano., “ Location privacy in pervasive computing” ,IEEE pervasive computing ,46-55, 200330. D.Nguyen, E. Mynatt., “Privacy mirrors: Understanding and shaping socio technical ubiquitous computing”, Technical report,

200231. X.Liang, J.Landay, “Modelling privacy control in context aware systems” , IEEE Pervasive computing ,59-63,200232. M.Tentori. ,J. Favela., “Privacy aware autonomous agents for privacy healthcare”, IEEE Computer Society, 55-62,200633. Al-Karkhi., A.Al-Yasiri, “Privacy,trust and identity in pervasive computing : A review of technical challenges and future research

, international journal of distributed and parallel systems” , vol 3 ,201234. A.Lee, , J.Boyer, C. Drexelius, P.Naldurg,, R.Hill, Campbell, Supporting dynamically changing authorizations in pervasive

communication systems, in the 2nd International Conference on Security in Pervasive Computing, 200535. J.M .Seigneur, S. Farrell, C.D. Jensen, , “Secure ubiquitous computing based on entity recognition”, in the UBICOMP2002 -

Workshop on Security in Ubiquitous Computing, (Goteborg, Sweden),200236. 36. F. Stajano, R. Anderson, “The resurrecting duckling: Security issues for ad-hoc wireless networks”. in Security

Protocols, 7th International Workshop Proceedings, Lecture Notes in Computer Science, 172-194, B. Christianson, B. Crispo,and M. Roe (Eds.),1999

37. J. Al-Muhtadi, R.Hill, R. Campbell, “Context and location-aware encryption for pervasive computing environments”, in thePervasive Computing and Communications Workshops, PerCom Workshops2006. Fourth Annual IEEE International Conferenceon, (Pisa), 2006

38. A. Al-Karkhi and A. Al-Yasiri “Asserting User Identity in Pervasive Computing Environments Using a Non-IntrusiveTechnique”, ISBN: 978-1-902560-24-3 , 2010 PGNet.

39. S. Creese, M. Goldsmith, B. Rosco, I. Zakiuddin, “Authentication for pervasive computing, in the Proceedings of the FirstInternational Conference on Security in Pervasive Computing, (Boppard, Germany), 2003

40. T. Grandison, M.Sloman, , “A survey of trust in internet applications”, IEEE Communications Surveys and Tutorials, 3(4),200041. W.Wagealla, S.Terzis, “Trust based model for privacy control in context–aware systems”, in the 2nd Workshop on Security in

Ubiquitous Computing, Washington, USA,200342. L.Kagal, T.Finin, A.Joshi, Trust-based security in pervasive computing environments‖. IEEE Computer, 34(12), 154-157, 200143. Anas EL HUSSEINI, Abdallah M'HAMED, Bachar EL HASSAN, Mounir MOKHTARI, “A Novel Trust- Based Authentication

Scheme for Low-Resource Devices in Smart Environments, “The 2nd International Conference on Ambient Systems, Networksand Technologies (ANT-2011), Niagara Falls : Canada, Procedia CS”, vol.5, 362-369, 2011

44. M.Sharmin.S.I, Ahamed.., “ SSRD+ : A Privacy aware trust and security model for resource discovery in pervasive computingenvironment” , 30th Annual international computer software and applications conference, 2006

45. A.Boukerche., Y.Ren., “ A trust based security system for ubiquitous and pervasive computing environment, computercommunications”, 4343-4351, 2008

46. S.Singh, A.Katiyar, “Pervasive computing service discovery in secure framework environment”, International research journal ofEngineering and technology, (2018), 380- 387

47. A.Koehler, A.C. Som., “Effects of Pervasive computing on sustainable development”, IEEE Technology and Society magazine,15-23, 2005

48. Claudio Da Rold, Gilbert Vander Heiden, “Applying a Cloud First Checklist to ensure successful sourcing and Business ITAlignment”, Gartner Inc, G00296404, pp: 1-22, 2016

49. Claudia Loebbecke, Bernhard Thomas, “Assessing cloud readiness: Introducing the magic metrices used by Continental AG”,IFIP AICT 366, pp: 270-281, 2011

50. https://www.castsoftware.com/products/highlight51. http://codeload.github.com/smartuni/smartpowerreader/zip/maste52. S. Madan, “A benchmark for the artificial intelligence applications on parallel computers” , BEAP, Conference on

Communications, Power and computing, WESCANEX 97 Proceedings; Winnipeg, MB; pp.: 82-87,1997

53. https://codeload.github.com/Smartuni/SmartFarm/zip/master54. https://www.castsoftware.com/products/highlight

25. Authors: S.Jayakumar, M.Srivathsan, S.Samundar Ahmed, Dhanish Kumar S M, M.Karthikeyan

Paper Title:Ticket Collectionwith Destination Prediction in Bus Services in Urban Areas using Time BasedPredictive Algorithm

Abstract: In the new age of automation and machine assisted function of the human way of life peoplestill tend to notice verification and checking of tickets in local land transport such as trains and buses to still beoperated by man. This project is a proposal of a new platform and method to book these tickets of buses on alocal level. This can lead to decrease in the overcrowding of buses, easy time management of commuters, andsmooth functioning of the bus business. Initially the bank details of the passenger must be linked to theapp.Machine learning predictive parsing algorithm in combination with data mining features enable the

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prediction of the passengers to and fro details on a daily and timely basis. Then a SMS alert for ticket paymentproof is sent to the user. In admin side, they calculate amount details using this application. Per day amountdetails of specific route or bus can be calculated by accessing the database. There is also a provision where theIMEI numbers of the consumers is collected. Through GPS system the IMEI numbers of the mobiles inside thebus is checked with the IMEI numbers of those in the database. Ticket defaulters are identified if the IMEInumbers are not present in the database. The entire trail of the transit is on a non-paper sever.

Keyword: GPS,IMEI,Ticket.

References:1. S. Kazi, M. Bagasrawala, F. Shaikh And A. Sayyed, "Smart E-Ticketing System For Public Transport Bus," 2018international

Conference On Smart City And Emerging Technology (Icscet), Mumbai, 20182. A. Shingare, A. Pendole, N. Chaudhari, P. Deshpande And S.Sonavane, "Gps Supported City Bus Tracking & Smart Ticketing

System," 2015 International Conference On Green Computing And 3. Internet Of Things (Icgciot), Noida, 20154. N. Lathia, J. Froehlich And L. Capra, "Mining Public Transport Usage For Personalised Intelligent Transport Systems," 2010 Ieee

International Conference On Data Mining, Sydney, Nsw, 2010 S. Sankarananrayanan And P. Hamilton, "Mobile Enabled BusTracking And Ticketing System," 2014 2nd International Conference On Information And Communication Technology (Icoict),Bandung, 2014

5. D. K. Sharma And S. R. Ahuja, "A First- Come-First-Serve Bus-Allocation Scheme Using Ticket Assignments," In The BellSystem Technical Journal

6. G. Indrajithand K.Vijayakumar, “Automatic Mathematical and Chronological Prediction in Smartphone Keyboard” InternationalJournal of Engineering and Computer Science ISSN: 2319-7242Volume 5 Issue 5 May 2016, Page No. 16714-16718.

7. K. Vijayakumar and C. Arun, “A Survey on Assessment of Risks in Cloud Migration”, International Journal of Applied EngineeringResearch, ISSN 0973-4562 Vol. 10 No.66 May 2015.

8. K. Vijayakumar and C. Arun, “Continuous Security Assessment of Applications in Cloud Environment”, International Journal ofControl Theory and Applications, ISSN: 0974-5645 volume No. 9(36), Sep 2016, Page No. 533-541

26.

Authors: V Brindha Devi, R Skanda Gurunathan, N Keerthi vasan

Paper Title: De-Centralized Certificate Creation and Verification using Block Chain (DCCVuB)

Abstract: The rapid growth in the population has lead to generation of large amount of data from eachindividual. Each and every individual holds several physically signed documents. Currently, the documents,certificates, and contracts are all printed in papers and manually signed. It is difficult for other party say arecruiter, or a government official or any other custom officer to verify the validity of the certificates and otherdocuments of the individual. It consumes a tremendous amount of time for validating and verifying suchdocuments manually. Thus we propose a system to develop a Decentralized application (DApp) forimplementing a Blockchain[1] to store and verify the documents. By the nature of blockchain, the documents aresecurely stored with high integrity, and no further modifications can be done to the blocks in the chain which inturn reduces the creation of forged documents. Also using Distributed Ledger technology(DLT)[5] and IPFS thedata is decentralised so that it is readily available with integrity. Also, using MultiSig[3] concepts, the system ismore secured by two step authentication. Thus, blockchain creates trust and DLT provides integrity ease ofaccess. And with use of IPFS the DApp is decentralized4]

Keyword: Document Verification, Blockchain, Certificates, Smart Contracts, MultiSig

References:1. S. Nakamoto, Bitcoin: A Peer-to-Peer Electronic Cash System, pp. 9, 2008.2. L. Luu, D.-H. Chu, H. Olickel, P. Saxena, A. Hobor, "Making smart contracts smarter", Proceedings of the 2016 ACM SIGSAC

Conference on Computerand Communications Security, pp. 254-269, 2016.3. Okamoto, T., “A digital multisignature scheme using bijective public-key cryptosystems”, ACM Trans. Computer Systems, 1988, 6,

(8), pp.432–4414. Juan Benet, “IPFS - Content Addressed, Versioned, P2P File System”, ArXiv,20145. Klaithem Al Nuaimi, Nader Mohamed, Mariam Al Nuaimi, Jameela Al-Jaroodi, “A Survey of Load Balancing in Cloud Computing:

Challenges and Algorithms”, Second Symposium on Network Cloud Computing and Applications, 20126. "Blockchain", Wikidia, [online] Available: https://en.wikipedia.org/wiki/Blockchain#cite_note-te20151031-1.7. "AboutHypededger", Hyperledger, [online] Available: https://www.hypededger.org/about.8. C. Christian, A. Elli, Angelo De Caro, K. Andreas, O. Mike, S. Simon, S. Alessandro, V,. Marko et al., "Blockchain cryptography and

consensus", IBM Research Zurich, June 2017.9. Vitalik Buterin, "Ethereum and The Decentralized Future", Future Thinkers Podcast. 2015-04-21, 05 2016.10. Zheng, Y. Li, P. Chen and X. Dong, "An Innovative IPFS-Based Storage Model for Blockchain," 2018 IEEE/WIC/ACM International

Conference on Web Intelligence (WI), Santiago, 2018, pp. 704-708J. Jones. (1991, May 10). Networks (2nd ed.) [Online]. Available:http://www.atm.com

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27. Authors: M.Kiruthiga Devi, R.Kaviya

Paper Title: Vital Symptoms Monitoring and Classification of Bipolar Disorder

Abstract: Bipolar disorder is a mental illness that puts patients into extreme states of mind known asmania and depression. These mental states are very harmful to the lives of the patients as their day to day actionsare disrupted. This project aims at identifying the symptoms of the patients who have extreme moods todetermine if they are bipolar using sensors and smart phones. Patients with mental illness tend to exhibitsymptoms like reduced physical activity, changes in mood, drastic changes in sleep pattern, inability to copewith stress and withdrawn from socializing. These changes can be monitored using sensors and the datacollected is compared with the data collected from healthy individuals. A classification algorithm is applied to

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the data collected to classify the symptoms and detect if the person has bipolar disorder or is just showing subtlesigns of mood swings.

Keyword: Bipolar disorder, Random forest classification, Neural network, DDMLP, Machine learning.

References:1. Enrique Garcia Ceja, Michael Alexander Riegler,PetterJackobsen, T.Nordgreen “Motor activity based classification of depression in

unipolar and bipolar patients” IEEExplore International Conference,July 2018 .doi:10.1109/CBMS.2018.000622. Wouter B. Teeuw, Johan Koolwaaij, Arjan Peddemors “Monitoring human behaviour through mobile technology”,October

2018,MDPI journel,doi:10.3390/proceedings21912433. Sara Mariani, Matteo Migliorini, Giulia Tacchino, Claudio Gentili “Clinical state assessment in bipolar patients by means of HRV

features obtained with a sensorized T-shirt ”, August 2018,doi: 10.1109/EMBC.2012.63464084. Maria Faurholt-jepsen,maj Vinberg,mads Frost,sune Debel,ellen Margrethe Christensen,jakob E Bardram “Behavioral activities

collected through smartphones and the association with illness activity in bipolar disorder”,April 2016. doi: 10.1002/mpr.1502.5. A. Maxhuni, A. Munoz-Mel endez, V. Osmani, H. Perez, O. Mayora, and E. F. Morales “Classification of bipolar disorder episodes

based on analysis of voice and motor activity of patients”,Volume 31 Issue C, September 2016.doi:10.1016/j.pmcj.2016.01.008.6. A Liaw, M Weiner “Classification and regression by random forest”,R news, Vol. 2/3, December 2002.7. C. Burton, B. McKinstry, A.S.T̆atar, A. Serrano-Blanco, C. Pagliari, and M. Wolters, “Activity monitoring in patients with

depression: a systematic review,” Journal of affective disorders, vol. 145, no. 1, 2013.8. “Development and Validation of a Screening Instrument for Bipolar Spectrum Disorder: The Mood Disorder Questionnaire” Robert

M.A. Hirschfeld , M.D., Janet B.W. Williams, The American Journal of Psychiatry, Nov 2000.9. “Random Forest:  A Classification and Regression Tool for Compound Classification and QSAR Modeling” Vladimir Svetnik, Andy

Liaw, Journal of chemical information and modelling, Nov 2003. 10. “Automate the Internet With If This Then That (IFTTT)”, Steven Ovadia, Behavioural and Social Sciences Librarian, 2014. K.

Vijayakumar and C. Arun, “A Survey on Assessment of Risks in Cloud Migration”, International Journal of Applied EngineeringResearch, ISSN 0973-4562 Vol. 10 No.66 May 2015.

11. K. Vijayakumar and C. Arun, “Continuous Security Assessment of Applications in Cloud Environment”, International Journal ofControl Theory and Applications, ISSN: 0974-5645 volume No. 9(36), Sep 2016, Page No. 533-541.

12. R.Joseph Manoj, M.D.Anto Praveena, K.Vijayakumar, “An ACO–ANN based feature selection algorithm for big data”, ClusterComputing The Journal of Networks, Software Tools and Applications, ISSN: 1386-7857 (Print), 1573-7543 (Online) DOI:10.1007/s10586-018-2550-z, 2018.

28. Authors: V N Rajavarman , SrinivasaRao Madala , T.Venkata Satya Vivek

Paper Title: A Novel Method for Secure Outsourced in Cloud through KAE Authentication

Abstract: Development of The cloud computing technology establishes a huge paradigm shift in thefunctional process of the IT industry. Since this new handling technology needs clients to believe in their usefulinformation to reasoning providers, there have been enhancing security and comfort problems on shorteneddetails. A few strategies using quality based security (ABE) have been prescribed for access administration ofabbreviated subtle elements in thinking processing; be that as it may, the greater part of them experience fromresoluteness in actualizing complex accessibility administration rules. The significant ones are capable ofsumming up with each other to develop vital variables and it could develop as traditional self-contained key,however covering the substantial number of keys being accumulated. In different terms, the key proprietor candischarge consistent size of separate key with proceedings of data for flexible alternatives of data that iscomposed of content set residing within the distributed storage space; however more significant and secured datadocuments which stay outside the set however kept classified. The lightly loaded overall key set could be theappropriate global pass to users which could be associated with a smart card with extremely confinedsafeguarded storage room. In our experiments evaluation official security examinations will provide to officialcloud data storage events.

Keyword: Attribute based encryption , Cloud Computing, secure Hashing, Scalable and reliable dataencryption and decryption.

References:1. Mohamed Nabeel, Elisa Bertino Fellow, “privacy keeping Delegated entry control in Public Clouds”,” court cases in A preliminary

variation of this paper appears within the lawsuits of the IEEE international convention on information Engineering(IRI ’12)[1] as aninvited paper.

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29. Authors: V.Sabaresan , S.Godfrey Winster

Paper Title: Multicast Routing Protocols for Mobile ADHOC Networks

Abstract: Mobile Ad-hoc Networks (MANETs) is a self-configuring network that doesn’t follows anypredetermined infrastructure. In MANETs there are collections of independent nodes which are associatedthrough wireless and at the same instance it is allowed to change its links to other nodes frequently without anyfixed infrastructure. Multicast routing is concurrently sending a message from single source to severaldestinations and it is an efficient approach to establish the communication between groups of nodes in thewireless network. Multicast routing plays very important roles in the fields of video conferencing, co-operativeworks, distance education, video on demand, cyber database updating and querying, etc. These applications canprovide themselves to manage multicast operations. Almost all different type of multicast routing algorithms inMANETs are facing various issues like limited bandwidth, possibility packets loose, delay variance, energyconsumption and transmission overhead problem. There are several multicast routing protocol has beenproposed to overcome the challenges of multicast routing and play very significant roles in the current scenario.Nowadays, dissimilar multicast routing algorithms with unique characteristic have been introduced. In this papercomplete survey on multicast routing algorithm to highlight their advantages, limitation and also show the futuredirection in multicast routing in MANETs.

Keyword: MANETs, Wireless networks, Routing and multicast routing protocol

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ICACT, Year: 2005, Volume: 2, Page: 765–770.120. G. Wang, C. Guojun, and L. Zhang, et al., “A novel QoS multicast model in mobile ad-hoc networks”, IEEE-IPDPS, Year: 2005,

Pages: 206b - 206b.121. Adel Ben Mnaouer, Lei Chen, Chuan Heng Foh and Juki Wirawan Tantra, " OPHMR: An Optimized Polymorphic Hybrid Multicast

Routing Protocol for MANET", IEEE Transactions on Mobile Computing, Year: 2007, Volume: 6, Issue: 5, Pages: 551 – 562.122. Tracy Camp, and Yu Liu, "An adaptive mesh-based protocol for geocast routing", Journal of Parallel Distributed Computing, Year:

2003, Volume: 63, Pages:196–213.

30. Authors: M. Senthil Murugan, T. Sasilatha

Paper Title:Hardware Implementation of Hybrid Model Encryption Algorithm for Secure Transmission ofMedical Images

Abstract: The advancement of medical field needs a more secure function for sharing the medical relatedimages in the present environment. For making secure transmission of medical images the best solution iscryptography algorithm. There are many cryptography algorithm existing in the market out of the entirealgorithm we are select the best encryption algorithm which is really suitable for medical images transmission.Transmission of data is easy at the same time secure transmission are meet the different challenges. To full fillthe all the challenges we concentrating the encryption algorithm which is highly secure. For making more securetransmission hybrid model encryption algorithm support more compare to single encryption algorithm. Itshaving capable of providing confidentiality, authenticity and integrity services to medical images exchanged intelemedicineapplications. The same hybrid model encryption may implement in real time application usingFPGA device. While implementing in hardware the following factors need to be concentrate more such aspower, area, throughput, PSNR, Sensitivity etc. keeping all the factors in mind the hybrid model encryptionalgorithm are developed for secure transmission of medical images. The aim of the research is to encrypt anddecrypt medical images efficiently and effectively protect the transmitted data. This research paper presents amodel for encrypting transmitted medical image data. This model uses the following encryption algorithm suchas Advanced Encryption Standard, Rivest Cipher 4.

Keyword: Hybrid Cryptography algorithm, Advanced Encryption Standard, Rivest Cipher 4, FieldProgrammable Gate Array, Block Cipher, Stream Cipher.

References:1. NIST -Advanced Encryption Standard, National Institute of Standards and Technology, FIPS-197, 2001. 2. M.-Y.Wang, C.-P.Su, C.-L.Horng, C. Wu, C.-T.Huang, Single and multi-core configurable AES architectures for flexible security,

IEEE Trans. Very Large Scale Integr. (VLSI) Syst. 18 (4) (2010) 541–552. ISSN 1063-8210.3. H. Li, Efficient and flexible architecture for AES, IEE Proc. Circuits Devices Syst. 153 (6) (2006) 533–538. 4. A. Hodjat, I. Verbauwhede, Area-throughput trade-offs for fully pipelined 30 to 70 Gbits/s AES processors, IEEE Trans. Comput. 55

(4) (2006) 366–372. ISSN 0018- 9340.5. T. Good, M. Benaissa, 692-nW advanced encryption standard (AES) on a 0.13-um CMOS, IEEE Trans. Very Large Scale Integr.

(VLSI) Syst. 18 (12) (2010) 1753–1757. 6. G. Saggese, A. Mazzeo, N. Mazzocca, A. Strollo, An FPGA-based performance analysis of the unrolling, tiling, and pipelining of the

AES algorithm, in: Field Programmable Logic and Application (FPL 2003), in: LNCS, vol. 2778, Springer, Berlin, Heidelberg, Lisbon,Portugal, 2003, pp. 292–302. ISBN 978-3-540-40822-2.

7. S. Drimer, T. Güneysu, C. Paar, DSPs, BRAMs, and a pinch of logic: extended recipes for AES on FPGAs, ACM Trans.Reconfigurable Technol. Syst. 3 (1) (2010) 3:1–3:27. ISSN 1936–7406.

8. P. Bulens, F.-X. Standaert, J.-J.Quisquater, P. Pellegrin, G. Rouvroy, Implementation of the AES-128 on Virtex-5 FPGAs, in: Progressin Cryptology, AFRICACRYPT 2008, in: LNCS, vol. 5023, Springer, Berlin, Heidelberg, 2008, pp. 16–26. ISBN 978-3- 540-68159-5.

9. A. Aziz, N. Ikram, A Look-Up Table implementation of AES, in: International Conference on High Performance Computing,Networking and Communication Systems (HPCNCS-07), Orlando, Florida, USA, 2007, pp. 187–191.

10. F.R. Henrquez, A.D. Prez, N.A. Saqib, C.K. Koc, Cryptographic Algorithms on Reconfigurable Hardware, Signals andCommunication Technology, Springer, 2007.

11. D.-S. Kundi, A. Aziz, N. Ikram, Resource efficient implementation of T-boxes in AESon Virtex-5 FPGA, Inf. Process. Lett. 110 (10)

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(2010) 373–377. ISSN 0020-0190.12. K. Latif, A. Arshad, A. Mahboob, Optimal utilization of available reconfigurablehardware resources, Comput. Electr. Eng. 37 (6)

(2011) 1043–1057.13. L. Ali, I. Aris, F.-S. Hossain, N. Roy, Design of an ultra-high speed AES processor fornext generation IT security, J. Comput. Electr.

Eng. 37 (6) (2011) 1160–1170.14. K. Gupta and S. Silakari, "Novel Approach for fast Compressed Hybrid color image Cryptosystem", Advances in Engineering

Software, vol. 49, pp. 29-42, 2012.15. J. Herranz, D. Hofheinz and E. Kiltz, "Some (in) sufficient conditions for secure hybrid encryption", Information and Computation,

vol. 208, no. 11, pp. 1243-1257, 2010.16. S. Priya, P. Karthigaikumar, N. Siva Mangai and P. Kirti Gaurav Das, "An Efficient Hardware Architecture for High Throughput AES

Encryptor Using MUX Based Sub Pipelined S-Box", Wireless PersCommun, 2016.17. A. Hodjat and I. Verbauwhede, "Area-throughput trade-offs for fully pipelined 30 to 70 Gbits/s AES processors", IEEE Transactions

on Computers, vol. 55, no. 4, pp. 366-372, 2006.18. Hua Li and Jianzhou Li, "A high performance sub-pipelined architecture for AES", 2005 International Conference on Computer

Design.19. U. Farooq and M. Aslam, "Comparative analysis of different AES implementation techniques for efficient resource usage and better

performance of an FPGA", Journal of King Saud University - Computer and Information Sciences, 2016.

31.

Authors: M.Baskaran, K.Vijayakumar

Paper Title: Optical Milli-Meter Wave Signal Generation using Frequency Octupling Without Optical Filter

Abstract: In this paper, we show a practical technique to produce a great octupledfrequency opticalMillimeter-Wave (MMW) signal utilizing two Mach-Zehnder modulators associated in course. A ContinuousWave (CW) Laser, two LiNb Mach-Zehnder Modulators(MZM) and a PIN Photodiode are utilized in the MMWsignal age A careful change of Direct Current (DC) inclination, tweak index(MI) and the presentation of a 90°stage move between the 2 fell stages guarantees the age of recurrence octupled MMW signal.80 GHz RF signalis produced from a 10 GHz RF Local Oscillator (LO) with a most extreme reachable with Optical SidebandSuppression Ratio (OSSR) of 57 dB and a Radio Frequency Spurious Suppression Ratio (RFSSR) of beyondwhat 42 dB can be gotten. An optical bearer is tweaked, and unadulterated ±fourth-request sidebands areproduced. The whole connection is reproduced utilizing Optisystem Software.

Keyword: Optical millimeter-wave, Mach-Zehnder Modulator, Frequency-Octupling, Radio over Fiber,Laser Diode.

References:1. A. J. Seeds K. J. Williams, "Microwave photonics", J. Lightw. Technol. Vol. 24, no. 12, 4628-4641, Dec. 2006.2. A. Wiberg, P. Perez-Millan, M. V. Andres, P. O. Hedekvist, "Microwave-photonic frequency multiplication utilizing optical four-wave

mixing and fiber Bragg gratings", J. Lightw. Technol. Vol. 24, no.1,329-334, Jan. 20063. Kumar, A., B. Suthar, V. Kumar, K. S. Singh, and A. Bhargava,“Tunable wavelength demultiplexer for DWDM application using 1-D

photonic crystal," Progress In Electromagnetics ResearchLetters, Vol. 33, 27-35, 2012.4. Par, Vol. 25k, C., C. G. Lee, C. S. Park, “Photonic frequency Up conversion by SBS-based frequency tripling," Journal of

LightwaveTechnology, No. 7, 1711-1718, 2007.5. Wang, Q., H. Rideout, F. Zeng, and J. Yao, “Millimeter-wave frequency tripling based on four-wave mixing in a semiconductor optical

amplifier," IEEE Photonics Technology Letters, Vol. 18,No. 23, 2460-2462, 2006.6. Yu, J., Z. Jia, L. Yi, Y. Su, G. K. Chang, T. and Wang, “Optical millimeter-wave generation or up-conversion using external

modulators," IEEE Photonics Technology Letters, Vol. 18, No. 1, 265-267, 2006.7. Shi, P.Et Al, “A frequency sextupling scheme for high-quality optical millimeter-wave signal generation without optical filter," Optical

Fiber Technology, Vol. 17, 236-241, 2011.8. N. A. Al-Shareefi, S. I. S. Hassan, M. F. B. A. Malek, R. Ngah, S. A. Abbas, and S. A. Aljunid, “A cost- effective method for high-

quality 60 GHZ optical millimetre wave signal generation based on frequency quadrupling”, Progress In Electromagnetics Research,Vol. 137, 255-274, 2013

9. Chun-Ting Lin, Yu-Min Lin, Jason (Jyehong) Chen, Sheng-Peng Dai, Po Tsung Shih, Peng-Chun Peng, and Sien Chi,” Optical direct-detection OFDM signal generation for radio-over-fiber link using frequency doubling scheme with carrier suppression”OSA Publishing, Optics Express ,Volume 16 ,Issue 9,2008

10. G. Qi J. P. Yao J. Seregelyi C. Belisle S. Paquet, "Generation and distribution of a wide-band continuously tunable millimeter-wavesignal with an optical external modulation technique" IEEE Trans. Microw. Theory Tech., Vol. 53 no. 10 pp. 3090-3097 Oct. 2005.

11. J. Zhang H. Chen M. Chen T. Wang S. Xie "A photonic microwave frequency quadrupler using two cascaded intensity modulatorswith repetitious optical carrier suppression",IEEE Photon. Technol. Lett., Vol. 19, no. 14, 1057-1059 Jul. 2007.

12. M. Mohamed X. Zhang B. Hraimel K. Wu, "Frequency sixupler for millimeter-wave over fiber systems" Opt. Express, Vol. 16, no. 14,10141-10151, Jun. 2008.

13. J. Zhang H. Chen M. Chen T. Wang S. Xie ,"Photonic generation of a millimeter-wave signal based on sextuple frequencymultiplication" Opt. Lett., Vol. 32, no. 9, 1020-1022, Apr. 2007.

168-171

32. Authors: Haripriya P, Porkodi R

Paper Title:An Insight Framework for Content based Medical Image Retrieval using Deep Convolutional NeuralNetwork

Abstract: Content Based Image Retrieval system is essentially proven techniques for retrieving theDICOM based images. DICOM format is use to acquire, store and shared medical images. Typically, retrieval ofimage under query upon the medical image databases is performed by fusing the low-level and high-leveldescriptors along with DCNN features. In this paper, DICOM image meta data are extracted by using dicomfunction and create the local database for reserve the information. To accomplish the initial search and retrievethe images by using extraction of Semantic information. The DICOM tags are extracted from the DICOMimages and use DCNN feature to build a feature vector database. Subsequently prediction is done based on theby leveraging the convolution layers based on the meta data along with DCNN image features. This paperattempts to implement pre filter method to all DICOM images which further decrease the searching no ofimages, searching time and ultimately gives fast processing. DCNN based prediction model was constructed and

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finest results are accomplished. Average accuracy of precision and recall up to0.80 and 0.87 respectively isachieved based on precision value which would be suitable for high quality image retrieval based on semanticinformation confined in the image.

Keyword: CBMIR, DICOM, DCNN, Semantic Gap

References:1. W. D. Bidgood, S. C. Horii, F. .Prior and D. E. V. Syckle, "Understanding and Using DICOM, the Data Interchange Standard for

Biomedical Imaging," Journal of the American Medical Informatics Association, vol. 4, no. 3, pp. 199-212, 1997.2. M. &. E. K. &. O. M. &. A. B. Ouhda, Content-Based Image Retrieval Using Convolutional Neural Networks, 2019.3. S. . Khan and S. . Khan, "An Efficient Content based Image Retrieval: CBIR," International Journal of Computer Applications, vol.

152, no. 6, pp. 33-37, 2016.4. A. . Kumar, J. . Kim, W. .Cai, M. J. Fulham, M. J. Fulham, D. .Feng and D. .Feng, "Content-Based Medical Image Retrieval: A Survey

of Applications to Multidimensional and Multimodality Data," Journal of Digital Imaging, vol. 26, no. 6, pp. 1025-1039, 2013.5. Pilevar, Abdol Hamid. “CBMIR: Content-based Image Retrieval Algorithm for Medical Image Databases” Journal of medical signals

and sensors vol. 1,1 (2011): 12-8.6. Ramamurthy, Byrav and K. R. Chandran. “CBMIR: Content Based Medical Image Retrieval Using Multilevel Hybrid Approach.”

(2015).7. Mohamed, O., Khalid, E. A., Mohammed, O., &Brahim, A. (2019). Content-Based Image Retrieval Using Convolutional Neural

Networks. In J. Mizera-Pietraszko, P. Pichappan, & L. Mohamed (Eds.), Lecture Notes in Real-Time Intelligent Systems(pp. 463–476).Cham: Springer International Publishing.

8. Chung, Y.-A., &Weng, W.-H. (2017). Learning Deep Representations of Medical Images using Siamese CNNs with Application toContent-Based Image Retrieval.CoRR, abs/1711.08490. Retrieved from http://arxiv.org/abs/1711.08490

9. Ma, L., Liu, X., Gao, Y., Zhao, Y., Zhao, X., & Zhou, C. (2017). A new method of content based medical image retrieval and itsapplications to CT imaging sign retrieval. Journal of Biomedical Informatics, 66, 148–158.https://doi.org/https://doi.org/10.1016/j.jbi.2017.01.002

10. Qayyum, A., Anwar, S. M., Awais, M., &Majid, M. (2017). Medical image retrieval using deep convolutional neuralnetwork.Neurocomputing. https://doi.org/10.1016/j.neucom.2017.05.025

11. Kumar, A., Dyer, S., Kim, J., Li, C., Leong, P. H. W., Fulham, M., &Feng, D. (2016). Adapting content-based image retrievaltechniques for the semantic annotation of medical images. Computerized Medical Imaging and Graphics, 49, 37–45.https://doi.org/https://doi.org/10.1016/j.compmedimag.2016.01.001

12. Artificial Intelligence for Maximizing Content Based Image Retrieval, Zongmin Ma Northeastern University, China, InformationScience Reference (an imprint of IGI Global) 701 E. Chocolate Avenue, Suite 200, Hershey PA 17033, 2009

13. Kim, P. (2017). MATLAB Deep Learning: With Machine Learning, Neural Networks and Artificial Intelligence(1st ed.). Berkely, CA,USA: Apress.

14. Clark, K., Vendt, B., Smith, K., Freymann, J., Kirby, J., Koppel, P., … Prior, F. (2013). The Cancer Imaging Archive (TCIA):maintaining and operating a public information repository. Journal of Digital Imaging, 26(6), 1045–1057.https://doi.org/10.1007/s10278-013-9622-7

15. G., Gong, E., Wintermark, M., Rubin, D., &Langlotz, C. P. (2018). Deep Learning in Neuroradiology.American Journal ofNeuroradiology.https://doi.org/10.3174/ajnr.A5543

16. Mattsson, N. (2016). Classification Performance of Convolutional Neural Networks.17. LeCun, Y. (1992). A theoretical framework for Back-Propagation. In P. Mehra& B. Wah (Eds.), Artificial Neural Networks: concepts

and theory. Los Alamitos, CA: IEEE Computer Society Press.

18. Güler, R. A., Neverova, N., & Kokkinos, I. (2018). DensePose: Dense Human Pose Estimation In The Wild. CoRR, abs/1802.00434.Retrieved from http://arxiv.org/abs/1802.00434

19. Amari, S. (1993). Backpropagation and stochastic gradient descent method.Neurocomputing, 5, 185–196. https://doi.org/10.1016/0925-2312(93)90006-O

20. Kunihiko Fukushima (1980),Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffectedby shift in position,Volume 36, Issue 4, pp 193–202

21. K. He, X. Zhang, S. Ren and J. Sun, "Deep Residual Learing for Image Recognition," 2016 IEEE Conference on Computer Vision andPattern Recognition (CVPR), Las Vegas, NV, 2016, pp. 770-778.doi: 10.1109/CVPR.2016.90

22. Pierre Sermanet, David Eigen, Xiang Zhang, Michael Mathieu, Rob Fergus, YannLeCun, “OverFeat: Integrated Recognition,Localization and Detection using Convolutional Networks, 2014 doi:https://arxiv.org/abs/1312.6229.

33. Authors: P.Valarmathie, A.Manju, R.Mervin

Paper Title: Digital Ownership by Biometric Watermarking

Abstract: Digital watermarking is a digital data that can be embedded in digital documents, images, audioand video. This watermark technique uses the unique biometric data of individuals. The individuals biometric ofeye, vein and fingerprint is embedded in the digital image. These unique data are fused and encrypted. Thefusion of three biometric data is output as a digital image. The host image is processed for embedding the fusionimage in it. The process of embedding image in another image is watermarking. It is also the domain of imageprocessing. Generally it means hiding an image in the host image. It is a technique to secure the unique data ofowner in an image to prove the ownership of the owner. Watermarking the unique features in a digital imageestablish that the digital image is belongs to the user whose biometric data watermarked. The watermarkingprocess is robust and perceptual transparency. The unique biometric data will logically prove the ownership ofdigital media.

Keyword: Watermarking, Digital ownership, Multimodel biometric, Image processing.

References:1. Agbaje MO, Awodele O, Ogbonna AC (2015) “Applications of digital watermarking to cyber security (Cyber Watermarking)”,

Proceedings of Informing Science & IT Education Conference (InSITE), pp 1–11 2. Chenglong Chen, Jiangqun Ni, Zhaoyi Shen, and Yun Qing Shi (2017) “Blind Forensics of Successive Geometric Transformations in

Digital Images”, IEEE Transactions on Image Processing, Vol.26, pp.2811 – 28243. Emanuele Maiorana, Patrizio Campisi, Julian Fierrez, Javier Ortega-Garcia, and Alessandro Neri (2010) “Templates for Sequence-

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Based Biometrics with Application to On-line Signature Recognition”, IEEE, Vol.40, pp.525 - 5384. Georgios Goudelis, Georgios Tsatiris, Kostas Karpouzis, Stefanos Kollias (2017) “Transform based feature extraction for effective

human action”, Computational Intelligence and Games (CIG)5. K. Nandakumar, A. K. Jain and S. Pankanti (2007) “Fingerprint-based Fuzzy Vault: Implementation and Performance”, IEEE

Transactions on Information Forensics and Security, vol. 2, no. 4, pp. 744–757 6. Lai C-C, Tsai C-C (2010) Digital image watermarking using discrete wavelet transform and singular value decomposition. IEEE Trans

Instrum Meas 59(11):3060–30637. Meta Malonia, Surendra Kumar Agarwal (2016) "Digital Image Watermarking using Discrete Wavelet Transform and Arithmetic

Progression Technique”, IEEE8. Malay Kishore Dutta, Phalguni Gupta and Vinay K.Pathak (2010) “Blind Watermarking in Audio Signals using Biometric Features in

Wavelet Domain”, IEEE Region 10 Conference9. P.Vignitha, Ch. Sai Theja Swaroopa, TVL. Kalyani (2013) "Digital Watermarking using Biometric Feature", Contemporary Computing

(IC3), Sixth International Conference

10. SaeidFazli, MasoumehMoeini (2016) “A robust image watermarking method based on DWT, DCT, and SVD using a new techniquefor correction of main geometric attacks”, Vol.127, pp.964-97

11. V. Evelyn Brindha (2011) "Biometric Template Security using Fuzzy Valut”, Consumer Electronics (ISCE), IEEE 15th InternationalSymposium.

34. Authors: Sivaranjanee R, Senthil Rathi B, Shriram M R

Paper Title: Utilization of Solid Waste for Removal of Pollutants

Abstract: Pollutants such as dye and heavy metal are a great threat to the society and hence these pollutantsare taken for study. Now-a-days the use of pesticide in agricultural farms and dye at textile industry is increasingday by day and their toxic levels too. The extensive use of these pollutants with mutagenic, carcinogenic andtoxic effects causes the increased contamination of water and soil bodies. In the present study, to mitigate theharmful effects of these pollutants the rural strong waste Citrullus vulgaris (water melon) rind, previouslyutilized as basic raw material for the preparation the activated carbon (AC) that is used for the removal ofpollutants. The adsorption characteristics of the pollutants onto the activated carbon produced from watermelonrind was studied. Batch experiments were conducted to remove the adsorbates from synthetic solution using ACsample. Characterization of the AC was done using TGA, SEM and FTIR analysis. Parameters such as the effectof contact time, initial concentration, pH and AC loading on the adsorption capacity of the adsorbents have beenstudied. Kinetics and isotherm studies were carried out. The data were fitted to the kinetic model such as pseudofirst-order and pseudo second-order. The isotherm models such as Langmuir and Freundlich models were usedto analyse the equilibrium data

Keyword: pollutants, watermelon rind, characterization, isotherms, kinetics

References:1. , S., Namasivayam, C., Kadirvelu, K., 2001. “Orange peel as an adsorbent in the removal of acid violet 17 (acid dye) from aqueous

solutions”. Waste manages, 21. 105-110.2. Drinking Water standards – In the Krewski et al Report “Managing Health Risks from Drinking water: A Background paper for the

Walkerton Inquiry”. Prepared on behalf of the Ontorio water works association and the Ontorio municipal water association (2001)3. K.Kathirvelu, K.Thamaraiselvi, C.Namasivayam, “Removal of heavy metals from industrial wastewaters by adsorption onto activated

carbon prepared from an agricultural solid waste”, Bioresource Technology, (2001), Vol: 76, pp. 63-654. K. Kadirvelu, M. Kavipriya, C.Karthika, M.Radhika, N. Vennilamani, S.Pattabhi, “Utilization of various agricultural wastes for

activated carbon preparation and application for the removal of dyes and metal ions from aqueous solutions”. Bioresource Technology,(2003), Vol: 87, pp.129-132.

5. Fahim bin AbdurRahman, Maimuna Akter, M.Zainal Abedin, “Dyes removal from textile wastewater using orange peels”.International journal of scientific and technology research, (2013), Vol.2, Issue 9.

6. Eng-cheong Koo, Siew-Teng Ong, “Utilization of agricultural waste as a bio sorbent for the removal of dye from aqueoussolution”, (2011).

7. Nilay Sharma, Barun Kumar Nandi, “Utilization of sugarcane bagasse, an agricultural waste to remove malachite green dye fromaqueous solutions”. J.Mater.Environ.Sci. (2013), 4(6), pp.1052-1065. ISSN: 2028-2508

8. Behisht Ara, Jasmin shah, M.Rasul Jan, Sobia Aslam, “Removal of Metribuzin herbicide from aqueous solution using corn cob”.International journal of science, environment and technology, (2013), Vol.2, No 2, 146-161.

9. Lee CK and Low KS, “Removal of copper from solution using moss”. Environ Technol 10:395 (1989)10. Saucedo I, Guibal E, Roulph CH and Cloirec P, “Sorption of uranyl ions by a modified chitosan: kinetic and equilibrium studies”.

Environ Technol 13:1101 (1992)11. Smila jenifer, M.Srihari, V.Shanmugam, Helen Kalavathy, Lima Rose Miranda, “Production of activated carbon from biomass

residues”, International Journal of Bioprocess Technology (2014) 1(1), 1-912. Agnes, M.R., Penelope, M.P.V., 2005. “Determination of citrulline in watermelon rind”. J. Chromatogr. A 1078, 196–200.13. T. Szabo, O. Berkesi, P. Forgo, K. Josepovits, Y. Sanakis, D. Petridis, and I. Dekany, Chem. Mater. 2006, 18, 2740-2749.14. D. Sud, G. Mahajan, M.P. Kaur, “Agricultural waste material as potential adsorbent for sequestering heavy metal ions from aqueous

solutions – a review”, Bioresour. Technol. 99 (2008) 6017–6027.15. M. Iqbal, A. Saeed, S.I. Zafar, “FTIR spectrophotometry, kinetics and adsorption isotherms modelling, ion exchange, and EDX

analysis for understanding the mechanism of Cd2+ and Pb2+ removal by mango peel waste”, J. Hazard. Matter. 164 (2009) 161–171.16. C.P.J. Isaac, A. Siva kumar, “Removal of lead and cadmium from water using annona squamosa shell; kinetic and equilibrium

studies”; Desalin. Water treat (2013) ; DOI: 10.1018/ 19443994.2013.77821817. Deng, H., Li, G., Xu, X., Tao, G., Dai, J., “Optimization of preparation of activated carbon from cotton stalk by microwave assisted

phosphoric acid – chemical activation”, Journal of hazardous materials 182, 217-22418. Bello, O.S, O.M. Adelaide, M.A. Hammed and O.A.M. Popoola 2010. “Kinetic and equilibrium studies of methylene blue removal

from aqueous solution by adsorption on treated saw dust”. Macedonian journal of chemistry and chemical engineering, 29, 77-8519. Benassa H, “Removal of acid dyes from aqueous solutions using orange peel as a sorbent material”. Ninth International Water

Technology Conference, IWTC9, Sharm El-Sheikh, Egypt, (2005) , pp 1175–118520. Salleh MAM, Mahmoud DK, Karim WAWA, Idris A (2011) “Cationic and anionic dye adsorption by agricultural solid wastes: a

Comprehensive review”. Desali 280:1–1321. Langmuir I, “The adsorption of gases on plane surfaces of glass, mica and platinum”. J AmChemSoc (1918) 40:36122. Freundlich HZ, die. Adsorption in losungen (Z).Phys Chem (1985) 57:387-470.23. S.Lagergren, Zur theorie der sogenannten adsorption geloster stoffe, Kungliga Svenska, Ventenskapsakademiens, Handlingar (1898)

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24 (4) 1.24. [24] Y.S.Ho, G.McKay, “The kinetics of sorption of basic dyes from aqueous solution by sphagnum moss peat”, Canadian Journal of

Chemical Engineering (1998) 76 822.25. C.A.Toles, W.E.Marshell, M.M. Johns, “Granular AC from nutshells for the uptake of metal ions and organic compounds”, carbon

(1997) 35 1407.26. Smisek M and Cerny S 1970, Activated carbon, Elsevier, Amsterdam.27. J.B. Mathangi, M. Helen Kalavathy. "Study of mathematical models for the removal of Ni from aqueous solutions using rind, an

agro‐ based waste", Water and Environment Journal, 2018.28. Helen Kalavathy M., Iyyaswami Regupathi, Magesh Ganesa Pillai, Lima Rose Miranda. "Modelling, analysis and optimization of adsorption

parameters for H3PO4 activated rubber wood sawdust using response surface methodology (RSM)", Colloids and Surfaces B: Biointerfaces,2009.

29. Lakshmipathy, R., and N.C. Sarada. "Adsorptive removal of basic cationic dyes from aqueous solution by chemically protonated watermelon(Citrullus lanatus) rind biomass", Desalination and Water Treatment, 2014

30. Magesh Ganesa Pillai. "Optimization and analysis of nickel adsorption on microwave irradiated rice husk using response surfacemethodology (RSM)", Journal of Chemical Technology & Biotechnology, 02/2009.

31. Nimmala Anvesh Reddy, R. Lakshmipathy, N.C. Sarada. "Application of Citrullus lanatus rind as biosorbent for removal oftrivalent chromium from aqueous solution", Alexandria Engineering Journal, 2014.

32. I.D. Mall, V.C. Srivastava, G.V.A. Kumar, I.M. Mishra. "Characterization and utilization of mesoporous fertilizer plant wastecarbon for adsorptive removal of dyes from aqueous solution", Colloids and Surfaces A: Physicochemical and EngineeringAspects, 2006.

35.

Authors: A.Annamalai Giri , P Yogananda, Nithya.M

Paper Title: Intrusion Detection System for Detecting Cyber Attacks

Abstract: The proposed topology for a wireless networked control system is learnt under several cyber-attack situations, and a distributed intrusion detection system (IDS) is created to see the presence of attacks.More particularly, the paper shows a modeling structure for the closed-loop control system with the IDS, and acomputational method to create and compute the IDS. The computational procedure delivers a stable closed-loopcontrol system with the IDS being sensitive to cyber-attacks. Also, a simulation example is used to demonstratethe application of the proposed procedure as well as its effectiveness.

Keyword: cyber physical system,WCN,FDI.

References:1. Cárdenas, Saurabh Amin , Zong-Syun Lin , Yu-Lun Huang , Chi-Yen Huang† and Shankar Sastry, Fujitsu Laboratories of

America , University of California, Berkeley , National Chiao Tung University, Taiwan” Attacks Against Process ControlSystems: Risk Assessment, Detection, and Response”-2011.

2. Yichi Zhang The University of Toledo.“Distributed intrusion detection system in a multilayer network architecture of smartgrids” 2011

3. International Journal of Network Security & Its Applications (IJNSA), Vol.4, No.2, March 2012 DOI: 10.5121/ijnsa.2012.4208109 “AN IMPLEMENTATION OF INTRUSION DETECTION SYSTEM USING GENETIC ALGORITHM” Lecturer,Department of Computer Science and Engineering, Shahjalal University of Science and Technology, Sylhet, Bangladesh.

4. “Mutual information-based feature selection for intrusion detection systems” Fatemeh Amiri a,n , Mohammad Mahdi RezaeiYousefi a , Caro Lucas a , Azadeh Shakery b , Nasser Yazdani b a Center of Excellence, Control and Intelligent Processing,School of Electrical and Computer Engineering, University of Tehran, Tehran, Iran b School of Electrical and ComputerEngineering, University of Tehran, Tehran, Iran-2011.

5. IEEE TRANSACTIONS ON RELIABILITY, VOL. 62, NO. 1, MARCH 2013 “Effect of Intrusion Detection and Response onReliability of Cyber Physical Systems Robert Mitchell and Ing-Ray Chen”, Member, IEEE.

196-199

36. Authors: D.Susitra , Suragani Thirupathi Rao , Pasala Bobby Sudheer

Paper Title: Implementation of Controller for PMBLDC Motor Drive based Electric Vehicle

Abstract: The entire research community working on automobiles have turned their focus on electricvehicle. PMBLDC motor is very popular in industrial and household applications due to its high efficiency, highpower density and low maintenance cost. The rapid advancement in power electronics devices and controllersaids the use of PMBLDC motor in electrical drives system instead of conventional DC motor and AC motor. Inthis paper we have discussed a Simulink model of Bi-directional speed control for high torque to volume ratioPMBLDC motor to control the speed of PMBLDC motor in electric vehicle application. In this scheme thedirection of motor is changed only with change in switching pattern. So this method is a cost effective method.The same strategy has been implemented using DSPIC4011, 16 bit microcontroller. The Raspberry Pi acts as agateway to configure the unique ID for the application and connect it with the web server. The device is capableof sending the sensor value to the database and the user can able to monitor the value in the web page.

Keyword: Electric Vehicle, PMBLDC Motor, DSPIC Controller, IoT Tech

References:1. IEEE TRANSACTIONS ON MAGNETICS, VOL. 41, NO. 1, JANUARY 2005 509 A Controller of Brushless DC Motor for Electric

Vehicle Hong-xing Wu, Shu-kang Cheng, and Shu-mei Cui.2. Energy-Regenerative Braking Control of Electric Vehicles Using Three-Phase Brushless Direct-Current Motors Bo Long 1, Shin Teak

Lim 2, Ji Hyoung Ryu 2 and Kil To Chong 2,Energies 2014, 7, 99-114; doi:10.3390/en7010099 .3. Journal for Research | Volume 02 | Issue 04 | June 2016 ISSN: 2395-7549 All rights reserved by www.journalforresearch.org

Brushless DC Motor Design for Electric Traction System Md. Bashir Sheikh Priti. S. Manware Priyadarshini Institute of Engineering& Technology Priyadarshini Indira Gandhi College of Engineering.

4. Middle-East Journal of Scientific Research 24 (3): 734-739, 2016 ISSN 1990-9233 © IDOSI Publications, 2016 DOI:10.5829/idosi.mejsr.2016.24.03.23070Corresponding Author: Aarim C. Sijini, PG scholar, Power Electronics and Drives, Dr. SivanthiAditanar College of Engineering, India. 734 Switched Reluctance Motor for Hybrid Electric Vehicle Aarim C. Sijini, E. Fantin and L.Prakash Ranjit PG Scholar, Dr. Sivanthi Aditanar.

200-205

5. Energies 2014, 7, 99-114; doi:10.3390/en7010099energies ISSN 1996-1073 www.mdpi.com/journal/energies Article Energy-Regenerative Braking Control of Electric Vehicles Using Three-Phase Brushless Direct-Current Motors Bo Long 1, Shin Teak Lim 2,Ji Hyoung Ryu 2 and Kil To Chong 2.*.

6. Journal for Research | Volume 02 | Issue 04 | June 2016 ISSN: 2395-7549 All rights reserved by www.journalforresearch.orgBrushless DC Motor Design for Electric Traction System Md. Bashir Sheikh Priti. S. Manware Priyadarshini Institute of Engineering& Technology Priyadarshini Indira Gandhi College of Engineering.

7. 0-7803-6401 -5/00/$10.00 Q 2000 IEEE Advanced PM Brushless DC Motor Control & System for Electric Vehicles Fang Lin &,IEEE Senior Member and Hock Guan Yea Nanyang Technological University, Nanyang Avenue, Singapore 639798 Tel: (65) 790-5023, Fax: (65) 792-0415, email address: eflluo@ntu.edu.sg.

8. 2011 International Conference on Recent Advancements in Electrical, Electronics and Control Engineering Practical Implementationof Four Quadrant Operation of Three Phase Brushless DC Motor using dsPIC, Sheeba Joice, C. Dept. of EEE Dr. S.R. ParanjothiDept. of EEE Dr. Jawahar Senthil Kumar .

9. IJCA Special Issue on “Computational Science - New Dimensions & Perspectives” NCCSE, 2011, DSP based Speed Control ofPermanent Magnet Brushless DC Motor, Vandana Govindan , Anish Gopinath , S.Thomas George Assistant Professor .

10. Proc. IEEE Conference on Emerging Devices and Smart Systems (ICEDSS 2017) 3-4 March 2017, Mahendra Engineering College,Tamilnadu, India. 978-1-5090-5555-5/17/$31.00 ©2017 IEEE 229 Modeling and Simulation of Closed Loop Speed Control forBLDC Motor P.Suganthi, S. Nagapavithra, S. Umamaheswari .

11. IEEE TRANSACTIONS ON MAGNETICS, VOL. 45, NO. 10, OCTOBER 2009, PM Synchronous Motor Speed Control UsingHybrid Fuzzy-PI With Novel Switching Functions Amit Vilas Sant and K. R. Rajagopal.

12. 10.1109/TIA.2016.2522943, IEEE Transactions on Industry ApplicationsBLDC Motor Driven Solar PV Array Fed Water PumpingSystem Employing Zeta Converter Rajan Kumar, Member, IEEE, and Bhim Singh, Fellow, IEEE.

13. M.Vanitha*et al. /International Journal of Pharmacy & Technology IJPT| Dec-2016 | Vol. 8 | Issue No.4 | 26426-26432Page 26426ISSN: 0975-766X .CODEN: IJPTFI Available Online throughResearch Article www.ijptonline.co EMERGENCY INFORMATION SHARING VIA MOBILE ALERT USING ARDUINOM.Vanitha School of Information Technology and Engineering, VIT University, Vellore, India. Email:mvanitha@vit.ac.in Receivedon 25-10-2016 Accepted on 02-11-2016

14. International Journal of Engineering Technology Science and Research IJETSR www.ijetsr.com ISSN 2394 – 3386 Volume 4, Issue 4April 2017 Arduino based Car Security System Sayali Waranka Suraj Nawale Twinkle Bardeskar Amruta Mhatre Electronics andtelecommunication Engineering Department,

15. ISSN XXXX XXXX © 2017 IJESC Development of System for Early Fire Detection using Arduino UNO Digvijay Singh1, NeetikaSharma2, Mehak Gupta3, Shubham Sharma4 Student1. 2. 3. 4 Department of Electronics and Communication MIET Jammu, JammuUniversity, J&K, India

37.

Authors: B. Deepa , V. Maheswari, V. Balaji

Paper Title: Creating Ciphertext and Decipher using Graph Labeling Techniques

Abstract: In this paper, we design plaintext through product edge labeling for aCyclegraph. The edgelabels thus obtained is further processed throughCaesar cipher method. The ciphertext thus obtained is subjectedto Bifid Cipherfor which we apply matrix multiplication. The decryption is performed usingInverse matrixmultiplication and subsequent application of Bifid cipher and Caesar Cipher yields our plaintext. Thismethodology is considered highly efficient and secure as it combines Cryptographic technique together withGraph Labeling thereby making it difficult for any Adversary to hack the plaintext. We investigate the abovetechnique using Non- singular square matrices.

Keyword: Plaintext, Ciphertext, Encryption, Decryption, Bifid cipher, Rotation letters,Inverse matrix,CycleGraphs. AMS subject classification MSC (2010) No: 05C78

References:1. Dinesh Goyal, Naveen Hemrajani, Kritika Paliwal “GPH Algorithm: Improved CBC improved BIFID cipher Symmetric Key

Algorithm,International Journal of Communication and Computer Technologies2. Volume 01 – No.60 Issue: 07 Aug 2013, ISSN NUMBER: 2278-9723 David Kahn, The codebreakers: The story of secret writing,

Revised ed.1996. ISBN 0-684-83130-9.

3. Lester S. Hill, Concerning Certain Linear Transformation Apparatus of Cryptography, The American Mathematical MonthlyVol.38,1931, pp.135-154.

4. Lester S. Hill, Cryptography in a Algebraic Alphabet, The American Mathematical Monthly Vol.36, June-July 1929, pp.306-312.5. Jan CA. Van Dev Lubbe, Basic Methods of Cryptography, Cambridge University Press, United Kingdom (2002).6. Chris Christensen, Caesar Ciphers, Spring 2010, HNR 304.7. J. Basker Babujee, V. Vishnupriya, encrypting number using pair labeling in path graph, IJPAM: Volume 114, No.2 (2017)

206-212

38. Authors: Manimala.G, Kavitha.V, Abinaya.V, Nisha G.Mathur

Paper Title: Data Capture from Medical Device using Augmented Reality

Abstract: In order to develop medical devices that connect to hand held devices via Bluetooth or USB, anew approach is followed by using Augmented Reality (AR) and machine vision which is utilized to identifydigitally a biomedical device and capture reading using Augmented Reality to create 3D imaging of tumors andhuman organs accurately. By superimposing anatomic structures segmented from tomography images (e.g., CT,MR) on the intraoperative video images. It is integrated with EMR’s, clinical information feeds and medicalimaging systems to support clinical decision making through a combined AR view. The output will be in theform of labels; 3D rendered models, or shaded modifications. It uses everyday technology – computers, tabletsor smart phones with cameras can be used to connect surgeons in real time, anywhere in the world.AR can beused to capture medical device information on a mobile device and can automate the data collection tasks byhealth workers in all the developing countries.

Keyword: Augmented Reality, Electronic Medical Record (EMR),3D imaging

References:

213-217

1. F. Wenzel, F. Recchi, T. Rohlfing, C. M. Brown, R. S. Bakos, R. J. Maciunas, and A. Bani-Hashemi, C. R. Maurer Jr., F. Sauer, B.Hu, B. Bascle, B. Geiger, “Augmented reality visualization of brain structures with stereo and kinetic depth cues: System descriptionand initial evaluation with head phantom,” in Proc. SPIE, K. S. Mun, Ed., 2001, vol. 4319, Medical Imaging 2001: Visualization,Display, and Image Guided Procedures, pp. 445–456.

2. Azuma. R. T., “A survey of augmented reality,” Presence: Tele operators Virtual Environ., vol. 6, no. 4, pp. 355–386, 1997.3. L. Preston & S. Bangay, “An investigation into factors influencing immersion in interactive virtual reality environments,” Stud.

Health Technol. Inform., vol. 58, pp. 43–51, 1998.4. S. L. Tang, Chee-KeongKwoh and Keck Voon Ling “Augmented reality systems for medical applications.5. LanceLiotta, David B Douglas, Clifford A Wilke, David Gibson, Emanuel F Petricoin, “Virtual reality and augmented reality:

Advances in surgery”6. FrancescoVolonté,FrançoisPugin,PascalBucher,Maki Sugimoto “Augmented reality and image overlay navigation with OsiriX in

laparoscopic and robotic surgery”7. “Augmented Reality Through Wearable Computing” Thad Starner, Steve Mann, Bradley Rhodes, Jeffrey Levine Jennifer Healey,

Dana Kirsch, Roz Picard, and Alex Pentland.8. Salb T, Brief J, Burgert O, Gockel T, Hassfeld S, et al. (2002) Intraoperative augmented reality for craniofacial surgery: the INPRES

system.9. Edwards PJ, King AP, Maurer CR, Jr., de Cunha DA, Hawkes DJ, et al. Design and evaluation of a system for microscope-assisted

guided interventions (MAGI). IEEE Trans Med Imaging 19: 1082-1093.10. Nicholas A. Kalogriopoulos, Jonathan Baran, Amit J. Nimunkar and John G. Webster, Electronic Medical Record Systems for

Developing Countries

39.

Authors: S.Bhavanisankari, G.T.Bharathy, T.Tamilselvi

Paper Title: Error Detection in Turbo Decoding using Neural Network

Abstract: In this paper reduction of errors in turbo decoding is done using neural network. Turbo codeswas one of the first thriving attempt for obtaining error correcting performance in the vicinity of the theoreticalShannon bound of –1.6 db. Parallel concatenated encoding and iterative decoding are the two techniquesavailable for constructing turbo codes. Decrease in Eb/No necessary to get a desired bit-error rate (BER) isachieved for every iteration in turbo decoding. But the improvement in Eb/No decreases for each iteration.From the turbo encoder, the output is taken and this is added with noise, when transmitting through the channel.The noisy data is fed as an input to the neural network. The neural network is trained for getting the desiredtarget. The desired target is the encoded data. The turbo decoder decodes the output of neural network. Theneural network help to reduce the number of errors. Bit error rate of turbo decoder trained using neural networkis less than the bit error rate of turbo decoder without training.

Keyword: Turbo Codes, Neural Networks, Encoder, Decoder, Bit Error Rate.

References:1. “ Techniques for early stopping and error detection in turbo decoding” in IEEE transaction of communication,vol51,no.10, October

2003.2. 2.“ A neural network for predicting decoder error in turbo decoders” in IEE communication letters, vol 3,no.5, may1999.3. 3. “The design and performance of neural network for predicting turbo decoding error with application to hybrid ARQ protocols” in

IEEE transactions of communication, vol 48,april 2000.4. “ Near Shannon limit error correcting coding and decoding in Proc. IEEE Conf. Communications,1993.5. J. Hagenauer, E. Offer, and L. Papke,“Iterative decoding of binary block and convolutional codes,” IEEE Trans.Inform. Theory, vol.

42,pp. 429–445 Mar. 1996.6. R. Y. Shao, S. Lin, and M. P. C.Fossorier, “Two simple stopping criteria for turbo decoding,” IEEE Trans. Commun., vol47, pp. 1117–

1120,Aug.1999.7. A. Ambroze, G.Wade, and MTomlinson, “Practical aspects of iterative decoding,” Elect. Eng. Proc.-Commun., vol. 147, pp.69– 74,

Apr.2000.8. Y. Wu, B. D. Woerner, and W. J. Ebel “A simple stopping criterion for turbodecoding,” IEEE Commun. Lett., vol. 4,pp.258–260,

Aug. 2000.9. Benedetto, S., et al., “Soft Output Decoding Algorithm in Iterative Decoding of Turbo Codes,” TDA Progress Report 42-124, Jet

Propulsion Laboratory, Pasadena, California, Vol. 18 pp. 63-87, February 15, 201710. Thomas Jerkovits, balazs Matuz “Turbo Code Design For Short Blocks”, Proceedings IEEE, International Conference on

Communications, Vol. 19 Feb. 2016.

218-221

40. Authors: R.Jayakarthik, M.S.Nidhya, K.Parvathavarthini

Paper Title: Usage Analysis of Smartphone with Hierarchical Clustering

Abstract: Functional and Non Functional attributes are the important factors of the Smartphone. Thispaper deals with how to group the characteristics of mobile phone using the clustering algorithms and thenobtained the results after classify the related algorithms. Classify all the functional and non-functional attributesof the smartphone using the latest data mining algorithms using WEKA (Waikato Environment for KnowledgeAnalysis) open-source software to analyze data.

Keyword: Usage Survey, Hierarchical Clustering. WEKA tool.

References:1. Borst, S., “User-level performance of channel-aware2. scheduling algorithms in wireless data networks”, IEEE INFOCOM, 2003.3. Balazinska, M., and Castro, P., "CharacterizingMobility and Network Usage in a Corporate Wireless Local-Area Network," ACM MobiSys, May 2003. Bai, F., and Helmy, A., "A Survey of Mobility Modeling and Analysis in

Wireless Adhoc Networks",4. Book chapter, Springer, Oct 06, ISBN: 978-0-387-25483-8.5. Woan-Rou Tseng and Kuo-Wei Hsu, "Smartphone App Usage Log Mining ", International Journal of Computer and Electrical

Engineering, Vol. 6, No. 2, April 2014.

222-227

6. Bai, F., Sadagopan, N., and Helmy, A., "The IMPORTANT Framework for Analyzing the Impact of Mobility on Performance ofRouting for Ad Hoc Networks", AdHoc Networks Journal - Elsevier, Vol. 1, Issue 4, pp. 383 -- 403, Nov. 2003.

7. Tang, D., and Baker, M., "Analysis of a Metropolitan-Area Wireless Network," Wireless Networks, vol. 8, no. 2-3, pp. 107 -- 120,Nov. 2004.

8. Kotz, D., Essien, K., "Analysis of a Campus-wide Wireless Network," ACM MobiCom, September, 2002. Christian Montag1,3*,Konrad Błaszkiewicz2, Rayna Sariyska1, Bernd Lachmann1, Ionut Andone2, Boris Trendafilov2, Mark Eibes2 and AlexanderMarkowetz2, "Smartphone usage in the 21st century: who is active on WhatsApp?". Montag et al. BMC Res Notes (2015) 8:331DOI 10.1186/s13104-015-1280-z.

9. S.Saraswathi "Smartphone Usage Among Students", E-ISSN No: 2454-9916 | Volume : 3 | Issue: 6 | June 2017.10. Ms. Kiruthika M, Mr. Rahul Jadhav, Ms. Dipa Dixit, Ms. Rashmi J, Ms. Anjali Nehete, Ms. Trupti Khodkar, "Pattern Discovery

Using Association Rules" (IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 2, No. 12, 2011.11. JinHyun Jooa, SangWon Bangb, GeunDuk Parka, "Implementation of a Recommendation System using Association Rules and

Collaborative Filtering" 1877-0509 © 2016 Published by Elsevier B.V. This is an open-access article under the CC BY-NC-NDlicense http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the Organizing Committee of ITQM2016 DOI: 10.1016/j.procs.2016.07.115.

12. G. Maggiore, C. Santos, A. Plaat, Smarter smartphones: understanding and predicting user habits from GPS sensor data, 1877-0509© 2014 Elsevier B.V. This is an open-access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/). Selection and peer-review under responsibility of Conference Program Chairs DOI: 10.1016/j.procs.2014.07.027.

13. Data mining with WEKA, Part 1 Introduction and regression Michael Abernethy Published on April 27, 2010.https://www.ibm.com/developerworks/library/os-weka1/

14. Gabriel Marquesa, Ana Respícioa,b*, Ana Paula Afonso, A mobile recommendation system supporting group collaborativedecision making, 1877-0509 © 2016 The Authors. Published by Elsevier B.V. This is an open-access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of KES International DOI:10.1016/j.procs.2016.08.235

41.

Authors: D.Leela,Dasarathy, A . K. , Leshmi mohan,,S.Kabileshwar

Paper Title: Static and Dynamic Analysis of Transmission Line Tower

Abstract: The towers of the to the most powerful life-line buildings that support electrical transmissionline are peculiar transmission. Transmission towers are essential for providing energy to various parts of thenation. The transmission line should be stable and carefully structured with the objective that they do not fizzlein the midst of a cataclysmic event and adapt to the national and universal standard. This present paper managesthe investigation of static and dynamic examination of transmission line tower . A run of the mill sort of transmission line tower is picked as contextual analysis . The examination and displaying of pinnacle is doneutilizing FE based ANSYS programming. The heaps following up on the pinnacle reflected are numb burden,live burden and dynamic burdens .in this Paper the most extreme twisting joined anxieties and characteristicfrequencies ,bowing minute shear constrain are gotten.

Keyword: static analysis, dynamic analysis, ANSYS, transmission tower References:1. Karthik S. Sowjanya G.V [2015] “Static and dynamic analysis of transmission line tower under seismic loads”, International Journal of

Engineering Research & Technology (IJERT), Vol. 4 Issue 08, pp no 29-33. 2. Mujamil, Banulatha G. N, Narayan. G, Rajeeva S.J [2015] “Dynamic analysis of transmission line tower for different sectional

properties with fluid viscous dampers” International Journal of Advanced Research in Education Technology (IJARET), Vol. 2, Issue2, pp no 134-137.

3. Sakthivel. T and Sanjeevi R [2015] “Analysis and Design of multi circuit transmission line tower” International Journal of EmergingTechnology in Computer Science & Electronics (IJETCSE), Volume 13 Issue 1, pp no 5661.

4. GopiSudamPunse [2014] “Analysis and design of transmission line tower” International OPEN ACCESS Journal of ModernEngineering Research (IJMER), Vol. 4, Issue.1, pp no 116-138.

5. Srikanth L, Neelima Satyam D [2014] “Dynamic analysis of transmission line towers” International Journal of Civil, Environmental,Structural, Construction and Architectural Engineering Vol:8, No:4, pp no 434-437.

6. Pahwa S, Tiwari V, Jatwa H [2014] “Analytical study of transmission tower subjected to wind and seismic loads optimizationtechnique” International Journal of Emerging Technology and Advanced Engineering, Volume 4, Issue 9, pp no 375-383.

7. Panchal D. R. [2014] “Various transmission tower analysis and design using professional software” International journal of innovativeresearch & development (IJIRD), Vol 3 Issue 7, pp no 107-112.

8. McClure G, Lapointe M [2003] “Modeling the structural dynamic response of overhead transmission lines” Computers and Structures81, pp no 825–834.

9. Raghavendra T [2012] “Computer aided analysis and structural optimization of transmission line towers” International Journal ofAdvanced Engineering Technology, (IJAET), Vol.3, Issue 3, pp no 44-50.

10. Addala G, Satyam D. N and Kumar R. P [2010] “Dynamic Analysis of transmission towers under strong ground motion” Proceedings,3rd International Earthquake Symposium, Bangladesh, Dhaka.

11. SudheerCh, Rajashekar K, Reddy P. P, BhargavaGopi Krishna Y [2013] “Analysis And Design Of 220kv Transmission Line Tower InDifferent Zones I & V With Different Base Widths – A Comparative Study”, International Journal of technology enhancements andemerging engineering research, vol 1, issue 4, pp no 3543.

12. Chandran R, Mathew L. A [2016] “Model and Seismic Analysis of Transmission Tower with I, C and Circular Sections Using FiniteElement Analysis”, International Journal of Science and Research (IJSR), Volume 5 Issue 7, July 2016, pp no-1297-1300.

13. Varakavi V. T. R, Arunakanthi E [2016] “Dynamic Analysis of Electrical Transmission Line Towers”, International Journal ofEngineering Science and Computing (IJESC), Volume 6 Issue No. 7, pp no 1739-1744.

14. ANSYS Mechanical APDL Structural Analysis guide & ANSYS Reference Guide. 15. IS: 5613 (Part 3/Sec 1): 1989 Code Of Practice For Design, installation And Maintenance For Overhead Power Lines, Part 3 - 400 KV

Lines, Section 1 - Design.16. Indian Standard “Code of practice for design loads (Other than earthquake) for buildings and structures” IS: 875 (Part 3) -

1987(Reaffirmed 1997).17. IS 1893 (Part 1): 2002 Criteria for earthquake resistant design Structures. 18. IS 1893 (Part 4): 2005 Criteria for earthquake resistant design Structures. Part 4- industrial structures including stack-like Structures.19. IS 802(Part 1/Section 1)-1995, Bureau of Indian Standards Use of structural steel in over head transmission line towers – code of

practice (materials, loads and permissible stresses), Sec.1 Materials and Loads.

228-233

Authors: Rajeshkannan S, Kerthana R

42.

Paper Title:A Modified Digital Image Processing Enabled Stroke Rehabilitation System based on WaveletTransform

Abstract: stroke rehabilitation therapy is for people suffering from paralysis. Regular procedures need theinvolvement of analyst for the duration of the plenary, requires excessive amount. Recently, many approacheshave been proposed with control strategy through gesture recognition. The main objective of this work is handgesture control for stroke rehabilitation based on dwt based feature extraction method. Establishment of artificialmachine based computer system assistance provides instruction for paralysis recovery system. The proposedsystem uses reverse biorthogonal wavelet with two-level decomposition. Preprocessing and image segmentationof real time movement of the hand gestures. Feature extraction is done using discrete wavelet transform (dwt)based approach which has very flexible and adaptable even at the cost of imperfect reconstruction. Theprosthetic based robotic hand for human finger movement recovery function. Human being finger movementprocess helps to rotate motors packed inside the robotic finger section. Therefore, the five finger machine fingermovement can imitate the real time human being gesture in a regular manner, which indicates the newlymodified machine represents as a education device to provide recovery for the persons affected after paralysis.

Keyword: discrete wavelet transform, paralysis, robot, stroke.

References:1. Anu Maria k Jose, Vigi elezebeth Wilson, mithu susan renji, vijay jose,”Hand gesture controlled robot”, International Journal of

Scientific & Engineering Research, Volume 7, Issue 3, March -2016.2. Durga.s,Gabrielaroselene,m.harshini,m.haneesha,prof.raghavendra mulimani,”Digital image processing based hand gesture recognition

robot for multiple applications”, International journal of innovative research in electrical, electronics, instrumentation and controlengineering,Vol. 4, Issue 5, May 2016.

3. Geng yang et al,”An iot enabled stroke rehabilitation system based on smart wearable armband and machine learning”, IEEE, 2017.4. Kerthana R, Dr.Rajeshkannan S,”A modified BLE enabled stroke rehabilitation system based on machine learning”, Fifth international

conference on green technologies for power generation, communication and instrumentation(ICGPC’19),St.Peter’s Institute of highereducation and research,Chennai,p. 32,April 2019,unpublished.

5. Kerthana R, Dr.Rajeshkannan S,”A modified zigbee enabled stroke rehabilitation system based on discrete wavelet transform”,International Conference on Recent trends in Electronics, Computing and Communication Engineering(ICRTECC’19),SaveethaSchool of Engineering,SIMATS,Chennai,April 2019,unpublished.

6. Dr.Rajeshkannan S, Kerthana R,”A Modified Digital image processing enabled stroke rehabilitation system based on wavelettransform”, Fourth International Conference on Recent Trends in Engineering and Technology(ICRTET-2019),St.joseph’s institute oftechnology,Chennai,pp. 66-71,May 2019,submitted for publication.

7. Rajkumar goel,vineet kumar,saurabh srivastava,a.k.sinha,”A review of feature extraction techniques for image analysis”, InternationalJournal of Advanced Research in Computer and Communication Engineering,Vol. 6, Special Issue 2, February 2017. Sana’a KhudayerJadwaa et al,”Feature extraction for hand gesture recognition review”, International Journal of Scientific & Engineering Research,Volume 6, Issue 7, July-2015.

8. Shilpa L khandade et al,”MATLAB based gesture recognition”, 2016.9. Soeb hussain, rupal saxena,xie han,jameel ahmed khan,prof.hynchul shin, “Hand gesture recognition using deep learning”,IEEE,2017.

234-239

43. Authors: Archana K C, Harini N

Paper Title: Mitigation of Spoofing Attacks on IOT Home Networks

Abstract: internet of things is now everywhere and even if people are aware of it or not, it is part of oureveryday life. For something that is so much in pace with our life, iot collects a lot of information about our daytoday life, which in case of a data leak or hijacking could lead to catastrophic effects in the society. Still iotdevices are not manufactured keeping in mind the security factor. This paper dives into the problem of spoofingattacks dealt by iot devices and comes up with an authentication mechanism, which uses variants of ellipticcurve cryptography to protect against such said attacks without exhausting the devices in case of computationalpower and storage area. The experimentation clearly revealed the strength of the scheme to mitigate spoofingattacks on the iot home networks.

Keyword: MQTT, IoT, ECC, PKG, ECDH, CoAP, REST, ECDSA

References:1. M. Singh, M. A. Rajan, V. L. Shivraj and P. Balamuralidhar, "Secure MQTT for Internet of Things (IoT)," 2015 Fifth International

Conference on Communication Systems and Network Technologies, Gwalior, 2015, pp. 746-7512. Singh, S., Sharma, P.K., Moon, S.Y. et al. J Ambient Intell Human Comput (2017). https://doi.org/10.1007/s12652-017-0494-4.3. Isha and Ashish Kr. Luhach, “Analysis of Lightweight Cryptographic Solutions for Internet of Things.4. M. S. Henriques and N. K. Vernekar, "Using symmetric and asymmetric cryptography to secure communication between devices in

IoT," 2017 International Conference on IoT and Application (ICIOT), Nagapattinam, 2017, pp. 1-45. P. N. Mahalle, B. Ang gorojati, N. R. Prasad and R. Prasad, "Identity establishment and capability based access control (IECAC)

scheme for Internet of Things," The 15th International Symposium on Wireless Personal Multimedia Communications, Taipei, 2012,pp. 187-191

6. Borgohain, T., Kumar, U. and Sanyal, S. (2018). Survey of Security and Privacy Issues of Internet of Things.7. Diego Mendez, Ioannis Papapanagiotou, Baijian, Internet of Things: Survey on Security and Privacy, Purdue University8. Rodrigo Roman, Jianying Zhou, Javier Lopez, On the features and challenges of security and privacy in distributed internet of things,

Computer Networks, Volume 57, Issue 10, 20139. C. Lesjak et al., "Securing smart maintenance services: Hardware-security and TLS for MQTT," 2015 IEEE 13th International

Conference on Industrial Informatics (INDIN), Cambridge, 2015, pp. 1243-125010. Md Tanvir Arafin, Dhananjay Anand, and Gang Qu. 2017. A Low-Cost GPS Spoofing Detector Design for Internet of Things (IoT)

Applications. In Proceedings of the on Great Lakes Symposium on VLSI 2017(GLSVLSI '17). ACM, New York, NY, USA, 161-166.DOI: https://doi.org/10.1145/3060403.3060455

11. Dr T.R Padmanabhan and Dr.C.K.Shyamala, N.Harini, “Cryptography and security”, Wiley India, First Edition, 201112. Kamakshi Devisetty R N, Aruna D, Harini.N, “Secure Proxy Blind ECDS Algorithm for IoT”, International Journal of Pure and

240-245

Applied Mathematics, Volume 118, No. 7 2018, 437-44513. D. Locke, ”MQ Telemetry Transport (MQTT) V3.1 Protocol Spec-ification,” IBM DeveloperWorks Technical Library, August 201014. T. Dierks and E. Rescorla, ”The Transport Layer Security (TLS) Protocol Version 1.2,” IETF RFC 5246, Standards Track, August

200815. R. Ostrovsky, A. Sahai, and B. Waters, “Attribute-based Encryption with Non-monotonic Access Structures,” in Proceedings of the

14th ACM Conference on Computer and Communications Security, ser. CCS ’07, 2007, pp. 195–20316. M. Ion, G. Russello, and B. Crispo, “Supporting Publication and Subscription Confidentiality in Pub/Sub Networks,” in Security and

Privacy in Communication Networks, ser. Lecture Notes of the Institute for Computer Sciences, Social Informatics andTelecommunications Engineering, vol. 50, 2010, pp. 272–289

17. P. T. Eugster, P. A. Felber, R. Guerraoui, and A.-M. Kermarrec, “The many faces of publish/subscribe,” ACM Comput. Surv., vol. 35,no. 2, pp. 114–131, Jun. 2003

18. D. Thangavel, X. Ma, A. Valera, H. X. Tan and C. K. Y. Tan, "Performance evaluation of MQTT and CoAP via a commonmiddleware," 2014 IEEE Ninth International Conference on Intelligent Sensors, Sensor Networks and Information Processing(ISSNIP), Singapore, 2014, pp. 1-6

44.

Authors: Manidipa Roy, Ashok Mittal

Paper Title:A Slotted Single Feed Circularly Polarized Microstrip Patch Antenna with Suppressed SurfaceWave Losses for WLAN Applications

Abstract: The circularly polarized microstrip antenna has been of great importance in WLAN applications.A circularly polarized slotted circular patch antenna with co-axial feed geometry has been designed to meet therequirements. The antenna designed has been slotted at several locations to make it radiate circularly polarizedradiation. Two metallic cylindrical vias have been inserted near the two diametric ends of the slot to improvethe realized gain of the antenna. The antenna structure is resonating at 6.4 GHz with 3dB axial ratio bandwidthof 200MHz and gain of 9.8dB has been observed.

Keyword: circular polarization, circular patch, slots, axial ratio bandwidth, cylindrical metallic pins

References:1. Ramesh Garg, Prakash Bhartia, “Microstrip Antenna Design Handbook”, Artech House, London2. J.R.James, P.S.Hall, “Handbook of Microstrip Antenna”, Peter Peregrinus, London3. C. A. Balanis, “Modern Antenna Handook”, Wiley & Sons, Hoboken, NJ, 20084. Varada Rajan Komanduri, David R. Jackson, Jeffery T. Williams and Amit R. Mehrotra, “A General Method for Designing

Reduced Surface Wave Microstrip Antennas”, IEEE Transactions On Antennas And Propagation, Vol. 61,. 6, June 20135. He-Xiu Xu, Student Member, IEEE, Guang-Ming Wang, Jian-Gang Liang, Mei Qing Qi, and Xi Gao,” Compact Circularly

Polarized Antennas Combining Meta-Surfaces and Strong Space-Filling Meta-Resonators”, IEEE Transactions On Antennas AndPropagation, Vol. 61, No. 7, July 2013

6. J.Brown, M.A., “Artificial Dielectrics Having Refractive Indices Less Than Unity”,Proc. IEE, Radio Section, Monograph.62, 19537. Walter Rotman, ”Plasma Simulation By Artificial dielectrics and Parallel Plate Media”, IRE Transactions on Antennas and

Propagation,January 19618. R.J.King, David V. Thiel, Kwang S. Park,”The Synthesis of Surface Reactance using an Artificial Dielectric”, IEEE Transactions

on Antennas and propagation, Vol. AP-31, No. 3, May19839. 9. Dan Seivenpiper, Zhang, Broas, “High Impedance Electromagnetic Surfaces with a Forbidden Frequency band”, IEEE

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Propagation, Vol.Ap-56,No.2, February 200811. 11. Keith R. Carver, James W. Mink,”Microstrip Antenna technology”, IEEE Transactions on Antennas and Propagation, Vol.Ap-

29,No.1, January 198112. 12. Marija M. Nikolic´, Antonije R. Djordjevic´, and Arye Nehorai, Microstrip Antennas With Suppressed Radiation in Horizontal

Directions and Reduced Coupling Fellow, IEEE Transactions On Antennas and Propagation, Vol. 53, No. 11, November 200513. 13. Manidipa Roy, Prateek Juyal, “Surface Wave Reduction in Circularly Polarized Microstrip Patch Antenna mounted on

Textured Pin Substrate”, IEEE AEMC 201114. 14. D.M.Pozar,”Rigorous Closed Form Expressions for the Surface Wave Loss of Printed Antennas”, Electronic Letters, Vol.26,

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246-248

45. Authors: Danish Faraaz S, Desmond Savio Rozario, Dr.K. Arun Vasantha Geethan

Paper Title: ROS Based Autonomous Robot with Open CV

Abstract: In this project, we have designed and developed an autonomous robot that is powered by RobotOperating System (ROS). The capabilities of the robot include autonomous navigation, image tracking andmapping. OpenCV has been implemented in the on-board microprocessor to process the images that are capturedby the general purpose webcams on the robot. A microcontroller has also been used to control the motors. Theultimate aim of this project is to develop a mobile robot capable of making its own decisions based on theimages received.

Keyword: Autonomous, Robot, ROS, Tracking

References:1. Haidi Jaber, Dr.Luc Jaulin, "Robot Path Planning using Interval Analysis", ENSTA Bretagne/OSM, 2012.2. R. Negenborn, "Robot Localization and Kalman Filters". Master's Thesis, Utrecht University, 2003.3. A. Kelly. "General solution for linearized systematic error propagation in vehicle odometry in Intelligent Robots and Systems", 2001.

Proceedings. 2001 IEEE/RSJ International Conference on, volume 4, pages 1938 - 1945 vol.4, 2001.4. Hugh Durrant-Whyte and Tim Bailey,

―Simultaneous Localization and Mapping (SLAM), Part I The Essential Algorithms‖, IEEE Robotics & AutomationMagazine, June 2006.

249-251

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6. André Gonçalves Araújo, ―ROSint - Integration of a mobile robot in ROS architecture‖, University of Coimbra, July 2012.7. S.Roland, Illah R. Nourbakhsh, Introduction to Autonomous Mobile Robots, Text Book, pp. 12- 45, 2004.8. Aaron Martinez, Enrique Fernandez, " Learning ROS for Robotics Programming", Text Book, pp. 7-9, September, 2013.9. Video Tutorial by Dr. Magnus Egerstedt, Georgia Institute of Technology - Control of Mobile Robots10. ROS Tutorial, ROS Documentation, Official Website: http://www.ros.org/11. OpenCV Tutorial, OpenCV Documentation, Official Website: https://opencv.org/12. B Abhishek; S Gautham; D Varun Rufus Raj Samuel; K Keshav; U.P. Vignesh; Shyam R Nair, ―ROS based Stereo vision system

for autonomous vehicle,‖ Robotics and Automation (ICRA), 2017 IEEE International Conference on Power, Control, Signals andInstrumentation Engineering (ICPSI).

46.

Authors: Natteshan N V S, N. SureshKumar

Paper Title: An Effective Hybrid Algorithm for De-Speckling the Speckle Noise in SAR Images

Abstract: Image Processing has emerged as an essential lookup domain in circuit branches for quite a fewdecades. SAR is a type of Radar. It is connected to pics of articles like scene. It applies eSAR receiving widedevelopment to the target for acquiring better spatial resolution than what is got through customary beam-scanning radars. In digital image processing field, processing Synthetic Aperture Radar is unexpectedly gainingfocus. Very similar to all image processing methods, issues such as edge detection, enhancement and noisereduction are vital lookup troubles in SAR images also. Of late, speckle noise has emerged as a massive issue.Because of its presence, the SAR Images are classified as robust and multiplicative noises. Effecting specklenoise reduction in Synthetic Aperture Radar photographs is pretty challenging. Synthetic Aperture radar and itsdescription is used in the utility of Flood prediction mapping. In this paper we implemented a bettermethodology for de-speckling Synthetic Aperture Radar imagery by way of using Fuzzy Discontinuity adaptiveNon neighborhood potential filter. Application of fuzzy strategy to Importance sampling unscented kalman filterproduces better end result than compared with fuzzy rost filter and also the TMAV and ATMAV produceshigher end result therefore it can be a satisfactory filter for de-speckling.

Keyword: Fuzzy approach, SAR, de-speckling, unscented kalman filter.

References:

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8. Subrahmanyam G, Rajagopalan AN, Aravind R ‘A recursive filter for despeckling SAR images’ ,IEEE Trans Image Processing,17(10),pp.1969–74, (2010) ENL SI MEAN STDE V RMSE S/MSE F+TMAV 0.56237 1.333492 0.1479 0.1972 0.250145 2.425351F+TMED 2.483 0.634620 0.5030 0.3192 0.647311 0.585201 F+ATMAV 0.19593 1.129581 0.2208 0.2494 0.331427 0.053722F+ATMED 4.211 0.487314 0.4488 0.2187 0.458055 3.555728 ORIGINAL FROST 0.54746 1.351519 0.2195 0.2967 0.2194226.865021 An Effective Hybrid Algorithm for De-Speckling the Speckle Noise in SAR Images 255 Published By: Blue EyesIntelligence Engineering & Sciences Publication Retrieval Number: A10511091S19/2019©BEIESPDOI:10.35940/ijeat.A1051.1091S19

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