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ISSN : 2249 - 8958 Website: www.ijeat.org Volume-8 Issue-5S3, JULY 2019 Published by: Blue Eyes Intelligence Engineering and Sciences Publication

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www.ijeat.org Exploring Innovation Editor-In-Chief Chair Dr. Shiv Kumar Ph.D. (CSE), M.Tech. (IT, Honors), B.Tech. (IT), Senior Member of IEEE Professor, Department of Computer Science & Engineering, Lakshmi Narain College of Technology Excellence (LNCTE), Bhopal (M.P.), India

Associated Editor-In-Chief Chair Dr. Dinesh Varshney Professor, School of Physics, Devi Ahilya University, Indore (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.

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

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 Engineeering, 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. Sameh Ghanem Salem Zaghloul Department of Radar, Military Technical College, Cairo Governorate, Egypt.

Editorial Members Dr. Uma Shanker Professor, Department of Mathematics, Muzafferpur Institute of Technology, Muzafferpur(Bihar), India

Dr. Rama Shanker Professor & Head, Department of Statistics, Eritrea Institute of Technology, Asmara, Eritrea

Dr. Vinita Kumar Department of Physics, Dr. D. Ram D A V Public School, Danapur, Patna(Bihar), India

Dr. Brijesh Singh Senior Yoga Expert and Head, Department of Yoga, Samutakarsha Academy of Yoga, Music & Holistic Living, Prahladnagar, Ahmedabad (Gujarat), India.

S. No Volume-8 Issue-5S3, July 2019, ISSN: 2249-8958 (Online) Page No. Published By: Blue Eyes Intelligence Engineering & Sciences Publication

Authors: B.Deepa ,V.Maheswari ,V.Balaji Paper Title: Encoding And Decoding Using Edge Labeling Of A Path Graph Abstract: In this paper,we make plaintext through product edge labeling by using a path graphand Caeser cipher method.We dividetheplaintext into two letter blocks. The formatted two letter blocks are encrypted by matrix multiplication and we decipher the message by using the Inverse atrix multiplication with additive three concept.We investigate the above technique using square matrices.

Keywords: Plaintext,Ciphertext, Encryption,Decryption, Matrices, Inverse Matrices, Rotation letters. AMS subject classification MSC (2010) No: 05C78 References: 1. [1]1 Davidkahn, The codebreakers: The story of secret writing, Revised ed.1996. ISBN 0-684-83130-9. [2]. LesterS.Hill, Concerning Certain Linear Transformation Apparatus of Cryptography, The American 1-4 Mathematical Monthly Vol.38,1931,pp.135-154. [3] LesterS.Hill, Cryptography in a Algebraic Alphabet, The American Mathematical Monthly Vol.36, June- July 1929, pp.306-312. [4] JanCA.Van Dev Lubbe, Basic Methods of Cryptography, Cambridge University Press, United Kingdom (2002). [5] Chris Christensen ,Caesar Ciphers, Spring 2010, HNR 304. [6] J.BaskerBabujee, V.Vishnupriya, Encrypting number using pair labeling in path graph, IJPAM: Volume 114, No.2 (2017) Kuppala Sushmanth Sai,Yaramala Srinath,P.Sabitha,Bejawada Venkatesh,RaghavendranR Authors:

Paper Title: Mood Sensing using Facial Landmarks Abstract: Communication plays a pivotal role in every person’s life.There are various types of communications in which some are verbal and some are non-verbal. Expressions on a person’s face are a type of non-verbal communication.Expressions on the face can be used to define how the person is feeling, recognizing them helps to enhance the human-machine interaction.Thus we propose a system that is un-affected by the illumination changes or the light changes. Expressions on the human face can be computed by using CLM,constrained local models inserts a dense model to a new input image to get the emotions stats .SVM classifier is used to distinguish the input image into different emotion categories. Results showed a remarkable increase in efficiency and performance.Change in lighting conditions will have a very little effect on the efficiency of the system.

Keywords: SVM, MEM, CLM 1. References: [1] Kaihao Zhang, Yongzhen Huang “Facial Expression Recognition Based on Deep Evolutional Spatial- Temporal Networks”,IEEE Transactions on Image processing 2017. 1-4 [2] Gozde Yolcu, Ismail Oztel, Serap Kazan, Cemil Oz, Kannappan Palaniappan, Teresa E. Lever, Filiz Bunyak “Deep Learning-based Facial Expression Recognition for Monitoring Neurological Disorders”IEEE international conference on bio informatics” [3] Su-Jing Wang ; Wen-Jing Yan ; Xiaobai Li ; Guoying Zhao ; Chun-Guang Zhou ; Xiaolan Fu ; Minghao Yang ; Jianhua Tao, “Micro-Expression Recognition Using Color Spaces, IEEE Transactions Image Processing 2015 [4] Washef Ahmed ; Soma Mitra ; Kunal Chanda ; Debasis Mazumdar “Assisting the autistic with improved facial expression recognition from mixed expressions” IEEE international conference on computer vision 2013” [5] S. Ramanathan ; Ashraf Kassim ; Y.V. Venkatesh ; Wu Sin Wah “Human Facial Expression Recognition using a 3D Morphable Model” IEEE conference on Image Processing 2006. [6] M. Pantic ; I. Patras“Dynamics of facial expression: recognition of facial actions and their temporal segments from face profile image sequences” IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 2006. A. Shiny, Reuma Akhtar, Saurav Singh, K. Sujana, D.V. Neelesh 2. 3Authors: Paper Title: An Efficient Method to Extract Geographic Information

Abstract: A ton of vital geographic information about spots, including points of interest, areas and personal information such as neighborhoods, phone numbers etc. can be found on the Internet. However, such information is not openly available using legitimate means. Furthermore, the given information is temperamental as it is static and not refreshed every now and again enough. In this paper, using the results of an internet list, an effective method to manage and collect datasets of spot names is demonstrated. The strategy proposed is to use the Google web crawler Application Programming Interface in order to recoup site pages related with express territory names and types of spots and after that analyses the resultant website pages to remove addresses and names of places. Using the data gathered from internet, the final result compiled is a dataset of spot names. We survey our philosophy by using accumulated data found using street view of Google Maps by examining signs belonging to businesses found in images. The conclusion exhibited by the results was that the modelled procedure efficiently created spot datasets on par with Google Maps and defeated the results of OSM.

Index Terms: datasets, geographic information, points of interest, spot names. References: [1] Raeymaekers S., Bruynooghe M., Van den Bussche J. Learning (k,l)-Contextual Tree Languages for Information Extraction. In: Gama J., Camacho R., Brazdil P.B., Jorge A.M., Torgo L. (eds) Machine 5-10 Learning: ECML 2005. Lecture Notes in Computer Science, vol 3720. Springer, Berlin, Heidelberg, 2005. [2] Skoutas, Dimitrios, Dimitris Sacharidis, and Kostas Stamatoukos. "Identifying and Describing Streets of Interest." In EDBT, pp. 437-448, 2016. [3] Keßler C, Maué P, Heuer JT, Bartoschek T. Bottom-up gazetteers: Learning from the implicit semantics of geotags. In International Conference on GeoSpatial Sematics (pp. 83-102). Springer, Berlin, Heidelberg, 2009. [4] Al-Olimat HS, Thirunarayan K, Shalin V, Sheth A. Location name extraction from targeted text streams using gazetteer-based statistical language models. arXiv preprint arXiv:1708.03105, Aug 2017 [5] Arampatzis A, Van Kreveld M, Reinbacher I, Jones CB, Vaid S, Clough P, Joho H, Sanderson M. Web- based delineation of imprecise regions. Computers, Environment and Urban Systems, 2006. [6] Zhang Y, Chiang YY, Knoblock C, Zhang X, Yang P, Gao M, Ma Q, Hu X. Extracting geographic features from the Internet: A geographic information mining framework. Knowledge-Based Systems, Mar 2019. [7] Goldberg DW, Wilson JP, Knoblock CA. Extracting geographic features from the internet to automatically build detailed regional gazetteers. International Journal of Geographical Information Science, Jan 2009. [8] Brindley P, Goulding J, Wilson ML. Generating vague neighbourhoods through data mining of passive web data. International Journal of Geographical Information Science, Mar 2018. [9] Beitzel SM, Jensen EC, Frieder O, Lewis DD, Chowdhury A, Kolcz A. Improving automatic query classification via semi-supervised learning. In Fifth IEEE International Conference on Data Mining (ICDM'05), Nov 2005. [10] Nguyen HT, Cao TH. Named entity disambiguation: A hybrid statistical and rule-based incremental approach. InAsian Semantic Web Conference (pp. 420-433). Springer, Berlin, Heidelberg, Dec 2008

Ms. P. Suganya, R. Chandu, RVSN VamsiKrishn, M. Pavan Kumar, Cherukuri milind Authors:

Paper Title: Secure Cloud Data Integrity Using Auditing And Ring Signatures For User Identity Abstract –The main aim of the project is to check the integrity of the data that is present in the cloud. There is a tend to show that a way to firmly, flexibly share data with others data. The project mainly deals with integrity checking of the data that the user obtain from the data owner. A Symmetric key is generated by the data owner in order to maintain the data integrity in a particular file using AES. This particular symmetric key is associated with the data owner and the user only. When the cloud file is shared the sensitive information should not be exposed to others except data owner and user. This process helps in hiding the sensitive information from other 4. users. Usually digital signature are used to verify the users and grant them access to the particular file that the user needs to share the data. So as to properly maintain the integrity of the whole data, public auditing is performed. A public verifier needs to choose for the acceptable public key to verify the integrity of the data. As 15-19 the result, the integrity of the data is maintained in the process via digital signatures and public keys under PKI.

Keywords: PKI, AES.

References:

[1] P. Mell and T. Grance, “Draft NIST Working Definition of Cloud Computing,” Nat’l Inst. of Standards and Technology, 2009. [2] A. Mishra, R. Jain, and A. Durresi, “Cloud Computing: Networking and Communication Challenges,” [3] IEEE Comm. Magazine, vol. 50, no. 9, pp. 24-25,Sept. 2012 [4] R. Moreno-Vozmediano, R.S. Montero, and I.M. Llorente, “Key Encounters in Cloud Computing to Empower the Future Internet of Services,” IEEE Internet Computing,v ol.17,no. 4,pp. 18-25July/Aug.2013. [5] K. Hwang and D. Li, “Trusted Cloud Computing with Secure Resources and Data Coloring,” IEEE Internet Computing, vol. 14, no. 5, pp. 14-22, Sept. [6] K. Ren, C. Wang, Q. Wang, "Security challenges for the public cloud", IEEE Internet Comput., vol. 16, no. 1, pp. 69-73, Jan. 2012. [7] G. Ateniese et al., "Provable data possession at untrusted stores", Proc. 14th ACM Conf. Comput. Commun. Secur., pp. 598-609, 200 [8] A. Juels, B. S. Kaliski, "Pors: Proofs of retrievability for large files", Proc. 14th ACM Conf. Comput. Commun. Secur., pp. 584-597, 2007 [9] K.Vijayakumar·C,Arun,Continuous security assessment of cloud based applications using distributed hashing algorithm in SDLC,Cluster Computing DOI 10.1007/s10586-017-1176-x,Sept 2017 [10] K.Vijayakumar·C,Arun, Analysis and selection of risk assessment frameworks for cloud based enterprise applications”, Biomedical Research, ISSN: 0976-1683 (Electronic), January 2017

Authors: Vinita Malik, Sukhdip Singh Assessing Risks and Cloud Readiness of Pervasive Applications Paper Title: Abstract: The evolution of revolutionary computational paradigms has been witnessed in terms of the pervasive systems which not only offer evolving service portfolio but also integrated sensor data from variable sources but surfaces various challenges of data interaction, integration and adaptation. This research has been provisioned with the comprehensive focus on pervasive devices user interaction, access control modeling, identity management, trust and service discovery modeling. In addition, we also proposed a deep insight into Pervasive computing characteristics, risks, risks models, environmental, ethical and social impacts. We have explored pervasive computing environment risk models by considering social, human and environmental 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 research also deals with cloud readiness of a pervasive power reader application for PaaS environment using a smart vendor solution. The cloud readiness score is evaluated by questionnaire qualitative information and source code scan. Various boosters and roadblocks are found out to give score to cloud readiness for any application.

Index Terms: Pervasive/Ubiquitous Computing, Risks, Trust, Access Control, Privacy, Identity Management, Cloud Readiness

References: [1] 1.A.A.H.Mousa.A.A.H, “Ubiquitous/Pervasive Computing” , International Journal of Innovative research & development‖,vol 2 ,pp:276-282,2013 5. [2] M.Weiser, “ The computer for the 21st century, Scientific American”, 265(No. 3), pp : 94 –104 [3] Y.Ren, A.Boukerche., “ Modelling and managing trust for wireless and mobile adhoc networks”, In: proceedings of 20-25 [4] IEEE conference on Communications (ICC), 2008 [5] J.M.Seigneur, “ Trust, Security and Privacy in global computing” , PhD theses , Trinity College Dublin , 2005 [6] 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. 105-109,2005 [7] Z Hayat, J.Reeve., “Ubiquitous security for ubiquitous computing” ,Information Security technical report ,12, pp. 172-178,2007 B. Abdulrazak., Y. Malik, “Review of challenges, Requirements and approaches of pervasive computing system Evaluation”, IETE Technical Review, 29,6, pp.506-522,2012 [8] L.M.Hilty, C.Som , “ Assessing the Human, Social and Environmental risks of pervasive computing” ,Human and Ecological Risk Assessment ,10, PP. 853-874,2004 [9] J.Sen, “Ubiquitous Computing: Applications, Challenges and future trends”, PP. 1-41,2012 [10] C.A.D Costa., “Towards a General software Infrastructure for ubiquitous Computing, Journal of Pervasive Computing” , IEEE CS , pp.64-73,2008 [11] R. Richardson., “CSI Computer Crime and Security Survey”, Computer security institute, 2009 [12] CarnegieMellon University, http://www.cert.org/octave/download/intro.html 9. Insight Consulting, http://dtps.unipi.gr/files/notes/ 2009 [13] Clusif, [14] http://www.clusif.asso.fr/fr/production/ouvrages/pdf/MEHARI- 2010-Overview.pdf [15] Insight Consulting, http://dtps.unipi.gr/files/notes/ 2009- 2010/eksamino_5/politikes_kai_diaxeirish_asfaleias/ egxeiridio_cramm.pdf [16] T. Ledermuller, N.L.Clarke, “ Risk assessment for mobile devices”, Lecture notes in computer science ,(2011), 210-221 [17] F.Liu,Y.Chen., K.Dai.,Z.Wang, “ Research on risk probability estimating using fuzzy clustering for dynamic security assessment” ,LNAI,3642,pp. 539-547,2005 [18] R.Sandhu, P. Samarati, “Access control: principles and practice. IEEE Communications Magazine”, 32(9),pp. 40–48.,1994 [19] R.Sandhu, E.Coyne, H. Feinstein, C. Youman, , “Rolebased access control models”. IEEE Computer, [20] 29(2), 38–47,1996 [21] S.Park,Y. Han, & T. Chung, , “Context-role based access control for context-aware application”. In Lecture [22] notes in computer science: Vol. 4208. High performance computing and communications (pp. 572– 580). Berlin/Heidelberg: Springer,2006 [23] 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 Computer Society,2002 [24] J.Joshi, E.Bertino, A.Ghafoor,. “Hybrid role hierarchy for generalized temporal role based access [25] control model.”, InProc. 26th international 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 [26] H.Zhang, Y.He, Z., “ Spatial context in rolebased access control .In Lecture notes in computer science”:Vol. 4296. Information Security and Cryptology—ICISC2006, November 2006 (pp. 166– 178),2006 [27] Z.Guangsen, P.Manish, Context-aware dynamic access control for pervasive applications. In Proc. communication networks and distributed systems modeling and simulation conference, SanDiego, California (pp.219–225) ,January 2004 Y.Kim, C.Mon, D. Jeong ,”Context-aware access contro mechanism forubiquitous applications”. In Lecture notes in computer science: Vol. 3528. Advances in web intelligence (pp. 236– [28] Berlin/Heidelberg: Springer,2005 [29] A. Ahmed, N. Zhang., “ Towards the realization of context risk aware access control in pervasive computing, Telecommunication Systems”, 45:127-137,2010 [30] M.Langheinrich., “ A Privacy awareness system for ubiquitous computing environments”, In: Proceedings of UbiComp ,LNCS , 237-245,2002 [31] U. Jendriche, M. Kreutzer, “ Pervasive privacy with identity management”,In : Proceedings of the workshop on Security in ubiquitous computing , 2002 [32] J.A.Muhtadi, R. Campbell., “ Routing through the mist Privacy preserving communication in ubiquitous computing environment”, In : Proceedings of the international conference on distributed computing systems ,2002 [33] A.Beressford, F.Stajano., “ Location privacy in pervasive computing” ,IEEE pervasive computing ,46-55,2003 [34] D.Nguyen.E. Mynatt., “ Privacy mirrors: Understanding and shaping socio technical ubiquitous computing”, Technical report ,2002 [35] X.Liang, J.Landay, “Modelling privacy control in context aware systems” , IEEE Pervasive computing ,59-63,2002 [36] M.Tentori. ,J. Favela., “Privacy aware autonomous agents for privacy healthcare”, IEEE Computer Society, [37] 55-62,2006 [38] A. 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 ,2012 [39] 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,2005 [40] 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),2002 [41] 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 [42] J. Al-Muhtadi, R.Hill, R. Campbell , “―Context and location-aware encryption for pervasive computing environments‖, in the Pervasive Computing and Communications Workshops, PerCom Workshops2006. Fourth Annual IEEE International Conference on, (Pisa),2006 [43] A. Al-Karkhi and A. Al-Yasiri “Asserting User Identity in Pervasive Computing Environments Using a Non-Intrusive Technique”‖, ISBN: 978-1-902560-24-3 ,2010 PGNet. [44] S. Creese, M. Goldsmith, B. Rosco, I. Zakiuddin, “Authentication for pervasive computing‖. ,in the Proceedings of the First International Conference on Security in Pervasive Computing, (Boppard, Germany),2003 [45] T. Grandison, M.Sloman, , “A survey of trust in internet applications”, IEEE Communications Surveys and Tutorials, 3(4),2000 [46] 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,2003 [47] L.Kagal, T.Finin, A.Joshi, Trust-based security in pervasive computing environments‖. IEEE Computer, 34(12), 154-157,2001 [48] 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, Networks and Technologies (ANT-2011), Niagara Falls : Canada, Procedia CS,vol.5,362-369,2011 [49] M.Sharmin.S.I, Ahamed.,S. Ahemed., “ SSRD+ : A Privacy aware trust and security model for resource discovery in pervasive computing environment” , 30th Annual international computer software and applications conference,2006 [50] A.Boukerche., Y.Ren., “ A trust based security system for ubiquitous and pervasive computing environment, computer communications”, 4343-4351, 2008 [51] S.Singh, A.Katiyar, “Pervasive computing service discovery in secure framework environment”, International research journal of Engineering and technology , (2018), 380- 387 [52] A.Koehler, A.C. Som., “ Effects of Pervasive computing on sustainable development” ,IEEE Technology and Society magazine ,(2005),15-23 [53] Claudio Da Rold, Gilbert Vander Heiden, “Applying a Cloud First Checklist to ensure successful sourcing and Business IT Alignment”, Gartner Inc, G00296404, pp: 1-22, 2016 [54] Claudia Loebbecke, Bernhard Thomas, “Assessing cloud readiness: Introducing the magic metrices used by Continental AG”, IFIP AICT 366, pp: 270-281, 2011 [55] https://www.castsoftware.com/products/highlight [56] .http://codeload.github.com/smartuni/smartpowerreader/zip/maste [57] 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. [58] 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. Shiny A, Rikhil G R, Namita Vagdevi Cherukuru, Alen Mammen Ivan, Sunil Prakash J Authors:

Paper Title: Complex Event Processing Of Market Data through Data Modeling In Big Data Abstract: Mathematical Finance utilizes advance refined mathematic models and advanced computer techniques to forecast the movement of worldwide markets. To possess an ability to react intelligently to the fast-paced changes in the business is a winning factor. Complex event processing with advanced toolchains plays a crucial role in the explosive growth and diversified forms of market data. To resolve such issues, we have developed a model based on Big Data that processes the intricate tasks to assess the market data. The model executes complex events in a data-driven mode in parallel computing on copious data sets, this model is known as StatCloud. To implement StatCloud, we have used datasets from the Bombay Stock Exchange to determine the performance. We execute the model with the help of Data analysis techniques and Data Modelling. The experiment results show that this model obtains high throughput and latency. It executes data dependent tasks through a data-driven strategy and implements a standard style approach for developing Mathematical Finance analysis models. This integrated model facilitates the work process of complex events in a financial organization to enhance the efficiency to implement the right strategies by the financial engineers.

3. Index Terms: Mathematical Finance, Big Data Analysis, Complex Event Processing, Data parallelization, Parallel Computing, Statistics. 11-17 References: [1] W. Härdle, T. Kleinow, and G. Stahl, Applied Quantitative Finance: Springer Berlin, 2002. [2] E. Thorp, "A perspective on quantitative finance: Models for beating the market," The Best of Wilmott, p. 33, 2005. [3] B. Fang and P. Zhang, "Big Data in Finance," in Big Data Concepts, Theories, and Applications, S. Yu and S. Guo, Eds., ed Cham: Springer International Publishing, 2016, pp. 391-412. [4] X. Shi, P. Zhang, and S. U. Khan, "Quantitative Data Analysis in Finance," in Handbook of Big Data Technologies, A. Y. Zomaya and S. Sakr, Eds., ed Cham: Springer International Publishing, 2017, pp. 719-753. [5] J. Chevalier and G. Ellison, "Risk taking by mutual funds as a response to incentives," Journal of Political Economy, vol. 105, pp. 1167-1200, 1997. [6] R. A. Brealey and E. Kaplanis, "Hedge funds and financial stability: An analysis of their factor exposures," International Finance, vol. 4, pp. 161-187, 2001. [7] T. J. Chemmanur and P. Fulghieri, "Investment bank reputation, information production, and financial intermediation," The Journal of Finance, vol. 49, pp. 57- 79, 1994. [8] B. Brown, M. Chui, and J. Manyika, "Are you ready for the era of ‘big data’," McKinsey Quarterly, vol. 4, pp. 24- 35, 2011. [9] BSE (2016-2018). BSE India Market Data. Available: https://www.bseindia.com/market_data.html [10] P. Brandimarte, Numerical methods in finance and economics: a MATLAB-based introduction: John Wiley & Sons, 2013 [11] X. Dong, "New development on market microstructure and macrostructure: Patterns of US high frequency data and a unified factor model framework," State University of New York at Stony Brook, 2013. [12] O. E. Barndorff‐Nielsen, "Econometric analysis of realized volatility and its use in estimating stochastic volatility models," Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol. 64, pp. 253-280, 2002. [13] 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. [14] 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. [15] 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 Manidipa Roy, Ashok Mittal Authors:

Paper Title: Compact Circular Polarization Design for Equilateral Triangular Micro strip Antenna Abstract: This article focuses on designing a single-feed circularly polarized equilateral triangular microstrip patch antenna. The axial ratio bandwidth of the antenna is around 190 MHz. The antenna has been etched at specific locations for achieving circular polarization. The suppression of surface waves is also being focused upon for gain enhancement. The array of cylindrical metallic pins is embedded near the radiating side of the patch antenna. The gain enhancement of around 3.23 dB is observed. The antenna is designed for use in satellite communications.

Index Terms: compact, circular polarization, slots, axial ratio.

References:

[1] Ramesh Garg, Prakash Bhartia, Microstrip Antenna Design Handbook, Artech House, London [2] J.R.James, P.S.Hall, Handbook of Microstrip Antenna, Peter Peregrinus, London 4. [3] C. A. Balanis, Modern Antenna Handook, Wiley & Sons, Hoboken, NJ, 2008 [4] Keith R. Carver, James W. Mink, Microstrip Antenna technology, IEEE Transactions on Antennas and Propagation, Vol.Ap-29,No.1, January 1981 18-20 [5] G. Steven, Q.Luo and F. Zhu, Circularly Polarized Antennas, John Wiley & Sons, UK, 2014. [6] T. Cai, G. Wang, X. Zhang and J. Shi, Low-profile compact circularly polarized antenna based on fractal metasurface and fractal resonator, IEEE Antennas Wireless Propagation Letters, Vol. 14, pp. 1072-1076, 2015 [7] Nasimuddin, X. Qing and Z. N. Chen, Compact circularly polarized slotted patch antenna for GNSS applications, IEEE Trans. Antennas Propagat., Vol. 62, No. 12, pp. 6506-6509, 2014. [8] V.V. Reddy and N.V.S.N. Sarma, Compact circularly polarized asymmetrical fractal boundary microstrip antenna for wireless applications, IEEE Antennas Wireless Propag. Lett., vol. 13, pp. 118-121, 2014 [9] W.S. Chen, C.K. Wu, K.L. Wong, Inset microstripline-fed circularly polarized microstrip antennas, IEEE Trans. Antennas Propag., vol. 48, no. 8, pp. 1253- 1254, 2000 [10] Rajkishor Kumar and Raghvendra Kumar Chaudhary, Circularly polarized rectangular DRA coupled through orthogonal slot excited with microstrip circular ring feeding structure for Wi-MAX applications, International Journal of RF and Microwave Computer-Aided Engineering, Volume 28, Issue 1, January 2018 [11] Manidipa Roy, Prateek Juyal, “Surface Wave Reduction in Circularly Polarized Microstrip Patch Antenna mounted on Textured Pin Substrate”, IEEE AEMC 2011 Manidipa Roy, Ashok Mittal Authors:

Paper Title: A Slotted Tri-Band Patch Antenna Embedded On Textured Pin Dielectric Abstract: The propagation of surface waves in the microstrip patch antenna proves to be proves to serious 5. hindrance to radiation mechanism of the antenna. The periodic arrangement of shorting pins is embedded in the dielectric substrate at specific location to enhance the gain by around 4-5dB. The slotted perturbations have been done for achieving tri-band characteristics. The antenna is suitable for operation at three resonant frequency bands centered at 2.2421 GHz, 5.7632GHz and 7.7633GHz, which makes it suitable for WLAN applications. 21-22 Index Terms: microstrip, slots, side patch, tri- band, WLAN

References: [1] Ramesh Garg, Prakash Bhartia, “Microstrip Antenna Design Handbook”, Artech House, London [2] J.Brown, M.A., “Artificial Dielectrics Having Refractive Indices Less Than Unity”,Proc. IEE, Radio Section, Monograph.62, 1953 [3] Walter Rotman, ”Plasma Simulation By Artificial dielectrics and Parallel Plate Media”, IRE Transactions on Antennas and Propagation,January 1961 [4] R.J.King, David V. Thiel, Kwang S. Park,”The Synthesis of Surface Reactance using an Artificial Dielectric”, IEEE Transactions onAntennas and propagation, Vol. AP-31, No. 3, May1983 [5] Dan Seivenpiper, Zhang, Broas, “High Impedance Electromagnetic Surfaces with a Forbidden Frequency band”, IEEE Transactions on Antennas and Propagation, Vol.-47,No.11, November 1999 [6] Silveinha, ”Electromagnetic Characterization of textured surfaces using textured Pins”, IEEE Transactions on Antennas and Propagation, Vol.Ap-56,No.2, February 2008 [7] 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 2013 [8] Keith R. Carver, James W. Mink,”Microstrip Antenna technology”, IEEE Transactions on Antennas and Propagation, Vol.Ap-29,No.1, January 1981 [9] D.M.Pozar,”Rigorous Closed Form Expressions for the Surface Wave Loss of Printed Antennas”, Electronic Letters, Vol.26, No.13, June 1990 Deekonda Sai Manish, Nabeel Shahid, Anand Raj, Dr.P.Mohamed Fathimal Authors:

Paper Title: A Novel Method to Perform Search Query Using Brainwave Signals

Abstract— The main aim of the proposed paper is to search the information using brain waves instead of searching using the text query. Electroencephalography (EEG) is an electrophysiological checking strategy to record the electrical activity of the mind. EEG estimates voltage changes coming about because of ionic current inside the neurons of the mind. EEG alludes to the account of the mind’s unconstrained electrical movement over a time frame, as recorded from different nodes put on the scalp. For training, When the user has read a question, the brain waves are recorded with the help of sensors like Neurosky device in the form of EEG signals values like Alpha, Beta, and Gamma and are stored in a dataset. The brainwaves of different persons are recorded for different questions and are stored. The features are reduced using PCA and the centroid of the values are calculated using the K-means clustering algorithm. For testing, when the user thinks about a question in the list, the brain waves are recorded and compared with the values available in the dataset. Using KNN Algorithm, the proposed system outputs the respective question which will be submitted to the search engine. K- Means clustering algorithm is used to calculate the cluster centroid. Once the centroid is calculated for each question, we plot each centroid in a 2-D plane. For a random question from the pool of existing questions, we 6. use the KNN algorithm to find the nearest match. When the match is found, the question corresponding to it is submitted to the search engine. 23-27

Index Terms- Brain Waves, EEG(Electroencephalography), PCA, K-Means, KNN, Neurosky Device.

References: [1] Separation Of Alpha, Beta, Gamma & Theta Activities In EEG To Measure The Depth Of Sleep And Mental Status Shah Aqueel Ahmed, Syed Abdul Sattar, D. Elizabath Rani-2013 [2] Brain Wave Recognition of Words Article in Proceedings of the National Academy of Sciences · January 1998 by PATRICK SUPPES, ZHONGLIN LU AND BING HAN [3] BRAIN WAVE SENSOR SYSTEM FOR ACCIDENT PREVENTION IN VEHICLES S. Pradeep Kumar and A. Wisemin Lins, VELS University, Pallava-8, April 2017 [4] STUDY AND APPLICATION OF BRAIN WAVES (ALPHA, BETA) FOR USER AMBIENT ENVIRONMENT CONTROL G. Ambica et al, International Journal of Computer Science and Mobile Computing, Vol.4 Issue.10, October- 2015. [5] Ahmad Azhari, Leonel Hernandez,(2016)-Brainwaves feature classification by applying K-Means clustering using single-sensor EEG, International Journal of Advances in Intelligent Informatics, ISSN: 2442-6571. [6] A Survey on Thoughts to Text Using Brainwave Pattern Dipti Pawar, Kapil Kamale, Ishaan Singh, Vishwajeet Kharote, Prajwal Goswami Assistant Professor, Dept. of Computer, Pune, India May-2017. [7] Ahmad Azhari, Murein Miksa Mardhia,(2017)-Principal component analysis implementation for brainwave signal reduction based on cognitive activity, International Journal of Advances in Intelligent

Informatics,ISSN: 2442-6571. [8] Mind Wave Neurosky device description: https://store.neurosky.com/pages/mindwave Rahul Kshetri, Ajay, Shivasheesh Kaushik, Vinay Sati Authors:

Paper Title: Tool Wear Analysis during Turning of Hard Material by Simulink Abstract- Present work is an attempt to develop a simulink model of tool wear by machining of Bearing Steel (62 HRC) using cubic boron nitride (CBN) tool. The available mathematical model in the scholarly literature is used to make the simulation model using MATLAB software. Three components of tool wear adhesive wear, abrasive wear & diffusion wear are considered separately for their modeling and later modeling of total wear is done. Variation of tool wear is studied with respect to cutting speed. The developed simulink model is capable to do the similar type of study by changing the workpiece and tool material combination.

Keywords: Simulink, Tool wear, Mathematical modeling, Turning.

References: [1] Rabinowicz, E., Dunn, L. A., and Russell, P. G., 1961, ‘‘A Study of Abrasive Wear under Three-Body Conditions,’ Wear, 4, pp. 345–355. 7. [2] Williams, J. A., 1994, Engineering Tribology, Oxford University Press, NY [3] Archard, J. F., 1953, ‘‘Contact and Rubbing of Flat Surfaces,’’ J. Appl. Phys., 24, pp. 981–988.Wear of Carbide Tool,’’ ASME J. Eng. Ind., 100, pp. 236–243. [4] Kannatey-Asibu, E., Jr., 1985, ‘‘A Transport-diffusion Equation in Metal Cutting and Its Application to 28-31 Analysis of the Rate of Flank Wear,’’ASME J. Eng. Ind., 107, pp. 81–89. [5] Loladze, T. N., 1981, ‘‘Of the Theory of Diffusion Wear,’’ CIRP Ann., 30~1!, pp. 71–76. [6] Usui, E., Shirakashi, T., and Kitagawa, T., 1978, ‘‘Analytical Prediction of Three Dimensional Cutting Process, Part 3: Cutting Temperature and Crater [7] Kramer, B. M., and Judd, P. K., 1985, ‘‘Computational Design of Wear Coating,’’ J. Vac. Sci. Technol. A, A3~6!, pp. 2439–2444. [8] Kramer, B. M., 1986, ‘‘Predicted Wear Resistances of Binary Carbide Coatings,’’ J. Vac. Sci. Technol. A, A4~6!, pp. 2870–2873. [9] Mamalis, A. G., J. Kundrak and M. Horvath, 2002. “Wear and Tool Life of CBN CuttingTools”. International Journal Advance Manufacturing Technology 20:475–479. [10] Rabinowicz, E., 1977. “Abrasive wear resistance as a materials test”, Lubrication Engineering, Vol. 33, pp.378–381 [11] Kannatey, 1985. “A Transport-diffusion Equation in Metal Cutting and Its Application to Analysis of the Rate of Flank Wear”, ASME J. Eng. Ind., 107, pp. 81–89. [12] Huang, Yong, and Steven Y. Liang. "Modeling of CBN tool flank wear progression in finish hard turning." TRANSACTIONS-AMERICAN SOCIETY OF MECHANICAL ENGINEERS JOURNAL OF MANUFACTURING SCIENCE AND ENGINEERING 126.1 (2004): 98-106. [13] Mathworks, Matlab User manual Shivasheesh Kaushik, Satyendra Singh Authors:

Analysis on Heat Transmission and Fluid Flow Attributes in Solar Air Accumulator Passage with Paper Title: Diverse Faux Jaggedness Silhouettes on Absorber Panel Abstract: There is a necessity to investigate the heat transmission and fluid cascade peculiarities of solar air convectors using varying faux irregular surface and shapes on absorber sheet, so that the solar devices utilize maximum amount of available solar radiated heat energy during day time. These artificial roughness shapes are use for the enhancement of thermal performance. This need arises from the fact that the heat circulation and liquid cascade trait have been investigated by the previous investigators only for the cases that differ considerably from those relevant to solar air brazier having different screen matrix placed in the planes parallel 8. to the flow direction and that the radiant energy being absorbed in depth. In our present research paper we investigating experimentally the behavior of artificial irregularities located over absorber platter of solar air heater vessel of varying shapes like trapezoidal, sin wave, rectangular, alternative elliptical shape pattern etc, 32-41 with different Reynolds Number range 4000 to 24000, mass flow rate on Nusselt Number and Friction Factor and also find the suitable optimum shape for heat transmission enhancement. The results indicated the best heat transfer enhancement results for the alternative elliptical shape pattern among other artificial roughness with range of 0.0786kg/s – 0.475kg/s mass flow rate with thermal efficiency near about 78%.

Index Terms: Aluminum sheet, artificial roughness, heat transfer characteristics.

References: [1] Rahimi M, Shabanian S.R. and Alsairafi A.A., 2009. Experimental and CFD studies on heat transfer and friction factor characteristics of a tube equipped with modified twisted tape inserts. Chemical Engineering and Processing 48, 762–770. [2] Eiamsa-ard S, Wongcharee K, and Sripattanapipat S, 3-D, 2009. Numerical simulation of swirling flow and convective heat transfer in a circular tube induced by means of loose-fit twisted tapes. International Communications in Heat and Mass Transfer 36, 947–955. [3] Zhang Z, Ma D, Fang X, and Gao X,. 2008. Experimental and numerical heat transfer in a helically baffled heat exchanger combined with one three-dimensional finned tube. Chem. Eng. Process 47, 1738–1743. [4] Munoz-Esparza D, and Sanmiguel-Rojas E., 2011. Numerical simulations of the laminar flow in pipes with wire coil inserts. Computers & Fluids 44, 169–177. [5] Kumar A., Chamoli S. and Kumar M., 2016. Experimental investigation on thermal performance and fluid flow characteristics in heat exchanger tube with solid hollow circular disk inserts. Applied Thermal Engineering 100, 227–236. [6] Bhuiya M.M.K., Sayem A.S.M., Islam M., Chowdhury M.S.U., Shahabuddin M., 2014. Performance assessment in a heat exchanger tube fitted with double counter twisted tape inserts. International Communications in Heat and Mass Transfer 50, 25–33. [7] Bhuiya M.M.K., Chowdhury M.S.U., Shahabuddin M., Saha M., Memone L.A., 2013. Thermal characteristics in a heat exchanger tube fitted with triple twisted tape inserts. International Communications in Heat and Mass Transfer 48, 124–132. [8] Bas H. and Ozceyhan V., 2012. Heat transfer enhancement in a tube with twisted tape inserts placed separately from the tube wall. Experimental Thermal and Fluid Science 41, 51–58. [9] Eiamsa-ard S. and Promvonge P., 2011. Influence of Double-sided Delta-wing Tape Insert with Alternate- axes on Flow and Heat Transfer Characteristics in a Heat Exchanger Tube. Chinese Journal of Chemical Engineering, 19(3), 410-423. [10] Eiamsa-ard S., Chinaruk T., Eiamsa-ard P. and Promvonge P., 2010. Thermal characteristics in a heat exchanger tube fitted with dual twisted tape elements in tandem. International Communications in Heat and Mass Transfer 37, 39–46. [11] Promvonge P., Eiamsa-ard S., 2007. Heat transfer and turbulent flow friction in a circular tube fitted with conical-nozzle turbulators. International Communications in Heat and Mass Transfer 34, 72–82. [12] Chang S. W., Yang T. L. Liou J. S., 2007. Heat transfer and pressure drop in tube with broken twisted tape insert. Experimental Thermal and Fluid Science 32,489–501. [13] L. Varshney and J. S. Saini, 1998. Heat transfer and friction factor correlations for rectangular solar air heater duct packed with wire mesh screen matrices. Solar energy 62, 255-262. [14] V.S. Hans, R.P. Saini, J.S. Saini, 2010. Heat transfer and friction factor correlations for a solar air heater duct roughened artificially with multiple v-ribs. Solar Energy 84, 898–911. [15] Brij Bhushan, Ranjit Singh, 2012. Thermal and thermohydraulic performance of roughened solar air heater having protruded absorber plate, Solar Energy 86, 3388–3396. [16] Prashant Dhiman, N.S. Thakur, Anoop Kumar, Satyender Singh,2011. An analytical model to predict the thermal performance of a novel parallel flow packed bed solar air heater. Applied Energy 88, 2157–2167. [17] A.A. El-Sebaii, S. Aboul-Enein, M.R.I. Ramadan, S.M. Shalaby, B.M. Moharram, 2011. Thermal performance investigation of double pass-finned plate solar air heater. Applied Energy 88, 727–1739. [18] Mohitkumar G. Gabhane, Amarsingh, Kanase-Patil, 2017. Experimental analysis of double flow solar air heater with multiple C shape roughness, Solar Energy 155, 1411–1416. [19] S.S. Krishnananth, K. Kalidasa Murugavel, 2013. Experimental study on double pass solar air heater with thermal energy storage. Journal of King Saud University – Engineering Sciences 25, 135–140. [20] Khushmeet Kumar, D.R. Prajapati, Sushant Samir, 2016. Heat Transfer and Friction Factor Correlations Development for Solar Air Heater Duct Artificially Roughened with ‘S’ Shape Ribs. Experimental Thermal and Fluid Science, http://dx.doi.org/10.1016/j.expthermflusci.2016.11.012 [21] Fouad Menasria, Merouane Zedairia, Abdelhafid Moummi, 2017. Numerical study of thermohydraulic performance of solar air heater duct equipped with novel continuous rectangular baffles with high aspect ratio. Energy, 10.1016/j.energy.2017.05.002 [22] L. Varshney, A.D. Gupta, 2017. Performance prediction for solar air heater having rectangular sectioned tapered rib roughness using CFD. Thermal Science and Engineering Progress, http://dx.doi.org/10.1016/j.tsep.2017.09.005 [23] Smith Eiamsa-ard , Pongjet Promvonge(2009)."Thermal characteristics in round tube fitted with serrated twisted tape".Applied Thermal Engineering 30, 1673e1682 [24] Abhishek Gautam, Lokesh Pandey, Satyendra Singh, 2017. Influence of perforated triple wing vortex generator on a turbulent flow through a circular tube, https://doi.org/10.1007/s00231-018-2296-4 [25] N.T. Ravi Kumar, P. Bhramara, A. Kirubeil, L. Syam Sundar, Manoj K. Singh, Antonio C.M. Sousa(2018).Effect of twisted tape inserts on heat transfer, friction factor of Fe3O4 nanofluids flow in a double pipe U-bend heat exchanger."International Communications in Heat and Mass Transfer 81, 155–163 [26] Alok Kumar, Sunil Chamoli, Manoj Kumar, 2016. Experimental investigation on thermal performance and fluid flow characteristics in heat exchanger tube with solid hollow circular disk inserts. Applied Thermal Engineering 100, 227–236. [27] Seyfi Şevika, Mesut Abuşkab, 2019. Thermal performance of flexible air duct using a new absorber construction in a solar air collector. Applied Thermal Enginering 146, 123-134. [28] Alok Kumar, Sunil Chamoli, Manoj Kumar, Satyendra Singh, 2016. Experimental investigation on thermal performance and fluid flow characteristics in circular cylindrical tube with circular perforated ring inserts. Experimental Thermal and Fluid Science 79, 168–174. Kartik Arora, Sanjay Kumar Singh Authors:

Paper Title: IOT Based Portable Medical Kit Abstract: Medication requirements and health issues have been on the rise in the last decade and to cater to this numerous technologies have been introduced to the health sector. Internet of Medical Things (IoMT) has been on the rise and providing medical care to the much needed through services, smart medical devices that not only provides an enhanced platform to the doctor but also to the patients to maintain better healthcare levels. This paper addresses the implementation of a multilayered architecture for a health monitoring kit incorporated with sensors and alert mechanism to activate a pill dispenser. The sensor data is relayed to a personalized android application and care has been taken to make sure that the patient receives the medicine on time and in the required dosage.

Index Terms: Android application, Dispenser, Health sensors Medicine, Monitor

References: [1] Prosanta Gope and Tzonelih Hwang, “BSN-Care: A Secure IoT-Based Modern HealthcareSystem Using Body Sensor Network” 1368IEEE SENSORS JOURNAL, VOL. 16, NO. 5, MARCH 1, 2016 9. [2] Kaleem Ullah,Munam Ali Shah,Sijing Zhang,”Effective Ways to Use Internet of Things in the Field of Medical and Smart Health Care” 978-1-4673-8753-8/16/$31.00 ©2016 IEEE [3] Punit Gupta, Deepika Ahrawal“IoT Based Smart Health Care Kit”. 2016 ICCTICT 42-46 [4] Abdelrahman Rashed, Ahmed Ibrahim, Ahmed Adel, Bishoy Mourad, Ayman Hatem, Mostafa Magdy,Nada Elgaml, Ahmed Khattab “Integrated IoT Medical Platform for RemoteHealthcare and Assisted Living”. 978-1-5386-1359-7$31.00c©2017 IEEE [5] Gulraiz J. Joyia, Rao M. Liaqat, Aftab Farooq, and Saad Rehman"Internet of Medical Things (IOMT): Applications, Benefits and Future Challenges in Healthcare Domain” [6] M.Shamim Hossaina,Ghulam Muhammad“Cloud assisted Industrial Internet of Things (IIoT) – Enabled framework for health monitoring” [7] “Combination of Cloud Computing and Internet of Things (IOT) in Medical Monitoring Systems” [8] D. Wan, “Magic medicaine cabinet: A situated portal for consumer healthcare,” in Proc. First Int. Symp. Handheld and Ubiquitous Com-puting (HUC ’99), Sep. 1999. [9] Kuperman GJ, Bobb A, Payne TH, et al. “Medication Related Clinical Decision Support in Computerized Provider Order Entry Systems: A Review” Journal of American Medical Informatics Association, 2007. [10] S. Mukund1 and N.K.Srinath “Design of Automatic Medication Dispenser” [11] “Automatic Pill Dispenser”, Mrityunjaya D H1, Kartik J Uttarkar2, Teja B3, KotreshHiremathInternational Journal of Advanced Research in Computer and Communication Engineering CertifiedVol. 5, Issue 7, July2016 C.Kishore Kumar, V.Venkatesh Authors:

Paper Title: Design and Development of IOT Based Intelligent Agriculture Management System in Greenhouse Environment Abstract: The introduction of Internet of Things (IoT) has a significant impact on shaping the communication and internetworking landscapes. The upcoming IoT researches are linked with design of standards and open architectures still requiring a global attention before deployment. The main objective is to design and develop a framework on Internet of Things (IoT) for precision agriculture using Machine learning techniques, where it surges the efficiency in farming by minimizing the loss of water and studying the fertility of the field. Libelium 10. Smart Agriculture is used to connect to the IoT which uses Waspmote module. Waspmote is the plug and sense platform which is programmed using Waspmote IDE configured to connect with the available Local Area Network (LAN). With the help of Machine learning techniques like Classification And Regression Technique (CART) and Linear Support Vector Machine (SVM), the amount of water required by the crops can be 47-52 estimated. In this paper, various regression such as stochastic gradient decent and boosted tree regression techniques are compared and results were obtained. Although each model applied in this paper performed well in predicting whether the crop needs to be irrigated, the optimal prediction accuracies were acquired by Boosted Tree Regression (BTC). It is compared by the fold numbers, Root Mean Squared Error (RMSE) and coefficient of Determination (CoD). The accuracy of the boosted tree regression came out to be 91.93% and the stochastic gradient descent prediction model delivered 62.95% accuracy. The amount of water required for the irrigation is then sent to appropriate actuator like solenoid valve and motor can be turned on for that particular period of time. Calibration test results and Measurements are represented to enhance the accuracy and success rates of Precision Agriculture (PA).

Index Terms: Decision Support System (DSS), Libelium, Machine learning, Smart Agriculture.

References: [1]. Tomo Popovic, Nedeljko Latinovic, Ana Pešic´,Zarko Zecˇevic´, Bozˇo Krstajic´, Slobodan Djukanovic., “Architecting an IoT-enabled platform for precision agriculture and ecological monitoring: A case study”, Computers and Electronics in Agriculture, vol.no: 140, 2017, pp. 255-265. [2]. Evans, R., & Bergman, J., “Relationships between cropping sequences and irrigation frequency under self- propelled irrigation systems in the Northern Great Plains (NGP)”, USDA Annual Report, 2007, pp. 466- 479. [3]. Anat Goldstein et.al, “Applying machine learning on sensor data for irrigation recommendations: revealing the agronomist’s tacit knowledge”, Precision Agriculture, vol.no: 19, 2018, pp.421-444. [4]. Kim, William M.Iverson, Robert G.Evans, “Remote Sensing control of an irrigation system using a distributed wireless sensor Network”, vol.no:57, , 2008, pp.1379-1387. [5]. Junyan Ma, Xingshe Zhou, Shining Li , Zhigang Li, “Connecting Agriculture to the Internet of Things through Sensor Networks”, 2011, pp.19-29. [6]. Allen, R. G., “Crop evapotranspiration: Guidelines for computing crop water requirements”, (FAO) irrigation and drainage paper. Rome: FAO, 1998, pp.344-356. [7]. Vellidis, G., Tucker, M., Perry, C., Reckford, D., Butts, C., Henry, H., et al.,”A soil moisture sensor-based variable rate irrigation scheduling system”, Precision agriculture ‘13. The Netherlands: Wageningen Academic Publishers, 2013, pp. 487-609. [8]. Thysen, I., & Detlefsen, N. K.,“Online decision support for irrigation for farmers”, Agricultural Water Management, vol.no: 86, 2006, pp.269–276. [9]. Mateos, L., Lopez-Cortijo, I., & Sagardoy, J. A., “SIMIS: The FAO decision support system for irrigation scheme management”, Agricultural Water Management, vol.no: 56, 2002 , pp.193–206. [10]. Y. Liu, Z-H. Ren, D. M. Li, X. K. Tian, Z. N. Lu, "The research of precision irrigation decision support system based on genetic algorithms," in Proc. 5 Int. Conf. on Machine Learning and Cybernetics, Dalian, 2006, pp.56-67. [11]. Hedley, C. B., Roudier, P., Yule, I. J., Ekanayake, J., & Bradbury, S., “Soil water status and water table depth modeling using electromagnetic surveys for precision irrigation scheduling”, Geoderma, vol. no:199, 2013, pp.22–29. [12]. Breiman,L., “Random Forests and Machine Learning”, vol. 45, 2001, pp. 5–32. [13]. Hill, T., Lewicki, P.,"STATISTICS: Methods and Applications", Statsoft, 2007, pp.678-690. [14]. Andriyas, S., & McKee, M., “Recursive partitioning techniques for modeling irrigation behavior”, Environmental Modeling & Software, vol.no:47, 2013, pp.207–217. Jaihind G, Ezhilarasie R, Umamakeswari A Authors:

Paper Title: Water Quality Monitoring and Prediction of Water Quality at College Premises Using Internet of Things Abstract: IoT is becoming more popular and effective tool for any real time application. It has been involved for various water quality monitoring system to maintain the water hygiene level. The main objective is to build a system that regularly monitors the water quality and manages the sustainability. This system deals with specific standards like low cost background and system efficiency when compared to other studies. In this paper, IoT based real time monitoring of water quality system is implemented along with Machine learning techniques such as J48, Multilayer Perceptron (MLP), and Random Forest. These machine learning techniques are compared based on the hyper-parameters and the results were obtained. The attributes such as pH, Dissolved Oxygen (DO), turbidity, conductivity obtained from the corresponding sensors are used to create a prediction model which classifies the quality of water. Measurement of water quality and reporting system is implemented by 11. using Arduino controller, GSM/GPRS module for gathering data in real time. The collected data are then analyzed using WEKA interface which is a visualization tool used for the analysis of data and prediction modeling.The Random forest technique outperforms J48 and Multilayer perceptron by giving 98.89% of correctly classified instances. 53-57

Index Terms: Internet of Things (IoT), Machine learning techniques, Random Forest, water quality, WEKA.

References: [1] Yiheng Chen. etal.,, “Water quality monitoring in smart city: A pilot project”, Automation in Construction, Vol.no: 89 (307–316), 2018. [2] Mompoloki Pule. etal.,, “Wireless sensor networks: A survey on monitoring water quality”, Journal of Applied Research and Technology, Vol.no:15 (562–570), 2017. [3] Dung Nguyen. etal.,, “A Reliable and Efficient Wireless Sensor Network System for Water Quality Monitoring”,13th International Conference on Intelligent Environments, 2017. [4] ArchanaSolanki. etal.,, “Predictive Analysis of Water Quality Parameters using Deep Learning”, International Journal of Computer Applications (0975 – 8887) Volume 125 – No.9, 2015. [5] Md. Omar Faruq. etal.,, “Design and Implementation of Cost Effective Water Quality Evaluation System”, IEEE Region 10 Humanitarian Technology Conference (R10-HTC) 21 - 23 Dec 2017. [6] Paul B. Bokingkito Jr. etal.,, “Design and Implementation of Real-Time Mobile-based Water Temperature Monitoring System”, Procedia Computer Science, Vol.no: 124, (698–705), 2017. [7] Yang Fu. etal.,, “Behavioural informatics for improving water hygiene practice based on IoT environment, Journal of Biomedical Informatics”, Vol.no: 78,(156–166), 2018. [8] Cristina Polonschiia, Eugen Gheorghiua, “A multitiered approach for monitoring water quality”, Energy Procedia,Vol.no: 112, (510–518), 2017. [9] M. Parameswari, M. Balasingh Moses, “Online measurement of water quality and reporting system using prominent rule controller based on aquacare-IOT”, Des Autom Embed Syst, DOI 10.1007/s10617-017- 9187-7. [10] Vesna Rankovic. Etal.,,, “Neural network modeling of dissolved oxygen in the Gruža reservoir, Serbia”, Ecological Modelling, Volume No: 221, (Pages 1239-1244) April 2010. [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. [13] 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. S. Shakthi Priyadharshini, A.Umamakeswari Authors:

Paper Title: Human Identification In Smart Home Environment Abstract: People moved their life as automatic systems, like smart home. But here major issues is security, the sensitive personal information have been raised the privacy concerns such as (modifying the data, unauthorized access of data) among peoples would like to share their personal information are only utilized for their benefits, rather than being utilized for malignant purpose. Person Identification in smart home with real time is the major challenging in monitoring system. Capturing the still and live images with high efficiency is the complex task. This system provides the efficiency in higher rate with quick response. Data are lively gathered and lively compared with the data, so the process is undergoing the HAAR and the Convolutional Neural Network (CNN). Capturing and labeling the images is done with the live processing then the live video processing is taken the frames of images. So, the data are not stored for long time, and compared the still and live frames in the known frame databases it matches the features and notify the system with two categories as known and unknown.

Index Terms: Human Classification, Face Recognition, HAAR Cascade, CNN, Person Re-Identification

References: [1] Shih-Chung Hsu and Yu-Wen Wang, Chung-Lin Huang” Human Object Identification for Human-Robot Interaction by using Fast R-CNN 2018 Second IEEE International Conference on Robotic Computing-2018. [2] Javed Iqbal, Mihai TeodorLazarescu, Osama Bin Tariq, “Capacitive Sensor for Tag less Remote Human Identification Using Body Frequency Absorption Signatures,” IEEE Transactions On Instrumentation And Measurement, Volume: 67, Issue: 4, April 2018. 12. [3] RaduProdan, IoanNascu, “Identifying Patterns for Human Activities of Daily Living in Smart Homes,” IEEE International Conference on Automation, Quality and Testing, Robotics, 2014. [4] S. Pisa, E. Pittella, E. Piuzzi, M. Cavagnaro, and P. Bernardi., “Design of a UWB Radar System for 58-62 Remote Breath Activity Monitoring” IEEE/MTT-S International Microwave Symposium Digest,2012. [5] M. Umair Bin Altaf∗, TarasButko, and Biing-Hwang (Fred) Juang, “Acoustic Gaits: Gait Analysis with Footstep Sounds,” IEEE Transactions on Biomedical Engineering, Vol. 62, NO. 8, AUGUST 2015. [6] G. Mokhtari,Q. Zhang,S. Ball, M. Karunanithi “BLUESOUND: A New Resident Identification Sensor – Using Ultrasound Array and BLE Technology for Smart Home Platform,” DOI 10.1109/JSEN.2017.2647960, IEEE Sensors Journal. [7] Xiangbo He, Ting Jiang, “Target identification in foliage environment using UWB radar with hybrid wavelet-ICA and SVM method,” Journal Physical Communication, Feb 2014. [8] Adnan Farooq, Ahmad Jalal, and Shaharyar Kamal, “Dense RGB-D Map-Based Human Tracking and Activity Recognition using Skin Joints Features and Self-Organizing Map,” KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS VOL. 9, NO. 5, May 2015. [9] Marco Teran, Juan Aranda and Henry Carrillo, Diego Mendez and Carlos Parra “IoT-based System for Indoor Location using Bluetooth Low Energy, count, location, track, and identity,” IEEE Colombian Conference on Communications and Computing (COLCOM),2017. [10] S. Pisa, E. Pittella, E. Piuzzi, M. Cavagnaro, and P. Bernardi., “Design of a UWB Radar System for Remote Breath Activity Monitoring” IEEE/MTT-S International Microwave Symposium Digest,2012. [11] Dai Sasakawa, Naoki Honma, Takeshi Nakayama, “Human Identification Using MIMO Array”, IEEE Sensors Journal, Volume: 18, Issue: 8, April15, 15 2018 [12] victoria moreno ; miguel a. zamora ; antonio f. skarmeta, a low-cost indoor localization system for energy sustainability in smart buildings ieee sensors journal, 2016. [13] ibrahim al-naimi ; chi biu wong ; philip moore ; xi chen, “indoor identification and tracking using advanced multimodal approach”, 10th international symposium on mechatronics and its applications (isma), 2015. [14] pirehpirzada ; neil white, adriana wilde, “sensors in smart homes for independent living of the elderly” 5th international multi-topic ict conference (imtic), 2018 [15] Alina Roitberg, Alexander Perzylo, Nikhil Somani, Manuel Giuliani, “Human Activity Recognition in the Context of Industrial Human-Robot Interaction,” Signal and Information Processing Association Annual Summit and Conference (APSIPA), 2014. [16] Long Xiao ; Bo Cheng ; Bo Yang ; Rong Du ; Wenbin Yu ; Xinping Guan, “A context-aware entrance guard in smart home: An event-driven application based on the human motion and face recognition” 5th International Conference on Automation, Robotics and Applications, 2011. [17] Javed Iqbal, Mihai TeodorLazarescu, ArslanArif and Luciano Lavagno, “High sensitivity, low noise front- end for long range capacitive sensors for tag less indoor human localization,” IEEE 3rd International Forum on Research and Technologies for Society and Industry (RTSI), 2017. [18] Ching-Hu Lu, Student Member, IEEE, Chao-Lin Wu, Member, IEEE, and Li-Chen Fu, Fellow, IEEE, “A Reciprocal and Extensible Architecture for Multiple-Target Tracking in a Smart Home” IEEE transactions on systems, man, and cybernetics—part c: applications and reviews, Vol. 41, no. 1, January 2011. [19] Frederic Bergeron ; Kevin Bouchard ; Sylvain Giroux ; Sebastien Gaboury ; Bruno Bouchard “Simple objects tracking system for smart homes,” IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2015. [20] Juan A. Fraile, Javier Bajo, Juan M. Corchado, and Ajith Abraham, Senior Member, IEEE, “Applying Wearable Solutions inDependent Environments”, IEEE transactions on information technology in biomedicine, vol. 14, no. 6, november 2010. [21] QinyiXu,YanChen,BeiBei Wang: “Radio-Shot Through-The-Wall Human Recognition”.Intelligent Environment, IEEE Global Conference on Signal and Information Processing (GlobalSIP), 2016. [22] ShijunZhai∗,Ting Jiang, “Target detection and classification by measuring and processing bistatic UWB radarsignal”https://doi.org/10.1016/j.measurement.2013.08.031, Volume 47, 2014. [23] Li Cuimei, Qi Zhiliang , Jia Nan , Wu Jianhua “Human face detection algorithm via Haar cascade classifier combined with threeadditional classifiers” IEEE 13th International Conference on Electronic Measurement & Instruments 2007.

Authors: P Vinod Kumar, Snivash, Mohammed Fahimullah A, K Vinay Gokul

Paper Title: Single Layer Coating Photonic Crystal Fiber Biosensor Based On Surface Plasmon Resonance Abstract: This paper is a demonstration for the design of photonic crystal fiber biosensor which is based on the phenomenon of surface plasmon resonance. The plasmonic coating covers the outer layer of the Photonic Crystal fiber to ease the fabrication process, we choose Gold. To quantitatively measure the represented design, we utilize a technique called as the Finite element method (FEM). To accomplish the sensitivities of 4300nm/RIU and 408.468 RIU-1 we apply the methodologies of wavelength and amplitude interrogation models. This design yielded a resolution of 2.33×10-5 respectively. A fluctuation from 1.33 to 1.39 in the analyte refractive index can be identified and measured by this design. The sensing range of the design is wide and may also be used in biological detection.

Keywords: Biosensor, Resolution, PCF, SPR

References: [1] Shaimaa I Azzam, Mohamed Farhat O Hameed, Rania Eid Shehata, AM Heikal, and Salah SA Obayya. 13. Multichannel photonic crystal fiber surface plasmon resonance based sensor. Optical and Quantum Electronics, 48(2):142, 2016. [2] MS Aruna Gandhi, S Sivabalan, P Ramesh Babu, and K Senthilnathan. Designing a biosensor using a photonic quasi-crystal fiber. IEEE Sensors Journal, 16(8):2425–2430, 2016. 63-66 [3] Md Hasan, SanjidaAkter, Ahmmed Rifat, Sohel Rana, and Sharafat Ali. A highly sensitive gold-coated photonic crystal fiber biosensor Multidisciplinary Digital Publishing Institute, 2017. based on surface plasmon resonance. In Photonics, volume 4, page 18. [4] Itaru Ishida, Tsuyoshi Akamatsu, Zhaoyang Wang, Yusuke Sasaki,Katsuhiro Takenaga, and Shoichiro Matsuo. Possibility of stack and draw process as fabrication technology for multi-core fiber. In 2013 Optical Fiber Communication Conference and Exposition and the National Fiber Optic Engineers Conference (OFC/NFOEC), pages 1–3.IEEE, 2013. [5] Duanming Li, Wei Zhang, Huan Liu, Jiangfei Hu, and GuiyaoZhou.High sensitivity refractive index sensor based on multicoating photonic crystal fiber with surface plasmon resonance at near-infrared wavelength. IEEE Photonics Journal, 9(2):1–8, 2017. [6] Chao Liu, WeiquanSu, Qiang Liu, Xili Lu, Famei Wang, Tao Sun,and [7] Paul K Chu. Symmetrical dual d-shape photonic crystal fibers for surface [8] plasmon resonance sensing. Optics express, 26(7):9039–9049,2018. [9] Min Liu, Xu Yang, Ping Shum, and Hongtao Yuan. High-sensitivity birefringent and single-layer coating photonic crystal fiber biosensor based on surface plasmon resonance. Applied optics, 57(8):1883–1886. [10] Min Liu, Xu Yang, Bingyue Zhao, Jingyun Hou, and Ping Shum. Square array photonic crystal fiber-based surface plasmon resonance refractive index sensor. Modern Physics Letters B, 31(36):1750352, 2017. [11] AA Rifat, GhafourAmouzadMahdiraji, YM Sua, YG Shee, Rajib Ahmed, Desmond M Chow, and FR MahamdAdikan. Surface plasmon resonance photonic crystal fiber biosensor: a practical sensing.approach.IEEE Photonics Technology Letters, 27(15):1628–1631, 2015. AhmmadRifat,GMahdirajiChow,YuShee,Rajib Ahmed and Faisal Adikan.Photonic Crystal Fiber based surface plasmon resonance sensor with selective analyte channels and graphene silver deposited core,Sensors,15(5):11499-11510,2015.

Authors: Pooja Arora, Dr. Anurag Dixit

Paper Title: The Hybrid Optimization Algorithm for Load Balancing In Cloud Abstract:The advancements in the cloud computing has gained the attention of several researchers to provide on-demand network access to users with shared resources. Cloud computing is important a research direction that can provide platforms and softwares to clients using internet. But, handling huge number of tasks in cloud infrastructure is a complicated task. Thus, it needs a load balancing method for allocating tasks to Virtual Machines (VMs) without influencing system performance. This paper proposes a load balancing technique, named Elephant Herd Grey Wolf Optimization (EHGWO) for balancing the loads. The proposed EHGWO is designed by integrating Elephant Herding Optimization (EHO) in Grey Wolf Optimizer (GWO) for selecting the optimal VMs for reallocation based on newly devised fitness function. The proposed load balancing technique considers different parameters of VMs and PMs for selecting the tasks to initiate the reallocation for load balancing. Here, two pick factors, named Task Pick Factor (TPF) and VM Pick Factor (VPF), are considered for allocating the tasks to balance the loads.

Keywords: Cloud computing, load balancing, Elephant Herding Optimization, Grey Wolf Optimizer, reallocation, pitch factors.

References: [1] Shang-Liang Chen, Yun-Yao Chen , Suang-Hong Kuo,” CLB: A novel load balancing architecture and algorithm for cloud services”, Computers & Electrical Engineering,Vol:58, pp:154-160, February 2017. [2] Qi Liu, Weidong Cai, Jian Shen, Xiaodong Liu, Nigel Linge, "An Adaptive Approach to Better Load Balancing in a Consumer-centric Cloud Environment," IEEE Transactions on Consumer Electronics, Vol: 62, no: 3, pp: 243 - 250, August 2016. [3] Jia Zhao, Kun Yang, Xiaohui Wei, Yan Ding, Liang Hu, and Gaochao Xu, "A Heuristic Clustering-based Task Deployment Approach for Load Balancing Using Bayes Theorem in Cloud Environment", IEEE Transactions on Parallel and Distributed Systems, Vol: 27, no: 2, pp: 305 -316, February 2016. 14. [4] Shiva Razzaghzadeh, Ahmad Habibizad Navin, Amir Masoud Rahmani, and Mehdi Hosseinzadeh, "Probabilistic Modeling to Achieve Load balancing in Expert Clouds", Ad Hoc Networks, January 2017. [5] Ranesh Kumar Naha and Mohamed Othman, "Cost aware service brokering and performance sentient load balancing algorithms in the cloud", Journal of Network and Computer Applications, Vol: 75, pp: 47–57, 67-71 November 2016. [6] Weihua Huang, Zhong Ma, Xinfa Dai, Mingdi Xu and Yi Gao, "Fuzzy Clustering with Feature Weight Preferences for Load Balancing in Cloud", International Journal of Software Engineering and Knowledge Engineering,Vol: 28, no:5,pp:593–617,2018. [7] Narander Kumar and Diksha Shukla,” Load Balancing Mechanism Using Fuzzy Row Penalty Method in Cloud Computing Environment”, Information and Communication Technology for Sustainable Development, pp: 365-373,2017. [8] Santanu Dam, Gopa Mandal, Kousik Dasgupta and Parmartha Dutta,” An Ant-Colony-Based Meta-Heuristic Approach for Load Balancing in Cloud Computing”, Applied Computational Intelligence and Soft Computing in Engineering, pp: 29,2018. [9] Shalini Joshi and Uma Kumari, "Load Balancing in Cloud Computing:Challenges & Issues," In proceedings of 2nd International Conference on Contemporary Computing and Informatics (IC3I), December 2016. [10] Mahfooz Alam and Zaki Ahmad Khan," Issues and Challenges of Load Balancing Algorithm in Cloud Computing Environment", Indian Journal of Science and Technology, Vol: 10, no:25, July 2017. [11] Wayne Jansen and Timothy Grance, ”Guidelines on security and privacy in public cloud computing”,pp: 800-144, 2011. [12] Jianting Ning, Zhenfu Cao, Xiaolei Dong, Kaitai Liang, Hui Ma and Lifei Wei," Auditable -Time Outsourced Attribute-Based Encryption for Access Control in Cloud Computing",IEEE Transactions on Information Forensics And Security, 2017. [13] Zenon Chaczko, Venkatesh Mahadevan, Shahrzad Aslanzadeh and Christopher Mcdermid," Availability and Load Balancing in Cloud Computing", In proceedings of International Conference in Computer and Software Modeling, IPCSIT, Vol:14 , 2011. [14] Daraghmi, E.Y. and Yuan, S.M.,”A small world based overlay network for improving dynamic load- balancing”, Journal of Systems and Software, Vol: 107, pp:187-203,2015. [15] A. S. Milani and N. J. Navimipour,” Load balancing mechanisms and techniques in the cloud environments: Systematic literature review and future trends”, J. Netw. Comput. Appl. Vol:71, no:1, pp: 86–98,2016. [16] Y. C. Jiang, “A survey of task allocation and load balancing in distributed systems”, IEEE Trans. Parallel Distrib. Syst.,Vol: 27no:2,pp: 585–599,2016. [17] G. Mateusz, G. Alicja and B. Pascal,” Cloud brokering: Current practices and upcoming challenges”, IEEE Cloud Comput.,Vol: 2,no:2,pp: 40–47,2015. [18] I. D. Falco, E. Laskowski, R. Olejnik, U. Scafuri, E. Tarantino and M. Tudruj, “Extremal optimization applied to load balancing in execution of distributed programs”, Appl. Soft Comput.,Vol: 30,no:3,pp: 501– 513,2015. [19] Y. Jiang, “Concurrent collective strategy diffusion of multi agents: The spatial model and case study”, J. Netw. Comput. Appl.,Vol: 39,no:4,pp: 448–458,2009. [20] Mirjalili, S., Mirjalili, S.M. and Lewis, A.,”Grey wolf optimizer” Advances in engineering software,Vol: 69, pp.46-61,2014. [21] G. Wang, S. Deb and L. d. S. Coelho, "Elephant Herding Optimization, "In proceedings of 3rd International Symposium on Computational and Business Intelligence (ISCBI),pp:1-5,2015. Garima Sharma, Vikas Tripathi Authors:

Paper Title: Recent Trends In Big Data Ingestion Tools Abstract: In big data era, data is flooding in at unparalleled, inflexible rate making collection and processing of data a bit hard and unmanageable without using appropriate data handling tools. Selecting the correct tool to meet the current as well as future requirement is a heavy task, and it became more strenuous with lack of awareness of all the available tools of this area. With right tools, one can rapidly fetch, import, process, clean, filter, store, export data from variety of sources with different frequency as well as capacity of data generation. A comprehensive survey and comparative study of performance, merits, demerits, usage of various ingestion tools in existence for frequent data ingestion activities (keeping volume, variety, velocity, and veracity in mind) have been presented in this paper.

Index Terms: Data Ingestion Tools, Apache NIFI, Apache Kafka, Apache Flink, Apache Kinesis, Apache Gobblin

References: [1] Rajiv Ranjan, “Streaming Big Data Processing in Datacenter Clouds”, 2014 IEEE. [2] KHIN ME ME THEIN, "Apache Kafka: Next Generation Distributed Messaging System" in International Journal of Scientific Engineering and Technology Research ,Vol 3, Issue 47,2014, pp 9478- 9483, [3] Roger Young, Sheila Fallon, Paul Jacob,” Dynamic Collaboration of Centralized & Edge Processing for Coordinated Data Management in an IoT Paradigm” in 32nd International Conference on Advanced Information Networking and Applications, 2018 [4] Jianlei Liu, Eric Braun, Clemens D upmeier,Patrick̈ Kuckertz, D. Severin Ryberg, Martin Robinius, Detlef Stolten ,Veit̃ Hagenmeyer, A Generic and Highly Scalable Framework for the Automation and Execution ofScientific Data Processing and Simulation Workflows” in IEEE International Conference on 18. Software Architecture, 2018. [5] Andreea MĂTĂCUȚĂ, Cătălina POPA, " Big Data Analytics: Analysis of Features and Performance of Big Data Ingestion Tools",in Informatica Economică, vol. 22, 2018. 87-91 [6] Rustem Dautov, Salvatore Distefano, Dario Bruneo, Francesco Longo, Giovani Merlino, Antonio Puliafito, “Data Processing in Cyber-Physical-Social Systemsthrough Edge Computing”, 2018 [7] M. Sarnovsky, P. Bednar and M. Smatana,” Data integration in scalable data analytics platform for process industries”,in 21st International Conference on Intelligent Engineering Systems,2017 [8] J.Amudhavel et.al, "Perspectives, Motivations, and Implications of Big Data Analytics", in Proc. International Conference on Advanced Research in ComputerScience Engineering & Technology, Newyork, USA, Mar. 06-07, 2015,vol. 9, pp. 344-352. [9] Dung Nguyen, Edward B. Duffy, Andre Luckow, Ken Kennedy, Amy Apon," Evaluation of Highly Available Cloud StreamingSystems for Performance and Price,” in 18th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, 2018, [10] Ehab Qadah, Michael Mock “A Distributed Online Learning Approach for PatternPrediction over Movement Event Streams with Apache Flink", in Vienna,Austria,2018. [11] M. Ficco,“ Aging-related performance anomalies in the apache storm stream processing system, Future Generation”,2017. [12] Siwoon Son, Sanghun Lee, Myeong-Seon Gil, Mi-Jung Choi, and Yang-Sae Moon," Locality Aware Traffic Distribution in Apache Storm for Energy Analytics Platform"in IEEE International Conference on Big Data and Smart Computing, 2018. [13] Cornelius C. Agbo, Qusay H. Mahmoud, J. Mikael Eklund, “A Scalable Patient Monitoring System Using Apache Storm”, in IEEE Canadian Conference on Electrical & Computer Engineering (CCECE), 2018 [14] Avantika Dixit,JaytrilokChoudhary,DP Singh,”Survey of Apache Storm Scheduler”, in 3rd International Conference on Internet of Things and Connected Technologies (ICIoTCT), 2018. [15] Thomas Lindemann, Jonas Kauke, Jens Teubner, “Efficient Stream Processing of Scientific Data” in IEEE 34th International Conference on Data Engineering Workshops, 2018 [15]. Gautam Pal, Ganagmin Li, Katie Atkinson,” Big Data Real Time Ingestion and Machine Learning ", in IEEE Second International Conference on Data Stream Mining & Processing, 2018.

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

Ticket Collectionwith Destination Prediction In Bus Services In Urban Areas Using Time Based Paper Title: Predictive Algorithm Abstract—- In the new age of automation and machine assisted function of the human way of life people still tend to notice verification and checking of tickets in local land transport such as trains and buses to still be operated by man. This project is a proposal of a new platform and method to book these tickets of buses on a local level. This can lead to decrease in the overcrowding of buses, easy time management of commuters, and smooth functioning of the bus business. Initially the bank details of the passenger must be linked to the app.Machine learning predictive parsing algorithm in combination with data mining features enable the prediction of the passengers to and fro details on a daily and timely basis. Then a SMS alert for ticket payment proof is sent to the user. In admin side, they calculate amount details using this application. Per day amount details of specific route or bus can be calculated by accessing the database. There is also a provision where the IMEI numbers of the consumers is collected. Through GPS system the IMEI numbers of the mobiles inside the bus is checked with the IMEI numbers of those in the database. Ticket defaulters are identified if the IMEI numbers are not present in the database. The entire trail of the transit is on a non-paper sever.

Keywords—GPS,IMEI,Ticket.

19. 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, 2018 92-96 [2] 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 Internet Of Things (Icgciot), Noida, 2015 [3] 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 Bus Tracking And Ticketing System," 2014 2nd International Conference On Information And Communication Technology (Icoict), Bandung, 2014 [4] D. K. Sharma And S. R. Ahuja, "A First- Come-First-Serve Bus-Allocation Scheme Using Ticket Assignments," In The Bell System Technical Journal [5] 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. [6] 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. [7] 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.

Authors: Priyanka Honale, Prof. Arunkumar G

Paper Title: Opinion Mining and Sentiment Analysis of Social Networking Data Abstract -- The important element of the information period had to try to find the opinion & view of another entity. In the invention when there wasn’t an internet possession, it was regular for an character to ask his/her friends and relatives for their opinion before making a final decision. institute conduct viewpoint of poll, surveys to understand the sentiment & opinion of general public towards it product/services Sentiment analysis is a type of data taking out that procedures the inclination of people's opinions through linguistic communication process , linguistics & texts scrutiny, that are familiar mine & explore subjective data from the net - largely social media like twitter & related sources. The analyzed knowledge quantifies the overall public sentiments/reactions toward 20. sure merchandise, people/ideas & reveals the contextual polarity of the information. There has been a large figure of research study & developed applications in the region of open reaction tracking & modeling opinion. Opinion study is the method of determining whether or not a section of writing is helpful, unhelpful/ impartial. Its is conjointly referred to as view Mining, derivation the opinion perspective of a speaker. In case of Twitter 97-103 exact skin texture such as hash tags, specific to the topic analyze can be old to get better the accurateness of sentiment predictions Feature like neutralization, negation handling, & capitalization internationalization as they need recently become an enormous a part of the web. Future opinion-mining systems would like broader & deeper common & sensible databases.

Index Terms— FEATURE based sentiment analysis , supervise Machine Learning Approach.

References: [1] Tan, Shulong, et al. "Interpreting the public sentiment variations on twitter." IEEE transactions on knowledge and data engineering 26.5 (2014): 1158-1170. [2] Gautam, Geetika, and Divakar Yadav. "Sentiment analysis of twitter data using machine learning approaches and [3] semantic analysis." Contemporary Computing (IC3), 2014 Seventh International Conference on. IEEE, 2014. [4] Jha, Vandana, et al. "HOMS: Hindi opinion mining system." Recent Trends in Information Systems (ReTIS), 2015 IEEE 2nd International Conference on. IEEE, 2015. [5] Larsen, Mark E., et al. "We Feel: mapping emotion on Twitter." IEEE journal of biomedical and health informatics 19.4 (2015): 1246-1252. [6] Luo, Yan, and Wei Huang. "Product Review Information Extraction Based on Adjective Opinion Words." Computational Sciences and Optimization (CSO), 2011 Fourth International Joint Conference on. IEEE, 2011. [7] 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. [8] 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. [9] K. Vijayakumar, S. Suchitra and P. Swathi Shri, "A secured cloud storageauditing with empiricaloutsourcing of key updates", International Journal Reasoning-based Intelligent Systems, Vol. 11, No. 2, 2019. Rehan Ahmad Khan, Agastya Gogoi, Rahul Srivastava, Shubham Kumar Tripathy, Authors: S.Manikandaswamy

Automobile Collision Warning and Identification System Using Visible Light and Wi-Fi Paper Title: Communication Abstract: This paper introduces a vehicle-to-vehicle (V2V) communication system based on visible light communication technology. A vehicle will transmit the data continuously to another vehicle in front of it using head light and the data is stored in the Secure Digital (SD) Card in comma separated value for future reference in case of emergency at the same time the data is stored in the cloud server for government reference for locate the most accident areas. Nowadays, people readily use internet in their day-to-day activities to accomplish their task by means of wireless or wired network. As users are increasing manifold, data transmission rate consequently decreasing. However, Wi-Fi imparts data rate of 150Mbps as per IEEE 802.11n, this speed is still not enough to suffice the needs of a user. Considering this, Visible light communication concept has been introduced. In this project, a comparative and analytic study about the speed of visible light and Wi-Fi communication is being done and also reduction of network jamming problem due to increasing users demand is also being done.

Index Terms: vehicle-to-vehicle (V2V), LED, Wi-Fi, SD card, Visible light communication

References: [1] Noof Al Abdulsalam, Raya Al Hajri, Zahra Al Abri, Zainab Al Lawati, and Mohammed M. Bait- Suwailam, "Design and Implementation of a Vehicle to Vehicle Communication System Using Li-Fi Technology", International Conference on Information and Communication Technology Research (ICTRC), 2015. [2] Pooja Bhateley, Ratul Mohindra, S.Balaji, "Smart Vehicular Communication System Using LI-FI 15. Technology", International Conference on Computation of Power, Energy Information and Communication (lCCPEIC), 2016. [3] Harald Haas ; Liang Yin ; YunluWang ; Cheng Chen, "What is Li-Fi", Journal of Lightwave Technology 72 -77 (Volume: 34 , Issue: 6 , March 15, 2016 ). [4] N. G. Ghatwai, V. K. Harpale and M. Kale, "Vehicle to vehicle communication for crash avoidance system," 2016 International Conference on Computing Communication Control and automation (ICCUBEA), Pune, 2016, pp. 1-3. doi: 10.1109/ICCUBEA.2016. [5] J. Lianghai, M. Liu, A. Weinand and H. D. Schotten, "Direct vehicle-to-vehicle communication with infrastructure assistance in 5G network," 2017 16th Annual Mediterranean Ad Hoc Networking Workshop (Med-Hoc-Net), Budva, 2017, pp. 1-5. doi: 10.1109/MedHocNet.2017 [6] Isamu Takai, Member, IEEE, Tomohisa Harada, Michinori Andoh, Keita Yasutomi, Member, IEEE, Keiichiro Kagawa, Member, IEEE, Shoji Kawahito, Fellow, IEEE "Optical Vehicle to Vehicle Communication System Using LED Transmitter and Camera Receiver", IEEE Photonics Journal, DOI 10.1109/JPHOT.2014.2352620, 2014. [7] 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. [8] 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. [9] 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 Vinay Sati, Shivasheesh Kaushik, Dr. Satyendra Singh, Dr. Rahul Kshetri, Rahul Pandey Authors:

Paper Title: Reduction of Losses in 90 Degree Pipe Bends By Varying Design Parameters Using CFD Software Abstract: Fluid plays a vital role in various fields of application like industries or domestic use. The efficient transportation of fluid from one location to another has been a major concern persistently. There are certain energy losses occurring in the flow of fluid whenever there is a change in the path of its flow. In this paper an analysis has been performed across the various general methods employed to generate bent sections, on a 2-D geometric model of a pipe designed using Autodesk Auto CADD 2017. Every alteration of path of fluid flows leads to the loss of momentum of the fluid particles present on the outer layer. This loss of momentum by the particles in turn lead to variation among the fluid parameter like velocity and pressure. These parameters been analyzed in this paper. All the calculations and simulations have been performed on the 2-D axis-symmetric sketches of pipe models, i.e. mesh models under the section of advanced numerical methods using ANSYS R16.0. The fluid being considered in this research activity has been water.

Index Terms: K-Epsilon, Naiver-Stokes equation, Advanced Numerical Methods, FLUENT Flow, fillet.

References: [1] S K Som, Suman Chakraborty- “Introduction to fluid mechanics and fluid machines” 3e, McGraw Hill International- ISBN 978-0-07-132919-4 [2] Yunus A. Cengel, John M. Cimbala- “Fluid Mechanics Fundamentals and Applications” 3e, McGraw Hill International-ISBN 978-93-392-0465-5 16. [3] Fox R. W., A. T. McDonald, and P. J. Pritchard, “Introduction to Fluid Mechanics”, 5e, John Wiley & Sons, Inc., 2004. [4] Laufer, J., “The structure of turbulence in fully developed pipe flow”, NACA Report, NACA-TN-2954 78-87 (1953). [5] Vinay Sati et al, “Hydrodynamic and Thermal Analysis in Pipe Flow using ANSYS Software”, International Journal of Research and Technology, ISSN: 2278-0181, March 2015. [6] John D. Anderson Jr, Computational Fluid Dynamics, McGraw Hill Book Company . [7] Literature Review of accelerated CFD Simulation Methods towards Online Application, Md Lokman Hosain and Rebei Bel Fdhila. [8] McKee, S., Review the MAC Method, Computer & Fluids 37 (2008) p.907-930. [9] Valizadeh, A., et. al. Modeling Two-Phase Flows Using SPH Method, J. of Applied Sciences 8(21): p.3817- 3826, (2008). [10] Greengard, L., Kropinski, M. C., An Integral Equation Approach to the Incompressible Navier-Stokes Equations in Two Dimensions, SIAM J. Sci. Comput. Vol. 20, No. 1, p. 318-336 (1998). [11] Kuhnert, J., Tiwari, S., Finite pointset method based on the projection method for simulations of the incompressible Navier Stokes equations, Fraunhofer Institute for Industrial Mathematics, Kaiserslautern, Germany. [12] Succi, S, The Lattice Boltzmann Equation for Fluid Dynamics and Beyond, ISBN-13: 978-0198503989, Oxford University Press, Oxford, New York, 2001 [13] Tokura, S., Comparison of Particle Methods: SPH and MPS, 13th International LS-DYNA User Conference [14] Rahul Pandey, Vinay Sati, Shivasheesh Kaushik, Dr. Anirudh Gupta, Hydrodynamic flow analysis across bent sections of pipe using CFD software, Vol 4 Issue 10 IJERT ISSN No- 2278-0181 [15] Vinay Sati, Shivasheesh Kaushik, Dr. Anirudh Gupta, Hydrodynamic and Thermal Analysis in Pipe Flow using ANSYS Software, Vol 4 Issue 03 IJERT ISSN No- 2278-0181

Authors: Elakya R, Himanshu Sinha, Prince Kumar, Singh Anubhav Gajendra, Shubham Gupta

Paper Title: Compositional Nature of Language to Represent Bimodal Visual- Audial Percepts Abstract: We describe the perceptual domain which have a composition domain and also which is rarely ever captured in the existed system. This has happened because they started to learn the composition structure directly. Compositional structures can be divided into separate domains. Keeping that in mind, we propose another way to deal with demonstrating bimodal perceptual areas that expressly relates unmistakable projections 17. over every methodology and after that mutually learns a bimodal meager portrayal. Presently this model will empower compositionality crosswise over particular projections and sum up to percept's traversed by this compositional premise. For instance, our model can be prepared on red triangles and blue squares; yet, certainly will likewise have learned red squares and blue triangles. To test our model, we have procured another bimodal 88-91 dataset including pictures and spoken articulations of hued shapes (hinders) in the table top setting.

Index Terms: Compositionality, Bimodal, Sparsity, Modality

References: 1. Learning to Interpret Natural Language Navigation Instructions from Observations : David L. Chen and Raymond J. Mooney ;Journal of Artificial Intelligence Research :2011. 2. A Joint Model of Language and Perception for Grounded Attribute Learning :Cynthia Matuszek, Nicholas FitzGerald, Luke Zettlemoyer, Liefeng Bo, Dieter Fox ;The Journal of Machine Learning Research:2012 Karen Vanessa Pennefather, Rajesh Masilamani A, S. Vasanth. Authors:

Paper Title: Machine Vision System for Evaluating Performance Measure of Laser Beam on Leather Abstract: The manual inspection of leather is sluggish and labor-intensive tasks, they can become precarious in the entire production process. In the present study an attempt has been made to investigate the Heat Affected Zone (HAZ) of leather during the laser cutting process due to the thermal effect caused by a laser at the cut contour edges. To inspect this, a machine vision system is used to capture images of the laser cut edges and then process it using MATLAB to acquire the required information for assessing the quality of leather. The leather can be categorized as good, average or poor quality based on the acquired information for the respective application. As a result, the degree of burn of the leather was determined.

Index Terms: HAZ (Heat Affected Zone), Laser, Leather, Machine Vision, MATLAB, Quality.

References: [1] Alexander Stepanov, Matti Manninen, Inni Pärnänen, Marika Hirvimäki, Antti Salminen “ Laser cutting of leather: tool for industry or designers?”15th Nordic Laser Materials Processing Conference, Nolamp 15, 25-27 August 18. [2] K.Hoang, A.Nachimuthu, “Image processing techniques for leather hide ranking in the footwear industry”, The University of New South Wales Sydney,2052, Australia. [3] L. Duval1,3, M. Moreaud1, C. Couprie1, D. Jeulin2, H. Talbot3, J. Angulo2, “Image Processing for 92-96 Materials Characterization, issues,Challenges and opportunities”, IEEE International Conference on Image Processing (ICIP) 2014. [4] Mohamed Rafiuddin and Dr.G.Satyanarayana, “Challenges in Exports: A Study of India’s Leather Industry”, International Conference on Science Engineering and Management Research 2014. [5] Murali Krishna Kasi,Member,J Bhaskara Rao ,Vijay Kumar Sahu, “Identification of Leather Defects Using an Auto adaptive Edge Detection Image Processing Algorithm”, 2014 International Conference on High Performance Computing and Applications (ICHPCA). [6] Parag Kohli and Ms. Shalvi Garg ,” Leather QualityEstimation Using an Automated Machine Vision System ”,Sangrur, Punjab INDIA Volume 6, Issue 3,May. - June. 2013, PP 44-47. [7] S.Vasanth and T.Muthuramalingam, “A study of Machinability of leather using CO2-Based Laser beam Machining process”, Advances in Manufacturing Processes pp 239-244,11 September 2018 [8] Umar Farooq, Muhammad Usman Asad, Faiqa Rafiq, Ghulam Abbas, Athar Hanif,”Application of Machine Vision for Performance Enhancement of Footing Machine Used in Leather Industry of Pakistan” ,Islamabad, Pakistan 10-12 September 2013. [9] U Venkateswarlu1*, M Muthukrishnan1, R Ramesh2 and NK Chandrababu2,“Effect of CO2 Laser on morphological properties of Leather”,International Research Journal of Engineering and Technology (IRJET) , Volume: 02 Issue: 06,Sep-2015. [10] Nasim, H., Jamil, Y.: Diode lasers: From laboratory to industry. Opt. Laser Technol. 56, 211– 222 (2014) Authors: Anjali Patil, Prof. Arunkumar G.

Paper Title: Real Time Pedestrian Detection And Tracking For Driver Assistance Systems Abstract- In Autonomous driving technology detecting pedestrians and vehicles should be fast and efficient in order to avoid accidents. Pedestrian detection and tracking is challenging for complex real world scenes. In proposed system Kalman filter has been used to detect and track the pedestrians. From three frames initially eigen object is computed in video sequences for detection of moving objects, then shape information is used to classify humans and other objects. Moreover with the help of continues multiple frames occlusion between objects get calculated. In the proposed system an application is developed which gives automatic warning in case of doubtful activities 19. performed by pedestrian of zone monitoring which can be used in various domains like defence and traffic monitoring. Proposed algorithm gives accurate moving object detection and advanced sensors are used to detect human movements ahead and alert the driver by using buzzer, result does not affect by body pose of individual. 97-101

Indexed Terms- Pedestrian detection, Tracking, ACF

References: [1] Dalal, Navneet, and Bill Triggs. "Histograms of oriented gradients for human detection." Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on. Vol. 1. IEEE, 2005. [2] Hong-Son Vu, Jia-Xian Guo et.al.,”A Real time Moving objects Detection and Classification Approach for Static cameras, 2016. International Conference on Consumer Electronics-Taiwan [3] Wren, Christopher Richard, et al. "Pfinder: Real-time tracking of the human body." IEEE Transactions on pattern analysis and machine intelligence 19.7, pp. 780-785 1997 [4] Chien, Shao-Yi, et al. "Video object segmentation and tracking framework with improved threshold decision and diffusion distance." IEEE Transactions on Circuits and Systems for Video Technology 23.6, pp. 921-934, 2013. [5] S. Azmat, Linda Wills, Scott Wills, ”Parallelizing Multimodel Background Modelling on Low Power Integrated”‖, Journal of Signal Processing System, pp. 1-11, February 2016. [6] y.w. Xu , X.B. Cao and T. Li “Extended Kalman Filter Based Pedestrian Localization for Collision Avoidance”, Proceedings of the 2009 IEEE International Conference on Mechatronics and Automation August 9 - 12, Changchun, China. [7] Anjana Das K M and O V Ramana Murthy “Optical Flow Based Anomaly Detection in Traffic Scenes”, 2017 IEEE International Conference on Computational Intelligence and Computing Research. [8] Gurunathan, A., & Viswanatham, V. M. (2017) “Autonomic Performance Enhancement Environment for Websphere Application Server. International Journal of Pure and Applied Mathematics, 116(23), 719-731”. [10] Arunkumar, G. and Madhu Viswanatham, V. (2017). “Autonomic Performance Management Approach for Business Product: Action Planner for Internal Change Management”, Journal of Advanced Research in Dynamical and Control Systems, 113(13), 482—493 Authors: A. Shiny, Mrinmoy Kumar Das, Divyam Kumar Mishra, Manish Kumar Singh, Suman Maitra

Paper Title: Deep Learning Based Remote Sensing Using Convolutional Neural Networks Abstract: We describe our achievements in collecting alternating convergence points with a thickness of 7 μm and focal lengths of 200 and 350 mm, combined with shadow correction, deconvolution and significant neural frame training for transmission close to photography. Visual quality image. Although images taken using diffractive optics have been shown in previous papers, important neural structures have been used in the recovery phase. We use the imagery component of our imaging structure to activate the rise of ultralight cameras with remote identification for Nano and pico satellites, as well as small drones and solar-guided aircraft for aeronautical remote identification systems. . We extend the customizability of the liquid center focus on non- circular surfaces, forcing movement at the liquid convergence point of the surface. We study their trends and whether we can use them in optical structures.

Index Terms: deconvolution, neural framework, picosatellites, ultra-lightweight remote.

Keywords: vehicle-to-vehicle (V2V), LED, Wi-Fi, SD card, Visible light communication.

References: 20. [1] A. Nikonorov, R. Skidanov, V. Fursov, M. Petrov, S. Bibikov, and Y. Yuzifovich, “Fresnel lens imaging with post-capture image processing,” in Proc. IEEE Conf. Comput. Vis. Pattern Recog. Workshops, Boston, MA, USA, 2015, pp. 33–41. 102-106 [2] Y. Peng, Q. Fu, F. Heide, and W. Heidrich, “The diffractive achromat full spectrum computational imaging with diffractive optics,” in Proc. SIGGRAPH ASIA: Virtual Reality Meets Physical Reality: Model. Simulating Virtual Hum. Environ., 2016, Art. no. 4. [3] Y. Peng, Q. Fu, H. Amata, S. Su, F. Heide, and W. Heidrich, “Computational imaging using lightweight diffractive-refractive optics,” Opt. Express, vol. 23, no. 24, pp. 31393–31407, 2015. A. Chambolle and T. Pock, “A first-order primal-dual algorithm for convex problems with applications to imaging,” J. Math. Imaging Vis., vol. 40, pp. 120–145, 2011. [4] A. Nikonorov et al., “Comparative evaluation of deblurring techniques for fresnel lens computational imaging,” in Proc. Int. Conf. Pattern Recog., Cancun, Mexico, 2016, pp. 775–780. [5] P. Atcheson, J. Domber, K. Whiteaker, J. A. Britten, S. N. Dixit, and B. Farmer, “MOIRE – Ground demonstration of a large aperture diffractive transmissive telescope,” Proc. SPIE, vol. 9143, 2014, Art. no. 91431W. [6] C. Guo et al., “High-performance etching of multilevel phase-type Fresnel zone plates with large apertures,” Opt. Commun., vol. 407, pp. 227–233, 2018. [7] C. J. Schuler, M. Hirsch, S. Harmeling, and B. Scholkopf, “Non-stationary ¨ correction of optical aberrations,” in Proc. Int. Conf. Comput. Vis., 2011, pp. 659–666. [8] F. Heide, M. Rouf, M. B. Hullin, B. Labitzke, W. Heidrich, and A. Kolb, “High-quality computational imaging through simple lenses,” ACM Trans. Graph., vol. 32, no. 5, 2013, Art. no. 149.

Authors: Junaid Ahmad, Bhanu Bhaskar, Haresh Seetharaman, Ajay Kumar, J. Arunnehru

Paper Title: 3DMSNET: 3D CNN Based Brain MRI Segmentation 21. Abstract—Segmentation of the brain images has become an important task to analyze the abnormality in infants. Automatic methods are important as the infant brain growth has to be tracked and it is almost impossible 107-110 for an individual to manually segment the MRI data on particular intervals. The manual segmentation tasks are time-consuming and require highly skilled professionals to segment images. Automatic segmentation methods have gained huge support for segmenting MRI images. Several segmentation methods lack accuracies due to nearest neighbor or self-similarity problems. The CNNs have outperformed the traditional methods and are proving to be more reliable day by day. The proposed method is a patch-based method which uses 3DMSnet (3D Multi-Scale Network) for segmentation. The model is evaluated on BrainWeb and other publicly available datasets.

Index Terms- segmentation, nearest-neighbor, self-similarity, patch-based, BrainWeb, CNN, MRI, 3DMSnet

References: [1] D.L. Pham, C. Xu, and J.L. Prince, Current methods in medical image segmentation, Annual Review of Biomedical Engineering, vol.2, no.2000, pp.315337,2000 [2] N. Passat, C. Ronse, J. Baruthio, J.-P. Armspach, C. Maillot, and C. Jahn, Region-growing segmentation of brain vessels: an atlas based automatic approach, Journal of Magnetic Resonance Imaging, vol.21, no.6, pp.715725,2005 [3] T. H. Lee, M. F. A. Fauzi and R. Komiya, “Segmentation of CT Brain Images Using K-Means and EM Clustering,” 2008 Fifth International Conference on Computer Graphics, Imaging and Visualization, Penang, 2008, pp. 339-344. [4] C. Wan, M. Ye, C. Yao and C. Wu, “Brain MR image segmentation based on Gaussian filtering and improved FCM clustering algorithm,” 2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), Shanghai, 2017, pp. 1-5. [5] A. Gholipour, N. Kehtarnavaz, R. Briggs, M. Devous and K. Gopinath, “Brain Functional Localization: A Survey of Image Registration Techniques,” in IEEE Transactions on Medical Imaging, vol. 26, no. [6] 4, pp. 427-451, April 2007. [7] N. Torbati and A. Ayatollahi, “A new method for non-rigid registration of MRI images,” 2017 Iranian Conference on Electrical Engineering (ICEE), Tehran, 2017, pp. 91-94. [8] N. H. Rajini and R. Bhavani, “Classification of MRI brain images using k-nearest neighbor and artificial neural network,” 2011 International Conference on Recent Trends in Information Technology (ICRTIT), Chennai, Tamil Nadu, 2011, pp. 563-568. [9] P. Moeskops, M. A. Viergever, A. M. Mendrik, L. S. de Vries, M. J. [10] N. L. Benders and I. Igum, “Automatic Segmentation of MR Brain Images With a Convolutional Neural Network,” in IEEE Transactions on Medical Imaging, vol. 35, no. 5, pp. 1252-1261, May 2016. [11] McConnell brain imaging center, BrainWeb: simulated brain database, http://www.bic.mni.mcgill.ca/brainweb/. [12] N4ITK: improved N3 bias correction IEEE transactions on medical imaging vol. 29,6 (2010): 1310-20. [13] K. Kamnitsas, C. Ledig, V. F. Newcombe, J. P. Simpson, A. D. Kane, D. K. Menon, D. Rueckert, B. Glocker, Ecient multi-scale 3D CNN with fully connected crf for accurate brain lesion segmentation, arXiv preprint arXiv:1603.05959. [14] K. He, X. Zhang, S. Ren and J. Sun, “Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification,” [15] 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, 2015, pp. 1026-1034. doi: 10.1109/ICCV.2015.123 [16] L. R. Dice, Measures of the amount of ecologic association between species, Ecology, vol.26, no.3, pp.297302, 1945 N. Narayanan, Ch.Surya Pradeep, Piyush Gulati, G. Raj Bharath, S.Nivash Authors:

Paper Title: Design of Highly Secured Biometric Voting System Abstract— Voting is the most basic right of every citizen and is one of the most important responsibilities of every citizen. The votes which we cast decide the future of our country, and we must vote honestly and not succumb to any sort of pressure as the phrase goes "With great power comes great responsibility". Now once our votes are cast, we should ensure they are not manipulated in any way. The current voting system (EVM) is easy to manipulate i.e., there is a lot of human intervention which in turn could compromise the results of the elections, hence in this project we will try to solve this vulnerability by introducing a two-step verification which 22. will help avoid the middle man attack i.e. only when the voter is physically present the fingerprint of the voter can be registered and only then the OTP will be sent to user’s mobile number this in turn prevents fake voters. 111-114

Keywords: EVM, Arduino, Fingerprint Scanning, OTP, Biometrics, GSM, Aadhaar

References: [1] Secured Online Voting System with Aadhaar Linking International Journal for Research in Engineering Application & Management (IJREAM) ISSN: 2454-9150 Special Issue - iCreate April – 2018. [2] Biometrically Secured Electronic Voting Machine 2017 IEEE Region 10 Humanitarian Technology Conference (R10-HTC) 21 - 23 Dec 2017, Dhaka, Bangladesh. [3] J. Deepika, S. Kalaiselvi, S. Mahalakshmi, S. Agnes Shifani, "Smart electronic voting system based on biometrie identification-survey", Science Technology Engineering & Management (ICONSTEM) 2017 Third International Conference on, pp. 939-942 [4] Development of a Credible and Integrated Electronic Voting Machine Based on Contactless IC Cards, Biometric Fingerprint Credentials and POS Printer 2016 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE). [5] N. S Aranganadhan M. DhineshKumar Praveen kumar A DSanthosh "Embedded System based Voting Machine System using Wireless Technology" International journal of innovative research in electrical instrumentation and control engineering vol. 4 no. 2 2016 pp. 127-130. [6] S. Sridhar CH. Manjulathal "Electronic Voting Machine Using Finger Print" International Journal of Professional Engineering Studies vol. 7 no. 4 pp. 274-277 2016. [7] Mobile Based Facial Recognition Using OTP Verification for Voting System 978-1-4799-8047- 5/15/$31.00c 2015 IEEE. [8] E-Voting System with Physical Verification Using OTP Algorithm, 2015 International Journal of Hybrid Information Technology Vol.8, No.8 (2015), pp.161-166. [9] R. Karpagavani, M. Mangai, D. Meena, E. Poonguzhali, Chitravalavan ., "Aadhaar Identity Based Electronic Voting Machine With Instant Result Announcement", i-manager's Journal on Embedded Systems, vol. 4, pp. 26, 2015. [10] B. Divya Soundarya Sai M. Sudhakar "Biometric System Based Electronic Voting Machine Using Arm9 Microcontroller" IOSR Journal of Electronics and Communication Engineering (IOSR-JECE) vol. 10 no. 1 pp. 57-65 2015. [11] D. Krishna "Aadhar Based Electronic Voting System and Providing Authentication" International journal of engineering and advanced technology vol. 4 no. 2 pp. 27-240 2013. [12] R. Alaguvel G. Gnanavel "Offline and Online E-Voting System with Embedded Security for Real Time" International Journal of Engineering Research (ISSN: 2319-6890) vol. 2 no. 2 pp. 76-82 April 2013 [13] B. Gomathi S. Veena priyadarshini "Modernized Voting Machine using Finger Print Recognition" International Journal of Scientific & Engineering Research vol. 4 no. 5 May 2013. [14] D. Ashok Kumar T. Ummal Sariba Begum "A Novel design of Electronic Voting System Using Fingerprint" International Journal of innovative technology & creative engineering (issn:2045-8711) vol. 1 no. 1 pp. 12-19 January 2011. [15] M Khasawneh M Malkawi O Al-Jarrah "A Biometric-Secure e-Voting System for Election Process" Proceeding of the 5th International Symposium on Mechatronics and its Applications (ISMA08). 2008. Pooja Manisha Rahate, M. B. Chandak Authors:

Paper Title: Text Normalization and Its Role In Speech Synthesis Abstract: As the technology is developing day-by-day and most of the human work is done by the machine or systems, it is the need of the today’s world to develop systems that can read informal text or words in a proper and standard way even though the format of writing these words or text does not match the standard English words. The informal texts types that exists are the dates, currencies, abbreviations and acronyms of standard words, measurements, URLs, phone numbers etc. This paper focuses on the normalization of such text that converts the informal text into their equivalent standard form which is called text normalization. To produce the equivalent speech form of these non-standard words is the necessity of the today’s system. Text normalization is pre-processing step of the natural language processing system. The paper discusses various techniques and methods for the conversion of the non-standard words into standard words. The methods used for classification of the token are regular expressions, used for simple patter match of the token. Naïve Bayes classification for number sense disambiguity and Stochastic Gradient Descent for resolving acronym and class ambiguity .The result and analysis are also mentioned in the form of error-rate of the system, which shows the area for the scope of more improvement in the system. 23. Index Terms: Naive Bayes, stochastic gradient descent, text normalization, translation memory 115-122

References: [1] Emma Flin et al, "A Text Normalization System for Non-Standard EnglishWords",Proceedings of the 3rd Workshop on Noisy User-generated Text, pages 107–115, Copenhagen, Denmark, September 7, 2017. [2] Meenakshi Sharma,"Text Normalization Using Hybrid Approach", International Journal of Computer Science and Mobile Computing, Vol.4 Issue.1, January- 2015, pg. 544-554. [3] Hay Mar Htun, Theingi Zin, Hla Myo Tun, "Text To Speech Conversion Using Different Speech Synthesis", NTERNATIONAL JOURNAL OF SCIENTIFIC & TECHNOLOGY RESEARCH VOLUME 4, ISSUE 07, JULY 2015. [4] Anand Arokia Raj et al, "Text Processing for Text-to-Speech Systems in Indian Languages", 6th ISCA Workshop on Speech Synthesis, Bonn, Germany, August 22-24, 2007. [5] Richard Sproat, Navdeep Jaitly, "RNN Approaches to Text Normalization: A Challange", arXiv preprint arXiv:1611.00068, Jan 2017. [6] Shaurya Rohatgi, Maryam Zare, "DeepNorm - A Deep Learning approach to Text Normalization", arXiv preprint arXiv:1712.06994, Dec 2017. [7] Chen Li, Yang Liu, "Improving Text Normalization via Unsupervised Model and Discriminative Reranking", Proceedings of the ACL 2014 Student Research Workshop, pages 86–93, 2014. [8] Dileep Kini, Sumit Gulwani, "FlashNormalize: Programming by Examples for Text Normalization", Proceeding IJCAI'15 Proceedings of the 24th International Conference on Artificial Intelligence, Pages 776- 783, 2015. [9] Suhas R. Mache et al, "Review on Text-To-Speech Synthesizer", International Journal of Advanced Research in Computer and Communication Engineering, Vol. 4, Issue 8, pg. 54-59, August 2015. [10] Lopez Ludeña, V., San Segundo, R., Montero, J. M., Barra Chicote, R., & Lorenzo, J. (2012). "Architecture for text normalization using statistical machine translation techniques." In IberSPEECH 2012 (pp. 112 – 122). Madrid, Spain, 2012. [11] Conghui Zhu et al, "A Unified Tagging Approach to Text Normalization", Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics, pg. 688–695, Prague, Czech Republic, 2007. [12] Chris Lin, Qian (Sarah) Mu, Yi Shao, "iTalk A 3-Component System for Text-to-Speech Synthesis", unpublished. [13] Slobodan Beliga, Miran Pobar and Sanda Martincic-Ipšic, "Normalization of Non-Standard Words in Croatian Texts", arXiv preprint arXiv:1503.08167v2, 30 Mar 2015. [14] Gokul P., Neethu Thomas, Crisil Thomas and Dr. Deepa P. Gopinath, "Text Normalization and Unit Selection for a Memory Based Non Uniform Unit Selection TTS in Malayalam", Proceedings of the 12th International Conference on Natural Language Processing, pg. 172-177, Trivandrum, India, 2015. [15] Richard Sproat et al, "Normalization of non-standard words", Computer Speech and Language (2001) 15, pg. 287–333. [16] Subhojeet Pramanika, Aman Hussaina, “Text Normalization using Memory Augmented Neural Networks”, arXiv preprint arXiv: 1806.00044v2, July 2018. [17] Daniel Jurafsky, James Martin, Speech and Language Processing, Pearson India, 2nd Edition. B.Surya Sai, P.Vamsi Mohan,Pijush Meher,M.Azhagiri Authors:

Paper Title: A Culling System For A Recipe Using Sentiment Analysis Techniques Abstract :This System helps the user for providing the recipes in which they are particularly interested in a mentioned criteria of ingredients on which the user wants to prepare a recipe. The input given by the user like the ingredient on which he/she wants to prepare a recipe can be of text, speech or visual. The process of providing a list of gathered recipes from different trusted and verified sources is performed with the help of Sentiment Analysis(SA), Watson Text-to-Speech and Watson Visual Recognition(WVR). The recipe extraction from different sources which are required for the user is retrieved with the help of a standard Web Crawler. The tools and technologies used for the proposed system are from Artificial Intelligence(AI), Natural Language Processing(NLP). The system proposed assists the user in providing a list of recipes in a prioritized order based on the optimization process performed by the Naïve Bayes Algorithm(NBA) of Sentiment Analysis. In addition, the displayed results of recipes have been reviewed and rated by different users from different sources.

Keywords : Naïve Bayes Algorithm(NBA) ,Natural Language Processing(NLP) , Recipe extraction , Sentiment Analysis (SA) , Web Crawler.

References:

24. [1] KeijiYanai, Takuma Maruyama and Yoshiyuki Kawano “ A Cooking Recipe Recommendation System with Visual Recognition of Food Ingredients” International Journal of Interactive Mobile Technologies, Vol 8,No 2, 2014. 123-127 [2] M.B. Vivek, N. Manju and M.B. Vijay “Machine Learning Based Food Recipe Recommendation System” D.S. Guru et al. (eds.), Proceedings of International Conference on Cognition and Recognition, Lecture Notes in Networks and Systems 14, Springer Nature Singapore Pte Ltd. 2018. [3] PakawanPugsee and MonsineeNiyomvanich “Sentiment Analysis of Food Recipe Comments”ECTI Transactions on Computer And Information Technology Vol.9, No.2 November 2015. [4] Rui Xia, Feng Xu, ChengqingZong, Qianmu Li, Yong Qi, and Tao Li “Dual Sentiment Analysis: Considering Two Sides of One Review” IEEE Transactions on Knowledge And Data Engineering, Vol. 27, No. 8, August 2015. [5] Kim Schouten and Flavius Frasincar “Survey on Aspect-Level Sentiment Analysis” IEEE Transactions on Knowledge AndData Engineering, Vol. 28, No. 3, March 2016. [6] Lakshmish Kaushik, Abhijeet Sangwan and John H. L. Hansen “Automatic Sentiment Detection in Naturalistic Audio” IEEE/ACM Transactions on Audio,Speech, And Language Processing, Vol. 25, No. 8, August 2017. [7] Sujata Rani and Parteek Kumar “A Sentiment Analysis System to Improve Teaching and Learning” IEEE Computer Society, Vol 50,No.5 May 2017. [8] Farkhund Iqbal , Jahanzeb Maqbool Hashmi, Benjamin C. M. Fung , Rabia Batool, Asad Masood Khattak, Saiqa Aleem , And Patrick C. K. Hung “A HybridFramework for Sentiment Analysis Using Genetic Algorithm Based Feature Reduction” IEEE Access , Vol 7,2019. A.Revathi, Dr.S.G.Santhi, 25. Authors: Paper Title: Energy Consumption Based Low Energy Aware Gateway (LEAG) Protocol In Wireless Sensor Networks Abstract: Wireless Sensor Networks (WSN) consists of a large amount of nodes connected in a self-directed manner. The most important problems in WSN are Energy, Routing, Security, etc., price of the sensor nodes and renovation of these networks is reasonable. The sensor node tools included a radio transceiver with an antenna and an energy source, usually a battery. WSN compute the environmental conditions such as temperature, sound, pollution levels, etc., WSN built the network with the help of nodes. A sensor community consists of many detection stations known as sensor nodes, every of which is small, light-weight and portable. Nodes are linked separately. Each node is linked into the sensors. In recent years WSN has grow to be an essential function in real world. The data’s are sent from end to end multiple nodes and gateways, the data’s are connected to other networks such as wireless Ethernet. MGEAR is the existing mechanism. It works with the routing and energy consumption. The principal problem of this work is choosing cluster head, and the selection is based on base station, so the manner is consumes energy. In this paper, develop the novel based hybrid protocol Low Energy Aware Gateway (LEAG). We used Zigbee techniques to reduce energy consumption and routing. Gateway is used to minimize the energy consumption and data is send to the base station. Nodes are used to transmit the data into the cluster head, it transmit the data into gateway and gateway compress and aggregate the data then sent to the base station. Simulation result shows our proposed mechanism consumes less energy, increased throughput, packet delivery ration and secure routing when compared to existing mechanism (MGEAR).

Index Terms: WSN, LEAG, Nodes, Energy, MGEAR.

References: [1] Dhaigude Tanaji Anand Rao, Dr. Latha Parthiban “A survey on Energy Efficient Routing Protocols” International Journal of Pure and Applied Mathematics, vol. 118, pp. 3109–3113, 2018. [2] Zhao Han, Jie Wu, Member, IEEE, Jie Zhang, Liefeng Liu, and Kaiyun Tian, A General Self-Organized Tree-Based Energy-Balance Routing Protocol for Wireless Sensor Network, IEEE Transactions on Nuclear Science,., vol. 61. April 2014, pp.732–740. [3] Amandeep Kaur, Er. Swaranjeet Singh, Navjot Kaur, “Review of LEACH Protocol and Its Types,” in International Journal of Emerging Engineering Research and Technology, vol. 3, May 2015, pp. 20–25. [4] Monika, Sneha Chauhan, Nishi Yadav, “LEACH-I Algorithm for WSN" in International Journal of Innovative Research in Computer and Communication Engineering, vol.4, Issue..3, March 2016, pp. 3459 – 3466. [5] Amrit Singh, “A Novel Routing Protocol (M-Gear) Using Gateway Based Energy-Efficient Scheme For Wireless Sensor Networks (Wsns)”, International Journal of Advanced Research in Computer Science, 128-132 vol.7, issue.6, November 2016, pp. 361-363. [6] Hassan Oudani, Salahddine Krit, Mustapha Kabrane, Kaoutar Bandaoud, Mohamed Elaskri, Khaoula Karimi, Hicham Elbousty, Lahoucine Elmaimouni, “Energy Efficient in Wireless Sensor Networks Using Cluster-Based Approach Routing” International Journal of Sensors and Sensor Networks”, International Journal of Sensors and Sensor Networks, vol.5, Issue.1, May 2017, pp.6-12. [7] Gurpreet Kaur, Sukhpreet Kaur, “Enhanced M-Gear Protocol for Lifetime Enhancement in Wireless Clustering System”, International Journal of Computer Applications, vol.147, issue.14, August 2016, pp.30- 34. [8] Kusum Lata, Kusum Dalal, “Performance Analysis of LEACH and M-GEAR Routing Protocols for WSN”, International Journal of Electronics, Electrical and Computational System, vol.6, Issue.6, June 2017,pp. 164-169. [9] Neeraj Dewli, Mrs. Rashmi Saini, “A Comparative Analysis of M-GEAR and MODLEACH Energy Efficient WSN Protocols”, International Journal of Computer Science and Information Technologies, vol.6, Issue.3, 2015,pp. 2641-2644. [10] Rahul Priyadarshi, Surender Kumar, SoniPrashant Sharma, “An Enhanced GEAR Protocol for Wireless Sensor Networks”, Springer Nanoelectronics, Circuits and Communication Systems, vol.511,pp. 287-297. [11] Velanati Mohana Gandhi, M.V.H.Bhaskara Murthy, M.Lakshmu Naidu, “Performance Analysis of Multihop-Gateway Energy Aware Routing (M-Gear) Protocol for Wireless Sensor Networks”, IOSR Journal Of Humanities And Social Science, vol.21,Issue.11,November 2016, pp.1-7. [12] Walid Abushiba, Princy Johnson, Saad Alharthi, Colin Wright, “An Energy Efficient and Adaptive Clustering for Wireless Sensor Network (CH-leach) using Leach Protocol” IEEE 13th International Computer Engineering Conference, December 2017, pp.50-54. [13] Mohammed Abo Zahhad, Sabah M. Ahmed, N. Sabor, and Shigenobu Sasaki, “Mobile Sink-Based Adaptive Immune Energy Efficient Clustering Protocol for Improving the Lifetime and Stability Period” IEEE Sensors Journal,Vol. 15, No. 8, pp 4576-4586, Aug 2015. [14] Gaurav Srivastav(2013), “Effective Sensory Communication using GEAR Protocol “ International Journal of Science and Research (IJSR) vol 9, issue 4 , Pp 1809-1815. [15] Gaurav Srivastav(2013), “Effective Sensory Communication using GEAR Protocol “ International Journal of Science and Research (IJSR) vol 9, issue 4 , Pp 1809-1815. [16] C. Divya (2015), “Analysis of GFEAR Protocol “International Journal of Emerging Research in Management &Technology, Volume-4, Issue-5, Pp 38-42. S. Rajarajeswari, Dhamini Poorna Chandra Authors:

Paper Title: Role Of Stop Word Removal In Sentiment Analysis Abstract: The advent of the internet has led to the explosion of interaction and expressing one’s opinion on social platforms. This poses a fertile ground for sentiment analysis, to understand the users’ opinion about the subject of interest. Content present online contain lot of noise. Stop words are present at multiple locations and usually add less semantic significance to the document itself which need to be removed before they can be analyzed. One such pre-processing technique for sentiment analysis is stop word removal. Stop word removal enhances the result of sentiment analysis. In the past decade, a considerable amount of research has been done to extract sentiments. There are also numerous commercial companies that provide sentiment analysis services. Stop word removal can enhance the performance of these tools in providing better results. In this paper, we demonstrate how stop word removal enhances sentiment analysis.

Index Terms: Pre-processing, Sentiment Analysis, Stop word.

References: [1] W. Medhat et al., Sentiment analysis algorithms and applications: a survey, Ain Shams Eng. J. (2014). 32. [2] M.R. Saleh, M.T. Martín-Valdivia, A. Montejo-Ráez, L.A. Ureña-López, Experiments with SVM to classify opinions in different domains, Expert Syst. Appl. 38 (2011) 14799–14804. [3] Manning, Christopher D., Prabhakar Raghavan, and Hinrich Schütze. Introduction to information retrieval. Vol. 1. No. 1. Cambridge: Cambridge university press, 2008. 167-172 [4] P. Tetlock, M. Saar- rnal of Finance 63 (3) (2008) 1437 1467. [5] T. Wilson, J. Wiebe, P. Hoffmann, Recognizing contextual polarity in phrase-level sentiment analysis, in: Proceedings of the Human Language Technology Conference and the Conference on Empirical Methods in Natural Language Processing (HLT/EMNLP), 2005, pp. 347 354. [6] H. Yu, V. Hatzivassiloglou, Towards answering opinion questions: separating facts from opinions and identifying the polarity of opinion sentences, in: Proceedings of the conference on Empirical methods in natural language processing, EMNLP-2003, 2003, pp. 129 136. [7] L. Tan, J. Na, Y. Theng, K. Chang, Sentence-level sentiment polarity classification using a linguistic approach, Digital Libraries: For Cultural Heritage, Knowledge Dissemination, and Future Creation (2011) 77 87. [8] S. R. Das, News Analytics: Framework, Techniques and Metrics, Wiley Finance, 2010, Ch. 2, the Handbook of News Analytics in Finance. [9] B. Pang, L. Lee, S. Vaithyanathan, Thumbs up? sentiment classification using machine learning, Association for Computational Linguistics, 2002, pp. 97 86, conference on Empirical Methods in Natural Language processing EMNLP. [10] C.J. Hutto, Eric Gilbert. VADER: A Parsimonious Rule-based Model for Sentiment Analysis of Social Media Text. Dr. Punith Kumar M B, Dr.T Shreekanth,Anupama M1,Sarsawath S2 Authors: An Ann Based Real Time System For Classification Of Normal And Abnormal Cries Of Pre-Term Paper Title: And Neonates Abstract: Infants communicate with the external world through cry. Most of the problems in the infants can be explored through their cry within first year. Variations in cry can sometimes indicate the neurological disorders, genetic problems and many more. Classification of the infant cry as normal and abnormal at the early stages can reduce the course of action or any casualty. Hence this work proposes a computational approach for the early diagnosis of pre-term and neonates' infant cry. The previous works include various algorithms for classification, however the novelty in this work can be attributed to processing only voiced part of the cry signal. The cry signal is first preprocessed by decomposing it into three levels using db13 wavelet in order to remove

any noise that has been inherited during signal acquisition. This signal is further processed to extract only voiced

part of the speech by identifying the endpoints through Zero Crossing Rate and Energy. Then the MFCC features

26. are extracted, as this kind of signal envelop is best estimated eventually using these kind of features and are used

to train feed forward neural networks based on back propagation algorithm. In order to train the network 100

normal and 100 abnormal samples were used. The database has been obtained from the neonatal ward of JSS

Hospital, Mysuru. The algorithm has been tested on the test dataset consisting of 50 samples. The performance

of the proposed method has been evaluated on only voiced part of the cry signal using the diagnostic test

measures and the efficiency is found to be 98%as compared to 90% efficiency if the same procedure is applied

on the entire cry signal. 133-138

Index Terms: Back Propagation, DCT, FFT, MFCC, STFT, Neural Network, Pre-term, Neonates, Hamming Window, Wavelet.

References: [1] Hofer M. A., “Unexplained infant crying: An evolutionary perspective”, ActaPaediatrica, 91, 491-496, 2002. [2] Chittora, Anshu and Patil, Hemant, “Newborn infant’s cry analysis”, International Journal of Speech Technology. 19. 10.1007/s10772-016-9379-8, 2016 [3] Ryuichi Kusaka,, Shohei Ohgi, Kenta Shigemori,and Tetsuya Fujimoto, “Crying and Behavioral Characteristics in Premature Infants”, J Jpn Phys TherAssoc ,v.11(1); 2008 [4] Orlandi, Silvia and Reyes-Garcia, Carlos Alberto & Bandini, Andrea & Donzelli, Gianpaolo & Manfredi Claudia, “Application of Pattern Recognition Techniques to the Classification of Full-Term and Preterm Infant Cry”. Journal of voice: official journal of the Voice , 2015 [5] [Hesam Farsaie Alaie, Lina Abou-Abbas, Chakib Tadj, “Cry-based infant pathology classification using GMMs” , Speech Communication, Volume 77, March 2016, Pages 28-52 [6] C.Manfredi, L.Bocchi, S.Orlandia, L.Spaccaterrab, G.P.Donzellib, “High-resolution cry analysis in preterm newborn infants”,Medical Engineering and Physics ,Volume 31, Issue 5, June 2009, Pages 528-532 [7] Dhanashri U.S. Talauliker, NayanaShenvi, “Analysis of Cry in New Born Infants”, International Journal of Advanced Research in Computer and Communication Engineering Vol. 4, Issue 3, March 2015 [8] Neema Verma, “Performance Analysis Of Wavelet Thresholding Methods In Denoising Of Audio Signals Of Some Indian Musical Instruments”, International Journal of Engineering Science and Technology (IJEST), Vol. 4 No.05 May 2012. [9] Parwinder Pal Singh, Pushpa Rani, “An Approach to Extract Feature using MFCC”, IOSR Journal of Engineering (IOSRJEN), Vol. 04, Issue 08, August. 2014 [10] Hassan Ramchoun, Mohammed Amine 11 JanatiIdrissi, Youssef Ghanou, Mohamed Ettaouil, “ Multilayer Perceptron: Architecture Optimization and Training”, International Journal of Interactive Multimedia and Artificial Intelligence, Vol. 4, No1,2016 [11] Prashant Arora1, Kulwinder Singh, “Denoising of Speech Signals Using Wavelets”, International Journal for Research in Applied Science & Engineering Technology (IJRASET), Volume 5 Issue I, January 2017. [12] Goberman AM, Robb MP, “Acoustic examination of preterm and full-term infant cries: the long-time average spectrum”, Journal of Speech Language and Hearing Research. 1999 Aug; 42(4):850-61. [13] Johnston, C., Stevens, B., Craig, K., & Grunau, R., “Developmental changes in pain expression in premature, full-term, two- and four-month-old infants”, Pain, 52, 1993, 201-208. [14] Li-mei Chen, Yu-Hsuan Yang, Chyi-Her Lin, Yuh-Jyh Lin, Yung-Chieh Lin, “Spectrum Analysis of Cry Sounds in Preterm and Full-Term Infants”, Conference on Computational Linguistics and Speech Processing ROCLING 2014, pp. 193-203 [15] Jashanpreet Kaur, Seema, Sunil Kumar, “Audio Noise Reduction Using Discrete Wavelet Transform and Filters”,International Journal of Advanced Research in Computer Science & Technology,Vol. 4, Issue 2, Apr. - Jun. 2016. [16] [Bachu R.G., Kopparthi S., Adapa B., Barkana B.D. “Separation of Voiced and Unvoiced using Zero crossing rate and Energy of the Speech Signal”, Advanced Techniques in Computing Sciences and Software Engineering , by Elleithy, Khaled, ISBN 978-90-481-3659-9. [17] T. Shreekanth and S Saraswathi, " Acoustic analysis and classification of Infant Cry: A new approach", Journal on Digital Signal Processing, Vol.5, No.2, 2017, pp.1-7. Dr.R.Geetha Ramani , S Suresh Kumar Authors:

Paper Title: Non-Volatile Kernel Root kit Detection And Prevention In Cloud Computing Abstract: The field of web has turned into a basic part in everyday life. Security in the web has dependably been a significant issue. Malware is utilized to rupture into the objective framework. There are various kinds of malwares, for example, infection, worms, rootkits, trojan pony, ransomware, etc. Each malware has its own way to deal with influence the objective framework in various ways, in this manner making hurt the framework. The rootkit may be in some arbitrary records, which when opened can change or erase the substance or information in the objective framework. Likewise, by opening the rootkit contaminated record may debase the framework execution. Hence, in this paper, a Kernel Rootkit Detection and Prevention (KRDP) framework is proposed an avert the records. The avoidance system in this paper utilizes a calculation to forestall the opening of the rootkit influenced record as portrayed. By and large, the framework comprises of a free antivirus programming which is 27. restricted to certain functionalities. The proposed model beats the functionalities by utilizing a calculation, in this way identifying the rootkits first and afterward cautioning the client to react to the rootkit tainted record. In this way, keeping the client from opening the rootkit contaminated record. Inevitably, in the wake of expelling 139-144 the tainted document from the framework will give an improvement in the general framework execution.

Index Terms: Cloud; file; malware; port; rootkits; process; prevention;

References: [1] R.Geetharamani, S.Sureshkumar, ShomonaGracia Jacob, “Rootkit (Malicious Code affecting Kernel) Prediction through Data Mining Methods and Techniques” , IEEE-ICCIC-2013. [2] LeianLin , Zuanxing Yin, Yuli Shen Haitao Lin, “Research and Design of Rootkit Detection Method” , Elsevier, ICMPBE, 2012. [3] Safaa Salam Hatem, Dr.Maged H, wafy, Dr.Mahamoud M. El-Khouly. “Malware Detection in Cloud Computing”, .IJACSA, Vol.5, No. 4, 2014. [4] Gerard wagener,Radu State, Alexandre Dulauno, “Malware Behaviour analysis”, Springer. [5] Sebastian Eresheim. “The Evolution of process Hiding Techniques in Malware-Current Thread and Possible Countermeasures”, Information Processing Society of Japan Sep 2017. [6] Diana Toma, Dominique Borrione, “SHA Formalization”, TIMA Laboratory, Grenoble, France. [7] http://www.offensivecomputing.net/ [8] https://www.virustotal.com/#/home/upload [9] 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. [10] 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. [11] 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 Authors: N.Tamilarasi, S.G.Santhi Secure Routing with Improved Medium Access Control (SRI-MAC) Protocol for Wireless Sensor Paper Title: Network using Particle Swarm Optimization Abstract:A set of wireless sensor nodes comprises to form a sensor field called Wireless Sensor Networks (WSN).The main purpose of using the sensor node is to collect information from the ambience process it and send to a common gateway interface called Base Station(BS). The major problems that we face while using WSN are limited battery power, bandwidth, security issues and transmission delay etc. Many algorithms and protocols were developed in order to solve the above issues. Therefore, better solutions are required to face the improvements and challenges in the current technologies. In WSN, the sensor node highly loses its energy during communication period. One of the major issues of Medium Access Control (MAC) layer is collision. Collision increases the energy consumption and delay of the sensor node. So we have to conserve the energy of the sensor node in order to extend the lifetime of the network. At the same time it is also important to transmit the data through secure path and identify the malicious node. In this paper, we propose a novelty approach called Secure Routing with Improved Medium Access control (SRI –MAC) Protocol to solve the issues. SRI-MAC identifies packet precedence sets using Fuzzy Implication System (FIS) to avoid packet collision in MAC layer and also it detects wormhole attacks and selects secure path among k-paths using Particle Swarm Optimization (PSO) algorithm. By simulation results, we show that the proposed approach is efficient in terms of energy consumption and secure routing.

Keywords: WSN, Fuzzy Implication System(FIS), Particle Swarm Optimization(PSO), MAC and Secure Routing with Improved –Medium Access Control (SRI-MAC).

References: [1] C. Y. Wan, S. B. Eisenman, and A. T. Campbell, ”CODA: congestion detection and avoidance in sensor 28. networks,” in Proceedings of the first International Conference on Embedded Networked Sensor Systems, pp.266–279, CA, USA, Nov 2003. [2] [M. A. Kafia, D. Djenourib, J. B. Othmanc, A. Ouadjaouta, and N.Badachea, ”Congestion detection 145-150 strategies in wireless sensor networks: a comparative study with testbed experiments,” Procedia Computer Science, vol. 37, pp. 168–175, 2014. [3] A. Mohajerani and D. Gharavian, "An ant colony optimization based routing algorithm for extending network lifetime in wireless sensor networks", Wireless Networks, vol. 22, no. 8, pp. 2637-2647, 2015.. [4] Prabha VR, Latha P. Enhanced multi-attribute trust protocol for malicious node detection in wireless sensor networks. Sadhana. 2017 Feb 1; 42(2):143-51. [5] M. Hatamian, H. Barati, and A. Movaghar, ”A new greedy geographical routing in wireless sensor networks,” Journal of Advances in Computer Research, vol. 6, no. 1, pp. 9–18, 2015. [6] A. Bhatia and R. Hansdah, "TRM-MAC: A TDMA-based reliable multicast MAC protocol for WSNs with flexibility to trade-off between latency and reliability", Computer Networks, vol. 104, pp. 79-93, 2016. [7] N.Thangamani, S.John Grasias, Dr.G.Dalin “Ucon-Ipso: Usage Control with Improved Particle Swarm Optimization (Ipso) Based Hierarchical Security Framework for Attack Detection in Wireless Sensor Networks” IJETST- Vol.||04||Issue||08||Pages 5681-5691||August||ISSN 2348-9480 [8] Sunita Rani and Jaya “Wireless Sensor Network: Black Hole Attack Detection Using BFO-FUZZY” International Journal of Computer Science and Mobile Applications, Vol.3 Issue. 9, September- 2015, pg. 42-52 [9] E. Vaidhegi, C. Padmavathy, T. Priyanka and A. Priyadharshini , ” Delay Sensitive Packet Scheduling Algorithm for MANETs by Cross Layer”, IJIRAE – International Journal of Innovative Research in Advanced Engineering, vol .1,Issue 1,2014 [10] Ahmed, K. Bakar, M. Channa and A. Khan, "A Secure Routing Protocol with Trust and Energy Awareness for Wireless Sensor Network", Mobile Networks and Applications, vol. 21, no. 2, pp. 272-285, 2016. [11] J. Wei, B. Fan, and Y. Sun, ”A congestion control scheme based on fuzzy logic for wireless sensor networks,” in Proceedings of the 9th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2012), pp. 501–504, Sichuan, China, May 2012. [12] O. B. Akan, and I. F. Akyildiz, ”Event-to-sink reliable transport in wireless sensor networks,” IEEE/ACM Transactions on Networking, vol.13, no. 5, pp. 1003–1016, 2005. [13] Ewa Niewiadomska-Szynkiewicz and Filip Nabrdalik, “Secure Low Energy AODV Protocol for Wireless Sensor Networks”, ITNAC – International Telecommunication Networks and Applications Conference , 2017. [14] S. Sarang, M. Drieberg, A. Awang and R. Ahmad, "A QoS MAC protocol for prioritized data in energy harvesting wireless sensor networks", Computer Networks, vol. 144, pp. 141-153, 2018. [15] Binitha S, “A Survey of Bio inspired Optimization Algorithms” International Journal of SoftComputing and Engineering ISSN: 2231-2307, Volume-2, Issue-2, May 2012. [16] Kiran Narang, “Black Hole Attack Detection using Fuzzy Logic” International Journal of Science and Research, Volume 2 Issue 8, August 2013. [1] Yash Pal Singh, “A Survey on Detection and Prevention of Black Hole Attack in AODV- based MANETs” journal of information, knowledge and research in computer engineering, nov 12 to oct 13 ,volume – 02, issue – 02 S.Vishnukkumar, Sibi N.R, R.Balamurugan, S.Vasanth, T. Muthuramalingam Authors:

Paper Title: Machine ability On Leather Using Co2 Laser And Power Diode Laser Abstract: Leather is a versatile, robust and trendy material and therefore its applications are nearly endless. The conventional method of leather cutting takes a lot of man power. Power diode-based Laser technology has grown significantly during recent years due to numerous advantages over conventional cutting methods. The conventional Lasers also have some drawbacks in cutting such as Geometrical inaccuracies, Carbonization, Overcut etc. This can be reduced by the use of Laser diodes. The main purpose of using Laser diode is to reduce power consumption. In the present study, an attempt has been made to develop laser diode-based Laser beam machining (LBM) and CO2 based LBM, to compare the performance measures of Carbonization and Geometrical inaccuracy. The main objective of this work is to enhance the machining process using Laser diode, to make it eco-friendly through the different duty cycles of Pulse Width Modulation (PWM) which can be used to control the intensity of the Laser beam.

Keywords —Laser Beam Machining (LBM), Pulse Width Modulation (PWM), Geometrical Inaccuracies, 29. Carbonization.

References: 151-154 [1] Renann G. Baldovino, Jayson P. Rogelio, M.S., “A Pulse Width Modulation (PWM) LASER Power Controller for the 3-Axis Computer Numerically Controlled (CNC) LASER Machine”,7th IEEE International Conference Humanoid,Nanotechnolgy,Information Technology, Communication and Control,Environment and Management (HNICEM),12-16 November, 2013. [2] P. Jamaleswara kumar, A Siva Sai Tarun, M.Gowtham, P. Thamma Rao, G. Yaswanth, “Design and fabrication of portable Laser cutting and engraving machine”, in International Journal of Engineering and Technology, 2018. [3] Nayanthara Bhatta, M.D., R. Rox Anderson, M.D., Keith Isaacson, M.D., Isaac Schiff, M.D., Krishna M Bhatta, M.D. in “Comparative study of different Laser systems”, vol61, no.4, April 1994. [4] Anizah khalid, Irraivan Elamvazuthi, Susana, Kamaruddin, Mohd Shahrul Azmi and Mohd Nidzam, “Productivity Improvement Through Automation of Leather Cutting Process”, Asia SENSE 2003. [5] Nasim, H., Jamil, Y.: Diode Lasers: From laboratory to industry. Opt. Laser. Technol. 56, 211-222 (2014). [6] Vasanth, S., Muthuramalingam, T., “A Study on Machinability of Leather Using CO2 Based Laser Beam Machining Process”. Advances in Manufacturing Processes, Lecture Notes in Mechanical Engineering, 239-244 (2019). T.R.Kowshika, K.Geetha Authors:

Paper Title: Dual Protectıon for Data usıng Steganographıc Technıques with Embedded Framework Abstract: As the world is getting digitalized, the rush for need of secured data communication is overtop. Provoked by the vulnerability of human visual system to understand the progressive changes in the scenes, a new steganography method is proposed. The paper represents a double protection methodology for secured transmission of data. The original data is hidden inside a cover image using LSB substitution algorithm. The image obtained is inserted inside a frame of the video producing a stego-video. Stego-video attained is less 30. vulnerable to attacks. After decryption phase, the original text is obtained which is error-free and the output image obtained is similar as the cover image. The quality of stego-video is high and there is no need for additional bandwidth for transmission. The hardware implement is required in order to calculate the 155-159 corresponding analytical results. The proposed algorithm is examined and realized for various encryption standards using Raspberry Pi3 embedded hardware. The results obtained focuses on the attributes of the proposed model. On comparing with other conventional algorithms, the proposed scheme exhibits high performance in both encryption and decryption process with increase in efficiency of secured data communication.

Index Terms: Data Hiding, Data Security, Steganography.

References: [1] MrithaRamalingam, Nor Ahidi Mat Isa, "A data hiding technique using scene-change detection for video steganography", Computers and Electrical Engineering,pp1-12,2015. [2] Mahdi Hashemzadeh, "Hiding information in videos using motion clues of feature points", Computers and Electrical Engineering, Vol 68 pp 14-25, 2018. [3] Xinpeng Zhang, "Separable Reversible Data Hiding in Encrypted Image", IEEE Transaction on Information Forensics and Security, Vol 7 No.2, pp 826-833. [4] Yunxia Liu, Shuyang Liu, Yonghao Wang and Hongguo Zhao, Si Liu (2019) Video Steganography", Neurocomputing, 335, 238-250. [5] Rupali Bhradwaj, Vaishali Sharama, "Image Steganography Based on Complemented Message and Inverted bit LSB Substitution ", Procedia Computer Science 93, 832-838., 2016. [6] Poonam, Shafali M. Arora, (2018) A DWT-SVD based robust Digital Watermarking for Digital Images, Procedia Computer Science, 251, 1441-1448. [7] Dawen Xu, Rangding Wang (2016) Separable and error-free reversible data hiding in encrypted image, Signal Processing, 123, 9-21. [8] Rakesh Mehta, Jaume Amores (2018) Improving detection speed in video by exploiting frame correlation, Pattern Recognition Letters, 112, 303-309. [9] Ayhan Yilmaz, A. Aydin Alatan, (2008) Error detection and concealment for video transmission using information hiding, 23, 298-312. [10] Wien Hong, Tung-Shou Chen and Han-Yan Wu (2012) An improved Reversible Data Hiding in Encrypted Images Using Side Match, Signal processing,,19, 199-202, [11] Soumitra Roy, Arup Kumar Pal, (2012) A blind DCT based color watermarking algorithm for embedding multiple watermarks, Signal processing, 19, 199-202. [12] GuruPrasad .K.Basavaraju, "Introduction to Raspberry Pi with Raspbian OS" in [13] https://www.codeproject.com/Articles/839230/Introduction-to-Raspberry-Pi-with-Raspbian-OS. [14] Image fusion using approximation and detail, http://shodhganga.inflibnet.ac.in/bitstream/10603/20682/13/13_chapter%204.pdf M.Praveen Kumar, P.Maheeth, M.Sai Krishna Reddy, S.Ravi Teja Authors:

A Renewable Energy Fed Non-Isolated Inverse Output Voltage DC-DC Converter With Broad Paper Title: Range Of Conversion Abstract: Renewable Energy fed non-isolated negative output Converter with dc-dc conversion is proposed which employed for various applications. In industrial purposes only few converters are available for wide conversion ratio, the proposed design has come up with wide range negative voltage load applications. The proposed converter is analyzed and design for continuous condition mode. For verification of theoretical analysis, the proposed converter is simulated using PSIM 9.0.

Index Terms: Negative output, Wide Gain, Buck-Boost, Non-isolated.

References: [1] F.l. luo, “inverse output luo converter; for voltage lift technique,” iee pioc. Electr.power appl., vol.146, no: -2, pp.208-224, mar-1999. [2] M. Zhu and f.l. luo, “enhanced self-lift cuk converter for inverse output voltage conversion,” ieee trans. On power electron., vol.25, no:9, pp,2227-2233, sep,2010. [3] K.i. hwu, y.t. yau and j.j. shieh, “inverse output resonant voltage divi der”in proc.ieee power electronics 31. and driver systems [4] a. Cocor, a. Baeseu, and a. Florescu,” elementary and self-lift inverse output luo dc-dc converters used in hybrid cars,” u.p.b. sci.bull., series c, vol,77, iss,4, pp,179-190,2015. 160-163 [5] B.axelrod,y,berkovich,anda,ioinovici“switched-capacitor/switched-inductor structures for getting transformer less dc-dc pwm converters,” ieee trans, circuits syst i, fundam. Theory appl., vol.55, n0.2, pp.687-696, mar.2008 [6] O. Abutbul, a gherlitz, y. Berkovich, and a. Ioniovici,” step-up switching-mode converter with high voltage gain using a switched capacitor circuit,” ieee trans. Circuits syst. I, fundamental theory appl., vol. No.8, pp.1098-1102, aug.2003. [7] Y. Tang, t. Wang, and y. He, “soft switching inverse output ky buck boost converter,” in proc. Ieee trans. Power electron.vol.29, no.6, pp.1053-1060. [8] k. I hwu and y. T yau, “a swithed capacitor based active network converter with high voltage gain,” iee trans.power electron., vol.29 n0.6, pp.2959-2968, jun.2014 [9] K. I hwu, y.t yau and z.f. lin, “inverse output buck boost converter,” in proc. Ieee industrial electronics and applications conf., may 2009, pp.3347-3350 [10] K. I hwu, w.c tu and y.h. chen, “a novel inverse output ky buck-boost converter”, in proc. Ieee power electronics and drive sys2009, pp....v.2009, pp.1155-1157 [11] m. Sai krishna reddy and elangovan. “analysis and simulation of zcs current fed full bridge converter with synchronous rectification” ieee pccctsg-2015, 183-185 [12] .senthil kumar k, m. Sai krishna reddy, d elangovan and dr. R. Saravan kumar “ interleave isolated boost converter as front end converter for fuel cell application” ieee, icees-2014, 202-205 Aashita Tiwari, Ahitagnee Paul, P. Suganya, A.P(O.G) Authors: A Smart Bot as an Interactive Medical Assistant with Voice-based System using Natural Language Paper Title: Processing Abstract-In this fast-moving world, people are ignorant about their health issues and avoid routine check-ups. It is very difficult for users to spend longer time on-line and explore health information. To solve the problem, voice-based application is provided to the user where user can interact with system and get inference of diseases and their remedies by giving the symptoms as input.For processing the given input, the data is normalized by using noun phrase extraction and medical term identifier. For getting more precise result, the system generates relevant questions to the user and accordingly provide remedy for problem. Question is generated by mapping the user input array with question generation matrix. The project is based on a digital medical aid through a smart bot using machine learning and optical character recognition techniques. The user can just talk to the bot and get to know what the possible causes and effects of the particular symptoms are, determine the illness and take appropriate actions. Basically, the purpose of this bot is to act as a friendly healthcare assistant that helps in all the work that people need to take care of their health.

Keywords— Friendly Healthcare Assistant, Machine Learning, Medical term identifier, Natural language processing, Normalization, Optical Character Recognition, Question-Answer System, Smart Bot, Voice based.

References: [1] LiqiangNie, Yi-Liang Zhao, Mohammad Akbari, Kialie Shen and Tat-Seng Chua, “Bridging the Vocabulary 32. Gap between Health Seekers and Healthcare Knowledge”, IEEE Transactions on Knowledge and Data Engineering, Vol. 27, No. 2, February 2015. 164-166 [2] LiqiangNie, Meng Wang, Luming Zhang and Shuicheng Yan, “Disease Inference from Health-Related Questions via Sparse Deep Learning”, IEEE Transactions on knowledge and Data Engineering, Vol.27, No. 8, August 2015. [3] Mi-Young Kim and Randy Goebel,” Detection and Normalization of Medical Terms Using Domain-specific Term Frequency and Adaptive Ranking”, IEEE,978-1-4244-6561-3 2010. [4] Yuya Yokoyama, Teruhisa Hochin and Hiroki Nomiya,” Estimation of Factor Scores from Feature Values of English Question and Answer Statements”, ICIS Japan, June 2016. [5] Zhou Zhao, Lijun Zhang, Xiaofei He and Wilfred Ng,” Expert Finding for Question Answering Via Graph Regularized MatrixCompletion”, IEEETransactions on Knowledge and Data Engineering, Vol. 27, No.4, April 2015. [6] Paya J Biswas, Aditi Sharan and Nidhi Malik,” A Framework for Restricted Domain Question Answering System”, IEEE International Conference on Issues and Challenges in Intelligent Computing Techniques (ICICT), September 2014. [7] Varsha Bhoir and M. A. Potey,” Question Answering System: A Heuristic Approach”, 978-1-4799-225 September 2014. [8] Ming Liu, Vasile Rus and Li Liu,” Automatic Chinese Factual Question Generation”, IEEE Transactions on Journal Name [9] WebMD (URL): http://www.webMD.com,2016.

[10]MedLinePlus (URL):http://www.nlm.nih.gov/medlineplus, 2016. K.Senthil Kumar, M. Palanivelan, M. Sivaram, P. Shanmugapriyaand V. Bakyalakshmi Authors:

Paper Title: A Cyclotomic Lattice Based Closed Loop QOSTBC for Four Transmit Antennas Abstract: Drawback of the Space Time Block Code (STBC) is, for complex constellations, full rate and full diversity design exist only for two transmit antennas. In this paper, we propose a novel closed loop Quasi Orthogonal Space Time Block Code (QOSTBC) system based on cyclotomic lattices for four transmit antennas. It is a full rate and full diversity design where the information bits are mapped into four dimensional (4-D) lattice points. The bit error and symbol error performance of the system are evaluated by simulation.

Index Terms: MIMO, QOSTBC, cyclotomic, lattice, diversity product. 33. References: 167-171 [1] Sundberg, C.,E.W. and Seshadri, “ Digital Cellular Systems for North America” , IEEE Globecom, pp.533-7,Dec. 1990. DOI:10.1109/GLOCOM.1990.116568 [2] Balaban, N and Salz, J. “Dual Diversity combining and equalization in digital cellular mobile radio ”, IEEE Transactions on vehicular Technology, pp.342- 54, May 1994. DOI: 10.1109/25.289415 [3] Winters,J., Salz, J and Gitlin, R.D., “ The impact of antenna diversity on the capacity of wireles communication systems”, IEEE Transactions oncommunications, pp. 1740-51, April 1994. DOI: 10.1109/TCOMM.1994.582882 [4] Witteneben, A. “A new bandwidth efficient transmit antenna modulation diversity scheme for linear digital modulation”, IEEE International Conference on communications (ICC) pp.1630-4, May 1993,.DOI: 10.1109/ICC.1993.397560

[5] Seshadri,N. and Winters.J.H. “Two signaling schemes for improvingthe error performance of frequency division duplex (FDD) transmission systems using transmitter antenna diversity” IEEE Vehicular Technology conference, pp.508- 11,May 1993.DOI: 10.1109/VETEC.1993.507522 [6] Tarokh, V., Seshdri, N. and calderbank, “ Space time codes for high datarate wireless communication: Performance analysis and code construction”, IEEE Trans. Inform. Theory, pp.744- 65, Mar. 1998.DOI: 10.1109/18.661517 [7] S. M. Alamouti, “A simple transmit diversityTechnique for wireless communications,” IEEE J. Select . Areas Commun. vol. 16, pp. 1451- 1458, Oct. 1998. [8] W.Su and X.G. Xia, “Signal constellations for quasi-orthogonal space time block codes with full diversity,” IEEE Trans. inform. Theory, vol.50, pp. 2331-2347, Oct. 2004.DOI: 10.1109/TIT.2004.834740 [9] Zhu Chen and Moon Ho Lee, “One bit feedback for Quasi- Orthogonal Space Time Block Codes Based on Circulant Matrices”, IEEE Transactions onWireless Communications,Vol.8, No.7, July 2009. DOI: 10.1109/TWC.2009.080667Wei Liu, Mathini Sellathurai and Jibo Wei, “ A Cyclotomic Lattice Based Quasi - Orthogonal STBC for Eight Transmit Antennas [10] ”, IEEE Signal Processing Letter, Vol.17, No.4, April 2010. DOI: 10.1109/LSP.2009.2039951 [11] Asher Shaji, Anchana C K. Senthil Kumar, R. Amutha, M. Palanivelan, D. Gururaj, S. Richard Jebasingh, M. Anitha Mary, S. Anitha, V. Savitha, R. Priyanka, Amruth Balachandran, H. Adithya, “Receive diversity based rate optimization for improved network lifetime and delay efficiency of wireless body area networks”, PLoS One, vol. , no. , pp. 1-20, October 2018. DOI: 10.1371/journal.pone.0206027 [12] M. Kanthibathi, R. Amutha, K. Senthil Kumar, “Energy efficient differential cooperative MIMO algorithm for wireless sensor network” Wireless Personal Communications (Springer), vol. 103, no. 4, pp. 2715-2728, December 2018.DOI: 10.1007/s11277-018-5957-1 [13] K. Senthil Kumar, R. Amutha, “Energy efficient wireless body area network using receive diversity” Journal of Engineering Science and Technology, vol.13, no. 8, August 2018. [14] K. Senthil Kumar, R. Amutha, “An algorithm for energy efficient cooperative communication in wireless sensor networks”, KSII Transactions on Internet and Information Systems, vol. 10, no. 7, pp. 3080-3099, August 2016.DOI: 10.3837/tiis.2016.07.012,ISSN: 1976-7277 [15] T.L.K. Sneha Piriya, K. Senthil Kumar, R. Amutha, “Energy Efficient V-MIMO using Turbo Codes in Wireless Sensor Networks” in the Second IEEE International Conference on Computing and Communication Technologies (ICCCT’17), on 24th Feb 2017. DOI: 10.1109/ICCCT2.2017.7972288 [16] H.Jafarkhani, “A quasi orthogonal space – Time block code,” IEEE Trans. Commun. vol. 49, pp. 1-4, Jan. 2001. [17] K. Senthil Kumar, R. Amutha, “Energy efficient cooperative communication using QOSTBC in wireless sensor networks”,International Journal of Advanced Engineering Technology,vol. 7, no. 1, pp. 244-251, March 2016. EISSN: 0976-3945 [18] J.H.Conway and N.J.A.Sloane, “ Sphere packing, lattices and groups”, 3rd edition, NewYork: Springer - verlag 1998. [19] Genyuan Wang, Huiyong Liao, Haiquan Wang and Xiang- Gen Xia, “Systematic and optimal cyclotomic lattices and diagonal space time block code designs ”, IEEE Trans. Information Theory, Vol 50, pp. 3348 - 3360, Dec.2004.DOI: 10.1109/TIT.2004.838096 [20] G.Wang and X.G.Xia, “ On optimal cyclotomicLattices and diagonal / single layer space time block codes”, Proc. ISIT 2004, June 27 - July 2, Chicago, USA, 2004. [21] G.Wang and X.G.Xia, “On optimal multi - layer cyclotomic Space –time code designs ” IEEE Trans. Inform. Theory, vol. 51, no. 3, pp. 1102-1135, Mar. 2005. Kiran Kumar Patro, M.Jayamanmadha Rao, P.Rajesh Kumar Authors: Swarm based Intelligent Feature Optimization technique for ECG based Biometric Human Paper Title: Recognition Abstract: Recently, rapid growth in network technology and communication leads to widening human activities, so that a strong identification system is necessary. This work aims to build an accurate identification technology based on unique physiological characteristics of ECG. The Biometric recognition of ECG primarily

depends on the quality of its features. Feature extraction is performed on parameters of a cardiac cycle based on 34. the fiducial approach and large data sets of features have been extracted. The extracted dataset contains

irrelevant, correlated and over-fitted features, which misleads the biometric system performance so that an

effective feature optimization is needed to sort out those features to avoid redundancy in the data. In this paper, a

novel swarm based intelligent feature optimization method; Particle Swarm Optimization (PSO) is used to

generate feature subset based on a fitness function with joint entropy. The dataset from optimization phase are 172-178 fed to classifiers such as ANN, K-NN and SVM for recognition. The proposed approach is tested with available open source MIT-BIH ECG ID database. Finally, a comparison is made with and without feature optimization in which PSO with KNN shows recognition accuracy of 97.8931%.

Index Terms: Biometric, Electrocardiogram (ECG), Entropy,SVM, Swarm.

References: [1] A.Ross, A.K Jain, “Human recognition using biometrics”, Annals of Telecommunications, vol.62, pp.11-35, 2007.. [2] Irvine M, Israel A and Cheng A, “ECG to Identify Individuals”, Pattern recognition, vl.38, pp.133-142, 2005. [3] L Khalil, F.Sufi and J.Hu, “ ECG based authentication”, Handbook of Information and Communication security Springer , pp.309-331, 2010. [4] C. Ye, M. Coimbra and B. Kumar, “ Investigation of human identification using two lead ECG signals” Proceedings of 4th IEEE Int. Conference on Biometrics Theory and Applications (IEEE BTAS), pp.01-08, 2010. [5] B. Nasri, M Guennoun and K. El-Khatib, “ Using ECG as a measure in Biometric Identification systems”, Proceedings IEEE Toronto Int. Conf. Science and Technology for humanity, pp.28-33, 2009. [6] Tilendra Choudhary, M. Sabarimalai Manikandan “A Novel Unified Framework for Noise-Robust ECG- Based Biometric Authentication” IEEE 2nd International Conference on Signal Processing and Integrated Networks (SPIN), pp.186-191, 2015. [7] Sairul Safie, M I Yusof, Kushsairy Kadir, Haidawati Nasir “Bipolar Pulse Active Features for ECG Biometric Application” International Conference on BioSignal Analysis, Processing and Systems (ICBAPS), pp. 01-05, 2015. [8] Md. Khayrul Bashar, Yuji Ohta, and Hiroaki Yoshida “ECG-based Biometric Authentication Using Mulscale Descriptors” Signal Processing, Computer Networks and Telecommunications, pp.01-04, 2015. [9] Abhijit Sarkar, A. Lynn Abbott, Zachary Doerzaph “ECG Biometric Authentication Using a Dynamical Model”, 7th IEEE Int. Conf. on Biometrics Theory, Applications and Systems, pp.01-06, 2015. [10] Yue Zhang, Youqun Shi, “A New Method for ECG Biometric Recognition using a Hierarchical Scheme Classifier”,6th IEEE International Conference on Software Engineering and Service Sciences,pp.457-460, 2015. [11] Fatema-tuz-Zohra Iqbal, Khairul Azami Sidek, “Cardioid Graph Based ECG Biometric Recognition Incorporating Physiological Variability” Proc. of the IEEE Int. Conf. on Smart Instrumentation, Measurement and Applications , pp.01-05, 2014. [12] Hatzinakos D, Agrafioti F, Gao J, “Heart Biometrics: Theory, methods and applications” Biometrics B , vl.3, pp.199-216, 2011. [13] Wan Y, Yao J, “ A Neural network to identify human subjects with Electrocardiogram signals”, World congress on Engineering and Computer Science; 2008, USA. [14] Belgcem N, Ali A, Fouenier, “ ECG based human authentication using wavelets and random forests”, International Journal of Cryptography Information Security , vol.2, pp.01-11, 2012. [15] Kiran Kumar Patro, P. Rajesh Kumar, “ A Novel Frequency-Time Based Approach for the Detection of Characteristic Waves in Electrocardiogram Signal”, Springer India , vol.372, no.1, pp.57-67, 2015. [16] LugovayaT.S.,“Biometric human identification based on electrocardiogram”, [Master's thesis] Faculty of Computing Technologies and Informatics, Electrotechnical University "LETI", Saint-Petersburg, Russian Federation; June 2005. [17] Kiran Kumar Patro, P. Rajesh Kumar, “ Effective Feature Extraction of ECG for Biometric Application”, Journal of Procedia, Elsevier, vol.115, pp.296-306, 2017. [18] Khan M, Quadri. “Effects of using filter based feature selection on the performance of Machine learners using different datasets”, BVICAM’S International Journal of Information Technology, vol.5, no.2, pp.597- 603, 2013. [19] Adejoke B, Omotoyo M. Review of Feature selection Methods in Medical Image Processing, vol.4, no.1, pp.01-05, 2014. [20] Kennedy J. Particle Swarm Optimization, Encyclopedia of Machine Learning, Springer USA: 760-766. [21] I. Defalco, A D Cioppa, E. Tarantino, “ Facing Classification Problems with Particle Swarm Optimization”, Applied soft Computing, vol.7, pp.652-658, 2007. [22] Xu Y, Jones G, Wang B. A Study on Mutual Information based Feature selection for text Categorization, International Journal of Computational Information Systems, vol.3, no.3, pp.1007-1012, 2007. [23] http://www.physionet.org/cgi-bin/atm/ATM – “MIT-BIH ECG ID Database. [24] T.S Lugovaya, AP Nemriko, “ Biometric human identification based on Electrocardiogram”, Proc. XII-th Russian Conference on Mathematical Methods of Pattern recognition, Moscow 2005:387-390. [25] Adrina, D.C Chan, M Hamdy, “Wavelet distance measure for person identification using electrocardiograms”, IEEE transactions on Instrumentation and measurement, vol.57, no.2, pp.248-253, 2008. [26] O. Boumbarov, Y.Velchev, S.Sokolov, “ ECG Personal identification in subspaces using Radial Basis Nueral Networks”, IEEE Int. workshop on Intelligent data acquisition and Advanced Computing systems, pp.446-451, 2009. [27] W D Shen, W J Tompkins, and Y H Hu, “ Implementation of a one-lead ECG human identification system on a normal population”, Journal of Engg. Comput. Innovations, vol.2, no.1, pp.12-21 ,2011. [28] K.Azmi, I Khalil, M Smole, “ ECG Biometric Recognition in different physiological conditions using robust normalized QRS” Complexes.cinc.org Computing in Cardiology, vol.39, pp.97-100, 2012. [29] S Gutta, Qi Cheng, “ Joint Feature Extraction and Classifier design for ECG based Biometric Recognition”, IEEE Journal of Biomedical and Health Informatics, vol.20, no.2, pp.460-468, 2016.

Ms.V.Preethi, Hani Saravanan, Vijay Balaji Authors:

Paper Title: Real Time Drowse Alert System Abstract -- In this project we have contrived a real time drowse alert system. This system focusses on observing the eyelid movements of an automobile driver and affirms that the driver is drowsy if the eyelids are closed or are stationery for more than a stipulated time. So apparently our system involves affixing cameras in cars and discerning the eyelid movements. The system uses a shape predictor for marking the face landmarks and predicting the eyes of the user and quantifies the eye aspect ratio using OpenCV. This ratio is computed for both the left eye and the right eye and the average value is ascertained as the net eye aspect ratio. This ratio plays a crucial role in pinpointing the closed eyes from open eyes. If the eyes are closed for more than the threshold time of the system an alert and a high-pitched alarm is set to blare to wake the driver up the alarm continues to blare until the driver’s open eyes are detected.

Keywords: OpenCV, eyes predictor, eye aspect ratio

References: [1] P. R. Tabrizi and R. A. Zoroofi, “Drowsiness detection based on brightness and numeral features of eye image,” in Proceedings of the 5th International Conference on Intelligent Information Hiding and Multimedia Signal [2] Processing, pp. 1310–1313, Kyoto, Japan, September 2009.ViewatPublisher· ViewatGoogleScholar· ViewatScopus de la Escalera, M. J. Flores, and J. M. Armingol, “Driver drowsiness warning system using visual information for both diurnal and nocturnal illumination conditions,” EURASIP Journal on Advances in Signal Processing, vol. 2010, Article ID 438205, 2010.Viewat 42. [3] GoogleScholar· ViewatScopus [4] D. Wenhui, Q. Peishu, and H. Jing, “Driver fatigue detection based on fuzzy fusion,” in Proceedings of the Chinese Control and Decision Conference (CCDC '08), pp. 2640–2643, Shandong, China, July 2008.Viewat 222-224 [5] Publisher· ViewatGoogleScholar· ViewatScopus [6] Rau P. Drowsy Driver Detection and Warning System for Commercial Vehicle Drivers: Field Operational Test Design, Analysis, and Progress. National Highway Traffic Safety Administration; Washington, DC, USA: 2005. [7] Zhang Z., Zhang J. A new real-time eye tracking based on nonlinear unscented Kalman filter for monitoring driver fatigue. J. Contr. Theor. Appl. 2010;8:181–188. [8] Portouli E., Bekiaris E., Papakostopoulos V., Maglaveras N. On-road experiment for collecting driving behavioural data of sleepy drivers. Somnology. 2007;11:259–267. [9] Qiang Ji, Zhiwei Zhu and Peilin Lan ―IEEE transactions on Vehicular Technology Real Time Nonintrusive Monitoring and Prediction of Driver Fatigue, vol. 53, no. 4, July 2004. [10] Weirwille, W.W. (1994). ―Overview of Research on [11] Driver Drowsiness Definition and Driver Drowsiness Detection,‖ 14th International Technical Conference on Enhanced Safety of Vehicles, pp 23-26. [12] Perez, Claudio A. et al. ―Face and Eye Tracking [13] Algorithm Based on Digital Image Processing‖,IEEE System, Man and Cybernetics 2001 Conference, vol. 2 (2001), pp 1178-1188. [14] Gonzalez, Rafel C. and Woods, Richard E. ―Digital Image Processing‖, Prentice Hall: Upper Saddle River, N.J., 2002. [15] An Analysis of Viola Jones algorithm for face detection by Yi-Quin Wang, University of Malaysia Phang, 2014, pp: 15-20. [16] K. C. Yowand, R. Cipolla, “Feature-based human face detection, “Image Vision Comput., vol.15, no.9, 1997, pp.713–735. V Brindha Devi , R Skanda Gurunathan , N Keerthi vasan Authors:

Paper Title: De-Centralized Certificate Creation And Verification Using Blockchain (DCCVUB) Abstract: The rapid growth in the population has lead to generation of large amount of data from each individual. 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 a 43. recruiter, or a government official or any other custom officer to verify the validity of the certificates and other documents of the individual. It consumes a tremendous amount of time for validating and verifying such documents manually. Thus we propose a system to develop a Decentralized application (DApp) for 225-229 implementing a Blockchain[1] to store and verify the documents. By the nature of blockchain, the documents are securely stored with high integrity, and no further modifications can be done to the blocks in the chain which in turn reduces the creation of forged documents. Also using Distributed Ledger technology(DLT)[5] and IPFS the data is decentralised so that it is readily available with integrity. Also, using MultiSig[3] concepts, the system is more secured by two step authentication. Thus, blockchain creates trust and DLT provides integrity ease of access. And with use of IPFS the DApp is decentralized4]

Index Terms: 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.432–441 [4] Juan Benet, “IPFS - Content Addressed, Versioned, P2P File System”, ArXiv,2014 [5] 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, 2012 [6] "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 Kalpana Shinde, Vini Kale, Dr, C.N.Kayte, Dr.Shobha Bawiskar Authors:

Paper Title: Determining the Probability of Recovering Data from Damaged USB Flash Drive Abstract: Habit of storing digital data is becoming a common practice. To cope this need lots off secondary memory devices are commercially available in cheap prices. Important factor is significance of data, its dependability in human daily life. Hence looking at this scenario the cyber crime rates are at hike. Mostly after committing the crime intentionally or unintentionally criminals try to destroy the digital evidence by doing damage to e- device. Basic aim is to check whether damaged secondary can recover the data or not. For this purpose various damaged setup are done and by using software’s results are analyzed.

Keywords: Pen Drive(PD), Pen Drive Models, Data Recovery , Damaged PD .

References: [1] Recuva software https://recuva.en.softonic.com/downloadStellar Phoneix [2] 7 Data Recovery Software https://7datarecovery.com/#forwardPhotrec data recovery [3] Photorec 7.0 Data Recovery -https://downloads.tomsguide.com/PhotoRec,0301-32874.html [4] Stellar Phoneix : https://www.stellarinfo.com/ [5] https://www.google.com/search?rlz=1C1CHBD_enIN764IN764&biw=1200&bih=733&tbm=isch&sa=1&ei =s9yIXMGYD9q7rQHTtrnABw&q=images+of+smart+toy+pen+drive+&oq=images+of+smart+toy+pen+d rive+&gs_l=img.3...9508.9508..10000...0.0..0.148.148.0j1...... 0....1..gws-wiz-img.IOMhl- 35. e9HT8#imgrc=4e9pT38M_LYKGM:[ [6] https://www.google.com/search?rlz=1C1CHBD_enIN764IN764&biw=1200&bih=733&tbm=isch&sa=1&ei 179-191 =Z9eIXKuIGZa9rQHiw7eABA&q=images+of+smart+toy+pen+drive++bracelets&oq=images+of+smart+t oy+pen+drive++bracelets&gs_l=img.3...11277.16608..16992...0.0..0.1263.2965.0j9j0j1j7-1...... 0....1..gws- wiz-img.RNbWXgRt-eI#imgrc=vW9w3N0V6az4CM: [7] https://www.google.com/search?rlz=1C1CHBD_enIN764IN764&biw=1200&bih=733&tbm=isch&sa=1&ei =Z9eIXKuIGZa9rQHiw7eABA&q=images+of+smart+toy+pen+drive+&oq=images+of+smart+toy+pen+dr ive+&gs_l=img.3...4164.4164..4496...0.0..0.0.0...... 0....1..gws-wiz- img.GpD7xMICc2E#imgrc=uGg_4WVwMi_R1M: [8] https://www.google.com/search?rlz=1C1CHBD_enIN764IN764&biw=1200&bih=733&tbm=isch&sa=1&ei =Z9eIXKuIGZa9rQHiw7eABA&q=images+of+smart+toy+pen+drive++bracelets&oq=images+of+smart+t oy+pen+drive++bracelets&gs_l=img.3...11277.16608..16992...0.0..0.1263.2965.0j9j0j1j7-1...... 0....1..gws- wiz-img.RNbWXgRt-eI#imgrc=vW9w3N0V6az4CM: [9] https://www.google.com/search?rlz=1C1CHBD_enIN764IN764&biw=1200&bih=733&tbm=isch&sa=1&ei =Z9eIXKuIGZa9rQHiw7eABA&q=images+of+smart+toy+pen+drive+&oq=images+of+smart+toy+pen+dr ive+&gs_l=img.3...4164.4164..4496...0.0..0.0.0...... 0....1..gws-wiz- img.GpD7xMICc2E#imgrc=_2MGQbESmY_QvM: [10] https://www.wantitall.co.za/pchardware/8gb-usb2-0-memory-stick-creative-metal-wrench-usb-flash-drive- kepmem-funny-gift-pen-drive__b06xv88cj7 [11] Sneha Pandhare , Dr.Shobha Bawiskar,” Recovery Of Data From Damaged Disks”.(Online-Oral Presentation), International Conference on “Innovations in Engineering, Technology and Sciences”- (ICIETS2018) with catlog “CFP18Q63-PRJ:978-1-5386-7321-8” held on September 21-22 ,2018, NIE Institute of Technology, Mysore, Karnataka, (Bangalore)India, will be published in IEEE Xplore Digital Library [12] Alpna, Dr. Sona Malhotra ,” Cyber Crime-Its Types, Analysis and Prevention Techniques” International Journal of Advanced Research in Computer Science and Software Engineering Research Volume 6, Issue 5, May 2016 ISSN: 2277 128X ,page no 145. Paper Available online at: www.ijarcsse.com [13] Madison, Alex (2016-07-09). "Keychain Not Included: The Five Highest-Capacity USB Flash Drives for Your Digital Life". Digital Trends. Retrieved 17 October 2016. [14] Jump up to:a b Athow, Desire (2016-07-04). "The best USB flash drives 2016". Tech Radar. Retrieved 17 October 2016. [15] "The Largest Flash Drives | Digital Trends". Digital Trends. 2018-07-23. Retrieved 2018-10-09. [16] G. I. A. Incorporated, "USB Flash Drive Market Trends," Global Industry Analyst Inc., 20 March 2017.[Online].Available:http://www.strategyr.com/MarketRe search/USB_Flash_Drives_Market_Trends.asp. [Accessed 20 March 2017 [17] Parthasarathy, M., & Parthasarathy, S. (2017). Performance Analysis of USB Flash Memory Devices on Linux vs. Windows XP. [18] JUANCHO D. ESPINELI, 2 JASMIN NIQUIDULA, “INFORMATION THEORY IN USB FLASH MEMORY DEVICE ANALYSIS” Proceedings of Academics World 63 rd International Conference, Manila, Philippines, 28th -29th April 2017, http://www.worldresearchlibrary.org/up_proc/pdf/834- 14997520531-6.pdf [19] International Journal of Engineering Research and Development. ISSN: 2278-067X. 2012; 1(6): 25-34. [20] 20 B Naresh Kumar Reddy, N Venktram, Sireesha, “An Efficient Data Transmission by using Modern USB Flash Drive” International Journal of Electrical and Computer Engineering (IJECE) Vol. 4, No. 5, October 2014, pp. 730~740 ISSN: 2088-8708 [21] Oka Mahendra,Djohar Syamsi,Ade Ramdan,Marcella Astrid,"Design and implementation of data storage system using USB flash drive in a microcontroller based data logger" , DOI 10.1109/ICACOMIT.2015.7440175,Electronic ISBN: 978-1-4673-7408-8 CD-ROM ISBN: 978-1-4673- 7407-1,https://ieeexplore.ieee.org/abstract/document/7440175. [22] PNY USB Flash Drive – CES 2006 – LetsGoDigital. Ces-show.com. Retrieved on 2011-05-18. [23] BlueTrek Bizz – an expandable USB and a Bluetooth headset in one Archived 2014-08-29 at the Wayback Machine. TechChee.com (2008-05-20). Retrieved on 2011-05-18. Madhwaraj Kango Gopal, Amirthavalli M. Authors:

Paper Title: Applying Machine Learning Techniques to predict the maintainability of Open Source Software

Abstract – Software maintainability is a vital quality aspect as per ISO standards. This has been a concern since decades and even today, it is of top priority. At present, majority of the software applications, particularly open source software are being developed using Object-Oriented methodologies. Researchers in the earlier past have used statistical techniques on metric data extracted from software to evaluate maintainability. Recently, machine learning models and algorithms are also being used in a majority of research works to predict maintainability. In this research, we performed an empirical case study on an open source software jfreechart by applying machine learning algorithms. The objective was to study the relationships between certain metrics and maintainability.

Index Terms— Machine Learning, Maintainability, Open Source Software, Object-Oriented, Design Metrics 36. References: [1] K.D. Welker, The software maintainability index revisited, Journal 192-195 of Defense Software Engineering, pp. 18-21, 2001. [2] P. Oman and J. Hagemeister, Metrics for assessing a software system’s maintainability, Proceedings of the IEEE International Conference on Software Maintenance, pp. 337- 344, 1992. [3] P. Oman and J. Hagemeister, Constructing and testing of Polynomials predicting software maintainability, Journal of Systems and Software, Vol. 24, No 3, pp. 251- 266, 1994. [4] M.M.T. Thwin and T.S. Quah, Application of neural networks for software quality prediction using object- oriented metrics,Journal of Systems and Software, Vol 76, No 2, pp. 147-156, 2005. [5] Y. Zhou and X.U. Baowen, Predicting the maintainability of open source software using design metrics, Wuhan University Journal of Natural Sciences, Vol 13, No 1, pp. 14-20, 2008. [6] Y.Zhou and H.Lueng, Predicting object-oriented software Maintainability using multivariate adaptive regression splines, Journal of Systems and Software, Vol 80, No 8, pp. 1349-1361, 2007. [7] K.K. Aggarwal, Y. Singh, A. Kaur and R. Malhotra, Application of artificial neural network for predicting maintainability using object-oriented metrics, World Academy of Science, Engineering and Technology, No 22, 2006. [8] P. Niemeyer and J. Knudsen , Learning Java, O’Reilly & Associates : 2005. [9] S.C. Misra, Modeling design/coding factors that drive maintainability of software systems, Software Quality Journal, Vol 13, pp. 297-320, 2005. [10] R. Martin, Agile Software Development Principles : Principles, Patterns and Practices, Prentice Hall, 2003. [11] Robert C Martin,“UML 0.9 Lexicon”, B Class, Citeseer. [12] http://clarkware.com/software/JDepend.html. [13] H.M. Halstead, Elements of Software Science, N. Holland : Elsevier, 1997. [14] S. Muthanna, K. Kontogiannis, K. Ponnambalam and B. Stacey, A maintainability model for industrial software systems using design level metrics, Proceedings of the Seventh Working Conference on Reverse Engineering, pp. 248-256, 2000. [15] S.S. Yau and J.S. Collofello, Some stability measures for software Maintenance, IEEE Transactions on Software Engineering, Vol 6, No 6, pp. 545-552, 1980. [16] F. Zhuo, B. Lowther, P. Oman and J. Hagemeister, Constructing and testing software maintainability assessment models, Proceedings of the First International Software Metrics Symposium, IEEE CS Press, pp. 61-70, 1993. [17] M.O. Elish, A.H.A. Yafei and M.A. Mulhem, Empirical comparison of three metrics suites for fault prediction inpackages of object-oriented systems : a case study of eclipse, Advances in Engineering Software, Vol 42, No 10, pp. 852-859, 2011. [18] IEEE, IEEE Standard Glossary of Software Engineering Terminology, Report IEEE Std 610.12-1990, IEEE, 1990. [19] Madhwaraj K.G., Empirical comparison of two metrics suites for maintainability prediction in packages of object-oriented systems : A case study of open source software, Journal of Computer Science, Vol 10, No. 11, pp. 2330-2338, 2014. [20] Madhwaraj K.G., Predicting the maintainability of object oriented software using design metrics – An evolutionary case study of open source software, International Review on Computers and Software, Vol 9, No. 6, 2014. [21] Malhotra, R., & Bansal, A.J. (2012). Fault Prediction Using Statistical and Machine Learning Methods for Improving Software Quality JIPS, 8, 241-262. [22] Arunima Jaiswal, Ruchika Malhotra, Software reliability prediction using machine learning techniques, International Journal of System Assurance Engineering and Management, 2018, Volume 9, Number 1, Pages 230-244. [23] Padhy, N., Panigrahi, R., & Neeraja, K. (2019). Threshold estimation from software metrics by using evolutionary techniques and its proposed algorithms, models. Evolutionary Intelligence, 1-15. Mrs. S Meena, Dr.K.Chitra, Mr. T Ramkumar, Mr. G Richie Roberts Authors:

Paper Title: Implementation of Controller structures in FPGA Platform Abstract: Field Programmable Gate Arrays are recently replacing general purpose microcontrollers in implementation of digital control systems. This paper includes the proposal of implementing complex controller structures in a Field Programmable Gate Array (FPGA). Till recent, PID controllers are implemented in FPGA Platform. PID controllers are simple, reliable, versatile feedback mechanisms used in most control systems. To reduce various undesirable effects on the output such as overshoot, some variants in the conventional PID controllers, such as the I-PD and IMC are also used. Here all these control controller structures are implemented in MATLAB, compared for best performance and run in the FPGA.

Index Terms: PID controller, I-PD controller, IMC controller, FPGA, Xilinx ISE 14.7 Design tools.

37. References: [1] V.Rajinikanth and K.LathaI-PD Controller Tuning For Unstable System Using Bacterial Foraging Algorithm:A Study Based On Various Error Criterion, Hindawi Publishing Corporation Applied 196-200 Computational Intelligence And Soft Computing Volume 2012, Article Id 329389, 10 Pages Doi:10.1155/2012/329389. [2] P.A. BalakrishnanOptimization Of I-PD Controller For A FOLIPID Model Using Particle Swarm Intelligence, International Journal Of Computer Applications (0975 – 8887) Volume 43– No.9, April 2012.J. [3] Morimasa Ogawa And Tohru Katayama A Robust Tuning Method For I-PD Controller Incorporating A Constraint On Manipulated Variable, Trans. Of The Society Of Instrument And Control Engineers Vol.E-1, No.1, 265/273 (2001). [4] S J Suji Prasad Optimization Of I-PD Controller Parameters With Multi Objective Particle Swarm Optimization, Journal Of Theoretical And Applied Information Technology 20th August 2014. Vol. 66 No.2. [5] Gao, Ruiyao And O'dwyer, Aidan And Coyle, Eugene: A Non-Linear PID Controller For CSTR Using Local Model Networks. Proceedings Of The IEEE 4th World Congress On Intelligent Control And Automation (Wcica 2002), Shanghai, China, 10-14 June. [6] A Nassirharand, N Hoq,H S TzouDesign Of Nonlinear PID Controllers Using System Step Response. [7] Poonam M BaikarDesign Of PID Controller Based Information Collecting Robot In Agricultural Field ,International Journal Of Advanced Research In Electrical, Electronics And Instrumentation Engineering, Vol. 3, Issue 8, August 2014. [8] Meena S, Chitra K. A New approach of PID tuning for Nonlinear SISO system based on Particle Swarm Optimization Techniques.International Journal of Applied engineering Research 2014;9(23):-21701-11. [9] S.Meena ,K.Chitra, R.VijayAnand. Development of I-PD controller on Embedded platform. International Conference on Signal Processing , Communication, Power and Embedded Sysytems. (Scopes- 2016). [10] K.J. Astrom and B. Wittenmark, Computer Controlled Systems, Prentice Hall, New Jersery, USA, 1997. [11] F. Krach, B. Frackelton, J. Carletta and R. Veillette,“FPGA-Based Implementation of Digital Control for a Magnetic Bearing,” American Control Conference, Vol.2, pp. 10801085, June 2003. [12] S.L. Jung, M.Y. Chang, J.Y. Jyang, L.C. Yeh, Y.Y. Tzou, “Design and Implementation of an FPGA-Based Control IC for AC-Voltage Regulation,” IEEE Trans. on Power Electronics, Vol. 14, pp. 522-532, May 1999. [13] S. Ferreira, F. Hafner, L.F Pereira, F. Moraes, “Design and Prototyping of Direct Torque Control of Induction Motors in FPGAs,” IEEE Symposium on Integrated Circuits and Systems Design, pp. 105-110, Sept. 2003. [14] F. Ricci, H. Le-Huy, “An FPGA-Based Rapid Prototyping Platform For Variable-Speed Drives”, IEEE Industrial Electronics Society Annual Conference, vol.2, pp. 1156-1161, Nov. 2002. D.Karthikeyan Authors:

Research on Metal Doped Zeolite As Catalyst To Reduce NOX Emission From Lean Burn Gasoline Paper Title: Engines Abstract— Lean burn gasoline direct injection (GDI) engines are the most preferred gasoline engines because of their low fuel consumption and high thermal efficiency. However, these engines produce exhaust gases that are particularly rich in oxygen and therefore the present three-way catalytic converter (TWC) is not suitable for converting the generated NOX emission into Nitrogen gases. In this present work, a new method of reducing Nitrogen Oxides emission in a gasoline engine is attempted by using an ordinary oxidation catalyst together with a deNOX(zeolite-based) catalyst. In this work, Na-form of ZSM-5 zeolite was used as a catalyst and 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 the catalyst. 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 with inlet and outlet cones ( forming catalytic convertor ). Experiments were conducted on a 2 cylinder Multi Point Port Fuel Injection engine along with a dynamometer. Exhaust emissions such as NOX, CO, HC, O2, CO2 were measured with AVL Di-gas-444 Analyzer. Exhaust gas temperature is measured with the use of a thermocouple. Firstly load tests (4, 7, 10, 13, and 16KW) were conducted on the engine without catalytic convertor was fixed close to the outlet pipe and the test were conducted again with same loading condition as mentioned above. Then by the same above procedure is followed to conduct test with Cu- ZSM5 and Fe-ZSM5 catalytic convertors. From the results it is observed that both Cu and Fe zeolite catalyst 38. minimize emissions than the commercial catalytic converter.

Index Terms: catalytic converter, NOX reduction, oxidation catalyst, ZSM5 zeolite. 201-206

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 Catalytic Reduction technique- SAE Paper980935. [5] Randip K. Das, Souvik Bhattacharyya, B.B. Ghosh and Maya Dutta Gupta- “Development and Performances Studies for SI Engine Emission 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 for the 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 Journal of 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 emission Control: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 Synthesized From 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 SI Engine 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 bimetals as 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 using Cu- 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: Performance Assessment” – SAE Paper No.2008- 01-0810. [14] Joseph R. Theis – “Selective Catalytic Reduction for Treating the NOx Emissions from Lean-Burn Gasoline Engines: Durability Assessment” – SAE Paper No.2008-01- 0811. [15] Elavarasan. G, Thiagarajan. L, Kannan. M, Karthikeyan. D, “Finding the Lubrication oil properties of an Internal Combustion Engine using 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 environmental applications” – 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 NOx under lean-burn conditions, Chinese Science Bulletin, Vol.49(21), pp.2231-2241. R.Sangeetha, Dr.S.Sathappan Authors:

An Enhanced Classification Based Outlier Detection using Decision Tree for Multi class in Data Paper Title: Stream Abstract :Data Stream has continual, unbound, large and unstable records. The processing in data streams involves extracting significantidea in primary data of the kind static and dynamic with one sweep. In streaming, records are generated by thousands of data sources continuously and simultaneously. These data normally won’t have common range. Some data will be deviated from the rest in terms of variant factors. These are considered as outliers and it is tough to find those in data stream as they have multi dimensionality. Outliers, being the most abnormal observations, may include the sample maximum or sample minimum. E-commerce is an application or category of data stream that is generated from millions of sources at a time. It includes multiple products and transactions. Some products are cancelled during transactions and some are infrequent and these are termed as outliers. This paper focus on the challenge in E-Commerce and the objective is to aid the agencies in taking fine decisions in right time by finding the outliers using supervised learning scheme. This work is carried out in two phases. In first phase the outliers are detected and classified as cancelled and delivered products. In the second phase the least transaction is found as an outlier by the enhanced methodology for multi-class classification. The work is implemented in WEKA 3.9.6 and is compared with the existing works with evaluation metrics.

39. Index terms: DataStream, Outlier, Decision Tree, Random Correction Code, Log loss.

207-213

References: [1] Anurag Bejju, “ Sales Analysis of E-CommerceWebsites using Data Mining Techniques”, International Journal of Computer Applications (0975 – 8887) Volume 133, No.5, January 2016. [2] Cao Lijun 1 , Liu Xiyin 2 , Zhou Tiejun 1 , Zhang Zhongping 3, Liu Aiyong, “A Data Stream Outlier Delection Algorithm Based On Reverse K Nearest Neighbors”, 2010 International Symposium on Computational Intelligence and Design IEEE. [3] DASH, M., & LIU, H (1997) Feature selection for classification. Intelligent Data Analysis, 131- 156. [4] Ding Xiang-wu and Wang Bin, "An Improved Pre-pruning Algorithm Based on ID3," JisuanjiYuxiandaihua,Vol.9, pp. 47,2008. [5] Han, Jiawei, Micheline Kamber, and Jian Pei. “Data mining: concepts and techniques”, Morgankaufmann, 2006. [6] Kurian M.J and Gladston Raj S,(2015) ” Outlier Detection in Multidimensional Cancer Data Using Classification Based approach “ International Journal of Applied Engineering Research ,Vol.10, No.79,pp. 342-348 , 2015. [7] Kurian M.J., Gladston Raj S., PhD, “ An Analysis on the Performance of a Classification based Outlier Detection System using Feature Selection”, International Journal of Computer Applications”, Volume 132 – No.8, December2015 . [8] Mani Mehrotra1, Nakul Joshi, “Anomaly Detection in Temporal data Using Kmeans Clustering with C5.0”, The International Journal of Engineering and Science (IJES) Volume 6, Issue 5, PP 77-81, 2017. [9] Quinlan J. R. (1986). “Induction of decision trees. Machine Learning,” Vol.1-1, pp. 81-106. [10] Rutkowski, L. Pietruczuk, P. Duda, and M. Jaworski,z“Decision Trees for Mining Data Streams Based on the McDiarmid’s Bound,” IEEE Trans. Knowledge and Data Eng., vol. 25, no. 6, pp. 1272- 1279, 2013. [11] Sanizahahmed, Norazon Mohamed Ramli, Habshah midi, , “Outlier detection in logistic regression and its application in medical data analysis”, in: 2012 IEEE colloquium on humanities, science and engineering. [12] Somkidamornsamankul, jairajpromrak, pawalaikraipeerapun, “solving multiclass classification problems using combining complementary neural networks and error-correcting output codes”, international journal of mathematics and computers in simulation, issue 3, volume 5, 2011. [13] Victoria J. Hodge and Jim Austin, “An Evaluation of Classification and Outlier Detection Algorithms”, archive.org, 2018. [14] https://towardsdatascience.com/understanding-binary-cross-entropy-log-loss-a-visual-explanation. [15] https://en.wikipedia.org/wiki/Hamming_distance.

Shakkeera L , Saranya A,Sharmasth Vali Y Authors:

Paper Title: Secure Collaborative Key Management System for Mobile Cloud Data Storage Abstract: Mobile Cloud Computing (MCC) is the combination of mobile computing, cloud computing and wireless networks to make mobile thin client devices resource-rich in terms of storage, memory computational power and battery power by remotely executing the wide range of mobile application’s data in a pay-per-use cloud computing environment. In MCC, one of the primary concern is the security and privacy of data stored in cloud. The existing techniques are not efficient to manage secret keys during key generation and key distribution processes. The objective of this project work is to develop a secure collaborative key management system (SCKMS) for mobile cloud data storage by implementing by the cryptographic techniques for file encryption and file decryption, key generation, key encryption, key distribution and key decryption processes. In our proposed methodology, DriverHQ public cloud infrastructure is used for accessing the secure file as Storage as a Service (SaaS) mechanism. For generating the secret key, the proposed work implemented with Pseudo Random Number Generator (PRNG) algorithm, it produces the sequence of random numbers for every time. The keys are distributed using general Secret key Sharing Scheme (SSS). The key pattern matching process is implemented to spilt the secret key into three partitions and sent it to client (mobile devices), cloud server and decryption server. The decryption server key and cloud sever key are mapped with client key. The key shares are grouped together using key-lock pair mechanism and it achieves key integrity during untrusted medium communication. The proposed work also eliminates key escrow and key exposure problems. The files are encrypted and decrypted using Rivest-Shamir-Adleman (RSA) algorithm. The RSA algorithm is more vulnerable against the brute force attack, because of using larger key size. Thus, the proposed SCKMS achieves data confidentiality and data integrity in mobile cloud storage data when compared to existing Key Management System (KMS). The work also reduces encryption & decryption computation and storage overhead in client mobile devices, and minimizes the energy consumption of the mobile devices efficiently. 40. Index Terms: Mobile Cloud Computing; Key Management; Secret Sharing Scheme; Pattern Matching. 214-225 References: [1] Hlatshwayo M, and Tranos Zuva, “Mobile Public Cloud Computing, Merits and Open Issues”, International Conference on Advances in Computing and Communication Engineering (ICACCE), 2016. [2] Santosh Kumar, and Gouda R H, “Cloud Computing – Research Issues, Challenges, Architecture, Platforms and Applications: A Survey “, International Journal of Future Computer and Communication, Vol. 1, No. 4, December 2012. [3] Masoud Nosrati, Ronak Karimi H, “Mobile Computing: Principles, Devices and Operating Systems”, World Applied Programming, Vol .2, No. 7, 2014. [4] Jia, and Cong Wang, “Enabling Cloud Storage Auditing With Verifiable Outsourcing of Key Updates”, IEEE Transactions on Information Forensics and Security, Vol. 11, No. 6, 2016. [5] Pratima Popat Gutai, Rutuja S Kothe, and Jahveri S B, “Efficient Hierarchical Cloud Storage Data Access Structure with KDC”, IEEE International Conference in Electronics, Communication and Computer Technology (ICAECCT), 2016. [6] Sikhar Patranabis, Yash Shrivastava, “Provably Secure Key-Aggregate Cryptosystems with Broadcast Aggregate Keys for Online Data Sharing on the Cloud”, Vol. 66, No. 5, 2017. [7] Yuqi Wang, and Kun She, “A Practical Quantum Public-key Encryption Model”, Third International Conference on Information Management (ICIM), 2017. [8] TalariBhanu Teja, Vootla Hemalatha, and Priyanka K, “Encryption And Decryption – Data Security For Cloud Computing – Using AES Algorithm”, SSRG International Journal of Computer Trends and Technology (IJCTT) - Special Issue, April 2017. [9] Mahesh Kumar K M, and Sunitha N R, “Hybrid Cryptographically Secure Pseudo-Random Bit Generator”, Second International Conference on Contemporary Computing and Informatics (IC3I), 2016. [10] Nasrollah Pakniat, Mahnaz Noroozi, and Ziba Eslami, “Distributed Key Generation Protocol with Hierarchical Threshold Access Structure”, IET Information Security, Vol. 9, No. 4, 2015 [11] Preeti Garg, and Vineet Sharma, “An Efficient and Secure Data Storage in Mobile Cloud Computing through RSA and Hash Function”, International Conference on Issues and Challenges in Intelligent Computing Techniques (ICICT), 2014 [12] Prasoon Raghav, Rahul Kumar, and Rajat Parashar, “Securing Data in Cloud Using AES Algorithm”, International Journal of Engineering Science and Computing, Vol. 6, No. 4, 2016 [13] Zheng Yan, Xueyun Li, Mingjun Wang, and Athanasios V Vasilakos, Senior Member, Vasilakos, “Flexible Data Access Control Based on Trust and Reputation in Cloud Computing”, IEEE Transactions on Cloud Computing, Vol. 5, No. 3, 2017 [14] Giwon Lee, Haneul Ko, and Sangheon Pack, “An Efficient Delta Synchronization Algorithm for Mobile Cloud Storage Applications”, IEEE Transactions on Services Computing, Vol. 10, No 3, 2017. [15] Guanlin Wu, Junjie Chen, Weidong Bao, Xiaomin Zhu, Wenhua Xiao, Ji Wang, and Ling Liu, “Collaborative Storage Algorithm Based on Alternating Direction Method of Multipliers on Mobile Edge Cloud”, IEEE International Conference on Edge Computing (EDGE), 2017 [16] Debiao He, Neeraj Kumar, Mohammad Khurram Khan, Lina Wang, and Jian Shen, “Efficient Privacy-Aware Authentication Scheme for Mobile Cloud Computing Services”, IEEE System Journal, Vol. 12, No. 2, 2018. [7] 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 A.Chinnasamy., P.Selvakumari, V.Pandimurugan Authors:

Paper Title: Vehicular Adhoc Network Based Location Routing Protocol For Secured Energy Abstract: ALERT chiefly uses irregular message routing copy to supply namelessness protection. The projected protocol provides the ALERT routing high namelessness protection, EALERT additionally dynamically partitions a network field into zones and haphazardly chooses nodes in zones as intermediate relay nodes, that Anon-traceable anonymous route. It then haphazardly chooses a node inside the choice zone as a result of subsequent relay node and uses the EGPSR formula as a variant of GPSR in awake to send the information to the relay node. Within the last step, the information is broadcasted to k nodes at intervals the destination zone, providing k-anonymity to the destination. In addition, the protocol contains a method to hide the information instigator among sort of initiators to strengthen the k- anonymity protection of the provision. The projected Energy aware ALERT detects the Sybil attack within the network, Routing protocol, geographical routing.

Index Terms:: Vehicular ad hoc networks, anonymity, routing protocol, geographical routing.

References: [1] B. Karp and H. Kung,. “GPSR: Greedy Perimeter Stateless routingfor Wireless Networks” in: Proceedings 41. of ACM MobiCom, 2000,pp. 243–254 [2] yih-Chun Hu David B. Johnson, “Secure Efficient Distance Vector Routing for Mobile Wireless Ad Hoc Networks,” in Proc.IEEE Workshop on Mobile Computing Systems and Application, 2002. 226-231 [3] Xiaoxin Wu and Bharat Bhargava “Ad HocOn-Demand Position-BasedPrivate Routing Protocol” in Proc. IEEE Transactionson Mobile Computing, 2005. [4] Zhou Zhi and Yow Kin Choong “Anonymizing Geographic Ad Hoc Routing for Preserving Location Privacy” in Proc IEEE Transactions.2005. [5] VivekPathak and Danfeng Yao “Securing Location Aware Services Over VANET UsingGeographical Secure Path Routing” IEEE Trans. Veh. Technol., 2008. [6] [6]LanjunDang andjixeu ,“Distributed Anonymous Secure Routing with Good Scalability for Mobile Ad Hoc Networks” in Proc.IEEE Transactions.2010. [7] ElaheSheklabadi, and Mehdi Berenjkou“An Anonymous Secure Routing Protocol for MobileAd Hoc Networks” in Proc. IEEEInternational Symposium on Computer Networks and Distributed Systems .2011 [8] Tong Zhou, Romit Roy Choudhur “Sybil Attacks Detection in VehicularAd Hoc Networks” in Proc. IEEE Journal on Selected Areas In Communications, VOL. 29, NO. 3, 2011 [9] Karim El Defrawy “ALARM: Anonymous Location-Aided [10] Routing in Suspicious MANETs” in proc IEEE Transactions On Mobile Computing, VOL. 10, NO. 9, 2011 [11] Rui Jiang and Yuan Xing “Anonymous On-demand Routing and Secure Checking of Traffic Forwarding for Mobile Ad Hoc Networks”31st International Symposium on Reliable Distributed Systems 2012. [12] Haiying Shen and Lianyu Zhao“ALERT: An Anonymous Location-BasedEfficient Routing Protocol in MANETs” in Proc. [13] IEEE transactions on mobile computing, vol. 12, no. 6, 2013 SohailAbbas,MadjidMerabti“Lightweight Sybil Attack Detection in MANETs” in Proc. IEEE systems journal, vol. 7, no. 2, 2013. Kuen-Han Li Jenq-ShiouLeu “Ant-based On-demand Clustering Routing Protocolfor Mobile Ad-hoc Networks,”Seventh International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing 2013. [14] Kim KyuSeok and NavratiSaxena “Analysis of a Novel Advanced Greedy Perimeter Stateless Routing Algorithm” in proc IEEE transaction 2013 [15] Seryvuth Tan and Keecheon Kim “Secure Route Discovery for Preventing Black Hole Attacks on AODV- based MANETs “ In procIEEE International Conference on Embedded and Ubiquitous Computing 2013 [16] BehnamHassanabadi and ShahrokhValaee “Reliable Periodic Safety Message Broadcasting in VANETs Using Network Coding” In proc IEEE transactions on wireless communications, vol. 13, no. 3, 2014 [17] Wei Liu and Ming Yu “AASR: Authenticated Anonymous Secure Routingfor MANETs in Adversarial Environments,”IEEE transactions on vehicular technology, vol. 63, no. 9, November 2014. [18] K.Vijayakumar·C,Arun,Continuous security assessment of cloud based applications using distributed hashing algorithm in SDLC,Cluster Computing DOI 10.1007/s10586-017-1176-x,Sept 2017 [19] K.Vijayakumar·C,Arun, Analysis and selection of risk assessment frameworks for cloud based enterprise applications”, Biomedical Research, ISSN: 0976-1683 (Electronic), January 2017 [20] 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.

M.Kiruthiga Devi, R.Kaviya Authors:

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 as mania and depression. These mental states are very harmful to the lives of the patients as their day to day actions are disrupted. This project aims at identifying the symptoms of the patients who have extreme moods to determine if they are bipolar using sensors and smart phones. Patients with mental illness tend to exhibit symptoms like reduced physical activity, changes in mood, drastic changes in sleep pattern, inability to cope with stress and withdrawn from socializing. These changes can be monitored using sensors and the data collected is compared with the data collected from healthy individuals. A classification algorithm is applied to the data collected to classify the symptoms and detect if the person has bipolar disorder or is just showing subtle signs of mood swings.

Key Words: 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.2018.00062 [2] Wouter B. Teeuw, Johan Koolwaaij, Arjan Peddemors “Monitoring human behaviour through mobile technology”,October 2018,MDPI journel,doi:10.3390/proceedings2191243 [3] 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.2012.6346408 [4] 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.T̆ atar, 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 Engineering Research, 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 of Control 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”, 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.

S.Hemavathi, K.Jayasakthi Velmurugan Authors:

Paper Title: Segmentation Of Affected Crops Using Deep Learning

Abstract - Deep Learning technology can accurately predict the presence of diseases and pests in the agricultural farms. Upon this Machine learning algorithm, we can even predict accurately the chance of any disease and pest attacks in future For spraying the correct amount of fertilizer/pesticide to elimate host, the normal human monitoring system unable to predict accurately the total amount and ardent of pest and disease attack in farm. At the specified target area the artificial percepton tells the value accurately and give corrective measure and amount of fertilizers/ pesticides to be sprayed.

Index Terms – deep learning, perception, host

42. References: [1] E. H. Miller, “A note on reflector arrays (Periodical style—Accepted for publication),” IEEE Trans. Antennas Propagat., to be published. 232-234 [2] M. Young, The Techincal Writers Handbook. Mill Valley, CA: University Science, 1989. [3] Y. Yorozu, M. Hirano, K. Oka, and Y. Tagawa, “Electron spectroscopy studies on magneto-optical media and plastic substrate interfaces(Translation Journals style),” IEEE Transl. J. Magn.Jpn., vol. 2, [4] B. Smith, “An approach to graphs of linear forms (Unpublished work style),” unpublished. [5] M. Suresh Anand, N.Mohankumar,A.Kumaresan,"Developing Indian Sign Language Recognition System for Recognizing English Alphabets with Hybrid Classification Approach",Indian Journal of Public Health Research & Development, Volume 9, Issue no. 2, Feb 2018 [6] J. Wang, “Fundamentals of erbium-doped fiber amplifiers arrays” IEEE J. Quantum Electron., submitted for publication. [7] G. O. Young, “Synthetic structure of industrial plastics (Book style with paper title and editor),” in Plastics, 2nd ed. vol. 3, J. Peters, Ed. New York: McGraw-Hill, 1964, pp. 15–64. [8] C. J. Kaufman, Rocky Mountain Research Lab., Boulder, CO, private communication, May 1995. [9] H. Poor, An Introduction to Signal Detection and EstimationW.-K. Chen, Linear Networks and Systems (Book style). Belmont, CA: Wadsworth, 1993, pp. 123–135. A. Sangeerani Devi, Sonia Prakash, K. Laavanya, A.Shali, D.Sathish Kumar Authors:

Paper Title: Violence Detection and Target Finding Using Computer Vision Abstract: Recognizing savagery in recordings through CCTV is basic for requirement and investigation of reconnaissance cameras with the plan of keeping up open wellbeing. Moreover, it will be an incredible device for securing kids and help guardians settles on a superior educated choice about their children. However, this can be a difficult drawback since detecting certain nuances with no human administration isn't entirely technical however also a conceptual problem.So, our idea is to use computer vision to develop an automated technique in detecting the violent behavior/street crime criminals through surveillance cameras installed in cities and towns. When the surveillance cameras detect the abnormal behavior, it captures the scene and generates an alert by 43. sending the captured image to the nearby police station. Further, the CCTV cameras using Cloud trigger the near-by cameras to track the particular target and its location. 235-238 Keywords: ViolenceDetection, Surveillance Cameras, Capture, Trigger,Google Cloud.

References: [1] Febin I P, Jayasree K ,“A Survey of Computer Vision Based Methods for Violent Action Representation and Recognition”. [2] Mike Haldas ,“Send Security Camera Photos From Raspberry Pi Via Mms Text Message”. [3] Malavika Nair, Mathew Gillroy, Neethu Jose, Jasmy Davies, “I-Surveillance Crime Monitoring and Prevention Using Neural Networks”. [4] Peipei Zhou ,Qinghai Ding, Haibo Luo,Xinglin Hou,“Violence Detection in Surveillance Video Using Low- Level Features”. [5] E. Bermejo , O. Deniz , G. Bueno and R. Sukthankar,“Violence Detection in Video Using Computer Vision Techniques”. [6] Q. Dai, J. Tu, Z. Shi, Y.-G. Jiang, and X. Xue., “Violent Scenes Detection Using Motion Features and Part- Level Attributes”. [7] Daniel Moreira, Sandra Avila, Mauricio Perez, Daniel Moraes, ” Temporal Robust Features for Violence Detection”. [8] A. Datta, M. Shah, and N. da Vitoria Lobo, ” Person-on-person violence detection in video data.”. [9] F. D. M. de Souza, G. C. Chavez, E. do Valle, D. A. Araujo, “Violence detection in video using spatio- temporal features“. [10] M. Suresh Anand, M. Balamurugan,"Sugar Level Detection Using Thermal Images",International Journal of Engineering & Technology, Volume 7, Issue no. 4.39, Dec 2018 [11] T. Hassner, Y. Itcher, and O. Kliper-Gross, “Violent flows: Real-time detection of violent crowd behaviour.“ [12] R. Mehran, A. Oyama, and M. Shah, “Abnormal crowd behaviour detection using social force model. “. [13] H. Mousavi, S. Mohammadi, A. Perina, R. Chellali, and V. Murino, “Analysing tracklets for the detection of abnormal crowd behaviour”. [14] 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. [15] 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. [16] 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 Haripriya P, Porkodi R Authors:

Paper Title: An Enhanced Framework for Content Based Medical Image Retrieval Using Deep Neural Network Abstract: As the technology growth fuelled by low cost tech in the areas of compute, storage the need for faster retrieval and processing of data is becoming paramount for organizations. The medical domain predominantly for medical image processing with large size is critical for making life critical decisions. Healthcare community relies upon technologies for faster and accurate retrieval of images. Traditional, existing problem of efficient and similar medical image retrieval from huge image repository are reduced by Content Based Image Retrieval (CBIR) . The major challenging is an semantic gap in CBIR system among low and high level image features. This paper proposed, enhanced framework for content based medical image retrieval using DNN to overcome the semantic gap problem. It is outlines the steps which can be leveraged to search the historic medical image repository with the help of image features to retrieve closely relevant historic image for faster decision making from huge volume of database. The proposed system is assessed by inquisitive amount of images and the performance efficiency is calculated by precision and recall evaluation metrics. Experimental results obtained the retrieval accuracy is 79% based on precision and recall and this approach is preformed very effectively for image retrieval performance.

Keywords: CBIR, DICOM, DNN, Semantic gap 44. References: [1] Amol Bhagat, M. A. (2013). DICOM Image Retrieval Using Novel Geometric Moments and Image 239-244 Segmentation Algorithm . International Journal of Advanced Computer Research, 37-46. [2] S.Malar Selvi1 and Mrs.C.Kavitha,, Content Based Medical Image Retrieval System (CBMIRS) Using Patch Based Representation, IOSR Journal of Computer Engineering (IOSR-JCE) e-ISSN: 2278-0661, p- ISSN: 2278-8727 PP 24-36 [3] Arvind Nagathan, M. I. (July 2013). Content-Based Image Retrieval System using Feed-Forward Backpropagation Neural Network. International Journal of Computer Science Engineering (IJCSE), 2, 143- 151. [4] Ceyhun Burak Akgül, D. L. (2010). Content-Based Image Retrieval in Radiology: Current Status and Future Directions. Journal of Digital Imaging, 208-222. [5] M. Flickner, H. S. (1995). Query by image and video content: the QBIC system. Computer, 28(9), 23-32. [6] W. Dean Bidgood, J. M. (1997). Understanding and Using DICOM, the Data Interchange Standard for Biomedical Imaging. Journal of American Medical Informatics Association, 199–212. [7] Ziegler, S. E. (2012). Development of a Next-Generation Automated DICOM Processing System in a PACS-Less Research Environment. Journal of Digital Imaging, 25(5), 670-677. Retrieved 12 25, 2018, from https://link.springer.com/article/10.1007/s10278-012-9482-6 [8] Gunjanbhai Patel, DICOM Medical Image Management the challenges and solutions: Cloud as a Service (CaaS), IEEE Third International Conference on Computing, Communication and Networking Technologies (ICCCNT'12), Coimbatore, 2012, pp. 1-5.2012, DOI: 10.1109/ICCCNT.2012.6396083 [9] B. K. Sahu and R. Verma, "DICOM search in medical image archive solution e-Sushrut Chhavi," IEEE 2011 3rd International Conference on Electronics Computer Technology, Kanyakumari,2011, pp. 256- 260.doi: 10.1109/ICECTECH.2011.5942093 [10] Alexandra La Cruz, Alexander Baranya, Maria-Esther Vidal, Medical Image Rendering and Description Driven by Semantic Annotations, Springer Berlin Heidelberg, Resource Discovery, 2013, pp.123-149 [11] Adnan Qayyum, 2a*Syed Muhammad Anwar, 3bMuhammad Awais, 1aMuhammad Majid, “Medical Image Retrieval using Deep Convolutional Neural Network”, Elsiver, 2017 [12] Alexander Selvikvåg Lundervold, Arvid Lundervold , An overview of deep learning in medical imaging focusing on MRI, Computer Vision and Pattern Recognition, 2018 [13] Kim, P. (2017). MATLAB Deep Learning: With Machine Learning, Neural Networks and Artificial Intelligence(1st ed.). Berkely, CA, USA: Apress. [14] Zaharchuk, G., Gong, E., Wintermark, M., Rubin, D., & Langlotz, C. P. (2018). Deep Learning in Neuroradiology. American Journal of Neuroradiology. https://doi.org/10.3174/ajnr.A5543 [15] 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 [16] R. Joseph Manoj1 • M. D. Anto Praveena2 • K. Vijayakumar, An ACO–ANN based feature selection algorithm for big data, Cluster Computing , Springer, https://doi.org/10.1007/s10586-018-2550 [17] 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. [18] 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. [19] 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 Authors: G. Suganya, R. Porkodi

Paper Title: Design of A Rule Based Bio Medical Entity Extractor

Abstract: The field of Biomedical Entity Extraction / Identification plays a vital role in Bioinformatics and rapidly growing to meet the needs of different text mining tasks. Many biomedical entity extraction tools have been developed so far. This research work has focused to develop a Rule based Biomedical Entity Extraction and tested with PubMed Medline abstracts of Colon cancer and Alzheimer disease categories. The proposed Biomedical Entity Extractor gives promising result when compared with existing tools. The proposed method is incorporating of two phases such as preprocessing the input text document using NLP techniques and create the rules to find out the biomedical entities using regular expression. The results of Rule based Biomedical Entity Extractor are validated with the well-known Biomedical Genia tagger and Genecards Database. The method proposed in this paper almost good as genia tagger. The evaluation results on Colon cancer and Alzheimer disease abstracts corpus of Biomedical Entity Extraction achieve an accuracy of 92% and 88% respectively which identifies more number of entities compared to other existing tools.

45. Index Term: Medline Abstracts, Genia tagger, Pubtator, BCC-NER, Biomedical text mining

References: 245-249 [1] Sakthi Murugan R, P. Shanthi Bala, G. Aghila, “Ontology based information retrieval- an analysis”, International journal of advanced research in computer science and software engineering, Vol 3, Issue 10, pp 486-493, 2013. [2] Saurav Sahay, Baoli Li, ernesst V.Garcia, Eugene Agichtein, Ashwin ram, “ Domain ontology construction from biomedical text”, International Conference on Artificial Intelligence (ICAI’07), Las Vegas, Nevada, USA, 2007. [3] Aarti Singh, Poonam Anand, “Automatic domain ontology construction mechanism”, IEEE Recent Advances in Intelligent Computational Systems (RAICS) pp 304-309, 2013. [4] Annalakshmi V, Bhuvaneswari V, Aruna L, “Dictionary Based Approaches in Protein Name Recognition”, International Research Journal of Engineering and Technology (IRJET), Vol 04, Issue 02, Feb -2017, pp 94- 98, 2014. [5] [5]. Burr Settles, “ABNER: an open source tool for automatically tagging genes, proteins and other entity names in text”, Bioinformatics, Vol 21, Issue 14, pp 3191–3192, 2005. [6] Gurusamy Murugesan, Sabenabanu Abdulkadhar , Balu Bhasuran and Jeyakumar Natarajan, “BCC-NER: bidirectional, contextual clues named entity tagger for gene/protein mention recognition”, EURASIP Journal on Bioinformatics and systems biology, Vol 7, pp 1-8, 2017. [7] Raja K, Subramani S, Natarajan J. “A hybrid named entity tagger for tagging human proteins/genes”, International Journal of Data Mining Bioinformatics, Vol 10, Issue 3, 2014. [8] Robert Leaman , Graciela Gonzalez , “Banner: An Executable Survey of Advances in Biomedical Named Entity Recognition “, Pacific Symposium on Biocomputing, Vol 13, pp 652-663, 2008. [9] D Campos, s Matos, JL Oliveira, “Gimli:open source and high-performance biomedical name recognition”, BMC Bioinformatics, 14, 2013. [10] Chih-Hsuan Wei, Hung-Yu Kao, and Zhiyong Lu “GNormPlus: An Integrative Approach for Tagging Genes, Gene Families, and Protein Domains”, BioMed Research International Vol 1, 2015. [11] C-H Wei, R Leaman, Z Lu, “SimConcept: A Hybrid Approach for Simplifying Composite Named Entities in Biomedicine”, Proceedings of the ACM Conference on Bioinformatics Computational Biology and Health Informatics, Newport Beach, CA, p138-146, 2014. [12] Sohn S., Comeau D.C., Kim W., “Abbreviation definition identification based on automatic precision estimates”, BMC Bioinformatics, 9, 402, 2008. [13] R Porkodi and B L Shivakumar, “Design and Development of Integrated Biomedical ontology for information extraction from Medline abstract”, International Journal of Engineering Research and Development, Vol 1, Issue 11, pp 01-10, July 2012. [14] Xu Wang, Chen Yang, Renchu Guan, “A Comparative study for biomedical named entity recognition”, International Journal of Machine Learning and Cybersecurity, Sep 2015. [15] Tsuruoka Y, “GENIA tagger: Part-of-speech tagging, shallow parsing, and named entity recognition for biomedical text”, 2006. [16] J D Kim, T Ohta, Y Tateisi and Tsujii, “GENIA corpus- a semantically annotated corpus for bio- textmining”, Bioinformatics, Vol 19, Suppl 1,2003. [17] Chih-Husan Wei, Hung-Yu Kao and Zhiyong Lu, “Pubtator: a web-based text mining tool for assisting biocuration”, Nucleic Acids Research, Vol 41, 2013. Authors: Nandhini K, Porkodi R

Paper Title: The Novel Gravitational Mass Weighted PCA Technique for Feature Extraction in Hyperspectral Data Classification Abstract: Recent advancements in the imaging spectrometer collect both spatial and spectral information which creates a huge dimensionality. The heavy spectral information creates to build a classifier for discerning between the materials in the scene. The minimum number of training labels always in an exchange between the spectral information and the performance is called the Hughes effect. Also the redundant of spectral information and noisy data presents in the hyperspectral scene. The above issues are overcome using feature extraction and feature selection methods which play a major role in the reduction of dimensionality. This paper proposes the novel fusion Gravitational Mass Weighted Principal Component Analysis (GMWPCA) techniques for hyperspectral data dimensionality. Also, this paper presents the deep insight about the feature extraction techniques in hyperspectral data of both supervised and unsupervised learning methods and experimental analysis in AVIRIS Indian Pines hyperspectral dataset by employing PCA, Probability PCA, LDA, and proposed techniques. The 93.63 % high accuracy achieved by using a novel proposed method.

Index Terms: Feature Extraction, Gravitational search algorithm, Gravitational weighted Mass PCA, Hyperspectral data, PCA

References: 46. [1] Imani, Maryam, and Hassan Ghassemian. "Binary coding based feature extraction in remote sensing high dimensional data." Information Sciences 342 (2016): 191-208. [2] Alakkari, Salaheddin, and John Dingliana. "Principal Component Analysis Techniques for Visualization of Volumetric Data." Advances in Principal Component Analysis. Springer, Singapore, 2018. 99-120. 250-255 [3] “Principal Component Analysis”, http://kiwi.bridgeport.edu/cpeg540/PrincipalComponentAnalysis_Tutorial.pdf. [4] Rodarmel, Craig, and Jie Shan. "Principal component analysis for hyperspectral image classification." Surveying and Land Information Science 62.2 (2002): 115-122. [5] Kasai, Mai. "In Vivo Tumor Spatial Classification using PCA and K-Means with NIR-Hyperspectral Data." Journal of Biomedical Engineering and Medical Imaging 3.1 (2016): 45. [6] Farrell, Michael D., and Russell M. Mersereau. "On the impact of PCA dimension reduction for hyperspectral detection of difficult targets." IEEE Geoscience and Remote Sensing Letters 2.2 (2005): 192- 195. [7] Licciardi, Giorgio, et al. "Linear versus nonlinear PCA for the classification of hyperspectral data based on the extended morphological profiles." IEEE Geoscience and Remote Sensing Letters 9.3 (2012): 447-451. [8] Ghamisi, Pedram, et al. "Hyperspectral data classification using extended extinction profiles." IEEE Geoscience and Remote Sensing Letters 13.11 (2016): 1641-1645. [9] Kang, Xudong, et al. "PCA-based edge-preserving features for hyperspectral image classification." IEEE Transactions on Geoscience and Remote Sensing 55.12 (2017): 7140-7151. [10] Lazcano, Raquel, et al. "Porting a PCA-based hyperspectral image dimensionality reduction algorithm for brain cancer detection on a many-core architecture."Journal of Systems Architecture 77 (2017): 101-111. [11] Báscones, Daniel, Carlos González, and Daniel Mozos. "Hyperspectral Image Compression Using Vector Quantization, PCA and JPEG2000." Remote Sensing 10.6 (2018): 907. [12] Pozo, Francesc, and Yolanda Vidal. "Damage and fault detection of structures using principal component analysis and hypothesis testing." Advances in Principal Component Analysis. Springer, Singapore, 2018. 137-191. [13] Deville, Yannick, et al. "Application and Extension of PCA Concepts to Blind Unmixing of Hyperspectral Data with Intra-class Variability." Advances in Principal Component Analysis. Springer, Singapore, 2018. 225-252. [14] Nascimento, José MP, and Jose MB Dias. "Does independent component analysis play a role in unmixing hyperspectral data?." IEEE Transactions on Geoscience and Remote Sensing 43.1 (2005): 175-187. [15] Bayliss, Jessica D., J. Anthony Gualtieri, and Robert F. Cromp. "Analyzing hyperspectral data with independent component analysis." 26th AIPR Workshop: Exploiting New Image Sources and Sensors. Vol. 3240. International Society for Optics and Photonics, 1998. [16] Villa, Alberto, et al. "On the use of ICA for hyperspectral image analysis." Geoscience and Remote Sensing Symposium, 2009 IEEE International, IGARSS 2009. Vol. 4. IEEE, 2009. [17] Kosaka, Naoko, Kuniaki Uto, and Yukio Kosugi. "ICA-aided mixed-pixel analysis of hyperspectral data in agricultural land." IEEE Geoscience and Remote Sensing Letters 2.2 (2005): 220-224. [18] Moussaoui, Saïd, et al. "On the decomposition of Mars hyperspectral data by ICA and Bayesian positive source separation." Neurocomputing 71.10-12 (2008): 2194-2208. [19] Shah, Chintan A., Manoj K. Arora, and Pramod K. Varshney. "Unsupervised classification of hyperspectral data: an ICA mixture model based approach." International Journal of Remote Sensing 25.2 (2004): 481- 487. [20] Du, Qian, and Chein-I. Chang. "A linear constrained distance-based discriminant analysis for hyperspectral image classification." Pattern Recognition 34.2 (2001): 361-373. [21] Ly, Nam Hoai, Qian Du, and James E. Fowler. "Sparse graph-based discriminant analysis for hyperspectral imagery." IEEE Transactions on Geoscience and Remote Sensing 52.7 (2014): 3872-3884. [22] Li, Wei, Jiabin Liu, and Qian Du. "Sparse and low-rank graph for discriminant analysis of hyperspectral imagery." IEEE Transactions on Geoscience and Remote Sensing 54.7 (2016): 4094-4105. [23] Baudat, Gaston, and Fatiha Anouar. "Generalized discriminant analysis using a kernel approach." Neural computation 12.10 (2000): 2385-2404. [24] Yu, Bin, et al. "Penalized discriminant analysis of in situ hyperspectral data for conifer species recognition." Ieee transactions on Geoscience and remote sensing 37.5 (1999): 2569-2577. [25] Imani, Maryam, and Hassan Ghassemian. "Feature space discriminant analysis for hyperspectral data feature reduction." ISPRS Journal of Photogrammetry and Remote Sensing 102 (2015): 1-13. [26] Shahdoosti, Hamid Reza, and Fardin Mirzapour. "Spectral–spatial feature extraction using orthogonal linear discriminant analysis for classification of hyperspectral data." European Journal of Remote Sensing 50.1 (2017): 111-124. [27] Li, Fan, et al. "Feature extraction for hyperspectral imagery via ensemble localized manifold learning." IEEE Geoscience and Remote Sensing Letters 12.12 (2015): 2486-2490. [28] Rashedi, Esmat, Hossein Nezamabadi-Pour, and Saeid Saryazdi. "GSA: a gravitational search algorithm." Information sciences 179.13 (2009): 2232-2248. Vinaya Singh P, P. A. Vijaya, Ravikumar Authors:

Paper Title: Automatic Vehicle Identification for Multiple Purposes at Toll Collection System

Abstract:: With the major growth in roadways, there is a raise in the number of toll booths. These toll booths have lengthy queues, the time consumed in paying cash and returning change causes additional delay. In this paper, a system uses Radio Frequency Identification (RFID) technology, ARM LPC2148, GSM, relay and computer host. RFID is used to obtain the vehicle number. RFID card with unique id is mounted on every vehicle, data contain on the card is examined by the RFID scanner placed at the toll gate. If the vehicle belongs to authorized person or registered, fixed money is automatically deducted from the owners account, message will be send to registered mobile number and automatically toll gate is opened. In this project Rs.50 is deducted 47. for car and Rs.100 is deducted for bus. If the account balance becomes insufficient then buzzer is alarmed, message will be sent to the owner that she/he has insufficient balance and should use manual toll payment. If 256-260 the vehicle belongs to higher officials such as VIP, police, army, ministers or ambulance the toll gate is opened automatically and the amount is not deducted. For upcoming situation all vehicle information that passes the toll will be stored. Stolen vehicle directory is also present, if the card number of the vehicle matches then buzzer is alarmed, message will be sent to the registered police station, amount is deducted from owner’s account and gate is opened. The advantage is that message is sent both to the owner and police station. Coding is done in Embedded C. This system eliminate the manual cash handling, reduces traffic congestion and help in lesser fuel utilization. This makes automatic toll collection more convenient for the public use.

Keywords: Automatic vehicle identification, Embedded C, RFID, GSM, authorized user, VIP vehicle, stolen vehicle.

References: [1] Amol A. Chapate, D.D. Nawgaje, “Electronic Toll Collection System Based on ARM”, International Journal of Science, Engineering and Technology Research (IJSETR), Volume 4, Issue 1, January 2015. [2] Sangeeta Pandey, Yamuna Rai and Shipra Mishra, “Fully Automated Toll Tax Collection Using RF Technology”, VSRD-IJEECE, Vol 2, Issue 6, 2012, 417-420. [3] K. Kamarulazizi, dr.W. Ismail, “Electronic Toll Collection System Using Passive RFID Technology”, Journal of Theoretical and Applied Information Technology. [4] Aniruddha Kumawat1, Kshitija Chhandramore, “Automatic Toll Collection system Using Rfid”, International Journal of Electrical and Electronics Research, Vol 2, Issue 2, 2014, 64-72. [5] Preeti Giri, Priyanka Jain, “Automated Toll Collection Using RFID Technology”, IJARSE, Vol 2, Issue 9, 2013. [6] Zhengang. R and Yingbo. G. (2009). “Design of electronic toll collection system in expressway based on RFID”, ESIAT 2009, International Conference on, 2 (4-5July 2009), 779-782. Mr.Bennilo Fernandes.J, Mr. Kasi Prasad Mannepalli, Mr. Agilesh Saravanan, Mr. KTPS Kumar Authors:

Paper Title: Fuzzy Utilization in Speech Recognition and its Different Application Abstract—Talk affirmation is one among the basic zones in cutting edge talk process. The examination of talk affirmation may be a bit of an examination for "artificial intelligence" machines that may "hear" and "appreciate" the verbally communicated data. The customary ways for talk affirmation like HMM and DTW, are outrageously inconvenient and time excellent. As such formal Fuzzy justification may be an endeavor in cutting edge talk process for the convincing portrayal of talk affirmation in a couple of utilization. The approach masterminded in the midst of this paper streamlines the utilization of fuzzy in talk affirmation and make the data dealing with time shorter. The case considered in the midst of this paper is that the least mind boggling, i.e., the example of speaker dependence, little vocabulary and disconnected words. There are various spectral and common choices isolated from human talk. The present ways for tendency acknowledgment from voice use basically MFCC and Energy feature. This paper briefs an overview concerning the present work on talk feeling ID strong for completing more examination by feathery approach.

Keywords—Emotional Speech Recognition, Fuzzy, HMM, NN, , Applications.

References: [1] Mohammad Savargiv , Mohammad Reza Keyvanpour, Soheila MehrMolaei. “A structural method based on fuzzy approach at producing emotional data”. 6th Iranian Joint Congress on Fuzzy and Intelligent Systems (CFIS), IEEE,12 April 2018. [2] Akhmedova, Shakhnaz & Stanovov, Vladimir & Semenkin, Eugene. “Cooperation of Bio-inspired and Evolutionary Algorithms for Neural Network Design”. Journal of Siberian Federal University - Mathematics and Physics. May 2018. 48. [3] Asemi, Adeleh & Salim, Siti Salwah & Shahamiri, Seyed Reza & Asemi, Asefeh & Houshangi, Narjes. “Adaptive Neuro-fuzzy Inference System for Evaluating Dysarthric Automatic Speech Recognition (ASR) Systems: A case study on MVML based ASR. Soft Computing”. 2018. 10.1007/s00500-018-3013-4. 261-266 [4] Nancy Bansal, Amit Verm, Iqbaldeep Kaur, Dolly Sharma. “Multimodal biometrics by fusion for security using genetic algorithm”. 4th International Conference on Signal Processing, Computing and Control (ISPCC), IEEE, 25 January 2018. [5] Win Chit, Yin & Khaing, Soe. “Myanmar Continuous Speech Recognition System Using Fuzzy Logic Classification in Speech Segmentation”, Association for Computing Machinery, 2018. [6] Semiye Demircan, Humar Kahramanli. “Application of fuzzy C-means clustering algorithm to spectral features for emotion classification from speech”. Neural Computing and Applications, Volume 29, Number 8, Page 59, 2018. [7] Fan, M., Hu, J., Cao, R., Ruan, W., Wei, X., A review on experimental design for pollutants removal in water treatment with the aid of artificial intelligence, Chemosphere (2018), doi: 10.1016/j.chemosphere.2018.02.111. [8] Ghoniem, Ranya & Shaalan, Khaled. “FCSR - Fuzzy Continuous Speech Recognition Approach for Identifying Laryngeal Pathologies Using New Weighted Spectrum Features”. Springer International Publishing AG , 384-395. 10.1007/978-3-319-64861-3_36, 2018. [9] Khanum, Seema & Abdul, Firos. “A novel speaker identification system using feed forward neural networks”, IEEE, 2017 3045-3047. 10.1109/ICECDS.2017.8390014. [10] Lazli, Lilia & Laskri, Mohamed Tayeb & Boudour, Rachid. “Discriminant learning for hybrid HMM/MLP speech recognition system using a fuzzy genetic clustering”. Intelligent Systems Conference, IEEE, 2017, 76-81. 10.1109/IntelliSys.2017.8324351. [11] Yenjerappa, Vani & A. Anusuya, M. “Noisy Speech Recognition Using Kernel Fuzzy C Means”. Springer Nature Singapore Pte Ltd. 2018, 10.1007/978-981-10-9059-2_29. [12] Abdelkefi, Mouna & Kallel, Ilhem. “Towards a fuzzy multiagent tutoring system for M-learners' emotion regulation”. 2017 IEEE, 1-6. 10.1109/ITHET.2017.8067821. [13] Akil, Muhammad & Nurtanio, Ingrid & Samsoe'oed Sadjad, Rhiza. “A DC motor speed control using the LPC-ANFIS speech recognition system”. 2017 IEEE. 215-220. 10.1109/QIR.2017.8168484. [14] Y. Dai, X. Wang, P. Zhang, W. Zhang. “Wearable Biosensor Network Enabled Multimodal Daily-life Emotion Recognition Employing Reputation-driven Imbalanced Fuzzy Classification, Measurement (2017), doi: http://dx.doi.org/10.1016/j.measurement.2017.06.006 [15] Faiyaz & Karyotis, Charalampos & Iqbal, Rahat & James, Anne. “An intelligent framework for emotion aware e-healthcare support systems”. 2016 IEEE. 1-8. 10.1109/SSCI.2016.7850044. [16] Ben Fredj, Ines & Ouni, Kais. (2017). “Fuzzy k-Nearest Neighbors applied to phoneme recognition”. 2016 IEEE.422-426. 10.1109/DT.2017.8012141. [17] Ghoniem, Ranya & Shaalan, Khaled. “A Novel Arabic Text-independent Speaker Verification System based [18] emotion recognition from face”. International Conference on Computer and Knowledge Engineering (ICCKE), IEEE 2017, 10.1109/ICCKE.2017.8167879. [19] Lazli, Lilia & Boukadoum, Mounir & Ait Mohamed, Otmane. (2017). “Fuzzy clustering optimized with [20] genetic algorithms: Application for hybrid speech recognition system”. 0567-0572. 10.1109/CoDIT.2017.8102654. [21] Yanti Liliana, Dewi & Widyanto, Rahmat & Basaruddin, T. “Human emotion recognition based on active appearance model and semi-supervised fuzzy C-means”. 2016 439-445. 10.1109/ICACSIS.2016.7872744. [22] Motamed, Sara & Setayeshi, Saeed & Rabiee, Azam. “Speech emotion recognition based on a modified brain emotional learning model”. Biologically Inspired Cognitive Architectures. 2017. 19. 10.1016/j.bica.2016.12.002. [23] Shing-Tai, PanCheng-Yuan, ChangYi-Heng Tsai. “FPGA-Based Robust Wireless Speech Motion Control for Home Service Robot Subject to Environmental Noises”. International Journal of Fuzzy Systems 2016. DOI: 10.1007/s40815-016-0222-9. [24] Gautam, Sumanlata & Singh, Latika. (2015). Developmental pattern analysis and age prediction by extracting speech features and applying various classification techniques. International Conference on Computing, Communication and Automation, ICCCA 2015. 83-87. 10.1109/CCAA.2015.7148348. [25] Paul Choudhury, Suman & Misra, Songhita & Hussain, Rabul & Shome, Nirupam & Kanti Das, Tushar. (2015). Effects of fuzzy parameter on text dependent speaker verification under uncontrolled noisy environment. 10.1109/GCCT.2015.7342703. [26] Mirlab Audio Signal Processing tutorials, “Speech feature MFCC Calculation guide”, (Browsing Date: 26th February 2016). [27] Elizabeth D. Casserly and David B. Pisoni, 'Speech perception and production'. Wiley Interdiscip Rev Cogn Sci. Author manuscript; available in PMC 2013 Aug 12. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3740754/, (Browsing Date: 25th October 2015). [28] Anila, R., Revathy, A.: Emotion recognition using continuous density HMM. Communications and Signal Processing (ICCSP), 2015 International Conference on. IEEE (2015) [29] Yogesh, C.K., et al.: Hybrid BBO_PSO and higher order spectral features for emotion and stress recognition from natural speech. Appl. Soft Comput. 56, 217–232 (2017) [30] Al-Naser, Mustafa, Elshafei, Moustafa, Al-Sarkhi, Abdelsalam: Artificial neural network application for multiphase flow patterns detection: a new approach. J. Petrol. Sci. Eng. 145, 548–564 (2016) [31] M. Savargiv and A. Bastanfard, “Persian speech emotion recognition”,Proceedings of Information and Knowledge Technology (IKT), 2015 7th Conference on, (2015), 1-5. Dhanasekaran Sundararaj, Padmanabhan K, Ananthi Sabapathy Authors:

Paper Title: Microstrip Antenna Design by Entrenching the Ground Plane Around the Patch Abstract—A new approach to enhance the Front-To-Back Ratio (FTBR) of aninset fed microstrip patch antennabyentrenching the ground plane encircling the patch is described in this paper.By entrenching the groundplane around patch,backlobe of the antenna getssuppressed.The FTBRhas been improvedto a value of 48.25 dBi, which is very much higher compared to the FTBR of reference microstrip antenna 13.29 dBi.

Index terms—Microstrip Antenna,Backlobe Suppression, Front-To-Back Ratio.

49. References: [1] Randy Bancroft, “Microstrip and Printed Antenna Design”, Scitech Publishing ,Inc,Raleigh ,NC.2009,pp.10-75. 267-269 [2] C.A.Balanis, “Antenna Theory : Analysis and Design”, Wiley India P Ltd, 2005.pp 811-882. [3] Qinjiang Rao, T.A.Denidhi,R.H.Johnston, “A new aperture coupled microstrip slot antenna”, IEEE Transactions on Antennas and Propagation,Vol 53,No.9,Sep 2005. [4] Zhi Ning Chen,Qing Xianming,Jin Shi, “Compact Substrate Integrated Waveguide Slot Antenna Array With Low Back Lobe”,IEEE Antennas and Wireless Propagation letters ,Vol 12,2013 [5] Jie Wei,Shaoweri Liao,Jianhua Xu ,Zhi Ning Chen,Xianming Qing,Jin Shi, “SIW slot antenna array with low back lobe”,IEE Asia-Pacific Conference on Antennas and Propagation,Aug 2013. [6] T.J.Cho,H.M. Lee, “Front-to-Back ratio improvement of a microstrip antenna by ground plane edge shaping” ,IEEE 2010 [7] Hong-Min Lee , “Front-to-Back ratio improvement of a Microstrip Patch Antenna Loaded with Surface Structure in a Partial Removed Grond Plane”, Journal of Electromagnetic Engineering and Science ,Vol.12 ,No.4,247-253,Dec 2012. [8] Rafael A.Rodriguez Solis, Ana Medina,Nestor Lopez, “Microstrip Patch Encircled by Trench”,IEEE 2000. [9] Siew Bee Yeap,Zhi Ning Chen, “Microstrip Patch Antennas with Enhanced gain by Partial substrate removal”,IEEE Transactions on Antennas and Propagation,Vol 58,No.9, Sep 2010. [10] Won-Gyu Lim , “New method for backlobe suppression of microstip patch antenna for GPS”.Proceedings of 40th European Microwave Conference,28-30September2010,Paris,France Sudhamathi. K,Deepa, S.Sabapathy, Ananthi, Krishnasamy ,Padmanabhanand, J.Rama Authors:

Paper Title: Bit Error Performance of Variations along the Spectrum for the Several Sub Carrier Abstract—Present scenario of mobile communication is to meet the demands of Error free and high data rate communication. This can be achieved with multi carrier modulation technique. In Multi carrier modulation, the transmitting data is split into several components which is modulated and transmitted with different carriers which leads to less Inter Symbol Interference, multipath fading and impulse noise. A novel method of multi carrier modulation and demodulation was done by multiplying user symbols with several sub carriers then combining the several modulated signals and transmitted over a noisy channel. Analysis was carried out after demodulation by comparing received symbols with transmitted symbols. Simulation was carried out to analyze the Bit error rate (BER) performance of multi carrier modulation system as a function of Number of sub carriers and spacing between them. It is found that BER performance is not consistent for all sub carriers. BER performance degrades as sub carriers increases from 1 to n, where n=fs/2. It is also observed that the BER performance is approximately stable throughout the specified spectrum for a given sub carrier.

Keywords—Bit Error Rate, Filter Bank Technique, Multi Carrier Modulation Technique

References: [1] S. Hassan, “Multicarrier Communication Systems with examples in Matlab -A New perspective”, Auerbach 50. Publications, Taylor and Francis Group, USA,2016. [2] A.C.Bingham, “Multicarrier modulation for data transmission. An idea whose time has come”, IEEE Communication Magazine,1990. 270-277 [3] S. Hara and R. Prasad, “Overview of Multi carrier CDMA”, IEEE Commn. Mag.,Vol. 35, No. 12, pp126- 133,1997. [4] W.C.Jake, “Microwave Mobile communications”, New York: Wiley, 1974. [5] R.Saltzbery,”Performanceofanefficientparalleldatatransmission”,IEEE Transaction on Communication Technology, Vol. 15, No. 6, 1967. [6] N.Yee, J.P. Linnartz and G.Fettweeis, “Multi-carrier CDMA in indoor wireless radio networks”, IEICE Trans. Commn, Vol. 77, No. 7, pp. 109-113,1994. [7] S. Rappaport, “Wireless Communications: Principle and Practice”, Prentice Hall,1995. [8] K.Sudhamathi, S.Ananthi and K.Padmanabhan, “Performance Analysis of MC-CDMA System with Trellis based Modulation in a Radio Channel”, JI of Instrumentation Society of India, Vol. 46, No. 1, pp. 52- 56,2016. [9] http://wcsp.eng.usf.edu/OFDM_links.html [10] Q.Shi and M. Ltvaaho, “Performance analysis of MC-CDMA in Rayleigh fading channels with correlated envelopes and phases”, In:IEE Proceedings – Communications,Vol. 50, No. 3, pp. 214 -220,2003. [11] T.LajosHanzo, B. Muzster, J. Choi, and Thomas Keller, “OFDM and MC-CDMA for Broadband Multi- User Communications WLANs and Broadcasting”, Wiley-IEEE Press, Sep.2003. [12] http://www.nutaq.com/blog/filter-bank-multicarrier-fbmc-%E2%80%93-potential-concept-5g- [13] B.F. Boroujeny, “OFDM VersusFilterBank Multicarrier”, Signal Processing Magazine, IEEE, Vol. 28, No. 3,pp. 92-112,2011.of 40th European Microwave Conference,28-30September2010,Paris,France Authors: Ananthi S, K.Padmanabhan, , Bhagvan Shree Ram, Arun Ananthanarayanan

Paper Title: Research on Speech Codec with Compression using Legendre Polynomials Abstract- Speech Codecs have been developed in several forms over the past 30 years and are used in Cellular digital telephony. Based on the throat excitation pulses and mouth cavity filter model, all the methods aim to encode the speech samples without much loss of clarity. The complexity of the methods using codebook searching, filtering and matrix inversion, all need heavy computations, leading to timing constraints. After 51. describing a tutorial elucidating the essentials of the present methods, a different direct method of encoding, suggested by the spherical harmonic oscillator functions of Legendre, is introduced. Its performance and simplicity are such that the same may find its applications soon. 278-284

Keywords: Speech Processing, Legendre Polynomial, Audio codecs, CELP, LPC, Speech Compression

References: [1] Jacob Benesty, M. Mohan Sondhi, Yiteng Huang (Eds.), Handbook Springer of Speech Processing Springer-Verlag Berlin Heidelberg, 2008. [2] Rabiner .R. and Schafer,.R.W., Digital Processing of Speech Signals, Prentice Hall Inc., Englewood Cliffs, N.J., 1978. [3] Atal and Schroeder B.S.: Predictive coding of speech signals and subjective error criteria, IEEE Trans. Acoust. Speech Signal Process. 3, 247–254 (1979). [4] C. Laflamme, J.-P. Adoul, H.Y. Su, S. Morisette: On reducing computational complexity of codebook search in CELP coder through the use of algebraic codes, Proc. IEEE Int. Conf. Acoust. Speech Signal Process. (1990) pp. 177–180. [5] J.H. Chen, M.S. Rauchwerk: An 8 kb/s low-delay CELP speech coder, Proc. IEEE Global Communication. Conf. (1991) pp. 1894– 1898. [6] B.S. Atal: The history of linear prediction, IEEE Signal Proc. Mag. 23(2), 154–161, (2006). [7] K.Padmanabhan ,S.Ananthi, R.Vijayarajeswaran, A practical Approach to Digital Signal Processing, New Age International Publishers, New Delhi, revised edition 2002. [8] www.datacompression.com. [9] Voice and Audio Compression for Wireless Communication, Book, Lagos, Hanco, Clare Sommerville and Jasan Woodland. Wiley IEEE Press, 2007. [10] 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. [11] Speigel, Math. Handbook of Formulas and Tables, Schaum Series Publications, 1990. [12] Attewell. P.B. and Sandford M.R, On the Creiterion of failure for Geological Rock Crack Distribution, Symposium on Anisotropic Rocks, 1974. [13] Techniques for Noise Robustness in Automatic Speech Recognition, edited by Tuomas Virtanen, Rita Singh, Bhiksha Raj, Wiley Inc.,2007. [14] http:/opus-codec.org. [15] Mouser.com, DS70146A – Code for DSPIC 30 Family for CELP. Authors: Tulasi Radhika Patnala, Sankararao Majji, Gopala Krishna Pasumarthi.

Paper Title: Optimization of CSA for Low power and High speed using MTCMOS and GDI techniques Abstract: The basic operation involved in any analog, digital, control system, DSP’s is addition. Performance and reliability of almost every digital system is depends on performance of adder. Over the decade, many adder architectures are proposed and still research work is going on adder to obtain the best results in power, delay and power delay product (PDP). In this paper we proposed one of the fastest adder architecture called Carry Select adder (CSA) and optimization is done for performance parameters like delay and power using GDI (Gate Diffused Input) and MTCMOS techniques. Implementation has been done in standard gpdk 90nm technology using Cadence tool.

Index Terms: carry select adder, Gate Diffused input, power delay product and MTCMOS.

References:

[1] A.Mitra; A.Bakshi; B.Sharma; N.Didwania” Design of a High Speed Adder” International journal of Scientific & Engineering Research, 2015.

[2] Abba, Akhila; K.Amarendar” Improved Power Gating Technique for Leakage Power Reduction”, IJES, 2014.

[3] B.Ramkumar; Kittur; M, Harish” Low Power and Area-Efficient carry select adder, IEEE Transactions on Very Large Scale 52. Integration Systems,2012.

[4] Kumre, Laxmi; Ajay.S, Ganga agnihotry” Analysis of GDI technique for Digital circuit Design, International journal of Computer 285-288 Applications, 2013.

[5] Liu, Feng; Song, Xiaoyu; Tan, Qingping; Chen, Gang” Formal Analysis of Hybrid Prefix/Carry -Select Arthimetic Systems. The Computer Journal, 2011.

[6] Morgenshtein, Arkadiy; Isreal, and Alexander Fish” Gate-Diffusion Input (GDI)-A Technique for Low Power Design of Digital Circuits: Analysis and Characterization”, IEEE, 2000.

[7] Sangeetha Parshionikar, Dr Deepak V.Bhior” Leakage Power Reduction Using Multi Threshold CMOS Technique”, IJSER, 2013.

[8] Verma, Pooja; Machandana, Rachna” Review Of Various GDI for Low Power Digital Circuits”, IJAETAE, 2014.

[9] Y.Wang; C.Pai; X.Song” The Design of Hybrid Carry Select Adder”, IEEE Transactions on Circuits and Systems: Analog and Digital Signal Processing, 2012.

[10] Saxena, Pallavi; Purohit, Urvashi; Joshi, Priyanka” Analysis of Low Power, Area Efficient and High speed Fast Adder”, International Journal of Advanced Research on Computer and Communication Engineering, 2013.

[11] Kiat-Seng Yeo, Kaushik Roy, “Low-Voltage, Low-Power VLSI Subsystems”, Indian edition.

[12] A. P. Chandrakasan, R. W. Brodersen, “Minimizing Power Consumption in Digital CMOS Circuits”, Proceedings of the IEEE, vol. 83, no. 4, pp. 498-523, April 1995.

53. Authors: Nikhil S Damle, Gauri Damle, Rushikesh Deshmukh Paper Title: Predicting Reliability of Open Source Hardware Platform Abstract: Embedded system today are influencing every sphere of life and all application domains. These systems were initially designed according to the application requirement as special hardware. This approach of embedded system design was influenced by particular family of processor. The new approach of embedded system design envisages the use of general purpose on the shelf hardware modules to be incorporated. These on the shelf hardware modules are also referred open source hardware platforms as their entire hardware module design in available in public domain. Thus populating such hardware platforms by different individuals becomes easy. At instances these open source modules are not as good as the original ones. This is because calculating and predicting the successes achieved or failure caused by the copy is not tested. This can be verified by the performance of the module designed in terms of reliability.

Keywords: Open source, firmware Software Reliability; Reliability Testing; module

References: 1. P. J. Gil, G. Benete, J.J. Serrano “Design of an Intelligent Stepper Motor Controller Module into a Distributed Process Control Architecture” MELECON '94. Mediterranean Electro technical Conference 12-14 April 1994 2. Erich Sulzer, Michael Bertsch, “Design & Engineering of Modern Process Control System” 1997 IEEE/PCA Cement Industry Technical Conference. XXXIX Conference Record (Cat. No.97CH36076), 20-24 April 1997 1-5 3. Fabiano Hessel, Vitor M. da Rosa, Igor M. Reis, “Abstract RTOS Modeling for Embedded Systems” 15th IEEE International Workshop on Rapid System Prototyping (RSP’04) PP 210-216 4. Semiconductor Device Reliability Failure Models Technology Transfer # 00053955A-XFR International SEMATECH May 31,2000 5. Dr. Peter Tröger, Krishna B. Misra: Reliability Prediction -Handbook of Performability Engineering. Springer. 2008 (p. 265ff) 6. Andreas Gerstlauer Haobo Yu Daniel D. Gajski “RTOS Modeling for System Level Design” Design, Automation and Test in Europe Conference and Exhibition (DATE’03) 7. Zhengting He “Timed RTOS Modeling for Embedded System Design” 11th IEEE Real Time and Embedded Technology and Applications Symposium (RTAS’05) 8. Cyprian F. Ngolah, Yingxu Wang, and Xinming Tan “Implementing Task Scheduling and Event Handling In RTOS+”CCECE 2004- CCGEI 2004, Niagara Falls, May 2004 9. Nikhil Shirish Damle, Avinash G Keskar “Co-simulation of Networked Embedded System: Verification Approach” SCIS & ISIS SCIS & ISIS 2008, pp 2086-2090 10. Chen, Hui & Qin, Zhidong. (2010). Reliability demonstration testing method for embedded operating systems. 2nd International Conference on Software Engineering and Data Mining, SEDM 2010. 11. Farah Lakhani, Michael J. Pont Farah Lakhani, Michael J. Pont Applying design patterns to improve the reliability of embedded systems through a process of architecture migration, IEEE 14th International Conference on High Performance Computing and Communications 978-0-7695-4749-7/12 $26.00 © 2012 IEEE DOI 10.1109/HPCC.2012.228 12. MIL-HDBK-217 (Electronics Reliability Prediction) Handbook. 2007 Authors: Damandeep Kaur,. Surender Singh

Paper Title: An Analysis of Detection of Brain Tumor using Image Processing Techniques Abstract: Image processing in biomedical field is being increasingly used for the detection and diagnosis of various abnormalities in the body parts. The detection of brain tumours using image processing on MRI images is one such field where better results are obtained as comparative to CT-scan and x-ray. Prior detection of the brain tumour is desirable and possible with the help of machine learning and image processing techniques. These techniques detect even a small abnormality in the human brain following a four-stage process which includes pre-processing, segmentation, feature extraction and optimization. Different parameters such as accuracy, PSNR, MSE are calculated to find out the efficiency of process and to compare it with other methods. This paper reviews about various different approaches which are used to detect the brain tumor using image processing techniques.

Keywords: Types of tumors, feature extraction, classification, optimisation, image processing

References: 54. 1. G Riddhi.S.Kapsa, S.S. Salankar, Madhuri.Babar “Literature Survey on Detection of Brain Tumor from MRI Images”IOSR Journal of Electronics and Communication Engineering (IOSR-JECE) e-ISSN: 22782834,p- ISSN: 2278-8735.Volume 10, Issue 1, Ver. II (Jan - Feb. 2015), PP 80-86 2. A.Sindhu1, S.Meera2 “A Survey on Detecting Brain Tumor in mri Images Using Image Processing Techniques ”International Journal of Innovative Research in Computer and Communication Engineering Vol. 3, Issue 1, January 2015 3. Umit Ilhan et al. “ Brain tumor segmentation based on a new threshold approach” / Procedia Computer Science 120 (2017) 580–587. 4. Quratul Aina, M. Arfan Jaffar and Tae-Sun Choic, “Fuzzy anisotropic diffusion based segmentation and texture based ensemble classification of brain tumor”, ELSEVIER Applied Soft Computing, 21, 330– 340, 2014. 5. B. Devkota et al. ‘ Image Segmentation for Early Stage Brain Tumor Detection using Mathematical Morphological Reconstruction’ / Procedia Computer Science 125 (2018) 115–123 6-9 6. Nilesh.L.Shimpi, G Ahmed Zeeshan, Dr. R Sundaraguru “BRAIN TUMOR DETECTION AND EXTRACTION” in International Journal of Advance Research in Engineering, Science & Technology (IJAREST) Volume 4, Issue 10, October 2017, e-ISSN: 2393- 9877, print-ISSN: 2394-2444 7. AMRUTA PRAMOD HEBLI et al. “BRAIN TUMOR DETECTION USING IMAGE PROCESSING: A SURVEY” Proceedings of 65 th IRF International Conference, 20th November, 2016, Pune, India, ISBN: 978-93-86291-38-7 8. 5. Tian Xia et al. “Patch-level Tumor Classification in Digital Histopathology Images with Domain Adapted Deep Learning” 978-1- 5386-3646-6/18/ ©2018 IEEE 9. Reema Mathew A et al. “TUMOR DETECTION AND CLASSIFICATIONOF MRI BRAIN IMAGE USING WAVELET TRANSFORM AND SVM” International Conference on Signal Processing and Communication (ICSPC’17) – 28th & 29th July 2017. 10. Aby Elsa Babu et al. “A Survey on Methods for Brain Tumor Detection” Proc. IEEE Conference on Emerging Devices and Smart Systems (ICEDSS 2018) 2-3 March 2018, Mahendra Engineering College, Tamilnadu, India. 11. Luxit Kapoor et al. “A Survey on Brain Tumor Detection Using Image Processing Techniques “ 978-1-5090-3519-9/17/ 2017 IEEE. 12. Miss. Shrutika Santosh et al. “Implementation of Image Processing for Detection of Brain Tumors” Proceedings of the IEEE 2017 International Conference on Computing Methodologies and Communication (ICCMC) 978-1-5090-4890-8/17/2017 IEEE 13. Geert Litjens et al. “A survey on deep learning in medical image analysis”. / Medical Image Analysis 42 (2017) 60–88, 1361-8415/© 2017 Elsevier 14. Olfa Limam, Fouad Ben Abdelaziz “Multicriteria Fuzzy Clustering for Brain Image Segmentation”, Institut Sup´erieur de Gestion, University of Tunis. 15. Nilesh Bhaskarrao Bahadure et al.” Image Analysis for MRI Based Brain Tumor Detection and Feature Extraction Using Biologically Inspired BWT and SVM” International Journal of Biomedical Imaging Volume 2017, Article ID 9749108. 16. G Rajesh Chandra et al. “TUMOR DETECTION IN BRAIN USING GENETIC ALGORITHM” / Procedia Computer Science 79 ( 2016 ) 449 – 457 . 17. J. Amin, M. Sharif, M. Yasmin, S.L. Fernandes, Big data analysis for brain tumor detection: Deep convolutional neural networks, Future Generation Computer Systems (2018), https://doi.org/10.1016/j.future.2018.04.065 18. P. Shanthakumar, P. Ganeshkumar/Computers and Electrical Engineering (2015) “performance analysis of classifier for brain tumor detection and diagnosis “0045-7906/ 2015 Elsevier Ltd 19. O. N. Pandey et al. “Review on Brain Tumor Detection Using Digital Image Processing” International Journal of Scientific & Engineering Research, Volume 5, Issue 5, May-2014 1352 ISSN 2229- 5518 20. Munmun Saha et al. “A Review on Various Image Segmentation Techniques for Brain Tumor Detection “International Journal of Scientific Research in Computer Science, Engineering and Information Technology © 2018 IJSRCSEIT | Volume 3 | Issue 1 | ISSN : 2456-3307 21. Alexander Zotin et al. “Edge detection in MRI brain tumor images based on fuzzy C-means clustering” / Procedia Computer Science 126 (2018) 1261–1270. 22. Iván Cabria et al. “MRI segmentation fusion for brain tumor detection” In information fusion , 36,1-9 (2017) 23. R. Pradeep Kumar Reddy et al. “Brain Tumor MRI Using Gradient Profile Sharpness” Int. J. Advanced Networking and Applications Volume: 09 Issue: 05 Pages: 3557-3562 (2018) ISSN: 0975-0290 24. Lei Guo, Youxi Wu, Xuena Liu and Xuena Liu, Research on the Segmentation of MRI Image Based on Multi-Classification Support Vector Machine, in the website http://www.paper.edu.cn. 25. VaradaS.Kolge and.Kulhalli K.V, PCA and PNN assisted automated brain tumor classification, IOSR Journal of Electronics and Communication Engineering (IOSR-JECE) ISSN: 2278-2834, ISBN: 2278-8735, PP: 19-23. 26. Takate V. S. and Vikhe P. S, Classification of MRI Brain Images using K-NN and k-means, Department of Instrumentation & Control, Pravara Rural Engineering College, Loni Ahmednagar, Maharashtra. 27. Ahmad Mubashir, Mahmood ul-Hassan, Imran Shafi, and Abdelrahman Osman, Classification of Tumors in Human Brain MRI using Wavelet and Support Vector Machine, IOSR Journal of Computer Engineering (IOSRJCE), 2012, ISBN: 2278-8727, Volume 8, Issue 2, PP 25-31. 28. Pankaj Sapra, Rupinderpal Singh, and ShivaniKhurana, Brain Tumor Detection Using Neural Network, International Journal of Science and Modern Engineering (IJISME) ISSN: 2319-6386, 2013, Volume-1, Issue-9. 29. Vipin Y. Borole, Sunil S. Nimbhore, Dr. Seema S. Kawthekar, “Image Processing Techniques for Brain Tumor Detection: A Review”, in International Journal of Emerging Trends & Technology in Computer Science (IJETTCS), ISSN 2278-6856, 2015. Authors: Nikhil Sharma Techniques of Enhancing Usage Management of A Local Area Network of A Higher Educational Paper Title: Institution Abstract: Managing the usage of Local Area Network is an important task as it helps in maintaining the productivity of the LAN There exists tools for this purpose and most of them are web interface based, they can control and monitor the usage only when connected to internet. Moreover proper usage logging and usage control based on the already set schedule is also absent. Practically, this management do not suffice the need of higher educational institutions because the areas like accountability, usage logging and control is not addressed in this approach. Both are important factor of enhancing management of LAN. This research paper proposes a tool-LABGUARD to incorporate these features. Further the tool has been implemented using .Net Technology. As a result, the usage of systems was accountable, only the authorized user could access the assigned sites with 55. granted privileges at the retrospective time. Thus the system became more accountable and secured. It also enhanced the control of administrator on entire lab setup. It also facilitates the administrator to retrieve log details. 10-15

Keywords: Open source, firmware Software Reliability; Reliability Testing; module

References: 1. Yang Chenghui,”A Design of Laboratory Information Management System” IEEE Xplore 2010 2. Chang Sheng, “Security of office management information system analysis” IEEE Xplore 2011. 3. Wu, Ziyan & Wu, Jinlang & Sun, Dan & Wu, Xiaoya. (2006). “Remote measurement platform based on DataSocket and .NET framework”. 63581W-63581W. 10.1117/12.717947. 4. Davis, Keir & W. Turner, John & Yocom, Nathan. (2004). “Securing Network Communication” 10.1007/978-1-4302-0748-1_10. 5. Buis, P. (2002). Socket-level server programming & .NET. Doctor Dobbs Journal. 27. 25-32. Authors: Atul Rawat, Sumeet Gupta, T. Joji Rao, Sushil Kumar Rai

Paper Title: Natural Gas Demand Estimation for India: Error Correction Modelling Abstract: Share of fossil fuel in India’s primary energy mix is around 92% with natural gas contributing 6% in 56. it. The power, fertilizer and city gas distribution (CGD) sector are the major gas-consuming sector in India. Despite the government efforts to increase the share of natural gas in the primary energy mix, the country still has low per capita gas consumption. In order to enhance natural gas consumption in the country, the Indian 16-24 government has set up a target to increase natural gas share in the energy mix to 15% by 2022. Therefore, the issue of estimation of the natural gas demand is addressed in the present paper to understand the dynamics of the natural gas market. The error correction model (ECM) is applied at a national and sectoral level to examine the domestic gas demand in India. The study reveals the following findings: (a) At the national and sectoral level, the last year gas consumption is an only statistically significant factor; (b) Price, population and income are not statistically significant at national and sectoral level and (c) Demand for natural gas is price inelastic at the national level.

Keywords: Demand Estimation, Error Correction, India, Natural Gas

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Available: https://www.weforum.org/agenda/2017/10/eight-key-facts-about-indias-economy-in-2017/. 9. W. Bank, "Data Bank," World Bank, 12 26 2018. [Online]. Available: https://databank.worldbank.org/data/home.aspx. [Accessed 25 2 2019]. 10. A. Wright, "8 things you need to know about India’s economy," 1 October 2017. [Online]. Available: https://www.weforum.org/agenda/2017/10/eight-key-facts-about-indias-economy-in-2017/. [Accessed 2 1 2019]. 11. BP, "Statistical Review of World Energy," BP, London, 2018. 12. PPAC, "Petroleum Planning & Analysis Cell," 31 12 2018. [Online]. Available: https://www.ppac.gov.in/content/153_1_ImportNAturalgas.aspx. [Accessed 8 01 2019]. 13. S. Cornot-Gandolphe, "India’s vision to a gas-based economy Drivers and Challenges," CEDIGAZ, 10 October 2017. [Online]. Available: https://www.cedigaz.org/indias-vision-gas-based-economy-drivers-challenges/. [Accessed 08 01 2019]. 14. P. Bodger and J. T. Baines, "Further Issues in Forecasting Primary Energy Consumption," TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE, vol. 26, no. 3, pp. 267-280, 1984. 15. S. C. Bhattacharyya and G. R. Timilsina, "Modelling energy demand of developing countries: Are the specific features adequately captured?," Energy Policy, vol. 38, no. 4, pp. 1979-1990, April 2010. 16. S. Jebaraj and S. Iniyan, "A review of energy models," vol. 10, no. 4, pp. 281-311, August 2006. 17. F. Urban, H. C. Mall and R. M. Benders, "Modelling energy systems for developing countries," Energy policy, vol. 35, no. 6, pp. 3473-3482, June 2007. 18. Thomas, Wim; Haigh, Martin, "World Energy Model A View To 2010," Shell International BV, The Hague, 2017. 19. E. Erdogdu, "Natural Gas Demand In Turkey," Applied Energy, vol. 87, no. 1, pp. 211-219, 2010. 20. L. Junchen, D. Xiucheng, S. Jianxin and M. Hook, "Forecasting the growth of China’s natural gas consumption," Energy, vol. 36, no. 3, pp. 1380-1385, March 2011. 21. S. K. Mukherjee, "Energy policy and planning in India," Energy, vol. 6, no. 8, pp. 823-851, 1981. 22. R. D. Rao and J. K. Parikh, "Forecast and analysis of demand for petroleum products in India," Energy Policy, vol. 24, no. 6, pp. 583- 592, June 1996. 23. B. S. Reddy and P. Balachandra, "Integrated energy-environment-policy analysis — a case study of India," Utilities Policy, vol. 11, no. 2, pp. 59-73, 2003. 24. J. Parikh, P. Purohit and P. Maitra, "Demand projections of petroleum products and natural gas in India," Energy, vol. 32, no. 10, pp. 1825-1837, 2007. 25. S. Jebaraj, S. Iniyan and H. Kota, "Forecasting of commercial energy consumption in India using Artificial Neural Network," International Journal of Global Energy Issues, vol. 27, no. 3, pp. 276-301, 2007. 26. C. K. R. S. D. Cleveland, "Aggregation and the role of energy in the economy," Ecological Economics, vol. 32, no. 2, pp. 301-317, 2000. 27. R. W. B. Ayres, "Accounting for growth: the role of physical work," Structural Change and Economic Dynamics, vol. 16, no. 2, pp. 181-209, 2005. 28. Chien-ChiangLee, "Energy consumption and GDP in developing countries: A cointegrated panel analysis," Energy Economics, vol. 27, no. 3, pp. 415-427, 2005. 29. I. M. W. Huson Joher Ali Ahmed, "Role of oil price shocks on macroeconomic activities: An SVAR approach to the Malaysian economy and monetary responses," Energy policy, vol. 39, no. 12, pp. 8062-8069, 2011. 30. J. B. Alam, Z. Wadud and J. W. Polak, "Energy demand and economic consequences of transport policy," International Journal of Environment Science and Technology, vol. 10, no. 5, pp. 1075-1082, 2013. 31. G. J. Daniel and S. Glaister, "The Demand for Automobile Fuel: A Survey of Elasticities," Journal of Transport Economics and Policy, vol. 36, no. 1, pp. 1-25, 2002. 32. D. Gately and H. Huntington, "The Asymmetric Effects of Changes in Price and Income on Energy and Oil Demand," The Energy Journal, vol. 23, no. 1, pp. 19-55, 2002. 33. P. I. Bureau, "Revision of Domestic Gas Prices," Government of India, New Delhi, 2014. 34. M. o. S. a. P. Implementation, "Energy Statistics," Government of India, New Delhi, 2018. 35. RBI, "Handbook of Statistics on Indian States," Reserve Bank of India, 15 3 2018. [Online]. Available: https://rbi.org.in/Scripts/AnnualPublications.aspx?head=Handbook%20of%20Statistics%20on%20Indian%20States. [Accessed 15 1 2019]. 36. R. F. Engle and C. W. Granger, "Co-Integration and Error Correction: Representation, Estimation, and Testing," Econometrica, vol. 55, no. 2, pp. 251-276, 1987. Authors: Pooja Dudhal, H.B.Chaudhari, Vipin Mishra, Bhanwar Lal Bishnoi

Paper Title: Numerical Differential Protection of Power Transformer using innovative algorithm 57. Abstract: This paper presents a new innovative algorithm for Numerical Differential Relay design of transformer. Fault information is critical for operating and maintaining power networks. This algorithm provides 25-34 accurate performance for transformer by which is independent of system conditions such as: External fault, Inrush current, CT saturation. Locating transformer faults quickly and accurately is very important for economy, safety and reliability point of view. Both fault-detection and protection indices are derived by using Numerical Differential Relay algorithm design of transformer. The embedded based differential and operating current measurement device is called numerical differential relay is among the most important development in the field of instantaneous fault operation. Numerical relay provides measurement of differential current and operating current at power transformer above 5MVA in substation. Simulation studies are carried out using MATLAB Software show that the proposed scheme provides a high accuracy and fast relay response in internal fault conditions. Current transformers form an important part of protective systems. Ideal Current Transformers (CTs) are expected to reflect the primary current faithfully on the secondary side. Under conditions the CT saturates, and hence it cannot reproduce the primary current faithfully. This paper deals with simulation methods for determining CT performance under different factor. A Simulink model has been developed to observe CT response under steady state w.r.t Burden, Turns ratio, Asymmetrical current, Hysteresis conditions. Thus, it is now possible to evaluate the CT performance under these factors.

Keywords: DC offset, FFT, Hysteresis, Burden

References: 1. Dr. Juergen Holbach, Siemens PT&D “Modern Solutions to Stabilize Numerical Differential Relays for Current Transformer Saturation during External Fault” 2. “Guide For The Application Of Current Transformers Used For Protection Relaying Purposes, 1996.”IEEE Std. C37.110 3. Piotr Sawko Wroclaw University of Technology, Faculty of Electrical Engineering “Impact of Secondary Burden and X/R Ratio on CT Saturation” 4. Omar.G.Mrehel Khaled Esmail. SH.Ghambirlou Mahmud .M. Alforjani, EEE Dept., University of Tripoli “Investigating Factors Affecting CT Saturation Using MATLAB” 1st Conference of Industrial Technology ( CIT2017) 5. Badri Ram ,D N Vishwakarma, “Power system protection and switchgear” 6. Fallahi, N. Ramezani, I. Ahmadi ”Current Transformers’ Saturation Detection and Compensation Based on Instantaneous Flux Density Calculations”, Online ISSN 1848-3380 7. R. P. Pandey, Dr. R. N. Patel PG Student [PSE], Professor Department of EEE, SSTC Bhilai Chhattisgarh, India”A CT Saturation Detection Algorithm Using Secondary Current Third Difference Function” © 2014 IJEDR | Volume 2, Issue 2 | ISSN: 2321-9939 Authors: Anubha Agrawal, Sakshi Saraswat, Hira Javed

Paper Title: A Pointer Generator Network Model to Automatic Text Summarization and Headline Generation Abstract: In a world where information is growing rapidly every single day, we need tools to generate summary and headlines from text which is accurate as well as short and precise. In this paper, we have described a method for generating headlines from article. This is done by using hybrid pointer-generator network with attention distribution and coverage mechanism on article which generates abstractive summarization followed by the application of encoder-decoder recurrent neural network with LSTM unit to generate headlines from the summary. Hybrid pointer generator model helps in removing inaccuracy as well as repetitions. We have used CNN / Daily Mail as our dataset.

Keywords: LSTM Encoder Decoder Model, Natural Language Processing, Pointer generator network and Coverage Mechanism, Text Summarization

References: 1. Lopyrev, K. (2015). Generating news headlines with recurrent neural networks. arXiv preprint arXiv:1512.01712. 2. Alexander M. Rush, Sumit Chopra, and Jason Weston. A neural attention model for abstractive sentence summarization. CoRR, abs/1509.00685, 2015. 3. See, A., Liu, P. J., & Manning, C. D. (2017). Get to the point: Summarization with pointer-generator networks. arXiv preprint arXiv:1704.04368. 58. 4. DeepMind Q&A Dataset: https://cs.nyu.edu/~kcho/DMQA/ cited on: 5. Chin-Yew Lin. 2004b. Rouge: A package for automatic evaluation of summaries. In Text summarization branches out: ACL workshop. 35-39 6. How to Configure the Learning Rate Hyperparameter When Training Deep Learning Neural Networks: https://machinelearningmastery.com/learning-rate-for-deep-learning-neural-networks/ (cited on: 22-May-2019) 7. Introduction toWord Vector: https://medium.com/@jayeshbahire/introduction-to-word-vectors-ea1d4e4b84bf (cited on: 22-May- 2019) 8. Recurrent neural networks and LSTM https://towardsdatascience.com/recurrent-neural-networks-and- lstm-4b601dd822a5 (cited on: 22-May-2019) 9. Understanding Encoder-Decoder Sequence to Sequence Model https://towardsdatascience.com/understanding-encoder-decoder- sequence-to-sequence-model-679e04af4346 cited on: 10. Python Language: https://www.geeksforgeeks.org/python-programming-language(cited on: 22-May-2019) 11. Machine learning course: https://www.coursera.org/learn/machine-learning 12. Oriol Vinyals, Meire Fortunato, and Navdeep Jaitly. 2015. Pointer networks. In Neural Information Processing Systems. 13. Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2015. Neural machine translation by jointly learning to align and translate. In International Conference on Learning Representations. 14. Stephen Merity, Caiming Xiong, James Bradbury, and Richard Socher. 2016. Pointer sentinel mixture models. In NIPS 2016 Workshop on Multi-class and Multi-label Learning in Extremely Large Label Spaces. 15. Caglar Gulcehre, Sungjin Ahn, Ramesh Nallapati, Bowen Zhou, and Yoshua Bengio. 2016. Pointing the unknown words. In Association for Computational Linguistics. 16. Wenyuan Zeng, Wenjie Luo, Sanja Fidler, and Raquel Urtasun. 2016. Efficient summarization with read-again and copy mechanism. arXiv preprint arXiv:1611.03382 . 17. Yishu Miao and Phil Blunsom. 2016. Language as a latent variable: Discrete generative models for sentence compression. In Empirical Methods in Natural Language Processing. 18. Ramesh Nallapati, Feifei Zhai, and Bowen Zhou. 2017. SummaRuNNer: A recurrent neural network based sequence model for extractive summarization of documents. In Association for the Advancement of Artificial Intelligence. 19. Ramesh Nallapati, Feifei Zhai, and Bowen Zhou. 2017. SummaRuNNer: A recurrent neural network based sequence model for extractive summarization of documents. In Association for the Advancement of Artificial Intelligence. 20. Ramesh Nallapati, Bowen Zhou, Cicero dos Santos, Çaglar Gulçehre, and Bing Xiang. 2016. Abstractive text summarization using sequence-to-sequence RNNs and beyond. In Computational Natural Language Learning. 21. Philipp Koehn. 2009. Statistical machine translation. Cambridge University Press. 22. Haitao Mi, Baskaran Sankaran, Zhiguo Wang, and Abe Ittycheriah. 2016. Coverage embedding models for neural machine translation. In Empirical Methods in Natural Language Processing. 23. Zhaopeng Tu, Zhengdong Lu, Yang Liu, Xiaohua Liu, and Hang Li. 2016. Modeling coverage for neural machine translation. In Association for Computational Linguistics. 24. Kelvin Xu, Jimmy Ba, Ryan Kiros, Kyunghyun Cho, Aaron C Courville, Ruslan Salakhutdinov, Richard S Zemel, and Yoshua Bengio. 2015. Show, attend and tell: Neural image caption generation with visual attention. In International Conference on Machine Learning. 25. Qian Chen, Xiaodan Zhu, Zhenhua Ling, Si Wei, and Hui Jiang. 2016. Distraction-based neural networks for modeling documents. In International Joint Conference on Artificial Intelligence. Authors: Harleen Kaur, Gourav Bathla

Paper Title: Group Recommendation for Cold Start Users Using Hybrid Recommendation Technique Abstract: Recommender system is an data retrieval system that gives customers the recommendations for the items that a customer may be willing to have. It helps in making the search easy by sorting the huge amount of data. We have progressed from the era of paucity to the new era of plethora due to which there is lot of development in the recommender system. In today’s scenario the interaction between the groups of friends, family or colleagues has increased due to the advancement in mobile devices and the social media. So, group recommendation has become a necessity in all kinds of domains. In this paper a system has been proposed using the group recommendation system based on hybrid based filtering method to overcome the cold start user issue which arises when a new user signs in and he/she doesn’t have any past records. So, the recommender system does not have enough information related to the user to recommend an item which will be of his/her interest. The dataset has been taken from the MovieLens is used in the experiment.

Keywords: Cold start problem, Group recommendation system, Hybrid filtering approach

References: 1. Ghodsad, P. R., & Chatur, P. N. (2018, August). Handling User Cold-Start Problem for Group Recommender System Using Social Behaviour Wise Group Detection Method. In 2018 International Conference on Research in Intelligent and Computing in Engineering (RICE) (pp. 1-5). IEEE. 2. Katarya, R., & Verma, N. (2017, December). Automatically detection and recommendation in collaborative groups. In 2017 International Conference on Intelligent Sustainable Systems (ICISS) (pp. 218-222). IEEE. 3. Dara, S., Chowdary, C. R., & Kumar, C. (2019). A survey on group recommender systems. Journal of Intelligent Information Systems, 1-25. 4. Patel, A., Thakkar, A., Bhatt, N., & Prajapati, P. (2019). Survey and Evolution Study Focusing Comparative Analysis and Future Research Direction in the Field of Recommendation System Specific to Collaborative Filtering Approach. In Information and Communication Technology for Intelligent Systems (pp. 155-163). Springer, Singapore. 5. Boratto, L., & Carta, S. (2015). ART: group recommendation approaches for automatically detected groups. International Journal of 59. Machine Learning and Cybernetics, 6(6), 953-980. 6. Yanxiang, L., Deke, G., Fei, C., & Honghui, C. (2013, January). User-based clustering with top-n recommendation on cold-start problem. In 2013 Third International Conference on Intelligent System Design and Engineering Applications(pp. 1585-1589). IEEE. 40-44 7. Fletcher, K. K. (2017, June). A Method for Dealing with Data Sparsity and Cold-Start Limitations in Service Recommendation Using Personalized Preferences. In 2017 IEEE International Conference on Cognitive Computing (ICCC) (pp. 72-79). IEEE. 8. Schein, A. I., Popescul, A., Ungar, L. H., & Pennock, D. M. (2002, August). Methods and metrics for cold-start recommendations. In Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval (pp. 253- 260). ACM. 9. Ortega, F., Hurtado, R., Bobadilla, J., & Bojorque, R. (2018). Recommendation to groups of users using the singularities concept. IEEE Access, 6, 39745-39761. 10. Hawashin, B., Mansour, A., Kanan, T., & Fotouhi, F. (2018, October). An efficient cold start solution based on group interests for recommender systems. In Proceedings of the First International Conference on Data Science, E-learning and Information Systems (p. 26). ACM. 11. Revathy, V. R., & Anitha, S. P. (2019). Cold Start Problem in Social Recommender Systems: State-of-the-Art Review. In Advances in Computer Communication and Computational Sciences (pp. 105-115). Springer, Singapore. 12. Delic, A., & Masthoff, J. (2018, July). Group Recommender Systems. In Proceedings of the 26th Conference on User Modeling, Adaptation and Personalization (pp. 377-378). ACM. 13. Amatriain, X., Jaimes, A., Oliver, N., & Pujol, J. M. (2011). Data mining methods for recommender systems. In Recommender systems handbook (pp. 39-71). Springer, Boston, MA. 14. Yang, W., Fan, S., & Wang, H. (2018, October). An item-diversity-based collaborative filtering algorithm to improve the accuracy of recommender system. In 2018 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (pp. 106-110). IEEE. 15. Yang, X., Guo, Y., Liu, Y., & Steck, H. (2014). A survey of collaborative filtering based social recommender systems. Computer Communications, 41, 1-10. 16. Nehete, S. P., & Devane, S. R. (2018, August). Recommendation Systems: past, present and future. In 2018 Eleventh International Conference on Contemporary Computing (IC3) (pp. 1-7). IEEE. 17. Schafer, J. B., Frankowski, D., Herlocker, J., & Sen, S. (2007). Collaborative filtering recommender systems. In The adaptive web (pp. 291-324). Springer, Berlin, Heidelberg. 18. Ambulgekar, H. P., Pathak, M. K., & Kokare, M. B. (2019). A Survey on Collaborative Filtering: Tasks, Approaches and Applications. In Proceedings of International Ethical Hacking Conference 2018 (pp. 289-300). Springer, Singapore. 19. Guo, L., Liang, J., Zhu, Y., Luo, Y., Sun, L., & Zheng, X. (2018). Collaborative filtering recommendation based on trust and emotion. Journal of Intelligent Information Systems, 1-23. 20. Thorat, P. B., Goudar, R. M., & Barve, S. (2015). Survey on collaborative filtering, content-based filtering and hybrid recommendation system. International Journal of Computer Applications, 110(4), 31-36. 21. Mustafa, N., Ibrahim, A. O., Ahmed, A., & Abdullah, A. (2017, January). Collaborative filtering: Techniques and applications. In 2017 International Conference on Communication, Control, Computing and Electronics Engineering (ICCCCEE) (pp. 1-6). IEEE. 22. Han, D., Li, J., Li, W., Liu, R., & Chen, H. (2019). An app usage recommender system: improving prediction accuracy for both warm and cold start users. Multimedia Systems, 1-14. 23. Zhu, Y., Lin, J., He, S., Wang, B., Guan, Z., Liu, H., & Cai, D. (2019). Addressing the Item Cold-start Problem by Attribute-driven Active Learning. IEEE Transactions on Knowledge and Data Engineering. 24. Belém, F. M., Heringer, A. G., Almeida, J. M., & Gonçalves, M. A. (2019). Exploiting syntactic and neighbourhood attributes to address cold start in tag recommendation. Information Processing & Management, 56(3), 771-790. 25. Silva, N., Carvalho, D., Pereira, A. C., Mourão, F., & Rocha, L. (2019). The Pure Cold-Start Problem: A deep study about how to conquer first-time users in recommendations domains. Information Systems, 80, 1-12. 26. Li, J., Zhang, K., Yang, X., Wei, P., Wang, J., Mitra, K., & Ranjan, R. (2019). Category Preferred Canopy–K-means based Collaborative Filtering algorithm. Future Generation Computer Systems, 93, 1046-1054. Authors: Nupur Kalra, Deepak Yadav, Gourav Bathla

Paper Title: SynRec: A Prediction Technique Using Collaborative Filtering and Synergy Score Abstract: Recommender systems are techniques designed to produce personalized recommendations. Data sparsity, scalability cold start and quality of prediction are some of the problems faced by a recommender system. Traditional recommender systems consider that all the users are independent and identical, its an assumption which leads to a total ignorance of social interactions and trust among user. Trust relation among users ease the work of recommender systems to produce better quality of recommendations. In this paper, an effective technique is proposed using trust factor extracted with help of ratings given so that quality can be improved and better predictions can be done. A novel-technique has been proposed for recommender system using film-trust dataset and its effectiveness has been justified with the help of experiments.

Keywords: Collaborative Filtering, Recommender System, Social Trust, Synergy Score

References: 1. G. Bathla,” A graph-based model to improve social trust and influence for social recommendation,” The Journal of Supercomputing, 2017,1-19. 2. D.Li., “ Item-based top-N recommendation resilient to aggregated information revelation,” Knowledge-Based Systems, 67,2014, 290- 304. 3. G. Bello-Orgaz “Social big data: Recent achievements and new challenges”. Information Fusion, 28, 2016,45-59 4. X.Yang, “A product recommendation approach based on the latent social trust network model for collaborative filtering,” In 2016 IEEE International Conference on Software Quality, Reliability and Security Companion (QRS-C) ,2016,pp. 178-185. IEEE. 5. P.Moradi, ” A reliability-based recommendation method to improve trust-aware recommender systems,” Expert Systems with Applications, 42(21), 2015,7386-7398. 6. P.Moradi,” An effective trust-based recommendation method using a novel graph clustering algorithm,” Physica A: Statistical Mechanics and its Applications, 436, 2015,462-481. 7. M.G.Ozsoy, “Trust based recommendation systems,” In 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013) ,pp. 1267-1274, IEEE. 8. N.Sodera, “ Open problems in recommender systems diversity,” In 2017 International Conference on Computing, Communication and Automation (ICCCA),2017 pp. 82-87, IEEE. 60. 9. H.Ma, “ Sorec: social recommendation using probabilistic matrix factorization,” In Proceedings of the 17th ACM conference on Information and knowledge management ,2008,pp. 931-940, ACM. 10. G.Guo, “ A novel recommendation model regularized with user trust and item ratings” IEEE Transactions on Knowledge and Data 45-51 Engineering, 28(7), 2016,1607-1620. 11. B.Yang,”Social collaborative filtering by trust,” IEEE transactions on pattern analysis and machine intelligence, 39(8), 2017,1633- 1647. 12. M,Deshpande,” Item-based top-n recommendation algorithms,” ACM Transactions on Information Systems (TOIS), 22(1),2004, 143- 177. 13. P.Sun, (2018, May)” Research of Personalized Recommendation Algorithm Based on Trust and User's Interest,” In 2018 International Conference on Robots & Intelligent System (ICRIS) 2018,pp. 153-156, IEEE. 14. W.Wenjuan, (2015, July)”A personalized recommendation strategy based on trusted social community,” In 2015 10th International Conference on Computer Science & Education (ICCSE), 2015,pp. 496-499, IEEE. 15. H.Zhao, ”A New Collaborative Filtering Algorithm with Combination of Explicit Trust and Implicit Trust,” In 2018 13th International Conference on Computer Science & Education (ICCSE),2018, pp. 1-5,IEEE. 16. F,Isinkaye,” Recommendation systems: Principles, methods and evaluation,” Egyptian Informatics Journal, 16(3), 2015,261-273. 17. M. Vlachos,” Addressing Interpretability and Cold-Start in Matrix Factorization for Recommender Systems,” IEEE Transactions on Knowledge and Data Engineering,2018. 18. A.Davoudi, (2016, January)” Product rating prediction using trust relationships in social networks,” In 2016 13th IEEE Annual Consumer Communications & Networking Conference (CCNC),2016, pp. 115-118, IEEE. 19. C.Park,” Improving top-K recommendation with truster and trustee relationship in user trust network”. Information Sciences, 374,2017, 100-114. 20. Y.Wang,” A trust-based probabilistic recommendation model for social networks,” Journal of Network and Computer Applications, 55, 2015,59-67. 21. J.Golbeck, “Filmtrust: Movie recommendations using trust in web-based social networks,” In Proceedings of the IEEE Consumer communications and networking conference ,Vol. 96, No. 1,2016, pp. 282-286. 22. F.Walter,” A model of a trust-based recommendation system on a social network,” Autonomous Agents and Multi-Agent Systems, 16(1),2008, 57-74. 23. J.Herlocker ,” Evaluating collaborative filtering recommender systems,” ACM Transactions on Information Systems (TOIS), 22(1),2004, 5-53. 24. R.Katarya, “ An effective collaborative movie recommender system with cuckoo search,” Egyptian Informatics Journal, 18(2),2017, 105-112. 25. N.de Mello, ” Insights on social recommender system,” In Proceedings of the Workshop on Recommendation Utility Evaluation: Beyond RMSE, at ACM RecSyS12 ,2012, pp. 33-38. 26. X.Wu,” Data mining with big data,” IEEE transactions on knowledge and data engineering, 26(1),2014 97-107. 27. R. Mukkamala, ” Fuzzy-set based sentiment analysis of big social data,” In 2014 IEEE 18th International Enterprise Distributed Object Computing Conference ,2014, pp. 71-80, IEEE. 28. L.Xiong,” An Approach for Top-k Recommendation Based on Trust Information,” In 2017 IEEE 10th Conference on Service- Oriented Computing and Applications (SOCA), 2017,pp. 198-205, IEEE. 29. L. Sheugh,” A novel 2D-Graph clustering method based on trust and similarity measures to enhance accuracy and coverage in recommender systems,” Information Sciences, 432,2018, 210-230. Authors: Basant Kumar

Paper Title: The (in) security of smart cities: vulnerabilities, risks, mitigation and prevention Abstract: Smart cities represent the overall development in an urban model utilizing human, and technological enhancement leading to an increase in economic and social opportunities. However, the significant challenges were observed with the rise of smart cities. A comprehensive review is conducted on the study of different approaches used for mitigation of the crime scenarios in smart cities in perspective of hacker’s view on hashing and thereby protecting the integrity of the data in heterogeneous devices on a network of smart city. This paper also proposes the ICT architecture of a smart city which is encompassed with numerous security layers in onion model to integrate secure framework for future smart city, for better city living and governance, based on cloud computing IoT and distributed computing in accordance with salted hash value added as a prefix and postfix in a generated password.

Keywords: Encryption, Hashing, Privacy, Smart City, Security

References: 1. Mohanty, S. P., Choppali, U., & Kougianos, E. (2016). Everything you wanted to know about smart cities: The internet of things is the backbone. IEEE Consumer Electronics Magazine, 5(3), 60-70. 2. Ijaz, S., Shah, M. A., Khan, A., & Ahmed, M. (2016). Smart cities: A survey on security concerns. Int. J. Adv. Comput. Sci. Appl, 7(2), pp. 612-625. 3. Thomas, S. R., Veitch, C. K. K. and Woodard, L. , "Categorizing Threat: Building and Using a Generic Threat Matrix.," Sandia Report SAND2007-5791, Sandia National Laboratories, Albuquerque, New Mexico(2007) 4. Mark Mateski, Cassandra M. Trevino, Cynthia K. Veitch, John Michalski, J. Mark Harris, Scott Maruoka, Jason Frye, "Cyber Threat Metrics," SANDIA REPORT, Sandia National Laboratories(2012) 5. Achieving Network Security - An AT&T survey and white paper in cooperation with the Economist Intelligence Unit. 2003 6. OWASP (2017a). White paper: OWASP Top 10 2017. OWASP (USA). Available at: https://www.owasp.org/images/b/b0/OWASP_Top_10_2017_RC2_Final.pdf. Visited on 22nd October 2017. 7. Johnson, Jerry. (2008). Network Defense Requires Layers of Strategic Thinking. Information Week (USA). Feb 25. Iss. 1174, pp. 43 - 49. 8. Fadia, A. (2006). The Unofficial Guide to Ethical Hacking. 2ndedition. Thomson Course Technology (Canada). 9. Shen, J., Liu, D., Shen, J., Liu, Q., & Sun, X. (2017). A secure cloud-assisted urban data sharing framework for ubiquitous-cities. Pervasive and mobile Computing, pp. 219-230. 10. Modi, C., Patel, D., Boris Aniya, B., Patel, H., Patel, A., & Maharajan, M. (2013). A survey of intrusion detection techniques in cloud. 61. Journal of Network and Computer Applications, 36(1), pp. 42-57. 11. Zhou, Q., & Luo, J. (2017). The study on evaluation method of urban network security in the big data era. Intelligent Automation & Soft Computing, pp. 1-6. 52-58 12. Tang, B., Chen, Z., Hefferman, G., Wei, T., He, H., & Yang, Q. (2015, October). 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(2011). Computer Security. rd edition. Wiley (USA). 18. Kanneganti, R. and Chodavarapu, P. (2008). SOA Security. st edition. Manning Publications CO (USA). 19. Norman, S. (2010). Metrics for Mitigating Cyber security Threats to Networks. IEEE Internet Computing. IEEE (USA). January/February. Vol. 14, Iss. no. 1, pp. 64-68. 20. Norman, S. (2010). Metrics for Mitigating Cyber security Threats to Networks. IEEE Internet Computing. IEEE (USA). January/February. Vol. 14, Iss. no. 1, pp.64-68. 21. Skulmoski, Gregory J., Hartman, Francis T. and Krahn, J. (2007). The Delphi Method for Graduate Research. Journal of Information Technology Education. Volume 6, 2007. 22. Jangbok, K., Kihyun, C. and Kyunghee, C. (2007). Spam Filtering With Dynamically Updated URL Statistics. IEEE Security and Privacy. July/August. Vol. 5, no. 4, pp. 33- 39. 23. Jangbok, K., Kihyun, C. and Kyunghee, C. (2007). Spam Filtering With Dynamically Updated URL Statistics. IEEE Security and Privacy. July/August. Vol. 5, no. 4, pp. 33-49. 24. Gai, K., Qiu, M., Zhao, H., & Xiong, J. (2016 June). Privacy-aware adaptive data encryption strategy of big data in cloud computing. In Cyber Security and Cloud Computing (CSCloud), 2016 IEEE 3rd International Conference, pp. 273-278. 25. Hedieh, S. and Mansour, J. (2011). HYSA: Hybrid steganographic approach using multiple steganography methods. Security and Communication Networks. John Wiley & Son Ltd (USA). October. Volume 4. Issue 10, pp. 1173– 1178 26. Gai, K., Qiu, M., Zhao, H., & Xiong, J. (2016 June). Privacy-aware adaptive data encryption strategy of big data in cloud computing. In Cyber Security and Cloud Computing (CSCloud), 2016 IEEE 3rd International Conference on (pp. 273-278). IEEE.Damiani, M. L., Bertino, E. and Perlasca, P. (2007). Data security in location-aware applications. an approach based on RBAC. International Journal of Information and Computer Security (Italy). Vol. 1, No.1/2, pp. 5– 8. 27. Tutton, J. (2010). 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Paper Title: Understanding users Display-Name Consistency across Social Networks Abstract: Online users create their profiles on numerous social platforms to get benefits of various types of social media content. During online profile creation, the user selects a username and feeds his/her personal details like name, location, email, etc. As different social networking services acquire common personal attributes of the same user and present them in a variety of formats. To understand the availability and similarity of personal attributes across various social networking services, we propose a method that uses the different distance measuring algorithms to determine the display-name similarity across social networks. From the experimental results, it is found that at least twenty percent GooglePlus-Facebook and Facebook-Twitter users select the same display name, while forty five percent Google and Twitter user select identical name across both the social networks.

Keywords: Cross link posts, Personal Information, Social Account, User Identity

References: 1. https://www.smartinsights.com/sarocial-media-marketing/social-media-strategy/new-global-social-media-resech/ (last accessed 05/18/2019). 2. https://www.pewinternet.org/2018/03/01/social-media-use-in-2018/.(last accessed 05/18/2019). 3. L. Humphreys, P. Gill, B. Krishnamurthy “Privacy on Twitter: how much is too much? Privacy issues on Twitter,”The annual meeting of the international communication Association, Singapore, pp. 1-29, 2010. 4. J. Vosecky, D. Hong, and V. Y. Shen, User identification across multiple social networks. In First International Conference on 62. Networked Digital Technologies, pages. 360-365. 2009. 5. A. Esfandyari, M. Zignani, S. Gaito, G. P. Rossi, “User identification across online social networks in practice:Pitfalls and solution,” Journal of Information Science, Vol. 44, no. 3, pages, 377-391,2018. 59-64 6. K. Shu, S. Wang, J. Tang, R. Zafarani, and H. Liu, User Identity Linkage across Online Social Networks: A Review. ACM SIGKDD Explorations Newsletter vol. 18, no. 2: pages 5-17, 2017. 7. A. Malhotra, L. Totti, W. J. Meira, P. Kumaraguru, and V. Almeida, Studying user footprints in different online social networks. In International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pages 1065-1070, 2012 8. M. Marti, and G. Varghese, "I seek you: searching and matching individuals in social networks," Proceedings of the eleventh international workshop on Web information and data management, ACM.67-75, 2009. 9. E. Raad, R. Chbeir, & 2010. 10. Z. Cheng ,J. Caverlee and K. Lee “You are Where you Tweet: a Content-Based Approach to Geo-locating Twitter Users’,” In Proceedings, of the 19th ACM International Conference on Information and Knowledge Management, Toronto, Canada, pp. 759–768, 2010. 11. O. Goga, H. Lei, S.H.K. Parthasarathi, G. Friedland, R. Sommer, and R. Teixeira, "Exploiting innocuous activity for correlating users across sites," In Proceedings of the 22nd international conference on World Wide Web, 447-458, 2013. 12. Li, G. A. Wang, and H. Chen, "Identity matching using personal and social identity features," Information Systems Frontiers, vol. 13, no. 1, pages 101-113, 2011. 13. O. Goga, , D. Perito, H. Lei, R. Teixeira, and R. Sommer, "Large-scale correlation of accounts across social networks," University of California at Berkeley, Berkeley, California, Tech. Rep. TR-13-002, 2013. 14. O. Peled, M. Fire, L. Rokach, and Y.Elovici, "Entity matching in online social networks," In International Conference on Social Computing (SocialCom), pages 339-344, 2013P. 15. O. Goga, H. Lei, S.H.K. Parthasarathi, G. Friedland, R. Sommer, and R.Teixeira, Exploiting innocuous activity for correlating users across sites.In Proceedings of the 22nd international conference on World Wide Web, pages 447-458, 2013 16. A. Narayanan and V. Shmatikov, De-anonymizing social networks. In Proceedings Of the 30th IEEE Symposium on Security and Privacy, pages 173-187, 2009 17. S. Bartunov, A. Korshunov , S. Park, W, Ryu., and H. Lee, Joint link-attribute user identity resolution in online social networks. The 6th SNA-KDD Workshop, 2012 18. N. Korula, and S. Lattanzi, An efficient reconciliation algorithm for social networks.In proceedings of the VLDB Endowment, vol. 7, no. 5, pages377-388, 2014 19. K. Shu, S. Wang, J. Tang, R. Zafarani, and H. Liu, User Identity Linkage across Online Social Networks: A Review. ACM SIGKDD Explorations Newsletter vol. 18, no. 2: pages 5-17, 2017 20. S. Tan, Z. Guan, D. Cai, X. Qin, J. Bu, and C. Chen, Mapping users across networks by manifold alignment on hypergraph, In AAAI, vol. 14, pages 159-165. 2014 21. X. Zhou, X. Liang, H. Zhang, and Y. Ma, "Cross-platform identification of anonymous identical users in multiple social media networks," IEEE transactions on knowledge and data engineering, vol. 28, no. 2, pages.411-424, 2016. 22. X. Zhou, X. Liang, X. Du, and J. Zhao, "Structure based user identification across social networks," IEEE Transactions on Knowledge and Data Engineering, 30(6),1178-1191,2018. 23. Yongjun L.I., and Su, Z. A Comment on “Cross-Platform Identification of Anonymous Identical Users in Multiple Social Media Networks,” IEEE Transactions on Knowledge and Data Engineering, 30(7),1409-1410,2018. 24. P. Jain, P. Kumaraguru and A. Joshi, @ i seek'fb.Me': "Identifying users across multiple online social networks," In Proceedings of the 22nd international conference on World Wide Web ,pages 1259-1268, 2013. 25. W. Cohen, P. Ravikumar, and S.Fienberg, A comparison of string metrics for matching names and records. In Kdd workshop on data cleaning and object consolidation Vol. 3, pages. 73-78, 2003. 26. http://nptlab.di.unimi.it/?page_id=360. (last accessed 02/29/2019). Authors: Smriti Bhatnagar, Shubham Sharma

Paper Title: Brain Tumor Extraction and Classification from MRI Images Abstract: This paper proposes a methodology in which detection, extraction and classification of brain tumour is done with the help of a patient’s MRI image. Processing of medical images is currently a huge emerging issue and it has attracted lots of research all over the globe. Several techniques have been developed so far to process the images efficiently and extract out their important features. The paper describes certain strategies including some noise removal filters, grayscaling, segmentation along with morphological operations which are needed to extract out the features from the input image and SVM classifier for classification purpose.

63. Keywords: Grayscaling, MRI, Morphological operations, MATLAB, Segmentation, SVM Classifier

References: 65-69 1. Ehab F. Badran, Esraa Galal Mahmoud, and Nadder Hamdy, ‘‘An Algorithm for Detecting Brain Tumours in MRI Images” Department of Electronics and Communications Engineering Arab Academy for Science and Technology & Maritime Transport Alexandria, November 2010. 2. Chang Wen Chen, Jiebo Luo and Parker, K.J, “Image segmentation via adaptive K-mean clustering and knowledge-based morphological operations with biomedical applications”, IEEE Transactions on Image Processing, Vol. 7, Issue 12, 1998. 3. W. Gonzalez, “Digital Image Processing”, 2nd ed. Prentice Hall, Year of Publication 2008, Page no 378. 4. Y. Zhang, L. Wu, “An MRI Brain Image Classifier via Principal Component Analysis and Kernel Support Vector Machine”, Progress In Electromagnetic Research, Vol. 130, 369-388, 2012. 5. Vijay Kumar and Priyanka Gupta, “Importance of Statistical Measures in Digital Image Processing”, International Journal of Emerging Technology and Advanced Engineering, Volume 2, Issue 8, August 2012. Authors: Manish Sharma, Prakash Bahrani Issues and handy Solutions addressed at every stage in real time data warehousing, i.e. ETL Paper Title: (Extraction, Transformation & Loading) Abstract: In the standard ETL (Extract Processing Load), the data warehouse refreshment must be performed outside of peak hours. iIt implies ithat the ifunctioning and ianalysis has stopped in their iall actions. iIt causes the iamount of icleanness of idata from the idata Warehouse which iisn't suggesting ithe latest ioperational transections. This iissue is iknown as idata ilatency. The data warehousing is iemployed to ibe a iremedy for ithis iissue. It updates the idata warehouse iat a inear real-time iFashion, instantly after data found from the data source. Therefore, data ilatency could ibe reduced. Hence the near real time data warehousing was having issues which was not identified in traditional ETL. This paper claims to communicate the issues and accessible options at every point iin the inear real-time idata warehousing, i.e. iThe iissues and Available alternatives iare based ion ia literature ireview by additional iStudy that ifocus ion near real-time data iwarehousing issue.

Keywords: Business Intelligence, Data Latency, Data Warehouse, Data Warehousing, ETL, Near Real Time 64. Data Warehousin

References: 70-74 1. E. Low, L. No, B. Windows, and L. Costs, “Efficient and Real Time Data Integration With Change Data Capture,” Integr. Vlsi J., no. Cdc, pp. 1–20, 2009. 2. R. J. Davenport, “ETL vs ELT,” no. June, 2008. 3. G. Swetha, D. Karunanithi, and K. A. Lakshmi, “Data Integration Models for Operational Data Warehousing,” vol. 3, no. 2, pp. 508– 516, 2014. 4. R. S, S. Balaji. B, and N. K. Karthikeyan, “From Data Warehouses to Streaming Warehouses: A Survey on the Challenges for Real- Time Data Warehousing and Available Solutions,” Int. J. Comput. Appl., vol. 81, no. 2, pp. 15–18, 2013. 5. A. A. Wani and B. L. Raina, “Data in Data Warehouse and its Qualities Issues,” no. 9, pp. 1753–1756, 2019. 6. K. Kakish and T. A. Kraft, “ETL Evolution for Real-Time Data Warehousing,” Proc. Conf. Inf. Syst. Appl. Res., p. 12, 2012. 7. N. Rahman, “Refreshing Data Warehouses with Near Real-Time Updates,” J. Comput. Inf. Syst., vol. 4417, no. Spring, p. 70, 2007. 8. A. A. Wani, U. Chandra, P. Bansi, and L. Raina, “Security Challenge in Big Data for Behaviour Analytics,” vol. 5, no. 7, pp. 578–581, 2018. 9. R. J. Santos and J. Bernardino, “Real-time data warehouse loading methodology,” p. 49, 2008. 10. A. A. Wani, A. Khan, A. Jamal, and P. K. Gupta, “Cost Efficient Media Cloud Storage and Systematic Risks Involved in the Cloud Computing,” no. 9, pp. 2466–2469, 2019. 11. A. A. Wani, “Discovery of knowledge by using Data warehousing as well as ETL processing.” 12. M. A. Naeem, G. Dobbie, and G. Weber, “An event-based near real-time data integration architecture,” Proc. - IEEE Int. Enterp. Distrib. Object Comput. Work. EDOC, no. April, pp. 401–404, 2008. 13. D. Agrawal, “The reality of real-time business intelligence,” Lect. Notes Bus. Inf. Process., vol. 27 LNBIP, pp. 75–88, 2009. 14. J. Zuters, “Near real-time data warehousing with multi-stage trickle and flip,” Lect. Notes Bus. Inf. Process., vol. 90 LNBIP, pp. 73– 82, 2011. Authors: Sukhmeet Singh, Paras Chawla, Ishbir Singh

Paper Title: A Robotic Automated Vegetable Making Machine Abstract: The process of cooking by robot has been drawing the attention of professionals and researchers. But not many researches have been done on this topic all over the world. The main purpose of this paper is to find the gaps from the systematic review on cooking robot. This systematic review was conducted according to PRISMA guidelines. For evidence acquisition we searched IEEE, Science Direct and Web of Science libraries. The last search was performed on March 29, 2019 by us. We identified 3416 publications from initial search, out of which 19 publications were retrieved. Out of these 19 publications 10 publications were excluded due to duplicate publication. But only 6 publications are of our interest.

Keywords: Robot, cook, dish

References: 1. Y. Nakauchi, T. Fukuda, K. Noguchi, and T. Matsubara, “Intelligent kitchen: Cooking support by LCD and mobile robot with IC- labeled objects,” in 2005 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS, VOLS 1- 65. 4, 2005, pp. 2464–2469. 2. H. Rong, X. Dai, and X. Liu, “Trajectory Planning Method of the Pot in Cooking Robot,” in 2010 2ND INTERNATIONAL ASIA CONFERENCE ON INFORMATICS IN CONTROL, AUTOMATION AND ROBOTICS (CAR 2010), VOL 1, 2010, pp. 129–132. 75-76 3. Y. Chen, Z. Deng, and B. Li, “Numerical simulations of motion behaviors of pan mechanism in a cooking robot with granular cuisine,” J. Mech. Sci. Technol., vol. 25, no. 3, pp. 803–808, Mar. 2011. 4. S. Taki, K. Fujimoto, H. Osaki, Y. Munesawa, and Y. Kajihara, “Generating method of procedure for cutting ingredient by cooking robot,” in ICIM’ 2004: PROCEEDINGS OF THE SEVENTH INTERNATIONAL CONFERENCE ON INDUSTRIAL MANAGEMENT, 2004, pp. 32–37. 5. D. Sakamoto, Y. Sugiura, M. Inami, and T. Igarashi, “Graphical Instruction for Home Robots,” 2016. 6. W. X. Yan et al., “A novel automatic cooking robot for Chinese dishes,” Robotica, vol. 25, no. 4, pp. 445–450, 2007. 7. K. Yamazaki, Y. Watanabe, K. Nagahama, K. Okada, and M. Inaba, “Recognition and Manipulation Integration for a Daily Assistive Robot Working on Kitchen Environments,” pp. 196–201, 2010. 8. J. Wei and A. D. Cheok, “Foodie : Play with Your Food Promote Interaction and Fun with Edible Interface,” vol. 58, no. 2, pp. 178– 183, 2012. 9. Y. Matsushima, N. Funabiki, T. Okada, T. Nakanishi, and K. Watanabe, “A Cooking Guidance Function on Android Tablet for Homemade Cooking Assistance System,” pp. 249–254, 2013. 10. N. Yoshida, T. Yoshimi, M. Mizukawa, and Y. Ando, “A study of egg breaking motion by single robot arm,” in 2013 32ND CHINESE CONTROL CONFERENCE (CCC), 2013, pp. 6057–6062. 11. M. Sutiono, H. Nugroho, and K. Karyono, “Appliance Hub : A Wireless Communication System for Smart Devices ( Case Study : Smart Rice Cooker ),” no. 978, pp. 125–130, 2016. Authors: Prashant Kamidi, Vamshi Krishna Sabbi, Ramakrishna Sanniti

Paper Title: IoT Based Smart Water Quality Monitoring and Prediction System Abstract: As indicated by Human Rights Watch, twenty million individuals in our nation are as yet drinking water defiled with arsenic. The World wellbeing Organization (WHO) has likewise expressed this emergency as "the biggest mass harming of a populace ever". To diminish the water related ailments and avoid water populace, we need to quantify water parameters, for example, ph, turbidity, conductivity, temperature and so on. Conventional approach of water observing requires gathering information from different sources physically. A while later examples will send lab for testing and breaking down. So as to spare time utilization and diminishing manual exertion my testing supplies will be put in any water source. Thus, this model can distinguish contamination remotely and take essential activities. The primary objective of this paper to assemble a Sensor- based Water Quality Monitoring System. Arduino Mega 2560 go about as a base station and information from sensor hubs will be send to it. For the scholastic reason, this paper exhibits a little model of sensor systems comprising of temperature, water level, stream and ph. At that point ph and temperature sensor esteems were 66. sent cloud stage (ARTIK cloud) and showed as a graphical portrayal on a neighborhood PC. In addition, GSM shield (SIM808) is associated with Arduino Mega which thinks about sensor esteems to edge esteems and sends 77-82 a text-based notification to the operator if the got esteem is above or underneath the edge esteem. The aftereffects of this undertaking are talked about in the outcome area of the paper. We tried three water tests from three diverse water sources, (for example, modern water, faucet water and pool water). Three water tests gathered from three distinctive swimming pools.(Except one example) Ph esteem found in rest of the examples were in typical range (temperature esteem between 26-27'C). Result segment (in page 20) clarifies our venture discoveries in subtleties.

Keywords: Artefacts; Framework; Feature Extraction; Ontology; Query Processing

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