Technology and Engineering International Journal of Recent

International Journal of Recent Technology and Engineering

ISSN : 2277 - 3878 Website: www.ijrte.org Volume-7 Issue-5S, FEBRUARY 2019 Published by: Blue Eyes Intelligence Engineering and Sciences Publication

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www.ijrte.org Exploring Innovation Editor-In-Chief Chair Dr. Shiv Kumar Ph.D. (CSE), M.Tech. (IT, Honors), B.Tech. (IT) Director, Blue Eyes Intelligence Engineering & Sciences Publication, Bhopal (M.P.), India 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 Director of College Development Counseling, Devi Ahilya University, Indore (M.P.), Professor, School of Physics, Devi Ahilya University, Indore (M.P.), and Regional Director, Madhya Pradesh Bhoj (Open) 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.

Convener Chair Mr. Jitendra Kumar Sen International Journal of Soft Computing and Engineering (IJSCE)

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.

Dr. J. Gladson Maria Britto Professor, Department of Computer Science & Engineering, Malla Reddy College of Engineering, Secunderabad (Telangana), India.

Dr. Sunil Tekale Professor, Dean Academics, Department of Computer Science & Engineering, Malla Reddy College of Engineering, Secunderabad (Telangana), India.

Dr. K. Priya Professor & Head, Department of Commerce, Vivekanandha College of Arts & Sciences for Women (Autonomous, Elayampalayam, Namakkal (Tamil Nadu), India.

Dr. Pushpender Sarao Professor, Department of Computer Science & Engineering, Hyderabad Institute of Technology and Management, Hyderabad (Telangana), India.

S. Volume-7 Issue-5S, February 2019, ISSN: 2277-3878 (Online) Page No. No Published By: Blue Eyes Intelligence Engineering & Sciences Publication

Authors: Swati Chauhan, G. Jims John Wessley Paper Title: Thermodynamic Performance Analysis of a Micro Turbojet Engine for UAV and Drone Propulsion Abstract: A thermodynamic performance analysis of a single spool micro turbojet engine that can produce thrust to a maximum of 4 kN is performed using Gas Turbine Simulation Programme (GSP). There is a huge gap in the availability of micro gas turbine engines in the thrust range of 0.13 to 4.45 kN to power UAVs and drones. This analysis brings out the feasibility of thrust generation in a micro turbojet engine so as to fill the gap. The engine is analyzed for four different mass flow rates, Mach numbers ranging from 0.3 to 0.9 with the operating altitude between 5000 m to 9000 m. The nett thrust produced for a pressure range of 2.5 and is in the range of 3.9 to 4 kN at a mass flow rate of 7.6 kg/s which satisfies the expected design requirement of the engine. Also, the maximum turbine inlet temperature is less than 1000 K so that there is no need for a new material required in the combustor and turbine where the high temperaure exists. The outcomes of this analysis form a strong base for further analysis, design and fabrication of micro gas turbine engines to propel future UAVs and drones.

References: 1. Janina Ferreira da Silva, Cleverson Bringhenti and Jesuino Takachi Tomita, "Turbojet Transient Performance Simulation", 22nd International congress of Mechanical Engineering, pp. 9748-9759, 2013. 1. 2. Soo Yong Kim and B. Soudarev, "Transient Analysis of a Simple Cycle Gas Turbine Engine", KSAS International Journal, Vol, No. 2, pp. 22-29, 2000. 3. M. H. Gobran, "Off-design performance of solar Centaur-40 gas turbine engine using Simulink," Ain Shams Engineering Journal 4, pp. 285-298, 1-6 2013. 4. Dinesh Kumar and R.K. Gupta, "Parametric and performance analysis of Turbojet engine through MATLAB," International Journal for Technological Research in Engineering 3(10), pp.2653-2657, 2016. 5. G. Torella, "Transient Performance and Behaviour of Gas Turbine engines", The Gas turbine and Aeroengine 6. Congress and Exposition, Brussels, Belgium, pp. 1-8, June 11-14, 1990. 7. Syed Ihtsham-ul-Haq Gilani, Aklilu Tesfamichael Baheta, and Mohad. Amin A.Majid, "Thermodynamics Approach to Determine a Gas Turbine Components Design Data and Scaling Method for Performance Map Generation", 1st International Conference on Plant Equipment and Reliability (ICPER), Selangor, Malaysia, March 27-28, 2008. 8. A. J. Ujam, "Parametric analysis of a Turbojet engine with reduced inlet pressure to compressor", IOSR Journal of Engineering 3(8), 29-37, 2015. 9. Saeed Farokhi, "Aircraft Propulsion", Second Edition, John Wiley & Sons, Ltd., May 2014, ISBN: 978-1-118-80677-7. 10. Irwin E. Treager, "Aircraft Gas Turbine Engine Technology", Third Edition, Tata McGraw-Hill Publishing Company Limited, New Delhi, 2009, ISBN-13:978-0-07-463111-9. 11. HIH Saravanamuttoo, H.Cohen, GFC Rogers, "Gas Turbine Theory", Fifth Edition, Pearson, 2014, ISBN 978-81-7758-902-3. 12. Jack D. Mattingly, "Elements of Gas Turbine Propulsion", Indian Edition, Tata McGraw-Hill Publishing Company Limited, New Delhi, 2014, ISBN-13:978-0-07-060628-9. 13. Philip G Hill, Carl Peterson, "Mechanics and Thermodynamics of Propulsion", Second Edition, Addison Wesley Longman, Inc., 1999, ISBN:0- 201-52483-X. 14. A.S. Rangwala, "Theory and Practice in Gas Turbines", New Age International (P) Limited, 2010, ISBN:978-81-224-2809. 15. Gitin M. Maitra, L. V. Prasad, "Handbook of Mechanical Design", Second Edition, Tata McGraw-Hill, 1985, ISBN: 0074517589. Authors: Krishnamoorthy, T. Jayakumar Paper Title: Design and Development of Aluminum-based Heat Sink for Electronic Gadgets Abstract: The aim of the present study to investigate the optimum weight and maximum heat dissipation of the heat sink. The materials and fin design as a two-major factor to increase the heat dissipation from the electronic chip. Show 3D analysis is verified with accessible exploratory information in the existing data for a continues finned heat sink. To identify the heat dissipation and mean temperature distribution of the heat sink for natural convection. Results reveal that the 6063 aluminum alloys with case II fin design have maximum heat dissipation of the heat sink compared to 6061, 7071 alloys. The heat dissipation of heat sink case II fin design is greater than the case I fin design under all the material condition, due to the airflow of the design. The mass of the case 1 fin design is lesser than the case II fin design. In addition to, the case I fins increase the heat transfer rate and have a higher weight than regular case II fin design. Moreover, it was concluded that case II fin design has the lowest temperature without raising the weight of the heat sink, which implies that the better performance in comparison to the other designs.

Keywords: Heat Sink, Aluminum, Heat Dissipation, Heat Flux, natural convection, radiation.

2. References: 1. N.H. Naqiuddin, L.H. Saw, M.C. Yew, F. Yusof, T.C. Ng, Overview of micro-channel design for high heat fl ux application, Renew. Sustain. 7-10 Energy Rev. 82 (2018) 901-914. doi:10.1016/j.rser.2017.09.110. 2. C.J. Kroeker, H.M. Soliman, S.J. Ormiston, Three-dimensional thermal analysis of heat sinks with circular cooling micro-channels, 47 (2004) 4733-4744. doi:10.1016/j.ijheatmasstransfer.2004.05.028. 3. D. Yang, Z. Jin, Y. Wang, G. Ding, G. Wang, International Journal of Heat and Mass Transfer Heat removal capacity of laminar coolant flow in a micro channel heat sink with different pin fins, Int. J. Heat Mass Transf. 113 (2017) 366-372. doi:10.1016/j.ijheatmasstransfer.2017.05.106. 4. X. Wang, B. An, L. Lin, D. Lee, Inverse geometric optimization for geometry of nano fl uid-cooled microchannel heat sink, Appl. Therm. Eng. 55 (2013) 87-94. doi:10.1016/j.applthermaleng.2013.03.010. 5. S. Romana, S.S. Banu, I. Ali, M.A.M. Iqbal, ScienceDirect, Mater. Today Proc. 5 (2018) 5481-5486. doi:10.1016/j.matpr.2017.12.137. 6. C. Chen, C. Ding, International Journal of Thermal Sciences Study on the thermal behavior and cooling performance of a nano fl uid-cooled microchannel heat sink, Int. J. Therm. Sci. 50 (2011) 378-384. doi:10.1016/j.ijthermalsci.2010.04.020. 7. Y.K. Prajapati, M. Pathak, M.K. Khan, Transient heat transfer characteristics of segmented finned microchannels, Exp. Therm. Fluid Sci. (2016). 8. A. Abdoli, G. Jimenez, G.S. Dulikravich, International Journal of Thermal Sciences Thermo- fl uid analysis of micro pin- fi n array cooling con fi gurations for high heat fl uxes with a hot spot, 90 (2015) 290-297. 9. J. Li, Z. Shi, 3D numerical optimization of a heat sink base for electronics cooling ?, 39 (2012) 204-208.. 10. D. Yang, Y. Wang, G. Ding, Z. Jin, J. Zhao, G. Wang, Numerical and experimental analysis of cooling performance of single-phase array microchannel heat sinks with different pin-fin configurations, Appl. Therm. Eng. 112 (2017) 1547-1556. doi:10.1016/j.applthermaleng.2016.08.211. 11. C.A. Rubio-jimenez, A. Hernandez-guerrero, J.G. Cervantes, D. Lorenzini-gutierrez, C.U. Gonzalez-valle, CFD study of constructal microchannel networks for liquid-cooling of electronic devices, Appl. Therm. Eng. 95 (2016) 374-381. doi:10.1016/j.applthermaleng.2015.11.037. 12. R. Brinda, R.J. Daniel, K. Sumangala, International Journal of Heat and Mass Transfer Ladder shape micro channels employed high performance micro cooling system for ULSI, Int. J. Heat Mass Transf. 55 (2012) 3400-3411. doi:10.1016/j.ijheatmasstransfer.2012.03.044. 13. M. Asadi, G. Xie, B. Sunden, International Journal of Heat and Mass Transfer A review of heat transfer and pressure drop characteristics of single and two-phase microchannels, Int. J. Heat Mass Transf. 79 (2014) 34-53. doi:10.1016/j.ijheatmasstransfer.2014.07.090. 14. C. Huang, Y. Chen, H. Li, International Journal of Heat and Mass Transfer An impingement heat sink module design problem in determining optimal non-uniform fin widths, Int. J. Heat Mass Transf. 67 (2013) 992-1006. doi:10.1016/j.ijheatmasstransfer.2013.08.103. 15. S. Das, D.P. Mondal, S. Sawla, N. Ramakrishnan, Synergic effect of reinforcement and heat treatment on the two body abrasive wear of an Al-Si alloy under varying loads and abrasive sizes, Wear. 264 (2008) 47-59. 16. G. V Kewalramani, A. Agrawal, S.K. Saha, International Journal of Heat and Mass Transfer Modeling of microchannel heat sinks for electronic cooling applications using volume averaging approach, Int. J. Heat Mass Transf. 115 (2017) 395-409. doi:10.1016/j.ijheatmasstransfer.2017.08.041. 17. E. Rasouli, C. Naderi, V. Narayanan, International Journal of Heat and Mass Transfer Pitch and aspect ratio effects on single-phase heat transfer through microscale pin fin heat sinks, Int. J. Heat Mass Transf. 118 (2018) 416-428. doi:10.1016/j.ijheatmasstransfer.2017.10.105. 18. J. Zhao, S. Huang, L. Gong, Z. Huang, Numerical study and optimizing on micro square pin-fin heat sink for electronic cooling, Appl. Therm. Eng. 93 (2016) 1347-1359. doi:10.1016/j.applthermaleng.2015.08.105. 19. R. Ricci, S. Montelpare, An experimental IR thermographic method for the evaluation of the heat transfer coefficient of liquid-cooled short pin fins arranged in line, Exp. Therm. Fluid Sci. 30 (2006) 381-391. doi:10.1016/j.expthermflusci.2005.09.004. H.E. Ahmed, B.H. Salman, A.S. Kherbeet, M.I. Ahmed, International Journal of Heat and Mass Transfer Optimization of thermal design of heat sinks?: A review, Int. J. Heat Mass Transf. 118 (2018) 129-153. doi:10.1016/j.ijheatmasstransfer.2017.10.099. 20. H.E. Ahmed, Optimization of thermal design of ribbed flat-plate fin heat sink, Appl. Therm. Eng. 102 (2016) 1422-1432. doi:10.1016/j.applthermaleng.2016.03.119. 21. D.R.S. Raghuraman, R. Thundil Karuppa Raj, P.K. Nagarajan, B.V.A. Rao, Influence of aspect ratio on the thermal performance of rectangular shaped micro channel heat sink using CFD code, Alexandria Eng. J. 56 (2017) 43-54. doi:10.1016/j.aej.2016.08.033. 22. D. Kim, International Journal of Heat and Mass Transfer Thermal optimization of branched-fin heat sinks subject to a parallel flow, HEAT MASS Transf. 77 (2014) 278-287. doi:10.1016/j.ijheatmasstransfer.2014.05.010. Authors: C. Rajesh, N. Venkata Niranjan Kumar, G. Gowthami Evaluation of Wear Behaviour OFPLA & Abs Parts Fabricated by Operate FDM Technique with Distinct Paper Title: Orientations Abstract: Soft rapid tooling is one of the technology especially implemented to produce plastic components out of low melting point polymer by introducing it in to high melting polymer mould. By this technique parts or prototypes will be produced in less number for design verification, getting approval for actual production of product and as well as mould etc. Fused Deposition Modelling(FDM) is one of the additive manufacturing technology produced to parts in an additive manner. So far, several polymers like Acrylonitrile Butadiene Styrene (ABS), polyamide, poly lactic acid (PLA) were used for parts production in this domain. During the course of process engineering polymers may undergo wear because of the processing conditions like pressurized material in let, temperature etc. Choosing correct polymer for such application is a very important aspect. In the paper a comparison between 3D printed poly lactic acid polymer specimens and Acrylonitrile Butadiene styrene specimens fabricated through FDM technique will be tested for evaluation of difference between its wear rate, frictional force and friction coefficient. when printed with different printing orientations.

Keywords: Acrylonitrile Butadiene styrene, 3D printed poly lactic, FDM technique

References: 1. Gibson I, Rosen D, Stucker B (Ed.),(2014) Additive manufacturing technologies : 3D printing,rapid prototyping, and direct digital manufacturing. Springer 2. S.H. Masood, Advances in Fused Deposition Modeling, in: S.H. Masood (Ed) Advances in Rapid Manufacturing and Tooling, Elsevier, UK, 3. 10(2014) pp 69-91. 3. .Kanchanomai C, Rattananon S, Soni M. Effects of loading rate on fracturebehavior and mechanism of thermoset epoxy resin. Polym Test2005;24(7):886-92. 11-16 4. Kanchanomai C, Noraphaiphipaksa N, Mutoh Y. Wear characteristic of epoxy resin filled with crushed-silica particles [J]. Composites: Part B, 2011, 42: 1446-1452.CrossRefGoogle Scholar 5. Karsli N G, Yilmaz T, Aytac A, Ozkoc G. Investigation of erosive wear behavior and physical properties of SGF and/or calcite reinforced ABS/PA6 composites [J]. Composites: Part B, 2013, 44: 385-393.CrossRefGoogle Scholar 6. Afrose MF, Masood SH, Nikzad M (Ed.), (2014) Effects of buildorientations on tensile properties of PLA material processed byFDM. Adv Mater Res 1044-1045 :(pp.31-34) 7. Anoop Kumar Sood, Asif Equbal, Vijay Toppo, Ohdar R.K. and Mahapatra S.S.(Ed.),(2012) An investigation on sliding wear of FDM built parts, CIRP Journal of Manufacturing Science and Technology vol. 5, (pp.48-54). 8. Tian X, Liu T, Yang C, Wang Q, Li D (Ed.), (2016) Interface and performance of 3D printedcontinuous carbon fiber reinforced PLA composites. Compos Part A 9. Appl SciManuf; 88(pp.198-205). 10. Gurrala PK, Regalla SP(Ed.),(2014) Friction and wear behavior of abs polymer parts madeby fused deposition modeling (FDM). In : International Conference on Advancesof Tribology. 11. A. Standard, G99. Standard test method for wear testing with a pin-on-diskapparatus. West Conshohocken, PA : ASTM International ; 2006. 12. Zmitrowicz A. Wear patterns and laws of wear-A review [J]. Journal of Theoretical and Applied mechanics, 2006, 44(2): 219-253.Google Scholar 13. Mejia O O, Brostow W, Buchman E. Wear resistance and wear mechanisms in polymer + metal composites [J]. Journal of Nanoscience and Nanotechnology, 2010, 10: 1-6. CrossRefGoogle Scholar 14. Durand JM, Vardavoulias M, Jeandin M. Role of reinforcing ceramic particles inthe wear behaviour of polymer-based model composites. Wear 1995;181-183:833-9. 15. Umanath K, Selvamani S T, Palanikumar K. Friction and wear behavior of Al6061alloy (SiC+Al2O3) hybrid composites [J]. International Journal of Engineering Science and Technology, 2011, 3(7): 5441-5445 16. Aigbodion V S, Hassan S B, Agunsoye J O. Effect of bagasse ash reinforcement on dry sliding wear behaviour of polymer matrix composites [J]. Materials and Design, 2012, 33: 322-327.CrossRefGoogle Scholar. Authors: Y. Krishna Priya, M. Vijaya Kumar Comparative Study of Fault Mitigation Techniques of Level Three Neutral Clamped Inverter Fed Paper Title: 4. Induction Motor Drive Abstract: For power applications having high voltages multi-level inverters gain more attentiveness in comparison to 17-21 conventional two level power converters due to less harmonic deformation, elevated DC link voltage, low EMI. But faults can affect the performance of multi-level inverters. By the large, 70% of the occuring faults in multi-level inverter circuits are open circuit faults. When faults exist in the system, fault mitigation is also a prior important task for the efficient operation of the system. In this paper, a comparison between two fault mitigation techniques, former the conventional asymmetrical PWM and latter the leg four implementation were provided which were simulated in the software MATLAB/SIMULINK and the results were shown at the instants of prior to fault, during fault and fault after mitigation.

Keywords: Neutral Clamped Multi level inverter, induction motor drive, open fault, asymmetrical sinusoidal pulse width modulation, mitigation.

References: 1. Preeti Soni , Kavita Burse, "Analysis of Voltage Source Inverters using Space Vector PWM for Induction Motor Drive," IOSR Journal of Electrical and Electronics Engineering (IOSR-JEEE), Volume 2, year:2012, pp: PP 14-19. 2. Muhammad Farrukh Yaqub, Muhammad Safian Adeel, Tahir. Izhar, "Variable Voltage Source Inverter with Controlled Frequency Spectrum Based on Random Pulse Width Modulation," International Journal of Power Electronics and Drive Systems, year: 2011. 3. F. W. Fuchs, "Some diagnosis methods for voltage source inverters in variable speed drives with induction machines-A survey," in Proc. IEEE Ind. Electron. Conf., 2003, pp. 1378-1385. 4. E. Najafi and A. H. M. Yatim, "Design and Implementation of a New Multilevel Inverter Topology," in IEEE Transactions on Industrial Electronics, vol. 59, no. 11, pp. 4148-4154, Nov. 2012. 5. Paavo Rasilo, Aboubakr Salem, Ahmed Abdallh, Frederik De Belie, Luc Dupré and Jan A. Melkebeek, "Effect of Multilevel Inverter Supply on Core Losses in Magnetic Materials and Electrical Machines", Energy Conversion IEEE Transactions on, vol. 30, pp. 736-744, 2015. 6. L. M. Tolbert, F. Z. Peng, and T. G. Habetler, "Multilevel converters for largeelectricdrives,"IEEE Trans. Ind. Appl., vol. 35, no. 1, pp. 36- 44,Jan./Feb. 1999. 7. J. A. Ferreira, "The multilevel modular DC converter", IEEE Trans. Power Electron., vol. 28, no. 10, pp. 4460-4465, Oct. 2013 8. Nakul Thombre, Ratika singh Rawat, Priyanka Rana, Umashankar S, "A Novel Topology of Multilevel Inverter with Reduced Number of Switches and DC Sources", International Journal of Power Electronics and Drive System (IJPEDS), Vol. 5, No. 1, pp. 56~62, July 2014. 9. H. W. Ping, N. A. Rahim and J. Jamaludin, "New three-phase multilevel inverter with shared power switches", J. Power Electron., vol. 13, pp. 787-797, 2013. 10. S. Mekhilef, "Digital control of three phase three-stage hybrid multilevel inverter", IEEE Trans. Ind. Electron., vol. 9, no. 2, pp. 719-727, May 2013. 11. A. M. S. Mendes, A. J. M. Cardoso, and E. S. Saraiva, "Voltage source inverter fault diagnosis in variable speed AC drives, by the average current Park's vector approach," IEEE IEMDC Proc., 1999, pp.704-706. 12. R. L. A. Ribeiro, C. B. Jacobina, E. R. C. Silva, and A. M. N. Lima, "Fault detection of open-switch damage in voltage-fed PWM motor drive systems," IEEE Trans. Power Electron., vol. 18, no. 2, pp. 587-593, Mar. 2003. 13. C. Kral and K. Kafka, "Power electronics monitoring for a controlled voltage source inverter drive with induction machines," in Proc. IEEE 31st Annu. Power Electron. Spec. Conf., 2000, vol. 1, pp. 213-217. 14. Dupont L., Khatir Z, Lefebevere S., Bontemps S., Effects of metallization thickness of ceramic substrates on the reliability of power assemblies under high temperature cycling, Microelectronics reliability,46(9-11), pp.1766-1771, 2006. 15. K. Rothenhagen and F. W. Fuchs, "Performance of diagnosis methods for IGBT open circuit faults in voltage source active rectifiers," IEEE PESC proc., 2004, pp.4348-4354. 16. A. M. S. Mendes, A. J. M. Cardoso, and E. S. Saraiva, "Voltage source inverter fault diagnosis in variable speed AC drives, by the average current Park's vector approach," IEEE IEMDC Proc., 1999, pp.704-706. 17. R. L. A. Ribeiro, C. B. Jacobina, E. R. C. Silva, and A. M. N. Lima, "Fault detection of open-switch damage in voltage-fed PWM motor drive systems," IEEE Trans. Power Electron., vol. 18, no. 2, pp. 587-593, Mar. 2003. Authors: A. Krishnamoorthy, V. Vijayarajan, R. Kannadasan, Shikhar Johari Robotic-Wheelchair Control Head Orientation and Eye Ball Movement Control with Health Monitor Paper Title: System Abstract: Nowadays many people have lost ability to control their upper and lower parts of the body. Due to ageing and also some health issue like paralyses. In order to overcome these problem we need electrical control wheel chair instead of older joystick. Initially only the control of wheel chair mechanism is done. Which is more complex and takes more time. In order to do the automatic control of wheel chair and the body parts are done. If the person heart beat raises then the normal rate means then it is monitored and then if the body temperature of the person raises means it will be also monitored and displayed. If any condition the blood pressure of the person increases means it is monitored and displayed on LCD. In order to control the hand movement of the person the MEMS accelerometer is used and the eye blink is used to monitor the eye movement of the person. The wheel chair mechanism is controlled separately if any object come in front while operating wheel chair means then the ultrasonic sensor is used to detect that. It reduces the complexity and time and also easily operated by paralyses person’s. 5. Keywords: Wheel chair mechanism, Temperature sensor, Eye blink sensor, Blood pressure sensor, Ultrasonic sensor, 22-23 MEMS accelerometer, Heart rate.

References: 1. Legged Mobility Legged Mobility A Wheelchair Alternative A Wheelchair Alternative Drew R. Browning John Trimble Shin-Min Song Roland Priemer Chang-de Zhang UniversityIllinois at Chicago 2. International Journal of Electrical, Electronics and Data Communication, ISSN: 2320-2084 Volume-3, Issue-6, June-2015 Patient Monitoring Smart Wheelchair patient monitoring smart wheelchair 1manpreet singh minhas, jeevanchavan, ujwal singh. 3. International Advanced Research Journal in Science, Engineering and Technology Vol. 2, Issue 6, June 2015 Copyright to IARJSETDOI10.17148/IARJSET.2015.2619 84 Head Motion Controlled Wheel Chair using MEMS 4. Robotics and Biomimetics (ROBIO), 2010 IEEE International Conference on Continuous blood pressure monitor. 5. Inventive Computation Technologies (ICICT), International Conference on Sensor networks based healthcare monitoring system Sign In or Purchase. 6. IEEE Journal on Robotics and Automation (Volume: 4, Issue: 2, Apr 1988 )Obstacle avoidance with ultrasonic sensors.

Authors: A. Krishnamoorthy, V. Vijayarajan, G. Sivashanmugam, N. Visakeswaran 6. Paper Title: Real Time Bus Tracking System Using Linkit One Abstract: The public transport system is growing at a very rapid pace to meet the needs of the overgrowing 24-27 population, each day thousands of buses are carrying millions of people to their destinations, there is an increased burden on the public transport system. The situations created difficulty in managing and tracking the public transport. Since there are a lot of buses travelling through same route and it creates immense amount of congestion and thereby the buses fail to reach their destinations at promises time, and this has become a major problem especially in cities, where most of the bus users are office going people, who need to keep up with time. With this kind of uncertainty in the transport system, people have to trade off with their time standing and waiting at the bus stop, for their bus to reach. It would be better if user is certain about when the bus is about to reach, investing his time in something productive rather than wasting it standing at the bus stop. In this paper, we use the power of user’s smartphone combined with cloud computing to create a IOT system to provide certainty in the transport system. We use a very efficient and powerful IOT prototyping platform LinkIt ONE board to achieve this, integrate GPS, GPRS and other sensors are makes it stand out from other platforms such as Raspberry Pi and Arduino. We use the GPS and GPRS on the board to get the co- ordinates of the bus, these co-ordinates are sent to a Firebase cloud service and then co-ordinates are fetched in real- time to a React-Native mobile application installed on user’s device. It was observed that, the proposed system brings down the waiting time of the user. This system can be used as a base to solve much more complex problems in transport system, like scheduling the buses and avoiding congestion.

Keywords: real-time tracking, LinkIt One, IOT, cloud computing, react-native, Firebase

References: 1. Dr. Saylee Gharge, Manal Chhaya, Gaurav Chheda, Jitesh Deshpande, "Real time bus monitoring system using GPS," An International Journal of Engineering Science and Technology, Vol. 2, Issue 3, June 2012. 2. Abid Khan, Ravi Mishra, "GPS-GSM based tracking system," International Journal of Engineering Trends and Technology, Vol. 3, Issue 2, pp: 161-164, 2012. 3. A. Krishnamoorthy & V. Vijayarajan (2017) Energy aware routing technique based on Markov model in wireless sensornetwork, International Journal of Computers and Applications, DOI: 10.1080/1206212X.2017.1396423. 4. S. P. Manikandan, P. Balakrishnan, "An Efficient real time query system for public transportation service using Zigbee and RFID," International Journal of Research in Communication Engineering, Vol. 2, No. 2, June 2012. 5. Swati Chandurkar, Sneha Mugade, Sanjana Sinha, Pooja Borkar, "Implementation of real time bus monitoring and passenger information system," International Journal of Scientific and Research Publications, Vol. 3, Issue 5, May 2013. 6. Pankaj Verma, J. S. Bhatia, "Design and development of GPS-GSM based tracking system with Google map based monitoring," International Journal of Computer Science, Engineering and Applications, Vol. 3, No.3, June 2013. 7. Madhu Manikya Kumar, K. Rajesekhar, K. Pavani, "Design of punctually enhanced bus transportation system using GSM and Zigbee," International Journal of Research in Computer and Communication Technology, Vol. 2, Issue 12, December 2013. 8. R.Maruthi, C.Jayakumari "SMS based Bus Tracking System using Open Source Technologies," International Journal of Computer Applications (0975 - 8887) Volume 86 - No 9, January 2014 College of Engineering,Chennai SSN College of Engineering, Chennai. 9. N.Vijayalashmy, V. Yamuna, G. Rupavani, A. Kannaki@VasanthaAzhagu," GNSS based bus monitoring and sending SMS to the passengers," International Journal of Innovative Research in Computer and Application Engineering, Vol. 2, Special Issue 1, March 2014. 10. R. Manikandan, S. Niranjani, "Implementation on real time transportation information using GSM query response system," Contemporary Engineering Sciences, Vol. 7, No.11, pp: 509-514, 2014. 11. G. Raja, D. NaveenKumar, G. Dhanateja, G. V. Karthik, Y. Vijay Kumar, "Bus Position monitoring system to facilitate the passengers," International Journal of Engineering Science and Advanced Technology(IJESAT), Volume-3, Issue-3, pp: 132-135, 2014. 12. S. Eken, A. Sayar, "A Smart bus tracking system based on location aware services and QR codes," IEEE International Symposium on Innovations in Intelligent and Applications Proceedings, pp: 299-309, 2014. [13] Vishal Bharte, Kaustubh Patil, Lalit Jadhav, Dhaval Joshi , " Bus Monitoring System Using Polyline Algorithm ," International Journal of Scientific and Research Publications, Volume 4, Issue 4, April 2014. 13. Shekhar Shinde, Vijaykumar Nagalwar, Nikhil Shinde, B.V. Pawar, "Design of E-City Bus Tracking System ,"Int. Journal of Engineering Research and Applications, ISSN : 2248-9622, Vol. 4, Issue 4( Version 9), April 2014, pp.114-117.

Authors: Anjalee Menen, R. Gowtham Paper Title: An Efficient Ransomware Detection System Abstract: Cyber security protects the system from unauthorized access and destruction of data. The intention is to provide security to the system by blocking attackers. Malware or malicious software is any kind of program which is developed with the aim of doing harm to victim’s data. Viruses, worms, Trojan horses, Ransomware, and spyware are different types of malware. When malicious software enters into the system, it will encrypt the user data, deletes or modifies the data. This type of software also used to steal the user data. Ransomware is one of the types of malware which was developed with the intention of getting money from the victims. When Ransomware starts executing in our system, it will start encrypting, deleting and modifying files. The user will get decryption key only after paying the claimed money. Many have found some solutions for detecting some specific Ransomware. The existing technique includes Static based technique which uses signature analysis which can only detect known Ransomware since it compares the extracted code snippet of the target executable with the database of known malware samples. The existing technique is based on the known input and known output and can only detect known Ransomware samples. In this 7. paper we have proposed an efficient Ransomware detection system based on the analysis of behavior with the help of machine learning technique. In the proposed technique, we analyzed the possible behavior of Ransomware based on the changes to user’s files, addition of registry key, stopping the active processes. Based on this behavior, the decision is 28-31 made using Machine learning technique.

Keywords: Ransomware, Cybersecurity, Malware, Machine Learning

References: 1. Ali, Azad. "Ransomware: a research and a personal case study of dealing with this nasty malware." Issues in Informing Science and Information Technology 14 (2017): 087-099. 2. Ransomware Damage Report, Cybersecurity Ventures, 2017. . [Visited on April 2018] 3. cybersecurityventures.com-ransomware-damage-report-2017-5-billion 4. Venugopal, Deepak, and Guoning Hu. "Efficient signature based malware detection on mobile devices." Mobile Information Systems 4.1 (2008): 33-49. 5. Moore, Chris. "Detecting ransomware with honeypot techniques." Cybersecurity and Cyberforensics Conference (CCC), 2016. IEEE, 2016. 6. Cabaj, Krzysztof, and WojciechMazurczyk. "Using software-defined networking for ransomware mitigation: the case of cryptowall." IEEE Network 30.6 (2016): 14-20. 7. Scaife, N., Carter, H., Traynor, P., & Butler, K. R. (2016, June). Cryptolock (and drop it)- stopping Ransomware-attacks on user-data. In Distributed Computing Systems- (ICDCS), 2016- IEEE -36th International Conference on (pp. 303-312)- IEEE. 8. Kharraz, Amin, et al. "Cutting the gordian knot: A look under the hood of ransomware attacks." International Conference on Detection of Intrusions and Malware, and Vulnerability Assessment. Springer, Cham, 2015. 9. TPSC Forums. [Visited on August 2017] https://forum.thepcsecuritychannel.com/c/malware/malware-samples 10. ytisf/theZoo. [Visited on August 2017] http://bit.ly/2KI3CqQ 11. RISS-Ransomware Dataset. [Visited on October 2017] http://rissgroup.org/ransomware-dataset/ 12. File forum. [Visited on August 2017] https://fileforum.betanews.com/ 13. Major Geeks. [Visited on January 2018] http://www.majorgeeks.com/ 14. Softpedia. [Visited on August 2017] http://www.softpedia.com/ 15. Ramesh, Gowtham, Jithendranath Gupta, and P. G. Gamya. "Identification-of- phishing- webpages- and -its -target- domains -by -analyzing the - feign- relationship." Journal of Information- Security and Applications 35- (2017)-75-84. 16. Support vector machines for machine learning, [Visited on January 2018], machine learning mastery.com-support-vector-machines-for-machine- learning 17. Hampton, Nikolai, ZubairBaig, and SheraliZeadally. "Ransomware behavioural analysis on windows platforms." Journal of Information Security and Applications 40 (2018): 44-51. 18. Aravind, V., and M. Sethumadhavan. "A-framework-for-analysing-the-security of-chrome-extensions." Advanced Computing, Networking-and- Informatics-Vol-2. Springer, Cham, 2014. 267-272. 19. Dr. M. Sethumadhavan and H. VaNath, Gangadharan, Kb, and, "Reconciliation engine and metric for network vulnerability assessment", in ACM- International Conference- Proceeding- Series, Kerala, 2012, pp. 9-21.

Authors: V.B. Huddar, M.A. Kamoji Paper Title: Feasibility of Introducing Solar Air Dryer for Drying Process in Cashew Industries Abstract: Drying is an energy intensive processes in cashew processing industry. High prizes of electricity and interrupted power supply affecting the production is the motivation for this research. The objective is to develop a solar dryer and check its feasibility to adapt in cashew drying. The end conditions of industrial drying are to bring the moisture content from initial ±13% to ±5% maintaining its color and taste. An electrical heater drying system is developed to find energy required to dry one kg of cashew kernel. A solar air heater developed replaces only electrical heater. Experimental results ensure drying within stipulated time of 6 hours and energy consumption of 255 kJ against 270 kJ and 251 kJ of electrical heater drying and industrial steam drying. Study suggests design is feasible to small and cottage industries. The energy savings up to 25,000 kJ per day for a batch of 100 kg is possible.

Keywords: Cashew kernels, drying, energy, steam, electricity, solar.

References: 1. Atul Mohod , Sudhir Jain, Ashok Powar, Naren Rathore, Anilkumar Kurchania, Elucidation of unit operations and energy consumption pattern in small scale cashew nut processing mills, Elseveer, Journal of Food Engineering, 2010, 99 184-189 2. Dr. T V Ramachandra, CES technical report 88, Energy Alternatives: Renewable energy and energy conservation technologies, 2000, page # 25 3. T V Ramachandra, Solar energy potential assessment using GIS, Energy Education Science and Technology, 2007, Volume (issue) 18(2): 101- 114 4. C.L. Hii, S.V. Jangam, S.P. Ong and A.S. Mujumdar, Solar Drying: Fundamentals, Applications and Innovations,2012, ISBN: 978-981-07-3336- 0, page # 25 - 30 5. Ali Zomorodian and Maryam Zamanian, Designing and Evaluating an Innovative Solar Air Collector with Transpired Absorber and Cover, Renewable Energy, 2010, Volume 2012, Article ID 282538, 5 pages 8. 6. A.Anuradha & Dr (Mrs) Rachel Oommen, Fabrication and Performance evaluation of a v-groove solar air heater, International Journal of Scientific & Engineering Research, 2013, Volume 4, Issue 6, 2072 ISSN 2229-5518 7. Abhishek Saxena & Harish Yadav, (Jan. 2013), Thermal Performance of a Hybrid Solar Air Heater, MIT International Journal of Mechanical 32-37 Engineering, Vol. 3, No. 1, pp. 57-62, ISSN No. 2230-7680 © MIT Publications 8. M. Joseph Stalin & P. Barath, Effective Utilization of Solar Energy in Air Dryer, International Journal of Mechanical and Production, Engineering Research and Development (IJMPERD), 2013, ISSN 2249-6890, Vol. 3, Issue 1, 133-142 9. Salwa Bouadila, Sami Kooli, Mariem Lazaar, Safa Skouri, Abdelhamid Farhat, Performance of a new solar air heater with packed bed latent storage energy for nocturnal use, Applied Energy, 2013, 110, 267-275 10. Bashria Abdrub alrasoul Abdallah Yousef and Nor Mariha Adam, Performance Analysis for V-Groove Absorber, Sci. Technol. 14(1):39-52 11. Ahmad Fudholi, Mohd Hafidz Ruslan, Lim Chin Haw, Sohif Mat, Mohd Yusof Othman, Azami Zaharim & Kamaruzzaman Sopian, Performance of Finned Double-Pass Solar Air Collector, Recent Advances in Fluid Mechanics, Heat & Mass Transfer and Biology, ISBN: 978-1-61804-065-7 12. Jose E. Quiñonez, Alejandro L. Hernandez, Silvana Flores Larsen, Development and Experimental Evaluation of a Vertical Solar Air Collector for Heating of Buildings, PLEA 2012 - 28th Conference, Opportunities, Limits & Needs Towards an environmentally responsible architecture Lima, Perú, 2012, 7-9 13. K. Sopian, M.A. Alghoul, Ebrahim M. Alfegi, M.Y. Sulaiman, E.A. Musa, Evaluation of thermal efficiency of double-pass solar collector with porous-nonporous media, Renewable Energy, 2009, 34, 640-645 14. S.S.Kashinath & K. Kalidasa M, Experimental Study of The Double-Pass Solar Air Heater with Thermal Energy Storage, Journal of King Saud University-Engineering Sciences, 2013, 25, 135-140 15. Hiroshi Tanaka, Theoretical analysis of solar thermal collector with a flat plate bottom booster reflector, Energy Science & Technology, 2011, Vol 2, No. 2, pp26-34 16. S.P. Sukhatme, Solar energy, "Principles of thermal collection and storage" 2nd edition, Tata McGrow Hills, 173-200. 17. P. Velmurgan & R Kalaivanan, "Thermal performance studies on multi pass flat plate solar air heater with longitudinal fins: An analytical approach", Springer, 2015, 40, 1141-1150 18. G.O.I. Ezeike, Development and performance of a triple-pass solar collector and dryer system, ELESEVIER, Energy in agriculture, 1986, Vol.5, Issue 1, 1-20. 19. Vivekanand B. Huddar and Dr. Mahesh A Kamoji, Experimental investigations on electrical heat-assisted drying of cashew kernels, IOP conference series (In Press).

Authors: Geetha Reddy Evuri, G. Srinivasa Rao, T. Ramasubba Reddy, K. SrinivasaReddy Simulation of Hybrid Electric Energy Storage System (HESS) for Hybrid Electric Vehicle for Power Paper Title: Applications 9. Abstract: In recent years, the use of hybrid energy storage systems has become a global solution for delivering energy-efficient and reliable energy. With the use of these technologies, many of the energy-saving systems may be suitable for this and sufficient power may be provided for specific applications. In today's world, the need to continue 38-42 using the power of the world should be cleaner than traditional technology. This need helps to Widespread use of energy in today's energy sources will reduce global warming and climate change hazards. Use of renewable energy sources and the advantages of using energy derived from the fuel described in this clause. This paper examines the various energy conservation tools and examines their strengths and weaknesses. Some energy storage systems are designed and modeled in MATLAB/Simulink. The necessary electrical circuits are included in the hybrid storage system. Lastly, it presents, model of the hybrid power system (such as the Battery-Superpacator).

Keywords: Ultra-capacitor, HESS, hybrid power system, dc/dc converter, supercapacitor.

References: 1. Supercapacitors: Materials, Systems, and Applications, edited by F. Beguin and E. Frackowiak, published by Wiley-VCH, 2013. 2. Technologies and Materials for Large Supercapacitors, edited by A. Nishino and K. Naoi, published by CMC International,2010. 3. Linden's Handbook of Batteries (Fourth Edition), edited by T.B. Reddy, Chapter 39: Electrochemical Capacitors by A.F.Burke, published by McGraw-Hill, 2011. 4. Burke,A.F,Ultracapacitor technologies and applications in hybrid and electric vehicles, international journal of energy and research (Wiley), Vol. 34, issue 2, 2011. 5. Electrochemical Double Layer Capacitors of Low Internal Resistance, Energy and Environmental Research, Vol. 3, No. 2, 156-165, 2013. 6. Burke, A.F., Advanced Batteries for Vehicle Applications, article in Encyclopedia of Automotive Engineering, Wiley, published online December 2012. 7. Maletin, Y., etals., Carbon Based Electrochemical Double Layer Capacitors of Low Internal Resistance, Energy and Environmental Research, Vol. 3, No. 2, 156-165, 2013. 8. Burke, A. and Miller, M., Lithium batteries and ultracapacitors alone and in combination in hybrid vehicles: Fuel economy and battery stress reduction advantages, paper presented at the Electric Vehicle Symposium 25, Shenzhen, China, November 2010. 9. Zhao, H. and Burke, A.F., Fuel Cell Powered Vehicles using Ultracapacitors, Fuel Cells, Vol. 10, Issue 5, September 2010. 10. [10] B. Lequesne, "Automotive Electrification: The Nonhybrid Story", Transportation Electrification, IEEE Transactions novel. 1, no 11. K. Kusakana and H. J. Vermaak, "Hybrid diesel generator/renewable energy system performance modeling," Renew Energy, vol. 67, pp. 97-102, Jul. 2014. 12. J. E. Paiva and A. S. Carvalho, "Controllable hybrid power system based on renewable energy sources for modern electrical grids," Renew. Energy, vol. 53, pp. 271-279, May 2013. 13. Y.-C. Kuo, Y.-M. Huang, and L.-J. Liu, "Integrated circuit and system design for renewable energy inverters," Int. J.Electr. Power Energy Syst., vol. 64, pp. 50-57, Jan. 2015. 14. H. Belmili, M. Haddadi, S. Bacha, M. F. Almi, and B. Bendib, "Sizing stand-alone photovoltaic-wind hybrid system: Techno-economic analysis and optimization," Renew. Sustain. Energy Rev., vol. 30, pp. 821-832, Feb. 2014.

Authors: Rijo Varghese, Senthilkumar Mathi Latency Reduction in Ethernet Open - Audio Video Bridging Streams for Automotive Infotainment Paper Title: Network Abstract: Ethernet is one of the most widely used high-speed interfaces at homes and offices, recently the trend is catching up towards its adoption to the automotive world where a high amount of data to be transported at very high speeds mostly in the infotainment domain. As with Controller Area Network (CAN) which is an automotive communication protocol, Ethernet is also packetized data communication system, where information is transferred in packets between nodes on various parts of the network. Ethernet would be the best candidate for replacing CAN in the future, but the high cost per node is a limiting factor for widespread use of it in an automotive environment. Due to this, it may probably not replace CAN networks in the near future but rather augment it. Reliability and fault-resilience being a characteristic for any automotive domain, the high data rate is an additional requirement for domains such as infotainment because the data that is transported is primarily multimedia. CAN network fail in this aspect, as the supported data rate is not sufficient for multimedia and high quantity sensor data. The alternative, OpenAVB is an open source Audio Video Bridging (AVB) system. In this paper, the customizing OpenAVB software for automotive use cases is discussed and analyzed. Also, it presents at enhancing the OpenAVB stack’s stream reservation process to suit the automotive scenarios to reduce the streaming latency with stream reservation protocol at network startup by performing bandwidth reservation.

Keywords: Ethernet, audio-video streams, infotainment, reservation, bandwidth

10. References: 1. Ashjaei M, Patti G, Behnam M, Nolte T, Alderisi G, Bello LL, Schedulability analysis of Ethernet Audio Video Bridging networks with scheduled traffic support, Real-Time Systems, 53(4) pp. 526-77, 2017. 43-56 2. IEEE Standards Association, Timing and Synchronization for Time-Sensitive Applications in Bridged Local Area Networks, In IEEE, Vol. 30, p. 292, 2011. 3. Gayathri S, Radhika N. Greedy hop algorithm for detecting shortest path in vehicular networks, International Journal of Control Theory and Applications, 9(2), pp. 1125-1133, 2016. 4. Qian K, Zhang T, Ren F. Awakening Power of Physical Layer: High Precision Time Synchronization for Industrial Ethernet, In Real-Time Systems Symposium, pp. 147-156, 2017. 5. Zhao L, He F, Lu J, Comparison of AFDX and audio video bridging forwarding methods using network calculus approach, In 36th Digital Avionics Systems Conference (DASC), IEEE/AIAA, pp. 1-7, 2017. 6. Wee J, Park K, Kwon K, Song B, Kang M. Design and Implementation of an Embedded Audio Video Bridging Platform for Multichannel Multimedia Transmission, Journal of Internet Computing and Services, 16(2):1-6, 2015. 7. Bello LL. Novel trends in automotive networks: A perspective on Ethernet and the IEEE Audio Video Bridging, In Emerging Technology and Factory Automation, pp. 1-8, 2014. 8. Kleineberg O, Fröhlich P, Heffernan D. Fault-tolerant ethernet networks with audio and video bridging. InEmerging Technologies & Factory Automation (ETFA), 2011 IEEE 16th Conference on 2011 Sep 5 (pp. 1-8). IEEE. 9. Abraham KT, Ashwin M, Sundar D, Ashoor T, Jeyakumar G, An evolutionary computing approach for solving key frame extraction problem in video analytics, In Communication and Signal Processing (ICCSP), pp. 1615-1619, 2017. 10. He F, Zhao L, Li E. Impact analysis of flow shaping in ethernet-avb/tsn and AFDX from network calculus and simulation perspective. Sensors. 2017 May 22;17(5):1181. 11. Higher Layer LAN Protocols Working Group, 802.1 Q-2014-IEEE Standard for local and metropolitan area networks-bridges and bridged networks, IEEE 802.1 Q, 2014. 12. Garner GM, Ryu H, Synchronization of audio/video bridging networks using IEEE 802.1 AS, IEEE Communications Magazine, 49(2), 2011. 13. Nasrallah A, Thyagaturu A, Alharbi Z, Wang C, Shao X, Reisslein M, ElBakoury H. Ultra-Low Latency (ULL) Networks: A Comprehensive Survey Covering the IEEE TSN Standard and Related ULL Research. arXiv preprint arXiv:1803.07673. 2018 Mar 20. 14. Tuohy S, Glavin M, Hughes C, Jones E, Trivedi M, Kilmartin L. Intra-vehicle networks: A review. IEEE Transactions on Intelligent Transportation Systems, 16(2):534-45, 2015. 15. Teener MD, Fredette AN, Boiger C, Klein P, Gunther C, Olsen D, Stanton K. Heterogeneous networks for audio and video: Using IEEE 802.1 audio video bridging, Proceedings of the IEEE, 101(11), pp. 2339-2354, 2013. 16. Melvin H, Shannon J, Stanton K. Time, Frequency and Phase Synchronisation for Multimedia - Basics, Issues, Developments and Opportunities, In MediaSync, pp. 105-146, Springer, Cham, 2018. 17. Moreno MF, de Resende Costa RM, Soares LF, Interleaved time bases in hypermedia synchronization, IEEE MultiMedia, 22(4), 68-78, 2015. 18. Teener MD, Automotive Ethernet AVB Landscape, SAE International Journal of Passenger Cars-Electronic and Electrical Systems, 8(2015-01- 0223), pp.156-160, 2015. 19. Imtiaz J, Jasperneite J, Schriegel S, A proposal to integrate process data communication to IEEE 802.1 Audio Video Bridging (AVB), In Emerging Technologies & Factory Automation (ETFA), 2011 IEEE 16th Conference, pp. 1-8, 2011. 20. Naman AT, Wang Y, Gharakheili HH, Sivaraman V, Taubman D. Responsive high throughput congestion control for interactive applications over SDN-enabled networks. Computer Networks, 134:152-66, 2018. 21. Hank P, Müller S, Vermesan O, Van Den Keybus J, Automotive Ethernet: in-vehicle networking and smart mobility, In Proceedings of the Conference on Design, Automation and Test in Europe, pp. 1735-1739, 2013. 22. Gothard A, Kreifeldt R, Turner C, Ames B, Bechtel G, Bergen J, Chang A, Gale B, Huotari AJ, Kim Y, Lewis K, AVB for automotive use, AVnu Alliance White Paper, 2014. 23. Huang Q, Chen Y, Zhou P, Yue T, Xiao Q, Design of AVB Node Based on R-Car T2, In Proceedings of the 2018 2nd International Conference on Management Engineering, Software Engineering and Service Sciences, pp. 192-196, 2018.

Authors: Sabapathy, T. Nithila, K. Vaishnavi, A. Shrinidhi, V. Srilekha, A. Jai Vigneshwar Paper Title: In-Plane Shear Behaviour of Unreinforced Brick Masonry Strengthened by Bio-Composite Fabrics Abstract: Unreinforced Masonry structures are subjected to failure due to in plane and out plane loads from the wind and earthquakes. Therefore strengthening of Unreinforced Masonry structures (URM) is vital to make them seismic resistant. This paper describes an experimental investigation on the in-plane behaviour of the URM panels strengthened with composite fabrics namely glass fiber and jute fiber respectively. These URM panels were fabricated and provided with two different orientations of the said fibers. Diagonal compression test was conducted and the crack patterns were analyzed. The test results showed that strengthening URM with jute fiber can be used as an effective alternative for glass fiber for retrofitting.

References: 1. Khalifa, A., W.J. Gold, A. Nanni, and M.I. Abdel Aziz, "Contribution of externally bonded frp to shear capacity of flexural members," ASCE- Journal of Composites for Construction, vol. 2, No.4, pp. 195- 203, Nov. 1998. 2. M. Corradi, A. Borri, A. Vignoli, "Experimental evaluation of in- plane shear behaviour of masonry walls retrofitted using conventional and 11. innovative methods," Masonry International- Journal of British Masonry Society, vol. 21, No. 1, pp.1-48, 2008. 3. Thanasis C. Triantafillou, "Strengthening of masonry structures using epoxy bonded FRP laminates," Journal of Composites for Construction, vol. 2, No. 2, May. 1998. 57-59 4. Giancarlo Marcari, Gaetano Manfredi, Andrea Prota, Marisa Pecce, "In-plane shear performance of masonry panels strengthened with FRP," Composites Part B: Engineering, No. 38, pp. 887-901, Jan. 2007. 5. Josh Lombard, David T Lau, Jag L Humar, Simon Foo And M S Cheung, "Seismic strengthening and repair of reinforced concrete shear walls," 12th World Conference on Earthquake Engineering, Auckland, 2000. 6. J.G. Tumialan, A. Morbin, A. Nanni, and C. Modena, "Shear strengthening of masonry walls with frp composites," Composites, No. 6, October, 2001. 7. Ahmad A. Hamid, Wael W. El-Dakhakhni, M.ASCE, Zeyad H. R. Hakam, Mohamed Elgaaly, F.ASCE, "Behaviour of composite unreinforced masonry-fiber-reinforced polymer wall assemblages under in-plane loading," Journal of Composites for Construction, Vol. 9, No. 1, pp. 73-83, Feb. 2005. 8. ASTM (2002). ASTM E 519-02, Standard Test Method for Diagonal Tension (Shear) in Masonry Assemblages. ASTM International, West Conshohocken, PA 9. Indian Standard Specification of Sand for Masonry Mortars, Indian Standard 2116, 1980. 10. Ordinary Portland Cement, 33 grade- Specification, Indian Standard 269, 2013. 11. Methods of Test for Aggregates for Concrete- Specific Gravity, Density, Voids, Absorption and Bulking, Indian Standard 2386 (Part III) - 1963. 12. Indian Standard Code of Practice for Preparation and Use of Masonry Mortars, Indian Standard 2250, 1981.

Authors: Rohith Krishnan, Hari Narayanan Paper Title: User-Space Authorization of Creat System Call in Linux Abstract: Authorization is a hot topic for several decades’ right from the time people started using information technology. It still continues to be. All the operating systems have some or other ways of authorizing user processes. Linux uses identity-based authorization by default which is not fine-grained. The architecture discussed in this paper provides an additional layer of authorization using security tickets over and above the userid and groupid based authorization. A Secure Daemon which is running in the user space is responsible for this second level of authorization. This new architecture is designed in such a way that all the critical system call invocations like creat are routed to the Secure Daemon which verifies the attached security ticket for authorization. This routing of the creat system call invocation is achieved by the use of wrapper function for C library function creat. This architecture ensures Principle of Least Privilege which is essential to prevent attacks from malicious programs. The processes are executed in a 12. sandboxed environment to protect the system from potential attacks. Since the Secure Daemon is running in the user space itself, this is also portable across different Linux platforms. 60-64 Keywords: Authorization, Principle of Least Privilege, Sandbox, System Calls, Secure Daemon, Wrapper Functions, Security Tickets.

References: 1. Hari Narayanan, Vivek Radhakrishnan, Shiju Sathyadevan, and Jayaraj Poroor. Architectural design for a secure linux operating system,accepted at International Conference on Wireless Communications Signal Processing and Networking (WISPNET), Chennai, 2017. Vivek Radhakrishnan, Hari Narayanan, and Shiju Sathyadevan.System Call Authorization in Linux by a Secure Daemon, accepted at International Conference on Operating system security (ICOSS'17). 2. Michael Wikberg. Secure computing: Selinux. http://www.tml.tkk./Publications/C/25/papers/Wikberg nal. pdf, 2007 3. Schreuders, Z. Cliffe, Tanya McGill, and Christian Payne. "Empowering end users to confine their own applications: The results of a usability study comparing SELinux, AppArmor, and FBAC-LSM." ACM Transactions on Information and System Security (TISSEC) 14(2)19.2011 4. Garnkel, Tal. "Traps and Pitfalls: Practical Problems in System Call Interposition Based Security Tools." NDSS. Vol. 3. 2003. 5. Robert NM Watson, Jonathan Anderson, Ben Laurie, and Kris Kennaway. Capsicum: Practical capabilities for unix. In USENIX Security Symposium, volume 46, 2010. 6. Farley, Benjamin. "Analyzing Capsicum for Usability and Performance." (2010). 7. Pawel Jakub Dawidek and Mariusz Zaborski.Sandboxing with Capsicum,www.usenix.org VOL. 39, NO. 6 DECEMBER 2014 8. William R. Harris?, Benjamin Farley?, Somesh Jha?, Thomas Reps?†?Computer Sciences Department; University of Wisconsin-Madison; Madison, WI, USA. Programming for a Capability System via Safety Games 2011. 9. Harada, T. Horie, and K. Tanaka, "Task oriented management obviates your onus on linux," in Linux Conference, vol. 3, 2004. 10. A P Murray and Duncan A Grove. Pulse: A pluggable user-space linux security environment. In Proceedings of the sixth Australasian conference on Information security-Volume 81, pages 19-25. Australian Computer Society, Inc., 2008. 11. Charles Jacobsen, Muktesh Khole, Sarah Spall, Scotty Bauer, and Anton Burtsev. Lightweight capability domains: towards decomposing the linux kernel. ACM SIGOPS Operating Systems Review, 49(2):44-50, 2016. 12. Adwitiya Mukhopadhyay, V Srinidhi Skanda, and CJ Vignesh. An analytical study on the versatility of a linux based firewall from a security perspective. International Journal of Applied Engineering Research, 10(10): 26777-26788, 2015. 13. S. P and K. P. Jevitha. Static analysis of firefox os privileged applications to detect permission policy violations. International Journal of Control Theory and Applications, 9(7):3085-3093, 2016. 14. D. Nidhin, Praveen, I., and Praveen, K., "Role-Based Access Control for Encrypted Data Using Vector Decomposition", in Proceedings of the International Conference on Soft Computing Systems: ICSCS 2015, Volume 2, P. L. Suresh and Panigrahi, K. Bijaya New Delhi: Springer India, 2016, pp. 123-131. 15. I. Ray, Alangot, B., Nair, S., and Dr. Krishnashree Achuthan, "Using Attribute-Based Access Control for Remote Healthcare Monitoring", in 2017 4th International Conference on Software Defined Systems, SDS 2017, 2017, pp. 137-142.

Authors: Saeed Ahmed, Nirmal Krishnnan, Thanmay Ganta, Gurusamy Jeyakumar Paper Title: A Video Analytics System for Class Room Surveillance Applications Abstract: Using video analytics to give insights about events happening in classroom is a very important task in classroom surveillance systems. This paper proposes a new algorithmic frameworkto identify abrupt changes in a class room video and thenevaluate the attention level of students.The proposed algorithm is implemented with and without video key frame extraction approaches. The SSIM (Structural Similarity Index) approach for key frame extraction is used in this study. After extracting the key frames, the detection of face and upper body of the students to evaluate their attention level is performed on the key frames. The results comparing thealgorithms with and without SSIM reveals that the SSIM based algorithm gives better results. The algorithmic design of the proposed approach, the results obtained and sample cases are presented in this paper.

Keywords: Video Analytics, Key Frame Extraction,SSIM values,Computer vision, Face Detection, Upper Body Detection.

References: 1. Guozhu Liu, and Junming Zhao, "Key Frame Extraction from MPEG Video Stream", In Proceedings of Second Symposium International Computer Science and Computational Technology, 2009. 2. Qiang Zhang, Shulu Zhang and Dongsheng Zhou, "Keyframe Extraction from Human Motion Capture Data Based on a Multiple Population Genetic Algorithm", Symmetry Open Access Journal, Vol. 6, No. 4, 2014. 3. Huayong Liu and Wenting MengZhi Liu, "Key Frame Extraction of Online Video Based on Optimized Frame Difference", In Proceedings 9th International Conference on Fuzzy Systems and Knowledge Discovery, 2012. 13. 4. Ramender, G, MovvaPavani and Kishore Kumar, G, "Evolving optimized video processing and wireless transmission system based on arm- cortex-a8 and gsm", International journal of computer networking, wireless and mobile communications, Vol. 3, No. 5, 2013. 65-69 5. Huayong Liu, LingyunPan and WentingMeng, "Key Frame Extraction from Online Video Based on Improved Frame Difference Optimization", In Proceedings of 14th International Conference on Communication Technology (ICCT), 2012. 6. Ran Zheng ,Chuanwei Yao ,Hai Jin ,Lei Zhu,Qin Zhang and Wei Deng, "Parallel Key Frame Extraction for Surveillance Video Service in a Smart City" Plos One, Vol. 10 No.8, 2015. 7. Sheena C.V and N,K.Narayanan, "Key Frame Extraction by Analysis of Histograms of Video Frames using Statsistical Videos", Procedia Computer Science, Vol. 70, pp. 36-40, 2015. 8. Zhang Ronghua and Liu Changzheng, "The Key Frame Extraction Algorithm Based on the Indigenous Disturbance Variation Difference Video", The Open Cybernetics &Systemics Journal, Vol. 9, pp. 36-40, 2015. 9. Z. Wang and A. C. Bovik, "A universal image quality index", IEEE Signal Processing Letters, Vol. 9, pp. 81-84, 2002. 10. SharanjeetKaurSandhu and AnupamAgarwal "Summarizing Videos by Key frame extraction using SSIM and other Visual Features", In Proceedings of - ICCCT '15 - Sixth International Conference on Computer and Communication Technology, 2015. 11. Kevin Thomas Abraham., Ashwin M., DharsakSundar., TharicAshoor and Jeyakumar G, "An Evolutionary Computing Approach for Solving Key Frame Extraction Problem in Video Analytics", In Proceedings of - ICCSP'17 - International Conference on Communication and Signal Processing, 2017. 12. Abraham K.T., Ashwin M., Sundar D., Ashoor T and Jeyakumar G, "Empirical Comparison of Different Key Frame Extraction Approaches with Differential Evolution Based Algorithms", In Advances in Intelligent Systems and Computing, Vol 683, Thampi S., Mitra S., Mukhopadhyay J., Li KC., James A., Berretti S. (Eds), 2017. 13. Senthil Kumar T and Narmatha G, "Video Analysis for Malpractice Detection in Classroom Examination". In: Suresh L., Panigrahi B. (eds) Proceedings of the International Conference on Soft Computing Systems. Advances in Intelligent Systems and Computing, Vol 397, 2016. 14. Jian Han Lim., EngYeow The., Ming Han Geh and Chern Hong Lim, "Automated classroom monitoring with connected visioning system", In Proceedings of Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, 2017. 15. Mehul K Dabhi and Bhavna K Pancholi, "Face Detection System Based on Viola - Jones Algorithm", International Journal of Science and Research, Vol. 5., No. 4., 2016.

Authors: Arvind P. Jayan, Ashwin Balasubramani, Akshay Kaikottil, N. Harini Paper Title: An Enhanced Scheme for Authentication Using OTP and QR code for MQTT Protocol Abstract: The pervasive nature of computing makes classical solutions inapplicable for handling communication between peers in the IOT platform. With enormous number of users and devices becoming a part of this network, Authentication of com-munication devices has become more challenging. The work presented in this paper discusses an 14. authentication scheme with reduced overhead based on multi factors, barcodes and One Time Password. The outcome of the experimentation revealed the effi-ciency of the scheme in terms of guaranteeing enhanced security without 70-77 compromise in versatility.

Keywords: MQTT (Message Queuing Telemetry Protocol), IoT (Internet of Things), AES (Advanced Encryption Standard), sniffing, authentication, QR code (Quick Response Code).

References: 1. Kinjal H. Pandya and Hiren J. Galiyawala.(2014). A Survey on QR Codes: in context of Research and Application. International Journal of Emerging Technology and Advanced Engineering,. 4(3): 258-262. 2. Shyamala C.K., Harini N. and Padmanabhan T.R. (2011) Cryptography and security, First Edition, Wiley India, 560p. 3. Kamakshi Devisetty R N, Aruna D. and Harini.N. (2018) Secure Proxy Blind ECDS Algorithm for IoT?, International Journal of Pure and Applied Mathematics. 118 (7): 437-445. 4. Sklavos, Nicolas and Zaharakis, I. (2016). Cryptography and Security in Internet of Things (IoTs): Models, Schemes, and Implementations. 8th IFIP International Conference on New Technologies, Mobility and Security (NTMS), Larnaca, pp. 1-2. DOI: 10.1109/NTMS.2016.7792443 5. Singh, G. (2013). A study of encryption algorithms (RSA, DES, 3DES and AES) for information security. International Journal of Computer Applications, 67(19): 33-38. 6. Lee, Young-Sil, Hyun Kim, Nack, Lim, Hyotaek, HeungKuk, and Lee Hoonjae. (2010) Online banking authentication system using mobile-OTP with QR-code. 5th International Conference on Computer Sciences and Convergence Information Technology, Seoul, pp. 644-648. DOI: 10.1109/ICCIT.2010.5711134. 7. Oh DS., Kim BH., Lee JK. (2011) A Study on Authentication System Using QR Code for Mobile Cloud Computing Environment. In: Park J.J., Yang L.T., Lee C. (eds) Future Information Technology. Communications in Computer and Information Science, Vol 184. Springer, Berlin, Heidelberg. Pp.500-507. 8. Preneel B. (2010) Cryptographic Hash Functions: Theory and Practice. In: Soriano M., Qing S., López J. (eds) Information and Communications Security. ICICS 2010. Lecture Notes in Computer Science, vol 6476. Springer, Berlin, Heidelberg. Pp. 1-3. 9. Yassein, M. B., Shatnawi, M. Q. Aljwarneh S. and R. Al-Hatmi. (2017) Internet of Things: Survey and open issues of MQTT protocol," International Conference on Engineering & MIS (ICEMIS), Monastir, pp. 1-6. DOI: 10.1109/ICEMIS.2017.8273112 10. Stallings, W. (2006). Cryptography and Network Security: Principles and practices. Fifth Edition. Pearson Education, India. 900p. 11. Gowthaman, A and Manickam, Sumathi. (2015), Performance study of enhanced SHA-256 algorithm. International Journal of Applied Engineering Research 10 (4):10921-10932. 12. Soni, Dipa and Makwana, Ashwin. (2017). A survey on MQTT: a protocol of Internet of Things (IOT). International conference on telecommunication, power analysis and computing techniques (ICTPACT - 2017), Chennai, India. 13. Syed Zulkarnain Syed Idrus, Estelle Cherrier, Christophe Rosenberger and Jean-Jacques Schwartzmann. (2013). A Review on Authentication Methods. Australian Journal of Basic and Applied Sciences,7(5):95-107. 14. Qadeer, M.A., A. Iqbal, M. Zahid and M. R. Siddiqui (2010) Network Traffic Analysis and Intrusion Detection Using Packet Sniffer. Second International Conference on Communication Software and Networks, Singapore, pp. 313-317.

Authors: K.A. Dhanya, T. Gireesh Kumar Paper Title: Efficient Android Malware Scanner Using Hybrid Analysis Abstract: Mobile Malicious applications are great threat to digital world as it is increasing tremendously along with benign applications. Main approaches for analysing the malware are static, dynamic and hybrid analysis. In this paper hybrid analysis is proposed with permission features accessed from applications statically, dynamic features like network activities, file system activities, cryptographic activities, information leakage etc. are dynamically accessed using Android Droid box and dynamic API calls are analysed using API Monitor tool. Separability assessment Criteria is used for relevant feature selection which had improved the performance. In this paper, hybrid features are used to characterize the malware along with learning algorithms such as Naïve Bayes, J48 and Random Forest. Random Forest classifier had produced TPR of 1, FPR of 0 with 77 best features.

Keywords: Mobile Malware, Droid box, API Monitor, Hybrid Analysis, Machine learning.

References: 1. Apktool, https://ibotpeaches.github.io/Apktool/. 2. Virustotal, https://www.virustotal.com, accessed Feb2, 2017 3. WEKA, http://www.cs.waikato.ac.nz/ml/weka/. 4. Google play, https://play.google.com/store. 5. Drebin Dataset. http://user.cs.uni-goettingen.de/~darp/drebin/, Accessed 2 Jan 2017 6. DroidboxTool, https://github.com/pjlantz/droidbox, Accessed Feb 15, 2017 7. Zhenlong Yuan, Yongqiang Lu, and YiboXue., Droid Detector: Android Malware Characterization and Detection Using Deep Learning, ISSN1007-021410/10pp114-123 Volume 21, Number 1, February 2016. 8. Dong-Jie Wu, Ching-Hao Mao, Te-En Wei, Hahn-Ming Lee, Kuo-Ping Wu, DroidMat: Android Malware Detection through Manifest and API 15. Calls Tracing, 2012 Seventh Asia Joint Conference on Information Security, DOI 10.1109/AsiaJCIS.2012.18. 9. MengyuQiao, Andrew H. Sung, and Qingzhong Liu, Merging Permission and API Features for Android Malware Detection, 2016 5th IIAI International Congress on Advanced Applied Informatics, DOI 10.1109/IIAI-AAI.2016.237. 78-80 10. Shree Garg, Sateesh K. Peddoju, Anil K. Sarje, Network-based detection of Android malicious apps, Int. J. Inf. Secur. (2017) 16:385-400 DOI 10.1007/s10207-016-0343-z. 11. Ali Feizollah, Nor BadrulAnuar, RosliSalleh, A inuddin Wahid Abdul Wahab. A review on feature selection in mobile malware detection. Digital Investigation 13(2015) 22-37. 12. M. V. Varsha, P. Vinod & K. A. Dhanya, Identification of malicious android app using manifest and opcode features, Journal of Computer Virology and Hacking Techniques, Volume 13. 13. P. Vinod, P. Viswalakshmi. "Empirical Evaluation of a System Call-Based Android Malware Detector", Arabian Journal for Science and Engineering, 2017 14. Narudin, Fairuz Amalina, Ali Feizollah, Nor BadrulAnuar, and Abdullah Gani. "Evaluation of machine learning classifiers for mobile malware detection", Soft Computing, 2016. 15. Varsha M. V., Vinod P. and Dhanya K. A., "Heterogeneous Feature Space for Android Malware Detection," In Proceedings of 8th IEEE International Conference on Contemporary Computing (IC3-2015), 20-22 August, 2015. 16. Kimberl T Tam, AliFeizollah, NorBadrulAnuar, RosliSalleh, LorenzoCavallaro., The Evolution of Android Malware and Android Analysis Techniques., ACM Computing Surveys, Vol. 0, No. 0, Article 00, Publication date: 0 17. Daniel Arp, Hugo Gascon, Konrad Rieck, et al.: DREBIN: E?ective and Explainable Detection of Android Malware in Your Pocket, NDSS '14, 23-26 February 2014, San Diego, CA, USA. 18. Dong-Jie,Wu, Ching-Hao Mao, Te-En Wei, Hahn-Ming Lee, Kuo-Ping WC., Droid Mat: Android Malware Detection through Manifest and API call Tracing., Information Security (ASIA JCIS), 2012. 19. Alessandro Reina, Aristide Fattori, Lorenzo Cavallaro., A System Call-Centric Analysis and Stimulation Technique to Automatically Reconstruct Android Malware Behaviors., EuroSec '13, April 14 2013, Prague, Czech Republic. 20. Mingshen Sun , John C. S. Lui., Droid Analytics: A Signature Based Analytic System to Collect, Extract, Analyze and Associate Android Malware. 12th IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom), 2013. 21. Paul, TG Gregory, and T. Gireesh Kumar. "A Framework for Dynamic Malware Analysis Based on Behavior Artifacts." Proceedings of the 5th International Conference on Frontiers in Intelligent Computing: Theory and Applications. Springer, Singapore, 2017. 22. R.Vinayakumar ; K.P.Soman ; Prabaharan Poornachandran., Deep android malware detection and classification., International Conference on Advances in Computing, Communications and Informatics (ICACCI), 2017.

Authors: T. Jenitha, S. Amutha, I. Shri Ram Paper Title: Enhanced Three Factor Security Protocol for Storage Devices Abstract: The Universal Serial Bus (USB) is eshtabilised for connection, communication and power supply between peripherals, also acts as an extremely popular interface standard for computer peripheral connections and is widely used in consumer Mass Storage Devices (MSDs). Eventhough current consumer USB MSDs provide relatively high transmission speed and are convenient to carry, the use of USB MSDs has been prohibited in many commercial and everyday applications primarily due to security problems. Security protocols have been previously proposed and a recent approach for the USB MSDs is to utilize multi-factor authentication. This paper proposes significant enhancements to the three-factor control protocol such as user identity, password and one-time-password (OTP) being sent to the e-mail that now makes it secure under many types of attacks including the password guessing attack, denial- of-service attack, and the replay attack. The proposed solution is given with a rigorous security analysis and practical computational cost analysis to demonstrate the usefulness of this new security protocol for consumer USB MSD.

Keywords: USB, MSD, Password guessing attack, ECC, OTP.

References: 1. Debiao He, Neeraj Kumar, Jong-Hyouk Lee, Senior Member, IEEE, and R. Simon Sherratt, Fellow, IEEE, “Enhanced Three-factor Security Protocol for Consumer USB Mass Storage Devices”, IEEE Transactions on Consumer Electronics, Vol. 60, No. 1, February 2014. 2. B. Chen, C. Qin, and L. Yu, "A Secure Access Authentication Scheme for Removable Storage Media," Journal of Information & Computational 16. Science, Binary Information Press, vol. 9, no. 15, pp.4353-4363, Nov. 2012. 3. C. Lee, C. Chen, and P. Wu, "Three-factor control protocol based on elliptic curve cryptosystem for universal serial bus mass storage devices," 81-84 IET Computers & Digital Techniques, vol. 7, no. 1, pp. 48-55, Jan. 2013. 4. F. Y. Yang, T. D. Wu, and S. H. Chiu, "A secure control protocol for USB mass storage devices," IEEE Trans. Consumer Electron., vol. 56, no. 4, pp. 2339-2343, Nov. 2010. 5. Mohamed Hamdy Eldefrawy, Muhammad Khurram Khan, Hassan Elkamchouchi, "The Use of Two Authentication Factors to Enhance the Security of Mass Storage Devices", 11th International Conference on Information Technology: New Generations, 2014. 6. M.H. Eldefrawy, M.K. Khan, K. Alghathbar, T.H. Kim, and H.Elkamchouchi, "Mobile one-time passwords: two-factor authentication using mobile phones," Security and Communication Networks, vol. 5, pp. 508-516, 2012. 7. Alzarouni. M.: 'The reality of risks from consented use of USB devices", Proc. 4th Australian Information Security Conf., pp. 312-317, 2006. 8. Yang, G., Wong, D.S., Wang, H., Deng, X.: 'Two-factor mutual authentication based on smart cards and passwords', J. Computer. System.Science , vol. 74, pp. 1160-1172, 2008. 9. Lauter, K.: 'The advantages of elliptic curve cryptography for wireless security', IEEE Wirel. Commun., pp. 1536-1284, 2006. 10. Schneier, B.: 'Applied cryptography, protocols, algorithms, and source code', Wiley, 2nd edn, 1996. 11. M. N. Rani, A. Kaushik, and M. Kumar, "A Review Based Study of Key Exchange Algorithms," International Journal of Recent Trends in Mathematics & Computing, vol. 1, 2013. 12. C. Wu, W. Lee, and W. J. Tsaur, "A secure authentication scheme with anonymity for wireless communications," IEEE Communications Letters, vol. 12, no. 10, pp. 722-723, Oct. 2008. 13. D. Hankerson, S. Vanstone, and A. Menezes, "Guide to elliptic curve cryptography", Lecture Notes in Computer Science, 2004. 14. W. Yuan, L. Hu, H. Li, and J. Chu, "An Efficient Password-based Group Key Exchange Protocol Using Secret Sharing," Appl. Math, vol. 7, pp. 145-150, 2013. 15. Suratose Tritilanunt, Napat Thanyamanorot, Nattawut Ritdecha, "A Secure Authentication Protocol using HOTP on USB Storage Devices", Information Science, Electronics and Electrical Engineering(ISEEE), International Conference, vol. 3, pp. 1908-1912, 2014.

Authors: B.J. Bipin Nair, Gopi Krishna Ashok, N.R. Sreekumar Paper Title: Classification of Autism based on Feature Extraction from Segmented Brain MRI Abstract: The Autism Spectrum Disorder is a neurological irregularity with multiple behavioral symptoms. It includes Asperger syndrome and pervasive developmental disorders. It is called as "spectrum" disorder because an individual with ASD might have a wide range of symptoms. People with ASD will have communication trouble, low eye contact, limited attentiveness and tedious behaviors. Various researches on structural MRI have mostly concentrated on the detection of autism in people with ASD. This study’s aim is to classify the type of Autism Spectrum Disorder for various body movements. Here we use supervised classification algorithms like ID3.For our study, we are considering the datasets which consists of 50 normal and 50 autistic brain MRI. Here, we are mainly focusing on effective classification of ASD using a classifier with a class label.

Keywords: ASD (Autism Spectrum Disorder), SVM- (Support Vector Machine), ELM-(Extreme Learning Machine), H-ELM-(Hierarchical Extreme Learning Machine), GPC-(Gaussian Process Classification), GM-(Grey Matter).

17. References: 1. Heinsfeld, A. S., Franco, A. R., Craddock, R. C., Buchweitz, A., &Meneguzzi, F. (2018). Identification of autism spectrum disorder using deep 85-89 learning and the ABIDE dataset. NeuroImage: Clinical, 17, 16-23. 2. Nair, B. B., &,Rahul Raghunath (2018). Coding and functional defect region prediction of placental protein in an embryo cell of first trimester using ANN approach. In Recent Findings in Intelligent Computing Techniques (pp. 167-170). Springer, Singapore. 3. Rani, N. S., &Vasudev, T. (2016). A Comparative Study on Efficiency of Classification Techniques with Zone Level Gabor Features towards Handwritten Telugu Character Recognition. International Journal of Computer Applications, 148(1). 4. Chanel, G., Pichon, S., Conty, L., Berthoz, S., Chevallier, C., &Grèzes, J. (2016). Classification of autistic individuals and controls using cross- task characterization of fMRI activity. NeuroImage: Clinical, 10, 78-88. 5. Qureshi, M. N. I., Min, B., Jo, H. J., & Lee, B. (2016). Multiclass classification for the differential diagnosis on the ADHD subtypes using recursive feature elimination and hierarchical extreme learning machine: structural MRI study. PloS one, 11(8), e0160697. 6. Lim, L., Marquand, A., Cubillo, A. A., Smith, A. B., Chantiluke, K., Simmons, A., ... &Rubia, K. (2013). Disorder-specific predictive classification of adolescents with attention deficit hyperactivity disorder (ADHD) relative to autism using structural magnetic resonance imaging. PloS one, 8(5), e63660. 7. Peng, X., Lin, P., Zhang, T., & Wang, J. (2013). Extreme learning machine-based classification of ADHD using brain structural MRI data. PloS one, 8(11), e79476. 8. Cheng, W., Ji, X., Zhang, J., &Feng, J. (2012). Individual classification of ADHD patients by integrating multiscale neuroimaging markers and advanced pattern recognition techniques. Frontiers in systems neuroscience, 6, 58. 9. Calderoni, S., Retico, A., Biagi, L., Tancredi, R., Muratori, F., &Tosetti, M. (2012). Female children with autism spectrum disorder: an insight from mass-univariate and pattern classification analyses. Neuroimage, 59(2), 1013-1022. 10. Chen, R., Jiao, Y., & Herskovits, E. H. (2011). Structural MRI in autism spectrum disorder. Pediatr Res, 69(5 Pt 2), 63R-8R. 11. Ecker, C., Rocha-Rego, V., Johnston, P., Mourao-Miranda, J., Marquand, A., Daly, E. M., ... & MRC AIMS Consortium. (2010). Investigating the predictive value of whole-brain structural MR scans in autism: a pattern classification approach. Neuroimage, 49(1), 44-56. 12. Ecker, C., Marquand, A., Mourão-Miranda, J., Johnston, P., Daly, E. M., Brammer, M. J., ... & Murphy, D. G. (2010). Describing the brain in autism in five dimensions-magnetic resonance imaging-assisted diagnosis of autism spectrum disorder using a multiparameter classification approach. Journal of Neuroscience, 30(32), 10612-10623.

Authors: B.J. Bipin Nair, Mathews Jose, S. Harikrishna Paper Title: To Predict the Gender and Fracture from Skull X-ray Image from Various Image Analysis Abstract: In our proposed work we are going to predict the gender and detect fracture from archeological skull image using various image analysis techniques. Due to the huge technological advancements in forensics as well as healthcare, determining the gender and predicting the fracture is easy with X-rays images. However, the major drawback of existing work is that conclusions are drawn based upon the prediction made by doctors manually. But from our system, using various medical image processing techniques like Histogram Equalization, Prewitt, Sobel and Canny Edge Detection algorithm we can predict the skull bones fracture, and by feature extraction we can predict gender using ROI. Our system works on the various efficient methods and algorithms developed to perform various operations on skull images, but these operations make life easy for the surgeons.

Keywords: Histogram Equalization, Sobel, Prewitt and Canny Edge Detection, Region of Interest (ROI).

References: 1. Gludovatz, B., Hohenwarter, A., Catoor, D., Chang, E. H., George, E. P., & Ritchie, R. O. (2014). A fracture-resistant high-entropy alloy for cryogenic applications. Science, 345(6201), 1153-1158 2. Tejaswi, H. L., & Dakshayani, K. R. (2014). of the Article: Anatomical Variations in the Arterial Supply of Gall Bladders in South Indian Cadavers. Indian Journal of Forensic Medicine & Toxicology, 8(1), 1208 18. 3. Rani, C. D. S., Cheema, C. C. D. G. S., Singh, A. K. G. E. A., Kumar, K. O. S. M. M., Divesh, G. E. S. K. E., Randhir, K. E. A. S. E., & Singh, S. D. A. K. Advancements in Engineering and Technology 4. Bakthula, R., & Agarwal, S. (2014). Automated human bone age assessment using image processing methods-survey. International Journal of 90-94 Computer Applications, 104(13) 5. Dimililer, K. (2017). IBFDS: intelligent bone fracture detection system. Procedia Computer Science, 120, 260-267. 6. Segmentation, M. G. B. I. Radius Bone Fracture Detection Using Morphological Gradient Based Image Segmentation. 7. Mungona, S. S., &Sawarkar, M. N. M. (2018). Diagnosis of X-Ray Using Gabor Wavelet Transform 8. Zaki, W. M. D. W., Fauzi, M. F. A., &Besar, R. (2009, November). A new approach of skull fracture detection in CT brain images. In International Visual Informatics Conference (pp. 156-167). Springer, Berlin, Heidelberg 9. Sawadkar, M., Keni, N., & Agarwal, R. (2016). Machine Learning Based Gender Detection Using Craniometric Analysis. International Journal of Engineering Science, 1773. 10. Pietka, E., Pospiech-Kurkowska, S., Gertych, A., & Cao, F. (2003). Integration of computer assisted bone age assessment with clinical PACS. Computerized medical imaging and graphics, 27(2-3), 217-228 11. Al-Ayyoub, M., Hmeidi, I., &Rababah, H. (2013). Detecting Hand Bone Fractures in X-Ray Images. JMPT, 4(3), 155-168. 12. Gajjar, B., Patel, S., &Vaghela, A. (2017). Fracture detection in X-ray images of long bone 13. Kaur, A., & Mann, K. S. (2018). Segmenting Bone Parts for Bone Age Assessment using Point Distribution Model and Contour Modelling. In Journal of Physics: Conference Series (Vol. 933, No. 1, p. 012004). IOP Publishing. 14. Xue, Z., Rajaraman, S., Long, R., Antani, S., &Thoma, G. (2018, June). Gender Detection from Spine X-ray Images Using Deep Learning. In 2018 IEEE 31st International Symposium on Computer-Based Medical Systems (CBMS)(pp. 54-58). IEEE 15. Rani, N. S., CH, P., & Sharan, J. (2015). Segmentation and Recognition of Touching Characters In Machine Printed Telugu Documents Using Average Character Widths and Central Moments Features. International Journal of Applied Engineering Research, 10(8), 19575-19583. 16. Bipinnair B J, Bhuvana P and PriyankaG(2015) Prediction Of Genetic Disorder By Using Phenotypic And Genotypic Information Using Clustering Approach. International Journal of Applied Engineering Research, 10(4), 10783-10793

Authors: Maharshi Shah, Priyanka Kumar Paper Title: Tamper Proof Birth Certificate Using Blockchain Technology Abstract: With the advancement of technology in modern era everything needs to be digitalized to make it more secure and reliable. Nowadays birth certificate is the only age proof of an individual and can be used to apply for a job, for admissions in collages/universities and basis of all the important government document identities like Aadhar card, Pan Card, Passport and other related matters. So identifying the correct birth certificate of any person is a major challenge. In the current system, afterbirth of any individual the birth has to be registered with the concerned local authorities within 21 days of its occurrence, then it should be filled up the form prescribed by the Registrar and then Birth Certificate is issued after verification with the actual records of the concerned hospital. However, current methodology used for birth certificate verification is costly and very time-consuming. Therefore, our objective is to propose a theoretical model for issuing birth certificate and verification of genuine birth records using blockchain 19. technology. This technology uses several functions including hash, public/private key cryptography, digital signatures, peer-to-peer networks and proof of work. So in this paper we have developed an efficient and more secure way of storing birth certificate by using Inter Planetary File System (IPFS) and most demanded “blockchain” technology. 95-98

Keywords: Birth certificate, birth certificate verification, birth certificate authentication, blockchain technology, IPFS.

References: 1. M. Warasart and P. Kuacharoen, "Paper-based Document Authentication using Digital Signature and QR Code," ICCET, 2012. 2. Nicolas Buchmann, Christian Rathgeb, Harald Baier, Christoph Busch and Marian M: Enhancing Breeder Document Long-Term Security using Blockchain Technology, IEEE 41st Annual Computer Software and Applications Conference, 2017. 3. Yongle Chen, Hui Li, Kejiao Li and Jiyang Zhang : An improved P2P File System Scheme based on IPFS and Blockchain, IEEE International Conference on Big Data (BIGDATA), 2017. 4. Sin Kuang Lo, Xiwei Xu, Yin Kia Chiam, Qinghua Lu : Evaluating Suitability of Applying Blockchain , International Conference on Engineering of Complex Computer Systems , 2017. 5. Safdar Hussain Shaheen, Muhammad Yousaf, Mudassar Jalil : Temper Proof Data Distribution for Universal Verifiability and Accuracy in Electoral Process Using Blockchain, IEEE Conference, 2017. 6. T.D. Smith : The Blockchain Litmus Test, IEEE International Conference on Big Data (BIGDATA), 2017. 7. http://startupmanagement.org/blog 8. H. Hou, "The application of blockchain technology in E-government in China," 2017 26th Int. Conf. Comput. Commun. Networks, ICCCN 2017, 2017. 9. J. Sidhu, "Syscoin : A Peer-to-Peer Electronic Cash System with Blockchain-Based Services for E-Business," 2008. 10. N. Smolenski and D. Hughes, "Academic Credentials In An Era Of Digital Decentralization Academic Credentials In An Era Of Digital Decentralization Learning Machine Cultural Anthropologist contents preface," 2016. 11. S. Nakamoto, "Bitcoin: A Peer-to-Peer Electronic Cash System," Www.Bitcoin.Org, p. 9, 2008. 12. Nomura Research Institute, "Survey on Blockchain Technologies and Related Services," 2016. 13. S. Thompson, "The preservation of digital signatures on the blockchain - Thompson - See Also," Univ. Br. Columbia iSchool Student J., vol. 3, no. Spring, 2017. 14. C. F. Bond, F. Amati, and G. Blousson, "Blockchain, academic verification use case," 2015. 15. J.-F. Blanchette, "The digital signature dilemma Le dilemme de la signature numérique," Ann. Des Télécommunications, vol. 61, no. 7, pp. 908- 923, 2006. 16. MIT Media Lab, "What we learned from designing an academic certificates system on the blockchain," Medium, no. December, p. 2016, 2016. 17. P. Schmidt, "Certificates, Reputation, and the Blockchain," MIT Media Lab, 2015.

Authors: B.J. Bipin Nair, Navaneeth Vijayan Paper Title: Employing Thresholding and Sobel Technique to Detect Autism from MRI Abstract: In our proposed work we are doing various Segmentation methods to detect the autistic disorders using cerebral brain MRI. In this work we are collecting brain MRI image for autistic disorders and uses an efficient preprocessing technique to remove the noise from brain MRI, then for locating the damaged tissue from brain MRI we are using an efficient segmentation techniques like thresholding and sobel to extract the affected region, then proceeds with checking how efficiently we are detecting the affected region.

Keywords: ABIDE, MRI, Sobel edge detection, Digital Image Processing (DIP).

References: 1. Bala, M., & Yasmin, S. (2016). Study the Corpus Callosum of Brain to Explore Autism Employing Image Segmentation. 2. R.Geetha Ramani, R.Sahayamary Jabarani(2017).Detection of Autism Spectrum Disorder and Typically Developing Brain from Structural Connectome through Feature Selection and Classification. International Journal of Innovations & Advancement in Computer Science IJIACS,2347 - 8616 3. Ahuja, R., & Kaur, D. (2014). Neuro-Fuzzy methodology for diagnosis of Autism. IJCSIT) Int J Comput Sci Inf Tech, 5, 2171-2172. 4. Mahajan, R., & Mostofsky, S. H. (2015). Neuroimaging endophenotypes in autism spectrum disorder. CNS spectrums, 20(4), 412-426. 5. Lim L, Marquand A, Cubillo AA, Smith AB,Chantiluke K, et al. (2013) Disorder-Specific Predictive Classification of Adolescents with Attention Deficit Hyperactivity Disorder (ADHD) Relative to Autism Using Structural Magnetic Resonance Imaging. 6. Anderson, J. S., Nielsen, J. A., Froehlich, A. L., DuBray, M. B., Druzgal, T. J., Cariello, A. N., ... & Alexander, A. L. (2011). Functional connectivity magnetic resonance imaging classification of autism. Brain, 134(12), 37423754. 20. 7. Wall, D. P., Kosmicki, J., Deluca, T. F., Harstad, E., & Fusaro, V. A. (2012). Use of machine learning to shorten observation-based screening and diagnosis of autism. Translational psychiatry, 2(4), e100.6 99-103 8. Hahn, H.K. (2010, April). Computer-assistance in neuroimaging: From quantitative image analysis to computer aided diagnosis. In Biomedical Imaging: From Nano to Macro, 2010 IEEE International Symposium on (pp. 275275). IEEE. 9. Lei, H., Zhao, Y., Wen, Y., & Lei, B. (2017, December). Adaptive Sparse Learning for Neurodegenerative Disease Classification. In Multimedia (ISM), 2017 IEEE International Symposium on (pp. 292-295). IEEE. 10. Piven, J., Bailey, J., Ranson, B. J., & Arndt, S. (1997). An MRI study of the corpus callosum in autism. American Journal of Psychiatry, 154(8), 1051-1056. 11. Li, D., Karnath, H. O., & Xu, X. (2017). Candidate biomarkers in children with autism spectrum disorder: a review of MRI studies. Neuroscience bulletin, 33(2), 219-237. 12. Anagnostou, E., & Taylor, M. J. (2011). Review of neuroimaging in autism spectrum disorders: what have we learned and where we go from here. Molecular autism, 2(1), 4. 13. Ali, H., Elmogy, M., El-Daydamony, E., & Atwan, A. (2015). Multi-resolution mri brain image segmentation based on morphological pyramid and fuzzy c-mean clustering. Arabian Journal for Science and Engineering, 40(11), 3173-3185. 14. Scassellati, Brian. "Quantitative metrics of social response for autism diagnosis." Robot and Human Interactive Communication, 2005. ROMAN 2005. IEEE International Workshop on. IEEE, 2005. 15. Momeni, N., Bergquist, J., Brudin, L., Behnia, F., Sivberg, B., Joghataei, M. T., & Persson, B. L. (2012). A novel blood-based biomarker for detection of autism spectrum disorders. Translational psychiatry, 2(3), e91. 16. Nair, B. B., Bhaskaran, V., & Arunjit, K. (2017). Structural designing of suppressors for autisms spectrum diseases using molecular dynamics sketch. International Journal of Drug Delivery, 8(4), 142-146. 17. Sreelakshmi, U. K., Akash, V. G., & Rani, N. S. (2017, April). Detection of variable regions in complex document images. In Communication and Signal Processing (ICCSP), 2017 International Conference on (pp. 0807-0811). IEEE. 18. Bipin nair b.j., Arunjit k., & Vijesh Bhaskaran(2017). Melatonin and fluoxetine interaction with shank3 protein gene for autism spectrum disorder. Pakistan journal of Biotechnology

Authors: B.J. Bipin Nair, K.K. Nidheesh, M. Vishnudev Comparative Sequence Analysis of Lymphoma Using A Hybrid Approach of Waterman Smith and Paper Title: Needleman WUNSCH Abstract: Bioinformatics is considered as the computational technique where in which it helps to solve the biological based issues. In computer science it helps to improve methods for organizing, analyzing the data, storage and retrieval of the data. In bioinformatics, sequence alignment is one of the major real-time application where in which it helps to compare two or more sequences and the similarity in these sequence can be later used for understanding the 21. relationships. In this work we are developing a computational tool which can predict the 3D structure of different stages of lymphoma. In sequence alignment we are following algorithms such as Needleman Wunch, Smith-Waterman. In 104-108 each case we are trying to find out best sequence compatibility. Lymphoma is a type of cancer which is mainly affecting in the lymphatic system. The cancer is affecting the white blood cells known as lymphocytes which performs a major role in the immune system. There are mainly two types of Lymphoma namely Hodgkin and Non Hodgkin. The presence of specific type of cell called a Reed-Sternberg cell will identify the presence of Hodgkin, and if that cell is not present it is considered as Non Hodgkin. There are basically four levels of structure prediction namely Primary, Secondary, Tertiary and Quaternary structures. In this work we are making the prediction of these various stages of lymphoma using an hybrid approach of Needleman Wunsch and Waterman smith algorithm.

References: 1. Kumar, S., Tamura, K., &Nei, M. (2004). MEGA3: integrated software for molecular evolutionary genetics analysis and sequence alignment. Briefings in bioinformatics, 5(2), 150-163. 2. Zhou, Z. M., & Chen, Z. W. (2013). Dynamic programming for protein sequence alignment. International Journal of Bio-Science and Bio- Technology, 5(2), 141-150. 3. Alsmadi, I., & Nuser, M. (2012). String matching evaluation methods for DNA comparison. International Journal of Advanced Science and Technology, 47(1), p13-32. 4. KHAMARUDHEEN, K., & HS, R. (2016). AN APPROACH FOR IDENTIFYING THE PRESENCE OF FACTOR IX GENE IN DNA SEQUENCES USING POSITION VECTOR ANN. Journal of Theoretical & Applied Information Technology, 87(3). 5. Gonnet, Gaston H., Mark A. Cohen, and Steven A. Benner."Exhaustive matching of the entire protein sequence database." Science 256.5062 (1992): 1443- 1445. 6. Lipman, David J., and William R. Pearson. "Rapid and sensitive protein similarity searches." Science 227.4693 (1985): 1435- 1441. 7. Smith, Temple F., Michael S. Waterman, and Walter M. Fitch. "Comparative biosequence metrics." Journal of Molecular Evolution18.1 (1981): 38-46. 8. Travis, Lois B., et al. "Lung cancer following chemotherapy and radiotherapy for Hodgkin's disease." Journal of the National Cancer Institute 94.3 (2002): 182-192 9. Chandrakala, D., et al. "Optimization of Process Parameters of Global Sequence Alignment Based Dynamic Program-an Approach to Enhance the Sensitivity of Alignment." (2016). 10. Notredame, Cédric, Desmond G. Higgins, and JaapHeringa. "TCoffee: A novel method for fast and accurate multiple sequence alignment." Journal of molecular biology 302.1 (2000): 205- 217. 11. Lee, Christopher, Catherine Grasso, and Mark F. Sharlow. "Multiple sequence alignment using partial order graphs." Bioinformatics 18.3 (2002): 452-464. 12. Satra, Ramdan, WisnuAnantaKusuma, and HeruSukoco. "Accelerating computation of DNA multiple sequence alignment in distributed environment." Telkomnika Indonesian Journal of Electrical Engineering 12.12 (2014): 8278-8285. 13. Li, Limin, et al. "Predicting enzyme targets for cancer drugs by profiling human metabolic reactions in NCI-60 cell lines." BMC bioinformatics 11.1 (2010): 501. 14. Umesh, C. V., et al. "Perusal of Mbl2 Gene-Susceptibility to Tuberculosis in Different Indian Populations." (2015). 15. Ashok, Sreeja, and M. V. Judy. "Process Flow for Information Visualization in Biological Data." Proceedings of the International Congress on Information and Communication Technology. Springer Singapore, 2016. 16. Nair, B. B., Shibin, K., &Shamcy, O. (2017, April). An hybrid method for comparing brainstem glioma sequences using needlemanwunsch and waterman smith algorithms. In Convergence in Technology (I2CT), 2017 2nd International Conference for (pp. 867-872).IEEE. Authors: B.J. Bipin Nair, K. Sahith Kumar Paper Title: Assessment of Morphological Markers from Autistic MRI Abstract: Our proposed work we are predicting the earlier stage developmental disorder autism through morphological markers .it can be performed through the analysis of cortical thickness from MRI with various parameter like thickness, gyrification, volume etc. from our work we are considering the MRI data set from the age between 15 to 30 year .in our work we are using various segmentation technique to calculate the various parameters like cortical thickness, gyrification, etc. from brain MRI. Through the parameter we are predicting the autism in the range of age with the experimentation of morphological markers.

Keywords: ASD -Autism Spectrum Disorder, MRI, Voxel Based Morphometry-VBM, gray matter-GA, white matter- WM.

References: 1. Yang, D. Y. J., Beam, D., Pelphrey, K. A., Abdullahi, S., & Jou, R. J. (2016). Cortical morphological markers in children with autism: a structural magnetic resonance imaging study of thickness, area, volume, and gyrification. Molecular autism, 7(1), 11. 2. Hyde, K. L., Samson, F., Evans, A. C., & Mottron, L. (2010). Neuroanatomical differences in brain areas implicated in perceptual and other core features of autism revealed by cortical thickness analysis and voxel-based morphometry. Human brain mapping, 31(4), 556-566. 3. Shi, F., Wang, L., Peng, Z., Wee, C. Y., & Shen, D. (2013). Altered modular organization of structural cortical networks in children with autism. PLoS One, 8(5), e63131. 4. Dierker, D. L., Feczko, E., Pruett Jr, J. R., Petersen, S. E., Schlaggar, B. L., Constantino, J. N., ... & Van Essen, D. C. (2013). Analysis of cortical shape in children with simplex autism. Cerebral Cortex, 25(4), 1042-1051. 5. Mak-Fan, K. M., Taylor, M. J., Roberts, W., & Lerch, J. P. (2012). Measures of cortical grey matter structure and development in children with 22. autism spectrum disorder. Journal of autism and developmental disorders, 42(3), 419-427. 6. Zubiaurre-Elorza, L., Soria-Pastor, S., Junque, C., Sala-Llonch, R., Segarra, D., Bargallo, N., & Macaya, A. (2012). Cortical thickness and behavior abnormalities in children born preterm. PLoS One, 7(7), e42148. 109-114 7. Hazlett, H. C., Poe, M. D., Gerig, G., Styner, M., Chappell, C., Smith, R. G., ... & Piven, J. (2011). Early brain overgrowth in autism associated with an increase in cortical surface area before age 2 years. Archives of general psychiatry, 68(5), 467-476. 8. Lee, P. S., Yerys, B. E., Della Rosa, A., Foss-Feig, J., Barnes, K. A., James, J. D., ... & Kenworthy, L. E. (2008). Functional connectivity of the inferior frontal cortex changes with age in children with autism spectrum disorders: a fcMRI study of response inhibition. Cerebral Cortex, 19(8), 1787-1794. 9. Herbert, M. R., Ziegler, D. A., Deutsch, C. K., O'brien, L. M., Lange, N., Bakardjiev, A., ... & Kennedy, D. (2003). Dissociations of cerebral cortex, subcortical and cerebral white matter volumes in autistic boys. Brain, 126(5), 1182-1192. 10. Ecker, C., Ginestet, C., Feng, Y., Johnston, P., Lombardo, M. V., Lai, M. C., ... & Williams, S. C. (2013). Brain surface anatomy in adults with autism: the relationship between surface area, cortical thickness, and autistic symptoms. Jama Psychiatry, 70(1), 59-70. 11. Hazlett, H. C., Poe, M. D., Gerig, G., Styner, M., Chappell, C., Smith, R. G., ... & Piven, J. (2011). Early brain overgrowth in autism associated with an increase in cortical surface area before age 2 years. Archives of general psychiatry, 68(5), 467-476. 12. Raznahan, A., Toro, R., Daly, E., Robertson, D., Murphy, C., Deeley, Q., ... & Murphy, D. G. (2009). Cortical anatomy in autism spectrum disorder: an in vivo MRI study on the effect of age. Cerebral cortex, 20(6), 1332-1340. 13. Cheng, W., Rolls, E. T., Gu, H., Zhang, J., & Feng, J. (2015). Autism: reduced connectivity between cortical areas involved in face expression, theory of mind, and the sense of self. Brain, 138(5), 1382-1393. 14. Hazlett, H. C., Gu, H., Munsell, B. C., Kim, S. H., Styner, M., Wolff, J. J., ... & Collins, D. L. (2017). Early brain development in infants at high risk for autism spectrum disorder. Nature, 542(7641), 348. 15. Schumann, C. M., Bloss, C. S., Barnes, C. C., Wideman, G. M., Carper, R. A., Akshoomoff, N., ... & Courchesne, E. (2010). Longitudinal magnetic resonance imaging study of cortical development through early childhood in autism. Journal of Neuroscience, 30(12), 4419-4427. 16. Zhou, Y., Yu, F., & Duong, T. (2014). Multiparametric MRI characterization and prediction in autism spectrum disorder using graph theory and machine learning. PLoS One, 9(6), e90405. 17. Pardakhti, N., & Sajedi, H. (2017, September). Age prediction based on brain MRI images using feature learning. In Intelligent Systems and Informatics (SISY), 2017 IEEE 15th International Symposium on (pp. 000267-000270). IEEE. 18. Huang, T. W., Chen, H. T., Fujimoto, R., Ito, K., Wu, K., Sato, K., ... & Aoki, T. (2017, April). Age estimation from brain MRI images using deep learning. In Biomedical Imaging (ISBI 2017), 2017 IEEE 14th International Symposium on (pp. 849-852). IEEE. 19. Nair, B. B., Bhaskaran, V., & Arunjit, K. (2017). Structural designing of suppressors for autisms spectrum diseases using molecular dynamics sketch. International Journal of Drug Delivery, 8(4), 142-146. 20. Pawar, M. S., Perianayagam, L., & Rani, N. S. (2017, June). Region based image classification using watershed transform techniques. In Intelligent Computing and Control (I2C2), 2017 International Conference on (pp. 1-5). IEEE. 21. Bipin nair b.j., Arunjit k., & Vijesh Bhaskaran(2017). Melatonin and fluoxetine interaction with shank3 protein gene for autism spectrum disorder. Pakisthan journal of Biotechnology Authors: Anjani Mamidala, Akanksh Mamidala, Divya Sai Nemmani, P.V. Naga Prapurna Paper Title: Transparent Solar Cells as Economic and Effective Alternative in the Field of Excitonics Abstract: Energy is essential for the economic development and growth of any society. Depletion of conventional sources and growing demands of rapid urbanization and industrialization are met through effective alternate sources like solar, wind and tidal energy. Solar energy is the most prolific method of energy capture in nature through a photovoltaic (PV) packaged module. In the recent past, for commercial and residential applications, BIPV - Building integrated photovoltaics (BIPV) are developed. Transparent solar cells are integrated with the existing window panes by absorbing and utilizing unwanted light energy through windows in buildings and automobiles. Such an efficient use of architectural space can prove to be economic for operation and maintenance but calls for installation cost. These cells allow every visible photon to pass through it and absorb all the photons in the infrared and ultraviolet range but are transparent to visible light. Anti-reflective coatings on the outside surface can further increase the efficiency by reducing reflections 23.

References: 115-119 1. Nancy W.Stauffer, Spring 2013 issue of Energy Futures, the magazine of the MIT Energy Initiative 2. Alaa A.F. Husain, Science Direct, Volume 94, pages 779-791, October 2018 3. Xinyuan Xia, Journal of Materials Chemistry 20(39): 8478-8482, September 2010 4. Jamie Lendino, Extreme Tech, April 2015 5. Alaa Husain, Pertanika Journal of Science and Technology 25(S)(2017):225-234, February 2017 6. Fraunhofer- Gesellschaft, Transparent Solar Cells, 2009 7. Effect of the Changeable Organic Semi-Transparent Solar Cell Window on Building Energy Efficiency and User Comfort, SehyunTak, MDPI, June 2017 8. Spectral Response of Polycrystalline Silicon Photovoltaic Cells Under Real-Use Conditions, EvaldoC.Gouvêa, MDPI, August 2017 9. Subhash Chander, International Journal of Renewable Energy Research 5(1):41-45, December 2014 10. K. Zwiebel, Technologies Critical Role in Energy and Environmental Markets, October 1998 11. Senthilarasu Sundaram, A Comprehensive Guide to Solar Energy Systems With Special Focus on Photovoltaic Systems, Pages 361-370, 2018 12. Giles E Eperon, ACS Nano 8(1), 591-598, 2013 Authors: A. Murugan, V. Ramakrishnan Modeling of HPFC Controller Using Multi - Machine Power System with Acting as Different Control Paper Title: Modes Abstract: Mainly this paper is focusing about the power flow model of the HPFC created for insistent state investigations of intensity frameworks with the help of different control system model. Then numerical investigations exhibited here are completed utilizing a two-area network model. This HPFC controller is using for controlling Real Power, Voltage control, Power Changes from the system parameters. In FACTS device the HPFC controller has been multitalented to its capability control of Real and Reactive Power. Also HPFC controller modeling has been done with the help of mathematical equations for different modes of operation as per kundur 2 area 4 machine 12 bus system. With the help of 2 area power system model is shown about PVV, PQQ, shunt Susceptance and Impedance control of system model. Each and every method is showing different kind of performance including of HPFC Controller. For better performance of power system only has been used the HPFC Controllers comparing other Controllers in FACTS Device. These and all is considering about different modes of operation. With the help of MATLAB Software different modes of modeling has been simulated. Transmission frameworks are normally substantial and complex electrical circuits consisting of several age/utilization hubs and a large number of transmission lines. Controlling the flow of intensity between the hubs in such complex circuits is a testing issue. It is additionally confused by the need to control the voltage at every hub to inside a little resistance of the appraised esteem. The strategy is approved by multi-machine- 9 bus system utilizing MATLAB content record.

24. Keywords: FACTS, UPFC, HPFC, Power Flow, Voltage Magnitude, PVV, PQQ, Modeling of HPFC. 120-124 References: 1. X. P. Zhang, C. Rehtanz, and B. Pal,Flexible AC Transmission Systems, Modelling and Control. Berlin, Germany: Springer, 2006. 2. D. Divan and H. Johal, Distributed FACTS|A new concept for realizing grid power ow control," IEEE Trans. Power Electron., vol. 22, no. 6, pp. 2253{2260, Nov. 2007. 3. J. Z. Bebic, P. W. Lehn, and M. R. Iravani, \The Hybrid Power Flow Controller - a new concept for exible ac transmission," in Proc. IEEE PES General Meeting, Montreal, Quebec, Canada, pp. 1{8, 2006. 4. N. R. Merritt and D. Chatterjee, Performance improvement of power systems using Hybrid Power Flow Controller," in Proc. Int. Conf. Power and Energy Systems (ICPS), pp. 1{6, Dec. 2011. 5. X. Wei, J. H. Chow, B. Fardanesh, and A. Edris, \A common modelling framework of voltage-sourced converters for power ow, sensitivity, and dispatch analysis," IEEE Trans. Power Syst., vol. 19, no. 2, pp. 934{941, May 2004. 6. Kazemi and E. Karimi, "The Effect of an Interline Power Flow Controller (IPFC) on Damping Inter?area Oscillations in Interconnected Power Systems" in Scientia Iranica, Vol. 15, No. 2, pp 211{216} Sharif University of Technology, April 2008. 7. I. Axente, R. K. Varma, and W. Litzenberger, "Bibliography of FACTS: 2000-Part I IEEE working group report," in Proc. IEEE Power & Energy Soc. General Meeting, 2011, pp. 1-6. 8. S. Arabi, P. Kundur, and R. Adapa, "Innovative techniques in modeling UPFC for power system analysis,"IEEE Trans. Power Syst., vol. 15,no. 1, pp. 336-341, Feb. 2000. 9. F. Milano, \An open source power system analysis toolbox," IEEE Trans. Power Syst., Vol. 20, No. 3, pp. 1199{1206, Aug. 2005 10. J. Z. Bebic, P. W. Lehn, and M. R. Iravani, "The hybrid power flow controller-A new concept for flexible AC transmission," inProc. IEEE Power Eng. Soc. General Meeting, Montreal, QC, Canada, 2006, pp. 1-8. 11. Satheesh and T. Manigandan, "Improving Power System Stability using PSO and NN with the Aid of FACTS Controller" European Journal of Scientific Research ISSN 1450-216X Vol.71 No.2 (2012), pp. 255- 264. 12. BindeshwarSingh, V.Mukherjee and PrabhakarTiwari, A survey on impact assessment of DG and FACTS controllers in power systems Vol.42 (2015),pp. 846-882 13. M. Farrokhabadi, S. Koenig, C. A. Ca~nizares, K. Bhattacharya, and T. Leibfried,\Battery energy storage system models for microgrid stability analysis and dynamic simulation," IEEE Trans. Power Syst., vol. 32, no. 5, pp. 1{13, Aug. 2017 14. A.Murugan and V.Ramakrishnan, "Modeling and Control of GSO Method Based on HPFC Using an Interconnected Hybrid Power Generation Systems" journal of Advanced Research in Dynamical and Control System, 15 Special Issue (2017). 15. R. M. Mathur and R. K. Varma, Thyristor-Based FACTS Controllers for Electrical Transmission Systems. Piscataway, NJ: Wiley-IEEE Press, 2002. 16. P. Kundur, Power System Stability and Control. New York, NY, USA:McGraw-Hill, 1994. 17. M. Eslami, H. Shareef, A. Mohamed, and M. Khajehzadeh, "A survey on flexible AC transmission systems (FACTS),"Przeglad Elektrotech.,vol. 88, no. 01A, pp. 1-11, Jan. 2012. R. M. Mathur and R. K. Varma,Thyristor-Based FACTS Controllers for Electrical Transmission Systems. New York, NY, USA: Wiley, 2002. Authors: R.P. Jaia Priyankka, S. Arivalagan, P. Sudhakar Efficient Collaborative Filtering based Recommendation System for Business Promotions Using Deep Paper Title: Neural Network Abstract: In the last decade, recommendation system (RS) has become popular due to its capability to foresee whether the specific customer would have preference for the item or not depending over the customer profile. Collaborative filtering methods in RS constructs the model after the consideration of the history of the user such as ratings given by the user to particular products, previously bought, wish list, etc. In addition, it also considers the identical decisions made by various users and then employs the model to determine the product or rating in which the user may be interested in. As the user's rating plays a major part in collaborative filtering, it is needed to develop a classification model to classify the product reviews. In this paper, we introduce a collaborative filtering method using deep neural network (DNN) to classify the online produce reviews. Based on the classification of the reviews, the products will be properly recommended to the user. The proposed DNN model is validated using a set of four dataset collected from online product reviews from Amazon namely Canon dataset, iPod dataset, DVD and Nokia dataset. The experimental values proves that the DNN model is effective than the compared methods.

Keywords: Classification, Collaborative Filtering, Deep Neural Network, Recommendation System.

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In: Proceedings of the international conference on intelligent user interfaces; 2002. p. 127-34. 7. Schafer JB, Konstan J, Riedl J. Recommender system in ecommerce. In: Proceedings of the 1st ACM conference on electronic commerce; 1999. p. 158-66. 125-133 8. Resnick P, Varian HR. Recommender system's. Commun ACM 1997;40(3):56-8. 9. Acilar AM, Arslan A. A collaborative filtering method based on Artificial Immune Network. Exp Syst Appl 2009;36(4):8324-32. 10. Chen LS, Hsu FH, Chen MC, Hsu YC. Developing recommender systems with the consideration of product profitability for sellers. Int J Inform Sci 2008;178(4):1032-48. 11. Jalali M, Mustapha N, Sulaiman M, Mamay A. WEBPUM: a web-based recommendation system to predict user future movement. Exp Syst Applicat 2010;37(9):6201-12. 12. Adomavicius G, Tuzhilin A. Toward the next generation of recommender system. A survey of the state-of-the-art and possible extensions. IEEE Trans Knowl Data Eng 2005;17(6):734-49. 13. Ziegler CN, McNee SM, Konstan JA, Lausen G. Improving recommendation lists through topic diversification. In: Proceedings of the 14th international conference on World Wide Web; 2005. p. 22-32. 14. Min SH, Han I. Detection of the customer time-variant pattern for improving recommender system. Exp Syst Applicat 2010;37(4):2911-22. 15. Celma O, Serra X. FOAFing the Music: bridging the semantic gap in music recommendation. Web Semant: Sci Serv Agents World Wide Web 2008;16(4):250-6 16. Pazzani MJ. A framework for collaborative, content-based and demographic filtering. ArtificIntell Rev 1999;13:393-408, No. 5(6). 17. Jennings A, Higuchi H. A personal news service based on a user model neural network. IEICE Trans Inform Syst 1992;E75- D(2):198-209 18. J. Vieira, F. M. Dias, and A.Mota, "Neuro-fuzzy systems: a survey," In 5thWSEASNNA International Conference on Neural Networks and Applications, Udine, Italia, 2004. 19. M. Krstic, and M. Bjelica, "Context-aware personalized program guide based on neural network," IEEE Transactions on Consumer Electronics 58, no. 4, 2012, pp. 1301-1306. 20. P. H. Chou, P.H., Li, K. K. Chen, K.-K., and M. J. Wu, "Integrating web mining and neural network for personalized e-commerce automatic service," Expert Systems with Applications 37, no. 4, 2010, pp. 2898-2910. 21. N. Kano, N. Seraku, F. Takahashi, & S. Tsuji, "Attractive quality and must be quality", Quality, 14, 1984, pp. 39-48. 22. C. C. Chang, P.L. Chen, F. R. Chiu, and Y. K. Chen, "Application of neural networks and Kano's method to content recommendation in web personalization," Expert Systems withApplications36, no. 3, 2009, pp. 5310-5316. 23. C. Biancalana, F. Gasparetti, A. Micarelli, A. Miola, and G. J. Zhang, Z. H. Zhan, Y. L. N. Chen, Y.J. Gong, J.H. Zhong, H. S. H Chung, Y. Li, and Y.H. Shi, "Evolutionary computation meets machine learning: A survey," IEEE Computational Intelligence Magazine 6, no. 4, 2011, pp. 68- 75. 5-10. 24. M. K. K. Devi, R. T. Samy, S. V. Kumar, and P. Venkatesh, "Probabilistic neural network approach to alleviate sparsity and cold start problems in collaborative recommender systems," In IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), 2010, pp. 1-4. Authors: M. Sneha, M. Vibin, T. Krishnaprabu, Aishwarya Mohan, R. Kanmani, S. Bhuvana Paper Title: Identity Secured Sharing Using Blockchain Abstract: Frequent cases of personal data leakage has brought back into the focus the security issues with the different 26. identity sharing mechanisms. A customer is expected to provide his personal identity for the authentication by different agencies. The KYC procedures which are used by the banks is completely dependent on the encryption which is slow 134-136 and it can lead to the loss of customer details to other theirs party financial institutions. This system can be efficient by using the Blockchain technology, which has the potential to automate a lot of manual process and it is also resistant to hacks of any sort. The immutable blockchain block and its distributed ledger is the perfect complement to the opaque process of KYC. With the addition of the smart contacts fraud detection can be automated. For KYC identity details storage, the banks can develop a shared private blockchain within the bank premise and the same can be used for verifying the documents. This allows the user to get control of their sensitive documents and also makes it easier for banks to obtain the documents they need for compliance.

References: 1. Identity-Based Encryption - Applied Cryptography Group https://crypto.stanford.edu/ibe/ 2. Identity Based Encryption - Applied Cryptography Group - Stanford crypto.stanford.edu/~dabo/cs355/lectures/IBE.pdf by D Boneh 3. Identity-Based Encryption | Computerworldhttps://www.computerworld.com/article/2551479/.../identity-based-encryption.html 4. Identity Based Encryption: An Overview - CSE@IIT Delhi www.cse.iitd.ernet.in/~ssen/csl863/ibeIntro.pdf 5. Githeub - decrypto-org/blockchain-papers: A curated list of academic https://githeub.com/decrypto-org/blockchain-papers 6. Applications of Blockchain Technology beyond Cryptocurrency - arXiv https://arxiv.org/pdf/1801.03528 7. Public Blockchain White Paper - OneLedger https://oneledger.io/whitepaper/oneledger-whitepaper.en.pdf 8. Introduction to Solidity Programming and Smart Contracts (For ... https://medium.com/.../introduction-to-solidity-programming-and-smart- contracts-for-... 9. Solidity Documentation - Read the Docs https://media.readthedocs.org/pdf/solidity/develop/solidity.pdf 10. What is Ethereum? the Most Comprehensive Beginners Guide https://blockgeeks.com/guides/ethereum/ Authors: S. Maruthi Srinivas, Bharath Manchikanti, G. Chetan Babu, N. Harini Paper Title: A Secure Scheme to Manage Complex Password Capable of Overcoming Human Memory Limitations Abstract: Security policies are required that protect information from illegal access, and also respect challenges users face in creating, and particularly managing, increasing numbers of passwords. With the increase in dependence of people on internet services for their day to day activities the number of passwords in use is more, the relationship between the passwords and the services is more complex. Each of the users follows their own strategy to remember and manage. Clearly, managing multiple passwords requires effort for creation, encoding, retrieval and execution. Most of us rely on the memory or reuse of passwords for easy management. The intricate characteristics of secure passwords, however, posit an unfortunate problem for password users. That is, whereas such passwords are difficult to be guessed by intruders, they are in general considerably difficult to be remembered by authorized users. This paper proposes a highly secure scheme that facilitates complex password management at ease without lack of user-friendliness.

Keywords: Encryption, Finger print, Biometric, Authentication, Single factor, Multi factor

References: 1. Katha Chanda. Password Security: An Analysis of Password Strengths and Vulnerabilities, I. J. Computer Network and Information Security, 2016, 7, 23-30. DOI: 10.5815/ijcnis.2016.07.04 27. 2. J Alex Halderman, Brent Waters, Edward W. Felten. A Convenient Method for Securely Managing Passwordshttps://jhalderm.com/pub/papers/password-www05.pdf 137-143 3. Anatomy of a hack: even your 'complicated' password is easy to crack. 4. .Mouad M H Ali, Vivek H Mahale, Pravin Yannawar, A. T. Gaikwad. Overview of Fingerprint Recognition System, International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT)-2016,DOI:10.1109/ICEEOT.2016.7754902. 5. Aleksandr Ometov, Sergey Bezzateev, Niko Mäkitalo 3, Sergey Andreev, TommiMikkonen and Yevgeni Koucheryavy , Multi-Factor Authentication: A Survey, doi:10.3390/cryptography2010001 6. Harini, N.; Padmanabhan, T.R. 2CAuth: A new two factor authentication scheme using QR-code. Int. J. Eng. Technol. 2013, 5, 1087-1094 7. Sattar B Sadkhan, Baheeja K Al Shukur, Ali K Mattar, Biometric voice authentication auto-evaluation system, 2017 Annual Conference on New Trends in Information & Communications Technology Applications (NTICT)DOI: 10.1109/NTICT.2017.7976100 8. Kavita, Ms. Manjeet Kaur, A Survey paper for Face Recognition Technologies, International Journal of Scientific and Research Publications, July 2016 441 ISSN 2250-3153http://www.ijsrp.org/research-paper-0716/ijsrp-p5564.pdf 9. Mauro Conti, Nicola Dragoni, and Viktor Lesyk,A Survey of Man In The Middle Attacks, IEEE Communications Surveys and Tutorials, 2016 DOI: 10.1109/COMST.2016.2548426 10. Zhizheng Wu, Sheng Gao, EngSiongChng and Haizhou Li, A study on replay attack and anti-spoofing for text-dependent speaker verification, Signal and Information Processing Association Annual Summit and Conference (APSIPA), 2014, DOI: 10.1109/APSIPA.2014.7041636 11. Peng Foong Ho, Yvonne Hwei-Syn Kam, Chin Wee Yu Nam Chong and Lip Yee Por, Preventing Shoulder-Surfing Attack with the Concept of Concealing the Password Objects' Information, Hindawi Publishing Corporation, Scientific World Journal. DOI: 10.1155/2014/838623 12. M Preetha1, M Nithya,A study and performance analysis of RSA algorithm. 2013, IJCSMC, ISSN 2320-088X 13. Himja Agrawal, ProfPRBadadapure, Survey Paper On Elliptic Curve Cryptography. International Research Journal of Engineering and Technology (IRJET) (2016). e-ISSN: 2395 -0056 14. Dr.N.Harini, Dr T.R Padmanabhan and Dr.C.K.Shyamala , ?Cryptography and security?, Wiley India, First Edition, 2011 Authors: B.J. Bipin Nair, C. Adith, S. Saikrishna A Comparative Approach of CNN Versus Auto Encoders to Classify the Autistic Disorders from Brain Paper Title: MRI Abstract: In the present study, we are going to apply the deep learning methods to create a trained predictive model to recognize the neuro developmental diseases such as ASD, FASD. Deep learning is a dominant ML technique in classification. It will extract all kind (low to high) feature from a digital image. Classifying medical images tends to develop a predictive model in order to predict the neuro developmental disease. Classification of medical data for a medical condition for example ASD always a challenging task and selecting the important feature is also a difficult task. Using the deep learning techniques, we can successfully classify the MRI data of the neurodegenerative disease. 28. Feature extracted by Convolution Neural Network and classification using the same advise a most robust method of classifying medical data especially MRI images. Stacked Auto encoders another better way for the feature extraction 144-149 and prediction clinical data.

Keywords: ASD-Autism spectrum disorder, ABIDE-Autism Brain Imaging Data Exchange

References: 1. Heinsfeld, A. S., Franco, A. R., Craddock, R. C., Buchweitz, A., &Meneguzzi, F. (2018). Identification of autism spectrum disorder using deep learning and the ABIDE dataset. NeuroImage: Clinical, 17, 16-23. 2. Bi, X. A., Wang, Y., Shu, Q., Sun, Q., & Xu, Q. (2018). Classification of Autism Spectrum Disorder Using Random Support Vector Machine Cluster. Frontiers in genetics, 9, 18. 3. Katuwal, G. J. (2017). Machine Learning Based Autism Detection Using Brain Imaging. 4. Kassraian-Fard, P., Matthis, C., Balsters, J. H., Maathuis, M. H., &Wenderoth, N. (2016). Promises, pitfalls, and basic guidelines for applying machine learning classifiers to psychiatric imaging data, with autism as an example. Frontiers in psychiatry, 7, 177. 5. Duda, M., Ma, R., Haber, N., & Wall, D. P. (2017). Use of machine learning for behavioraldistinction of autism and ADHD. Translational psychiatry, 6(2), e732. 6. Zhou, Y., Yu, F., & Duong, T. (2014). Multiparametric MRI characterization and prediction in autism spectrum disorder using graph theory and machine learning. PLoS One, 9(6), e90405. 7. Deshpande, G., Libero, L., Sreenivasan, K. R., Deshpande, H., & Kana, R. K. (2013). Identification of neural connectivity signatures of autism using machine learning. Frontiers in human neuroscience, 7, 670. 8. Wall, D. P., Kosmicki, J., Deluca, T. F., Harstad, E., &Fusaro, V. A. (2012). Use of machine learning to shorten observation-based screening and diagnosis of autism. Translational psychiatry, 2(4), e100. 9. Ecker, C., Marquand, A., Mourão-Miranda, J., Johnston, P., Daly, E. M., Brammer, M. J., ...& Murphy, D. G. (2010). Describing the brain in autism in five dimensions-magnetic resonance imaging-assisted diagnosis of autism spectrum disorder using a multiparameter classification approach. Journal of Neuroscience, 30(32), 10612-10623. 10. Jamal, W., Das, S., Oprescu, I. A., Maharatna, K., Apicella, F., & Sicca, F. (2014). Classification of autism spectrum disorder using supervised learning of brain connectivity measures extracted from synchrostates. Journal of neural engineering, 11(4), 046019. 11. Tseng, P. H., Paolozza, A., Munoz, D. P., Reynolds, J. N., &Itti, L. (2013, October). Deep learning on natural viewing behaviors to differentiate children with fetal alcohol spectrum disorder. In International Conference on Intelligent Data Engineering and Automated Learning (pp. 178- 185). Springer, Berlin, Heidelberg. 12. Luo, S., Li, X., & Li, J. (2017). Automatic Alzheimer's Disease Recognition from MRI Data Using Deep Learning Method. Journal of Applied Mathematics and Physics, 5(09), 1892. 13. Summers, M. J., Madl, T., Vercelli, A. E., Aumayr, G., Bleier, D. M., &Ciferri, L. (2017). Deep Machine Learning Application to the Detection of Preclinical Neurodegenerative Diseases of Aging. DigitCult-Scientific Journal on Digital Cultures, 2(2), 9-24. 14. Sarraf, S., &Tofighi, G. (2016, December). Deep learning-based pipeline to recognize Alzheimer's disease using fMRI data. In Future Technologies Conference (FTC) (pp. 816-820). IEEE. 15. Singh, G., Vadera, M., Samavedham, L., & Lim, E. C. H. (2016). Machine Learning-Based Framework for Multi-Class Diagnosis of Neurodegenerative Diseases: A Study on Parkinson's Disease. Age, 65(02.83), 64-94. 16. Sen, B., Borle, N. C., Greiner, R., & Brown, M. R. (2018). A general prediction model for the detection of ADHD and Autism using structural and functional MRI. PloS one, 13(4), e0194856. 17. Dessouky, M. M., Elrashidy, M. A., Taha, T. E., &Abdelkader, H. M. (2014). Dimension and complexity study for Alzheimer's disease feature extraction. International Journal Of Engineering And Computer Science, 3(07). 18. Nair, B. B., Bhaskaran, V., &Arunjit, K. (2017). Structural designing of suppressors for autisms spectrum diseases using molecular dynamics sketch. International Journal of Drug Delivery, 8(4), 142-146. 19. Shobha Rani, N., Chandan, N., Sajan Jain, A., & R. Kiran, H. (2018). Deformed character recognition using convolutional neural networks. International Journal of Engineering & Technology, 7(3), 1599. 20. Bipinnairb.j., Arunjit k., &VijeshBhaskaran(2017). Melatonin and fluoxetine interaction with shank3 protein gene for autism spectrum disorder. Pakisthan journal of Biotechnology. Authors: B. Divya, M. Santhi Paper Title: SVM-based Pest Classification in Agriculture Field Abstract: Integrated Pest Management is currently used to reduce the use of harmful pesticides and chemicals in the agriculture environment. However, the early detection of pest and controlling of the pest population is the critical task and time consuming process and the judgment is mostly based on the manual process which is highly prone for error. In this paper, an image based classification is used to for detection and classification of the pest species which is commonly available in the felid. Digital images were obtained. Detection of pest in the images, segmentation, feature extraction was performed by the algorithms for the detected pest. Finally, SVM was used for classification and results were compared with K-Neural Network. Compared to the KNN, SVM achieved accurate results with combined features with accuracy as its metrics.

Keywords: IPM, Pest, GLCM, LTP, CCV, SVM.

References: 1. Ziyi Liu, Junfeng Gao, Guoguo Yang, Huan Zhang, Yong He, "Localization and Classification of Paddy Field Pests using a Saliency Map and Deep Convolutional Neural Network", Scientific Reports ,vol. 6, February 2016. 2. Chenglu Wen, Daoxi Wu, Huosheng Hu, Wei Pan, "Pose Estimation-dependent Identification Method for Field Moth Images using Deep Learning Architecture", Journal of Biosystems Engineering, Vol. 136, August 2015. 3. SitiN.A.Hassan, NadiahS.A.Rahman, ZawZawHtike and Shoon Lei Win , "Advances in Automatic Insect Classification", Electrical and 29. Electronics Engineering: An International Journal (ELELIJ) Vol 3, No 2, May 2011. 4. Ying Yang, Bo Peng, and Jianqin Wang, "System for Detection and Recognition of Pests in Stored-Grain Based on Video Analysis", pp. 119- 124, International Conference on Computer and Computing Technologies in Agriculture ,2011. 150-155 5. Katarina Mele, "Insect Soup Challenge: Segmentation, Counting, and Simple Classification", IEEE International Conference on Computer Vision Workshops ,2013. 6. Zhu, L. & Zhang, Z., "Auto-classification of insect images based on color histogram and GLCM" International Conference on Fuzzy Systems and Knowledge Discovery, 2010. 7. Solis-Sánchez, L. O., García-Escalante, J., Castañeda-Miranda, R., Torres-Pacheco, I. & Guevara-González, R. "Machine vision algorithm for whiteflies (BemisiatabaciGenn.) scouting under greenhouse environment", Journal of Applied Entomology, vol. 133,pp. 546-552,2009. 8. Zhang, H. & Mao, H, "Feature selection for the stored-grain insects based on PSO and SVM", International Workshop on Knowledge Discovery and Data Mining ,2009. 9. Yang, H. et al., "Research on insect identification based on pattern recognition technology.", International Conference on Natural Computation, 2010. 10. Do, M., Harp, J. & Norris, K, "A test of a pattern recognition system for identification of spiders.", B. Entomol. Res 89, pp.217-224,1999. 11. Yu, Z. & Shen, X.," Application of Several Segmentation Algorithms to the Digital Image of Helicoverpaarmigera", Journal of China Agricultural University 5, 2001. 12. Arbuckle, T., Schroder, S., Steinhage, V. &Wittmann, D. "Biodiversity informatics in action: identification and monitoring of bee species using ABIS.", International Symposium Informatics for Environmental Protection, Zurich, Switzerland. Marburg: Metropolis-Verlag., 2001. 13. Yao, Q. et al. An insect imaging system to automate rice light-trap pest identification. J INTEGR AGR 11, pp.978-985,2012. 14. Kumar, R., Martin, V. &Moisan, S., "Robust insect classification applied to real time greenhouse infestation monitoring", International Conference on Pattern Recognition: Visual Observation and Analysis of Animal and Insect Behavior, IEEE. 2010. 15. Solis-Sánchez, L. O. et al., "Scale invariant feature approach for insect monitoring",Comput. Electron. Agr75, pp.92-99,2011. 16. Cheng, L. &Guyer, D.," Image-based orchard insect automated identification and classification method",Comput. Electron. Agr, pp.110-115, 2012. 17. Xia, C., Lee, J., Li, Y., Chung, B. & Chon, T.," In situ detection of small-size insect pests sampled on traps using multifractal analysis.", Opt. Eng,2012. 18. Venugoban, K. &Ramanan,, "A. Image classification of paddy field insect pests using gradient-based features.", International Journal of Machine Learning and Computing, 2014. 19. Zhang, J., Wang, R., Xie, C. & Li, R.," Crop pests image recognition based on multifeatures fusion.", Journal of Computational Information Systems, pp.5121-5129, 2014. 20. Yao, Q. et al.," Automated counting of rice planthoppers in paddy fields based on image processing.", J INTEGR AGR, pp.1736-1745, 2014. 21. Sivic, J. & Zisserman," A. Video Google: a text retrieval approach to object matching in videos", International Conference on Computer Vision (ICCV 2003), IEEE, 2003. 22. Ojala, T., Pietikäinen, M. &Mäenpää, T.," Multiresolution gray-scale and rotation invariant texture classification with local binary patterns.", IEEE T PATTERN ANAL,pp. 971-987, 2002. 23. Lowe, D. G.," Distinctive image features from scale-invariant keypoints", Int. J. Comput. Vision, pp. 91-110, 2004. 24. Dalal, N. &Triggs, B.," Histograms of oriented gradients for human detection.", IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2005), IEEE, 2005. 25. Zhang, W., Deng, H., Dietterich, T. G. & Mortensen, E. N.," A hierarchical object recognition system based on multi-scale principal curvature regions.", International Conference on Pattern Recognition (ICPR 2006), IEEE. 2006. 26. Larios, N. et al. Automated insect identification through concatenated histograms of local appearance features: feature vector generation and region detection for deformable objects. Mach. Vision. Appl19,pp. 105-123 2008. 27. XiChengYouhua, ZhangYiqiong, ChenYunzhi, WuYiYue, "Pest identification via deep residual learning in complex background" Computers and Electronics in Agriculture, Vol. 141, pp. 351-356,2017, 28. LimiaoDeng, YanjiangWang, ZhongzhiHan, RenshiYu," Research on insect pest image detection and recognition based on bio-inspired methods" Biosystems Engineering Volume 169, Pages 139-148,May 2018. 29. ChengjunXie, JieZhang,RuiLi, JinyanLicPeilinHong, JunfengXiaaPengChena" Automatic classification for field crop insects via multiple-task sparse representation and multiple-kernel learning Computers and Electronics in Agriculture, pp.123-132,2015. 30. A. Ebrahimi a, M.H. Khoshtaghazaa, S. Minaei a, B. Jamshidi b ", Vision-based pest detection based on SVM classification method", Computers and Electronics in Agriculture, pp. 52-58,2017. 31. He-Ping Yang, Chun-Sen Ma, Hui Wen, Qing-Bin Zhan &Xin-Li Wang," A tool for developing an automatic insect identification system based on wing outlines", Scientific Reports volume 5, 2015. 32. Manisha Bhangea, H.A.Hingoliwalab, "Smart Farming: Pomegranate Disease Detection Using Image Processing", Procedia Computer Science, pp.280 - 288,2015. 33. Zalak R. Barot, NarendrasinhLimbad, "An Approach for Detection and Classification of Fruit Disease: A Survey", International Journal of Science and Research (IJSR), pp.44-50,2017. 34. HalilBisgin, TanmayBera, Hongjian Ding, "Comparing SVM and ANN based Machine Learning Methods for Species Identification of Food Contaminating Beetles", Scientific Reports,2018. Authors: Gino Sinthia, M. Balamurugan Paper Title: Analyzing Student’s Academic Performance Using Multilayer Perceptron Model Abstract: Identification of the student’s behavior in the class room environment is very important. It helps the lecturer to identify the needs of the students. It also aids in identifying the strength and weakness of the individual and guide them to improve on their performance. Observing and supervising the students regularly can improve their performance. The data has been collected from 120 students who took the common the course taught by two different lectures. The students were observed based on the internal assignments and quizzes and the model exam given by the respective lecturers. In this paper the students are categorized into different groups based on their performance using Multilayer Perceptron (MLP) and also different factors which are influencing the performance of the students are identified.

Keywords: Student’s Performance, Machine Learning, Multilayer Perceptron, k- Nearest Neighbor (KNN), K-means.

References: 1. C. Brooks, G. Erickson, J. Greer, and C. Gutwin, "Modelling and quantifying the behaviours of students in lecture capture environments," Computers & Education, vol. 75, pp. 282-292, 2014.K. Elissa, 2. Liansheng Jia,Sannyuya Liu,Yangjun Chen, Hercy N.HCheng,Wang-ChenChang,JianwenSun," Integrating clustering and sequential analysis to 30. explore students' behaviors in an online Chinese reading assessment system", 6th IIAI International Congress on Advanced Applied Informatics,2017 3. Febrianti Widyahastuti, Viany Utami Tjhin," Predicting Students Performance in Final Examination 156-160 4. using Linear Regression and Multilayer Perceptron",10th International Conference on Human System Interactions (HSI), 2017 5. Tao Zhang, Yihua Xu,Peng Qi,Xin Li,Guoping Hu," Predicting the Performance Fluctuation of Students Based on the Long-term and Short-term Data", The Sixth International Conference of Educational Innovation through Technology,2017M. Young, The Technical Writer's Handbook. Mill Valley, CA: University Science, 1989. 6. Xi Zhang, Guangzhong Sun§,Yigong Pan, Hao Sun, Jiali Tan," Poor Performance Discovery of College Students Based on Behavior Pattern", IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computed, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation, 2017 7. Shadi Esnaashariors, Lesley Gardner, Michael Rehm," Characterizing Students' Behavior based on their Participation in Property Course in New Zealand", 32nd International Conference on Advanced Information Networking and Applications Workshops , 2018 8. J. James Manoharan, Dr. S. Hari Ganesh, M. Lovelin Ponn Felciah, A.K. Shafreen Banu," Discovering Students' Academic Performance Based on GPA using K-Means Clustering Algorithm", World Congress on Computing and Communication Technologies, 2014. 9. Arto Vihavainen, "Predicting Students' Performance in an Introductory Programming Course using Data from 10. Students'ownProgrammingProcess",13th International Conference on Advanced Learning Technologies, 2013. 11. Kin Fun Li, David Rusk, Fred Song," Predicting Student Academic Performance", Seventh International 12. Conference on Complex, Intelligent, and Software Intensive Systems,2013. 13. Ishwank Singh, A Sai Sabitha, Abhay Bansal," Student Performance Analysis Using Clustering Algorithm", 6th International Conference - Cloud System and Big Data Engineering, 2016. 14. Mazwin Tan, Halina Hassan, Nurul Na'imy Wan," Statistical analysis on students' performance and their preference of course in bachelor of Engineering Technology programmes in Malaysian Spanish 15. Institute, Universiti Kuala Lumpur", International Conference on Statistics in Science, Business and Engineering,2012. Authors: R. Susithra, A. Mahalakshmi, Judith Justin Paper Title: A Robust Adaptive Multi-Scale Superpixels Segmentation in SEM Image 31. Abstract: This work presents a region-growing image segmentation approach based on superpixels SEM Image segmentation decomposition. From a first contour- constrained over-segmentation of the input image, the image 161-166 segmentation into parts is achieved by again and again merging like superpixels SEM image segmentation into parts into fields, regions. This approach raises two key issues: how to compute the similarity between superpixels SEM Image segmentations in order to perform accurate merging and in which order those superpixels SEM Image segmentations must be merged together. In this perspective, we firstly introduce a robust adaptive multi-scale superpixels SEM Image segmentation similarity in which region comparisons are made both at content and common border level. Secondly, we offer a complete merging worked design to small amount of support guide the field, region merging process. Such strategy uses an adaptive merging criterion to ensure that best region aggregations are given highest priorities. This lets to get stretched to a final segmentation into harmony regions with strong division line take as rule. We act experiments on the BSDS500 image dataset to high-light to which extent our method compares favorably against other well-known image segmentation algorithms.

Keywords: Scanning Electron Microscope (SEM), adaptive multi scale superpixel, image segmentation.

References: 1. R. Achanta, A. Shaji, K. Smith, A. Lucchi, P. Fua, and S. Su¨sstrunk. Slic superpixels SEM Image segmentations compared to state-of-the-art superpixels SEM Image segmentation methods. IEEE transactions on pattern analysis and machine intelligence, 34(11):2274-2282, 2012. 2. R. Adams and L. Bischof. Seeded region growing. IEEE Transactions on pattern analysis and machine intelligence, 16(6):641-647, 1994. 3. P. Arbelaez, M. Maire, C. Fowlkes, and J. Malik. Contour detection and hierarchical image segmentation. IEEE transactions on pattern analysis and machine intelligence, 33(5):898-916, 2011. 4. S. Arora, J. Acharya, A. Verma, and P. K. Panigrahi. Multilevel thresholding for image segmentation through a fast statistical recursive algorithm. Pattern Recognition Letters, 29(2):119- 125, 2008. 5. A. Baâzaoui, W. Barhoumi, A. Ahmed, and E. Zagrouba. Semi-automated segmentation of single and multiple tumors in liver ct images using entropy-based fuzzy region growing. IRBM, 2017. 6. M.-H. Chen, J. Wen, Y. Zhu, H.-Y. Xing, and Y. Wang. Multi- level thresholding for pupil location in eye-gaze tracking systerm. In Machine Learning and Cybernetics (ICMLC), 2016 International Conference on, volume 2, pages 1009-1014. IEEE, 2016. 7. G. A. Castillo. A k-means based watershed imaging segmentation algorithm for banana cluster quality inspection. 2016. 8. D. Comaniciu and P. Meer. Mean shift: A robust approach toward feature space analysis.IEEE Transactions on pattern analysis and machine intelligence, 24(5):603-619, 2002. 9. P.-H. Conze, V. Noblet, F. Rousseau, F. Heitz, V. de Blasi, R. Memeo, and P. Pessaux. Scale- adaptive supervoxel-based random forests for liver tumor segmentation in dynamic contrast- enhanced CT scans. International Journal of Computer Assisted Radiology and Surgery, 12(2):223-233, 2017. 10. P. Dollár and C. L. Zitnick. Structured forests for fast edge detection. In Proceedings of the IEEE International Conference on Computer Vision, pages 1841-1848, 2013]. 11. J. Freixenet, X. Munõz, D. Raba, J. Mart´?, and X. Cuf´?. Yet another survey on image segmentation: Region and boundary information integration. In European Conference on Computer Vision, pages 408-422. Springer, 2002. 12. R. M. Haralick and L. G. Shapiro. Image segmentation techniques. Computer vision, graphics, and image processing, 29(1):100-132, 1985. 13. R. Hettiarachchi and J. F. Peters. Vorono¨? region-based adaptive unsupervised color image segmentation. Pattern Recognition, 65:119-135, 2017. 14. A. Mehnert and P. Jackway. An improved seeded region growing algorithm. Pattern Recogni- tion Letters, 18(10):1065-1071, 1997. Authors: G. Pavithra, N.M. Dhanya Curve Path Prediction and Vehicle Detection in Lane Roads Using Deep Learning for Autonomous Paper Title: Vehicles Abstract: There is always a huge demand for the development of the self-driving cars since they are the future of the autonomous vehicles. In the field of autonomous vehicles, problems still remains unsolved when there occurs any obstacle in the road lane while driving. In Self-driving cars, Lane detection is considered to be the most important part in reducing the number of accidents and risks. In this paper we have discovered the methodologies existing in the lane detection, the advantages and disadvantages of models. We have proposed a model that can detect lane in the straight and curved roads and detect vehicle existing in the lane. We have implemented a deep learning algorithm for the Vehicle Detection. The proposed methodology has been successfully applied to the dataset, the results are recorded and the performance metrics are tabulated. We have also discussed on the future scope of the Lane detection.

Keywords: Lane Detection, Deep Learning, Convolutional neural network.

References: 32. 1. Annamalai, Jayalakshmi, and C. Lakshmikanthan. "An Optimized Computer Vision and Image Processing Algorithm for Unmarked Road Edge Detection." Soft Computing and Signal Processing. Springer, Singapore, 2019. 429-437. 2. A.Assidiq, O.Khalifa, R. Islam, and S. Khan, "Real time lane detection for autonomous vehicles," in Computer and Communication Engineering, 167-170 2008. ICCCE 2008. 3. Z.Kim, "Robust lane detection and tracking in challenging scenarios," IEEE Trans. Intell. Transp. Syst. Mar. 2008. 4. P.Daigavane and P. Bajaj, "Road Lane Detection with Improved Canny Edges Using Ant Colony Optimization," in 3rd International Conference on Emerging Trends in Engineering and Technology International Journal of Computer Science & Information Technology (IJCSIT) 5. Young UkYim, & Se-Young Oh. (2003). Three-feature based automatic lane detection algorithm (tfalda) for autonomous driving. IEEE Transactions on Intelligent Transportation Systems, 4(4), 219-225. doi:10.1109/tits.2003.821339 6. Kuo-Yu Chiu, & Sheng-Fuu Lin. (2005). Lane detection using color-based segmentation. IEEE Proceedings. Intelligent Vehicles Symposium, 2005. doi:10.1109/ivs.2005.1505186 7. N M Dhanya, Veerakumar S, "Performance analysis of various regression algorithms for time series temperature prediction", Journal of Advanced Research in Dynamical and Control Systems, 18 May 2018, Pages 175-194, Scopus-August-2018, Publisher: IGI Global 8. N.M. Dhanya and Harish, U. C., "Sentiment analysis of twitter data on demonetization using machine learning techniques", Lecture Notes in Computational Vision and Biomechanics, vol. 28, pp. 227-237, 2018. 9. Aarthi, R., and S. Harini. "A Survey of Deep Convolutional Neural Network Applications in Image Processing." Int. J. Pure Appl. Math 118 (2018): 185-190. 10. https://www.kaggle.com/jessicali9530/stanford-cars-dataset/home 11. https://www.gti.ssr.upm.es/data/Vehicle_database.html Authors: R. Janaki, R. Hemalatha, T. Janani, M. Kavipriya, S. Srinithi Performance of Cooperative Transmission Schemes in Industrial Wireless Sensor Network Using S-AODV Paper Title: Protocol 33. Abstract: In this paper, we investigate the impact of propagation channel conditions including both line of sight (LOS) and non line of sight(NLOS) environment. The major issue identified in wireless sensor network is data 171-175 transmission rate redundancy which is resolved by proposing a new protocol namely S-AODV (Statistical Adhoc On- demand Distance Vector routing) protocol. This protocol is designed to improve the AIDV protocol by developing adaptive methods by considering the statistical parameters. The coorelation coefficient is the prominent parameter used for selection of routes in S-AODV protocol. The routing algorithm is designed by considering the propagation environment, energy efficiency, outage probability and throughput efficiency.

Keywords: cooperative communication, industrial wireless communication, adaptive methods, energy efficiency, outage probability, throughput performance.

References: 1. P. T. A. Quang and D.-S. Kim, "Throughput-aware routing for industrial sensor networks: Application to ISA100.11a," Industrial Informatics, IEEE Transactions on, vol. 10, no. 1, pp. 351-363, February 2014. 2. Z. Zhang and T. Lok, "Performance comparison of conventional and cooperative multihop transmission," in Wireless Communications and Networking Conference, 2006. WCNC 2006. IEEE, vol. 2, April 2006, pp. 897-901. 3. H.-C. Liu, J. Min, and H. Samueli, "A low-power baseband receiver ic for frequency-hopped spread spectrum communications," Solid-State Circuits, IEEE Journal of, vol. 31, no. 3, pp. 384-394, March 1996. 4. K. Pentikousis, "In search of energy-efficient mobile networking," IEEE Communications Magazine, vol. 48, no. 1, pp. 95-103, January 2010. 5. C. K. Lo, S. Vishwanath, and R. W. Heath, "An energy-based comparison of long-hop and short-hop routing in mimo networks," IEEE Transactions on Vehicular Technology, vol. 59, no. 1, pp. 394-405, January 2010. 6. J. Laneman, D. Tse, and G. W. Wornell, "Cooperative diversity in wireless networks: Efficient protocols and outage behavior," Information Theory, IEEE Transactions on, vol. 50, no. 12, pp.3062-3080, December 2004.https://www.gti.ssr.upm.es/data/Vehicle_database.html Authors: C.P. Chaithanya, N. Manohar, Ajay Bazil Issac Paper Title: Automatic Text Detection and Classification in Natural Images Abstract: Text detection is the method of locating areas in a picture wherever, text is present. Text detection and classification in natural pictures is very important for several computer vision applications like optical character recognition, distinguish between human and machine inputs and spam removal. Currently the challenge in text identifying is to detect the text in natural pictures due to many factors like, low-quality image, unclear words, typical font, image having a lot of color stroke than the background color, blurred pictures due to some natural problems like rain, sunny, snow, etc. The main aim of this work is to identify and classify the text in natural pictures. Here system detects the text and finds the connected regions, chainthem together in their relative position. Uses a text classification engine to filter chains with low classification confidence scores.

Keywords: CNN, OCR, Extraction.

References: 1. Amritha S Nadarajan, Thamizharasi A, "A Survey on Text Detection in Natural Images", International Journal of Engineering Development and Research (IJEDR), ISSN: 2321-9939, Volume.6, Issue 1, pp.60-66, January 2018. 2. Chandio, A. A., Pickering, M., & Shafi, K. (2018, March). Character classification and recognition for Urdu texts in natural scene images. In Computing, Mathematics and Engineering Technologies (iCoMET), 2018 International Conference on (pp. 1-6). IEEE. 3. Tridib Chakraborty et al, (2017), Text recognition using image processing, International Journal of Advanced Research in Computer Science, 8 (5), May-June 2017, 765-768 4. Karaoglu, S., Tao, R., van Gemert, J. C., & Gevers, T. (2017). Con-Text: Text Detection for Fine-Grained Object Classification. IEEE Transactions on Image Processing, 26(8), 3965-3980. 5. Guan, L., & Chu, J. (2017, June). Natural scene text detection based on SWT, MSER and candidate classification. In Image, Vision and Computing (ICIVC), 2017 2nd International Conference on (pp. 26-30). IEEE. 6. Zhu, Q. H., Zhu, R., Li, N., & Yang, Y. B. (2017, October). Deep metric learning for scene text detection. In Systems, Man, and Cybernetics (SMC), 2017 IEEE International Conference on (pp. 1025-1029). IEEE. 34. 7. Shi, B., Bai, X., & Belongie, S. (2017). Detecting oriented text in natural images by linking segments. ArXiv preprint arXiv: 1703.06520. 8. Zhong, Z., Jin, L., & Huang, S. (2017, March). Deeptext: A new approach for text proposal generation and text detection in natural images. In Acoustics, Speech and Signal Processing (ICASSP), 2017 IEEE International Conference on (pp. 1208-1212). IEEE. 176-180 9. Karaoglu, S., Tao, R., Gevers, T., & Smeulders, A. W. (2017). Words matter: Scene text for image classification and retrieval. IEEE Transactions on Multimedia, 19(5), 1063-1076. 10. Pirker J, Wurzinger G. Optical Character Recognition of Old Fonts - A Case Study. The IPSI BgD Transactions on Advanced Research. 2016. 11. Jeong, M., & Jo, K. H. (2015, January). Multi language text detection using fast stroke width transform. In Frontiers of Computer Vision (FCV), 2015 21st Korea-Japan Joint Workshop on (pp. 1-4). IEEE. 12. Jacob, J., & Thomas, A. (2015, December). Detection of multioriented texts in natural scene images. In Control, Instrumentation, Communication and Computational Technologies (ICCICCT), 2015 International Conference on (pp. 625-628). IEEE. 13. Rani, N. S., & Vasudev, T. (2015, December). Automatic detection of Telugu single and multi-character text blocks in handwritten words. In 2015 International Conference on Computing and Network Communications (CoCoNet) (pp. 234-240). IEEE. 14. Rani, N. S., & Vasudev, T. (2015, December). Post-processing methodology for word level Telugu character recognition systems using Unicode Approximation Models. In 2015 International Conference on Trends in Automation, Communications and Computing Technology (I-TACT-15) (pp. 1-7). IEEE. 15. Rong, L., Suyu, W., & Shi, Z. (2014, April). A two level algorithm for text detection in natural scene images. In Document Analysis Systems (DAS), 2014 11th IAPR International Workshop on (pp. 329-333). IEEE. 16. Yao, C., Bai, X., & Liu, W. (2014). A unified framework for multioriented text detection and recognition. IEEE Transactions on Image Processing, 23(11), 4737-4749. 17. Pise, A., & Ruikar, S. D. (2014, April). Text detection and recognition in natural scene images. In Communications and Signal Processing (ICCSP), 2014 International Conference on (pp. 1068-1072). IEEE. 18. Meng, Q., Song, Y., Zhang, Y., & Liu, Y. (2013, September). Text detection in natural scene with edge analysis. In Image Processing (ICIP), 2013 20th IEEE International Conference on (pp. 4151-4155). IEEE. 19. A. Coates et al., "Text Detection and Character Recognition in Scene Images with Unsupervised Feature Learning," 2011 International Conference on Document Analysis and Recognition, Beijing, 2011, pp. 440445. 20. B. Epshtein, E. Ofek and Y. Wexler, "Detecting text in natural scenes with stroke width transform," 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Francisco, CA, 2010, pp. 2963-2970. 21. Shao Y., Wang C., Xiao B., Zhang Y., Zhang L., Ma L. (2010) Text Detection in Natural Images Based on Character Classification. In: Qiu G., Lam K.M., Kiya H., Xue XY., Kuo CC.J., Lew M.S. (Eds) Advances in Multimedia Information Processing - PCM 2010. PCM 2010. Lecture Notes in Computer Science, vol 6298. Springer, Berlin, Heidelberg. 22. Park, J., & Lee, G. (2008). A robust algorithm for text region detection in natural scene images. Canadian Journal of Electrical and Computer Engineering, 33(3/4), 215-222. 35. Authors: Jahanara Khanam, K. Ananya, B.J. Santhosh Kumar Paper Title: Efficient Authenticated Service Mechanism Over Cloud based for HMS Abstract: While affiliations by and by put countless in PHRs, the best PHR structures, motivators, and depictions are not all around settled upon. Regardless of, no matter how you look at it premium and activity, little PHR inspect has been done to date, and concentrated on research enthusiasm for PHRs appears to be lacking. In a database the individual information gets stored using encryption techniques that are the reason it is progressively secure and other good position relies upon attribute sort of the encryption methodology that gets changed with the objective that it get logically secure and powerful. In our proposed work we are using Policy Match quality based encryption (Policy Match - ABE) it is a promising cryptographic response to the passage control issues. Indeed, the issue of applying Policy Match - ABE portrays a couple of security and assurance challenges as for the quality renunciation, essential security, and coordination of attributes issued from different specialists. The proposed instrument secures data about recuperation plan using Policy Match - ABE, where various key specialists comprehend with their dangers independently. This proposed segment how securely and adequately comprehends with the characterized data scattered in the work.

Keywords: HMS-Hospital management system, ABE- Attribute based encryption, PM-ABE- Policy based attribute encryption, PHR- patient health record, EMR- Electronic medical record.

References: 1. M. Chen, J. Yang, Y. Hao, S. Mao, and K. Hwang, ``A 5G cognitive system for healthcare,'' Big Data Cognit. Comput., vol. 1, no. 1, p. 2, 2017,doi: 10.3390/.bdcc1010002. 2. R. Zhang and L. Ling, "Security Models and Requirements for Healthcare Application Clouds", IEEE 3rd International Conference on Cloud Computing (CLOUD), 2010, pp. 268-275. 3. M. Pirretti, P. Traynor, P.McDaniel, and B. Waters,"Secure attribute-based systems" Journal of Computer Security, vol. 18, no 5,pp 799- 837,2010. 4. Microsoft HealthVault (2015) http://www.healthvault.com. Accessed May 1, 2015 Google Health (2013) https://www.google.com/health. Accessed Jan. 1, 2013 5. M. Bishop, "Computer Security Art and Science" , Pearson Education, 2003, India.V. Hu, R. Kuhn, D. Ferraiolo, "Attribute-Based Access 181-186 Control", Computer Magazine, 15 February 2015. 6. K. Liang, J. K. Liu, D. S. Wong, and W. Susilo, "An efficient cloudbased revocable identity-based proxy re-encryption scheme for public clouds data sharing," in Proc. 19th Eur. Symp. Res. Comput. Secur. Sep. 2014. 7. Matthew Green, Susan Hohenberger, and Brent Waters. Outsourcing the decryption of ABE cipher-texts. In Proceedings of USENIX Security 2011. 8. Jin, X., Krishnan, R. and Sandhu, R., 2012, July. A unified attribute-based access control model covering DAC, MAC and RBAC. In IFIP Annual Conference on Data and Applications Security and Privacy (pp. 41-55).Springer Berlin Heidelberg. 9. Adwitiya Mukhopadhyay,. QoS based telemedicine technologies for rural healthcare emergencies, Global Humanitarian Technology Conference (GHTC),December, 2017 IEEE. 10. Santhosh Kumar B J, An Advanced Hierarchical Attribute Based Encryption Access Control in Mobile Cloud Computing, International Journal of Engineering & Technology, 7 (3.10) (2018) 18-22. 11. S Manishankar, R Sandhya, S Bhagyashree, Dynamic load balancing for cloud partition in public cloud model using VISTA scheduler algorithm, Journal of Theoretical and Applied Information, 2016/5/1. 12. Zhiguo Wan, Jun?e Liu, and Robert H. Deng, "HASBE: A Hierarchical Attribute-Based Solution for Flexible and Scalable Access Control in Cloud Computing" IEEE Transactions On Information Forensics And Security, Vol. 7, No. 2, April 2012. 13. D. Dunaev and L. Lengyel. "An intermediate level obfuscation method", 2014. 14. LinkeGuo,Chi Zhang, JinyuanSun, Yuguang Fang, "A Privacy-Preserving Attribute-Based Authentication System for Mobile Health Networks" IEEE TRANSACTIONS ON MOBILE COMPUTING, VOL. 13, NO. 9, SEPTEMBER 2014. 15. M. Li, S. Yu,Y. Zheng, ,K. Ren, &W. Lou, Scalable and secure sharing of personal health records in cloud computing using attribute-based encryption, IEEE Transactions on Parallel and Distributed Systems, vol. 24(1), pp. 131-143, 2013. thereby reducing the complexity of key management. 16. Josh Benaioh, Melissa Chase, Eric Horvitz, and Kristin Lauter. Patient controlled encryption: Ensuring privacy of electronic medical records. In ACM workshop on Cloud Computing Security CCSW 09, pages 103{114. ACM, 2009.} 17. J. Marconi, "E-Health: Navigating the Internet for Health Information Healthcare", Advocacy White Paper. Healthcare Information and Management Systems Society, May, 2002, as cited in Broderick M, Smaltz DH. E-Health Defined. E-Health Special Interest Group, Healthcare Information and Management Systems Society, 2003 May 5. [updated 2003 May 5; cited 2008 Jan 21]. 18. Faysel, M. A. (2015). Evaluation of a Cyber Security System for Hospital Network. Studies in health technology and informatics, 216,915. Authors: G. Valarmathi, K. Gayathri, B. Sai Kruti, A. Tabitha Jerusha Paper Title: A Modern Technique Enabling Social Interactions for Paralyzed People with Talk Back System Abstract: It is known that the technological advancements are increasing at a faster pace. But the utilization of these technologies in various sectors is very low. Communications between paralyzed and a standard person have invariably been a difficult task. The project aims to facilitate individuals by means of a human activities based communication interpreter system. For every specific gesture produces a proportional amendment in resistance and measures the orientation of hand. This will be useful for person who had leg paralyzed. Eyeball and head movements are also monitored. The process of those human movements finished in controller. This section is useful for fully paralyzed persons.

36. Keywords: Paralysis, Sign Language, Eyeball Movement. 187-190 References: 1. AsfandAteem, Zeeshan Ali Akbar,Mairaj Ali, Muhammad Asad Bashir, -"Eye Monitored Device for disable People" 2. Thanmay Rajpitak Ratnesh Kumar Eric Scwarts "Eye Detection Using Morphological and Color Image Processing" Florida Conference on Recent Advances in Robotics FCRAR 2009. 3. S.Tameemsultana and N. Kali Saranya, "Implementation of Head and Finger Movement Based Automatic Wheel Chair", Bonfring International Journal of Power Systems and Integrated Circuits, vol. 1, Special Issue, pp 48-51, December 2011. 4. Anbarasi Rajamohan, Hemavathy R., Dhanalakshmi M."Deaf-Mute Communication Interpreter", International Journal of Scientific Engineering and Technology Volume 2 Issue 5, pp: 336-341 (ISSN: 2277-1581) 5. Kuldeep Singh Rajput, Shashank Deshpande, Uma Mudenagudi, "INTERACTIVE ACCELEROMETRIC GLOVE FOR HEARING IMPAIRED". 6. Nikolaos Bourbakis, Anna Esposito, D. Kabraki, "Multimodal Interfaces for Interaction-Communication between Hearing and Visually Impaired Individuals: Problems & Issues", 19th IEEE International Conference on Tools with Artificial Intelligence. 7. Netchanok Tanyawiwat and Surapa Thiemjarus, Design of an Assistive Communication Glove using Combined Sensory Channels, 2012, Ninth International Conference on Wearable and Implantable Body Sensor Networks. 8. N.Bourbakis, An SPNG based method for image to NL text conversion, PR Journal. 9. G. Grimes, Digital Data Entry Glove Interface Device, AT & T Bell Labs, 1983. 10. D. Sturman and D. ZeIter, -A survey of glove-basedinput, II IEEE Computer Graphics and Applications, vol. 14, no. l, pp. 30-39, 1994. 11. M. Mohandes and S. Buraiky, -Automation of the Arabicsign language recognition using the powerglove, II AIMLJournal, vol. 7, no. 1, pp. 41- 46,2003. Authors: R. Lakshmi Devi, S. Vaishali, S. Vishalini Paper Title: Real Time Driver Somnolence Alert System Using Web Application Abstract: The number of accidents that has occurred in India due to driver fatigue has been alarmingly high due to continuous driving, throughout day and night. According to the statistical data of 2017, approximately 80,000 deaths are occurring each year and 1.47 lakhs of passengers with an accuracy of about 80% in a non- real time implementation and intrusive method based detection were found to be drowsy drivers. The aim of our project is to detect the driver’s drowsiness with the help of Computer Vision based technology and to alert the driver through a stimulator and a voice playback system. This project describes an efficient method for drowsiness detection by three well defined phases. These three phases are facial features detection, the eye tracking and yawning detection. Once the face is detected, the system is made illumination invariant by segmenting the skin part alone and considering only the chromatic components to reject most of the non face image backgrounds based on skin colour. The tracking of eyes and yawning detection are done by correlation coefficient template matching and it is processed in the MATLAB using Support Vector Machine Algorithm and the Object Detection library to segregate face and non face regions and disintegration the left eye, right eye and mouth images from the face region to analyze the three different facial parameters separately. If the reference template matches with the current frame in real time then the speed of the engine is reduced automatically and the driver is alerted using the playback system and the vibrator under his seat. In addition to this, the driver’s sleep status will be updated on the travel agent’s personal login. The entire process such as detecting, processing, alerting takes place at the faster rate and the driver is alerted within a second. This method gives an accuracy of about 99% so that the driver fatigue is to be detected accurately. It can be translated into a mobile app to provide additional information to the passengers such as vehicle information, live vehicle tracking, driver details and the time of arrival to reach the destination.

Keywords: Computer vision, Support vector machine algorithm, Vibrator, Automatic speed control, Playback system, 37. Linear regression, Ada Boosting algorithm, Facial Adaptive coding. 191-195 References: 1. Association for Safe International Road Travel. (Jun. 2017). Annual Global Road Crash Statistics. [Online]. Available: http://asirt.org/initiatives/informing-road-users/road-safety-facts/roadcrash- statistics 2. Center of Disease Control and Prevention. (Nov. 2015). Drowsy Driving: Asleep at the Wheel. [Online]. Available: http://www.cdc.gov/features/dsdrowsydriving/ 3. M. Murphy. (Jun. 2015). "Google's self-driving cars are now on the street of California." Quart. Accessed: Jun. 2017. [Online]. Available: https://qz.com/437788/googles-self-driving-cars-are-now-onthe-streets-of-california/ 4. W. Pa. NHTSA Adopts SAE International Standard Defining Autonomous Vehicles. Accessed: Nov. 16, 2018. [Online]. Available: https://www.prweb.com/releases/2016/10/prweb13732945.htm 5. T. C. Frankel. (Feb. 2016). What it feels like to drive a tesla on autopilot. The Washington Post. [Online]. Available: https://www.washingtonpost.com/news/the-switch/wp/2016/02/01/whatit- feels-like-to-drive-a-tesla-on-autopilot/ 6. D. Muoio. (2017). These 19 companies are racing to build selfdriving cars in the next 5 years. Business Insider. [Online]. Available: http://www.businessinsider.com/companies-making-driverless-carsby-2020-2017-1/tesla-recently-made-a-big-move-to-meet-its-goal-ofhaving- a-fully-self-driving-car-ready-by-2018-1 7. C. Woodyard. (Oct. 2015). Study: Self-driving cars have higher accident rate. USA Today. [Online]. Available: http://www.usatoday. com/story/money/cars/2015/10/31/study-self-driving-carsaccidents/74946614/ 8. P. LeBeau. (Oct. 2015). Crash data for self-driving cars may not tell whole story. CNBC. [Online]. Available: http://www. cnbc.com/2015/10/29/crash-data-for-self-driving-cars-may-not-tellwhole- story.html 9. E. Zolfaghrifard. (Oct. 2015). When Tesla's autopilot goes wrong: Owners post terrifying footage showing what happens when brand new autonomous driving software fails. Dailymail.com. [Online]. Available: http://www.dailymail.co.uk/sciencetech/article-3281562/Tesla- autopilotfail-videos-emerge-Terrifying-footage-shows-happens-autonomousdriving- goes-wrong.html 10. D. Tran, E. Tadesse, W. Sheng, Y. Sun, M. Liu, and S. Zhang, "A driver assistance framework based on driver drowsiness detection," in Proc. IEEE Int. Conf. Cyber Technol. Automat., Control, Intell. Syst. (CYBER), Jun. 2016, pp. 173-178. 11. D. Osipychev, D. Tran, W. Sheng, and G. Chowdhary, "Human intentionbased collision avoidance for autonomous cars," in Proc. Amer. Control Conf. (ACC), May 2017, pp. 2974-2979. 12. D. Tran et al., "A collaborative control framework for driver assistance systems," in Proc. IEEE Int. Conf. Robot. Automat. (ICRA), May/Jun. 2017, pp. 6038-6043. 13. Autonomous Car. Accessed: Nov. 16, 2018. [Online]. Available: http://en.wikipedia.org/wiki/Autonomous_car. Authors: S. Kalaiselvi, S. Tamilselvan Paper Title: Cognitive Wireless Sensor Network Merits, Applications, Practical Difficulties and Research Trends References: 1. G. P. Joshi, S. Y. Nam, and S. W. Kim, "Cognitive radio wireless sensor networks: Applications, challenges and research trends," Sensors, vol. 13, no. 9, pp. 11196-11228, 2013. 2. M. Shafiee and V. T. Vakili, "An approach to efficient spectrum sensing in cognitive wireless sensor networks (C-WSNs)," Appl. Mech. Mater., vols. 256-259, pp. 2303-2306, Dec. 2013. 38. 3. S. Naik and N. Shekokar, "Conservation of energy in wireless sensor network by preventing denial of sleep attack," Procedia Comput. Sci., vol. 45, pp. 370-379, Jan. 2015. 4. Y. Jiao and I. Joe, "Markov model-based energy efficiency spectrum sensing in cognitive radio sensor networks," J. Comput. Netw. Commun., 196-201 vol. 2016, 2016, Art. no. 7695278. 5. J. Mitola and G. Q. Maguire, Jr., "Cognitive radio: Making software radios more personal," IEEE Pers. Commun., vol. 6, no. 4, pp. 13-18, Apr. 1999. 6. P. Prakash, S.-R. Lee, S.-K. Noh, and D.-Y. Choi, "Issues in realization of cognitive radio sensor networks," Int. J. Control Autom., vol. 7, no. 1, pp. 141-152, 2014. 7. http://www.nytimes.com/2012/05/03/world/asia/south-korea-accused-north-accused-of-jamming-signals.html?_r=0 (accessed on 26 June 2013). 8. Wang, J.; Ghosh, M.; Challapali, K. Emerging cognitive radio applications: A survey. IEEE Commun. Mag. 2011, 49, 74-81. 9. Rehmani, M.H.; Lohier, S.; Rachedi, A. Channel Bonding in Cognitive Radio Wireless Sensor Networks. In Proceedings of the International Conference on Selected Topics in Mobile and Wireless Networking (iCOST), Avignon, France, 2-4 July 2012; pp. 72-76. 10. Ghandour, A.J.; Fawaz, K.; Artail, H. Data Delivery Guarantees in Congested Vehicular Ad Hoc Networks Using Cognitive Networks. In Proceedings of the IEEE IWCMC 2011, Istanbul, Turkey, 4-8 July 2011; pp. 871-876. 11. Di Felice, M.; Doost-Mohammady, R.; Chowdhury, K.R.; Bononi, L. Smart radios for smart vehicles:Cognitive vehicular networks. IEEE Veh. Technol. Mag. 2012,7,26-33. 12. Hoblos, G.; Staroswiecki, M.; Aitouche, A. Optimal Design of Fault Tolerant Sensor Networks. In Proceedings of the IEEE International Conference on Control Applications, Anchorage, AK, USA, 25-27 September 2000; pp. 467-472. 13. Sim, Z.W. Radio Frequency Energy Harvesting for Embedded Sensor Networks in the Natural Environment; Ph.D. thesis, University of Manchester: Manchester, UK, 17 April 2012. 14. Xu, J., Jin, N., Lou, X., Peng, T., Zhou, Q. and Chen, Y., 2012, May. Improvement of LEACH protocol for WSN. In 2012 9th International Conference on Fuzzy Systems and Knowledge Discovery (pp. 2174-2177). IEEE. 15. Manjeshwar, A. and Agrawal, D.P., 2001, April. TEEN: a routing protocol for enhanced efficiency in wireless sensor networks. In null (p. 30189a). IEEE. Authors: K. Sreelakshmi, Santosh Anand, Somnath Sinha Paper Title: Black Hole Attack in Mobile Ad Hoc Network – Analysis and Detection Abstract: Mobile Ad Hoc Network (MANET) is susceptible to different type of attacks due to its mobility and other limitation. Black hole attacks are one type of severe active type of attack which occurs in network layer and directly affect the network parameter. This paper analyzes the severity of this attack by implementing the attack using NS2 simulator. Also the available prevention mechanisms and their effectiveness are discussed. This paper also suggests a new approach for detection and prevention of MANET against black hole attack.

Keywords: MANET, AODV Routing Protocol, Ad hoc network, Black hole.

References: 39. 1. Acharya A. A., K. M. Arpitha K.M., Santhosh Kumar B. J., " An intrusion detection system against UDP flood attack and ping of death attack (DDOS) in MANET", International Journal of Engineering and Technology (IJET), 2016, 8(2). 2. S. Anand, Akarsha, R. R., "A Protocol for the Effective Utilization of Energy in Wireless Sensor Network", International Journal of Engineering 202-205 & Technology, 2018, 7(3.3), 93-98. 3. M. Y. Su, "Prevention of selective black hole attacks on mobile ad hoc networks through intrusion detection systems", Computer Communications, 2011, 34(1), 107-117. 4. P. N. Raj, P. B. Swadas, "Dpraodv: A dyanamic learning system against blackhole attack in aodv based manet", 2009, arXiv preprint, arXiv:0909.2371. 5. V. Khandelwal, D. Goyal, "Blackhole attack and detection method for AODV routing protocol in 6. MANETs", International Journal of Advanced Research in Computer Engineering & Technology, 2013, 2(4). 7. F. H. Tseng, L.D. Chou, H. C. Chao, "A survey of black hole attacks in wireless mobile ad hoc networks", Humancentric Computing and Information Sciences,2011, 1(1), 4. 8. B. Sun, Y. Guan, J. Chen, U.W. Pooch, "Detecting black-hole attack in mobile ad hoc networks", presented at 5th European Personal Mobile Communications Conference, Glasgow, United Kingdom, 22-25 April 2003. 9. N.H. Mistry, D.C. Jinwala, M.A. Zaveri, "MOSAODV: solution to secure AODV against blackhole attack", IJCNS) International Journal of Computer and Network Security, 2009, 1(3), 42-45. Authors: B.R. Pushpa, P.S. Amal, Nayana.P.Kamal Paper Title: Detection and Stagewise Classification of Alzheimer Disease Using Deep Learning Methods Abstract: Alzheimer disease is a neuro degenerative disease that affects memory, thinking and cognitive behavior. It is one of the leading disease all over the world. AD leads to death of neurons in various brain regions like hippocampus, enlarged ventricles, entorhinal cortex, temporal and parietal lobes. Currently there is no medicine that can cure the disease but it can slower or stops neural damage. The diagnosis of AD involves heterogeneous clinical assessment such as patient medical history, neuropsychological test, family history, blood test etc. are conducted. Diagnosis of AD is important and challenging, with the early prediction of AD the treatment can be efficiently introduced in the early stages. The proposed work begins noise removal of MRI brain images which includes denoising using Median filtering and DnCNN.Further brain tissue are segmented based on voxel based that is white matter and grey matter and cerebrospinal fluid and region based segmentation and finally a deep convolutional neural network for classifying the different phases of AD

Keywords: Alzheimer’s Disease (AD), Deep Leaning, MRI Images.

References: 40. 1. Chen, T., Ma, K. K., & Chen, L. H. (1999). Tri-state median filter for image denoising. IEEE Transactions on Image processing, 8(12), 1834- 1838. 206-212 2. Tian, C., Xu, Y., Fei, L., & Yan, K. (2018). Deep Learning for Image Denoising: A Survey. arXiv preprint arXiv:1810.05052. 3. Hirata, Y., Matsuda, H., Nemoto, K., Ohnishi, T., Hirao, K., Yamashita, F., ... & Samejima, H. (2005). Voxel-based morphometry to discriminate early Alzheimer's disease from controls. Neuroscience letters, 382(3), 269-274. 4. Tsai, C., Manjunath, B. S., & Jagadeesan, B. (1995). Automated segmentation of brain MR images. Pattern recognition, 28(12), 1825-1837. 5. Resmi, A., Thomas, T., & Thomas, B. (2013). A novel automatic method for extraction of glioma tumour, white matter and grey matter from brain magnetic resonance images. Biomed Imaging Interv J 2013; 9 (2) 6. Sun, T., & Neuvo, Y. (1994). Detail-preserving median based filters in image processing. Pattern Recognition Letters, 15(4), 341-347. 7. Sujji, G. E., Lakshmi, Y. V. S., & Jiji, G. W. (2013). MRI brain image segmentation based on thresholding. International Journal of Advanced Computer Research, 3(1), 97. 8. Thompson, P. M., Hayashi, K. M., de Zubicaray, G. I., Janke, A. L., Rose, S. E., Semple, J., ... & Toga, A. W. (2004). Mapping hippocampal and ventricular change in Alzheimer disease. Neuroimage, 22(4), 1754-1766. 9. Lee, H. Y., Codella, N. C., Cham, M. D., Weinsaft, J. W., & Wang, Y. (2010). Automatic left ventricle segmentation using iterative thresholding and an active contour model with adaptation on short-axis cardiac MRI. IEEE Transactions on Biomedical Engineering, 57(4), 905-913. 10. Mahmood, R., & Ghimire, B. (2013, July). Automatic detection and classification of Alzheimer's Disease from MRI scans using principal component analysis and artificial neural networks. In Systems, Signals and Image Processing (IWSSIP), 2013 20th International Conference on (pp. 133-137). IEEE. 11. Savio, A., García-Sebastián, M., Hernández, C., Graña, M., & Villanúa, J. (2009, September). Classification results of artificial neural networks for alzheimer's disease detection. In International Conference on Intelligent Data Engineering and Automated Learning (pp. 641-648). Springer, Berlin, Heidelberg. 12. Islam, J., & Zhang, Y. (2018). Early Diagnosis of Alzheimer's Disease: A Neuroimaging Study with Deep Learning Architectures. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (pp. 1881-1883). 13. Ding, Y., Zhang, C., Lan, T., Qin, Z., Zhang, X., & Wang, W. (2015, November). Classification of Alzheimer's disease based on the combination of morphometric feature and texture feature. In Bioinformatics and Biomedicine (BIBM), 2015 IEEE International Conference on (pp. 409-412). IEEE. 14. Gunawardena, K. A. N. N. P., Rajapakse, R. N., & Kodikara, N. D. (2017, November). Applying convolutional neural networks for pre-detection of alzheimer's disease from structural MRI data. In Mechatronics and Machine Vision in Practice (M2VIP), 2017 24th International Conference on (pp. 1-7). IEEE. 15. Bäckström, K., Nazari, M., Gu, I. Y. H., & Jakola, A. S. (2018, April). An efficient 3D deep convolutional network for Alzheimer's disease diagnosis using MR images. In Biomedical Imaging (ISBI 2018), 2018 IEEE 15th International Symposium on (pp. 149-153). IEEE. 16. Mahanand, B. S., Suresh, S., Sundararajan, N., & Kumar, M. A. (2011, July). Alzheimer's disease detection using a Self-adaptive Resource Allocation Network classifier. In Neural Networks (IJCNN), The 2011 International Joint Conference on (pp. 1930-1934). IEEE. 17. Menon, H. P., Hiralal, R., Kumar, A. A., & Devasia, D. (2018). Segmentation of MRI Brain Images and Creation of Structural and Functional Brain Atlas. In Computational Vision and Bio Inspired Computing (pp. 366-383). Springer, Cham. 18. Bhavana, V., & Krishnappa, H. K. (2016, March). A survey on multi-Modality medical image fusion. In 2016 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET) (pp. 1326-1329). IEEE. Authors: M. Smruthi, N. Harini Paper Title: A Hybrid Scheme for Detecting Fake Accounts in Facebook Abstract: A social networking service serves as a platform to build social networks or social relations among people who, share interests, activities, backgrounds, or real life connections. A social network service is generally offered to participants who registers to this site with their unique representation (often a profile) and one’s social links. Most social network services are web-based and provide means for users to interact over the Internet. Nevertheless these sites are also constantly preyed by hackers raising various problems related to threats and attacks such as disclosure of information, identity thefts etc. One of the most common ways of performing a large-scale data harvesting attack is the use of fake profiles, where malicious users present themselves in profiles impersonating fictitious or real persons. An attempt has been made in this work to use a hybrid model based on machine learning and skin detection algorithms to detect the existence of fake accounts. The experimentation process clearly brought out the strength of the proposed scheme in terms of detecting fake accounts with high accuracy.

Keywords: Social Media, Facebook, Privacy, Social Network Analysis, Fake Profiles, Machine Learning, Skin Detection.

References: 1. Wani, Suheel Yousuf, Ahmad Wani, Mudasir and Ahmad Sofi, Muzafar. Why Fake Profiles: A study of Anomalous users in different categories of Online Social Networks. International Journal of Engineering, Technology, Science and Research Vol-4 (September 2017). 41. 2. Bhardwaj, Akashdeep, Goundar, Sam and Avasthi, Vinay. Impact of Social Networking on Indian Youth-A Survey. International Journal of Electronics and Telecommunications Vol-7(September 2017). 213-217 3. Persia and D. D'Auria, "A Survey of Online Social Networks: Challenges and Opportunities," 2017 IEEE International Conference on Information Reuse and Integration (IRI), San Diego, CA, 2017, pp. 614-620. 4. H. Gao, J. Hu, T. Huang, J. Wang and Y. Chen, "Security Issues in Online Social Networks," in IEEE Internet Computing, vol. 15, no. 4, pp. 56- 63, July-Aug. 2011. 5. A. Gupta and R. Kaushal, "Towards detecting fake user accounts in facebook," 2017 ISEA Asia Security and Privacy (ISEASP), Surat, 2017, pp. 1-6. 6. MR, Neethu.; HARINI, N. “ Safe sonet: a framework for building trustworthy relationships”. International Journal of Engineering & Technology, [S.l.], v. 7, n. 2.26, p. 57-62, may 2018. ISSN 2227-524X. 7. E. Van Der Walt and J. Eloff, "Using Machine Learning to Detect Fake Identities: Bots vs Humans," in IEEE Access, vol. 6, pp. 6540-6549, 2018. 8. Neethu M.R. and Harini N. “Securing Image Posts in Social Networking Sites”. In: Hemanth D., Smys S. (eds) Computational Vision and Bio Inspired Computing. Lecture Notes in Computational Vision and Biomechanics, vol 28. Springer, Cham (2018) 9. M. Priyadharshini, V and Valarmathi, A. “Breast and nipple line localization for adult image identification in online social networks”. 10.1109/ICETETS.2016.7603006 (February 2016). 10. Y. Lei, W. Xiaoyu, L. Hui, Z. Dewei and Z. Jun, "An algorithm of skin detection based on texture," 2011 4th International Congress on Image and Signal Processing, Shanghai, 2011, pp. 1822-1825. 11. Tan, Wei Ren, Chee Seng Chan, PratheepanYogarajah, and Joan Condell.: "A fusion approach for efficient human skin detection", Industrial Informatics, IEEE Transactions on 8, no. 1,138-147(2012) 12. Patil, Prajakta M., and Y. M. Patil, "Robust Skin Colour Detection and Tracking Algorithm", International Journal of Engineering Research and Technology Vol. 1. No.8 (October-2012), ISSN: 2278- 0181 (2012). 13. X. Zhou, W. Gong, W. Fu and F. Du, "Application of deep learning in object detection," 2017 IEEE/ACIS 16th International Conference on Computer and Information Science (ICIS), Wuhan, 2017, pp. 631-634. Authors: L. Prabhavathi, Dr.S. Baskar Paper Title: Design and Implementation of Novel controllers in Digital Circuits Using GDI Technology Abstract: A New model of an approach presents GDI full adder based on the reflected binary gray code design of multiplexer. The Weighted Code Approach has been Existed to generate Gray Code that Gray code is basically reflected binary code in which two successive values differ in only one bit. In general there are different methods of converting as a decimal number to Gray code is performed by converting decimal to binary and then binary to Gray code.. In this proposed Design and Implementation of Novel controllers in digital circuits using GDI technology is consist of three novel circuits one is gray code conversion of “multiplexer circuit” and other one is” logic circuit ” and 42. last one is” reflected binary code circuit” and all novel circuits produce gray code output. Low complexity of GDI technology to be suitable design for power-efficient as well as for power-delay products and also used to reduce power 218-221 consumption in area of digital circuits .All simulations are done by Tanner using TSMC BSIM 0.25um technology. This approach is proposed for fast design, low power circuits, improve power characteristics.

Keywords: GDI(gate diffusion input),low power, shift right operation, delay, digital circuits,, XOR-gate, AND- gate , OR -gate, gray code conversion 2x1 MUX.

References: 1. ISSN (print): 2278-8948, Volume-1 Issue-3,2012 "GDI Technique: A Power Method for Digital Circuits "by Kuna l & Nidhi Kedia. 2. A.R, Saberkari, SH. Shokouhi, A Novel Low-power-Voltage Cmos 1-Bit Full Adder Cell with the GDI Technology”, proceeding of the 2006 IJME-INTERTECH conference. 3. K. Navi, O. Kavehei, M. Ruholamini, A Sahafi, Sh. Mehrabi and N. Dadkahahi ,”Low power and High Performance 1-Bit Cmos Full Adder Cell’ 4. F, GRAY. March 17, 1953,”Weighted Code Approach for generation of gray Code”. 2,632,058. 5. ISSN:0976-1353 Volume 25 Issue 5-APRIL 2018 “A Survey On Different Architecture For XOR Gate”, by, Rajarajeshwari, V . Vaishali and C. Saravan kumar. 6. ISSN: 2278-5841, Vol 1, Issue 6, November 2012.IJRCCT,” Energy Efficient Full-Adder using GDI Technology” by Balakrishna. Batta 7. ISSN(PRINT):2394-3408,2394-3416,Volme-3.ISSUE-52016”Design of Area Efficient Binary to gray code Converter” by Sowmya Bhat, Avinash, Rajashree Nambiar Kushuma Prabhu Authors: Anup S Kumar, N.R. Sathish Kumar, Arun Das, Shubam, S.P. Mani Raj Paper Title: Monitoring Cyber Attacks and Analysis of Breaches Abstract: Analyzing cyber incident data sets is an important method for deepening our understanding of the evolution of the threat situation. In present generation we come to know about many cyber breaches and hacking taking place. In this paper work, we research about the various cyber-attacks and breaches and study the way these attacks are done and find an alternative for the same. We show that rather than by distributing these attacks as because they exhibit autocorrelations, we should model by stochastic process both the hacking breach incident inter-arrival times and breach sizes. We draw a set of cyber securities insights, including that the threat of cyber hacks is indeed getting worse in terms of their frequency.

Index Terms: Hacking, cyber-attacks, cyber threats, breach prediction, times series, cybersecurity data analytics.

43. References: 1. P. R. Clearinghouse. Privacy Rights Clearinghouse’s Chronology of Data Breaches. Accessed: Nov. 2017. [Online]. 222-225 Available:https://www.privacyrights.org/data-breaches 2. ITR Center. Data Breaches Increase 40 Percent in 2016, Finds New Report From Identity Theft Resource Center and CyberScout. Accessed: Nov. 2017. [Online]. Available: http://www.idtheftcenter.org/ 2016databreaches.html 3. Leigh and L. Harding, Wikileaks: Inside Julian Assange‟s war on secrecy. US: Guardian Books, 2011. 4. Ming-Chien Yang and Ming-Hour Yang April(2012),”RIHT: A Novel Hybrid IP Scheme,” IEEE Transactions on Information Forensics and Security vol. 7 5. C. Gong and K. Sarac, “Toward a practical packet marking approach for IP traceback,” Int. J. Network Security, Mar. 2009. 6. Baumgärtner L., Strack C., Hoßbach B., Seidemann M., Seeger B., and Freisleben B. (2015)., “Complex event processing for reactive security monitoring in virtualized computer systems”, In Proceedings of the 9th ACM International Conference on Distributed Event-Based Systems (DEBS '15). ACM, New York, NY, USA, 22-33. 7. Seymour E. Goodman and Herbert S. Lin, editors. Toward a Saferand More Secure Cyberspace. National Academies Press, 2007 8. D. Howard and K. Prince, Security 2020: Reduce security risks this decade. US: Wiley, 2010. 9. Dmitrieva, “Stealing information: Application of a criminal anti theft statute to leaks of confidential government information,” 55 Fla. L. Rev. 1043. Authors: M. Jeyakarthic, S. Selvarani Paper Title: An Efficient Approach Using FM-Weight for Revenue Prediction on Rare Itemsets Abstract: A pattern is a social event of exercises that happen together in a database. Past examinations in the field are normally committed to the issue of regular pattern investigation where just precedents that appear as frequently as conceivable in the data are mined. In this manner, structures including exercises that appear in couple of instructive accumulations are not retrieved. We propose a structure to address different orders of intriguing precedents and after that instantiate it to the specific occasion of extraordinary models. Our focus is strived towards rare itemsets mining. The goal of this work is to show that applying additional measures support, confidence and lift framework to disclosure of rare association rules. Moreover, we proposed new algorithm for finding rare itemsets with high revenues from FM- weighted transactions with client analysis. The experimental results outperformed those found utilizing the traditional approach in the prediction of revenue from clients in next-period transactions.

Keywords: Association rule mining, Rare Association Rule mining, RFM Analysis, Weighted Association Rule.

References: 1. Agrawal, R., Imieliński, T., Swami, A. 1993. Mining association rules between sets of items in large databases. In: Proceedings of ACM SIGMOD. Washington, DC, USA. pp. 207–216. 44. 2. Han, J.W., Kamber, M., 2006. Data Mining: Concepts and Techniques. Morgan Kaufmann, San Francisco. 3. R.C. Blattberg B.D. Kim S.A. Neslin. 2008. Database Marketing: Analyzing and Managing Clients (Chapter 12, Series eds. J. Eliashberg). 226-232 Springer. New York, USA. 4. Shim, B., Choi, K., Suh, Y., 2012. CRM strategies for a small-sized online shopping mall based on association rules and sequential patterns. Expert Syst. Appl. 39 (9), 7736–7742. 5. Dursun, A., Caber, M., 2016. Using data mining techniques for profiling profitable hotel clients: an application of RFM analysis. Tour. Manag. Perspect. 18, 153–160. 6. Szathmary L, Valtchev P, Napoli A(2010) Generating rare association rules using the minimal rare itemsets family. Int J Softw Inf 4(3):219-238. 7. Koh YS, Rountree N(2005) Finding Sporadic rules using apriori inverse. Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics), 3518:97-106 8. J.M.Luna, J.R.Romero, S. Ventura 2014, On the adaptablility of G3PARM to the extraction of rare association rules. Knowl. Inf Syst 38: 391- 418. 9. Lu, S., Hu, H., Li, F., 2001. Mining weighted association rules. Intell. Data Anal. 5 (3), 211–225. 10. Wang, W.,Yang, J.,Yu, P., 2004. WAR:weighted association rules for item in-tensities. Knowl. Inf. Syst.6(2),203–229. 11. Yun, U., Leggett, J.J., 2005. WFIM: weighted frequent itemset mining with a weight range and a minimum weight. In: Proceeding of the 2005 SIAM International Conference on Data Mining (SDM’05). Newport Beach, CA. pp. 636–640. 12. Vo, B., Coenen, F., Le, B., 2013. A new method for mining frequent weighted itemsets based on WIT-trees. Expert Syst. Appl. 40 (4), 1256– 1264. 13. Kalakota, R., Robinson, M., 1999. e-Business Roadmap For Success. Addison Wesley Longman Inc, New York, USA. 14. Peppard, J., 2000. Customer relationship management (CRM) in financial services. Eur. Manag. J. 18 (3), 312–327. 15. Hughes, A.M., 2006. Strategic Database Marketing. McGraw-Hill. 16. Linoff, G.S., Berry, M.J.A., 2002. Mining the Web: Transforming Client Data into Client Value. John Wiley and Sons, New York, NY. 17. Liu, D.R., Shih, Y.Y., 2005. Integrating AHP and data mining for product re-commendation based on client lifetime value. Inf. Manag. 42 (3), 387–400. 18. Li, L.H., Lee, F.M., Liu, W.J., 2006. The timely product recommendation based on RFM method. In: Proceedings of International Conference on Business and In-formation. Singapore. 19. Kim, H.K., Im, K.H., Park, S.C., 2010. DSS for computer security incident response applying CBR and collaborative response. Expert Syst. Appl. 37 (1), 852–870. 20. Chan, C.C.H., 2008. Intelligent value-based client segmentation method for campaign management: a case study of automobile retailer. Expert Syst. Appl. 34 (4), 2754–2762. 21. Hsieh, N., 2004. An integrated data mining and behavioral scoring model for analyzing bank clients. Expert Syst. Appl. 27 (4), 623–633. 22. Lin, C.S., Tang, Y.Q., 2006. Application of incremental mining and client's value analysis to collaborative music recommendations. J. Inf. Technol. Soc. 6 (1), 1–26. 23. Chiang, W.Y., 2011. To mine association rules of client values via a data mining procedure with improved model: an empirical case study. Expert Syst. Appl. 38 (3), 1716–1722. 24. Hu, Y.H., Yeh, T.W., 2014. Discovering valuable frequent patterns based on RFM analysis without client identification information. Knowl. Based Syst. 61, 76–88. 25. Cheng-Hsiung Weng 2017 Revenue prediction by mining frequent itemsets with customer analysis, Engineering applications of artificial intelligence 63 (2017) 85-97. Authors: R. Aylmer Britto, Shreya Sinha, Swastika Palit, Arockia Selvakumar Paper Title: pH Monitoring IOT Controlled Biomimetic Robotic Fish Abstract: Autonomous Underwater Vehicles (AUVs) find a wide range of applications in marine geosciences and are increasingly being used for search and rescue, health monitoring, data collection and naval surveillance. In this research, design, fabrication and IOT control of a pH level measuring robotic fish (Fanny) is proposed. The developed robotic fish has three parts body, abdomen and tail, apart from that two hinges are provided for the independent motions of the abdomen and tail. The developed robotic fish uses a gear train for motion transmission. The modelling of Fanny is done using 3D printing using ABS material. An IOT control system is developed with our own platform using HTTP protocol. To monitor the pH level of water a pH sensor SEN0161 is used. The Chemical nature of the water was observed and was constantly pushed to the cloud on a regular time interval. Based on IOT, the response time varies according to the behaviour of the water body and the degree of turn of robotic fish is found to be dependent on the efficiency of the design.

References: 1. Techet A H, Triantafyllou M S. Boundary layer relaminariza-tion in swimming fish. Proceedings of the Ninth International Offshore and Polar Engineering Conference, Brest, France, 1999 2. Lighthill, M. (1960). Note on the swimming of slender fish J. Fluid Mech., 305-317. 45. 3. Hu, H. (2006). Biologically inspired design of autonomous robotic fish at Essex. Proceedings of the IEEE SMC UK-RI, 1-8 4. Yu, J., Wang, S., and Tan, M. (2005). A simplified propulsive model of bio-mimetic robot fish and its realization. Robotica, 23(1), 101-107 5. Chen, Z., Shatara, S., and Tan, X. (2010). Modeling of biomimetic robotic fish propelled by an ionic polymermetal composite caudal fin. 233-237 Mechatronics, IEEE/ASME Transac-tions on, 15(3), 448-459. 6. Kelly, S., and Murray, R. (2000). Modeling efficient pisciform swimming for control. International Journal of Robust and Nonlinear Control, 10(4), 217-241. 7. Hong, C., and Chang-an, Z. (2005). Modeling the dynamics of biomimetic underwater robot fish. Robotics and Biomimetics (ROBIO). 2005 IEEE International Conference on, 478-483. 8. Shang, L., Wang, S., Tan, M., and Dong, X. (2009). Motion control for an underwater robotic fish with two undulating long-fins. Decision and Control, 2009., 6478- 6483. 9. Sardorbek Muminov, Nam-Yeol Yun, Seung-Won Shin, Soo-Hyun Park, Jun-Ho Jeon, Chang-Gi Hong, Sung-Joon Park, Chang-Hwa Kim, Gi- Hun Yang, Young-Sun Ryuh . (2012).Biomimetic Fish Robot Controlling System by using Underwater Acoustic Signal. 2012 9th Annual IEEE Commu-nications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks (SECON) 10. En-Cheng Liou, Chien-Chi Kao, Ching-Hao Chang, Yi-Shan Lin, Chun-Ju Huang. (2018). Internet of Underwater Things: Challenges and Routing Protocols. Proceedings of IEEE In-ternational Conference on Applied System Innovation 2018 IEEE ICASI 2018- Meen, Prior and Lam (Eds) 11. Redouane Es-sadaoui, Lahoucine Azergui, Youssef Ghanam, Jamal Khallaayoune.(2017). Design and experimentation of a low-power IoT embedded system for wireless underwater sensing. 12. Asma Fatani, Ahmed Bensenouci, SIEEE, Tayeb Brahimi, Hedaih Alshami, Mohamed-Amine Bensenouci. (2018.)Dual pH Level Monitoring and Control using IoT Application. 13. Thongchai Photsathian, Thitiporn Suttikul, Worapong Tangsrirat (2018). Design and Improvement of Wireless Crayfish Breeding System by Controlling Water Temperature and Monitoring pH via Cloud System Services. Authors: Suresh B. Rathod, V. Krishna Reddy Paper Title: Decision Making Framework for Decentral-ized Virtual Machine Placement in Cloud Computing Abstract: In decentralized cloud architecture, the host’s configured with an autonomous local resource manager (ALRM) which takes decisions for Virtual Machine (VM) migration if it is over utilized. The ALRM takes decision for migrating its one of the VM to other peer host, by considering the peer host’s utilizations received after fixed interval. This autonomous decision making results in same host identification by multiple hosts. The VM placement might results in the identified server to get over utilized and it might initiate the process of VM migration. During migration the VM and its content migrated to the identified host in plaintext form. This involves the user credentials and VM’s kernel state information. Hence fault tolerance aware secure VM migration for decentralized cloud computing is 46. introduced which avoids the over utilization of the identified server by considering its future CPU utilization, avoids the same host identification by hybrid decentralized decision making and it also ensures the VM data remain protected 238-244 during migration. If failure in the decision making model the fault tolerance mechanism is introduced that helps to maintain system up for longer time. Experimental results revels that the proposed solution helps in providing security to VM’s data during VM migration and avoids same destination host selection during VM placement.

Keywords: Cloud computing (CC), Virtual Machine (VM), Controlling Host (CH), Host controller (HC).

References: 1. “National Institute of Standards and Technology | NIST.” [Online]. Available: https://www.nist.gov/. [Accessed: 29-Dec-2017]. 2. E. Feller, C. Morin, and A. Esnault, “A case for fully decentralized dynamic VM consolidation in clouds”, Proceedings of the conference Cloud Com 2012 - Proc. 2012 4th IEEE Int. Conf. Cloud Comput. Technol. Sci.,pp. 26–33, https://doi.org/10.1109/CloudCom.2012.6427585. 3. Cloud computing dened Characteristics service levels- Cloud computing news[Online].Available https://www.ibm.com/blogs/cloud- computing/2014/01/cloud-computing-defined-characteristics-service-levels/ (25 April 2018). 4. W.T. Wen, C.D. Wang, D.S. Wu, and Y.Y. Xie, “An ACO-based Scheduling Strategy on Load Balancing in Cloud Computing Environment”, Proceedings of the conference Ninth Int. Conf. Front. Comput. Sci. Technol., pp. 364–369, https://doi.org/10.1109/FCST.2015.41 5. D. Grygorenko, S. Farokhi, and I. Brandic, “Cost-aware VM placement across distributed DCs using Bayesian networks”, Proceedings of the conference Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 9512, pp. 32–48, https://doi.org/10.1007/978-3-319-43177-2_3.. 6. R. Benali, H. Teyeb, A. Balma, S. Tata, and N. Ben Hadj Alouane, “Evaluation of traffic-aware VM placement policies in distributed cloud using Cloud Sim”, Proceedings of the conference Proc. - 25th IEEE Int. Conf. Enabling Technology Infrastructure. Collab. Enterp. WETICE 2016,pp.95–100, http://ieeexplore.ieee.org/document/7536438/. 7. Bagheri Z, Zamanifar K.. “Enhancing energy efficiency in resource allocation for real-time cloud services,”7th Int Symp Telecommun IST 2014,http:// DOI: 10.1109/ISTEL.2014.7000793. 8. Ferdaus MH, Murshed M, Calheiros RN, Buyya R. “An algorithm for network and data aware placement of multitier applications in cloud data centers,” Journal of Network and Computer Applications,vol.98,pp.65-8398, https://doi.org/10.1016/j.jnca.2017.09.009. 9. Pantazoglou M, Tzortzakis G, Delis A. Decentralized and Energy-Efficient Workload Management in Enterprise Clouds. IEEE Transaction on Cloud Computing,vol.4,no.2, pp196-209,2015. http://DOI: 10.1109/TCC.2015.2464817 10. Nikzad S. “An Approach for Energy Efficient Dynamic Virtual Machine Consolidation in Cloud Environment,” International Journal of Advanced Computer Science and Applications,vol.7 No.9, 2016. 11. Zhao Y, Huang W., “Adaptive Distributed Load Balancing Algorithm Based on Live Migration of Virtual Machines in Cloud,” In Proceedings of 5th International Joint Conference on INC, IMS and IDC, Nexus, pp.170-175, South korea, November 2009, http://doi>10.1109/NCM.2009.350. 12. Fu X, Zhou C.Virtual machine selection and placement for dynamic consolidation in Cloud computing environment, Frontiers of Computer Science, Vol.9 no.2,pp.322-330,http://doi10.1007/s11704-015-4286-8. 13. Teng F, Yu L, Li T, Deng D, Magouls F..Energy efficiency of VM consolidation in IaaS clouds, Journal of Supercomputing, vol.73 no.2pp782- 809,http:// DOI 10.1007/s11227-016-1797-5. 14. Arianyan E, Taheri H, Sharian S(2016). Multi target dynamic VM consolidation in cloud data centers using genetic algorithm, Journal of Information Science Engineering,vol.32 no4,pp. 1575-1593,2016. 15. Double exponential smoothing Insight Central (2017)[Online].Availablehttps://analysights.wordpress.com/tag/datodouble-exponential- smoothing/.(25 April 2018). 16. Mukhtarov M(2012). “Cloud Network Security Monitoring and Response System,” International Transactions on Systems Science and Applications, vol.8 no.3,pp.181-185, sai: itssa.0008.2012.020,2012. 17. Anala M.R., Kashyap M., Shobha G.(2013) Application performance analysis during live migration of virtual machines, Advance Computing Conference (IACC), 2013 IEEE 3rd International,pp.366-373,Mysore,India, February 2013. 18. Tavakoli, Zahra,Meier, Sebastian,Vensmer, Alexander.A framework for security context migration in a firewall secured virtual machine environment, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics),7479 LNCS,41-51. 19. Suresh B. Rathod and Vuyyuru Krishna Reddy (2018).Decentralized Predictive Secure VS Placement in Cloud Enviornment, Journal of computer science, vol.14no.3,2018. 20. Suresh B. Rathod and Vuyyuru Krishna Reddy. N Dynamic framework for secure VM migration over cloud computing, Journal of Information Processing Systems,vol.13no. 3,2017. 21. Suresh B. Rathod and Vuyyuru Krishna Reddy (2014), secure Live VM Migration in Cloud Computing: A Survey, International Journal of Computer Applications, vol.103no.2, 2018. 22. A. Shribman and B. Hudzia, “Pre-copy and post-copy VM live migration for memory intensive applications,” Lect. Notes Computer. Science (including Subser. Lecture Notes Artificial Intelligence. Lecture Notes Bioinformatics), vol. 7640 LNCS, 2013, pp. 539–547, available online: https://link.springer.com/chapter/10.1007/978-3-642-36949-0_63: 28.02.2015. 23. Amazon Web Services – AWS Well-Architected Framework October 2015. 24. Michael Pantazoglou, Gavriil Tzortzakis, and Alex Delis. Decentralized and Energy-Efficient Workload Management in Enterprise Clouds, IEEE Transactions on Cloud Computing,pp.196-209,vol. 4 no.2,http:// DOI 10.1109/TCC.2015.2464817,2016. 25. Xiaoying Wang, Xiaojing Liu, Lihua Fan, and Xuhan Jia. Research Article: A Decentralized Virtual Machine Migration Approach of Data Centers for Cloud Computing, Hindawi Publishing Corporation Mathematical Problems in Engineering Volume 2013, Article ID 878542,2013. 26. Santosh Kumar Majhi, Padmalochan Bera. A security context migration framework for Virtual Machine migration,015 International Conference on Computing and Network Communications, CoCoNet 2015,pp.452-456, http:// DOI:978-1-4673-7309-8/15/$31.00 ©2015 IEEE 452February 2015. 27. Xin Wan, XinFang Zhang, Liang Chen; JianXin Zhu(2012).An improved vTPM migration protocol based trusted channel,2012 International Conference on Systems and Informatics (ICSAI2012),pp.870 – 875,China, http://DOI: 978-1-4673-0199-2/12,June 2012. 28. Fengzhe Zhang, Haibo Chen. Security-Preserving Live Migration of Virtual Machines in the Cloud, Journal of Network and Systems Management, vol.21no.4,pp.562-587, DOI 10.1007/s10922-012-9253-1.2013. 29. Suresh B. Rathod and Vuyyuru Krishna Reddy(2018),Decision Making Framework for Decentralized Virtual Machine Placement in Cloud Computing, International Journal of Engineering and Technology,vol7no.2.7,2018. Authors: S. Pandiaraj, T. Sudalai Muthu Paper Title: Prioritization of Replica for Replica Replacement in Data Grid Abstract: Datagrid provides storage facility to data grid user to store and process huge amount of data. Replication plays a major role in optimizing the Datagrid. The replacement of replica in datagrid is to be optimal to improve the performance of the Datagrid. The replacement of replica is decided based on the priority of the replica in future. The priority of the replica is based on its importance in the near future. In this paper, an approach is proposed to evaluate the priority of the replica by considering the importance of that replica in the near future. The proposed approach is evaluating the priority using the rubrics on the parameters; Frequency of access (F), Number of Access (N), and Recent access (R). The rubrics on each parameter are constructed empirically. The priority of the replica (PR) is determined by using the rubrics values and weights. The proposed approach is simulated in the OptorSim simulator. The simulation 47. result shows that the proposed approach yielded the Hit Rate as 95% consistently performed well on various file sizes. 245-248 Keywords: Replica Replacement algorithms, Data Replications, Data Grid.

References: 1. T. S. Muthu and K. R. Kumar, "A Log-based predictive approach for replica replacement in data grid," 2017 4th International Conference on Advanced Computing and Communication Systems (ICACCS), Coimbatore, 2017, pp 1-6. 2. Ranjana P, George, “A Improving network capacity for effective traffic management using graphs”, International Journal of Applied Engineering Research, vol. 10, no.7, pp. 16853-16863, 2015. 3. Sathish N, Ranjana P, “Secure remote access fleet entry management system using UHF band RFID”, Advances in Intelligent Systems and Computing, vol. 216, pp. 141-149, 2014. 4. Tos, Uras, Riad Mokadem, Abdelkader, Hameurlain, Tolga Ayav, and Sebnem Bora, "Dynamic replication strategies in data grid systems: a survey," The Journal of Supercomputing, vol. 71, no. 11, pp. 4116-4140 , 2015. 5. T. Amjad, "A survey of dynamic replication strategies for improving data availability in data grids," Future Generation Computer Systems, vol. 28, no.2, pp. 337-349, February, 2012. 6. T. S. Muthu, R. Vadivel, A. Ramesh and G. Vasanth, "A novel protocol for secure data storage in Data Grid environment," Trendz in Information Sciences & Computing(TISC2010), Chennai, 2010, pp. 125-130. 7. T. Sudalai Muthu and R. Masillamani, "A novel protocol for secure data storage in Data Grid," 2010 First International Conference On Parallel, Distributed and Grid Computing (PDGC 2010), Solan, 2010, pp. 184-189. 8. S. Nazanin and M. Amir, “PDDRA: A new pre-fetching based dynamic data replication algorithm in data grids,” International Journal of Future Generation Computer Systems, vol. 28, no. 1, pp. 666-681, 2012. 9. M. Bsoul, A. Al-Khasawneh, E. Eddien Abdallah, and Y. Kilan, “Enhanced fast spread replication strategy for data grid,” Journal of Network and computer applications, vol. 12, no. 4, pp. 113-121, 2010. 10. Leyli Mohammad Khanli, Ayaz Isazadeh, Tahmuras N. Shishavan, “PHFS: A dynamic replication method, to decrease access latency in the multi-tierdata grid”, Future Generation Computer Systems, vol. 1, no. 27, pp. 233-244, 2011. 11. J. M. Pérez, Félix García-Carballeira, Jesús Carretero, Alejandro Calderón, Javier Fernández, “Branch replication scheme: A new model for data replication in large scale data grids,” Journal Future Generation Computer Systems, vol. 26, no.1, pp.12-20, 2010. 12. K. Sashi and A.S. Thanamani, “A new dynamic replication algorithm for European data grid,” Third Annual ACM Bangalore Conference, pp. 17-25, 2010. 13. K. Sashi and A. S. Thanamani, "Dynamic replication in a data grid using a Modified BHR Region Based Algorithm," Future Generation Computer Systems, vol. 27, pp. 202-210, 2011. 14. V. Andronikou, K. Mamouras, K. Tserpes, D. Kyriazis, T. Varvarigou, “Dynamic QoS-aware data replication in grid environments based on data importance,” Future Gener. Comput. Syst, vol. 28, pp. 544–553, 2012. 15. N. Mansouri, "A Threshold-based dynamic data replication and parallel job scheduling strategy to enhance datagrid," Journal of Cluster Computing, vol. 17, no. 3, pp. 957-977, 2014. 16. T. SudalaiMuthu and K. RameshKumar, “A Value based dynamic replica replacement strategy in datagrid,” International Journal of Control Theory and Applications, vol. 10, no. 26, pp. 448-462, 2017. 17. T. SudalaiMuthu and K. RameshKumar, "Hybrid predictive approach for replica replacement in data grid," 2017 International Conference on Computer Communication and Informatics (ICCCI), Coimbatore, 2017. 18. T. SudalaiMuthu, K. RameshKumar and K. Sarukesi, “A Weight based replica replacement algorithm in datagrid”, Journal of Advanced Research in Dynamical and Control Systems, vol. 6, pp. 1164-1178, 2017. Authors: D.S. Shylu Sam, S. Radha, D. Jackuline Moni, P. Sam Paul, J. Jecintha Paper Title: Design of 1-V, 12-Bit Low Power Incremental Delta Sigma ADC for CMOS Image Sensor Applications Abstract: This work describes a 12-bit low power incremental delta sigma analog to digital converter (ADC) suitable for CMOS image sensor applications. The resolution of the delta sigma ADC is improved by sharing an op-amp between two stages of the modulators. Op-amp is the main building block of delta sigma ADC and the power consumption is reduced by self-biasing amplifier. The prime conversion is done by using comparator. The 12-bit incremental delta sigma ADC is designed in 90nm CMOS process. Simulation result shows that the power consumption for 12-bit incremental delta sigma ADC is 0.001mW.

Keywords: Delta-sigma ADC, op-amp sharing, self-biased amplifier, low power, comparator.

References: 1. Ilseop Lee, Byoungho Kim and Byung-GeunLee, “A Low-Power Incremental Delta–Sigma ADC for CMOS Image Sensors” IEEE transactions on circuits and systems-II: express briefs,Vol.63,no.4,2016,pp.371-375. 2. AdiXhakoni, Ha Le-Thai, and Georges G. E. Gielen, “A Low-Noise High-Frame-Rate 1-D Decoding Readout Architecture for Stacked Image Sensors,” IEEE Sensors Journal ,vol.4,issue.6,2014,pp.1966 – 1973. 3. Theuwissen, “CMOS image sensors: State-of-the art and future perspectives,” in Proc. 33rd Eur. Solid State Circuits Conf, pp.,2007,pp. 21–27. 4. L.Ferrigno, S. Marano, V. Paciello, and A. Pietrosanto, “Balancing computational and transmission power consumption in wireless image sensor networks,” in Proc. IEEE Int. conf. VECIMS,2005,pp.66. 5. J. Guo and S. Sonkusale, “A high dynamic range CMOS image sensor for scientific imaging applications,” IEEE Sensors J., vol. 9, no.10, 2009,pp.1209–1218. 6. Cheng and K. Tsai,“ Distributed barrier coverage in wireless visual sensor networks with β-QoM,” IEEE Sensors J., vol. 12, no. 6,2012, pp. 1726–1735. 7. X. Wang, S. Wang, and D. Bi,“ Compacted probabilistic visual target classification with committee decision in wireless multimedia sensor networks,” IEEE Sensors J., vol. 9, no. 4, 2009,pp. 346–353. 48. 8. MengyunYue, Dong Wu and Zheyao Wang, “Data Compression for Image Sensor Arrays Using a 15-bit Two-Step Sigma–Delta ADC ”, IEEE sensors journal, vol. 14, no. 9, 2014,pp. 2989-2998. 249-253 9. M. S. Shin et al.,“ CMOS X-Ray detector with column-parallel 14.3-bit extended-counting ADCs,” IEEE Trans. Electron Devices, vol. 60, no. 3,2013, pp. 1169– 1177. 10. E. Kotter and M. Langer, “Digital radiography with large-area ﬂat-panel detectors,” Eur. Radiol., vol. 12, no. 10, 2002,pp. 2562–2570. 11. K.-P. Pun, S. Chatterjee, and P. R. Kinget, “A 0.5-V 74-dB SNDR 25-kHz continuous-time delta-sigma modulator with a return-to-open DAC,” IEEE J. SolidState Circuits, vol. 42, no. 3, 2007,pp. 496-507. 12. Y. Chae and G. Han, “Low voltage, low power, inverter-based switched capacitor delta-sigma modulator,” IEEE J. Solid-State Circuits, vol. 44, no. 2, 2009,pp. 458–472. 13. M. Bazes, “Two novel fully complementary self-biased CMOS differential amplifiers,” IEEE J. Solid-State Circuits, vol. 26, no. 2, 1991,pp. 165– 168. 14. S. U. Ay, “A sub-1 volt 10-bit supply boosted SAR ADC design in standard CMOS,” Int. J. Analog Integr. Circuits Signal Process., vol. 66, no. 2, 2011,pp. 213–221. 15. Mesgarani, M. N. Alam, F. Z. Nelson, and S. U. Ay, “Supply boosting technique for designing very low-voltage mixed-signal circuits in standard CMOS,” in Proc. IEEE Int.Midwest Symp.Circuits Syst.Dig.Tech.Papers, 2010,pp.893-896 . 16. B.J.Blalock,“Body-driving as a Low –Voltage Analog Design Technique for CMOS Technology” in Proc. IEEE Southwest Symp.Mixed-Signal Design,2000,pp.113-118. 17. Samaneh Babayan-Mashhadi, Reza Lotfi., “Analysis and Design of a Low voltage low-power double- tail comparator”, IEEE Transactions on Very Large Scale Integration (VLSI) Systems ,vol.22 No. 2,2014, 343–352. 18. M. Maymandi-Nejad and M. Sachdev,“1-bit quantiser with rail to rail input range for sub-1V modulators,” IEEE Electron. Lett., vol. 39, no. 12, 2003,pp. 894–895. 19. Masaya Miyahara, Yusuke Asada, Daehwa Paik and Akira Matsuzawa, “A Low-Noise Self-Calibrating Dynamic Comparator for High-Speed ADCs,” in IEEE A-SSCC, 2008,pp. 269-272. 20. Chi-Hang Chan, Yan Zhu, U-Fat Chio, Sai-Weng Sin, Seng-Pan U, R.P.Martins, “Reconfigurable Low-Noise Dynamic Comparator with Offset Calibration in 90nm CMOS”, IEEE Asian Solid-State Circuits Conference,2011,pp. 14-16. 21. Mohamed Abbas, Yasuo Furukawa, Satoshi Komatsu, Takahiro J. Yamaguchi, and Kunihiro Asada, “Clocked Comparator for High Speed Applications in 65nm Technology” in IEEE A-SSCC,2010,pp.1-4. 22. Bernhard Goll and Horst Zimmermann, “A Clocked, Regenerative Comparator in 0.12 μm CMOS with tunable Sensitivity”, In IEEE ESSCIRC, 2007, pp.408-411. Authors: K.P. Sivagami, S.K. Jayanthi Paper Title: Automatic Water Body Extraction using Multispectral Thresholding Abstract: Industrialization and urbanization lead to change in land use patterns and increase in utilization of water resources. Timely monitoring of surface water and delivering data on the dynamics of surface water are essential for policy and decision making processes. Change detection based on multispectral and multi temporal remote sensing data is one of the most acceptable and ever growing surface water change detection mechanisms in recent years. In this paper, a study has been conducted to detect the water bodies present in Erode region of Tamil Nadu based on Resourcesat-2 LISS-III November 2011 data using Normalized Difference Water Index and Thresholding based techniques. The result illustrates the effectiveness of the Bi-level Bi-stage Multispectral Thresholding approach for identification of water bodies and hence applied to detect the changes in water bodies during the period of 2011 to 2014.

Keywords: Bi-level Bi-Stage Multispectral Thresholding, Change Detection, Surface Water Body Extraction, Water Indices.

References: 1. Amare Sisay, 'Remote sensing based water surface extraction and change detection in the Central Rift Valley Region of Ethiopia,' American Journal of Geographic Information System, 2016, 5(2), 33-39. 2. Geophysical products development division technical report, “Surface water layer products from OCM2 for BHUVAN NOEDA,’ Geophysical and spectral products, SDAPSA, NRSC, September 2014 (available online: 49. bhuvan.nrsc.gov.in/data/download/tools/.../ocm2_water_layer_BHUVAN_NOEDA.pdf). 3. Gulcan Sarp, Mehmet Ozcelik, 'Water body extraction and change detection using time series: A case study of Lake Burdur, Turkey,' 254-260 ScienceDirect, Journal of Taibah University for Science, 2017, 11, 381-391 (open access: http://creativecommons.org/licenses/by-nc-nd/4.0/). 4. Himanshu Rana, Nirvair Neeru, ‘Water Detection using Satellite Images Obtained through Remote Sensing,’ Advances in Computational Sciences and Technology, 2017, 10(6), 1923-1940. 5. Komeil Rokni , Anuar Ahmad, Ali Selamat, Sharifeh Hazini, 'Water Feature Extraction and Change Detection Using Multitemporal Landsat Imagery,' Remote Sensing, 2014, 6, 4173-4189. 6. Lillesand, T.M., Kiefer, R.W., Chipman, J.W., ‘Remote Sensing and Image Interpretation,’ Fifth Edition, John Wiley & Sons (Asia) Pte. Ltd., Singapore, 2004; 586-593. 7. Satya Prakash Maurya, Ashwani Kumar Agnihotri, ‘Evaluation of course change detection of Ramganga river using remote sensing and GIS, India,’ Weather and Climate Extremes, 2016, 13, 68–72. 8. Shridhar D. Jawak, Alvarinho J. Luis, 'A Semiautomatic Extraction of Antarctic Lake Features Using Worldview-2 Imagery,' Photogrammetric Engineering & Remote Sensing, 2014, 80(10). 9. Sivagami K P, Jayanthi S K, Aranganayagi S, 'Detection of Image Edges using B2MST', International Journal of Computational Intelligence and Informatics, 2016, 6(1), 42-52. 10. Sivagami K P, Jayanthi S K, Aranganayagi S, 'Monitoring Land Cover of Google Web Service Images through ECOC and ANFIS Classifiers,' International Journal of Computer Sciences and Engineering, 2017, 5(8), 9-16 (open access: https://doi.org/10.26438/ijcse/v5i8.916). 11. Stuart K. McFeeters, 'Using the Normalized Difference Water Index (NDWI) within a Geographic Information System to Detect Swimming Pools for Mosquito Abatement: A Practical Approach,' Remote sensing, 2013, 5, 3544-3561. 12. Tri Dev Acharya, In Tae Yang , Anoj Subedi, Dong Ha Lee, ' Change detection of lakes in Pokhara, Nepal using in Landsat data,' Proceedings 2017, 1(17), 1-6. 13. Wenbo Li, Zhiqiang Du, Feng Ling, Dongbo Zhou, Hailei Wang, Yuanmiao Gui, Bingyu Sun, Xiaoming Zhang , 'A comparison of land surface water mapping using the normalized difference water index from TM, ETM+ and ALI,' Remote sensing, 2013, 5, 5530-5549. 14. Yan Zhou, Jinwei Dong, Xiangming Xiao, Tong Xiao, Zhiqi Yang, Guosong Zhao, Zhenhua Zou, Yuanwei Qin, "Open Surface Water Mapping Algorithms: A comparison of water-related spectral indices and sensors," Water, 2017, 9(256), 1-16. Authors: T. Nathiya and G. Suseendran Paper Title: An Improved HNIDS in Cloud Real Time Prediction Using Fuzzy Decision Making Combination Rule Abstract: Fuzzy Decision Making (FDM) combination rule can be used to improve the real time prediction in cloud. Needs the dynamic approach in day to day to monitor the traffic and notify the illegal problems into system administrator. This type of approach is known as HNIDS (Hybrid Network Intrusion Detection System). So our model HNIDS is introduce the FDM rule in this paper. Not only FDM we are using new upcoming classification learner XGBoost and SVM. SVM is functional dependent and XGBoost is decision tree type of classification. So that the two different type of classification model to predict the cloud network packets whether the packets are normal or abnormal. Finally FDM combination rule to take the decision using belief probability evidences. This is new type of prediction method. Result and Discussion have shown that Our HNIDS using the method to predict the network packets with high accuracy value and minimum computation cost efficiency.

Keywords: HNIDS, SVM, XGBoost, Fuzzy Decision Making Rule, NSL- KDD Datasets, Python, Azure Cloud.

50. References: 1. A. Kumbhare and M. Chaudhari, “IDS : Survey on Intrusion Detection System in Cloud Computing,” International Journal of Computer Science 261-267 and Mobile Computing, vol. 3, no. 4, pp. 497–502, 2014. 2. B. Mahalakshmi and G. Suseendran, “Effectuation of Secure Authorized Deduplication in Hybrid Cloud,” Indian Journal of Science and Technology, vol. 9, no. 25, Jul. 2016. 3. T. Nathiya and G. Suseendran, “An Effective Hybrid Intrusion Detection System for Use in Security Monitoring in the Virtual Network Layer of Cloud Computing Technology,” Data Management, Analytics and Innovation, Advances in Intelligent Systems and Computing, vol. 839, pp. 483–497, 2019. 4. J. Song, “Feature Selection for Intrusion Detection System Jingping Song Declaration and Statement,” Department of Computer Science Institute of Mathematics, Physics and Computer Science, Aberystwyth University , Ph.D. Thesis, p. 132, 2016. 5. T. Nathiya and G. Suseendran, “An Effective Way of Cloud Intrusion Detection System Using Decision tree , Support Vector Machine and Naïve Bayes Algorithm,” International Journal of Recent Technology and Engineering (IJRTE), vol. 7, no. 4S2, pp. 38–43, 2018. 6. T. Nathiya, “Reducing DDOS Attack Techniques in Cloud Computing Network Technology,” International Journal of Innovative Research in Applied Sciences and Engineering (IJIRASE), vol. 1, no. 1, pp. 23–29, 2017. 7. M. Alauthman, O. Dorgham, A. Almomani, F. Albalas, and A. Obeidat, “An Online Intrusion Detection System to Cloud Computing Based on Neucube Algorithms,” International Journal of Cloud Applications and Computing, vol. 8, no. 2, pp. 96–112, 2018. 8. W. Feng, Q. Zhang, G. Hu, and J. X. Huang, “Mining network data for intrusion detection through combining SVMs with ant colony networks,” Future Generation Computer Systems, vol. 37, pp. 127–140, 2014. 9. M. Mazini, B. Shirazi, and I. Mahdavi, “Anomaly network-based intrusion detection system using a reliable hybrid artificial bee colony and AdaBoost algorithms,” Journal of King Saud University - Computer and Information Sciences, 2018. 10. K. Siddique, Z. Akhtar, M. A. Khan, Y. H. Jung, and Y. Kim, “Developing an intrusion detection framework for high-speed big data networks: A comprehensive approach,” KSII Transactions on Internet and Information Systems, vol. 12, no. 8, pp. 4021–4037, 2018. 11. M. S. Moorthy Manthira and M. Rajeswari, “Virtual host based intrusion detection system for cloud,” International Journal of Engineering and Technology, vol. 5, no. 6, pp. 5023–5029, 2013. 12. M. Almseidin, M. Alzubi, S. Kovacs, and M. Alkasassbeh, “Evaluation of machine learning algorithms for intrusion detection system,” SISY 2017 - IEEE 15th International Symposium on Intelligent Systems and Informatics, Proceedings, pp. 277–282, 2017. 13. M. Raza, I. Gondal, D. Green, and R. L. Coppel, “Fusion of FNA-cytology and Gene-expression Data Using Dempster-Shafer Theory of Evidence to Predict Breast Cancer Tumors,” Bioinformation, vol. 1, no. 5, pp. 170–175, 2012. 14. A. Verma and V. Ranga, “Statistical analysis of CIDDS-001 dataset for Network Intrusion Detection Systems using Distance-based Machine Learning,” Procedia Computer Science, vol. 125, pp. 709–716, 2018. 15. G. Ansari, “Framework for Hybrid Network Intrusion Detection and Prevention System,” Interational journal of computer Technology & Application, vol. 7, no. August, pp. 502–507, 2016. 16. L. Hoang Son et al., “APD-JFAD: Accurate Prevention and Detection of Jelly Fish Attack in MANET,” IEEE Access, vol. 6, no. Issue(1), pp. 56954–56965, 2018. 17. F. H. Botes, L. Leenen, and R. De La Harpe, “Ant colony induced decision trees for intrusion detection,” European Conference on Information Warfare and Security, ECCWS, no. June, pp. 53–62, 2017. 18. H. Liu, Z. Wu, C. Peng, F. Tian, and L. Lu, “Adaptive gaussian mechanism based on expected data utility under conditional filtering noise,” KSII Transactions on Internet and Information Systems, vol. 12, no. 7, pp. 3497–3515, 2018. 19. S. S. Dhaliwal, A. Al Nahid, and R. Abbas, “Effective intrusion detection system using XGBoost,” Information (Switzerland), vol. 9, no. 7, 2018. 20. G. Suseendran, E. Chandrasekaran and Anand Nayyar ,“Defending Jellyfish Attack in Mobile Ad hoc Networks via Novel Fuzzy System Rule G.,” Data Management, Analytics and Innovation, Advances in Intelligent Systems and Computing, vol. 839, pp.437-455, 2019.. 21. T. Reineking, “Belief functions: theory and algorithms,” 2014. Authors: S. Shivashankara and S. Srinath An American Sign Language Recognition System using Bounding Box and Palm FEA-TURES Extraction Paper Title: Techniques Abstract: The sign language is absolutely an ocular interaction linguistic over and done with its built-in grammar, be nothing like basically from that of spoken languages. This research paper presents, an inventive context, whose key aim is to achieve the transmutation of 24 static gestures of American Sign Language alphabets into human or machine identifiable manuscript of English language. The gestures sets considered for cognition and recognition process are purely invariant to location, Background, Background color, illumination, angle, distance, time, and also camera resolution in nature. The gesture recognition process is carried out after clear segmentation and preprocessing stages. As an outcome, this paper yields an average recognition rate of 98.21%, which is an outstanding accuracy comparing to state of art techniques.

Keywords: American Sign Language, Bounding Box Technique, Canny Edge Detector, CIE Color Model, Gesture Recognition.

References: 1. B.M.Chethana Kumara, H.S.Nagendraswamy, R.Lekha Chinmayi, “Spatial Relationship Based Features for Indian Sign Language Recognition”, International Journal of Computing, Communications & Instrumentation Engineering, Vol. 3, Issue 2, (2016), pp.206-212, available online: http://dx.doi.org / 10.15242 / IJCCIE.IAE0516005. 2. Rachael Locker McKee, David McKee, “What's so hard about learning ASL?: Students' AND Teachers' perceptions”, Sign Language Studies, Linstok Press Inc., (1992), pp.129-157, 3. Srinath.S, Ganesh Krishna Sharma, “Classification approach for sign language recognition”, International Conference on Signal, Image Processing, Communication & Automation, (2017), pp.141-148. 4. Shivashankara S, Srinath S, “A comparative Study of Various Techniques and Outcomes of Recognizing American Sign Language: A Review”, International Journal of Scientific Research Engineering & Technology, Vol 6, Issue 9, (2017), pp.1013-1023, available online: http://www.ijsret.org 5. Shivashankara S, Srinath S, “A Review on Vision Based American Sign Language Recognition, its Techniques, and Outcomes”, 7th IEEE 51. International Conference on Communication Systems and Network Technologies, (2017), pp293-299, DOI 10.1109 / CSNT.2017.58 6. Dr.Roger Sapsford, Victor Jupp, Data Collection and Analysis, Sage Publishing Ltd, (2006) 7. URL: http://www.asluniversity.com 268-281 8. URL: http://www.asl.tc 9. URL: http://www.lifeprint.com 10. URL: http://www.youtube.com 11. Tülay Karay1lan, Özkan K1l1ç, “Sign Language Recognition”, 2nd International Conference on Computer Science and Engineering, (2017), pp.1122-1126. 12. N.S Sreekanth, N.K Narayanan, “Static Hand Gesture Recognition using Mon-vision Based Techniques”, International Journal of Innovative Computer Science & Engineering, Vol. 4, Issue 2, (2017), pp.33-41, 2017, available online: http://www.ijicse.in. 13. Miguel Rivera-Acosta, Susana Ortega-Cisneros, Jorge Rivera, Federico Sandoval-Ibarra, “American Sign Language Alphabet Recognition Using a Neuromorphic Sensor and an Artificial Neural Network”, Sensors, (2017), pp.1-17, available online: http://www.mdpi.com / journal / sensors. 14. Nitesh S. Soni, Prof. Dr. M. S. Nagmode, Mr. R. D. Komati, Online Hand Gesture Recognition & Classification for Deaf & Dumb, International Conference on Inventive Computation Technologies, 2017. 15. Amit Kumar Gautam, Ajay Kaushik, “American Sign Language Recognition System Using Image Processing Method”, International Journal on Computer Science and Engineering, Vol. 9, No.07, (2017), pp.466-471, available online: http://www.enggjournals.com/ijcse/doc/IJCSE17-09-07- 028.pdf 16. Fifin Ayu Mufarroha, Fitri Utaminingrum, “Hand Gesture Recognition using Adaptive Network Based Fuzzy Inference System and K-Nearest Neighbor”, International Journal of Technology, (2017), pp.559-567, available online: https://www.researchgate.net / publication / 316581665 17. Huiwei Zhuang, Mingqiang Yang, Zhenxing Cui, Qinghe Zheng, “A Method for Static Hand Gesture Recognition Based on Non-Negative Matrix Factorization and Compressive Sensing”, IAENG International Journal of Computer Science, (2017), available online: http://www.iaeng.org / IJCS / issues_v44 / issue_1 / IJCS_44_1_07.pdf 18. Neethu P S, Dr. R Suguna, Dr.Divya Sathish, “Real Time Hand Gesture Recognition System”, Taga Journal, Vol. 14, (2018), pp.7982-792, available online: http://www.tagajournal.com 19. Shivashankara S, Srinath S, "American Sign Language Recognition System: An Optimal Approach", International Journal of Image, Graphics and Signal Processing, Vol.10, No.8, (2018), pp. 18-30, available online: http://www.mecs-press.org/ 20. Victor Lempitsky, Pushmeet Kohli, Carsten Rother, Toby Sharp, “Image Segmentation with A Bounding Box Prior”, Microsoft Research Cambridge, (2009), available online: https:// www.microsoft.com / en-us / research / publication / image-segmentation-with-a-bounding-box- prior/ 21. Nobuyuki Otsu, "A threshold selection method from gray-level histograms", IEEE Trans. Sys., Man., Cyber, Vol 9, Issue 1, (1979), pp.62–66, available online: https:// ieeexplore.ieee.org / document / 4310076/ 22. John Canny, “A Computational Approach to Edge Detection”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol PAMI-8, No.6, (1986), pp.679-698, available online: https:// ieeexplore.ieee.org / abstract/document/4767851/ Authors: Dr.P. Suresh, P. Anuradha Paper Title: A Critical Study on Psychological Trauma in Alice Walker's Possessing the Secret of Joy Abstract: African American women have been discriminated throughout the ages. They have been considered has least being by their own community people. These women were marginalized in the society and are treated as slaves, they undergo struggles in class, race, gender discrimination. African American women were represented in the way of the oppressed class who have always been a subject of men's domination. They are suppressed in many aspects, they are also misrepresented in society. African American women are denied from their basic rights, they are totally excluded from social, economic and political rights. Women of African American were kept dump by which they forget the value of their self. They have been affected psychologically and in need of emancipation. African American women writers contributed their writing for racial and gender equality. They fought bravely against the structures of patriarchy through there writings. The main theme of their writings is self-esteem, self-realization, the importance of regaining their rights and achieving their ‘self'. There are many black women writers in literature, some of them are Toni Morrison, Maya Angelou, Maria Stewart, Deborah Gray-White, Zora Neal Hurston, Alice Walker and so many. These writers paved way for the liberation of African American women, some of the writers are activist who fought for the freedom of women domination. This paper is going to deal with one of the writers mentioned above; Alice Walker, not only a 52. writer but also an activist fought for the Southern women freedom. This paper focuses on the issue of removing female circumcision in Alice Walker's Possessing the Secret of Joy. The author portrays her fictional women character Tashi in 282-286 this novel, who face endless struggle after female circumcision. The aim of this paper is to show how these African women are traumatized by the name of tradition and culture.

Keywords: Gender discrimination, sexual identity, subjugation, oppression, awakening, female circumcision.

References: 1. Alice.Walker, Pratibha Parmar.,'Warrior Marks: Female Genital Mutilation and the Sexual Blinding of Women'.New York: A Harvest Book, Harcourt Brace & Company,1996,p. 2. Barbara Christian. Black Women Novelists: The Development of a Tradition.1892-1976.Westport: Greenwood Press, 1980. 3. Christian, Barbara T. Black Feminist Criticism. New York: Pergamon,1985. 4. Dieke, Ikenna Critical Essays on Alice Walker.Greenwood:1999. 5. Frank, Katherine, Women Without men, The Feminist Novel in Africa in women in African Literature Today, vol.15.1987 6. Frank, Katherine, Women Without men, The Feminist Novel in Africa in women in African Literature Today, vol.15.1987 7. Hurston, Zora Neale. Their Eyes Were Watching God. 1937. New York: Harper Perennial, 1990. Print. 8. Singh, Amrithit,'The Novels of the Harlem Renaissance'. United States of America.1976. 9. Walker Alice. Possessing the Secret of Joy. New York: Pocket Books, 1992. Print. 10. Walker, Melissa. ‘Down from the Mountaintop: Black Women's Novels in the Wake of Civil Right Movement, 1966-1989'. 1991. Authors: Bangala Sairam, S.P.V. Subba Rao, T. Ramaswamy Paper Title: Multi Utility Equipment for Assisting Disable Persons Abstract: In our society disable people faces lot of problems in terms of communication and literacy. When we deals with blind people they faces difficulty in reading the normal books and newspapers which are not Braille scripted and we face lot of problems while communicating with deaf and dumb people. So, this project focuses to find the unique solution to these problems. The project mainly uses four techniques such as image to text, text to speech, speech to text and sign to speech conversion techniques. Visually impaired people can hear the text on paper using image to text and text to speech techniques this is can achieved by Tessaract OCR (Optical Character Recognition) technique and espeak speech synthesizer. Vocally and hearing impaired people can express their views by using sign to speech and speech to text conversion techniques. This makes use of Raspberry pi, camera, microphone, GSM, GPS and speakers to build the system. This is a method where a computer is made to speak.

Keywords: Optical Character Recognition (OCR), Raspberry pi(RPI), espeak, GSM, GPS, Speakers, Microphone, camera etc.

53. References: 1. Chucai Yi, Student Member, IEEE , Yingli Tian, Senior Member, IEEE , and Aries Arditi “Portable Camera-Based Assistive Text and Product 287-291 Label Reading From Hand-Held Objects for Blind Persons” 2013 IEEE/ASME transactions on mechatronics 2. S. Venkateswarlu, D. B. K. Kamesh, J. K. R. Sastry, Radhika Rani”Text to Speech Conversion”. Indian Journal of Science and Technology, Vol 9(38), DOI: 10.17485/ijst/2016/v9i38/102967, October 2016. 3. K Nirmala Kumari, Meghana Reddy J. “Image Text to Speech Conversion Using OCR Technique in Raspberry Pi”. International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering Vol. 5, Issue 5, May 2016. 4. Nagaraja L, Nagarjun R S, Nishanth M Anand, Nithin D, Veena S Murthy. “Vision based Text Recognition using Raspberry Pi”. International Journal of Computer Applications (0975 – 8887) National Conference on Power Systems & Industrial Automation 2015. 5. Poonam S. Shetake, S. A. Patil, P. M. Jadhav “Review of text to speech conversion methods” 2014. 6. THOMAS, S. Natural Sounding “Text-To-Speech Synthesis Based On Syllable-Like Units”, Department Of Computer Science And Engineering Indian Institute Of Technology Madras, 2007. 7. DUTOIT, T. “A Short Introduction to Text-to-Speech”, Kluwer Academic Publishers, Dordrecht, Boston, London, 1997. 8. Geethu G Nath, Anu V S “Embedded Sign Language Interpreter System For Deaf and Dumb People” Department of Electronics and Communication Sree Buddha College of Engineering, Pattoor Kerala, India International Conference on Innovations in information Embedded and Communication Systems (ICIIECS) 2017. 9. Vision impairment and hearing impairment @http://who.int/en/news-room/fact-sheet/deatials 10. Ray Smith “An Overview of the Tesseract OCR Engine” Google Inc, Ninth international conference on Document Analysis and Recognition (ICDAR), IEEE 2007. Authors: Perala Vinay Kumar, B. Saritha 54. Paper Title: Wireless Arm based Automatic Meter Reading & Control System Abstract: Automatic meter reading (AMR) is the technology of automatically collecting data from energy metering 292-294 devices (water, gas, and electric). This concept provides a cost efficient and secure manner of electricity billing. At the end of every billing cycle the person from service provider has to visit the place where the meter is placed to get the reading and either note it down or takes an image of energy meter for further data processing (i.e. for generating the bill). By using this system save the time required for conventional billing system and minimized human work load. User and service provider both are get correct reading and bill amount. AMR (Automatic meter reading) System can provide message at hourly, daily and monthly by the request. This technology reduced the man power, reading collection time, theft of electricity also avoids late bill payment. By apply this system data security improved. And improve customer and service provider services the information also provided to the electricity department and to the user using GPRS technology for bill payment purpose.

Keywords: Microcontroller, Energy Meter, Opto Coupler, GPRS, RTC, Current Sensors.

References: 1. Miura, N., Sato, H., Narita, H., and Takaki, M., "Automatic meter-reading system by power line carrier communications," in Proc. C 1990 IEEE Trans Generation, Transmission and Distribution, Vol. 137 Issue: 1, pp. 25 -31. 2. C.P. Young and M.J. Devaney, "Digital power metering manifold," in Proc.1997 IEEE Instrumentation and Measurement Technology Conference,Vol.2, pp. 1403-1406 3. Loss, P et al., “A Single Phase Microcontroller Based Energy Meter,” IEEE Instrumentation and Measurement Technology Conference. St. Paul Minnesota, USA, May 18-21, 1998. 4. AliZaidi.S.K., “Design and implementation of low cost electronic prepaid energy meter”, Multitopic Conference, 2008. INMIC 2008.IEEE International 2008. 5. Bharath P, Ananth N, Vijetha S, JyothiPrakash K. V. ,“Wireless automated digital Energy Meter”, ICSET 2008 6. Chih-hsien Kung and Devaney, M.J., "Multi-rate digital power metering, “Instrumentation and Measurement Technology Conference, 1995, pp. 179-182. 7. Anderson, H.R., "Measured data transmission performance for AM broadcast-VHF radio distribution 2000 IEE 3G Mobile Communication Technologies Conference, pp. 426-430. 8. Misra, R.B. and Patra, S., "Tamper detection using Neuro-fuzzy logic [static energy meters]," in Proc. 1999 IEE Metering and Tariffs for Energy Supply Conference, pp. 101-108. 9. Patrick, A., Newbury, J., and Gargan, S., "Two-way communications systems in the electricity supply industry," IEEE Trans. Power Delivery, Vol.13, pp. 53 -58, Jan. 1998. 10. SrideviChitti, P. Chandrasekhar, M. Asharani, “A Unique Test Bench for Various system-on-a-Chip”, International Journal of Electrical and Computer Engineering (IJECE), Vol. 7, No. 6, December 2017, pp. 3318~3322, ISSN: 2088-8708, DOI: 10.11591/ijece.v7i6.pp3318-3322. Authors: A. Benjamin Franklin, T. Sasilatha Paper Title: Design and Analysis of Low Power Full Adder for Portable and Wearable Applications Abstract: In this paper, our objective is to design a low power full adder with minimum number of transistors and to analysis the calculated values such as Power, Delay and Power Delay-product (PDP) using 45 nm CMOS process technology. The adder cell is compared with several types widely used adders with different configuration of transistors. The proposed full adder cell has low power consumption, better area efficiency. Designed full adders were evaluated through post-layout Spectre simulations with a 45 nm CMOS technology using Cadence tool. This result shows 20% to 30% improvement in power consumption designed adder that makes it to be used for wide range of applications.

Keywords: Full Adder, power delay product (PDP) and area, CMOS LOGIC, Low power, Transistor configuration. 55.

References: 295-299 1. Keivan Navi,. Mehrdad Maeen, Vahid Foroutan, Somayeh Timarchi, Omid Kavehei ‘A novel low-power full-adder cell for low voltage’, Integration, the VLSI journal 42 (2009) 457–467. 2. B. Ramkumar and Harish M Kittur ‘Low-Power and Area-Efficient Carry Select Adder’, IEEE Transactions on Very Large Scale Integration (VLSI) Systems, Vol. 20, No. 2, February 2012 371. 3. Shahzad Asif, Mark Vesterbacka ‘Performance analysis of radix-4 adders’, Integration the VLSI journal 45 (2012) 111–120 4. M. Aliotoa, G. Di Cataldob, G. Palumbo ‘Mixed Full Adder topologies for high-performance low-power arithmetic circuits’, Microelectronics Journal 38 (2007) 130–139 5. K.Navi, V.Foroutan, M.RahimiAzghadi, M.Maeen, M.Ebrahimpou, M.Kaveh, O.Kavehei ‘A novel low-powerfull-adder cell with new technique in designing logical gates based on static CMOS inverter’, Microelectronics Journal 40 (2009) 1441–1448 6. Kiat-Seng Yeo and Kaushik Roy ‘Low voltage low power VLSI subsystem,’ Tata McGraw Hill Edition 2009. 7. Sasilatha T. and Raja J., ‘A 1.2V, 2.4 GHz, Low power 120nm CMOS ASK Transceiver for WSN’ International Journal of Engineering Research and Applications (IJERIA), Ascent Publications Vol.2, No.7 ,2009,pp:257-278. Authors: Godasu Swetha, T. Ramaswamy, S.P.V. Subba Rao Paper Title: Automation of Telecom Networks by Using Robot Framework Abstract: Telecom industry is becoming a top performing industry in the last few years. It is facing a unique set of challenges in order to meet the customer demands and the technology front due to its wide range of sectors. To meet those challenges till date a lot of human/manual intervention is required for Telecom services. Quality of service is not good, it consumes more time and cost is more due to human/manual intervention Telecom services. To overcome all these problems most of the industries approach Automation methods. But till date, only commercial automation tools are available in the market to automate SIP. License cost of commercial automation tools create a major problem and 56. requires skilled automation engineers who should have knowledge of the script programming. This paper presents an Open source Robot Framework automation solution for Telecom Services. In this project Telecom libraries are 300-305 implemented by using Python and Robot framework scripts are written for automation of Telecom Networks. Also implemented virtualized IPPBX, Xlite (Softphone), Cisco7940 as part of this project. As Robot framework is an open source, the cost of the Telecom network automation is reduced and it is keyword driven framework so that less a number of skilled engineers are required these are the main advantages of this project.

Keywords: Telecom Networks, Robot Framework, IPPBX, Xlite, Cisco7940, Python.

References: 1. Sarwar Khan, Nouman Sadiq. "Design and configuration of VoIP based PBX using asterisk server and OPNET platform", 2017 International Electrical Engineering Congress (iEECON), 2017 2. Burnstein, Ilene, Practical Software Testing: a process-oriented approach. 709, Springer, New York, 2003. 3. Pettichord, “Seven steps to test automation success” in Proceedings of the Software Testing, Analysis & Review Conference (STAR), 1999. 4. M. J. Harrold, “Testing: a roadmap” In Proceedings of ICSE, pages 61–72, 2000. 5. Laukkanen, Pekka, “Data-Driven and Keyword-Driven Test Automation Frameworks”, Master’s Thesis, Software Business and Engineering Institute, Department of Computer Science and Engineering, Helsinki University of Technology, 2006. 6. Edward Kit, "Integrated, Effective Test Design and Automation”, February 1999. 7. "Media Protocols and Applications", Information Technology Transmission Processing and storage, 2005 8. J. Rosenberg, H. Schulzrinne, G. Camarillo, A. Johnston, J. Peterson, R. Sparks, M. Handley, and E. Schooler, “RFC 3261: SIP - Session Initiation Protocol.” 9. Benbin Chen. "Innovative application of SIP protocol for communication platform", 2010 International Conference on Anti-Counterfeiting Security and Identification, 07/2010 10. A Jhonston, M Handley, S Donovan, R Sparks, C Cunniangham, Session Initiation Protocol basic call flow”, RFC 3665, Network Working Group, December 2003, pp.2-93. 11. Prasad, J.K., Kumar, B.A, Analysis of SIP and realization of advanced IP-PBX features, Vol 6, IEEE, 2011 12. "Asterisk (PBX)", En.wikipedia.org, 2016. [Online]. Available: http://en.wikipedia.org/wiki/Asterisk_PBX. [Accessed: 12- Jan-2016]. 13. Datarkar, Trupti, N. P. Bobade, and M. A. Gaikwad. "Voice over Internet Protocol (VOIP) Based On Asterisk." IJAR 1.8 (2015):499-502. 14. Jianfeng Zhu, Zhuang Li, Yuchun Ma, Yulin Huang, Realization of Extended Functions of SIP-Based IP-PBX, Vol 3, IEEE, 2010 15. ]Robot Framework homepage, http://code.google.com/p/robotframework/.Cited Mar. 2011. 16. Jian-Ping, Liu, Liu Juan-Juan, and Wang Dong-Long. "Application Analysis of Automated Testing Framework Based on Robot", 2012Third International Conference on Networking and Distributed Computing, 2012. 17. http://robotframework.org/ Authors: Venakapalli Tejasree, T. Ramaswamy, S.P.V. Subba Rao Paper Title: Deployment of IP Multimedia Subsystem by Using Open IMS Abstract: Telecom industry is becoming a top performing in the last few years. So that industry is facing unique challenges on maintaining separate technologies like for 2G(GSM), 3G(UMTS), 4G(LTE/WiMax) and the expand perception of internet protocol (IP) technologies and the huge growth in wireless features dealing to reduce that advance approach AII-IP based Next Generation Networks (NGN). The third generation partnership project (3GPP) has designated an IP multimedia Subsystem (IMS) in 3GPP Release 5 to carry converged multimedia application across both wireless and wire line devices. IMS Provides full packets call control ability by utilizing session initiation protocol (SIP). SIP has been culled by 3GPP as the gesture protocol to guide utilizer registrations and multimedia session management in the IMS. Utilizing IP protocols described by the Internet Engineering Task Force (IETF), IMS will consolidate cellular networks and the cyber world, contribution incipient accommodation capabilities for expeditious accommodation engenderment and deployment of integrated IP multimedia applications. The practical bit of evaluate the open source IMS core platform [9]with reverence to CSCF which is predicated on the SIP Express Router (SER). The study relates the incipient module and the proceed function of SER, required to facsimile with the elongated version of SIP and to act as a CSCF for IMS purposes.

Keywords: IMS, Internet Protocols, NGN, Open IMS, SIP, 3GPP.

References: 1. B. Han, V. Gopalakrishnan, L. Ji, and S. Lee, “Network function virtualization: Challenges and opportunities for innovations,” IEEE Commun. 57. Mag., vol. 53, no. 2, pp. 90–97, Feb. 2015. 2. R. Babbage. "A Traffic Model for the IP Multimedia Subsystem (IMS)", 2007 IEEE 65th Vehicular Technology Conference - VTC2007Spring, 306-310 04/2007 3. Ashraf Ali, AlhadKuwadekar, Khalid Al-Begain. "IP Multimedia Subsystem SIP Registration Signaling Evaluation for Mission Critical Communication Systems", 2015 IEEE International Conference on Data Science and Data Intensive Systems, 2015 4. L. Veltri, S. Salsano, G. Martiniello. "Wireless LAN-3G Integration: Unified Mechanisms for Secure Authentication based on SIP", 2006 IEEE International Conference on Communications, 2006 5. Ashraf A. Ali, Khalid Al-Begain. "chapter 2 IP Multimedia Subsystem and SIP Signaling Performance Metrics", IGI Global, 2017 6. Juan Miguel Espinosa Carlin, Rene Herpertz. "Performance Benchmark of a distributed Multimedia service delivery framework", 2009 IEEE Asia-Pacific Services Computing Conference (APSCC), 2009 7. Wang Jianhui. "A novel queuing model for IMSbased IPTV system", 2009 2nd IEEE International Conference on Broadband Network & Multimedia Technology, 10/2009 8. Copeland, "Session Control", Converging NGN Wireline and Mobile 3G Networks with IMS Converging NGN and 3G Mobile, 2008 9. Baudoin, Cédric, Mathieu Gineste, Chaput Emmanuel, Patrick Gelard, and Julien Bernard. "Dynamic satellite system QoS architecture integrated with IP Multimedia Subsystem core network : DYNAMIC SATCOM QOS ARCHITECTURE INTEGRATED WITH IMS CORE NETWORK", International Journal of Satellite Communications and Networking, 2014 10. Markakis, Evangelos, AnargyrosSideris, PetrosAnapliotis, Georgios Alexiou, CharalabosSkianis, and EvangelosPallis. "IMS-enabled interactive broadcasting network utilizing peer to peer constellations", 2012 International Conference on Telecommunications and Multimedia (TEMU), 2012 11. Ivica Cubic, Ivan Barbaric. "Usability of Mobile Ad hoc Networks for IP Services", 2006 International Conference on Software in Telecommunications and Computer Networks, 2006 12. Wei Wu. "SIP-based vertical handoff between WWANs and WLANs", IEEE Wireless Communications, 6/200 13. Pesch, D. "Performance evaluation of SIPbased multimedia services in UMTS", Computer Networks, 20051019 14. Janevski, “Mobile Broadband: Next Generation Mobile Networks", NGN Architectures Protocols and Services, 2014 15. Technical Specification Group Services and System Aspects (2006), IP Multimedia Subsystem (IMS), Stage 2, TS 23.228, 3rd Generation Partnership Project TS 23.228 v8.2.0, 2007 Authors: S.P.V. Subba Rao, T. Ramaswamy, Karanam Jaya Chandra Paper Title: Flood Alerting Structure for Parking Lots with Wireless Remote Monitoring Assistance Abstract: Modern coastal cities all around the world are affected due to flash floods and non coastal urban cities are 58. of no exception, small drizzles can lead to a huge accumulation of water on the streets which submerge the vehicles in parking lots and in low lying areas causing huge loss of assets. The proposed system provides efficient real time 311-314 monitoring of parking area and parking lots. In the proposed system to monitor the flood condition we are using water level sensor which is gives us actual water level in the parking area. Also we are using slot sensor for checking availability of vehicle slots, if slots are available then system will allocate that slot to the new user who wants to park their vehicle. For real time monitoring of parking area we are using camera, by using camera we can see live video stream of parking area at the time of flood. Here we are using IOT technology, so the system will feed the video stream to the cloud and user can easily see the live video stream of the parking area by using particular IP address.

Keywords: Water level sensor, Slot sensor, Zigbee, Arduino, Bluetooth, IR sensor, Raspberry pi, camera etc.

References: 1. BELL V.A., KAY A.L., JONES R.G., AND MOORE R.J. 2007. Development of a high resolution grid-based river ow model for use with regional climate model output. Hydrology and Earth System Sciences. 11(1), 532–549 2. CASTILLO-EFFER, M., QUINTELA, D.H., MORENO, W., JORDAN, R., WESTHOFF, W., 2004. Wireless Sensor Networks for Flash-Flood Alerting. In Proceedings of the 5th IEEE International Caracas Conference on Devices, Circuits and Systems, Caracas, Venezuela, 4–5 November 2004; Volume 1, pp. 142–146. 3. MOORE R.J., COLE S.J., BELL V.A., JONES D.A., 2006. Issues in flood forecasting: ungauged basins, extreme floods and uncertainty. In Frontiers in Flood Research, 8th Kovacs Colloquium, TchiguirinskaiaI, TheinKNN, HubertP (eds). IAHS Publ. 305, UNESCO: Paris; 103–122. 4. SUNKPHO, J. AND OOTAMAKORN, C., 2011. Real-time flood monitoring and warning system. Songklanakarin Journal of Science and Technology, 2011, 33(2), 227-235. 5. Julien Cartigny, David Simplot, and Jean Carle, “Stochasticflooding broadcast protocols in mobile wireless networks,” LIFLUniv. Lille, vol. 1, 2002. 6. K. Andersson and M. S. Hossain, “Heterogeneous wireless sensor networks for flood prediction decision support systems,” in Proc. 2015 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS). IEEE, 2015, pp. 133–137. 7. S. Hallegatte, C. Green, R. J. Nicholls, and J. Corfee-Morlot, “Future flood losses in major coastal cities,” Nature climate change, vol. 3, no. 9, pp. 802–806, 2013. 8. M. Ancona, A. Dellacasa, G. Delzanno, A. La Camera, and I. Rellini, “An Internet of Things vision of the flood monitoring problem,” in Proc. Fifth International Conference on Ambient Computing, Applications, Services and Technologies (AMBIENT), 2015, pp. 26–29. Authors: S. Surendran, E. Sivasenthil, V. Senthil Kumar Paper Title: A Study on Prepared Copper Zinc Ferrites Nano Powder by Sol-gel Technique Abstract: The nanoparticle called “Copper zinc ferrite” and its preparation is said to be a very exhausting subject for academics, and the most appreciable thing is that it is treated as an application-oriented research in the field of Nano science. In this preparation, a variety of physical as well as chemical methods can be employed for nanoparticle synthesis. Hence, Sol-gel method is adopted for the preparation of copper zinc ferrite. The adopted method is efficient for fiber and fine powder preparation by permitting the distinct access to various oxides, and it can be processed under low temperature. Hence the preparation of tests is isolated by the X-ray powder diffraction pattern technique. The meticulous metric is calculated by exploiting the Scherer’s concept, which involves spinal cubic framework. The second feature called “scanning electron microscope” (SEM) has paid more attention to electron beam scanning above the surface for image creation. The electrons present in the beam cooperate with the model, generating different signals which are employed to acquire data regarding the composition and topology of the surface and help in gathering the resulting electron signals. The third feature is the utilization of Energy Dispersive X-ray Analyzer (EDAX), which is referred to as an x-ray technique utilized to categorize the material composition of the elements. The final method is Fourier Transform infrared spectroscopy (FTIR), which measures the vibrational frequencies by permitting the valuable imminent into the diverse functional groups and the chemical bonds involved in the existing system. The vibrational frequency of the spinal framework is present in the CuZn functional group, and it is recognized with the help of FTIR. The value of coercivity starts decreasing when CuZn is carried out in various annealing temperatures. The energy level 59. band gap of the copper zinc ferrites nano samples can be determined by the FTIR. The projected work is performed for computing the crystalline framework and its chemical composition. 315-318

Keywords: Sol-gel, Nano particle, XRD, EDAX, FTIR

References: 1. Tahseen H. Mubarak et al (2017), “Preparation and study structure properties of Zinc-Copper ferrite nanoparticles”, IOSR Journal of Applied Physics (IOSR-JAP) e-ISSN: 2278-4861.Volume 9, Issue 6 Ver.III (Nov. - Dec. 2017), PP 08-12. 2. V. V. AWATI (2015)”, Synthesis and Characterization of Ni-Cu-Zn Ferrite Materials by 3. Auto Combustion Technique”, International Journal of Chemical and Physical Sciences, ISSN:2319-6602IJCPS Vol. 4 Special Issue ETP – 2015. 4. Arifa Sheikh et-al (2016), “A Thorough Study of Zinc Ferrite Nanoparticles with Reference to Green Synthesis”, International Journal of Nanomedicine and Nano surgery, ISSN 2470-3206, Volume: 2.3. 5. P M PRITHVIRAJ SWAMY et al (2011), “Synthesis and characterization of zinc ferrite nanoparticles obtained by self-propagating low- temperature combustion method”, Indian Academy of Sciences, Vol. 34, No. 7, December 2011, pp. 1325–1330. 6. Aurelija GATELYTĖ (2011),” Sol-Gel Synthesis and Characterization of Selected Transition Metal Nano-Ferrites”, MATERIALS SCIENCE (MEDŽIAGOTYRA). Vol. 17, No. 3. 2011. 7. W. C. Kim, S. J. Kim, S. W. Lee, and C. S. Kim, “Growth of ultrafine NiCuZn ferrite and magneticproperty by sol-gel method,” J. Magn. Magn. Mater, vol. 226, pp. 1418 - 1420, 2001. 8. W. Y. Lucas, J. W. Moon, C. Rawn, L. J. Lane, A. Rondinone, J. R. Tompson, B. C.Chakoumakis, and T. J. Phelps, “Magnetic properties of bio- synthesized zinc ferrite nanoparticles,” J.Magn. Magn. Mater., vol. 323, pp. 3043 - 3048, 2011. 9. M. Srivastava, S. Chaubey, and A. K. Ojha, “Investigation on size dependent structural and magnetic behavior of nickel ferrite nanoparticles prepared by sol-gel and hydrothermal method,” Mater. Chem.Phys., vol. 118, pp. 174 - 180, 2009 Authors: S. Sri Hari, C. Kavinkumar, Gurram Keshav Niketh, N. Harini Paper Title: Enhancing Security of One Time Passwords in Online Banking Systems Abstract: The advancement in the technology has led to introduction of many popular Internet applications. Internet Banking also called as e-banking is one such indispensable application which has a major impact on our modern life. 60. Although banks strongly encourage their customers to do online transactions and advertise it as a very safe and secure mode of transacting, in reality there is a huge risk associated. The continuous growth of online banking application 319-324 brings with it several security issues and increased cost of implementing higher security systems for customers and banks. Online banking system maybe compromised in a wide variety of ways like using trojan horse, botnets, Phishing etc. Although multifactor authentication schemes exist to verify the authenticity of the client, the drawbacks with these schemes is that they work at transaction level rather than at the authentication level. This paves way for so called for man in the middle attack between the user and security mechanisms of browsers, smartphones etc. With online banking security becoming a critical requirement one needs to find better usable and workable solution based on transaction cum authentication level. An attempt has been made in this work to propose an authentication that enhances the security of the online banking systems.

Keywords: Authentication, e-Banking, One Time Password, Fingerprint, Biometrics, Short Message Service, Subscriber Identity Module.

References: 1. https://www.moneycontrol.com/news/trends/current-affairs-trends/indian-banks-lost-rs-109-75-crore-to-theft-and-online-fraud-in-fy18- 2881431.html 2. Jun Lu, Bingjun Zhang. Security product research in the Internet banking based on OTP, Financial electronic .J.China, 2009.11.pp80-81 3. C. Grier, S. Tang, S. King, “Secure Web Browsing with the OP Web Browser”, in IEEE Symp. on Security and Privacy (SP 2008), pp. 402-416, 2008. 4. K.Chikomo, M. K. Chong, A. Arnab, A. Hutchison (2006), ―Security of mobile banking‖, University of Cape Town,South Africa, Tech. Rep. [Online]. Available: http://pubs. cs.uct.ac.za/archive/ 00000341/01/Security of Mobile Banking paper.pdf. 5. N. Croft, M. Olivier, ―Using an approximated One-Time Pad to Secure Short Messaging Service (SMS)‖, in Proceedings of the Southern African Telecommunication Networks and Applications Conference (SATNAC), 2005, pp. 71–76. 6. Xing Fang, Justin Zhan. Online Banking Authentication Using Mobile Phones,IEEE 2010. [18]. A. Hisamatsu, D. Pishva, and G.G.D. Nishantha. 7. Online Banking and Modem Approaches Toward its Enhanced Security, ICACT 2010. 8. Online Banking: Threats and Countermeasures Revised Version: 1.3 Release Date:June, 2010 AhnLab, Inc. 9. Singhal, D and V. Padhmanabhan (2008). A Study on Customer Perception Towards internet Banking: Identifying major contributing factors. The Journal of Nepalese Business Studies. V (1), 101 – 111. 10. Dr.N.Harini, Dr T.R Padmanabhan and Dr.C.K.Shyamala , ―Cryptography and security‖, Wiley India, First Edition, 2011 11. N. Harini and Dr. T.R. Padmanabhan, “2CAuth: A New Two Factor Authentication Scheme Using QR-Code”, International Journal of Engineering and Technology (IJET),Vol. 5:2 Apr-May 2013, Pages: 1087 -1094 Authors: K. Rameshwaraiah, Srinivasa Babu Kasturi, M. Swapna Paper Title: Outsource Key Updates for Cloud Storage Auditing with Key-Exposure Resilience Abstract: Cloud Storage has also been increasing in recognition these days because of a few of the identical motives as Cloud Computing. Cloud Storage promises virtualized storage on demand, over a network based on a request for a given Quality-of-Service (QoS). Although cloud storage provides super benefit to users, it brings new safety hard issues. One essential safety hassle is a way to efficiently check the integrity of the statistics saved in cloud. In current years, many auditing protocols for cloud storage had been proposed to cope with this trouble. The key publicity problem, as another vital hassle in cloud storage auditing, has been taken into consideration recently. Hence, the intention of this paper is to design a cloud storage auditing protocol that may fulfill above requirements to acquire the outsourcing of key updates. We suggest a novel paradigm known as cloud storage auditing with verifiable outsourcing of key updates. In this new paradigm, key-update operations aren't performed by the client, but by way of a Third Party Auditor (TPA).

Keywords: Cloud Storage, Auditing, Third party Auditor, Key Exposure.

61. References: 1. Jia Yu, Kui Ren, and Cong Wang, "Enabling Cloud Storage Auditing With Verifiable Outsourcing of Key Updates", IEEE Transactions On 325-328 Information Forensics And Security, Vol. 11, No. 6, June 2016 2. J. Yu, K. Ren, C. Wang, and V. Varadharajan, "Enabling cloud storage auditing with key-exposure resistance," IEEE Trans. Inf. Forensics Security, vol. 10, no. 6, pp. 1167-1179, Jun. 2015. 3. F. Sebe, J. Domingo-Ferrer, A. Martinez-balleste, Y. Deswarte, and J. Quisquater, "Efficient remote data possession checking in critical information infrastructures," IEEE Trans. Knowl. Data Eng., vol. 20, no. 8, pp. 1034-1038, Aug. 2008 4. Y. Zhu, H. Wang, Z. Hu, G.-J. Ahn, H. Hu, and S. S. Yau, "Efficient provable data possession for hybrid clouds," in Proc. 17th ACM Conf. Comput. Commun. Secur., 2010, pp. 756-758. 5. C. Wang, K. Ren, and J. Wang, "Secure and practical outsourcing of linear programming in cloud computing," in Proc. IEEE INFOCOM, Apr. 2011, pp. 820-828 6. K. Yang and X. Jia, "An efficient and secure dynamic auditing protocol for data storage in cloud computing," IEEE Trans. Parallel Distrib. Syst., vol. 24, no. 9, pp. 1717-1726, Sep. 2013. 7. J. Yu, F. Kong, X. Cheng, R. Hao, and G. Li, "One forward-secure signature scheme using bilinear maps and its applications," Inf. Sci., vol. 279, pp. 60-76, Sep. 2014. 8. H. Shacham and B. Waters, "Compact proofs of retrievability," in Advances in Cryptology. Berlin, Germany: Springer-Verlag, 2008, pp. 90-107. 9. G. Ateniese, R. Di Pietro, L. V. Mancini, and G. Tsudik, "Scalable and efficient provable data possession," in Proc. 4th Int. Conf. Secur. Privacy Commun. Netw., 2008, Art. ID 9 10. G. Ateniese et al., "Provable data possession at untrusted stores," in Proc. 14th ACM Conf. Comput. Commun. Secur., 2007, pp. 598-609. Authors: C. Sivakumar, P. Latha Parthiban Paper Title: An Improved Location based Routing Protocol for WSN Using Novel Location Proximity Algorithm Abstract: The main characteristics of Wireless Sensor Network (WSN) include its: dynamic changing topology, multihop connections between the nodes and an effective dynamic routing protocol. These concerns are addressed in current research and it aims to improve the effective utilization of nodes in WSN. To improve the location search information of next hop, a novel Location Proximity (LP) search algorithm is used. Hence, proposed system addresses 62. the concerns related to high energy consumption in WSN network characteristics under balanced and unbalanced network condition. The simulation results proved that proposed method fits well to changes in network than 329-334 conventional algorithms. Also, the proposed method adopts well to dynamic change in network that results in increased throughput (23%) with improved lifetime (10%)and reduced energy consumption (reduced to 78%).

Keywords: Location based routing, wireless sensor network, increased throughput, energy efficiency.

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M., Ramadan, R. A., & El Boghdadi, H. M. (2014). Finding the best sink location in WSNs with reliability route analysis. Procedia Computer Science, 32, 1160-1167. 17. Long, J., Dong, M., Ota, K., & Liu, A. (2014). Achieving source location privacy and network lifetime maximization through tree-based diversionary routing in wireless sensor networks. IEEE Access, 2, 633-651. 18. Bhuiyan, M. Z. A., Wang, G., Cao, J., & Wu, J. (2015). Deploying wireless sensor networks with fault-tolerance for structural health monitoring. IEEE Transactions on Computers, 64(2), 382-395. 19. Lin, H., &Üster, H. (2014). Exact and heuristic algorithms for data-gathering cluster-based wireless sensor network design problem. IEEE/ACM Transactions on Networking (TON), 22(3), 903-916. 20. Qiu, C., & Shen, H. (2014). A delaunay-based coordinate-free mechanism for full coverage in wireless sensor networks. IEEE Transactions on Parallel and Distributed Systems, 25(4), 828-839. 21. Wang, N. C., & Chiang, Y. K. (2011). Power-aware data dissemination protocol for grid-based wireless sensor networks with mobile sinks. IET communications, 5(18), 2684-2691. 22. Yun, Y., & Xia, Y. (2010). Maximizing the lifetime of wireless sensor networks with mobile sink in delay-tolerant applications. IEEE Transactions on mobile computing, 9(9), 1308-1318. 23. Misra, S., Hong, S. D., Xue, G., & Tang, J. (2010). Constrained relay node placement in wireless sensor networks: Formulation and approximations. IEEE/ACM Transactions on Networking (TON), 18(2), 434-447. 24. Wang, W., Srinivasan, V., & Chua, K. C. (2008). Extending the lifetime of wireless sensor networks through mobile relays. IEEE/ACM Transactions on Networking (TON), 16(5), 1108-1120. 25. Wang, Z. M., Basagni, S., Melachrinoudis, E., &Petrioli, C. (2005, January). Exploiting sink mobility for maximizing sensor networks lifetime. In System Sciences, 2005. HICSS'05. Proceedings of the 38th Annual Hawaii International Conference on (pp. 287a-287a). 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Arun Kumar Paper Title: A Practical Implementation Smart Farming Using Recommendation Routing in WSN Abstract: The objective of this paper is to automate crop supervision using Wireless Sensor Network (WSN). The main importance of the work is to help the agriculturalists improve the yield of the crop by trying to eliminate the human errors and the consequences which follow them. This is done by trying to maintain the optimum crop requirements in the Green house. The automated system also enhances measures for burst nature of data being transmitted the amount of traffic exceeds the speed of a link. At this point vulnerabilities of a network, causes failure at a moment, delay in delivery, reduced throughput and loss of packets in network. The framework takes intelligent decisions in order to help maintain the required conditions by proposing routing mechanism called recommendation routing. The Wireless mode of data transfer establishes a Wireless Sensor Network (WSN) which nullifies the problems 63. that arise due to wired mode of data transmission. The main aim of this work is to automate the crop management using wireless sensor networks and proposing a dynamic routing mechanism to address the efficient data transfer in node. 335-345

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Glan Devadhas Paper Title: Increasing the Coverage Area Using Microcells in Hybrid GFDM System based on RoF Technology Abstract: Hybrid architecture based on Wavelength Division Multiplexing Passive Optical Networks (WDM-PON) and Radio-over-Fiber (RoF) technology to deploy Generalized Frequency Division Multiplexing (GFDM) signals in 5G Heterogeneous Networks (HetNet) is proposed in this paper. The proposed RoF technology is the combination of Optical and Wireless communications that is used to reduce the base stations and to provide feasibility in high capacity connections and flexibility over long distance. This paper mainly focuses on increasing the coverage area without much path loss in the densely populated areas. By using microcells in GFDM system, this technology significantly enhances the data capacity of the users and also provides wider coverage area. The performance of GFDM is analyzed by computing the throughput and various parameters that affect the capacity of the system. The obtained simulation results proved that the proposed technique performs much better than conventional techniques.

64. Keywords: GFDM, GFDM Improved Proportional Fair, Microcells, Coverage area, Pathloss. 346-352 References: 1. Abdoli J, Jia M and Ma J (2015), "Filtered OFDM: A new waveform for future wireless systems", pp. 66-70, IEEE 2015. 2. Al-Hourani A, Chandrasekhran S, Kandeepan S, Jamalipour A (2017), "Aerial platforms for public safety networks and performance optimization", Wireless Public Safety Networks, pp. 133-153, 2017. 3. Bi M, Jia W, Li L, Miao X and Hu W (2017) , "Investigation of F-OFDM in 5G fronthaul networks for seamless carrier-aggregation and asynchronous transmission", pp.W1C-6, Optical Fiber Communication, March 2017. 4. Deniz C, Uyan O G, Gungor V C (2018), "On the performance of LTE downlink scheduling algorithms: A case study on edge throughput", Computer Standards & Interfaces, Elsevier, pp. 96-108, August 2018. 5. Fettweis G, Krondorf and Bittner S (2009), "GFDM- Generalized Frequency Division Multiplexing", pp.1-4, IEEE 2009. 6. Ferreira J S, Rodrigues H D, Gonzalez A A, Nimr A, Matthe M, Zhang D, Mendes L L and Fettweis G (2017), "GFDM frame design for 5G applications scenarios", Journal of Communication and Information Systems, July 2017. 7. Garcia-Morales J, Femenias G and Riera-Palou F (2016), "Analysis and Optimization of FFR-aided OFDMA-based heterogeneous cellular networks", pp. 5111-5127, IEEE 2016. 8. Hojeij M R, Nour C A, Farah J and Douillard C (2018), "Weighted Proportional Fair Scheduling for downlink Non Orthogonal Multiple Access", Wireless Communication and Mobile Computing, 2018. 9. Japertas S, Grimaila V (2017), "Mobile Signal Path losses in microcells behind buildings", Radio Engineering, pp.191, April 2017. 10. Kelly F P, Maulloo A K and Tan D (1997), "Rate control for communication networks: shadow prices, proportional fairness and stability", Journal of the Operational Research Society, pp. 206-217, 1997. 11. Koudouridis G P, Lundqyist H, Li H and Gelabert X (2018), "Energy Efficiency of network coding enabled mobile small cells", Computer Communications, pp. 50-58, 2018. 12. Michailow N, Krone S, Lentmaier M and Fettweis G (2012), "Bit error rate performance of Generalized Frequency Division Multiplexing", pp.1- 5, IEEE 2012. 13. Ngo T T, Pham T A and Nguyen N D (2018), "Hybrid OFDM RoF-Based WDM-PON/MMW Backhaul Architecture for heterogeneous wireless networks", Journal on Electronics and Communications, March 2018. 14. Niknam S, Nasir A A, Mehrpouyan H and Natarajan B (2016), "A multiband OFDMA heterogeneous network for millimeter wave 5G wireless applications", pp.5640-5648, IEEE 2016. 15. Schaepperle J (2010), "Throughput of a wireless cell using superposition based multiple access with optimized scheduling", pp.212-217, IEEE, September 2010. 16. Sivakumar S, Louisa A, Pavithra K R and Revathy K (2018), "Dynamic Load Balancing in LTE Network", IRJET 2018. 17. Tian F, Guo D, Liu B, Zhang Q, Tian Q, Ullah R and Xin X (2018), "A novel concatenated coded modulation based on GFDM for access optical networks", pp.1-8, IEEE, April 2018. 18. Umezawa, Toshimasa, Jitsuno K, Kanno A, Yamamoto N and Kawanishi T (2017), "30-GHz OFDM Radar and Wireless Communication experiment using Radio Over Fiber tehnology", pp. 3098-3101, IEEE 2017. 19. Viswanath P, Tse D N and Laroia R (2002), "Opportunistic beam forming using dumb antennas", pp.1277-1294, Transactions on Information Theory, 2002. 20. Xu Z (2017), "Coverage and capacity comparison between mmwave and lower frequency bands for 5G communications", Boletin Tecnico, September 2017. 21. Zhang X, Jia M, Chen L, Ma J and Qiu J (2015), "Filtered OFDM enabler for flexible waveform in the 5th generation cellular networks", pp.1-6, IEEE 2015. Authors: M. Aniber Benin, B. Stanly Jones Retnam, M. Edwin Sahayaraj Paper Title: Analysis of Mechanical Strength of Processed Aluminum Laminates Abstract: The aim of this research work is to prepare the titanium ceramic powder reinforced into the matrix of polypropylene and polyvinylchloride foam and analyze the mechanical behavior of these composites and their laminates combinations. The laminates combination has three layers. The center core is aluminum sheet and the others are either polypropylene or polyvinylchloride. The reinforcement of titanium ceramic powder with the thermoplastic polymer has more interest in this research work because it replaces the conventional reinforcement of synthetic fibers in the polymer matrix. The mechanical behavior of the composites and their laminates were analyzed by tensile, flexural and impact test. The physical morphology of the composites was studied using scanning electron microscope. The test result showed that the addition of titanium ceramic powder enhanced the above said properties in polypropylene matrix and the rigidity of the polyvinylchloride increased to reduce its impact strength. The detailed analysis is discussed in the result and discussion section.

Keywords: Aluminum Laminates, Mechanical behavior, Polypropylene composite, Polyvinylchloride composite, Titanium ceramic powder.

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Cho D, Zhou H.J, Cho Y, Andus D, Joo Y.L, Structural properties and superhydrophobicity of electrospun polypropylene fibers from solution and melt., Polymer, 2010, 51, 6005 - 6012. 13. Xiao J.M, Chen Y.A, new micro-structure designs of a polypropylene (PP) composite with improved impact property., Materials letters, 2015, 152, 210 - 212. 14. Ari G.A, Aydin I, Nanocomposites prepared by solution blending: microstructure and mechanical properties., Journal of Macromolecular Science; Part B, 2008, 47(2), 260-267. 15. Sarkar M, Dana K, Ghatak S, Banerjee A., Polypropylene-clay composites prepared from indian bentonite. Bulletin of Material Science, 2008, 31(1), 23 - 31. 16. Straznicky P.V, Laliberte J.F, Poon C, Fahr A., Applications of fiber-metal laminates, Polymer Composites., 2000, 21, 558 - 567. 17. Zhou J, Gual Z.W, Cantwell W.J., The influence of strain-rate on the perforation resistance of fiber metal laminates., Composite structures, 2015, 125, 247 - 255. 18. Syed Ahmed, Anil Kumar C, Mechanical characterization and analysis of perforated fiber metal laminates, International Journal of Engineering trends and technology, 2014, 13(1), 17 - 24. 19. Murtatha M Jamel, Parisa Khoshnoud, Subhashini Gunashekar, Nidal Abu-Zahra, Mechanical properties and dimensional stability of rigid PVC foam composites filled with high aspect ratio phlogopite mica, Journal of Minerals and Materials Charcterization and Engineering, 2015, 3, 237- 247. Authors: S. Subash, B. Stanly Jones Retnam, J. Edwin Raja Dhas, M. Sivapragash 66. Paper Title: Morphological and Comparative Characterization of Silk/Bamboo Fiber Reinforced Epoxy Composite Abstract: Composites are basically a combination of numerous components combined together to form a single bonding structure. Different types of composites are found around us in the form of concrete, balloons etc. are made up of multiple components which are reinforced in to matrix materials which posses exactly different nature. Though there are many types of composites available, polymer (epoxy) Matrix composites plays a very vital role. Basically the reinforcement in composites are made in order to increase the comfort level, reduce weight, increase strength, reduce cost and increase the durability of the material in turn it helps to serve the purpose. It’s being said that most of the fibers used in the reinforcement posses less weight and are stronger than metals. In recent days the vehicle manufacturing Industries make use of this composite materials in order to increase the fuel efficiency by reducing the overall weight. This material gives a very appealing appearance to the exterior and good comfort in the interior at lower cost. In the past few decades the natural fibers started replacing the glass fiber reinforced polymer composites as they can be easily decomposed which does no harm to the ecological system of earth. Silk/Bamboo which is a natural fiber composite can be used as a good alternate for glass fiber or can be used along with the glass fiber in turn reducing the percentage composition of glass fiber. In this work silk/bamboo and glass fiber are used as reinforcement along with epoxy thermosetting plastic as matrix material are synthesized and tensile and SEM were done for the tensile fractured specimens. The properties are compared with each other to show the betterment. 358-361 Keywords: Fiber, Silk/Bamboo, Reinforced, composite, Epoxy.

References: 1. T. Gurunathan, Smita Mohanty and K .Sanjay, Composites: Part A, 77 (2015) 1-25. 2. R. Satheesh Raja, K. Manisekar and V. Manikandan, Key Engg. Mater.,471-472 (2011) 26-30. 3. A.V. Ratna Prasad and K. Mohana Rao, Materials and Design, 32 (2011) 4658-4663. 4. H.P.S. Abdul Khalil, I.U.H. Bhat , M. Jawaid , A. Zaidon , D. Hermawan and Y.S. Hadi, Mater. and Design, 42 (2012) 353-368. 5. Zou Meng , WeiCan-gang , LiJian-qiao , XuShu-cai , Zhang Xiong, Thin-WalledStructures, 95(2015)255-261. 6. I.M.Alhuthali, C. Dong, Composites: Part B, 43 (2012) 2772-2781. 7. Fenglei Lyu , Qingfa Wang , Han Zhu , Mingliang Du, Li Wang and Xiangwen Zhang, Green Energy & Environment, (2017) 151-159. 8. Mei-po Ho, Hao Wanga, Kin-tak Lau, Joong-hee Lee and David Hui, Composites: Part B 43 (2012) 2801-2812. 9. L.M Matuana and J.J.Balatinecz, Wood Sci Technol., 35 (2001) 191-201. 10. D.N Saheb and J.P. Jog. ,Adv Polym.Technol. 18(4) (1999) 351-63. 11. A.V.Gonzaleza , J.M.Cervantes-Uca, R.Olayob., Composites:part B, 30 (1999) 309-20. 12. A.M. Mohanty and L.T. Drazal. J Mater Sci Lett., 21 (2002) 1885-8. 13. R.Zulkifli, C.H.Azhari, M.J.Ghazali, A.R.Ismail. Eur J Sci.Res. (2009)27:454. 14. B.Stanly Jones Retnam, M. Sivapragash, P. Pradeep, Bulletin of mater. Sci., 37(5) (2014) 1059-1064. Authors: T. Ramani, P. Sengottuvelan QOS Development based on Link Prediction with Time Factor for Clustering the Route Optimization and Paper Title: Route Selection in Mobile Ad Hoc Network Abstract: The clustering has been broken down for some issues in Mobile ad hoc network, and there are numerous methodologies has been talked about for the issue of node determination and travel time prediction, however, endures with the exactness and time prediction issues for QoS improvement in the network. So only we propose a novel approach which performs Link Prediction Based Route Clustering Optimization (LPRCO) for QoS Improvement utilizing which a single route will be chosen for better data transmission. The proposed technique keeps up a record about the route at each time window for every node. We assess the activity design at every node at each time window utilizing for the time factor, which the route movement factor will be processed for each route accessible for different goal from a beginning stage. The strategy keeps up different data about the network like the number of nodes, number of the link at every node and the separation between the nodes. Every one of these components is utilized to process the link movement factor at a specific activity channel at any point in time. Based on the element, we record the transmission time at each link at various time window to choose the single route to achieve any goal. The proposed approach has created effective outcomes in route choice and travel time prediction for improving QoS in the network.

Keywords: Route, Cluster, Link, Optimization, Network, Traffic, Time.

References: 67. 1. Tamil selvi," Enhancing Security in Optimized Link State Routing Protocol for Manet Using Threshold Cryptography Technique," IEEE International Conference on Recent Trends in Information Technology, Vol-4, Issue-12, 2014. 2. AydinGuney, BarisAtakan and Ozgur B. Akan, "Mobile Ad Hoc Nanonetworks with Collision-Based Molecular Communication," IEEE 362-368 Transactions on Mobile Computing, Vol. 11, No. 3, pp. 353-366, 2012 3. Zhiguo Ding and Huaiyu Dai," Relay Selection for Cooperative Noma,"IEEE Wireless Communications Letters, Vol-4, Issue-12, 2016. 4. Ranjan, R. Swaminathan, M. Uysal, and E. Knightly,"DDOS-Resilient Scheduling To Counter Application Layer Attacks Under Imperfect Detection," IEEE, Vol-25, Issue-42, 2006. 5. Bahi C and Guyeux A, "Efficient and Robust Secure Aggregation of Encrypted Data in Sensor Networks," Fourth International Conference on Sensor Technologies and Applications (SENSORCOMM'10), pp. 472-477, July 2010. 6. BhushanChaudhari and PrathmeshGothankar, "Wireless Network Security Using Dynamic Rule Generation of Firewall," Information & Computing Technology (ICCICT), pp 190-200, 2012. 7. BarisAtakan, "Nanoscale Communication with Molecular Arrays in Nanonetworks," IEEE Transactions on Nano Bioscience, Vol. 11, No. 2, pp. 149-160, 2012. 8. Cao, J, Zhang, Y, Cao, G &Xie, L,' Data consistency for cooperative caching in mobile environments,' Computer, vol. 40, no. 4, pp. 60-66, 2007. 9. Murat Kuscu and OzgurB.Akan, "A Physical Channel Model and Analysis for Nanoscale Molecular Communications with Forester Resonance Energy Transfer (FRET)," IEEE Transactions on Nanotechnology, Vol. 11, No. 1, pp. 200-207, 2012. 10. Hyun-Ho Choi & Dong-Ho Cho 'On the Use of Ad Hoc Cooperation for Seamless Vertical Handoff and Its Performance Evaluation, Journal of Mobile Network Applications,' vol. 15, 2010, pp. 750-766. 11. Hong KunHo, Lee SuKyoung, Lae Young Kim &Pyung Jung Song 'Cost-Based Vertical Handover Decision Algorithm for WWAN/WLAN Integrated Networks,' 2009, EURASIP Journal on Wireless Communications and Networking. 12. A.G. Kasyanov and Gim, "Proximity-Based Groupcast in MANET," Journal of communication technology and electronics, Springer, Vol. 57, No. 12, pp. 1303-1313, 2012. 13. David, P &Marila C, 'Onto- Scalable Ad-hoc networks: Deferred routing,' Science direct on Computer communication, vol. 35, pp. 1574-1589, 2012. 14. Antonopoulos, A &Verikoukis, C, 'Traffic-Aware Connection Admission Control Scheme for Broadband Mobile Systems', Communications Letters, IEEE, vol.14, no.8, pp.719-721, 2010. 15. Erik Kuiper &SiminNadjim-Tehrani, 'Geographic Routing with Location Service in Intermittently Connected MANETs,' IEEE Transactions on Vehicular Technology, vol.60, no.2, pp.592-604, 2011. 16. Friedman, R &Kliot, G, 'Location Services in Wireless Ad-hoc and Hybrid Networks: A survey,' Department of Computer Science, Technion, Haifa, Israel, no. 4, pp. 1-15, 2006. 17. Bsoul, M, Al-Khasawneh, A, Kilani, Y &Obeidat, I,' A threshold-based dynamic data replication strategy.' The Journal of Supercomputing, vol.60, no.3, pp. 301-310, 2012. 18. Ashok Kumar, R &Baskaran, P, 'A Co-Operative Cluster-Based Data Replication Technique for Improving Data Accessibility and Reducing Query Delay in MANETs', International Journal of Scientific and Research Publications,vol.3.no.11.pp.1-5, 2013. 19. Andronikou, V, Mamouras, K, Tserpes, K, Kyriazis, D &Varvarigou, T,'DynamicQoS-aware data replication in grid environments based on data importance', Future Generation Computer Systems, vol.28, no 3, pp.544-553, 2012. 20. Al-Omari, SAK &Sumari, P, an 'An overview of mobile ad hoc networks for the existing protocols and applications,' International Journal on applications of graph theory in wireless ad hoc networks and sensor networks, vol.2,no.1,pp.87-110, 2010. Authors: M. Hariprabhu, K. Sundararaju Performance Improvement of Grid Connected Photovoltaic Power Generation System Using Robust Power Paper Title: Balanced Control (RPBC) Technique with Active Power Line Conditioning Abstract: This work displays a grid-connected photovoltaic (PV) framework given a global maximum power point tracking (MPPT) system, which is performed by methods for the Robust Power Balanced Control (RPBC) strategy. The RPBC based MPPT method is utilized to tackle issues identified with mismatching marvels, for example, partial shading, in which the PV exhibits are normally submitted. Considering the inquiry of the global maximum power point under partial shading, the adequacy of the RPBC - based MPPT procedure is featured when contrasted, and the notable bother and watch MPPT system, since both the said RPBC method is utilized to decide the dc bus voltage reference to guarantee a proper grid-tied inverter task. A current generator method given a synchronous reference outline is proposed, which works in conjunction with a dc-bus controller and MPPT techniques, computing the reference current of the grid-tied inverter. Furthermore, the present generator controls the vitality prepared by the PV framework to keep away from overpower rating of the grid-tied inverter, since the dynamic power infusion into the grid, reactive power compensation and harmonic currents suppression are completed all the while utilizing RPBC technique. The execution and feasibility of the grid-tied PV framework are assessed by methods for simulation and experimental results.

68. Keywords: Maximum Power Point Tracking, Grid, Inverter, Robust Power Balanced Controller. 369-378 References: 1. S Punitha, K Sundararaju, "Voltage stability improvement in power system using optimal power flow with constraints", 2017 IEEE International Conference on Electrical, Instrumentation and Communication Engineering (ICEICE),pp 1-6. 2. K.Sundararaju, A.Nirmalkumar, April 2012,"Cascaded and Feed forwarded Control of Multilevel Converter Based STATCOM for Power System Compensation" International Review on Modeling and Simulation, Vol.5, No.2, PP.609-615. 3. K. Sundararaju, A. Nirmalkumar,February 2012,"Cascaded Control of Multilevel Converter based STATCOM for Power System Compensation of Load Variation, International Journal of Computer Applications,Vol.40,No.5,PP.30-35. 4. RS Kumar, S Kirthika, K Sundararaju, "Analysis Of Single- Stage High-Frequency Resonant AC/AC Converter Using Artifical Neural Networks" International Journal of Pure and Applied Mathematics 117 (8), 161-164. 5. R Karthikeyan & Dr S Chenthur Pandian "Reducing THD in Hybrid Multilevel Inverter with Varying Voltage Steps under Space Vector Modulation ", European Journal of Scientific and Research(EJSR), Volume 54 Issue 02 June 2011, pp: 198-206, ISSN: 1450-216X 6. M Jeyapriya, S Banumathi, "Comparable Action of single phase Transformer less GRID-Coupled PV Inverters with Common AC and DC load", Journal of Chemical and Pharmaceutical Sciences, 2017, pp 299-304. 7. K.Sundararaju, M.Hariprabhu, "Implementation of Intelligent Grid-Interfaced PV with DC-DC Boost Converter Topology for Agricultural Water Pumping System" International Journal of Pure and Applied Mathematics, ISSN: 1314-3395, pp:2147 - 2160. 8. M.Hariprabhu, Dr.K.Sundararaju, "Sophisticated Fuzzy Rule Set (SFRS) based MPPT Technique for Grid-Connected Photovoltaic Power Plant with DC-DC Boost Converter" , Journal of Advanced Research in Dynamical and Control Systems Vol. 9. Sp- 18 / 2017, PP: 2612 - 2637. Authors: D. Himabindu, G. Sreenivasan, R. Kiranmayi Paper Title: ‘Fractional-order-PID-controlled QZSI-Fed Induction-Motor-Drive-System with OTT-Filter’ Abstract: QZS(Quasi-Z-Source) is an upright choice amid rectifier & TPIM(three-phase-induction-motor) since QZSI can boost the low voltage to the entailed-level. This effort deals with enhancement in dynamic-response of QZSI-fed IM-framework. The purpose of this effort is to plan a closed-loop-controlled-QZSI-fed-IMS(-induction-motor–system) that affords a steady-rotor-speed. This has been comprehended using closed-loop-controlled-QZSI-IMD-system. The Alternating-Current(AC)gets converted into Direct-Current(DC)by utilizing a rectifier. The ‘QZSI’ is exploited to switch it to TPAC(three-phase-AC). The yield of TPI(three-phase-inverter) is filtered before it is applied to a ‘TPIM(three-phase-Induction-motor)’. The ‘FOPID-controller’ is recommended to keep-constant value-of-speed. The outcomes attained using PI-controlled QZSI-IMD framework is evaluated with FOPID-controlled-QZSI-IMD framework. The recommended-FOPID-controlled QZSI-IMD-system has benefits like diminished settling-time &diminished- peak –over-shoot. 69. Keywords: ‘QZSIFIM’,’ FO-PID’, ‘OTT- filter’, ‘THD’. 379-383 References: 1. Y.Shuitao, -Q.Lei,& -F. Z. Peng, "-Current-Fed-quasi-Z-source-inverter-with-voltage- buck-boost&-regeneration-capability," -IEEE-Trans. -Ind. -Appl., -vol-47, -no-2, -pp-882-892, -2011. 2. M.Adamowicz, -R.Strzelecki, -F.Z.Peng, -J.Guzinski,&-H.A.Rub, "-New-type -LCCT-Z-source-inverters,"-in-Proc-EPE, -2011, -pp-1-10. 3. Y.Zhou &-W.Huang, "-Single-stage-boost-inverter-with-couple-inductor," -IEEE Trans.-PE.,-vol-27, -no-4,-pp-1885-1893, -Apr-2012. 4. M.K.Nguyen, -Y.C.Lim, & -G.B.Cho, "-Switched-inductor-quasi-Z-source--inverter,"-IEEE-Trans.-PE.,-vol-26,-no-11, -pp-3183- 3191, -Nov- 2011. 5. C.J.Gajanayake, -F.L.Luo, -H.B.Gooi, -P. L.So, &-L.K.Siow, "-Extended-boost-Z-source-inverters," -IEEE-Trans..PE., -vol-25, -no-10, -pp- 2642-2652, -Oct- 2010. 6. D.Vinnikov, -I.Roasto, -R.Strzelecki, & -M.Adamowicz,"-Step-up-DC/DC -converters-with-cascaded-quasi-Z-source-network," -IEEE-Trans.- Ind.-Electron., -vol-59, -no-10, -pp-3727-3736, -Oct-2012. 7. J.C.Rosas-Caro,-F.Z.Peng,-H.Cha& C.Rogers,"-Z-source-converter-based-energy-recycling-zero-voltage-electronic-load," -IEEE-Trans.-Ind.- Electron.,-vol-56, -no- 12,- pp-4894-4902, -Dec- 2009. 8. S.Rajakaruna &-L.Jayawickrama, "-Steady-state-analysis&-designing-impedance- network-of-Z-source-inverters," IEEE-Trans.-Ind.-Electron.,- vol-57,-no-7,-pp-2483-2491, -Jul-2010. 9. R.Lai,-F.Wang,-R.Burgos,-Y.Pei, -D.Boroyevich, -B.Wang, -T.A.Lipo, -V.D. Immanuel,&-K.J.--Karimi,"-A systematic topology-evaluation- methodology-for-high-density-three--phase-PWM-AC-AC-converters,"-IEEE-Trans.-P.E., -vol-23, -no-6, -pp- 2665-2680, -Nov- 2008. 10. P.Sun, -C.Liu, -J.-S.Lai, -C.-L.Chen, &-N Kees,"-Three-Phase-dual-buck-inverter -with-unified-pulse-width-modulation," -IEEE-Trans.-PE,- volum-27, -num-3, -PP. -Num- 1159-1167, [-Marc-2012]. Authors: V. Usha, R. Soundarya, R. Sathya, C. Saranya Jothi Paper Title: Secure Cloud Data Storage Using Hybrid Encryption Algorithm Abstract: Information transmitted through web is getting bigger consistently .Therefore a calculation is required by which our information can be exchanged quickly and safely. The primary point of this specific research is to ensure the transmitted information with the assistance of encryption and decoding systems. This exploration paper shows a model for encoding the information transmitted through cloud. As use of frameworks and web is ending up fast and identifying with well ordered, information security is transformed into an essential stress in PC frameworks. There is reliably chance dismissing framework security which drives a need of a successful and direct technique for tying down the electronic documents from being scrutinized or used by people other than who are affirmed to do it. Encryption is one of the security strategy broadly used to guarantee anonymity. Different types of Encryption algorithms are given below Triple DES, RSA, AES, Blowfish, MD5, SHA, HMAC and Steganography utilized to expand encryption speed, lessen handling time and furthermore gives greater security, verification, approval, Combination of information and furthermore looks after privacy.

Keywords: Security, DES, RSA, AES, Blowfish, MD5, SHA, HMAC, Steganography.

References: 1. Md. Martuza Ahamad* and Md. Ibrahim Abdullah, "Comparison of Encryption Algorithms for multimedia", Rajshahi University Journal of Science & Engineering ,Vol. 44:131-139, 2016. 2. Mehul Batra, Prayas Dixit, Lalit Rawat, Rohini Khalkar, "SECURE FILE STORAGE IN CLOUD COMPUTING USING HYBRID 70. ENCRYPTION ALGORITHM", International Journal of Computer Engineering and Applications, Volume XII, Issue VI, June 18, www.ijcea.com ISSN 2321-3469 3. Dr. S.H Patil and Rohini Khalkar, "Data Security Technique In Cloud Storage", International Journal of Computer Engineering and Technology, 384-387 vol. 4, Issue. 2, pp:373-375, June 2013. 4. Rohini Khalkar and Dr. S.H Patil, "Data Integrity Proof Techniques In Cloud Storage", International Journal of Computer Engineering and Technology, vol. 4, Issue.2, pp:454-458, April 2013. 5. V.S. Mahalle and A. K. Shahade, "Enhancing the Data Security in Cloud by Implementing Hybrid (Rsa & Aes) Encryption Algorithm", IEEE, INPAC,pp 146-149,Oct. 2014. 6. P. S. Bhendwade and R. T. Patil, "Steganographic Secure Data Communication", IEEE, International Conference on Communication and Signal Processing, pages 953-956,April 2014. 7. Kamara S., Lauter K.: Cryptographic cloud storage. In: 14th international conference on Financial cryptograpy and Data security, pp. 136-149 (2010). 8. Yi-Ruei Chen, Cheng-Kang Chu, Wen-Guey Tzeng, and Jianying Zhou :Cloud HKA: A Cryptographic approach for hierarchical access control in cloud computing, Proc. of 11th international conference on Applied Cryptographyand Network Security (ACNS'13), pp. 37-52, Springer- Verlag, Berlin, Heidelberg, (2013). 9. Peter Mel and Tim Grace, "The NIST Definition of Cloud Computing", NIST, 2010. 10. Achill Buhl, "Rising Security Challenges in Cloud Computing", in Proc. of World Congress on Information and correspondence Technologies ,pp. 217-222, Dec. 2011. 11. Srinivasarao D et al., "Breaking down the Superlative symmetric Cryptosystem Encryption Algorithm", Journal of Global Research in Computer Science, vol. 7, Jul. 2011 12. Jitendra Singh Adam et al.," Modified RSA Public Key Cryptosystem Using Short Range Natural Number Algorithm" , International Journal of Advanced Research in Computer Science and Software Engineering ,vol. 2,Aug. 2012. 13. Tingyuan Nye and Tang Zhang "An investigation of DES and Blowfish encryption algorithm? , in Proc. IEEE Region 10 Conference, pp. 1-4 ,Jan. 2009. 14. Manikandan.G et al., "A changed cryptographic plan improving information", Journal of Theoretical and Applied Information Technology, vol 35,no.2,Jan.2012. Authors: M. Pushpalatha, Antony Selvadoss Thanamani Paper Title: Statistical Dictionary with Conditional Random Fields to Identify the Kannada Named Entities Abstract: We present an algorithm to recognize and identify the named entities of Kannada text document. The Kannada text document is collected from Central Institute of Indian Languages has many issues to be addressed. The proposed method has addressed the objective of algorithm is to determine and recognize the Kannada Named Entities like name of a person, designation of a person and place needs to be identified and recognized. The proposed statistical dictionary with conditional random fields in deep neural networks have been used to achieve the task of recognition of Kannada Named Entities The dictionary of Kannada words is formed from the statistical approach of matching patterns of Unicode values of individual words of a document. The sequence of Unicode values are considered for matching of patterns with deep architecture of neural networks has helped us in recognizing the Kannada word items from a dictionary formed from the proposed method. Finally the proposed method has achieved an accuracy of 84.46% from 71. the proposed statistical dictionary of Kannada words with Conditional Random Fields. 388-392 Keywords: CRF, Dictionary, Deep Learning, KNER.

References: 1. Amarappa, S. and S.V. Sathyanarayana, 2013. Namedentity recognition and classification in Kannadalanguage. Int. J. Electron. Comput. Sci. Eng., 2:281-289. 2. Amarappa, S. and S.V. Sathyanarayana, 2013. A hybrid approach for Named Entity Recognition, Classification and Extraction (NERCE) in Kannada documents.Proc. Int. Conf. Multimedia Process.Commun.Info.Tech. 3. Amarappa, S. and S.V. Sathyanarayana, 2015. Kannada Named entity recognition and classification (nerc) based on multinomial naïve bayes (mnb) classifier. Int. J. Natural Language Comput. DOI: 10.5121/ijnlc.2015.4404 4. Bhat, S., 2012. Morpheme segmentation for Kannada standing on the shoulder of giants. Proceedings of the 3rd Workshop on South and Southeast Asian Natural Language Processing, (NLP' 12), pp: 79-94. 5. Bhuvaneshwari, C.M., 2014. Rule based methodology for recognition of kannada named entities. Int. J. Latest Trends Eng. Technol., 3: 50-59. 6. Cucerzan, S. and D. Yarowsky, 1999. Language independent named entity recognition combining morphological and contextual evidence. Proceedings of the 1999 Joint SIGDAT Conference on Empirical Methods in Natural Language Processing and Very Large Corpora, (VLC' 99), pp: 90-99. 7. Curran, J.R. and S. Clark, 2003. Language independent NER using a maximum entropy tagger. Proceedings of the 7th Conference on Natural Language Learning at HLT-NAACL, (LLH' 03), Edmonton, pp: 164-167. DOI: 10.3115/1119176.1119200 8. Ekbal, A., R. Haque and S. Bandyopadhyay, 2008. Named entity recognition in Bengali: A conditional random field approach. IJCNLP. 9. Ekbal, A. and S. Bandyopadhyay, 2008. Bengali named entity recognition using support vector machine. IJCNLP. 10. Gali, K., H. Surana, A. Vaidya, P. Shishtla and D.M. Sharma, 2008. Aggregating machine learning and rulebased heuristics for named entity recognition. IJCNLP. 11. James, H., 1995. Natural Language Understanding. 1stEdn., Dorling Kindersley pvt.Ltd., New Delhi, India. 12. Lafferty, J., A. McCallum and F.C. Pereira, 2001.Conditional random fields: Probabilistic models for segmenting and labeling sequence data. 13. Malarkodi, C.S., R.K. Pattabhi and L.D. Sobha, 2012. Tamil NER - coping with real time challenges. Proceedings of the Workshop on Machine Translation and Parsing in Indian Languages, (PIL' 12), pp: 23-23. 14. Murthy, V., M. Khapra and P. Bhattacharyya, 2016. Sharing network parameters for crosslingual named entity recognition. Comput.Sci. Nadeau, D. and S. Sekine, 2007.A survey of named entity recognition and classification. Lingvisticae Investigationes, 30: 3-26. 15. Nayan, A., B.R.K. Rao, P. Singh, S. Sanyal and R. Sanyal, 2008. Named entity recognition for Indian languages. Proceedings of the IJCNLP-08 Workshop on NER for South and South East Asian Languages, (EAL' 08), pp: 97-104. 16. Noor, N.M., J. Sulaiman and S.A. Noah, 2016. Malay name entity recognition using limited resources. Adv. Sci. Lett., 22: 2968-2971. DOI: 10.1166/asl.2016.7124 17. Nothman, J., N. Ringland, W. Radford, T. Murphy and J.R. Curran, 2013. Learning multilingual named entity recognition from Wikipedia. Artificial Intell., 194: 151-175. DOI: 10.1016/j.artint.2012.03.006 18. Pallavi, K.P. and A.S. Pillai, 2015. Kannpos-kannada parts of speech tagger using conditional random fields. Proceedings of the Emerging Research in Computing, Information, Communication and Applications, (ICA' 15), Springer India, pp: 479-491. 19. Pandian, S., K.A. Pavithra and T. Geetha, 2007. Hybrid Three-stage named entity recognizer for Tamil. INFOS. 20. Pattabhi, R.K., T. Rao, S.R.R.R. Vijay, Vijayakrishna and L. Sobha, 2007. A text chunker and hybrid POS tagger for Indian languages. Shallow Parsing South Asian Languages. Riaz, K., 2010. Rule-based named entity recognition in Urdu. Proceedings of the 2010 Named Entities Workshop, Jul. 16-16, Uppsala, pp: 126-135. 21. Saha, S.K., S. Sarkar and P. Mitra, 2008a. A hybrid feature set based maximum entropy Hindi named Entity recognition. IJCNLP. 22. Saha, S.K., S. Chatterji, S. Dandapat, S. Sarkar and P. Mitra, 2008b. A hybrid approach for named entity Recognition in Indian languages. Proc. IJCNLP. 23. Shishtla, P., P. Pingali and V. Varma, 2008a. A character N-gram based approach for improved recall in Indian Language NER. IJCNLP. 24. Shishtla, P., K. Gali, P. Pingali and V. Varma, 2008b. Experiments in Telugu NER: A conditional random Field approach. IJCNLP, (2008, January) pp: 105-110. Authors: E. Prabakaran, Jessy Rooby Experimental Investigation on Concrete Using Flyash As a Partial Replacement for Cement with Grey Paper Title: Water in Magnetic Field Abstract: The performance of the concrete in structural aspect is been taken in mind when we go for the large structures and as well as the durability which plays a major role on it. India is fast growing country with more unskilled laborers and the lack of knowledge in concrete behavior which leads to complications in the performance of concrete. The replacement of industrial by-products has become common in the past two decades. But the need of super plasticizer is important to maintain the required water binder ratio. To avoid the failure of fresh concrete properties by using super plasticizer, the magnetic water addition recommended with various strength of magnetic field for the required slump value. The grey water can be reused by treatment with magnetic field. The variation of pH and chemical property produces more durable concrete. The present study aims to find out the physical, chemical, mechanical and durability properties of M20 grade concrete using grey water and 20% flyash with a magnetic power of 0T, 0.8T, 1T and 1.2T respectively. Specimens were also cast with normal water for comparisons. It is found that magnetized grey water with 1.2T shows good improvement in compressive strength, corrosion resistance.

Keywords: Magnetic water, grey water, fly ash, super plasticizer, durability.

References: 1. Nan Su, Yeong-Hwa Wu, Chung-Yo Mar, "Effect of magnetic water on the engineering properties of concrete containing granulated blast- furnace slag", Cement and Concrete Research ,30 (2000) 599-605 72. 2. UN Water Statistics, the United Nations Inter-agency Mechanism on all Freshwater Related Issues, Including Sanitation, available at: http://www.unwater.org/statistics/en/ , 2014 (accessed November 11th, 2016). 3. Pang Xiao-Feng and Zhu Xing-Chun, The Magnetization of Water Arising From a Magnetic-Field and Its Applications in Concrete Industry, 393-396 International Journal of Engineering Research and Applications. 3(5) (2013)1541-1552. 4. Neville AM. Properties of concrete. New York: John Wiley and Sons, Inc.; 1996. 5. H H. Afshin, M. Gholizadeh and N. Khorshidi, "Improving Mechanical Properties of High Strength Concrete by Magnetic Water Technology", Scientica Iranica, Vol. 17, No. 1, Feb 2010, pp. 74-79 6. Banejad 1 and E. Abdosalehi, "The effect of magnetic field on water hardness reducing", Thirteenth International Water Technology Conference, IWTC 13 2009, Hurghada, Egypt, PP 117-128 7. S. Bharath, S. Subraja, P. Arun Kumar, "Influence of magnetized water on concrete by replacing cement partially with copper slag", Journal of Chemical and Pharmaceutical Sciences, Volume 9, Issue 4, DEC.,2016, PP2791-2795 8. M Gholizadeh, H Arabshahi, "The effect of magnetic water on strength parameters of concrete", Journal of Engineering and Technology Research, Vol. 3(3), pp. 77-81, March 2011 9. IS: 456:2000, Indian Standard Code for Plain and reinforced concrete-code of practice, BIS, New Delhi 10. Kezhen Yan, Hongbing Xu, Guanghui Shen and Pei Liu, "Prediction of splitting tensile strength from cylinder compressive strength of concrete by support vector machine", Advances in Materials Science and Engineering, Volume 2013, Article ID 597257, PP 1-13 ,2013 11. Kompally Laxminarayana, Md.Subhan, "Experimental Studies on Durability of Magnetic Water Concrete", International Journal of Research, Volume 04 Issue 10 September 2017 PP703-717 12. Mehta, P. K., and Monteiro, P. M., Concrete: Structure, Properties, and Materials, Prentice Hall, PP 559, 1993 13. D.Rajender Babu, Etaveni Madhavi, Surabhi Haritha, "Durability Studies on Magnetic Water Concrete (M30 &M40 Grade)", International Journal of Innovative Research in Science, Engineering and Technology, Vol. 5, Issue 11, November 2016 14. B. Siva Konda Reddy, Vaishali G Ghorpade, H.Sudarsana Rao, "Use of magnetic water for mixing and curing of concrete", International Journal of Advanced Engineering Research and Studies, Vol-IV, Issue I, Oct.-Dec, 2014, 93-95 15. Taghried Isam Mohammed Abdel-Magid. et. al., "Effect of magnetized water on workability and compressive strength of concrete", Procedia Engineering, 193 ( 2017 ) 494 - 500 73. Authors: K. Lalithamani, A. Punitha Paper Title: Detection of Oral Cancer Using Deep Neural Based Adaptive Fuzzy System in Data Mining Techniques Abstract: Cancer has the highest growth rate among all diseases globally. Oral cancer is one of the most dangerous cancer which affects and originates from the oral cavity and neck. Overuse of tobacco and smoking cigarettes are the primary risk factor for developing oral cancer. Another habit which has a strong association with oral cancer is the consumption of alcohol. A large number of patient deaths were recorded from oral cancer as a result of lack of its identification and late treatment. Researchers in the medical community are making an effort to provide a system for effective diagnosis and prevention of the serious disease. In the present research, oral cancer patients can be identified through the use of data mining technology which includes detection, classification and clustering. A Deep Neural Based Adaptive Fuzzy System (DNAFS) is proposed in this paper which uses machine learning for the detection and identification of oral cancer. The two techniques which are part of DNAFS are based on fuzzy logic and neural networks. DNAFS and methods for data mining are explored for the identification of suitable techniques which are helpful in classifying data efficiently. The stages included in the proposed mechanism include data collection, pre processing, Fuzzy C-Means for clustering data, feature selection, classification and identification. Meaningful relationships can be extracted effectively from data using data mining techniques. About 96.29 % accurate results are available from experiments. There is less than 5 ms incidence of error in the result. The datasets are required to be investigated further in daily clinical practice.

Keywords: Data mining, feature selection, Machine Learning, Fuzzy C-means, Oral cancer, medical data set.

References: 1. R. Suganya, R. Shanthi "Fuzzy C- Means Algorithm- A Review "International Journal of Scientific and Research Publications, Volume 2, Issue 11, November 2012 1 ISSN 2250-3153. 2. Gopi Krishna., Sunitha K.V.N and Mishra S," BRAIN TUMOR CLASSIFICATION USING HYBRID Fuzzy C MEANS BASED RADIAL BASIS FUNCTION NEURAL NETWORK", International Journal of Recent Scientific Research Vol. 9, Issue, 3(G), pp. 25119-25125, March, 2018. 3. Mr.S.P. shukla and Mrs. Ritu Dwivedi," Clustering and Classification of Cancer Data Using Soft Computing Technique", IOSR Journal of Computer Engineering (IOSR-JCE) e-ISSN: 2278-0661, p- ISSN: 2278-8727Volume 16, Issue 1, Ver. I (Jan. 2014), PP 32-36. 4. Godwin Ogbuabor, and Ugwoke, F. N," Clustering Algorithm For A Healthcare DATASET USING SILHOUETTE SCORE VALUE", International Journal of Computer Science & Information Technology (IJCSIT) Vol 10, No 2, April 2018. 5. Balasubramanian, T., & Umarani, R. (2012, March). An analysis on the impact of fluoride in human health (dental) using clustering data mining technique. In Pattern Recognition, Informatics and Medical Engineering (PRIME), 2012 International Conference on (pp. 370-375). IEEE. 397-404 6. Banu G. Rasitha & Jamala J.H.Bousal (2015). Perdicting Heart Attack using Fuzzy C Means Clustering Algorithm. International Journal of Latest Trends in Engineering and Technology (IJLTET). 7. Escudero, J., Zajicek, J. P., & Ifeachor, E. (2011). Early detection and characterization of Alzheimer's disease in clinical scenarios using Bioprofile concepts and K-means. In Engineering in Medicine and Biology Society, EMBC, 2011 Annual International Conference of the IEEE (pp. 6470-6473). IEEE. 8. Paul, R., & Hoque, A. S. M. L. (2010, July). Clustering medical data to predict the likelihood of diseases. In Digital Information Management (ICDIM), 2010 Fifth International Conference on (pp. 44-49). IEEE. 9. Barrios JA, Villanueva C, Cavazos A, Colas R (2016) "Fuzzy C-means Rule Generation for Fuzzy Entry Temperature Prediction in a Hot Strip Mill". J Iron Steel Res Int 23: 116-123. 10. Krishna PG, Bhaskari DL (2016). "Fuzzy C-Means and Fuzzy TLBO for Fuzzy Clustering." Proc Second Int Conf Comp Comm Technol: 479- 486. 11. Ghesmoune M, Lebbah M, Azzag H (2016). "A new growing neural gas for clustering data streams." Neural Networks 78: 36-50. 12. Kaur A., Kaur A.(2012), "Comparison of Mamdani-Type and Sugeno-Type Fuzzy Inference Systems for Air Conditioning System," International Journal of Soft Computing and Engineering, ISSN: 2231-2307, no. 2, pp. 323-325. 13. Efosa C., Akwukwuma N.( 2013), "Knowledge based Fuzzy Inference System for Sepsis Diagnosis," International Journal of Computational Science and Information Technology, Vol.1, No. 3, pp. 1-7. 14. Choi H., Yoo H., Jung H., Lim T., Lee K., Ahn K.( 2015), "An ANFIS-based Energy Management Inference Algorithm with Scheduling Technique for Legacy Device," International Conference on Artificial Intelligence, Energy and Manufacturing Engineering, pp. 80-82. 15. Uc T., Karahoca A., Karahoca D. (2013), "Tuberculosis disease diagnosis by using adaptive neuro-fuzzy inference system and rough sets," Neural Comput & Applications, Springer, pp. 471-483. 16. R Jaya Suji, SP Rajagopalan," Multi-ranked feature selection algorithm for effective breast cancer detection", Biomed Res- India 2016 Special Issue Special Section: Computational Life Science and Smarter Technological Advancement. 17. R.Jaya Suji , Dr.S.P.Rajagopalan," An Intelligent Oral Cancer Diagnosis System using Texture Analysis based Segmentation and 18. Correlated Fuzzy Neural Classifier Fuzzy Rough Set and LS-SVM", International Journal of Applied Engineering Research ISSN 0973-4562 Volume 10, Number 6 (2015). 19. Jaya Suji. R, Dr.Rajagopalan S.P, "An automatic Oral Cancer Classification using Data Mining Techniques", International Journal of Advanced Research in Computer and Communication Engineering, Vol.2, No.10, 2013. 20. Neha Sharma, Hari Om, "Extracting Significant Patterns for OralCancer Detection Using Apriori Algorithm", Intelligent Information Management, Vol. 6, pp. 30-37, 2014. 21. R. A. Mohammadpour, S. M. Abedi, S. Bagheri, and A. Ghaemian, "Fuzzy rule-based classification system for assessing coronary artery disease," Computational and Mathematical Methods in Medicine, vol. 2015, Article ID 564867, 8 pages, 2015. View at Publisher · View at Google Scholar · View at Scopus 22. D. Pal, K. Mandana, S. Pal, D. Sarkar, and C. Chakraborty, "Fuzzy expert system approach for coronary artery disease screening using clinical parameters," Knowledge-Based Systems, vol. 36, pp. 162-174, 2012. View at Publisher · View at Google Scholar · View at Scopus. Authors: Senthil Kumar Sundararajan, B. Sankaragomathi, D. Saravana Priya Channel Transformation Enhanced Deep Convolutional Neural Network enforced Image Retrieval Paper Title: Mechanism for Medical Image Applications Abstract: The diversified utilization of digital imaging data in the medical domain has in turn increased the size of the medical image repository. This increase in the size of the repository imposes huge challenges during the process of querying and handling huge databases will lead to the requirement of Content Based Medical Image Retrieval 74. Systems(CBMIR). In this paper, a Channel Transformation Enhanced Deep Convolutional Neural Network-based Image Retrieval Mechanism (CTEDCNN-IRM) is proposed for handling the issue of semantic gap that prevails 405-411 between human perceived high level semantic information and imaging devices’ captured low level visual information in medical imaging applications. The experimental results of the proposed CTEDCNN-IRM confirmed a mean classification accuracy and mean precision rate of 99.83% and 0.78 in the process of image retrieval. This proposed CTEDCNN-IRM is also determined to be well suited and applicable to the processing of multimodal medical images that relates to different body organs.

References: 1. Anthimopoulos, M., Christodoulidis, S., Ebner, L., Christe, A., & Mougiakakou, S. (2016). Lung Pattern Classification for Interstitial Lung Diseases Using a Deep Convolutional Neural Network. IEEE Transactions on Medical Imaging, 35(5), 1207-1216. 2. Zhang, F., Song, Y., Cai, W., Hauptmann, A. G., Liu, S., Pujol, S., … Chen, M. (2016). Dictionary pruning with visual word significance for medical image retrieval. Neurocomputing, 177(1), 75-88. 3. Quellec, G., Lamard, M., Cazuguel, G., Cochener, B., & Roux, C. (2012). Fast Wavelet-Based Image Characterization for Highly Adaptive Image Retrieval. IEEE Transactions on Image Processing, 21(4), 1613-1623. 4. Rahman, M., Antani, S., & Thoma, G. (2011). A Learning-Based Similarity Fusion and Filtering Approach for Biomedical Image Retrieval Using SVM Classification and Relevance Feedback. IEEE Transactions on Information Technology in Biomedicine, 15(4), 640-646. 5. Jiji, G. W., & Durai Raj, P. S. (2015). Content-based image retrieval in dermatology using intelligent technique. IET Image Processing, 9(4), 306- 317. 6. Cai, W., Zhang, F., Song, Y., Liu, S., Wen, L., Eberl, S., … Feng, D. (2014). Automated feedback extraction for medical imaging retrieval. 2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI), 1(1), 56-68. 7. Saritha, R. R., Paul, V., & Kumar, P. G. (2018). Content based image retrieval using deep learning process. Cluster Computing, 1(1), 67-78. 8. Wu, L., & Hoi, S. C. (2011). Enhancing Bag-of-Words Models with Semantics-Preserving Metric Learning. IEEE Multimedia, 18(1), 24-37. 9. Shin, H., Roth, H. R., Gao, M., Lu, L., Xu, Z., Nogues, I., … Summers, R. M. (2016). Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics 10. and Transfer Learning. IEEE Transactions on Medical Imaging, 35(5), 1285-1298. 11. Yogapriya, J., Saravanabhavan, C., Asokan, R., Vennila, I., Preethi, P., & Nithya, B. (2018). A Study of Image Retrieval System Based on Feature Extraction, Selection, Classification and Similarity Measurements. Journal of Medical Imaging and Health Informatics, 8(3), 479-484. 12. Markonis, D., Schaer, R., & Müller, H. (2015). Evaluating multimodal relevance feedback techniques for medical image retrieval. Information Retrieval Journal, 19(1-2), 100-112. 13. Seetharaman, K., & Sathiamoorthy, S. (2016). A unified learning framework for content based medical image retrieval using a statistical model. Journal of King Saud University - Computer and Information Sciences, 28(1), 110-124. 14. Zhang, X., Liu, W., Dundar, M., Badve, S., & Zhang, S. (2015). Towards Large-Scale Histopathological Image Analysis: Hashing-Based Image Retrieval. IEEE Transactions on Medical Imaging, 34(2), 496-506. 15. Li, P., & Ren, P. (2017). Partial Random Spherical Hashing for Large-Scale Image Retrieval. 2017 IEEE Third International Conference on Multimedia Big Data (BigMM), 1(2), 13-25. 16. Mengting, L., & Jun, L. (2017). Deep hashing for large-scale image retrieval. 2017 36th Chinese Control Conference (CCC), 1(1), 45-54. 17. Zhang, J., & Peng, Y. (2017). SSDH: Semi-supervised Deep Hashing for Large Scale Image Retrieval. IEEE Transactions on Circuits and Systems for Video Technology, 1(1), 1-1. 18. Balasundaram, R., & Sudha, G. F. (2017). An efficient and reduced memory indexing approach based on priority rank spectral hashing for multibiometric database. International Journal of Biometrics, 9(2), 113-122. 19. Revathi, B., & Sudha, G. (2018). Retrieval performance analysis of multibiometric database using optimized multidimensional spectral hashing based indexing. Journal of King Saud University - Computer and Information Sciences, 1(1), 67-78. 20. Shamna, P., Govindan, V., & Abdul Nazeer, K. (2018). Content-based medical image retrieval by spatial matching of visual words. Journal of King Saud University - Computer and Information Sciences, 1(2), 34-48. 21. Qayyum, A., Anwar, S. M., Awais, M., & Majid, M. (2017). Medical image retrieval using the deep convolutional neural network. Neurocomputing, 266(1), 8-20. Authors: M. Joseph Vishal Kumar, Krishna Samalla Paper Title: Design and Development of Water Quality Monitoring System in IOT Abstract: Due to the impact of polluted water globally tremendous changes are taking place towards development of a reconfigurable smart sensor interface device for water quality monitoring system in an IOT environment. Water quality monitoring system measures the water level parameters are collected by the sensors. The sensors are sending to the microcontroller board. We are using sensors like Co2, temperature, ph sensor, water level sensors and turbidity sensors. This sensor controls the whole operation and monitored by Cloud based wireless communication devices. The microcontroller system can be seen as a system that reads from the input perform processing and writes to output. For his Water monitoring system output will be in digital form. In this output of these sensors directly goes to the microcontroller. Whenever outputs of the other sensors are in analog form. Then we need to convert the analog values to digital values before connecting to the controller. In this paper water quality is pure as sensors play a major role for water quality monitoring system, the time and costs in detecting water quality of a reservoir as part of the environment.

Keywords: Microcontroller (RPI), Co2 sensor, Temperature sensor, Turbidity sensor, PH sensor, water level sensor etc. 75.

References: 412-418 1. Zulhani Rasin and Mohd Abdullah International Journal Engineering &Technology, "Water Quality Monitoring System Using ZigBee Based Wireless Sensor Network", 2016. 2. Ch. Pavankumar, S. Praveenkumar, "CPCB Real Time Water Quality Monitoring", Report: Centre for Science and Environment, 2013 3. R.M. Bhardwaj, "Overview of Ganga River Pollution", Report: Central Pollution Control Board, Delhi, 2011 4. Tuan Le Dinh, Wen Hu, PavanSikka, Peter Corke, L. Overs, Stephen Brosman, "Design and Deployment of a Remote Robust Sensor Network: Experiences from Outdoor Water", 32nd IEEE Conf. on Local, Computers, pp 799-806, Feb., 2007 5. Quio Tie-Zhn, Song Le, "The Design of Multi Parameter On Line Monitoring System of Water Quality based on GPRS", Report: Advanced Transducers and intelligent Control System Lab, Taiyuan Technical University, Taiyuan, China, 2010 6. AainaVenkateswaran, HarshaMenda P, Prof PritiBadar, "The Water Quality Monitoring System based on Wireless Sensor Network" Report: Mechanical and Electronic Information Institute, China University of GeoScience, Wu Hen, China, 2012 7. PavlosPapageorgiou, "Literature Survey on Wireless Sensor Networks", Report: University of Maryland, 16 July 2003 8. SatishTurken, AmrutaKulkarni, "Solar Powered Water Quality Monitoring System using Wireless Sensor Network", IEEE Conf. on Automation, Computing, communication, control, and compressed sensing, pp281-285, 2011 9. Liang Hu, Feng Wang, Jin Zhou and Kuo Zhao "A Survey from the Perspective of Evolutionary International Journal of Pure and Applied Mathematics Special Issue 1367 Process in the Internet of Things", International Journal of Distributed Sensor Networks, Article ID462752, 2015 10. Thing Speak-Understanding your Things-The open IoT Platform with MATLAB analytics, Math Works. 11. Nikhil Kedia entitled "Water Quality Monitoring for Rural Areas-A Sensor Cloud Based Economical Project"2015. Authors: P. Velmurugan, A. Kannagi, P. John Paul 76. Paper Title: Multi Slot TMI Measure based Machine Scheduling for CNC Applications with Improved Data Security Abstract: The modern manufacturing process has been adapted with computer numerical control machines. Number 419-423 of protocols has been discussed earlier for the improvement of production yield with secure access. However, the performance of scheduling and data security in CNC applications is still challenging due to the security issues. To overcome the deficiency, an multi slot TMI (Throughput-Makespan-Idle Time) based scheduling algorithm is presented. The proposed work performs scheduling of CNC machines and their operations to improve the scheduling performance. The presence of multi slot in CNC machines enables the processing of more than one operation in the batch process. By identifying a sequence which is efficient in terms of throughput, makespan and idle time makes the CNC machine to produce higher outcome. The data security has been enabled with attribute based encoding to improve the security of data in CNC applications. The method produces efficient results on scheduling as well as data security.

Index Terms: CNC, Machine Scheduling, Data Security, Multi Slot Machines, TMI.

References: 1. Chandranath, Raghavendra, Suresh and Ravishankar, May 2014, 'Part Localization For CNC Machining Using Discrete Point Set Approach,' ARPN Journal of Engineering and Applied Sciences, Vol.9, No.5, pp (719-723). 2. Mukul Gupta, Pretty Gupta, Mudita Singh, Yadav, 2018,' Low-Cost Mini Multi-Tool CNC Machine,' IOSR -Journal of Mechanical and Civil Engineering (IOSR-JMCE), Vol.15, PP (49-55). 3. Hesham, Ayman Mansour, Aug 2017,' Controlling Speed of Hybrid Cars using Digital Internal Model Controller,' IEEE -IOSR Journal of Mechanical and Civil Engineering, Vol.14, PP (12-22). 4. Asif Hussain Ansar Md, Mohd Abdul Hussain, Shaik Mahmood Alamoodi, Shanila Mahreen, Taskeen Sultan, Mohammed Abdul RahmanUzair, Mar 2016,'Features and applications of CNC machines and systems', International Journal of Science, Engineering and Technology Research (IJSETR), Volume 5, pp (1-10). 5. Dr.A. Kannagi, "Smart curiosity sink node prediction mining algorithm for path optimization in wireless sensor network", International Journal of Engineering and Technology (IJET) - Volume 7, No.2.21(2018) : Special Issue 21, Page No : 180-184. 6. Dmello, JebinBiju, Hegde, Ganoo,' Design and fabrication of automated 2-axis welding machine', International Journal of Mechanical Engineering and Technology (IJMET), Vol.8, pp (236-244). 7. Akhil, Ajith, Jun 2018, CNC Engraving Machine Based On Open Source Electronics', Journal of Engineering Research and Application, Vol.8, pp (43-50). 8. A.Kannagi, M.Muthuraja, "Data security Description of enhanced data mining analysis using Symmetric Inference Model", International Journal of Advanced Information and Communication Technology (IJAICT), Volume 1, Issue 5, Pages : 461-465, September 2014, ISSN : 2349-6339 (P). 9. Dinesh Kumar, Mar 2018,' Hacking and Cyber Security,' International Journal for Research in Applied Science & Engineering Technology (IJRASET), Vol.6, pp (244-256). 10. Prabhanjay Gadhe, Vikas Jangir, Mayur Yede, Wasim-Ul-Haq, Feb 2017,' Design and Implementation of PCB Using CNC,' International Research Journal of Engineering and Technology (IRJET), Vol.4, pp (1-4). 11. Saravana Kumar, Allen Jeffrey, Mohan Raj, 2017,' Production Lead Time Reduction in a Hydraulic Machine Manufacturing Industry by Applying Lean Techniques,' American Journal of Engineering Research (AJER), Vol.6,pp (365-373). 12. A.Kannagi, Dr.Vidushi Sharma, Dr.K.Ganesan "Database description of enhanced data mining analysis", International Journal of Emerging Innovations in Science and Technology (IJEIST), Vol.1, Issue - 3, May-2014, ISSN: 2348-4403. 13. A.Kannagi, Dr.Vidushi Sharma, Dr.K.Ganesan "Implementation of association rules research problem in data mining", International Journal of Emerging Innovations in Science and Technology(IJEIST), Vol.1, Issue - 2, April-2014, ISSN: 2348-4403. 14. Sangeetha, JanardhanaRao Rama Gowri, Oct 2016, 'Automatic medicine vending system medical ATM,' International Journal of Scientific Development and Research, Vol.1, pp (185-190). 15. Dalip Kumar, kulbhushan Sharma, Aug 2013, 'optimization of CNC turning parameters for surface Roughness by using Taguchi method,' International Journal of Mechanical and Production Engineering, Vol.4, pp (17-20). 16. VanitaThete, Vijay Kadlag, 'May 2015, 'Effect of Process Parameters of Friction Stir Welded Joint for Similar Aluminium Alloys H30', International Journal of Engineering Research and Applications, Vol.5, pp (10-17). 17. Suresh Kumar, 2016,' Analysis of hazard identification and health assessment using ultrasonic welding,' International journal chemistry science, Vol.14, No.3, pp (1211-1224). 18. Harshad Srinivasan, Ola Harrysson, Richard Wysk, May 2015,' Automatic part localization in a CNC machine coordinate system using 3D scans', Springer: International Journal Advanced manufacture Technology, pp (1-12). 19. Kentaroh Toyoda, Takis Mathiopoulos, Iwao Sasase, Tomoaki Ohtsuki, 'A Novel Blockchain-Based Product Ownership Management System (POMS) for Anti-Counterfeits in The Post Supply Chain,' IEEE Access, pp (1-14). 20. Vedprakash, Saurabh Sharma, Ashwani Kumar, Priya Kumari, Shyam Lal, Dec 2017, 'Design and implementation of CNC router: a review,' International Journal of Advanced Research in Science and Engineering, Vol.6, No.2, pp (485-494). 21. Iuon-Chang Lin, and Tzu-Chun Liao, Sept 2017,' A Survey of Blockchain Security Issues and Challenges', International Journal of Network Security, Vol.19, No.5, PP (653-659). 22. Sata, 2010,' Error Measurement and Calibration of Five Axis CNC Machine using Total Ball Bar Device,' International Conference and Workshop on Emerging Trends in Technology, pp (1-3). 23. Harshitha, Narasimhan, Jul 2014,'Implementation of PLC for CNC Flame cutting machine', International Journal of Scientific & Engineering Research, Volume 5, pp (277-286). 24. Sinan Gurel& M. Selim Akturk, Scheduling preventive maintenance on a single CNC machine, International Journal of Production Research Volume 46, 2008 25. Hong-seok Park, Development of smart machining system for optimizing feedrates to minimize machining time, Journal of Computational Design and EngineeringVolume 5, Issue 3, July 2018, Pages 299-304. 26. Jie Huang, Real-time feedrate scheduling for five-axis machining by simultaneously planning linear and angular trajectories, International Journal of Machine Tools and ManufactureVolume 135, December 2018, Pages 78-96. 27. Abbas Shahzadeh, Smooth path planning using biclothoid fillets for high speed CNC machines, International Journal of Machine Tools and Manufacture, Volume 132, September 2018, Pages 36-49Sarang Authors: R. Rajyashree, N.M. Balamurugan Paper Title: Routing For Power Controlled and Mobility Constraint Ad Hoc Networks Abstract: Time varying nature of wireless channels due to the dynamic establishment of node communication imposes significant challenge in data apportion especially if the sender follows static transmission strategy with respect to transmission rate and transmission power. An approach of Power Control and Rate Adaptation (PCRA) scheme that 77. makes adjustment of the parameters at the physical and application layer respectively proposed in this study to attain the required video quality while achieving fairness among communicating nodes. Mobility issue of the wireless network 424-429 need attention in improvising video delivery. In this paper an algorithm has been adapted in which data from the network layer is used for retaining the path without link deterioration. Performance evaluation have been carried out and found to have enhanced data relegation.

Keywords: wireless network, power control and rate adaptation, link deterioration, fairness.

References: 1. Ayaz Ahmad and Naveed Ul Hassan, "Joint Power Control and Rate Adaptation for Video Streaming in Wireless Networks With Time-Varying Interference", IEEE Transactions On Vehicular Technology, Vol 65, No. 8, pp.6315-6328, 2016. 2. Chao Chen and Xiaoqing Zhu, "Rate Adaptation and Admission Control for Video Transmission With Subjective Quality Constraints", IEEE Journal Of Selected Topics In Signal Processing, Vol 9,No.1, pp.22-34, 2015. 3. Jian Yang and Weizhe Cai, "Online Measurement-Based Adaptive Scalable Video Transmission in Energy Harvesting Aided Wireless Systems" IEEE Transactions On Vehicular Technology, Vol 66, No. 7, pp.6231-6334, 2017. 4. Liang Qian and Zheng Fang, "A QoE-Driven Encoder Adaptation Scheme for Multi-User Video Streaming in Wireless Networks", IEEE Transactions On Broadcasting, Vol 63, No. 1, pp.20-30, 2017. 5. Michael Seufert et al., "A Survey on Quality of Experience of HTTP Adaptive Streaming", IEEE Communication Surveys & Tutorials, Volume 17, No. 1, pp.469-488, First Quarter, 2015. 6. Esteban Egea-Lopez and Pablo Pavon-Mari (2016), "Distributed and Fair Beaconing Rate Adaptation for Congestion Control in Vehicular Networks" IEEE Transactions on Mobile Computing. 7. Euhanna Ghadimi, Francesco Davide Calabrese et al., "A Reinforcement Learning Approach to Power Control and Rate Adaptation in Cellular Networks" IEEE ICC Wireless Communications Symposium 2017. 8. Seyed Ehsan Ghoreishi et al. , "Power-Ef?cient QoE-Aware Video Adaptation and Resource Allocation for Delay-Constrained Streaming Over Downlink OFDMA", IEEE Communication Letters, Vol. 20, No. 3, pp.574-577, 2016. 9. Wenjie Li et al., "Rate-Selective Caching for Adaptive Streaming Over Information-Centric Networks", IEEE Transactions On Computers, Vol. 66, No. 9, pp.1613-1627, 2017. 10. Xiang Chen et al., "Quality-Driven Joint Rate and Power Adaptation for Scalable Video Transmissions Over MIMO Systems", IEEE Transactions On Circuits And Systems For Video Technology, Vol. 27, No. 2, pp.1549-1554, 2017. 11. Vemuri Sai Krishna and Manav R. Bhatnagar , "A Joint Antenna and Path Selection Technique in Single-Relay-Based DF Cooperative MIMO Networks" IEEE Transactions On Vehicular Technology, Vol. 65, No. 3,pp.1340-1353, 2016. 12. Lisa Pinals, Ahmad Abu Al Haija ," Link Regime and Power Savings of Decode-Forward Relaying in Fading Channels" IEEE Transactions On Communications, Vol. 64, No. 3, pp 931-945, 2016. 13. Jun Chen, and Thomas G. Pratt , "Packet-Based Energy Efficiency of MIMO Systems with Interference Avoidance over Frequency-Selective Fading Channels" IEEE Transactions On Wireless Communications, Vol. 15, No. 4, pp.2689-2710 , 2016. 14. Yanping Yang et.al , "Joint Rate and Power Adaptation for Amplify-and-Forward Two-Way Relaying Relying on Analog Network Coding" IEEE Access. 15. N.Changuel and B.Sayadi (2010), "Joint encoder and buffer control for statistical multiplexing of multimedia contents" IEEE GLOBECOM,pp.1- 6, 2016. 16. M.Vutukuru, H.Balakrishnan , "Cross- layer wireless bit rate adaptation" Proc.SIGCOMM, pp.3-14, 2009. 17. X. Ji, J.Huang (2009), "Scheduling and resource allocation for SVC streaming over OFDM downlink systems" IEEE Trans.Circuits.Syst.Video Technol., Vol.19,No.10,pp.1549-1555, 2009. 18. Maria Antonieta Alvarez, Umberto Spagnolini,Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano , "Distributed Time and Carrier Frequency Synchronization for Dense Wireless Networks" IEEE Transactions on Signal and Information Processing over Networks, pp-1, 2018. 19. Evsen Yanmaz "Mitigating effects of node failure via mobility in wireless networks" ieee wireless pervasive computing, 2009. 20. Sandip chakraborty, Sukumar nandi, and Subhrendu chattopadhyay "Alleviating hidden and exposed nodes in high-throughput wireless mesh networks" ieee transactions on wireless communications, vol. 15, no. 2,pp.928-937, 2016. 21. K.Seong, M.Mohseni, "Optimal resource allocation for ofdma downlink systems" ieee int. symp. inf. theory, pp.1394-1398, 2006. 22. H. Ahlehagh, S.Dey, "Video-aware scheduling and caching in the ran", ieee/acm trans. netw., vol 22, no.5,pp.1444-1462, 2014. Authors: J. Premalatha, P.M. Joe Prathap OARMIC - Obstacle Avoidance based Autonomous Robotics Movements with Interference Less Multi- Paper Title: Channel Underwater Sensor Network Abstract: Underwater (UW) sensor networks have been drawing additional attention investigation interests newly due to their diverse specialized submissions. Autonomous robots (ARs) function as unidentified UW surroundings must be able to keep away from flooded obstacle, for instance rock face, snow obstacle, and oceanic changes. The use of AR for data gathering presents significant recompense including elasticity in sensor deployment, substantial energy savings, and minimized collisions, hidden node issues, interference, and conflicts. We propose the use of AR to move along deep-sea segments and accumulate data onto the sensors. Moreover the methodology for obstacle prevention by ARs that are built with advanced cameras (AC). The data accumulated from the support of two ACs placed in vertical and horizontal directions are functioned in actual time to give obstacle discovery coordination as per the locations. Obstacle detection and avoidance in a different direction, computed based on fuzzy logic optimization using border detection, segments of route and curves. By using horizontal and vertical obstacle detection create a route to reach the destination without obstacle interruption. AR has the knowledge to justify the sea floor and angular changes equal to 20 meters ahead of its movement. We also present dissimilar AR mobility’s and inspect the result of diverse network size parameters on network outputs such as delay throughput and packet delivery ratio. Each AR then transports the received data to the outside base station (BS). In order, the outside BS transmits the received data from AR to the network tower 78. control server. Also, we focus on the condition based channel allocation for UW in order to avoid the transmission issues. We design an active and stretchy channel reuse plan for the condition channel allocation, and prepare the 430-434 interference situation as a flexible nosiness free chart as per the sensors current location sharing and a predetermined threshold of interference. Because of heavy interference the optimal output may not be possible for more data transmission due to its high computational cost. By using this proposed achievement we overcome all the issues.

Keywords: Autonomous Robots; Obstacles; Interference; Collision; Condition Channel Allocation; Noise Free;

References: 1. Mandar Chitre Shiraz Shahabudeen, Milica Stojanovic, "Underwater Acoustic Communications and Networking: Recent Advances and Future Challenges" Marine Technology Society Journal, Volume 42, Number 1, 2008 . 2. Zhaohui Wang, Shengli Zhou Josko Catipovic Peter Willett," Parameterized Cancellation of Partial-Band Partial-Block-Duration Interference for Underwater Acoustic OFDM"Dec. 1 - 2, 2011. 3. Jaime Lloret, Sandra Sendra, Miguel Ardid and Joel J. P. C. Rodrigues," Underwater Wireless Sensor Communications in the 2.4 GHz ISM Frequency Band"www.mdpi.com/journal/sensors, 28 March 2012. 4. Aparajita Nailwal, Mukul Varshney, Abha Kiran Rajpoot," Acoustic Underwater Wireless Sensor Networks: Channel Gain and Channel Communication" International Journal of Computer Science and Mobile Computing, Vol.5 Issue.6, June- 2016. 5. Subha M, Sivisona, Manasa, KS Divya, savitha," Technologies Used In Underwater Wireless Communication" International Journal of Innovative Research in Computer and Communication Engineering, Vol. 5, Special Issue 6, July 2017. 6. Wei Zhang, Shilin Wei, Yanbin Teng, Jianku Zhang, Xiufang Wang and Zheping Yan, "Dynamic Obstacle Avoidance for Unmanned Underwater Vehicles Based on an Improved Velocity Obstacle Method"www.mdpi.com/journal/sensors, 27 November 2017. 7. Nasir Saeed, Member, IEEE, Abdulkadir Celik, Member, IEEE, Tareq Y. Al-Naffouri, Member, IEEE, Mohamed-Slim Alouini,"Underwater Optical Wireless Communications, Networking, and Localization: A Survey"28 Feb 2018. 8. Waqas Aman, Muhammad Mahboob Ur Rahman, Junaid Qadir, Haris Pervaiz, Qiang Ni," Impersonation Detection in Line-of-Sight Underwater Acoustic Sensor Networks"7 Aug 2018. Authors: Ankita Jain, Arun Kumar Yadav, Brijesh Kumar Chaurasia Paper Title: A Proactive Approach for Resource Provisioning in Cloud Computing Abstract: Cloud computing has become a preferred service supplier for information technology, on the users demands resources can be provided there over internet. On the internet workload are changing frequently but continuously changing pattern is still there. Currently for automatic scaling of resource management, low cost and improving resource utility in the cloud, workload prediction scheme has become a very bright packer. Recently, there are various approaches available for workload prediction which is based on the single model prediction approach. However because of the internet providing a large scale heterogeneity data over the cloud, it is very difficult to find out a satisfactory result by mean of a traditional model. We have proposed a proactive approach for resource allocation by analyzing large scale heterogeneity data in cloud. Our implementation shows a better result of resource prediction accuracy with low cost and less time consuming than previous approaches.

Keywords: Master Server, Slave Server, Workload, Resource utilization, Virtual Machine.

References: 1. Fox, Armando, Rean Griffith, Anthony Joseph, Randy Katz, Andrew Konwinski, Gunho Lee, David Patterson, Ariel Rabkin, and Ion Stoica. "Above the clouds: A Berkeley view of cloud computing." Dept. Electrical Eng. and Comput. Sciences, University of California, Berkeley, Rep. UCB/EECS 28, no. 13 (2009): 2009. 2. Roy, Nilabja, Abhishek Dubey, and Aniruddha Gokhale. "Efficient autoscaling in the cloud using predictive models for workload forecasting." In Cloud Computing (CLOUD), 2011 IEEE International Conference on, pp. 500-507. IEEE, 2011. 3. Chen, Zhijia, Yuanchang Zhu, Yanqiang Di, and Shaochong Feng. "Self-adaptive prediction of cloud resource demands using ensemble model and subtractive-fuzzy clustering based fuzzy neural network." Computational intelligence and neuroscience 2015 (2015): 17. 4. Sapankevych, Nicholas I., and Ravi Sankar. "Time series prediction using support vector machines: a survey." IEEE Computational Intelligence Magazine 4, no. 2 (2009). 5. Bankole, Akindele A., and Samuel A. Ajila. "Cloud client prediction models for cloud resource provisioning in a multitier web application environment." In Service Oriented System Engineering (SOSE), 2013 IEEE 7th International Symposium on, pp. 156-161. IEEE, 2013. 6. Caron, Eddy, Frédéric Desprez, and Adrian Muresan. "Forecasting for Cloud computing on-demand resources based on pattern matching." PhD diss., INRIA, 2010. 7. Ghorbani, Mahboobeh, Yanzhi Wang, Yuankun Xue, Massoud Pedram, and Paul Bogdan. "Prediction and control of bursty cloud workloads: a fractal framework." In Proceedings of the 2014 International Conference on Hardware/Software Codesign and System Synthesis, p. 12. ACM, 2014. 79. 8. Calzarossa, Maria Carla, Luisa Massari, and Daniele Tessera. "Workload characterization: A survey revisited." ACM Computing Surveys (CSUR) 48, no. 3 (2016): 48. 9. Yin, Jianwei, Xingjian Lu, Xinkui Zhao, Hanwei Chen, and Xue Liu. "BURSE: A bursty and self-similar workload generator for cloud 435-444 computing." IEEE Transactions on Parallel and Distributed Systems 26, no. 3 (2015): 668-680. 10. Eldin, Ahmed Ali, Ali Rezaie, Amardeep Mehta, Stanislav Razroev, Sara Sjostedt de Luna, Oleg Seleznjev, Johan Tordsson, and Erik Elmroth. "How will your workload look like in 6 years? analyzing wikimedia's workload." In Cloud Engineering (IC2E), 2014 IEEE International Conference on, pp. 349-354. IEEE, 2014. 11. Wang, Kai, Minghong Lin, Florin Ciucu, Adam Wierman, and Chuang Lin. "Characterizing the impact of the workload on the value of dynamic resizing in data centers." Performance Evaluation 85 (2015): 1-18. 12. Calzarossa, Maria Carla, and Daniele Tessera. "Modeling and predicting temporal patterns of web content changes." Journal of Network and Computer Applications 56 (2015): 115-123. 13. Calheiros, Rodrigo N., Enayat Masoumi, Rajiv Ranjan, and Rajkumar Buyya. "Workload prediction using ARIMA model and its impact on cloud applications' QoS." IEEE Transactions on Cloud Computing 3, no. 4 (2015): 449-458. 14. Islam, Sadeka, Jacky Keung, Kevin Lee, and Anna Liu. "Empirical prediction models for adaptive resource provisioning in the cloud." Future Generation Computer Systems 28, no. 1 (2012): 155-162. 15. Jiang, Yexi, Chang-Shing Perng, Tao Li, and Rong N. Chang. "Cloud analytics for capacity planning and instant vm provisioning." IEEE Transactions on Network and Service Management 10, no. 3 (2013): 312-325. 16. Yang, Jingqi, Chuanchang Liu, Yanlei Shang, Bo Cheng, Zexiang Mao, Chunhong Liu, Lisha Niu, and Junliang Chen. "A cost-aware autoscaling approach using the workload prediction in service clouds." Information Systems Frontiers16, no. 1 (2014): 7-18. 17. Liu, Chunhong, Yanlei Shang, Li Duan, Shiping Chen, Chuanchang Liu, and Junliang Chen. "Optimizing workload category for adaptive workload prediction in service clouds." In International Conference on Service-Oriented Computing, pp. 87-104. Springer, Berlin, Heidelberg, 2015. 18. Patel, Jemishkumar, Vasu Jindal, I-Ling Yen, Farokh Bastani, Jie Xu, and Peter Garraghan. "Workload estimation for improving resource management decisions in the cloud." In Autonomous Decentralized Systems (ISADS), 2015 IEEE Twelfth International Symposium on, pp. 25- 32. IEEE, 2015. 19. Gong, Zhenhuan, Xiaohui Gu, and John Wilkes. "Press: Predictive elastic resource scaling for cloud systems." In Network and Service Management (CNSM), 2010 International Conference on, pp. 9-16. Ieee, 2010. 20. Zia Ullah, Qazi, Shahzad Hassan, and Gul Muhammad Khan. "Adaptive Resource Utilization Prediction System for Infrastructure as a Service Cloud." Computational intelligence and neuroscience 2017 (2017). 21. Panneerselvam, J., Liu, L., Antonopoulos, N., Bo, Y., 2014. Workload analysis for the scope of user demand prediction model evaluations in cloud environments. In: Utility and Cloud Computing (UCC), 2014 IEEE/ACM 7th International Conference on. IEEE, pp. 883-889. 22. Kaur, Parmeet, and Shikha Mehta. "Resource provisioning and work flow scheduling in clouds using augmented Shuffled Frog Leaping Algorithm." Journal of Parallel and Distributed Computing 101 (2017): 41-50. 23. Seth, Sonam, and Nipur Singh. "Dynamic Threshold-Based Dynamic Resource Allocation Using Multiple VM Migration for Cloud Computing Systems." In International Conference on Information, Communication and Computing Technology, pp. 106-116. Springer, Singapore, 2017. 24. Gill, Sukhpal Singh, and Rajkumar Buyya. "Resource Provisioning Based Scheduling Framework for Execution of Heterogeneous and Clustered Workloads in Clouds: from Fundamental to Autonomic Offering." Journal of Grid Computing (2018): 1-33 Authors: M. Sindhana Devi, M. Soranamageswari Paper Title: A Denoising of Images in Frequency Domain Using Optimized Neuro Hybrid Fuzzy Filter 80. Abstract: In image processing domain, the image is corrupted by several types of noises especially when the final product is used for edge detection, image segmentation and data compression. So image de-noising has become a very 445-452 essential exercise all through diagnose. In the gray scale image the impulse noise can be removed by using the neuro fuzzy (NF) network based impulse noise filtering approach. The each NF filtering approach is a first order sugeno type fuzzy inference system. Since the Sugeno type is not intuitive technique and it also less accurate. In order to improve the accuracy of the NF filtering approach, utilized the hybrid technique of Mamdani and Sugeno based fuzzy interference system approach and an optimized intelligent water drop technique (IWD) in the spatial domain. However the hybridized Sugeno-Mamdani based fuzzy interference system implemented in the spatial domain this leads to reduce the accuracy of the removal of noise. Also the IWD has the issues in selection domination and in ability to handle indistinguishable fitness. In order to overcome these issues in this paper proposed a denoising of images in frequency domain using optimized neuro hybrid fuzzy filter. The optimised Fuzzy intelligence noise filters approach the pixels in the image are converted into frequency domain by using discrete Fourier transform. The noise present in the pixels is filtered by using fuzzy intelligence noise filter. The modified intelligent water drop algorithm applied for frequency domain. After that by using inverse discrete Fourier transform frequency domain pixels of images are converted to original image. In that optimised method noise present in the images are fully eliminated. The performance of the proposed approach evaluated in terms of Mean Squared Error (MSE) and Peak Signal–to–noise Ratio (PSNR), Structural Similarity (SSIM), Mean Absolute Error (MAE) and Maximum Difference value (MD).

Keywords: Fuzzy inference system, Neuro Fuzzy system, Sugeno type, intelligent water drop algorithm.

References: 1. Lu, C. T., & Chou, T. C. (2012). Denoising of salt-and-pepper noise corrupted image using modified directional-weighted-median filter. Pattern Recognition Letters, 33(10), 1287-1295. 2. Duan, F., & Zhang, Y. J. (2010). A highly effective impulse noise detection algorithm for switching median filters. IEEE Signal Processing Letters, 17(7), 647-650. 3. Li, Y., Sun, J., & Luo, H. (2014). A neuro-fuzzy network based impulse noise filtering for gray scale images. Neurocomputing, 127, 190-199. 4. Sindhana Devi, M., & Soranamageswari, M. (2017, July). A sugeno and tsukamoto fuzzy inference system for denoising medical images, International journal of Recent Scientific Research, 8(7), (pp.18074-18078). 5. Sindhana Devi, M., & Soranamageswari, M. (2016, March). A Hybrid technique of Mamdani and Sugeno based fuzzy interference system approach. In Data Mining and Advanced Computing (SAPIENCE), International Conference on (pp. 340-342). IEEE. 6. Alijla, B. O., Wong, L. P., Lim, C. P., Khader, A. T., & Al-Betar, M. A. (2014). A modified intelligent water drops algorithm and its application to optimization problems. Expert Systems with Applications, 41(15), 6555-6569. 7. Haji, M., Bui, T. D., & Suen, C. Y. (2012). Removal of noise patterns in handwritten images using expectation maximization and fuzzy inference systems. Pattern Recognition, 45(12), 4237-4249. 8. Sadeghi, S., Rezvanian, A., & Kamrani, E. (2012). An efficient method for impulse noise reduction from images using fuzzy cellular automata. AEU-International Journal of Electronics and Communications, 66(9), 772-779. 9. Toh, K. K. V., & Isa, N. A. M. (2010). Noise adaptive fuzzy switching median filter for salt-and-pepper noise reduction. IEEE signal processing letters, 17(3), 281-284. 10. Latifo?Lu, F. (2013). A novel approach to speckle noise filtering based on artificial bee colony algorithm: an ultrasound image application. Computer methods and programs in biomedicine, 111(3), 561-569. 11. Ahmed, F., & Das, S. (2014). Removal of high-density salt-and-pepper noise in images with an iterative adaptive fuzzy filter using alpha- trimmed mean. IEEE Transactions on fuzzy systems, 22(5), 1352-1358. 12. Verma, O. P., Hanmandlu, M., Sultania, A. K., & Parihar, A. S. (2013). A novel fuzzy system for edge detection in noisy image using bacterial foraging. Multidimensional Systems and Signal Processing, 24(1), 181-198. 13. Agrawal, A., Choubey, A., & Nagwanshi, K. K. (2011). Development of adaptive fuzzy based Image Filtering techniques for efficient Noise Reduction in Medical Images. IJCSIT) International Journal of Computer Science and Information Technologies, 2(4), 1457-1461. 14. Hussain, A., Bhatti, S. M., & Jaffar, M. A. (2012). Fuzzy based impulse noise reduction method. Multimedia Tools and Applications, 60(3), 551- 571. 15. SOYTÜRK, M. A., BA?TÜRK, A., & YÜKSEL, M. E. (2014). A novel fuzzy filter for speckle noise removal. Turkish Journal of Electrical Engineering & Computer Sciences, 22(5), 1367-1381. 16. Nair, M. S., & Raju, G. (2012). A new fuzzy-based decision algorithm for high-density impulse noise removal. Signal, Image and Video Processing, 6(4), 579-595. Authors: T. Manoj Kumar, N. Albert Singh Space Reduction Strategy based Particle Swarm Optimization Technique for the Solution of Paper Title: Environmental/Economic Dispatch Problem Abstract: Proposed for the ideal arrangement of eco-friendly economic power dispatch problem of thermal units, an expert release of particle swarm optimization technique is presented in this paper. A space reduction strategy based PSO is analyzed here to solve the EED problem to acquire the Pareto ideal arrangement in the recommended pursuit space by upgrading the speed of optimization process. PSO is one of the nature based algorithm enlivened by the sociological behavior which can be utilized in variety of engineering applications. So many papers are available nowadays for the proper analysis and some of the papers are utilized for literature review. Search space reduction strategy can be applied to our existing particle swarm optimization to increase the speed of moving particles in order to attain Pareto optimal global solution. Here the validation of SR-PSO is demonstrated with an Indian test system with 6 generators. Our main aim is to dispatch the system with minimum operating fuel cost with least emission.

81. Keywords: Environmental/Economic Dispatch problem, Pareto Optimal solution, Search Space Reduction Particle Swarm optimization. 453-457 References: 1. A. Farag, S. Al-Baiyat, and T. C. Cheng, “Economic load dispatch multi-objective optimization procedures using linear programming techniques, ”IEEE Trans. Power Syst., vol. 10, no. 2, pp. 731–738, May1995. 2. W. R. Barcelo and P. Rastgoufard, ''Dynamic economic dispatch using the extended security constrained economic dispatch algorithm,'' IEEE Trans. Power Syst., vol. 12, no. 2, pp. 961-967, May 1997. 3. Z.-X. Liang and J. D. Glover, ''A zoom feature for a dynamic programming solution to economic dispatch including transmission losses,'' IEEE Trans. Power Syst., vol. 7, no. 2, pp. 544-550, May 1992. 4. D. W. Ross and S. Kim, ''Dynamic economic dispatch of generation,'' IEEE Trans. Power App. Syst., vol. PAS-99, no. 6, pp. 2060-2068,Nov. 1980. 5. A. M. Sasson, ''Nonlinear programming solutions for load-flow,minimum-loss, and economic dispatching problems,'' IEEE Trans. Power App. Syst., vol. PAS-88, no. 4, pp. 399-409, Apr. 1969. 6. R. J. Ringlee and D. D. Williams, `Èconomic system operation considering valve throttling losses II-distribution of system loads by the method of dynamic programming,'' Trans. Amer. Inst. Elect. Eng. III, Power App.Syst., vol. 81, no. 3, pp. 615-620, Apr. 1962. 7. J.-Y. Fan and L. Zhang, ``Real-time economic dispatch with line flow and emission constraints using quadratic programming,'' IEEE Trans. Power Syst., vol. 13, no. 2, pp. 320-325, May 1998. 8. R. R. Shoults, R. K. Chakravarty, and R. Lowther, ``Quasi-static economic dispatch using dynamic programming with an improved zoom feature,'' Elect. Power Syst. Res., vol. 39, no. 3, pp. 215-222, 1996. 9. J. Nanda, L. Hari, and M. L. Kothari, `Èconomic emission load dispatch with line flow constraints using a classical technique,'' IEE Proc.Generat., Transmiss. Distrib., vol. 141, no. 1, pp. 1-10, Jan. 1994. 10. L. G. Papageorgiou and E. S. Fraga, `À mixed integer quadratic programming formulation for the economic dispatch of generators with prohibited operating zones,'' Electr. Power Syst. Res., vol. 77, no. 10, pp. 1292-1296, 2007. 11. JiejinCai, Xiaoqian Ma, Qiong Li, Lixiang Li, HaipengPeng“A multi-objective chaotic ant swarm optimization for environmental/economic dispatch” ElsvierElect. Power and Energy Systems 32 (2010) 337-344. Authors: P. Kavipriya, K. Karthikeyan Paper Title: Comparative Analysis of Features Extraction Strategies for Classification in Educational Data Mining Abstract: In this era of automation, education has also smartened up itself and is not imperfect to old lecture method. The expected search is on to find out innovative ways to build it more useful and proficient in developing students’ performance. Currently, huge of data are gathered in educational databases, other than it remains unutilized. In order to obtain essential benefits from such big data, strong tools are required. Data mining is alarmed with the development of methods and techniques for making use of data analysis and prediction. This is the course of pattern detection as well as extraction where the vast amount of data is concerned. Both the data mining and education industry have emerged some of the reliable systems. In regard to this emerge; we present an approach for handling feature extraction by utilizing data mining algorithms for educational system and systematically evaluate the performance of the algorithms to find best-fit features for the further classification process. Results are discussed for selected papers and a summary of the finding is presented to conclude the paper.

Keywords: Feature Extraction, Genetic Algorithm, Feature Selection, Classifiers, Support Vector Machine

References: 1. Mamta Sharma, MonaliMavani "Accuracy Comparison of Predictive Algorithms of Data Mining: Application in Education Sector" ICAC3 2011, CCIS 125, pp. 189–194, 2011. © Springer-Verlag Berlin Heidelberg 2011. 2. Aparna.U.R, Shaiju Paul "Feature Selection and Extraction in Data mining" 978-1-5090-4556-3, 2016 Online International Conference on Green Engineering and Technologies (IC-GET), IEEE. 3. Sewell, Martin, 2007, "Feature Selection" http://machine-Iearning.martinsewell. Com/feature-selection/ (viewed January 15, 2012). 82. 4. M. Dorigo and G. Di Caro, “The ant colony optimization meta-heuristic,” in New Ideas in Optimization, D. Corne, M. Dorigo, and F. Glover, Eds. New York: McGraw-Hill, 1999, pp. 11–32. 5. Dorigo M., Stützlet T, “Ant Colony Optimization, MIT Press”, ISBN 0-262-04219-3, 2004. 458-463 6. Ant Colony Optimization (ACO) Submitted by: Subvesh Raichand MTech, 3rd Semester 07204007 Electronics and Communication optimization-23928078. 7. G. Y. Yu, Y. Z. Wang, “Applied Research of improved genetic algorithms,” Machinery, vol. 5, 2007, pp. 58-60. 8. M. Pei, E.D. Goodman, and W.F. Punch. Feature extraction using genetic algorithms. Technical report, Michigan State University: GARAGe, June 1997. 9. J. Yang and V. Honoavar. Feature Extraction, Construction and Selection: A data Mining Perspective, chapter 1: Feature Subset Selection Using a Genetic Algorithm, pages 117–136. H. Liu and H. MotodaEds, massachussetts: kluwer academic publishers edition, 1998. 10. Maryam Zaffar, Manzoor Ahmed Hashmani, K.S.SAVITA "Performance Analysis of Feature Selection Algorithm for Educational Data Mining" 2017 IEEE Conference on Big Data and Analytics (ICBDA). 11. K. Patel, J. Vala, and J. Pandya, "Comparison of various classification algorithms on iris datasets using WEKA," Int. J. Adv. Eng. Res. Dev.(IJAERD), vol. 1, 2014. 12. Aparna.U.R, Shaiju Paul "Feature Selection and Extraction in Data mining" 2016 Online International Conference on Green Engineering and Technologies,978-1-5090-4556-3/16/$31.00, IEEE 13. P.Kavipriya, Dr.K.Karthikeyan "A Comparative Study of Feature Selection Algorithms in Data Mining" International Journal of Advanced Research in Computer and Communication Engineering, ISO 3297:2007, Vol. 6, Issue 11, November 2017. 14. P.Kavipriya, Dr.Karthikeyan.” Case Study: On Improving Student Performance Prediction in Education Systems using Enhanced Data Mining Techniques. ”- International Journal of Advanced Research in Computer Science and Software Engineering,- Volume 7, Issue 5, May 2017 15. K. Patel, J. Vala, and J. Pandya, "Comparison of various classification algorithms on iris datasets using WEKA," Int. J. Adv. Eng. Res. Dev. (IJAERD), vol. 1, 2014. 16. M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann, and I. H. Witten, "The WEKA data mining software: an update," ACM SIGKDD explorations newsletter, vol. 11, pp. 10-18, 2009. 17. P.Kavipriya, Dr.Karthikeyan, “A Hybridization of Enhanced Ant Colony Optimization (ACO) and Genetic Algorithm (GA) for Feature Extraction in Educational Data Mining,” Journal of Advanced Research in Dynamical & Control Systems, Vol. 10, 12-Special Issue, Pages: 1278-1284, 2018. Authors: M. Jamuna Rani, V. Geethalakshmi, K. Sindhumitha Paper Title: Hybrid Evolutionary Techniques for Ultra Wide Band Sensor Network Localization Abstract: Localization of sensors is an important and an integral issue for wireless sensor networks control and operation. Definite self-localization competence is immensely desirable in a wireless sensor network. A prominent complication in distance oriented localization of wireless sensor network is whether a given sensor network is cognizable or not. This paper introduces an innovative and computationally proficient localization method for WSN that uses Tabu Search (TS) based global optimization on the results of Hill Climbing based local optimization for the location computation and optimization of sensor nodes. From the performance analysis of this integrated method it is prominent that in spite of memory demands, TS-based method has superior convergence characteristics compared to 83. other earlier proposed WSN localization techniques. Also, trends in the recent years have shifted to the hybrid optimization methods i.e. optimization by hybridization of metaheuristics and other techniques. This greatly reduces 464-467 localization error and computation time for large networks. In the proposed system hill climbing local search method is implemented along with tabu search algorithm for efficient localization.

Keywords: Localization, Tabu search, Hill climbing, Optimization, Meta-heuristics.

References: 1. Akyildiz, I.F., W. Su, Y. Sankarasubramaniam, E. Cayirci, "A Surveyon sensor Network”, IEEE Communications Magazine, August, 102- 114(2002). 2. K.Yu and I. Oppermann, “Performance of UWB position estimation based on TOA measurements,” in Proc. Joint UWBST & IWUWBS, (Kyoto, Japan), pp. 400–404, 2004. 3. L. Doherty, K. pister, and L. El Ghaoui, “Convex position estimation in wireless sensor networks,” in IEEE INFOCOM, vol. 3, 2001, pp. 1655– 1663. 4. P. Biswas and Y. Ye, “Semidefinite programming for ad hoc wireless sensor network localization,” in Third International Symposium on Information Processing in Sensor Networks, 2004, pp. 46–54. 5. Anushiya A. Kannan, Guoqiang Mao and Branka Vucetic, “Simulated annealing based wireless sensor network localization”, Journal of Computers, Issue 2, pp. 15-22, May 2006 6. Chi-Chang Chen, Yan Nong Li, Chi Yu Chang, “A novel range-free localization scheme for wireless sensor networks”, International journal on applications of graph theory in wireless ad hoc networks and sensor networks(GRAPH-HOC) September 2012, Vol.4, No.2/3, pp. 1-13. 7. Kuo-FengSsu, Chia-Ho Ou, and Hewijin Christine Jiau, “Localization With Mobile Anchor Points In Wireless Sensor Network”, IEEE Transactions on Vehicular Technology, May 2005. 8. Hongyang Chen, Qingjiang Shi, Pei Huang, H.Vincent Poor, and Kaoru Sezaki, “Mobile Anchor Assisted Node Localization For Wireless Sensor Network”, International Conference on Communications and Mobile Computing., August 2009, Vol.1, pp. 1-5 9. Nabil Ali Alrajeh, Maryam Bashir, Bilal Shams, “Localization techniques in wireless sensor networks”, International journal of distributed sensor networks, 2013, Vol.10, pp.304-468. 10. Priti Narwal, Dr.S.S.Tyagi, “Position Estimation Using Localization Technique In Wireless Sensor Networks”,International Journal of Application or Innovation in Engineering & Management (IJAIEM), June 2013, vol. 2, iss. 6, pp.110-115. 11. Lovepreet Singh, Sukhpreetkaur,“ Techniques of node localization in wireless sensor networks: Review”, International Journal of innovative Research in Computer and Communication Engineering, May 2014, Vol.2, Iss. 5, pp. 4143-4148. 12. W.-H. Liao, Y.-C. Lee, S. P. Kedia, “Mobile Anchor Positioning for Wireless Sensor Networks”, The Institution of Engineering and Technology Communications2011, Vol.5, Iss.7, pp.914-921. 13. Binwei Deng, Guangming Huang,Lei Zhang,Hao Liu,“Improved Centroid Localization Algorithms in WSNs”, 3rd International Conference on Intelligent System and Knowledge Engineering (ISKE 2008), Nov 2008,Vol.1,pp.1260-1264. 14. Zhang Zhao-yang, Gou Xu, Li Ya-peng, Shan-shan Huang, “ DV Hop Based Self-Adaptive Positioning in Wireless Sensor Networks”, 5th International Conference on Wireless Communications, Networking and Mobile Computing (WiCom 2009), Sept 2009, pp. 1-4. 15. Jiaqiang Yang, Shiyou Yang, and Peihong Ni, “A Vector Tabu Search Algorithm with Enhanced Searching Abilityfor Pareto Solutions and its Application to Multi objective Optimizations”, IEEE Transactions on Magnetics 10.1109/TMAG.2015. 16. Ji Zeng Wang, Hongxu Jin, “Improvement on APIT Localization Algorithms for Wireless Sensor Networks”, International Conference on Networks Security, Wireless Communications and Trusted Computing (NSWCTC ’09), April 2009, Vol.1, pp. 719-723. 17. Mohammad Shehab, Mohammed Azmi Al-Betar3 Ahamad Tajudin Khader2 Laith Mohammad Abualigah4 Hybridizing cuckoo search algorithm with hillclimbing for numerical optimization problems 8th International Conference on Information Technology (ICIT)2017(978-1-5090-6332- 1/17/ IEEE) Authors: A.S. Keerthy, S. Manju Priya Complexity Analysis of Compressing Genomic Sequence Data with Chained Hash Indexing in Multiple Paper Title: Dictionary-based LZW Abstract: Data compression is the most discussed topic among the researchers as well as people working in the data industry. Huge volume of data comes from different sources and in a variety of formats like audio, video, pictures, text data, numeric data, etc. Among the variety of data available for researchers to work on, the most prominent are the genomic data produced by biological research labs. With the advent of high speed sequencing machinery and techniques, the amount of genomic data being produced is surpassing the Moore’s Law. To store data proficiently and use it efficiently, compression of data is the best choice that researchers can opt for. Considering the specialty of genomic data, the compression methodology must be lossless. Keeping all these factors in consideration, a multiple dictionary based LZW compression technique was proposed and implemented. This paper computes the complexity analysis of the methodology and compares it with the currently existing ones.

Keywords: Complexity Analysis, Compression, Genomic Data, Lossless, MDLZW.

References: 1. Sayood, Khalid, Introduction to data compression, (2012), 4th ed. Morgan Kaufmann, Elsevier. 2. Wandelt, Sebastian, Marc Bux, and Ulf Leser., Trends in genome compression, Curr Bioinform 9.3, (2014), 315-326. 3. Ziv, J., and Lempel A., "A universal algorithm for sequential data compression, IEEE Trans. Inf. Theory (1977), 23.3, 337-343. 4. Keerthy A S and Dr Manju Priya S., Lempel- Ziv-Welch Compression Using Multiple Dictionaries, KJCS,(2017) Vol 11, Issue 2, 11-15. 5. Keerthy, A. S., and S. Manju Priya,(2017)Lempel-Ziv-Welch Compression of DNA Sequence Datawith Indexed Multiple Dictionaries.IJAER,12.16, 5610-5615. 6. Canovas R., and Alistair M.,2013, Practical compression for multi-alignment genomic files, Proceedings of theThirty-Sixth Austalasian 84. Computer Science Conference-Volume 135. Australian Computer Society, 51-60. 7. Grumbach S., and FarizaT., 1993, Compression of DNA sequences, Proceedings of Data Compression Conference, DCC’93, IEEE, 340-350. 468-471 8. Kotze H.C., and G.J. Kuhn., 1989, An evaluation of the Lempel-Ziv-Welch data compression algorithm, in Proc Communications and Signal Processing, COMSIG. Southern African Conference on IEEE, p.65. 9. Keerthy A S, Manju Priya S, 2016, Comparative analysis of Data and Compression and Pattern Matching Techniques for Biological Big Data. Available athttp://ijarcet.org, Accessed April 30, 2018 10. Keerthy A S, Appadurai, 2015, An empirical study of DNA compression using dictionary methods and pattern matching in compressed sequence, IJAER, vol 10, pg 35064-35067. 11. Ming-Bo Ling, 1997, A parallel VLSI Architecture for the LZW Data Compression Algorithm, International Symposium on VLSI Technology, Systems and Applications, 98-101. 12. MLin, Ming-Bo, Jang-Feng Lee, and Gene Eu Jan, 2006, A lossless data compression and decompression algorithm and its hardware architecture, IEEE Trans. Very Large Scale Integr. (VLSI) Syst 14, no. 9, 925 -936 13. Vichitkraivin, Perapong, and Orachat Chitsobhuk., 2009, An Improvement of PDLZW implementation with a Modified WSC Updating Technique on FPGA. World Acad Sci Eng Technol, 36,611-615. 14. Nishad, P. M., and Manicka Chezian R., 2012, Enhanced lzw (lempel-ziv-welch) algorithm by binary search with multiple dictionary to reduce time complexity for dictionary creation in encoding and decoding, IJARCSSE 2.3,192 -198. 15. Nishad, P. M. and R. Manicka Chezhian, 2012, Optimization of LZW(Lempel-Ziv-Welch) Algorithm to Reduce Time Complexity for Dictionary Creation in Encoding and Decoding, AJCSIT, 114-118 16. Nishad, P. M., and R. Manicka Chezian., 2012, Avital approach to compress the size of DNA sequence using LZW (Lempel-Ziv-Welch) with fixed length binary code and tree structure, IJCA, 43.1, 7-9. 17. Kuruppu, Shanika, et al., 2012, Iterative dictionary construction for compression of large DNA data sets. IEEE/ACM TCBB 9.1, 137-149. 18. Jones, Daniel C., et al., 2012, Compression of next-generation sequencing reads aided by highly efficient de novo assembly, Nucleic Acids Res 40.22, 171-171. 19. Pinho, Armando J., Diogo Pratas, and Sara P. Garcia., 2011, GReEn: a tool for efficient compression of genome resequencing data., Nucleic acids Res 40.4, 27-27. 20. Nishad P M, 2014, A novel approach to reduce computational complexity of multiple dictionary Lempel ziv welch mdlzw using indexed k nearest neighbor ikntn clustering and binary insertion sort algorithm, [Ph.D Dissertation], Bharathiyar University, TN,India. 21. Huang W., Weimin W., and Hui Xu, 2006, A Lossless Data Compression Algorithm for Real-time Database, Intelligent Control and Automation, 2006. WCICA 2006. The Sixth World Congress on. Vol. 2. IEEE, 6645 – 6648. 22. Jiang, J., and Jones S., 1992, Word-based dynamic algorithms for data compression, IEEE Proceedings I (Communications, Speech and Vision) 139.6, 582-586. Authors: S. Swapna, K. Siddappa Naidu Speed Characteristics of Brushless DC Motor Using Adaptive Neuro Fuzzy PID Controller under Different Paper Title: Load Condition Abstract: The increasing development towards usage of accurately controlled, high starting torque, high efficiency and low noise motors for devoted applications has fascinated the attention of researcher in permanent magnet brushless direct current (PMBLDC) motor. BLDC motors can act as suitable option to the conventional motors like permanent magnet direct current motor (PMDC), Switched Reluctance Motor (SRM) etc. This research paper analysis and compares the performance of a BLDC motor supplying various types of loads, and at the same time, implementing different control techniques such as fuzzy PID and ANFIS PID (Adaptive Neuro-Fuzzy Inference System PID). A comparison has been made in this research paper by observing the various speed response of brushless direct current motor at the time of application of load as well as at the time of removal of the load. The efficiency of the proposed method such as Adaptive Neuro-Fuzzy Inference System has been verified in terms of rise time, settling time and peak overshoot by developing the simulation model using MATLAB/SIMULINK.

Keywords: BLDC motor; Fuzzy Proportional-integral-derivative (fuzzy PID) controller; Adaptive Neuro-Fuzzy Inference System PID (ANFIS PID) controller; MATLAB/SIMULINK

References: 1. B.Indu Rani, Ashly Mary Tom, “Dynamic Simulation of Brushless DC Drive Considering Phase Commutation and Backemf Waveform for Electromechanical Actuator”, IEEE TENCON 2008, Hyderabad. ISBN: 978-1-4244-2408-5. 85. 2. Balogh Tibor,Viliam Fedak, Frantisek Durovsky., “Modeling and Simulation of the BLDC Motor in MATLAB GUI”, Proceedings of the IEEE Fifth International Conference on Fuzzy Systems and Knowledge Discovery, US, pp. 1403- 1407, 2011 3. J.Shao, D.Nolan, and T.Hopkins, “A Novel Direct Back EMF Detection for Sensorless Brushless DC (BLDC) Motor Drives,” Applied Power 472-479 Electronic Conference (APEC 2002), pp33-38. 4. D. M. Erdman, “Control system, method of operating an electronically commutated motor, and laundering apparatus,” US Patent 4654566, March 31, 1987. 5. T.Endo, F.Tajima, et al., “Microcomputer Controlled Brushless Motor without a Shaft Mounted Position Sensor,” IPEC-Tokyo, 1983. 6. D.Erdman, US Patent No.4654566, “Control system, method of operating an electronically commutated motor, and laundering apparatus,” March 1987. 7. K.Uzuka, H.Uzuhashi, et al., “Microcomputer Control for Sensorless Brushless Motor,” IEEE Trans. Industry Application, vol.IA-21, MayJune, 1985. 8. Muhammad Firdaus Zainal Abidin, Dahaman Ishak, Anwar Hasni Abu Hassan, “Comparative Study of PI, Fuzzy and Hybrid PI Fuzzy Controller for Speed Control of Brushless DC Motor Drive”, Proceedings of the IEEE International Conference in Computer Application and Industrial Electronics Application, Malaysia, pp.189-195, December 2011. 9. P. Pillay and R. Krishnan, “Modeling of permanent magnet motor drives,”IEEE Trans. Ind. Electron., vol. 35, no. 4, pp. 537–541, Nov. 1988. 10. P. Pillay and R. Krishnan,"Modeling simulation and analysis of a permanent magnet brushless dc motor drive," presented at the IEEE IAS Annual Meeting, Atlanta, 1987. 11. Vashist Bist and Bhim Singh, “An Adjustable-Speed PFC Bridgeless Buck–Boost Converter-Fed BLDC Motor Drive,” IEEE Trans. Ind. Appl., vol. 61, no. 6, pp. 2665–2676, JUNE. 2014. 12. Md Mustafa kamal, Dr.(Mrs.)Lini Mathew and Dr. S. Chatterji “Speed Control of Brushless DC Motor Using Fuzzy Based Controllers” 2014 IEEE Students’ Conference on Electrical, Electronics and Computer Science. 13. K.S. Tang, Kim Fun Man, Guanrong, Sam Kwong., “An Optimal Fuzzy PID Controller”, IEEE Transactions on Industrial Electronics Vol. 48, No. 4, pp.757-765, August 2001. 14. PetarCmosija, RamuKrishnant, ToniBjazic., “Optimization of PM Brushless DC Motor Drive Speed Controller using Modification of Ziegler- Nichols Methods Based on Bode- Plots EPE-PEMC”, Proceedings of the IEEE International Conference on Power Electronics, Slovenia, pp 343- 348, 2006. Authors: S. Maria Glammi, K. Meena Alias Jeyanthi Paper Title: Design of Wide Band and Rectangular Microstrip Patch Antenna for Breast Tumor Detection Abstract: In the worldwide, the most common cancer for women is affected by Breast cancer and faces many miserable problems because of not determining early. MRI is the another approach for breast cancer, but there is no indication right now that, but it is an efficient screening tool for women at usual risk. While MRI is more responsive than mammograms, it also has a higher false positive rate. Microwave detection of the breast cancer is the best analytical method for prior detection of breast cancer. Malignant tumors will have a high dielectric constant as a result of enormous water content than the ordinary tissue. Electromagnetic waves get distributed when it is transmitted over the malignant tumor tissue; these dispersed signals is back propagated and analyzed in order to find the location and size of the cyst. Microstrip patch antenna and wide band antenna is designed for breast cancer detection based on the variations in dielectric constant between the ordinary and tumor tissues which is operating at microwave frequencies. 86. The antenna operating at 2.4GHz is designed on a FR4 substrate and the return loss performance is analyzed in ADS software. The fundamental of matching liquid is set to the microwave signal into breast tissues. The mixture of the matching liquid is 80% sunflower oil with 20% distilled water into the breast tissues. Both measurements and 480-484 simulation results suggest that this matching liquid provides, nearly, the maximum dispersed power from inner most of breast tissues in interval [2.4 GHz] that is applicable frequency range for breast imaging. Thus the method has to be done using network analyzed, RF transmitter and RF receiver.

Keywords: Return loss, microstrip patch, breast cancer, advanced design system

References: 1. Magthoom Fouzia Y, Dr.K.Meena alias Jeyanthi Design of a Novel Microstrip Patch Antenna for Microwave Imaging Systems International Journal of Engineering and Technical Research (IJETR) ISSN: 2321-0869, Volume-2, Issue-3, March 2014 2. Rabia Çalisan A Microstrip Patch Antenna Design for Breast Cancer Detection Procedia - Social and Behavioral Sciences 195 ( 2015 3. Tayebeh Gholipur Optimized matching Liquid with Wide-Slot Antenna for Microwave Breast Imaging International Journal of Electronics and Communications 2017 4. sanyog rawat Annular ring microstrip patch antenna with finite ground plane for ultra-wideband applications International Journal of Microwave and Wireless Technologies 2014 5. Liang Wang Direct Extraction of Tumor Response Based on Ensemble Empirical Mode Decomposition for Image Reconstruction of Early Breast Cancer Detection by UWB IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS, VOL. 9, NO. 5, OCTOBER 2015 6. 4. A.M. Abbosh Differential Microwave Imaging of the Breast Pair IEEE Antennas and Wireless Propagation Letters 2015 7. Sakshi Bohra UWB Microstrip Patch Antenna for Breast Cancer Detection International Journal of Advanced Research in Electronics and Communication Engineering (IJARECE) Volume 5, Issue 1, January 2016 8. Ahmed Mirza An Active Microwave Sensor for Near Field IEEE Sensors Journal 2017 9. Djamila Ziani A Compact Modified Square Printed Planar Antenna for UWB Microwave Imaging Applications(IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 9, No. 2, 2018 10. G.P.GaneshVarma Early Detection of Breast Cancer Using Patch Antenna International Journal of Pure and Applied Mathematics Volume 119 No. 15 2018, 751-759 11. V.Kannagi Microstrip patch antenna as a sensor for breast cancer detection International Journal of Pure and Applied Mathematics Volume 118 No. 24 2018 12. Azzeddin Naghar Breast Tumor Detection System Based on a Compact UWB Antenna Design Progress In Electromagnetics Research M, Vol. 64, 123–133, 2018 Authors: M. Sathya, S. Manju Priya Paper Title: PSO Search-based Feature-selection Method for High Dimensional Data Abstract: The growth of gene expression data from various techniques continues to expand. Addressing problems associated with high dimensional data and selecting relevant features have become more essential. Selection of relevant genes allows researchers to computationally explore gene expression to find functional genes, disease-causing genes and drug interactions to target specific genes. In the data mining community several feature-selection methods and techniques are continuously being studied and introduced. Selecting the best feature ranking method is still challenging. Feature-selection methods combine search methods and feature evaluation to find relevant features. The choice of search method has a significant relationship with feature ranking scores. In this paper, a new feature-selection method using PSO search strategy to derive high ranking feature subset is introduced. The extracted feature subset is experimentally studied on classification of Colon tumor using Colon dataset. The findings of the study show that PSO based search strategy shows better results than other methods. The study concludes that the proposed method can be used for high dimensional and classification problems on microarray dataset.

Keywords: Microarray dataset, Feature-selection, Classification, Search Strategy, Feature Ranking, Particle Swarm Optimization, High Dimensional Problem.

References: 1. Arunkumar C, & Ramakrishnan, S. (2018). Attribute selection using fuzzy roughset based customized similarity measure for lung cancer microarray gene expression data. Future Computing and Informatics Journal. 2. Aziz R, Verma, C. K., & Srivastava, N. (2016). A fuzzy based feature-selection from independent component subspace for machine learning classification of microarray data. Genomics data, 8, 4-15. 87. 3. Dashtban M., Balafar M., & Suravajhala, P. (2018). Gene selection for tumor classification using a novel bio-inspired multi-objective approach. Genomics, 110(1), 10-17. 4. Gao W., Hu, L., Zhang, P., & Wang, F. (2018). Feature-selection by integrating two groups of feature evaluation criteria. Expert Systems with 485-488 Applications. 5. Ge R., Zhou, M., Luo Y., Meng, Q., Mai, G., Ma, D., & Zhou, F. (2016). McTwo: a two-step feature-selection algorithm based on maximal information coefficient. BMC bioinformatics, 17(1), 142. 6. Jain I., Jain, V. K., & Jain R. (2018). Correlation feature-selection based improved-Binary Particle Swarm Optimization for gene selection and cancer classification. Applied Soft Computing, 62, 203-215. 7. Nagpal A., & Singh V. (2018). A Feature-selection Algorithm Based on Qualitative Mutual Information for Cancer Microarray Data. Procedia Computer Science, 132, 244-252. 8. Qi M., Wang T., Liu F., Zhang, B., Wang, J., & Yi, Y. (2018). Unsupervised feature-selectionby regularized matrix factorization. Neurocomputing, 273, 593-610. 9. Sahu B., Dehuri S., & Jagadev A. K. (2017). Feature-selection model based on clustering and ranking in pipeline for microarray data. Informatics in Medicine Unlocked, 9, 107-122. 10. Shukla A. K., Singh P., & Vardhan, M. (2018). A hybrid gene selection method for microarray recognition. Biocybernetics and Biomedical Engineering 11. Wang, A., An, N., Yang, J., Chen, G., Li, L., & Alterovitz, G. (2017). Wrapper-based gene selection with Markov blanket. Computers in biology and medicine, 81, 11-23. 12. Wang, H., Ke, R., Li, J., An, Y., Wang, K., & Yu, L. (2018). A correlation-based binary particle swarm optimization method for feature-selection in human activity recognition. International Journal of Distributed Sensor Networks, 14(4), 13. Sun, L., Zhang, X., Xu, J., Wang, W., & Liu, R. (2018). A Gene selection approach based on the fisher linear discriminant and the neighborhood rough set. Bioengineered, 9(1), 144-151. 14. Tabakhi, S., Najafi, A., Ranjbar, R., & Moradi, P. (2015). Gene selection for microarray data classification using a novel ant colony optimization. Neurocomputing, 168, 1024-1036. 15. Dai, J., & Xu, Q. (2013). Attribute selection based on information gain ratio in fuzzy rough set theory with application to tumor classification. Applied Soft Computing, 13(1), 211-221. 16. Dash R., & Misra B. (2017). Gene selection and classification of microarray data: a Pareto DE approach. Intelligent Decision Technologies, 11(1), 93-107. Authors: V. Kalpana Implementation of Vehicle Speed Reducing System at Speed Breaks by Detecting Patholes and Humps Paper Title: Using Ultrasonic Sensor Abstract: In many developing countries the maintenance of roads are the major problem. A country’s economy also determined by the safe roads and the road conditions of the country. India, as a second populous country in the world, 88. also a fast growing country. There are huge network of roads. Even from Kashmir to Kanyakumari broad roads are the dominant means of transportation in India. This work discusses merits and demerits of previous pothole detection 489-495 methods that have been developed and proposes a cost effective solution to identify potholes and humps on roads and provide timely alerts to drivers to avoid accidents or vehicle damages. Ultrasonic sensors are used to identify potholes and humps. They also measure their depth and height respectively. The proposed system captures the geographical location coordinates of potholes and humps using GPS receiver. The sensed-data includes pothole depth, height of hump and geographic location, which is stored in the database(cloud). This serves as a valuable source of information to the Government authorities and to vehicle drivers. An android application is used to alert drivers so that precautionary measures can be taken to evade accidents. Alerts are given in the form of a flash messages with an audio beep and long beep alarm to the drivers. The Arduino UNO is an open-source microcontroller board based on the Microchip ATmega328P microcontroller and developed by Arduino.cc. Ultrasonic sensors work by emitting sound waves at a frequency too high for humans to hear. They then wait for the sound to be reflected back, calculating distance based on the time required. This is similar to how radar measures the time it takes a radio wave to return after hitting an object..

Keywords: Ardunio UNO, Ultrasonic sensors, patholes, humps, IoT.

References: 1. Artist Mednis, Girts Strazdins, Reinholds Zviedris, Georgijs Kanonirs and Leo Selavo, (2011) “Real Time Pothole Detection using Android Smartphones with Accelerometers”, Conference Paper IEEE Explore. 2. S.P. Bhumkar, V.V. Deotare, R.V. Babar, (2012) “Accident Avoidance and Detection On Highways” International Journal of Engineering Trends and Technology. 3. Carullo and Parvis, (2001) “An ultrasonic sensor for distance measurement in automotive applications”, IEEE Sensors Journal. 4. Eriksson, Girod, Hull, Newton, Madden, and Balakrishnan, (2008) “The pothole patrol: Using a mobile sensor network for road surface monitoring”, International conference on Mobile Systems. 5. Gnanapriya V.B, Padmashree.V, Bagyalakshmi and G.A. Pravallikha, (2017) “IOT Based Pothole Detection and Notification System”, American-Eurasian Journal of Scientific Research. 6. Gunjan Chugh, Divya Bansal and Sanjeev Sofat, (2014) “Road Condition Detection Using Smartphone Sensors”, International Journal of Electronic and Electrical Engineering. 7. Prachi,More, S.Surendran, S.Mahajan, S.K.Dubey, “Patholes and pitfalls spotter”,International Journal of Research in Engineering and Technology,2014. 8. Rajeshwari Madli, Santosh Hebbar, Praveenraj Pattar and G.V. Prasad, (2015) “Automatic Detection and Notification of Potholes and Humps on Roads to Aid Drivers”, IEEE Sensors Journal. 9. S. S. Rode, S. Vijay, P. Goyal, P. Kulkarni, and K. Arya, (2009) “Pothole detection and warning system: Infrastructure support and system design,” international conference on electronics computer technology. 10. Sachin Bharadwaj, Sundra Murthy, Golla Varaprasad, (2013) “Detection of potholes in autonomous vehicle”, IET Intelligent Transport Systems. 11. Sudarshan S. Rode, Shonil Vijay, Prakhar Goyal, Purushottam Kulkarni, Kavi Arya, (2009) “Pothole Detection and Warning System”, In Proceedings of International Conference on Electronic Computer Technology. 12. Stepheena Joseph and K. Edison Prabhu, (2017) “Role of ultrasonic sensor in Automatic Pothole and Hump Detection System”, International Journal of Scientific & Engineering Research. 13. Sundar, S. Hebbar and V. Golla, (2015) “Implementing intelligent traffic control system for congestion control, ambulance clearance, and stolen vehicle detection”, IEEE Sensors Journal. 14. Sundaram A, Ashenafi Paulo’s Forsido and Dawid Adane, (2016) “Ultrasonic Sensor Based Obstacle Detection for Automobiles”, International Journal of Recent trends in Engineering and research. 15. Sundaram A, Ashenafi Paulo’s Forsido and Dawid Adane, (2016) “Ultrasonic Sensor Based Obstacle Detection for Automobiles”, International Journal of Recent trends in Engineering and research. Authors: Joyce. C.K. Mani, R. Anandan Paper Title: Slot Allocation and Reservation of Parking System Using IOT Abstract: Now days in many multiplex buildings, MNC companies and shopping malls have the severe problem in parking the car. Though the car users in the urban area have been dramatically increased over the last decade, there had been no smart facilities for parking their car in the respective areas. To overcome this drawback we have developed a smart parking system with slot reservation using IOT module, Bluetooth, Ultrasonic sensors and GSM. An android application is developed with the webpage accessibility for doing the same in reduced time.

Keywords: Slot reservation; Internet Of Things IOT; Bluetooth technology; Mobile application; Global System for Mobile Communication GSM; Radio Frequency Identification RFID; Smart parking Management System SPARK; Wireless Sensor Network WSN; Speed Measurement Sub-system SMS; Weather information Providing Sub-system WPS; Peripheral Interface Controller PIC; Integrated Circuit IC; Liquid Crystal Display LCD

References: 1. Bansal Neha1, Patel Sushil, Panchal Saishav, “Traffic Congestion And Fuel Wastage Due To Idling Vehicles At Crossroads”, National Journal of Community Medicine 2010, Vol. 1, Issue 1 2. Ndayambaje Moses, Y. D. Chincholkar “Smart Parking System for Monitoring Vacant Parking”, International Journal of Advanced Research in Computer and Communication Engineering Vol. 5, Issue 6, June 2016 89. 3. M.Y.I. Idris, Y.Y. Leng, E.M. Tamil, N.M. Noor and Z. Razak, “Car Park System: A Review of Smart Parking System and its Technology”. Information Technology Journal, 8: 101-113. 4. YusnitaRahayu and Fariza N. Mustapa, “A Secure Parking Reservation System Using GSM Technology”, International Journal of Computer and 496-502 Communication Engineering, Vol. 2, No. 4, July 2013 5. LambrosLambrinos and AristotelisDosis, “Applying Mobile and Internet of Things Technologies in Managing Parking Spaces for People with Disabilities”, UbiComp’13, September 8–12, 2013, Zurich, Switzerland 6. Satish V.Reve, SonalChoudhri, “Management of Car Parking System Using Wireless Sensor Network”, International Journal of Emerging Technology and Advanced Engineering ISSN 2250-2459, Volume 2, Issue 7, July 2012. 7. S.V.Srikanth; PramodP.J; Dileep K.P; Tapas S; Mahesh U. Patil; Sarat Chandra Babu N, “Design and Implementation of a Prototype Smart PARKing (SPARK) System Using Wireless Sensor Networks”, Advanced Information Networking and Applications Workshops, 2009. WAINA '09. 8. Renuka R. and S. Dhanalakshmi, “Android Based Smart Parking System Using Slot Allocation & Reservations”, ARPN Journal of Engineering and Applied Sciences,VOL. 10, NO. 7, APRIL 2015 9. Seong-eunYoo, Poh Kit Chong, Taisoo Park, “DGS: Driving Guidance System Based on Wireless Sensor Network”, Advanced Information Networking and Applications,10.1109/WAINA.2008.184 10. T. Pham, M. Tsai, D. Nguyen, C. Dow, and D.Deng, “A Cloud-Based Smart-Parking System Based on Internet-of-Things Technologies”, IEEE Access: Emerging Cloud-Based Wireless Communications and Networks,10.1109/ACCESS.2015.2477299 11. H.ShruthiNandhini, C.P.Sunandha, S.Yamuna,“Smart Parking System and Slot Allocation with Congestion Avoidance Technique”Vol. 5, Issue 3, March 2016 12. Nikhil Palde, ChhayaNawale, SunitaKute, “Car Parking System an Android Approach”, Vol. 4, Issue 3, March 2016 13. Anish Vahora, SiddharajGogre, Palash Gandhi,PratikVaswani, “Comprehensive study of Smart Parking System”, International Journal of Emerging Trends & Technology in Computer Science (IJETTCS) Volume 6, Issue 1, January - February 2017 14. http://www.microcontrollerboard.com/pic_memory_organization.html 15. http://www.sunrom.com/p/20x4-lcd-black-on-yellowgreen 16. https://www.mechaterrain.com/ultrasonic-sensors 17. Gobhinath.S, Gunasundari.N, Gowthami.P, “Internet of Things (IOT) Based Energy Meter”, International Research Journal of Engineering and Technology (IRJET) Volume: 03 Issue: 04| Apr-2016 18. MVSS Babu, VS Hari Chandana, K Gayathri, TV Anil Kumar, “Smart Human Health Monitoring System By Using IoT”, SSRG International Journal of Electronics and Communication Engineering-March 2017 19. Gianni Pasolini, Marco Chiani, Roberto Verdone, “Performance Evaluation of a Bluetooth-Based WLAN Adopting a Polling Protocol Under Realistic Channel Conditions”, International Journal of Wireless Information Networks,Vol. 9, No. 2, April 2002 20. Akshay Singh, Sakshi Sharma, Shashwat Singh, “Android Application Development using Android Studio and PHP Framework, International Journal of Computer Applications 21. E.Anitha, Anitha M.A, N.Blessita Gracelyn, S.T. Santhanalakshmi, “Automatic Free Parking Slot Status Intimating System”, International Research Journal of Engineering and Technology, Volume: 04 Issue: 03 | Mar -2017 Authors: A. Saranya, R. Anandan Paper Title: Survey Using Big Data Tools for Doing Rainfall Prediction Abstract: Big data collect large volume of data and gives the analysis of the data obtained using the Hadoop Framework in a fast and more efficient manner which is very accurate and makes our work very easy. Rainfall data is collected from the data set obtained and using Sqoop tool we get the data from MySql to HDFS architecture. The HDFS is mainly the database of Hadoop architecture which stores the data and distributes it to the various tools of Hadoop. It performs the accumulation of data in a way which makes decision analysis for the final output easy to obtain. Hence in our project we are focusing on getting the data from the data set and storing it in HDFS to get the analysis by Hadoop framework using Hive, Map Reduce and Pig to get the output of result which consists of all the analysis of rainfall in a city for all the years and give a clear perspective of the state of rainfall in the city at any moment of the year and also the analysis is shown through bar graph and pie chart which makes the understanding of analysis a little easier and it brings a little more significance to our work.

Keywords: Hadoop, MySql, HDFS, Hive, MapReduce, Pig, Sqoop. 90. References: 503-506 1. Rainfall analysis and rainstorm prediction using MapReduce Framework C.P Shabariram ; K.E. Kannammal ; T. Manoj praphakar Computer Communication and Informatics (ICCCI), 2016 International Conference on 30 May 2016. 2. Rainstorm Prediction using Support Vector Machine in Hadoop Cluster C.P Shabariram data mining and knowledge engineering, vol7 2015. 3. Depth-Duration Frequency of Precipitation for Texas, U.S. Department of the Interior, U.S. Geological Survey, Page Contact Information: Contact USGS, December 07 2016. 4. Map Reduce: Simplified Data Processing on Large Clusters, Jeffrey Dean and Sanjay Ghemawat, OSDI 2004. 5. Extracting storm-centric characteristics from raw rainfall data for storm analysis and mining, Kulsawasd Jitkajornwanich, Ramez Elmasri, John McEnery, Chengkai Li, Univ. of Texas at Arlington, Arlington, TX, ACM Digitstal library, nov 2012. 6. Shabariram, C.P. (2015). Rainstorm Prediction using Support Vector Machine in Hadoop Cluster. Data Mining and Knowledge Engineering, 7(9), 316-320. 7. Jitkajornwanich, K., Elmasri, R., McEnery, J., & Li, C. (2012, November). Extracting storm-centric characteristics from raw rainfall data for storm analysis and mining. In Proceedings of the 1st ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data (pp. 91- 99). ACM. 8. Dean, J., & Ghemawat, S. (2008). MapReduce: simplified data processing on large clusters. Communications of the ACM, 51(1), 107-113. 9. Cleveland, T. G., & Thompson, D. B. (2008). Rainfall Intensity in Design. In World Environmental and Water Resources Congress 2008: Ahupua'A (pp. 1-10). 10. Asquith, W. H. (1998). Depth-duration frequency of precipitation for Texas. US Department of the Interior, US Geological Survey. Authors: Afshankaleem, I. Santi Prabha Paper Title: Enhancement of Urban Sound Classification Using Various Feature Extraction Techniques Abstract: In this paper we describe few methods of extracting features from sound data, one commonly used feature extraction technique in speech recognition is isolating the Mel Frequencies Cepstral Coefficients (MFCC). The accuracy of speech recognition systems, to a large extent, depends on the feature sets used for representing the recorded speech data. It has been a continuous process to derive better feature sets for more accurate speech recognition using ASR (Automatic Speech Recognition) systems. Many feature sets and their different combinations have been tried to achieve better accuracy but a feature set providing completely accurate results has not yet been formulated. These large feature sets consume significant amount of memory, together with computing and power requirements and they do not always contribute to improve the recognition rate. There are few commonly used features extraction methods, such as Mel-scaled spectrogram, Chroma gram, spectral-contrast, and the tonal centroid features We go on to detail the effectiveness of different models on each method, including tests of Random Forests, Naïve Bayes,J48, SVM, Machines architectures 91. Keywords: Mel Banks Cepstral Coefficients (MFCC), Sound Classification, Feature Extraction. 507-514 References: 1. Justin Salamon, Christopher Jacoby, and Juan Pablo Bello. 2014. A Dataset and Taxonomy for Urban Sound Research. In Proceedings of the 22nd ACMinternational conference on Multimedia (MM '14). ACM, New York, NY, 2. James Lyons. 2013. Mel Frequency Cepstral Coefficient (MFCC) tutorial. In Practical Cryptography Online. URL=http://www.practicalcryptography.com/miscellaneous/machine-learning/guide-mel-frequency-cepstral-coefficientsmfccs/ 3. Hibare, Rekha, and Anup Vibhute. "Feature Extraction Techniques in Speech Processing: A Survey." International Journal of Computer Applications 107.5 (2014) 4. Dan-Ning hang*, Lie Lu**, Hong-Jiang Zhang**, Jian-Hua Tao*, Lian-Hong Cui* “Music type Classification by Spectral contrast feature” Proceedings. IEEE International Conference on Multimedia and Expo 5. Aaqid Sayeed. 2016 Urban Sound Classification, Part I, Part II. 6. Hibare, Rekha, and Anup Vibhute. "Feature Extraction Techniques in Speech Processing: A Survey." International Journal of Computer Applications 107.5 (2014). 7. Dan-Ning hang*, Lie Lu**, Hong-Jiang Zhang**, Jian-Hua Tao*, Lian-Hong Cui*“Music type Classification by Spectral contrastfeature” Proceedings. IEEE International Conference on Multimediaand Expo 8. Christopher Harte and Mark Sandler Martin Gasser “Detecting Harmonic Change In Musical Audio “AMCMM’06,October 27, 2006, Santa Barbara, California, USA. 9. L. Breiman, “Pasting small votes for classification in large databases and on-line”, Machine Learning, 36(1), 85-103, 1999. 10. Sonia Suuny, David Peter S, K. Poulose Jacob, “Performance of Different Classifiers In Speech Recognition”, 2013 IJRET. 11. Justin Salmon, Christopher Jacoby, Juan Pablo Bello”Music and Audio research Laboratory, New York University, Centre of Urban Science and Progress”,2014, USA 12. Huan Zhou, Ying Song, Haiyan Shu,”Smart Energy and Environment Cluster Institute for Infocomm Research”, IEEE Region Conference(TENCON), Malaysia,November 5-8,2017 13. N. R. Fatahillah, P.Suryati and C. Haryawan, "Implementation of Naive Bayes classifier algorithm on social media (Twitter) to the teaching of Indonesian hate speech," 2017 International Conference on Sustainable Information 14. N. Albarakati and V. Kecman, "Fast neural network algorithm for solving classification tasks: Batch error back-propagation algorithm," 2013 Proceedings of IEEE Southeastcon, Jacksonville, FL, 2013, pp. 1-8 15. A. B. Kandali, A. Routray and T. K. Basu, "Emotion recognition from Assamese speeches using features and GMM classifier," TENCON 2008- 2008 IEEE Region 10 Conference, Hyderabad, 2008, pp. 1-5. Authors: M. Divya, K.R. Kavitha, A.P. Jaya Krishna Paper Title: Energy Efficient Data Transmission in Wireless Sensor Network Using Cross Site Leaping Algorithm Abstract: The sink mobility along a controlled path can get better energy efficiency in wireless sensor networks. To remain incident delay to the ground value, an application that requires instance data snapshots, it is for all time desirable to decrease the duration of a data collection process and during the attack between source to destination. Due to the path constraint, a sink with constant speed has limited communication time to collect data from the sensor nodes deployed randomly. These significant challenges in jointly improving the amount of data collected and reducing energy consumption. To address this issue, Cross Site Leaping Algorithm (CSLA) to improve energy consumption. To monitor the performance of all nodes in the network we use angular node. From this angular node will monitor the energy level of all other nodes. Before the data transmission, the transmitter power, receiver power and bandwidth of the signal are calculated by CSLA. The CSLA will collect the information about the data which need to send from source to destination and it will find the efficient path to send a data. And also introduced attack and packet issue, we proposed a linear discriminant packet flow analysis system (LDPA) to promote energy consumption and detect the attack. We have presented LDPA for WSNs that is secure and robust against malicious insider attack by any compromised or faulty node in the network. In contrast to the traditional snapshot aggregation approach in WSNs, a node in the proposed algorithm instead of unicasting its sensed information to its parent node, broadcasts its estimate to all its neighbors. This makes the system more fault-tolerant and increase the information availability in the network. The simulations conducted on the proposed algorithm have produced results that demonstrate its effectiveness.

Keywords: WSN, linear discriminant packet flow analysis system, optimized rout path switch, cross-site leaping algorithm.

References: 1. A.K.M. Azad, Joarder Kamruzzaman "Energy-Balanced Transmission Policies for Wireless Sensor Networks," IEEE 2011, Pg.No:927-940. 2. Antonis Kalis, Athanasios G. Kanatas, George P. Efthymoglou" A Co-operative Beam forming Solution for Eliminating Multi-Hop Communications in Wireless Sensor Networks," IEEE 2010, Pg. No:909-917. 92. 3. Antonis Kalis, Athanasios G. Kanatas "Cooperative Beam Forming in Smart Dust: Getting Rid of Multihop Communications," IEEE 2010, Pg. No:47-53. 4. Christy Nadar, Rasika Patil, Siddhi Raut, Kavita Mhatre"Energy Efficient Optimal Opportunistic Routing Using Sleep Mode for Wireless Sensor 515-521 Network," IEEE 2017, Pg. No:656-660. 5. Emma Jones "Distributed Cooperative Sensor Networks using Intelligent Adaptive Antennas," IEEE 2007, Pg. No: 2931-2934. 6. Fan Jing, Wu Qiong, Hao JunFeng "Optimal Deployment of Wireless Mesh Sensor Networks based on Delaunay Triangulations," IEEE 2010, Pg. No:370-374. 7. Francisco Luna, Juan F. Valenzuela-Valdés, Sandra Sendra, and Pablo Padilla "Intelligent Wireless Sensor Network Deployment for Smart Communities," IEEE 2018, Pg.No:176-182. 8. Hasan Khodashahi M, Ali Norouzi, Fatemeh Amiri, Mehdi Dabbaghian" A novel optimal routing algorithm by creating sectors concentrically in wireless sensor networks," IEEE 2010, Pg.No:168-173. 9. Jon R. Ward and Mohamed Younis "An Energy-Efficient Cross-Layer Routing Approach for Wireless Sensor Networks Using Distributed Beamforming", IEEE 2016, Pg.No:574-579. 10. Muhammad Farrukh Munir, Agisilaos Papadogiannis, and Fethi Filali "Cooperative Multi-Hop Wireless Sensor-Actuator Networks: Exploiting Actuator Cooperation and Cross-Layer Optimizations," IEEE 2008, Pg.No:2881-2886. 11. Madiha Razzaq, Devarani Devi Ningombam, Seokjoo Shin "Energy Efficient K-means Clustering-based Routing Protocol for WSN Using Optimal Packet Size," Pg. No:632-635. 12. Rong Du, Ayca Ozcelikkale, Carlo Fischione, Ming Xiao "Optimal Energy Beamforming and Data Routing for Immortal Wireless Sensor Networks," IEEE 2017, Pg. No:6785-6790. 13. Sakthidasan Renu, Lei Yen, Abebe Belay, Hsin-Piao Lin+, Shiann-Shiun Jeng "Hybrid Beam Forming with Data Logging Scheduling (DLS) for Wireless Sensor Network (WSN)," IEEE 2017, Pg. No:1603-1606. 14. Songtao Guo, Cong Wang, and Yuanyuan Yang "Joint Mobile Data Gathering and Energy Provisioning in Wireless Rechargeable Sensor Networks," IEEE 2014, Pg. No:678-691. 15. Vahid Shah-Mansouri and Vincent W.S. Wong "Distributed Maximum Lifetime Routing in Wireless Sensor Networks Based on Regularization," IEEE 2007, Pg. No:598-603. 16. Wael Ali Hussein, Burhanuddin M Ali, MFA Rasid, Fazirulhisyam Hashim "Design and Performance Analysis of High Reliability-optimal Routing protocol for Mobile Wireless Multimedia Sensor Networks," IEEE 2017, Pg. No:136-140. 17. Wayes Tushar, David Smith and Tharaka Lamahewa" Distributed Transmit Beamforming: Data Funneling in Wireless Sensor Networks," IEEE 2012, Pg. No:49-54. 18. Wenjing Guo, Wei Zhang, Gang Lu" A Comprehensive Routing Protocol in Wireless Sensor Network Based on Ant Colony Algorithm," IEEE 2010, Pg. No:41-44. 19. Xu Xu and Weifa Liang" Placing Optimal Number of Sinks in Sensor Networks for Network Lifetime Maximization," IEEE 2011, Pg. No: 3524- 3529. 20. Yong Ma, Haifeng Jiang "Max-min based Optimal Routing Algorithm for Wireless Sensor Networks," IEEE 2010, Pg. No:79-82. Authors: Jobins George and Dr.B. Lethakumari Paper Title: Analysis of Microstrip Antenna Array with Dumbbell Defected Ground Structure 93. Abstract: The ground plane of microwave planar circuits is etched with slots or defects which are referred as Defected 522-526 Ground Structure. By adopting DGS, the various parameters of microwave circuits such as narrow bandwidth, cross polarization, low gain, etc can be improved. This paper proposes design and stimulation of a 2x2 rectangular micro strip patch antenna array with dumbbell shaped slot defected ground structure. The operating frequency for the implemented antenna array is 10 GHz. The corporate-series feed network is implemented for feeding patch elements. The 2x2 antenna array is implemented on FR4 substrate with 1.588mm thickness and dielectric constant of 4.4.The stimulation and performance analyze of antenna is done using An soft High frequency structure simulator software. Finally, comparison of antenna array characteristics with and without DGS is presented.

Keywords: High frequency system stimulator (HFSS), Defeated ground structure (DGS), Optoelectronics integrated circuits (OEIC), Microwave monolithic integrated circuits (MMIC), Photonics band gap (PBG)

References: 1. Constantine A. Balanis, Antenna Theory Analysis and Design, 3rd edition, John Wiley & Sons, Inc., Publication, 2005. 2. Ramesh Garg, Prakash Bhartia, InderBahl, and ApisakIttipiboon, Microstrip antenna design handbook, Artech House, 2001. 3. David M Pozar, Microwave Engineering, 4th edition, John Wiley & Sons, Inc., Publication, 2012. 4. Muhammad MahfuzulAlam, Md. Mustafizur Rahman Sonchoyand Md. Osman Goni, “Design and Performance Analysis of Microstrip Array Antenna”, Progress In Electromagnetics Research Symposium Proceedings, Moscow, Russia, 2009. 5. Md. TanvirIshtaique-ulHuque, Md. Al-Amin Chowdhury Md. Kamal Hosain and Md. Shah Alam "Performance Analysis of Corporate Feed Rectangular Patch Element and Circular Patch Element 4x2 Microstrip Array Antennas", in International Journalof Advanced Computer Science and Applications (IJACSA), Vol.2, No.8, 2011. 6. T. A. Millikgan, Modern Antenna Design, 2nd ed., IEEE Press, John Wiley & Sons inc., 2007. 7. H. J. Visser, Array and Phased Array Antenna Basics, ed: John Wiley & Sons, 2005. 8. Garg, R., P. Bhatia, I. Bahl, and A. Ittipibon, (2001) Microstrip Antenna Design Handbook, Artech House, Boston, London. 9. Kim, J. P. and Park,W.S., “Microstriplowpass filter with multislots on ground plane,” ElectronicsLetters, Vol. 37, No. 25, pp. 1525 –1526, Dec. 2001. 10. Yang, F., and RahmatSamii, Y., “Electromagnetic band gap structures in antenna engineering”,Cambridge University press, USA,2009. 11. A.K Arya,M.Vkartikeyan, A.pattnaik “Defected ground structures in micreostrip antenna: a review” in frequenz 64(2010) Authors: M. Joseph Vishal Kumar, Krishna Samalla Paper Title: Design and Development of Water Quality Monitoring System in IOT Abstract: Due to the impact of polluted water globally tremendous changes are taking place towards development of a reconfigurable smart sensor interface device for water quality monitoring system in an IOT environment. Water quality monitoring system measures the water level parameters are collected by the sensors. The sensors are sending to the microcontroller board. We are using sensors like Co2, temperature, ph sensor, water level sensors and turbidity sensors. This sensor controls the whole operation and monitored by Cloud based wireless communication devices. The microcontroller system can be seen as a system that reads from the input perform processing and writes to output. For his Water monitoring system output will be in digital form. In this output of these sensors directly goes to the microcontroller. Whenever outputs of the other sensors are in analog form. Then we need to convert the analog values to digital values before connecting to the controller. In this paper water quality is pure as sensors play a major role for water quality monitoring system, the time and costs in detecting water quality of a reservoir as part of the environment.

Keywords: Microcontroller (RPI), Co2 sensor, Temperature sensor, Turbidity sensor, PH sensor, water level sensor etc. 94. References: 527-533 1. Rasin, Z. and Abdullah M.R. Water quality monitoring system using zigbee based wireless sensor network. International Journal of Engineering & Technology, vol. 9, no.10, pp.24-28, 2009. 2. Pavankumar, C.H. and Praveenkumar, S. CPCB Real Time Water Quality Monitoring. Report: Centre for Science and Environment, 2013. 3. Bhardwaj, R.M. Overview of Ganga River Pollution. Report: Central Pollution Control Board, Delhi, 2011, pp.1-23. 4. Le Dinh, T., Hu, W., Sikka, P., Corke, P., Overs, L. and Brosnan, S. Design and deployment of a remote robust sensor network: Experiences from an outdoor water quality monitoring network. Conference on Local Computer Networks,2007, pp. 799-806. 5. Qiao, T.Z. and Song, L. The design of multi-parameter online monitoring system of water quality based on GPRS. International Conference on Multimedia Technology, 2010, pp. 1-3. 6. Venkateswaran, A., HarshaMenda, P. and PritiBadar, P. The Water Quality Monitoring System based on Wireless Sensor Network. Report: Mechanical and Electronic Information Institute, China University of GeoScience, Wu Hen, China, 2012. 7. Papageorgiou, P. Literature survey on wireless sensor networks, 2003, pp.1-17. 8. Turken, S. and Kulkarni,A. Solar Powered Water Quality Monitoring System using Wireless Sensor Network”, IEEE Conf. on Automation, Computing, communication, control, and compressed sensing, 2011, pp.281-285, 9. Wang, F., Hu, L., Zhou, J. and Zhao, K.A survey from the perspective of evolutionary process in the internet of things. International Journal of Distributed Sensor Networks, vol.11, no.3, 2015, p.462752. 10. Thing Speak-Understanding your Things-The open IoT Platform with MATLAB analytics, Math Works 11. Kedia, N. Water quality monitoring for rural areas-a Sensor Cloud based economical project. Internationals Conference on Next Generation Computing Technologies (NGCT), 2015,pp. 50-54. Authors: V. Vanitha Paper Title: Rice Disease Detection Using Deep Learning Abstract: Rice bacterial leaf blight, Rice sheath bight and rice blast are the commonly occurring pathology in rice. Early identification and accurate diagnosis can help to limit the spread of diseases and ensure the quality of crop. Automatic detection of the commonly occurring plant diseases are desirable to support farmers. This paper proposes an automatic plat disease identification approach using deep convolutional neural network. A dataset of 500 images of 95. healthy and diseased samples were collected and the model is trained to identify the three common diseases on paddy. We have experimented with the convolutional neural networks to improve the accuracy for identification of rice 534-539 diseases. The results show that we can effectively detect and recognize three classes of rice diseases best accuracy of 99.53% on test set.

Keywords: Identification of rice diseases, Convolutional neural networks, Deep learning, Image recognition.

References: 1. Gupta, T., 2017, ‘Plant leaf disease analysis using image processing technique with modified SVM-CS classifier’, Int. J. Eng. Manag. Technol, 5, pp.11-17. 2. Es-saady, Y., El Massi, I., El Yassa, M., Mammass, D. and Benazoun, A., 2016,’ Automatic recognition of plant leaves diseases based on serial combination of two SVM classifiers’, In 2016 International Conference on Electrical and Information Technologies (ICEIT) pp. 561-566. 3. P. B. Padol and A. A. Yadav, "SVM classifier based grape leaf disease detection," 2016 Conference on Advances in Signal Processing (CASP), Pune, 2016, pp. 175-179. 4. Liu, L. and Zhou, G., 2009, ‘Extraction of the rice leaf disease image based on BP neural network’, In 2009 International Conference on Computational Intelligence and Software Engineering (pp. 1-3). IEEE. 5. Dhau, I., Adam, E., Mutanga, O., Ayisi, K., Abdel- Rahman, E.M., Odindi, J. and Masocha, M., 2018,’. Testing the capability of spectral resolution of the new multispectral sensors on detecting the severity of grey leaf spot disease in maize crop’, Geocarto international, 33(11), pp.1223-1236. 6. Shicha, Z., Hanping, M., Bo, H. and Yancheng, Z., 2007,’ Morphological feature extraction for cotton disease recognition by machine vision’, Microcomputer information, 23(4), pp.290-292. 7. Arivazhagan, S. and Ligi, S.V., 2018. Mango Leaf Diseases Identification Using Convolutional Neural Network. International Journal of Pure and Applied Mathematics, 120(6), pp.11067-11079. 8. Liu, B., Zhang, Y., He, D. and Li, Y., 2017,’ Identification of apple leaf diseases based on deep convolutional neural networks’ Symmetry, 10(1), p.11. 9. Fuentes, A., Yoon, S., Kim, S. and Park, D., 2017, ‘A robust deep-learning-based detector for real-time tomato plant diseases and pests recognition’, Sensors, 17(9), p.2022. 10. Lu, Y., Yi, S., Zeng, N., Liu, Y. and Zhang, Y., 2017,’ Identification of rice diseases using deep convolutional neural networks’, Neurocomputing, 267, pp.378-384. 11. Jin, X., Jie, L., Wang, S., Qi, H. and Li, S., 2018,’ Classifying wheat hyperspectral pixels of healthy heads and Fusarium head blight disease using a deep neural network in the wild field’, Remote Sensing, 10(3), p.395. 12. Anthonys, G. and Wickramarachchi, N., 2009, ‘ An image recognition system for crop disease identification of paddy fields in Sri Lanka’, In 2009 International Conference on Industrial and Information Systems (ICIIS) (pp. 403-407). IEEE. 13. Yao, Q., Guan, Z., Zhou, Y., Tang, J., Hu, Y. and Yang, B., 2009, ‘ Application of support vector machine for detecting rice diseases using shape and color texture features’, In 2009 international conference on engineering computation pp. 79-83. 14. Maharjan, G., Takahashi, T. and Zhang, S.H., 2011,’ Classification methods based on pattern discrimination models for web-based diagnosis of rice diseases’, Journal of Agricultural Science and Technology, 1(1), pp.48-56. 15. N. N Kurniawati, and S.Abdullah. "Texture analysis for diagnosing paddy disease." In International Conference on Electrical Engineering and Informatics, 2009. ICEEI'09., vol. 1, pp. 23-27. IEEE, 2009. 16. Singh, A.K., Rubiya, A. and Raja, B.S., 2015,’ Classification of rice disease using digital image processing and svm classifier’, International Journal of Electrical and Electronics Engineers, 7(1), pp.294-299. 17. Phadikar, S., Sil, J. and Das, A.K., 2012,’ Classification of rice leaf diseases based on morphological changes’, International Journal of Information and Electronics Engineering, 2(3), pp.460-463. 18. Orillo, J.W., Cruz, J.D., Agapito, L., Satimbre, P.J. and Valenzuela, I., 2014, ‘Identification of diseases in rice plant (oryza sativa) using back propagation Artificial Neural Network’, In 2014 International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM) (pp. 1-6). IEEE. 19. Phadikar, S. and Sil, J., 2008, ‘ Rice disease identification using pattern recognition techniques’, In 2008 11th International Conference on Computer and Information Technology (pp. 420-423). IEEE. 20. Majid, K., Herdiyeni, Y. and Rauf, A., 2013, ‘ I-PEDIA: Mobile application for paddy disease identification using fuzzy entropy and probabilistic neural network’, In 2013 International Conference on Advanced Computer Science and Information Systems (ICACSIS) (pp. 403-406). IEEE. Authors: G. Hariharan, P. Suresh, C. Sagunthala Paper Title: Critical Success Factors for the Implementation of Supply Chain Management in SMEs Abstract: The aim of this study is to find the critical success factors (CSFs) for the successful implementation of supply chain management in auto-components-manufacturing units Small Medium Enterprises--(SMEs), in Coimbatore district. The research method used was descriptive analysis, and the primary data were by structured questionnaires collected from 60 respondents of SMEs. The data were analyzed using mean, standard deviation, rank and Cronbach’s Alpha. The findings show that ten CSFs namely, involvement of top management, collaboration with supply chain partners, information sharing, use of sophisticated technologies, less rigid production system, improvement in competitive priorities, long term goals, product differentiation, innovation as well as inventory Management, are identified for a successful execution of SCM in SMEs. The result also shows that SMEs should focus more on product differentiation, innovation and inventory management.

Keywords: Supply Chain Management, SMEs, Auto components manufacturing companies

References: 1. Bauer, M. J. (2000). The Effect of the Internet on supply chain &logistics.World Trade, 13, 71-78. 2. Bianchi, C., and Saleh, A. (2010). On importer trust and commitment: a comparative study of two developing countries. International Marketing 96. Review, 27(1), 55-86. 3. Dinter, B. (2013). Success factors for information logistics strategy an empirical investigation. Decision Support Systems. 54 (3)1207-1218. 540-543 4. Ganesan, K., and Saumen, B. (2005). Corporate turnaround through effective supply chain management: the case of a leading Jewellery manufacturer in India. Supply Chain Management: An International Journal, 10(5), 340-348 5. Gunasekaran, A (1997). Performance Measure and Metrics in A Supply Chain Environment. International Journal Operation and Production Management, 28, (4):71-81. 6. Gunasekaran, A.,and Ngai, E. W. T. (2003). The successful management of a small logistics company. International Journal of Physical Distribution & Logistics Management.33(9),825-42. 7. Gunasekaran, A. and Ngai, E. W. (2004). Virtual supply chain management. Production Planning & Control.15 (6)584-595. 8. Hariharan Ganeshan and Dr P Suresh. (2016). Strategy Development by SMEs’ While Practicing Supply Chain With Respect To South Indian Textile Sectors. International Journal of Management Research & Review, 6(6):861-870. ISSN:2249-7196. 9. Hariharan Ganeshan, and Dr P Suresh. (2017). An Empirical Analysis on Supply Chain Problems, Strategy, and Performance with Reference to SMEs. Prabandhan: Indian Journal of Management. 10 (11), 19-30 10. Hariharan Ganeshan, S. Nagarajan and Dr P Suresh. (2018). Supply Chain Risk Mitigation Strategies and Its Performance of SMEs. International Journal of Pure and Applied Mathematics. 119 (15), 741-749. 11. Huan, S., Sheoran, S dan and Wang, G., 2004. A Review and Analysis of Supply Chain Operations Reference (SCOR) Model. Supply Chain Management: an International Journal, 9, (1): 23-29 12. Hwang, B. N. and Lu, T. P. (2013). Key success factor analysis for e-SCM project implementation and a case study in semiconductor manufacturers. International Journal of Physical Distribution & Logistics Management. 43 (8)657-683. 13. Kim, J. and Rhee, J. (2012). An empirical study on the impact of critical success factors on the balanced scorecard performance in Korean green supply chain management enterprises. International Journal of Production Research. 50, (9). 2465-83. 14. Kuei, C. H., Madu, C. N. and Lin, C. (2008). Implementing supply chain quality management. Total Quality Management.19 (11)1127-1141. 15. Kumar S.R. &Pugazhendhi S. (2012). Information Sharing in Supply Chains: An Overview. Procedia Engineering. Vol.38, 2147–2154. 16. Kumar, S., Luthra, S., and Haleem, A. (2014). Critical success factors of customer involvement in greening the supply chain: an empirical study. International Journal of Logistics System and Management. 19(3)283-310. 17. Kumar R., Singh R K. & Shankar R. (2015). Critical success factors for implementation of supply chain management in Indian small and medium enterprises and their impact on performance. IIMB Management Review. 18. Lin, C., Kuei, C. H. and Chai, K. W. (2013).Identifying critical enablers and pathways to high performance supply chain quality management. International Journal of Operations & Production Management.33 (3)347-370. 19. Luthra, S., Garg, D., and Haleem, A. (2014b)Critical success factors of green supply chain management for achieving sustainability in Indian automobile industry. Production Planning & Control. 20. Mentzer, J. T., DeWitt, W., Keebler, J. S., Min, S., Nix, N. W., Smith, C. D. and Zacharia, Z. G. (2001). Defining supply chain management. Journal of Business Logistics,22 (2) pp. 1-25. 21. More D. &Basu P. (2013). Challenges of supply chain finance: A detailed study and a hierarchical model based on the experiences of an Indian firm. Business Process Management Journal, Vol. 19, No. 4, 624-647. 22. Mothilal, S., Gunasekaran, A., Nachiappan, S. P. and Jayaram, J. (2012). Key success factors and their performance implications in the Indian third party logistics (3PL) industry. International Journal of Production Research.50(9)2407-2422. 23. Ngai, E. W. T., Cheng, T. C. E. and Ho, S. S. M. (2004). Critical success factors of web based supply chain management systems: an exploratory study. Production Planning &Contro. 15(6),622-630. 24. Power, D. J., Sohal, A. S., and Rahman, S. U. (2001). Critical success factors in agile supply chain management. International Journal of Physical Distribution & Logistics Management, 31, 247-265. 25. Saxena, K. B. C., & Sahay, B. S. (2000). Managing IT for world class manufacturing: The Indian scenario. International Journal of Information Management, 20, 29-57. 26. Singh, R. K., Garg, S. K., and Deshmukh, S. G. (2008a). Challenges and strategies for competitiveness of SMEs: a case study. International Journal for Services and Operations Management, 4(2), 181-200. 27. Stanley, E. F., Cynthia, W., Chad, A., and Gregory, M. (2009). Supply chain information-sharing: benchmarking a proven path. Benchmarking: An International Journal, 16(2), 222-246. 28. Symbiosis Centre for Management. (2013). India Retail Supply Chain Study. Retailers Association of India. 29. Thoo, A. C., Huam, H. T., Rosman, M. Y., Rasli, A. M. and Hamid, A. B. A. (2011). Supply chain management: success factors from the Malaysian manufacturer’s perspective. African Journal of Business Management.5 (17)7240-7247. 30. Wagner, B. A., Fillis, I., and Johansson, U. (2003). E-business and e-supply in small and medium sized businesses. Supply Chain Management: An International Journal, 8(4), 343-354. 31. Wittstruck, D., andTeuteberg, F. (2012). Understanding the success factors of sustainable supply chain management: Empirical evidence from the electrics and electronics Industry.Corporate Social Responsibility and Environmental Management. 19(3)141-158 Authors: J.A. Raja, V. Krishnaveni A Study on the Factors for Low Literacy Rate among the Tribal Tea Labourers of the Nilgiris District, Paper Title: Tamilnadu, India Abstract: Education is considered the basic right of every citizen, irrespective of his social and economic status. But, it has been seen that some people are denied their right to education due to oppression prevailing in society even now. It might be due to lack of awareness among the people or some other economic and political factors. The paper deals with the issue on the poor literacy rate of the tribes belonging to the Nilgiris district of Tamilnadu. The tribes constitute six to seven per cent of the population in the district. The researcher tries to bring out the factors and reasons which prove to be barriers for the education of the children of the tribal community. The study speaks about the tribal tea labourers’ problems which deprive them of the minimum level of education. There are a number of reason like poverty, indirect human rights violation in the work place, lack of awareness, custom of the tribal community etc. Recommendation and suggestions were given on the basis of the above mentioned issues. The steps which have to be taken by the NGOs, Social activists and Government bodies have been suggested in the paper. The paper concludes that education, being a 97. birth right should not be denied for the citizens in a democratic country like India. 544-546 Keywords: Tea Labours, Tribal Tea Labours, Literacy rate, Higher education.

References: 1. Jayaswal, M., Sinha, S.K., Kumari, K. and Arora, A. (2003), Parental Support and Academic Achievement in Tribal School Students of Jharkhand, Journal of All India Association for Educational Research, September, Vol. 15, No.3, Pp: 9-16. 2. Singh and Ohri (1993), “Status of Tribal Women in India” Social Change, December, Vol.23, No.4, Pp: 21-26. 3. UshaSree, S., (1980): Social Disadvantage, Academic Adjustment and Socialistic Achievement, Social Change, Vol. 10. No.1& 2. 4. Jha and Jhingran(2002),“Education of Tribal Children in India and Issue of Medium of Instruction”, Government Janashala Programme, Delhi 5. Srivastava,(2004), “Identification of Educational needs of SAORAS”,NCERT New Delhi. 6. Ambasht NK (2001). Tribal Education:Problems and Issues. New Delhi, Venkatesh Prakashan. 7. Sarkar (1979), “A Critical Study of the Impact of Western Education on the AO Tribe of Mokokchung (Gauhati University)” 8. Rami, Gaurang. (2012) ‘Status of Primary Education in the Tribal District of Gujarat: A Case Study of the Dangs District’, International Journal of Rural Studies, 19(1):1-6. 9. Joshi, Ira (2009), “Quality Education for all –The New National Agenda” Kurushetra, 57(9), pp.29-31 Authors: A. Vijay Vasanth,.K. Venkatachalapathy Paper Title: QoS Improvement through Enhanced Reactive Routing Protocol in MANET Abstract: RAMP is an effective protocol for congestion control and traffic management while passing data in between nodes in MANET but at the same time the standard technology used for searching the neighbor node and the complexity in delivering the packets from the root node to the destination differs in different complexities. To overcome the problem, an hybrid combination to improve the QoS service is proposed in this research paper. To improve the searching methodology, Genetic algorithm is proposed and to improve the better quality in the transmission of packets, 98. cooperative caching is proposed and this approach certainly proves worthier than the initial versions of reactive protocols. 547-550

Keywords: RAMP, Congestion Control, MANET, Trusted value, ERAMP,MRAMP, QoS, MANET, Adhoc Networks, Route cache, routing over head, Reactive routing protocols.

References: 1. Ying-Hong Wang, Jenhui Chen, Chih-Feng Chao and Chien-Min Lee,” A Transparent Cache-based Mechanism for Mobile Ad Hoc Networks”, Proceedings of the Third International Conference on Information Technology and Applications (ICITA’05),IEEE 2005 2. Hao-jun Li, Fei-yue Qiu, Yu-jun Liu,” Research on Mechanism Optimization of ZRP Cache Information Processing in Mobile Ad Hoc Network”, IEEE 2007 3. Wenbo Zhu, Xinming Z hang,Yongzhen Liu, Nana Li, “ Improve Preemptive Routing Performance in Mobile Ad hoc Networks with Cache- enabled Method”, National Natural Science Foundation 2008 4. P. Sateesh Kumar and S. Ramachandram,” The Performance Evaluation of Cached Genetic Zone Routing Protocol for MANETs”, IEEE 2008 5. Fenglien Lee, Carl T. Swanson and Jigang Liu, “ Efficient On-Demand Cache Routing for Mobile Ad Hoc Networks”, IEEE 2009 6. Vineet Joshi, Xuefu Zhu, and Qing-An Zeng,” Caching-based Multipath Routing Protocol”, Proceedings of International Conference on Computational Science and Engineering 2009. 7. Anjum A.Mohammed, Gihan Nagib,” Optimal Routing In Ad-Hoc Network Using Genetic Algorithm”, International Journal of Advanced Networking and Applications Vol :03,Issue:05,Pages:1323-1328(2012) 8. G. Santhi and Alamelu Nachiappan,” A Survey of QoS routing protocols for Mobile Ad HocNetworks”, International journal of computer science & information Technology (IJCSIT) Vol.2, No.4, August 2010 9. R.K.Nadesh, D.Sumathy, M. B. Benjula Anbu Malar “Performance Analysis of MANET (WLAN) Using Different Routing Protocols in Multi service Environments-An Quantitative Study” International Journal Advanced Networking and Applications Volume: 03, Issue: 02, Pages: 1076- 1079 (2011) 10. Sanjeev Gangwar and Dr. Krishan Kumar,” Mobile Ad Hoc Networks: A detailed Survey of QoS Routing Protocols”, International Journal of Distributed and Parallel Systems (IJDPS) Vol.2, No.6, November 2011 11. Chao-Tsun Chang,” Hash Caching mechanism in source-based routing for wireless ad hoc networks”, Journal of Network and Computer Applications, October 2011 12. Narinderjeet Kaur, Maninder Singh, “Caching Strategies in MANET Routing Protocols”,International Journal of Scientific and Research Publications, Volume 2, Issue 9, September 2012 13. CH. V. Raghavendran, G. Naga Satish, P.SureshVarma and K.N.S.L. Kumar, “Challenges and Advances in QoS Routing Protocols for Mobile Ad Hoc Networks”, International Journal of Advanced Research in Computer Science and Software Engineering, Volume 3, Issue 8, August 2013 14. Vivek Arya and Charu, “A survey of Enhanced Routing protocols for MANETs”, International Journal on AdhocNetworking Systems, Vol.3, issue No.3, July 2013. 15. Mostafa Dehghan, Anand Seetharam, Ting He, Theodoros Salonidis, Jim Kurose, and Don Towsley,” Optimal Caching and Routing in Hybrid Networks”, IEEE Military Communications Conference,2014 16. Y.J.Sudha Rani, Dr.M.Seetha “Caching Mechanisms in MANET for Data Availability and Performance Improvement: A Review”, International Journal of Electrical Electronics & Computer Science Engineering Volume 1, Issue 6,December 2014. 17. J. Bibiana Jenifer, M. Manikandan. (2014). “Efficient Cache Consistency in Server- Based MANET with Cache Replication”, ISSN. 3 (2), p5462-5466. 18. Mayur Bhalia, “ Analysis of MANET Characteristics, Applications and its routing challenges”, International Journal of Engineering Research and General Science, Volume 3, Issue 4, Part-2, July-August, 2015 19. Dr.K.Venkatachalapathy, A.Vijay Vasanth, T.Sowmia, V.Ohmprakash,” Cache Routing and Cache optimization technique to improve Time-To- Live quality of notes in the Mobile Adhoc Networks (MANET) using Proactive Routing Protocol”, International Conference on Informatics and Analytics (ICIA’16), Pondicherry Engineering College, August 25-26,2016. 20. T.Senthil Murugan, A.Sarkar, “ Routing protocols for wireless sensor networks: What the literature says?”, Alexandria Engineering Journal, Volume 55, Issue 4, pp:3173 – 3183, 2016. 21. Mahantesh.H.M, Shivakeshi.C, Naveen Kumar.B, Pampapathi.B.M, “Optimization of Link Cache in DSR over MANETs”, International Journal of Computer Science Trends and Technology, volume 4 Issue 3, May - Jun 2016. 22. T.Senthil Murugan, T.Kalaiselvi, “ An improved load balanced distributed weightbased energy-efficient clustering hierarchy routing protocol for military application in MANET”, Indian Journal of Science and TechnologyVolume 9, Issue 32, pp: 1-9, 2016. 23. V.V. Mandhare, R.C. Thool, “Improving QoS of Mobile Ad-hoc Network using Cache Update Scheme in Dynamic Source Routing Protocol”, 7th International Conference on Communication, Computing and Virtualization 2016, Procedia Computer Science 79 (2016), pp- 692 – 699. 24. A.VijayVasanth,K.Venkatachalapathy, T.P.Latchoumi, Latha Parthiban,” A Survey on Cache Route schemes to improve QoS in Ad-Hoc Networks”, International Conference on Intelligence Computing and Control(I2C2’17), Karpagam College of Engineering, Coimbatore, June 23- 24,2017 25. A.VijayVasanth,K.Venkatachalapathy, T.P.Latchoumi, Latha Parthiban,” An Efficient Cache Refreshing Policy to Improve QoS in MANET through RAMP”, Second International conference on Computational Intelligence and Informatics (ICCII’17),JNTUH College of Engineering, Hyderabad, September 25-27,2017 26. A.VijayVasanth,K.Venkatachalapathy, T.P.Latchoumi, Latha Parthiban,” Improving QoS in MANET by adopting effective Cache optimization policy in ADSR”, Journal of Advanced Research in Dynamical & Control Systems, Vol. 10, 06-Special Issue, 2018 Authors: A. Manimuthu, G. Murugaboopathi A Proposed “Modified Filtering Algorithm” (MFA) for Secured Data by Identifying the Delay and Paper Title: Effective Completion Time of Tasks in Cloud Abstract: In today’s era, cloud computing has played a major role in providing various services and capabilities to a number of researchers around the globe. One of the major problems we face in cloud is to identify the various constraints related with the delay in the Task accomplishment as well as the enhanced approach to execute the task with high throughput. Many researches have shown that although having an optimum solution is almost impossible but having a sub-optimal solution using heuristic algorithms seems possible. In this paper, we propose “Modified Filtering Algorithm” for task scheduling on cloud environment, compared with previously used particle swarm optimization (PSO), heuristic approaches, and modified PSO algorithm for efficient task scheduling. Comparing all these three algorithms, we aim to generate an optimal schedule in order to minimize completion time of task execution.

Keywords: Cloud environment, Modified Filtering Algorithm (MFA), Heuristic algorithms, PSO, Task scheduling, 99. Quantum Time;

References: 551-558 1. China cloud computing. Peng Liu:cloud computing definition and characteristics, http://www.chinacloud.cn/.2009-2-25. 2. R. Buyya, C. S. Yeo, S. Venugopal, J. Broberg, I. Brandic, “Cloud Computing and Emerging IT Platforms”, Vision, Hype, and Reality for Delivering Computing as the 5th Utility, Future Generation Computer Systems 25(6), 599–616 (2016). http://dx.doi.org/10.1016/j.future.2008.12.001 3. P. Kumar, A. Verma, “Independent Task Scheduling in Cloud Computing by Improved Genetic Algorithm”, International Journal of Advanced Research in Computer Science and Software Engineering,Vol2, Issue 5, May 2017. 4. Z. Yingfeng, L. Yulin, “Grid Computing Resource Management Scheduler Based on Evolution Algorithm [j]”, Computer Engineering Conference, 2016, 29(15):1102175. 5. P. Roy, M. Mejbah, N. Das. “Heuristic Based Task Scheduling in Multiprocessor Systems with Genetic Algorithm by choosing the eligible processor”, International Journal of Distributed and Parallel Systems (IJDPS), Vol3, No.4, July 2015. 6. Abraham, R. Buyya, and B. Nath.” Nature’s heuristics for scheduling jobs on computational Grids”, 8th IEEE International Conference on Advanced Computing and Communications (ADCOM 2000), India, 2014. 7. H. Yin, H. Wu, J. Zhou, “An Improved Genetic Algorithm with Limited It rat ion for Grid Scheduling”, IEEE Sixth International Conference on Grid and Cooperative Computing, GCC 2007, Los Alamitos, CA, pp. 221-227, 2017. 8. R. Verma, S. Dhingra, “Genetic Algorithm for Multiprocessor Task Scheduling”, IJCSMS International Journal of Computer Science and Management Studies, Vol.1, Issue 02, pp. 181-185, 2011 9. J. Kennedy, R.C. Eberhart, “Jobs swarm optimization”, Proc, IEEE Conf. Neural Netw., vol. IV, IEEE, Piscataway, NJ, 2014,pp.1942-1948. 10. L. Zhang, Y. Chen, B. Yang “Task Scheduling Based on MFA Algorithm in Computational Grid”, 2006 Proceedings of the 6th International Conference on Intelligent Systems Design and Applications, vol-2, 16-18 Oct, 2016,Jinan, China. 11. T. Chen, B. Zhang, X. Hao, Y. Dai, “Task scheduling in grid based on jobs swarm optimization”, The Fifth International Symposium on Parallel and Distributed Computing, ISPDC '06. pp. 238-245, 2016. 12. Widrow Bernard, Samuel Stearns D. Adaptive Signal Processing. – Pearson Education, Delhi, 2004. 13. Monson Hayes H. Statistical Digital Signal Processing and Modelling. – John Wiley & Sons Inc, Kundli, 2002. 14. Emmanuel Ifeachor C., Jervis Barrie W. Digital Signal Processing – A practical approach. – Pearson Education, Delhi, 2004. 15. Georgi Iliev, Nikola Kasabov. Adaptive filtering with averaging in noise cancellation for voice and speech recognition. – Department of Information Science, University of Otago, 2001. 16. Diniz P. Adaptive Filtering Algorithms and Implementation Issues. – Kluwer Academic Publishers, USA, 2002. 17. Xiao Hu, Ai-qun Hu, Li Zhao. A Robust Adaptive Speech Enhancement System // IEEE Int. Conference on Neural Networks and Signal processing. – Nanjing, China, December14-17, 2003. 18. Ikeda S., Sugiyama A. An adaptive noise canceller with low signal distortion for speech codecs // IEEE Trans. Signal Processing. – Vol. 47, Mar. 1999. – P. 665–674. 19. Julie E. Greenberg. Modified LMS Algorithms for Speech Processing with an Adaptive Noise Canceller // IEEE Trans. On Speech and Audio Processing. – Vol. 6, No. 4, July 1998. Authors: R. Elamaran, K. Srinivasan, S. Vimala Paper Title: Use of Copper Slag for Partial Replacement to Fine Aggregate in Concrete Abstract: Copper slag is an excellent by-product or waste material which retains its original properties. Due to its chemical composition which includes high iron, silica and aluminum oxide content, it can be used as a partial replacement for sand in concrete mixes. Mix design of concrete is done on weight basis, by adding various percentages of copper slag (10%, 15%, 20%, 25%, 30% and 35%) instead of sand and concrete mixtures were prepared based on it. The cube, beam and cylindrical specimens were then prepared, demoulded after 24 hours and properly cured. The specimens were subjected to compression testing, split tensile strength testing and flexural testing at 7 and 28 days. It was observed from the test results that the compressive strength of the specimens was higher than the control specimen on adding 10 to 30% of copper slag and on further increasing it, the compressive strength was observed to be reducing. In particular, 10% addition gave more strength than 30% addition. Delay in hardening of concrete specimens was observed. Replacement of copper slag increased the self-weight of the specimens of about 15%.

Keywords: Copper slag, compressive strength, fine aggregates, hardening, self-weight of concrete. 100. References: 559-564 1. Khalifa S Al-Jabri, Abdullah H Al-Saidy, Makoto Hisada, and Salem K Al-Oraimi, “Copper slag as a sand replacement for high performance concrete,” Construction and Building Materials 23(2009) 2132-40. 2. Caijun shi, Christian Meyer and Ali Behnood, “Utilization of copper slag in cement and concrete,” Resources, Conservation and Recycling 52(10), 1115 -1120, 2008. 3. M Najimi, J Sobhani and AR Pourkhorshidi, “Durability of copper slag contained concrete exposed to sulfate attack”, Construction and building materials 25 (4), 1895 – 1905, 2011. 4. Toshiki Ayano, Osamu Kuramoto and Kenji Sakata, “Concrete with copper slag fine aggregate,” Zairyo/Journal of the Society of Materials Science, Japan 49 (10), 1097 – 1102, 2000. 5. Wei Wu, Weide Zhang and Guowei Ma, “Mechanical properties of copper slag reinforced concrete under dynamic compression,” Construction and Building Materials 24 (6), 910 – 917, 2010. 6. Khalifa S Al-Jabri, Abdullah H Al-Saidy, Ramzi Taha, “Effect of copper slag as a fine aggregate on the properties of cement mortars and concrete,” Construction and Building Materials 25 (2), 933-938,2011. 7. Wei Wu, Weide Zhang and Guowei Ma, “Optimum content of copper slag as a fine aggregate in high strength concrete,” Materials & Design 31 (6), 2878-2883, 2010. 8. Washington Almeida moura, Jardel Pereira Gonclaves and Monica Batista Leite Lima, “Copper slag waste as a supplementary cementing material to concrete,” Journal of Material Science 42 (7), 2226, 2007. Authors: M. Sivasubramanian, M. Sivajothi, P. Kumar Paper Title: An Efficient Color Image Segmentation Using Texture Features and Improved Saliency Map Abstract: Image segmentation is the process of partitioning an image into many regions based on some characteristics like color, texture and intensity. It plays a very important role in image analysis. This paper presents an efficient technique for color image segmentation. Proposed technique utilizes Integer Wavelet Transform (IWT) and Self- Organizing Map (SOM) based Enhanced Adaptive Kernelized FCM (EAKFCM) algorithm. Low frequency components of color image are extracted using IWT. Five texture features are derived from the low frequency components. Improved saliency map is calculated. Texture features and ISM are used as an input to the SOM which is an unsupervised neural network. The segmentation of homogenous regions is obtained employing EAKFCM algorithm. Proposed segmentation technique is tested on natural images and Berkeley segmentation dataset. Efficiency of the proposed technique is measured using five widely used statistical measures such as precision, accuracy, recall, entropy 101. and time. Results demonstrated that the efficiency of the proposed color image segmentation technique is superior to other methods in terms of evaluation metrics. 565-570

Keywords: Color image segmentation, clustering, lifting scheme, Integer wavelet transform, self-organizing map, improved saliency map.

References: 1. A.B. Umredkar, A. R. Mahajan and Praful V. Barekar,' Segmentation of color image using saliency map technique', International Journal of Emerging Trend in Engineering and Basic Sciences, 2(2), (2015), 236-239. 2. B. Chitradevi and P.Srimathi,'An overview on image processing techniques',International Journal of Innovative Research in Computer and Communication Engineering,2(11),(2014),6466-6472. 3. Q.Zhao, Y.Hu and J.Cao, ' Automatic image segmentation based on saliency Map and fuzzy SVM', 2009 IET International communication conference on wireless mobile and computing, IEEE. 4. S. Vasuki and L.Ganesan,' An efficient approach to color image segmentation using intermediate features of maximum overlap wavelet transform in peak finding algorithm', IJ. On Image and Graphics, 9(1), (2009), 67-76. 5. N.Zhang, Q.Liu, W.Yang and S.Wang et al. ,' A Review of color image segmentation based on visual Attention', In: International conference on computer science and Technology, 2017, 231-240. 6. L. Zhang ,L.Yang and T.Luo ,'Unified Saliency Detection Model Using Color and Texture Features', PLoS ONE 11(2),(2016),doi:10.1371/ journal.pone.0149328 7. M.Tsaneva ,'Texture features for segmentation of satellite images', Cybernetics and information technologies, 8(3), (2008), 73-85. 8. H.C. S. Kumar, K. B. Raja and K.R. Venugopal, 'Automatic image segmentation using wavelets', International journal on computer Science and network security, 9(2), (2009), 305-313. 9. T.celik and T. Tjahjadi, 'Unsupervised color image segmentation using dual-tree complex wavelet transform', Computer Vision and Image understanding, 114, (2010), 813-826. 10. M.A. Jaffer, M. Ishtiaq, A. Hussain and A.M. Mirza, 'Wavelet based color image segmentation using self-organizing map Neural Network,' In: International Conference on computer engineering and applications, (2009), 430-434. 11. S.S.Ganesh, K. Mohanaprasad and Y. Karuna, 'Object identification using wavelet transform', Indian Journal of Science and Technology, 9(5), (2016). 12. B.M. Jabarulluh and C.N.K. Banu, 'segmentation using saliency - color mapping technique', Indian Journal of Science and Technology, 8(15), 2015. 13. P.Bhattacharya,A.Biswas A and S.P. Maity,'Wavelets-Based Clustering Techniques for Efficient Color Image Segmentation. In: Kumar Kundu M., Mohapatra D., Konar A., Chakraborty A. (eds) Advanced Computing, Networking and Informatics- Volume 1. Smart Innovation, Systems and Technologies, 27, (2014), Springer, Cham 14. C.Mythili,'Efficient technique for color image segmentation towards the enhancement of image retrieval',Phd thesis,2014. 15. D. Martin, C. Fowlkes, D. Tal, J. Malik, A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics, Proc. IEEE Int. Conf. Comput. Vision 2 (2001) 416-423. 16. Rajaby, Ahadi and Aghaeinia,H,' Robust color image segmentation using fuzzy c-means with weighted hue and intensity', Digital Signal Processing, 2016, http://dx.doi.org/10.1016/j.dsp.2016.01.010. 17. D.Chi," Self-Organizing Map-Based Color Image Segmentation with k -Means Clustering and Saliency Map",ISRN Signal Processing, Vol.2011, doi:10.5402/2011/393891 18. A.Singh andM. Joshi,'Image segmentation using Haar DWT &texture analysis in MATLAB', International Journal of Computer Science And Technology,5(3),(2014),22-25. 19. N. Valliammal and S.N.Geethalakshmi ,' Leaf Image Segmentation Based on the Combination of Wavelet Transform and K Means Clustering', International Journal of Advanced Research in Artificial Intelligence,1(3),(2012),37-43. 20. A.Halder and S.Hassan, 'Self-organizing Feature Map and Linear Discriminant Analysis based Image Segmentation',In:1st International Conference on Futuristic trend in Computational Analysis and Knowledge Management, 2015. Authors: K. Rajasekhar, B. Prabhakara Rao Paper Title: Comparison of SNR Estimation Methods of OFDM System Abstract: In this paper a new Signal-to-Noise-Ratio (SNR) estimation method is proposed for OFDM systems. The SNRs and NMSEs of different estimation techniques are determined for different number of symbols as 4, 8, 16, 32 and 64. From the results it is known that when number of symbols increases, the bit error rate increases which lead to the decrease of SNR. Simulation results shows that the performance of the OFDM System will be increased as the average SNR is high for the signal model method which is the proposed method and this method is better than the previous available methods. From the results, it is proved that the proposed signal model method bit error rates are low when compared to the existing methods.

Keywords: Signal to Noise Ratio, SNR estimation, MLE, MMSE, OFDM

References: 1. SNR Estimation Algorithm Based on the Preamble for OFDM Systems in Frequency Selective Channels Guangliang Ren, Member, IEEE, Huining Zhang, and Yilin Chang IEEE Transactions On Communications, Vol. 57, No. 8, August 2009. 2. X. Xu, Y. Jing, "Subspace-based noise variance and SNR estimation for OFDM systems," in Proc. IEEE Mobile Radio Applications Wireless Commun.2005. 102. 3. S. Boumard, "Novel noise variance and SNR estimation algorithm for wireless MIMO OFDM systems," in Proc. IEEE Global Tele communication. Conf., Dec. 2003 4. D. R. Pauluzzi and N. C. Beaulieu, "A comparison of SNR estimation techniques for the AWGN channel," IEEE Trans. communication. vol. 48, 571-573 pp.681-1691, Oct. 2000. 5. An iterative and joint estimation of SNR and frequency selective channel for OFDM systems Vincent Savaux, Yves Lou¨et, Mo¨?se Djoko- Kouam, Alexandre Skrzypczack 6. T. A. Summers and S. G. Wilson, "SNR mismatch and online estimation in turbo decoding," IEEE Trans. Com communication, vol. 46, pp. 421- 423, Apr. 1998. 7. G. Ren, Y. Chan, and H. Zhang, "A new SNR's estimator for QPSK modulations in an AWGN channel," IEEE Trans. Circuits and Syst. II, vol. 52, pp. 331-335, June 2005. 8. S. He and M. Torkelson, "Effective SNR estimation in OFDM system simulation," in Proc. IEEE Global Tele communication. Conf., Nov.1998,pp. 945-950. 60 9. D. Athanasios and G. Kalivas, "SNR estimation for low bit rate OFDM systems in AWGN channel," in Proc. International Conf. Syst. Mobile communication. Learning Technologies Networking, Apr. 2006, pp. 198-198. 10. H. Xu, G. Wei, and J, Zhu, "A novel SNR estimation algorithm for OFDM," in Proc. IEEE Vehicular Technology Conf., May 2005, pp.3068- 3071. 11. IEEE Working Group 802.16 on Broadband Wireless Access Standards Part 16: Air interface for fixed and mobile broadband wireless access system, IEEE Std 802.16e-2005, Feb. 2006. 12. D. C. Chu, "Polyphase codes with good periodic correlation properties IEEE Trans Inform. Theory, vol. 18, pp. 531-532, 1972. 13. ITU Radio communication Sector, Recommendation ITU-R M.1225.Guidelines for Evaluation of Radio Transmission Technologies for IMT2000, 1997. 14. M. R. Spiegel, Mathematical Handbook of Formulas and Tables. New York: McGraw-Hill, 1990. Authors: B. Shanmugam, R. Ganapathi A Study on Psychological Well Being and Job Satisfaction of Employees in Information Technology (IT) Paper Title: Sector in Coimbatore District Abstract: Psychological well being is the cognitive assessment of individuals on their satisfaction with entire life and 103. it is mixture of mental, physical, social and emotional aspects of individuals and it affects performance and satisfaction of employees and business success of organization. Significant difference is prevailing among psychological well being 574-577 and profile of employees of Information Technology (IT) sector. The employees are satisfied with work atmosphere, chance for career development and cooperation among employees in IT sector. The psychological well being has positive, significant and high degree of relation with job satisfaction of employees in IT sector. To enhance psychological well being of employees in IT sector, they must be pleased with things happened to them and they should change few things in them and they must always complete their work timely. Further, they should be very good listener when problems are discussing with their friends and they must obtain many things from others through managing better relation with them.

Keywords: Employees, IT Sector, Job Satisfaction, Psychological Well Being

References: 1. Green, C., & Heywood, J. (2011). Flexible contracts and subjective well-being. Economic Inquiry, 49(3), 716-729. 2. Ishmeet Singh, & Navjot Kaur. (2017). Contribution of information technology in growth of Indian economy. International Journal of Research - GRANTHAALAYAH, 5(6), 1-9. 3. Jaideep Kaur. (2013). Role of psychological well being and its impact on the motivational level of the employees in IT sector. International Journal of Advanced Research in Management and Social Sciences, 2(6), 43-51. 4. Kompal Wadhawan. (2016). Psychological well-being as a predictor to job performance and job satisfaction. International Journal of Academic Research and Development, 1(3), 1-3. 5. Laura Lorente, Nuria Tordera, & José María Peiro. (2018). How work characteristics are related to European workers' psychological well-being. A comparison of two age groups. International Journal of Environmental Research and Public Health, 15(127), 1-11.

6. Nagesh, K. (2002). Indian software industry development: International and national perspectives. Economic and Political Weekly, 36(45), 4278- 4290. 7. Najib Ahmad Marzuki. (2013). The impact of personality on employee well-being. European Scientific Journal, 9(20), 43-52. 8. Norizan Baba Rahim, & Siti Rohaida, M. Z. (2015). Career satisfaction and psychological well-being among professional engineers in Malaysia: The effect of career goal development. Asian Academy of Management Journal, 20(2), 127-146. 9. Ronald J. Burke, Scott Moodie, Simon Dolan, & Lisa Fiksenbaum. (2012). Job demands, social support, work satisfaction and psychological well-being among nurses in Spain. ESADE Working Papers Series, Barcelona. 10. Ryff, C. (1991). The structure of psychological well being. Journal of Personality and Social Psychology, 69, 719-727. 11. Sell, H., & Nagpal, R. (1992). Assessment of subjective well being: Subjective well being inventory, SUBI, Regional Health Paper, SEARO, 24, New Delhi. 12. Vijayasri, G. V. (2013). The role of information technology (IT) industry in India. Abhinav International Monthly Refereed Journal of Research In Management & Technology, 2(8), 54-64. 13. Thomas Parry, & Bruce Sherman. (2015). Workforce health-the transition from cost to outcomes to business performance. Benefits Quarterly, 12 pp. 32-38. 14. Thuso Baruti, & Calvin Gwandure. (2017). Psychological well-being, job satisfaction, and organisational commitment among employees in Botswana, An Unpublished Master Thesis, University of the Witwatersrand, Witwatersrand. 15. Trecy Emerald, & Genoveva. (2014). Analysis of psychological well-being and job satisfaction toward employees performance in Pt Aristo Satria Mandiri Bekasi, Indonesia. International Journal of Business, Economics and Law, 4(1), 22-30. Authors: Ziyoda G. Mukhamedova Paper Title: Energy Efficiency Review and Monitoring of Special Self - Propelled Railway Rolling Stock Abstract: Electric power installation of a special self-propelled rolling stock used in the power supply divisions is considered in the paper; the expediency of its use for the analysis of the main energy indices, in particular, the evaluation of the efficiency of electro-hydro-mechanical units at separate and joint operation, is shown. To assess the energy efficiency, an integrated efficiency factor has been proposed that allows accounting the energy parameters of electrical installations operating in various modes. Recommendations have been given to increase the power factor of electrical installations.

Keywords: special self-propelled rolling stock, assessment, power efficiency, efficiency factor, electrical installation, block-diagram, electric motor, synchronous generator.

104. References: 1. Rail service car diesel assembly manual ADM-1, manual for operation 77.020-00.00.000 RE, Russia, JSC "V. Tikhoretsk Machine-Building Plant named after V.V.Vorovsky" 2003. 578-582 2. Instructions for operation and maintenance of railroad handcars, low-capacity engines, rail service cars on the railways of JSC "Uzbekistan Railways" No. 388 - N, November 10, 2014. 3. Lyubarsky, B. G. Traction drive for high-speed rolling stock / B. G. Lyubarsky, D. Yu. Zyuzin et al. // Newsletter of National Technical University, Kharkov, Ukraine, New solutions in modern technologies. - 2006 - ?42, - Pp.72 - 77. 4. Efanov, A. N. Evaluation of economic efficiency of investment and innovation in rail transport. - SPb.: PGUPS. - 2001, - 149 p. 5. Zevenke, G.V. Basics of the theory of circuits: Textbook / P. A. Ionkin, A. V. Netushil, S. V. Strakhov. - M .: Energoatomizdat, 1989. - 528 p. 6. Epifanov, A. P. Fundamentals of the electric drive: Textbook. / A. P. Epifanov, 2nd ed., - SPb .: Lan publishing house, 2009. - 192 p. 7. Robert. B.Ash. Basic Probability Theory. University of Illinois. Published in New York, 2008. Poisson Process, pp 197 - 200. 8. Mukhamedova Z. G., Khromova G. A., Yutkina I. S. Mathematical Model of Oscillations of Bearing Body Frame of Emergency and Repair Railcar, Journal "Transport Problems", Volume 12, Issue 1, Gliwice 2017, pp. 93 - 103. 9. Popa G., Badea C. et al. Dynamic Oscillations Features of the Br 185 Locomotive Series. Journal of the Balkan Tribological Association, 2016, Volume 22, no.1, pp. 66 - 73. 10. Mukhamedova Z. G., Yakubov M. S. Analysis of Optimal Periodicity of Preventive Maintenance of Rail Service Car Taking into Account Operational Technology. European science review. 2018. Pp. 167 - 171. Said Sanatovich Shaumarov, Anvar Ishanovich Adilhodzhaev, Elena Vladimirovna Shipacheva, Sanjar Authors: Ishratovich Kandokhorov Paper Title: Development of New Constructive and Heat-Insulating Materials Abstract: The question of the development of a new methodological approach to the creation of a heat-insulating constructional building material for external enclosing structures of buildings and structures of railway transport is 105. considered. The results of numerical experimental studies of the cellular concrete macrostructure are presented. The dependence of the fractal dimension of the pore structure of cellular concrete on the magnitude of porosity for hexagonal and cubic types of laying particles of concrete was obtained. 583-586

Keywords: railway buildings and structures, structurally-insulating material, cellular concrete, macrostructure, modeling, optimization, fractal dimension.

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Authors: A. Balamurugan, R. Bhavya, K. Radhakrishnan, M. Kannan, N. Lalitha Paper Title: Substation Monitoring and Control based on Microcontroller Using IOT Abstract: As the complexity of distribution network has grown, automation of substation has become a need of every utility company to increase its efficiency and to improve the quality of power being delivered. The proposed project which is IOT based controlling of the substation will help the utility companies, by ensuring that their local-substation faults are immediately realized and reported to their concerned departments via IOT, to provide that term of intensity intrusion is decreased. The measured parameters will send as SMS messages. The microcontroller will cooperate with the sensors introduced at the nearby substation and perform a task as commanded. Electrical parameters like current, voltage will be compared continuously to its rated value will help protect the distribution and power transformer from burning due to overload, short circuit fault, overvoltage’s, and surges. Under such conditions, the whole unit is closed down through the control area including transfers detecting it, and instantly killing the electrical switch. SMS cautions can likewise be produced to demonstrate this. The utilization of GSM makes the substation astute in the sense that it can transmit signals and information and receive commands. This enables to reduce labor cost at the substation and spares time. In this manner, the observing and working effectiveness of the sub-station will definitely increment. 106. Keywords: Substation, SMS Messages, IOT, Relay, Monitoring. 587-591

References: 1. Amit Sachan, "Microcontroller based substation monitoring and control system with GSM modem" ISSN: 2278-1676 Volume 1, Issue 6 (July- Aug. 2012), PP 13-21 2. Natalie Matta, Rana Rahim-Amoud, Leila Merghem- Boulahia, AkilJrad, "A wireless sensor network for substation monitoring and control in the smart grid" (IEEE) 3. M. Kezunovic, Y. Guan, M.Ghavami, "New concept and solution for monitoring and control system for the 21 st century substation" (IEEE) 4. Jyotishman Pathak, Yuan Li, Vasant Honavar and James D. McCalley, "A Service-Oriented Architecture for Electric Power Transmission System Asset Management," In ICSOC Workshops, pp: 26-37, 2006. 5. B.A. Carreras, V. E. Lynch, D. E. Newman and I. Dobson, "Blackout Mitigation Assessment in Power Transmission Systems," Hawaii International Conference on System Science, January 2003. 6. Xiaomeng Li and Ganesh K. Venayaga moorthy, "A Neural Network-Based Wide Area Monitor for a Power System," IEEE Power Engineering Society General Meeting, Vol. 2, pp: 1455-1460, 2005. 7. Argonne National Laboratory, "Assessment of the Potential Costs and Energy Impacts of Spill Prevention, Control, and Countermeasure requirements for Electric Utility Substations," Draft Energy Impact Issue Paper, 2006.

Authors: R. Devarajan, M. Anandraj, G. Rameshkumar, S. Goplakrishnan, M. Subramanian Power Generation Using Hybrid Renewable Energy Resources GSM based Control Performance for Paper Title: Domestic Applications Abstract: Renewable power source innovations offer spotless, copious vitality assembled from self-reestablishing assets, for example, the sun, wind, and so forth. As the power request expands, control disappointment additionally 107. increments. In this way, sustainable power sources can be utilized to give consistent burdens. Another converter topology for hybrid wind/ photovoltaic energy system is proposed. Hybridizing sun oriented and wind control sources 592-598 provide a sensible type of intensity age. The main consideration of this project is to control the performance of the domestic application using GSM. This setup enables the two references to supply the heap independently or at the same time contingent upon the accessibility of the vitality sources.

Keywords: PV Solar, Wind, Controller, GSM, Relay, Fan, Light.

References: 1. A. Bakhshai et al., "A Hybrid Wind-Solar Energy System: A New Rectifier Stage Topology," IEEE Magazine, July 2010. 2. A. Nirmal Kumar and R. Bharani Kumar "Analysis of Wind Turbine Driven PM Generator with Power Converters," International Journal of Computer and Electrical Engineering, Vol. 2 [4], August 2010. 3. R. Karki and R. Billinton "Capacity Expansion of Small Isolated Power Systems Using PV and Wind Energy," IEEE Transactions on Power Systems, Vol. 16 [4], November 2001. 4. Chen et al., "Multi-Input Inverter for Grid-Connected Hybrid PV/Wind Power System," IEEE Transactions on Power Electronics, vol. 22, May 2007. 5. G. Adrian and D. C. Drago, "Modeling of renewable hybrid energy sources," Scientific Bulletin of the Petru Maior University of Tirgu Mures, Vol. 6, 2009. 6. J. Hui, "An Adaptive Control Algorithm for Maximum Power Point Tracking for Wind Energy Conversion Systems," Department of Electrical and Computer Engineering, Queen's University, December 2008. 7. V. Agarwal and S. Jain, "An Integrated Hybrid Power Supply for Distributed Generation Applications Fed by Nonconventional Energy Sources," IEEE Transactions on Energy Conversion, Vol. 22 [2], June 2008.Kim et al., "Dynamic Modeling and Control of a Grid-Connected Hybrid Generation System with Versatile Power Transfer," IEEE transactions on industrial electronics, VOL. 55 [4], April 2008. 8. K. Kalaitzakis and E. Koutroulis, "Design of a Maximum Power Tracking System for Wind-Energy- Conversion Applications," IEEE Transactions on Industrial Electronics, Vol. 53 [2], April 2006. 9. V. Lorentz, "Bidirectional DC Voltage Conversion for Low Power Applications," University Friedrich-Alexander of Erlangen-Nuremberg, 2009. Output Voltage [V] Output Current [A] Electrical and Electronics Engineering: An International Journal (EEEIJ) Vol.1, No.2, August 2012 10. Mahmoud et al.," A Simple Approach to Modeling and Simulation of Photovoltaic Modules," IEEE Transactions on Sustainable Energy, Vol. 3 [1], January 2012. 11. S. Rana and S. Nath, "The Modeling and Simulation of Wind Energy Based Power System using MATLAB," International Journal of Power System Operation and Energy Management, Vol 1, 2011. 12. S. K. Pradhan and D. Das, "Modeling And Simulation of PV Array with Boost Converter: An Open Loop Study," National Institute Of Technology, Rourkela, 2011. 13. F. Giraud and Z. M. Salameh, "Steady-State Performance of a Grid-Connected Rooftop Hybrid Wind-Photovoltaic Power System with Battery Storage," IEEE transactions on energy conversion, vol. 16 [1], March 2001. 14. R. Devarajan and S. Senthilkumar, "Performance Analysis of Three port Bidirectional DC to DC Converter," International Journal Applied Engineering Research, Vol. 10[10], 2015.

Authors: M. Ramzan Begam, P. Sengottuvelan A Law Enforcement for Crime Detection (RRL-PMD): Relative Record Linkage based Pattern Mining Paper Title: Algorithm Using Decision Classifier to Identify Crime Rates Abstract: Prediction of crime data become tremendous in automatic resultant process in law enforcement. Due to vast amount of information in crime records are processed automatically through knowledge mining techniques. The term prediction doesn’t make the relational terms to identify correct crime key terms. So the classification analysis based on statistical attribute crime weightage leads more complex to analyze. To solve this problem, we propose a Relative Record linkage based pattern mining algorithm using decision classifier to identify crime rates(RRL-PMD). By analyzing the real terms observed from crime case arein relative sentence format, the sentence case similarity measure predicts the crime key terms to form cluster. The record linkage generalize the frequency of count term measure to reduce the dimensionality make links based on relative closeness measure. The subset classifier make decision to categorize the risk analyzed from crime records. The proposed system produce higher efficiency to reduce the redundancy of complexity level make efficient crime analysis.

Keywords: Crime Analysis, Decision Classifier, Prediction, Cluster, Semantic Analysis, Record Linkage.

References: 1. R. Bolton and D. Hand, "Unsupervised Profiling Methods for Fraud Detection," Statistical Science, vol. 17, no. 3, pp. 235-255, 2001. 2. P. Brockett, R. Derrig, L. Golden, A. Levine, and M. Alpert, "Fraud Classification Using Principal Component Analysis of RIDITs," The J. Risk and Insurance, vol. 69, no. 3, pp. 341-371, 2002, 3. S. Bordag, "A comparison of co-occurrence and similarity measures as simulations of context," in CICLing, 2008. 4. Hsinchun Chen, Wingyan Chung, Yi Qin, Michael Chau, Jennifer JieXu, Gang Wang, RongZheng, HomaAtabakhsh, "Crime Data Mining: An 108. Overview and Case Studies", AI Lab, University of Arizona, proceedings National Conference on Digital Government Research, 2003, available at: http://ai.bpa.arizona.edu/ 5. Fan, C., Xiao, K., Xiu, B., &Lv, G. (2014, August). A fuzzy clustering algorithm to detect criminals without prior information. In Advances in 599-608 Social Networks Analysis and Mining (ASONAM), 2014 IEEE/ACM International Conference on (pp. 238-243). IEEE. 6. BUCZAK, A. L., AND GIFFORD, C. M. Fuzzy association rule mining for community crime pattern discovery. In ACM SIGKDD Workshop on Intelligence and Security Informatics (2010), ACM, p. 2. 7. Hsinchun Chen, Wingyan Chung, Yi Qin, Michael Chau, Jennifer JieXu, Gang Wang, RongZheng, HomaAtabakhsh, "Crime Data Mining: A General Framework and Some Examples", IEEE Computer Society April 2004. 8. Lin, S., & Brown, D. E. (2006). An outlier-based data association method for linking criminal incidents. Decision Support Systems, 41(3), 604- 615. 9. NAKAYA, T., AND YANO, K. Visualising crime clusters in a space-time cube: An exploratory data-analysis approach using space-time kernel density estimation and scan statistics. Transactions in GIS 14, 3 (2010), 223-239. 10. Clifton Phua, Member, IEEE, Kate Smith-Miles, Senior Member, IEEE, Vincent Lee, and Ross Gayler, "Resilient Identity Crime Detection", IEEE transactions on knowledge and data engineering ,vol.24 no.3 year 2012 11. A. Budanitsky and G. Hirst, "Evaluating wordnet-based measures of lexical semantic relatedness," Comput. Linguist., vol. 32, no. 1, pp. 13- 47, March 2006. 12. G. Gordon, D. Rebovich, K. Choo, and J. Gordon, "Identity Fraud Trends and Patterns: Building a Data-Based Foundation for Proactive Enforcement," Center for Identity Management and Information Protection, Utica College, 2007. 13. M. A. Salahli, "An approach for measuring semantic relatedness between words via related terms," Mathematical and Computational Applications, vol. 14, no. 1, pp. 55-63, April 2009 14. TOOLE, J. L., EAGLE, N., AND PLOTKIN, J. B. Spatiotemporal correlations in criminal offense records. ACM Transactions on Intelligent Systems and Technology (TIST) 2, 4 (2011) 15. X. Li and M. Juhola, "Crime and its social context: Analysis using self-organising map," Proceedings of the European Conference on Intelligence and Security Informatics, pp. 121-124, 2013 16. AniruddhaKshirsagar, Lalit Dole, "Recognizing the theft of identity using data mining" ,International Journal of Emerging Technology and Advanced Engineering (ISSN 2250-2459, ISO 9001:2008 Certified Journal, Volume 4, Issue 4, April 2014 17. K. R. HafedhShabat, Nazila Omar, "Named entity recognition in crime using machine learning approach," in Information Retrieval Technology, December 2014, pp. 280-288 18. Kiani, R., Mahdavi, S., &Keshavarzi, A. (2015). Analysis and Prediction of Crimes by Clustering and Classification. Analysis, 19. X. Li, H. Joutsijoki, J. Laurikkala, M. Siermala, and M. Juhola, "Crime vs. demographic factors revisited: Applications of data mining methods," Webology, Vol. 12, No. 1, Article 132, 2015. 20. GRAIF, C., GLADFELTER, A. S., AND MATTHEWS, S. A. Urban poverty and neighborhood effects on crime: Incorporating spatial and network perspectives. Sociology Compass 8, 9 (2014), 1140-1155. 21. Bin Pei, Xiuzhen Wang, Fenmei Wang, Parallelization of FP-growth Algorithm for Mining Probabilistic Numerical Data based on MapReduce,2016 9th International Symposium on Computational Intelligence and Design, 2473-3547/16 $31.00 © 2016 IEEE.

Authors: Dr.P. Selvam, G. Devan, M.P. Sakthivel, P. Anandan, E. Boopathy Enhance the Power Distribution Network in Three-Phase Three –Wire and Qualities Sag and Unbalance Paper Title: Mitigation Using DVR Abstract: Power quality is one of the real worries in the present period. It has become important, especially, with the introduction of sophisticated devices, whose performance is very sensitive to the quality of power supply. A dynamic voltage restorer (DVR) based on photovoltaic (PV) generation/battery units is proposed to improve voltage quality in a micro-grid. The restorer is connected with the grid by a rectifier, which is in series with the point of common coupling (PCC). Power quality problem is an occurrence manifested as a nonstandard voltage, current or frequency that fails end- use equipment's. One of the major issues dealt here is the voltage sag. To take care of this issue, custom power gadgets are utilized. One of those gadgets is the Dynamic Voltage Restorer (DVR), which is the most productive and successful present-day custom power gadget utilized as a part of intensity dissemination systems. Its appeal includes lower cost, smaller size, and its fast dynamic response to the disturbance. This paper introduces power quality problems and diagram of Dynamic Voltage Restorer with the goal that youthful electrical designers come to think about such a cutting-edge custom power gadget for control quality change in the future era.

Keywords: PV Solar, Micro-grid, DVR, PCC, Power Quality.

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P.Selvam, Dr. M. Y. Sanavullah, Dr. A. Nagappan "Analysis of Voice Signal recognition using embedded System," International Journal of Emerging Technologies and Applications in Engineering, Technology and Sciences, ISSN 0974-3588 Vol 3, Issue 1, pp 437- 439 Jan 2010 19. P.Selvam, Dr. M. Y. Sanavullah " A Novel Approach of Computing A Diagnoalisation Matrix Method for Solving An Evaluation Problem of Home Automation Speech Recognition System in Hidden Markov model," International Journal of Engineering Research and Industrial Applications, ISSN 0974-1518 Vol 3, No. 1, pp 351-362, Feb 2010 20. PK Kumaresan, P Selvam, KT Sikamani, M Kannan, P Senguttuvan "An Efficient Categorization Of Content Based Region Oriented Database Retrieval And Datamining," International Journal of Emerging Technologies and Applications in Engineering, Technology and Sciences, ISSN 0974-3588 Vol 3, Issue 1, pp 437- 439, 2010 21. 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Authors: G. Rama Krishnaprabu, G. Ramkumar, M. Jagadeesh, E. Senthilkumar, P. Ashok Kumar Paper Title: IOT based Interactive Industrial Energy Management System and Emergency Alert Using SMS & E-Mail Abstract: A hybrid energy system is a system that consists of two or more alternative energy sources (ex: solar and wind). In a modern industrial application that energy source can be utilized for the control application for real-time power system stabilization. The entire monitoring and control progress of the industrial utilities is an appropriate improvement in the industrial growth system. Here, the various industrial parameters are taken up for control such as gas, fire, machine, motor, in embedded based control. Solar, wind-based renewable power plant energy is stored in Battery. The Diesel generator is a backup sources Hybrid controller to implement the energy sources changeover logic based on optimal energy management strategy. The controller establishes an Automatic mode of operation in the hybrid controller for machine and motor power changeover operations. In this module, the fire and gas sensor will analyze its set range variation by the controller. If it exceeds it pre-defined values set in the controller the immediate indication and alert is arrived for to take necessary safety precaution and control in real time application. In industrial progress, the machine and motor are valuable sources for operating different load condition. Some mix-constrain load machine will be a failure. This effect will be rectified in this scheme through load analysis. The power control circuit provides a constant source depends upon load changes. All these variations will be monitored and control by IOT.

Keywords: Hybrid Energy, Monitoring, Embedded, Safety precaution, Load Analysis, IOT. 110. References: 616-624 1. I. Takahashi and T. Noguchi, "A new quick response and high-efficiency control strategy of an induction motor," IEEE Trans. Ind. Appl., vol. IA-22, no. 5, pp. 820-827, Sep./Oct. 1986. 2. M. Depenbrock, "Direct self-control (DSC) of the inverter-fed induction machine," IEEE Trans. Power Electron., vol. 3, no. 4, pp. 420-429, Oct. 1988. 3. A. Mir Sayeed, Donald, S. Zinger, and Malik Elbuluk, "Fuzzy controller for an induction machine," IEEE, Trans. On Industry Applications, Vol. 30, No 1, January February 1994. 4. G. Buja, D. Casadei, and G. Serra, "Direct torque control of induction motor drives," in Proceedings of the ISIE Conference, 1997, pp. TU2. 5. Domenico Casadei, Giovanni Serra, and Angelo Tani "FOC and DTC: Two Viable Schemes for Induction Motors Torque Control" IEEE, Trans. Power Electro. VOL. 17, NO. 5, pp.779-787 SEPTEMBER 2002. 6. T. Habetler, F. Profumo, M. Pastorelli, and L. M. Tolbert, "Direct torque control of induction machines using space vector modulation," IEEE Trans. Ind. Appl., vol. 28, no. 5, pp. 1045-1053, Sep./Oct. 1992. 7. S. Mir, M. E. Elbuluk, and D. S. Zinger, "PI and fuzzy estimators for tuning the stator resistance in direct torque control of induction machines," IEEE Trans. Power Electron., vol. 13, no. 2, pp. 279-287, Mar. 1998. 8. K. B. Lee and J. H. Song, "Torque ripple reduction in DTC of an induction motor driven by the three-level inverter with low switching frequency," IEEE Trans. Power Electron., vol. 17, no. 2, pp. 255-264, Mar. 2002. 9. C. Lascu, I. Boldea, and F. Blaabjerg, "Direct torque control of sensorless induction motor drives: A sliding-mode approach," IEEE Trans. Ind. Appl., vol. 40, no. 2, pp. 582- 590, Apr. 2004. 10. Manuel, A., Francis, J.: 'Simulation of direct torque controlled induction motor drive by using space vector pulse width modulation for torque ripple reduction,' Int. Int. J. Adv. Res. Electr. Electron. Instrum. Eng.., 2013, 2, (9), pp. 4471-4478.

Authors: R. Sathish, D. Kishor Kumar, K. Asokan, M. Jagathesan, C. Thangavel Paper Title: Evaluation of a New Nine-Level Cascaded Multi Level-Inverter with Reduced Number of Components Abstract: With the expanding requests for power supplies in PC, telecom, electric vehicle, and other comparable zones where low voltage and high current are required, the customary dc control circulation system (DC PDS) is gradually unable to meet the prerequisites because of its deficiencies, for example, more transformation stages, low effectiveness and poor transient reaction. High-frequency ac power distribution system (HFAC PDS) proposed it has become an alternative because of its merits such as fewer conversion stages, higher efficiency, faster response, high power density, distributed heat profile and potential for connector-less power transfer.

Keywords: Switched Capacitor, an Isolation Circuit, Buffer Circuit, Controller, H-bridge Circuit, a Driver Circuit, Load, Nine Level Inverter.

References: 1. E. Babaei, S. Laali, And Z. Bayat, "A Single-Phase Cascaded Multilevel Inverter Based On A New Basic Unit With Reduced Number Of Power 111. Switches," IEEE Trans. Ind. Electron., Vol. 62, No. 2, Pp. 922-929, Feb. 2015. 2. S. Fan, Kai Zhang, J. Xiong, And Y. Xue, "An Improved Control System For Modular Multilevel Converters With New Modulation Strategy And Voltage Balancing Control," IEEE Trans. Power Electron., Vol. 30, No. 1, Pp. 358-371, Jan. 2015. 625-630 3. J.-S. Choi And F.-S. Kang, "Seven-Level PWM Inverter Employing Series-Connected Capacitors Paralleled To A Single Dc Voltage Source," IEEE Trans. Ind. Electron., Vol. 62, No. 6, Pp. 3448-3459, Jun. 2015. 4. J. Mei, K. Shen, B. Xiao, L. M. Tolbert And J. Zheng, "A New Selective Loop Bias Mapping Phase Disposition PWM With Dynamic Voltage Balance Capability For Modular Multilevel Converter," IEEE Trans. Ind. Electron., Vol. 61, No. 2, Pp. 798-807, Feb. 2014. 5. G. Buticcchi, E. Lorenzani And G. Franceschini, "A Five-Level Single-Phase Grid-Connected Converter For Renewable Distributed Systems," IEEE Trans. Ind. Electron., Vol. 60, No. 3, Pp. 906-918, Mar. 2013. 6. Y. Hinago And H. Koizumi, "A Switched-Capacitor Inverter Using Series/Parallel Conversion With Inductor Load," IEEE Trans. Ind. Electron., Vol. 59, No. 2, Pp. 878-887. Feb. 2012. 7. E. Babaei, "A Cascade Multilevel Converter Topology With Reduced Number Of Switches," IEEE Trans. Power Electron., Vol. 23, No. 6, Pp. 2657-2664, Nov. 2008. 8. S. Kouro, P. Lezana, M. Angulo, And J. Rodríguez, "Multicarrier PWM With Dc-Link Ripple Feedforward Compensation For Multilevel Inverters," IEEE Trans. Power Electron., Vol. 23, No. 1, Pp. 52-59, Jan. 2008. 9. J. Drobnik, "High frequency alternating current power distribution," Proceedings of IEEE INTELEC, pp. 292-296, 1994. 10. P. Jain, H. Pinheiro, "Hybrid high-frequency AC power distribution architecture for telecommunication systems," IEEE Trans. PowerElectron., vol. 4, no.3, Jan. 1999. 11. B. K. Bose, M.-H. Kin and M. D. Kankam, "High-frequency AC vs. DCdistribution system for a next-generation hybrid electric vehicle," in Proc.IEEE Int. Conf. Ind. Electron., Control, Instrum, (IECON), Aug. 5-10,1996, vol.2, pp. 706-712. 12. S. Chakraborty, M. D. Weiss and M. G. Simões, "Distributed IntelligentEnergy Management System for a Single-Phase High-Frequency ACMicrogrid," IEEE Trans. Ind. Electron., vol. 54, no. 1, pp. 97-109, Feb.2007. 13. L. Zhou and S. A. Boggs, "High-Frequency Attenuating Cable for protection of Low-Voltage AC Motors Fed by PWM Inverters," IEEETrans. Power Del., vol. 20, no. 2, pp. 548-553, Apr. 2005. 14. A. Nabae, I. Takahashi, and H. Akagi, "A new neutral-point-clampedPWM inverter," IEEE Trans. Ind. Appl., vol. IA-17, no. 5, pp. 518-523, Sep. 1981. 15. T. A. Meynard and H. Foch, "Multi-level conversion: High voltage chopper and voltage-source inverters," in Proc. IEEE 23rd Annu.Power Electron. Spec. Conf., Jun. 29-Jul. 3, 1992, vol. 1, pp. 397-403.

Authors: P. Loganathan, N. Kokila, T. Revathy, S. Suresh Kumar, S. Ayyasamy Design and Implementation of Zigbee based Sensor Network in Smart Grid System for Power Management Paper Title: Using IOT Abstract: The current power grid is experiencing a massive change. Smart grid innovation is a radical approach for ad improvisation in the prevailing power grid. Integration of electrical and communication infrastructure is inevitable for the deployment of the smart grid organizes. Brilliant network innovation is described by full duplex correspondence, programmed metering framework, sustainable power source incorporation, appropriation computerization and finish checking and control of whole power matrix. Remote sensor systems (WSNs) are small small-scale electrical, mechanical systems that are sent to gather and convey the information from the environment. WSNs can be utilized for observing and control of grid resources. Security of remote sensor based correspondence arranges a noteworthy worry for scientists and designers. The constrained handling capacities of remote sensor systems make them more defenseless against cyber-attacks. The countermeasures against cyber-attacks must be less mind-boggling with a position to offer classification, information availability, and honesty. The address arranged outline and improvement approach for the regular correspondence organize a change of perspective to plan information situated WSN design. WSN security is an 112. inescapable piece of smart grid cybersecurity. This work is relied upon to fill in as a comprehensive assessment and analysis of communication standards, cybersecurity issues and solutions for WSN based intelligent grid infrastructure. 631-637

Keywords: Smart Grid, Integration, Renewable Energy, WSN.

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Authors: S. Noordeen, K. Dinakaran Multi Attribute Point of Randomized Key Service Level Attribute-based Encryption Standards for Secure Paper Title: Cloud Computing in Cloud Environment Abstract: The growing size of information, security became the risk of access from centralized resource providers deploy them into the cloud, where authorized users could access them. To provide security to the cloud resources there are many service level agreements (SLA) are provided, but the problem of public auditability and data dependence is unresolved due to challenge in key auditing. The service level needs the security based on key auditing by choosing the different service level. To propose a multi-level attribute based randomized key auditing encryption service (MARK- SLE) to improve the service level of security in the cloud environment. Additional to third part key aggregate level with prime factor verification. Then compute the encryption and decryption of attributes accessed at each level to provide cloud security. Whatever the data modified by any user will be updated so that to provide data audit and data dependence by swapping the reference of address in the block modified. The method maintains user access history, where the user access details are stored. The access history is being used to make decisions on providing service to the user request. The proposed method increases the efficiency of public auditability and tampers resistance.

113. Keywords: Cloud Computing, Data Security, Public Auditing, Service Level Encryption, Cloud Security.

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Authors: M.S. Harisha, D. Jayadevappa Paper Title: FPGA Implementation of Auto Switching in a Multicore Hybrid Processor Abstract: This paper proposes indigenously designed SMART processor core auto switching with two splitting strategies. First one is, Separating heterogeneous instructions based on functionality like hardware and software. Second one is, Checking out for FREE/BUSY status of individual core in a multi-core processor and allocate instructions or tasks to FREE status cores, thereby efficiently manage traffic and perform load balancing amongst various processor cores. I have also enclosed snapshots popular hardware interfacing & corresponding display results.

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