International Journal of Recent Technology and Engineering

ISSN : 2277 - 3878 Website: www.ijrte.org Volume-7 Issue-5S2, JANUARY 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), Senior Member of IEEE Professor, Department of Computer Science & Engineering, Lakshmi Narain College of Technology Excellence (LNCTE), Bhopal (M.P.), India

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

Associated Editor-In-Chief Members Dr. Hai Shanker Hota Ph.D. (CSE), MCA, MSc (Mathematics) Professor & Head, Department of CS, Bilaspur University, Bilaspur (C.G.), India

Dr. Gamal Abd El-Nasser Ahmed Mohamed Said Ph.D(CSE), MS(CSE), BSc(EE) Department of Computer and Information Technology, Port Training Institute, Arab Academy for Science, Technology and Maritime Transport, Egypt

Dr. Mayank Singh PDF (Purs), Ph.D(CSE), ME(Software Engineering), BE(CSE), SMACM, MIEEE, LMCSI, SMIACSIT Department of Electrical, Electronic and Computer Engineering, School of Engineering, Howard College, University of KwaZulu- Natal, Durban, South Africa.

Scientific Editors Prof. (Dr.) Hamid Saremi Vice Chancellor of Islamic Azad University of Iran, Quchan Branch, Quchan-Iran.

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

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

Dr. Veronica Mc Gowan Associate Professor, Department of Computer and Business Information Systems,Delaware Valley College, Doylestown, PA, Allman, China.

Dr. Fadiya Samson Oluwaseun Assistant Professor, Girne American University, as a Lecturer & International Admission Officer (African Region) Girne, Northern Cyprus, Turkey.

Dr. Robert Brian Smith International Development Assistance Consultant, Department of AEC Consultants Pty Ltd, AEC Consultants Pty Ltd, Macquarie Centre, North Ryde, New South Wales, Australia

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

Executive Editor Chair Dr. Deepak Garg Professor & Head, Department of Computer Science And Engineering, Bennett University, Times Group, Greater Noida (UP), India

Executive Editor Members Dr. Vahid Nourani Professor, Faculty of Civil Engineering, University of Tabriz, Iran.

Dr. Saber Mohamed Abd-Allah Associate Professor, Department of Biochemistry, Shanghai Institute of Biochemistry and Cell Biology, Shanghai, China.

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

Dr. Labib Francis Gergis Rofaiel Associate Professor, Department of Digital Communications and Electronics, Misr Academy for Engineering and Technology, Mansoura, Egypt.

Dr. Hugo A.F.A. Santos ICES, Institute for Computational Engineering and Sciences, The University of Texas, Austin, USA.

Dr. Sunandan Bhunia Associate Professor & Head, Department of Electronics & Communication Engineering, Haldia Institute of Technology, Haldia (Bengal), India.

Dr. Awatif Mohammed Ali Elsiddieg Assistant Professor, Department of Mathematics, Faculty of Science and Humatarian Studies, Elnielain University, Khartoum Sudan, Saudi Arabia.

Technical Program Committee Chair Dr. Mohd. Nazri Ismail Associate Professor, Department of System and Networking, University of Kuala (UniKL), Kuala Lumpur, Malaysia.

Technical Program Committee Members Dr. Haw Su Cheng Faculty of Information Technology, Multimedia University (MMU), Jalan Multimedia (Cyberjaya), Malaysia.

Dr. Hasan. A. M Al Dabbas Chairperson, Vice Dean Faculty of Engineering, Department of Mechanical Engineering, Philadelphia University, Amman, Jordan.

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

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

Dr. Thanhtrung Dang Associate Professor & Vice-Dean, Department of Vehicle and Energy Engineeering, HCMC University of Technology and Education, Hochiminh, Vietnam.

Dr. Wilson Udo Udofia Associate Professor, Department of Technical Education, State College of Education, Afaha Nsit, Akwa Ibom, Nigeria.

Convener Chair Mr. Jitendra Kumar Sen Blue Eyes Intelligence Engineering & Sciences Publication, Bhopal (M.P.), India

Editorial Chair Dr. Sameh Ghanem Salem Zaghloul Department of Radar, Military Technical College, Cairo Governorate, Egypt.

Editorial Members Dr. 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.

Dr. Nitasha Soni Assistant Professor, Department of Computer Science, Manav Rachna International Institute of Research and Studies, Faridabad (Haryana), India.

Dr. Siva Reddy Sheri Associate Professor, Department of Mathematics, School of Technology Hyderabad Campus, GITAM University, Visakhapatnam (Andhra Pradesh), India.

Dr. Nihar Ranjan Panda Associate Professor, Department of Electronics and Communication Engineering, Sanketika Vidya Parishad Engineering College, Visakhapatnam (Andhra Pradesh), India. Volume-7 Issue-5S2, January 2019, ISSN: 2277-3878 (Online) Pag S. No Published By: Blue Eyes Intelligence Engineering & Sciences Publication e No. Authors: G. Jegadeeswari. Paper Title: Performance Analysis of Power Quality Improvement using Shunt Active Power Filter Abstract: Nowadays, the usage of non-linear loads in power system is more sufficient. For example, UPS, inverters, converters, etc. These loads make the supply current as non-sinusoidal and distorted form, which is called harmonics. At this time Active power filters have been developed to improve power quality. In this Paper, a Shunt Active Power Filter (SAPF) control scheme has proposed to eliminate the current harmonics and improve the power quality. The shunt active power filter controlled by using the different controllers such as (PI, PID, Fuzzy logic, Pq Theory and hysteresis controller). In our proposed system, Hysteresis controller and Instantaneous power theory were used to reduce the harmonics current using the shunt active power filter. And both controllers’ results are compared, and then find which controller is most suitable to control the shunt active power filter in term of total harmonic reduction. MATLAB/SIMULINK power system toolbox is used to simulate the proposed system.

Keywords: Power Quality, Shunt Active Power Filter (SAPF), Hysteresis Current Controller, Harmonics, MATLAB/Simulink. 1. References: 1-3 1. Qian Liu, Li Peng, Yong Kang, Shiying Tang, Deliang Wu, and Yu Qi “A Novel Design and Optimization Method of anLCL Filter for a Shunt Active Power Filter” IEEE Transactions on industrial electronics, vol. 61, No. 8, pp:4000-4010,august 2014. 2. Anand Singh, Dr. Prashant Baredar,“Power Quality Analysis of Shunt Active Power Filter Based On Renewable Energy Source” IEEE International Conference on Advances in Engineering & Technology Research (ICAETR - 2014). 3. Jeevananthan.K.S, “Designing of Single Phase Shunt Active Filter Using Instantaneous Power Theory” International journel on Electric Engineering & Research ,Vol. 2, Issue 2, pp: (1-10), Month: April - June 2014. 4. Quoc-Nam Trinh and Hong-Hee Lee, Senior Member, IEEE “An Advanced Current Control Strategy for Three-Phase Shunt Active Power Filters” IEEE Transactions on industrial electronics, vol. 60, no. 12,pp:5400-5411 December 2013. 5. H. Sasaki and T. Machida, "A New Method to Eliminate AC Harmonic by Magnetic Compensation Consideration on Basic Design," IEEE Trans. on Power Apparatus and Syst., vol. 90, no. 5, pp. 2009-2019. 6. H. Akagi, Y. Kanazawa, K. Fujita And A. Nabae “Generalized Theory of Instantaneous Reactive Power and Its Application” Electrical Engineering in Japan, Vol. 103, No. 4 , 1983 7. H. Akagi “Control Strategy and Site Selection of a Shunt Active Filter for Damping of Harmonic propagation in Power Distribution Systems” IEEE Transactions on Power Delivery, Vol. 12, No 1, 1997 8. T. Narongrit, K-L. Areerak and K-N. Areerak “The Comparison Study of Current Control Techniques for Active Power Filters” 2011 9. H. Akagi “New Trends in Active Filter for Power Conditioing” IEEE Transactions On Industry Applications, Vol 32, No 6, 1996 10. H. Akagi, E. H. Watanabe, M. Aredes “Instantaneous Power Theory and Application to Power Conditioning” IEEE Press, 2007. Authors: S.V. Saravanan, S. Sindhuja. Miniaturisation Using Shorting Posts in C-Shaped and H-Shaped Microstrip Patch Antennas for GPS Paper Title: Applications Abstract: This paper presents the study of the effects of shorting posts for C-shaped and H- shaped microstrip patch antennas for GPS application. A C-shaped patch and H-shaped patch loadedmicrostrip patch antenna for GPS frequency (1.575 GHz) are designed and simulated. The shorted microstrip patch antenna is a compact antenna but it suffers from the disadvantage that more number of shorting pins is required thereby making fabrication process harder especially when manufactured in larger quantities. An alternate way to reduce the resonance frequency of the microstrip antenna is to increase the path length of the surface by cutting slots in the radiating patch. The slot is taken as the capacitive reactance in the patch.

Keywords: Slot-Loaded Patch, Microstrip Patch Antenna, Global Positioning Satellite 2. (GPS), Shorted. 4-8 References: 1. W.C. Liu and P.C. Kao, “Design of a probe-fed H-shaped microstrip antenna for circular polarization”, Journal of Electromagnetic Waves and Applications, vol. 21, pp. 857-864, 2007. 2. R. Porath, “Theory of miniaturized shorting-post micro-strip antennas,” IEEE Transactions, Antennas and Propagation, Vol. 48, No. 1, pp. 41-47, 2000. 3. R. Garg, P. Bhartia, I. Bahl, and A. Ittipiboon, “Micro-strip antenna design handbook,” Artech House: London, 2001. 4. M. Sanad, “Effect of the shorting posts on short circuit microstrip antennas,” Proceedings, IEEE Antennas and Propagation Society International Symposium, pp. 794- 797, 1994. 5. H. K. Kan and R. Waterhouse, “Size reduction technique for shorted patches,” Electronics Letters, Vol. 35, pp. 948-949, 1999. 6. Abdel Fattah Sheta and Samir F. Mahrnoud, “A novel H-shaped patch antenna,” Microwave Opt Technol Lett, vol. 31, pp. 62-65, 2001. 7. B. Davor, R. Bojan, “Small H-shaped shorted patch antennas,” Radio engineering, vol. 17, pp. 77, 2008. 8. A.A. Deshmukh, G. Kumar, “Compact Broadband C-shaped Stacked Microstrip Antennas”, IEEE Antenna and Propagation Society International Symposium, Vol.2, pp. 538-541, 2002. 9. Mohammad Tariqul Islam, Mohammed Nazbus, Shakib, Norbahiah Misran, Baharudin Yatim, “Analysis of Broadband Microstrip Patch Antenna,” Proc. IEEE, pp. 758-761, 2008. 10. C.A. Balanis, Modern Antenna Handbook, John Wiley & Sons, 2008. 11. Saravanan, S.V., Dheepak, M. “Design of SEA BALL for maritime security” Journal of Advanced Research in Dynamical and control systems,2017(Special Issue 11), pp. 492-495 12. Ahmed H. Raja “Study of Micro Strip Feed Line Patch Antenna”, Antennas and Propagation International Symposium, vol. 27, pp. 340-342 December 2008. Authors: T. Beni Steena, P. Indira, M. Geethalakshmi. Enhancing Performance of Optical Link using Integrated DWDM and Flip- OFDM for High Speed Paper Title: Optical Communication Systems Abstract: The main advantages of the optical transmission media such as wide bandwidth, high bit rate with large channel capacity made it most favorable delivering transmission media. In this paper we consider flip-OFDM along with DWDM. Both techniques have different hardware complexities. The Dense Wavelength Division Multiplexing (DWDM) network is reshaping the landscape of communication networks. To compensate dispersion effects in optical wireless communication we use OFDM. We convert bipolar OFDM signals to unipolar OFDM symbol is to add a DC bias. This is known as DC offset OFDM. DC bias depends on the value of PAPR, which is large for OFDM. To lower DC bias values we use clipped negative time samples. Which results in Inter-carrier Interference and out of band optical power. DC bias is avoided by Asymmetric clipped optical OFDM. Only positive (odd) subcarriers carry information and negative values are clipped at the transmitter. The performance were still improved by Flip-OFDM, the positive and negative parts are extracted from bipolar OFDM real time domain signal and transmitted in two consecutive OFDM symbols. Both frames are positive samples since negative part is flipped before transmission. Thus Flip OFDM is a unipolar technique that can be used in optical wireless communication. In this paper we review and analyse Flip-OFDM and suggests further improvements. The future based DWDM based integrated services are also discussed. It is clear that the proposed 3. system can provide tremendous improvement in BER performance and high data rate which is well suited for future applications. The simulations are performed by MATLAB. 9-12

Keywords: Flip-OFDM, DWDM, PAPR.

References: 13. R. Iftikhar, A.Muhammed, C.Mahwish,“Analyzing The Non LinearEffects At Various Power Levels and Channel Counts On ThePerformance Of DWDM Based Optical Fiber Communication System Emerging Techonolgies (ICET), International Conference IEEE,2012 14. Nuo Huang, Jun-Bo Wang, Cunhua Pan, Jin-Yuan Wang, Yijin Pan,and Ming Chen, “Iterative Receiver for Flip-OFDM in OpticalWireless Communication”, in IEEE Photonics Technology Letters,Vol. 27, No. 16, pp. 17-29, August 15, 2015. 15. N. Fernando, Y. Hong, and E. Viterbo, “Flip-OFDM for unipolar communication systems,” IEEE Trans. Commun., vol. 60, no. 12, pp.3726–3733, Dec. 2012. 16. M.A.J. John, P.G. Gokul ,“ Performance Evaluation and Simulation OfOFDM In Optical Communication Systems,” Journal of EngineeringResearch and Application, vol.5 , pp. 1- 4, February 2015 17. Malti, Meenakshi Sharma, AnuSheetal, “Comparison of CSRZ, DRZ and MDRZ Modulation Formats for High Bit Rate WDM-PON System using AWG”, International Journal of Emerging Technology and Advanced Engineering (IJETAE), Vol.6, no.2, pp.83-87, June 2012. 18. S. Parkash, A. Sharma, S Singh H.P. Singh.,“ PerformanceInvestigation of 40 GB/s DWDM over Free Space OpticalCommunication System Using RZ Modulation Format ,” Advances inOptical Technologies, pp. 1-8, January 2016 19. Fabio Cavaliere, Luca Giorgi, Roberto Sabella, “Overcoming the challenges of very high speed optical communication” Ericsson Review, Vol.11,pp.1-8, Oct. 14, 2013 20. J. Kahn and J. Barry, “Wireless infrared communications,” Proceedings of the IEEE, vol. 85, no. 2, pp. 265–298, 1997. 21. Liang Wu , Zaichen Zhang , Jian Dang , Jiangzhou Wang , and Huaping Liu, “Polarity Information Coded Flip-OFDM for IntensityModulated Systems,” IEEE Communications Letters , vol. 20, issue. 8, Aug. 2016. 22. J. Carruthers and J. Kahn, “Modeling of non directed wireless infrared channels,” Communications, IEEE Transactions on, vol.45, no.10, pp.1260–1268, 1999. Authors: C. Rajinikanth, S. Abraham Lincon. A Semi Supervised based Hyper Spectral Image (HSI) Classification Using Machine Learning Paper Title: Approach Abstract: In this paper, a new algorithm has been designated for classification of satellite remote sensing of hyperspectral image. The classification process is based on the three main categories: filtering, Clustering and

classified, in this process to achieve a new optimal image clustering to overcome the problem of multi-label images in satellite remote processing. Finally, it gets clustered and result in classified output. The proposed research contribution is validated by classification experiments using Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) image sensors from the results the overall accuracy of single and multi-label of Salinas A dataset.

Keywords: Hyperspectral Image, Clustering, Classification and Optimization.

References: 1. Bing, L., Xuchu,Y., Z.Pengqiang, Xiong,Y., Anzhu, Y.,&Zhixiang,X., 2017. “A semi-supervised convolutional neural network for hyperspectral image classification.” Remote Sensing Letters 8(9): 839-848. 2. Xiaochen, Lu., Junping, Z., Tong, L., and Ye.Z.,2017. “Hyperspectral image classification based on semi-supervised rotation forest.” Remote sens. 2017,9: 924. 3. Borja, A., and Manuel Grana,R., 2016. “Hyperspectral Image Analysis bySpectral-Spatial Processing and Anticipative Hybrid Extreme

Rotation Forest Classification.” IEEE Transactions on Geoscience and Remote Sensing 54(5). 4. Zhi, H., Han,L., Yiwen,W., and Jie.H., 2017. “Generative Adversarial Networks-Based Semi-Supervised Learning for Hyperspectral 4. Image Classification.” Remote Sens., 9: 1042. 13-16 5. Yushi, C.,Hanlu,J., Chunyang,L., Xiuping Jia, and Pedram,G., 2016. “Deep feature extraction and classification of hyperspectral images based on convolutionalneural networks.” IEEE Transactions on Geoscienceand Remote Sensing, 54(10). 6. Pedram, G., Yushi,C., and Xiao,X.,2016. “A Self-Improving convolution neural network for the Classification of Hyperspectral Data.”IEEE Geoscience and Remote Sensing Letters13(10):1537 -1541: 7. Jun, Y., Shanjun,M., and Mei.L., 2016. “A deep learning framework for hyperspectral image classification using spatial pyramid pooling.” Remote Sensing Letters 7(9): 875–884: 8. Gaangliang,C.,Feiyun,Z.,Shiming,X.,Ying,W., and Chunhong,P.,2015.”Semi-supervissed Hyperspectral Image Classification via discriminant analysis and roboust regression.” IEEE journal of selected topics in applied earth observation and remote sensing 9(2):595- 608, 9. Borja, A., Marques,M., and Manuel,G., 2015. “Spatially regularized semisupervised Ensembles of Extreme Learning Machines for hyperspectral image segmentation.” Neurocomputing 149: 373–386. 10. Kun, T., Jun,H., Jun,L., and Peijun.D., 2015. “A novel semi-supervised hyperspectral image classification approach based on spatial neighborhood information and classifier combination.” ISPRS Journal of photogrammetry and remote sensing 105: 19-29: Authors: K. Hemalatha, S. Sivajothi Kavitha, D. Usha. Paper Title: Self Regulated Deodorizing Lavatory System Abstract: The introduction of integrated robotics in the field of sanitation is the main motto of our project. In this paper, we have implemented a new idea of integrating a robot named Auto end- effect or which is used for improving the sanitation, efficiency and convenience of the cleaning process in the public lavatory systems. In the proposed system called Self–Regulated Deodorizing Lavatory System, a counter is used to record the number of times of usage of the lavatory system and initiates the cleaning process. Similarly a sensor module detects the sanitation level and the cleaning process is continued which is performed by a robotic arm called Auto end-effector. Thus SRDLS system will greatly eliminate the role of manpower in the maintenance of public lavatory system to a greater elevation and facilitate the preservation of hygienic standards.

Keywords: Self-Regulated Deodorizing Lavatory System (SRDLS), Auto End-effector, Lavatory Systems, Sanitation, Hygienic Standards.

References: 5. 1. C.Arun kumar, A. Adithya Bharadwaj, R.Balasubramanian, P.Gowtham, ”Autonomous lavatory cleaning system”, international journal of Robotics and Automation(IJRA),volume.6,issue.4,2015. 2. M.Karthick, S.Sakthi kannan, G.Velmathi, “odour sensor based solution for the sanitary problem faced by elderly people and 20-23 kindergarten children”, International Journal of Information Sciences and Techniques (IJIST) Vol.4, No.3,May 2014. 3. Cleophas D. K Mutepfe, Emanuel Rashayi, Elisha C Mabunda, “Intelligent Water Dispensing System Model Utilizing AAA framework”, International journal of science and research(IJSR),volume.2, issue .7,July 2013. 4. Dan Li, “The design of cleaning robot based on ARM microprocessor”, 6th international conference on Electronic, Mechanical, Information and Management (EMIM 2016). 5. Jaeseok Yun and Sang-Shin Lee, “Human Movement Detection and Identification Using Pyroelectric Infrared Sensor”, Multidisciplinary Digital Publishing Institute (MDPI), May 2014 6. Manya Jain, Pankaj Singh Rawat, “Automatic Floor Cleaner”, International Research Journal of Engineering and Technology(IRJET), volume.04, issue.04, April 2017. 7. Md.Anisur Rahman, Alimul Haque Khan, Dr.Tofayel Ahmed, Md.Mohsin Sajjad, “Design, Analysis and Implementation of a Robotic Arm- The Animator”, American Journal of Engineering Research (AJRE), 2013. 8. Jung-Young Lim,Sang-Young Kim, “Single phase Switched Reluctance motor for vacuum cleaner”, Industrial Electronics,2001. 9. Dhanashree Salunke, Sheetal Bhingardive, S.N.Rawat, “Embedded Based Autonomous Modular Lavatory System for Railways”, International Journal of Advanced Research in Electronics and Communication Engineering (IJARECE), volume.5, Issue.3, March 2016. Authors: K.R. Anupriya, T. Sasilatha. Paper Title: Epileptic Seizure Detection Using HWPT based ANFIS Classifier Abstract: Epilepsy patients experience challenges in everyday life due to precautions they have to take in

order to cope with this situation .When a seizure occurs it might cause injuries or endanger the lives of the patients or others when they are using heavy machinery or driving etc. Prediction of epileptic activities before they occur will enable the patients and caregivers to take appropriate precautions. This paper proposes a novel

patient-specific epileptic seizure detection using electroencephalogram (EEG). The proposed method combined both harmonic wavelet packet trans-form (HWPT) and fractal dimension (FD) to extract feature vectors from EEG signals effectively. Finally, the Adaptive Neuro-Fuzzy Inference System (ANFIS) is used to classify the

feature vectors obtained from the epileptic electroencephalogram (EEG) signals. The ANFIS classification

method combines both neural networks and the fuzzy logic principles together. Finally, the use of less computationally intensive feature extraction techniques facilitates speedy epileptic seizure detection when compared with existing techniques, signifying potential usage in real-time applications.

Keywords: Seizure, Classifier, EEG, ANFIS, HWPT, Fractal Dimension..

References: 1. A. S. Zandi, M. Javidan, G. A. Dumont, and R. Tafreshi, “Automated real-time epileptic seizure detection in scalp EEG recordings using an algorithm based on wavelet packet transform,” IEEE Trans. Biomed. Eng., vol. 57, no. 7, pp. 1639–1651, Jul. 2010.

2. R. B. Yaffe et al., “Physiology of functional and effective networks in epilepsy,” Clin. Neurophysiol., vol. 126, no. 2, pp. 227–236, Feb. 2015. 20-25 6. 3. J. Gotman, “A few thoughts on ‘What is a seizure?”’ Epilepsy Behavior, vol. 22, pp. S2–S3, Dec. 2011. 4. TP Runarsson, S Sigurdsson, On-line detection of patient specific neonatal seizures using support vector machines and half-wave attribute histograms, inThe International Conference on Computational Intelligence for Modeling, Control and Automation, and International Conference on Intelligent Agents, Web Technologies and Internet Commerce (CIMCA- IAWTIC) (Vienna), pp. 673– 677. 28–30 Nov 2005. 5. JYoo, L Yan, D El-Damak, MA Bin Altaf, AH Shoeb, AP Chandrakasan, An 8 channel scalable EEG acquisition SoC with patient- specific seizure classification and recording processor. IEEE J. Solid State Circuits 48(1), 214–228 (2013) 6. PRana, J Lipor, H Lee, WV Drongelen, MH Kohrman, BV Veen, Seizuredetection using the phase-slope index and multichannel ECoG. IEEE Trans.Biomed. Eng. 59(4), 1125– 1134 (2012) 7. M. J. Katz and E. B. George, “Fractals and the analysis of growth paths,”Bull. Math. Biol., vol. 47, no. 2, pp. 273–286, Jan. 1985. 8. A. Accardo, M. Affinito, M. Carrozzi, and F. Bouquet, “Use of the fractal dimension for the analysis of electroencephalographic time series,” Biological, vol. 77, no. 5, pp. 339–350, Nov. 1997. 9. T. Higuchi, “Approach to an irregular time series on the basis of the fractal theory,” Phys. D, Nonlinear Phenomena, vol. 31, no. 2, pp. 277–283, Jun. 1988. 10. Q. Yuan, W. Zhou, S. Li, and D. Cai, “Epileptic EEG classification based on extreme learning machine and nonlinear features,” Epilepsy Res., vol. 96, nos. 1–2, pp. 29–38, Sep. 2011. 11. R. G. Andrzejak, K. Lehnertz, F. Mormann, C. Rieke, P. David, and C. E. Elger, “Indications of nonlinear deterministic and finite- dimensional structures in time series of brain electrical activity: Depen- dence on recording region and brain state,” Phys. Rev. E, Stat. Phys. Plasmas Fluids Relat. Interdiscip. Top., vol. 64, no. 6, p. 061907, Nov. 2001. 12. A. Shoeb and J. Guttag, “Application of machine learning to epileptic seizure detection,” in Proc. 27th Int. Conf. Mach. Learn. (ICML), 2010, pp. 975–982. 13. A. L. Goldberger et al., “PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic sig- nals,” Circulation, vol. 101, no. 23, pp. e215–e220, Jun. 2000. 14. S. Santaniello, S. P. Burns, A. J. Golby, J. M. Singer, W. S. Anderson, and S. V. Sarma, “Quickest detection of drug-resistant seizures: An opti- mal control approach,” Epilepsy Behavior, vol. 22, pp. S49–S60, Dec. 2011. 15. U. R. Acharya, F. Molinari, S. V. Sree, S. Chattopadhyay, K.-H. Ng, and J. S. Suri, “Automated diagnosis of epileptic EEG using entropies,” Biomed. Signal Process. Control, vol. 7, no. 4, pp. 401–408, Jul. 2012. Authors: S. Usha, C. Subramani, T.M. Thamizh Thentral, A. Geetha, Ishwarya Ravi. Paper Title: Modified Energy Efficient Compact Fluorescent Lamp Abstract: In the current electricity demand, the usage of normal bulbs extensively consumes more power for the usage. India being a moderate country of power generation of about 335 GW, it would be very efficient to replace normal bulbs by CFL bulbs. This paper deals with reduction of electronic waste by replacing blow out cflinplace of tube light choke. CFLs can be screwed into the same sockets as other light bulbs and provide very comparable lighting. One of the greatest benefits of compact fluorescent light bulbs is energy efficiency. A CFL uses 50 to 80 percent less energy than other light bulbs.

Keywords: This, cflinplace, CFLs, efficiency. about 335, GW, India.

References: 1. Ayesha Muneeb*, Samia Ijaz, Souman Khalid and Aftab Mughal “Research Study on Gained Energy Efficiency in a Commercial Setup by Replacing Conventional Lights with Modern Energy Saving Lights “Journal of Architectural Engineering 7. Technology,vol.6(2),pp.1-2,2017. 2. Banwell, P., Kwartin R., 2006, Quality Assurance in ENERGY STAR Residential Lighting Programmes, presented at the 24-26 International Energy Efficiency in Domestic Appliances and Lighting Conference, June 2005, London 3. Global Network on Energy for Sustainable Development (GNESD), 2005, Political commitment and innovative policies are necessary for power sector reform programmed to benefit the poor, Newsletter (October) 4. Emmanuel T Ogbomida Appraising the Cost and Heat Emission Implications of Residential Energy Efficient Lighting in Benin City, Edo State Journal of Energy Engineering · January 2013 2013, 3(5): 234-241 5. Global Network on Energy for Sustainable Development (GNESD), 2006, Can Renewable Energy make a real contribution? Global Network on Energy for Sustainable Development (GNESD), Newsletter. 6. Bennet, K., 2001, Energy Efficiency in Africa for Sustainable Development: A South African Perspective, Energy Research Institute, University of Cape Town, South Africa 7. All China Market Research Co., Ltd. (ACMR), 2004, Survey Report for Annual Follow-up Evaluation of the Promotion Item of China Green Lighting Project, ACMR, Beijing 8. Illuminating Engineering Society of North America (IESNA) (2000) Lighting Handbook. 9. Industrial lighting ANSI/IESNA RP-7- 01 (1991) Recommended Practice for Industrial Facilities. Authors: S. Usha, C. Subramani, C. Vimala, M. Venkatesan, Nair Anirudh Murali. Paper Title: Wireless Energy Metering for IOT Application Abstract: In recent times, with the development of technology, and the ever increasing demands of the population in terms of electrical power in order to run the many home used appliance has increased, hence the need to effectively reach the masses in terms of informati0on of consumption and billing is also of great importance. The advent of IOT (internet of things) has led to minimization of many process related functions. This paper proposes wireless energy metering using RF Transceiver. It is a simple system which is used for measuring electrical bills through wireless communication and sends the information regarding consumed power & also sends the dead line for paying of electrical bill. The primary advantage of this system is its efficiency,

cost redundancy and portability, this system also gives a leading edge over online payment and approachability.

An overview of this technology is provided in detail in this paper, along with the simulation and feasibility.

Keywords: AMR, RF Transmitter, RF Receiver, Energy Meter, Automatic Energy Meter Devices.

References: 1. Prof. Dr. K. P. Sathyamoorthy, “Smart energy meter load control”, International Journal of Advanced Research in Electrical, 27-29 8. (IJAREEIE), ISSN (Online): 2278 – 8875, Vol. 2, Issue 8, August 2013 2. S. Arun, Dr. Sidappa “Design and Implementation of Automatic Meter Reading System Using GSM, ZIGBEE through GPRS”, International Journal of Advanced Research in Computer Science and Software Engineering Research Paper. Volume 2, Issue 5, May 2013. 3. Bharat Kulkarni “GSM Based Automatic Meter Reading System Using ARM Controller”, International Journal of Emerging Technology and Advanced Engineering Website, Volume 2, Issue 5, May 2012. 4. Mr. Rahul Ganesh Sarangle, Prof. Dr. UdaypanditKhot, Prof. JayanModi “Gsm Based Power Meter Reading and Control System”, International Journal of Engineering Research and Applications (IJERA) vol. 4, June- July 2012. 5. Abdollahi, M. Dehghani, and N. Zamanzadeh , “SMS based reconfigurable automatic meter reading system,” IEEE International Conference on Control Applications (CCA 2007), Oct, 2007, pp. 1103 - 110 T.M. Thamizh Thentral, R. Jegatheesan, K. Vijayakumar, S. Senthilnathan, T.V. Abhinav Authors: Viswanaath Implementation of Shunt Compensating Device for the Mitigation of Harmonic Current in Non-Linear Paper Title: Distributed System Abstract: With the extensive use of harmonic generating devices, the control of harmonic currents to maintain a high level of power quality is becoming increasingly important. An effective way to suppress harmonics is harmonic compensation by using passive filters, active power filters and custom power devices. In this paper one of the custom power device called Distribution static synchronous compensator (DSTATCOM) taken to improve the power quality in the distribution systems. This device provides reactive power compensation, load balancing and harmonic current compensation in ac distribution networks. By using this DSTATCOM harmonic current can also be compensated. The reference current is extracted with help of synchronous reference frame theory and control signal to the device is generated from the hysteresis current control method. Both the simulation and the experimental results were analyzed. Simulation results are obtained with a condition of balanced sinusoidal voltage source and balanced load.

Keywords: Total Harmonic Distortions, DSTATCOM, Hysteresis Current Controller, Synchronous Reference Frame Theory. References: 1. Alexandre B. Nassif, Student Member, IEEE, Wilsun Xu, Fellow, IEEE, and Walmir Freitas, Member, IEEE , ‘’An Investigation on the selection of filter topologies for passive filter applications’’, 0885-8977/$25.00 © 2009 IEEE. 2. J.Nastran , R. Cajhen, M. Seliger, and P.Jereb,”Active Power Filters for Nonlinear AC loads, IEEE Trans.on Power Electronics Volume 9, No.1, PP: 92-96, Jan 2004. 3. Mikko Routimo, Student Member, IEEE, Mika Salo, and Heikki Tuusa, “Comparison of Voltage-Source and Current-Source Shunt Active Power Filters”, IEEE transactions on power electronics, vol. 22, no. 2, march 2007, 0885-8993. 9. 4. Victor Fabián Corasaniti, Maria Beatriz Barbieri, Patricia Liliana Arnera, and María Inés Valla, “Hybrid Power Filter To Enhance Power Quality In A Medium-Voltage Distribution Network” At IEEE Transactions On Industrial Electronics, Vol. 56, No. 8, August 2009. 5. Papic, I.: ‘Power quality improvement using distribution static compensator with energy storage system’. Proc. Ninth Int. Conf. 30-35 Harmonics Quality Power, Orlando, 2000, pp. 916–920. 6. Kumbha, V., Sumathi, N.: ‘Power quality improvement of distribution lines using DSTATCOM under various loading conditions’, Int. J. Modern Eng. Res., 2012, 2, (5), pp. 3451–3457. 7. Topologies for Passive Filter ApplicationsKhadkikar. V.:‘Enhancing electric power quality using UPQC: A comprehensive overview’, IEEE Trans. Power Electron., 2012, 27, (5), pp. 2284–2297. 8. M.Mangaraj, A.K.Panda, “An efficient control algorithm based dstatcom for power conditioning”, 2015 International Conference on Industrial Instrumentation and Control (ICIC) College of Engineering Pune, India. May 28-30, 2015 9. Bhim Singh, Senior Member, IEEE, Jitendra Solanki, “A Comparison of Control Algorithms for DSTATCOM”, IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, VOL. 56, NO. 7, JULY 2009 10. Tejas Zaveri, Bhalja Bhavesh, Naimish Zaveri, “Control techniques for power quality improvement in delta connected load using DSTATCOM”, 2011 IEEE International Electric Machines& Drives Conference (IEMDC) 11. T.M.Thamizh Thentral, Arghya Dipta Banerjee, R.Jegatheesan, K.Vijayakumar “A Synchronous Reference Frame Theory Based Unified Power Quality Conditioner Designed by the Implementation of Active Filters”, published in Journal Of Advanced Research in Dynamical and Control System, 09-Special Issue, 2018 12. Wenyi Liang, Jianfeng Wang, Patrick Chi-Kwong Luk, Weizhong Fang, Weizhong Fei, “Analytical Modeling of Current Harmonic Components in PMSM Drive With Voltage-Source Inverter by SVPWM Technique”, Published in: IEEE Transactions on Energy Conversion (Volume:29, Issue:3, Sept.2014) 13. Fang Zheng Peng, Jih-Sheng Lai, “Generalized instantaneous reactive power theory for three-phase power systems”, Published in: IEEE Transactions on Instrumentation and Measurement (Volume: 45, Issue:1, Feb 1996) 14. Mrutyunjaya Mangaraj, Anup Kumar Panda, “NBP-based icosφ control strategy for DSTATCOM”, published in IET Power Electronics. 15. S. Buso, S. Fasolo, L. Malesani, P. Manavelli “A Dead Beat Adaptive Hysteresis Current Control,” IEEE Transactions on Industry Applicalions, vol.36, no.4,pp. 1174.1 180, July 2000. T.M. Thamizh Thentral, A. Geetha, S. Usha, Ayush Singh, Varoon Kannan, Kotikalapudi Authors: Kameshwari Vashini. Paper Title: Modelling and Emulation of Solar Powered Vehicle Abstract: With growing energy demands, switching to renewable energy is humanity’s last resort. This can be

achieved only if the people are ready to switch to renewable energy, right from powering their homes to replacing their fuel cars. A solar EV provides an answer to the latter. The current requirement is an efficient and economic version of a Solar Electric Vehicle. Nowadays, there is a growing market for electric vehicles given

the current scenario of global warming and the need to reduce it. Although electric vehicles have their advantages, especially in terms of traction efficiency, the major disadvantage is the shorter operating distance in comparison to a conventional vehicle. This is primarily due to the comparatively low energy density of the batteries that propel these vehicles. Hence they are apt for urban, short range purposes. For example, they may be used as taxis or as delivery vehicles. This paper focuses on the simulation of electric vehicles using a Hardware in the Loop (HiL) model of an electric vehicle traction system. The vehicle is tested under different conditions to analyze its energy consumption and other parameters.

Keywords: Hardware in Loop Simulation, Energy Storage System (ESS), Battery Management System(BMS), Boost Converter, Buck Boost Converter, Supervisory Control and Data Acquisition (SCADA).

References: 1. D. Maclay, “Simulation gets into the loop,” IEE Review, vol. 43, no. 3,pp. 109–112, 1997.

2. C. Dufour, V. Lapointe, J. Belanger, and S. Abourida, “Hardware-in-the loop closed-loop experiments with an FPGA-based permanent magnet synchronous motor drive system and a rapidly prototyped controller,” in Proc. IEEE Int. Symp. Industrial Electronics ISIE 10. 2008, pp. 2152–2158,2008. 3. S. Abourida and J. Belanger, “Real-time platform for the control, prototyping, and simulation of power electronics and motor drives, 36-41 ”in Proc. 3rd International Conference on Mondeling, Simulation, and Applied Optimization, 2009. 4. C. Bordas, C. Dufour, and O. Rudloff, “A 3-level neutral-clamped inverter model with natural switching mode support for the real-time simulation of variable speed drives,” in Planet-RT Opal White Paper,2009. 5. G. G. R. Walker, Evaluating MPPT converter topologies using amatlab PV model, Journal of Elect. Electron. Eng. , vol. 21, pp.49- 55,2001. 6. N. Jeddi, L. El Amraoui, Design of a photovoltaic system for constant output voltage and current, The Fifth International Renewable Congress, pp. 586-591, Hammet, Tunisia, March ,2014. 7. N. Hatziargyriou et. All, Modeling New Forms of Generation and Storage, CIGRE Technical Brochure, 2000. 8. M. G. Villalva, 1 R. Gazoli, and E. R. Filho, Comprehensive approach to modeling and simulation of photovoltaic arrays, IEEE Transaction on Power Electronics, vol. 24, no. 5, pp.1198-1208, May 2009. 9. H. Tian, F. Mancilla-David, K. Ellis, E. Muljadi, and P. Jenkins, A cell-to-module-to-array detailed model for photovoltaic panels, Solar Energy, vol. 86, no. 9, pp. 2695-2706, September2012. 10. I. Husain, Electric and Hybrid Electric Vehicles, CRC Press, 2003 11. B. Dunn, H. Kamath and J. Tarascon, “Electrical Energy Storage for the Grid: A Battery of Choices”, Science 18 Nov 2011, Vol. 334, Issue6058, pp. 928-935, 2011 12. Davide Cittanti, Alessandro Ferraris, AndreaAirale, Sabina Fiorot, Santo Scavuzzo and Massimiliana Carello, “Modeling Li-ion batteries for automotive application: A trade-off between accuracy and complexity”, IEEE International Conference of Electrical and Electronic Technologies for Automotive, pp. 1-8, 2017 13. J. Surya Kumari, Ch. Sai Babu, “Comparison of Maximum Power Point Tracking Algorithms for Photovoltaic System”, IJAET, Vol. 1, Issue 5, pp.133-148, Nov 2011. 14. M. H. Rashid, Power Electronics: Circuits, Devices and Applications, 3rd edition, Pearson, 2004 [15] V. R. Moorthi, Power Electronics: Devices, Circuits and Industrial Applications, Oxford University Press, 2007 15. Hairul Nissah Zainudin, Saad Mekhilef, “Comparison Study of Maximum Power Point Tracker Techniques for PSystems”,MEPCON’10, Cairo University, Egypt, December 2010. 16. Burri Ankaiah, Jalakanuru Nageswararao, “Enhancement of Solar Photovoltaic Cell by Using Short-Circuit Current Mppt Method”, IJESSI, Volume 2 Issue 2,PP.45-50, February 2013. Authors: M. Rama Sekhara Reddy, M. Vijaya Kumar, M. Mahesh. A Fuzzy based Synchronous Flux Weakening Control with Flux Linkage Prediction for Doubly-Fed Paper Title: Wind Power Generation Systems Abstract: With the expanding joining of DFIG based substantial breeze power plants, their effects on power regime inert and potent conduct must be examined. Amid grate voltage douses, it consists of DC;-ve succession parts within rotor and stator transition and ephemeral high current are created. Thus to conquer this, a concurrent motion debilitate manage regime with motion liaison forecast is proposed. In conventional manage, the bum prescient manage was exerted to realize fast synchronization and frail collaboration among rotor and stator transition by motion liaison expectation under grate voltage plunges. In advanced manage methodology a FLC is exerted to conquer every one of issues happened in conventional technique. The outcome denote thus advanced manage regime is viable in stifling high current in stator and rotor and decreasing motions in torsion, with to a great extent enhances the execution of DFIG amid grate voltage douses.

Keywords: Rotor Side Converter (RSC), Grid Side Converter (GSC), Low Voltage Ride through (UVRT), Electromagnetic Theory (EMT). References: 1. Y. Li; Z. Xu; and K. Meng, “Optimal power sharing control of wind turbines,” IEEE Transactions on Power Systems, vol. 32, no. 1, pp. 824-825, Jane 2017. 2. N. K. S. Naidu; B. Singh, “Grid-interfaced DFIG-based variable speed wind energy conversion system with power smoothening,” 11. IEEE Transactions on Sustainable Energy, vol. 8, no. 1, pp. 51-58, Jane 2017. 3. Y. Ju, F. Ge, W. Wu, Y. Lin, and J. Wang, “Three-phase steady-state model of doubly fed induction generator considering various 42-48 rotor speeds,” IEEE Access, vol. 4, pp. 9479-9488, 2016. 4. Y. Song; X. Wang; F. Blaabjerg, “High-frequency resonance damping of DFIG-based wind power system under weak network,” IEEE Transactions on Power Electronics, vol. 32, no. 3, pp. 1927-1940, March 2017. 5. P. Cheng; H. Nian; C. Wu; and Z. Q. Zhu, “Direct stator current vector control strategy of DFIG without phase-locked loop during network unbalance,” IEEE Transactions on Power Electronics, vol. 32, no. 1, pp. 284-297, Jane 2017. 6. Z. Zou; X. Xiao; Y. Liu; Y. Zhang; and Y. Wang, “Integrated protection of DFIG-based wind turbine with a resistive-type SFCL under symmetrical and asymmetrical faults,” IEEE Transactions on Applied Superconductivity, vol. 26, no. 7, pp. 5603005, October 2016. 7. K. E. Okedu, “Enhancing DFIG wind turbine during three-phase fault using parallel interleaved converters and dynamic resistor,” IET Renewable Power Generation, vol. 10, no. 8, pp. 1211-1219, September 2016. 8. W. Chen; D. Xu; N. Zhu; M. Chen; and F. Blaabjerg, “Control of doubly-fed induction generator to ride-through recurring grid faults,” IEEE Transactions on Power Electronics, vol. 31, no. 7, pp. 4831-4846, July 2016. 9. L. J. Cai and I. Erlich, “Doubly fed induction generator controller design for the stable operation in weak grids,” IEEE Transactions on Sustainable Energy, vol. 6, no. 3, pp. 1078-1084, July 2015. 10. J. Li and K. Corzine, “Harmonic compensation for variable speed DFIG wind turbines using multiple reference frame theory,” IEEE Applied Power Electronics Conference and Exposition, pp. 2974-2979, 2015. 11. L. Holdsworth, X. G. Wu, J. B. Ekanayake, and N. Jenkins, “Comparison of fixed speed and doubly-fed induction wind turbines during power system disturbances,” IEEE Proceedings-Generation, Transmission and Distribution, vol. 150, no. 3, pp. 343-352, May 2003. 12. D. W. Xiang, S. C.Yang, and L. Ran,“Ride-through control strategy of a doubly-fed induction generator for symmetrical grid fault,” Proceedings of the CSEE, vol. 26, no. 3, pp. 165-170, February 2006. Authors: Kanchamreddy Snehitha, R. Kiranmayi, K. Nagabhushanam. Design and Operation of Flyback CCM Inverter with Fuzzy based Discrete-Time Repetitive Control Paper Title: for PV Power Applications Abstract: In continuous conduction mode, A discrete-time repetitive controller (RC) is proposed for fly 12. back inverter with fuzzy controller. In this paper fuzzy based repetitive controller is used due to some advantages. Such as, it reduces ripples then THD will be reduced, which has simple structure, low cost, and high efficiency. Comparing to the conventional controller the repetitive controller obtain good tracking ability and 49-54 disturbance rejection and applied to flyback inverter in Continuous Conduction Mode operation. Conventional controller results in poor control performance due to the effect of the right-half-plane zero in CCM operation. To allow tracking and rejection of periodic signals within a specified frequency range the RC scheme, a low-pass filter is used. The stability of the closed loop system is derived and the zero tracking error is achieved with the stability of the closed loop system. By using the simulation results we can analyze the proposed method.

Keywords: Module-Integrated Converter, Right-Half-Plane Zero, Low-Pass Filter, Phase-Lead Compensation, Fuzzy Control.

References: 1. F. F. Edwin, W. Xiao, and V. Khankikar, "Dynamic displaying and control of interleaved flyback module-incorporated converter for PV control applications," IEEE Trans. Ind. Electron., vol. 61, no. 3, pp. 1377-1388, Mar. 2014. 2. S. B. Kjaer, J. K. Pedersen, and F. Blaabjerg, "An audit of single-stage framework associated inverters for photovoltaic modules," IEEE Trans. Ind. Appl., vol. 41, no. 5, pp. 1292-1306, Sep./Oct. 2005. 3. Y. H. Kim, J. W. Jang, S. C. Shin, and C. Y. Won, "Weighted-proficiency upgrade control for photovoltaic AC module interleaved flyback inverter utilizing a synchronous rectifier," IEEE Trans. Power Electron., vol. 29, no. 12, pp. 6481-6493, Dec. 2014. 4. G. Petrone, G. Spagnuolo, and M. Vitelli, "A simple system for conveyed MPPT PV applications," IEEE Trans. Ind. Electron., vol. 59, no. 12, pp. 4713-4722, Dec. 2012 5. Y. Li and R. Oruganti, "A flyback-CCM inverter conspire for photovoltaic AC module application," in Proc. Australasian Univ. Power Eng. Conf. (AUPEC), 2008, pp 1-6. 6. N. Kasa, T. Iida, and L. Chen, "Flyback inverter controlled by sensorless current MPPT for photovoltaic power framework," IEEE Trans. Ind. Electron., vol. 52, no. 4, pp. 1145-1152, Aug. 2005. 7. N. Sukesh, M. Pahlevaninezhad, and P. K. Jain, "Examination and execution of a solitary stage flyback PV microinverter with delicate exchanging," IEEE Trans. Ind. Electron., vol. 61, no. 4, pp. 1819-1833, Apr. 2014. 8. Z. Zhang, X. F. He, and Y. F. Liu, "An ideal control strategy for photovoltaic lattice tide-interleaved flyback microinverters to accomplish high productivity in wide load extend," IEEE Trans. Power Electron., vol. 28, no. 11, pp. 5074-5087, Nov. 2013. 9. H. Hu, S. Harb, N. H. Kutkut, Z. J. Shen, and I. Batarseh, "A singlestage microinverter without utilizing electrolytic capacitors," IEEE Trans. Power Electron., vol. 28, no. 6, pp. 2677-2687, Jun. 2013. 10. R. W. Erickson and D. Maksimovic, "Essentials of Power gadgets," Springer Science and Business Media, 2007. Authors: Thota Swetha, S. Srinivas. Paper Title: A Novel IEEE-754 Floating-Point Butterfly Architecture based on Multi Operand Adders Abstract: FFT (Fast Fourier Transform) is one of most efficient algorithm widely used in communication systems.FFT function consists of Butterfly units with multiply add operations over complex numbers. A floating point is applied to FFT design, mainly to butterfly units. The concentrated tasks are calculated from general purpose processor by order FP concerns. The significant drawback of FP butterfly is its slowness in its examination with its fixed point. In proposed FP butterfly uses a fused dot product add(FDPA) unit to calculate butterfly unit, depending on binary signed digit(BSD).A BSD adder is introduced and utilized as a part of the three operand adder and parallel BSD multiplier, in order to enhance the speed of the FDPA unit. A modified booth encoding is utilized to accelerate BSD multiplier. The results shows that proposed FP butterfly design is considerably speedier than past butterfly design.

Keywords: Binary-Signed Digit (BSD) Representation, Butterfly Unit, Complex Number System, Fast Fourier Transform (FFT), Floating-Point (FP), Redundant Number System, Three-Operand Addition. 13. References: 1. IEEE Standard for Floating-Point Arithmetic, ANSI/IEEE Standard 754-2008, Aug. 2008. 55-60 2. R.K. Montoye, E. Hokenek, and S.L. Runyon, “Design of the IBM RISC System/6000 Floating-Point Execution Unit,” IBM J. Research and Development, vol. 34, pp. 59-70, 1990. 3. E. Hokenek, R.K. Montoye, and P.W. Cook, “Second-Generation RISC Floating Point with Multiply-Add Fused,” IEEE J. Solid-State Circuits, vol. 25, no. 5, pp. 1207-1213, Oct. 1990. 4. D. Takahashi, “A Radix-16 FFT Algorithm Suitable for Multiply-Add Instruction Based on Goedecker Method,” Proc. Int’l Conf. Multimedia and Expo, vol. 2, pp. II-845-II-848, July 2003. 5. J.H. McClellan and R.J. Purdy, “Applications of Digital Signal Processing to Radar,” Applications of Digital Signal Processing, A.V. Oppenheim, ed., pp. 239-329, Prentice-Hall, 1978. 6. B. Gold and T. Bially, “Parallelism in Fast Fourier Transform Hardware,” IEEE Trans. Audio and Electroacoustics, vol. AU-21, no. 1, pp. 5-16, Feb. 1973. 7. H.H. Saleh and E.E. Swartzlander, Jr., “A Floating-Point Fused Dot-Product Unit,” Proc. IEEE Int’l Conf. Computer Design (ICCD), pp. 427-431, 2008. 8. M.P. Farmwald, “On the Design of High-Performance Digital Arithmetic Units,” PhD thesis, Stanford Univ., 1981. 9. P.-M. Seidel and G. Even, “Delay-Optimized Implementation of IEEE Floating-Point Addition,” IEEE Trans. Computers, vol. 53, no. 2, pp. 97-113, Feb. 2004. 10. H. Saleh and E.E. Swartzlander, Jr., “A Floating-Point Fused Add-Subtract Unit,” Proc. IEEE Midwest Symp. Circuits and Systems (MWSCAS), pp. 519- 522, 2008. Authors: Dileep Dharmappa, Mahalinga V Mandi, S. Ramesh. Binary Sequences having Good Correlation and Large Linear Complexity Properties for Satellite Paper Title: Navigation Applications Abstract: LFSR based binary sequences are known to have good correlation and better balance property and hence they are used in Satellite Navigation Applications as signature sequences. However, due to the code length requirements of length being multiple of on-board fundamental frequency 10.23MHz in GNSS systems, often the LFSR based codes have to be truncated (like 10230 bits). Due to the truncation the correlation property 14. and the balance property gets degraded. Apart from the correlation and balance properties of the binary

sequences the linear complexity property also plays an important role for GNSS applications where in users need 61-67 to be protected against unintended or unauthorized access like commercial applications or military applications. In this work the balance property, even correlation, odd correlation and linear complexity property of the state of the art binary sequences of length 10230 bits being used for one of the GNSS system namely Galileo E5b-I primary sequences of length 10230 bits are evaluated. A method for generation of binary sequences having properties better than Galileo E5b-I primary sequences are presented. Binary sequences generated from the proposed method is analyzed for balance, linear complexity and correlation properties. It is found that the proposed sequences have better balance, correlation properties and high linear complexity. Due to the high linear complexity property, the proposed sequences provide inherent security for the system against spoofing and hence make the GNSS system secure.

Keywords: Even Correlation, Odd Correlation, Linear Complexity, Chaotic Map, Binary Sequences, CDMA, GNSS. References: 1. Dudkov Alexey, Valery P Ipatov (2005) Signature-interleaved DS CDMA controlling odd correlation peaks. IEEE 16th International Symposium on Personal, Indoor and Mobile Radio Communications. 4:2527-2530. 2. Fukumasa Hidenobu, Ryuji Kohno, Hideki Imai (1994) Design of pseudonoise sequences with good odd and even correlation properties for DS/CDMA. IEEE Journal on Selected Areas in Communications. 12(5):828-836. 3. Galileo Interface Control Document (2016) Galileo Open Service Signal In Space Interface Control Document OS SIS ICD 1.3. Publishing European GNSS Agency. https://www.gsc-europa.eu/system/files/galileo_documents/Galileo-OS-SIS-ICD.pdf Accessed 26 January 2018 4. Heidari-Bateni G, McGillem C D (1994) A Chaotic Direct-Sequence Spread-Spectrum Communication System. IEEE Transactions on Communications. 42(234): 1524-1527. 5. Ling Cong, Li Shaoqian (2000) Chaotic Spreading Sequences with Multiple Access Performance Better than Random Sequences. IEEE Transactions on Circuits and Systems—I: Fundamental Theory and Applications. 47(3):394-397 6. Mahalinga V Mandi, K N HariBhat, R Murali (2010) Generation of Large Set of Binary Sequences derived from Chaotic Functions with Large Linear Complexity and Good Cross Correlation Properties. International Journal of Advanced Engineering and Applications (IJAEA) III:313-322. 7. Massey J L (1969) Shift Register Synthesis and BCH Decoding. IEEE Trans. on Information Theory. 15(1):122-127. 8. Robert M May (1976) Simple Mathematical Models with Very Complicated Dynamics. 261: 459–467. 9. Sarwate D V, Pursley M B (1980) Cross correlation Properties of Pseudorandom and Related Sequences. Proceedings of IEEE. 68(5):593-619 10. Wang D, Xue R, Sun Y (2017) A ranging code based on the improved Logistic map for future GNSS signals: code design and performance evaluation. J Wireless Com Network. 2017(1):57. Zhu Y, T T Tjhung, H K Garg (1999) A new family of polyphase sequences for CDMA with good odd and even correlation properties. 2nd IEEE Workshop on Signal Processing Advances in Wireless Communications (Cat. No.99EX304). Authors: K.M. Arun Kumar, T.C. Manjunath, G. Arun Kumar. Bearing Fault Diagnosis in IM Using STFT and J-48 Algorithm based on Vibration Signals in Paper Title: Dynamic Machines Abstract: The condition monitoring of bearing faults is carried out by analyzing the properties & the characteristics of the vibratory signal obtained from the machine. The detection of the fault from the signal which is extracted is still a challenging problem in the vibration control, which is one of the most important topics to be considered for the condition monitoring of machines and for research purpose. In this paper, diagnosis of bearing fault is done using STFT and J-48 algorithm. Short-Time-Fourier-Transform (STFT) can be used in order to identify the faults in the bearing from the vibration signal which is captured. STFT has the linear type phase characteristics and preserves the signal properties sharpness even when the sudden changes in the signal nature. Vibration signal is then divided into the different section so that relating to the ball bearing parts passage and thus exits from the bearing fault, allowing to estimating the faults occur in ball bearing element. The analysis of vibration signal is carried out in Lab VIEW. Machine learning is a method to enter the database for giving importance to the pleasant information. Machine learning consists of 3 stages, viz., FE, FS, FC. Then, the main important features were taken from the raw vibratory signal, selection of the features was obtained utilizing J-48 algorithm and to build the better classifier, the different parameter of J-48 algorithm are optimized. This algorithm is applied to the RT analysis & furthers the CMT is used as it is very much convenient since the time of computation required to analyze is very less, with an classification accuracy was found to be 94.5%.

Keywords: using STFT,FE, FS, FC. ,Then, CMT, RT ,analysis , Lab VIEW., Machine. 15. References: 1. Riddle J, “Ball bearing maintenance”,Norman, OK University of Oklohama Press, 1955. 2. Chow T.W.S. and Fei G., “Three-phase induction machines asymmetrical faults identification using bi-spectrum”, IEEE Trans. Energy 68-79 Conversion, Vol. 10, pp. 688-693, 1995. 3. Kilman G.B. and Stein J., “Methods of motor current signature analysis”,Elec. Mach. Power Syst., Vol. 20, pp. 463-474, 1992. 4. Vendrusculo E.A., Pomilio J.A., “Avoiding over-voltages in long distance driving of induction motors”, Applied Power Electronics Conference and Exposition, APEC 2001, Sixteenth Annual 2001 IEEE (Volume:1)., pp. 622 – 627, Mar. 2001. 5. Liu Yukun, Guo Liwei, Wang Qixiang, An Guoqing, Guo Ming, Lian Hao, “Application to induction motor faults diagnosis of the amplitude recovery method combined with FFT”, Mechanical Systems and Signal Processing, Vol. 24, Issue 8, pp. 2961–2971, Nov. 2010. 6. Boqiang Xu, Liling Sun, Lie Xu, Guoyi Xu, “Improvement of the Hilbert Method via ESPRIT for Detecting Rotor Fault in Induction Motors at Low Slip”, IEEE Transactions on Energy Conversion, Vol. 28, Issue 1, pp. 225 – 233, Mar. 2013. 7. Boqiang Xu, Liling Sun, Lie Xu, Guoyi Xu., “An ESPRIT-SAA-Based Detection Method for Broken Rotor Bar Fault in Induction Motors”, IEEE Transactions on Energy Conversion, Vol. 27, Issue 3, pp. 654 – 660, Sept 2012. 8. Benbouzid M.E.H. Benbouzid M.E.H., Nejjari H., Beguenane R., Vieira M., “Induction motor asymmetrical faults detection using advanced signal processing techniques”,IEEE Transactions on Energy Conversion, Vol. 14, Issue 2, pp. 147 – 152, Jun 1999. 9. Richard G. Lyons, “ Understanding digital signal processing’, Pearson Education, 2009 10. Neelam Mehala, Ratna Dahiya, “Diagnosis of rotor faults of induction motor using FFT Based power spectrum”, International Journal on Electronics of Engineering. 11. Jose A. Antonino-Daviu, Martin Riera-Guasp, Jose Roger Folch, and M. Pilar Kolina Palomares, “A method for the diagnosis of rotor bar failures in induction machines”,IEEE Transactions on Industry Applications, Vol.42, No. 4, pp. 990-996,2006. 12. Boaahash B., “Time frequency signal analysis in: Advances in spectrum estimation and array processing”, Ed. S. Haykin, Printice Hall, 1990. 13. Laon Cohen, “Time frequency Analysis”, Prentice Hall PTR, 1995. 14. L. Satish, “Short-time Fourier and wavelet transforms for fault detection in power transforms during impulse test”, IEEE proc. Science Measurement Technology, Vol.145,No. 2,pp.77-84, March 1998. 15. Mitchell Tom, “Machine Learning” [ed.] 1. s.l. : McGraw Hill, 1997. pp. Ch 1 , Page2. Vol. 1. ISBN 0070428077. 16. Breiman, Leo, et.al.,“Classification and Regression Trees”,. 1. s.l. : Wadsworth International Group, 1984. p. 50 Ch 3. 17. Witten Ian H., & Frank Eibe, “Data mining : practical machine learning tools and techniques”, 2. s.l. : Morgan Kaufmann Publishers(Imprint of Elsevier), pp. 150, Ch. 5,2002. 18. Mark et.al.,“The WEKA Data Mining Software: An Update”, Hall, 1, s.l. : ACM-2009, Special Interest Group on Knowledge Discovery and Data mining (SIGKDD) Explorations, Vol. 11, pp. 10-18, 2009. 19. Raileanu, Laura Elena and Stoffel, Kilian, “Theoretical Comparison between the Gini Index and Information Gain Criteria”,Annals of Mathematics and Artificial Intelligence, Vol. 41, Issue 1, pp. 77-93, 2004. 20. Rissanen J., “Modeling By Shortest Data Description”,Automatica, Vol. 14, pp. 465-471, 1978. 21. Grünwald P.D., “Model selection based on minimum description length”, Journal of Mathematical Psychology, Vol. 44, pp. 133-152, 2000. 22. Rissanen J., “A Universal Prior for Integers and Estimation by Minimum Description Length”, Annals of Statistics, Vol. 11, Issue 2, pp. 416-431, 1983. 23. Muralidharan V, Sugumaran V.,“Rough set based rule learning and fuzzy classification of wavelet features for fault diagnosis of monoblock centrifugal pump”, Measurements,Vol. 46, No. 9, pp. 3057-63, 30 Nov. 2013. Authors: J. Maheswarreddy, S.A.K. Jilani. Paper Title: Region of Interest Extraction based on Hybrid Salient Detection for Remote Sensing Image Abstract: Remote sensing images have huge amount of information in it due to use of high resolution cameras and sensors. Region of interest (ROI) is defined as the regions which draw the attention of viewer at first sight and they are the focal point of the image. ROI selection in remote sensing images allows the viewer to search for specific objects in the region. Traditional approaches for ROI selection are computationally complex and inaccurate. In this work, a hybrid approach which combines the best of frequency domain analysis and Super pixel based spatially weighted intensity contrasting is proposed for selecting the ROI in remote sensing images. Compared to previous methods the proposed hybrid ROI selection is able to extract the ROI accurately.

Keywords: ROI, Saliency Map, Gaussian Pyramid, Frequency Domain Analysis, Quaternion.

References: 1. L. Itti, C. Koch, and E. Niebur, “A model of saliency-based visual attention for rapid scene analysis,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 20, no. 11, pp. 1254–1259, Nov. 1998. 2. Z. Li and L. Itti, “Saliency and gist features for target detection in satellite images,” IEEE Trans. Image Process., vol. 20, no. 7, pp. 2017–2029, Jul. 2011. 3. N. Imamoglu, W. Lin, and Y. Fang, “A saliency detection model using low-level features based on wavelet transform,” IEEE Trans. Multimedia, vol. 15, no. 1, pp. 96–105, Jan. 2013. 4. B. Du and L. Zhang, “Target detection based on a dynamic subspace,” Pattern Rec fognit., vol. 47, no. 1, pp. 344–358, 2014. 16. 5. J. Harel, C. Koch, and P. Perona, “Graph-based visual saliency,” Adv. Neural Inf. Process. Syst., vol. 19, pp. 545–552, 2007. 6. N. Bruce and J. Tsotsos, “Saliency based on information maximization,” Adv. Neural Inf. Process. Syst., vol. 18, pp. 155–162, 2006. 7. Q. Yan, L. Xu, J Shi, and J Jia, “Hierarchical saliency detection,” in Proc. IEEE Conf. Comput. Vision Pattern Recognit., 2013, pp. 80-86 1155–1162. 8. L. Zhang, H. Li, P. Wang, and X. Yu, “Detection of regions of interest in a high-spatial-resolution remote sensing image based on an adaptive spatial subsampling visual attention model,” GISci. Remote Sens., vol. 50, no. 1, pp. 112–132, 2013. 9. L. Zhang, K. Yang, and H. Li, “Regions of interest detection in panchromatic remote sensing images based on multiscale feature fusion,” IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 7, no. 12, pp. 4704– 4716, Dec. 2014. 10. L. Zhang and K. Yang, “Region-of-interest extraction based on frequency domain analysis and salient region detection for remote sensing image,” IEEE Geosci. Remote Sens. Lett., vol. 11, no. 5, pp. 916–920, May 2014. 11. C. Kim and P. Milanfar, “Visual saliency in noisy images,” J. Vision, vol. 13, no. 4, pp. 1–14, 2013. 12. L. Mai, Y. Niu, and F. Liu, “Saliency aggregation: A data-driven approach,” in Proc. IEEE Conf. Comput. Vision Pattern Recognit., 2013, pp. 1131–1138. 13. H. Jiang et al., “Salient object detection: A discriminative regional feature integration approach,” in Proc. IEEE Conf. Comput. Vision Pattern Recognit., 2013, pp. 2083–2090. 14. C. Tao, Y. Tan, Z. R. Zou, and J. Tian, “Unsupervised detection of builtup areas from multiple high-resolution remote sensing images,” IEEE Geosci. Remote Sens. Lett., vol. 10, no. 6, pp. 1389–1393, Nov. 2013. 15. Z. Li and L. Itti, “Saliency and gist features for target detection in satellite images,” IEEE Trans. Image Process., vol. 20, no. 7, pp. 2017–2029, Jul. 2011. 16. I. Rigas, G. Economou, and S. Fotopoulos, “Low-level visual saliency with application on aerial imagery,” IEEE Geosci. Remote Sens. Lett., vol. 10, no. 6, pp. 1389–1393, Nov. 2013. 17. S. L. Moan, A. Mansouri, J. Y. Hardeberg, and Y. Voisin, “Saliency for spectral image analysis,” IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 6, no. 6, pp. 2472–2479, Dec. 2013. 18. S. Goferman, L. Zelnik-Manor, and A. Tal, “Context-aware saliency detection,” in Proc. IEEE Conf. Comput. Vision Pattern Recognit., 2010, pp. 2376–2383. Authors: T. Veeramani, P. Srinuvasarao, B. Rama Krishna, R. Thilagavathy. Paper Title: Impact of Social Media Networks Big Data Analysis for High-Level Business Abstract: Web based life systems are a noteworthy asset for both little and enormous organizations. These are share market is the user sellers of products, with represent the businesses over the ownership him the products. The public network with sharing any one product to fill the stock exchange, sale, with draw and incomplete as well as product that are traded every user private amount share in user. An enormous of amount that are hoping to advance their brands on the Internet. Internet based life strategy to the free service with created on business set are. Web application based life to live data storage with online social media service for 17. example like on user Gmail, yahoo, Facebook, Twitter, LinkedIn and so forth. In this social network one user to multiple user send from information for voice, video, message, data upload the page, and photo share our mutual 87-92 friends. Those are implementation software or application developed with in organization. For a number of page, different type of source code implement at the software. Through Social network add, one can make products of the preferences and internship of user and visited the most recent online application received by people in general information. Web based life use has additionally turned out to be progressively versatile, in extensive part on account of social applications.

Keywords: Social Media, Business. References: 1. https://blog.hootsuite.com/social-media-for-business/ 2. https://blog.hootsuite.com/social-media-for-business/ 3. https://www.lyfemarketing.com/blog/importance-social-media-business/ 4. https://www.smartinsights.com/social-media-marketing/social-media-strategy/new-global-social-media-research/ 5. https://www.statista.com/statistics/282846/regular-social-networking-usage-penetration-worldwide-by-country/ Authors: R. Dhanalakshimi, C. Geetha, T. Sethukarasi. Monitoringand Detecting Disease in Human Adults Using Fuzzy Decision Tree and Random Forest Paper Title: Algorithm Abstract: The traditional healthcare involves clinical diagnosis using doctor's expertise and knowledge. It is a challenge to provide proper healthcare in rural and remote areas since they are more likely to travel a long distance to access specialist diagnosis. The number of medical practitioners and facilities are low in these areas making it difficult to provide an expert diagnosis in a significant time interval. The problem can be solved by delivering expert systems to diagnose disease which is built using data mining method and fuzzy logic. The

decision trees are widely used in machine learning to predict results. These medical data and expert decision are best represented as the fuzzy data set. The fuzzy decision trees treat fuzzy data and produce simple decision trees. In this project, we built an expert system that diagnoses disease using the random forest algorithm. The

fuzzy decision trees are used to increase the accuracy of the diagnosis system. Thus we use Hybrid Fuzzy

Decision tree in Random forest algorithm to identify the disease by analyzing the medical records of the patient in this paper.

Keywords: Random Forest, Fuzzy Decision Trees, Health Care, Diagnosis System. References: 1. V. Podgorelec, P. Kokol, B. Stiglic, I. Rozman, “Decision trees: an overview and their use in medicine,” Journal of Medical Systems,

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Network-Conscious VM Placement for Energy Efficiency in Green Data Centres through Dynamic Paper Title: VM Consolidation Abstract: In the present scenario, cloud computing environment grants all the resources in scalable manner to every users in pay-per-use processing model over the Internet through various data centers. An energy consumption of these resources have to be addressed in many issues in the cloud. A key strategy of virtual machine (VM) management is a live VM migration in data center networks. One of the significant problems of

cloud provider is the energy cost. VM migration and placement has been shown as an efficient approach for energy saving. In this paper, we are proposing an algorithm, Modified Energy Conscious Greeny Cloud Dynamic Algorithm (MECGCD), goes for preventing unnecessary traffics in a datacenter network, and

excessive energy consumption (EC) started from wrong routing management and improper VM allocation. In this paper, we observe at the issue of how to choose the host for VM placement and to migrate VMs from abnormal loaded hosts such as under loaded or over loaded to another and switching off the idle host machine into sleep mode. VM placement be determined the host machines by shortest distance, minimum EC and maximum bandwidth usage in the cloud environment. The evaluation of experiments confirmed that the proposed algorithm minimizes EC and network traffic in a cloud data center in a quotable manner than other existing algorithms.

Keywords: Cloud Computing, Haversine, Data Center, Live VM Migration, Energy Consumption.

References: 1. Jansen, W., & Grance, T. (2011). Sp 800-144. guidelines on security and privacy in public cloud computing.(Accessed January 2018). 2. www.independent.co.uk/environment/global-warming-data-centres-to-consume-three-times-as-much-energy-in-next-decade-experts- warn-a6830086.html energy consumption of data centres 2016. 3. “Limiting Global Climate Change to 2 degrees Celsius –The way ahead for 2020 and beyond”, publications.europa.eu/ resource/ uriserv/l28188.ENG(Accessed September 20, 2018). 4. Abdelaal,M.A., Ebrahim,G.A. and Anis,W.R.: Network-aware resource management strategy in cloud computing environments. In Computer Engineering & Systems (ICCES), 2016 11th International Conference on, 26-31, IEEE, (2016). 5. Pantazoglou,M., Tzortzakis,G. and Alex Delis.: Decentralized and energy-efficient workload management in enterprise clouds. IEEE Transactions on Cloud Computing 4(2), 196-209(2016). 6. Monil,M.A.H.and Rahman,R.M. Vm consolidation approach based on heuristics fuzzy logic, and migration control. Journal of Cloud Computing 5(1), 1-18(2016). 101-106 7. Bari,M.F., Zhani,M.F., Zhang,Q., Ahmed,R. and Boutaba,R.: CQNCR: optimal VM migration planning in cloud data centers. In Networking Conference, 2014 IFIP, 1-9. IEEE.(2014). 19. 8. Murugesan, S., and Gangadharan, G. R. Harnessing green IT: Principles and practices. Wiley Publishing. ISBN: 978-1-119- 97005-7. (2012). 9. Buyya, R., Broberg, J., and Goscinski, A. M. Cloud computing,: Principles and paradigms (Vol. 87),. John Wiley & Sons,. ISBN: 978- 0-470-88799-8. (2010). 10. Nguyen,T.,H., Francesco,,M.D.and Yla-Jaaski,A.: Virtual Machine Consolidation, with Multiple Usage Prediction for Energy,- Efficient Cloud Data Centers. IEEE Transactions. on Services Computing (2017). 11. Tao,F., Li,C., Liao,T.W.and Laili,Y.: BGM-BLA: a new algorithm, for dynamic migration of virtual machines, in cloud computing." 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Duggan, M., Duggan, J., Howley, E., and Barrett, E. A network, aware approach for the scheduling of virtual, machine migration during, peak loads. Cluster Computing, 20(3), 2083-2094. (2017). 29. Sajitha, A.V, and Subhajini, A.C. Dynamic, VM Consolidation Enhancement, for Designing and, Evaluation of Energy Efficiency, in Green Data Centers Using Regression Analysis. International Journal of Engineering & Technology, 7(3.6), 179-186. (2018). Authors: V. Alan Gowri Phivin, A.C. Subhajini. Paper Title: Time Conserving CBIR System for Fabric Images based on Color and Texture Features

Abstract: Digital images play an inevitable role in human life and hence, the utilization of images grow day-by-day. Though the advanced storage technology helps in massive data storage, efficient retrieval system is the need of this hour and this issue is well-addressed by Content Based Image Retrieval (CBIR) systems. The

CBIR systems are widely present for healthcare and remote sensing domain. However, the presence of CBIR 20. systems is found to be limited for fabric images. Taking this as a challenge, this work presents a CBIR system exclusively meant for fabric images by extracting color and texture features. When the user passes the search query image to the CBIR system, the features of the query image is compared with the features of the images in 107-113 the dataset, which is performed by Extreme Learning Machine (ELM) classifier. The performance of the proposed CBIR system is found to be satisfactory in terms of retrieval accuracy and time consumption.

Keywords: CBIR, Color and Texture Feature, Image Retrieval.

References: 1. Jun Yue, Zhenbo Li, Lu Liu, Zetian Fu, "Content-based image retrieval using color and texture fused features", Mathematical and Computer Modelling, V.54, pp. 1121-1127, 2011. 2. ElAlami, M. Esmel. "A new matching strategy for content based image retrieval system", Applied Soft Computing, Vol.14, pp.407- 418, 2014. 3. Haralick, R.M., Shanmugam, K., Dinstein, I.H., "Textural features for image classification", IEEE Trans. Syst. Man Cybern. 6, 610– 621 (1973) 4. Ojala, T., Pietikäinen, M., Harwood, D., "A comparative study of texture measures with classification based on featured distributions", Pattern Recognit. 29(1), 51–59 (1996). 5. Osman Emre Dai ; Begüm Demir ; Bülent Sankur ; Lorenzo Bruzzone, "A Novel System for Content-Based Retrieval of Single and Multi-Label High-Dimensional Remote Sensing Images", IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol.11, No.7, July 2018 ) 6. Jatindra Kumar Dash ; Sudipta Mukhopadhyay ; Rahul Das Gupta, "Content-based image retrieval using fuzzy class membership and rules based on classifier confidence", IET Image Processing, Vol.9, No.9, pp.836-848, 2015. 7. Licheng Jiao ; Xu Tang ; Biao Hou ; Shuang Wang, "SAR Images Retrieval Based on Semantic Classification and Region-Based Similarity Measure for Earth Observation", IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol.8 , No.8, pp. 3876-3891, 2015. 8. Hatice Cinar Akakin ; Metin N. Gurcan, "Content-Based Microscopic Image Retrieval System for Multi-Image Queries", IEEE Transactions on Information Technology in Biomedicine, Vol.16, No.4, pp. 758-769, 2012. 9. Jing-Ming Guo, Heri Prasetyo, "Content-Based Image Retrieval Using Features Extracted From Halftoning-Based Block Truncation Coding", IEEE Transactions on Image Processing, Vol.24, No.3, pp. 1010-1024, 2015. 10. Wei Bian ; Dacheng Tao, "Biased Discriminant Euclidean Embedding for Content-Based Image Retrieval", IEEE Transactions on Image Processing, Vol.19, No.2, pp.545-554, 2010. 11. Ashnil Kumar ; Falk Nette ; Karsten Klein ; Michael Fulham ; Jinman Kim, "A Visual Analytics Approach Using the Exploration of Multidimensional Feature Spaces for Content-Based Medical Image Retrieval", IEEE Journal of Biomedical and Health Informatics, Vol.19, No.5, pp.1734-1746, 2015. 12. Gwénolé Quellec ; Mathieu Lamard ; Guy Cazuguel ; Béatrice Cochener ; Christian Roux, "Fast Wavelet-Based Image Characterization for Highly Adaptive Image Retrieval", IEEE Transactions on Image Processing, Vol.21, No.4, pp.1613-1623, 2012. 13. Liu Yang ; Rong Jin ; Lily Mummert ; Rahul Sukthankar ; Adam Goode ; Bin Zheng ; Steven C.H. Hoi ; Mahadev Satyanarayanan, "A Boosting Framework for Visuality-Preserving Distance Metric Learning and Its Application to Medical Image Retrieval", IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.32, No.1, pp.30-44, 2010. 14. Yibing Ma ; Zhiguo Jiang ; Haopeng Zhang ; Fengying Xie ; Yushan Zheng ; Huaqiang Shi ; Yu Zhao, "Breast Histopathological Image Retrieval Based on Latent Dirichlet Allocation", IEEE Journal of Biomedical and Health Informatics, Vol.21, No.4, pp.1114- 1123, 2017. 15. Jing-Ming Guo ; Heri Prasetyo ; Jen-Ho Chen, "Content-Based Image Retrieval Using Error Diffusion Block Truncation Coding Features", IEEE Transactions on Circuits and Systems for Video Technology, Vol.25, No.3, pp. 466-481, 2015. 16. Lelin Zhang ; Zhiyong Wang ; Tao Mei ; David Dagan Feng, "A Scalable Approach for Content-Based Image Retrieval in Peer-to-Peer Networks", IEEE Transactions on Knowledge and Data Engineering, Vol.28, No.4, pp. 858-872, 2016. 17. 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Jasperlin, T., & Dr. Gnanadurai, “Histopathological Image Analysis by Curvelet Based Content Based Image Retrieval System”, Journal of Medical Imaging and Health Informatics, Vol. 6, No. 8, pp. 2063-2068, 2016. 22. Jabid, T., Kabir, M. H., & Chae, O, Local directional pattern (LDP) forface recognition. In 2010 Digest of Technical Papers International Confer-ence on Consumer Electronics (ICCE) (pp. 329–330), 2010. 23. Do, M. N., & Vetterli, M., The contourlet transform: an efficient directional multiresolution image representation. IEEE Transactions on image processing, 14(12), 2091-2106, 2005. 24. Guang-Bin Huang, Hongming Zhou, Xiaojian Ding, and Rui Zhang, Extreme Learning Machine for Regression and Multiclass Classification, IEEE Transactions on systems, Man and Cybernetics - Part B, Vol.42, No.2, pp.513-529, 2012. 25. http://www.textures.com/browse/ Authors: P. Balamurugan, J. Santhosh G. Arulkumaran. Paper Title: Reliable and Energy Efficient Data Gathering Protocol in Wireless Sensor Networks Abstract: A sensor network is a set of small autonomous systems, called sensor nodes which co-operate to solve at least one common problem. Sensor nodes in Wireless Sensor Networks (WSN) are generally used to collect, aggregate and communicate the fused data to the Base Station (BS). In sensor networks, the nodes are having the limited energy which is one of the most critical issues in WSN. The energy gets reducing when the 21. nodes are collecting information. So data gathering is required to be efficient, adaptive and robust. The paper provides solution for the energy issues by developing the energy efficient data gathering algorithm called Mark 114-119 Based Data Gathering (MBDG) algorithm, which proposes to minimize the energy and delay in the process of gathering and communicating the fused data the BS in WSN. The proposed algorithm is compared with existing algorithms namely LEACH, PEGASIS, EMLN-DG and GBE-DG. The result shows that the proposed algorithm considerably improves network lifetime, reducing delay and energy consumption compared with existing algorithms.

Keywords: Energy, Delay, lifetime, WSN, Mark and Algorithm. References: 1. Heinzelman,W., Chandrakasan, A. and Balakrishnan, H. “Energy-Efficient Communication Protocol for Wireless Microsensor Networks”, Proceedings of the Hawaii Conference on System Sciences,Vol.2, pp 1-10, 2000. 2. Meghanathan, N. “An Algorithm to Determine Energy-aware Maximal Leaf Nodes Data Gathering Tree for Wireless Sensor Networks”, Journal of Theoretical and Applied Information Technology, Vol. 15, No. 2, pp. 96-107, 2010. 3. Meghanathan, N. “Grid Block Energy based Data Gathering Algorithms for Wireless Sensor Networks”, international journal communication networks and information technology, Vol. 2, No. 3, pp. 151-161, 2010. 4. S. Lindsey and C. Raghavendra, “PEGASIS: Power-Efficient Gathering in Sensor Information Systems,” in Proc. of IEEE Aerospace Conference, vol. 3, pp.1125–1130, March 9-16, 2002. 5. Al-Dhelaan, A. "Pyramid Based Data Gathering Scheme for Wireless Sensor Networks." Journal of Theoretical and Applied Information Technology, Vol. 29, No.2, 2011. 6. N. Aslam, W. Phillips, W.Robertson and Sh. Sivakumar, “A multi criterion optimization technique for energy efficient cluster formation in wireless sensor networks,” Information Fusion, vol. 12, Issue 3, pp. 202-212, July, 2011. 7. Bai, F. E., Mou, H. H., and Sun, J. “Power-Efficient Zoning Clustering Algorithm for Wireless Sensor Networks”, In IEEE International Conference on Information Engineering and Computer Science, pp. 1-4, 2009. 8. Bencan Gong Tingyao Jiang, “A Tree-Based Routing Protocol in Wireless Sensor Networks”, International Conference on Electrical and Control Engineering, pp. 5729-5732, 2011. 9. Chalak, A, R., Misra, S., and obaidat, M. S. “ A Cluster Head Selection Algorithm for Wireless Sensor Networks”, In IEEE International Conference on Electronics, Circuits and Systems, pp.130-133, 2010. 10. Chang, B., and Zhang, X. “An Energy Efficient Cluster Based Data Gathering Protocol for Wireless Sensor Networks”, In IEEE International Conference on Wireless Communications Networking and Mobile Computing, pp.1-5, 2010. 11. W. Heinzelman, A. Chandrakasan and H. Balakrishnan, “An application-specific Protocol Architecture for Wireless Microsensor Networks,” IEEE Transactions on Wireless Communications, vol. 1, no 4, pp. 660 -670, 2002. Authors: P. Muniappan, M. Ravithammal, S. Senthil. An Incentive Inventory Model for Exponential Function of Cost with Maximum Life Time of Paper Title: Deteriorating Products Abstract: This paper investigates an inventory model for deteriorating products with maximum lifetime and constant demand. Shortages are allowed and backlogged them completely. This model assumes that (i) deteriorating products not only deteriorate continuously, and has a maximum lifetime, and (ii) deteriorating products having exponential function of holding cost, shortage cost and purchasing cost. The goal of this model is to determine the optimal decisions so that the seller’s profit function is maximized. We provide simple analytical tractable procedures for deriving the model and give numerical examples to illustrate the solution procedure.

Keywords: Shortages, Deteriorating Items, Exponential Function, Inventory Costs.

References: 1. Bakker M, Riezebos J, and Teunter RH. Review of inventory systems with deterioration since 2001. European Journal of Operational Research. 2012; 221(2), 275–284. 2. Chen SC and Teng JT. Retailer’s optimal ordering policy for deteriorating items with maximum lifetime under supplier’s trade credit financing. Applied Mathematical Modelling. 2014;38, 4049-4061.

3. Ghosh SK, Chaudhuri KS. An EOQ model with a quadratic demand, time-proportional deterioration and shortages in all cycles. International journal of system sciences. 2006; 37,663–672. 4. Goyal SK and Giri B C. The production-inventory problem of a product with time varying demand, production and deterioration rates. European Journal of Operational Research. 2003; 147, 549–557. 5. Kreng VB and Tan SJ . Optimal replenishment decision in an EPQ model with defective items under supply chain trade credit policy. Expert Systems with Applications. 2011; 38, 9888–9899. 6. Muniappan P and Uthayakumar R. Mathematical analyze technique for computing optimal replenishment policies. International Journal of Mathematical Analysis. 2014; 8, 2979 – 2985. 22. 7. Muniappan P, Uthayakumar R and Ganesh S. An economic lot sizing production model for deteriorating items under two level trade credit. Applied Mathematical Sciences. 2014; 8, 4737 – 4747. 120-123 8. Sarkar B. An EOQ model with delay in payments and time varying deterioration rate. Mathematical and Computer Modelling. 2012; 55, 367–377. 9. Sarkar,B., and Sarkar, S. An improved inventory model with partial backlogging, time varying deterioration and stock-dependent demand. Economic Modelling, 2013; 30, 924-932. 10. Sarkar B, Saren S and Wee HM. An inventory model with variable demand, component cost and selling price for deteriorating items. Economic Modelling. 2013; 30, 306–310. 11. Teng, JT, Min J, and Pan Q. Economic order quantity model with trade credit financing for non-decreasing demand. Omega. 2012; 40, 328–335. 12. Wan-Chih Wang, Jinn-Tsair Teng and Kuo-Ren Lou. Seller’s optimal credit period and cycle time in a supply chain for deteriorating items with maximum lifetime. European Journal of Operational Research 2014; 232, 315–321. Authors: K. Kishore AnthuvanSahayaraj, K. Venkatachalapathy.

An Automatic Vehicle Type Classification and Counting based on Deep Learning in Traffic Paper Title: Environment Abstract: A model for automatic vehicle type classification and counting based on deep learning is proposed to handle complex traffic scene. This model covers of parts, vehicle detection model and vehicle detection and classification and counting model.Faster R-CNN method is implemented in vehicle detection model to extract vehicle images from an image with disorder background which may contains numerousvehicles. In vehicle classification model, an image contains only one vehicle is fed into a CNN model to produce a feature, then a Non negative matrix factorization is used to implement the classification process. Experiments show that vehicle’s detection and classification from traffic scenes can be recognized effectively by using our method. Furthermore, in order to build a large scale database easier, this paper comes up with a novel network collaborative annotation mechanism using iterative refinement in region proposal network. 124-128

23. Keywords: Faster R-CNN, Iterative, Non-Negative Matrix Factorization, Object Detection, Object Classification. References: 1. Editorial, Special issue on big data driven intelligent transportation system Neurocomputing (2016) . 2. D. Tao, Y. Rui, M. Wang, Learning to rank using user clicks and visual features for image retrieval, IEEE Transactions on Cybernetics (2015)767–779. 3. Q. Ge, T. Shao, C. Wen, R. Sun, Analysis on strong tracking filtering forlinear dynamic systems, Mathematical Problems in Engineering (2015) 1–9. 4. Z. Lu, L. Wang, J. Wen, Image classification by visual bag-of-words refinement and reduction, Neurocomputing (2016) 373–384. 5. L. Xie, J. Wang, B. Zhang, Q. Tian, Incorporating visual adjectives forimage classification, Neurocomputing (2016) 48–55 6. S. A. A. Shah, M. Bennamoun, F. Boussaid, Iterative deep learningimage set based face and object recognition, Neurocomputing (2016) 866- 874. 7. N. Nedjah, F. P. Silva, A. O. Sa, L. M.Mourelle, D. 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In Proceedings of the 2008 IEEE Workshop on Applications of Computer Vision, Copper Mountain, CO, USA, 7–9 January 2008; pp. 1–6. 23. Leotta, M.J.; Mundy, J.L. Vehicle surveillance with a generic, adaptive, 3D vehicle model. IEEE Trans. Pattern Anal. Mach. Intell. 2011, 33, 1457–1469. 24. Ma, X.; Grimson, W.E.L. Edge-based rich representation for vehicle classification. In Proceedings of the10th IEEE International Conference on Computer Vision, Beijing, China, 17–21 October 2005; Volume 2,pp. 1185–1192. 25. Messelodi, S.; Modena, C.M.; Zanin, M. A computer vision system for the detection and classification ofvehicles at urban road intersections. Pattern Anal. Appl. 2005, 8, 17–31. [CrossRef] 26. Alonso, D.; Salgado, L.; Nieto, M. Robust vehicle detection through multidimensional classification for onboard video based systems. In Proceedings of the 2007 IEEE International Conference on Image Processing,San Antonio, TX, USA, 16 September–19 October 2007. 27. Lou, J.; Tan, T.; Hu, W.; Yang, H.; Maybank, S.J. 3-D modelbased vehicle tracking. IEEE Trans. Image Proc. 2005, 14, 1561–1569. 28. Gentile, C.; Camps, O.; Sznaier, M. Segmentation for robust tracking in the presence of severe occlusion.IEEE Trans. Image Proc. 2004, 13, 166–178. 29. Song, X.; Nevatia, R. A model-based vehicle segmentation method for tracking. In Proceedings of the10th IEEE International Conference on Computer Vision, Beijing, China, 17–21 October 2005; Volume 2,pp. 1124–1131. 30. Liang, M.; Huang, X.; Chen, C.-H.; Chen, X.; Tokuta, A. Counting and Classification of Highway Vehicles by Regression Analysis. IEEE Trans. Intell. Transp. Syst. 2015, 16, 2878–2888. 31. R. S, H. K, G. R, Faster r-cnn: Towards realtime object detection withregion proposal networks, NIPS (2015) 442–451. 32. Y. Zhang, L. Zhang, P. Li, A novel bilogically inspired elm-based network for image recognition, Neurocomputing (2016) 286–298. 33. TolgaEnsari”Character Recognition Analysis with Nonnegative Matrix Factorization”International Journal of Computers, Volume 1, 2016,pp219-222. 34. J. Shotton, J. Winn, C. Rother, A. Criminisi, Textonboost: Joint appearance, shape and context modeling for multi-class object recognition andsegmentation, ECCV (2006) 1–15. 35. Rakesh N. Rajaram, EshedOhn-Bar, and Mohan M. Trivedi, RefineNet: Iterative Refinement for Accurate Object Localization016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC). Authors: S. Sweetlin Susilabai, D.S. Mahendran, S. John Peter. Paper Title: Interbit Exchange and Merge (IBEM) Pattern of Blowfish Algorithm Abstract: Nowadays security plays important role whenever there is communication between sender and receiver. To triumph over the issues of security intruders, various cryptographic algorithms are used. In this paper, authors have attempted to improve the security level of blowfish with proposed Inter bit exchange and merge (IBEM) pattern of data before applied which is fed into S-Boxes. Inter bit Exchange and Merge (IBEM) 24. pattern of data allows the intruders cannot easily find key mechanism what the user actually send. The results of all the tests conducted which leads to a common conclusion that the security of the Inter bit Exchange and 129-132 Merge process provides data in great secure manner when compared to original blowfish algorithm.

Keywords: AES, DES, Blowfish, Cryptography. References: 1. Behrouz A. Forouzan, “Cryptography and Network Security”, Tata McGraw-Hill, 2nd edition, 2008. 2. William stalling,” Cryptography and network security”, 3rd ed. 3. Manikandan G, Rajendran P, Chakarapani K, Krishnan G and Sundarganesh G "A Modified Crypto Scheme for Enhancing Data Security", Journal of Theoretical and Applied Information Technology, Vol. 35, No.2, pp.149-154, 2012. 4. Monika Agrawal, Pradeep Mishra, "A Modified Approach for Symmetric Key Cryptography Based on Blowfish Algorithm", International Journal of Engineering and Advanced Technology, Vol. 1, Issue 6, pp. 79-83, 2012. 5. B.Geethavani, E.V.Prasad and R.Roopa , “A New Approach for Secure Data Transfer in Audio Signals Using DWT”, 2013, IEEE. 6. Christina L , Joe Irudayaraj V S, “Optimized Blowfish Encryption Technique”, International Journal of Innovative Research in Computer and Communication Engineering,Vol. 2, Issue 7, July 2014. 7. Saikumar Manku and K. Vasanth “Blowfish encryption algorithm for information security”, ARPN journal of engineering, vol 10, June 2015 8. Vaibhav Poonia ,Dr. Narendra Singh Yadav, “Analysis of modified Blowfish Algorithm in different cases with various parameters”, International Conference on Advanced Computing and Communication Systems (ICACCS -2015), Jan. 05 – 07, 2015. Authors: J. Anitha. Paper Title: Zero Forcing in Snake Graph Abstract: A dynamic coloring of the vertices of a graph G starts with an initial subset S of colored vertices, with all remaining vertices being non-colored. At each discrete time interval, a colored vertex with exactly one non-colored neighbor forces this non-colored neighbor to be colored. The initial set S is called a forcing set (zero forcing set) of G if, by iteratively applying the forcing process, every vertex in G becomes colored. The zero forcing number of G, denoted Z(G), is the minimum cardinality of a zero forcing set of G. In this paper, obtain the zero forcing number for hexagonal chain torus, alternate quadrilateral snake and double quadrilateral snake. AMS Subject Classification--- 05C69, 05C85, 05C90 and 05C20.

Keywords: Zero Forcing Set, Hexagonal Chain Torus, Alternate Quadrilateral Snake, Double Quadrilateral Snake. 25.

References: 133-136 1. AIM Special Work Group, Zero forcing sets and the minimum rank of graphs. Linear Algebra Appl. 428(7), 1628-1648 (2008). 2. D. Burgarth, V.Giovannetti, L. Hogben, S. Severini, and M. Young, Logic circuits from zero forcing, Natural Computing 14(3), 485- 490 (2015). 3. M. Gentner, L.D. Penso, D. Rautenbach and U.S. Souza, Extremal values and bounds for the zero forcing number, Discrete Applied Mathematics, 214, 196-200 (2016). 4. K. F. Benson, D.Ferrero, M. Flagg, V. Furst, L. Hogben, V. Vasilevskak and B. Wissman, Zero forcing and power domination for graph products, https://arxiv.org/abs/1510.02421. 5. Randic, Milan, Tsukano, Yoko, Hosoya, and Haruo, On enumeration of kekule structures for benzenoid tori, http ://hdl.handle.net/10083/841. 6. S.K. Vaidya and R. M. Pandit, Edge domination in various snake graphs, International Journal of Mathematics and Soft Computing, 7(1) (2017), 43-50. 7. A. Manonmania and R. Savithiri, Double quadrilateral snakes on k-odd sequential harmonious labeling of graphs, Malaya Journal of Matematik, 3(4)(2015) 607611. Authors: R. Immanuel Rajkumar, G. Sundari.

Paper Title: Mobile Agents based Smart System for Accident Free Level Crossing in Railways

Abstract: Safety travel is an important motto of every journey. A massive improvement in technologies development which implemented in railway systems influenced travel a comfortable one. A track circuit, signaling system, data logging system and monitoring system makes a system a close monitored for every

moment. But due to manual operations, human mistakes, signaling failures, manual overrides and human innocence creates a large disaster by accidents which lead to maximum loss of life. Mobile agents is a concept which periodically collect the information from various nodes by having mobility characteristics and without

influence of any human operations, carry an information from one node to various nodes. Nodes like trains, signals, level crossing, track sensors which periodically update in the remote server, computing the actions based

on the data received from the mobile agents. This leads to provide a smart intelligent system to predict and to avoid chance of occurrence of accidents. Mobile agents can override the current operations in railways, and even predict the chance of any accident occurrence. The more advantage of the proposed system is, it can be easily

installed over the currently existing system which leads to better performance.

Keywords: Mobile Agent, Colision Avoidance, Level Crossing Sensors, Remote Tracking.

References: 1. B. Ai "Challenges toward wireless communications for high-speed railway" IEEE Transaction Intelligent Transportation Syst., vol. 15, no. 5, pp. 2143-2158, 2014 2. A. Verma, K. K. Pattanaik, and P. P. Goel “Mobile A gent-based CBTC System with Moving Block Signalling for Indian Railways ” 2nd International Conference on Rail way Technology: Research, Development, and Maintenance (Railways 2014), Civil-Comp Press, Stirlingshire, UK, Paper 278, 2014. 3. Johnny Wong *, Guy Helmer, Venkatraman Naganathan, Sriniwas Polavarapu, Vasant Honavar, Les Miller “SMART mobile agent facility”Elsevier, The Journal of Systems and Software ( 2001 ) Page No. 9-22

4. Anshul Verma*, K. K. Pattanaik “Mobile agent based train control system for mitigating meet conflict at turnout” Procedia Computer Science 32 ( 2014 ) Page No. 317 – 324 5. Anshul Verma*, K. K. Pattanaik “Multi-agent communi cation-based train control system for Indian railways: the behavioral analysis” 6. J. Mod. Transport. (2015) 23(4):272–286 DOI 10.1007 /s40534-015-0083-1 7. A. Anastasopoulos, K. Bollas, D. Papasalouros and D. Kourousis "Acoustic emission on-line inspection of rail wheels" Proc. 29th Eur. Conf. Acoustic. Emission Testing, pp. 1-8, 2010 8. P. Bennett "Wireless sensor networks for underground railway applications: Case studies in Prague and London" Smart Struct. Syst., vol. 6, no. 5/6, pp. 619-639, 2010 9. E. Berlin and K. van Laerhoven "Sensor networks for railway monitoring: Detecting trains from their distributed vibration footprints" IEEE Int. Conference Distributed Computation Sens. Syst., pp. 80-87, 2013 10. Xiaoqing Zeng, Chenliang Tao And Zhenyu Niu, Kai Zhang " The Study of Railway Control System Model " IEEE Int. on Industrial

Electronics and Applications. Syst., pp. 1424-1428, 2010. 11. Yashpal Sing, Kapil Gulati and S Niranjan" Dimensions And Issues Of Mobile Agent Technology " International Journal of Artificial Intelligence & Applications (IJAIA), Vol.3, No.5, September 2012, pp. 51-61, DOI: 10.5121/ijaia.2012.

12. Ali Pouyan, Momeneh Taban, Sadegh Ekrami " A Distributed Multi-Agent Control Model for Railway Transportation System" ICAS 2011: The Seventh International Conference on Autonomic and Autonomous Systems., pp. 24-28, 2011. 13. S. Bruni, R. Goodall, T. Mei and H. Tsunashima "Control and monitoring for railway vehicle dynamics" Vehicle System Division., vol. 45, no. 7/8, pp. 743-779, 2007 14. Deepti Singh*, Ankit Thakur, Deepak Gupta " A Review of Mobile Agent Security" International Journal of Advanced Research in Computer Science and Software Engineering, vol. 5,Issue 2,Feb 2015 no. 7/8, pp. 188-190, 2015 137-143 26. 15. P. Li "Estimation of railway vehicle suspension parameters for condition monitoring" Control Eng. Practice, vol. 15, no. 1, pp. 43-55, 2007 16. F. Marquez, P. Weston and C. Roberts "Failure analysis and diagnostics for railway trackside equipment" Eng. Failure Anal., vol. 14, no. 8, pp. 1411-1426, 2007 17. H. Tsunashima, T. Kojima, Y. Marumo, H. Matsumoto and T. Mizuma "Condition monitoring of railway track using in-service vehicle" Proc. 4th IET Int. Conf. Railway Condition Monitoring, pp. 1-6, 2008 18. F. Flammini "Towards wireless sensor networks for railway infrastructure monitoring" Proc. Electr. Syst. Aircraft, Railway Ship Propulsion, pp. 1-6, 2010 19. A. Wilkinson "Long range inspection and condition monitoring of rails using guided waves" Proc. 12th Int. Conf. Exhib., Railway Eng., 2013 20. Pandapotan Siagian; Kisno Shinoda “Web based monito ring and control of robotic arm using Raspberry Pi” International Conference on Science in Information Technology (ICSITech) Year: 2015 Pages: 192 - 196, DOI: 10.1109/ICSITech.2015.7407802 IEEE Conference Publications 21. Prachi H. Kulkarni; Pratik D. Kute; V. N.”IoT based data processing for automated industrial meter reader using Raspberry Pi” International Conference on Internet of Things and Applications (IOTA) Year: 2016 Pages: 107 - 111, DOI: 10.1109/IOTA.2016.7562704 IEEE Conference Publications 22. R.Immanuel Rajkumar, "An Approach to Implementation of Intelligent Signaling for Automatic Blocking System in Railway Sectors Using Mobile Agents" Procedia Computer Science Volume 46, 2015, Pages 337-345, Proceedings of the International Conference on Information and Communication Technologies, ICICT 2014, 3-5 December 2014. 23. K. Sekula and P. Kolakowski "Piezo-based weigh-in-motion system for the railway transport" Struct. Control Health Monitoring, vol. 19, no. 2, pp. 199-215, 2012 24. M. McHutchon, W. J. Staszewski and F. Schmid "Signal processing for remote condition monitoring of railway points" Strain, vol. 41, no. 2, pp. 71-85, 2005 25. J. Reason and R. Crepaldi "Ambient intelligence for freight railroads" IBM J. Res. Develop., vol. 53, no. 3, pp. 1-14, 2009 26. G. Scholl "SAW-based radio sensor systems for short-range applications" IEEE Microw., vol. 4, no. 4, pp. 68-76, 2003 27. Mamoru Sekiyama; Bong Keun Kim; Seisho Irie; Tamio Tanikawa “Sensor data processing based on the data l og system using the portable IoT device and RT-Middleware”,12th International Conference on Ubiquitous Robo ts and Ambient Intelligence (URAI) Year: 2015 Pages: 46 - 48, DOI: 10.1109/URAI.2015.7358925 IEEE Conference Publications 28. Altaf Hamed Shajahan; A. Anand “Data acquisition an d control using Arduino-Android platform: Smart plug” 2013 International Conference on Energy Efficient Technologies for Sustainability Year: 2013 Pages: 241 - 244, DOI: 10.1109/ICEETS.2013.6533389 IEEE Conference Publications 29. R.Immanuel Rajkumar, GPS & Ethernet Based Real Time Train Tracking System International Conference on Advanced Electronic Systems. p. 283-287 30. H. Yazdi. "Intelligent condition monitoring of railway signaling equipment using simulation" Proc. Inst. Elect. Eng.?Seminar Condition Monitoring Rail Transport Syst. (Ref. No. 1998/501), pp. 13-1-13-5, 1998 31. S. Zller "Efficient real-time monitoring of multimodal transports with wireless sensor networks", Proc. 36th IEEE Conf. Local Comput. Netw., 2011 32. Shubham N. Mahalank; Keertikumar B. Malagund; R. M. Banakar “Device to device interaction analysis in Io T based Smart Traffic Management System: An experimental approach” Symposium on Colossal Data Analysis a nd Networking (CDAN) Year: 2016 Pages: 1 - 6, DOI: 10.1109/CDAN.2016.7570909 33. M. F. AL. Faisal; S. Bakar; PS Rudati “The developmen t of a data acqusition system based on internet of things framework” International Conference on ICT For Smart Society (ICISS) Year: 2014 Pages: 211 - 216, DOI: 10.1109/ICTSS.2014.7013175 IEEE Conference Publications 34. Milan Matijevic; Vladimir Cvjetkovic “Overview of ar chitectures with Arduino boards as building blocks for data acquisition and control systems” 2016 13th Internat ional Conference on Remote Engineering and Virtual Instrumentation (REV), Year: 2016 Pages: 56 - 63, R.Immanuel Rajkumar, “Real Time Wireless based Train Tracking, Track Identification and Collision avoidance System for Railway Sectors”. International Journ al of advanced research in Computer Engineering & Technology:20143;. p. 2172-77. 35. R.Immanuel Rajkumar, “An Approach to Implementation o f Intelligent Tracking System for Railway Sectors using Mobile Agents” in “International Journal of Applied Engineering Research” Volume 9, Number 23 (2014) pp.18977-18989 36. R. Immanuel Rajkumar, "An approach to Avoiding Train Collision in Railway Sectors Using Multi Agent System" Procedia Computer Science Volume 57, 2015, Pages 1067-1073,3rd International Conference on Recent Trends in Computing 2015 (ICRTC-2015). 37. X. S. Li, et al., "Analysis and Simplification of Three-Dimensional Space Vector PWM for Three-Phase Four-Leg Inverters," IEEE Transactions on Industrial Electronics, vol. 58, pp. 450-464, Feb 2011. 38. R. Arulmozhiyal and K. Baskaran, "Implementation of a Fuzzy PI Controller for Speed Control of Induction Motors Using FPGA," Journal of Power Electronics, vol. 10, pp. 65-71, 2010. 39. D. Zhang, et al., "Common Mode Circulating Current Control of Interleaved Three-Phase Two-Level Voltage-Source Converters with Discontinuous Space-Vector Modulation," 2009 IEEE Energy Conversion Congress and Exposition, Vols 1-6, pp. 3906-3912, 2009. 40. Z. Yinhai, et al., "A Novel SVPWM Modulation Scheme," in Applied Power Electronics Conference and Exposition, 2009. APEC 2009. Twenty-Fourth Annual IEEE, 2009, pp. 128-131. Authors: S. Sridevi, R. Anandan. AORA-A Novel Optimized Intrusion Detection System for Identification of the Black Hole Attacks in Paper Title: Wireless Sensor Networks Abstract: The application of Wireless Sensor Networks finds its function in all the application areas like Health care, Automation, Agriculture and others. Along with the IoT (Internet of Things), WSN plays a very important role in data collection which is used for the monitoring and control. Even though WSN plays a more 27. noteworthy role in the collection, monitoring and control, WSN suffers a serious setback in the form of different

attacks which manipulates the data or even the nodes. To overcome this setback, IDS (Intrusion detection 144-151 System) has been placed to guarantee the stability and security of the Wireless Sensor Networks. Several IDS has been implemented, but challenges increases day by day.As first step towards intelligent IDS, this paper proposes the new algorithm AORA (Advanced Optimizer for Reliable Allocation) which mechanism on the powerful BAT optimizer integrated with Cognitive learning machines (CLM). The proposed algorithm has been tested with the two scenarios such as AODV and LEACH environment and accuracy of detection is determined for several test cases. The proposed algorithm has been compared by implementing the other optimization algorithms method such as different PSO and GA in which the proposed optimizer outperforms and other algorithms in terms of accuracy of detection (AID), and throughput.

Keywords: AORA, BAT, PSO, GA Cognitive Learning Machines (CLM), AODV, LEACH. References: 1. BarnaliSahu, Debahuti Mishra , “A Novel Feature Selection Algorithm using Particle Swarm”, Optimization for Cancer Microarray Data, International Conference on Modelling Optimization and Computing (ICMOC-2012), Procedia Engineering, Vol.38, pp. 27-31, 2012. 2. Pallavi Dixit, Dr. Santosh Kumar, “Novel Approach of Features selection by grey wolf optimization with SVM”, INTERNATIONAL JOURNAL OF RESEARCH IN ELECTRONICS AND COMPUTER ENGINEERING, VOL. 6, ISSUE 1, pp.321-324, JAN.-MAR. 3. SEDIGHEH KHAJOUEI NEJAD, SAM JABBEHDARI, MOHAMMAD HOSSEIN MOATTAR, “A Hybrid Intrusion Detection System Using Particle Swarm Optimization for Feature Selection”, International Journal of Soft Computing and Artificial Intelligence, Volume-3, Issue-2, pp.55-58, Nov-2015. 4. A. Rajagopal1, S. Somasundaram1, B. Sowmya, “Performance Analysis for Efficient Cluster Head Selectionin Wireless Sensor Network Using RBFO and HybridBFO-BSO”, International Journal of Wireless Communications and Mobile Computing,Vol.6, issue. No 1, pp.1-9, 2018. 5. NareshMallenahalli, T. HitendraSarma, “A Tunable Particle Swarm Size Optimization Algorithm for Feature Selection”, Journal of Neural and Evolutionary Computing, arXiv:1806.10551v1 [cs.NE] 20 Jun 2018. 6. Yu Xue , WeiweiJia, Xuejian Zhao and Wei Pang, “An Evolutionary Computation Based Feature Selection Method for Intrusion Detection”, Journal of Security and Communication Networks, Volume 2018, Article ID 2492956, pp.1-10, 2018. 7. AmandeepKaur, ParveenKaur, Harisharan Aggarwal, “Implementation of Black hole attacks in WSN using Genetic Algorithm and PSO”, Advances in Wireless and Mobile Communications, Volume 10, Number 4 , pp. 717-726, 2017. 8. ManizhehGhaemi, Mohammad-Reza Feizi-Derakhshi, “Feature selection using Forest Optimization Algorithm”, Pattern Recognition,http://dx.doi.org/10.1016/j.patcog.2016.05.012., 2016. 9. Ahmed Ibrahem Hafez1,∗, Hossam M. Zawbaa1,3, E. Emary4,5, Aboul Ella Hassanien, “Sine Cosine Optimization Algorithm for Feature Selection”, International Symposium on INnovations in Intelligent SysTems and Applications (INISTA), pp. 1-5, 2016. 10. Shih-Wei Lin a, Kuo-Ching Ying, Shih-Chieh Chen, Zne-Jung Lee, “Particle swarm optimization for parameter determination and feature selection of support vector machines”, Expert Systems with Applications, Vol.35, pp.1817-1824, 2008. 11. LucijaBrezocnik, “Feature selection for classification using particle swarm optimization”, IEEE EUROCON 2017, pp. 6-8 JULY 2017, OHRID, R. MACEDONIA 12. MarwaSharawi, Hossam M. Zawbaa, and E. Emary, “Feature Selection Approach Based on Whale Optimization Algorithm”, international conference advanced computational intelligence, pp.4-6, 2017. 13. Long Zhang, Linlin Shan, Jianhua Wang, “Optimal feature selection using distance-based discrete firefly algorithm with mutual information criterion”, Neural Computing& Applications, DOI 10.1007/s00521-016-2204-0, 2016. Authors: T. Pravin Rose, G. Glan Devadhas. Paper Title: Analysis of Fractional Order PI Controller with Cuckoo Optimization for Multi Tank Process Abstract: pH neutralization procedure is measured as a standard process for testing non-linear controllers. Thus the research of the evidence of identity and control in pH neutralization procedure is very important. Wide- ranging researches in the proof of identity of pH neutralization procedure have been done by many relative experts for many years. In this paper authors propose design methodology and application of Adaptive Neuro- Fuzzy Inference System (ANFIS) with optimization algorithm to improve the prediction based on fractional PI controller. Therefore, this paper deals with tank size and its quantity mainly concerning multiple tanks.

Keywords: Fractional Order PI Controller (FOPI), ANFIS and Cuckoo Search Optimization.

References: 1. Faisal, KP, FalahUmmer, Hareesh, KC, MunavirAyaniyat, Nijab K, Nikesh, P &Jibi, R 2015, ‘Application of Fmea Method in a Manufacturing Organization focused on Quality,’ International Journal of Engineering and Innovative Technology, vol. 4, no. 7, pp. 64-70. 2. Hamdan, H & Garibaldi, M 2010, ‘Adaptive Neuro-Fuzzy Inference System (ANFIS) in Modelling Breast Cancer Survival,’ WCCI 2010, IEEE World Congress on Computational Intelligence, pp. 18–23.

3. Katikar, R, Pawar, M &RamkrishnaDikkatwar 2014, ‘Analysis Of Risk By Fmea in Manufacturing Outsourcing For Batch Type Industries’, International Journal of Research in Engineering & Technology, vol. 2, no. 9, pp. 89-98. 4. 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. 5. Krishnaraj, C, Mohanasundram, KM, Devadasan, SR &Sivaram, NM 2012, ‘Total failure mode and effect analysis: a powerful technique for overcoming failures,’ Int. J. Productivity and Quality Management, vol. 10, no. 2, pp.131-147. 6. Liu, HC, Liu, L & Liu, N 2013, ‘Risk evaluation approaches in failure mode and effects analysis: A literature review,’ Expert Syst.

Appl., vol. 40, no. 2, pp. 828–838. 7. MustainBillah, Mohammad BadrulAlamMiah, Abu Hanifa&Ruhul Amin 2015, ‘Adaptive Neuro Fuzzy Inference System based Tea Leaf Disease Recognition using Color Wavelet Features’, Communications on Applied Electronics, vol. 3, no. 5, pp. 1-4. 8. NavneetWalia, Harsukhpreet Singh & Anurag Sharm 2015, ‘NFIS: Adaptive Neuro-Fuzzy Inference System- a Survey,’ International Journal of Computer Applications, vol. 123, no.13, pp. 32-38. 152-154 28. 9. Ping-Shun Chen &Ming-Tsung Wu 2013, ‘A modified failure mode and effects analysis method for supplier selection problems in the supply chain risk environment: A case study’, Computers & Industrial Engineering, vol. 66, pp. 634–642. 10. Qing Li 2013, ‘A novel Likert scale based on fuzzy sets theory,’ Expert Systems with Applications, Elsevier, vol. 40, pp.1609–1618. 11. Rezaei, K, Hosseini, R &Mazinani, M 2014, ‘A Fuzzy Inference System for Assessment of the Severity of the peptic ulcers,’ Computer Science & Information Technology, pp. 263-271. 12. Senthilmurugan, PR &Perarasu, JK 2014, ‘Modified Failure Mode And Effect Analysis (MFMEA) For Machineries In Sugar Industry’, International Journal of Engineering Sciences & Research Technology, vol. 3, no. 4, pp.7051-7055. 13. TejaskumarParsana, S &Mihir Patel, T 2014, ‘A Case Study: Process FMEA Tool to Enhance Quality and Efficiency of Manufacturing Industry,’Bonfring International Journal of Industrial Engineering and Management Science, vol. 4, no. 3, pp. 145-152. Zhang Yu, MengDawei, Zhou Meilan& Lu Dengke 2014, ‘Fuzzy Logic Control Strategy Based on Genetic Algorithm Optimization for PHEV’, Advanced Science and Technology Letters, vol. 53, pp. 373-376. Authors: V.S. Bibin Raj, G. Glan Devadhas Paper Title: Design of a Noval Controller to Maintain DC Level of PV System for Low Voltage Applications Abstract: The human exercises add to the worldwide temperature alteration of the planet. Thus, every nation endeavors to diminish carbon discharges. The world is standing up to the weariness of non-sustainable power sources, just as it's increasing costs which cause the worldwide money related shakiness. By the grouping it is resolved that the new enthusiasm for power has been compensated by the execution of sun based electric and photovoltaic development. These embed some assistance for the up and coming requirements for the monetary development of the country and the speed developing force age innovation. The central expect is to make another framework which joins the working PV System to stack and the power equipment and the logic to pursue the sun based route by introducing the MPP following. By this, the proficiency can be expanded further and can enhance the use factor. At that point fundamental conspicuousness will be put on the photovoltaic system, the demonstrating and reenactment of photovoltaic cluster, the MPP control and the DC/DC converter. The PV Simulink model could be utilized later on for broadened contemplate with various DC/DC converter topology. Advancement of MPPT algorithm can be actualized with the current Photovoltaic and DC/DC converter. This topology is most reasonable for the low voltage applications, for example, Health Monitoring systems (HMS), Bed Side Monitors and for some low voltage applications.

Keywords: PV, MPPT, Dc-Dc Converter, Inverter, Renewable Energy Sources, Control Algorithm. References: 1. Anand I Subramaniaom Senthil Kumar, Deepankar Wiswas M. Kaliamoort hy. "Dynamic Power Management Syst em Employing a Single-St age Power Converter f or St andalone Solar PV Applicat ions", IEEE Transact ions on Power ectronics, 2018 2. Yongheng Yang, , and F. Blaabjerg. "A modif ied P&O MPPT algorit hm f or single-phase PV syst ems based on deadbeat cont rol", 6t h IET Int ernat ional Conf erence on Power Elect ronics Machines and Drives (PEMD 2012), 2012. 3. Anand I, Sent hilkumar Subramaniam, Dipankar Biswas, Kaliamoort hy M. "Dynamic Power Management Syst em employing single st age Power Converter for St andalone Solar PV Applicat ions", IEEE Transact ions on Power Elect ronics, 2018 4. V. S Bibin Raj, Glan Devadas. "Implement at ion of renewable resources f or increased power demand in modern era", 2014 Int ernat ional Conf erence on Cont rol, Inst rument at ion, Communicat ion and Comput at ional Technologies (ICCICCT), 2014 5. Aswat hy Sukumaran, G. Devadhas Glan, S.S. Kumar. "An improved t umor segment at ion algorithm from T2 and f lair mult imodalit y MRI brain images by support vector machine and genet ic algorithm", Cogent Engineering, 2018. 29. 6. M. Ito, K. Kato, H. Sugihara, T. Kichimi, J. Song, and K. 7. Kurokawa, “A preliminary study on potential for very large-scale photovoltaic power generation (VLS-PV) system in the gobi desert from economic and environmental viewpoints,” Sol. Energy Mater. Sol. Cells, vol. 75, nos. 3/4, pp. 507–517, 2003. 155-159 8. X. Sun, Y. Shen, W. Li, and H. Wu, “A PWM and PFM hybrid modulated three-port converter for a standalone PV/battery power system,” IEEE J.Emerg. Sel. Topics Power Electron., vol. 3, no. 4, pp. 984–1000, Dec. 2015. 9. Y. C. Liu and Y. M. Chen, “A systematic approach to synthesizing multiinput dc-dc converters,” IEEE Trans. Power Electron., vol. 24, no. 1,pp. 116–127, Jan. 2009. 10. Y. M. Chen, A. Q. Huang, and X. Yu, “A high step-up three-port dc-dc converter for stand-alone PV/battery power systems,” IEEE Trans. Power Electron., vol. 28, no. 11, pp. 5049–5062, Nov. 2013. 11. O. Ray, A. P. Josyula, S. Mishra, and A. Joshi, “Integrated dual-output converter,” IEEE Trans. Ind. Electron., vol. 62, no. 1, pp. 371– 382, Jan. 2015. 12. H. Wu, J. Zhang, and Y. Xing, “A family of multiport buck-boost converters based on dc-link-inductors (DLIS),” IEEE Trans. Power Electron.,vol. 30, no. 2, pp. 735–746, Feb. 2015. 13. W.-H. Ki and D. Ma, “Single-inductor multiple-output switching converters,” in Proc. IEEE 32nd Annu. Power Electron. Spec. Conf., 2001, vol. 1,pp. 226–231. 14. S. Bandyopadhyay and A. P. Chandrakasan, “Platform architecture for solar, thermal, and vibration energy combining with MPPT and single inductor,” IEEE J. Solid-State Circuits, vol. 47, no. 9, pp. 2199–2215,Sep. 2012. 15. L. Benadero, V. Moreno-Font, R. Giral, and A. E. Aroudi, “Topologies and control of a class of single inductor multiple-output converters operating incontinuous conduction mode,” IET Power Electron., vol. 4, no. 8, pp. 927–935, Sep. 2011. 16. W. Jiang and B. Fahimi, “Multiport power electronic interface—Concept,modeling, and design,” IEEE Trans. Power Electron., vol. 26, no. 7,pp. 1890–1900, Jul. 2011. 17. Y. J. Moon, Y. S. Roh, J. C. Gong, and C. Yoo, “Load-independent current control technique of a single-inductor multiple-output switching dc-dc converter,” IEEE Trans. Circuits Syst. II, Express Briefs, vol. 59, no. 1,pp. 50–54, Jan. 2012. 18. A. Nami, F. Zare, A. Ghosh, and F. Blaabjerg, “Multi-output dc-dc convert ers based on diode-clamped converters configuration: Topology and control strategy,” IET Power Electron., vol. 3, no. 2, pp. 197–208, Mar. 2010. 19. A. Khaligh, J. Cao, and Y.-J. Lee, “A multiple-input DC–DC converter topology,” IEEE Trans. Power Electron., vol. 24, no. 3, pp. 862–868,Mar. 2009. 20. Z. Rehman, I. Al-Bahadly, and S. Mukhopadhyay, “Multiinput dc–dc converters in renewable energy applications—An overview,” Renew. Sustain.Energy Rev., vol. 41, pp. 521–539, 2015. 21. M. H. Huang and K. H. Chen, “Single-inductor multi-output (SIMO)DC-DC converters with high light-load efficiency and minimized crossregulation for portable devices,” IEEE J. Solid-State Circuits, vol. 44, no. 4, pp. 1099–1111, Apr. 2009. 22. H. Shao, X. Li, C. Y. Tsui, and W. H. Ki, “A novel single-inductor dualinput dual-output dc-dc converter with PWM control for solar energ harvesting system,” IEEE Trans. Very Large Scale Integr. (VLSI) Syst.,vol. 22, no. 8, pp. 1693–1704, Aug. 2014. 23. E. Babaei and O. Abbasi, “Structure for multi-input multi-output dc-dc boost converter,” IET Power Electron., vol. 9, no. 1, pp. 9–19, 2016. Authors: S. Reeba Rex, D.M. Mary Synthia Regis Praba. Paper Title: Analysis of EMI Reduction in Optimised Boost Converter Using Analogue PWM Chaotic Technique Abstract: These days the EMI creates a high trouble in power electronic circuits mainly in excessive frequency relevance, those trouble is greater extreme. To lessen the EMI this paper introduce some methods. However in excessive frequency relevance the effectiveness of preceding techniques are small. This paper introduces the PWM Chaotic control which is used to decrease the EMI in excessive frequency relevance. This 30. paper offers with hide of Electromagnetic Interference in optimised Boost converter. The outcome of this work suggests that high of the EMI is decreased through the whole band of frequency and additionally it decrease the 160-164 complete variety of frequency band. Simulation consequences are evaluated to support the ordinary conventional PWM converter and PWM chaotic pulse converter. ORCAD/PSPICE simulation implements be applied to create the circuit. This paper Optimised Boost converters are prepared to carry out in chaotic mode; consequently spread spectra is produce this chaotic mode which might be apply to lessen EMI successfully. Fast Fourier Transform (FFT) technique is applied to examine the spectrum. At remaining, the simulation moulds are offered this simulation, this work to investigate the competence of decreasing EMI in proposed circuit.

Keywords: Optimised Boost Converter, PWM Chaotic Control, Electromagnetic Interference (EMI), Fast Fourier Transform (FFT). References: 1. Natarajan, Sudhakar, and Rajasekar Natarajan. "An FPGA chaos-based PWM technique combined with simple passive filter for effective EMI spectral peak reduction in DC-DC converter." Advances in Power Electronics 2014 (2014). 2. Zhang, Haoran, and Shaoan Dai. "A reduced-switch dual-bridge inverter topology for the mitigation of bearing currents, EMI, and DC- link voltage variations." IEEE Transactions on Industry Applications 37, no. 5 (2001): 1365-1372. 3. Mainali, Krishna, and Ramesh Oruganti. "Conducted EMI mitigation techniques for switch-mode power converters: A survey." IEEE Transactions on Power Electronics 25, no. 9 (2010): 2344-2356. 4. Ashritha, M., and M. L. Sudheer. "Mitigation of High-Frequency CM Conducted EMI in Offline Switching Power Supplies." In 2018 IEEE Symposium on Electromagnetic Compatibility, Signal Integrity and Power Integrity (EMC, SI & PI), pp. 317-321. IEEE, 2018. 5. Bogónez-Franco, Paco, and Josep Balcells Sendra. "EMI comparison between Si and SiC technology in a boost converter." In Electromagnetic Compatibility (EMC EUROPE), 2012 International Symposium on, pp. 1-4. IEEE, 2012. 6. Wang, Chen, James L. Drewniak, D. Wang, Ray Alexander, James L. Knighten, and David M. Hockanson. "Grounding of heatpipe/heatspreader and heatsink structures for EMI mitigation." (2001). 7. Hasan, Saad Ul, Yuba Raj Kafle, and Graham E. Town. "Simple spread-spectrum pulse-modulation technique for EMI mitigation in power converters." In Universities Power Engineering Conference (AUPEC), 2017 Australasian, pp. 1-5. IEEE, 2017. 8. So, Roger, Chi Fai Shek, Stanton Lui, and Eddie Kwok. "Partnering for EMI mitigation-a case study from the LokMa Chau Spur Line." In International Conference on Railway Engineering-Challenges for Railway Transportation in Information Age, 2008International Conference on Requirements Engineering Institute of Electrical and Electronics Engineers (IEEE). 2008. 9. Ramachandran, A., and M. Channa Reddy. "Novel method of mitigation of conducted EMI in PWM inverter fed induction motor Adjustable speed drives." In Industrial Technology, 2006. ICIT 2006. IEEE International Conference on, pp. 2337-2342. IEEE, 2006. 10. Wu, Sau-Mou, and Kai-Hsiang Chang. "An LED driver with active EMI mitigation scheme." In Electron Devices and Solid State Circuit (EDSSC), 2012 IEEE International Conference on, pp. 1-4. IEEE, 2012. 11. S. Reeba Rex and D.M. Mary Synthia Regis Praba, “Controller Design for Boost Converter Using Soft Computing Techniques Based Optimization Algorithms” Authors: B.R. Aravind, V. Rajasekaran. Technological Modality to Influence Persuasive and Argumentative Vocabulary for Effective Paper Title: Communication with reference to Selected TED Talk Videos Abstract: English as the Second language learning recently gained attention in the field of research. The ESL (English as Second Language) learners need vocabulary enhancement and fluency for proficiency of the language which can be achieved through training. By learning and practicing a language with enhanced vocabulary will increase the vocabulary. TED (Technology, Entertainment, and Design) talks are a world’s biggest digital platform for public speaking. Vocabulary elements given in the TED talks can be defined as a lexicon of language which plays a significant role in communication. The aim of this research conveys the significant role of TED talk videos’ speaker and its influence towards its audiences. This can be achieved only by practical use of vocabulary by the ESL and EFL learners. In this research work, Persuasive and Argumentative vocabulary in the transcript of random 25 TED talk videos with time frame of 0-6mins, and sorted by ‘newest’ tab are analyzed. Also, in-depth analyses of both Persuasive and Augmentative keywords used and its frequencies are listed out from the 25 videos. This research significantly concludes that for effective communication, the learner has to be proficient in vocabulary acquisition.

Keywords: English Learning, Vocabulary Enhancement, ELS, TED, Lexicon, Persuasive, Augmentative.

References: 1. TED Blog website (2012) What talks have resonated most with you? Tweet TED’s billionth video view. 2. Tsou, A., Demarest, B., & Sugimoto, C. R. (2015). How Does TED Talk? A Preliminary Analysis. iConference 2015 Proceedings. 3. Laufer, B., & Girsai, N. (2008). Form-focused instruction in second language vocabulary learning: A case of contrastive analysis and translation. Applied Linguistics, 29, 649–716. doi:10.1093/applin/amn018 4. Webb, S., & Kagimoto, E. (2009). The effects of vocabulary learning on collocation and meaning. TESOL Quarterly, 43, 55–77. doi:10.1002/j.1545-7249.2009. tb00227.x 5. Zimmerman, C. (2008). Word knowledge: A vocabulary teacher’s handbook. New York, NY: Oxford University Press. 6. Li, Y., Gao, Y., & Zhang, D. (2016). To Speak Like a TED Speaker--A Case Study of TED Motivated English Public Speaking Study in EFL Teaching. Higher Education Studies, 6(1), 53-59. 7. Arnaud, P. & Bejoint, H. (1992).Vocabulary and Applied Linguistic. Basingstoke: Macmillan. Berne, J. I., & Blachowicz, C. L.

Z.,(2008)What reading teachers say about vocabulary instruction: Voices from the classroom. The Reading Teacher 62 (4).314-323. 8. Cameron, L. (2001). Teaching languages to young learners. Cambridge: Cambridge University Press. 9. Coady, J., & Huckin, T. (Eds.). (1997). Second language vocabulary acquisition. Cambridge: Cambridge University Press. 10. Carter, R., & McCarthy, M. (Eds.). (1988). Vocabulary and language teaching. London: Longman. 11. Gu, Y. (2003a). Vocabulary learning in second language: person, task, context andstrategies.Electronic Journal. TESL-EJ, 7, 2, 1-26. 12. Gu, Y. (2003b). Fine brush and freehand: The vocabulary learning art of two successful Chinese EFL learners. TESOL Quarterly, 37, 73-104. 13. Harmon, J. M., Wood, K. D.,, & Keser, K. (2009) Promoting vocabulary learning with interactive word wall. Middle School Journal, 40(3), 58-63. 14. Linse, C. T. & Nunan, D. (Ed). (2005). Practical english language teaching: Young learners. New York: McGraw- Hill ESL/ELT. 15. Maximo, R. (2000). Effects if rote, context, keyword, and context/ keyword method on retention of vocabulary in EFL classroom, Language Learning, 50, 2, 385-412. 16. Meara, P. (1980). Vocabulary acquisition: A neglected aspect of language learning. Language Teaching and Linguistics Abstracts, 13, 221-246. 31. 17. Oxford, R. L. (1990). Language Learning Strategies. What Every Teacher should know. Boston: Heinle and 323 Heinle. 165-170 18. Read, J. (2000). Assessing vocabulary. United Kingdom: Cambridge University Press. 19. Schmitt, N., & Meara, P. (1997).Researching vocabulary through a word knowledge framework: word association and verbal suffix. Studies in Second Language Acquisition 19, 17-36. 20. Schmitt, N. (2000). Vocabulary in language teaching. Cambridge: Cambridge University Press. 21. Huffman, S. R. (2010). The influence of collaboration on attitudes towards English vocabulary learning. 22. Sung, C. C. M. (2016). ESL university students’ perceptions of their global identities in English as a lingua franca communication: A case study at an international university in Hong Kong. The Asia-Pacific Education Researcher, 25(2), 305-314. 23. Richards, J. C., & Rodgers, T. S. (2014). Approaches and methods in language teaching. Cambridge university press. 24. Taka, V. P. (2008). Vocabulary learning strategies and foreign language acquisition. Multilingual matters. 25. Janzen, J. (2008). Teaching English language learners in the content areas. Review of Educational research, 78(4), 1010-1038. 26. Taylor, A. (2014). The people's platform: Taking back power and culture in the digital age. Metropolitan books. 27. Cook, V. (2016). Second language learning and language teaching. Routledge. 28. Pulido, D. (2003). Modeling the role of second language proficiency and topic familiarity in second language incidental vocabulary acquisition through reading. Language learning, 53(2), 233-284. Authors: M.K. Nallakaruppan, U. Senthil Kumaran. Paper Title: IoT based Machine Learning Techniques for Climate Predictive Analysis Abstract: The continuous research in the fields of Internet of Things and Machine Learning has offered ascend to various weather forecast models. However, the issue of precisely foreseeing or anticipating the weather still perseveres. This paper is an application of Internet of Things and Machine Learning algorithms like Decision Tree and Time Series Analysis. The Internet of Things actually signifies 'things' (e.g. sensors and other shrewd gadgets) which are associated with the web. Despite the fact that this may appear to be irrelevant, 'things' represent a new and progressively, critical foundation requiring their own particular devoted technological system. The obtained results from the Machine Learning demonstrated that the time series method forecasts the weather more accurately for a larger duration of time.

Keywords: Weather Prediction, Machine Learning, Internet of Things, Decision Tree, Support Vector Machines, Time Series. References: 1. Deshmukh A. D. &Shinde U. B. 2016, August. A low cost environment monitoring system using raspberry Pi and arduino with Zigbee. In: Inventive Computation Technologies (ICICT), International Conference on. 3: 1-6. IEEE. 2. Jindarat S. &Wuttidittachotti P. 2015, April. Smart farm monitoring using Raspberry Pi and Arduino.In: Computer. Communications, and Control Technology (I4CT), 2015 International Conference on . IEEE. pp. 284-288. 3. Savić T. &Radonjić M. 2015, November. One approach to weather station design based on Raspberry Pi platform. In: Telecommunications Forum Telfor (TELFOR), 23rd . IEEE. pp. 623-626. 4. Wang Y. & Chi Z. 2016, July. System of Wireless Temperature and Humidity Monitoring Based on Arduino Uno Platform. In: Instrumentation & Measurement, Computer, Communication and Control (IMCCC), 2016 Sixth International Conference on. IEEE. pp. 770-773. 32. 5. Saini H., Thakur A., Ahuja S., Sabharwal N. & Kumar N. 2016, February. Arduino based automatic wireless weather station with remote graphical application and alerts. In: Signal Processing and Integrated Networks (SPIN), 2016 3rd International Conference on. IEEE. pp. 605-609. 171-175 6. Kumar N. P. &Jatoth R. K. 2015 May. Development of cloud based light intensity monitoring system using raspberry Pi. In: Industrial Instrumentation and Control (ICIC), 2015 International Conference on.IEEE. pp. 1356-1361. 7. Srinivasan V. S., Kumar T. &Yasarapu D. K. 2016, May. Raspberry Pi and iBeacons as environmental data monitors and the potential applications in a growing BigData ecosystem. In: Recent Trends in Electronics, Information & Communication Technology (RTEICT), IEEE InternationalConference on. IEEE. pp. 961-965. 8. Ibrahim M., Elgamri A., Babiker S. & Mohamed A. 2015, October. Internet of things based smart environmental monitoring using the raspberry-pi computer. In: Digital Information Processing and Communications (ICDIPC), 2015 Fifth International Conference on. IEEE. pp. 159-164. 9. Folea S. C. &Mois G. 2015. A low-power wireless sensor for online ambient monitoring. IEEE Sensors Journal. 15(2): 742-749 10. Sandeep V., Gopal K. L., Naveen S., Amudhan A. & Kumar L. S. 2015, August. Globally accessible machine automation using Raspberry pi based on Internet of Things. In: Advances in Computing, Communications and Informatics (ICACCI), 2015 International Conference on. IEEE. pp. 1144-1147. 11. Princy S. E. & Nigel K. G. J. 2015, November. Implementation of cloud server for real time data storage using Raspberry Pi. In: Green Engineering and Technologies (IC-GET), 2015 Online International Conference on . IEEE. pp. 1-4. 12. Chapman, L. & Thornes, J.E. (2011) What resolution do we need for a route-based road weather decision support system? Theoretical & Applied Climatology 104:551-559. 13. Chapman, L. (2012) Probabilistic road weather forecasting. Proceedings of the 16th SIRWEC Conference, Helsinki, Finland, May 2012. 14. Chapman, L. & Thornes, J.E. (2006) A geomatics based road surface temperature prediction model. Science of the Total Environment 360:68-80 15. Mahoney, W.P. & O'Sullivan, J.M. (2013) Realizing the Potential of Vehicle-Based Observations. Bulletin of the American Meteorological Society 94:1007–1018. 16. Box,G.E.P. and G.M.Jenkins, 1976.Time Series Analysis : Forecasting and Control. Holden Day Inc. San Francisco,CA. 17. Cook, D.F. and Wolfe, M.L., 1991, “A backpropagation neural network to predict average air temperatures”, AI Applications, 5, p.p.40- A.Geetha, T.M. ThamizhThentral, SnehalArvindSilekar, GracyKatiyar, Gaurav Mishra, Authors: SharadBhowmick. Paper Title: Analyzing the Characteristics of Different Types of Motors Used in Electric Vehicles Abstract: This document deals with work done by the students on analyzing the characteristics of different types of motor used in an hybrid electric vehicle, and to find out the most efficient motor for the same. Characteristics such as losses, efficiency, cost etc. are taken into consideration and compared, calculated to find out the efficiency of different types of motors under different conditions. The performance of motors under two major conditions is taken into account. Two different types of motors are used for the purpose and a better one is 33. found out. All this is done by using MATLAB software and simulation of the motors are carried out and their

torque is calculated using respective formulas, as a result the efficiency calculation is also carried out using 176-179 suitable formulae. Two main types of motors such as BLDC and PMSM are used for the purpose, they are simulated on different conditions i.e once on a highway and another on a city road, their efficiency is calculated is on the same basis.

Keywords: Electric Motors, Efficiency Calculation, Comparison, Electric Vehicle. References: 1. Comparison Of Electric Motors used for Electric Vehicle Propulsion by Adrain BALTATANU, Leonard Marin FLOREA at INTERNATIONAL CONFERENCE OF SCIENTIFIC PAPER AFASES 2013. 2. Comparative Study of Using Different Electric Motors in the Electric Vehicles by Nasser Hashernnia and BehzadAsaei at 2008 International Conference of Electrical machines. 3. Gaurav Nanda and Narayan C. Kar."A Survey and Comparison of Characteristics of Motor Drives Used in Electric Vehicles".Canadian Conference on Electrical and Computer Engineering. 2006. 4. Comparison Of Electric Motors For Electric Vehicle Application by SwarajRavindra Jape and Archana Thosar2 5. D. van Niekerk, M. Case, D.V. Nicolae, “BrushlessDirect Current Motor EfficiencyCharacterization”,978-1-4763-7239-8/15/$31.00 ' 2015 IEEE 6. https://www.tesla.com/support/model-s-specifications. Authors: Rejeesh Rayaroth, G. Sivaradje. Paper Title: Grey Wolf Optimization based Sensor Placement for Leakage Detection in Water Distribution System Abstract: Water Distribution System (WDS) are employed in everyday life either for domestic or for industrial purpose. WDS are large scale systems that need the design of better leak detection methods to avoid water waste. Recently, researchers concerned about WDS have focused their research on water leakage detection techniques. However, the different existing techniques failed to improve the performance of accuracy and time consumption during water leakage detection. In order to address the above mentioned issues, Bivariate Correlation and Sensitivity Analysis based Meta-Heuristic Grey Wolf Optimization (BCSA-MHGWO) Technique is introduced. The main aim of the BCSA-MHGWO technique is to detect the water leakage with a minimal number of sensor placed nodes. Initially, WDS is represented in graph model comprising a set of vertices (i.e., nodes) and set of edges (i.e., pipes). The sensitivity and entropy value is calculated for all nodes based on the pressure and flow rate. After calculating the sensitivity value, the correlation value of all nodes is measured by using bivariate correlation coefficient based on the pressure series. Finally, grey wolf optimization process is carried out in BCSA-MHGWO technique to select the optimal nodes for sensor placement based on the sensitivity, entropy and correlation value for water leakage detection. In this way, water leakage detection accuracy and time performance get improved using BCSA-MHGWO technique. The performance of BCSA- MHGWO Technique is measured in terms of water leakage detection accuracy, water leakage detection time, and false positive rate. The simulation results show that BCSA-MHGWO Technique improves the performance of water leakage detection accuracy and also reduces water leakage detection time when compared to state-of- the-art works.

Keywords: Water Distribution Systems, Leak Detection, Bivariate Correlation, Sensitivity, Entropy, Meta- Heuristic, Grey Wolf Optimization. References: 1. Jiheon Kang, Youn-Jong Park, Jaeho Lee, Soo-Hyun Wang and Doo-Seop Eom “Novel Leakage Detection by Ensemble CNN-SVM and Graph-Based Localization in Water Distribution Systems”, IEEE Transactions on Industrial Electronics, Volume 65, Issue 5, May 2018, Pages 4279 - 4289 2. G. R. Anjana, K. R. Sheetal Kumar, M. S. Mohan Kumar and Bharadwaj Amrutur, “A Particle Filter Based Leak Detection Technique

for Water Distribution System”, Procedia Engineering, Elsevier, Volume 119, 2015, Pages 28-34. 3. Yeonsoo Kim, Shin Je Lee, Taekyoon Park, Gibaek Lee, Jung Chul Suh and Jong Min Lee, “Robust Leakage Detection and Interval Estimation of Location in Water Distribution Network”, IFAC-Papers, Elsevier, Volume 48, Issue 8, 2015, Pages 1264–1269 4. Aravind Rajeswaran, Sridharakumar Narasimhan and Shankar Narasimhan, “A graph partitioning algorithm for leak detection in water distribution networks”, Computers and Chemical Engineering, Elsevier, Volume 108, 2018, Pages 11–23 5. Ramon Perez, Vicenc- Puig, Josep Pascual, Joseba Quevedo, Edson Landeros, Antonio Peralta, “Methodology for leakage isolation using pressure sensitivity analysis in water distribution networks”, Control Engineering Practice, Elsevier, Volume 19, 2011, Pages 1157–1167

6. Jordi Meseguer, Josep M. Mirats-Tur, Gabriela Cembrano and Vicenc Puig, “Model-based Monitoring Techniques for Leakage 180-188 34. Localization in Distribution Water Networks”, Procedia Engineering, Elsevier, Volume 119, 2015, Pages 1399-1408 7. A. Agathokleous, S. Xanthos and S. E. Christodoulou, “Real-time monitoring of water distribution networks”, Water Utility Journal, Volume 10, 2015, Pages 15-24 8. Dileep Kumar, Dezhan Tu, Naifu Zhu, Dibo Hou, and Hongjian Zhang, “In-Line Acoustic Device Inspection of Leakage in Water Distribution Pipes Based on Wavelet and Neural Network”, Journal of Sensors, Hindawi Publishing Corporation, Volume 2017, 2017, Pages 1-10 9. Alemtsehay G. Seyoum and Tiku T. Tanyimboh, “Integration of Hydraulic and Water Quality Modelling in Distribution Networks: EPANET-PMX”, Water Resource Management, Springer, 2017, Volume 31, Pages 4485-4503 10. D. Wachla, P. Przystalka and W. Moczulsk, “A Method of Leakage Location in Water Distribution Networks using Artificial Neuro- Fuzzy System”, IFAC, Volume 48, Issue 21, 2015, Pages 1216-1223 11. Alain Prodon, Scott DeNegre and Thomas M. Liebling, “Locating leak detecting sensors in a water distribution network by solving prize-collecting Steiner arborescence problems”, Mathematical Programming, Springer, Volume 124, Issue 1–2, July 2010, Pages 119– 141 12. David B. Steffelbauer and Daniela Fuchs-Hanusch, “Efficient Sensor Placement for Leak Localization Considering Uncertainties”, Water Resource Management, Springer, Volume 30, 2016, Pages 5517–5533 13. Gaudenz Moser, Stephanie German Paal and Ian F.C. Smith “Performance comparison of reduced models for leak detection in water distribution networks”, Water Resources Management, Springer, Volume 30, Issue 14, November 2016, Pages 5517–5533 14. Suzhen Li, Yanjue Songb and Gongqi Zhou, “Leak detection of water distribution pipeline subject to failure of socket joint based on acoustic emission and pattern recognition”, Measurement, Elsevier, Volume 115, February 2018, Pages 39-44 15. Paul Irofti and Florin Stoican, “Dictionary learning strategies for sensor placement and leakage isolation in water networks”, IFAC Papers OnLine, Elsevier, Volume 50, Issue 1, 2017, Pages 1553–1558 16. Albert Rosich, Ramon Sarrate and Fatiha Nejjari, “Optimal Sensor Placement for Leakage Detection and Isolation in Water Distribution Networks”, IFAC Proceedings Volumes, Elsevier, Volume 45, Issue 20, January 2012, Pages 776-781 17. Alberto Martini, Marco Troncossi, and Alessandro Rivola, “Automatic Leak Detection in Buried Plastic Pipes of Water Supply Networks by Means of Vibration Measurements”, Hindawi Publishing Corporation, Shock and Vibration, Volume 2015, 2015, Pages 1-13 18. Nourhan Samir, Rawya Kansoh, Walid Elbarki and Amr Fleifle, “Pressure control for minimizing leakage in water distribution systems”, Alexandria Engineering Journal, Elsevier, 2017, Volume 56, Pages 601-612 19. Corneliu T.C. Arsene, Bogdan Gabrys and David Al-Dabass, “Decision support system for water distribution systems based on neural networks and graphs theory for leakage detection”, Expert Systems with Applications, Elsevier, Volume 39, 2012, Pages 13214–13224 20. Zhang Hongwei and Wang Lijuan, “Leak Detection in Water Distribution Systems using Bayesian Theory and Fisher’s Law”, Transactions of Tianjin University, Springer, Volume 17, June 2011, Pages 181-186 Authors: Saritha Reddy Venna, Ramesh Babu Inampudi. MMBAS-NS: Multimodal Biometric Authentication System and Key Generation Algorithm for Paper Title: Network Security on Mobile Phones Abstract: Nowadays mobile devices are an important part of our everyday lives since they enable us to access a large variety of ubiquitous services. In recent years, the availability of these ubiquitous and mobile services has significantly increased due to the different form of connectivity provided by mobile devices. In the same trend, the number and typologies of vulnerabilities exploiting these services and communication channels have increased as well. As the number of vulnerabilities and, hence, of attacks increase, there has been a corresponding rise of security solutions proposed by researchers. To overcome these issues in security solutions, we introduce a new method based on cryptographic generation system. We proposed a new multimodal biometric authentication system, here key values are created via the use of multiple biometrics instead of a single biometric, in an effort to generate strong and repeatable cryptographic keys. In this work, a multimodal biometric authentication system (MMBAS) is developed using face, fingerprint and retina images and key generation is also done using these images. Initially images are pre-processed using adaptive median filtering and Otsu’s segmentation algorithm for background subtraction. Then minutiae feature of these images are extracted with the use of Local Binary Pattern (LBP) algorithm and then the feature vectors of face, fingerprint and retina are fused using XOR operation. Later the fused feature vector is used for cryptographic key generation. The evaluation is performed on network security for showing the reliability of the newly introduced approach in terms of Precision, Recall, Accuracy and false rejection rate.

Keywords: Module-Integrated Converter, Right-Half-Plane Zero, Low-Pass Filter, Phase-Lead Compensation, Fuzzy Control.

References: 1. La Polla, M., Martinelli, F., & Sgandurra, D. (2013). A survey on security for mobile devices. IEEE communications surveys & tutorials, 15(1), 446-471. 2. Jobanputra, N., Kulkarni, V., Rao, D., & Gao, J. (2008). Emerging security technologies for mobile user accesses. The electronic Journal on E-Commerce Tools and Applications. 3. Dedo, D. (2004). Windows mobile-based devices and security: Protecting sensitive business information. Microsoft Corporation Apr. 4. Schneider, K. N. (2013). Improving data security in small businesses. Journal of Technology Research, 4, 1. 5. Mahmood, S., Amen, B., & Nabi, R. M. (2016). Mobile Application Security Platforms Survey. International Journal of Computer Applications, 133(2), 40-46. 6. Sabhanayagam, T., Venkatesan, V. P., & Senthamaraikannan, K. (2018). A Comprehensive Survey on Various Biometric Systems. International Journal of Applied Engineering Research, 13(5), 2276-2297. A. K. Jain, A. Ross and S. Pankanti, “Biometrics, A Tool for Information Security”, IEEE Transactions on Information Forensics And Security, 2006, vol.1, no.2, pp. 125 – 144. 7. Kataria, A. N., Adhyaru, D. M., Sharma, A. K., & Zaveri, T. H. (2013, November). A survey of automated biometric authentication techniques. In Engineering (NUiCONE), 2013 Nirma University International Conference on (pp. 1-6). IEEE 8. Mushtaq, M. F., Jamel, S., Disina, A. H., Pindar, Z. A., Ahmad, N. S., & Shakir, M. M. D. (2017). A Survey on the Cryptographic Encryption Algorithms. Proceeding of (IJACSA) International Journal of Advanced Computer Science and Applications, 8(11). 9. James Wayman, Anil Jain, Davide Maltoni and Dario Maio, "An Introduction to Biometric Authentication Systems". In Biometrics:Technology, Design and performance evaluation. Springer Publications. ISBN 978-0-7923-8345-1. 10. Kapoor, V., & Verma, S. “A survey of various Cryptographic techniques and their Issues” International Journal of Advanced Research in Computer Engineering & Technology (IJARCET) Volume 3 Issue 12, December 2014. 11. Parvathi Ambalakat, "Security of Biometric Authentication Systems", in proceedings of 21st Computer Science Seminar, 2005. 12. AlMahafzah, H., & AlRwashdeh, M. Z. (2012). A survey of multibiometric systems. arXiv preprint arXiv:1210.0829. 13. EL-SAYED, A. Y. M. A. N. (2015). Multi-biometric systems: a state of the art survey and research directions. IJACSA) International Journal of Advanced Computer Science and Applications, 6. 14. Jagadeesan, A., Thillaikkarasi, T., & Duraiswamy, K. (2010). Cryptographic key generation from multiple biometric modalities: Fusing minutiae with iris feature. Int. J. Comput. Appl, 2(6), 16-26. 15. Feng, Q., He, D., Zeadally, S., & Wang, H. (2018). Anonymous biometrics-based authentication scheme with key distribution for mobile multi-server environment. Future Generation Computer Systems, 84, 239-251. 16. Naidu, P. A., Prasad, C. H. G. V. N., Prasad, B., & Bodla, B. “Fingerprint and Palmprint Multi-Modal Biometric Security System”.

International Journal of Engineering and Applied Computer Science Volume: 02, Issue: 05, May 2017. 17. Jagadiswary, D., & Saraswady, D. (2016). Biometric authentication using fused multimodal biometric. Procedia Computer Science, 85, 189-199 35. 109-116. 18. Lalithamani, N., & Sabrigiriraj, D. M. (2014). Technique to generate a face and palm vein-based fuzzy vault for a multi-biometric cryptosystem. Machine Graphics and Vision, 23(1/2), 97-114. 19. Kanade, S., Petrovska-Delacrétaz, D., & Dorizzi, B. (2009, September). Multi-biometrics based cryptographic key regeneration scheme. In Biometrics: Theory, Applications, and Systems, 2009. BTAS'09. IEEE 3rd International Conference on (pp. 1-7). IEEE. 20. Sanjay Kumar, Surjit Paul, Dilip Kumar Shaw “Real-Time Multimodal Biometric User Authentication for Web Application Access in Wireless LAN” Journal of Computer Science 2017, 13 (12): 680.693 21. Sarier, N. D. (2018). Multimodal biometric identity based encryption. Future Generation Computer Systems, 80, 112-125. 22. Ali, Z., Hossain, M. S., Muhammad, G., Ullah, I., Abachi, H., & Alamri, A. (2018). Edge-centric multimodal authentication system using encrypted biometric templates. Future Generation Computer Systems, 85, 76-87. 23. Saevanee, H., Clarke, N., Furnell, S., & Biscione, V. (2015). Continuous user authentication using multi-modal biometrics. Computers & Security, 53, 234-246. 24. Gomez-Barrero, M., Galbally, J., & Fierrez, J. (2014). Efficient software attack to multimodal biometric systems and its application to face and iris fusion. Pattern Recognition Letters, 36, 243-253. 25. Vazquez-Fernandez, E., & Gonzalez-Jimenez, D. (2016). Face recognition for authentication on mobile devices. Image and Vision Computing, 55, 31-33. 26. Galdi, C., Nappi, M., & Dugelay, J. L. (2016). Multimodal authentication on smartphones: Combining iris and sensor recognition for a double check of user identity. Pattern Recognition Letters, 82, 144-153. 27. Snelick, R., Uludag, U., Mink, A., Indovina, M. and Jain, A., 2005. Large-scale evaluation of multimodal biometric authentication using state-of-the-art systems. IEEE transactions on pattern analysis and machine intelligence, 27(3), pp.450-455. 28. Li, S.Z. (ed.): Encyclopedia of Biometrics (First Edition), Springer Reference (2009). 29. Henniger, O., Scheuermann, D. and Kniess, T., 2010, March. On security evaluation of fingerprint recognition systems. In Internation Biometric Performance Testing Conference (IBPC), pp. 1-10. 30. Kanade, S., Camara, D., Krichen, E., Petrovska-Delacrétaz, D. and Dorizzi, B., 2008, Three factor scheme for biometric-based cryptographic key regeneration using iris. In Biometrics Symposium, pp. 59-64. 31. Kanade, S., Petrovska-Delacrétaz, D. and Dorizzi, B., 2010, Generating and sharing biometrics based session keys for secure cryptographic applications. Fourth IEEE International Conference on Biometrics: Theory Applications and Systems (BTAS), pp. 1-7. 32. Dierks, T., Rescorla, E.: The Transport Layer Security (TLS) Protocol – Version 1.2”,RFC 5246, IETF Network Working Group, August (2008). 33. Salles-Loustau, G., Berthier, R., Collange, E., Sobesto, B., Cukier, M.: Characterizing Attackers and Attacks: An Empirical Study. IEEE 17th Pacific Rim International Symposium on Dependable Computing (PRDC), pp.174-183, 2011. 34. Krishna, N.M. and Reddy, P.C.S., 2014. A Dimensionality Reduced Iris Recognition System with Aid of AI Techniques. Global Journal of Research in Engineering, pp.1-17. 35. He K., J. Sun, and X. Tang, “Guided image filtering,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35,no. 6, pp. 1397–1409, 2013. 36. Yang J., J. Yang, and Y. Shi, “Finger-vein segmentation based on multichannel even-symmetric gabor filters,” IEEE International Conference on Intelligent Computing and Intelligent Systems, Vol. 4. 2009, pp. 500–503. 37. Abhilash Sharma1, and Ms. Rajani Gupta, 2015. Iris recognition based learning `vector quantization and local binary patterns on iris matching. International Journal of Technical Research and Applications, vol.3, no. 5 , pp. 7-14. 38. Xu, J., Cha, M., Heyman, J.L., Venugopalan, S., Abiantun, R. and Savvides, M., 2010, September. Robust local binary pattern feature sets for periocular biometric identification. Fourth IEEE International Conference on Biometrics: Theory Applications and Systems (BTAS), pp. 1-8. 39. Sahu, D.K. and Parsai, M.P., 2012. Different image fusion techniques–a critical review. International Journal of Modern Engineering Research (IJMER), 2(5), pp.4298-4301. 40. Chang, Y.J., Zhang, W. and Chen, T., 2004, Biometrics-based cryptographic key generation. IEEE International Conference on Multimedia and Expo, 2004. pp. 2203-2206. 41. Nandakumar, K., Jain, A.K. and Pankanti, S., 2007. Fingerprint-based fuzzy vault: Implementation and performance. IEEE transactions on information forensics and security, 2(4), pp.744-757. Authors: Kanchamreddy Snehitha, R. Kiranmayi, K. Nagabhushanam. Design and Operation of Flyback CCM Inverter with Fuzzy based Discrete-Time Repetitive Control Paper Title: for PV Power Applications Abstract: In continuous conduction mode, A discrete-time repetitive controller (RC) is proposed for fly back inverter with fuzzy controller. In this paper fuzzy based repetitive controller is used due to some advantages. Such as, it reduces ripples then THD will be reduced, which has simple structure, low cost, and high efficiency. Comparing to the conventional controller the repetitive controller obtain good tracking ability and

disturbance rejection and applied to flyback inverter in Continuous Conduction Mode operation. Conventional controller results in poor control performance due to the effect of the right-half-plane zero in CCM operation. To allow tracking and rejection of periodic signals within a specified frequency range the RC scheme, a low-pass

filter is used. The stability of the closed loop system is derived and the zero tracking error is achieved with the

stability of the closed loop system. By using the simulation results we can analyze the proposed method.

Keywords: Module-Integrated Converter, Right-Half-Plane Zero, Low-Pass Filter, Phase-Lead

Compensation, Fuzzy Control.

References:

11. F. F. Edwin, W. Xiao, and V. Khankikar, "Dynamic displaying and control of interleaved flyback module-incorporated converter for PV control applications," IEEE Trans. Ind. Electron., vol. 61, no. 3, pp. 1377-1388, Mar. 2014. 12. S. B. Kjaer, J. K. Pedersen, and F. Blaabjerg, "An audit of single-stage framework associated inverters for photovoltaic modules," IEEE Trans. Ind. Appl., vol. 41, no. 5, pp. 1292-1306, Sep./Oct. 2005. 13. Y. H. Kim, J. W. Jang, S. C. Shin, and C. Y. Won, "Weighted-proficiency upgrade control for photovoltaic AC module interleaved flyback inverter utilizing a synchronous rectifier," IEEE Trans. Power Electron., vol. 29, no. 12, pp. 6481-6493, Dec. 2014. 14. G. Petrone, G. Spagnuolo, and M. Vitelli, "A simple system for conveyed MPPT PV applications," IEEE Trans. Ind. Electron., vol. 59, no. 12, pp. 4713-4722, Dec. 2012 15. Y. Li and R. Oruganti, "A flyback-CCM inverter conspire for photovoltaic AC module application," in Proc. Australasian Univ. Power Eng. Conf. (AUPEC), 2008, pp 1-6. 36. 16. N. Kasa, T. Iida, and L. Chen, "Flyback inverter controlled by sensorless current MPPT for photovoltaic power framework," IEEE 200-204 Trans. Ind. Electron., vol. 52, no. 4, pp. 1145-1152, Aug. 2005. 17. N. Sukesh, M. Pahlevaninezhad, and P. K. Jain, "Examination and execution of a solitary stage flyback PV microinverter with delicate exchanging," IEEE Trans. Ind. Electron., vol. 61, no. 4, pp. 1819-1833, Apr. 2014. 18. Z. Zhang, X. F. He, and Y. F. Liu, "An ideal control strategy for photovoltaic lattice tide-interleaved flyback microinverters to accomplish high productivity in wide load extend," IEEE Trans. Power Electron., vol. 28, no. 11, pp. 5074-5087, Nov. 2013. 19. H. Hu, S. Harb, N. H. Kutkut, Z. J. Shen, and I. Batarseh, "A singlestage microinverter without utilizing electrolytic capacitors," IEEE Trans. Power Electron., vol. 28, no. 6, pp. 2677-2687, Jun. 2013. 20. R. W. Erickson and D. Maksimovic, "Essentials of Power gadgets," Springer Science and Business Media, 2007. Authors: S. Lavanya Devi, S. Nagarajan, R. Vaishali. Paper Title: Design, Analysis and Comparison of Suitable Converters for Photovoltaic System Abstract: This paper deals with the design, analysis and comparison of power electronic converter that suits the best for photovoltaic system. Renewable energy has become the center of rising interest now-a-days. They are the sustainable energy sources that come from the natural environment. It is a clean alternative to fossil fuels. Therefore, power electronics application to field such as photovoltaic generation, wind power generation etc. has 37. become vital. Converters and inverters are used so that the generation using these renewable resources is carried out easily and efficiently. This project deals with the comparison of design, modeling, simulation and 205-211 implementation of DC to DC converters used for photovoltaic. The performance of the system in terms of Total harmonic distortions and efficiency of the output produced by the converter are compared by using various converter topologies namely, buck-boost converter, Cuk converter and Sepic converter. By choosing the best efficient and ripple-free converter, the need of filter circuits can be reduced or eliminated significantly. Based on the total harmonic distortion and efficiency, the best suitable converter for the photovoltaic is concluded. A photovoltaic panel rated 24v delivers 250W Power, with a switching frequency of 20 KHz for the converters.

Keywords: Power Electronic Converters, Photovoltaic, Total Harmonic Distortion, Efficiency. References: 1. Ahmed saidi, benachaiba chellali “simulation and control of solar wind hybrid renewable system”, IEEE proceedings, 2017. 2. Azadeh Safari, Saad Mekhilef “simulation and hardware implementation of incremental conductance method using cuk converter“, IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS ,Vol No 58,2011. 3. B.Pakkiraiah and G.Durga ,”various MPPT issues to improve solar PV sytem efficiency” ,Hindawi journal of solar energy, june 2016. 4. M.M.Rajan singaravel and S.Arul Daniel ”MPPT with songle dc-dc converter and inverter for grid connected hybrid wind-driven PMSG-PV system”, IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS ,June 2015 5. Ersan Kabalci “Design and analysis of a hybrid renewable energy plant with solar and wind power”, Energy conversion and management, April 2013. 6. K.Sarvanan,N.Nandini ,”design and simulation of hybried renewable energy system using fuzzy logic controller”, journal of chemical and pharmaceutical sciences , vol 9 ,December 2016. 7. Mustafa Engin Basoglu, Bekir Cakir, “comparisons of MPPT performances of isolated and non-isolated converters”, Renewable and sustainable energy reviews, ELSEVIER, January 2016. 8. Onur Kircioglu, Sabri Camur ,“modelling and analysis of sepic converter with coupled inductors”, IEEE Transactions of power electronics ,2016 9. Sajib Chakraborty, sudipta dey, ”Design of a transformerless grid connected hybrid system”, International forum on Strategic Technology, 2014. 10. Sajib chakraborty, Razzak ,” Design of a transformerless grid tie inverter using Dual stage buck-boost converters“, International journal of renewable energy research,2014. 11. Sidharth samantara, Renu sharma “Modelling and simulation of cuk converter with Beta MPPT for standalone system”, IET International Summit, september 2015. 12. Sumit wagh, Dr.P.V.Walke “Review on wind – solar hybrid power system”, International journal of research in science and engineering, Volume 3, Issue 2, April 2017. Authors: P. Rathnavel, T. Baldwin Immanuel, P. Rayavel. Paper Title: Energy Efficient Light Monitoring and Control Architecture Using Embedded System Abstract: In this paper, we propose an energy efficient RF-based outdoor light monitoring and control system that can monitor and handle outdoor lights more efficiently as compared to the conventional systems. The proposed system uses the RF-based wireless devices which allow more efficient lamps management. The designed system uses sensors to control and guarantee the optimal system parameters. To realize effectiveness of the proposed system, the prototype has been installed inside the University, where the experimental results proved that the proposed system saves around 70.8% energy for the outdoor street environment because of using sensors, LED lamps, and RF based communication network. To implement wireless control system of lights, several comparable architectures have been applied for outdoor lighting. In the design of the intelligent lighting system by considering the system cost as the main factor beside the energy saving. In, the author tries to reduce the number of sensors on each lighting nodes, but this reduction will result in less accuracy of the system due to more packet loss and hence will result in performance degradation. Furthermore, the authors in and designed the energy efficient lighting controls system by utilizing the WIMAX and GPRS as backbone technology, respectively, to communicate with the control center. One of the drawbacks of utilizing WIMAX and GPRS is the utilization of licensed spectrum, which will result in interference with the existing WIMAX and GPRS users. Hence, the lighting system will also require efficient interference avoiding algorithms to cope with interference, but this is not suitable for the lighting systems. These systems also have no capability to change the light intensity according to the users’ requirement because they statically control the energy consumption and do not consider the user requirements in the sense of light intensity and the user’s presence while dimming or turning

off the lamps. In order to fill this research hole, we design the energy efficient RF TRANSRECEIVER-based outdoor light monitoring and control system. In addition to all these things ,an additional led is given as backup light, which will be used during main led light failure or when the operating temperature of main led exceeds the

optimum range.

Keywords: WSN (Wireless sensor Network), MSD (Mass Storage Device), HID (Human Interface

Device), LDR (Light Depended Resistor). 212-215 38. References: 1. W. Yue, S. Changhong, Z. Xianghong, and Y. Wei, "Design of new intelligent street light control system," in Proc. IEEE International Conference on Control Automation, 2010, pp. 1423–1427. 2. C.Ozcelebi, and J. Lukkien, "Exploring user-centered intelligent road lighting design: a road map and future research directions," IEEE Trans. Consum. Electron, vol. 57, pp. 788-793, May 2011. 3. P.Rathnavel, S.Surendernath and S.Saravanan, "An Interleaved High-Power Flyback Inverter for Standalone Application Using MPPT Algorithm” Journal of Advanced Research in Dynamical & Control Systems, 11-Special Issue, November 2017. 4. L. Chushan, W. Jiande, and H. Xiangning, "Realization of a general LED lighting system based on a novel Power Line Communication technology," in Proc. IEEE Applied Power Electronics Conference and Exposition, 2010, pp. 2300-2304. 5. P. Elejoste et al., "An Easy to Deploy Street Light Control System Based on Wireless Communication and LED Technology," Sensors (Basel), vol. 13, no. 5, pp. 6492–6523, May 2013. 6. F. Leccese, M. Cagnett, and D. Trinca, " A Smart City Application: A Fully Controlled Street Lighting Isle Based on Raspberry-Pi Card, a ZigBee Sensor Network and WiMAX," Sensors (Basel), vol. 14, no. 12, pp. 24408–244424, Dec. 2014. 7. L. Yongsheng, L. Peijie, and C. Shuying, “Remote Monitoring and Control System of Solar Street Lamps Based on ZigBee Wireless Sensor Network and GPRS,” Electronics and Signal Processing Lecture Notes in Electrical Engineering, vol. 97, Springer, 2011, pp. 959-967. 8. Z. Kaleem, I. Ahmad, and C. Lee, “Smart and Energy Efficient LED Street Light Control System using ZigBee Network,” in Proc. FIT, 2014, pp. 361-365. Ember Corporation, "EM250 single-chip ZigBee/802.15.4 solution," 120-0082-000V datasheet, May 2013. 39. Authors: Geetha, SubhomoyGanguly, Dipanjan Paul, ShubhamSahu, Shreyas Mehta,ShashankTiwari Paper Title: Calculation and Profiling of Energy Consumption Rate of an Electric Vehicle Abstract: Electric vehicle (EV) energy consumption is different and dependent on number of external factors such as road topology, city driving, highway driving, driving style, ambient temperature etc. Improving battery performance is one of the most important factors in promoting the EV market by prolonging battery life reducing the cost of ownership and giving confidence in the product to potential customer. This paper consider a method of improving battery performance and its characteristics on different load in different vehicles and thus increase holding capacity. This will be realized by finding energy consumption rate(ECR), voltage of battery at different stage of vehicles.The goal of this paper is to detect its power consumption on different vehicle speed, torque, voltage output of the battery. All the testing has been done in an ambient temperature and in a plane road (alpha=0, alpha-inclination). The roads considered for testing are tarmac and gravel road.All the testing has been performed in matlab simulation. For the testing three vehicles have been considered which are Toyota, Tesla-s, and Mercedes Benz. All the three vehicles have their own fixed specification i.e. frontal area, air lag, tire radius etc. at last simulation graph has been compared for all three different vehicles and their battery characteristics. Keywords: Energy Consumption Rate, Simulation, Battery, Electric Vehicle. References: 1. Don Sherman. ‘Five Slippery Cars, Enters a Wind Tunnel; One Sinks Out a Winner’, Drag Queens, no.-12, 2017. 2. Motion and dynamic equations for vehicles, NPTEL- Elec. Engineer and electronics- “Intro. to hybrid and electric vehicle,” in 216-219 modern Tech. ,2013.; 94(2): pp846-639. 3. Yao, E.; Yang, Z.; Song, Y.; Zuo, T. Comparison of electric vehicle’s energy consumption factors for different road types. Discret.Dyn. Nat. Soc. 2013, 2013, 328757: 1-328757:7. 4. Shanker, R.; Marco, J. Method for estimating the energy consumption of electric vehicles and plug-in hybrid electric vehicles under real-world driving conditions. Intell. Tramp. Syst. IET 2013, 7, 138-150. 5. AIS-039 (Revision1):2015, Electric Power Train Vehicles – Measurement of Electric Energy Consumption, 2012, 302867:1- 302867:11,pp 121-130. 6. E. Vinot and R. Trigui, “Optimal energy management of hevs with hybrid storage systems”, Energy Conversion and Management, vol. 76, pp.437-453, 2013. 7. O. Tremblay, L.-A.Dessaint, and A.-I.Dekkiche, “A generic battery model for the dynamic simulation of hybrid electric vehicles,” in Vehicle Power and Propulsion Conference, 2007.VPPC 2007.IEEE.Ieee, 2007, pp. 284-289. 8. Piller S, Perrin M, Jossen A. Methods of State of Charge determination and their applications. Journal of Power Sources 2001; 96(1): 113-120. 9. Piccolo A, Ippolito L, Galdi V, Vaccaro A. Optimization of energy flow management in hybrid electric vehicles via genetic algorithms. IEEE/ASME International Conference on Advanced Intelligent Mechatronics 2001, Como, Italy 8-12 July, pp. 434- 439. Authors: Kanchamreddy Snehitha, R. Kiranmayi, K. Nagabhushanam. Design and Operation of Flyback CCM Inverter with Fuzzy based Discrete-Time Repetitive Control Paper Title: for PV Power Applications Abstract: In continuous conduction mode, A discrete-time repetitive controller (RC) is proposed for fly back inverter with fuzzy controller. In this paper fuzzy based repetitive controller is used due to some advantages. Such as, it reduces ripples then THD will be reduced, which has simple structure, low cost, and high efficiency. Comparing to the conventional controller the repetitive controller obtain good tracking ability and disturbance rejection and applied to flyback inverter in Continuous Conduction Mode operation. Conventional controller results in poor control performance due to the effect of the right-half-plane zero in CCM operation. To allow tracking and rejection of periodic signals within a specified frequency range the RC scheme, a low-pass filter is used. The stability of the closed loop system is derived and the zero tracking error is achieved with the stability of the closed loop system. By using the simulation results we can analyze the proposed method.

Keywords: Module-Integrated Converter, Right-Half-Plane Zero, Low-Pass Filter, Phase-Lead Compensation, Fuzzy Control.

References: 1. Liu, B.; Yan, Z.; Chen, C.W. Medium Access Control for Wireless Body Area Networks with QoS Provisioning and Energy Efficient Design. IEEE Trans. Mob. Comput. 2017, 2, 422–434. 2. Raffaele, G.; Parastoo, A.; Hassan, G.; Giancarlo, F. Multi-sensor fusion in body sensor networks: State-of-the-art and research

challenges. Inf. Fusion 2017, 5, 68–80. 3. Fortino, G.; Gravina, R.; Raffaele, G.; Philip, K.; Roozbeh, J. Enabling Effective Programming and Flexible Management of Efficient Body Sensor Network Applications. IEEE Trans. Hum. Mach. Syst. 2013, 1, 115–133. 4. George, S.; Nikos, D.; Rosario, S.; Valeria, L.; Fortino, G.; Yiannis, A. Decentralized Time-Synchronized Channel Swapping for Ad Hoc Wireless Networks. IEEE Trans. Veh. Technol., 2016, 10, 8538–8553. 5. IEEE Standard for Local and Metropolitan Area Networks Part 15.6: Wireless Body Area Networks. In IEEE Std. 802.15.6-2012; IEEE: Piscataway, NJ, USA, 2012; pp.1–271. 6. Youssef, M.; Younis, M.; Arisha, K.A. A constrained shortest-path energy-aware routing algorithm for wireless sensor networks. In Proceedings of the 2002 IEEE Wireless Communications and Networking Conference (WCNC2002), Orlando, FL, USA, 17–21 March 220-224 40. 2002; pp. 794–799. 7. Haibo, Z.; Hong, S. Balancing energy consumption to maximize network lifetime in data gathering sensor networks. ACM Trans. Sens. Netw. 2009, 2, 1–25. 8. Yasaman, K.; Rashid, A.; Ashfaq, K. Energy efficient decentralized detection based on bit-optimal multi-hop transmission in onedimensional wireless sensor networks. In Proceedings of the 2013 ITIP Wireless Days (WD), Valencia, Spain, 13–15 November 2013. Authors: M. Senthil Murugan, T. Sasilatha Paper Title: Implementation of Advanced Encryption Standard Algorithm on Steganography 41. Abstract: In continuous conduction mode, A discrete-time repetitive controller (RC) is proposed for fly

back inverter with fuzzy controller. In this paper fuzzy based repetitive controller is used due to some 225-230 advantages. Such as, it reduces ripples then THD will be reduced, which has simple structure, low cost, and high efficiency. Comparing to the conventional controller the repetitive controller obtain good tracking ability and disturbance rejection and applied to flyback inverter in Continuous Conduction Mode operation. Conventional controller results in poor control performance due to the effect of the right-half-plane zero in CCM operation. To allow tracking and rejection of periodic signals within a specified frequency range the RC scheme, a low-pass filter is used. The stability of the closed loop system is derived and the zero tracking error is achieved with the stability of the closed loop system. By using the simulation results we can analyze the proposed method.

Keywords: Module-Integrated Converter, Right-Half-Plane Zero, Low-Pass Filter, Phase-Lead Compensation, Fuzzy Control.

References: 21. F. F. Edwin, W. Xiao, and V. Khankikar, "Dynamic displaying and control of interleaved flyback module-incorporated converter for PV control applications," IEEE Trans. Ind. Electron., vol. 61, no. 3, pp. 1377-1388, Mar. 2014. 22. S. B. Kjaer, J. K. Pedersen, and F. Blaabjerg, "An audit of single-stage framework associated inverters for photovoltaic modules," IEEE Trans. Ind. Appl., vol. 41, no. 5, pp. 1292-1306, Sep./Oct. 2005. 23. Y. H. Kim, J. W. Jang, S. C. Shin, and C. Y. Won, "Weighted-proficiency upgrade control for photovoltaic AC module interleaved flyback inverter utilizing a synchronous rectifier," IEEE Trans. Power Electron., vol. 29, no. 12, pp. 6481-6493, Dec. 2014. 24. G. Petrone, G. Spagnuolo, and M. Vitelli, "A simple system for conveyed MPPT PV applications," IEEE Trans. Ind. Electron., vol. 59, no. 12, pp. 4713-4722, Dec. 2012 25. Y. Li and R. Oruganti, "A flyback-CCM inverter conspire for photovoltaic AC module application," in Proc. Australasian Univ. Power Eng. Conf. (AUPEC), 2008, pp 1-6. 26. N. Kasa, T. Iida, and L. Chen, "Flyback inverter controlled by sensorless current MPPT for photovoltaic power framework," IEEE Trans. Ind. Electron., vol. 52, no. 4, pp. 1145-1152, Aug. 2005. 27. N. Sukesh, M. Pahlevaninezhad, and P. K. Jain, "Examination and execution of a solitary stage flyback PV microinverter with delicate exchanging," IEEE Trans. Ind. Electron., vol. 61, no. 4, pp. 1819-1833, Apr. 2014. 28. Z. Zhang, X. F. He, and Y. F. Liu, "An ideal control strategy for photovoltaic lattice tide-interleaved flyback microinverters to accomplish high productivity in wide load extend," IEEE Trans. Power Electron., vol. 28, no. 11, pp. 5074-5087, Nov. 2013. 29. H. Hu, S. Harb, N. H. Kutkut, Z. J. Shen, and I. Batarseh, "A singlestage microinverter without utilizing electrolytic capacitors," IEEE Trans. Power Electron., vol. 28, no. 6, pp. 2677-2687, Jun. 2013. 30. R. W. Erickson and D. Maksimovic, "Essentials of Power gadgets," Springer Science and Business Media, 2007. Authors: M. Parameswari T. Sasilatha, K. Mahalakshmi, P. Uma, P. Kokila. Paper Title: Real Time Brain Computer Interface System Abstract: Brain System Interface (BSI) innovation is a part of science characterizing how PCs and the human cerebrum can work together. It is a mind embed framework. The sensor, which is embedded into the mind, screens cerebrum action in the patient and changes over the expectation of the client into PC directions or to the subject's coveted development. It is intended to encourage individuals or patient who has lost control of their appendages or the individuals who have been deadened by extreme spinal-string wounds. It is possessed by Cyber kinetics and is a work in progress and in clinical preliminaries. The PCs interpret mind movement and

make the correspondence yield utilizing custom deciphering programming. Vitally, the whole Brain System Interface (BSI) framework was particularly intended for clinical use in people and subsequently, its produce, get together, and testing is planned to meet human wellbeing prerequisites. In this paper, we discuss on the

development, components, working principle, advantages, drawbacks and solid association between the cerebrum of an extremely crippled individual and a PC.

42. Keywords: Brain System Interface, Sensor, Cyber Kinetics, Cerebrum, PCs. 231-233 References: 1. Manjunatha. V “Brain Gate Technology” IIMCA vol.2, special issue 1, March2014. 2. “Mind Control Wired”, March 2005. 3. “People with paralysis control robotic arms using brain computer interface (BCI)” Brown University. 4. “The annual BCI Research award 2014- The winners”. Authors: S. Balaji, A. Bharat Raj, T. Sasilatha. Paper Title: A Peer to Peer Botnet Framework for Network Threat Detection in Wireless Networks Abstract: In recent era, botnets have turned into the main cause of numerous web attacks in wireless networks. A botnet comprises of a system of bargained nodes controlled by single or various intruders. To be all around arranged for future attacks, it isn't sufficient to examine how to identify and guard against the botnets that has showed up before. All the more essentially, we should examine progressed botnet plans that could be produced by botmasters sooner rather than later. In this paper, we present a framework of a propelled peer to peer distributed botnet. Contrasted and current botnets, the proposed botnet is harder to be closed down, observed, and seized. It gives vigorous system network, individualized encryption and control activity which is scattered , restricted botnet presentation by every bot, and simple checking and recuperation by its botmaster. 43. The simulation results demonstrate the utilization of the transfer speed and the drop of data by the malicious nodes which will be viably high of the various nodes in the system of devices. 234-236

Keywords: To be all around arranged for future attacks References: 1. S. Kandula, D. Katabi, M. Jacob, and A. Berger, “Botz-4-Sale: Surviving Organized DDOS Attacks That Mimic Flash Crowds,” Proc. Second Symp. Networked Systems Design and Implementation (NSDI ’05), May 2005. 2. C.T. News, Expert: Botnets No. 1 Emerging Internet Threat, http:// www.cnn.com/2006/TECH/internet/01/31/furst/, 2006. 3. F. Freiling, T. Holz, and G. Wicherski, “Botnet Tracking: Exploring a Root-Cause Methodology to Prevent Distributed Denial-of- Service Attacks,” Technical Report AIB-2005-07, CS Dept. RWTH Aachen Univ., Apr. 2005. 4. D. Dagon, C. Zou, and W. Lee, “Modeling Botnet Propagation Using Time Zones,” Proc. 13th Ann. Network and Distributed System Security Symp. (NDSS ’06), pp. 235-249, Feb. 2006. 5. A. Ramachandran, N. Feamster, and D. Dagon, “Revealing Botnet Membership Using DNSBL Counter-Intelligence,” Proc. USENIX Second Workshop Steps to Reducing Unwanted Traffic on the Internet (SRUTI ’06), June 2006. [6] E. Cooke, F. Jahanian, and D. McPherson, “The Zombie Roundup: Understanding, Detecting, and Disrupting Botnets,” Proc. USENIX Workshop Steps to Reducing Unwanted Traffic on the Internet (SRUTI ’05), July 2005. 6. N. B. Salem, J.-P. Hubaux, and M. Jakobsson. Reputation based wi-fi deployment. SIGMOBILE Mob. Comput. Commun. Rev., 9(3):69–81, 2005. 7. W. Xu, W. Trappe, Y. Zhang, and T. Wood. The feasibility of launching and detecting jamming attacks in wireless networks. In MobiHoc ’05: Proceedings of the 6th ACM international symposium on Mobile ad hoc networking and computing, pages 46–57, Urbana-Champaign (IL), USA, 2005. 8. Y. Zhang, W. Lee, and Y.-A. Huang. Intrusion detection techniques for mobile wireless networks. Wireless Networks. 9. Li Zhao and José G. Delgado-Frias “MARS: Misbehavior Detection in Ad Hoc Networks”, in proceedings of IEEE Conference on Global Telecommunications Conference, November 2007. 10. A.Patwardhan, J.Parker, M.Iorga, A. Joshi, T.Karygiannis and Y.Yesha “Threshold-based Intrusion Detection in Adhoc Networks and Secure AODV” Elsevier Science Publishers B. V., Ad Hoc Networks Journal (ADHOCNET), June 2008. 11. S.Madhavi and Dr. Tai Hoon Kim “AN INTRUSION DETECTION SYSTEM IN MOBILE ADHOC networks” International Journal of Security and Its Applications Vol. 2, No.3, July, 2008. 12. Afzal, Biswas, Jong-bin Koh, Raza, Gunhee Lee and Dong-kyoo Kim, "RSRP: A Robust Secure Routing Protocol for Mobile Ad Hoc Networks", in proceedings of IEEE Conference on Wireless Communications and Networking, pp.2313-2318, April 2008. 13. Bhalaji, Sivaramkrishnan, Sinchan Banerjee, Sundar, and Shanmugam, "Trust Enhanced Dynamic Source Routing Protocol for Adhoc Networks", in proceedings of World Academy Of Science, Engineering And Technology, Vol. 36, pp.1373-1378, December 2008. 14. Meka, Virendra, and Upadhyaya, "Trust based routing decisions in mobile ad-hoc networks" In Proceedings of the Workshop on Secure Knowledge Management, 2006. 15. Muhammad Mahmudul Islam, Ronald Pose and Carlo Kopp, "A Link Layer Security Protocol for Suburban Ad-Hoc Networks", in proceedings of Australian Telecommunication Networks and Applications Conference, December 2004. Authors: A. Arikesh, Maumita Saha . Design and Operation of Flyback CCM Inverter with Fuzzy based Discrete-Time Repetitive Control Paper Title: for PV Power Applications Abstract: This paper deal with the Fuzzy based simulation of bi-directional converter suitable for renewable energy based energy storage applications. A control algorithm for bidirectional power flow management connected with a grid based or renewable energy based power system with a three phase bi- directional converter and battery charging and discharging with DC – DC converter is proposed with considering AC-DC and DC-AC filter design. The proposed system with fuzzy controller and energy storage is simulated in SIMULINK platform and the outputs are plotted. For the proposed system LC filter is designed for the charging and discharging modes of energy storage and the values are tested in the MATLAB simulation.

44. Keywords: DC, AC-DC, SIMULINK , LC Filter, MATLAB. References: 1. “David Borge-Diez,Ana-Rosa Linares-Mena”,Energy-efficient three phase bi-directional converter for grid connected storage 237-242 applications, (Sept. 21,2016) 2. “RadakBlange, ChitralekhaMahanta and Anup Kumar Gogoi”,Control of DC-DC Buck Boost Converter Output Voltage Using Fuzzy Logic Controller,(Nov.25, 2017) 3. “PraneydeepRastogi, Mangesh Borage and Vineet Kumar Dwivedi”,Estimation of Size of Filter Inductor and Capacitor in 6-Pulse and 12-Pulse Diode Bridge Rectifier,(May 2015 ) 4. “SudeepPyakuryal, Mohammad Matin”,Filter Design for AC to DC Converter,(ISSN (Online) 2319-183X ,Volume 2, Issue 6 (June 2013), PP. 42-49,) 5. “JunhongZhang”, Bidirectional DC-DC Power Converter Design Optimization, Modeling and Control,(Jan. 30, 2008) 6. “Arpita K, Dr. P Usha”,Fuzzy logic controlled Bi-directional DC-DC Converter for Electric Vehicle Applications,(e-ISSN: 2278- 1676,p-ISSN: 2320-3331, Volume 12, Issue 3 Ver. IV (May – June 2017),PP 51-55) 7. “Prasoon ChandranMavila, Nisha B. Kumar”,Integrated Bidirectional DC-DC converter for EV charger with G2V, V2G and V2H capabilities, (Vol. 3, Special Issue 1, February 2016). Authors: D. Santhosh Kumar, R.Sanjutha, S. Subashini, K.H. Sowmmya, V.P. Subhashree. Paper Title: Landing Aerodynamics and Adequate Power Plant Using LPWT for Airport Lighting Scheme Abstract: Renewable energy source are more crucial for our country because renewable energy produce endless power supply. In this project we use input as wind energy. The wind which is produced in the aircraft while takeoff and launching the plane. The aircraft which produce high pressure while takeoff and launching. Using the wind turbine which is placed near the airport which produce power. The turbines start rotating with the help of high pressure air it produced from the aircrafts. The LPWT which can be used on the both sides of the airport where it need some energy to rotate. While dealing with wind energy we are connected with the surface wind. The aircraft which travels nearly around 400 nautical miles per hour. During the aircraft launching or take off which produce high rotates the turbines which produce energy. During this process a proposed controller is used to display the landing and takeoff process in the monitor . The power which produced from the turbines is used for lighting in the airport. 45.

Keywords: GPWS, Piezoelectric Pads, LPWT. 243-246 References: 1. American Wind Energy Association. ”State -level Renewable Energy Portfolio Standards Standards (RPS)”. 2. Touchdown activation system-IJARIIT journal htps:// www.ijariit.com 3. C.Wan,Z.Xu,P.Pinson, Z.Y Dong and K.P.Wong,”Optical prediction intervals of wind power generation, ”IEEE Transactions on Power Systems,vol.29,no.3,pp.1166-1174,May 2014. 4. Sensor senses: Piezoelectric Force Sensors at machines design.com. Retreived at 2012.05.04 5. Touchdown analysis-ISSN:2456-8619 www.jiser.net 6. History of Wind Energy in Cutler J. Cleveland,(ed) Encyclopedia of Energy Vol.6. 7. Sathyajith, Mathew (2006).Wind Energy Fundamentals , Resources Analysis and Economics. Springer Berlin Heidelberlg. 8. R.J. Bessa, V. Miranda, A .Botterud, Z. Zhou, and J.Wang, "Time adaptive quantile-coupla for wind power probablastic forecasting," Renewable Energy, vol. 40, no. 1 pp.29-39,2012. 9. Zavadil, Robert. Nicholas Miller, Abraham Ellis, and Eduard Muljadi. "making Connections." IEEE Power and energy magazine, Vol.3, number 6., Nov./Dec. 2005 Authors: B.tulasiramarao, p. Ramreddy, k. Srinivas, a.raveendra. Paper Title: Effect of Tool Overhang Length on Turning Operation Using Finite Element Model Abstract: Turning surface accuracy and high productivity rates have become the vital determinants and both the accuracy and surface quality plays vital role. In this paper the cutting tool modeled with finet element model and for different tool overhanging lengths analytical modal has prepared. The modal and stiffness data of the tool are extracted from ANSYS software also the mode shapes were drawn. Tool overhang was selected as input and the influences of tool overhang on the stability of turning using finite element was obtained. The stability lobe diagrams corresponding to different tool overhangs different stiffness, tool frequencies and damping ratios were presented.

Keywords: Ansys software, finite element model ,tool over hang length and SLD. References: 1. D.B. Welboum and J.D. Smith, “Machine-tool Dynamics: An introduction”, Cambridge, 1970. 46. 2. J.Cook and H. Nathan, “Self-Excited Vibrations in Metal Cutting,” ASME Transactions, Journal of Engineering for industry, Vol. 81, pp. 183- 186,1979. 3. S.F.Bao, W.G. Zhang, S.Y. Yu, S.M. Qiao, and F.L.Yang, “A New Approach to the Early Prediction of Turning Chatter”, Journal of 247-249 Vibration and Acoustics, Vol. 116, pp. 485- 488, 1994. 4. Y.S.Tarng, H.T.Young and B.Y.Lee, “An analytical model of chatter vibration in metal cutting”, International Journal of Machine Tools and Manufacture, vol.34, pp.183-197, 1994. 5. Iturrospe , V. Atxa, and J.M. Abete, “State-space analysis of mode-coupling in orthogonal metal cutting under wave regeneration”, International Journal of Machine Tools &manufacture, vol. 47, pp.1583–1592, 2007. 6. I.E.Minis, E.B. Magrab, and I.O.Pandelidis, “Improved Methods for the Prediction of Chatter in Turning, Part3: A Generalized Linear Theory”, Trans. ASME Journal of Engineering for Industry, Vol. 112, pp. 28-35, 1990. 7. D.W.Liu and C.R. Liu, “An Analytical Model of Cutting Dynamics. Part 1: Model Building”, Trans. ASME, Journal of Engineering for Industry, Vol. 107, pp. 107- 111, 1995. 8. M.N.Hamdon and A.E.Bayoumi, “Analysis for regenerative machine tool chatter”, Journal of Manufacturing Science and Engineering, vol. 11, pp. 345-349,1997 9. M.N.Hamdon and A.E.Bayoumi, “An approach to study the effects of tool geometry on the primary chatter vibration in orthogonal cutting”, Journal of Sound and Vibration, vol. 128(3) pp. 451-469, 1999. 10. J.R.Pratt and A.H. Nayfeh, “Design and Modeling for Chatter Control”, Nonlinear Dynamics, Vol. 19, pp. 49-69, 1999. Authors: R. Vanitha. Paper Title: Conveyors Monitoring, Control and Protection Using Programmable Logic Controller Abstract: Conveyors have been the most important transport media in transferring the coal from coalmines / storage areas to Boilers in thermal power stations. The monitoring and protection of these conveyors are very important as the occurrence of faults may affect the whole power generation. The protection of the conveyors is carried out using Relay Logic methods, that have several disadvantages and hence there is a need for a new method. This paper focuses on the monitoring, controlling and protecting the conveyors from varies types of faults occurring in conveyors using programmable logic controller (PLC). Four important types of faults that occurs frequently in conveyors, such as belt sway fault, pull chord fault, zero speed fault and fire protection are considered in this work. These faults are sensed and rectified by programmable logic controller which has a high degree of safety, accuracy and easy to maintain and monitor.

Keywords: Programmable Logic Controller, Conveyors, Interlock Mode and De-Interlock Mode. References: 1. Maria G. Ioannides “Design and Implementation of PLC-Based Monitoring Control System for Induction Motor” IEEE Transactions on Energy Conversion, Vol. 19, No. 3, pp.469 -476, Sep 2004. 2. Ahmad Fouad Alwan, “Project Design and Management of Programmable logic Controllers for Electrical Technology”, International Journal of Emerging Sciences, Vol. 2, No. 3, pp. 322-333, September 2012 3. Mehmet Fatih Isik, Mustaf Resit Haboglu, Hilmi Yanmaz, “Monitoring and control of PLC based motion control systems via device- net “, IEEE procedings of 16th International conference on Power Electronics and Motion Control Conference and Exposition (PEMC), 2014. 4. Joanna Marie M. Baroro, Melchizedek Alipio, Michael LawrenceT. Huang, Teodoro M. Ricamara, AngeloA. Beltran Jr., “Automation of Packaging and Material Handling Using PLC”, International Journal of Scientific Engineering and Technology, Vol. 3, No. 6, pp: 767 –770, June 2014. 5. M.Kanmani, J.Nivedha, G.Sundar, “Belt Conveyor Monitoring and Fault Detecting Using PLC and SCADA”, International Journal of 47. Advanced Research in Electrical, Electronics and Instrumentation Engineering, Vol. 3, Special Issue 4, pp.243-248, May 2014, 250-254 6. R.Keerthika, M.Jagadeeswari,” Coal Conveyor Belt Fault Detection and Control in Thermal power plant using PLC and SCADA”, International Journal of Advanced Research in Computer Engineering & Technology, Vol. 4, No.4, pp.1649 -1652, April 2015. 7. Rajnikanth, “Control of Conveyor using PLC”, International Journal of Innovative Research in Technology”, Vol.3, No.1, pp.356 – 359, June 2016. 8. Amandeep Kaur , Dipti Bansal ,”Monitoring and controlling of continue furnace line using PLC and SCADA”, in IEEE proc of 5th International Conference on Wireless Networks and Embedded Systems (WECON), 2016 9. Frank.D.Petruzella, Programmable Logic Controllers, reprinted by 2005, ISBN- 10: 0073510882 | ISBN-13: 9780073510880 10. Thomas.A.Hughes, Book on Programmable Logic Controllers,4th edition, Product ISBN/ID:978-1-55617-899-3 Authors: Srinivasa Babu Kasturi, V. Divya Vani Linking Social Media to E-Commerce: Cold-Start Synthetic Items Inspiration through Micro Blogging Paper Title: Data Abstract: In latest years, the bounds among e-trade and social networking have emerged as an increasing 48. number of blurred. Many e-commerce websites aid the mechanism of social login in which users can sign up the websites the usage of their social network identities which includes their Face book or Twitter money owed. Users can also post their newly bought merchandise on micro blogs with hyperlinks to the e-trade product web 255-257 pages. In this paper, we recommend a story resolution for move-web site bloodless-start item for consumption reference which ambitions to propose products from e-commerce websites to users at social network web sites in “bloodless-begin” situations, a hassle which has hardly ever been explored earlier than. A principal project is a way to leverage understanding extracted from social networking sites for the move-site cold-begin product advice. We suggest using the related customers throughout social networking web sites and e-commerce web sites (customers who've social networking debts and feature made purchases on e-commerce web sites) as a bridge to map users’ social networking capabilities to another feature representation for a product advice. In specific, we propose mastering each users’ and merchandise’ feature representations (called consumer embeddings and product embeddings, respectively) from records accrued from e-commerce web sites the usage of recurrent neural networks and then follow a changed gradient boosting timber method to convert customers’ social networking features into consumer embeddings. We after that develop a feature-based environment factorization approach which could force the found out person embeddings for the cold-begin item for consumption recommendation. Investigational outcomes on a massive dataset made from the prime Chinese micro blogging provider SINA WEIBO and the largest Chinese B2C e-commerce internet site JINGDONG have proven the usefulness of our future structure.

Keywords: SINA WEIBO, JINGDONG , Face book , Twitter money owed., We, B2C. References: 1. J. Wang and Y. Zhang, “Opportunity model for e-commerce recommendation: Right product; right time,” in SIGIR, 2013. 2. M. Giering, “Retail sales prediction and item recommendations using customer demographics at store level,” SIGKDD Explor. Newsl., vol. 10, no. 2, Dec. 2008. 3. G. Linden, B. Smith, and J. York, “Amazon.com recommendations: Item-to-item collaborative filtering,” IEEE Internet Computing, vol. 7, no. 1, Jan. 2003. 4. V. A. Zeithaml, “The new demographics and market fragmentation,” Journal of Marketing, vol. 49, pp. 64–75, 1985. 5. W. X. Zhao, Y. Guo, Y. He, H. Jiang, Y. Wu, and X. Li, “We know what you want to buy: a demographic-based system for product recommendation on microblogs,” in SIGKDD, 2014 6. J. Wang, W. X. Zhao, Y. He, and X. Li, “Leveraging product adopter information from online reviews for product recommendation,” in ICWSM, 2015. 7. Y. Seroussi, F. Bohnert, and I. Zukerman, “Personalised rating prediction for new users using latent factor models,” in ACM HH, 2011. 8. T. Mikolov, I. Sutskever, K. Chen, G. S. Corrado, and J. Dean, “Distributed representations of words and phrases and their compositionality,” in NIPS, 2013. 9. Q. V. Le and T. Mikolov, “Distributed representations of sentences and documents,” CoRR, vol. abs/1405.4053, 2014. 10. J. Lin, K. Sugiyama, M. Kan, and T. Chua, “Addressing coldstart in app recommendation: latent user models constructed from twitter followers,” in SIGIR, 2013. 11. T. Mikolov, K. Chen, G. Corrado, and J. Dean, “Efficient estimation of word representations in vector space,” CoRR, vol. abs/1301.3781, 2013. 12. Y. Koren, R. Bell, and C. Volinsky, “Matrix factorization techniques for recommender systems,” Computer, vol. 42, no. 8, pp. 30–37, Aug. 2009. 13. J. H. Friedman, “Greedy function approximation: A gradient boosting machine,” Annals of Statistics, vol. 29, pp. 1189–1232, 2000. 14. L. Breiman, J. Friedman, R. Olshen, and C. Stone, Classification and Regression Trees. Monterey, CA: Wadsworth and Brooks, 1984. 15. L. Breiman, “Random forests,” Mach. Learn., vol. 45, no. 1, Oct. 2001. Authors: U.M Prakash, Pratyush, Pranshu Dixit, Anamay Kumar Ojha. Paper Title: Emotional Analysis Using Image Processing Abstract: In machine learning, a convolutional neural network (CNN or ConvNet) is a part of deep and feed-forward artificial neural networks that has successfully visualized images.CNNs use a variation of multilayer perceptron designed to require minimal pre-processing. CNNs use relatively little pre-processing compared to other image classification algorithms. This means that the network learns the filtering that was hand-engineered in other algorithms. This independence of human efforts for feature design is a major advantage due to which we are using it in our paper. In the context of machine vision, image recognition is the capability of software to identify people, places, objects, actions and writing in images. When we are using our algorithm train the model from our data set of around 600 images, we are getting an accuracy of 85.23%. We can also use other methods for modelling in for this problem set. 49.

Keywords: Filter, kernel size, convolving, activation map, feature map, stride, max pool, activation 258-262 function, reception field, epoch cycles. References: 1. https://adeshpande3.github.io/A-Beginner%27s-Guide-To-Understanding-Convolutional-Neural-Networks/ 2. https://s-media-cache-ak0.pinimg.com/originals/df/47/a7/df47a74b5e1514dcc03a700bd1634a9d.jpg 3. https://c.wallhere.com/photos/91/73/home_alone_macaulay_culkin_kevin_mccallister_boy_fear_shout_fright-795921.jpg!d 4. https://www.askideas.com/media/13/Crying-Baby-Funny-Face.jpg 5. http://img1.showmoreimages.com/pic/21lZGlhLmdldHR5aW1hZ2VzLmNvbS92aWRlb3MvZmFjZS1vZi1hLWhhcHB5LWJveS12a WRlby1pZDEwNDMzODg4Nz9zPTY0MHg2NDAlog 6. https://i.pinimg.com/736x/a1/7b/2d/a17b2dbcaf08929c6c62920db3d44b8e--anger-management-management-tips.jpg 7. http://www.apa.org/science/about/psa/2011/05/facial-expressions.aspx 8. For the video tutorialhttps://youtu.be/27FPv1VHSsQ000000 Authors: E. Bijolin Edwin, M. RoshniThanka, Shiny Deula. Paper Title: An Internet of Drone (IOD) Based Data Analytics in Cloud for Emergency Services Abstract: In the fast-emerging world internet has become the part of life in people’s life, since everyone and everything are connected to internet. The recent technology behind Internet of Drone (IOD) the safe 50. operations on commercial and public use presents communication and computational challenges in the real- world aspects. The usage of multidrone in which the tasks allocated for each drone and the values from the 263-267 datacentre transferred to the cloud gives the data balancing techniques from an unreachable area. The cameras used in today’s drone can process images quickly in each frame by frame. The drone data analytics methods with high efficiency in the areas of progress monitoring, inspections, and surveying to analyse the data for making key decisions are being identified. The key services includes Pre-visualizations for few concepts, Analysis of Unmanned Aerial Vehicles (UAV) based engineering, grade aerial images,3D point cloud analysis, improved and efficient co-ordination and communication, issue faster resolution. The data viewed and observed through drones are yet to be captured and given to the cloud data centre. 3DPath Planning Algorithm using visibility graph proposed to know the shortest path and the amount of data collected are being measured under visibility graph method. SPF learning algorithm which makes the data to move quickly under each scenario. Drone data processing just expanding into the cloud with bandwidth management for data processing and based on the image detection effective decision will be taken to safeguard the human life and property. This focus on infinitely scalable computational power with web data analytics algorithm having end to end process automation. Performance of the model is evaluated with the given types of resources and the number of nodes utilized.

Keywords: Pre-Visualizations, UAV, Cloud Data Centre, Cloud Computing.

References: 1. Adrian Carrio, Carlos Sampedro, Alejandro Rodriguez-Ramos, and PascualCampoy, A Review of Deep Learning Methods and Applications for Unmanned Aerial Vehicles, Computer Vision and Aerial Robotics Group, April 2017. 2. BasitQureshiAnisKoubâa Mohamed-FouedSritiYasirJavedMaramAlajlan, Dronemap - A Cloud-based Architecture for the Internet-of- Drones, International Conference on Embedded Wireless Systems and Networks (EWSN) 2016, 15–17 February, Graz, Austria. 3. Lan Zhang, Bing Wang, WeilongPeng, Chao Li, Zeping Lu and Yan Guo, Forest Fire Detection Solution Based on UAV Ariel Data, International Journal of Smart Home, vol. 9, no. 8 (2015), pp.239-250. 4. EniDwiWardihani, MagfurRamdhani, Amin Suharjono, Thomas AgungSetyawan, SidiqSyamsulHidayat, Helmy, SaronoWidodo, Eddy Triyono, FirdanisSaifullah, Real-Time Forest Fire Monitoring System Using Unmanned Aerial Vehicle, Journal Of Engineering Science And Technology, vol. 13, no. 6 (2018), pp. 1587 – 1594. 5. J. Li and Y. Li, “Dynamic analysis and pid control for a quadrotor,” in Mechatronics and Automation (ICMA), 2011 International Conference on IEEE, 2011, pp. 573–578. 6. K. U. Lee, H. S. Kim, J. B. Park, and Y. H. Choi, “Hovering control of a quadrotor,” in Control, Automation and Systems (ICCAS), 2012 12th International Conference on. IEEE, 2012, pp. 162–167. 7. E.Bijolin Edwin, Dr.P.UmaMaheswari, M.RoshniThanka, Fragmentation and Dynamic Replication Model in Multicloud by Data Hosting with Secured Data Sharing, Asian Journal of Research in Social Sciences and Humanities, Feb 2017, vol. 7, pp. 459-474. 8. E.Bijolin Edwin, Dr.P.UmaMaheswari, M.RoshniThanka, A Survey on Security Assurance Architecture in Virtualization implementation on Cloud, International Journal of Science, Engineering and Technology Research, Nov 2012, vol. 1, issue 5, pp. 154- 159. 9. M.RoshniThanka, Dr.P.UmaMaheswari, E.Bijolin Edwin, An improved efficient: Artificial Bee Colony algorithm for security and QoS aware scheduling in cloud computing environment Cluster Computing, Springer September 2017, vol. 23. 10. [10] R.Srimathi, E.Bijolin Edwin, Data Hosting in Multi Cloud using Fragmentation and Dynamic Replication, International Journal of Engineering Science and Technology (IJEST), vol. 8, March 2016, pp. 46-51. 11. M.RoshniThanka, Dr.P.UmaMaheswari,E.Bijolin Edwin, An Optimized Multi-Objective Job Scheduling in Cloud Environment, Asian Journal of Research in Social Sciences and Humanities, vol. 6, August 2016, pp. 818-828. 12. P.BrightPrabakar, E.Bijolin Edwin, Energy Efficient Virtual Machine Monitoring Architecture for Green Cloud Computing, International Journal of Computer Applications, vol. 65, March 2013, pp.15-18. 13. Gowsic, K, Shanthi, N &Preetha, B 2017, ‘Firefly Resources Optimization Technique for Data Delivery in Wireless Multimedia Sensor Networks’, Asian Journal of Research in Social Sciences and Humanities, vol. 7, no. 1, pp. 1011-1029 Online ISSN : 2249- 7315. Article DOI : 10.5958/2249-7315.2017.00039.9. 14. Gowsic, K, Shanthi, N &Preetha, B 2016, ‘Resource Optimized Spectral Route Selection Protocol For WMSN Surveillance Application’ Asian Journal of Information Technology, vol. 15, no. 19, pp. 3734-3741 online ISSN: 1682-3915. 15. E. Bijolin Edwin, P. Umamaheswari& M. RoshniThanka, ”An efficient and improved multi-objective optimized replication management with dynamic and cost aware strategies in cloud computing data center”, Cluster Computing, Springer, ISSN 1386-7857, DOI 10.1007/s10586-017-1313-6, 21 November 2017. 16. Guang Yang, XingqinLin,Yan Li, Hang Cui, Min Xu, Dan Wu, HenrikRydén,Sakib Bin Redhwan, A Telecom Perspective on the Internet of Drones, 2018. 17. Lin Mengc, Takuma Hirayamab, Shigeru Oyanagi, The Development of Underwater-Drone equipped with 360-degreePanorama Camera in Opensource Hardware,2017 International Conference on Identification, Information and Knowledge in the Internet of Things, Science Direct, Procedia Computer Science, vol. 129 (2018), pp.438–442. Authors: Abhishek Thakur, Rajeev Ranjan. Paper Title: Image Segmentation and Semantic Labeling using Machine Learning Abstract: In this paper image color segmentation is performed using machine learning and semantic labeling is performed using deep learning. This process is divided into two algorithms. In the first algorithm machine learning is used to detect super pixels. These super pixels are segmented on the basis of colors. In the second algorithm deep learning is used to train color categories. This algorithm classify each object into semantic labels. Experiment is performed on BSDS300, CASIA v1.0, CASIA v2.0, DVMM and SegNetVGG16CamVid.

Keywords: Feature Extraction; Machine Learning; Deep Learning; Convolution Neural Network; Image 51. Forensic. References: 268-272 1. Badrinarayanan, Vijay, Alex Kendall, and Roberto Cipolla. "Segnet: A deep convolutional encoder-decoder architecture for image segmentation." arXiv preprint arXiv:1511.00561 (2015). 2. Brostow, Gabriel J., Julien Fauqueur, and Roberto Cipolla. "Semantic object classes in video: A high-definition ground truth database." Pattern Recognition Letters 30.2 (2009): 88-97. 3. Li, Zhenguo, Xiao-Ming Wu, and Shih-Fu Chang. "Segmentation using superpixels: A bipartite graph partitioning approach." Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on. IEEE, 2012. 4. Krizhevsky, Alex, Ilya Sutskever, and Geoffrey E. Hinton. "Imagenet classification with deep convolutional neural networks." Advances in neural information processing systems. 2012. 5. Simonyan, Karen, and Andrew Zisserman. "Very deep convolutional networks for large-scale image recognition." arXiv preprint arXiv:1409.1556 (2014). 6. Szegedy, Christian, et al. "Going deeper with convolutions." Proceedings of the IEEE conference on computer vision and pattern recognition. 2015. 7. Visin, Francesco, et al. "Renet: A recurrent neural network based alternative to convolutional networks." arXiv preprint arXiv:1505.00393 (2015). 8. Yosinski, Jason, et al. "How transferable are features in deep neural networks?." Advances in neural information processing systems. 2014. 9. Garcia-Garcia, Alberto, et al. "A survey on deep learning techniques for image and video semantic segmentation." Applied Soft Computing 70 (2018): 41-65. 10. Guo, Yanming, et al. "A review of semantic segmentation using deep neural networks." International Journal of Multimedia Information Retrieval 7.2 (2018): 87-93. 11. Thakur, Abhishek, and Neeru Jindal. "Image forensics using color illumination, block and key point based approach." Multimedia Tools and Applications (2018): 1-21. 12. Kim, Tae Hoon, Kyoung Mu Lee, and Sang Uk Lee. "Learning full pairwise affinities for spectral segmentation." IEEE transactions on pattern analysis and machine intelligence 35.7 (2013): 1690-1703. 13. Cour, Timothee, Florence Benezit, and Jianbo Shi. "Spectral segmentation with multiscale graph decomposition." Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on. Vol. 2. IEEE, 2005. Authors: T. Harikala, RVS SatyaNarayana. Power Efficient Technique for MIMO radar using Co-operative and Non co-operative game theory in Paper Title: Wireless Applications Abstract: Multiple Input Multiple Output (MIMO) is receiving the massive attention in the field of communication. MIMOradars aresimultaneously transmitted multiplelinearly independent wave form and receive their reflected signals. In MIMO communications, Radar offers as a paradigm for signal processing research.The power allocation is the major concern in the MIMO radar network. In this paper, the game theory of Nash equilibrium and Pareto optimality is introduced for non-cooperative and cooperative network of distributive clusters respectively. Based on the distance analysis, the MIMO radar network is decided either it is a non-cooperative or cooperative. These power allocation strategies are introduced to allot the specified power for each antennas within the cluster. The clustering of the network is occurred by using K-means clustering. The proposed method is named as Cooperative and Non Cooperative Game Theory based Power Allocation (CNCG- PA) in MIMO radars. The performance of the CNCG-PA is analysed by the power consumption and it is compared with GNG PA method. Assume both the GNG PA method and CNCG-PA methods have 3 clusters and 3 users in each clusters. The power consumption of cluster 2 of the GNG PA of 0.1191 is more when compared to the CNCG-PA of 0.1167.

Keywords: MIMO radar, power allocation, Nash equilibrium and Pareto optimality game theory, Power consumption.

References: 1. Song, Xiufeng, Shengli Zhou, and Peter Willett. "Reducing the waveform cross correlation of MIMO radar with space–time coding." IEEE Transactions on Signal Processing 58.8 (2010): 4213-4224. 2. Song, X., Willett, P., Zhou, S. and Luh, P.B., 2012. The MIMO radar and jammer games.IEEE Transactions on Signal Processing, 60(2), pp.687-699. 3. Cui, G., Kong, L. and Yang, X., 2012.GLRT-based detection algorithm for polarimetric MIMO radar against SIRV clutter.Circuits, Systems, and Signal Processing, 31(3), pp.1033-1048.

4. Tang, Bo, Jun Tang, and YingningPeng."MIMO radar waveform design in colored noise based on information theory." IEEE Transactions on Signal Processing 58.9 (2010): 4684-4697. 52. 5. Jie, Lu. "Space-Time Signal Processing for MIMO Radar Target Detection."Advanced Research on Computer Science and Information 273-278 Engineering.Springer, Berlin, Heidelberg, 2011.172-176. 6. Godrich, Hana, Alexander M. Haimovich, and Rick S. Blum. "Target localization accuracy gain in MIMO radar-based systems." IEEE Transactions on Information Theory 56.6 (2010): 2783-2803. 7. Chen, Haowen, Shiying Ta, and Bin Sun. "Cooperative game approach to power allocation for target tracking in distributed MIMO radar sensor networks." IEEE Sensors Journal 15.10 (2015): 5423-5432. 8. Godrich, Hana, Athina P. Petropulu, and H. Vincent Poor. "Power allocation strategies for target localization in distributed multiple- radar architectures." IEEE Transactions on Signal Processing 59, no. 7 (2011): 3226-3240. 9. Ma, B., Chen, H., Sun, B. and Xiao, H., 2014.A joint scheme of antenna selection and power allocation for localization in MIMO radar sensor networks.IEEE Communications Letters, 18(12), pp.2225-2228. 10. Yan, J., Liu, H., Pu, W., Zhou, S., Liu, Z. and Bao, Z., 2016. Joint beam selection and power allocation for multiple target tracking in netted colocated MIMO radar system. IEEE Transactions on Signal Processing, 64(24), pp.6417-6427. Authors: Amrutha Patil, Shashikumar G. Totad. Paper Title: Non-invasive Soya Bean Seed Analysis Using Machine Learning Abstract: The soya bean is economically the most important legume in the world. Therefore, it is important to grow good quality seeds for a better yield. Identifying the right set of seeds is a difficult task when done manually since, there are no definite external characteristics of soya bean that correlate with its germination potential. Therefore, in this work an attempt is made at correlating the physical properties of soya bean with its germination potential using the concepts of machine learning and image processing. The input here being images, there are different methods to take images of soya bean, that is by using digital camera or radiography. 53. The pros and cons of these methods are discussed. Since, using radiography images is not cost-efficient and its

local availability for research purpose is scarce, a digital camera is used to take soya bean images. Once the 279-282 image dataset is available, different classification methods are employed to classify the images into ‘germinating’ and ‘non-germinating’ seeds. The classifiers used are CNN, KNN and SVM and the average accuracy of the classifiers is 66.17%. The performance of different classifiers is analyzed to find the most suitable classifier. It is observed that most of the ‘germinating’ seeds have intact seed coat, elongated spherical shape, smooth texture and are evenly colored. Whereas, the other half has damaged seed coat, flat shape or not completely spherical, are unevenly textured and discolored at parts. Finally, the suggestions are made to improvise the results.

Keywords: CNN, germination potential, KNN, machine learning, non-invasive, radiography, seed analysis, soya bean, SVM.

References: 1. T.Y. Tunde-Akintunde, J.O. Olajide, B.O. Akintunde. (2016). Mass-Volume-Area Related and MechanicalProperties of Soybean as a Function of Moisture and Variety 2. Engr. Onu John Chigbo. (2008). Selected Physical Properties of Soybean In Relation To Storage Design 3. H. Kibar, T. Öztürk. (2005). Physical and mechanical properties of soybean 4. Sachin Vilas Wandkar, Pravin Dhangopal Ukey, Dilip Ananda Pawar. (2012). Determination of physical properties of soybean at different moisture levels 5. Ilse Krannera, Gerald Kastbergerb, Manfred Hartbauerb, and Hugh W. Pritcharda. (2010). Noninvasive diagnosis of seed viability using infrared thermography 6. Dr. Henry Bruggink. (2012). X-ray based seed analysis and sorting 7. Bruggink, Henry & Van Duijn, Bert. (2017). X-ray based seed analysis. Seed Testing International. 45-50. 8. G. Gomes jr, Francisco & Van Duijn, Bert. (2017). Three-dimensional (3-D) X-ray imaging for seed analysis. Seed Testing International. 154. 48-52. 9. Lester W. Young, Christopher Parham, Zhong Zhong, Dean Chapman, Martin J. T. Reaney; Non-destructive diffraction enhanced imaging of seeds, Journal of Experimental Botany, Volume 58, Issue 10, 1 July 2007, Pages 2513–2523 Authors: Nupura Torvekar, Pravin S. Game Paper Title: Predictive Analysis of Credit Score for Credit Card Defaulters Abstract: Risk management has always been an important aspect of the financial institutions. Apart from the consumer frauds that cause huge losses, one more source of credit risk is nothing but the loan defaulters. Appropriate loan granting decisions therefore play an important role in avoiding these losses. Credit score and credit scoring which depends upon the credit history of a customer is one among the many factors that contribute to the loan granting decisions. Prediction of the loan defaulters in advance can help the financial institutions in undertaking some preventive measures to avoid granting loans to customers with potential risk and thereby reducing the amount of bad loans. Various machine learning techniques can play an important role in the identification of loan defaulters. The proposed work aims to identify and distinguish the good customers from bad customers by using different machine learning techniques. Two different tools Waikato Environment for Knowledge Analysis (WEKA) and KNIME (Konstanz Information Miner) are used for analyzing the performance of the classifiers. The main focus of this work is the prediction of credit card defaulters and hence two data sets relating to the credit card data of customers have been used for the purpose of this study. The results obtained from the proposed work can help the financial institutions in the identification and control of credit risk.

Keywords: Classification; machine learning techniques; risk management.

References: 1. Zhou H, Lan Y, Soh Y, Huang G and Zhang R (2012), "Credit risk evaluation with extreme learning machine", IEEE International Conference on Systems, Man, and Cybernetic(SMC), Seoul, 2012, pp. 1064-1069. 2. Butaru F,Chen Q,Clark B, Das S, Loc AW, Siddique A, (2016), "Risk and risk management in the credit card industry", Journal of 54. Banking Finance, Vol.72, pp.218-239. 3. Koklu M, Sabanci K (2016), "Estimation of Credit Card Customers Payment Status by Using kNN and MLP", International Journal of 283-286 Intelligent Systems and Applications in Engineering,Vol.4 (Special Issue) , pp.249-251 . 4. Venkatesh A, Gracia A, Shomona (2016), "Prediction of Credit-Card Defaulters: A Comparative Study on Performance of Classifiers", International Journal of Computer Applications,145, pp.36-41. 5. Yeh, Ivy Lien, Che-Hui, "The comparisons of data mining techniques for the predictive accuracy of probability of default of credit card clients", Expert Systems with Applications. 36,pp. 2473-2480, 10.1016/j.eswa.2007.12.02 6. Paulius D, Gintautas G (2012), "Credit risk evaluation modeling using evolutionary linear SVM classifiers and sliding window approach", Proceedings of the International conference on computational science ,ICCS 2012 Book Series: Procedia Computer Science 7. Mulhim Al, Beyrouti B (2014), “Credit Scoring Model Based on Back Propagation Neural Network Using Various Activation and Error Function”, International Journal of Computer Science and Network Security, Vol.14 No.3, pp. 16-24. 8. Paulius D, Gintautas G, Gudas (2011), "Credit Risk Evaluation Model Development Using Sup- port Vector Based Classifiers", Proceedings of the International conference on Computational Science ICCS 2011, Vol.4, pp.1699-1707. 9. Yao J, Lian C (2016), "A New Ensemble Model based Support Vector Machine for Credit Assessing", International Journal of Grid and Distributed Computing, Vol. 9, No.6, pp.159-168. 10. Hamid AJ, Ahmed TM (2016), "Developing prediction model of loan risk in banks using data mining”, Machine Learning and Applications: An International Journal (MLAIJ), Vol.3, No 11. Brown I, Mues C (2012), "An experimental comparison of classification algorithms for imbalanced credit scoring data sets", Expert Systems with Applications, Vol. 39,Issue 3,pp. 3446-3453. 12. Tripathi D, Edla DR, Kuppili V, Bablani A (2018), "Credit Scoring Model based on Weighted Voting and Cluster based Feature Selection", Procedia Computer Science, Vol.132, pp.22-31. 13. Lessmann S, Baesens B, Seow V,Thomas L(2015), 14. Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research", European Journal of Operational Research, Vol. 247, Issue 1, pp. 124-136. 15. Weka; Machine Learning Group, Waikato University, New Zealand; http://www.cs.waikato.ac.nz/ml/weka/ 16. KNIME; KNIME.com AG, Germany; http://www.kni me.org/ 17. J. K. Han, M. Pei, Jian, “Data Mining: Concepts and Techniques”, Elsevier Publishers, Third Edition. 18. UCI – Datasets Repository, Machine Learning Center from California University; http://archive.ics.uci.edu/ml/ Authors: Nagaraja SR, Nalini N, Mohan BA, Sarojadevi H. Paper Title: Dynamic Signalling System for Vehicular Traffic Control Using Density Based Approach Abstract: Objectives: Designing a Dynamic Signaling System algorithm based on vehicular density to 55. control vehicular traffic and En-hancement of effective designing guidelines for congestion control mechanism. Methods/Statistical analysis: Design congestion control technique for VANETs (Vehicular Ad-Hoc Networks) 287-290 consists of three important steps as follows i. Congestion Detection, ii. Congestion Notification. iii. Rate Adjustment. Implementation of Dynamic Signaling System Algorithm based on density of vehicles by using IR sensors and ARM processor. Findings: Existing signaling system is based on static time slot allotted to traffic lights. The traffic lights cannot be changed as per changing traffic density. Dynamic Signaling System [DSS] will solve this problem by continuously sensing the density of the vehicles and adjusting the timing of traffic lights. Statistical analysis results shows that dynamic signaling system is better compare to existing signaling system. Application/Improvements: Dynamic signaling system helps to avoid the vehicular traffic and to minimize the traveling time, waiting time of traveler. This will help to enhance future research work in VANETs.

Keywords: VANETs, Signaling System, Short Range Communication, Wireless Access. References: 1. Nagaraja SR, Nalini N. Performance analysis of proactive congestion control techniques for VANETs. IEEE – Wispnet. 2016, pp. 352-356. 2. Nagaraja SR, Nalini N, Rama Krishna K, Satish EG. Alternative Path Selection Through Density Based Approach To Controlling The Vehicular Traffic In VANETS. International Journal of Advanced Research in Computer and Communication Engineering. 2015, 4 (10), pp. 1-5. 3. Nagaraja SR, Nalini, AshwiniG. Alternate Path Selection Algorithm By Virtue Of Proactive Congestion Control Technique for VANETS. International Journal of Computer Science Trends and Technology (IJCST). 2015, 3 (2), pp. 1-5. 4. Jabbarpour R, Noor RM, Ghahremani S. Dynamic Congestion Control Algorithm for Vehicular Ad-hoc Networks. International Journal of Software Engineering and Its Applications. 2013, 7 (3), pp. 95-108. 5. Piran MJ, Murthy GR, Babu GP. Vehicular Ad Hoc And Sensor Networks Principles And Challenges. International Journal of Ad hoc Sensor & Ubiquitous Computing (IJASUC). 2011, 2 (2), pp. 1-12. 6. Darus MY, Bakar KA. Congestion Control Algorithm in Vanets. World Applied Sciences Journal. 2013, 21 (7), pp. 1057-1061. 7. Konur S, Fisher M. Formal Analysis of a VANET Congestion Control Protocol through Probabilistic Verification. In Proc. 73rd IEEE Vehicular Technology Conference (VTC2011-Spring)Budapest, Hungary. 2011, pp. 1-11. 8. Sepulcre M, Gozalvez J, Harri J, Hartenstein H. Application-Based Congestion Control Policy for the Communication Channel in VANETs. IEEE COMMUNICATIONS LETTERS. 2010, 14 (10), pp. 1-3. 9. Darus MYB, Bakar KA. Congestion Control Framework for Disseminating Safety Messages in Vehicular Ad-Hoc Networks (VANETs). International Journal of Digital Content Technology and its Applications. 2011, 5 (2), pp. 173-180. 10. Nagaraja SR, Nalini N, Ashwini G. Congestion Control in VANETs using ReRouting Algorithm. IEEE-Wispnet. 2016, pp. 297- 300. Authors: Anuradha D. Thakare. Paper Title Data Clustering for Optimized Information Search with Hybrid Evolutionary Approaches Abstract: Clustering is an important data analysis technique which reveals the relationships among unexplored data objects. Cluster initialization and selection of seeds in first iteration contributes to the quality of clustering. The prime objective is to find best cluster with some quality measure. K-means is prone to local optima since initial centroids are selected randomly. In order to evaluate this problem, some heuristic clustering algorithms are introduced along with evolutionary approaches like Genetic Algorithms and Swarm Intelligence. Genetic Algorithms are the heuristic search techniques and are found to be robust to envisage the optimal or near optimal combination of weights in a multidimensional space. This article presents comparative analysis of various hybrid evolutionary approaches developed for clustering to find the optimal cluster center. The objective is to improve the quality of clusters. From the analytical and experimental results, it is observed that the proposed hybrid evolutionary algorithms perform satisfactorily over the existing approaches. As compared to hybrid PSOBA, Multi Stage Genetic Clustering results into reduced error rate by 30 to 50 percent for thyroid and iris dataset respectively. The clustering results vary with respect to dataset and the internal spread.

Keywords: evolutionary algorithms; Genetic Algorithms(GA); Particle Swarm Optimization(PSO); Bee Algorithm(BA); K- means(KM). References: 1. A. D. Thakare, C.A. Dhote, An Improved k-means Algorithm with simultaneous optimization of clustering objectives, International Conference on Emerging Research in Computing, Information, Communication and Applications’ - ERCICA-2014. Publication in 56. Elsevier and Elsevier digital library, 2014. 2. A. D. Thakare, C.A. Dhote, Novel Multi Stage Genetic Clustering method for multi-objective optimization in Data Clustering, ICCUBEA 2015, Scopus Indexed, IEEE Xplore 291-294 3. A. D. Thakare, C.A. Dhote, A Two-Stage Genetic K-harmonic means method for data clustering, Third International Symposium on Intelligent Informatics (ISI' 2014), Advances in Intelligent and Soft Computing (Springer) Series. Volume Title: Advances in Intelligent Informatics. 4. A. D. Thakare, S. M. Chaudhari, Introducing a Hybrid Swarm Intelligence Based Technique for Document Clustering, International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622, Vol. 2, Issue 6, November- December 2012, pp.1455- 1459 1455 5. C.A. Dhote, A. D. Thakare, S. M. Chaudhari, Data Clustering Using Particle Swarm Optimization and Bee Algorithm, Internaional Conference on Computing Communication and Networking Technologies,2013, IEEE, DOI: 10.119/ICCCNT.2013.6726828 , Page(s): 1-5 6. A.D. Thakare, Dr. C.A. Dhote, S. M. Chaudhari, Intelligent Hybrid Approach for Data Clustering, Advances in Recent Technology in Computing- 2013, IEEE 7. Yanping Lu, Shengrui Wang, Shaozi Li, change, Particle Swarm optimizer for variable weighing in clustering high-Dimensional data, Zhou January 2011, Machine Learning. 8. ftp://ftp.ics.uci.edu/pub/machine-learning-databases/ 9. S. Bandyopadhyay and U. Maulik, ``Nonparametric genetic clustering: Comparison validity indices'', IEEE Transactions on Systems, Man and Cybernetics, Part C, vol. 31, no. 1, pp. 120-125, 2001 10. S. Bandyopadhyay and U. Maulik, ``Genetic Clustering for Automatic Evolution of Clusters and Application to Image Classification'', Pattern Recognition, vol.35, pp. 1197-1208, 2002 11. S. Bandyopadhyay and S. K. Pal, ``Classification and Learning Using Genetic Algorithms: Applications in Bioinformatics and Web Intelligence", Springer, Heidelberg, 2007 12. S. Bandyopadhyay, C. A. Murthy and S. K. Pal, ``Pattern Classification Using Genetic Algorithms'', Pattern Recognition Letters, vol. 16, pp. 801-808, August 1995 13. Xiaoyan CAI, Wenjie Li “ A spectral analysis approach to document summarization: Clustering and ranking sentences simultaneously”, Information Sciences 181 (2011) 3816–3827, ElsevierMenendez H.D.; Barrero D.F.; Camacho, D., A Multi-Objective Genetic Graph- Based Clustering algorithm with memory optimization, IEEE Congress on Evolutionary Computation (CEC), 2013. 14. Clustering data set to categorical feature using a multi-objective genetic algorithm, Dutta, D.; University Institute of Technology., Golapbug, India; Dutta, P.; Sil, J., International Conference on Data Science& Engineering (ICDSE), 2012 Authors: Sujatha Rajkumar, Arun.M, Jagriti Hirwani, Sirohi Sajal Sanjeev. Paper Title: Predictive Analysis of Crops Cultivation for a Smart Green Environment Using Azure Services Abstract: Internet of Things is a pervasive field and can be efficiently implemented in the agriculture sector. Internet of things can revolutionize the world today making it more efficient and smart. Agriculture is very essential in a country and integrating it with Internet of things (IoT) technology can take automation in agriculture to a whole another level. Ever increasing population brings with an increase in demand for food and to sustain the farming must be made more productive. IoT enhances the agricultural productivity by providing the farmer with information about soil moisture, temperature, humidity and acidity of the soil. This research work implements a practical system which deals with monitoring the crop field through a wireless network of sensors (light, humidity, temperature, soil moisture, water level indicator etc.) along with automating the irrigation system based on several field constraints. The farmers can monitor the farm conditions through a web app from anywhere, anytime and receive timely notification about the changes in the farm. This makes IoT based farming highly efficient when compared with the conventional cultivation approach. IoT technology and predictive data analytics can be used to enhance the agricultural productivity by providing the farmer, information about soil moisture, temperature, humidity and acidity of the soil. In IoT-based farming, the crops are monitored with the help of light, humidity, temperature, soil moisture and Ultrasonic sensors along with automating the irrigation system. Even in case of environmental issues, IoT based farming provides great benefits like more efficient water usage and optimization of fertilizers and plant treatments. The article aims in making use of IoT technology for smart agriculture. ThingSpeak Math works IoT platform is used for analyzing and presenting agriculture fields sensor data. The major objective of this paper is to collect real-time sensor data of a green environment and make predictions on crops cultivation pattern based on the weather condition 57. through MS Azure IFTTT services. Keywords: Module-Integrated Converter, Right-Half-Plane Zero, Low-Pass Filter, Phase-Lead 295-298 Compensation, Fuzzy Control.

References: 31. F. F. Edwin, W. Xiao, and V. Khankikar, "Dynamic displaying and control of interleaved flyback module-incorporated converter for PV control applications," IEEE Trans. Ind. Electron., vol. 61, no. 3, pp. 1377-1388, Mar. 2014. 32. S. B. Kjaer, J. K. Pedersen, and F. Blaabjerg, "An audit of single-stage framework associated inverters for photovoltaic modules," IEEE Trans. Ind. Appl., vol. 41, no. 5, pp. 1292-1306, Sep./Oct. 2005. 33. Y. H. Kim, J. W. Jang, S. C. Shin, and C. Y. Won, "Weighted-proficiency upgrade control for photovoltaic AC module interleaved flyback inverter utilizing a synchronous rectifier," IEEE Trans. Power Electron., vol. 29, no. 12, pp. 6481-6493, Dec. 2014. 34. G. Petrone, G. Spagnuolo, and M. Vitelli, "A simple system for conveyed MPPT PV applications," IEEE Trans. Ind. Electron., vol. 59, no. 12, pp. 4713-4722, Dec. 2012 35. Y. Li and R. Oruganti, "A flyback-CCM inverter conspire for photovoltaic AC module application," in Proc. Australasian Univ. Power Eng. Conf. (AUPEC), 2008, pp 1-6. 36. N. Kasa, T. Iida, and L. Chen, "Flyback inverter controlled by sensorless current MPPT for photovoltaic power framework," IEEE Trans. Ind. Electron., vol. 52, no. 4, pp. 1145-1152, Aug. 2005. 37. N. Sukesh, M. Pahlevaninezhad, and P. K. Jain, "Examination and execution of a solitary stage flyback PV microinverter with delicate exchanging," IEEE Trans. Ind. Electron., vol. 61, no. 4, pp. 1819-1833, Apr. 2014. 38. Z. Zhang, X. F. He, and Y. F. Liu, "An ideal control strategy for photovoltaic lattice tide-interleaved flyback microinverters to accomplish high productivity in wide load extend," IEEE Trans. Power Electron., vol. 28, no. 11, pp. 5074-5087, Nov. 2013. 39. H. Hu, S. Harb, N. H. Kutkut, Z. J. Shen, and I. Batarseh, "A singlestage microinverter without utilizing electrolytic capacitors," IEEE Trans. Power Electron., vol. 28, no. 6, pp. 2677-2687, Jun. 2013. 40. R. W. Erickson and D. Maksimovic, "Essentials of Power gadgets," Springer Science and Business Media, 2007. Authors: Pykhtin A.I, Emelianov I.P. Paper Title: Prospects of Creating a Unified Information System for Admission To Universities In Russia Abstract: The article deals with the problems faced by entrants for admission to Russian universities in modern conditions: the need for decision-making under uncertainty, the compressed time frame when moving between selected educational organizations. An approach to solving these problems is proposed, which consists in creating a unified information system that integrates information resources of admission commissions of all universities of the Russian Federation with the portal of state and municipal services. To serve applicants, it is proposed to use a network of multifunctional centers for the provision of state and municipal services. The proposed approach will provide applicants with the opportunity to remotely quickly change their decisions without a personal visit to the admission commission, while its implementation does not require significant costs from universities or the Ministry of science and high education of Russia. The creation of a unified system of 58. admission to universities to increase the openness and accessibility of the admission campaign for higher education programs will allow more objective distribution of applicants between universities, as much as 299-301 possible to satisfy their wishes in accordance with the scores on the results of entrance examinations. In creating a unified information and communication system of admission to universities are interested not only applicants and their parents who receive timely information about the recruitment process and their chances of admission to the University, but also the University management, who need operational information to analyze the course of the admission company and strategic planning, as well as the staff of the technical staff of the admission commission, receiving effective tools for the performance of duties and local operational decisions.

Keywords: Entrant, university, information system. References: 1. Epanchinceva O.L., Pogromskaya T.A., “Formirovanie edinogo 2. konkursnogo prostranstva Omskogo regiona”, / O.L. Epanchinceva, Matematicheskie struktury i modelirovanie. vol 16, pp. 5-10, 2006. 3. Kostjushina E.A. “Organizacija edinogo konkursnogo prostranstva regiona,” Otkrytoe i distancionnoe obrazovanie, vol. 3, pp. 35-41, 2003. 4. Pyhtin A.I., Emel'yanov I.P., “Koncepciya organizacii priema v 5. vuzy na osnove provedeniya edinogo vserossijskogo konkursa po 6. napravleniyam podgotovki i special'nostyam,” Izvestiya 7. YUgo-Zapadnogo gosudarstvennogo universiteta, vol. 2 (47), pp. 8. 086-088, 2013. 9. Mezenceva A.G., Ovchinkin O.V., Pyhtin A.I., “Osnovnye moduli avtomatizirovannoj informacionnoj sistemy dlya upravleniya priyomnoj kampaniej v usloviyah edinogo konkursnogo prostranstva Rossii,” Sovremennye instrumental'nye sistemy, informacionnye tekhnologii i innovacii, pp 138–140, 2018. 10. Pyhtin A.I., “Etapy sozdaniya edinoj informacionnoj sistemy upravleniya priyomom v vuzy Rossii,” Sbornik statej VII mezhdunarodnoj nauchno-prakticheskoj konferencii, pp 105-106, January 2017. 11. Pyhtin A.I., Mezenceva A.G., “Funkcional'naya model' centralizovannoj priemnoj kampanii v vuzy Rossii,” Sovremennye naukoemkie tekhnologii, vol. 2, pp. 63-68, 2017. 12. Mnogofunkcional'nye centry predostavleniya gosudarstvennyh i municipal'nyh uslug, URL: https://ru.wikipedia.org/wiki/Mnogofunkcional'nye centry predostavleniya gosudarstvennyh i municipal'nyh uslug#cite_note-1, 2018. 13. Pyhtin A.I., Spirin E.A., Zaharov I.S., “Metod i algoritm resheniya zadachi konkursnogo otbora i zachisleniya v vuz”, Telekommunikacii, vol. 5, pp. 12-19, 2008. 14. Tan C., “Tensions and challenges in China’s education policy borrowing,” Educational Research, vol. 58, pp. 195-206, 2016. 15. Kan M.V., “Mezhvuzovskoe edinoe informacionnoe prostranstvo konkursnogo otbora abiturientov na primere Kirgizii,” Sovremennye informacionnye tekhnologii i IT-obrazovanie, vol. 7, pp. 357-368, 2011.

Authors: Mukti Fajar ND, Reni Budi Setianingrum. The Disruptive Innovation in Competition Law: Regulation Issues Of On Line Transportation in Paper Title: Indonesia Abstract: The rapid development of digital technology encourages businesses to innovate their products and services. But these business innovations often create an unexpected leap leading to disruptive innovation, for example, the growth of online transportation business. As a result, the existing regulation cannot reach this leap. This study aims to study: (1) the legal position of disruptive innovation in competition law; and (2) analyzing the status of application-based transportation in competition law. The method of this research is normative legal research, which examines various legal principles, legal theories, and legislation. Findings from this study are, first: disruptive innovation indeed creates chaos in business competition, but as long as it does not violate regulation about (1) activities that are prohibited; (2) agreements that are prohibited; and (3) abuse of dominant position and run fairly, obey the law and doesn’t inhibit the entry of competitors, it does not violate the competition law. Second, application-based transportation business raises new problems concerning with the regulation that must be applied. Though the business platform is completely different from conventional transport companies, this new business platform does not violate business competition law.

Keywords: Disruptive Innovation, Competition Law, Online transportation

References: 1. Achmad Ali, 2002, Menguak Tabir Hukum, Jakarta: Gunung 2. Agung Ari Siswanto, 2004, Hukum Persaingan Usaha, Jakarta:Ghalia Indonesia Doni Wijayanto, 2018, Legal Startup Business, Solo: Metagraf 3. Hans Kelsen, 2006, Teori Hukum Murni: Dasar Dasar Ilmu Hukum Normatif, Bandung: Nusamedia 4. Moegni Djojodirjo, 1982, Perbuatan Melawan Hukum, Tanggung Gugat (aanspraakelijkheid) untuk Kerugian, yang Disebabkan karena Perbuatan Melawan Hukum, Jakarta: Pradnya Paramita 5. Ningrum Natasya Sirait, 2003, Asosiasi & Persaingan Usaha Tidak Sehat Medan: Pustaka Bangsa Press 6. Rhenald Kasali, 2017, Disruption: Menghadapi Lawan Lawan Tak Kelihatan Dalam Peradaban UBER , Jakarta: Gramedia 7. Satjipto Rahardjo, 2000, Ilmu Hukum, Bandung: Citra Aditya Bakti 306-311 8. Sudikno Mertokusumo, 2003, Mengenal Hukum: Suatu Pengantar ( Edisi Kelima), Yogyakarta: Penerbit Liberty 9. Sudikno Mertokusumo, Penemuan Hukum: Suatu Pengantar ( Edisi Kedua), Yogyakarta: Penerbit Liberty 10. Alexandre de Streel and Pierre Larouche ,2015, Disruptive Innovation And Competition Policy Enforcement, Global Forum on Competition 2015 www.oecd.org/competition/globalforum 11. Australian Government, 2016, Productivity Commission 12. Damien Geradin. 2015. “Should Uber be Allowed to Compete in Europe? And if so How?”. Competition Policy International. Inc 59. 13. Djoko Wintoro, Dampak Inovasi Pemasaran Terhadap Struktur Modal Dan Kinerja Perusahaan, Jurnal Keuangan dan Perbankan, Vol. 12, No.1 Januari 2008, 14. Edy Suandi Hamid. 2017, Disruptive Innovation: Manfaat Dan Kekurangan Dalam Konteks Pembangunan Ekonomi, Universitas Islam Indonesia 15. Florian Baumann and Klaus Heine, 2012, Innovation, Tort Law, and Competition, Düsseldorf Institute for Competition Economics (DICE), 2012 16. Gestiar Yoga Pratama, Suradi, Aminah, 2016, Perlindungan Hukum Terhadap Data pribadi pengguna Jasa Transportasi Online Dari Tindakan Penyalahgunaan Pihak Penyedia Jasa Berdasarkan Undang Undang Nomor 8 Tahun 1999 Tentang Perlindungan Konsumen, Diponegoro Law Journal 17. Gilbert Holland Montague, 1915, Unfair Methods of Competition, The Yale Law Journal 18. Han Li ToH, Disruptive Innovation: Implications for Enforcement of Competition Law, 14th OECD Global Forum on Competition , www.ccs.gov.sg 19. Hsin Fang Wei, 2016, Does Disruptive Innovation “Disrupt” Competition Law Enforcement ? The Review and Reflect, Paper on Taiwan International Conference Competition Policy in Global and Digital Economy 20. Liya Sukma Mulia, “Promosi Pelaku Usaha Yang Merugikan Konsumen”,download.portalgaruda.org/article.php?...PROMOSI%20PELAKU Ngo, Victor. 2015. Transportation Network Companies And The Ridesourcing Industry, Review of Impacts and Emerging Regulatory Frameworks for Uber. Report for City of Vancouver 21. Schneider, Allison. 2015. Uber Takes The Passing Lane, Disruptive Competition and Taxi-Livery Service Regulations. Elements 22. Tucker, Eric. 2017. Uber and the Unmaking and Remaking of Taxi Capitalisms: Technology, Law and Resistance in Historical Perspective. Osgoode Hall Law School of York University. 23. Undang Undang Nomor 5 tahun 1999 tentang Larangan Praktek Monopoli dan Persaingan Usaha Tidak Sehat 24. 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Interview with Ir Nawir Messi the ex chairman of KPPU Authors: Srinivas Kumar P, Raja Reddy K. Development of Semantic Enabled Engineering Soil Classification Along With Visualisation of Particle Paper Title: Size Distribution Curve Application Abstract: Soil classification is the basic knowledge which a geotechnical engineer needs to have before embarking on the construction of projects like highway or metro construction. With the advent of semantic web technologies it is now possible for the humans and machines to collaborate by way of understanding the underlying meaning of the soil classification concepts. An innovative approach is discussed in this paper where artificial intelligence enabled soil classification is developed along with the visualization of particle size distribution curve using R language and owl technologies.

Keywords: Ontology; owl; R; rdf; semantic web.b.

References: 1. Indian Roads Congress, “Quality Assurance Handbook For Rural Roads”,Vol -1,pp31,May 2007 Stephen Balkirsky et al,”Towards A Robot Task OntologyStandard”,Proceedings of ASME 2017 International Manufacturing Science and Eng.Conference, MSEC 2017 June 4-8,2017,Los Angeles,CA,USA. 2. Pankesh Patel et all, From raw data to smart manufactur-ing,IEEE Intelligent Systems, Volume 33,Issue 4 3. Jimi Condilite,”Next Up in Driverless Vehicles:Automous Excavators”. Technology Review, https://www.technologyreview.com/the- download/609174/next-up-in-driverless-vehicles-autonomous-excavators/,Oct 19,2017 4. G.Kandaswamy, Ministry of road transport and highways specification for road and bridge works,Indian Roads Congress, Fifth 60. revision,pp62,Jan 2013 5. Deccan Chronicle,”At last: Godavari TBM’s tryst with Benga-luru's Majestic station” https://www.deccanchronicle.com/nation/current-affairs/200416/at-last-godavari-tbm-s-tryst-with-bengaluru-s-majestic- 312-318 station.html”,20 Apr 2016 6. Kaklamanos, KT Elmy, Development of a Geotechnical En-gineering Software Package in R and Its Implementation in the Civil Engineering CurriculumGeotechnical and Structural Engineering Congress 2016, 635-647, 7. R.G. Raskin and M.J Pan. Knowledge representation in seman-tic web for earth and environmental terminology(SWEET), com- puters & geosciences,31(9):1119-1125,2005. 8. M. Zhao, Q. Zhao, D. Tian, P. Qian, and X. Zhang. Ontology-based intelligent retrieval system for soil knowledge. WSEAS Transactions on Information Science and Applications, 6(7):1196–1205, 2009. 9. T. Heeptaisong and A. Shivihok. Soil Knowledge-based Sys-tems Using Ontology.In Proceedings of the International Multi- Conference of Engineers and Computer Scientists, pages 1–5, 2012. 10. P. L. Buttigieg, N. Morrison, B. Smith, C. J. Mungall, and S. E. Lewis. The environment ontology: contextualising biological and biomedical entities. Journal of Biomedical Semantics, 4:43, 2013. 11. P. Shivananda and P. Srinivas Kumar. Building Rules Based Soil Classification Ontology. International Journal of Computer Science and Information Technology &Security, 3(2), 2013. 12. C. Deb, S. Marwaha, P. Malhotra, S. Wahi, and R. Pandey. Strengthening soil taxonomy ontology software for description and classification of USDA soil taxonomy up to soil series. In Proceedings of the 2nd International Conference on Computingfor Sustainable Global Development, pages 1180–1184, 2015. 13. H Du, V Dimitrova, D Magee, R Stirling, G Curioni, H Reeves, B Clarke ,An ontology of soil properties and processes, International Semantic Web Conference, 30-37 14. D.H. Deng,Z.Y. Gong, Z.L. Guo and S. Phillip. (2008). Semantic programming of Web-enabled database applications, Proceedings of 1st IEEE International Workshop on Semantic Computing and Applications, IEEE Press,2008,51-60. 15. A. Sheth, “Internet of Things to Smart IoT Through Semantic, Cognitive, and Perceptual Computing,” IEEE Intelligent Systems, vol. 31, no. 2, 2016, pp. 108–112. 17] I. Grangel-González et al., “The Industry 4.0 Standards Landscape from a Semantic,Integration Perspective,” Proc. 22nd IEEE Int’l Conf. Emerging Technologies and Factory Automation (ETFA), 2017, pp. 1–8. 16. A. Gyrard et al., “Building the Web of Knowledge with Smart IoT Applications,”IEEE Intelligent Systems, vol. 31, no. 5, 2016, pp. 83–88. 17. Shiny server Introduction, Jeff Allan, https://www.rstudio.com/products/shiny/shiny-server/,25-Feb-2014 18. Postgresql Introduction https://www.postgresql.org/about/ 19. OntorionIntroductionhttp://www.cognitum.eu/semantics/FluentEditor/rOntorionFE.aspx Authors: Kailash Kumar, Mohammed Alawairdhi. Paper Title: Overlap Analysis of Major Search Engines Abstract: This paper examines the overlapping of the results retrieved between three major search engines namely Google, Yahoo and Bing. A rigorous analysis of overlap among these search engines is conducted on 125 random queries. The overlap of first five page results, i.e., 50 results from each search engines and only non- sponsored results across these 3 major search engines are taken into consideration. Search engines have their own frequency of updates and ranking of results based on their relevance. Moreover, sponsored search advertisers are different for different search engines. Single search engine cannot index all Web pages. In this research paper, the overlapping analysis of the results were carried out between January 1, 2017 to January 31, 2018 among 3 major search engines, Google, Yahoo and Bing. A framework is built in java to analyze the overlap among these search engines. This framework eliminates the common results and merges them in a unified list. It also uses the ranking algorithm to re-rank the search engine results and displays it back to the user.

Keywords: Search Engines, Google, Yahoo, Bing, ResultOverlap, Merging and Ranking Algorithms.

References: 1. Cody Hansen, Feifei Li, “ColumbuScout: Towards Building Local Search Engines over Large Databases”, SIGMOD’12, May 20–24, 2012, Scottsdale, Arizona, USA. 2. J.L. Wolf, M.S. Squillante, J. Sethuraman, L. Ozsen, “Optimal Crawling Strategies for Web Search Engines”, ACM 1- 58113-449-5/02/0005. 3. B.ChaitanyaKrishna,C.Niveditha, G.Anusha, U.Sindhu ,Sk.Silar, “Analysis of Data Mining Techniques for Increasing Search Speed In Web, International Journal of Modern Engineering Research (IJMER), Vol.2, Issue.1, Jan-Feb 2012 pp- 375-383. 4. https://www.statista.com/statistics/264473/number-of-internet-hosts-in-the-domain-name-system/ 5. http://www.newmediatrendwatch.com/world-overview/34-world-usage-patterns-and-demographics. 6. Yi Shang and Longzhuang Li, “Precision Evaluation of Search Engines”, 2002. 7. MiladShokouhi, “Segmentation of Search Engine Results for Effective Data-Fusion”, 8. Luo Si and Jamie Callan, “Relevant Document Distribution Estimation Method for Resource Selection”, SIGIR ’03, July 61. 28-Aug 1, 2003, Toronto, Canada. Copyright 2003 ACM 1-58113-646-3/03/0007. 319-328 9. Su, Chen and Dong, “Evaluation of Web-Based Search Engines from the End-User's Perspective: A Pilot Study”, Proceedings of the ASIS Annual Meeting, v35 p348-61 1998. 10. Bar-Ilan, J. (2005). “Comparing Rankings of Search Results on the Web” in Information Processing and Management 2005 v.41 n.6 p.973-986. 11. Spink, A., Jansen, B.J., Koshman, S., and Blakely, C. (2006). “A Study of Results Overlap and Uniqueness and Among Major Web Search Engines” in Information Processing and Management 2006 v.42 n.5 p.1379-1390.

Authors: Y. Manas Kumar, L.Yamuna, S.R.Y .Himatej. Paper Title: Application of Modified Memetic Algorithm to Uncover Authorship Styles in Software Forensics Abstract: Our paper sincerely advocates a memetic algorithm to uncover authorship styles. For software forensics experts our proposed mechanism will greatly reduce the time, effort whenever a malicious job is done to break into a software system. We have considered three factors, namely the variable naming convention, usage of comment styles. We have considered three factors, namely the variable naming convention, usage of comment styles, usage of data structures. We observe that these 3 factors can greatly help to uncover authorship style of a pro-grammer thus saving us from further damage in this technologically dependant society.

Keywords: Software forensics, Memetic, Authorship, nearness value, genetic. References: 1. H (2001) A memetic Pareto evolutionary approach to artificial neural networks. Lecture Notes in Computer Science 2256: 1–12. 2. Aggarwal C, Orlin J, Tai R (1997) Optimized crossover for the independent set problem. Operations Research 45: 226–234. 62. 3. Aguilar J, Colmenares A (1998) Resolution of pattern recognition problems using a hybrid genetic/random neural network learning algorithm. Pattern Analysis and Applications 1: 52–61. 4. Beasley J, Chu P (1996) A genetic algorithm for the set covering problem. European Journal of Operational Research 94:393–404 329-333 5. Beasley J, Chu P (1998) A genetic algorithm for the multidimensional knapsack problem. Journal of Heuristics 4: 63–86 6. Becker B, Drechsler R (1994) Ofdd based minimization of fixed polarity Reed-Muller expressions using hybrid genetic algorithms. Proceedings of the IEEE International Conference on Computer Design: VLSI in Computers and Processor, pp 106– 110 7. Berger J, Salois M, Begin R (1998) A hybrid genetic algorithm for the vehicle routing problem with time windows. Proceedings of the 12th Biennial Conference of the Canadian Society for Computational Studies of Intelligence, pp 114–127 8. Berretta R, Cotta C, Moscato P (2001) Forma analysis and new heuristic ideas for the number partitioning problem. Proceedings of the 4th MIC — Metaheuristic International Conference, pp 337–341 9. Frederick Mosteller and David L. Wallace. Applied Bayesian and Classical Inference: The Case 0/ the Pcdcmlist PapfTs. Springer Series in Statistics. Springer-Verlag, 1D64 10. Herbert Solomon. Confidencc Intervals in Legal Settings1 pages 455-473. John Wiley & Sons, 1986. 11. Eugene H. Spafford. The Internet worm program: an analysis. Computer Communication RC1Jiew, 190), January 1989. Also issued as Purdue CS technical report TR•CSD-82:3. Authors: Azhar Talha Syed, Suresh Merugu, Vijaya Kumar Koppula. 63. Paper Title: Plant Recognition using Spatial Transformer Network Abstract: Agriculture is one of the most prominent work sectors in countries like India. However, the majority of farmers are unaware of the modern plant diseases and the methods are to be followed to expect a 334-333 better yield from their crops. Data science and Machine Learning have made a great progress in recent years for providing a solution to problems like these. Findings: By developing a system which will help the farmers in getting aware about the different species of plants without having a need for definite education would be very helpful to them. Objective: In this paper, we propose an efficient way of recognizing plants using cell phone cameras, as it will be very easy for the farmers and also other people who have their work involving plants, to get information about a plant which will help them in their work. We also provide a performance analysis on our solution and the previous work in this paper. Methods/Statistical Analysis: In Machine Learning terminology this is a multiclass classification problem where the input is an image and the expected output is the class of which the plant in the image belongs to. There are several ways of solving a multi-class classification problem such as using K nearest neighbors, Multiclass Support Vector Machines, Neural Networks, and Convolutional Neural Networks. But for this problem, we also take user convenience into consideration and we suggest the use of Spatial Transformer Network as the classification will still be accurate whilst the image is not properly aligned and has a lot of noise in it.

Keywords: Plant Recognition; Deep Learning; Convolutional Neural Networks; Spatial Transformer Network.

References: 1. Alex Krizhevsky, Ilya Sutskever, Geoffrey E. Hinton in “ImageNet Classification with Deep Convolutional Neural Networks” Advances in Neural Information Processing Systems 25 (NIPS 2012). Max Jaderberg, Karen Simonyan, Andrew Zisserman, Koray

Kavukcuoglu in “Spatial Transformer Networks” [Online]. Available:https://arxiv.org/pdf/1506.02025.pdf. 2. Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, Ruslan Salakhutdinov in “Dropout: A Simple Way to Prevent Neural Networks from Overfitting” The Journal of Machine Learning Research, 2014, pp 1929-1958. 3. Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun in “Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification”. 4. Matthew D. Zeiler, Rob Fergus, 2013, “Visualizing and Understanding Convolutional Networks”. 5. Hervé Goëau, Pierre Bonnet, Alexis Joly, Julien Barbe, Souheil Selmi, Vera Bakic, Vera Bakic, Jennifer Carré, Daniel Barthelemy, Nozha Boujemaa. “Pl@ntNet Mobile App” . 6. Michael Nielsen, in is book Neural Networks and Deep learning - Chapter 3 - Overfitting and Regularization. [Online].Available: http://neuralnetworksanddeeplearning.com/chap3.html#other_techniques_for_regularization. Jyotismita Chaki and Ranjan Parekh. “Plant Leaf Recognition using Shape based Features and Neural Network classifiers” in International Journal of Advanced Computer Science and Applications, Vol. 2, No. 10, 2011. 7. Wang-Su Jeon and Sang-Yong Rhee. “Plant Leaf Recognition Using a Convolution Neural Network ” in International Journal of Fuzzy Logic and Intelligent Systems Vol. 17, No. 1, March 2017, pp. 26-34. 8. Jonathan P. Caulkins (2010) in his research work “Estimated Cost of Production for Legalized Cannabis” at RAND, Drug Policy Research Center. Authors: K.L.S. Soujanya, Sri Sai Rajasekhar Gutta. Paper Title: Accident Alert System With IOT And Mobile Application Abstract: The most common thought we have for our close ones is their safety. With cities expanding rapidly and seeing a substantial rise in traffic and road accidents, the safety of our close one’s commuting on daily basis or taking long journeys is uncertain. People keep in touch with them in order to know their journey and whether the person has reached safely to the destination. The present work is to develop an em-bedded system powered by IOT technology through which we can constantly track and ensure safety of the people we care for. The system is equipped with a gyro sensor and multiple collision-detection sensors which are attached all around and under the chassis of the vehicle to detect abnormal tilt of the vehicle and/or collision if there occurs any. Location of the vehicle is constantly reported to the application and upon occurrence of an anomaly detected by the gyro-sensor or collision sensors, the occurrence of an accident and location of the vehicle is immediately notified to the family members on their application. The objective of this project is to ensure safety and be aware of the people and their situation if there occurs an accident. 64.

Keywords: Accident Alert System; Accident Alert IOT; Family Accident Alert Application; Vehicle Collision 337-340 Detection; Vehicle Accident Alert.

References: 1. Elie Nasr, Elie Kfoury, David Khoury, “An IOT approach toVehicle Accident Detection, Reporting and Navigation”, IEEE IMCET, Document Number: 7777457, available at online: http://www.ieeexplore.ieee.org/document/7777457 2. Abdulrahman Taha Mohammed, Noor Ain Kamsani, “Automatic Accident Detector and reporting system”, IEEE SCOReD 2017, Document Number: 8305425, available at online: http://www.ieeexplore.ieee.org/document/8305425 3. Ashish Kushwaha, Gaurav Katiyar, Harshita Katiyar, Hemant Yadav, Saxena, ‘GPS And GSM Based Accident Alarm System’ National Student Conference On “Advances in Electrical & Information Communication Technology”, AEICT-2014. 4. Dinesh Kumar, Shreya Gupta, Sumeet Kumar, Sonali Srivastava, “Accident detection and reporting system using GPS and GSM module”, JETIR 2015, ISSN: 2349-5162, available at online: http://www.jetir.org/papers/JETIR1505018.pdf 5. A. App and P. LLC, "Auto Accident App dans l’ App Store", App Store, 2016. Available: https://itunes.apple.com/ca/app/auto-accident- app/id515255099?l=fr. Authors: Ramya Kondapi, Rahul Kumar Katta, Sirisha Potluri. Paper Title: Pacifiurr: An Android Chatbot Application for Human Interaction Abstract: The objective of this paper is to build an Android Application based on Virtual voice and chat 65. Assistant. The current study focuses on development of voice and text/chat bot specifically. It is specially being

built for people who feel depressed and insists them to talk open mindedly which in turn pacifies them. As the 341-344 name of the application suggests, Pacifiurr: An application to pacify people and make them as happy as a cat would be with his or her mother (the reason why a cat purrs). We will be using Android Studio for the application design and Machine Learning as a part of Artificial Intelligence for Natural Language Processing (NLP), an easiest way to use Machine Learning libraries. At the back-end we will be using a database to store the communication history between the user and the bot. This application will only work on devices with Android operating system version-5.0 and above.

Keywords: Artificial Intelligence, Virtual Assistant, Software Agent, Machine Learning, Natural Language Processing, Android Studio, Chatbot.

References: 1. Tobias Kowatsch, Dirk Volland, Iris Shih, et al. Design and Evaluation of a Mobile Chat App for the Open Source Behavioral Health Intervention Platform MobileCoach 2. Alexandros Ronioti, Manolis Tsiknakis. Detecting Depression Using Voice Signal Extracted by Chatbots: A Feasibility Study 3. Pratik Kataria, Kiran Rode, Akshay Jain User Adaptive Chatbot for Mitigating Depression 4. Alison Darcy, Andrew Ng, Athena Robinson et al. Woebot 5. Simon D'Alfonso, Olga Santesteban-Echarri, Simon Rice et al. Artificial Intelligence-Assisted Online Social Therapy for Youth Mental Health. 6. Gillian Cameron, David Cameron, Gavin Megaw et al. Towards a chatbot for digital counselling 7. Simon Hoermann, Kathryn L McCabe, David N Milne, et al. Application of Synchronous Text-Based Dialogue Systems in Mental Health Interventions: Systematic Review. 8. Robert R Morris, Kareem Kouddous, Rohan Kshirsagar, et al. Towards an Artificially Empathic Conversational Agent for Mental Health Applications: System Design and User Perceptions. 9. Kathleen Kara Fitzpatrick, Alison Darcy, Molly Vierhile. Delivering Cognitive Behavior Therapy to Young Adults With Symptoms of Depression and Anxiety Using a Fully Automated Conversational Agent (Woebot): A Randomized Controlled Trial. 10. Caryn Kseniya Rubanovich, David C Mohr, Stephen M Schueller. Health App Use Among Individuals With Symptoms of Depression and Anxiety: A Survey Study With Thematic Coding. Authors: Sudhir Kumar Mohapatra,Bimal Prasad Kar,Befkadu Belete,Tarini Prasad Panigrahy. Paper Title: Extraction of Association Rules Using Chemical Reaction Optimization Abstract: This paper explores the applicability of chemical reaction optimization in association rule mining. We apply CRO on transactional database. Our algorithm generates N number of rules from the given database. The proposed algorithm is tested on real-life data from friendship mall, Addis Ababa, Ethiopia. From the results, we find it to be the best alternative to the existing popular algorithm like apriori algorithm and the FP-growth algorithm.

Keywords: CRO, Association rule mining, Apriori, FP-growth, Chemical Reaction optimizations. 66.

References: 345-348 1. Chen, C.-H., Hong, T.-P., & Tseng, Vincent S. (2006). A clus-ter-based fuzzy-genetic min-ing approach for association rules and membership functions. In IEEE. 2. Kayaa, M., & Alhajj, R. (2005). Genetic algorithm based framework for mining fuzzy as-association rules. Fuzzy Sets and Systems, 152(3), 587–601. 3. Tsay, Y. J., & Chiang, J. Y. (2005). CBAR: An efficient me-thod for mining association rules. Knowledge-Based Systems, 99–105. 4. Saggar,M.,Agrawal,A.K.,Lad,A.,2004.Optimization of associa-tion rule mining using im-proved genetic algorithms. In: Proceeding of the IEEE International Conference on Systems Manand Cybernetics, vol.4, pp.3725–3729. 5. Waiswa,P.P.W.,Baryamureeba,V.,2008.Extraction of interesting association rules using genetic algorithms. Int. J. Comput. ICT Res. 2(1), 26–33. Authors: CH. Neelima, Vandana Khare Paper Title: Ultra-Fast Streaming Camera Platform for ICU Applications Abstract: This paper explores the applicability of chemical reaction optimization in association rule mining. We apply CRO on transactional database. Our algorithm generates N number of rules from the given database. The proposed algorithm is tested on real-life data from friendship mall, Addis Ababa, Ethiopia. From the results, we find it to be the best alternative to the existing popular algorithm like apriori algorithm and the FP-growth algorithm.

Keywords: CRO, Association rule mining, Apriori, FP-growth, Chemical Reaction optimizations.

References: 1. Chen, C.-H., Hong, T.-P., & Tseng, Vincent S. (2006). A clus-ter-based fuzzy-genetic min-ing approach for association rules and membership functions. In IEEE. 67. 2. Kayaa, M., & Alhajj, R. (2005). Genetic algorithm based framework for mining fuzzy as-association rules. Fuzzy Sets and Systems, 152(3), 587–601. 3. Tsay, Y. J., & Chiang, J. Y. (2005). CBAR: An efficient me-thod for mining association rules. Knowledge-Based Systems, 99–105. 349-351 4. Saggar,M.,Agrawal,A.K.,Lad,A.,2004.Optimization of associa-tion rule mining using im-proved genetic algorithms. In: Proceeding of the IEEE International Conference on Systems Manand Cybernetics, vol.4, pp.3725–3729. 5. Waiswa,P.P.W.,Baryamureeba,V.,2008.Extraction of interesting association rules using genetic algorithms. Int. J. Comput. ICT Res. 2(1), 26–33. 6. K. Sarath and V. Ravi, “Association rule mining using binary particle swarm optimization,” Engineering Applications of Artificial Intelligence, vol. 26, no. 8, pp. 1832–1840, 2013. 7. A. Y. Lam and V. O. Li, “Chemical-reaction-inspired metaheuristic for optimization,” IEEE Transactions on Evolutionary Computation, vol. 14, no. 3, pp. 381–399, 2010. 8. Anandhaval-li,M.,SurajKumar,S.,Ayush,Kumar,Ghose,M.K.,2009.Optimized association Rule mining using geneticalgo- rithm. Adv. Inf.Min.,01–04, ISSN: 0975-3265. 9. Hadian, A.,Nasiri ,M.,Bidgoli, B.M.,2010. Clusteringbasedmulti-objectiverule mining usinggeneticalgorithm. Int.J.DigitalContent Technol.Appl.4,37–42. 10. Kuo,R.J,Chao,C.M.,Chiu,Y.T.,2011.Applicationofparticleswarmoptimizationto association rulemining. Appl .Soft Comput. 11,326– 336. 11. Gupta, M.,2012. Application of weighted particle swarm optimization in association rule mining. Int.J. Comput. Sci.Inf.1, 2231–5292. 12. Asadi, A.,Afzali,M.,Shojaei,A.,Sulaimani,S,2012.New binary PSO based method for find-ing best thresholds in association rule mining . Appl. Soft Comput., 260–264. 13. Nandhni,M.,Janani,M.,Sivanandham,S.N.,2012.Associaitonruleminingusing swarm intel-ligence and domain ontology .In: IEEE International Conference on Recent Trends in In-formation Technology(ICRTIT), Coimbatore, India, pp.537– 541. Authors: Sai Spandhana Reddy Emmadi, Sirisha Potluri. Paper Title: Android Based Instant Messaging Application Using Firebase Abstract: Communication through internet is becoming vital these days. An online communication allows the users to communicate with other people in a fast and convenient way. Considering this, the online communication application must be able share the texts or images or any other files in a faster way with minimum delay or with no delay. Firebase is one of the platforms which provides a real-time database and cloud services which allows the developer to make these applications with ease. Instant messaging can be considered as a platform tomaintain communication. Android provides better platform to develop various applications for instant messaging compared to other platforms such as iOS. The main objective of this paper is to present a software application for the launching of a real time communication between operators/users. The system developed on android will enable the users to communicate with another users through text messages with the help of internet. The system requires both the device to be connected via internet. This application is based on Android with the backend provided by google Firebase.

Keywords: communication; firebase; android; Instant messaging; real-time databases; group messaging..

References: 1. Anon., 2015. Development of a Health Care Assistant App for the Seniors. International Journal of Applied Science and Engineering, pp. 3-5. 2. Jianye Liu; Jiankun Yu, Research on Development of Android Applications, 4th International Conference on Intelligent Networks and Intelligent Systems, 15 December 2011 3. Abhinav Kathuria et al, Challenges in Android Application Development: A Case Study, Vol.4 Issue.5, May- 2015, pg. 294-299 4. Li Ma et al, Research and Development of Mobile Application for Android Platform, International Journal of Multimedia and Ubiquitous Engineering 9(4):187-198 • April 2014 5. Nikhil M. Dongre, Nikhil M. Dongre, Journal of Computer Engineering (IOSR-JCE), Volume 19, Issue 2, Ver. I (Mar.-Apr. 2017), PP 65-77 6. Javed Ahmad Shaheen et al, Android OS with its Architecture and Android Application with Dalvik Virtual Machine Review, International Journal of Multimedia and Ubiquitous Engineering Vol. 12, No. 7 (2017), pp. 19-30 7. Sajid Nabi Khan, Ikhlaq Ul Firdous, Review on Android App Security, International Journal of Advanced Research in Computer Science and Software Engineering, Volume 7, Issue 4, April 2017 68. 8. Lazarela Lazareska, Kire Jakimoski et al, Analysis of the Advantages and Disadvantages of Android and iOS Systems and 352-355 Converting Applications from Android to iOS Platform and Vice Versa, American Journal of Software Engineering and Applications 2017; 6(5): 116-120 9. Bin Peng et al, The Android Application Development College Challenge, 2012 IEEE 14th International Conference on High Performance Computing and Communication & 2012 IEEE 9th International Conference on Embedded Software and Systems, 18 October 2012 10. Shao Guo-Hong, Application Development Research Based on Android Platform,2014 7th International Conference on Intelligent Computation Technology and Automation, 08 January 2015 11. S Karthick, Android security issues and solutions, 2017 International Conference on Innovative Mechanisms for Industry Applications (ICIMIA), 13 July 2017 12. Pravin Auti, Sangam Mahale, Vikram Zanjad, Madhuri Dangat, n.d. An Android Based Global Chat Application. 4(1), pp. 1-2. 13. Pravin Auti, Sangam Mahale, Vikram Zanjad, Madhuri Dangat, n.d. An Android Based Global Chat Application. 4(1). 14. S, A. K., n.d. Mastering Firebase for Android Development: Build real-time, scalable, and cloud-enabled Android apps with Firebase. s.l.: s.n Authors: Kavita Shinde, Sarita Patil. Paper Title: Discovering CQA post voting prediction using Artificial Neural network and Entropy Analysis Abstract: As the whole world is aware of education and its importance the knowledge in web pages also grows due to crowd sourcing. Many web portals are running because of the good amount of the investment of the user's knowledge for free and in a well desired manner makes the portals make hefty business. Some web portals like stack overflow, yahoo and even some social media sites like twitter and all are completely relying on crowdsourcing data. Most of the time it is hard to identify the best answer from the users for a question that was raised by the other user in the portal. Some methodologies are existed to achieve this where they are using the scores that are given by the other users or likes. This many times yield in loss of precision and never cross check the validation of the answers with their contents. So this paper puts forwards an idea of identifying the bag of word technique along with the Artificial neural network and entropy analysis of for nonlinear and unplanned distribution of data. Finally, by using the Bayesian law along with the fuzzy classification model for predicting 69. degree yields the best prediction of question and answers. 356-361 Keywords: CQA, ANN, Bayesian Probability, Entropy Evaluation, Fuzzy Logic, Bag of words.

References: 1. GengZhang, Han-Xiong Li, A Probabilistic Fuzzy Learning System for Pattern classification, DOI: 978-1-4244-6588-0/10, IEEE, 2010. 2. Keeley Crockett, Annabel Latham, David Mclean, Zuhair Bandar, James O’Shea, On Predicting Learning Styles in Conversational Intelligent Tutoring Systems using Fuzzy Classification Trees, DOI 978-1-4244-7317-5/11,IEEE,2011. 3. Jianping Gou, Wenmo Qiu, Qirong Mao, Yongzhao Zhan, Xiangjun Shen and Yunbo Rao, A Multi-Local Means Based Nearest Neighbor classifier, DOI 10.1109/ICTAI.2017.00075, IEEE, 2017. 4. Prithwish Jana, Soulib Ghosh, Suman Kumar Bera and Ram Sarkar, Handwritten Document Image Binarization: An Adaptive K- Means Based Approach, DOI: 978-1-5386-3745-6/17, 2017. 5. Jie Sun, Zhi-Min Liu, K-Means Clustering Algorithm for Full Duplex Communication, DOI 978-1-4673-9026-2/ 16, IEEE, 2016. 6. G. Zweig, O. Siohan, G. Saon, B. Ramabhadran, D. Povey, L. Mangu and B. Kingsbury, 2d Color Barcodes For Mobile Phones Automated Quality Monitoring In The Call Center With ASR And Maximum Entropy, DOI: 1-4244-0469-X, IEEE, 2006. 7. Giuseppe Bianchi, Chiara Carusi, Lorenzo Bracciale, An online approach for joint task assignment and worker evaluation in crowd- sourcing, DOI: 978-1-5090-4260-9/17, IEEE, 2017. 8. Maorong Shao, Ying Zhang, Ying Jiang, Lingxuan Zhu, A Refugee Crisis System Based on Entropy AHP and Dynamic Programming, 978-1-5090-0729-5/16, IEEE, 2016. 9. Driss El Hannach, Rabia Marghoubi, Mohamed Dahchour, Project Portfolio Management Information Systems(PPMIS), DOI: 978- 1-5090-0751-6/16,IEEE, 2016. 10. Ji Zhang, Zhi Du, Dong Xie, Shouxia Jiang, Yang Liu, Jin Ma and Yanbo Chen, Improved SEC Model Based Evaluation Approach for Design Scheme of The New Generation Smart Substation, DOI: 978-1-5090-5417-6/16, IEEE, 2016. 11. Samuel Jonathan Slade, A Reactively Learning Neural Network that Decides Behaviors for an Artificial Life System with Homogeneous Agents, DOI: 978-1-5090-4093-3/16, IEEE, 2016. 12. Utku Kose, An Artificial Neural Networks based Software System for Improved Learning Experience, DOI: 10.1109/ICMLA.2013.175, IEEE, 2013. 13. . Peixoto, A. A. R. Diniz, N. C. Almeida, J. D. de Melo, A. D. Dória Neto, A. M. G. Guerreiro, Modeling a System for Monitoring an Object Using Artificial Neural Networks and Reinforcement Learning,, DOI: 978-1-4244-9637-2/11, IEEE, 2011. 14. H. M. Peixoto, A. A. R. Diniz, N. C. Almeida, J. D. de Melo, A. D. Dória Neto, A. M. G. Guerreiro, Learning Ensembles of Neural Networks by Means of a Bayesian Artificial Immune System, DOI: 10.1109/TNN.2010.2096823, IEEE, 2010. 15. H. M. Peixoto, A. A. R. Diniz, N. C. Almeida, J. D. de Melo, A. D. Dória Neto, A. M. G. Guerreiro, Bayesian Learning of Neural Networks by Means of Artificial Immune Systems, DOI: 0-7803-9490-9/06, IEEE, 2006. 16. Toby O'Hara, Larry Bull, Building Anticipations in an Accuracy-based Learning Classifier System by use of an Artificial Neural Network, DOI: 0-7803-9363-5/05, IEEE, 2005. 17. Kim Schouten, Onne van der Weijde, Flavius Frasincar, and Rommert Dekker, " Supervised and Unsupervised Aspect Category Detection for Sentiment Analysis With Co-Occurrence Data ", IEEE TRANSACTIONS ON CYBERNETICS,2017 Authors: Sara Kutty T K , Hanumanthappa M. Paper Title: An Implementation of Differential Evolution Algorithm for Optimal Water Allocation Problem Abstract: Differential evolution algorithm is an optimization technique which is very efficient and simple for global optimization over continuous spaces. This paper applies differential evolution algorithm in water resource allocation and distribution problems in order to allocate water resources in an optimal way. The algorithm considers the optimal allocation as a simulated biological evolution process. The main aim of this paper is to implement differential evolution algorithm, to allocate water resources optimally and to check its efficiency through a case study. The objective is to meet the water demand of the users by minimizing the total water supply from public water source and to encourage the use of other water sources especially rain water harvesting. An optimal water allocation model is considered and the results show that it is simple, accurate in producing the results, adaptable and reliable.

Keywords: Evolutionary Algorithms, Differential Evolution Algorithm, Mutation.

References: 1. F. Xiao, W. Huang and Z. Zhigang, Optimal Allocation of Water Resources Based on Differential Evolution Algorithm, 2009, International Conference on Environmental Science and Information Application Technology, Wuhan, 2009, pp. 587-592, doi: 10.1109/ESIAT.2009.143 2. Xianfeng HUANG, Guohua FANG, Water Resources Allocation Effect EvaluationBased on Chaotic Neural Network Model, JOURNAL OF COMPUTERS, VOL. 5, NO. 8, AUGUST 2010 3. J A Adeyemo, F.A.O.Otieno, Multi Objective Differential Evolution algorithm for solving Engineering Problems, Journal of Applied 70. Sciences, 9(20):3652-3661, 2009 4. M. Janga Reddy and D. Nagesh Kumar, Multiobjective Differential Evolution with Application to Reservoir System Optimization, JOURNAL OF COMPUTING IN CIVIL ENGINEERING © ASCE / MARCH/APRIL 2007 362-366 5. Josiah Adeyemo, FaizalBux and Fred Otieno, Differential evolution algorithm for crop planning: Single and multi-objective optimization model, International Journal of the Physical Sciences Vol. 5 (10), pp. 1592-1599, 4 September, 2010, ISSN 1992 - 1950 ©2010 Academic Journals 6. Y.P. Chang, C.J. Wu, Optimal multi-objective planning of large scale passive harmonic filters using hybrid differential evolution method considering parameter and loading uncertainty, IEEE Trans on Power Delivery, vol. 20, 2005. 7. Feng, KePeng, and Jun CangTian, Water Resources Optimal Allocation Based on MultiObjective Differential Evolution Algorithm, Applied Mechanics and Materials, 2013. 8. Leandro dos Santos Coelho. A Hybrid Method of Differential Evolution and SQP for Solving the Economic Dispatch Problem with ValvePoint Effect, Advances in Intelligent and Soft Computing, 2006 9. Sushruta Mishra, Brojo Kishore Mishra, Hrudaya Kumar Tripathy. chapter 6 Significance of Biologically Inspired Optimization Techniques in Real-Time Applications , IGI Global, 2017 10. Deng, Hai Ying, Zhi Gang Zhang, and Yi Gang Yu. "The Differential Evolution and its Application in Short-Term Scheduling of Hydro Unit" , Advanced Materials Research, 2011. 11. Bach Hoang Dinh, ThangTrung Nguyen and CuongDuc Minh Nguyen, Modified Differential Evolution for Multi-objective Load Dispatch Problem Considering Quadratic Fuel Cost Function, International Journal of Advanced Science and Technology Vol.90 (2016), pp.25-40 12. Proceedings of the 18th Asia Pacific Symposium on Intelligent and Evolutionary Systems - Volume 2 , Springer Nature America, Inc, 2015 13. Y. Lou, J. Li and Y. Shi, A Differential Evolution based on individual-sorting and individual-sampling strategies," 2011 IEEE Symposium on Differential Evolution (SDE), Paris, 2011, pp. 1-8, doi: 10.1109/SDE.2011.5952052 14. Z.Y. Yang, K. Tang and X. Yao,Self-adaptive Differential EvolutionwithNeighborhood Search, Proc. of the 2008 IEEE Congress on EvolutionaryComputation, pp.1110–1116, 2008. Authors: Sayyad Layak B, Ch. Sanjay, Sher Afghan Khan. Paper Title: Optimization and Analysis of Super Finishing Lathe Attachment Abstract: This paper presents the research work done to solve the problem faced by super-finishing attachment used in lathe machine, which cannot be continuously operated for mass production as it is possible in 71. a full-fledged super finishing machine. Research work is implemented in 50LT attachment and converted it into continuous working machine there by making it suitable for mass production without having to purchase costly 367-372 Super finishing machine and getting similar production only with an attachment on lathe machine. The research work also provides solution for the problem faced by attachment when operated in cold working condition wherein the shrinking of the part takes place and the machine gets jam and it becomes impossible to operate, the solution thereby makes the attachment to work in adverse environment also.

Keywords: Friction, heat generated, super-finishing machine, OHNS, Ebonite Coating. References: 1. M. Amiri and Michael M. Khansari “the thermodynamics of friction and wear” –(Published 27 April 2010) www.mdpi.com/journal/entropy-on 2. B.Ivkovig M. Djurdjanovic, D.Stamenkovic “The Influence of the contact surface roughness on the static friction coefficient” – (Tribology in industry, Volume 22 no 3 and 4,2000). (The paper was published at the first Mediterranean conference on tribology in Israel) 3. Prof. Prasanta Sahoo – “Engineering Tribology”PHI Learning Private Limited Delhi- 110092 , 2015 edition. 4. Mohammad A. Chowdhury, Soma Chakraborty “Sliding Friction of Steel Combinations” The Open Mechanical Engineering Journal, 2014, 8, 364-369 Authors: Mahua Biswas, Suplab Kanti Podder, Shalini R, Debabrata Samanta. Paper Title: Factors that influence Sustainable Education with respect to Innovation and Statistical Science Abstract: Education is a systematic and collective process of acquiring knowledge and skills to develop the members of the executive or administration of an organization for managing and controlling the professional requirements of individuals, organizations and society at large. This research paper unfolded the contribution from Innovation and Statistical Science in Sustainable Management Education that can ensure the managerial Skills up gradation, Technical acquisition, Skilled employment, Direct Link to Productive Industries, Advanced technological knowledge and Discovering various fields of environmental scenario. The study is empirical in nature and the requisite data was collected both from primary and secondary sources. Total 800 respondents were considers from divers background of Teachers, Decision-makers and Students and the semi-structured interview schedules of randomly selected 120 stakeholders were employed and make an attempt to assess the contribution from Innovation and Statistical Science in Sustainable Management Education. Data so collected was carefully collated and analyzed for hidden patterns. Based on the results, suggestions and recommendations were listed.

Keywords: Sustainable Management Education, Innovation, Statistical Science, managerial or administrative skills and advanced technological knowledge..

References: 1. Cheryl Kerr and Cathryn Lloyd (2008). “Pedagogical learnings for management education: Developing creativity and innovation”, Journal of Management & Organization, 14(5), pp. 486-503 2. Greer, Youngblood, and Gray (1999) Human Resource Management: The Make or Buy Decision. J of Academy of Management

Executive, 13(3), pp 85–96. 3. M.E. Mogee, Mogee Res. and Anal. S (1993). “Educating innovation managers: strategic issues for business and higher education”, IEEE Transactions on Engineering Management 40(4), pp 410 – 417 373-376 4. Maryam Alavi and Douglas R. (2017). “Using Information Technology to Add Value to Management Education”, Academy of Management Journal, 40(6), pp 89-97 5. MaikAdom and Daniel Fischera (2014), “Emerging areas in research on higher education for sustainable development – management education”, Journal of Cleaner Production, 62 (1), pp 1-7. 6. Suplab Kanti Podder , Arun B K, "Comparison of effectiveness of employee engagement through permanent employees or outsourced employees"", International Journal of Academic Research and Development, Volume-3, Issue-2, March 2018; pp 1406- 72. 1408. ISSN: 2455-4197, 2018-06-07. 7. Suplab Kanti Podder, Arun B K, "Contribution of HR sub-functions outsourcing in the improvement of quality and innovation of education", International Journal of Commerce and Management Research, Volume-III, Issue-1, 2015-06-16. 8. Suplab Kanti Podder , , "Impact of Differential Compensation Patterns Due To HR Outsourcing On Motivational Levels", Primax International Journal of Human Resource, Volume-II, Issue-2, 2015-01-06. 9. [9] Porter, Lyman W. and McKibbin (1999), Management Education and Development: Drift or Thrust into the 21st Century?, Information Analyses; Reports - Research; Books,pp 125-134 10. Suplab Kanti Podder, , "Contribution from Outsourcing of HR Functions Towards Innovation and Quality Improvement in Higher Education for Holistic Development of Society", International Conference on Responsible Management Education - Key to Holistic Development of Society on 21- 22 October organized by Dayananda Sagar Business Academy, Bangalore, 2016. 11. Suplab Kanti Podder , Mukesh Soni, "Factors that Influence Responsible Management Education towards HRM Ethics and its Practices in Respect of Educational Institutions in Bangalore", International Conference on Responsible Management Education - Key to Holistic Development of Society on 21- 22 October organized by Dayananda Sagar Business Academy, Bangalore , 2016. 12. Suplab Kanti Podder , Arun B K, "Comparison of Effectiveness of Employee Engagement Through Permanent Employees or Outsourced Employees", National Conference on Changing Role of HRM - The Strategic Opportunities and Challenges on 3 March , 2016. 13. RayIsona and NielsRöling (2007), “Challenges to science and society in the sustainable management and use of water: investigating the role of social learning”, Environmental Science & Policy, 10(6), pp 499-511. Authors: B.praveen, Umarani Nagavelli, Anand Thota, Debabrata Samanta. Paper Title: Cardinal Digital Image Data Fortification Expending Steganography Abstract: In the present advanced world applications from a PC or a cell phone reliably used to complete each sort of work for expert and also amusement reason. In any case, one of the significant issues that a product distributer will confront is the issue of theft. All through the most recent few decades, all-major or minor programming has been pilfered and unreservedly flowed over the web. The effect of the uncontrolled programming theft has been gigantic and keeps running into billions of dollars consistently. For an autonomous designer or a software engineer, the effect of robbery will be colossal. Enormous organizations that make

specific programming regularly utilize complex equipment strategies, for example, utilization of dongles to stay away from programming robbery. Be that as it may, this is absurd to expect to improve the situation a typical autonomous software engineer of a little organization. As a feature of the exploration, another technique for

programming security that does not require restrictive equipment and other complex strategies are proposed in this paper. This technique utilizes a blend of inbuilt equipment includes and in addition steganography and encryption to secure the product against theft. The properties or strategies utilized incorporate uniqueness of

equipment, steganography, solid encryption like AES and geographic area. To abstain from hacking the proposed system additionally makes utilization of self-checks in an irregular way. The procedure is very easy to actualize for any designer and is usable on both customary PCs and also versatile conditions. - Steganography is

the science that includes conveying mystery information in a suitable interactive media transporter, e.g., picture, sound, and video documents. It is dependably non obvious. In this message more critical than unique flag. Steganography has different helpful applications. The fundamental targets of steganography are un perceptibility, strength (protection from different picture preparing techniques and pressure) and limit of the concealed information. These are the primary variables which make it not quite the same as different strategies watermarking and cryptography. This paper incorporates the essential steganography techniques and the primary spotlight is on the survey of steganography in computerized pictures.

Keywords: Data protection, Steganography, Stego Image, Cover Image, Software Protection, Encipher, AES, Stegano DB, LSB. Steganography, Histogram, Adjacent Pixel Difference (APD), PSNR, Capacity. 73. 377-380 References: 1. Zhang and Wang, “Binary power data hiding scheme”1434-8411/@2015 Elsevier. 2. R.Rathna Krupa, “An overview of image hiding techniques in image processing”ISSN:2321-2381@2014 Published by the standard international journals . 3. Vipul Sharma and Sunny Kumar, “A new approach to hide text in images using steganography”ISSN: 2277 128X@2013,IJARCSSE. 4. W-C Kuo and C-C Wang, “Data hiding based on generalized exploiting modification direction method” TheImaging Science Journal Vol 61 IMAG 324 @ RPS 2013. 5. Aarti Mehndiratta, “Data hiding systemusingcryptography and steganography:A comprehensive modern investigation.”e-ISSN:2395- 0056,p-ISSN:2395-0072@2015,IRJET.NET. 6. B.Subramanan “Image encryption based on aes key expansion” in IEEE applied second international conferenceon emerging applicaton of information technology,978-0-7695-4329-1/11,2011. 7. Vipul Madhukar Wajgade,Dr. Suresh Kumar,Stegocrypto – A Review of Steganography techniques usinCryptography”,International Journal.OfComputeengineeringTechnology,ISSN:22229-3345,vol. 4,2013,pp. 423-426 8. T. Sharp, An implementation of key-based digital signal steganography, Proc. of the 4th Information Hiding Workshop, vol. 2137, pp. 13-26, Springer, 2001. 9. J. Mielikainen, LSB matching revisited, IEEE Signal Processing Letters, vol. 13, no. 5, pp. 285-287,2006. 10. X. Li, B. Yang, D. Cheng, and T. Zeng, A generalization of LSB matching, IEEE Signal Processing Letters, vol. 16, no. 2, pp. 69-72, 2009. 11. Syed K A Khadri,, Debabrata Samanta, M Paul,” Message Encryption Using Text Inversion plus N Count: In Cryptology”, International Journal of Information Science and Intelligent System (IJISIS), pp. 71-74, Volume 3, Number 2, 2014. 12. Syed K A Khadri,, Debabrata Samanta, M Paul,” Novel Approach for Message Security”, International Journal of Information Science and Intelligent System (IJISIS), pp. 47-52,Volume 3, Number 1, 2014. 13. Syed K A Khadri,, Debabrata Samanta, and M Paul, "Approach of Message Communication Using Fibonacci Series: In Cryptology", Lecture Notes on Information Theory, Vol. 2, No. 2, pp. 168-171, June 2014. doi: 10.12720/lnit.2.2.168-171. 14. Syed K A Khadri,, Debabrata Samanta, M Paul,” Message communication using Phase Shifting Method (PSM )”,International Journal of Advanced Research in Computer Science (IJARCS), Volume 4, Number 11, pp.9-11 ,November-December 2013. 15. Syed K A Khadri,, Debabrata Samanta, M Paul,” Secure Approach for Message Communication”, International Journal of Advanced Research in Computer and Communication Engineering (IJARCCE), pp. 3481-3484, Vol. 2, Issue 9, September 2013. 16. Dipti Kapoor Sarmah, Neha bajpai, “ Proposed System for Data Hhiding Using Cryptography and Steganography”, International Journal of Computer Applications (0975 – 8887), Volume 8 – No. 9, October 2010. 17. R. Rejani, D. Murugan and Deepu V. Krishnan, “Novel Software Protection Framework Using Steganography, Cryptography, Uniqueness of Hardware and Self-Checks”. 18. Bertrand Anckaert, Bjorn De Sutter and Koen DeBosschere, “Software Piracy Prevention through Diversity”,Proceedings of the 4th ACM workshop on Digital rights management, pp. 63-71, 2004. Authors: Jolly Upadhyaya, Neelu Jyothi Ahuja, Kapil Dev Sharma. Evaluating User Expectations and Quality of Service: A Novel Approach to Understanding Cloud Paper Title: Services Abstract: Cloud Computing technology has revolutionized over the past decade as one of the fastest growing and adopted paradigm especially in the higher education sector. Its impact and popularity as a support system for learning is primarily based on the fact that it provides fast access to educational services and resources with high performance and support. At the same time, lack of institutional budgets, concerns for cyber security, and cost of technical and computer support continue to impact educational administration decision while adopting this enriched service. There is consensus that not enough consideration is given to the quality of cloud services experienced at the users’ end while pursuing such methods to fulfil the academic requirements. Currently, unavailability of a reliable standard model that effectively defines the “Quality of Experience,” QoE parameters from the users’ point of view impacts the recommendation of use for the cloud service across various educational institutions. Hence, it has become increasingly necessary to monitor, track, and quantify the variables influencing QoE for cloud computing-based e-learning applications and develop a new QoE Metrics Model. The current study was performed implementing quantitative method to collect and analyze the data received from various levels of educational institutions. The participants surveyed for study in the current work include students, librarians and faculties that were aware of cloud computing applications and services for higher 381-385 74. education. Our study emphasized on variables like accessibility, demographics, age, income, educational status etc. and were statistically analyzed. The results of our study identified a correlation between the research questions and inferred hypotheses from them, leading to create an instrument that could be helpful in future as a diagnostic tool for the customer of cloud services, in academia. The implication of this study is to further help improve the qualitative process needed to identify the gap between user expectations and the experience of real quality of service (QoS)leading to build a reliable conceptual model for service evaluation in cloud computing.

Keywords: Higher Education; Metrics, Quality of Service (QoS), Quality of Experience (QoE). References: 1. Akpan, Helen Anderson and M. R. Sudha.Monitoring Performances of Quality of Service in Cloud with System of Systems.International Journal of New Technologies in Science and Engineering, 2, 3, Sep2015.www.researchgate.net/publication/282571588_Monitoring_Performances_of_Quality_of_Service_in_Cloud_with_System_of _Systems 2. Almajalid, Rania. “A Survey on the Adoption of Cloud Computing in Education Sector.” CoRR abs/1706.01136 (2017): n. pag. 3. Bardsiri, Amid Khatibi, and Syed Mohsen Hashemi. “QoS Metrics for Cloud Computing Services Evaluation.” I.J. Intelligent Systems and Applications, 12, 27-33, MECS, Nov. 2014, www.mecs-press.org/ijisa/ijisa-v6-n12/IJISA-V6-N12-4.pdf, DOI: 10.5815/ijisa.2014.12.04. 4. Chen, Xiaoyu, et al. June, 2010. Using Cloud For Research: A Technical Review. TECIRES REPORT, School of Electronic and Computer Science, University of Southampton. eprints.soton.ac.uk/id/eprint/271273 5. Choudaha, Rahul. “Latest Data and Statistics on Indian Higher Education and New Regulatory Reform”June 2017. https://www.dreducation.com/2017/06/indian-universities-colleges-latest-data-statistics-heera-aicte-ugc.html 6. Farokhi, Soodeh. “Quality of Service Control Mechanisms in Cloud Computing Environments.” LinkedIn SlideShare, Vienna University of Technology, Austria, 22 Jan. 2016, www.slideshare.net/soodehfarokhi/quality-of-service-control-mechanisms-in-cloud- computing-environments. 7. Gupta, Prerita, et al. “Quality of Services in Cloud Computing: Issues, Challenges and Analysis.” International Journal of New Innovations in Engineering and Technology, 3,3., IJNIET, July 2015, www.ijniet.org/wp-content/uploads/2018/09/3312.pdf. 8. Hamidi, Homa, and Saeed Rouhani. “The Effects of Cloud Computing Technology on E-Learning: Empirical Study.” Robotics and Automation Engineering Journal, 2, 5, Juniper Publishers, Apr. 2018,juniperpublishers.com/raej/pdf/RAEJ.MS.ID.555596.pdf, DOI: 10.19080/RAEJ.2018.02.5. 9. Hignite, Karla, et al. “Shaping the Higher Education Cloud.” EDUCAUSE, EDUCAUSE, May 2010, library.educause.edu/resources/2010/5/shaping-the-higher-education-cloud.

10. Ishaq, Atif, and Mohammad Nawaz Brohi. “Cloud Computing In Education Sector With Security and Privacy Issue: A Proposed Framework.” International Journal of Advances in Engineering & Technology, IJAET, Dec. 2015, www.ijaet.org/media/2I30- IJAET0830208-v8-iss6-pp889-898.pdfhttp://www.ijaet.org/media/2I30-IJAET0830208-v8-iss6-pp889-898.pdf. 11. Jain, Anjali, and U S Pandey. “International Journal of Advanced Research in Computer Science and Software Engineering.” IJARCSSE, IJARCSSE, July 2013, International Journal of Advanced Research in Computer Science and Software Engineering. 12. Jalgaonkar, Meghna, and Ashok Kanojia. “Adoption of Cloud Computing in Distance Learning.” International Journal of Advanced Trends in Computer Science and Engineering, Vol.2, No.1, Pages: 17-20, Special Issue of ICACSE, 2013,pdfs.semanticscholar.org/0b4e/fc468cd26dabc05c87fe144e3543dd7cbf1b.pdf. 13. Katz, Richard, et al. “Demystifying Cloud Computing for Higher Education.” EDUCAUSE Center for Analysis and Research (ECAR), EDUCAUSE, Sept. 2009,library.educause.edu/resources/2009/9/demystifying-cloud-computing-for-higher education. 14. Pardeshi, Vaishali H. “Cloud Computing for Higher Education Institutes: Architecture, Strategy and Recommendations for Effective Adaptation.” Procedia Economics and Finance, Science Direct, 2014, core.ac.uk/download/pdf/82674946.pdf. 15. “The NMC Horizon Report: 2015 Higher Education Edition.” Horizon Report > 2015 Higher Education Edition, NMC, 2015, files.eric.ed.gov/fulltext/ED559357.pdf 16. Upadhyaya, Jolly and Neelu Jyothi Ahuja. “Cloud Computing in Libraries and Higher Education: An Innovative User-Centric Quality of Service Model.” Oct. 2017. 17. Yadav, Kiran. “Role of Cloud Computing in Education.” IJIRCCE, IJIRCCE, Feb. 2014, www.ijircce.com/upload/2014/february/21_Role.pdf. Authors: SrinivasNagaballi, Vijay S. Kale. Paper Title: Assessment of Voltage Stability Indices to Predict the Line Close to Voltage Collapse Abstract: Voltage stability is the integral part of the power system stability. In this paper, assessment of various voltage stability indices (VSIs) are presented to predict the proximity of the distribution line close to voltage collapse. These line VSIs are based on the concept of voltage quadratic equation of the two bus system. The behaviour of VSIs have been tested on two test systems, i.e. IEEE 12-bus and IEEE 33-bus radial distribution systems (RDS) with increasing penetration of base load. These indices are differentiated to resolve their effectiveness in identifying the weakest line in the system. Results show that these indices evaluation can be used for placing Distributed Generation (DG) and capacitors in the system.

Keywords: distribution system, voltage stability indices, voltage collapse.

References: 1. KundurP, Paserba J, AjjarapuV, Andersson G, Bose A, Canizares C, et al. “Definition and classification of power system stability IEEE/CIGRE joint task force on stability terms and definitions,” IEEE Trans. Power Syst., Vol. 19, pp.1387–401,2004. 2. Pereira RMM, Ferreira CMM, Barbosa FPM, “Comparative study of STATCOM and SVC performance on dynamic voltage collapse of an electric power system with wind generation,” Latin Am. Trans., Vol. 12, pp.138–45,2014. 3. Ettehadi M, GhasemiH, Vaez-Zadeh S, “Voltage stability-based DG placement in distribution networks,” IEEE Trans. Power Deliv., Vol. 28, pp. 171–178,2013. 4. Zeinalzadeh A, MohammadiY, Morad MH, “Optimal multi objective placement and sizing of multiple DGs and shunt capacitor banks simultaneously considering load uncertainty via MOPSO approach,” Int. Jou. Electric Power Energy Syst., Vol.67, pp.336–349,2015. 5. Nisam M, Mohamed A, Hussain A, “ Performance evaluation of voltage stability indices for dynamic voltage collapse 386-392 prediction,”JournalAppl. Sci., Vol.6, pp.1104–1113,2006. 75. 6. Abhyankar SG, Flueck AJ., “A new confirmation of voltage collapse via instantaneous time domain simulation,” In: Proceedings of the North American power symposium, pp.1–9,2009. 7. Cupelli M, Cardet CD, Monti A., “Comparison of line voltage stability indices using dynamic real time simulation,” In: IEEE PES innovative smartgridtechnologiesEurope,pp.1–8,2013. 8. Vu, K., Begovic, M.M., Novosel, D., Saha, M.M., “Use of local measurements to estimate voltage-stability margin,” IEEE Trans. Power Syst., Vol.14, pp.1029-1035,1999. 9. Zhou, D.Q., Annakkage, U.D., Rajapakse, A.D., “Online monitoring of voltage stability margin using an artificial neural network,” IEEE Trans. Power Syst., Vol.25, pp.1566-1574,2010.

10. Gao, B., Morison, G.K., Kundur, P., “Voltage stability evaluation using modal analysis,” IEEE Trans. Power Syst., Vol.7, pp.1529– 1542,1992. 11. Iba, K., Suzuli, H., Egawa, M., Watanabe, T., “Calculation of the critical loading with nose curve using homotopy continuation method,” IEEE Trans. Power Syst., Vol.6, pp.584–593, 1991. 12. Musirin I, KhawaT, Rahman A, “Novel fast voltage stability index (FVSI)for voltage stability analysis in power transmission system,” In:Proceedings of the student conference on research and development proceedings, pp.265–268,2002. 13. MoghavvemiM,OmarFM,“Techniqueforcontingencymonitoringand voltage collapse prediction,” IEE Proc. Gener. Transm. Distrib. Vol.145, pp.634–640,1998. 14. Mohamed A, Jasmon G B, YusoffS, “A static voltage collapse indicator using line stability factors,”Journal Ind. Technol. Vol.7, pp.73– 85,1989. 15. Moghavvemi M, Faruque M O, “Technique for assessment of voltage stabilityinill- conditionedradialdistributionnetwork,”IEEEPowerEng. Rev. Vol.21, pp.58–60,2001. 16. Yazdanpanah-Goharrizi A, Asghari R, “Anovel line stability index (NLSI) for voltage stability assessment of power systems,” In: Pro- ceedings of the international conference on power systems, pp.165–167, 2007. 17. ParthaKayal and Chandan Kumar Chanda, “A simple and fast approach forallocationandsizeevaluationofdistributedgeneration,”International Journal of Energy and Environmental Engineering, Vol.4(1), pp.1– 9, 2013. 18. Sahari S, AbidinAF, Rahman T K A, “Development of artificial neural network for voltage stability monitoring,” In: Proceedings of the power and energy conference, pp.37–42,2003. 19. Eminoglu U, Hocaoglu M H, “A voltage stability index for radial distri- butionnetworks,”In:ProceedingsoftheUniversities’powerengineering conference, pp.408–413,2007. 20. JavadModarresi, EskandarGholipour, Amin Khodabakhshian, “A com- prehensive review of the voltage stability indices,” Renewable and SustainableEnergyReviews,Vol.63,pp.1–12,2016. Authors: Sudesh Sharma, Sarvesh Kumar, Chandra Prakash, Verma Hemant Gaur. Paper Title: Harmony Search Algorithm for solving m Connected Coverage Problem in WSN Abstract: Addressing the coverage problem is not a complete set of tasks for solving data aggregation in Wireless Sensor Networks. Since the collected information of each sensor node to reach the base station the deployment of sensors plays a critical role in WSN. This paper addresses m connected coverage problem which covers all the given targets and provide a complete connectivity between the sensors for effective data aggregation of data to the base station. A widespread Harmony Search Algorithm which is a metaheuristic algorithm for solving optimization problems is imposed in this sensor deployment concept. The results of the proposed algorithm have been compared with other existing techniques and the results shows that proposed algorithm outperforms existing algorithms.

Keywords: Wireless Sensor Network, Harmony Search, m connected coverage. References:

1. Akyildiz, I. F., Su, W., Sankarasubramaniam, Y., &Cayirci, E. (2002). Wireless sensor networks: a survey. Computer networks, 38(4), 393-422. 2. Jain, E., & Liang, Q. (2005). Sensor placement and lifetime of wireless sensor networks: theory and performance analysis. In Global Telecommunications Conference, 2005. GLOBECOM'05. IEEE(Vol. 1, pp. 5-pp). IEEE. 3. Liu, Z. (2007). Maximizing network lifetime for target coverage problem in heterogeneous wireless sensor networks. Mobile Ad-Hoc and Sensor Networks, 457-468. 76. 4. Heinzelman, W. R., Chandrakasan, A., & Balakrishnan, H. (2000, January). Energy-efficient communication protocol for wireless 393-396 microsensor networks. In System sciences, 2000. Proceedings of the 33rd annual Hawaii international conference on(pp. 10-pp). IEEE. 5. Costa, D. G., & Guedes, L. A. (2010). The coverage problem in video-based wireless sensor networks: A survey. Sensors, 10(9), 8215- 8247. 6. Cheng, Xiuzhen, Liran Ma, Baogui Huang, Ying Chen, and Jiguo Yu. "On Connected Target k-Coverage in Heterogeneous Wireless Sensor Networks." (2016). 7. Wang, Yun, Xiaodong Wang, Dharma P. Agrawal, and Ali A. Minai. "Impact of heterogeneity on coverage and broadcast reachability in wireless sensor networks." In Computer Communications and Networks, 2006. ICCCN 2006. Proceedings. 15th International Conference on, pp. 63-67. IEEE, 2006. 8. Lazos, Loukas, and Radha Poovendran. "Stochastic coverage in heterogeneous sensor networks." ACM Transactions on Sensor Networks (TOSN) 2, no. 3 (2006): 325-358. 9. Du, Xiaojiang, and Fengjing Lin. "Maintaining differentiated coverage in heterogeneous sensor networks." EURASIP Journal on Wireless Communications and Networking 2005, no. 4 (2005): 565-572. 10. Zorbas, Dimitrios, and Christos Douligeris. "Connected coverage in WSNs based on critical targets." Computer Networks 55, no. 6 (2011): 1412-1425. 11. Cardei, Mihaela, and Ding-Zhu Du. "Improving wireless sensor network lifetime through power aware organization." Wireless Networks 11, no. 3 (2005): 333-340. 12. Cardei, Mihaela, My T. Thai, Yingshu Li, and Weili Wu. "Energy-efficient target coverage in wireless sensor networks." In INFOCOM 2005. 24th annual joint conference of the ieee computer and communications societies. proceedings ieee, vol. 3, pp. 1976-1984. IEEE, 2005. Authors: Abhishek Thakur, Neeru Jindal. Paper Title: Geometrical Attack Classification using DCNN and Forgery Localization using Machine Learning Abstract: Manipulation of images is frequently happening nowadays for false propaganda and also for illegal advantage. Only the manipulation of images are not sufficient as an evidence. These are considered only after valuable forensic investigation. The most common forgeries are copy move and splicing. It is very important to

detect the realness of digital images which cause a grave threat to the society. This paper is about copy move, splicing forgery classification of various geometrical attacks. The deep convolution neural network is used to classify images into forged or not forged and also classify which type of forgery is present.

Keywords: Image Forensics (IF), Deep Learning (DL), Convolution Neural Network (CNN), Color Illumination (CI), Copy-move Forgery (CMF), Splicing Forgery (SF).

References: 397-401 77. 1. T.J. Carvalho, C. Riess, E. Angelopoulou, E. Pedrini, H., & A., Rocha, IEEE Transactions on Information Forensics and Security, Exposing Digital Image Forgeries by Illumination Color Classification, (2013); 8:1182-1194. 2. A. Thakur, & N. Jindal, Multimedia Tools and Application, Image Forensics Using Color Illumination, Block and Key Point Based Approach, (2018); 77: 26033. 3. C.S. Prakash, A. Kumar, S. Maheshkar et al., Multimedia Tools and Application, An integrated method of copy-move and splicing for image forgery detection, (2018). 4. D. Tralic, I. Zupancic, S. Grgic, M. Grgic, 55th International Symposium ELMAR, CoMoFoD - New Database for Copy Move Forgery Detection, (2013); 49-54. 5. P. Gao, H. Zhang, R. Guo, J. Liu, L. Ma, J. Zhang and Q. He., National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Science, CASIA Image Tempering Detection Evaluation Database (CAISA TIDE) V1.0 and v2.0, http://forensics.idealtest.org. 6. A. Nadig and W. Harwell George et al., DVMM Laboratory of Columbia University, Columbia Image Splicing Detection Evaluation Dataset, http://www.ee.columbia.edu/ln/dvmm/downloads/ AuthSplicedDataSet/photographers.htm. 7. Y. Rao, J. Ni. A., IEEE Int. Workshop on Information Forensics and Security, Deep Learning Approach to Detection of Splicing and Copy-Move Forgeries in Images, (2016). Authors: Yashvi Jain, NamrataTiwari, ShripriyaDubey,Sarika Jain. Paper Title: A Comparative Analysis of Various Credit Card Fraud Detection Techniques Abstract: Fraud is any malicious activity that aims to cause financial loss to the other party. As the use of digital money or plastic money even in developing countries is on the rise so is the fraud associated with them. Frauds caused by Credit Cards have costs consumers and banks billions of dollars globally. Even after numerous mechanisms to stop fraud, fraudsters are continuously trying to find new ways and tricks to commit fraud. Thus, in order to stop these frauds we need a powerful fraud detection system which not only detects the fraud but also detects it before it takes place and in an accurate manner. We need to also make our systems learn from the past committed frauds and make them capable of adapting to future new methods of frauds.In this paper we have introduced the concept of frauds related to credit cards and their various types. We have explained various techniques available for a fraud detection system such as Support Vector Machine (SVM), Artificial Neural Networks (ANN), Bayesian Network, K- Nearest Neighbour (KNN), Hidden Markov Model, Fuzzy Logic Based System and Decision Trees. An extensive review is done on the existing and proposed models for credit card fraud detection and has done a comparative study on these techniques on the basis of quantitative measurements such as accuracy, detection rate and false alarm rate. The conclusion of our study explains the drawbacks of existing models and provides a better solution in order to overcome them.

Keywords: Neural Network, Genetic Algorithm, Support Vector Machine, Bayesian Network, K- Nearest Neighbour, Hidden Markov Model, Fuzzy Logic Based System, Decision Trees.

References: 1. d. l. g. s. chandrahas mishra, “credit card fraud detection using neural networks,” international journal of comoputer science, vol. 4, no. 7, July 2017. 2. h. s.,. j. g. d.,. b. snehal patil, “credit card fraud detection using decision tree induction algorithm,” international journal of computer science and mobile computing, vol. 4, no. 4, pp. 92-95. 3. a. a. pansy khurana, “credit card fraud detection using fuzzy logic and neural network,” SpringSim, 2016. 4. a. a. nancy demla, “credit card fraud detection using svm and reduction of false alarms,” inyternation journal of innovations in engineering and technology, vol. 7, no. 2, 2016. 5. D. S. G. S.Saranya, “fraud detection in credit card transaction using bayesian network,” international research journal of engineering and technology, vol. 4, no. 4, April 2017. 6. T. R. C.Sudha, “credit card fraud detection in internet using k nearest neighbour algorithm,” IPASJ international journal of computer science, vol. 5, no. 11, 2017. 7. a. k. s.,. m. abhinav srivastava, “credit card fraud detection using hidden markov model,” IEEE, vol. 5, no. 1, 2008. 8. E. D. Yusuf Sahin, “detecting credit card fraud by ann and logistic regression,” 2011. 402-407 9. R. M. jamail esmaily, “Intrusion detection system based on multilayer perceptron neural networks and decision tree,” in International conference on Information and Knowledge Technology, 2015. 10. S. J. K. T. J. C. W. Siddhatha Bhattacharya, “Data Mining for credit card fraud: A comparative study,” Elsevire, vol. 50, no. 3, pp. 602- 613, 2011. 11. “Raghavendra Patidar and Lokesh Sharma,” International Journal of soft computing and engineering, vol. 1, no. NCAI2011, 2011. 12. s. p. tanmay kumar behera, “credit card fraud detection: a hybrid approach using fuzzy clustering and neural network,” in international conference on advances in computing and communication Engineering, 2015. 13. N. W. Wen -Fang Yu, “Research on credit card fraud detection model based on distance sum,” in International joint conference on artificial intelligence, Hainan Island,China, 2009. 14. S. k. A. K. M. Ayushi agarwal, “Credit card fraud detection: A case study,” in IEEE, New Delhi, India, 2015. 15. K. T. B. V. Sam Maes, “Credit cards fraud detection using bayesian and neural networks,” p. 7, August 2002. 78. 16. P. K. D. K. R. D. A. A. Thuraya Razoogi, Credit card fraud detection using fuzzy logic and neural networks, Society for modelling and simulation International(SCS), 2016. 17. E. D. Y. Sahin, “Detecting credit card fraud by decision trees,” in Proceedings of the international multiconference of engineers and computer science, Hong Kong, 2011. 18. P. M.,. S. H. Geoffrey F.Miller, “Designing Neural networks using genetic algorithms,” [Online]. Available: https://static1.squarespace.com/static/58e2a71bf7e0ab3ba886cea3/t/5909113c1b631b40f8137956/1493766462349/1989+neural+netwo rks.pdf. 19. M. H. O. Ekrem duman, “Detecting credit card fraud by genetic algorithm and scatter search,” Expert Systems with applications: An International Journal, vol. 38, no. 10, pp. 13057-13063, 2011. 20. M. K. A. N. Alireza Pouramirarsalani1, “Fraud detection in E- Banking by using the hybrid feature selection and evolutionary algorithms,” International Journal Of Computer Science and Network Security, vol. 17, no. 8. 21. A. T. P. K. M. G. P. N. Priya Chougle, “Genetic K- Means Algorithm for credit card fraud detection,” International journal of computer science and information technologies, vol. 6, pp. 1724-1727, 2015. 22. O. O. O. A.,. W. Stephen Fashoto, “Hybrid Methods for credit card fraud detection,” Kampala International University, Kampala, Uganda, University of Abuja, Nigeria, Redeemer's University, Ede, Osun State, Nigeria, [Online]. Available: http://www.journalrepository.org/media/journals/BJAST_5/2015/Dec/Fashoto1352015BJAST21603.pdf. 23. M. A. S. K. M. S. MR HaratiNik, “FUZZGY model,” [Online]. Available: https://ieeexplore.ieee.org/document/6483148. 24. M. A. P. Krishna K. Tripathi, “Survey on credit card fraud detection methods,” 1Computer Engg., M.E Computer, TERNA Engg College NERUL, Mumbai University, Mumbai, Maharashtra, India., [Online]. Available: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.414.3256&rep=rep1&type=pdf. 25. A. O. A. S. A. O. John o. Awoyemi, “credit cars fraud detection using machine learning techniques: A comparative analysis,” in International conference on computing networking and infomatics. 26. E. Aji M. Mubarek, “Multilayer perceptron neural network technique for fraud detection,” in International Conference on Computer Science and Engineering(UBMK), 2017. 27. S. K. M. A. Masoumeh Zareapoor, “analysis of credit card fraud detection techniques: based on design criteria,” in international journal of computer applications, 2012. 28. J. I. T. L. N. de Castro, “Artificial immune systems as a novel soft computing paradigm,” Journal of Soft Computing, vol. 7, p. 526– 544, 2003. 29. A. S. Wheeler R, “ Multiple algorithms for fraud detection. Knowledge-Based Systems,” no. S0950-7051(00)00050-2, 2000. Authors: Uma Meena, Anand Sharma. An Efficient Hop-by-Hop Message Authentication Scheme and Secure Location Privacy in Wireless Paper Title: Sensor Networks Abstract: Wireless sensor network in recent days affected with two main research problem such as message authentication and location privacy. This paper present an Elliptic Curve ElGamal Signature Algorithm scheme (ECESA) for message authentication and Euclidean Zigzag Bidirectional Tree (EZBT) for location privacy of both source and sink. ECESA involves three phase: (i) private and public key generation using Elliptic Curve Cryptography (ECC), (ii) ElGamal signature arrangement for effective message encryption and (iii) matching the decrypted result with MD5 hash value for authentication of the authorized person. The most important privacy preserving techniques are the EZBT to send the messages either sink to source or from source to sink with the location privacy scheme. On account of this, the proxy source and sink is selected while using the Euclidean distance technique. Finally, the efficiency of the work has been demonstrated through the simulation results of location privacy and message verification. Then the performance are validated in terms of quality of service (QoS).

Keywords: Elliptic curve cryptography, ElGamal encryption, MD5 hash algorithm, location privacy, Euclidean distance, Zigzag bidirectional tree.

References: 1. Franklin M, Gelles R, Ostrovsky R, Schulman LJ (2015 Jan) Optimal coding for streaming authentication and interactive communication. IEEE Transactions on Information Theory, 61(1):133-45. 2. Sultana S, Ghinita G, Bertino E, Shehab M (2015 May) A lightweight secure scheme for detecting provenance forgery and packet dropattacks in wireless sensor networks. IEEE transactions on dependable and secure computing, 12(3):256-69. 3. Rivest RL, Shamir A, Adleman L(1983 Jan 1)A method for obtaining digital signatures and public-key cryptosystems. Communications of the ACM, 26(1):96-9. 4. Du W, Deng J, Han YS, Varshney PK, Katz J, Khalili A (2005 May) A pairwise key predistribution scheme for wireless sensor networks. ACM Transactions on Information and System Security (TISSEC), 1; 8(2):228-58. 5. Pointcheval D, Stern J (2000 Dec 24) Security arguments for digital signatures and blind signatures. Journal of cryptology, 13(3):361- 96. 6. Karlof C, Wagner D (2003 Sep 30)Secure routing in wireless sensor networks: Attacks and countermeasures. Ad hoc networks, 1(2):293-315. 7. Chaum D (1988 Jan 1) The dining cryptographers problem: Unconditional sender and recipient untraceability. Journal of cryptology, 1(1):65-75. 8. Lu R, Lin X, Zhu H, Liang X, Shen X (2012 Jan) BECAN: a bandwidth-efficient cooperative authentication scheme for filtering injected false data in wireless sensor networks. IEEE transactions on parallel and distributed systems, 23(1):32-43. 9. ElGamal T (1985 Jul)A public key cryptosystem and a signature scheme based on discrete logarithms. IEEE transactions on information theory, 31(4):469-72. 408-413 79. 10. Zhu S, Setia S, Jajodia S, Ning P. Interleaved hop-by-hop authentication against false data injection attacks in sensor networks. ACM Transactions on Sensor Networks (TOSN) 3(3):14. 11. Fouda MM, Fadlullah ZM, Kato N, Lu R, Shen XS (2011 Dec) A lightweight message authentication scheme for smart grid communications. IEEE Transactions on Smart Grid, 2(4):675-85. 12. Fouda MM, Fadlullah ZM, Kato N, Lu R, Shen XS (2011 Dec) A lightweight message authentication scheme for smart grid communications. IEEE Transactions on Smart Grid, 2(4):675-85. 13. Reiter MK, Rubin AD(1998 Nov1). Crowds: Anonymity for web transactions. ACM transactions on information and system security (TISSEC), 1(1):66-92. 14. Liu D, Ning P, Li R (2005 Feb 1) Establishing pairwise keys in distributed sensor networks. ACM Transactions on Information and System Security (TISSEC), 8(1):41-77. 15. Dolev S, Ostrobsky R (2000 May1)Xor-trees for efficient anonymous multicast and reception. ACM Transactions on Information and System Security (TISSEC), 3(2):63-84. 16. Wang Q, Wang C, Ren K, Lou W, Li J. Enabling public auditability and data dynamics for storage security in cloud computing. IEEE transactions on parallel and distributed systems 22(5):847-59. 17. He X, Niedermeier M, De Meer H (2013 Mar 31) Dynamic key management in wireless sensor networks: A survey. Journal of Network and Computer Applications, 36(2):611-22. 18. Hu YC, Johnson DB, Perrig A (2012 Jul) SEAD: Secure efficient distance vector routing for mobile wireless ad hoc networks. Ad hoc networks, 1(1):175-92. 19. PV G, Rajesh S (2014May 29) Hop-by-Hop Message Validation and Source Privacy in Wireless Sensor Networks. IJITR 2014 May 29, 2(3):923-32. 20. Ren K, Lou W, Zhang Y (2008 May) LEDS: Providing location-aware end-to-end data security in wireless sensor networks. IEEE Transactions on Mobile Computing, 7(5):585-98. 21. Li Y, Ren J, Wu J (2012 Jul) Quantitative measurement and design of source-location \privacy schemes for wireless sensor networks. IEEE Transactions on Parallel and Distributed Systems, 23(7):1302-11. 22. Li H, Lin K, Li K (2011 Apr 1) Energy-efficient and high-accuracy secure data aggregation in wireless sensor networks. Computer Communications, 34(4):591-7. 23. Lu R, Lin X, Zhu H, Liang X, Shen X (2012 Jan) BECAN: a bandwidth-efficient cooperative authentication scheme for filtering injected false data in wireless sensor networks. IEEE transactions on parallel and distributed systems, 23(1):32-43. 24. Chen H, Lou W (2015 Jan 31)On protecting end-to-end location privacy against local eavesdropper in wireless sensor networks. Pervasive and Mobile Computing, 16:36-50. Authors: S.Naveen Kumar, Manoj. Kumar. Rath, P.Markandeya Raju. Paper Title: A Review on Utilization of Crumb rubber in various ingredients of Concrete Abstract: Urbanisation and the day to day exponential increase in the number of automobileshas 80. increased the usage of rubber. Due to this, the amount of scrap rubber is also increasingwhich is generally left for scrap deposition in landfills. According to a recent survey, it is estimated that the rubber scrap will reach 24-26 nearly 1.2 billion tonnes annually by end of 2030. Scrap tyres are also harm to environment as they are non– biodegradable and a good catchment area for breading of mosquitoes androdents. Large amounts of cross-ply rubber are also being deposited along the path of aircraft runways which is a huge threat in terms of skid resistance factor of aircrafts.Further, there is a limitation in recycling of these in the use of crumb rubber as well as polymer fibre material. As anattempt to reusethis waste, many experimental studies are carried out using it as a filler material in concrete industry. This paper presents a review of the work carried out by the past and recent researchers who studied the fresh and hardened properties of concrete with crumb rubber as anauxiliary material.

Keywords: Bias tyre, Crumb rubber, Pre-treatment, Radial tyre.

References: 1. Musa Adamu, Bashar S Mohammed and Nasir Sharif, “Effect of Polycaboxylate super plasticizer dosage on the mechanical performance of roller Compacted Rubbercrete for Pavement Applications”, Journal of Engineering and Applied Sciences, pg.no.5253- 5260, 2017. 2. Musa Adamu, Bashar S Mohammed and Nasir Sharif, “Mechanical Performance of Roller Compacted Rubbercrete with different mineral filler”, Journal Technology (Science & Engineering), pg.no.75-88, Aug 2017. 3. J. Retama & A.G. Ayala, “Influence of Crumb rubber in Mechanical response of Modified Portland Cement Concrete”, Advances in Civil Engineering, vol.17, 2017. 4. Mohammed Safan, Fatma M. Eid & Mahamoud Aweal, “Enhanced Properties of Crumb Rubber and its applications in Rubberized Concrete”,vol.7,pg.no.1784-1790,Sep/Oct 2017. 5. Khubshbu Tak & Uttam Panchori, “Surface Modification of Crumb rubber & its influence on the Mechanical Properties of Rubber- Cement Concrete”, International Conference on Communication & Computational Technologies, pg.404-411, Dec, 2017. 6. Mwaya Temina Sendwa, Mohd Nizam Shakimon “Replacing Fine Aggregate with Tyre Rubber Pre-treated in Sodium Hydroxide”, vol.3, issue.1, IJSREST, 2017. 7. Samaneh Pourmohammadimojaveri, B.Samali, G. Adam, “An Investigation on waste tyre rubber treatment to use as Aggregate in Concrete material”, Current Trends in Bio Medical Engineering & Bio Science, July 2017. 8. Alsayed M.Abdullah,Ghada s. Mousa, Zainab E. Abd El-Shafy,Mohamed Ashour Mohamed, “ Investigation on improving Rigid Pavement Properties by adding recycled rubber”,vol.46,pg.no.1-11,Jan 2017. 9. N. Nilesh & Rathi, “Experimental study on Concrete by Partial Replacement of Fine Aggregate with Pre-treated Crumb Rubber”, International Journal of Innovations in Engineering Sciences & Technology, vol.3, issue.2, pg.no.10-20, 2017. 10. Nikhil Ramachandra Pardeshi, Dig Vijay P. Singh, Sakshi Ramesh Patil,Pravin J. Gorde, Prachity P. Janrao ,“ Performance and Evolution of Rubber as Concrete Material ”,vol.4,issue.1,Jan 2017. 11. Hanbing Liu, Xianqiang Wang, Yubo Jiao and Tao Sha, “Experimental Investigation of the Mechanical and Durability properties of Crumb Rubber Concrete”, Materials 2016,9,172,March 2016. 12. Musa Adamu, “Nano Silica modified roller Compacted rubbercrete-An overview”, Taylor & Francis group, pg. no.484-487, 2016. 13. Nabeel Hamid Shah,B.K.Singh, M.S.Yati Agarwal, “Use of tyre rubber crumb as replacement of Fine Aggregate in Cement Concrete”, International Journal of Innovative research in Technology,vol.3,issue4,pg.no.123-129,Sep-2016. 14. Osama Youssf, Julie E. Mills, Reza Hassanli, “Assessment of Mechanical Performance of Crumb rubber Concrete”, Construction and Building Materials,vol.125,pg.175-183,2016. 15. Anne & Russ Evans, “The Composition of a Tyre: Typical Components”, Waste & RESOURCES Action Programme, May 2016. Authors: Sahith Reddy Madara , Chithirai Pon Selvan , Sampath SS , Swaroop Ramaswamy Pillai. Impact of Process Parameters on Surface Roughness of Hastelloy using Abrasive Waterjet Machining Paper Title: Technology Abstract: Abrasive waterjet cutting is one of the unconventional cutting processes capable of cutting extensive range of difficult-to-cut materials. This paper assesses the impact of process parameters on surface roughness which is a significant machining performance measure in abrasive waterjet cutting of hastelloy. The experimental parameters were selected based on Taguch’s design of experiments. Experiments were conducted in varying nozzle traverse speed, abrasive mass flow rate and standoff distance for cutting hastelloy using abrasive waterjet cutting process. The effects of these parameters on surface roughness have been discussed.

Keywords: Hastelloy; mass flow rate; traverse speed; standoff distance; garnet . 81. References: 1. Muath Al-Falahi, B. T. Baharudin, Tang Sai Hong & Khamirul Amin Matori, “Surface Defects in Groove Milling of Hastelloy- C276 24-26 Under Fluid Coolant”, Taylor & Francis - Materials and Manufacturing Processes, 1-9, 2016. 2. Sk.Khadar Basha, Murahari Kolli, M.V.Jagannadha Raju, “Parametric optimization of Edm on Hastelloy C-276 Using Taguchi L18 Technique”, International Journal of Engineering & Technology, vol.7, 714-716, 2018. 3. Chithirai Pon Selvan M, Sahith Reddy Madara, Sampath S, Sarath Raj N S, “Effects of Process Parameters on Depth of Cut in Abrasive Waterjet Cutting of Phosphate Glass”, IEEE Xplore, 1-6, 2018. 4. S.Jaya Kishore, K.Siva Kumar, Dr.M.Narayana Rao, “Experimental Parametric Studies on Hastelloy Using Abrasive Water Jet Machining”, International Conference on Precision, Mesco, Micro and Nano Engineering – Indian Institute of Technology Madras, 1-5, 2017. 5. Guo Y, Wu D, Ma G, “Trailing heat sink effects on residual stress and distortion of pulsed laser welded Hastelloy C-276 thin sheets”, Journal of Materials Processing Technology, vol.214, iss.12, 891-2899, 2014. Authors: G.Niranjana, V. Aumugam. Paper Title: Improvement of Single Page Application in responsive design using Web API and Angular JS Abstract: In traditional web applications, the communication with the server is initiated by the client by sending a page request. The request is processed by the server and the resulting webpage is sent to the client. For subsequent interactions within the page like a link is navigated or a form is submitted with data, a new request is 82. generated and sent to the server. The server repeats the sequence of action for processing the request and the response is generated by sending a new page to the client. When using Single Page Applications (SPAs), the 24-26 initial request is processed and the entire page is sent to the client/browser, and further interaction takes place through Aax requests. This means the entire page is not reloaded only the portion of the page that has changed is updated by the browser. This approach increases the response time of the application. Emerging technologies like ASP.NET Web API, JavaScript frameworks like AngularJS and new styling features provided by HTML5 & CSS3 make it really easy to design and build SPAs. Our result shows 10 times improvement in initial loading time and the server response time is reduced to half.

Keywords: ASP.NET Web API, HTML5 And CSS3. References: 1. Yanyan Lu, Haiyan Wu, Yingxue Wang , “ Web application performance analysis based on comprehensive load testing “, IET International Conference on Wireless Mobile and Multimedia Networks Proceedings (ICWMMN 2006), 2006, p. 386 - 386 2. Ye Zhou, Yang Ji, “ Design of rest APIS for the exposure of IMS capabilities towards Web services”, IET International Conference on Communication Technology and Application (ICCTA 2011), 2011, p. 526 - 530 3. L. Baresi, D. Bianculli, C. Ghezzi, S. Guinea, P. Spoletini, “ Validation of web service compositions “, IET Software, Volume 1, Issue 6, 2007 , p. 219 - 232 4. Ali Mesbah, Arie van Deursen, “Migrating Multi-page Web Applications to Single-page Ajax Interfaces” Report TUD-SERG-2006-018 2nd revision. 5. http://www.w3schools.com/angular, “Angular JS Tutorial” w3schools.com. 6. http://www.asp.net/web-api, “Web API tutorial” Microsoft asp.net site. 7. http://www.asp.net/single-page-application, “Single page application tutorial” 8. http://www.w3schools.com/bootstrap/, “Bootstrap Tutorial” w3schools.com. 9. https://en.wikipedia.org/wiki/Single-page_application, “About Single page application” Wikipedia.org. 10. http://www.w3schools.com/ajax/, “Ajax tutorial” w3schools.com. 11. https://msdn.microsoft.com/en-us/library/hh833994(v=vs.108).aspx, “Web API tutorial” Microsoft MSDN site. 12. http://blogs. msdn. com /b /martinkearn /archive /2015 /01/05 /introduction -to-rest-and-net-web-api.aspx, “Introduction to REST and .net Web API” Microsoft MSDN site. 13. https://angularjs.org/, “Angular JS Introduction” angularjs.org. 14. https://docs.angularjs.org/api, “Angular JS documentation” docs.angularjs.org. 15. Madhuri A. Jadhav Balkrishna R. Sawant, Anushree Deshmukh, “Single Page Application using AngularJS”, International Journal of Computer Science and Information Technologies, Vol. 6 (3) , 2015, 2876-2879 Authors: K.m.hemambaran, s.a.k. Jilani. Paper Title: Mask-Nha Based Image Denoising with Random Walker Segmentation Abstract: The search for well-organized image de-noising techniques is still a valid challenge at the crossing of functional learning and statistics. In spite of the refinement of the currently proposed methods, most algorithms have not yet succeeded a desirable level of applicability. In order to reduce the drawbacks in the earlier methods, a novel algorithm probabilistic method is associated as two-dimensional non-harmonic analysis called mask non-harmonic analysis such a way that the noise is degraded in the input image. In this, the entire region of the image is considered as homogeneous texture. But when the noise content is more, the segmentation of a noisy image into original images become more complex. Hence, Random walker segmentation is implemented for segmentation with canny detection algorithm in order to preserve edges. Then the regions obtained from the segmentation are analyzed using mask NHA algorithm. Theoretical analysis and experimental results are reported to illustrate the usefulness and potential applicability of our algorithm on various computer vision fields, including image enhancement, edge detection, image decomposition, and other applications.

Keywords: Image de-noising, 2D-NHA, Segmentation, Random Walker, Canny edge detection, Mask NHA..

References: 1. Zhu, L., Fu, C.W., Brown, M.S., Heng, P.A.: A non-nearby low-rank framework for ultrasound speckle discount. In: CVPR. (2017) 56505658 2. Granados, M., Kim, K., Tompkin, J., Theobalt, C.: Automatic noise modeling for ghost-free hdr reconstruction. ACM Trans. Graph. 32(6) (2013) 3. Nguyen, A., Yosinski, J., Clune, J.: Deep neural networks are effortlessly fooled: High confidence predictions for unrecognizable 83. pictures. In: CVPR. (2015) 427436 1 4. Elad, M., Aharon, M.: Image denoising via sparse and redundant representations over discovered dictionaries. IEEE Transactions on Image Processing 15(12) (2006) 37363745 24-26 5. Xu, J., Zhang, L., Zhang, D., Feng, X.: Multi-channel weighted nuclear norm minimization for actual coloration photo denoising. In: ICCV. (2017) 6. Kai Zhang, Wangmeng Zuo et.Al, FFDNet: Toward a Fast and Flexible Solution for CNN based Image Denoising, in part supported with the aid of the National Natural Scientific Foundation of China (NSFC) beneath Grant No. 61671182 and 61471146 7. K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, “Image denoising by sparse three-D remodel-domain collaborative filtering,” IEEE Transactions on Image Processing, vol. Sixteen, no. 8, pp. 2080–2095, 2007. 8. Fei Xu, Yongyong Chen, Chong Peng (2017). Denoising of Hyperspectral Image Using Low-Rank Matrix Factorization, IEEE Geoscience and Remote Sensing Letters. 9. Leo Grady: Random Walks for Image Segmentation, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 28, No. Eleven, Nov. 2006 10. K. Sinop and L. Grady. A seeded picture segmentation framework unifying graph cuts and random walker which yields a brand new set of rules. In ICCV, 2007. 11. Yoshizawa, T., Hirobayashi, S., & Misawa, T. (2011). Noise discount for periodic alerts using high-decision frequency evaluation. EURASIP Journal on Audio, Speech, and Music Processing, 2011(1), 5. 12. Chunhua Dong, Xiangyan Zeng: An Improved Random Walker with Bayes Model for Volumetric Medical Image Segmentation, Journal of Healthcare Engineering Volume 2017, Article ID 6506049 13. Hasegawa, M., Kako, T., Hirobayashi, S., Misawa, T., Yoshizawa, T., & Inazumi, Y. (2013). Image inpainting on the basis of spectral structure from 2-D nonharmonic evaluation. IEEE Transactions on Image Processing, 22(eight), 3008-3017. 14. Marc Lebrun (2012). An Analysis and Implementation of the BM3D Image Denoising Method.IPOL magazine photograph processing online,2012(2). 15. Fei Xu, Yongyong Chen, Chong Peng (2017). Denoising of Hyperspectral Image Using Low-Rank Matrix Factorization, IEEE Geoscience and Remote Sensing Letters . 16. Ma, H., & Nie, Y. (2016). An side fusion scheme for photograph denoising primarily based on anisotropic diffusion fashions. Journal of Visual Communication and Image Representation, 40, 406-417. 17. K.M.Hemambaran, Dr.S.A.K.Jilani (2018). HRFA Noise Removal and Segmentation: A Review, International Journal of Emerging Technologies and Innovative studies, 5(7), 464-468. 18. K.M.Hemambaran , Dr. S.A.K. Jilani, HRFA Based Image Denoising With Edge Preserve Segmentation IOSR Journal of Electronics and Communication Engineering (IOSR-JECE), ISSN: 2278-8735.Volume thirteen, Issue 6, PP 27-34. Authors: K Rajasekar, S.Sunithamani. Paper Title: Low setting time offering series capacitive RF MEMS switch for Wi-Fi applications Abstract: In this paper, a novel cantilever series capacitance based RF MEMS switch proposed for Wi-Fi applications. We have extended the analysis on the effect of quality factor on the switch on settling time. The holes in the cantilever help the switch to reduce the settling time. The quality factor of the switch designed with Al cantilever is 0.49. Low quality factor indicated damping effect is reduced. Si3N4 is used as dielectric material, with this the switch is offering high capacitance ratio. The series switch is offering high isolation at 2.4 GHz, so the proposed switch can be used in Wi-Fi applications as an isolator. The isolation losses of the switch is -30 dB, insertion loss is -0.34 dB and pull-in voltage required for the switching is 20 V.

Keywords: Series Switches, Dielectric Losses, Cantilever, CPW Transmission Line. References: 1. Tejinder Singh, Alaa Elhady, Huayong Jia, Ali Mojdeh, Cemalettin Kaplan, Vipul Sharm, Mohamed Bashab, Eihab Abdel-Rahmanb, “Modeling of low-damping laterally actuated electrostatic MEMS”, Mechatronics, https://doi.org/10.1016/j.mechatronics.2018.03.009, 52 (2018) 1–6. 2. Jasmina Casals-Terré, Marco A. Llamas, David Girbau, Lluís Pradell, Antonio Lázaro, Flavio Giacomozzi, and Sabrina Colpo, “Analytical Energy Model for the Dynamic Behavior of RF MEMS Switches Under Increased Actuation Voltage”, Journal of Microelectromechanical Systems, Vol. 23, No. 6, December 2014. 3. Cuong Do, Maryna Lishchynska, Marcin Cychowski, Kieran Delaney, and Martin Hill,” Energy-Based Approach to Adaptive Pulse Shaping for Control of RF-MEMS DC-Contact Switches”, Journal of Microelectromechanical Systems, Vol. 21, No. 6, December 2012. 4. Subrata Halder, Cristiano Palego, Zhen Peng, James C. M. Hwang, David I. Forehand, and Charles L. Goldsmith, ” Compact RF Model for Transient Characteristics of MEMS Capacitive Switches”, IEEE Transactions On Microwave Theory And Techniques, Vol. 57, No. 1, January 2009. 5. Feixiang Kea, Jianmin Miaob, Chee Wee Tanb, “Reduction of squeeze-film damping in a wafer-level encapsulated RF MEMS DC shunt switch”, Sensors and Actuators A, doi:10.1016/j.sna.2011.07.015, 171 (2011) 118– 125. 84. 6. Peter Kolis, Anil K. Bajaj, and Marisol Koslowski,” Quantification of Uncertainty in Creep Failure of RF-MEMS Switches”, Journal of Microelectromechanical Systems, Doi:10.1109/JMEMS.2016.2636841, 2016. 24-26 7. M. Niessnera, G. Schraga, G. Wachutkaa, J. Iannaccib, T. Reuttera, H. Mulatzc,” Non-linear model for the simulation of viscously damped RF-MEMS switches at varying ambient pressure conditions”, Procedia Chemistry, doi:10.1016/j.proche.2009.07.154, 1 (2009) 618–621. 8. M. Niessnera, J. Iannaccib, A. Pellera, G. Schraga, G. Wachutkaa,” Macromodel-based simulation and measurement of the dynamic pull-in of viscously damped RF-MEMS switches”, Procedia Engineering, doi:10.1016/j.proeng.2010.09.052, 5 (2010) 78–81. 9. Martin Niessnera, Gabriele Schraga, Jacopo Iannaccib, Gerhard Wachutkaa,” Macromodel-based simulation and measurement of the dynamic pull-in of viscously damped RF-MEMS switches”, Sensors and Actuators A, doi:10.1016/j.sna.2011.04.046, 172 (2011) 269– 279. 10. Ryan C. Tung, Adam Fruehling, Dimitrios Peroulis, and Arvind Raman,” Multiple Timescales and Modeling of Dynamic Bounce Phenomena in RF MEMS Switches”, Journal Of Microelectromechanical Systems, Vol. 23, No. 1, February 2014. 11. G.M. Rebeiz. “RF MEMS Theory design and technology,” Wiley publishers, 2003. 12. Maher Bakri-Kassem and Raafat R. Mansour,” High Power Latching RF MEMS Switches” IEEE Transactions on Microwave Theory and Techniques, vol. 63, no. 1, January 2015. 13. Anna Persano, Fabio Quaranta, Maria Concetta Martucci, Pietro Siciliano, Adriano Cola,“On the electrostatic actuation of capacitive RF MEMS switches onGaAs substrate”, Sensors and Actuators A, 232,202–207, 2015. 14. Changwei Chen, Yonhua Tzeng, Erhard Kohn, Chin-Hung Wang, Jun-Kai Mao,” RF MEMS capacitive switch with leaky nanodiamond dielectric film”, Diamond & Related Materials 20, 546–550, 2011. 15. Rajesh Saha, Santanu Maity, Chandan Tilak Bhunia,” Design and characterization of a tunable patch antenna loaded with capacitive MEMS switch using CSRRs structure on the patch”, Alexandria Engineering Journal, http://dx.doi.org/10.1016/j.aej.2016.05.002, 2016. 16. Vikas, K., Sunithamani, S., Yagnika, M., Siva Krishna, S., Avanthi, S, “Study and analysis of novel RF MEMS switched capacitor” International Journal of Engineering and Technology (UAE), 2018. 17. Vikas, K., Sunithamani, S., Yagnika, M., Siva Krishna, S., Avanthi, S, “Design and simulation of low actuation voltage RF MEMS shunt capacitive switch with serpentine flexures & rectangular perforations” 18. International Journal of Engineering and Technology (UAE), 2018. Authors: V. Kanimozhi, Prem Jacob. Paper Title: UNSW-NB15 Dataset Feature Selection and Network Intrusion Detection using Deep Learning Abstract: Anomaly detection system in network, monitors and detects intrusions in the networking area, which is referred to as NIDS, the Intrusion Detection System in Networks. There are numerous network datasets available in networking communications with relevant and irrelevant features drastically decreases the rate of intrusion detection and increases False Alarm Rate. The benchmark network dataset available is UNSW-NB15 dataset was created in 2015. The top significant features are proposed as feature selection for dimensionality reduction in order to obtain more accuracy in attack detection and to decrease False Alarm Rate. We apply a combination fusion of Random Forest Algorithm with Decision Tree Classifier using Anaconda3 (free and open- source distribution of Python3) and package management system Conda in which 45 features have been 85. decreased to the strongest four features. The proposed system detects normal and attacks with a better accuracy using Deep Learning technique. 24-26

Keywords: data visualization; feature selection; intrusion detection; Artificial Neural Network; UNSW- NB15 Dataset. References: 1. “Network Intrusion Detection and Prevention: Concepts and Techniques ”, by Ghorbani A., Lu W., and Tavallaee M., 2010, Springer Science, LLC. 2. "Feature selection and intrusion classification in NSL-KDD cup 99 datasets employing SVMs", by Pervez M. S. and Farid D. M. The 8th International Conference on Software, Knowledge, Information Management and Applications (SKIMA 2014), Dhaka, 2014, pp.1-6. 3. “Unsw-nb15: A comprehensive data set for network intrusion detection,” in MilCIS-IEEE Stream, Military Communications and Information Systems Conference by Moustafa N. and Slay J., Canberra, Australia, IEEE publication,2015 4. The significant features of the UNSW-NB15 and the KDD99 sets for Network Intrusion Detection Systems”, by Moustafa N. and Slay J., the 4th International Workshop on Building Analysis Datasets and Gathering Experience Returns for Security (BADGERS 2015), collocated with RAID 2015, 2016, [Online]available: 5. "Feature selection and intrusion classification in NSL-KDD cup 99 datasets employing SVMs,"by Pervez M. S. and Farid D. M., The 8th International Conference on Software, Knowledge, Information Management and Applications (SKIMA 2014), Dhaka, 2014, pp.1-6. 6. An investigation into discrepancies in findings with the KDDCUP'99 data set and multi-objective evolution of neural network classifier ensembles from imbalanced data, by Engen, Vegard. Machine learning for network-based intrusion detection: Diss. Bournemouth University,2010. 7. “Robust Preprocessing and Random Forests Technique for Network Probe Anomaly Detection.," by Kumar, G. Sunil, and C. V. K. Sirisha, International Journal of Soft Computing and Engineering (IJSCE) ISSN: 2231-2307, Volume-1, Issue-6, January 2012. 8. “Improving the Intrusion Detection using Discriminative Machine Learning Approach and Improve the Time Complexity by Data Mining Feature Selection Methods”,by Bajaj and Arora, International Journal of Computer Applications (0975-8887), Volume 76-No.1, August 2013. [Onine] available: http://research.ijcaonline.org/volume76/number1/pxc3890587.pdf 9. “Toward Generating a New Intrusion Detection Dataset and intrusion Traffic Characterization”,by Iman Sharafaldin, ArashHabibiLashkari, and Ali A. Ghorbani, 4th International Conference on Information Systems Security and Privacy (ICISSP), Portugal, January 2018. 10. “Intelligent intrusion detection system using artificial neural networks,” by Alex Shenfield, David Day, and Aladdin Ayesh, vol. 4, no.2, pp. 95-99, June 2018. 11. Feature Selection in UNSW-NB15 and KDDCUP’99 datasets by JANARTHANAN, Tharmini and ZARGARI, Shahrzad (2017) In: 2017 IEEE 26th International Symposium on Industrial Electronics (ISIE),. IEEE. 12. “Intrusion detection system: A comprehensive review”J.Netw. Comput. Appl., Rev., 36 (1), pp. 16-24,2013 by Liao H.-J., Lin C.-H.R., Lin Y.-C., and Tung K.-Y. [Online]. Available https://www.kdnuggets.com/2016/10/beginners-guide-neural-networks- python-scikit-learn.html 13. “The trends of intrusion prevention system network, in: 2010” by D. Stiawan, A.H. Abdullah, and M.Y. Idris, 2nd International Conference on Education Technology and Computer, vol. 4, pp. 217-221, June 2010. 14. “Network intrusion detection based on a general regression neural network optimized by an /5improved artificial immune algorithm.”Rev.,10 (3), 2015 by Wu J., Peng D., Li Z., Zhao L., and Ling H. Wu J., Peng D., [Online] Available https://www.ncbi.nlm.nih.gov/pubmed/25807466. 15. “Neural networks for classification: A survey” by Zhang G.P. IEEE Trans. Syst. Man Cybern. C, Rev., 30 (4), pp. 451-462, 2000. Authors: Bode VenkataKavyateja, J. Guru Jawahar, C. Sashidhar. Paper Title: Investigation on Ternary Blended Self Compacting Concrete using fly ash and Alccofine Abstract: Self compacting concrete is one of the new concepts, without using any external vibrations and labors can easily fill the formwork even in difficulty places without segregation. For such cases, SCC possesses good flowability and cohesiveness. In this study, two mineral admixtures were used to improve the required quality of concrete. The main aim of this study is to evaluate the workability and compressive strength property of SCC containing mineral admixtures such as fly ash and alccofine. In this study the replacement of cement with fly ash was kept at 30% for all concrete mixes with varying dosages of alccofine (0%, 5%, 10% & 15%). Different tests such as slump flow, V-funnel & L-box tests were conducted to check the workability of SCC. Compressive strength values of SCC mixes were determined at different curing periods. From the test results, it is observed that the optimum replacement of alccofine can be taken as 10%. The test results indicate that the combination of fly ash and alccofine in cement replacement produce M25 grade concrete.

Keywords: Self Compacting Concrete, Fly Ash, Alccofine 1203, Super Plasticizer and Compressive Strength..

References: 1. S.K. Saxena, M. Kumar, N.B. Singh, "Effect of Alccofine powder on the properties of Pond fly ash based Geopolymer mortar under different conditions", Environ. Technol. Innov. 9 (2018) 232–242. doi:10.1016/j.eti.2017.12.010. 2. A. Jariwala, D. Dipak, A. Rana, S. Jaganiya, P. Rathod, "Experimental Study on the Enhancement in Concrete Due to the Ultra-Fine Particles", (2016) 138–141. 86. 3. S. Kavitha, T. Felix Kala, "Evaluation of strength behavior of self-compacting concrete using alccofine and GGBS as partial replacement of cement", Indian J. Sci. Technol. 9 (2016) 1–5. doi:10.1055/s-0028-1117451. 24-26 4. R. Challagalli, M. Tech, "Comparative Study on Fresh and Hardened Concrete Properties of Ternary Blend Self Compacting Concrete", 3 (n.d.) 13–17. 5. Guru Jawahar J, Sashidhar C, Ramana Reddy IV, Annie Peter J. Micro and macrolevel properties of fly ash blended self compacting concrete. Mater Des 2013;46:696–705. 6. B. Baby, J. Anto, "Study of Properties of Self Compacting Concrete with Micro Steel Fibers and Alccofine", 2 (2017) 83–87. 7. S. Pal, J. Maitra, "Effect of Alccofine and Metakaolin on the Performance of SCC", (2018). 8. M.S. Pawar, A.C. Saoji, "Effect of Alccofine on Self Compacting Concrete", Int. J. Eng. Sci. 2 (2013) 5–9. 9. O.P. Cement, Ql2ffs, (2013). 10. ASTM C 642-13, Standard Test Method for Density , Absorption , and Voids in Hardened Concrete, Am. Soc. Test. Mater. (2013) 11– 13. doi:10.1520/C0642-13.5. 11. BIS:516-1959, Indian Standard Methods of Tests for Strength of Concrete. Bureau of Indian Standards, New Delhi, India, IS 516(Reaffirmed). 1959 (1959) New Delhi.India. doi:10.3403/02128947. 12. R. Challagalli, G.S. Hiremath, "Comparative Study on Durability Properties of Self-Compacting Concrete Produced Using Different Pozzolanas", (2017). 13. A. Narender Reddy, T. Meena, "A Study on Compressive Behavior of Ternary Blended Concrete Incorporating Alccofine", Mater. Today Proc. 5 (2018) 11356–11363. doi:10.1016/j.matpr.2018.02.102. 14. T.M. A. NarenderReddy, Available Online through ISSN : 0975-766X CODEN : IJPTFI Review Article A COMPREHENSIVE OVERVIEW ON PERFORMANCE OF ALCCOFINE CONCRETE, Int. J. Pharm. Technol. 9 (2017) 5500–5506. 15. A. Mohan, K.M. Mini, "Strength and durability studies of SCC incorporating silica fume and ultra fine GGBS", Constr. Build. Mater.171 (2018) 919–928. doi:10.1016/j.conbuildmat.2018.03.186. 16. A.P. Report, B.O.F. Technology, FORMULA TO DETERMINE CONCRETE STRENGTH, (2013). 17. S. Sunthornjittanon, Linear Regression Analysis on Net Income of an Agrochemical Company in Thailand, (2015). 18. T. Krishnan, R. Purushothaman, "Optimization and influence of parameter affecting the compressive strength of geopolymer concrete containing recycled concrete aggregate": Using full factorial design approach, IOP Conf. Ser. Earth Environ. Sci. 80 (2017). doi:10.1088/1755-1315/80/1/012013. Authors: Gopichand G, Sailaja G, N. VenkataVinod Kumar, T. Samatha. Paper Title: Digital Signature Verification Using Artificial Neural Networks Abstract: Identification and verification of hard written signature from images is major issue. This is very difficult as even human eye does not have that much visual ability to identify every detail of the in handwritten.Signature changes every time so it is difficult for humans to identify the original and forged ones. By using deep learning which uses the sophisticated is digital configured replica of human brain, we can identify the forgery done in signature with higher accuracy. Keywords: deep learning, digital configured replica, forgery, signature.

References: 1. S. Yin, A. Jin, Y. Han, and B. Yan, “Image-based handwritten signature verification using hybrid methods of discrete Radon transform , principal component analysis and probabilistic neural network,” Appl. Soft Comput. J., vol. 40, pp. 274–282, 2016. 2. K. Wrobel, R. Doroz, P. Porwik, J. Naruniec, and M. Kowalski, “Engineering Applications of Artificial Intelligence Using a Probabilistic Neural Network for lip-based biometric verification,” Eng. Appl. Artif. Intell., vol. 64, no. January, pp. 112–127, 2017.

3. D. Suryani, E. Irwansyah, R. Chindra, D. Suryani, E. Irwansyah, and R. Chindra, “ScienceDirect O fflfflineine Signature Signature 87. Recognition Recognition and and Verification Verification System System using usingfficient Fuzzy Kohonen Clustering Network ( EFKCN ) Algorithm E fficient Fuzzy Kohonen Clustering Network ( EFKCN ) Algorithm,” ProcediaComput. Sci., vol. 116, pp. 621– 628, 2017. 24-26 4. Y. Serdouk, H. Nemmour, and Y. Chibani, “New off-line Handwritten Signature Verification method based on Artificial Immune Recognition System,” Expert Syst. Appl., vol. 51, pp. 186–194, 2016. 5. Y. Serdouk, H. Nemmour, and Y. Chibani, “Handwritten signature verification using the quad-tree histogram of templates and a Support Vector-based artificial immune classification ଝ,” Image Vis. Comput., vol. 66, pp. 26–35, 2017. 6. P. Porwik, R. Doroz, and T. Orczyk, “Signatures veri fi cation based on PNN classi fi eroptimised by PSO algorithm,” vol. 60, pp. 998–1014, 2016. 7. S. Kumar, D. Prosad, and P. Pratim, “Fast recognition and verification of 3D air signatures using convex hulls,” Expert Syst. Appl., vol. 100, pp. 106–119, 2018. 8. N. Khera and S. A. Khan, “Microelectronics Reliability Prognostics of aluminum electrolytic capacitors using arti fi cial neural network approach,” Microelectron. Reliab., vol. 81, no. October 2017, pp. 328–336, 2018. 9. A. Fallah, M. Jamaati, and A. Soleamani, “A new online signature verification system based on combining Mellintransform , MFCC and neural network,” Digit. Signal Process., vol. 21, no. 2, pp. 404–416, 2011. 10. D. Dabrowski, “Condition monitoring of planetary gearbox by hardware implementation of artificial neural networks,” Measurement, vol. 91, pp. 295–308, 2016. Authors: Bhanupriya K, Hemachandra Reddy K. Multi Dimensional Modeling of The In-Cylinder Fuel Sprays Combustion and Emission Formation in Paper Title: D.I. Diesel Engine Abstract: D.I. Diesel engines are the monetarily utilized vehicles in day by day practice. The execution of D.I. Diesel engine to a great extent relies upon the ignition elements inside the cylinder. Thus, such combustion is affected by the shower qualities, fuel substance and afterward the movement of the cylinder. The primary issue with diesel engines is discharges of nitrogen oxides (NOx) and particulates. So as to limit the discharges, it is important to structure the diesel engine with better in-cylinder stream (air-fuel blending) and combustion procedure. Computational Fluid Dynamics (CFD) reproduction comprehends the Diesel engine temperature circulation and NOx species fixations as for time. A little direct injection (DI) engine was picked for the examination. CFD re-enactment results were contrasted and that of engine emissiontests. This paper likewise shows the reproduction after effects of direct infusion diesel motor in-chamber stream (air fuel blending) and combustion. Keywords: CFD, Diesel engine, combustion modelling, Turbulent In-cylinder Flow Modeling..

References: 88. 1. Y. Kidoguchi, M. Sanda and K. Miwa, “Experimental and theoretical optimization of combustion chamber and fuel distribution for the Low Emission Direct – Injection Diesel Engines,” Engg for Gas turbines & Power, Vol.125/351, 2003 2. S. Shundoh, T. Kakegawa, K. Tsujimura, and S. Kobayashi, 1991, “The Effect of Injection Parameters and Swirl on Diesel 24-26 Combustion with High Pressure Fuel Injection,” SAE Paper No. 910489. 3. D. A. Pierpont, and R.D. Reitz, 1995, “Effects of Injection Pressure and Nozzle Geometry on D.I. Diesel Emissions and Performance,” SAE Paper No. 950604. 4. K. Nakakita, 1994, “Optimization of Pilot Injection Pattern and Its Effect on Diesel Combustion with High-Pressure Injection,” JSME Int. J. Ser. B.37, pp. 966–973. [ISI] 5. I.D. Middlemiss, 1978, “Characteristics of the Perkins `Squish Lip' Direct Injection Combustion System,” SAE Paper No. 780113. 6. Y. Daisho, 1986, “Effects of Combustion Chamber Geometry in a Direct-Injection Diesel Engine,” Trans. Jpn. Soc. Mech. Eng., Ser. B 52(479), pp. 2768–2773. 7. M. Konno, 1991, “Reduction of Smoke and NOx Emissions by Active Turbulence Generated in the Late Combustion Stage in Diesel Engines — 2nd Report,” Trans. Jpn. Soc. Mech. Eng., Ser. B 57(534), pp. 773–777. 8. L. Zhang, 1996, “Effect of Chamber Geometry on Flame Behavior in a DI Diesel Engine,” Trans. Jpn. Soc. Mech. Eng., Ser. B 62(600), pp. 3213–3219. 9. K. Sakata, 1990, “Development of Toyota Reflex Burn (TRB) System in DI Diesel Engine,” SAE Paper No. 900658. 10. R.S.G. Baert, D.E. Beckman and R.P. Verbeek, 1996, “New EGR Technology retains HO Diesel Economy with 21stCentury Emissions,” SAE paper No. 960848. Authors: ShaikSabiyaSulthana, O. Nagaraju, J. Guru Jawahar. Paper Title: Fresh Properties of Self Compacting Concrete using fly ash and Alccofine Abstract: Self compacting concrete (SCC) is emerging technology in the construction industry. SCC has

the ability to flow and fill the formwork without using any external vibrations. In this study, fresh properties of 89. ternary blended SCC using fly ash (FA) and alccofine (AF) are investigated. In this study, SCC mixes are 24-26 manufactured in two categories. In the first category, the replacement level of FA was kept at 30% for all concrete mixes with varying dosages of AF (0%, 5%, 10% & 15%). In the second category, the replacement level of mineral admixtures (FA and AF) was kept at 35% with varying dosages of AF (0%, 5%, 10% & 15%). SCC fresh properties were investigated using slump flow, V-funnel & L-box tests. From the first and second category test results, it is observed that the optimum replacement of alccofine can be taken as 10%.

Keywords: Self Compacting Concrete, Cement, Fly Ash, Alccofine, Fresh Properties. References: 1. S.K. Saxena, M. Kumar, N.B. Singh, "Effect of Alccofine powder on the properties of Pond fly ash based Geopolymer mortar under different conditions", Environ. Technol. Innov. 9 (2018) 232–242. doi:10.1016/j.eti.2017.12.010. 2. Jariwala, D. Dipak, A. Rana, S. Jaganiya, P. Rathod, "Experimental Study on the Enhancement in Concrete Due to the Ultra-Fine Particles", (2016) 138–141. 3. S. Kavitha, T. Felix Kala, "Evaluation of strength behavior of self-compacting concrete using alccofine and GGBS as partial replacement of cement", Indian J. Sci. Technol. 9 (2016) 1–5. doi:10.1055/s-0028-1117451. 4. R. Challagalli, M. Tech, "Comparative Study on Fresh and Hardened Concrete Properties of Ternary Blend Self Compacting Concrete", 3 (n.d.) 13–17. 5. Guru Jawahar J, Sashidhar C, Ramana Reddy IV, Annie Peter J. Micro and macrolevel properties of fly ash blended self compacting concrete. Mater Des 2013;46:696–705. 6. B. Baby, J. Anto, "Study of Properties of Self Compacting Concrete with Micro Steel Fibers and Alccofine", 2 (2017) 83–87. 7. S. Pal, J. Maitra, "Effect of Alccofine and Metakaolin on the Performance of SCC", (2018). 8. M.S. Pawar, A.C. Saoji, "Effect of Alccofine on Self Compacting Concrete", Int. J. Eng. Sci. 2 (2013) 5–9. 9. O.P. Cement, Ql2ffs, (2013). 10. ASTM C 642-13, Standard Test Method for Density , Absorption , and Voids in Hardened Concrete, Am. Soc. Test. Mater. (2013) 11–13. doi:10.1520/C0642-13.5. 11. EFNARC. Specification and guidelines for self-compacting concrete. European Federation of Producers and Applicators of Specialist Products for Structures, 2002. Authors: Pravin N.Kathavate, J.Amudhavel. Paper Title: Comparative Assessment on Privacy Preservation in Health Care Sectors coupled with IoT Abstract: Safe and high-quality healthcare service is of supreme significance to patients. Security and patients’ privacy of healthcare data are imperative problems that will have a large impact on the upcoming accomplishment of Healthcare with IoT. A major problem in the IoT dependent healthcare system is the fortification of privacy. Usually, a healthcare service contributor receives data from its patients and distributes them with healthcare experts or registered clinics. The contributor may perhaps share out the data to pharmaceutical companies and health insurance companies. Hence, for overcoming the challenges existing in security, this paper has come out with a privacy-preserving technique with significant data extraction from IoT devices linked with healthcare sector. According to the adopted scheme, the information obtained from IoT devices is processed for preserving the sensitive data, such that unknown people are prohibited to access them. Here, Grey Wolf Optimization (GWO) scheme is proposed to recognize the optimal key. The objective of the proposed scheme is to minimize hiding failure rate, modification degree, and true positive value for better preservation of sensitive data. Moreover, the implemented technique is distinguished with conventional schemes like Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Ant Bee Colony (ABC), Firefly (FF) and Differential Evolution (DE) algorithms in terms of performance. Also, the statistical analysis of the presented method is measured for three test cases, and the effectiveness of the implemented method is revealed.

Keywords: Internet of Things; Healthcare; Privacy Preservation; Sanitization; Hidden rate; Modification Degree, True Positive rate. References: 1. J. H. Abawajy and M. M. Hassan, "Federated Internet of Things and Cloud Computing Pervasive Patient Health Monitoring System," IEEE Communications Magazine, vol. 55, no. 1, pp. 48-53, January 2017. 2. Prosanta Gope and Tzonelih Hwang,”A Fog Based Middleware for Automated Compliance With OECD Privacy Principles inInternet 90. of Healthcare Things," IEEE Access, vol. 4, pp. 8418-8441, 2016. 3. M. S. Hossain and G. Muhammad, “Cloud-Assisted Industrial Internet of Things (IIoT)- Enabled Framework for Health Monitoring,” 24-26 Computer Networks, vol. 101, pp.192–202, June 2016. 4. K. Zhang, K. Yang, X. Liang, Z. Su, X. Shen and H. H. Luo, "Security and privacy for mobile healthcare networks: from a quality of protection perspective,"IEEE Wireless Communications, vol. 22, no. 4, pp. 104-112, August 2015. 5. Mahmud Hossain, S.M. Riazul Islam, Farman Ali, Kyung-Sup Kwak, Ragib Hasan,” An Internet of Things-based health prescription assistant and its security system design“,Future Generation Computer Systems, 2 December 2017. 6. M. A. Salahuddin, A. Al-Fuqaha, M. Guizani, K. Shuaib and F. Sallabi, "Softwarization of Internet of Things Infrastructure for Secure and Smart Healthcare," in Computer, vol. 50, no. 7, pp. 74-79, 2017. 7. Ming Tao, Jinglong Zuo, Zhusong Liu, Aniello Castiglione, Francesco Palmieri, “Multi-layer cloud architectural model and 8. ontology-based security service framework for IoT-based smart homes”, Future Generation Computer Systems, vol. 78, Part 3, pp. 1040-1051, January 2018.

9. D. He and S. Zeadally, "An Analysis of RFID Authentication Schemes for Internet of Things in Healthcare Environment Using Elliptic Curve Cryptography," IEEE Internet of Things Journal, vol. 2, no. 1, pp. 72-83, Feb. 2015. 10. Bahar Farahani, Farshad Firouzi, Victor Chang, Mustafa Badaroglu, Kunal Mankodiya, “Towards fog-driven IoT eHealth: Promises and challenges of IoT in medicine and healthcare”, Future Generation Computer Systems, vol. 78, Part 2, pp. 659-676, January 2018. 11. Min Woo Woo, JongWhi Lee, KeeHyun Park, “A reliable IoT system for Personal Healthcare Devices”, Future Generation Computer Systems, vol. 78, Part 2, pp. 626-640, January 2018. 12. YangSun Lee, Junho Jeong, Yunsik Son, “Design and implementation of the secure compiler and virtual machine for developing secure IoT services”, Future Generation Computer Systems, vol. 76, pp. 350-357, November 2017. 13. Munish Bhatia, Sandeep K. Sood, “A comprehensive health assessment framework to facilitate IoT-assisted smart workouts: A predictive healthcare perspective”, Computers in Industry, vol. 92–9, pp. 50-663, November 2017. 14. Suwon Kim, Seongcheol Kim, “User preference for an IoT healthcare application for lifestyle disease management Telecommunications Policy, 23 March 2017. 15. Sanaz Rahimi Moosavi, Tuan Nguyen Gia, Amir-Mohammad Rahmani, Ethiopia Nigussie, Hannu Tenhunen, “SEA: A Secure and Efficient Authentication and Authorization Architecture for IoT-Based Healthcare Using Smart Gateways”, Procedia Computer Science, vol. 52, pp. 452-459, 2015. 16. Sandeep K. Sood, Isha Mahajan, “Wearable IoT sensor based healthcare system for identifying and controlling chikungunya virus”, Computers in Industry, vol. 91, pp. 33-44, October 2017. 17. Gunasekaran Manogaran, R. Varatharajan, Daphne Lopez, Priyan Malarvizhi Kumar, Chandu Thota, “A new architecture of Internet of Things and big data ecosystem for secured smart healthcare monitoring and alerting system”, Future Generation Computer Systems, 16 November 2017. 18. Yi Liu, Yinghui Zhang, Jie Ling, Zhusong Liu, “Secure and fine-grained access control on e-healthcare records in mobile cloud computing”, Future Generation Computer Systems, vol. 78, Part 3, pp. 1020-1026, January 2018. 19. Richard K. Lomotey, Joseph Pry, Sumanth Sriramoju, “Wearable IoT data stream traceability in a distributed health information system”, Pervasive and Mobile Computing, vol. 40, pp. 692-707, September 2017. 20. Suwon Kim, Seongcheol Kim, “A multi-criteria approach toward discovering killer IoT application in Korea”, Technological Forecasting and Social Change, vol. 102, pp. 143-155, January 2016. 21. Sravani Challa, Ashok Kumar Das, Vanga Odelu, Neeraj Kumar, Athanasios V. Vasilakos, “An efficient ECC-based provably secure three-factor user authentication and key agreement protocol for wireless healthcare sensor networks”, Computers & Electrical Engineering, 18 August 2017. 22. Amir M. Rahmani, Tuan Nguyen Gia, Behailu Negash, Arman Anzanpour, Pasi Liljeberg, “Exploiting smart e-Health gateways at the edge of healthcare Internet-of-Things: A fog computing approach”, Future Generation Computer Systems, vol. 78, Part 2, pp. 641-658, January 2018. 23. Yuehong YIN, Yan Zeng, Xing Chen, Yuanjie Fan, “The internet of things in healthcare: An overview”, Journal of Industrial Information Integration, vol. 1, pp. 3-13, March 2016. 24. Samir V. Zanjal, Girish. R. Talmale, “Medicine Reminder and Monitoring System for Secure Health Using IOT”, Procedia Computer Science, vol. 78, pp. 471-476, 2016. 25. Sanaz Rahimi Moosavi, Tuan Nguyen Gia, Ethiopia Nigussie, Amir M. Rahmani, Jouni Isoaho, “End-to-end security scheme for mobility enabled healthcare Internet of Things”, Future Generation Computer Systems, vol. 64, pp. 108-124, November 2016. 26. Asif Qumer Gill, Nathan Phennel, Dean Lane, Vinh Loc Phung, “IoT-enabled emergency information supply chain architecture for elderly people: The Australian context”, Information Systems, vol. 58, pp. 75-86, June 2016. 27. Seyedali Mirjalili, Seyed Mohammad Mirjalili and Andrew Lewis, "Grey Wolf Optimizer", Advances in Engineering Software, vol.69, pp.46–61, 2014. 28. Holland, J.H.: Adaptation in Natural and Artificial Systems “Genetic Algorithm”, University of Michigan Press, Ann Arbor, Michigan; re-issued by MIT Press, 1992. 29. Kennedy, J.; Eberhart, R. . "Particle Swarm Optimization". Proceedings of IEEE International Conference on Neural Networks. IV. pp. 1942–1948, 1995. 30. S. Amendola, R. Lodato, S. Manzari, C. Occhiuzzi and G. 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Authors: P L Srinivasa Murthy, Balasubramanya H S, SaumyaSupratik, PavanJadhav. Paper Title: Characterization of Jute Frp Composite for Structural and Electrical Applications Abstract: The bio-based FRP composites are fast growing in industrial applications and fundamental research. Bio based fibers have been proved to be better alternative to synthetic fiber in automobiles, railway coaches and aerospace applications. Polymers are finding wide applications in our daily life due to their exclusive properties such as higher strength to weight ratio, lightness, economy, chemical resistance etc. Polymer composites are materials with many outstanding properties and in order to make them biodegradable or partially biodegradable, lot of efforts have been made to use bio based fibers as reinforcement. But the performance of these bio based composites depends on the properties of the reinforcing fibers and the bonding between the matrix and the fibers. The current research focuses on jute fiber reinforced polymer matrix composites that can be used for structural and electrical applications. In this work sawdust and wheat flour are used as filler material. Four samples of different compositions and fillers are developed by means of hand layup process. The investigation of tensile strength, flexural strength and impact energy are considered/taken up in present work. Surface morphology of composites have also been studied.

Keywords: LapoxL12, saw dust, hemicelluloses, flexural test. 91. References: 24-26 1. Dr P V Senthiil, AakashSirssht, “Studies on Material and Mechanical Properties of Natural Fiber Reinforced Composites”, The International Journal Of Engineering And Science (IJES) Volume 3Issue, 11,2014 2. AjithGopinatha, SenthilKumar.Mb, Elayaperumal, Experimental Investigations on Mechanical PropertiesOf Jute Fiber Reinforced Composites with Polyester and Epoxy Resin Matrices. 12th global congress on manufacturing and management, gcmm 2014 3. M. Ramesha, Sri AnandaAtreyaa, U. S. Aswina, H. Eashwara, C. Deepa, Processing and Mechanical Property Evaluation of Banana Fiber Reinforced Polymer Composites. 12th global congress on manufacturing and management, gcmm 2014 4. Moorthy M. Nair1, K. ShambhaviKamath, NagarajShetty, Study on Tensile and Hardness behavior of Sawdust Impregnated on Short Coir Fiber Reinforced Epoxy Composite. Indian Journal of Advances in Chemical Science S1 (2016) 118-121 5. SubhankarBiswasa, SweetyShahinura, MahbubHasana and QumrulAhsan, Physical, Mechanical and Thermal Properties of Jute and Bamboo Fiber Reinforced Unidirectional Epoxy Composites. 6th BSME International Conference on Thermal Engineering (ICTE 2014) 6. Vivek Mishra, SandhyaraniBiswas, Physical and Mechanical Properties of Bi-directional Jute Fiber epoxy Composites. Chemical, Civil and Mechanical Engineering Tracks of 3rd Nirma University International Conference on Engineering. 7. D. Chandramohan, John Presin Kumar, Experimental data on the properties of natural fiber particle reinforced polymer composite material. 8. J.F.Horta, F.J.Simões, A.Mateus, Study of Wood-Plastic Composites with reused High Density Polyethylene and Wood Sawdust. International Conference on Sustainable and Intelligent Manufacturing, RESIM 2016, 14-17 December 2016, Leiria, Portugal. 9. Md. RashnalHossain, Md. AminulIslama, Aart Van Vuureab, IgnaasVerpoest, Tensile behavior of environment friendly jute epoxy laminated composite. 5th BSME International Conference on Thermal Engineering. 10. EmadOmrani, Pradeep L. Menezes, Pradeep K. Rohatgi, State of the art on tribological behavior of polymer matrix composites reinforced with natural fibers in the green materials world. 11. TakianFakhrul, M.A.Islam, Degradation behavior of natural fiber reinforced polymer matrix composites. 5th BSME International Conference on Thermal Engineering. Authors: Hemavathy S, C N Chandrappa, Prashanth Kumar K C. Paper Title: Surface Roughness Study on Forged Al-TiB2 Composite by Regression Analysis Abstract: Aluminum composites are very rapidly replacing engineering metals and alloys because of its light weight and high strength in aerospace and biomedical applications etc. In the present work Al-TiB2 (Aluminum alloy A2024) composite is fabricated by In-Situ technique. The serious examination on impact of surface roughness of Aluminum TiB2 composite is done. The material is subjected for turning operation to contemplate the surface roughness. This investigation centers around building up an exact model for expectation of surface unpleasantness on manufactured composite. The working parameters are speed, feed, depth of cut and tool nose radius. One of the data mining techniques non-linear regression analysis is applied in developing the empirical model, this model is transferred to software by visual basic programming language. The test results show that the value of surface roughness is low at high cutting speed and comparatively high at low cutting speed. Surface roughness increases with increase in feed and depth of cut. However it decreases with increasing tool nose radius and surface roughness increases as what% of TiB2 increases in aluminum. The values of surface roughness of models compared with experimental results. This models developed in this study have a satisfactory compatibility in both model construction and verification and there is a scope for future work.

92. Keywords: Al 2024 alloy, In-situ technique, surface roughness, regression analysis, tool nose radius. 24-26 References: 1. J.E. Allison, G.S. Cole, “metal matrix composite in the automotive industry: opportunities and challenges”, JOM (Jan 1993) (19- 24). 2. Manna, B. Bhattacharyya, Investigation for effective tooling system to machine Al/SiC-MMC, in: Proceedings of the RAMP-2001, Department of Production Engineering, Annamalai University, India. (465-468). 3. El-Baradie, 1993. Surface roughness model for turning grey cast iron (154). Proc. IMechE, 207:(43-54). 4. Bandyopadhyay, B.P. and E.H. Teo, 1990. Application of factorial design of experiment in high speed tuning. (3-8). 5. Gorlenko, O.A., 1981. Assessment of surface roughness parameters and their interdependence. Precis. Eng., #:2. 6. K. Manjunath/Optimization and comparative study of cutting parameters foe aluminum composite material/MSRIT, Bangalore. 7. M.M.schwartz, “Composites Materials Handbook”, Mc Grew Hill Book Co..Pg.1.4-4.131. 8. R.M.Aikini Jr., “The Mechanical Properties of insitu composites”.(35-39). 9. J.M.Sanchez, I.Azcona, F.Castro, “Mechanical properties of Titanium Di boride based cerments”, JOM.Vol.Pg.9-14, 2000.(35- 39) 10. Minitab (2000) Meet Minitab, Release 13. Minitab Inc., State College, PA. Authors: Madan Mohan Rao. Nelluri, and Habibullah Khan. Spectral Efficient Massive Mimo Multi-Cell 5G Cellular Environment Using Optimal Linear Paper Title: Processing Schemes Abstract: In this paper, the optimal scheduling of UEs per cell is carried out to increase the spectral efficiency of 5G wireless networks with massive MIMO antennas in multi-cell systems. The scheduling of UEs is carried out in terms of several system parameters. The scheduling is carried out by considering a multi- objective function that optimizes of arbitrary pilot reuse, power control and random user locations. Expressions are derived to validate uplink and downlink transmission with power control and random user locations to increase the performance of UEs.The inter-cell interferences are suppressed using linear processing schemes in a coordinated beamforming fashion.

Keywords: UE, Massive MIMO Antennas, linear Processing Schemes, System Parameters

References: 1. Boccardi, F., Heath, R. W., Lozano, A., Marzetta, T. L., &Popovski, P. (2014). Five disruptive technology directions for 5G. IEEE Communications Magazine, 52(2), 74-80. 2. Hoydis, J., Ten Brink, S., &Debbah, M. (2013). Massive MIMO in the UL/DL of cellular networks: How many antennas do we need?. 93. IEEE Journal on selected Areas in Communications, 31(2), 160-171. 3. Björnson, E., Sanguinetti, L., Hoydis, J., &Debbah, M. (2015). Optimal design of energy-efficient multi-user MIMO systems: Is massive MIMO the answer?. IEEE Transactions on Wireless Communications, 14(6), 3059-3075. 24-26 4. Zhang, K., Tan, W., Xu, G., Yin, C., Liu, W., & Li, C. (2018). Joint RRH Activation and Robust Coordinated Beamforming for Massive MIMO Heterogeneous Cloud Radio Access Networks. IEEE Access, 6, 40506-40518. 5. Li, J., Wang, D., Zhu, P., Wang, J., & You, X. (2018). Downlink spectral efficiency of distributed massive MIMO systems with linear beamforming under pilot contamination. IEEE Transactions on Vehicular Technology, 67(2), 1130-1145. 6. Nguyen, T. M., Ha, V. N., & Le, L. B. (2015). Resource allocation optimization in multi-user multi-cell massive MIMO networks considering pilot contamination. IEEE Access, 3, 1272-1287. 7. Sanguinetti, Luca, Emil Bjornson, and JakobHoydis. "Fundamental Asymptotic Behavior of (Two-User) Distributed Massive MIMO." arXiv preprint arXiv:1811.03324 (2018). 8. Molisch, A. F., Ratnam, V. V., Han, S., Li, Z., Nguyen, S. L. H., Li, L., &Haneda, K. (2017). Hybrid beamforming for massive MIMO: A survey. IEEE Communications Magazine, 55(9), 134-141. 9. Saatlou, O., Ahmad, M. O., &Swamy, M. N. S. (2018). Spectral Efficiency Maximization of Multiuser Massive MIMO Systems With Finite-Dimensional Channel via Control of Users’ Power. IEEE Transactions on Circuits and Systems II: Express Briefs, 65(7), 883- 887. 10. Tekanyi, A. M. S., Waziri, S. M., Abdulrazaq, M. B., & Yusuf, S. M. (2018). Mitigating the Effect of Pilot Contamination in Massive Multiple Input Multiple Output System Using Pilot Allocation Protocol. ATBU Journal of Science, Technology and Education, 6(4), 97- 110. 11. Caire, G. (2018). On the ergodic rate lower bounds with applications to massive MIMO. IEEE Transactions on Wireless Communications, 17(5), 3258-3268. 12. Zhang, C., Jing, Y., Huang, Y., & Yang, L. (2018). Performance analysis for massive MIMO downlink with low complexity approximate zero-forcing precoding. IEEE Transactions on Communications. 13. Shao, L., &Zu, Y. (2018). Approaches of approximating matrix inversion for zero-forcing pre-coding in downlink massive MIMO systems. Wireless Networks, 24(7), 2699-2704. 14. Sadeghi, M., Björnson, E., Larsson, E. G., Yuen, C., &Marzetta, T. (2018). Joint unicast and multi-group multicast transmission in massive MIMO systems. IEEE Transactions on Wireless Communications, 17(10), 6375-6388. 15. Upadhya, K., Vorobyov, S. A., &Vehkapera, M. (2017). Superimposed pilots are superior for mitigating pilot contamination in massive MIMO. IEEE Transactions on Signal Processing, 65(11), 2917-2932. Authors: D.Sreenivasulu Reddy, Ram Mohan B. Paper Title: Structure and FE Examination of Hybrid Composite Motor Protective Helmet Abstract: The helmet is defensive thing, used to shield head from foremost injuries. The head protector fundamentally ensures the skull and mind amid lethal mishaps. So the fundamental subject of the protective helmet is to safe watch the rider (or) the talented operator amid mishaps. It is essential for the rider to wear head protector, amid the riding of the vehicle as its exceptionally basic nowadays, that the little to significant mishaps occurring, not on account of the riders speed, might be a result of the environment, current streets, busy works of the general public and a few exposures on street. So the mishaps are unavoidable however we must be progressively watchful. Yet at the same time we ought to have clear knowledge of the injuries those might cause deadly passing of the rider. Thus the head protector is must for rider security. From that point onward, the comfort of the rider all through the journey is additionally critical worry for the helmet business to increase best market for their own item. So to meet the fundamental worries of the rider, it's most vital to build up the best comfort with a light weight, high quality, and high effect safe and better feel for the rider. The present work manages the geometrical improvement of the current head protector utilizing CAD programming apparatus and after that the basic investigation of the current model utilizing ANSYS workbench, straight examination, the outcomes, distortion, stress, and strain plots was been contrasted and entrenched 94. outcomes. At that point the elective model with different mixes had been produced and broke down for the basic investigation and the outcomes had been contrasted and the current head protector. Toward the finish of the 24-26 protective helmet with predominant quality, attributes with low material cost will be accomplished through the exploration.

Keywords: helmet, deformation, stress, strain, structural analysis.

References: 1. Chia-Yuan Chang, Chih-Hsiang Ho et.al design of a helmet, M.E thesis 2003. 2. PuneetMahajan, Design of Motorcycle Helmets,IIT Delhi,2010. 3. Radha Raman Mishra, A. N. Veerendra Kumar, et.al, High Velocity Impact Analysis of Kevlar Composite by MATLAB,Indian journal of advances in chemical science,2014,68-71. 4. AmalThomas,Dr.P.Suresh, J.AnishJafrinThilak,N.Subramani, IMPACT ANALYSIS ON COMPOSITE HELMET BY USING FRC AND GLASS FIBER BY USINclehelmetGANSYS,International Research Journal of Engineering and Technology (IRJET). 5. Mohammad AghaeiAsl Mohammad AghaeiAsl, Finite Element Analysis Of Helmet Subjected To Bullet Impact, Indian Journal of Fundamental and Applied Life Sciences ISSN: 2231– 6345. Authors: S. Karunya, K. Kalaiselvi. Paper Title: A Persist Evaluation in Women Tracking System Based on Current Epoch Abstract: Women are an equal soul of men by comprises men in her name itself but really they are treated equal among men. There is a broad gap in between past and present centuries. Women are treated poorly on past centuries by getting huge works, asking more dowries and even killing female infant but in present century these has been reduced and crimes are increased more in numbers against women like abducted, murdered, raped and harassed in various ways. This assessment is on women’s tracking system which helps them in their safety and security. Although there are n numbers of tracking devices still crimes against women are in an increasing rate. These crimes have to be reduced in an effective ways of implementing versatile trackingWomen are an equal soul of men by comprises men in her name itself but really they are treated equal among men. There is a broad gap in between past and present centuries. Women are treated poorly on past centuries by getting huge works, asking more dowries and even killing female infant but in present century these has been reduced and crimes are increased more in numbers against women like abducted, murdered, raped and harassed in various ways. This assessment is on women’s tracking system which helps them in their safety and security. Although there are n

numbers of tracking devices still crimes against women are in an increasing rate. These crimes have to be 95. reduced in an effective ways of implementing versatile tracking system by combining various technologies into a single integrated unit. 24-26

Keywords: Audio and Image, GPS, GPRS, GSM, Sensors.

References: 1. Miss. Ashwini .P. Thaware,“A Safety Device for Women’s Security Using GSM/GPS”, International Journal on Recent and Innovation Trends in Computing and Communication Vol. 5 Issue 4,pp. 2321-8169, 2017. 2. P.Madhu Bala S.Sivaraman, “GPS Based Bus Tracking System “,International journal for electronics and communication engineering,pp.2348-8549,2017. 3. Priti Jadhav, Kajal Ingale, Shifa Asari1, Prof . Kalidas Bhawale, “Student Tracking System using GSM and GPS Technology”, International Journal of Innovative Research in Computer and Communication Engineering ,Vol.5 Issue 3,pp.2320-9801,2017. 4. Miss.Trupti R.Chandhari,Dr.A.J.Patil,” CHILDREN TRACKING SYSTEM USING VOICE RECOGNITION , Global Journal of Advanced Engineering Technologies,Vol.6 Issue 1,pp.2277-6370,2017. 5. Karunya Sundaraganapathy, S.Nirmala Sugirtha Rajini , S.Ramamoorthy, “Embedded Lockets for Multipurpose Tracking System using GPS, GPRS and GSM, Indian Journal of Science and Technology,Vol.10 Issue 3,pp. 0974-6846 ,2017. 6. D. G. Monisha, M. Monisha, G. Pavithra, R. Subhashini, “Women Safety Device and Application-FEMME, Indian Journal of Science and Technology, Vol. 9(10), pp. 0974-6846, 2016. 7. K. Kalaiselvi, S. Karunya, “TRACKING SYSTEM – A PROPOSED MODEL ON LITERATURE REVIEW”, IEEE Xplore Library, PP.CFP17L34-ART, ISBN: 978-1-5386-4031-9, 2018. 8. S. Karunya, K. Kalaiselvi, “Integrated proposition on tracking environment”, International Journal of Engineering & Technology, Vol.7 Issue 2.33, pp. 653-656, 2018. 9. Geetha Pratyusha Miriyala,P.V.V.N.D.P.Sunil, Ramya Sree Yadalapalli, Vasantha rama Lakshmi pasam, Tejaswri Kondapalli, Anusha Miriyala,” Smart Intelligent, Security System for women”, International Journal of Electronics and Communication Engineering & Technology ,Vol.7 Issue 2,pp. 0976-6464,2016. 10. A.Berthibella,R.Gowrishankari, B.Kiruthika A.Lisyamary R.Saraswathi,” Development Of Gps Gsm Based Tracking System With Google Mapbased Monitoring”,SSRG International Journal of Electronics and Communication Engineering(ICCREST),special issue,pp.2348-8549,2017. 11. Hazza Alshamisi,Veton Kepuska, “Real Time GPS Vehicle Tracking System”, International Journal of Advanced Research in Electronics and Communication Engineering(IJARECE), Vol.6(3), pp.2278-909X, 2017. 12. Roshni S. Sune, M. H. Nerkar, “IOT Based Women Tracking and Security with Auto Defender System: A Review”, International Journal of Innovative Research in Computer and Communication Engineering, Vol. 6(1), pp. 2320-9801, 2018. 13. Sriranjini R, “GPS and GSM Based Self Defense System for Women Safety”, Journal of Electrical & Electronic Systems, Vol. 6(2), PP. 2332-0796, 2017. 14. B.Umadevi, Dr.P.Eswaran, Dr.N.Manoharan, “WOMENS SECURITY SOLUTION USING: IOT”, International Journal of Pure and Applied Mathematics, Vol.119(10), PP. 1871-1874, 2018. 15. Stephenraj S, Sripriya P, ACCIDENT PREVENTION EYE TIREDNESS DETECTION USING IMAGE MINING, International Journal of Mechanical and Production Engineering Research and Development(IJMPERD), Vol. 8, Issue 2, Apr 2018, 363-368.

Authors: J. Selwyn Babu, J. Rex. Paper Title: Experimental Investigation on Lightweight Concrete Slabs Abstract: The popularity of Lightweight Concrete (LWC) is due to its least density factor and high insulation capability. Use of LWC can diminish the null load of structural members significantly. The rise in the price of civil construction materials, depletionand environmental exploitation has set an alarm for an alternative material. In this study, the normal coarse particulates (CA) was replaced by coconut shell (CS). Since specific gravity of both the materials is different the replacement was done on the volume basis. The properties of coconut shell material which is available in surplus amount and concrete ingredients were studied. Coconut Shell used in concrete has high effectuality on account of its flat surface on one side. In this paper, a study has been made on the flexural performance of lightweight concrete slabs. Slab specimens of size 1300 x 500 x 70mm were designed and casted for various replacement ratios (0%, 25%, 50%, 75% and 100%) of CS. Four point loading test was performed on slabs and parameters such as ultimate moment capacity, ductility factor, energy absorption, stiffness, and cracking pattern were observed.

Keywords: Light weight Concrete, Coconut shell, CA Replacement, Four point Load Test. References: 1. AbdulkadirKanand, RamazanDemirbog, “A novel material for lightweight concrete fabrication”, Elsevier Cement & Concrete 96. Composites, Vol. 31 (2009), PP. 489–495. 2. AmarnathYerramala and Ramachandrudu, “Properties of Concrete with Coconut Shells as Particulate Spare”, International 24-26 Journal of Engineering Inventions(2012), Volume 1, Issue 6, PP: 21-31. 3. ASTM C330-99, “Standard specification for lightweight particulates for structural concrete”, Annual Book of ASTM Standards, United States, 522-525. 4. GeethaKumari, C. G. Puttappaand C. Shashidar, “Flexural Appearances of SFRSCC and SFRNC One Way Slabs”, IJRET (2013), Volume.2, Issue.7, pp. 220-229. 5. Hassan Mohamed Ibrahim, “Experimental Investigation of Capacity of Mesh-Reinforced Cementitious Slab”, Elsevier Construction and Building Materials (2010), pp. 251-259. 6. Jihad Sawan and Mohamed Abdel-Rohman.Impact Effect on R.C. Slabs Experimentations Journal of Structural Engineering, ASCE(1986), Vol. 106, No. PP: 2057-2065. 7. Kayali and Zhu .Chloride Induced Reinforcement Corrosion In Lightweight Particulate High Strength Fly Ash Concrete blocks. Elsevier Construction and Building Materials(2004),PP: 327–336. 8. Miguel Fernandez Ruiz and Aurelio Muttoni.Shear Strength of Rc Slabs under Concentrated Loads near Clamped Linear Supports. Elsevier Engineering Structures (2013), PP: 10–23. 9. Randy D. Martin and Thomas H.-K. Kang .Structural Design and Construction Issues of Approach Slabs. ASCE, Journal of Structural Design And Construction (2013) , PP: 12-20. 10. ShettyM. S.(2005),“Concrete technology theory and practice”, 3rd Multicolor illustrative revised ed., India. 11. Yushun li and weishen,“Flexural behavior of lightweight bamboo – steel composite slabs”, Elsevier Thin-Walled Structures(2012), pp. 83–90. Authors: ShaikHussain, Sanam Ravi Teja. Paper Title: Experimental Investigations on Modified Combustion Chamber Geometry in Diesel Engine Abstract: Today the two disturbing conditions in front of the engineers worldwide are to decrease the utilization of conventional fuels and to downscale the ever rising environmental pollution.The performance characteristics and emission characteristics of single cylinder water cooled diesel engine with the effect of piston crown geometries such as HCC (Hemispherical combustion chamber) and RCC (Re-entrant combustion chamber) are evaluated. The tests are conducted with diesel and Rice Bran Methyl Ester and Diesel blends as 97. fuels with different loading conditions. Rice bran methyl ester is prepared by using transesterification process. Without modifying the compression ratio and cylindrical volume of the engine the baseline hemispherical type 24-26 piston is replaced with Re-entrant type piston. All the engine tests were conducted with diesel and 20% blend with diesel [RBOME20] indiesel engine with HCC and RCC. From the investigations it is observed that the brake point thermal efficiency is increased and specific fuel consumption proportion is decreased for re-entrant combustion chamber. Further thenormal pollutants emissions are reduced. But slightly increase in nitrogen oxides is detected compared to base fuel for re-entrant combustion.

Keywords: Diesel engine, biodiesel, re-entrant combustion chamber, Hemispherical Combustion chamber and Rice bran methyl ester.

References: 1. Umesh T and Manjunath HN Rukmangadha P, Dr.Madhu D., “Experimental Study of Performance & Emission Analysis of Rice bran oil as an Alternative fuel for an I.C Engine”, IOSR-JMCE, Volume 11, PP 130-134,2014. 2. Deepa.D, Karuppasamyl..,” Performance and emission characteristics of diesel engine using rice bran oil methyl ester blend with additive diethyl ether”, Volume 3 ,2014. 3. Mir Mohsin John, Vineet Kumar.,” Effect of Load on the Performance of DI Diesel Engine Running on Rice Bran Bio-diesel and Its Blends”, Volume 1, Number 1; September, 2014pp. 14-17. 4. A.Ravichandran, K. Rajan, M.Rajaram Narayanan and K.R.Senthil Kumar et al..,“ Effect of piston bowl geometry on the performance of a diesel engine using Corn biodiesel and its diesel blends” International Journal of ChemTech Research, Volume 9, No.01 pp 105- 112,2016. 5. PalashChakma and HaengMuk Cho “Comparative Study on the Modified Combustion Chamber Geometries in Diesel Engine for Using Biodiesel to Achieve Emissions Standards ”,IJETAE, Volume 07 ,2017. 6. Dr. Abdul siddique.sk, shaikabdulazeeez and Raffimohammed” A review on c.i engine combustion chamber geometry and optimization”, Volume 3, Issue 5, August 2016. 7. Chetan S Bawankar and Rajesh Gupta,” Effects of piston bowl geometry on combustion and emission characteristics on diesel engine”, Volume 05,2016. 8. S. Jaichandar, K. Annamalai, “Influences of reentrant combustion chamber geometry on the performance of Pongamia biodiesel in a DI diesel engine”, Energy 44 (2012) 633-640. 9. S. Jaichandar , K. Annamalai , “Effects of open combustion chamber geometries on the performance of Pongamia biodiesel in a DI diesel engine”, Fuel 98 (2012) 272–279. 10. Banapurmath NR, Chavan AS, Bansode SB, SankalpPatil, Naveen G, SankethTonannavar, Keerthi Kumar N and TandaleMSet al.., “Effect of Combustion Chamber Shapes on the Performance of Mahua and Neem Biodiesel Operated Diesel Engines”, Volume 6,2015. 11. Chetan S Bawankar and Rajesh Gupta,” Effects of piston bowl geometry on combustion and emission characteristics on diesel engine”, Volume 05,2016. Authors: YogeshMadaria, Vijay Kanjarla. Effectiveness of a Dimpled Non-Even Surface For Oscillations Control For Flow Over Fissure: Paper Title: Numerical Analysis Abstract: To decay the pressure oscillation in the flow above an open crater, a passive control method, namely introduction of a dimpled non-even surface, is attempted. This paper presents the numerical analysis of the above system, which was undertaken to govern the effectuality of the said control modem. This work focuses on an open fissure with the length-to-depth ratio in proportions of 1: 2. To check the oscillation persuaded in the flow, a textured non-even surface is fitted at the upstream of the crater. The even and dimpled non even cases are compared for the flow instability and noise around fissure. Large eddy simulation coupled with acoustic model is utilized as a tool for this. The results obtained for even cases were compared with available experimental and computation data. On the basis of flow visualizations, it can be said that introduction of dimpled non-even surface upstream was significantly effective in suppressing the oscillations in fissure flow. Based on the comparison of flow filed structure in the even and dimpled non-even cases, the control mechanism of void oscillation technique is evaluated.

Keywords: fissure flow oscillation, passive control, numerical simulation, dimpled non-even surface.

References: 1. Chang, K., Constantinescu, G.., and Park, S.O., 2006, “Analysis of the flow featured mass transfer processes for the incompressible flow past an open fissure with a laminar and a fully turbulent incoming boundary state”, J. Fluid Mech., Vol. 561, pp 113-145. 98. 2. Rowley C, Williams R. Dynamics of high-Reynolds-number flow .Annu Rev Fluid Mech 2006;38:251–76. 3. Williams DR, Cornelius D, Rowley CW. Supersonic fissure response on open loop forcing. Active Flow Control Notes Numer Fluid MechMultidiscip Des 2007;95:230–43. 24-26 4. Alam MM, Matsuo S, Teramoto K, Setoguchi T, Kim HD. A computational control of fissure-induced pressure oscillations using subfissure. J ThermSci 2006;15(3):213–9. 5. Wang YP, Lee SC, Li KM, Gu Z, Chen J. Combined experimental and numerical study of flow over fissure and its application . ACTA Acust United Acust 2012;98(4):600–11. 6. Chokani N, Kim I. Suppression of pressure oscillations in an open fissure by passive pneumatic control. AIAA 91-1729, 1991. 7. Sarno R, Franke M. Suppression of flow-induced pressure oscillations in craters. J Aircr 1994;31(1):90–6. 8. Stallings RL, Plentovich EB, Tracy MB, Hemsch MJ. Effect of passive venting on static pressure distributions in transonic speeds. NASA Technical Memorandum 4549, 1994. 9. Zhang X, Chen X, Rona A, Edwards J. Attenuation of fissure flow oscillation through leading edge flow control. J Sound Vib 1999;221(1):23–47. 10. Ukeiley LS, Ponton MK, Seiner JM, Jansen B. Suppression of pressure loads in fissure flows. AIAA J 2004;42(1):70–9. 11. Li W, Taku N, Kozo F. Noise control of supersonic fissure flow with upstream mass blowing. Progress in hybrid RANS-LES modeling notes on numerical fluidmechanics , vol. 117. p. 315–24. 12. [18] Alam MM, Matsuo S, Teramoto K, Setoguchi T, Kim HD. A new method of controlling fissure-induced pressure oscillatons using sub-fissure. J MechSciTechnol 2007;21:1398–407. 13. Alam F, Steiner T, Chowdhury H, Moria H, Khan I, Aldawi F, et al. A study of golf ball aerodynamic drag. ProcEng 2011;13:226–31. 14. Lienhart H, Breuer M, Köksoy C. Drag reduction by dimples? A complementary experimental/numerical investigation. Int J Heat Fluid Flow 2008;29(3):783–91. 15. Tian LM, Ren LQ, Liu QP, Han ZW, Jiang X. The mechanism of drag reduction around bodies of revolution using bionic non-even surfaces. J Bionic Eng 2007;4(2):109–16. Authors: Deepak V Biradar, K.R. Nataraj. Paper Title: Node Recovery and Forward Node Move Algorithm for Network Lifetime Enhancement 99. Abstract: Wireless Sensor Networks are used for wide variety of applications ranging from monitoring of weather, enemy vehicles across the borders. The critical activities require the nodes to be used for a longer period of time and whenever nodes lose their energy network must be able to recover or replace nodes over a 24-26 period of time. Before transmitting the data multiple routes are found and then a route is chosen which has lowest distance or end to end delay and high residual energy which affect the life time ratio in the network. The main cause for life time expiring at the faster rate is improper selection of forward node and second important cause occurs if all neighbours are dead. The proposed method performs the Classification of neighbours into super healthy and non healthy nodes and picks the best node which has the highest residual energy. The proposed method also performs the relocation of forward node and recovery of dead nodes to improve network lifetime.

Keywords: WSN, Residual Energy, Forward Node and Recovery, Network Lifetime.

References: 1. Tin-Y Wu, Kai-Hua Kuo, Hua-Pu Cheng, Jen-Wen Ding and Wei-Tsong Lee, "Increasing the Lifetime of Ad Hoc Networks Using Hierarchical Cluster-based Power Management," KSII Transactions on Internet and Information Systems, vol. 5, no. 1, pp. 5-23, 2011. DOI: 10.3837/tiis.2011.01.001 2. Bandyopadhyay, S. and E.J. Coyle, 2003. “An energy efficient hierarchical clustering algorithm for wireless sensor networks.” in Proceedings of the IEEE Conference on Computer Communications (INFOCOM) 3. ChakerAbdelazizKerrache ; Andrea Lupia ; Floriano De Rango ; Carlos T. Calafate ; Juan-Carlos Cano ; Pietro Manzoni,” An energy-efficient technique for MANETs distributed monitoring”, Wireless Communications and Mobile Computing Conference (IWCMC), 2017 13th International 26-20 June 2017 4. JieHu ; Lie-Liang Yang ; Lajos Hanzo,"Energy-Efficient Cross-Layer Design of Wireless Mesh Networks for Content Sharing in Online Social Networks", IEEE Transactions on Vehicular Technology ( Volume: PP, Issue: 99 ),03 March 2017 5. AzadehSheikholeslami ; Majid Ghaderi ; Hossein Pishro-Nik ; Dennis Goeckel,"Energy-Efficient Secrecy in Wireless Networks Based on Random Jamming",IEEE Transactions on Communications ( Volume: 65, Issue: 6, June 2017 ) 6. M Selvi ; C Nandhini ; K Thangaramya ; K Kulothungan ; A Kannan,"HBO based clustering and energy optimized routing algorithm for WSN", Advanced Computing (ICoAC), 2016 Eighth International Conference on 19-21 Jan. 2017 7. YongjunSun ;Wenxin Dong ; YahuanChen.," An Improved Routing Algorithm Based on Ant Colony Optimization in Wireless Sensor Networks", IEEE Communications Letters ( Volume: 21, Issue: 6, June 2017 ),Page(s): 1317 - 1320 8. HamidMehboob,WalidMasoudimansour, Amir G. Aghdam, Kamran Sayrafian-Pour,"An Energy-Efficient Target-Tracking Strategy for Mobile Sensor Networks", IEEE Transactions on Cybernetics ( Volume: 47, Issue: 2, Feb. 2017 ),Page(s): 511 – 523 9. Muhammad Khalid ; Zahid Ullah ; Naveed Ahmad ; Huma Khan ; Haitham S. Cruickshank ; Omar Usman Khan, "A comparative simulation based analysis of location based routing protocols in underwater wireless sensor networks", 10. Recent Trends in Telecommunications Research (RTTR), Workshop on , 10-10 Feb. 2017 11. P. Vadivazhagu; P.D.Selvam, "Network lifetime enhancement method for sink relocation and packet drop detection in wireless sensor networks",Communications and Signal Processing (ICCSP), 2015 International Conference,2-4 April 2015 12. Pradeebaa, Nandita Lavanis, "Network lifetime improvement using routing algorithm with sleep mode in wireless sensor network",Wireless Communications, Signal Processing and Networking (WiSPNET), International Conference,23-25 March 2016.

13. R.D. Joshi ; P.P. Rege, "Implementation and analytical modelling of modified optimised link state routing protocol for network lifetime improvement", IET Communications Volume: 6, Issue: 10, July 3 2012 ) 14. Sung-Yeon Kim ; Jeong-AhnKwon ; Jang-Won Lee ; Chang Soon Park ; Youngsoo Kim ; HyosunHwang, "Network lifetime improvement by relaying node re-selection in the IEEE 802.15.6 BAN", Ubiquitous and Future Networks (ICUFN), 2012 Fourth International Conference on, 4-6 July 2012 15. XiaobingWu; GuihaiChen, "Dual-Sink: Using Mobile and Static Sinks for Lifetime Improvement in Wireless Sensor Networks", Computer Communications and Networks, 2007. ICCCN 2007. Proceedings of 16th International Conference on 13-16 Aug. 2007 16. Z. Han ; H. V. Poor, "Lifetime Improvement of Wireless Sensor Networks by Collaborative Beam forming and Cooperative Transmission", Communications, 2007. ICC '07. IEEE International Conference,13 August 2007 17. M. A. Matin ; Md. Nafees Rahman, "Lifetime improvement of wireless sensor network", Communication Software and Networks (ICCSN), 2011 IEEE 3rd International Conference on 27-29 May 2011 18. Fuu-Cheng Jiang ; Hsiang-Wei Wu ; Der-Chen Huang ; Chu-Hsing Lin, "Lifetime Security Improvement in Wireless Sensor Network Using Queue-Based Techniques”, Broadband, Wireless Computing, Communication and Applications (BWCCA), 2010 International Conference on 4-6 Nov. 2010 19. M. Marta and M. Cardei, “Improved sensor network lifetime withmultiple mobile sinks,” J. Pervas. Mobile Comput., vol. 5, no. 5,pp. 542–555, Oct. 2009. 20. L. Sun, Y. Bi, and J. Ma, “A moving strategy for mobile sinks in wireless sensor networks,” in Proc. 2nd IEEE Workshop Wireless Mesh Netw.,Sep. 2006, pp. 151–153. 21. [19] Y. Yang, M. I. Fonoage, and M. Cardei, “Improving network lifetimewith mobile wireless sensor networks,” Comput. Commun., vol. 33, no. 4, pp. 409–419, Mar. 2010. 22. Xiao-gang Qi ; Chen-xi Qiu,"An Improvement of GAF for Lifetime Elongation in Wireless Sensor Networks", Wireless Communications, Networking and Mobile Computing, 2009. WiCom '09. 5th International Conference on 24-26 Sept. 2009 23. Chu-Fu Wang ; Jau-Der Shih ; Bo-Han Pan ; Tin-Yu Wu,"A Network Lifetime Enhancement Method for Sink Relocation and Its Analysis in Wireless Sensor Networks", IEEE Sensors Journal ( Volume: 14, Issue: 6, June 2014 ), 14 February 2014,Page(s): 1932 – 1943 24. Hui Wang ; Nazim Agoulmine ; Maode Ma ; Yanliang Jin,"Network lifetime optimization in wireless sensor networks",IEEE Journal on Selected Areas in Communications ( Volume: 28, Issue: 7, September 2010 ) 25. S. Sara and D. Sridharan, “Routing in mobile wireless sensor network: A survey,” Telecommun. Syst., Aug. 2013. 26. F. Akyildiz, W. Su, Y. Sankarasubramaniam, and E. Cayiric, “Wireless sensor networks: A survey,” Comput. Netw., vol. 38, no. 4, pp. 393–422, Mar. 2002. 27. P. Ferrari, A. Flammini, D. Marioli, and A. Taroni, “IEEE802.11 sensor networking,” IEEE Trans. Instrum. Meas., vol. 55, no. 2, pp. 615–619, Apr. 2006 28. W. R. Heinzelman, A. Chandrakasan, and H. Balakrishnan, “Energy-efficient communication protocol for wireless microsensor networks” in IEEE Hawaii International Conference on Systems Sciences, 2000. 29. F. Shebli, I. Dayoub, A. Okassa M'foubat, A. Rivenq and J. M. Rouvaen, “Minimizing energy consumption within wireless sensors networks using optimal transmission range between nodes”, 2007 IEEE International Conference on Signal Processing and Communications 30. Y. Sun, W. Huangfu, L. Sun, J. Niu, and Y. Bi, “Moving schemes for mobile sinks in wireless sensor networks,” in Proc. IEEE IPCCC,Apr. 2007, pp. 101–108. 31. Luo and J. P. Hubaux, “Joint mobility and routing for lifetime elongation in wireless sensor networks,” in Proc. IEEE Inf. Commun. Conf., vol. 3. Mar. 2005, pp. 1735–1746. Authors: B.Tulasiramarao , P.Ramreddy, K.Srinivas, A.Raveendra. A Multivariate model of orthogonal turning operation on cutting tool dynamics modeled by optimum Paper Title: cutting parameters using genetic algorithm Abstract: Turning accuracy and high productivity rates have become the key determinants and both accuracy and surface quality plays vital role. In this publication a diversified multivariate model of an orthogonal turning operation has been formulated considering a series of turning experiments. Using the obtained experimental data, the cutting dynamics has been modeled with radial basis function neural network for different work piece materials. In par with basic cutting parameters, tool overhang and tool wear were selected as inputs and static cutting edge forces, average roughness values and critical chatter length on work piece were presented as outputs. For four work materials considered in experiments, four neural networks were trained. Using these neural network models, optimum cutting parameters such as speed, depth of cut, feed and tool- overhang lengths are projected by minimizing total cutting edge force with the help of genetic algorithms.

Keywords: cutting parameters, design parameters, neural network and genetic algorithm.

References: 1. J.P. Gurney and S.A. Tobias, “A graphical analysis of regenerative machine tool instability”, Transactions of the ASME Journal for Engineering Industry, pp.103–112, 1962. 2. J.Tlusty and M. Polacek, “The Stability of Machine Tool against Self-Excited Vibration in Machining”, Proceedings of International Research in Production Engineering, Pittsburgh, PA, pp. 465–474,1963. 100. 3. J.A. Tlusty, “A method of analysis of machine tool stability”, International Journal of Machine Design and Research, Vol. 6 pp.5–14, 1965. 4. S.A. Tobias, and W. Fishwick, “The chatter of lathe tools under orthogonal cutting conditions”, Transactions of the American Society of Mechanical Engineers, Vol. 80, pp.1079–1088,1968. 24-26 5. D.B. Welboum and J.D. Smith, “Machine-tool Dynamics: An introduction”, Cambridge, 1970. 6. J.Cook and H. Nathan, “Self-Excited Vibrations in Metal Cutting,” ASME Transactions, Journal of Engineering for industry, Vol. 81, pp. 183- 186,1979. 7. S.F.Bao, W.G. Zhang, S.Y. Yu, S.M. Qiao, and F.L.Yang, “A New Approach to the Early Prediction of Turning Chatter”, Journal of Vibration and Acoustics, Vol. 116, pp. 485- 488, 1994. 8. Y.S.Tarng, H.T.Young and B.Y.Lee, “An analytical model of chatter vibration in metal cutting”, International Journal of Machine Tools and Manufacture, vol.34, pp.183-197, 1994. 9. Iturrospe , V. Atxa, and J.M. Abete, “State-space analysis of mode-coupling in orthogonal metal cutting under wave regeneration”, International Journal of Machine Tools & manufacture, vol. 47, pp.1583–1592, 2007. 10. I.E.Minis, E.B. Magrab, and I.O.Pandelidis, “Improved Methods for the Prediction of Chatter in Turning, Part3: A Generalized Linear Theory”, Trans. ASME Journal of Engineering for Industry, Vol. 112, pp. 28-35, 1990. 11. D.W.Liu and C.R. Liu, “An Analytical Model of Cutting Dynamics. Part 1: Model Building”, Trans. ASME, Journal of Engineering for Industry, Vol. 107, pp. 107- 111, 1995. 12. M.N.Hamdon and A.E.Bayoumi, “Analysis for regenerative machine tool chatter”, Journal of Manufacturing Science and Engineering, vol. 11, pp. 345-349,1997 13. M.N.Hamdon and A.E.Bayoumi, “An approach to study the effects of tool geometry on the primary chatter vibration in orthogonal cutting”, Journal of Sound and Vibration, vol. 128(3) pp. 451-469, 1999. 14. J.R.Pratt and A.H. Nayfeh, “Design and Modeling for Chatter Control”, Nonlinear Dynamics, Vol. 19, pp. 49-69, 1999. 15. J.R.Pratt, M.A.Davies, C.J. Evans, and M.D.Kennedy, “Dynamic Interrogation of a Basic Cutting Process”, CIRP Annals, Vol. 48, pp. 39-42, 1999. Authors: A. Prashanth, P.Shiva Kumar. Paper Title: Experimental Evaluation and Fabrication of Composite Made Traction Gear Abstract: Apparatus is a toothed wheel that works with others to modify the connection between the speed of a driving component and the speed of the determined parts. Outfitted gadgets can change the speed, torque, and course of a power source. The most well-known circumstance is for a rigging to work with another apparatus. This paper displays a point by point and manufacture of a high quality and minimal effort Traction equip. First The Traction outfit is demonstrated in "CATIA V5" and imported to "ANSYS" for auxiliary examination and modular investigation to decide the characteristic frequencies and mode shapes. Examination is finished by the diverse materials for gears like Cast press, carbon steel, and composite materials like Aluminum Silicon carbide results are looked at as far as possible underlined on the near execution of Traction outfit having distinctive load conditions by deciding the mistakes produced and auxiliary pressure created in the Traction design for stack conditions Using ANSYS and furthermore assess the which one is better appropriate material and manufacture utilizing that material Traction adapt is withstand and give better execution because of impacts.

101. Keywords: footing gear plan; auxiliary investigation; display examination; cast press; carbon steel; Aluminum Silicon carbide; ansys; 24-26

References: 1. . Siva Prasad, Syed AltafHussain, V.Pandurangadu, K.PalaniKumar. Demonstrating and Analysis of Spur Gear for Sugarcane Juice Machine under Static Load Condition by Using FEA. Global Journal of Modern Engineering Research (2012), 2(4):2862-2866. 2. VivekKaraveer, AshishMogrekar and PremanReynold Joseph T (2013), "Demonstrating and Finite Element Analysis of Spur Gear", International Journal of Current Engineering and Technology, ISSN 2277-4106. 3. MahebubVohra, Prof. Kevin Vyas "Similar Finite Element Analysis of Metallic and non-Metallic goad adapt", May-June 2014, IOSR Journal of Mechanical and Civil Engineering, 11(3):136-145. 4. NitinKapoor, Pradeep Kumar, Rahul Garg and Ram Bhool. " ParametricModeling and Weight Analysis of Glass Filled Polyamide Composite Differential Gearbox", International Journal of Science, Engineering and Technology Research, 2014,3(6). 5. Yakut, H. Duzcukoglu, M. T. Demirci, " The heap limit of PC/ABS goad apparatuses and examination of rigging harm", Archives of Materials science and Engineering, November 2009, 6. M. Patil, S.Herakal, S. B. Kerur, "Dynamic Ana lysis of Composite goad adapt", May-2014, Proceeedings of third IRF International Conference.. 7. A.D. Dighe, A. K. Mishra, V. D. Wakchaure," Investigation of Wear Resistance and Torque Transmission Capacity of Glass Filled Polyamide and PEEK composite goad gears", Feb-2014, International Journal of Engineering and Advance Technology, Vol-3/3. 8. Pradeep Kumar Singh, M. Gautam, Gangasagar and ShyamBihariLal," July-2014, International Journal of Mechanical Engineering and Robotics Research, Vol 9. Mrs. C.M. Meenakshi, Akash Kumar, ApoorvaPriyadarshi,Digant Kumar Dash and Hare Krishna., Analysis of Spur Gear Using Finite Element Analysis, Middle-East Journal of Scientific Research (12): 1672-1674, 2012 ISSN 1990-9233 10. Atul Kumar, P. K. Jain and P. M. Patha., Comparative Finite Element Analysis of Reconstructed New and Worn Tooth of Spur Gear, Proceedings of the first International and sixteenth National Conference on Machines and Mechanisms (iNaCoMM2013), IIT Roorkee, India, Dec 18-20 2013 11. Raja Roy,S. PhaniKumar,D.S. Sai Ra vi K iran., Contact weight investigation of goad outfit utilizing FEAM., International Journal of Advanced Engineering Applications, Vol.7, Iss.3, pp.27-41 (2014) 12. Darle W Dudley (1954), Practical Gear Design, McGraw-Hill Book Company.. 13. Khurmi Gupta R S (2000), "Machine Design", Khanna Publication.. 14. Khurmi R S (1997), "Hypothesis of Machine", Khanna Publication.. 15. Machine Design Data Book (2003), PSG Publication. 16. Rattan S (1998), "Hypothesis of Machines", DhanpatRai Publication. 17. Romlay F R M (2008), "Displaying of a Surface Contact Stress for Spur Gear Mechanism Using Static and Transient Finite Element Method", Journal of Structural Durability and Health Monitoring (SDHM), Vol. 4, No. 1, Tech Science Press.. 18. Shanavas S (2013), "Stress Analysis of Composite Spur Gear", International Journal of Engineering Research and Technology (IJERT), ISSN: 2278-0181. 19. Shinde S P, N ikamAn and Mulla T S (2012), "Static Analysis of Spur Gear Using Finite Element Analysis", IOSR Journal of Mechanical and Civil Engineering (IOSR-JMCE), pp. 26-31, ISSN: 2278-1684. 20. Lin Tengjiao, Ou H., Li Runfang. 2007. A limited component strategy for 3D static and dynamic contact/affect investigation of rigging drives, Computer Methods in Applied Mechanics and Engineering, 196(9-12):1716-1728. Authors: Prakash K. Aithal, Dinesh Acharya U., Geetha M. Paper Title: Development of Real Time Analytics of Movies Review Data using PySpark Abstract: The data play the vital role in every organization. The data can be divided into structured, semi- structured and unstructured. One can not process the unstructured data in real-time using RDBMS or Hadoop. Spark is an extension of Hadoop architecture which clubs the goodness of both Hadoop and Storm. Spark supports languages such as Scala, Java, Python, and R. The proposed method uses PySpark to analyze the movies review dataset of 50000 reviews by 36409 peoplefor 1539 movies in real-time. Since movie reviews are written by many users in real-time, it is necessary for real-time data analysis. This method finds all the users who are very activein writing the reviews of the movies. This analytics may be used for giving incentives to the active reviewers. Further, the information about more popular movies based on reviews can be gained through analytics. To achieve these tasks basic map, reduce and filter functionalities have been applied. It is found from the analytics that the Movie code B002VL2PTU has been reviewed by the maximum number of people and also it is determined that maximum of 112 reviewswerewrittenbythe single user with code A3LZGLA88K0LA0. The frequency count of words in the movie review is accomplished, and sentiment of the user can be analyzed using unigrams. Keywords: Real-time Analytics; BigData; PySpark References: 1. https://snap.stanford.edu/data/webAmazon.html. 2. http://backtobazics.com/big-data/spark/understanding-apache-sparkarchitecture/. 102. 3. Ruining He and Julian McAuley. Ups and downs: Modeling the visual evolution of fashion trends with one-class collab- orative filtering. In proceedings of the 25th international conference on world wide web, pages 507–517. International World Wide Web Conferences Steering Committee,2016. 24-26 4. Julian McAuley, Christopher Targett, Qinfeng Shi, and Anton Van Den Hengel. Image-based recommendations on styles and substitutes. In Proceedings of the 38thInternational ACM SIGIR Conference on Research and Development in InformationRetrieval,pages43– 52.ACM,2015. 5. Timothy Wong. Exploratory Data Analysis of Amazon. comBook Reviews. PhD thesis,2009. 6. Sumit Kawate and Kailas Patil. An approach for reviewing and ranking the customers’reviews through quality of review (qor). ICTACTJournal on Soft Computing, 7(2),2017. 7. Li Zhuang, Feng Jing, and Xiao-Yan Zhu. Movie review mining and summarization. In Proceedingsofthe15thACM international conference on Information and knowledge management, pages 43–50. ACM,2006. 8. Lina L Dhande and Girish K Patnaik. Analyzing sentiment of movie review data using naive bayes neuralclassifier. 2014. 9. Callen Rain. Sentiment analysis in amazon reviews using probabilistic machine learning.2013. 10. Neelu Rani, Nishant Singh, SujayPawar, et al. Sentiment analysis by data mining of past movie reviews/ratings. Impe- rialJournalofInterdisciplinaryResearch,3(6),2017. 11. Xiaomeng Su. Introduction to bigdata.NTNU. 12. http://iihtofficialblog.blogspot.com/2014/07/5-vs-of-hadoop-big-data.html. 13. Debi PrasannaAcharjya. A survey on big data analytics: challenges, open research issues andtools. 2016. 14. LiranEinav and Jonathan Levin. The data revolution and economic analysis. Innovation Policy and the Economy, 14(1):1–24,2014. 15. ChristophLofi and Philipp Wille. Exploiting social judge- ments in big data analytics.2015. 16. B. Yadranjiaghdam, S. Yasrobi, and N. Tabrizi. Developing a real-time data analytics framework for twitter streaming data. In 2017 IEEE International Congress on Big Data (BigData Congress), pages 329–336, June2017. 17. http://theservicemanagement.blogspot.com/2016/09/research onion.html. Authors: Kailas Tambe, G. Krishna Mohan. Paper Title: Effect of Process Parameters on Pcbn Tool Wear Rate in Friction Stir Process of Aluminium 7075 Sic Abstract: Friction state process (FSP) a variant of friction stir welding process (FSW), is used to friction process of Metal Matrix composites. Since Aluminium Alloy (AA) 7075 SiC is not been premeditated on FSP,effort has been made to explore the result of a range of process parameters on tool wear rate(TWR)in FSP

by Taguchi method, research paper. The Process parameters that careful study of rotational speed, translational 103. speed and tool pin diameter. Polycrystalline Cubic Boron Nitride (PCBN) tool of 6 mm, 7 mm and 8 mm 24-26 diameters are used. The research is done by L9 (34) orthogonal array. The eroded length of tool, volume measurements and the tool wear rate is calculated for a variety of combinations of factors and levels. The results of experiments systematically discussed and to achieve process parameters on tool wear rate is determined.

Keywords: FSP, AA 7075SiC, TWR, Taguchi method and FSW.

References: 1. Nalbant, H.Gokkaya and G. Sur, “Application of Taguchi Method in the Optimization of cutting Parameters for Surface Roughness in Turning”, Materials and Design, Vol. 28, pp. 1379-1385, 2007 2. R.S. Mishra, P. Sarathi De, N. Kumar, Friction stir welding and processing, Science and Engineering, Springer, New York, New York, 2014. 3. M. Kaladhar, K. Venkatasubbaiah, andCh. Srinivasa Rao, “Determination of Optimum Process Parameter During Turning of AISI 304 Austenitic Stainless Steel using Taguchi Method and ANOVA”, International Journal of Lean Thinking, Volume 3, Issue 1, pp. 1- 19,2012. 4. Soheyl Soleymani, Amir Abdollah-zadeh, and Sima Ahmad Alidokht, Improvement in tribological properties of surface layer of an Al alloy by friction stir processing, Journal of surface engineered materials and advanced technology, (2011), 1, 95-100. 5. C. Lorenzo-Martin, O.O. Ajayi, Rapid surface hardening and enhanced tribological performance of 4140 steel by friction stir processing, to appear on Wear, 2015. 6. Ranganth M. s., Vipin, Nand Kumar and Rakesh Kumar, “Experimental Analysis of Surface Roughness in CNC Turning of Aluminium Using Response Surface Methodology”, International Journal of Advance Research and Innovation, vol 3, Issue 1, pp. 45- 49, 2015 7. Neelimadevi, C., Mahesh, V. and Selvaraj, N., Mechanical characterization of aluminium silicon carbide composite, Int. J. App. Eng. Res., 1(4), 793-799( 2011). Authors: Tony Manuel, Bhargavi H Goswami. Paper Title: Experimenting With Scalability of Beacon Controller in Software Defined Network Abstract: In traditional network, a developer cannot develop software programs to control the behavior of the network switches due to closed vendor specific configuration scripts. In order to bring out innovations and to make the switches programmable a new network architecture must be developed. This led to a new concept of Software Defined Networking(SDN). In Software defined networking architecture, the control plane is detached from the data plane of a switch. The controller is implemented using the control plane which takes the heavy lift of all the requests of the network. Few of the controllers used in SDN are Floodlight, Ryu, Beacon, Open Daylight etc. In this paper, authors are evaluating the performance of Beacon controller using scalability parameter on network emulation tool Mininet and IPERF. The experiments are performed on multiple scenarios of topology size range from 50 to 1000 nodes and further analyzing the controller performance.

Keywords: SDN, Beacon, Mininet, Controller.

References: 1. Goswami B., Asadollahi S.S. (2018) Enhancement of LAN Infrastructure Performance for Data Center in Presence of Network Security. In: Lobiyal D., Mansotra V., Singh U. (eds) Next-Generation Networks. Advances in Intelligent Systems and Computing, vol 638. Springer, Singapore. 2. Saleh Asadollahi, Bhargavi H Goswami, (2017) “Revolution in Existing Network under the Influence of Software Defined Network”, Proceedings of the 11th INDIACom, Pages: 1012-1017, IEEE, New Delhi, India. 3. S Das, B Goswami, S Asadollahi, Investigating Software-Defined Network and Networks-Function Virtualization for Emergent Network-oriented Services, IJIRCCE, Vol.5, Special Issue 2, April 2017, Pg. No. 201 – 205, DOI:10.15680. 4. Saleh Asadollahi ; Bhargavi Goswami ; Ahmad Sohaib Raoufy ; Hedmilson, (2017), “Scalability of software defined network on 104. floodlight controller using OFNet”, International Conference on Electrical, Electronics, Communication, Computer, and Optimization Techniques (ICEECCOT), Pages: 1 – 5, IEEE, Mysore, India. 24-26 5. Nick McKeown; et al. (April 2008). "OpenFlow: Enabling innovation in campus networks". ACM Communications Review. Retrieved 2018-09-01. 6. Andreas Voellmy, Hyojoon Kim, and Nick Feamster. Procera: a language for high-level reactive network control. In Proc. 1st workshop on Hot topics in software defined networks, HotSDN ’12, pages 43–48, New York, NY, USA, 2012. ACM.. 7. S Asadollahi ; B Goswami (2017) “Experimenting with Scalability of Floodlight Controller in Software Defined Networks”, International Conference on Electrical, Electronics, Communication, Computer, and Optimization Techniques (ICEECCOT), Pages: 1 – 5, IEEE, Mysore, India. 8. S Asadollahi, B Goswami, Investigating Software-Defined Network and Networks-Function Virtualization for Emergent Network- oriented Services, IJIRCCE, Vol.5, Special Issue 2, April 2017, Pg. No. 211 – 217. 9. OpenFlow: Beacon controller. Available at https://openflow.stanford.edu/display/Beacon/Home.html (last accessed on September 2018). 10. Mininet: Emulator. Available at http://mininet.org/ (last accessed on September 2018) 11. Justin Pettit (August 20, 2018). "[ovs-announce] Open vSwitch 2.10.0 Available". openvswitch.org. Retrieved September 01, 2018. 12. Python: Scripting network topologies. Available at https://www.python.org/ (last accessed on September 2018) 13. Xterm: Emulator. Available at https://invisible-island.net/xterm/ (last accessed on September 2018) 14. IPERF: Networks tool. Available at https://iperf.fr/ (last accessed on September 2018) 15. Gnuplot: Graph tool. Available at http://www.gnuplot.info/ (last accessed on September 2018) 16. S Asadollahi ; B Goswami ; MSameer (2018) “Ryu controller’s scalability experiment on software defined networks”, Proceedings of IEEE International Conference on Current Trends in Advanced Computing (ICCTAC), Pages: 1 – 5, IEEE, Bangalore, India. 17. B Goswami, S Asadollahi, Implementation of SDN using OpenDayLight Controller, IJIRCCE, Vol.5, Special Issue 2, April 2017, Pg. No. 218 – 227 18. S Asadollahi, B Goswami, Software Defined Network, Controller Comparison, IJIRCCE, Vol.5, Special Issue 2, April 2017, Pg. No. 211 – 217 Authors: Tony Manuel, Bhargavi H Goswami. Paper Title: Experimenting With Scalability of Beacon Controller in Software Defined Network Abstract: In traditional network, a developer cannot develop software programs to control the behavior of the network switches due to closed vendor specific configuration scripts. In order to bring out innovations and to make the switches programmable a new network architecture must be developed. This led to a new concept of 105. Software Defined Networking(SDN). In Software defined networking architecture, the control plane is detached from the data plane of a switch. The controller is implemented using the control plane which takes the heavy lift 24-26 of all the requests of the network. Few of the controllers used in SDN are Floodlight, Ryu, Beacon, Open Daylight etc. In this paper, authors are evaluating the performance of Beacon controller using scalability parameter on network emulation tool Mininet and IPERF. The experiments are performed on multiple scenarios of topology size range from 50 to 1000 nodes and further analyzing the controller performance.

Keywords: SDN, Beacon, Mininet, Controller. References: 1. Goswami B., Asadollahi S.S. (2018) Enhancement of LAN Infrastructure Performance for Data Center in Presence of Network Security. In: Lobiyal D., Mansotra V., Singh U. (eds) Next-Generation Networks. Advances in Intelligent Systems and Computing, vol 638. Springer, Singapore. 2. Saleh Asadollahi, Bhargavi H Goswami, (2017) “Revolution in Existing Network under the Influence of Software Defined Network”, Proceedings of the 11th INDIACom, Pages: 1012-1017, IEEE, New Delhi, India. 3. S Das, B Goswami, S Asadollahi, Investigating Software-Defined Network and Networks-Function Virtualization for Emergent Network-oriented Services, IJIRCCE, Vol.5, Special Issue 2, April 2017, Pg. No. 201 – 205, DOI:10.15680. 4. Saleh Asadollahi ; Bhargavi Goswami ; Ahmad Sohaib Raoufy ; Hedmilson, (2017), “Scalability of software defined network on floodlight controller using OFNet”, International Conference on Electrical, Electronics, Communication, Computer, and Optimization Techniques (ICEECCOT), Pages: 1 – 5, IEEE, Mysore, India. 5. Nick McKeown; et al. (April 2008). "OpenFlow: Enabling innovation in campus networks". ACM Communications Review. Retrieved 2018-09-01. 6. Andreas Voellmy, Hyojoon Kim, and Nick Feamster. Procera: a language for high-level reactive network control. In Proc. 1st workshop on Hot topics in software defined networks, HotSDN ’12, pages 43–48, New York, NY, USA, 2012. ACM.. 7. S Asadollahi ; B Goswami (2017) “Experimenting with Scalability of Floodlight Controller in Software Defined Networks”, International Conference on Electrical, Electronics, Communication, Computer, and Optimization Techniques (ICEECCOT), Pages: 1 – 5, IEEE, Mysore, India. 8. S Asadollahi, B Goswami, Investigating Software-Defined Network and Networks-Function Virtualization for Emergent Network-oriented Services, IJIRCCE, Vol.5, Special Issue 2, April 2017, Pg. No. 211 – 217. 9. OpenFlow: Beacon controller. Available at https://openflow.stanford.edu/display/Beacon/Home.html (last accessed on September 2018). 10. Mininet: Emulator. Available at http://mininet.org/ (last accessed on September 2018) 11. Justin Pettit (August 20, 2018). "[ovs-announce] Open vSwitch 2.10.0 Available". openvswitch.org. Retrieved September 01, 2018. 12. Python: Scripting network topologies. Available at https://www.python.org/ (last accessed on September 2018) 13. Xterm: Emulator. Available at https://invisible-island.net/xterm/ (last accessed on September 2018) 14. IPERF: Networks tool. Available at https://iperf.fr/ (last accessed on September 2018) 15. Gnuplot: Graph tool. Available at http://www.gnuplot.info/ (last accessed on September 2018) 16. S Asadollahi ; B Goswami ; MSameer (2018) “Ryu controller’s scalability experiment on software defined networks”, Proceedings of IEEE International Conference on Current Trends in Advanced Computing (ICCTAC), Pages: 1 – 5, IEEE, Bangalore, India. 17. B Goswami, S Asadollahi, Implementation of SDN using OpenDayLight Controller, IJIRCCE, Vol.5, Special Issue 2, April 2017, Pg. No. 218 – 227 18. S Asadollahi, B Goswami, Software Defined Network, Controller Comparison, IJIRCCE, Vol.5, Special Issue 2, April 2017, Pg. No. 211 – 217 Authors: P. Ashok, N. Swathi, M. Tirumala Devi, T. S. Uma Maheswari Paper Title: Reliability for a Multicomponent System Using Mixture of Two Weibull Distributions Abstract: The reliability is derived for a multi component Series system, Parallel system and standby system is considered for where stress-strength follows weibull distribution. The general expression for the reliability of a multi component standby system is obtained and the system reliability is computed numerically for different values and parameters.

Keywords: Weibull Distribution, Series system, Parallel system, Stress-strength Model, Standby System. 106. References: 556-559 1. Kapur, K.C. and Lamberson, L.R. (1977): Reliability in Engineering Design, John Wiley and Sons, Inc. 2. S.N.N and Sriwastav, G.L. (1975). Studies in Cascade Reliability-I, IEEE Transastions on Reliability, Vol.R-24.No.1p.53-57. 3. Raghava char, A. C.N. kesava Rao, B and pandit S.N.N. (1987).The Reliability of a cascade system with Normal Stress and Strength distribution, AsR.vol.2, p.49-54. 4. Sandhya, K. and Umamaheswari, T.S (2013) Reliability of a multicomponent stress strength model with standby system using mixture of two exponential. Journal of reliability and Statistical Studies, Vol, 6, Issue 2, p.105-113. 5. Sriwastav, G.L and Kakati, M.C. (1981).A Stress-Strength Model with Redundancy, IAPQR Trans.6 No .1, p.21-27.