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Total Received Papers: 612 | Total Accepted Papers: 106
Total Rejected Papers: 506 | Acceptance Rate: 17.32%

S. No

Volume-7 Issue-5S2, January 2019, ISSN: 2277-3878 (Online)
Published By: Blue Eyes Intelligence Engineering & Sciences Publication

Page No.

1.

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.

References:

  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. 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. 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. 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. 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. Narongrit, K-L. Areerak and K-N. Areerak “The Comparison Study of Current Control Techniques for Active Power Filters” 2011
  9. 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.

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2.

Authors:

S.V. Saravanan, S. Sindhuja

Paper Title:

Miniaturisation Using Shorting Posts in C-Shaped and H-Shaped Microstrip Patch Antennas for GPS 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 (GPS), Shorted.

References:

  1. 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. Porath, “Theory of miniaturized shorting-post micro-strip antennas,” IEEE Transactions, Antennas and Propagation, Vol. 48, No. 1, pp. 41-47, 2000.
  3. Garg, P. Bhartia, I. Bahl, and A. Ittipiboon, “Micro-strip antenna design handbook,” Artech House: London, 2001.
  4. Sanad, “Effect of the shorting posts on short circuit microstrip antennas,” Proceedings, IEEE Antennas and Propagation Society International Symposium, pp. 794- 797, 1994.
  5. 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. Davor, R. Bojan, “Small H-shaped shorted patch antennas,” Radio engineering, vol. 17, pp. 77, 2008.
  8. 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. 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.
  13. Xiaofei Shi, Zhihong Wang, Hua Su, Yun Zhao, “A H-type Microstrip Slot Antenna in Ku-band Using LTCC Technology with Multiple Layer Substrates,” Proc. IEEE, Vol. 978-1, pp. 7104 - 7106, 2011.
  14. Neenansha Jain, Anubhuti Khare, Rajesh Nema, “E-Shape Micro strip Patch Antenna on Different Thickness for pervasive Wireless Communication”, International Journal of Advanced Computer Science and Applications, Vol. 2, No. 4, 2011
  15. M Jeby Thomas Jacob,” A Novel Wave Bird Concept for Marine Surveillance”, Indian Journal of Science and Technology, Vol.7, pp-56-60, Oct2014.
  16. Dheepak, Dr.S.V.Saravanan,”RF Optimization for Quality improvement in GSM network”, International Journal of Electrical Engineering & Technology, Vol.6, Issue 8,pp-53-62,Oct 2015.
  17. Amit A. Rakholiya1 and Namrata V. Langhnoja “ A review on miniaturization techniques for microstrip patch antenna” International Journal of Advance Research and Innovative Ideas in Education, Vol. 3, Issue 2, 2017.
  18. S.Sindhuja, Dr. S.V. Saravanan “Design and simulation of miniaturized microstrip patch antennas using shorting techniques for GPS Applications”, Journal of Advanced Research in Dynamical and Control Systems, Vol-9, Issue-9, pp. - 220-227, 2017.

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3.

Authors:

T. Beni Steena, P. Indira, M. Geethalakshmi

Paper Title:

Enhancing Performance of Optical Link using Integrated DWDM and Flip- OFDM for High Speed 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 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.

Keywords: Flip-OFDM, DWDM, PAPR.

References:

  1. 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
  2. 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.
  3. Fernando, Y. Hong, and E. Viterbo, “Flip-OFDM for unipolar communication systems,” IEEE Trans. Commun., vol. 60, no. 12, pp.3726–3733, Dec. 2012.
  4. 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
  5. 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.
  6. 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
  7. 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
  8. Kahn and J. Barry, “Wireless infrared communications,” Proceedings of the IEEE, vol. 85, no. 2, pp. 265–298, 1997.
  9. 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.
  10. J. Carruthers and J. Kahn, “Modeling of non directed wireless infrared channels,” Communications, IEEE Transactions on, vol.45, no.10, pp.1260–1268, 1999.

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4.

Authors:

C. Rajinikanth, S. Abraham Lincon 

Paper Title:

A Semi Supervised based Hyper Spectral Image (HSI) Classification Using Machine Learning 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 Image Classification.” Remote Sens., 9: 1042.
  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:

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5.

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:

  1. 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. Karthick, S.Sakthi kannan, G.Velmathi, “odour sensor based solution for the sanitary problem faced by elderly people and 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. 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.

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6.

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. 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. B. Yaffe et al., “Physiology of functional and effective networks in epilepsy,” Clin. Neurophysiol., vol. 126, no. 2, pp. 227–236, Feb. 2015.
  3. 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
  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. 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. 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. 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. 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. 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. 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. 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. 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.

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7.

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 Technology,vol.6(2),pp.1-2,2017.

  1. Banwell, P., Kwartin R., 2006, Quality Assurance in ENERGY STAR Residential Lighting Programmes, presented at the International Energy Efficiency in Domestic Appliances and Lighting Conference, June 2005, London
  2. 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)
  3. 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
  1. 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.
  2. Bennet, K., 2001, Energy Efficiency in Africa for Sustainable Development: A South African Perspective, Energy Research Institute, University of Cape Town, South Africa
  3. 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
  4. Illuminating Engineering Society of North America (IESNA) (2000) Lighting Handbook.
  5. Industrial lighting ANSI/IESNA RP-7- 01 (1991) Recommended Practice for Industrial Facilities.

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8.

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. Dr. K. P. Sathyamoorthy, “Smart energy meter load control”, International Journal of Advanced Research in Electrical, (IJAREEIE), ISSN (Online): 2278 – 8875, Vol. 2, Issue 8, August 2013
  2. 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. 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

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9.

Authors:

T.M. Thamizh Thentral, R. Jegatheesan, K. Vijayakumar, S. Senthilnathan, T.V. Abhinav Viswanaath

Paper Title:

Implementation of Shunt Compensating Device for the Mitigation of Harmonic Current in Non-Linear 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. 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.
  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. 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. 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. 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. 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.

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10.

Authors:

T.M. Thamizh Thentral, A. Geetha, S. Usha, Ayush Singh, Varoon Kannan, Kotikalapudi 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. Maclay, “Simulation gets into the loop,” IEE Review, vol. 43, no. 3,pp. 109–112, 1997.
  2. 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 2008, pp. 2152–2158,2008.
  3. Abourida and J. Belanger, “Real-time platform for the control, prototyping, and simulation of power electronics and motor drives, ”in Proc. 3rd International Conference on Mondeling, Simulation, and Applied Optimization, 2009.
  4. 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. R. Walker, Evaluating MPPT converter topologies using amatlab PV model, Journal of Elect. Electron. Eng. , vol. 21, pp.49-55,2001.
  6. 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. Hatziargyriou et. All, Modeling New Forms of Generation and Storage, CIGRE Technical Brochure, 2000.
  8. 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. 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. Husain, Electric and Hybrid Electric Vehicles, CRC Press, 2003
  11. 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. 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. 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.

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11.

Authors:

M. Rama Sekhara Reddy, M. Vijaya Kumar, M. Mahesh

Paper Title:

A Fuzzy based Synchronous Flux Weakening Control with Flux Linkage Prediction for Doubly-Fed 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. 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. K. S. Naidu; B. Singh, “Grid-interfaced DFIG-based variable speed wind energy conversion system with power smoothening,” IEEE Transactions on Sustainable Energy, vol. 8, no. 1, pp. 51-58, Jane 2017.
  3. Ju, F. Ge, W. Wu, Y. Lin, and J. Wang, “Three-phase steady-state model of doubly fed induction generator considering various rotor speeds,” IEEE Access, vol. 4, pp. 9479-9488, 2016.
  4. 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. 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. 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. 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. 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. 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. 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. 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.

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12.

Authors:

Kanchamreddy Snehitha, R. Kiranmayi, K. Nagabhushanam

Paper Title:

Design and Operation of Flyback CCM Inverter with Fuzzy based Discrete-Time Repetitive Control 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. 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. 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. 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. 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. 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. 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. 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. 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. 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.

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13.

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.

References:

  1. IEEE Standard for Floating-Point Arithmetic, ANSI/IEEE Standard 754-2008, Aug. 2008.
  2. 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. 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. 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. 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. 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. 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. P. Farmwald, “On the Design of High-Performance Digital Arithmetic Units,” PhD thesis, Stanford Univ., 1981.
  9. -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.

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14.

Authors:

Dileep Dharmappa, Mahalinga V Mandi, S. Ramesh

Paper Title:

Binary Sequences having Good Correlation and Large Linear Complexity Properties for Satellite 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 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 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.
  11. 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).

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15.

Authors:

K.M. Arun Kumar, T.C. Manjunath, G. Arun Kumar

Paper Title:

Bearing Fault Diagnosis in IM Using STFT and J-48 Algorithm based on Vibration Signals in 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

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 Conversion, 10, pp. 688-693, 1995.
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  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.
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16.

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. 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. 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. 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. Du and L. Zhang, “Target detection based on a dynamic subspace,” Pattern Rec fognit., vol. 47, no. 1, pp. 344–358, 2014.
  5. Harel, C. Koch, and P. Perona, “Graph-based visual saliency,” Adv. Neural Inf. Process. Syst., vol. 19, pp. 545–552, 2007.
  6. Bruce and J. Tsotsos, “Saliency based on information maximization,” Adv. Neural Inf. Process. Syst., vol. 18, pp. 155–162, 2006.
  7. Yan, L. Xu, J Shi, and J Jia, “Hierarchical saliency detection,” in Proc. IEEE Conf. Comput. Vision Pattern Recognit., 2013, pp. 1155–1162.
  8. 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. 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.
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17.

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 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 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/

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18.

Authors:

R. Dhanalakshimi, C. Geetha, T. Sethukarasi

Paper Title:

Monitoringand Detecting Disease in Human Adults Using Fuzzy Decision Tree and Random Forest 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.

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Authors:

A.V. Sajitha and A.C. Subhajini

Paper Title:

Network-Conscious VM Placement for Energy Efficiency in Green Data Centres through Dynamic 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:

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20.

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 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 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. Md Mahmudur Rahman ; Sameer K. Antani ; George R. Thoma, "A Learning-Based Similarity Fusion and Filtering Approach for Biomedical Image Retrieval Using SVM Classification and Relevance Feedback", IEEE Transactions on Information Technology in Biomedicine, Vol.15, No.4, pp. 640-646, 2011.
  18. Lining Zhang ; Lipo Wang ; Weisi Lin, "Semisupervised Biased Maximum Margin Analysis for Interactive Image Retrieval", IEEE Transactions on Image Processing, Vol.21, No.4, pp.2294-2308, 2012.
  19. Peizhong Liu ; Jing-Ming Guo ; Chi-Yi Wu ; Danlin Cai , "Fusion of Deep Learning and Compressed Domain Features for Content-Based Image Retrieval", IEEE Transactions on Image Processing, Vol.26, No.12, pp.5706-5717, 2017.
  20. Xiaofan Zhang ; Wei Liu ; Murat Dundar ; Sunil Badve ; Shaoting Zhang, "Towards Large-Scale Histopathological Image Analysis: Hashing-Based Image Retrieval", IEEE Transactions on Medical Imaging, Vol.34, No.2, pp.496-506, 2015.
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21.

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 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 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, 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, 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, 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, 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.

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22.

Authors:

P. Muniappan, M. Ravithammal, S. Senthil 

Paper Title:

An Incentive Inventory Model for Exponential Function of Cost with Maximum Life Time of 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.
  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.
  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.

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23.

Authors:

K. Kishore AnthuvanSahayaraj, K. Venkatachalapathy

Paper Title:

An Automatic Vehicle Type Classification and Counting based on Deep Learning in Traffic 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

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. 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. Ge, T. Shao, C. Wen, R. Sun, Analysis on strong tracking filtering forlinear dynamic systems, Mathematical Problems in Engineering (2015) 1–9.
  4. Lu, L. Wang, J. Wen, Image classification by visual bag-of-words refinement and reduction, Neurocomputing (2016) 373–384.
  5. Xie, J. Wang, B. Zhang, Q. Tian, Incorporating visual adjectives forimage classification, Neurocomputing (2016) 48–55
  6. A. A. Shah, M. Bennamoun, F. Boussaid, Iterative deep learningimage set based face and object recognition, Neurocomputing (2016) 866- 874.
  7. Nedjah, F. P. Silva, A. O. Sa, L. M.Mourelle, D. A.Bonilla, A massivelparallel pipelined reconfigurable design for m-pln based neural networks for efficient image classification, Neurocomputing (2016) 39–55.
  8. Ge, D. Xu, C. Wen, Cubature information filters with correlated noisesand their applications in decentralized fusion, Signal Processing (2014) 434–444.
  9. Ge, C. Wen, S. Duan, Fire localization based on range-range-rangemodel for limited interior space, IEEE Transactions on Instrumentation and Measurement (2014) 2223–2237
  10. Traffic Scorecard. INRIX. Available online: http://inrix.com/ (accessed on 5 May 2017).
  11. Hsieh, J.-W.; Yu, S.-H.; Chen, Y.-S.; Hu, W.-F. Automatic traffic surveillance system for vehicle tracking and classification. IEEE Trans. Intell. Transp. Syst. 2006, 7, 175–187.
  12. Buch, N.; Velastin, S.A.; Orwell, J. A review of computer vision techniques for the analysis of urban traffic.IEEE Trans. Intell. Transp. Syst. 2011, 12, 920–939.
  13. Sermanet, P.; Eigen, D.; Zhang, X.; Mathieu, M.; Fergus, R.; LeCun, Y. OverFeat: Integrated Recognition,Localization and Detection using Convolutional Networks. arXiv. 2013. Available online: https://arxiv.org/abs/1312.6229 (accessed on 6 February 2017).
  14. Daigavane, P.M.; Bajaj, P.R. Real Time Vehicle Detection and Counting Method for Unsupervised TrafficVideo on Highways. Int. J. Comput. Sci. Netw. Secur. 2010, 10, 112–117.
  15. Chen, S.C.; Shyu, M.L.; Zhang, C. An Intelligent Framework for Spatio-Temporal Vehicle Tracking.In Proceedings of the 4th IEEE Intelligent Transportation Systems, Oakland, CA, USA, 25–29 August 2001.
  16. Gupte, S.; Masoud, O.; Martin, R.F.K.; Papanikolopoulos, N.P. Detection and Classification of Vehicles.IEEE Trans. Intell. Transp. Syst. 2002, 3, 37–47.
  17. Cheung, S.; Kamath, C. Robust Techniques for Background Subtraction in Urban Traffic Video. In Proceedingsof the Visual Communications and Image Processing, San Jose, CA, USA, 18 January 2004.
  18. Kanhere, N.; Pundlik, S.; Birchfield, S. Vehicle Segmentation and Tracking from a Low-Angle Off-AxisCamera. In Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and PatternRecognition, San Diego, CA, USA, 20–25 June 2005.
  19. Deva, R.; David, A.; Forsyth, D.A.; Andrew, Z. Tracking People by Learning their Appearance. IEEE Trans.Pattern Anal. Mach. Intell. 2007, 29, 65–81.
  20. Toufiq, P.; Ahmed, E.; Mittal, A. A Framework for Feature Selection for Background Subtraction.In Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition,New York, NY, USA, 17–22 June 2006.
  21. Gao, T.; Liu, Z.; Gao, W.; Zhang, J. Moving vehicle tracking based on sift active particle choosing.In Proceedings of the International Conference on Neural Information Processing, Bangkok, Thailand,1–5 December 2009.
  22. Jun, G.; Aggarwal, J.; Gokmen, M. Tracking and segmentation of highway vehicles in cluttered and crowded scenes. 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. S, H. K, G. R, Faster r-cnn: Towards realtime object detection withregion proposal networks, NIPS (2015) 442–451.
  32. 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. 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).

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24.

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) 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 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. 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.

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25.

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.

References:

  1. AIM Special Work Group, Zero forcing sets and the minimum rank of graphs. Linear Algebra Appl. 428(7), 1628-1648 (2008).
  2. Burgarth, V.Giovannetti, L. Hogben, S. Severini, and M. Young, Logic circuits from zero forcing, Natural Computing 14(3), 485-490 (2015).
  3. 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. 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/
  6. 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.

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26.

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. Ai "Challenges toward wireless communications for high-speed railway" IEEE Transaction Intelligent Transportation Syst., vol. 15, no. 5, pp. 2143-2158, 2014
  2. 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. Mod. Transport. (2015) 23(4):272–286 DOI 10.1007 /s40534-015-0083-1
  7. 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. 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. 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. 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
  15. Li "Estimation of railway vehicle suspension parameters for condition monitoring" Control Eng. Practice, vol. 15, no. 1, pp. 43-55, 2007
  16. 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. 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. Flammini "Towards wireless sensor networks for railway infrastructure monitoring" Proc. Electr. Syst. Aircraft, Railway Ship Propulsion, pp. 1-6, 2010
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  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. 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. 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. 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
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  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. Immanuel Rajkumar, GPS & Ethernet Based Real Time Train Tracking System International Conference on Advanced Electronic Systems. p. 283-287
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  33. 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.
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  37. 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.
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27.

Authors:

S. Sridevi, R. Anandan 

Paper Title:

AORA-A Novel Optimized Intrusion Detection System for Identification of the Black Hole Attacks in 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 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 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.

  1. 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, -MAR.
  2. 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.
  3. 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.
  4. 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.
  5. 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.
  1. 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,
  2. 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.
  3. 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,
  4. 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.
  5. LucijaBrezocnik, “Feature selection for classification using particle swarm optimization”, IEEE EUROCON 2017, pp. 6-8 JULY 2017, OHRID, R. MACEDONIA
  6. MarwaSharawi, Hossam M. Zawbaa, and E. Emary, “Feature Selection Approach Based on Whale Optimization Algorithm”, international conference advanced computational intelligence, pp.4-6, 2017.
  7. 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.

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28.

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, 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,’ 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.
  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, 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.
  14. 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.

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29.

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. 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.
  6. 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.
  8. 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. 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. 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. 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. 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. -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. 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. 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. 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. 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. 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. 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. 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. 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. 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.

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30.

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 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 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 Electronics2014 (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 Applications37, 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 Electronics25, 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 "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”

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31.

Authors:

B.R. Aravind, V. Rajasekaran 

Paper Title:

Technological Modality to Influence Persuasive and Argumentative Vocabulary for Effective 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.
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  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.

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32.

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.
  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.
  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. 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.40-46.

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33.

Authors:

A.Geetha, T.M. ThamizhThentral, SnehalArvindSilekar, GracyKatiyar, Gaurav Mishra, 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 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 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. 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.

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34.

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. 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 Localization in Distribution Water Networks”, Procedia Engineering, Elsevier, Volume 119, 2015, Pages 1399-1408
  7. 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. 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

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35.

Authors:

Saritha Reddy Venna, Ramesh Babu Inampudi

Paper Title:

MMBAS-NS: Multimodal Biometric Authentication System and Key Generation Algorithm for 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: Biometrics, Authentication System, Key Generation, Network Security, Face, Fingerprint, Retina.

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. 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.  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, 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.
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  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.
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Authors:

V. Karthikeyan, V. Jamuna 

Paper Title:

A Simplified Optimal THD Modulation Algorithm for Multi Level Inverter with Reduced Components

Abstract: Applications of multi level inverters have been broadly accepted for high power AC drives purposes. In this paper a multi level inverter with reduced number of components has been designed. The power quality of the output waveforms are controlled by proper selection of switching angles for different levels of the output. A simplified optimal THD modulation algorithm is used to calculate the proper switching angles of the output. This algorithm greatly reduces the computational efforts required than the conventional computation methods and also produces better output performances. The simulation model of 9-level asymmetrical inverter with reduced number of components is built to validate its operation and the results are presented.

Keywords: Multi Level Inverter, Reduced Number of Components, Optimal THD Modulation, and Harmonic Distortion.

References:

  1. Kouro S., Malinowski M., Gopakumar K., et al., “Recent advances and industrial applications of Multilevel Converters,” IEEE Transactions on Industrial Electronics, vol.57, no.8, pp. 2553-2580, August 2010.
  2. Rodriguez J., Lai J S., Peng F Z., “Multilevel Inverters: A Survey of Topologies, Control, and Applications,” IEEE Transactions on Industrial Electronics, vol.49, no.4, pp. 724-738, April 2002.
  3. Nabae A., Takahashi I., Akagi., “A New Neutral-Point-Clamped PWM Inverter,” IEEE Transactions on Industrial Applications, vol.IA-17, no.5, pp. 518-523, October 1981.
  4. Lai J S., Peng F Z., “Multilevel Converters: A New Breed of Power Converter,” IEEE Transactions on Industrial Applications, vol.32, no.3, pp. 509-517, May 1996.
  5. Hammond P W., “A New Approach to Enhance Power Quality for Medium Voltage AC Drives,” IEEE Transactions on Industrial Applications, vol.33, no.1, pp. 202-208, January 1997.
  6. Franquelo L G., Rodriguez J., Leon J I., et al., “The Age of Multilevel Converters Arrives,” IEEE Industrial Eelctronics Magazine, vol.2, pp. 28-39, June 2008.
  7. Mastromauro R A., Liserre M., Dell’ Aquila A., “Control Issues in Single-Stage Photovoltaic Systems: MPPT, Current and Voltage Control,” IEEE Transactions on Industrial Informatics, vol.8, no.2, pp. 241-254, May 2012.
  8. Karthikeyan V., Jamuna V., Abisha James., “Multilevel Inverter for Hybrid Energy Generation System,” Applies Mechanics and Materials, vol.622, pp. 127-131, August 2014.
  9. Malinowski M., Gopakumar K., Rodrigue J., Pérez M A., “A Survey on Cascaded Multilevel Inverters,” IEEE Transactions on Industrial Electronics, vol.57, no.7, pp. 2197-2206, July 2010.
  10. Najafi E., Yatim A H M., “Design and Implementation of a New Multilevel Inverter Topology,” IEEE Transaction on Industrial Electronics, vol.59, no.11, pp. 4148-4154, November 2012.
  11. Karthikeyan V., Jamuna V., “Hybrid Control Strategy for BCD Topology Based Modular Multilevel Inverter,” Circuits and Systems, vol. 7, no.8, pp. 1441-1454, June 2016.
  12. Banaei M R., Salary E., “Asymmetric Cascaded Multi-level Inverter: A Solution to Obtain High Number of Voltage Levels,” Journal of Electrical Engineering & Technology, vol.8, no.2, pp. 316-325, February 2013.
  13. Babaei E., Laali S., Bayat Z., “A Single-Phase Cascaded Multilevel Inverter Based on a New Basic Unit with Reduced Number of Power Switches,” IEEE Transactions on Industrial Eelctronics, vol.62, no.2, pp. 922-929, February 2015.
  14. Babaei E., Laali S., Alilu S., “Cascaded Multilevel Inverter with Series Connection of Novel H-Bridge Basic Units,” IEEE Transactions on Industrial Electronics, vol.61, no.12, pp. 6664-6671, December 2014.
  15. Ghasemi N., Zare F., Boora A A., Ghosh A., Langton C., Blaabjerg F., “Harmonic Elimination Technique for a Single-Phase Multilevel Converter with Unequal DC Link Voltage Levels,” IET Power Eelctronics, vol.5, no.8, pp.1418-1429, August 2012.
  16. Kumle A N., Fathi S H., Jabbarvaziri F., Jamshidi M., Yazdi S S H., “Application of Memetic Algorithm for Selective Harmonic Elimination in Multi-Level Inverters,” IET Power Electronics, vol.8, no.9, pp. 1733-1739, September 2015.
  17. McGrath B P., Holmes D G., Lipo T., “Multicarrier PWM Stategies for Multilevel Inverter,” IEEE Transactions on Industrial Electronics, vol.49, no.4, pp. 858-867, April 2002.
  18. Irfan A., Vijay B B., “Simplified Space Vector Modulation Technique for Seven-Level Cascaded H-Bridge Inverter,” IET Power Electronics, vol.7, no.3, pp. 604-613, March 2014.

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37.

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 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 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. Pakkiraiah and G.Durga ,”various MPPT issues to improve solar PV sytem efficiency ,Hindawi journal of solar energy, june 2016.
  4. 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. 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.

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38.

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).

References:

  1. 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. 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. 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. 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. 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. 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. 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.

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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- Engineer and electronics- “Intro. to hybrid and electric vehicle,” in 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. 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. 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. Vinot and R. Trigui, “Optimal energy management of hevs with hybrid storage systems”, Energy Conversion and Management, vol. 76, pp.437-453, 2013.
  7. 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.

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40.

Authors:

M. Parameswari, T. Sasilatha, S. Vijayalakshmi, P. Divya Bharathi, A. Aishwarya

Paper Title:

Extensible Network Lifetime Using Relay Selection Scheme on Wide Area Wireless Sensor Networks

Abstract: One of the most important metrics in wide area wireless sensor networking (WAWSN) is to maximize the network lifetime. In this paper, a relay selection scheme is proposed under the topology constraints to maximize the lifetime of WAWSNs through solving an optimization problem where relay selection of each node acts as optimization variable. Considering the diversity of the sensor nodes in WAWSNs, the optimization problem takes not only energy consumption rate but also energy difference among sensor nodes into account to improve the network lifetime performance. Since it is Non-deterministic Polynomial-hard (NP-hard) and intractable, a heuristic solution is then designed to rapidly address the optimization. The simulation results assumed indicates that the proposed relay selection scheme has better performance in network lifetime compared with existing algorithms and that the heuristic solution has low time complexity with only a negligible performance degradation gap from optimal value. Furthermore, we also assumed simulations based on a general WAWSN model to comprehensively illustrate the advantages of the proposed algorithm.

Keywords: WAWSNs, Lifetime, Energy Consumption, Residual Energy, Relay Selection, Optimization, Heuristic Solution.

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 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.

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41.

Authors:

M. Senthil Murugan, T. Sasilatha 

Paper Title:

Implementation of Advanced Encryption Standard Algorithm on Steganography

Abstract: It is sometimes not enough to keep the contents of a message secret. It may also be necessary to keep the existence of the message secret. The algorithm that is described here is about hiding an audio file into video frame. A video file is chosen first. The video file is then divided into frames. A particular frame is selected and skin tone detection is performed on it using HSV color space model. The skin region is taken as the Region of Interest (ROI). An audio file is the secret message to be transmitted. It is embedded into the frame using Advanced Encryption Standard (AES) algorithm. The secret audio file encrypted on a particular frame is transmitted with a key. The receiver can extract the secret audio file only if he has the key. This concept is based on visual method. The goal of using a video to encrypt the data is that it will be difficult to detect the frame in which the data is embedded. The secret data can be retrieved only by the user who has knowledge of the frame in which the data is embedded and the key which is known only by the sender and receiver. Thus a secured communication is established.

Keywords: AES, Steganography, Skin Tone Detection.

References:

  1. Basilio JAM, Torres GA, Sánchez Pérez G, Medina LKT, Meana HMP    (2011) Explicit image detection using YCbCr space color model as skin         Appl Math Comput Eng.
  2. Channalli S, Jadhav A (2009) Steganography an art of hiding data. Int J Comput Sci Eng 1(3):137–141
  3. Cheddad A, Condell J, Curran K, Mc Kevitt P (2009) A skin tone detection algorithm for an adaptive approach to steganography. Signal   Process 89(12):2465–2478
  4. Chen WY (2007) Color image steganography scheme using set partitioning              in hierarchical trees coding, digital Fourier transform and         adaptive phase modulation. Appl Math Comput 185(1):432–448
  5. Elgammal A, Muang C, Hu D (2009) Skin detection - a short tutorial. In Encyclopedia of Biometrics. 1218–1224. Springer US.
  6. Eltahir ME, Kiah LM, Zaidan BB, Zaidan AA (2009) High rate video streaming steganography. In: International Conference on Future Computer and Communication (ICFCC 2009) 672–675
  7. Farag H, El-Khamy SE (2014) Blind key steganography based on multilevel wavelet and CSF. Int Refereed J Eng Sci 3.
  8. Fleck MM, Forsyth DA, Bregler C (1996) Finding naked people. In Computer Vision – ECCV’96: 593–602
  9. Gong X, Lu HM (2008) Towards fast and robust watermarking scheme for H.264 video. In Proc. 10th IEEE ISM: 649–653
  10. Hamad SH, Khalifa AS (2013) A quantization-based image watermarking using multi-resolution wavelet decomposition. Egypt Comput Sci J.
  11. Hu S, KinTak U (2011) A novel video steganography based on non uniform rectangular partition. In: IEEE 14th International Conference on         Computational Science and Engineering (CSE) 57–61
  12. Kakumanu P, Makrogiannis S, Bourbakis N (2007) A survey of skin- color modeling and detection methods. Pattern Recogn 40(3):1106–1122         Multimed Tools Appl.

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42.

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 Inter-face (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 cere-brum of an extremely crippled individual and a PC.

Keywords: Brain System Interface, Sensor, Cyber Kinetics, Cerebrum, PCs.

References:

  1. 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”.

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43.

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. 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.

Keywords: To be all around arranged for future attacks

References:

  1. 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. T. News, Expert: Botnets No. 1 Emerging Internet Threat, http:// www.cnn.com/2006/TECH/internet/01/31/furst/, 2006.
  3. 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. 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. 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. B. Salem, J.-P. Hubaux, and M. Jakobsson. Reputation based wi-fi deployment. SIGMOBILE Mob. Comput. Commun. Rev., 9(3):69–81, 2005.
  7. 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. 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. 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. 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.

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44.

Authors:

A. Arikesh, Maumita Saha

Paper Title:

Fuzzy based Modular Bidirectional Energy Conversion for Hybrid Vehicle and Renewable Energy 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.

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 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)

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45.

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.

Keywords: GPWS, Piezoelectric Pads, LPWT.

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. 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. 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

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46.

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. B. Welboum and J.D. Smith, “Machine-tool Dynamics: An introduction”, Cambridge, 1970.
  2. Cook and H. Nathan, “Self-Excited Vibrations in Metal Cutting,” ASME Transactions, Journal of Engineering for industry, Vol. 81, pp. 183- 186,1979.
  3. 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.
  4. 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. 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. 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. 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. 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.

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47.

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. Kanmani, J.Nivedha, G.Sundar, “Belt Conveyor Monitoring and Fault Detecting Using PLC and SCADA”, International  Journal of Advanced Research in  Electrical, Electronics and Instrumentation Engineering, Vol. 3,  Special Issue 4, pp.243-248, May 2014,
  6. 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. 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

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Authors:

Srinivasa Babu Kasturi, V. Divya Vani

Paper Title:

Linking Social Media to E-Commerce: Cold-Start Synthetic Items Inspiration through Micro Blogging Data

Abstract: In latest years, the bounds among e-trade and social networking have emerged as an increasing 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 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. Wang and Y. Zhang, “Opportunity model for e-commerce recommendation: Right product; right time,” in SIGIR, 2013.
  2. Giering, “Retail sales prediction and item recommendations using customer demographics at store level,” SIGKDD Explor. Newsl., vol. 10, no. 2, Dec. 2008.
  3. Linden, B. Smith, and J. York, “Amazon.com recommendations: Item-to-item collaborative filtering,” IEEE Internet Computing, vol. 7, no. 1, Jan. 2003.
  4. A. Zeithaml, “The new demographics and market fragmentation,” Journal of Marketing, vol. 49, pp. 64–75, 1985.
  5. 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. Wang, W. X. Zhao, Y. He, and X. Li, “Leveraging product adopter information from online reviews for product recommendation,” in ICWSM, 2015.
  7. Seroussi, F. Bohnert, and I. Zukerman, “Personalised rating prediction for new users using latent factor models,” in ACM HH, 2011.
  8. Mikolov, I. Sutskever, K. Chen, G. S. Corrado, and J. Dean, “Distributed representations of words and phrases and their compositionality,” in NIPS, 2013.
  9. V. Le and T. Mikolov, “Distributed representations of sentences and documents,” CoRR, vol. abs/1405.4053, 2014.
  10. Lin, K. Sugiyama, M. Kan, and T. Chua, “Addressing coldstart in app recommendation: latent user models constructed from twitter followers,” in SIGIR, 2013.
  11. Mikolov, K. Chen, G. Corrado, and J. Dean, “Efficient estimation of word representations in vector space,” CoRR, vol. abs/1301.3781, 2013.
  12. Koren, R. Bell, and C. Volinsky, “Matrix factorization techniques for recommender systems,” Computer, vol. 42, no. 8, pp. 30–37, Aug. 2009.
  13. H. Friedman, “Greedy function approximation: A gradient boosting machine,” Annals of Statistics, vol. 29, pp. 1189–1232, 2000.
  14. 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.

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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.

Keywords: Filter, kernel size, convolving, activation map, feature map, stride, max pool, activation 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. https://i.pinimg.com/736x/a1/7b/2d/a17b2dbcaf08929c6c62920db3d44b8e--anger-management-management-tips.jpg
  6. http://www.apa.org/science/about/psa/2011/05/facial-expressions.aspx
  7. For the video tutorialhttps://youtu.be/27FPv1VHSsQ000000

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50.

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 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 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. 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. 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. 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. 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. 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] 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. 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. 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. 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.

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51.

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 Forensic.

References:

  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.

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52.

Authors:

T. Harikala, RVS SatyaNarayana

Paper Title:

Power Efficient Technique for MIMO radar using Co-operative and Non co-operative game theory in 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.
  5. Jie, Lu. "Space-Time Signal Processing for MIMO Radar Target Detection."Advanced Research on Computer Science and Information 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.

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53.

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. 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 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. 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. Onu John Chigbo. (2008). Selected Physical Properties of Soybean In Relation To Storage Design
  3. 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. 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. 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

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54.

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 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 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, 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. 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/

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55.

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 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) 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 –  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.

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56.

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: clustering; evolutionary algorithms; Genetic Algorithms(GA); Particle Swarm Optimization(PSO); Bee Algorithm(BA); K- means(KM).

References:

  1. 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 Elsevier and Elsevier digital library, 2014. 
  2. D. Thakare, C.A. Dhote, Novel Multi Stage Genetic Clustering method for multi-objective optimization in Data Clustering, ICCUBEA 2015, Scopus Indexed, IEEE Xplore
  3. 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. 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. 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. 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. 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. 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. Bandyopadhyay and S. K. Pal, ``Classification and Learning Using Genetic Algorithms: Applications in         Bioinformatics and Web Intelligence", Springer, Heidelberg,  2007
  12. 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

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57.

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 through MS Azure IFTTT services.

Keywords: Internet of Things, Smart agriculture, Cloud Computing, IFTTT, Wireless Sensor area network, Thing Speak cloud, Azure Web App

References:

  1.  Hemlata Channe, Sukhesh Kothari, Dipali Kadam, “Multidisciplinary Model for Smart Agriculture using Internet-of-Things (IoT), Sensors, Cloud-Computing, Mobile-Computing & Big-Data Analysis,” Int.J.Computer Technology & Applications, Vol 6 (3),374-382.
  2. Ojas Savale, Anup Managave, Deepika Ambekar, Sushmita Sathe, “Internet of Things in Precision Agriculture using Wireless Sensor Networks,” International Journal Of Advanced Engineering & Innovative Technology (IJAEIT).
  3. Raheela Shahzadi, Muhammad Tausif, Javed Ferzund, Muhammad Asif Suryani, “Internet of Things based Expert System for Smart Agriculture,” (IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 7, No. 9, 2016.
  4. Domain Mohanraj I, Kirthika Ashokumar, Naren J, ”Field Monitoring and Automation using IOT in Agriculture,” 6th International Conference On Advances In Computing & Communications, ICACC 2016, 6-8 September 2016.
  5. A V L N Sujith, K Chandra Sekha,” Automated Agriculture as a Service Using IoT,” International Journal of Advanced Research in Computer Science and Software Engineering, Volume 7, Issue 5, May 2017.
  6. Heble, S., Kumar, A., Prasad, K. V. V. D., Samirana, S., Rajalakshmi, P., & Desai, “A low power IoT network for smart agriculture,” 2018 IEEE 4th World Forum on Internet of Things (WF-IoT),2018.
  7. Andreas Kamilaris, Feng Gaoy, Francesc X. Prenafeta-Bold´u and Muhammad Intizar Aliy, “Agri-IoT: A Semantic Framework for Internet of Things-enabled Smart Farming Applications,” 2016 IEEE 3rd World Forum on Internet of Things (WF-IoT).
  8. Jayavardhana Gubbia, Rajkumar Buyyab, Slaven Marusic, Marimuthu Palaniswami , “Internet of Things (IoT): A vision, architectural elements, and future directions,” Future Generation Computer Systems 29 (2013) 1645–1660
  9. Georgakopoulos, D., & Jayaraman, P. P. (2016), "Internet of things: from internet scale sensing to smart services,” Computing,98(10),1041–1058.,doi:10.1007/s00607-016-0510-0
  10. Partha Pratim Ray, “Internet of things for smart agriculture: Technologies, practices and future direction,” Journal of Ambient Intelligence and Smart Environments 9 (2017) 395–420
  11. Li Li, Hu Xiaoguang, Chen Ke, He Ketai, “The Applications Of WiFi-based Wireless Sensor Network In Internet Of Things And Smart Grid,” 2011 6th IEEE Conference on Industrial Electronics and Applications.

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58.

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 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 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.

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59.

Authors:

Kaajal Nishandh, Sanjay Kumar P, P.K Krishnan Namboori

Paper Title:

Expression Profiling & Classification using Convolutional Neural Networks of Tumor Suppressor Genes Linked with Stress

Abstract: Tumor suppressor genes are always linked with stress, directly or indirectly which results in mutation. Therefore the probability of turning these mutations into cancer increases. Identification of major tumor suppressor genes and its presence among Indian population is analyzed. Due to great advancement in the field of deep learning, and wide variety of scopes in future, deep learning is incorporated in this project to perform the classification task .The requirement of large amount of data to perform classification task is one of the major drawback of deep learning. In order to solve this problem, one-shot learning algorithm is introduced which gave the accuracy of 70.2%. A secure data sharing platform has been developed using blockchain technique.

Keywords: Block Chain Technique, Deep Learning, Tumor suppressor genes.

References:

  1. Chen, B. Mulgrew, and J. Pflaum, S. Schlosser and M. MÃuller, "p53 Family and Cellular Stress Responses in Cancer", 2018. .
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  3. Gregory Koch et al (2015) Siamese Neural Networks for One-shot Image Recogni-tion.Proceedings of  the 32nd International  Conference  on  Machine Learning, Lille, France, 2015.37
  4. HimaVyshnavi A M, Lakshmi Anand C, Deepak O M, P K Krishnan Namboori.Evaluation of Colorectal Cancer (CRC)
  5. Epidemiology A Pharmacogenomic Approach.J Young Pharm, 2017; 9(1): 36-39.
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  7. org,2018.[Online].Available:https://www.annualreviews.org/doi/abs/10.1146/annurev-cancerbio-050216-121919.
  8. Preethi M. Iyer et al (2016) Brca1 responsiveness towards breast cancer-a population-wise pharmacogenomic analysis. IJPPS 8(9):267
  9. Zhang and Y. Ji, "Blockchain for healthcare records: A data perspective", 2018.
  10. Capgemini (2017) Blockchain: A Healthcare Industry View
  11. Sherry ST et al (2001) dbSNP: the NCBI database of genetic variation. Nucleic Acids Res 29(1):308-11
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  14. Indian Genome Variation Consortium (2005) The Indian Genome Variation database (IGVdb): a project overview. Hum Genet 118(1):1-11
  15. Li, F. Liang, M. Li, D. Zou, S. Sun, Y. Zhao, W. Zhao, Y. Bao, J. Xiao and Z. Zhang, "MethBank 3.0: a database of DNA methylomes across a variety of species", 2018.
  16. Iyer, Akshay & Vyshnavi A M, Hima & Namboori P K, Krishnan. (2018). Deep Convolution Network Based Prediction Model For Medical Diagnosis Of Lung Cancer - A Deep Pharmacogenomic Approach : deep diagnosis for lung cancer. 1-4. 10.1109/ICAECC.2018.8479499.
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60.

Authors:

Mukti Fajar ND, Reni Budi Setianingrum

Paper Title:

The Disruptive Innovation in Competition Law: Regulation Issues Of On Line Transportation in 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
  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
  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
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61.

Authors:

Srinivas Kumar P, Raja Reddy K.

Paper Title:

Development of Semantic Enabled Engineering Soil Classification Along With Visualisation of Particle 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.
  1. Pankesh Patel et all, From raw data to smart manufactur-ing,IEEE Intelligent Systems, Volume 33,Issue 4
  2. 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
  3. Kandaswamy, Ministry of road transport and highways specification for road and bridge works,Indian Roads Congress, Fifth revision,pp62,Jan 2013
  4. 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-station.html”,20 Apr 2016
  5. 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,
  6. 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.
  7. 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.
  8. 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.
  9. 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.
  10. Shivananda and P. Srinivas Kumar. Building Rules Based Soil Classification Ontology. International Journal of Computer Science and Information Technology &Security, 3(2), 2013.
  11. 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.
  12. 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
  13. 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.
  14. 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.
  15. Gyrard et al., “Building the Web of Knowledge with Smart IoT Applications,”IEEE Intelligent Systems, vol. 31, no. 5, 2016, pp. 83–88.
  16. Shiny server Introduction, Jeff Allan, https://www.rstudio.com/products/shiny/shiny-server/,25-Feb-2014
  17. Postgresql Introduction https://www.postgresql.org/about/
  18. OntorionIntroductionhttp://www.cognitum.eu/semantics/FluentEditor/rOntorionFE.aspx

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62.

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. L. Wolf, M.S. Squillante, J. Sethuraman, L. Ozsen, “Optimal Crawling Strategies for Web Search Engines”, ACM 1-58113-449-5/02/0005.
  3. 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 28-Aug 1, 2003, Toronto, Canada. Copyright 2003 ACM 1-58113-646-3/03/0007.
  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.

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63.

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. Abbass 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.
  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
  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.
  1. Herbert Solomon. Confidencc Intervals in Legal Settings1 pages 455-473. John Wiley & Sons, 1986.
  2. 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.

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64.

Authors:

Azhar Talha Syed, Suresh Merugu, Vijaya Kumar Koppula.

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 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.
  1. 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.
  2. Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun in “Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification”.
  3. Matthew D. Zeiler, Rob Fergus, 2013, “Visualizing and Understanding Convolutional Networks”.
  4. Hervé Goëau, Pierre Bonnet, Alexis Joly, Julien Barbe, Souheil Selmi, Vera Bakic, Vera Bakic, Jennifer Carré, Daniel Barthelemy, Nozha Boujemaa. “Pl@ntNet Mobile App” .
  5. 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.
  6. 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.
  7. Jonathan P. Caulkins (2010) in his research work “Estimated Cost of Production for Legalized Cannabis” at RAND, Drug Policy Research Center.

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65.

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.

Keywords: Accident Alert System; Accident Alert IOT; Family Accident Alert Application; Vehicle Collision 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. 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.

337-340

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66.

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 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 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.

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67.

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.

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.
  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.
  6. 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. 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. [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.

345-348

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68.

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.
  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.
  1. 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.
  2. 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.
  3. 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.
  4. Hadian, A.,Nasiri ,M.,Bidgoli, B.M.,2010. Clusteringbasedmulti-objectiverule mining usinggeneticalgorithm. Int.J.DigitalContent Technol.Appl.4,37–42.
  5. Kuo,R.J,Chao,C.M.,Chiu,Y.T.,2011.Applicationofparticleswarmoptimizationto association rulemining. Appl .Soft Comput. 11,326–336.
  6. Gupta, M.,2012. Application of weighted particle swarm optimization in association rule mining. Int.J. Comput. Sci.Inf.1, 2231–5292.
  7. 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.
  8. 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.

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69.

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. 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
  8. Lazarela Lazareska, Kire Jakimoski et al, Analysis of the Advantages and Disadvantages of Android and iOS Systems and 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

352-355

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70.

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 degree yields the best prediction of question and answers.

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. 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. 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. 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

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71.

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. 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 Sciences, 9(20):3652-3661, 2009
  4. 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
  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. 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. 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.

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72.

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 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 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. Amiri and Michael M. Khansari “the thermodynamics of friction and wear” –(Published 27 April 2010) www.mdpi.com/journal/entropy-on
  2. 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. 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

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73.

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. 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
  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 1406-1408. 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.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.

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74.

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.

References:

  1. Zhang and Wang, “Binary power data hiding scheme”1434-8411/@2015 Elsevier.
  2. 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. 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. Sharp, An implementation of key-based digital signal steganography, Proc. of the 4th Information Hiding Workshop, vol. 2137, pp. 13-26, Springer, 2001.
  9. Mielikainen, LSB matching revisited, IEEE Signal Processing Letters, vol. 13, no. 5, pp. 285-287,2006.
  10. 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. 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.

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75.

Authors:

Jolly Upadhyaya, Neelu Jyothi Ahuja, Kapil Dev Sharma

Paper Title:

Evaluating User Expectations and Quality of Service: A Novel Approach to Understanding Cloud 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 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: Cloud Computing; Higher Education; Metrics, Quality of Service (QoS), Quality of Experience (QoE).

References:

  1. Almajalid, Rania. “A Survey on the Adoption of Cloud Computing in Education Sector.” CoRR abs/1706.01136 (2017): n. pag.
  2. 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.
  3. 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
  4. 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
  5. 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.

381-385

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76.

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 , 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., 12, pp.138–45,2014.
  3. Ettehadi M, GhasemiH, Vaez-Zadeh S, “Voltage stability-based DG placement in distribution networks,” IEEE Trans. Power , 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,” 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 prediction,”Journal Sci., Vol.6, pp.1104–1113,2006.
  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, , 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., 25, pp.1566-1574,2010.
  10. Gao, B., Morison, G.K., Kundur, , “Voltage stability evaluation using modal analysis,” IEEE Trans. Power Syst., Vol.7, pp.1529–1542,1992.
  11. Iba, K., Suzuli, H., Egawa, M., Watanabe, , “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. 145, pp.634–640,1998.
  14. Mohamed A, Jasmon G B, YusoffS, “A static voltage collapse indicator using line stability factors,”Journal Ind. Technol. 7, pp.73–85,1989.
  15. Moghavvemi M, Faruque M O, “Technique for assessment of voltage stabilityinill-conditionedradialdistributionnetwork,”IEEEPower 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, 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.

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77.

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( 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.
  4. Heinzelman, W. R., Chandrakasan, A., & Balakrishnan, H. (2000, January). Energy-efficient communication protocol for wireless microsensor networks. In System sciences, 2000. Proceedings of the 33rd annual Hawaii international conference on( 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.

393-396

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78.

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:

  1. 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. Thakur, & N. Jindal, Multimedia Tools and Application, Image Forensics Using Color Illumination, Block and Key Point Based Approach, (2018); 77: 26033.
  3. 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. Tralic, I. Zupancic, S. Grgic, M. Grgic, 55th International Symposium ELMAR, CoMoFoD - New Database for Copy Move Forgery Detection, (2013); 49-54.
  5. 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. 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).

397-401

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79.

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. l. g. s. chandrahas mishra, “credit card fraud detection using neural networks,” international journal of comoputer science, vol. 4, no. 7, July 2017.
  2. 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. pansy khurana, “credit card fraud detection using fuzzy logic and neural network,” SpringSim, 2016.
  4. 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. 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. 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. k. s.,. m. abhinav srivastava, “credit card fraud detection using hidden markov model,” IEEE, vol. 5, no. 1, 2008.
  8. D. Yusuf Sahin, “detecting credit card fraud by ann and logistic regression,” 2011.
  9. 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. 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, 1, no. NCAI2011, 2011.
  12. 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. 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. k. A. K. M. Ayushi agarwal, “Credit card fraud detection: A case study,” in IEEE, New Delhi, India, 2015.
  15. T. B. V. Sam Maes, “Credit cards fraud detection using bayesian and neural networks,” p. 7, August 2002.
  16. 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.

402-407

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80.

Authors:

Uma Meena, Anand Sharma

Paper Title:

An Efficient Hop-by-Hop Message Authentication Scheme and Secure Location Privacy in Wireless 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.
  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.

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81.

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 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 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. 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. 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.