International Journal of Innovative Technology and Exploring Engineering

ISSN : 2278 - 3075 Website: www.ijitee.org Volume-8 Issue-4, FEBRUARY 2019 Published by: Blue Eyes Intelligence Engineering and Sciences Publication

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

Associated Editor-In-Chief Chair Dr. Vinod Kumar Singh Associate Professor and Head, Department of Electrical Engineering, S.R.Group of Institutions, Jhansi (U.P.), India

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Technical Program Committee Members Dr. Srilalitha Girija Kumari Sagi Associate Professor, Department of Management, Gandhi Institute of Technology and Management, Visakhapatnam (A.P.) India.

Dr. Vishnu Narayan Mishra Associate Professor, Department of Mathematics, Sardar Vallabhbhai National Institute of Technology, Ichchhanath Mahadev Dumas Road, Surat (Gujarat), India.

Dr. Sripada Rama Sree Vice Principal, Associate Professor, Department of Computer Science and Engineering, Aditya Engineering College, Surampalem (Andhra Pradesh), India.

Dr. Ramzi Raphael Ibraheem Al Barwari Assistant Professor, Department of Mechanical Engineering, College of Engineering, Salahaddin University – Hawler (SUH) Erbil – Kurdistan, Erbil Iraq.

Dr. Kapil Chandra Agarwal H.O.D. & Professor, Department of Applied Sciences & Humanities, Radha Govind Engineering College, U. P. Technical University, Jai Bheem Nagar, Meerut, (U.P). India.

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

Editorial Chair Dr. Saeed Balochian Associate Professor, Gonaabad Branch, Islamic Azad University, Gonabad, Iran.

Editorial Members Dr. Gyanesh Shrivastava Associate Professor, Department of Information Technology, MATS University, Raipur (Chhattisgarh), India.

Dr. Swapnil B. Mohod Assistant Professor, Department of Electrical Engineering, Prof. Ram Meghe College of Engineering & Management, Badnera, Amravati (Maharashtra), India.

Dr. Subramani Roychoudri Professor, Department of Computer Science and Engineering, Usha Rama College of Engineering and Technology, Telaprolu (Andhra Pradesh), India.

Dr. KPNV Satyasree Professor, Department of Computer Science and Engineering, Usha Rama College of Engineering and Technology, Telaprolu (Andhra Pradesh), India.

Dr. Parul Mishra Assistant Professor, Department of English, GD Goenka University Gurugram, Gurgaon (Haryana), India. S. Volume-8 Issue-4, February 2019, ISSN: 2278-3075 (Online) Page No Published By: Blue Eyes Intelligence Engineering & Sciences Publication No.

Authors: Ranjit Sadakale, R. A. Patil, N V K Ramesh Paper Title: An Efficient AODV Routing Protocol for Vehicular Ad hoc Network Abstract: Vehicular Ad-hoc Network (VANET) is considered as a sensor network with special characteristics and some advance features. For VANET nodes treated with high mobility and fast topology change. These nodes can sense its neighboring area to provide various services like traffic monitoring, speed of vehicle and some environmental parameters monitoring. One of the advance reactive routing protocol is Ad Hoc on-demand Distance Vector (AODV) is most commonly used routing protocol in topology based routing. This paper is presenting improved AODV protocol, in order to consider different parameters like node mobility, sent packet rate, delay and throughput. Results are implemented using Network Simulator-2.

Keywords: Cooperative Communication, Intelligent Transportation System (ITS), Packet combining, VANET.

References: 1. Jothi K R,Dr,Ebenezer Keyakumar A,”A Survey on Broadcasting Protocols in VANETs”,IJITEE, Vol.3 Nov 2013, ISSN 2278-3075. 2. Kulla E.,Morita S.,Katayama K., “Route lifetime prediction methos in VANET by using AODV routing protocol”, Advances in Intelligent systems and computing, 772 pp.3-11, 2019 3. Abbasi I.A., Khan A.S., Ali S., “A Reliable Path Selection and Packet Forwarding Routing for Vehicular Ad hoc Networks”, EJWCN, 2018(1), 236. 4. S. Peters, A. Panah, K. Truong, and R. Heath, “Relay Architectures for 3GPP LTE Advanced,”, EURASIP Journal on Wireless Communications and Networking, May 2009. 5. T. Beniero, S. Redana, J. Hmlinen, and B. Raaf, “Effect of Relaying on Coverage in 3GPP LTE-Advanced,” IEEE Vehicular Technology Conference, vol. 53, pp. 1–5, Apr. 2009. 6. J. Cho and Z. Haas, “On the Throughput Enhancement of the Downstream Channel in Cellular Radio Networks Through Multihop 1. Relaying,” IEEE Journal on Selected Areas in Communications, pp. 1206–1219, Sept. 2004. 7. R. Irmer and F. Diehm, “On coverage and capacity of relaying in LTE-advanced in example deployments,” IEEE Symposium on Personal, Indoor and Mobile Radio Communications, pp. 1–5, Sept. 2008. 1-4 8. Tarek Bejaoui,”Qos-Oriented High Dynamic Resource Allocation in Vehicular Communication Networks”, The Scientific World journal , vol 14 Article ID 718698. 9. IEEE 802.16 Broadband Wireless Access Working Group, “Amendment working document for Air Interface for Fixed and Mobile Broadband Wireless Access Systems,” June 2009. 10. J. Laneman, D. Tse, and G. Wornell, “Cooperative Diversity in Wireless Networks: Efficient Protocols and Outage Behavior,” IEEE Transactions on Information Theory, vol. 50, pp. 3062–3080, Dec. 2004. 11. LI Yong,Hou Yi-bin,HUANNG Zhang-qin, WEI yi-fei, “High Throughput relay policy in wireless cooperative relaying networks on stochastic control theory”, Elsevier, August 2011, 18(4). 12. Georgios Papadimitriou, Nikolas Pappas ,“ Network –level performance evaluation of a two-relay cooperative random access wireless system”, Computer networks 88 (2015) 187-201. 13. Mohmad Feteiha, Hossam S Hassanein, “Decode-and –Forward cooperative vehicular relaying for LTE-A MIMO-downlink”, Vehicular communications 3 (2016) 12-20. 14. G.G. Md.Nawaz Ali,Edward Chan,Wenzhong Li, “On scheduling data access witj cooperative load balancing in vehicular adhoc networks”, J Supercomput (2014) 67:438-468 15. Zeyu Zheng,Shengli Fu,Kejie Lu, “On the relay selection for cooperative wireless networks with physical layer network coding”, Wireless Netw (2012) 18:653-665. 16. Kai Liu,Joseph K Y Ng, “Cooperative Data scheduling in Hybrid VANETs: VANET as a software Defined Network”, ACM transactions on Networking, Vol 24, No 3 June 2016. 17. Suman Saha,”Research Challenges of Position Based Routing Protocol in Vehiculat Adhoc Networks”, IOSRJEN, ISSN(e): 2250- 3021,Nov 2016,Vol 06,Issue 11. 18. S. Meko and P. Chaporkar, “Channel Partitioning and Relay Placement in Multi-hop Cellular Networks,” International Symposium on Wireless Communication Systems, pp. 66–70, Sept. 2009. 19. J. Cioffi, “A Multicarrier Primer,” Nov. 1991. 20. Angelos Antonopolous, Christos Verikoukis, Charalabos Skianis and Ozgur B. Akan “Energy efficient network coding-based MAC for cooperative ARQ wireless networks” Ad Hoc Networks 11 (2016) 190–200 Authors: Hemant R. Deshmukh, Mahip M. Bartere Paper Title: Enhancement of Image Stegnography Technique for Improvement of Security Abstract: Steganography will pick up its significance because of the exponential development and mystery correspondence of potential PC clients over the web. It can likewise be characterized as the investigation of undetectable correspondence that ordinarily deals with the techniques for disguising the nearness of the bestowed message. For the most part information implanting is accomplished in correspondence, picture, content, voice or interactive media content for copyright, military correspondence, confirmation and numerous different purposes. In picture Steganography, riddle correspondence is expert to introduce a message into cover picture (used as the 2. transporter to embed message into) and deliver a stego picture (created picture which is passing on a covered message). In this paper we have on a very basic level researched diverse steganographic strategies. For hiding data 5-8 we used virtual key replacement technique which provides high data security in terms of payload, Image Quality etc.

Keywords: Data Hiding, Security, Payload capacity.

References: 1. Hong Cao and Alex C. Kot, On Establishing Edge Adaptive Grid for Bilevel Image Data Hiding”, IEEE transactions on information forensics and security, vol. 8, no. 9, September 2013. 2. Che-Wei Lee and Wen-Hsiang Tsai, A Secret-Sharing-Based Method for Authentication of Grayscale Document Images via the Use of the PNG Image With a Data Repair Capability, IEEE transactions on image processing, vol. 21, no. 1, January 2012. 3. Ming Li, Michel K. Kulhandjian, Dimitris A. Pados, Stella N. Batalama, and Michael J. Medley, Extracting Spread-Spectrum Hidden Data From Digital Media, IEEE transactions on information forensics and security, vol. 8, no. 7, July 2013. 4. Chunfang Yang, Fenlin Liu, Xiangyang Luo, and Ying Zeng, Pixel Group Trace Model-Based Quantitative Steganalysis for Multiple Least-Significant Bits Steganography, IEEE transactions on information forensics and security, vol. 8, no. 1, january 2013. 5. A. E. Mustafa, A.M.F. ElGamal, M.E. ElAlmi, Ahmed.BD, A Proposed Algorithm For Steganography In Digital Image Based on Least Significant Bit , Issue No. 21, April. 2011. 6. D. C. Wu and W. H. Tsai, “A steganographic method for images by pixel-value differencing”, Pattern Recognition Letters, vol. 24, no. 9- 10, pp. 1613–1626, 2003. 7. Weiqi Luo, Fangjun Huang, Jiwu Huang, “Edge Adaptive Image Steganography Based on LSB Matching Revisited”, IEEE Transactions on Information Forensics and Security, Vol. 5, No. 2, June 2010, pp. 201-214. 8. G.Karthigai Seivi, Leon Mariadhasan, K. L. Shunmuganathan, “Steganography using Edge Adaptive Image”, Proc. of the International Conference on Computing, Electronics and Electrical Technologies (ICCEET), pp. 1023-1027, 2012. 9. Cheng-Hsing Yang, Chi-Yao Weng, Shiuh-Jeng Wang , Hung-Min Sun, “Adaptive Data Hiding in Edge Areas of Images With Spatial LSB Domain Systems”, IEEE Transactions on Information Forensics and Security, Vol. 3, No. 3, September 2008, pp.488-497. 10. R. L. Tataru, D. Battikh, S. El Assad, H. Noura, O. Deforges, “Enhanced Adaptive Data Hiding in Spatial LSB Domain by using Chaotic Sequences”, Eighth International Conference on Intelligent Information Hiding and Multimedia Signal Processing, pp. 85-88, 2012. 11. Zhu Liehuang, Li Wenzhuo, Liao Lejian , Li Hong, “A Novel Algorithm for Scrambling Digital Image Based on Cat Chaotic Mapping”, International Conference on Intelligent Information Hiding and Multimedia Signal Processing, pp. 601-605, 2006. 12. Sahar Mazloom, Amir-Masud Eftekhari-Moghadam, “Color Image Cryptosystem using Chaotic Maps”, IEEE Symposium on Computational Intelligence for Multimedia, Signal and Vision Processing, pp. 142-147, 2011. 13. Qian-chuan Zhong, Qing-xin Zhu , Ping-Li Zhang ,“A Spatial Domain Color Watermarking Scheme based on Chaos”, International Conference on Apperceiving Computing and Intelligence Analysis (ICACIA), pp. 137-142, 2008. 14. Chen Wei-bin, Zhang Xin, “Image Encryption Algorithm based on Henon Chaotic System”, International Conference on Image Analysis and Signal Processing (IASP), pp. 94-97, 2009. 15. A. E. Mustafa, A.M.F. ElGamal, M.E. ElAlmi, Ahmed.BD, A Proposed Algorithm For Steganography In Digital Image Based on Least Significant Bit , Issue No. 21, April. 2011. 16. Anuja Yeole, Mahip Bartere ,”An X-Or Base Image Encryption and Data Security through Higher LSB Data Hiding Approach: Result Oriented”, International Journal of Engineering Science and Computing, April 2016 Volume 6 Issue No. 4. 17. Wu, D.C., and Tsai, W.H.: ‘A steganographic method for images by pixel-value differencing’, Pattern Recognit. Lett., 2003, 24, (9-10), pp. 1613–1626 18. H.-C. Wu, N.-I. Wu, C.-S. Tsai and M.-S. Hwang,”Image steganographic scheme based on pixel-value differencing and LSB replacement methods”IEE Proc.-Vis. Image Signal Process., Vol. 152, No. 5, October 2005. 19. Ran-Zan Wang and Yeh-Shun Chen,” High-Payload Image Steganography Using Two-Way Block Matching”, IEEE Signal Processing Letters, Vol. 13, No. 3, March 2006 161. 20. Cheng-Hsing Yang, Chi-Yao Weng, Shiuh-Jeng Wang,” Adaptive Data Hiding in Edge Areas of Images With Spatial LSB Domain Systems”, IEEE Transactions On Information Forensics And Security, Vol. 3, No. 3, September 2008. 21. M.B. Ould Medeni,” A Novel Steganographic Method for Gray-Level Images With four-pixel Differencing and LSB Substitution”,978- 1-61284-732-0/11/$26.00 ©2010 IEEE. Authors: P.Meghana, S. SagarImambi, P. Sivateja, K. Sairam Paper Title: Image Recognition for Automatic Number Plate Surveillance Abstract: Automatic number plate recognition is a well known proposal in todays world due to the rapid growth of cars, bikes and other vehicles. This automatic number plate recognition system uses image processing technology for identification of the vehicles. This system can be used in highly populated areas and higly restricted areas to easily identify traffic rule violated vehicles and owners name, address and other information can be retrieved using this system. This system can be automated and it is used to recognize vehicles without authorization ,vehicles that violated rules at populated areas like malls, universities, hospitals and other car parking lots. This can also be used in the case of car usage in terrorist activites, smuggling, invalid number plates, stolen cars and other illegal activities. It can also be used in highway electronic toll collection. Image of the car number plate is captured and detection is done by image processing ,character segmentation which locate the alpha numeric characters on a number plate.Then the segmented characters are translated into text entries using optical character recognition(ocr).ANPR systems are already available but efficiency is not gained thoroughly. These systems are developed using different methodologies butsome factors like vehicle speed, different font styles,font sizes, language of vehicle number and light conditions are required to be explored .These can affect a lot in the overall recognition rate. ANPR systems use (ocr) optical character recognition to scan the vehical number 3. plates, and it can be retrieved whenever required. The other details of the owners of the vehicles like address and mobile number can be manipulated whenever necessary by contacting the system administrative. The purpose of 9-12 this paper is to recognize a car number plate using ann, image segmentation. We intended to develop a system in mat lab which can perform detection as well as recognition of a car number plate.

Keywords: ANPR, histogram approach, OCR, template matching

References: 1. Rahim Panahi, Iman Gholampour. "Accurate Detection and Recognition of Dirty Vehicle Plate Numbers for High-Speed Applications", IEEE Transactions on Intelligent Transportation Systems, 2017 2. H. Caner, H. S. Gecim, and A. Z. Alkar, “Efficient embedded neural network- based license plate recognition system,” IEEE Trans. Veh. Technol., vol. 57, no. 5, pp. 2675–2683, Sep. 2008. 3. Unsupervised Category Modeling, Recognition, and Segmentation in Images Sinisa Todorovic, Member, IEEE, and Narendra Ahuja, Fellow, IEEE 4. V. Abolghasemi and A. Ahmadyfard, “An edge-based color-aided method for license plate detection,” Image Vis. Comput., vol. 27, no. 8, pp. 1134–1142, Jul. 2009. 5. Semantic Image Segmentation with Contextual Hierarchical Models Mojtaba Seyedhosseini and Tolga Tasdizen, Senior Member, IEEE. 6. A Complete System for Vehicle Plate Localization, Segmentation and Recognition in Real Life Scene A.Conci, J. E. R. de Carvalho, T. W. Rauber 7. M. H. Glauberman, “Character recognition for business machines,” Electronics, vol. 29, pp. 132–136, 1956. 8. Automatic License Plate Recognition Shyang-Lih Chang, Li-Shien Chen, Yun-Chung Chung, and Sei-Wan Chen, Senior Member, IEEE 9. Automatic License-Plate Location and Recognition Based on Feature Salience Zhen-Xue Chen, Cheng-Yun Liu, Fa-Liang Chang, and Guo-You Wang Authors: Maram AL Muhisen, Hüseyin Gökçekuş, Mohammad Abazid Paper Title: Study of Redesign for Commercial Environmental Building Abstract: In recent times, sustainable construction is universally considered essential in structure developments, specifically in the commercial fields. Moreover, a nationwide non-profit association, USGBC (United States Green Building Council), was capable of establishing regulations and an assessment system for the sustainable structures known as LEED or the Leadership in Energy and Environmental Design. The fundamental basis for green structures is utilization of sustainable proficiency techniques either in newly constructed developments or renovations of existing estates, so that the operating and maintenance expenditures are reduced. While the rental cost or value of the structure is increased, the energy cost is minimized. Conversely, practical verification affecting the valuing techniques of sustainable structures and properties is restricted. Hence, the objective of the following study is to acknowledge the concerns linked to sustainable commercial developments and the rate-added interval, in which the aspects that influence energy costs are examined. The rate- added interval depicts the variations among the high value of construction value and energy rates, where a green profit is resembled by a positive difference value.

Keywords: Sustainable, Structures, LEED, Rate-Added Interval, Green Building Council.

4. References: 1. Howe, J. C. (2010). Overview of green buildings. National Wetlands Newsletter,33 (1). 2. Samer, M. (2013). Towards the implementation of the Green Building concept in agricultural buildings: a literature review. Agricultural 13-17 Engineering International: CIGR Journal, 15 (2), 25-46. 3. Boschmann, E. E., & Gabriel, J. N. (2013). Urban sustainability and the LEED rating system: case studies on the role of regional characteristics and adaptive reuse in green building in Denver and Boulder, Colorado. Geographical Journal, 179 (3), 221-233 4. Ellison, L. and Sayce, S. (2007) Assessing Sustainability in The Existing Commercial Property Stock Establishing Sustainability Criteria Relevant for The Commercial Property Investment Sector. Journal of Property Management, Vol. 25 No. 3, pp. 287-304. 5. Lzkendorf, T. and Lorenz, D. (2005) Sustainable Property Investment: Valuing Sustainable Buildings Through Property Performance Assessment, Building research and information, 33(3), 212-234. 6. Mansfield, J. (2009). The Valuation of Sustainable Freehold Property: A CRE Perspective. Journal of Corporate Real Estate, Vol. 11 No. 2 pp. 91-105. 7. Almuhisen, M. & Gökçekuş, H. (2018). Climate Change Impact on Economy. International Journal of Scientific & Engineering Research, 9(6), 1661-1669. 8. Abazid, M., & Harb, H. (2018). An Overview of Risk Management in The Construction Projects. Academic Research International, 9(2), 73–79. 9. Abazid, M. (2017). The Quality Control Implementation in the Construction Projects in Saudi Arabia. 10. Nouban, F. & Abazid, M. (2017). An Overview of The Total Quality Management in Construction Management. Academic Research International, 8(4), 68-74. 11. Abazid, M., & Gökçekus, H. (2019). Application of Total Quality Management on The Construction Sector in Saudi Arabia. International Journal of Technology. 12. Abazid, M., Gökçekus, H. and Çelik, T. (2019). Study of the Quality concepts Implementation in the Construction of Projects in Saudi Arabia by using building information Modelling (BIM). International Journal of Innovative Technology and Exploring Engineering, 8(3), 84-87. Authors: Gopi Dattatreya and K. K. Naik Paper Title: Circular Patch on Rectangular Slits loaded Antenna with DGS for Biomedical Applications Abstract: In recent times, sustainable construction is universally considered essential in structure developments, specifically in the commercial fields. Moreover, a nationwide non-profit association, USGBC (United States Green Building Council), was capable of establishing regulations and an assessment system for the sustainable structures known as LEED or the Leadership in Energy and Environmental Design. The fundamental basis for green structures is utilization of sustainable proficiency techniques either in newly constructed developments or renovations of existing estates, so that the operating and maintenance expenditures are reduced. While the rental cost or value of the structure is increased, the energy cost is minimized. Conversely, practical verification affecting the valuing techniques of sustainable structures and properties is restricted. Hence, the objective of the following study is to acknowledge the concerns linked to sustainable commercial developments and the rate-added interval, in which the aspects that influence energy costs are examined. The rate- added interval depicts the 5. variations among the high value of construction value and energy rates, where a green profit is resembled by a positive difference value. 18-21

Keywords: Sustainable, Structures, LEED, Rate-Added Interval, Green Building Council.

References:

1. S. Yano and A. Ishimaru, "A theoretical study of the input impedance of a circular microstrip disk antenna," IEEE Transactions on Antennas and Propagation, vol. 29, pp. 77-83, 1981. 2. K. S. Kim, T. Kim, and J. Choi, "Dual‐frequency aperture‐coupled square patch antenna with double notches," Microwave and Optical Technology Letters, vol. 24, pp. 370-374, 2000. 3. G. D. Ntouni, A. S. Lioumpas, and K. S. Nikita, "Reliable and energy-efficient communications for wireless biomedical implant systems," IEEE journal of biomedical and health informatics, vol. 18, pp. 1848-1856, 2014. 4. K. K. Naik, P. A. V. Sri, and J. Srilakshmi, "Design of implantable monopole inset-feed c-shaped slot patch antenna for bio-medical applications," in Progress in Electromagnetics Research Symposium-Fall (PIERS-FALL), 2017, 2017, pp. 2645-2649. 5. A. Kiourti and K. S. Nikita, "Miniature scalp-implantable antennas for telemetry in the MICS and ISM bands: design, safety considerations and link budget analysis," IEEE Transactions on Antennas and Propagation, vol. 60, pp. 3568-3575, 2012. 6. X. Tong, C. Liu, X. Liu, H. Guo, and X. Yang, "Switchable ON-/OFF-Body Antenna for 2.45 GHz WBAN Applications," IEEE Transactions on Antennas and Propagation, vol. 66, pp. 967-971, 2018. 7. S. A. Kumar and T. Shanmuganantham, "Design of implantable CPW fed monopole H-slot antenna for 2.45 GHz ISM band applications," AEU-International Journal of Electronics and Communications, vol. 68, pp. 661-666, 2014. 8. Ketavath Kumar Naik and Dattatreya Gopi, "Flexible CPW-fed split-triangular shaped patch antenna for WiMAX applications, "Progress In Electromagnetics Research M, vol. 70, pp. 157–166, 2018. 9. C. Liu, Y.-X. Guo, and S. Xiao, "Compact dual-band antenna for implantable devices," IEEE Antennas and Wireless Propagation Letters, vol. 11, pp. 1508-1511, 2012. 10. H. Younesiraad, M. Bemani, and S. Nikmehr, "A Dual-Band Slotted Square Ring Patch Antenna for Local Hyperthermia Applications," Progress In Electromagnetics Research, vol. 71, pp. 97-102, 2017. Authors: K. Haribabu, Ch. Umashankar, S.V.S Prasad Paper Title: An IoT Detection of Milk Parameters using Raspberry PI and GSM for Diary Farmers Abstract: The Raspberry pi development board controller which based to measure some of the parameters. It will be very simple to measure the milk parameters of ph value fat and CLR value. The ph detector it will detects the ph value levels in the milk and similarly in the same way the lactometer will measure how the milk purity obtained. The milk purity will be studied deeply by purely qualitatively quantitatively. In this domain the sensors will be interfaced to the raspberry pi controller. Every farmer will have Rfid interface user id and it will be connected to farmer mobile number by the gsm module. The measured parameters of milk will be sms to the connected to the farmer mobile number. The measured content will be uploaded to the webpage through internet using the gprs with date and time it will be displayed in the lcd monitor. It can be a coffee price and economical tool to sight purityness of the milk. With the assistance of GSM and GPRS method the milk can be easily traded and reading parameter information of milk will be sent to the govt so it will be helpful to the govt about the illegal things can be overcome such as milk impurity. The farmers swipes RFID the cardboard it reads the Milk parameters like pH worth CLR and every RFID coupled with various farmer mobile variety, once mensuration done of the Milk parameters SMS the parameters information to the farmer. By exploitation the GPRS technology 6. the knowledge will transfer to the server for the longer term analysis and records. 22-24 Keywords: Raspberry Pi, Rfid Reader Module, GSM Module, Ph Sensor, CLR(Corrected lactometer Reading), IOT(Thing speak).

References: 1. Prof. S.V. Arote, Prof. S.B. Lavhate, Prof. V. S. 2. Phatangare, “Low value Milk Analyzing and asking System victimization Electronic Card”, International Journal of Computer Technology and physical science Engineering-Volume two, Issue 2.Page no 5 to 13. 3. Sheryl S. Chougule, Mahesh S. Kumbhar, “To Develop processing System for farm Auto ----mation”,International Journal of engineering and Electronics Engineering and Science Vol.No.05, May 2016. 4. Kejal monarch, Rajeshri Kelkar, Amruta fish genus, M .S. Chavan, “Photometric primarily based Sensor for Fat Detection in contemporary Milk”, International Journal of Innovative Research in pc and communication Engineering.vol 3,Issue 4,April 2015. 5. Prof.A.S.Mali1, Arena A. Chougale, “Low Budget 6. System for measure of Milk Parameters and asking for Dairy” SSRG International Journal of Electronics and Communication Engineering – Volume two, Issue 5, May 2015. 7. Ropak Chakravarty, a paper on IT at Milk Collection centres in cooperative Diaries:The National Dairy Development Board Experience,pp 37-47. Authors: Gousiya Begum, S. Zahoor Ul Huq, A.P. Siva Kumar Paper Title: Security Vulnerabilities in Hadoop Framework Abstract: Apache Hadoop emerged as the widely used distributed parallel computing framework for Big Data Processing. Apache Hadoop is an open source framework suitable for processing large scale data sets using clusters of computers. Data is stored in Hadoop using Hadoop Distributed File System. Though Hadoop is widely used for distributed parallel processing of Big Data, some security vulnerabilities does exist. As part of our research we have investigated Hadoop Framework for possible security vulnerabilities and also demonstrated the mechanism to address the identified security vulnerabilities. Our findings include the vulnerabilities in logging mechanism, file system vulnerabilities, and addition of external jar files to the framework. we have addressed these vulnerabilities using custom Map Reduce jobs. 7. Keywords: Custom Map Reduce, Hadoop Distributed File System, Hadoop Framework, Security vulnerabilities. 25-28

References: 1. Srinivasan, Madhan Kumar, and P. Revathy, "State-of-the-art Big Data Security Taxonomies," Proceedings of the 11th Innovations in Software Engineering Conference, ACM, 2018. 2. Wang, Jiayin, et al. "Seina: A stealthy and effective internal attack in hadoop system," Computing, Networking and Communications (ICNC), 2017 International Conference on. IEEE, 2017. 3. Parmar, Raj R., et al. "Large-scale encryption in the Hadoop environment: Challenges and solutions," IEEE Access 5 (2017): 7156-7163. 4. Rao, P. Ram Mohan, S. Murali Krishna, and AP Siva Kumar. "Privacy preservation techniques in big data analytics: a survey," Journal of Big Data 5.1 (2018): 33. 5. Dou, Zuochao, et al. "Robust insider attacks countermeasure for Hadoop: Design and implementation,." IEEE Systems Journal 12.2 (2018): 1874-1885. 6. Cloud Security Overview https://www.cloudera.com/documentation/enterprise/5-12-x/PDF/cloudera-security.pdf Authors: T. Raghavendra Vishnu, D. Venkata Ratnam, P. Bhanu Priyanka, M. Sridhar, K. Padma Raju 8. Paper Title: Detection and Analysis of Cycle Slips from GNSS Observations Abstract: Global Positioning System (GPS) receiver’s high precision and high reliability has gained importance in recent years as a result of continuous demand for GPS applications in various fields. In order to obtain accurate positioning information the GPS receivers use carrier phase measurements for high-precision applications. Carrier phase measurements are greatly affected by Cycle Slips (CS). In this paper, detection of the cycle slips analysis is carried out using the raw carrier phase data recorded for the solar maximum year 2013 at Koneru Lakshmaiah (K L) University, Guntur, India. Higher-order phase differencing scheme is used for the detection of the cycle slips. At higher-order differences, the amplification of sudden jumps associated with the cycle slips can be observed thereby improving the ability to detect cycle slips. It is found that cycle slips occurrence is high during the solar maximum year (2013). The connection of cycle slip occurrence with ionospheric scintillations is also investigated. During the geomagnetic storm event on 29 June, 2013, maximum S4 has been observed due to fall in C/N0 leading to occurrence of cycle slips. Empirical Mode Decomposition-Detrended Fluctuation Analysis (EMD-DFA) algorithm is used for mitigating the effects of ionospheric scintillations.

Keywords: Cycle slips, EMD-DFA, Scintillations

References: 1. Hoffmann-Wellenhof, B., H. Lichtenegger, and J. Collins (1994), GPS theory and practice, Springer-Verlag, New York. 2. Leick, A., L. Rapoport, and D. Tatarnikov (2015), GPS satellite surveying, John Wiley & Sons. 3. Dai, Z. (2012), MATLAB software for GPS cycle-slip processing, GPS solutions, 16(2), 267-272. 4. Skone, S., K. Knudsen, and M. De Jong (2001), Limitations in GPS receiver tracking performance under ionospheric scintillation conditions, Physics and Chemistry of the Earth, Part A: Solid Earth and Geodesy, 26 (6), 613-621. 5. Silva, P. (2013), Cycle Slip Detection and Correction for Precise Point Positioning, Proceedings of the Institute of Navigation ION GNSS, 22 (2015), 47. 6. Blewitt, G. (1990), Jet Propulsion Laboratory, California Institute of Technology, Pasadena, Geophysical Research Letters, 17(3), 199- 202. 7. de Lacy, M. C., M. Reguzzoni, F. Sansò, and G. Venuti (2008), The Bayesian detection of discontinuities in a polynomial regression and its application to the cycle-slip problem, Journal of Geodesy, 82(9), 527-542. 8. Sunda, S., R. Sridharan, B. Vyas, P. Khekale, K. Parikh, A. Ganeshan, C. Sudhir, S. Satish, and M. S. Bagiya (2015), Satellite‐based 29-34 augmentation systems: A novel and cost‐effective tool for ionospheric and space weather studies, Space Weather, 13 (1), 6-15. 9. Liu, Z. (2011), A new automated cycle slip detection and repair method for a single dual-frequency GPS receiver, Journal of Geodesy, 85(3), 171-183. 10. Dai, Z., S. Knedlik, and O. Loffeld (2009), Instantaneous triple-frequency GPS cycle-slip detection and repair, International Journal of Navigation and Observation. 11. Kim, D., and R. B. Langley, Instantaneous Real‐Time Cycle‐Slip Correction for Quality Control of GPS Carrier‐Phase Measurements (2002), Navigation, vol. 49, pp. 205-222. 12. Banville, S., R. Langley, S. Saito, and T. Yoshihara (2010), Handling cycle slips in GPS data during ionospheric plasma bubble events, Radio Science, vol. 45. 13. Zhang, D., L. Cai, Y. Hao, Z. Xiao, L. Shi, G. Yang, and Y. Suo (2010), Solar cycle variation of the GPS cycle slip occurrence in China low‐latitude region, Space Weather, 8(10). 14. Zhao, L., L. Li, Y. Liu, and N. Li (2014), Cycle slip detection and repair with triple frequency combination method, paper presented at 2014 IEEE/ION Position, Location and Navigation Symposium-PLANS 2014, IEEE. 15. Yue, X., W. S. Schreiner, N. M. Pedatella, and Y. H. Kuo (2016), Characterizing GPS radio occultation loss of lock due to ionospheric weather, Space Weather, 14 (4), 285-299. 16. Dejie Yu, Junsheng Cheng, Yu Yang (2003), Application of EMD method and Hilbert spectrum to the fault diagnosis of roller bearings, doi:10.1016/S0888-3270(03)00099-2. 17. Yih Jeng, Ming-Juin Lin, Chih-Sung Chen, and Yu-Huai Wang (2007), Noise reduction and data recovery for a VLF-EM survey using a nonlinear decomposition method, Geophysics,Vol. 72, No. 5, P. F223–F235. 18. Kantelhardt, J. W., S. A. Zschiegner, E. Koscielny-Bunde, S. Havlin, A. Bunde, and H. E. Stanley (2002), Multifractal detrended fluctuation analysis of nonstationary time series, Physica A: Statistical Mechanics and its Applications, 316(1), 87-114. 19. Saba, M. F., W. Gonzalez, and A. Clúa de Gonzalez (1997), Relationships between the AE, ap and Dst indices near solar minimum (1974) and at solar maximum (1979), Annales Geophysicae, pp. 1265-1270. 20. Afraimovich, E. L., V. V. Demyanov, T. N. Kondakova (2003), Degradation of GPS performance in geomagnetically disturbed conditions, GPS Solutions, No.7, 109–119. 21. Koster, J. R., Equatorial scintillation (1972), Planetary and Space Science, vol. 20, pp. 1999-2014. 22. Burke, W., L. Gentile, C. Huang, C. Valladares, and S. Su, Longitudinal variability of equatorial plasma bubbles observed by DMSP and ROCSAT‐1 (2004), Journal of Geophysical Research: Space Physics, vol. 109. 23. Tanna, H., and K. Pathak, Multifractality due to long-range correlation in the L-band ionospheric scintillation S 4 index time series (2014), Astrophysics and Space Science, vol. 350, pp. 47-56. Authors: Albert Allen D Mello, G. Ramanan, Dhanaya Prakash R Babu Effect of Carbon Nanotube Layers on Change in Mechanical Characteristic of E-Glass Fiber Paper Title: Reinforced Epoxy Composite Abstract: Polymer composite reinforced with fiber materials have always proven its superior significant enactment over numerous traditional materials, considering their incomparable strength to weight ratio and stiffness. The Carbon nanotubes (CNTs) usage in glass-fiber reinforced polymer (GFRP) has high potential in changing the characteristics of composite laminates. Carbon nanotubes (CNT) because of their outstanding mechanical, electrical and thermal properties have engrossed composite fraternity in exploring the opportunity of 9. utilizing them as a supplementary reinforcement in fiber reinforced polymer composites. Reports of the fabrication of GFRP with and without CNT are discussed in this paper. The target in this study is to examine the mechanical 35-40 characters of GFRP with and without Multi-walled carbon nanotubes (MWCNT). GFRP laminated composite are fabricated by hand lay-up technique. Composite laminated layers are fabricated using epoxy resin without CNT and with 0.5% and 1.5% MWCNT. The materials were tested to determine tensile, flexural and compression properties. It is observed that carbon nanotubes can enhance the mechanical properties in the composite laminates. Composite laminate with 1.5wt% MWCNT exhibited good mechanical properties compared to that with 0.5wt% MWCNT and without MWCNT.

Keywords: CFRP Composites, Carbon nanotubes, Mechanical Characteristics, Bending moment

References: 1. Friedrich, K. Polymer composites for tribological applications. Advanced Industrial and Engineering Polymer Research. 1(1), 2018, pp.3- 39. 2. Saba, N., & Jawaid, M. A Review on Thermo mechanical Properties of Polymers and Fibers Reinforced Polymer Composites. J. of Industrial and Engineering Chemistry, 67, 2018, pp.1-11. 3. Darwins, A. K., Satheesh, M., and Ramanan, G., Modelling and optimization of friction stir welding parameters of Mg-ZE42 alloy using grey relational analysis with entropy measurement. IOP Conference Series: Materials Science and Engineering, 402(1), 2018, pp.12162. 4. Islam, M. E., Mahdi, T. H., Hosur, M. V., and Jeelani, S. Characterization of carbon fiber reinforced epoxy composites modified with nanoclay and carbon nanotubes. Procedia Engineering, 105, 2015, pp.821-828. 5. Periyardhasan, R., and Devaraju, A. Mechanical Characterization of Steel Wire Embeded GFRP Composites. Materials Today: Proceedings, 5(6), 2018, pp.14339-14344. 6. Ramanan, G., Dhas, J. E. R. Multi Objective Optimization of Wire EDM Machining Parameters for AA7075-PAC Composite Using Grey- Fuzzy Technique. Materials Today: Proceedings, 5(2), 2018, pp.8280-8289. 7. Maciel, N. D. O. R., Ferreira, J. B., da Silva Vieira, J., Ribeiro, C. G. D., Lopes, F. P. D., Margem, and Silva, L. C. Comparative tensile strength analysis between epoxy composites reinforced with curaua fiber and glass fiber. Journal of Materials Research and Technology, 2018, pp.136-148. 8. Rana, R. S., Rana, S., and Purohit, R. Characterization of Properties of epoxy sisal/Glass Fiber Reinforced hybrid composite. Materials Today: Proceedings, 4(4), 2017, pp.5445-5451. 9. Sivasaravanan, S., and Raja, V. B. Impact characterization of epoxy LY556/E-glass fibre/nano clay hybrid nano composite materials. Procedia Engineering, 97, 2014, pp.968-974. 10. Ramanan, G., Samuel, G.D., Sherin, S.M., Samuel, K.., Modeling and prediction of machining parameters in composite manufacturing using artificial neural network, IOP Conference Series: Materials Science and Engineering, 402, 2018, pp.012163. 11. Naqi, A., Abbas, N., Zahra, N., Hussain, A., and Shabbir, S. Q. Effect of multi-walled carbon nanotubes (MWCNTs) on the strength development of cementations materials. Journal of Materials Research and Technology, 2018, pp.156-163. 12. Masoumeh Nazem Salimi, Mehdi Torabi Merajin and Mohammad Kazem Besharati Givi, Enhanced mechanical properties of multifunctional multiscale glass/carbon/epoxy composite reinforced with carbon nanotubes and simultaneous carbon nanotubes/nanoclays, Journal of Composite Materials, 2016, pp.1–14. Authors: K.S Rajasekhar, T Ranga Babu Paper Title: Analysis of Dermoscopic Images using Multiresolution Approach Abstract: Abnormal growth of cells in any part of the body is called cancer. Cancer that is formed on skin is called skin cancer. Life span of a cancer patient can be increased by the early detection of tumor part. This paper deals with classification of dermoscopic images, i.e. benign or malignant based on coefficients extracted from multiresolution analysis based wavelet functions and tetrolet transform. Statistical texture features such as Mean, Standard Deviation, Kurtosis and Skewness are calculated from the coefficients of the multiresolution transfroms. The Gray Level Co-occurence Matrix(GLCM) is calculated for the dermoscopic images from which features such as homogenity, energy and entropy are calculated. In addition to these shape features are also taken into consideration. K-Nearest Neighbor(KNN) classifier is used for classification of dermoscopic images. In this work, dermoscopic images are obtained from the International Skin Imaging Archive (ISIC). The performance of the system is evaluated using accu-racy, sensitivity and specificity. The area under the curve(AUC) demonstrates the superiority of tetrolet transform.

Keywords: Dermoscopic images, Texture features, GLCM features, Shape features, KNN classifier, Accuracy, Sensitivity, Specificity and AUC.

References: 1. http://www.skincancer.org/skin-cancer-information/skin-cancer-facts. 2. Sheha, Mariam A., Mai S. Mabrouk, and Amr Sharawy. "Automatic detection of melanoma skin cancer using texture analysis." 10. International Journal of Computer Applications 42.20 (2012): 22-26. 3. Dobrescu, Radu, et al. "Medical images classification for skin cancer diagnosis based on combined texture and fractal analysis." WISEAS Transactions on Biology and Biomedicine 7.3 (2010): 223-232. 41-48 4. Celebi, M. Emre, et al. "A methodological approach to the classification of dermoscopy images." Computerized Medical Imaging and Graphics 31.6 (2007): 362-373. 5. Lau, Ho Tak, and Adel Al-Jumaily. "Automatically early detection of skin cancer: Study based on nueral netwok classification." Soft Computing and Pattern Recognition, 2009. SOCPAR’09. International Conference of. IEEE, 2009. 6. Elgamal, Mahmoud. "Automatic skin cancer images classification." IJACSA) International Journal of Advanced Computer Science and Applications 4.3 (2013): 287-294.. 7. Yuan, Xiaojing, et al. "SVM-based texture classification and application to early melanoma detection." Engineering in Medicine and Biology Society, 2006. EMBS’06. 28th Annual International Conference of the IEEE. IEEE, 2006. 8. Yu, Lequan, et al. "Automated melanoma recognition in dermoscopy images via very deep residual networks." IEEE transactions on medical imaging 36.4 (2017): 994-1004. 9. https://isic-archive.com. 10. Krommweh, Jens. "Tetrolet transform: A new adaptive Haar wavelet algorithm for sparse image representation." Journal of Visual Communication and Image Representation 21.4 (2010): 364-374J. Jones. (1991, May 10). Networks (2nd ed.) [Online]. Available: http://www.atm.com 11. (Haenssle, H. A., et al. "Man against machine: diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists." Annals of Oncology (2018). 12. Bi, Lei, et al. "Dermoscopic image segmentation via multi-stage fully convolutional networks." IEEE Trans. Biomed. Eng 64.9 (2017): 2065-2074. 13. Rahman, Mahmudur, Nuh Alpaslan, and Prabir Bhattacharya. "Developing a retrieval based diagnostic aid for automated melanoma recognition of dermoscopic images." 2016 IEEE Applied Imagery Pattern Recognition Workshop (AIPR). IEEE, 2016 14. Sultana, Nazneen N., and N. B. Puhan. "Recent Deep Learning Methods for Melanoma Detection: A Review." International Conference on Mathematics and Computing. Springer, Singapore, 2018. 15. Adria Romero,Lopez et al. "Skin lesion classification from dermoscopic images using deep learning techniques." Biomedical Engineering (BioMed), 2017 13th IASTED International Conference on. IEEE, 2017. 16. Codella, Noel, et al. "Deep learning, sparse coding, and SVM for melanoma recognition in dermoscopy images." International Workshop on Machine Learning in Medical Imaging. Springer, Cham, 2015. 17. Yu, Lequan, et al. "Automated melanoma recognition in dermoscopy images via very deep residual networks." IEEE transactions on medical imaging 36.4 (2017): 994-1004. 18. Oliveira, Roberta B., Aledir S. Pereira, and JoÃ£o Manuel RS Tavares. "Skin lesion computational diagnosis of dermoscopic images: Ensemble models based on input feature manipulation." Computer methods and programs in biomedicine 149 (2017): 43-53. 19. Yi, Xin, Ekta Walia, and Paul Babyn. "Unsupervised and semi-supervised learning with Categorical Generative Adversarial Networks assisted by Wasserstein distance for dermoscopy image Classification." arXiv preprint arXiv:1804.03700 (2018). 20. Castillejos-FernÃ¡ndez, Heydy, et al. "An Intelligent System for the Diagnosis of Skin Cancer on Digital Images taken with Dermoscopy." Acta Polytechnica Hungarica 14.3 (2017). 21. Majtner, Tomas, Sule Yildirim-Yayilgan, and Jon Yngve Hardeberg. "Combining deep learning and hand-crafted features for skin lesion classification." Image Processing Theory Tools and Applications (IPTA), 2016 6th International Conference on. IEEE, 2016 Authors: K Ram Prasad, B Rajasekhar Reddy, C Hari Prasad, Dinakara Prasad Reddy P Paper Title: Monarch Butterfly Optimization Algorithm for Capacitor Placement in Radial Distribution Systems Abstract: Monarch butterfly optimization (MBO) is used for the optimal capacitor placement problem. Loss Sensitivity method is used for optimal locations of capacitors. Capacitor sizes by MBO algorithm in radial distribution systemscorresponding to maximum loss reductions are determined in this paper. The results are presented with test system15-bus, 34-bus and 69-bus.

Keywords: Monarch butterfly optimization algorithm, Loss Sensitivity Method.

References: 1. Karimianfard, Hossein, and Hossein Haghighat. "Generic Resource Allocation in Distribution Grid." IEEE Transactions on Power 11. Systems 34, no. 1 (2019): 810-813. 2. Mandal, S., K. K. Mandal, B. Tudu, and N. Chakraborty. "A New Improved Hybrid Algorithm for Multi-objective Capacitor Allocation in Radial Distribution Networks." In Soft Computing for Problem Solving, pp. 585-597. Springer, Singapore, 2019. 49-51 3. Cuevas, Erik, Emilio BarocioEspejo, and Arturo Conde Enríquez. "A Modified Crow Search Algorithm with Applications to Power System Problems." In Metaheuristics Algorithms in Power Systems, pp. 137-166. Springer, Cham, 2019. 4. Reddy, P., et al. "An Efficient Distribution Load Flow Method for Radial Distribution Systems with Load Models." International Journal Of Grid And Distributed Computing 11.3 (20Reddy, 5. Veera, DinakaraPrasasd Reddy P. VC, and Reddy T. Gowri. "Ant Lion optimization algorithm for optimal sizing of." Electrical Power & Energy Systems 28 (2017): 669-678. 6. DinakaraPrasasd Reddy, P. V. C., and T. Reddy Dr. "Optimal renewable resources placement in distribution." Electrical Power & Energy Systems 28 (2017): 669-678. 7. Dinakara Prasad Reddy. "Sensitivity based capacitor placement using cuckoo search algorithm for maximum annual savings." IOSR Journal of Engineering 4.4 (2014): 6. 8. G. G. Wang, X. Zhao and S. Deb, "A Novel Monarch Butterfly Optimization with Greedy Strategy and Self-Adaptive," 2015 Second International Conference on Soft Computing and Machine Intelligence (ISCMI), Hong Kong, 2015, pp. 45-50. Authors: Jyothi Budida, Sanjai Kumar Mortha, Sreerama Lakshmi Narayana Paper Title: Constrained Optimization of Linear Antenna Arrays using Novel Social Group Optimization Algorithm Abstract: Antenna array optimization is a major research problem in the field of electromagnetic and antenna engineering. The optimization typically involves in handling several radiation parameters like Sidelobe level (SL) and beamwidth (BW). In this paper, the linear antenna array (LAA) configuration is considered with symmetrical distribution of excitation and special distribution. The objective of the design problem considered involves in generating optimized patterns in terms of SLL and BW and check the robustness of the social group optimization algorithm (SGOA). The analysis of the design problem is carried out in terms of radiation pattern plots. The simulation is carried out in Matlab.

Keywords: Antenna array, optimization, SGOA

12. References: 1. Balanis, C. A., Antenna Theory: Analysis and Design, John Wileyand Sons, 1982 52-56 2. Cheng, K. D: Optimization techniques for antenna arrays. In: Proceedings of the IEEE, 59(12) 1664–1674 (1971) . 3. On the Linear Antenna Array Synthesis Techniques for Sum and Difference Patterns 4. Using Flower Pollination Algorithm V. V. S. S. S. Chakravarthy · P. S. R. Chowdary · Ganapati Panda ·Jaume Anguera · Aurora Andújar · Babita Majhi Proceedings of Arabian Journal for Science and Engineering – Springer Nature Hub. 5. Ram, G.; Mandal, D.; Ghoshal, S.P.; Kar, R.: Nature-inspired algorithm- based optimization for beamforming of linear antenna array system. In: Patnaik, S. et al. (eds.) Nature-Inspired Computing and Optimization 2017, pp. 185–215. Springer, Berlin. doi:10.1007/978-3- 319-50920-4_8 6. Performance of Beamwidth Constrained Linear Array Synthesis Techniques Using Novel Evolutionary Computing Tools CSR Paladuga, CV Vedula, J Anguera, RK Mishra, AAndújar, applied computational electromagnetics society journal 33 (3),273-278 7. Saxena, P.; Kothari, A.: Linear Antenna Array Optimization Using Flower Pollination Algorithm. Springer, Berlin(2016). 8. Suresh Satapathy and Anima Naik.:Social group optimization (SGO): a new population evolutionary optimization technique. In: Complex Intell. Syst., (2) 173–203 (2016). 9. Antenna Array Synthesis Using Social Group Optimization VS Chakravarthy, PSR Chowdary, SC Satpathy, SK Terlapu, J Anguera Microelectronics, Electromagnetics and Telecommunications,895-905. Authors: Suvarna Sharma, Amit Bhagat Paper Title: Automation of Manual Seed URLs Cull Approach for Web Crawlers 13. Abstract: Web mining has become a more emerging topic these days and is speedily increasing with the growth of data on web. It is playing an essential role in our life as it helps us providing quicker information by 57-63 using new trends and technologies to improve. Hyperlink structure analysis and web crawling provide scope for more advanced research topics. If a system coverers various most relevant web pages in search engine environment, then it can improve the result of search engine. This URL’s set may be useful for extracting more relevant information or improving on existing and may also be useful to manage crawling infrastructure to offer quicker responses. Today, web crawling is an emerging issue in search engine which considers search quality, accessing pages at various servers to extract features. In the current scenario, the user may only be interested in the best result with some specific constraints. The constraint may define to the domain of search or importance of relevant pages. Here, we consider important or useful pages for particular user in searching environment. We proposed a framework, namely BUDG (Base URL’s Set for Directed Graph) which deals with URL’s hyperlink structure and generates a min set of ‘K’ URLs and then discover the covered graph for directed graph. Experimental results show that the proposed framework is working properly for different domain.

Keywords: Information Retrieval, Seed URLs, Web crawler, Web graph analysis, Web Mining.

References: 1. S. Brin, and L. Page , “The anatomy of a large-scale hypertextual web 2. search engine,” Computer networks and ISDN systems, vol. 30, no. 1,pp.107-117,Apr. 1998. 3. S. Sharma, A. Bhagat, “Research on Ranking Algorithms in Web Structure Mining,” International Journal of Knowledge Based Computer Systems, vol. 3, no. 2, pp.13-20, Dec. 2015. 4. S. Mirtaheri, M. E. Dincturk, S. Hooshmand, G. Bochmann and G.-V. Jourdan, “A Brief History of Web Crawlers,” Proc. of the 2013 Conf. of the Center for Advanced Studies on Collaborative Research. IBM Corp, pp.40-54, Nov. 2013. 5. C. Olston and M. Najork, “Web crawling,” Foundations and Trends in Information Retrieval, vol. 4,no. 3, pp.175-246, Feb. 2010. 6. S. Zheng, P. Dmitriev, and C. Giles, “Graph based crawler seed selection,” In Proc. of the 18th ACM international Conf. on Information and knowledge management, ACM, pp.1089-1090, Nov. 2009. 7. P. Dmitriev, “Host-based seed selection algorithm for web crawlers,” US Patent App. 12/259,164, Oct. 2008. 8. P. N. Priyatam, A. Dubey, K. Perumal, S. Praneeth, D. Kakadia, and V. Varma, “Seed Selection for Domain-Specific Search,” In Proc. of the 23rd International Conf. on World Wide Web, ACM, pp.923-928, April 2014. 9. Y. J. Du, Y. F. Hai, C. Z. Xie, and X. M. Wang, “An approach for selecting seed URLs of focused crawler based on user-interest ontology,” Applied Soft Computing , (Elsevier) , vol.14, pp.663–676, Jan. 2014. 10. Weisstein and W. E., “Website of the Simple Directed Graph – from Wolfram Math world,” 1996 11. J. M. Kleinberg, “Authoritative sources in a hyperlinked environment,” Journal of the ACM, vol.46,no.5, pp.604-632, Sep. 1999. 12. TORONTO.EDU, “Website of the Datasets for Experiments on Link Analysis Ranking Algorithms,” http://www.cs.toronto.edu/tsap/experiments/datasets/index.html, 1986 Authors: Pranta Sutradhar, Pritam Maity, Sayan Kar, Sourav Poddar Modelling and Optimization of PSA (Pressure Swing Adsorption) Unit by using Aspen Plus® and Paper Title: Design Expert ® Abstract: Pressure swing adsorption (PSA) is a well-established technique for separation of components from air, which is commonly known as Air Separation Unit (ASU), drying of gas and nitrogen and hydrogen purification separation and etc. In PSA processes, the most important is adsorbent material depending upon its properties. Generally, ASU is difficult to operate due to high degree of energy integration into itself. This research article represents the separation of nitrogen from air. As separation of nitrogen is a very important in the field of chemical engineering as it has wide applications in the various process industries. There are various techniques for separation of nitrogen, amongst them the most common are reverse stirling cycle, LINDE-HAMPSON cycle, Joule Thompson effect and etc. This article mainly focusses on the separation of nitrogen using PSA unit only. The whole process was simulated using Aspen Plus ® and the simulated results were then optimized using Design Expert ®. Various flowrates ranging from 50 kg/h to 200 kg/h were selected, depending upon the process conditions. The output of the simulated results from Aspen Plus ® were then optimized using Box Behnken method, in order to obtain the optimized flowrate of Nitrogen. The response pattern suggest that the flowrates of nitrogen and other gases follows quadratic equation. The significance of the coefficients of the equation and the adequacy of the fit were determined using Student-t test and Fischer F-test respectively. The final flowrates obtained are interchanged in order to obtain the maximum conditions, except for nitrogen production other production rates remain the same. 14. Keywords: Nitrogen, PSA (pressure swing Adsorption), Aspen Plus®, Design Expert®. 64-69 References: 1. Ming-Lung Li, Hao-Yeh Lee, Ming-Wei Lee and I-lung Chien,“ Simulation and Formula Regressionof an Air Separation Unit in China Steel Corporation“ , ADCONP, 2014, pp. 213-218. 2. D.R.Vinson,“ Air separation control technology“, Computers and Chemical Engineering, 30, 2006, pp. 1436-1446 3. S.Ivanova, R. Lewis,“ Producing Nitrogen via Pressure swing Adsorption“, Chemical Engineering Progress,108(6), 2012, pp. 38 -42.. 4. Z. Xu, J. Zhao, X. Chen, Z. Shao, J. Qian, L. Zhu, Z. Zhou, H. Qin,“ Automatic load change system of cryogenic air separation process“, Separation and Purification Technology, 81, 2011, pp. 451-465. 5. Aspen Plus Tutorial #1: Aspen Basic. Available: https://www.aspentech.com 6. Aspen PlusTutorial #2: Thermodynamic Method. Available: https://www.aspentech.com 7. Stoecker W.F., “Design of Thermal stress”, Toronto, Tata McGraw Hill, 1986. 8. Aspen Tech, Aspen Physical Property System 11.1. Aspen Technology, Inc ,Cambridge, MA, USA, 2001, Available: https://www.aspentech.com 9. http://www.statease.com/training.html (Stat-Ease Webinars) 10. Marcos Almeida Bezerra, Ricardo Erthal Santelli, Eliane Padua Oliveira, Leonardo SilveiraVillar, Luciane Amélia Escaleira, “Response surface methodology (RSM) as a tool for optimization in analytical chemistry“, Talanta, 75(5), 2008, pp. 965 -977. 11. http://www.weibull.com/hotwire/issue130/hottopics130.htm (Box-Behnken Designs for optimizing Product Performance Designs for optimizing Product Performance) 12. Box, G. and Behnken, D., “Some New Three. Level Designs for the Study of Quantitative. Variables“, Technometrics, 2, 1960, pp. 455 – 475. 13. http://www.weibull.com/hotwire/issue130/hottopics130.htm (Box-Behnken Designs for optimizing Product Performance) 14. Chatterjee, S., B. Price, Regression Analysis by Example. 2nd Edition, John Wiley & Sons, New York, 1991, xvii, 278 pp., ISBN: 0‐471‐88479‐0, Available: https://onlinelibrary.wiley.com 15. W.F. Castle, “Air separation and liquefaction: recent developments and prospects for the beginning of the new millennium”, International Journal of Refrigeration, 25, 2002, pp. 158-172. 16. Randall F. Barron, Cryogenic systems, 2nd edition, Oxford University Press, 1985, ISBN-13: 978-0195035674, Available: https://www.amazon.com. Authors: N. Phani Madhuri, A. Meghana, PVRD. Prasada Rao, P.Prem Kumar Paper Title: Ailment Prognosis and Propose Antidote for Skin using Deep Learning Abstract: Nowadays The disease prediction by the using the machine learning has become very common. With the end goal to accomplish a compelling method to distinguish skin disease at a beginning period without playing out any pointless skin biopsies, advanced pictures of melanoma skin injuries have been explored. In this paper, distinctive computerized pictures have been investigated dependent on unsupervised division strategies. feature extraction systems are then connected on these portioned pictures. After this, a complete dialog has been investigated dependent on the outcomes. Melanoma spreads through metastasis, and along these lines it has been turned out to be exceptionally deadly. Feature that excess prologue to radiations from the sun dynamically disintegrate melanin in the skin. Likewise, such radiations invade into the skin thusly pulverizing the melanocyte cells. Melanomas are uneven and have sporadic edges, indented edges, and shading assortments, so examining the shape, shading, and surface of the skin sore is basic for melanoma early acknowledgment. In this work, the fragments of an advantageous steady non-invasive skin sore examination structure to help the melanoma abhorrence and early disclosure are proposed. The initial segment is a constant caution to help customers with anticipating skin duplicate caused by sunshine; a novel condition to enroll the perfect open door for skin to 15. duplicate is along these lines introduced. The second part is an automated picture examination including picture obtainment, hair area and dismissal, damage division, feature extraction, and plan. The framework has been 70-74 created in a propelled application in Matlab. The preliminary outcomes show that the proposed structure is compelling, achieving high plan correctness

Keywords: Melanoma, Skin Biopsies, Non-Invasive, Unsupervised Division Strategies, Sporadic Fringes.

References: 1. S. Suer, S. Kockara, and M. Mete, ``An improved border detection in dermoscopy images for density-based clustering,''BMC Bioinformat., vol. 12, no. 10, p. S12, 2011. 2. M. Rademaker and A. Oakley, ``Digital monitoring by whole body photography and sequential digital dermoscopy detect thinner melanomas,'‘ J. Primary Health Care, vol. 2, no. 4, pp. 268272, 2010. 3. O. Abuzaghleh, B. D. Barkana, and M. Faezipour, ``SKINcure: A real-time image analysis system to aid in the malignant melanoma prevention and early detection,'' in Proc. IEEE Southwest Symp. Image Anal. Interpretation (SSIAI), Apr. 2014, pp. 8588. 4. O. Abuzaghleh, B. D. Barkana, and M. Faezipour, ``Automated skin lesion analysis based on color and shape geometry feature set for melanoma early detection and prevention,'' inProc. IEEE Long Island Syst., Appl. Technol. Conf. (LISAT), May 2014, pp. 16. 5. (Mar. 27, 2014). American Cancer Society, Cancer Facts & Figures. [Online]. Available: http://www.cancer.org/research/cancerfactsstatistics/ cancerfactsgures2014/index Authors: Sabjan S.N, Maheshwar Pratap Paper Title: The Implementation of TPM on Manufacturing Performance at FMCG Company Abstract: The focus of this paper is to enlighten the commitments of Quality Maintenance Pillar of TPM in increasing the product quality in a FMCG industry involved in the manufacturing of HDPE bottles and coconut oil. QM pillar is a critical activity of the TPM approach which expects to delight the customer through zero defect manufacturing. TPM that is effectively implemented increases the production efficiency with an ultimate aim of achieving zero losses, zero breakdown and zero defects. The main aim of QM pillar is to eliminate the non- conformances in a methodical way and maintain the equipment for high quality products. Activities involved with QM pillar was able to decrease the customer complaints and regulatory complaints to zero. The targets put forward by the QM pillar was effectively achieved by the industry, the targets included maintaining the customer complaints at zero, reduce the in process defects by 50% and increase the production of Total value of goods worth 50 lakhs to one crore worth SKU.

Keywords: TPM, Quality Maintanance pillar

16. References: 1. Cua, K. O., McKone, K. E., & Schroeder, R. G. (2001). Relationships between implementation of TQM, JIT, and TPM and manufacturing 75-87 performance. Journal of operations management, 19(6), 675-694. 2. McKone, K. E., Schroeder, R. G., & Cua, K. O. (2001). The impact of total productive maintenance practices on manufacturing performance. Journal of operations management, 19(1), 39-58. 3. Ahuja, I. P. S., & Khamba, J. S. (2008). An evaluation of TPM initiatives in Indian industry for enhanced manufacturing performance. International Journal of Quality & Reliability Management, 25(2), 147-172. 4. Ahuja, I. P. S., & Khamba, J. S. (2007). An evaluation of TPM implementation initiatives in an Indian manufacturing enterprise. Journal of quality in maintenance engineering, 13(4), 338-352. 5. Ahuja, I. P. S., & Khamba, J. S. (2008). Strategies and success factors for overcoming challenges in TPM implementation in Indian manufacturing industry. Journal of Quality in Maintenance Engineering, 14(2), 123-147. 6. Chan, F. T. S., Lau, H. C. W., Ip, R. W. L., Chan, H. K., & Kong, S. (2005). Implementation of total productive maintenance: A case study. International journal of production economics, 95(1), 71-94. 7. Tangen, S. (2003). An overview of frequently used performance measures. Work study, 52(7), 347-354. 8. Brah, S. A., & Chong, W. K. (2004). Relationship between total productive maintenance and performance. International Journal of Production Research, 42(12), 2383-2401. 9. Blanchard, B. S. (1997). An enhanced approach for implementing total productive maintenance in the manufacturing environment. Journal of quality in Maintenance Engineering, 3(2), 69-80. 10. Seth, D., & Tripathi, D. (2006). A critical study of TQM and TPM approaches on business performance of Indian manufacturing industry. Total Quality Management & Business Excellence, 17(7), 811-824. 11. Eti, M. C., Ogaji, S. O. T., & Probert, S. D. (2004). Implementing total productive maintenance in Nigerian manufacturing industries. Applied energy, 79(4), 385-401. 12. McKone, K. E., Schroeder, R. G., & Cua, K. O. (1999). Total productive maintenance: a contextual view. Journal of operations management, 17(2), 123-144. Authors: Riktesh Srivastava, Mohd. Abu Faiz Paper Title: Reviews Analysis of Online Retail Stores in UAE: Analytical Study of Sentiments Through Social Media Abstract: Text mining for social media has now become decisive tool for marketing, and many businesses understand the supremacy of embracing technology into their marketing campaigns. These texts are the “Consumer language”, owing to its spread and reach. There is no reservation that use of user generated texts has stimulated the companies to identify them and use it for decision making, however, classifying sentiment analysis through these texts is still a fresh sensation. Online retail companies in UAE are an early adopter of social media, but how do they use text mining techniques is still a matter to wary upon. The study proposes a model to collect reviews from multiple sources and identify sentiments and topics simultaneously. The model is the tested on 3 online retail companies in UAE and the results depicts productive outcomes.

Keywords: Sentiment Analysis, Liu Hu algorithm, Plutchik modeling, Latent Semantic Indexing.

References: 1. J. Marshall, “Companies Increasingly Trademark Hashtags,” Wall Street Journal, 30-Mar-2016. 2. W. G. Mangold and D. J. Faulds, “Social media: The new hybrid element of the promotion mix,” Bus. Horiz., vol. 52, no. 4, pp. 357–365, Jul. 2009. 3. Read, “How to Increase Your Reach on Any Social Network,” 2015. [Online]. Available: https://blog.bufferapp.com/increase-reach. [Accessed: 10-Nov-2018]. 4. J. Marshall, “Companies Increasingly Trademark Hashtags,” Wall Street Journal, 30-Mar-2016. 5. N. Patel, “How to Use Hashtags to Increase Your Online Presence,” 2014. [Online]. Available: https://www.quicksprout.com/2014/04/04/how-to-use-hashtags-to-increase-your-online-presence/. [Accessed: 10-Nov-2018]. 6. Z. Yuzdepski, “Goodbye Stars, Hello Facebook Business Recommendations,” Vendasta Blog, 2018. 7. J. Jansen, M. Zhang, K. Sobel, and A. Chowdury, “Micro-blogging as online word of mouth branding,” in Proceedings of the 27th international conference extended abstracts on Human factors in computing systems - CHI EA ’09, Boston, MA, USA, 2009, p. 3859. 8. Y.-M. Li, C.-Y. Lai, and C.-W. Chen, “Identifying Bloggers with Marketing Influence in the Blogosphere,” in Proceedings of the 11th International Conference on Electronic Commerce, New York, NY, USA, 2009, pp. 335–340. 9. L. Kolowich, “22 Customer Review Sites for Collecting Business & Product Reviews,” 2018. [Online]. Available: https://blog.hubspot.com/service/customer-review-sites. [Accessed: 10-Nov-2018]. 17. 10. M. Hu and B. Liu, “Mining and Summarizing Customer Reviews,” in Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, New York, NY, USA, 2004, pp. 168–177. 11. N. Hu, I. Bose, N. S. Koh, and L. Liu, “Manipulation of online reviews: An analysis of ratings, readability, and sentiments,” Decis. 88-92 Support Syst., vol. 52, no. 3, pp. 674–684, Feb. 2012. 12. X. Hu, L. Tang, J. Tang, and H. Liu, “Exploiting Social Relations for Sentiment Analysis in Microblogging,” in Proceedings of the Sixth ACM International Conference on Web Search and Data Mining, New York, NY, USA, 2013, pp. 537–546. 13. W. Ding, S. Yu, S. Yu, W. Wei, and Q. Wang, “LRLW-LSI: An Improved Latent Semantic Indexing (LSI) Text Classifier,” in Rough Sets and Knowledge Technology, 2008, pp. 483–490. 14. R. Ortega Bueno, A. Fonseca Bruzón, C. Muñiz Cuza, Y. Gutiérrez, and A. Montoyo, “UO_UA: Using Latent Semantic Analysis to Build a Domain-Dependent Sentiment Resource,” in Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), Dublin, Ireland, 2014, pp. 773–778. 15. Chiru, T. Rebedea, and S. Ciotec, “Comparison between LSA-LDA-Lexical Chains,” in WEBIST-2014, 2014, p. 8. 16. Haddi, X. Liu, and Y. Shi, “The Role of Text Pre-processing in Sentiment Analysis,” Procedia Comput. Sci., vol. 17, pp. 26–32, 2013. 17. K. Kenyon-Dean et al., “Sentiment Analysis: It’s Complicated!,” in Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), New Orleans, Louisiana, 2018, pp. 1886–1895. 18. Krouska, C. Troussas, and M. Virvou, “The effect of preprocessing techniques on Twitter sentiment analysis,” in 2016 7th International Conference on Information, Intelligence, Systems Applications (IISA), 2016, pp. 1–5. 19. S. Guha, A. Joshi, and V. Varma, “Sentibase: Sentiment Analysis in Twitter on a Budget,” in Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), Denver, Colorado, 2015, pp. 590–594. 20. R. Srivastava, “3 years - 3 moves Government verdicts to renovate Customary Bharat to Contemporary India: Evaluation of opinions from citizens,” Int. J. Bus. Data Anal., vol. 1, no. 1, 2018. 21. B. Dickinson, M. Ganger, and W. Hu, “Dimensionality Reduction of Distributed Vector Word Representations and Emoticon Stemming for Sentiment Analysis,” J. Data Anal. Inf. Process., vol. 03, p. 153, Nov. 2015. 22. S. M. Arif and M. Mustapha, “The Effect of Noise Elimination and Stemming in Sentiment Analysis for Malay Documents,” in Proceedings of the International Conference on Computing, Mathematics and Statistics (iCMS 2015), 2017, pp. 93–102. 23. C. Manning, P. Raghavan, and H. Schuetze, Introduction to Information Retrieval, 1st ed. England: Cambridge University Press, 2009. 24. Dempsey, “Porter2 Stemmer Documentation,” 2016. 25. Y. Lin, J. Zhang, X. Wang, and A. Zhou, “An Information Theoretic Approach to Sentiment Polarity Classification,” in Proceedings of the 2Nd Joint WICOW/AIRWeb Workshop on Web Quality, New York, NY, USA, 2012, pp. 35–40. 26. R. Plutchik, “A psychoevolutionary theory of emotions,” Soc. Sci. Inf., vol. 21, no. 4–5, pp. 529–553, Jul. 1982. 27. R. Plutchik, “The Nature of Emotions: Clinical Implications,” in Emotions and Psychopathology, Springer, Boston, MA, 1988, pp. 1–20. 28. R. Srivastava and J. S. Rathore, “Content Analysis Concerning Online Shopping in UAE: Evaluation of Impact Score from News | International Journal of Business Analytics and Intelligence-Volume 6 Issue 1,” Int. J. Bus. Anal. Intell., vol. 6, no. 1, pp. 9–13, 2018. Authors: D. Rajesh, T. Jaya Paper Title: Exploration on Cluster Related Energy Proficient Routing in Mobile Wireless Sensor Network Abstract: Mobile Wireless Sensor Network is a encompassing of spatially conveyed self declaration frames works with a correspondence for examining and recording circumstances at conflicting areas. Mobility based 18. wireless sensor network includes thousands of mobile sensor nodes in the heterogeneous network, wherever each sensor nodes is associated with sensor node head. Mobility based wireless sensor network is arising and appealing 93-97 exploration region in which a few applications, for example, human services, agribusiness, and military are making utilization of it. Energy proficiency is a standout amongst the most critical problem in mobility based wireless sensor network. Clustering authorize high accessibility, overhead and parallel processing. A tactic is used in heterogeneous moveable sensor network is clustering to reduce the energy exploitation and boosts the duration of network. Clustering approach weaken mobility stream, restrict energy exploitation, develop remaining energy and increase the duration of the heterogeneous sensor network mobile sensor network. This article assimilates exploration of unusual energy productive clustering protocols in mobility based wireless sensor network.

Keywords: Mobile Wireless Sensor Network MWSN, clustering, Cluster-Head, Energy Effectiveness, Information gathering, Security.

References: 1. Vishal Singh, 2016, “A Survey of Energy-Efficient-Clustering Algorithms in Wireless Sensor Networks”, International Journal of Engineering and Computer Science. 2. M.Sheik Dawood et al, 2015, “A Survey on Energy-Efficient-Clustering Protocols for Wireless Sensor Networks,” International Journal of Computer Science and Mobile Computing. 3. Vinay Kumar, Sanjeey Jain and Sudarshan Tiwari, “Energy-Efficient-Clustering Algorithms in Wireless Sensor Networks: Survey, 2011,” IJCSI International Journal of Computer Science, Vol. 8, No 2, pg. 259-268. 4. Firoj Ahamad, Rakesh Kumar, 2015 “Energy-Efficient-Routing Protocols for Wireless Sensor Networks: A Review,” International Journal of Innovations & Advancement in Computer Science, Vol. 4, pg. 165-171. 5. Swati Shamkumar, Vimal Shukla, 2014, “A Review on Energy-Efficient Routing Protocols in Wireless Sensor Networks,” International Journal of Emerging Technology and Advanced Engineering, Vol. 4, Pg. 653-657. 6. Sissy Annamma Johnson, Josmy George, 2016 , “A Survey on Different Types of Clustering-Based-Routing Protocols in Wireless Sensor Networks,” Journal of Research, Vol. 2, pg. 13-16. 7. Santal Pal Singh, S.C. Sharma, 2015, “A Survey on Cluster-Based Routing-Protocols in Wireless Sensor Networks,” International Confrence in Advanced Computing Technologies and Applications, pg. 687- 695. 8. Sanjeev Kumar Gupta, Neeraj Jain, Poonam Sinha, 2013, “Clustering Protocols in Wireless Sensor Networks: A Survey”, International Journal of Applied Information, Vol. 5, No-2, pg. 41-50. 9. Xu-Xun Liu, 2012,“A Survey on Clustering-Routing Protocols in Wireless Sensor Networks”, School of Electronic and Information Engineering, ISSN 1424-8220. 10. Kunkunuru Udayakumar et al, 2015, “Analysis of Various Clustering-Algorithms in Wireless Sensor Networks”, International Journal of Computer Science and Information Technologies. 11. Vandna Sharma, Payal Jain, 2013, “Various Hierarchical-Routing Protocols in Wireless Sensor Network: A Survey,” IJCSMC, Vol. 2, Issue.5, pg. 63-72. 12. R.U. Anitha P. Kamalakkannan, 2013,“EEDBC-M: Enhancement of Leach-Mobile protocol with Energy-Efficient Density-based Clustering for Mobile Sensor Networks (MSNs)”, International Journal of Computer Applications (0975 – 8887), Volume 74– No.14. 13. Punret Gurbani, Hansa Acharya, Anurag Jain, 2016, “Hierarchical-Cluster Based Energy- Efficient Routing-Protocol for Wireless Sensor Networks: A Survey”, International Journal of Computer Science and Information Technologies, Vol.7(2), pg.682-687. 14. Sangeeta Badiger, Mohan B A, 2015, “Secure and Energy-Efficient Clustering-Scheme (SAEECS) With Data-Aggregation in Mobile Wireless Sensor Networks”, International Research Journal of Engineering and Technology (IRJET), Volume: 02 Issue: 03. 15. Dr. V. Ramesh, 2017, “Energy-Efficient Clustering Scheme (EECS) With Secure Data Aggregation for Mobile Wireless Sensor Networks”, International Journal of Electrical Electronics & Computer Science Engineering, Volume 4, Issue 5 16. Awatef Benfradj Guiloufi, Nejeh Nasri, Abdennaceur Kachouri, 2014, “Energy-Efficient Clustering Algorithms for Fixed and Mobile Wireless Sensor Networks”, IEEE. 17. ChanglinMa, Nian Liu, and Yuan Ruan, 2013, “A Dynamic and Energy-Efficient Clustering Algorithm in Large-Scale Mobile Sensor Networks”, International Journal of Distributed Sensor Networks. 18. Muhammad Arshad, Mohamad Y. Aalsalem, Farhan A. Siddiqui, 2014, “Energy-Efficient Cluster Head Selection In Mobile Wireless Sensor Networks”, Journal of Engineering Science and Technology. 19. Rajesh. D, M. Firoja Banu, D. Stella, Ansila. P. Grace, 2016 “Ch Panel Based Routing Scheme for Mobile Wireless Sensor Network”, International Journal of MC Square Scientific Research, Vol.8, No.1. Authors: Malan D. Sale, V. Chandra Prakash Paper Title: Dynamic Dispatching of Elevators in Elevator Group Control System: Research and Survey Abstract: With an increase in the population and demand for elevators in high-rise buildings, there is a need for installing more number of elevators to transport passengers efficiently. In tall buildings, Elevator Group Control System (EGCS) is the system for managing vertical transportation facility. The paper presents a survey of different techniques used to schedule and dispatch elevators in EGCS. The research study focuses on the dynamic scheduling of elevators for all up and down landing calls that aims to overcome the limitations and weaknesses of the existing works. The main aim of the research work is to reduce the waiting time of passengers for a car call on a specific floor and save power consumption of the elevators or lifts. Fuzzy algorithms, neural network algorithms, and genetic algorithms are the primary methods used to dispatch elevators in the control system. The study compares experimental results generated by various methods.

Keywords: EGCS, Elevators, up-peak traffic, down-peak traffic 19. References: 98-102 1. Fernandez, J., et al., "Dynamic Fuzzy Logic Elevator Group Control System with Relative Waiting Time Consideration," Industrial Electronics, IEEE Transactions on 61.9 2014: 4912-4919. 2. Fu, Lijun, and Tiegang Hao., "Analysis and simulation of passenger flow model of elevator group control system," Fuzzy F Systems S and Knowledge K Discovery D, 2012 9th International Conference on. IEEE, 2012. 3. Qiu, JianDong, and ZhaoYuan Jiang, "The research and simulation on the elevator group control system EGCS scheduling algorithm," Electrical and Control Engineering (ICECE), 2011 International Conference on. IEEE, 2011. 4. Yang, Suying, Jianzhe Tai, and Cheng Shao, "Dynamic partition of elevator group control system with destination floor guidance in up- peak traffic," journal of computers 4.1 2009: 45-52. 5. Fernández, Joaquín, et al., "Dynamic fuzzy logic (EGCS) elevator group control system for energy optimization," International Journal of Information Technology & Decision Making 12.03 (2013): 591-617. 6. Liting, Cao, Zhang Zhaoli, and Hou Jue, "Dynamic Optimized Dispatching System for Elevator Group Based on Artificial Intelligent Theory," Electronic Measurement and Instruments, 2007. ICEMI'07. Eighth International Conference on. IEEE 2007. 7. Sun, Jin, Qian-Chuan Zhao, and Peter B. Luh, "Optimization of group elevator scheduling with advance information," Automation Science and Engineering, IEEE Transactions on 7.2 2010: 352-363. 8. Wang, Donghua, and Baofeng Li., "An Optimization Model of Elevators Group Zoning Dispatching and It’s Application," Cryptography and Network Security, Data Mining and Knowledge Discovery, E-Commerce & Its Applications and Embedded Systems (CDEE), 2010 First ACIS International Symposium on. IEEE 2010. 9. Cortés, Pablo, et al., "Fuzzy logic based controller for peak traffic detection in elevator systems, "Journal of computational and theoretical nanoscience. 9.2 2012: 310-318. 10. Chen, Ta Cheng, et al., "GA Based Hybrid Fuzzy Rule Optimization Approach for Elevator Group Control System," Applied Mechanics and Materials. Vol. 284. 2013. 11. Cao, Liting, Shiru Zhou, and Shuo Yang, "Elevator Group Dynamic Dispatching System Based on Artificial Intelligent Theory," Intelligent Computation Technology and Automation (ICICTA), 2008 International Conference on. Vol. 1. IEEE 2008. 12. Liu, Yaowu, et al., "Energy saving of elevator group control based on optimal zoning strategy with interfloor traffic," Information Management, Innovation Management and Industrial Engineering (ICIII), 2010 International Conference on. Vol. 3. IEEE 2010. 13. Rashid, M. M., et al., "Design of fuzzy based controller for modern elevator group with floor priority constraints," Mechatronics (ICOM), 2011 fourth International Conference On. IEEE 2011. 14. Zhang, Yine, Yun Yi, and Jian Zhong, "The Application of the Fuzzy Neural Network Control in Elevator Intelligent Scheduling Simulation," Information Science and Engineering (ISISE), 2010 International Symposium on. IEEE 2010. 15. Sorsa, J., Ehtamo, H., Kuusinen, JM., et al.,” Modeling uncertain passenger arrivals in the elevator dispatching problem with destination control,” Optim Lett (2018) 12: 171. https://doi.org/10.1007/s11590-017-1130-0 16. Albert So, et. at.,” Traffic analysis of a three-dimensional elevator system,” building services engineering research and technology,2017 DOI: 10.1177/0143624417710106 17. You Zhou et al. ,“ An Elevator Monitoring System Based On the Internet of Things,” 8th International Congress of Information and Communication Technology (ICICT-2018) Procedia Computer Science 131 (2018) 541–544 18. Shuo-Yan Chou et al., ”Improving Elevator Dynamic Control Policies Based on Energy and Demand Visibility,” 2018 3rd International Conference on Intelligent Green Building and Smart Grid (IGBSG) 22-25 April 2018 19. Liu, Weipeng, et al.," Dispatching algorithm design for elevator group control system with Q-learning based on a recurrent neural network," Control and Decision Conference (CCDC), 2013 25th Chinese. IEEE, 2013. 20. Li, Zhonghua, Zongyuan Mao, and Jianping Wu. , "Research on dynamic zoning of elevator traffic based on an artificial immune algorithm," Control, Automation, Robotics, and Vision Conference, 2004. ICARCV 2004 8th. Vol. 3. IEEE, 2004. Authors: Manoj Kumar Shukla, Kamal Sharma Enhanced Dispersion and Tensile Properties of Graphene/CNT Epoxy Composites by Varying the Filler Paper Title: Ratio Abstract: In this study a three phase hybrid composite is fabricated comprising of graphene and carbon nanotube (CNT) nano-fillers reinforced in epoxy resin. The filler contents were maintained 0 and 1 wt. % and the ratio of graphene and CNT fillers were 1:1, 1:3 and 3:1. Effect of filler ratio on dispersion and tensile properties of hybrid composite mixture are investigated. Observations of the samples by Dynamic Light Scattering (DLS), Scanning Electron Microscopy (SEM), and Image Analysis (IA) confirmed formation 3-D hybrid nanostructure. The best dispersion is observed for graphene: CNT content 1:3 indicating good bonding between both the fillers and epoxy matrix. The maximum tensile strength of 50.28 MPa and elastic modulus of 2848 MPa is observed for filler ratio 1:3 (graphene: CNT) which is 57 and 40 % increase as compared with pristine epoxy composite. For this configuration homogeneous mixture with Poly Dispersity Index (PDI) of 0.513 is investigated for the sample. The value of PDI is observed to be lowest by both Particle Size Distribution (PSD) analysis methods which make agreement of results. Analysis of PSD of composite mixture provides a direction for selecting appropriate filler content and fabrication process.

Keywords: Particle size distribution (PSD), hybrid nano-composite, Image analysis (IA), tensile strength, elastic modulus.

References: 1. S. K. Srivastava and I. P. Singh, “Hybrid epoxy nanocomposites: lightweight materials for structural applications,” Polym. J., vol. 44, 20. no. 4, pp. 334–339, 2012. 2. V. Singh, D. Joung, L. Zhai, S. Das, S. I. Khondaker, and S. Seal, “Graphene based materials: Past, present and future,” Prog. Mater. Sci., vol. 56, no. 8, pp. 1178–1271, 2011. 103-107 3. S. Chatterjee, F. Nafezarefi, N. H. Tai, L. Schlagenhauf, F. A. Nüesch, and B. T. T. Chu, “Size and synergy effects of nanofiller hybrids including graphene nanoplatelets and carbon nanotubes in mechanical properties of epoxy composites,” Carbon N. Y., vol. 50, no. 15, pp. 5380–5386, 2012. 4. G. Zhang, F. Wang, J. Dai, and Z. Huang, “Effect of functionalization of graphene nanoplatelets on the mechanical and thermal properties of silicone rubber composites,” Materials (Basel)., vol. 9, no. 2, p. 92, 2016. 5. P. K. Singh and K. Sharma, “Mechanical and Viscoelastic Properties of In-situ Amine Functionalized Multiple Layer Grpahene / epoxy Nanocomposites,” pp. 1–11, 2018. 6. Y. J. Wan et al., “Grafting of epoxy chains onto graphene oxide for epoxy composites with improved mechanical and thermal properties,” Carbon N. Y., vol. 69, no. November, pp. 467–480, 2014. 7. J. Li, P. S. Wong, and J. K. Kim, “Hybrid nanocomposites containing carbon nanotubes and graphite nanoplatelets,” Mater. Sci. Eng. A, vol. 483–484, no. 1–2 C, pp. 660–663, 2008. 8. R. Pecora, “Dynamic light scattering measurements of nanometer particles in liquids,” J. Nan. Part. Res., vol. 2, pp. 123–131, 2000. 9. F. Ross Hallett, “Particle size analysis by dynamic light scattering,” Food Res. Int., vol. 27, no. 2, pp. 195–198, 1994. 10. G. A. Yakaboylu and E. M. Sabolsky, “Determination of a homogeneity factor for composite materials by a microstructural image analysis method,” vol. 00, no. 0, pp. 1–10, 2017. 11. A. Braun and V. Kestens, “RESEARCH PAPER A new certified reference material for size analysis of nanoparticles,” 2012. 12. J. A. V. Gonçalves, D. A. T. Campos, G. de J. Oliveira, M. de L. da S. Rosa, and M. A. Macêdo, “Mechanical properties of epoxy resin based on granite stone powder from the Sergipe fold-and-thrust belt composites,” Mater. Res., vol. 17, no. 4, pp. 878–887, 2014. 13. H. Nolte, C. Schilde, and A. Kwade, “Determination of particle size distributions and the degree of dispersion in nanocomposites,” Compos. Sci. Technol., vol. 72, no. 9, pp. 948–958, 2012. 14. B. Krause, M. Mende, P. Pötschke, and G. Petzold, “Dispersability and particle size distribution of CNTs in an aqueous surfactant dispersion as a function of ultrasonic treatment time,” Carbon N. Y., vol. 48, no. 10, pp. 2746–2754, 2010. 15. C. A. Schneider, W. S. Rasband, and K. W. Eliceiri, “NIH Image to ImageJ: 25 years of image analysis,” Nat. Methods, vol. 9, no. 7, pp. 671–675, 2012. Authors: Manoj Kumar Shukla, Kamal Sharma 21. Paper Title: Microstructure and Elemental Investigation of Graphene/ CNT Epoxy Composite Abstract: Epoxy based graphene/ CNT reinforced hybrid composite was prepared using sonication method with equal ratio of nano-fillers at weight percent of 0 and 0.25 wt. % are fabricated. In the present work, the influence of graphene/ CNT substitution on the microstructure and element distribution on hybrid epoxy composite is reported. The composite was characterized for their morphological properties by Scanning Electron Microscopy (SEM). The distribution of elements and elemental composition was also evaluated using Energy Dispersive X- Ray Spectroscopy (EDX). The reaction progress and compositions of elements were analyzed as a function of microstructure. The presence of functionalized filler and formation of copolymerization of polymer was confirming with the help of the EDX spectra of the hybrid composite. Hybrid composite confirmed the presence of Carbon, Chlorine, Silicon and other elements. Variation in the ratio of elements present in pristine and hybrid epoxy composite confirms the occurrence of chemical reaction during processing of composite sample. SEM-EDX analysis show better adhesion in hybrid composite as compared to pristine composite. The detailed results will be presented and discussed.

Keywords: Graphene, CNT, epoxy, hybrid composite, EDX, SEM.

References: 1. R. Atif and F. Inam, “Influence of Macro-Topography on Damage Tolerance and Fracture Toughness of Monolithic Epoxy for Tribological Applications,” World J. Eng. Technol., no. May, pp. 335–360, 2016. 108-111 2. Z. Anwar, A. Kausar, I. Rafique, and B. Muhammad, “Advances in Epoxy/Graphene Nanoplatelet Composite with Enhanced Physical Properties: A Review,” Polym. Plast. Technol. Eng., vol. 2559, no. January, p. 03602559.2015.1098695, 2015. 3. A. K. Geim and K. S. Novoselov, “The rise of graphene.,” Nat. Mater., vol. 6, no. 3, pp. 183–91, 2007. 4. H. Nolte, C. Schilde, and A. Kwade, “Determination of particle size distributions and the degree of dispersion in nanocomposites,” Compos. Sci. Technol., vol. 72, no. 9, pp. 948–958, 2012. 5. S. Chatterjee, F. Nafezarefi, N. H. Tai, L. Schlagenhauf, F. A. Nüesch, and B. T. T. Chu, “Size and synergy effects of nanofiller hybrids including graphene nanoplatelets and carbon nanotubes in mechanical properties of epoxy composites,” Carbon N. Y., vol. 50, no. 15, pp. 5380–5386, 2012. 6. U. Szeluga, B. Kumanek, and B. Trzebicka, “Synergy in hybrid polymer/nanocarbon composites. A review,” Compos. Part A Appl. Sci. Manuf., vol. 73, pp. 204–231, 2015. 7. J. Wang et al., “Graphene and Carbon Nanotube Polymer Composites for Laser Protection,” J. Inorg. Organomet. Polym. Mater., vol. 21, no. 4, pp. 736–746, 2011. 8. Z. A. Ghaleb, M. Mariatti, and Z. M. Ariff, “Synergy effects of graphene and multiwalled carbon nanotubes hybrid system on properties of epoxy nanocomposites,” J. Reinf. Plast. Compos., vol. 0(0) 1–11, 2017. 9. P. J. Lu et al., “Methodology for sample preparation and size measurement of commercial ZnO nanoparticles,” J. Food Drug Anal., vol. 26, no. 2, pp. 628–636, 2018. 10. C. Zhao, “Enhanced strength in reduced graphene oxide/nickel composites prepared by molecular-level mixing for structural applications,” Appl. Phys. A Mater. Sci. Process., vol. 118, no. 2, pp. 409–416, 2014. 11. C. C. C.S. Sipaut, N. Ahmed, R.Adnan, I. Rahman, MA Bakar, J Ismail, “2007 Properties and Morphology of Bulk Epoxy Composite filled with modified fumed silica-epoxy nanocomposite,” J. Appl. Sci., vol. 7, no. (1), pp. 27–34, 2007. Authors: Poornaiah Billa, Anandbabu Gopatoti 3D MR Images Denoising using Adaptive Blockwise Approached Non-Local Means (ABNLM) Filter for Paper Title: Spatially Varying Noise Levels Abstract: The uniform noise distribution over the image is assumed in most of the filtering techniques. The resulting filtering technique becomes problematic when noise not uniformly distributed. Magnetic Resonance images with spatially varying noise levels were produced by Sensitivity-encoded, intensity inhomogeneity and surface coil based acquisition techniques. To adapt these spatial variations in noise levels, we propose a new Adaptive Blockwise approached NL-Means Filter where denoising capability of filter is adjusted based on the local image noise level. Image Noise levels are spontaneously acquired from the MR images using a proposed new adaptive technique. To reduce the computational burden of NLM Filter, an Adaptive Blockwise Non-Local Means Filter is proposed to speed up the denoising process. With adaptive soft wavelet coefficient mixing, a multiresolution framework is adapted to ABNLM filter for denoising of 3-Dimensional MR images. The proposed Multiresolution filter adapts the filtering parameters automatically based on image space-frequency resolution. The outcome of the stated multiresolution Adaptive Blockwise Non-Local Means Filter shows better performance in considering the non uniform noise when compared to Rician NL-means filters where the noise parameters has to be specified initially. 22. Keywords: Non-Local Mean Filter, Blockwise approach, Magnetic Resonance (MR) Image, Wavelet Transform and denoising. 112-118

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Simoncelli, “Image quality assessment: from error visibility to structural similarity,” Image Processing, IEEE Transactions on, vol. 13, pp. 600–612, April 2004. 16. Samsonov A, Johnson C. Noise-adaptive nonlinear diffusion filtering of MR images with spatially varying noise levels. Magn Reson Med 2004;52:798–806. 17. Delakis I, Hammad O, Kitney RI. Wavelet-based de-noising algorithm for images acquired with parallel magnetic resonance imaging (MRI). Phys Med Biol 2007;52:3741–3751. 18. Mahmoudi M, Sapiro G. Fast image and video denoising via nonlocal means of similar neighborhoods. IEEE Signal Process Lett 2005;12:839–842. 19. Kervrann C, Boulanger J, Coupe´ P. Bayesian non-local means filter, image redundancy and adaptive dictionaries for noise removal. In: Proc Conf Scale-Space and Variational Meth, Ischia, Italy; 2007. p 520–532. 20. Brox T, Kleinschmidt O, Cremers D. Efficient nonlocal means for denoising of textural patterns. IEEE Trans Image Process 2008; 17:1083–1092. Authors: Bhupesh Kumar Dewangan, Amit Agarwal, Venkatadri M, Ashutosh Pasricha Paper Title: Sla-Based Autonomic Cloud Resource Management Framework By Antlion Optimization Algorithm Abstract: Service level agreement SLA is a key to attract the user to opt service from the cloud. The quality of service QoS and SLA plays vital role towards the trust to use the services of any application/infrastructure. If SLA violation rate is high then it directly affect to cost and user distraction. In this paper, we have done state-of-art survey on various SLA-aware resource management frameworks and obtain the different objective function and the utilization percentage from year 2014 to 2018. The objective of this paper is to propose SLA-based autonomic resource management technique SMART through antlion optimization algorithm to maximize the resource utilization based on SLA and QoS satisfaction. The execution time, cost and SLA violation rate, objective functions computed for this framework and compare with two existing frameworks. The framework is implements in cloudsim toolkit and the results recorded the utmost performance. The experimental results confirm that cost, execution time, and resource cost are increasing while SLA violation rate is increasing.

Keywords: Autonomic Computing, Resource Management, SLA Violation Rate, Resource Utilization.

23. References: 1. Wu L. et al., “SLA-based resource provisioning for hosted software-as-a-service applications in cloud computing environments,” IEEE 119-123 Transactions on services computing, vol. 7, no. 3, pp. 465-485, 2014. 2. Kohne A., “Evaluation of SLA-based decision strategies for VM scheduling in cloud data centers,” in 3rd Workshop on CrossCloud Infrastructures & Platforms, 2016. 3. Antonescu A. F., “Simulation of SLA-based VM-scaling algorithms for cloud-distributed applications,” Future Generation Computer Systems, vol. 54, no. 1, pp. 260-273, 2016. 4. Garg S. K., “SLA-based virtual machine management for heterogeneous workloads in a cloud datacenter,” Journal of Network and Computer Applications, vol. 45, pp. 108-120, 2014. 5. B. R. Zhao Y., “SLA-based resource scheduling for big data analytics as a service in cloud computing environments,” in 44th International Conference on Parallel Processing (ICPP), 2015. 6. S. P. Serrano D., “SLA guarantees for cloud services,” Future Generation Computer Systems, vol. 54, no. 1, pp. 233-246, 2016. 7. Singh S., “ STAR: SLA-aware autonomic management of cloud resources,” IEEE Transactions on Cloud Computing, pp. 1-22, 2017. 8. Cai X., “SLA-aware energy-efficient scheduling scheme for Hadoop YARN,” The Journal of Supercomputing, vol. 73, no. 38, pp. 3526- 3546, 2017. 9. Beloglazov A..Washington, DC: U.S. Patent and Trademark Office Patent 9,363,190, 2016. 10. Mosa A., “Optimizing virtual machine placement for energy and SLA in clouds using utility functions,” Journal of Cloud Computing, vol. 5, no. 1, pp. 1-17, 2016. 11. Panda S. K., “SLA-based task scheduling algorithms for heterogeneous multi-cloud environment,” The Journal of Supercomputing, vol. 73, no. 6, pp. 2730-2762, 2017. Authors: Mohan Gupta, Kamal Sharma Experimental Observation of Heat Exchange and Pressure Drop By Using Many Inserts in a Round Paper Title: Tube Abstract: The capability of a convectional heat exchanger (HE) in transferring heat requires improvement for conveying a considerable proportion of energy at cheaper rate and amount. For augmenting the heat transfer coefficient, different means have been employed. However, the use of inserts has become an assured method in 24. enhancing heat transfer through endurable escalation of frictional losses. The objective of the study is the examination of a round pipe fitted along with multiple inserts with regard to its characteristics related to energy transfer and water flow; these inserts are organized in clockwise and anticlockwise attitudes. 124-130

Keywords: ”Nu”,”Re”, “F”, “Twisted tape inserts”.

References: 1. S. E-ard, C. Thianpong, P. Promvonge, Experimental investigation of heat transfer and flow friction in a circular tube fitted with regularly spaced twisted tape elements, Int. Commun. Heat Mass Transfer 33 (2006) 1225–1233. 2. Smith E-ard , Pongjet Promvonge, Heat transfer characteristics in a tube fitted with helical screw-tape with/with no core-rod inserts, International Communications in Heat and Mass Transfer 34 (2007) 176–185. 3. Chinaruk Thianpong, Petpices E-ard, Khwanchit Wongcharee, Smith E-ard, Compound heat transfer enhancement of a dimpled tube with a twisted tape swirl generator, International Communications in Heat and Mass Transfer 36 (2009) 698–704. 4. S. E-ard, K. Wongcharee, P. E-ard, C. Thianpong, Heat transfer enhancement in a tube using delta-winglet twisted tape inserts, Applied Thermal Engineering 30 (2010) 310–318. 5. S. E-ard, C. Thianpong, P. 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E-ard, Friction and heat transfer characteristics of laminar swirl flow through the round tubes inserted with alternate clockwise and counter-clockwise twisted-tapes, International Communications in Heat and Mass Transfer 38 (2011) 348–352. 10. Smith E-ard, Khwanchit Wongcharee, Pongjet Promvonge, Influence of Nonuniform Twisted Tape on Heat Transfer Enhancement Characteristics, Chem. Eng. Comm., 199:1279–1297, 2012. 11. K. Nanan, C. Thianpong, P. Promvonge, S. E-ard, Investigation of heat transfer enhancement by penetrate helical twisted-tapes, International Communications in Heat and Mass Transfer 52 (2014) 106–112. 12. P. Promvonge, S. E-ard, Heat transfer behaviors in a tube with combined conical-ring and twisted-tape insert, International Communications in Heat and Mass Transfer 34 (2007) 849–859. 13. [V. Kongkaitpaiboon, K. Nanan, S. E-ard, Experimental investigation of heat transfer and turbulent flow friction in a tube fitted with perforated conical-rings, International Communications in Heat and Mass Transfer 37 (2010) 560–567. 14. Ji-An Meng, Xin-Gang Liang, Ze-Jing Chen, Zhi-Xin Li, Experimental study on convective heat transfer in alternating elliptical axis tubes, Experimental Thermal and Fluid Science 29 (2005) 457–465. 15. M. Faizal, M.R. Ahmed, Experimental studies on a corrugated plate heat exchanger for small temperature difference applications, Experimental Thermal and Fluid Science 36 (2012) 242–248. 16. Smith E-ard, Vichan Kongkaitpaiboon and Kwanchai Nanan, Thermohydraulics of Turbulent Flow Through Heat Exchanger Tubes Fitted with Circular-rings and Twisted Tapes, Chinese Journal of Chemical Engineering, 21(6) 585—593 (2013). 17. C. Thianpong, P. E-ard, P. Promvonge, S. E-ard, Effect of perforated twisted-tapes with parallel wings on heat transfer enhancement in a heat exchanger tube, Energy Procedia 14 (2012) 1117 – 1123. 18. S. E-ard, P. Somkleang, C. Nuntadusit, C. Thianpong, Heat transfer enhancement in tube by inserting uniform/non-uniform twisted-tapes with alternate axes: Effect of rotated-axis length, Applied Thermal Engineering 54 (2013) 289-309. 19. Smith E-ard, Pongjet Promvonge, Thermal characteristics in round tube fitted with serrated twisted tape, Applied Thermal Engineering 30(2010)1673-1682. 20. Jian Guo, Aiwu Fan, Xiaoyu Zhang, Wei Liu, A numerical study on heat transfer and friction factor characteristics of laminar flow in a circular tube fitted with center-cleared twisted tape, International Journal of Thermal Sciences 50 (2011) 1263-1270. 21. S. E-ard, P. Promvonge, Experimental investigation of heat transfer and friction characteristics in a circular tube fitted with V-nozzle turbulators, International Communications in Heat and Mass Transfer 33 (2006) 591–600. 22. S.W. Chang, K.-W. Yu, M.H. Lu, Heat transfers in tubes fitted with single, twin, and triple twisted tapes, Exp. Heat Transfer 18 (4) (2005) 279–294. 23. S.W. Chang, Y.J. Jan, J.S. Liou, Turbulent heat transfer and pressure drop in tube fitted with serrated twisted tape. Int. J. Therm. Sci. 46 (5) (2007) 506-518. 24. S.W. Chang, T.L. Yang, J.S. Liou, Heat transfer and pressure drop in tube with broken twisted tape insert. Exp. Therm. Fluid Sci. 32 (2) (2007) 489-501. 25. M. Rahimi, S.R. Shabanian, A.A. Alsairafi, Experimental, CFD studies on heat transfer and friction factor characteristics of a tube equipped with modified twisted tape inserts. Chem. Eng. Process. 48 (3) (2009) 762-770. 26. P. Bharadwaj, A.D. Khondge, A.W. Date, Heat transfer and pressure drop in a spirally grooved tube with twisted tape insert. Int. J. Heat Mass Transfer 52 (7e8) (2009) 1938-1944. 27. S. E-ard, P. Promvonge, Thermal characteristics in round tube fitted with serrated twisted tape, Appl. Therm. Eng. 30 (13) (2010) 1673– 1682. 28. S. E-ard, K.Wongcharee, P. E-ard, C. Thianpong, Thermohydraulic investigation of turbulent flow through a round tube equipped with twisted tapes consisting of centre wings and alternate-axes, Exp. Thermal Fluid Sci. 34 (8) (2010) 1151–1161. 29. S. E-ard, P. Seemawute, K. Wongcharee, Influences of peripherally-cut twisted tape insert on heat transfer and thermal performance characteristics in laminar and turbulent tube flows, Experimental Thermal and Fluid Science 34 (2010) 711–719. 30. P. Murugesan, K. Mayilsamy, S. Suresh, Turbulent heat transfer and pressure drop in tube fitted with square-cut twisted tape, Chin. J. Chem. Eng. 18 (4) (2010) 609–617. 31. [31] P. Murugesan, K. Mayilsamy, S. Suresh, P.S.S. Srinivasan, Heat transfer and pressure drop characteristics in a circular tube fitted with and with no V-cut twisted tape insert, International Communications in Heat and Mass Transfer 38 (2011) 329–334 32. K. Wongcharee and S. E-ard, Heat transfer enhancement by twisted tapes with alternate axes and triangular, rectangular and trapezoidal wings, Chemical Engineering and Processing 50 (2011) 211–219. Authors: Amandeep, Sanjeev Kumar, Vikas Chauhan, Prem Kumar Paper Title: LTE-A Heterogeneous Networks using Femtocells Abstract: For the improvement of coverage and services of quality, Femtocells play important role in heterogenous Networks in LTE-A networks. Femtocells are used to provide good indoor voice, increase network capacity and high data coverage in LTE-A. the problem of Cross-Tier interference is a large problem in Femtocells Networks. Cross-Tier interference is an interference between Femtocells base station and Microcell’s base station in a network structure. Throughput is increased while Cross-Tier interference can be decreased using Femtocell in 25. any Networks. In this paper, we also show experiment results obtain by a simulation framework which shows how Femtocells can increase the throughput and reduce the interference. 131-134 Keywords: Heterogeneous Network, Experiment, Femtocells, LTE, Interference, Throughput, Pathloss, SINR.

integrated femto and macrocell environments. In Wireless Days (WD), 2011 IFIP (pp. 1-5). IEEE. 10. Claussen, Holger. "Performance of macro-and co-channel femtocells in a hierarchical cell structure." In Personal, Indoor and Mobile Radio Communications, 2007. PIMRC 2007. IEEE 18th International Symposium on, pp. 1-5. IEEE, 2007. 11. https://en.wikipedia.org/wiki/LTE_(telecommunication) 12. http://www.3glteinfo.com/lte-advanced-heterogeneous-networks/ 13. http://www.2cm.com.tw/technologyshow_content.asp?sn=0912230018 14. De La Roche, G., Valcarce, A., López-Pérez, D., & Zhang, J. “Access control mechanisms for femtocells”. IEEE Communications Magazine, 2010. 15. Slamnik, N., Okic, A., & Musovic, J. “Conceptual radio resource management approach in LTE heterogeneous networks using small cells number variation”. In Telecommunications (BIHTEL), XI International Symposium, pp. 1-5, IEEE, 2016. 16. Seidel, E., & Saad, E. (2010). LTE Home Node Bs and its enhancements in Release 9. Nomor Research, 1-5. Authors: Vandana Agrawal Paper Title: Parameterization of Unorganized Point Cloud Data for B-Spline Surface Fitting Abstract: In the present work an algorithm is presented for the parameterization of unorganized point cloud data such that a smooth B-spline surface can be fitted. Points belonging to various surfaces and edges are identified during segmentation. Further edges bounding to segmented region are represented by curves. In the present work initially B-spline curves are constructed with C1 smoothness by interpolating the measured points lying on the edges. For each segmented region four such curves named as boundary curves are constructed to enclose it. Using these boundary curves Coons surface is constructed which serves as base surface for each segmented region. Each Coons surface is divided into grids and for each measured point the nearest grid vertex is found out. The parameters of this vertex are used as the parameters of the measured point. Finally, an algorithm using an iterative approach is given to further improve the parameterization.

Keywords: parameter, data points, curve, surface

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Keywords: Humanoid Robot, Transform approach, Partitioned-proportional derivative

References: 1. Jun Morimoto, Gordon Cheng,et al, “A Simple Reinforcement Learning Algorithm For Biped Walking” Proceedings of the 2004 IEEE International Conference on Robotics &Automation New Orleans. LA * April 2004 2. Marlon Fernando Velásquez-Lobo, Juan Manuel,etal, “ Modeling a Biped Robot on Matlab/SimMechanics” CONIELECOMP 2013, 23rd International Conference on Electronics, Communications and Computing, 11-13 March 2013 3. Amarpreet Singh & Ashish Sigla, [2017], Kinematic Modeling of Robotic Manipulators,Proceeding @ The National Academy of Scince, India ,Sect.A phys.Sci(July-September 2017) 87(3):303-319 4. M.Himanth & L.M Bharath, [2017], Intrenational Journal of Robotics & Automation, Vol3,Issue2, IJRA(2017)21-28 5. Zongxing Lu,1 Chunguang Xu,et al,[2015] Inverse Kinematic Analysis and Evaluation of a Robot for Nondestructive Testing Application, Journal of Robotics, Volume 2015, Article ID 596327, 7 pages, Hindawi Publishing Corporation 6. N. Latif A. Shaari, Ida S. Md Isa,et al [2015], Torque Analysis Of The lower limb exoskeleton robot design, ARPN Journal of Engineering and Applied Sciences, VOL. 10, NO. 19, OCTOBER 2015 141-146 7. C.Hernandez-Santos, E-Rodriguez_leal,et al,[2012]Kinematics and dynamics of a new 16-DOF Humanoid Biped Robot with active toe joint, Intrenational Journal of Advanced robotics system,INTECH,17 Aug,2012. 8. Zhe Li, Gongfa Li, Ying Sun,et al,[2017],Development of articulated robot trajectory planning,Int. J. Computing Science and Mathematics, Vol. 8, No. 1, 2017 9. GilJin Yang, Byoungwook Choi,et al.[2013], Implementation of Joint Space Trajectory Planning for Mobile Robots with Considering Velocity Constraints on Xenomai, International Journal of Control and Automation, 7(9):1-3 · October 2013 10. G Maliotis, “A Hybrid Model Reference Adaptive Control/Computed Torque Control Scheme for Robotic Manipulators”, Proceeding of the institute of mechanical engineers,PartI;Journal of systems and control engineering, Volume: 205 issue: 3, page(s): 215-221,Issue published: August 1, 1991 ,Received: November 21, 1990; Accepted: July 26, 1991 11. MD. Ahhtaruzzaman, Amir Akramin Shafie, [2016], Gait Analysis: Systems, Technologies, And Importance, Journal of Mechanics in Medicine and Biology, Vol. 16, No. 7 (2016) 1630003 (45 pages) °c World Scientific Publishing Company 12. .J-P ,Merlet, Jacobian, manipulability, condition number and accuracy of parallel robots, INRIA , BP 93,06902 Sophia-Antipolis, France 13. Anthony A. Maciejewski * Charles A.Klein,[1989], The Singular Value Decomposition: Computation and Application to Robotics, The international Journal of Robotics Research , Vol8, No 6, December1989,@1989 Massachusetts Institute of Technology 14. J.Denavit, R.S Hartenberg, et al.[2011], Velocity, Acceleration , and static forces analyses of Spatial Linkages, Journal of Applied Mechanics,Vol 32, Issue 4,903-910,sept15,2011 15. Nikos G.Tsagarakis and Bram Vanderborght, et al[2009], The Mechanical Design of the New Lower Body for the Child Humanoid robot ‘iCub, The 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems October 11-15, 2009 St. Louis, USA 16. Brenna D. Argall, Brett Browning, et al., Mobile Robot Motion Control from Demonstration and Corrective Feedback, The research is partly sponsored by the Boeing Corporation under GrantNo. CMU-BA-GTA-1, BBNT Solutions under subcontract No. 950008572, via prime Air Force contract No. SA-8650-06-C-7606, the United States Department of the Interior under Grant No.NBCH-1040007 17. Jianxian Cai, Lixin Li, [2013],Autonomous Navigation Strategy in Mobile Robot, Journal Of Computers, VOL. 8, NO. 8, AUGUST 2013, 18. Robert Platt, Robert Burridge, et al.[2013], Humanoid Mobile Manipulation Using Controller Refinement, Dexterous Robotics Laboratory Johnson Space Center, NASA,july 2013 Authors: M Sai Prasanthi,Venkata Bharath Katragadda, Hrushik Perumalla, Bandla Sowmya Paper Title: Hybrid Approach for Securing the IoT Devices Abstract: Today the Internet has turned out to be omnipresent, has touched every edge of the globe, and is influencing human life in incredible ways. We are presently entering a period, where different kind’s appliances are associated with the web. We are entering a time of the IoT. Internet of Things enables the appliances to communicate and perform their activities based on network activity. Today, a PC is substantially less helpful without an association of internet; tomorrow, that will be the situation with apparatuses like a fridge. To put it plainly, these apparatuses should convey to one another. Sensors in the perception layer gather the information from the sources. This information will be transmitted through the system layers over the web to the cloud. Today IoT deals with huge amount of data. This information may be exceptionally touchy and their protection and security must not be endangered. Here comes the requirement for security algorithms to protect the information. In this paper, we provide a hybrid approach of security algorithms (AES along with RSA) to secure the data in network layer. 28. Keywords: Cryptography, Symmetricencryption, Asymmetric encryption, AES,RSA,Image slicer. 147-151

References: 1. Abdelali El Bouchti, Samir Bahsani, Trik Nahhal “Encryption As A Service For Data Healthcare Cloudsecurity. “ 2. C. Perera, A. Zaslavsky, P. Christen, D. Georakopoulos,“Context Aware Computing For The Internet Of Things.” 3. J. Gubbi, R. Buyya, S. Marusic, And M. Palaniswami, “Internet Of Things (Iot): A Vision, Architectural Elements, And Future Directions.” 4. Ch. Qiang, G.Quan, B.Yu, L.Yang, “Research On Security Issues Of The Internet Of Things.” 5. M. Friedemann, And C. Floerkemeier. "From The Internet Of Computers To The Internet Of Things." 6. Y.Challal, E. Natalizio, S.Sen, And A.Maria Vegni “Internet Of Things Security And Privacy: Design Methods And Optimization”, Add Hoc Network 7. L. Tawalbeh, M. Mowafi And W. Aljoby, "Use Of Elliptic Curve Cryptography For Multimedia Encryption," In Iet Information Security. 8. L. A. Tawalbeh, Y. Jararweh And A. Moh’md. “An Integrated Radix-4 Modular Divider/Multiplier Hardware Architecture For Cryptographic Applications”. 9. Iot Ecosystem Components: The Complete Connectivity Layer 10. konink Lijke Phulips: Meethu Personal Wireless Light-ing,(2013). 11. "Cellular Automata For Dynamic S-Boxes In Cryptog-raphy." 12. Implementation Of Multi Mode Aes Algorithm Using Verilog" 13. A Novel Approach To Secure Data Sharing Scheme For Dynamic Members Through Different Secure Methods. 14. A Survey On Applications And Security Issues Of Internet Of Things 15. R. L. Rivest, A. Shamir And L. Adleman, "A Method For Obtaining Digital Signatures And Public-Key Cryptosys-tem”. 16. An Hybrid Of Rsa Token And Iterated Hash Algorithm For Secured Data Transfer Authors: N. Krishna Jyothi, V. Anitha Paper Title: Design of Multiple U Slotted Microstrip Antenna for Wimax and Wideband Applications Abstract: A novel miniaturized configuration of a different U-slotted micro-strip radio wire is outlined in view of focus recurrence about 4. 7 GHz with dielectric steady (εr) for 4. 4 also substrate thicknesses from claiming 2. 4mm. The suggested radio wire might meet the interest from claiming WiMax and wideband requisitions. The way parameters like return loss, VSWR, gain, directivity would simulated, broke down and optimized utilizing high back structure test system. The recommended radio wire is created and tried utilizing the Rhode Also Schwarz vector organizes analyzer R&S® ZVL-13 and its execution aspects would got. Those Outcomes indicate that the Inclination offers Inclination of the recommended radio wire could make incredibly progressed contrasted with customary micro-strip patavium antennas.

Keywords: Microstrip antennas, WiMaX, Return Loss, VSWR. 29. References: 1. Implementation and development of single feed design using multiple U slotted patch antenna for wireless applications Vikram Thakur, 152-155 International Journal of Engineering Research & technology (IJERT). 2. C. A. Balanis, “Antenna Theory, Analysis and Design”,John Wiley & Sons, New York, 1997. 3. Indrasen Singh, V.S. Tripathi, “Microstrip patch antenna and its applications: A Survey”, International journal of Computer Technology and Applications, Vol. 2(5), pp.1595-1599, 2011. 4. I.J. Bahl, P. Bhartia, “Microstrip Antennas”, Artech House, 1980. 5. Keith R. Carver, “James W. Mink, “Microstrip Antenna Technology”, IEEE Transactions on antennas and propogations,Vol.AP-29, No.1, January 1981. 6. R.E Collin, “Foundations for Microwave Engineering” ,IEEE Press 2nd Edition, 2002. 7. S. E James, M.A. Jusoh, M. H. Mazwir and S.N.S. Mahmud, “Finding The Best Feeding Point Location of Patch Antenna using HFSS”, ARPN Journal of Engineering and Applied Sciences, Vol.10, No. 23, December 2015 8. Vinaybankey,N.AnveshKumar,’’Designand performance issue of microstrip patch antennas’’,International journal of Scientific and Engineering Research volume6,Issue 3,March-2015 1572 Authors: Usha N., G. Devakumar Development of a Model for the Sustainability of Agri Engineering Manufacturing Companies in Paper Title: Karnataka, India Abstract: Indian agriculture sector contributes 18% of GDP to the country’s economy and provides employment about 50% of the workforce. Agriculture sector is facing challenges to get integrated with the business sector and to getting timely and convenient information to increase the productivity. Agricultural mechanization helps to overcome this problem. Agri Engineering Manufacturing Companies (AEMC) plays a major role in effective implementation of Agricultural mechanization. Agricultural mechanization has been accepted as an important element of modernization of agriculture by the world. Hence this article focused on the ways to address the contemporary issues for sustainability of AEMC. In this article quantitative research has been carried out and a thorough literature review has been carried out through scholarly Scopus Indexed journals to identify the factors for sustainability of AEMC for the purpose of conducting pilot study. The critical factors such as Entrepreneurial Competency (EC), Business Model (BM), Innovation and Technology (IT) were arrived based on the rating and ranking scale calculation. Survey questionnaire was developed and validated based on the feedback given by the entrepreneurs, academicians, subject experts and industry experts. A total population of 372 numbers of AEMC has been identified through agricultural department websites, trade websites and agricultural 30. events in the state of Karnataka. Census method of sampling has been adopted and the sample was categorised based on their manufacturing activity such as Equipment and implements, Irrigation, Farm Machineries and 156-164 Processing Machineries. The primary data has been collected through face to face interview, telephonic interview and Google spreadsheet. The collected data has been analysed using Statistical Package for the Social Sciences 25 (SPSS 25) and Analysis of Moment Structures 25 (AMOS 25) software. The data reliability and validity has been analysed through Cronbach alpha value of 0.785 and KMO value of 0.703 respectively which are well within the limit. Further Structural Equation Modelling (SEM) has been used to develop a model consisting of the identified factors such as EC, BM and IT. The obtained Goodness of fit statistics values are well within the acceptable limit. The output of this research is recommended to implement in AEMC such as farm equipment, machineries and irrigation equipment manufacturing companies. As per the research finding, it is recommended to concentrate on the unmet customer need so as to increase the market share and sustain business. Department restructuring enable the entrepreneurs to adopt the new technology as well as meet the growing needs of the customers. Adoption of technological forecasting helps the entrepreneurs to sense the future requirement of the market and be equipped to face the competition. It is suggested to the entrepreneurs to participate in the national and international trade fairs and exhibitions to secure maximum market share to attain sustainability.

Keywords: Agri Engineering Flexible Manufacturing Companies, Business Sustainability, Entrepreneurial Competency, Innovation and Technology.

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Ku, “Customer focus, service process fit and customer relationship management profitability: the effect of knowledge sharing”, The Service Industries Journal, vol. 30(2), 2010, pp. 203-223. Authors: P. Lakshmi Prasanna, D. Rajeswara Rao Paper Title: Probabilistic Recurrent Neural Network for Topic Modeling Abstract: Data storing, and retrieving is the most important task in the current situation. Storing can be done based on the topic that the document describes. To know the topics, we have to classify the documents, to classify we are using topic modeling. In this paper we proposed probabilistic recurrent neural network (PRORNN) gives the most prominent result in the classification. it's a Recurrent neural network (RNN)-based language model designed to directly capture the worldwide linguistics which means relating words during a document via latent topics. owing to their consecutive nature, RNNs square measure smart at capturing the native structure of a word sequence – each linguistics and syntactical – however would possibly face problem basic cognitive process long- range dependencies. As recurrent neural network fails to remember large dependencies, we are using topic modeling merged with probabilistic recurrent neural network which is called PRORNN. This PRORNN consists of all the merits of RNN and latent topic models. Thus, it gives most accurate classification as the result. The proposed PRORNN model integrates the merits of RNNs and latent topic models. In this paper we take the 20 news groups data set in that we take 2000 documents and we can labeled to two topics. to classify this 2000 documents and assigned 2 topics to for that documents and use the rnn package to execute recurrent neural network in R Tool.

Keywords: PRORNN, Classification, Topic Modeling, local, RNN.

References: 1. Topicrnn: A Recurrent Neural Network With Long-Range Semantic Dependency By Adji B. Dieng, Chong Wang, Jianfeng Gao, John Paisley. 31. 2. Recurrent And Convolutional Neural Networks By Ji Young Lee, Franck Dernoncourt. 3. Neural Network Approach For Text Classification Usinf+G Relevance Factor As Term Weighted Method By Anuradha Patra And 165-168 Divakar Singh. 4. Automatic Text Categerization Using Neural Networks By Mignel E.Ruiz. 5. Text Classification Using Artificial Neural Networks By Fraser Murray 6. Hierarchical Text Categorisation Based On Neural Networks And Dempster-Shafer Theory Of Evidence By Gertrud Jeschke And Mounia Lalmas 7. Generative And Discriminative Text Classification With Recurrent Neural Networks By Dani Yogatama, Chris Dyer, Wang Ling, And Phil Blunsom. 8. Fuzzy Approach Topic Discovery In Health And Medical 9. Corpora By Amir Karami _ Aryya Gangopadhyay _ Bin Zhou _ Hadi Kharrazi 10. Discovering Scientific Influence Using Cross-Domain Dynamic Topic Modeling By Jennifer Sleeman, Milton Halem, Tim Finin, Mark Cane 11. Textual Document Clustering Using Topic Models By Xiaoping Sun 12. Analysis Of Initialization Method On Fuzzy C-Means Algorithm Based On Singular Value Decomposition For Topic Detection By Ichsani Mursidah, Hendri Murfi 13. Analyzing Sentiments In One Go: A Supervised Joint Topic Modeling Approach By 14. Zhen Hai, Gao Cong, Kuiyu Chang, Peng Cheng, And Chunyan Miao 15. Topic Models For Unsupervised Cluster Matching By Tomoharu Iwata, Tsutomu Hirao, And Naonori Ueda. 16. Bag-Of-Discriminative-Words (Bodw) Representation Via Topic Modeling By Yueting Zhuang, Hanqi Wang, Jun Xiao, Fei Wu, Yi Yang,Weiming Lu, And Zhongfei Zhang. 17. Sequential Short-Text Classification With Recurrent And Convolutional Neural Networks By Ji Young Lee ,Franck Dernoncourt_ 18. An Unsupervised Cross-Lingual Topic Model Framework For Sentiment Classification By Zheng Lin, Xiaolong Jin, Xueke Xu, Yuanzhuo Wang, Xueqi Cheng, Weiping Wang, And Dan Meng. 19. Trending Topic Discovery Of Twitter Tweets Using Clustering And Topic Modeling Algorithms By Ma. Shiela C. Sapul, Than Htike Aung And Rachsuda Jiamthapthaksin. 20. Impact Of Topic Modelling Methods And Text Classification Techniques In Text Mining: A Survey By Mino George, P. Beaulah Soundarabai, Karthik Krishnamurthi Authors: Jayanti Mehra, RS Thakur Paper Title: Probability Density Based Fuzzy C Means Clustering for Web Usage Mining Abstract: The World Wide Web is huge repository and it is growing exponentially. It contains vast amount of information which is growing and updating rapidly. Various organizations, institutes, government agencies and service centers update their information regularly. The World Wide Web provides its services to the varieties of web users. Web users may have different interests, needs and backgrounds. Clustering is one of the most important tasks in the active areas of Web Usage Knowledge Discovery. It assures to handle the difficulty of information overload on the Internet while many users are connected on the social media. Clustering is utilized for grouping information into comparative access design for discovering client interest. There are two drawbacks of FCM algorithm, firstly the requirements of no. of clusters c and secondly assigning the primary relationship matrix. Due to these two drawbacks the FCM algorithm is hard to decide about the suitable no. of cluster and this algorithm is insecure. The determination of desirable preliminary cluster is an important problem, therefore a new technique called PDFCM algorithm is described.

Keywords: Clustering, FCM, Probability Based Fuzzy c means Clustering (PDFCM), Web Log Mining.

References: 1. A. Gupta and A. Khandekar, "Development of Weblog Mining Based on Improved Fuzzy C-Means Clustering Algorithm", International Journal of Science, Engineering and Technology Research, Vol.5 (3), pp.688-693, March 2016. 2. A. Kapoor and A. Singhal, "A comparative study of K-Means, K-Means++ and Fuzzy C-Means clustering algorithms", In Proc. of 3rd International Conference on Computational Intelligence & Communication Technology, IEEE, pp. 1-6, 2017. 3. A. Zahid, A. V. Babuy, W Ahmed and M F Azeemz, "A fuzzy set theoretic approach to discover user sessions from web navigational data", In Proc. of Recent Advances in Intelligent Computational Systems, IEEE, pp. 879-884, 2011. 4. B. Chandra, M. Gupta, and M.P. Gupta, "A multivariate time series clustering approach for crime trends prediction", In Proc of International Conference on Systems, Man and Cybernetics, IEEE, pp. 892-896, 2008. 5. B. Maheswari and P. Sumathi, "A New Clustering and Preprocessing for weblog mining" In Proc. of World Congress on Computing and Communication Technologies, IEEE, pp. 25-29, 2014. 32. 6. B. S. Shedthi, Shetty and M. Siddappa, "Implementation and comparison of K-means and fuzzy C-means algorithms for agricultural data", In Proc. of International Conference on Inventive Communication and Computational Technologies, IEEE, pp. 105-108, 2017. 7. C. Baviskar and S. Patil, "Improvement of data object's membership by using Fuzzy K-Means clustering approach", In Proc. of 169-173 International Conference on In Computation of Power, Energy Information and Communication, IEEE, pp. 139-147, 2016. 8. C. T. Baviskar and S. S. Patil, "Improvement of data object's membership by using Fuzzy K-Means clustering approach", In Proc. of International Conference on Computation of Power, Energy Information and Communication (ICCPEIC)IEEE, pp. 139-147, 2016. 9. C. Yanyun, Q. Jianlin, G. Xiang, C. Jianping, J. Dan and C. Li, "Advances in research of Fuzzy c-means clustering algorithm", In Proc. of International Conference on Network Computing and Information Security, IEEE, vol. 2, pp. 28-31, 2011. 10. Chen, Y.L. and Huang, C.K., “Discovering fuzzy time-interval sequential patterns in sequence databases”, IEEE Transactions on Systems, Man and Cybernetics, Vol. 35(5), pp. 959-972, 2005. 11. D. Koutsoukos, G. Alexandridis, G. Siolas, and A. Stafylopatis, "A new approach to session identification by applying fuzzy c-means clustering on weblogs", In Proc. of Symposium Series on Computational Intelligence, IEEE, pp. 1-8, 2016. 12. G. S. Chandel, K. Patidar and M. S. Mali, "A Result Evolution Approach for Web usage mining using Fuzzy C-Mean Clustering Algorithm", In Proc. of International Journal of Computer Science and Network Security, Vol.16(1). pp.135-140, 2016. 13. H. Gulat, and P. K. Singh, "Clustering techniques in data mining: A comparison", In Proc. of 2nd International Conference on Computing for Sustainable Global Development, IEEE, pp.410-415, 2015. 14. H. X. Pei, Z. R. Zheng, C. Wang, C. Li, and Y. H. Shao, "D-FCM: Density based fuzzy c-means clustering algorithm with application in medical image segmentation", Procedia Computer Science, Vol.122(1), pp. 407-414, 2017. 15. K. Suresh, R. M. Mohana, A. Rama Mohan Reddy, and A. Subramanyam, "Improved FCM algorithm for clustering on web usage mining." In Proc. of International Conference on Computer and Management, pp. 1-4. 2011. 16. P. Sampath and M. Prabhavathy, "Web Page Access Prediction Using Fuzzy Clustering by Local Approximation Memberships (Flame) Algorithm”, Vol.10 (7), pp.3217-3220, 2006. 17. S. K. Dwivedi and B. Rawat, "A review paper on data preprocessing: A critical phase in web usage mining process", In Proc. of International Conference on Green Computing and Internet of Things, IEEE, pp. 506-510, 2015. 18. V. Anitha and P. Isakki, "A survey on predicting user behavior based on web server log files in a web usage mining", In Proc. of International Conference on Computing Technologies and Intelligent Data Engineering, IEEE, pp. 1-4, 2016. 19. V. Chitraa, and A. S. Thanamani, "Weblog Data Analysis by Enhanced Fuzzy C Means Clustering”, International Journal on Computational Sciences & Applications, Vol.4 (2), pp. 81-95, 2014. 20. Y. Hu, Chuncheng Y. Y. Zuo, and F. Qu, "A cluster validity index for fuzzy c-means clustering", In System Science, In Proc. of International Conference on Engineering Design and Manufacturing Informatization (ICSEM) IEEE, vol. 2, pp. 263-266, 2011. 21. Z. Ansari, S. A. Sattar, A.V. Babu, and M. F. Azeem, “Mountain density-based fuzzy approach for discovering web usage clusters from weblog data, Fuzzy Sets and Systems”, Vol.279 (1), pp.40-63, 2015.

Authors: Bingu Rajesh, Puvvada Nagesh, Koppada Gowtham, Gorantla Vivek, N.Srinivasu Paper Title: A New Scheme to Safeguard Data for Cloud Integrated Internet Things Abstract: In our day-to-day life people use many electronic gadgets to control things around, which in turn those things communicate with other things around and get the requested work done, this is Internet of Things. As there would be enormous amount of data generated by Internet of Things why not we store it in cloud? Here, in this paper, we discuss how to secure data for cloud integrated Internet of Things. In two main steps we can ensure 33. the data cannot be tampered. First, the CP-ABE (Cipher text Policy – Attribute Based Encryption) produces a secret key and encrypts the data. The data can only be decrypted when the secret key is correctly produced. The 174-178 second way uses threshold cryptography where secret key is further encrypted by RSA and then generated key is divided internally and giving to a group of users. Shared key can be produced only if all the authorized users come together. Above proposed scheme not only provides confidentiality but also helps in reducing number of keys and prevents unauthorized/malicious users to access our data.

Keywords: CP-ABE (Cipher text Policy – Attribute Based Encryption), Threshold cryptography, Confidentiality, Malicious users.

References: 1. Jiguo Li, Wei Yao, Yichen Zhang, Huiling Qian, and Jinguang Han, “Flexible and Fine-Grained Attribute-Based Data Storage in Cloud Computing”, IEEE,2017. 2. Hongwei Li, Yuanshun Dai1, Ling Tian, “Identity based authentication for cloud computing”, Springer-Verlag Berlin Heidelberg. 3. Changji wang, Xuan Liu,Wentao Li,”Implementing a Personal Health Record Cloud Platform using Ciphertext-Policy Attribute Based Encryption”, International Confercne on Intelleigent Networking and Collaborative Systems. 4. Threshold cryptography-based data security in cloud computing. IEEE International Conferenceon Computational Intelligence & Communication Technology 2015. 5. A. Shamir. How to share a secret. Commun. ACM, 22, pp. 612-613, November 1979. 6. Ravleen Kaur, Pragya Kashmira, Kanak Meena, Dr. A.K.Mohapatra “Survey on Different Techniques of Threshold Cryptography”, IOSR Journal of Electronics and Communication Engineering (IOSR-JECE). 7. Achieving efficient and secure data acquisition for cloud-supported IoT in smart grid, 2017 IEEE. 8. Secure Data Access in Cloud Computing, Sunil Sanka ,2010. 9. Jitender Grover1, Shikha 2, Mohit Sharma3, “Cloud Computing and Its Security Issues - A Review “, IEEE – 33044 , Dec 2015. 10. H. Zhong, and H. Zhen, An Efficient Authenticated Group Key Agreement Protocol,” Security Technology, 2007 41st Annual IEEE International Carnahan Conference on, vol., no., pp.250-254, 8-11 Oct. 2007. Authors: Pushpendra Kumar, Ramjeevan Singh Thakur Paper Title: Early Detection of the Liver Disorder from Imbalance Liver Function Test Datasets Abstract: Aim of this research is to develop a model for early detection of liver disorder from imbalance Liver Function Test (LFT) results’ datasets that assists the practitioners in diagnosing the liver disease efficiently. Because in the initial stage symptoms of the diseases are vague so the medical practitioners often fail to detect the disease. This study used two datasets of Liver Function Test (LFT) for building the systems, one is ILPD dataset (secondary) taken from UCI repository and second dataset (Primary) is collected form Madhya Pradesh region of India. We have used Support Vector Machine and K-Nearest Neighbour (KNN) algorithms to implement the system and Synthetic Minority Oversampling Technique (SMOTE) to balance the datasets. We have compared the results of both the algorithm on the different parameter for both the imbalanced and balanced datasets. We get the improved result for accuracy, specificity, precision, false positive rate (FPR) parameters on balanced datasets using SVM whereas using KNN we get improve results for accuracy, specificity, sensitivity, FPR and FNR parameters on balanced datasets. We can conclude that the proposed system gives the improve result on balance dataset on most of the parameter. Proposed system helps the healthcare practitioners in diagnosing the liver disease efficiently at the early stage.

Keywords: K Nearest Neighbor (KNN), Liver Function Test (LFT), SMOTE, Support Vector Machine (SVM).

References: 1. J. Han, J. Pei, and M. Kamber, Data mining: concepts and techniques. Elsevier, 2011. 2. M. Abdar, M. Zomorodi-Moghadam, R. Das, and I.-H. Ting, "Performance analysis of classification algorithms on early detection of liver disease," Expert Systems with Applications, vol. 67, pp. 239-251, 2017. 3. M. Hassoon, M. S. Kouhi, M. Zomorodi-Moghadam, and M. Abdar, "Rule Optimization of Boosted C5. 0 Classification Using Genetic Algorithm for Liver disease Prediction," in Computer and Applications (ICCA), 2017 International Conference on, 2017, pp. 299-305: IEEE. 4. K. Nagaraj and A. Sridhar, "NeuroSVM: A Graphical User Interface for Identification of Liver Patients," arXiv preprint 34. arXiv:1502.05534, 2015. 5. J. Hopkins. (11/05/2018). Liver: Anatomy and Functions. 6. B. S. A. Benjamin Wedro. (11/05/2018). Liver Disease Facts. 179-186 7. K.-C. Cheng, W.-Y. Lin, C.-S. Liu, C.-C. Lin, H.-C. Lai, and S.-W. Lai, "Association of different types of liver disease with demographic and clinical factors," Biomedicine, vol. 6, no. 3, 2016. 8. M. S. P. B. a. N. B. V. Bendi Venkata Ramana. Machine Learning Repository [Online]. 9. S. Bahramirad, A. Mustapha, and M. Eshraghi, "Classification of liver disease diagnosis: a comparative study," in Informatics and applications (ICIA), 2013 second international conference on, 2013, pp. 42-46: IEEE. 10. M. ABDAR, "A survey and compare the performance of IBM SPSS modeler and rapid miner software for predicting liver disease by using various data mining algorithms," Cumhuriyet Science Journal, vol. 36, no. 3, pp. 3230-3241, 2015. 11. D. Alemayehu and M. L. Berger, "Big Data: transforming drug development and health policy decision making," Health services and outcomes research methodology, vol. 16, no. 3, pp. 92-102, 2016. 12. S. N. N. Alfisahrin and T. Mantoro, "Data Mining Techniques for Optimization of Liver Disease Classification," in Advanced Computer Science Applications and Technologies (ACSAT), 2013 International Conference on, 2013, pp. 379-384: IEEE. 13. A. Hammad and S. AbouRizk, "Knowledge discovery in data: A case study," Journal of Computer and Communications, vol. 2, no. 05, p. 1, 2014. 14. M. B. Priya, P. L. Juliet, and P. Tamilselvi, "Performance Analysis of Liver Disease Prediction Using Machine Learning Algorithms," 2018. 15. M. Abdar, N. Y. Yen, and J. C.-S. Hung, "Improving the Diagnosis of Liver Disease Using Multilayer Perceptron Neural Network and Boosted Decision Trees," Journal of Medical and Biological Engineering, pp. 1-13, 2017. 16. X. Zhou, Y. Zhang, M. Shi, H. Shi, and Z. Zheng, "Early detection of liver disease using data visualisation and classification method," Biomedical Signal Processing and Control, vol. 11, pp. 27-35, 2014. 17. . V. Ramana, M. P. Babu, and N. Venkateswarlu, "A critical comparative study of liver patients from USA and INDIA: an exploratory analysis," International Journal of Computer Science Issues, vol. 9, no. 2, pp. 506-516, 2012. 18. S. Kant and I. A. Ansari, "An improved K means clustering with Atkinson index to classify liver patient dataset," International Journal of System Assurance Engineering and Management, vol. 7, no. 1, pp. 222-228, 2016. 19. C. Cortes and V. Vapnik, "Support-vector networks," Machine learning, vol. 20, no. 3, pp. 273-297, 1995. 20. R. Saxena. (2017, 11/05/2018). SVM CLASSIFIER, INTRODUCTION TO SUPPORT VECTOR MACHINE ALGORITHM. 21. S. Ray. (2015 11/05/2018). Understanding Support Vector Machine algorithm. 22. R. Saxena. (2016, 11/05/2018). KNN CLASSIFIER, INTRODUCTION TO K-NEAREST NEIGHBOR ALGORITHM. 23. H. Patel and G. S. Thakur, "Classification of imbalanced data using a modified fuzzy-neighbor weighted approach," International Journal of Intelligent Engineering and Systems, vol. 10, no. 1, pp. 56-64, 2017. 24. [24] H. Patel and G. S. Thakur, "Improved Fuzzy-Optimally Weighted Nearest Neighbor Strategy to Classify Imbalanced Data," 2017. 25. [25] N. V. Chawla, K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer, "SMOTE: synthetic minority over-sampling technique," Journal of artificial intelligence research, vol. 16, pp. 321-357, 2002. 26. H. He and E. A. Garcia, "Learning from imbalanced data," IEEE Transactions on knowledge and data engineering, vol. 21, no. 9, pp. 1263-1284, 2009. 27. Y. Xu, C. Wu, K. Zheng, X. Niu, and Y. Yang, "Fuzzy–synthetic minority oversampling technique: Oversampling based on fuzzy set theory for Android malware detection in imbalanced datasets," International Journal of Distributed Sensor Networks, vol. 13, no. 4, p. 1550147717703116, 2017. 28. Z. Zheng, Y. Cai, and Y. Li, "Oversampling method for imbalanced classification," Computing and Informatics, vol. 34, no. 5, pp. 1017- 1037, 2016. 29. R. Das and A. Sengur, "Evaluation of ensemble methods for diagnosing of valvular heart disease," Expert Systems with Applications, vol. 37, no. 7, pp. 5110-5115, 2010. 30. J. Brownlee. (2016, 11/05/2018). Confusion Matrix in Machine Learning. Authors: Pankaj Kumar Sharma Paper Title: Model for Detection and Prevention of MANET Anomalies Abstract: MANET is a self-configured network of devices in wireless linked network, in an arbitrary topology. Each node is an independent node, which can play a role of host, router & receiver. The connectivity is established by operating system hosted on participating nodes. Routing algorithm establishes routes and forwarding information as packets to and from source to sink station. Many routing techniques attempt to achieve optimal performance, however modifications are still required in existing routing protocols to improve the performance of MANET. An efficient MANET leads to fulfillment of three key performance metrics (PDR, AE2ED, and Overhead). There exist some predominant anomalies in Mobile Ad-hoc Network in terms of above performance metrics. Anomalies in MANET arise due to various environmental factors like variation in number of connections among participating nodes, mobility of nodes, pause time of node, rate of data packet forwarded by nodes and total density of nodes, adversely affecting its performance. In order to overcome some predominant anomalies, in this research a systematic approach has been used to develop an intelligent system model, which controls the performance adaptively.

Keywords: MANET, PDR, AE2ED, Overhead, Fuzzy

References: 1. V. Venkata Ramana et al., ” Bio Inspired Approach to Secure Routing in MANETs”, International Journal of Artificial Intelligence & Applications (IJAIA), Vol.3, No.4, July 2012. 2. S. Umamaheswari and G. Radhamani,” An Improved ACO Based Algorithm for EnhancingPerformance in Wireless Adhoc Network”, American Journal of Scientific Research ;ISSN 1450-223X Issue 54 (2012), pp. 68-80; © EuroJournals Publishing, Inc. 2012 ; 3. Jun-Zhao Sun, Mobile Ad Hoc Networking: An Essential Technology for pervasive Computing Mediate team, Machine Vision and Media Processing unit ,info Tech Unit, InfoTech Oulu P.O.Box 4500, FIN-90014 University of Oulu, Finland. 4. Caixia li, Sreenatha Gopalarao Anavatti and Tapabrata Ray, “ Analytical Hierarchy Process using Fuzzy Inference Techniques for real – time route Guidance system , IEEE Transaction on Intelligent Transportation Systems , vol 15 No 1 February 2014 35. 5. C.-K Toh, Maximum Battery Life Routing to Support Ubiquitous Mobile Computing in Wireless Ad hoc Networks‖, , 2001 ,IEEE. 187-191 6. R. L. Flood and M.C. Jackson , "Creative problem solving". John Wiley and Sons, Chichester, (1991), ISBN 0-471-93052-0. 7. Siddesh Gundagatti Karibasappa , K.N Muralidhara, “ Neuro Fuzzy Based Routing International Conference on Industrial and Information Systems, ICIIS 2011, Aug. 16 19, 2011 IEEE 8. Dr .C. Suresh Gnana Dhass and N. Kumar, Power Aware Routing protocols in in Mobile Ad hoc Networks-Survey, International Journal of advanced research in Computer Science and Software Engineering, 2012,Vol. 2, Issue 9. 9. N. Battat and H. Kheddouci, “HMAN: Hierarchical Monitoring for Ad Hoc Network,” in IEEE/IFIP EUC, 2011. 10. K. Kwak, G. Huerta-Canepa, Y. Ko, D. Lee, and S. J. Hyun, “An Overlay-Based Resource Monitoring Scheme for Social Applications in MANET,” in IEEE COMPSAC, 2009. 11. K. Ramachandran, E. Belding-Royer, and K. Almeroth, “DAMON: A Distributed Architecture for Monitoring Multi-hop Mobile Networks,” in IEEE SECON, 2004. 12. R. Riggio, M. Gerola, D. Miorandi, A. Zanardi, and F. Jan, “A Distributed Network Monitoring Framework for Wireless Networks,” in IFIP/IEEE IM, 2011. 13. K. Graffi, D. Stingl, J. Rueckert, A. Kovacevic, and R. Steinmetz, “Monitoring and Management of Structured Peer-to-Peer Systems,” in IEEE P2P, 2009. 14. M. Jelasity, A. Montresor, and O. Babaoglu, “Gossip-Based Aggregation in Large Dynamic Networks,” ACM Transactions on Computer Systems, vol. 23, no. 3, pp. 219–252, 2005. 15. R. van de Bovenkamp, F. Kuipers, and P. Van Mieghem, “Gossip-based Counting in Dynamic Networks,” in IFIP NETWORKING, 2012. 16. P. Yalagandula and M. Dahlin, “A Scalable Distributed Information Management System,” ACM SIGCOMM Computer Communication Review, vol. 34, no. 4, pp. 379–390, 2004. 17. Muhammad Aamir_ and Mustafa A. Zaidi, A Buffer Management Scheme for Packet Queues in MANET, TSINGHUA SCIENCE AND TECHNOLOGY ISSNll1007- 0214ll01/10llpp543-553 Volume 18, Number 6, December 2013 18. Anita Yadav • Y. N. Singh • R. R. Singh, Improving Routing Performance in AODV with Link Prediction in Mobile Adhoc Networks, Wireless PersCommun DOI 10.1007/s11277-015-2411-5, Springer Science+Business Media New York 2015. 19. ShariqMahmood Khan • R. Nilavalan • Abdulhafid F. Sallama, A Novel Approach for Reliable Route Discovery in Mobile Ad-Hoc Network, Wireless PersCommun DOI 10.1007/s11277-015-2461-8, Springer Science+Business Media New York 2015. Networks, ACM MSWIM’04, October 4–6, 2004, Venezia, Italy. Authors: Geeta Chhabra, VasudhaVashisht, Jayanthi Ranjan 36. Paper Title: Improving Accuracy For Cancerclassification With Gene Selection Abstract: The article presents a detail overview of different classification techniques for colon cancer prediction 192-199 by gene expression dataand evaluated their performance based on classification accuracy, computational time &proficiency to reveal gene information. The gene selection methods have been introduced also and evaluated with respect to their statistical significance to cancer classifier.The purpose is to build a multivariate model for tumour classification with genetic algorithm.The multivariate models were constructed using nearest centroid, k- nearest neighbours, support vector machine, maximum likelihood discriminant functions, neural networks and random forest classifiers combined with genetic algorithm applied to the colon cancer publicly available dataset.It has been observed from the experimental analysis that Maximum Likelihood Discriminant Functions (MLHD) performs better and accuracy has been further been improved by using most frequent genes using the forward selection method. Also, maximum likelihood discriminant functions are cost effective and faster than neural networks (NNET), nearest centroid (Nearcent) and random forest (RF). Thus, the experiments show that classification accuracy is affected with the selection of genes that contributes to the accuracy of the model. It will remove the irrelevant genes thus will reduce the size and make the algorithm fast.

Keywords: data mining; genetic algorithm; machine learning algorithms.

References: 1. AdamsL. J., BelloG. A., Dumancas G.Development and Application of a Genetic Algorithm for Variable Optimization and Predictive Modeling of Five-Year Mortality Using Questionnaire Data.Bioinformatics and Biology Insights.2015;3(3):31-41. 2. Amancio D.R., Comin C.H., Casanova D., Travieso G., Bruno O.M., Rodrigues, A.F., Costa L. F. A Systematic Comparison of Supervised Classifiers. PLoS ONE. 2014; 9(4): e94137. Available from Doi:10.1371/journal.pone.0094137 3. Bennet J., Ganaprakasam C.,Kumar N. A. Hybrid Approach for Gene Selection and Classification using Support Vector Machine. The International Arab Journal of Information Technology. 2015;12(6A):695-700. 4. Bhola A., Tiwari A. K. Machine Learning Based Approaches for Cancer Classification Using Gene Expression Data. Machine Learning and Applications:An International Journal.2015;2(3/4). Available from DOI:10.5121/mlaij.2015.2401. 5. Chen H., Zhao H., Shen J., Zhou R., Zhou Q. Supervised Machine Learning Model for High Dimensional Gene Data in Colon Cancer Detection. IEEE International Congress on Big Data.2015;134-141. 6. Dagliyan O.,Uney-YuksektepeF., Kavakli IH, Turkay M. Optimization Based Tumor Classification from Microarray Gene Expression Data. PLoS ONE. 2011; 6(2). Available from https://doi.org/10.1371/journal.pone.0014579. 7. Galván-TejadaC., Zanella-Calzada L., Galván-Tejada J., Celaya-Padilla J.M., Gamboa-Rosales H., Garza-Veloz I., Martinez-Fierro M.L. Multivariate Feature Selection of Image Descriptors Data for Breast Cancer with Computer-Assisted Diagnosis. Diagnostics. 2017;7(1):9.Available from https://doi.org/10.3390/diagnostics7010009 8. Guia J. M. De, Devaraj M. Analysis of Cancer Classification of Gene Expression Data: A Scientometric Review. International Journal of Pure and Applied Mathematics. 2018; 119(12):12505-12513. 9. Kourou K., Exarchos T. P., Exarchos K. P., Karamouzis M. V., Fotiadis D. I. Machine learning applications in cancer prognosis and prediction. Computational and Structural Biotechnology Journal.2014; 13:8-17. 10. Lu Y., Han J., Cancer classiﬁcation using gene expression data.Information Systems. 2003; 28: 243–268. 11. Maher B. A., Mahmoud A. M., El-Horbaty El-S., SalemM. Abdel-B. Classification of Two Types of Cancer Based on Microarray Data. Egyptian Computer Science Journal. 2014; 38(2):56-66. 12. Merk S.colonCA: exprSet for Alon et al. (1999) colon cancer data. R package version 1.22.0. 2018. 13. Moorthy K., Mohamad M. S., Deris S. A Review on Missing Value Imputation Algorithms for Microarray Gene Expression Data.Current Bioinformatics.2014;9:18-22. 14. Mashhour M. E.,Houby E.M.F, Wassif T. K.,Salah A.I. Survey on different Methods for Classifying Gene Expression using Microarray Approach. International Journal of Computer Applications.2016; 150(1):12-21. 15. Novakovic J. Dj., Veljovic A., Ilic S.S., Papic Z., Tomovic M. 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Available from DOI:10.1371/journal.pone.0157484. 21. Venkatesan E.V., Velmurugan T.Performance Analysis of Decision Tree Algorithms for Breast Cancer Classification. Indian Journal of Science and Technology.2015;8(29). Available from DOI: 10.17485/ijst/2015/v8i29/84646. 22. Worrawat E., Chan J.H. Apriori gene set-based microarray analysis for disease classification using unlabeled data. Procedia Computer Science. 2013; 23:137-145. 23. Zhang H., Wang H., Dai Z., Chen M.,Chen M.S., Yuan Z. Improving accuracy for cancer classification with a new algorithm for genes selection. BMC Bioinformatics.2012;13:298. Available from https://doi.org/10.1186/1471-2105-13-298. 24. Meyer D., DimitriadouE., Hornik K., WeingesselA., LeischF. e1071: Misc Functions of the Department of Statistics, Probability TheoryGroup. 2017. Available fromhttps://CRAN.R-project.org/package=e1071. Authors: Venkatesh. P, R Sivaprakasam. Studies on the Effect of Turning Operation on Mean Cutting Force and Cutting Power of AISI 3415 Paper Title: Alloy Steel Abstract: This exploration is conceded to reveal the outcome of machining factors such as cutting velocity, depth of cut and feed rate on the mean cutting force and the cutting power on turning AISI 3415 cylindrical steel alloy components. The experiments are planned based on the (33) full factorial design and conducted on an All Geared Lathe with TiN coated cutting tool insert of 0.8mm nose radius, simultaneously cutting forces such as feed 37. force, thrust force and tangential force are observed using a calibrated lathe tool dynamometer adapted in the tool holder. A mathematical expression representing mean cutting force and cutting power is created by means of non- 200-204 linear regression examination. The outcome of each machining factors on the mean cutting force and the cutting power is studied and presented accordingly.

Keywords: AISI 3415 steel alloy; Cutting force; Cutting power; Full factorial design; Lathe; Regression analysis

References: 1. Tomé, L.I., Baião, V., da Silva, W. and Brett, C.M., 2018. Deep eutectic solvents for the production and application of new materials. Applied Materials Today, 10, pp.30-50. 2. Selvam, M.D., Senthil, P. and Sivaram, N.M., 2017. Parametric optimisation for surface roughness of AISI 4340 steel during turning under near dry machining condition. International Journal of Machining and Machinability of Materials, 19(6), pp.554-569. 3. Hassanalian, M. and Abdelkefi, A., 2017. Classifications, applications, and design challenges of drones: A review. Progress in Aerospace Sciences, 91, pp.99-131. 4. Selvam, M.D. and Senthil, P., 2016. Investigation on the effect of turning operation on surface roughness of hardened C45 carbon steel. Australian Journal of Mechanical Engineering, 14(2), pp.131-137. 5. Salamati, M., Soltanpour, M., Fazli, A. and Zajkani, A., 2018. Processing and tooling considerations in joining by forming technologies; part A—mechanical joining. The International Journal of Advanced Manufacturing Technology, pp.1-55. 6. Dennison, M.S., Sivaram, N.M. and Meji, M.A., 2018. A Comparative Study on the Tool-Work Interface Temperature Observed during the Turning Operation of AISI 4340 Steel in Flooded, Near Dry, and Dry, Machining Conditions. i-Manager's Journal on Future Engineering and Technology, 13(4), p.34. 7. DENNISON, M.S. and MEJI, M.A., 2018. A Comparative Study on the Surface Finish Achieved During Face Milling of AISI 1045 Steel Components. i-Manager's Journal on Mechanical Engineering, 8(2), p.18. 8. Stephenson, D.A. and Agapiou, J.S., 2016. Metal cutting theory and practice. CRC press. 9. Selvam, M.D., Dawood, D.A.S. and Karuppusami, D.G., 2012. Optimization of machining parameters for face milling operation in a vertical CNC milling machine using genetic algorithm. IRACST-Engineering Science and Technology: An International Journal (ESTIJ), 2(4). 10. Selvam, M.D. and Sivaram, N.M., 2017. The effectiveness of various cutting fluids on the surface roughness of AISI 1045 steel during turning operation using minimum quantity lubrication system. i-Manager's Journal on Future Engineering and Technology, 13(1), p.36. 11. Selvam, M.D., Srinivasan, V. and Sekar, C.B., 2014. An Attempt To Minimize Lubricants In Various Metal Cutting Processes. International Journal of Applied Engineering Research, 9(22), pp.7688-7692. 12. Selvam, M.D. and Sivaram, N.M., 2018. A comparative study on the surface finish achieved during turning operation of AISI 4340 steel in flooded, near-dry and dry conditions. Australian Journal of Mechanical Engineering, pp.1-10. 13. Khorasani, A.M., Gibson, I., Goldberg, M., Nomani, J. and Littlefair, G., 2016. Machinability of Metallic and Ceramic Biomaterials: A review. Science of Advanced Materials, 8(8), pp.1491-1511. 14. Thakur, A., Gangopadhyay, S., Maity, K.P. and Sahoo, S.K., 2016. Evaluation on effectiveness of CVD and PVD coated tools during dry machining of Incoloy 825. Tribology Transactions, 59(6), pp.1048-1058. 15. Bhattacharya, A., Das, S., Majumder, P. and Batish, A., 2009. Estimating the effect of cutting parameters on surface finish and power consumption during high speed machining of AISI 1045 steel using Taguchi design and ANOVA. Production Engineering, 3(1), pp.31-40. 16. Aggarwal, A., Singh, H., Kumar, P. and Singh, M., 2008. Optimizing power consumption for CNC turned parts using response surface methodology and Taguchi's technique—a comparative analysis. Journal of materials processing technology, 200(1-3), pp.373-384. 17. Nur, R., Noordin, M.Y., Izman, S. and Kurniawan, D., 2017. Machining parameters effect in dry turning of AISI 316L stainless steel using coated carbide tools. Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering, 231(4), pp.676-683. 18. Cakir, M.C., Ensarioglu, C. and Demirayak, I., 2009. Mathematical modeling of surface roughness for evaluating the effects of cutting parameters and coating material. Journal of materials processing technology, 209(1), pp.102-109. 19. Asiltürk, I., Neşeli, S. and Ince, M.A., 2016. Optimisation of parameters affecting surface roughness of Co28Cr6Mo medical material during CNC lathe machining by using the Taguchi and RSM methods. Measurement, 78, pp.120-128. Authors: Nadeem Gulzar Shahmir, Manzoor Ahmad Tantray Paper Title: Life Cycle Cost Analysis of Translucent Concrete Abstract: Translucent concrete permits the daylight specifically to go starting with one of its end then onto the next end. This is to be finished by embedding plastic optical filaments in concrete which is chiefly utilized for correspondence reason and the optical strands take a shot at the premise of Nano optics; in this paper cost examination on execution of translucent concrete in room (translucent concrete room) is talked about. The examination depends on the estimations and analyses, computations are done on the suppositions that plastic optical filaments of 2mm width are utilized in the room having specific measurements. By using these plastic optical filaments in concrete was checked for the expense as well as checked for the light force going crosswise over casted concrete blocks. Lux meter was utilized for estimating power of light and sizes of (150mm x 150mm) (22500mm sq. surface zone) with thickness of 75mm solid 3D shapes was cast to check the outcomes. By utilizing translucent concrete in room won't just keep up the quality of the room yet will likewise enable the light to go into the room bringing about immense measure of vitality sparing and giving different advantages of daylight. Base on the presumptions of utilizing these casted cuboids in the room the last expense of the room was determined and was contrasted with the expense of regular room and last outcomes rely upon the measure of vitality that gets spared by utilizing translucent concrete in room and different advantages of utilizing translucent concrete in room 38. and it was found to be economical and energy efficient source to utilize translucent concrete in rooms or buildings.

205-207 Keywords: Translucent solid, plastic optical filaments, lux meter, daylight, vitality sparing, concrete samples, translucent concrete room..

References: 1. Experimental Analysis of Translucent Concrete by using Optical Fibres by Nikhil, Umer farooq, Silal ahmed, Juraige, Shabeeba omar march 2016 SSRG International Journal of Civil Engineering. 2. Computational Modelling of Translucent Concrete Panels by Aashish Ahuja; Khalid M. Mosalam and Tarek I. Zohdi in November 2014 journal of architectural engineering. 3. Analysis of Transparent Concrete as an Innovative Material Used in Civil Engineering by Monika Zielińska, Albert Ciesielski in 2018 IOP Conference Series: Materials Science and Engineering. 4. Experimental study of light transmitting concrete by Abdulmajeed altomate, Faisal Alatshan , Mohmad Jadan in 2016 International Journal of Sustainable Building Technology and Urban Development. 5. Translucent Concrete: Test of Compressive Strength and Transmittance A Karandikar N. Virdhi A. Deep. 6. Effect of Plastic Optical Fibre on Some Properties of Translucent Concrete by Dr. Shakir Ahmed Salih, Dr. Hasan Hamodi Joni , Safaa Adnan Mohamed in November 2014 Eng. &Tech. Journal, Vol. 32, Part (A), No.12, 2014 7. Compressive strength of translucent concrete by Salmabanu Luhar, Urvashi Khandelwal in Sept 2015 International Journal of Engineering Sciences & Emerging Technologies 8. Litracon by Shreyas.K in Sept 2018 International Journal of New Technologies in Science and Engineering. 9. Translucent concrete: Test of compressive strength and transmittance by A. Karandikar in 2015 International journal of engineering research and technology 10. Experimental Study of Light Transmitting Concrete Using Optical Fibre by Sachin Sahu, Amlan Kumar Sahoo, Aman Kumar Singhal, Kuramana Stephen, Tamo Talom, Subham Saroj Tripathy, Sidhant Das in 2018 11. Experimental Evaluation on Light Transmittance Performance of Translucent Concrete by Awetehagn Tuaum, Stanley Muse Shitote and Walter Odhiambo Oyawa in 2018 international journal of applied engineering research. 12. A novel translucent concrete panel with waste glass inclusions for architectural applications by Valerio R.M. Lo Verso, Simonetta L. Pagliolico and Laura Ligi in july 2015 the indian concrete journal. 13. Evaluation of The Mechanical Properties of Translucent Concrete by Dr. Shakir Ahmed Salih , Dr. Hasan Hamodi Jonj , Safaa Adnan Mohamad in april 2018 International Journal of Engineering Trends and Technology (IJETT) 14. Study of Translucent Glass Concrete by Sisira Sugunan , Nisha Babu, Sowparnika M. in 2016 IOSR Journal of Mechanical and Civil Engineering Authors: Mohd Azlishah Othman, Abd Shukur Jaafar, Nurmala Irdawaty Hassan Paper Title: Development of Broadband EMF Sensors for Energy Harvesting using RF and Microwave Signals Abstract: Telecommunication Tower has been built for giving the wide coverage on UHF for communication devices. The radiation power from the tower gives awareness that radiation by the cellular tower might affect the human health. Hence, this contribution leads to invention of EMF Meter exists specifically focus on power radiation which is known as RF power meter. The RF power meter is use to detect broadband frequency of UHF in ranging from 300 MHz to 3 GHz radiation power. Within the UHF range, Radio Energy Harvesting technology was introduced. This gives the innovative opportunity of Radio Energy harvesting application on RF power meter. By combining both technologies, the RF power meter could detect the power radiation while harvesting RF energy at the same time. The solution provide on having devices able to power up with less consumption on power supply. In this project, RF power meter was programmed by Arduino and RF energy harvesting was designed. The RF power meter able to achieve 98.6% accuracy and at the input power level of -10 dBm, the measured result shows a RF to DC conversion efficiency achieving 63.3% with the corresponding DC output voltage of 2.11 V.

Keywords: About; Broadband EMF Sensor, Harvest RF Energy, 1.8 GHz to 2.4 GHz, Voltage Multiplier Circuit.

References: 1. Flint, X. Lu, N. Privault, D. Niyato, and P. Wang, “Performance Analysis of Ambient RF Energy Harvesting with Repulsive Point Process Modeling,” pp. 1–21, 2015. 2. X. Lu, P. Wang, D. Niyato, D. I. Kim, and Z. Han, “Wireless Networks with RF Energy Harvesting: A Contemporary Survey,” vol. 17, no. 2, pp. 757–789, 2014. 3. Q. M. Bashayreh, A. a. Omar, and A. M. Alshamali, “The effect of RF radiation on human health using stratified human head model,” 2010 IEEE Radar Conf., pp. 178–182, 2010. 4. Ahmad, R. Ariffin, N. M. Noor, and M. A. Sagiruddin, “1.8 GHz Radio Frequency signal radiation effects on human health,” 2011 IEEE 39. Int. Conf. Control Syst. Comput. Eng., pp. 546–550, 2011. 5. G. Kumar and I. I. T. Bombay, “Cell Phone / Tower Radiation Hazards & Solutions,” no. July, 2012. 6. M. M. Dawoud, “High Frequency Radiation and Human Exposure,” no. October, pp. 1–7, 2003. 208-211 7. T. Le, K. Mayaram, and T. Fiez, “Efficient far-field radio frequency energy harvesting for passive powered sensor networks,” IEEE J. Solid-State Circuits, vol. 43(5), no. 5, pp. 1287–1302, 2008. 8. H. Kanaya, “Multi-Band Miniaturized Slot Antenna with Multi-Band Impedance Matching Circuit,” vol. 0, pp. 551–554, 2014. 9. B. Dixon, “Radio Frequency Energy Harvesting,” pp. 2–3, 2014. 10. X. Lu, P. Wang, D. Niyato, and Z. Han, “Resource Allocation in Wireless Networks with RF Energy Harvesting and Transfer,” no. December, pp. 68–75, 2014. 11. N. Degrenne et al., “Self-Starting DC : DC Boost Converter for Low-Power and Low-Voltage Microbial Electric Generators To cite this version : Self-Starting DC : DC Boost Converter for Low-Power and Low-Voltage Microbial Electric Generators,” Ecce, pp. 889–896, 2011. 12. Q. Yuan and S. Suzuki, “B-21-2 Exact Approach to Design Matching Circuit with Element Ohmic Loss,” vol. 2, no. 3, p. 2016, 2016. 13. S. S. Chouhan and K. Halonen, “A modified cross coupled rectifier based charge pump for energy harvesting using RF to DC conversion,” Circuit Theory Des. (ECCTD), 2013 Eur. Conf., no. 1, pp. 1–4, 2013. 14. J. Emery, “Cockcroft-Walton Voltage Multiplier,” pp. 1–8, 2013. 15. N. M. Waghamare and R. P. Argelwar, “High Voltage Generation by using Cockcroft-Walton Multiplier,” vol. 4, no. 2, pp. 256–259, 2015. 16. R. Thakare, S. B. Urkude, and R. P. Argelwar, “Analysis of Cockcroft - Walton Voltage Multiplier,” vol. 5, no. 3, pp. 3–5, 2015. 17. P. Rengalakshmi, “Rectifier for RF Energy Harvesting,” vol. 143, no. 10, pp. 14–17, 2016. 18. Michelon et al., “Performance Analysis of Ambient RF Energy Harvesting with Repulsive Point Process Modeling,” 2016 17th Int. Symp. Antenna Technol. Appl. Electromagn. ANTEM 2016, vol. 17, no. 5, pp. 5–6, 2016. 19. Khansalee, Y. Zhao, and E. Leelarasmee, “A Dual-Band Rectifier for RF Energy Harvesting Systems,” pp. 0–3, 2014. 20. Chaour, S. Bdiri, A. Fakhfakh, and O. Kanoun, “Modified Rectifier Circuit for High Efficiency and Low Power RF Energy Harvester,” pp. 619–623. 21. P. Haddad, S. Member, G. Gosset, and J. Raskin, “Automated Design of a 13 . 56 MHz 19 µ W Passive Rectifier With 72 % Efficiency Under 10 µ A load,” vol. 51, no. 5, pp. 1290–1301, 2016. 22. C. Liou, S. Member, M. Lee, and S. Huang, “High-Power and High-Ef fi ciency RF Recti fi ers Using Series and Parallel Power-Dividing Networks and Their Applications to Wirelessly Powered Devices,” vol. 61, no. 1, pp. 616–624, 2013. 23. V. Kuhn, C. Lahuec, F. Seguin, and C. Person, “A Multi-Band Stacked RF Energy Harvester With RF-to-DC Efficiency Up to 84 %,” vol. 63, no. 5, pp. 1768–1778, 2015. 24. Michelon, E. Bergeret, A. Di Giacomo, and P. Pannier, “RF Energy Harvester with Sub-threshold Step-up Converter,” 2016. Authors: Yogesh Kumar, Rahul Rishi A Robust Pattern Based Re-engineering Model Guided by MODA and ELM for Software Testing Paper Title: Effort Estimation Abstract: Software Testing Effort (STE) plays a big role in code development method that highly contributes 40. in complete development effort. Reducing the testing effort while not altering the standard/quality of the final code is always imperative; thus, STE measure is incredibly essential to conduct code testing method in associate 212-218 economical manner. In this paper, a MODA aided Pattern based re-engineering (PBRE) model has been proposed for the selection of desirable number of projects with their respective features from within company and cross- company projects. The five input features selected by the MODA for Software Testing Effort (STE) estimation prior to development are Project Duration, Development Personnel, Test Cases, Function Points and Project Cost. We subjected the selected projects and features to train an ELM model for estimating STE using the k-fold cross validation approach. Outcomes shows that the anticipated model for estimating STE from cross-company projects and within-company projects yielded similar results to actual effort.

Keywords: Software Testing Effort (STE), Multi-objective Dragonfly algorithm (MODA), Pattern based reengineering (PBRE), Extreme Learning Machine (ELM), Root Means Square Estimation (RMSE).

References: 1. Chemuturi M, “Mastering software quality assurance: best practices, tools and techniques for software developers”, 2010. 2. Bardsiri VK, Jawawi DN, Hashim SZ, Khatibi E, “Increasing the accuracy of software development effort estimation using projects clustering”, IET software,Vol.6,No.6,pp.461-473,2012. 3. Benestad HC, Anda B, Arisholm E, “Understanding cost drivers of software evolution: a quantitative and qualitative investigation of change effort in two evolving software systems”, Empirical Software Engineering, Vol.15, No.2, pp.166-203, 2010. 4. Pai DR, McFall KS, Subramanian GH, “Software effort estimation using a neural network ensemble”, Journal of Computer Information Systems, Vol.53, No.4, pp.49-58, 2013. 5. Jorgensen M, Shepperd M, “A systematic review of software development cost estimation studies”, IEEE Transactions on software engineering, Vol.33, No.1, pp.33-53, 2007. 6. Jorgensen M, Shepperd M, “A systematic review of software development cost estimation studies”, IEEE Transactions on software engineering, Vol.33, No.1, pp.33-52, 2007. 7. Seyedali Mirjalili, “Dragonfly algorithm : a new meta-heuristic optimization technique for solving single-objective, discrete, and multi- objective problems”, Neural Computing and Application, Vol. 27, Issue 4, pp 1053-1073, May-2016. 8. Hieu, Trung, Huynh, Yonggwan and Won, “Regularized online sequential learning algorithm for single-hidden layer feedforward neural networks”, Pattern Recognition Letters, Volume 32, Issue 14, 15 October 2011, Pages 1930-1935. 9. WeiweiZong, Guang-BinHuang and Yiqiang Chen, “Weighted extreme learning machine for imbalance learning”, Neurocomputing, Volume 101, 4 February 2013, Pages 229-242. 10. Zhifei, Shao and Meng Joo, “An online sequential learning algorithm for regularized Extreme Learning Machine”, Neurocomputing, Volume 173, Part 3, 15 January 2016, Pages 778-788. 11. Yogesh Kumar, Rahl Rishi, “Dragonfly algorithm guided extreme learning machine based prediction model for software testing effort estimation”, in Journal of advanced research in dynamical and control system, Special Issue-07, 2018. Pp. 1948-1958. 12. Yogesh Kumar, “Comparative analysis of software size estimation techniques in project management”, in International journal for research in applied science & engineering technology, Vol. 5, Issue VIII, Aug-2017. Pg 1470-1477. 13. Tannu, Yogesh Kumar, “Comparative Analysis of Different Software Cost Estimation Methods”, International Journal of Computer Science and Mobile Computing, Volume 3, Issue 6, 04 July 2014, pg.547-557. Authors: Rajarajan.S, Sivaprakasam.R Optimisation of Machining Factors for Surface Roughness and Mean Cutting Force of AISI 52100 Steel Paper Title: During Turning Under Microlubrication Condition Abstract: This research work is conducted inorder to find the best practicable turning factors to achieve enhanced surface quality cylindrical AISI52100 steel components under microlubrication condition. The turning operation is performed in a turning centre (All Geared Lathe) with CBN insert of 0.8mm nose radius. The turning factors namely feed rate, cutting velocity and depth of cut are preferred to accomplish the experimentation based on Taguchi’s L25(53) orthogonal array, simultaneously the cutting forces such as feed force, tangential force and thrust force are observed using a calibrated lathe tool dynamometer adapted in the tool holder. The surface roughness of the turned steel alloy components is deliberated by means of a precise surface roughness apparatus. A prediction model in lieu of average surface roughness and mean cutting force is created by means of nonlinear regression examination with the aid of MINITAB software. The most favorable machining settings for surface roughness and mean cutting force are recognized by Taguchi’s method and verified with a confirmation trial.

Keywords: AISI52100; Microlubrication condition; Surface roughness; Cutting force; Lathe; Regression analysis; Taguchi method.

References: 41. 1. Ali, S.M., Dhar, N.R. and Dey, S.K., 2011. Effect of minimum quantity lubrication (MQL) on cutting performance in turning medium carbon steel by uncoated carbide insert at different speed-feed combinations. Advances in Production Engineering & Management, 6(3). 2. Selvam, M.D. and Senthil, P., 2016. Investigation on the effect of turning operation on surface roughness of hardened C45 carbon 219-225 steel. Australian Journal of Mechanical Engineering, 14(2), pp.131-137. 3. Leppert, T., 2011. Effect of cooling and lubrication conditions on surface topography and turning process of C45 steel. International Journal of Machine Tools and Manufacture, 51(2), pp.120-126. 4. Sharma, A.K., Tiwari, A.K. and Dixit, A.R., 2016. Effects of Minimum Quantity Lubrication (MQL) in machining processes using conventional and nanofluid based cutting fluids: A comprehensive review. Journal of Cleaner Production, 127, pp.1-18. 5. Selvam, M.D., Dawood, D.A.S. and Karuppusami, D.G., 2012. Optimization of machining parameters for face milling operation in a vertical CNC milling machine using genetic algorithm. IRACST-Engineering Science and Technology: An International Journal (ESTIJ), 2(4). 6. Dennison, M.S., Sivaram, N.M. and Meji, M.A., 2018. A Comparative Study on the Tool-Work Interface Temperature Observed during the Turning Operation of AISI 4340 Steel in Flooded, Near Dry, and Dry, Machining Conditions. i-Manager's Journal on Future Engineering and Technology, 13(4), p.34. 7. Selvam, M.D. and Sivaram, N.M., 2017. The effectiveness of various cutting fluids on the surface roughness of AISI 1045 steel during turning operation using minimum quantity lubrication system. i-Manager's Journal on Future Engineering and Technology, 13(1), p.36. 8. Kurgin, S., Dasch, J.M., Simon, D.L., Barber, G.C. and Zou, Q., 2012. Evaluation of the convective heat transfer coefficient for minimum quantity lubrication (MQL). Industrial Lubrication and Tribology, 64(6), pp.376-386. 9. Selvam, M.D., Srinivasan, V. and Sekar, C.B., 2014. An Attempt To Minimize Lubricants In Various Metal Cutting Processes. International Journal of Applied Engineering Research, 9(22), pp.7688-7692. 10. Debnath, S., Reddy, M.M. and Yi, Q.S., 2014. Environmental friendly cutting fluids and cooling techniques in machining: a review. Journal of cleaner production, 83, pp.33-47. 11. Dureja, J.S., Singh, R. and Bhatti, M.S., 2014. Optimizing flank wear and surface roughness during hard turning of AISI D3 steel by Taguchi and RSM methods. Production & Manufacturing Research, 2(1), pp.767-783. 12. Selvam, M.D., Senthil, P. and Sivaram, N.M., 2017. Parametric optimisation for surface roughness of AISI 4340 steel during turning under near dry machining condition. International Journal of Machining and Machinability of Materials, 19(6), pp.554-569. 13. Liao, Y.S., Liao, C.H. and Lin, H.M., 2017. Study of oil-water ratio and flow rate of MQL fluid in high speed milling of Inconel 718. International Journal of Precision Engineering and Manufacturing, 18(2), pp.257-262. 14. Ramasamy, K., Dennison, M.S. and Baburaj, E., 2018. Surface Finish Achieved in Producing Pneumatic Piston Rod: An Experimental Investigation. i-Manager's Journal on Mechanical Engineering, 8(3), p.9. 15. Sarhan, A.A., Sayuti, M. and Hamdi, M., 2012. Reduction of power and lubricant oil consumption in milling process using a new SiO 2 nanolubrication system. The International Journal of Advanced Manufacturing Technology, 63(5-8), pp.505-512. 16. Rahim, E.A. and Sasahara, H., 2011. A study of the effect of palm oil as MQL lubricant on high speed drilling of titanium alloys. Tribology International, 44(3), pp.309-317. 17. Boubekri, N., Shaikh, V. and Foster, P.R., 2010. A technology enabler for green machining: minimum quantity lubrication (MQL). Journal of Manufacturing Technology Management, 21(5), pp.556-566. 18. Vijayakumar, E. and Selvam, M.D., 2018. The Effect of Cutting Fluid on Surface Roughness of AISI 4340 Steel during Turning Operation. International Journal of ChemTech Research, 11(03), pp.227-230. 19. DENNISON, M.S. and MEJI, M.A., 2018. A Comparative Study on the Surface Finish Achieved During Face Milling of AISI 1045 Steel Components. i-Manager's Journal on Mechanical Engineering, 8(2), p.18. 20. Sharma, J. and Sidhu, B.S., 2014. Investigation of effects of dry and near dry machining on AISI D2 steel using vegetable oil. Journal of cleaner production, 66, pp.619-623. 21. Selvam, M.D. and Sivaram, N.M., 2018. A comparative study on the surface finish achieved during turning operation of AISI 4340 steel in flooded, near-dry and dry conditions. Australian Journal of Mechanical Engineering, pp.1-10. Authors: Sukanya Ledalla, Tummala Sita Mahalakshmi Paper Title: Sentiment Analysis using Legion Kernel Convolutional Neural Network with LSTM Abstract: Social media is growing as a communication medium where people can express their feelings online and opinions on a variety of topics in ways they rarely do in person. Detecting sentiments in texts have gained a considerable amount of attention in the last few years. Thus, the terms sentiment analysis have taken their own path to become essential elements of computational linguistics and text analytics. These terms are designed to detect peoples’ opinions that consist of subjective expressions across a variety of products or political decisions. In recent years, in India, opinions are expressed using multi-lingual words. This has become a new challenge in the area of sentiment analysis. Machine learning techniques, such as neural networks, have proven success in this task; however, there is room to advance to higher-accuracy networks. In this paper, a novel sentiment analysis system is developed which uses Legion Kernel Convolutional Neural Network with Long Short-Term Memory (LSTM). In this investigation U. S. English, Hindi dialects and datasets like twitter sentiment corpus, transliteration pairs, English word- frequency list, Hindi word-frequency list and various public opinion datasets are used. The proposed network can achieve the highest known accuracy of 92.25%. Thus the proposed network’s success can be extended to other fields also.

Keywords: Convolutional Neural Network; Long Short-Term Memory; Sentiment Analysis; Subjective Expressions; Multi-Lingual Sentence; F-Score

References: 42. 1. Szegedy, C., Ioffe, S., Vanhoucke, V., & Alemi, A. A. (2017). Inception-v4, Inception- ResNet and the Impact of Residual Connections on Learning. In AAAI (pp. 4278- 4284). 226-229 2. Tripathy, A., Agrawal, A., & Rath, S. K. (2016). Classification of sentiment reviews using n-gram machine learning approach. Expert Systems with Applications, 57, 117-126. 3. Verma, A. & Liu, Y. (2017). Hybrid Deep Learning Ensemble Model for Improved Large-Scale Car Recognition. IEEE Smart World Congress 4. Al-Barazanchi, H. A., Qassim, H., & Verma, A. (2016, October). Novel CNN architecture with residual learning and deep supervision for large-scale scene image categorization. In Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON), IEEE Annual (pp. 1-7). IEEE. 5. Vo, H. H., & Verma, A. (2016, December). New Deep Neural Nets for Fine-Grained Diabetic Retinopathy Recognition on Hybrid Color Space. In Multimedia (ISM), 2016 IEEE International Symposium on (pp. 209-215). IEEE. 6. Bojanowski, P., Grave, E., Joulin, A., & Mikolov, T. (2016). Enriching word vectors with subword information. arXiv preprint arXiv:1607.04606 7. Wang, J., Yu, L. C., Lai, K. R., & Zhang, X. (2016, August). Dimensional sentiment analysis using a regional CNN-LSTM model. In ACL 2016—Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics. Berlin, Germany (Vol. 2, pp. 225-230). 8. Zhang, K., Chao, W. L., Sha, F., & Grauman, K. (2016, October). Video summarization with long short-term memory. In European Conference on Computer Vision (pp. 766-782). Springer International Publishing. 9. Madhu Bala Myneni, L V Narasimha Prasad, J Sirisha Devi (2017). In A Framework for Sementic Level Social Sentiment Analysis Model. Journal of Theoretical and Applied Information Technology 10. Medel, J. R., & Savakis, A. (2016). Anomaly detection in video using predictive convolutional long short-term memory networks. arXiv preprint arXiv:1612.00390. 11. J Sirisha Devi, Siva Prasad Nandyala, P Vijaya Bhaskar Reddy (2019). A Novel Approach for Sentiment Analysis of Public Posts. In Innovations in Computer Science and Engineering 12. Rahman, L., Mohammed, N., & Al Azad, A. K. (2016, September). A new LSTM model by introducing biological cell state. In Electrical Engineering and Information Communication Technology (ICEEICT), 2016 3rd International Conference on (pp. 1-6). IEEE. Authors: M. Bindusri, S. Koteswara Rao Paper Title: Sunspot Data Denoising using Wavelet Abstract: In data analysis, signal processing plays a prominent role since the received sunspot data continuously 43. fluctuates. Sunspot number data is corrupted with Gaussian noise and for statistical analysis; the noise needs to be filtered using wavelet transform. Traditional methods, Fourier transform and Kalman filter has limitations when 230-236 analyzing the sunspot number data. A Wavelet transform is a promising tool that provides the time-frequency representation of the data. Daily sunspot number data from 2001 to 2018 is analyzed using Daubechies wavelet transform. Daubechies wavelet transform provides flexibility and is used for wide ranges of data using different denoising techniques such as Rigrsure, Sqtwolog, Heursure, Minimaxi thresholding methods. Results showed Sqtwolog (Universal (or) global threshold) and Heursure gave the better- denoised results compared with the other two denoising threshold methods for the sunspot number data.

Keywords: Denoising methods- Heursure, Minimaxi, Rigrsure, Sqtwolog, Sunspot number, wavelets.

References: 1. HAN YANBEN, HAN YONGGANG (30-Aug 2013). Wavelet analysis of sunspot relative numbers. 2. ASWATHY MARY PRINCE, Dr. SANISH THOMAS, Er. RAVI JOHN, Dr.D.P. JAYAPANDIAN (2013). A study on themed range periodicity of sunspot number during solar cycles 21, 22, 23and 24, International journal of scientific and research publications. 3. Sunspots essay research paper. 4. SATISH KUMAR KASDE, DEEPAK KUMAR SONDHIYA, ASHOK KUMAR GWAL (September 2016), Volume 5. Analysis of sunspot time series during the ascending phase of solar cycle24 using the wavelet transform 5. S. POSTALCLOGLU, K. ERKAN, E.D. BOLAT. Comparison of Kalman filter and wavelet filter for denoising. 6. P.M. BENTLEY, J.T.E. MC DONNEL, Wavelet transforms an introduction, Volume 6. 7. BABATUNDE S. EMMANUEL. Discrete wavelet mathematical transformation method for non-stationary heart sound signal analysis ( August 2012), Vol: 7, No: 8. 8. I.M. DREMIN, O.V. IVANOV, V.A.NECHITAILO LEBEDEV physical institute, Moscow117294, Russia. Wavelets and their use. 9. BURHAN ERGEN, FIRAT University, TURKEY. Signal and Image denoising using wavelet transform. 10. LEI LEI, CHAO WANG, XIN LIU (2013), Vol:7, No:9. Discrete wavelet transform decomposition level determination exploiting sparseness measurement. 11. C EDRIC VONNESCH, THIERRY BLU, MICHAEL UNSER (20-Aug-2007). Generalized Daubechies wavelet families, Volume 55. 12. S.C SHIRALASHETTI (2014). An application of the Daubechies orthogonal wavelets in power system engineering, Recent advances in Information technology. 13. LU JNG-YI, LIN HONG, YE DONG, ZHANG YAN-SHENG (2016). A new wavelet threshold function and denoising application, http://dx.doi.org/10.1155/2016/3195492. 14. BARTOSZ KOZLOWSKI, Journal of Telecommunications and Information technology, 2005. Time series denoising with wavelet transforms. 15. E.HOSTALKOVA, A. PROCHAZKA. Wavelet signal and Image denoising. 16. PIOTR LIPINSKI, MYKHAYLO YATSYMIRSKYY. Efficient 1D and 2D Daubechies wavelet transforms with application to signal processing. 17. M. PITCHAMMAL, N. RIGANA FATHIMA, S. SHAJUN NISHA (2016). Emprical evaluation of wavelet transforms using Shrinkage thresholding techniques with medical images., Vol:6. 18. MARIO MASTRIANI. Denoising and compression in wavelet domain via projection onto approximation coefficients. 19. YALI LIU (2015). Image denoising method based on threshold, wavelet transform and genetic algorithm, Vol: 8, No: 2. 20. VAISHALI V. THORAT, ELECTRONICS and TELECOMMUNICATION ENGINEERING department, SAVITRIBHAI PHULE Pune University. Study of Denoising algorithms- Review paper. 21. M. STNDAG, A. SENGR, M. GKBULUT and F. ATA, “PRZEGLD ELEKTRO TECHNICZNY (2012), Vol: 89, No 5, pp 2047-2052. Performance comparison of wavelet thresholding techniques on weak ECG signals denoising. 22. JEENA ROY, SALCE PETER, NEETHA JOHN (2013),Vol-2. Denoising using soft thresholding. 23. DANIEL VALENCIA, Member IEEE, DAVID OREJUALA, JEFERSON SALAZAR, JOSE VALENCIA, Member IEEE. Comparison analysis between Rigrsure, Sqtwolog, Heursure and Minimaxi techniques using Hard and Soft thresholding methods. Authors: Poonthamil R, Maheshwar Pratap Paper Title: “Optimization of Instrumental Workflow in CSSD” at Hospital Sector Abstract: Theobjective of this paper is to analyze the existing instrument workflow of CSSD [Central Sterile Supply Department] and to suggest the optimized workflow solutions for the hospital. We need to study about CSSDand what are the various activities which takes placethere along with the timings they required for each activity. By knowing those, we need to find out the critical and non-critical activities to create a map.The basic outline of the map is that the instruments from OT [Operation Theatre] to TSSD [Theatrical Sterile Supply Department], TSSD to CSSD, then CSSD to TSSD and TSSD to OT store. In detail, we will study about each area how the instruments are moving, and how much time it consumes.For that we need to create an existing workflow with the lean tool called VSM [Value Stream Mapping] and in that pick out the critical and non–critical activities. We can remove the non–critical activities and create a new workflow.With the new workflow we will form the Program Evaluation & Review Technique model which helps to know the percentage of efficiency has been improved in accordance to the existing workflow. With this solution, we can propose a new workflow of Instruments with the minimized critical activity and time period for the activities which takes place in CSSD of the hospital sector. 44. Keywords: Value Stream Mapping, Program Evaluation and Review Technique, Optimized Workflow. 237-242

References: 1. Ben-Tovim, D. I., Bassham, J. E., Bolch, D., Martin, M. A., Dougherty, M., &Szwarcbord, M. (2007). Lean thinking across a hospital: redesigning care at the Flinders Medical Centre. Australian Health Review, 31(1), 10-15. 2. Du, G., Zheng, L., & Ouyang, X. (2017). Real-time scheduling optimization considering the unexpected events in home health care. Journal of Combinatorial Optimization, 1-25. 3. Lummus, R. R., Vokurka, R. J., &Rodeghiero, B. (2006). Improving quality through value stream mapping: A case study of a physician's clinic. Total Quality Management, 17(8), 1063-1075. 4. Cima, R. R., Brown, M. J., Hebl, J. R., Moore, R., Rogers, J. C., Kollengode, A., ...& Team, S. P. I. (2011). Use of lean and six sigma methodology to improve operating room efficiency in a high-volume tertiary-care academic medical center. Journal of the American College of Surgeons, 213(1), 83-92. 5. Toussaint, J. S., & Berry, L. L. (2013, January). The promise of Lean in health care. In Mayo clinic proceedings (Vol. 88, No. 1, pp. 74- 82). Elsevier. 6. Gill, P. S. (2012). Application of value stream mapping to eliminate waste in an emergency room. Global Journal of Medical Research, 12(6). 7. Gwadz, M. V., Collins, L. M., Cleland, C. M., Leonard, N. R., Wilton, L., Gandhi, M., ...& Ritchie, A. S. (2017). Using the multiphase optimization strategy (MOST) to optimize an HIV care continuum intervention for vulnerable populations: a study protocol. BMC public health, 17(1), 383. 8. Van de Klundert, J., Muls, P., &Schadd, M. (2008). Optimizing sterilization logistics in hospitals. Health care management science, 11(1), 23-33. 9. Kushwaha, N., & Pant, M. (2018). Fuzzy magnetic optimization clustering algorithm with its application to health care. Journal of Ambient Intelligence and Humanized Computing, 1-10. 10. Lin, Q. L., Liu, H. C., Wang, D. J., & Liu, L. (2015). Integrating systematic layout planning with fuzzy constraint theory to design and optimize the facility layout for operating theatre in hospitals. Journal of Intelligent Manufacturing, 26(1), 87-95. 11. Schwarz, P., Pannes, K. D., Nathan, M., Reimer, H. J., Kleespies, A., Kuhn, N., ...&Zügel, N. P. (2011). Lean processes for optimizing OR capacity utilization: prospective analysis before and after implementation of value stream mapping (VSM). Langenbeck's archives of surgery, 396(7), 1047. 12. Doğan, N. Ö., &Unutulmaz, O. (2016). Lean production in healthcare: a simulation-based value stream mapping in the physical therapy and rehabilitation department of a public hospital. Total Quality Management & Business Excellence, 27(1-2), 64-80. 13. Henrique, D. B., Rentes, A. F., GodinhoFilho, M., &Esposto, K. F. (2016). A new value stream mapping approach for healthcare environments. Production Planning & Control, 27(1), 24-48. 14. Lorence, D., & Wu, L. F. (2012). Meeting US Health Reform Mandates with Computerized Health Services Utilization Matching and Optimization. Journal of medical systems, 36(3), 2047-2055. 15. Mallor, F., &Azcárate, C. (2014). Combining optimization with simulation to obtain credible models for intensive care units. Annals of Operations Research, 221(1), 255-271. 16. Masterson, B. J., Mihara, T. G., Miller, G., Randolph, S. C., Forkner, M. E., &Crouter, A. L. (2004). Using models and data to support optimization of the military health system: A case study in an intensive care unit. Health Care Management Science, 7(3), 217-224. 17. Meisami, A., Deglise-Hawkinson, J., Cowen, M. E., & Van Oyen, M. P. (2018). Data-driven optimization methodology for admission control in critical care units. Health care management science, 1-18. 18. Goh, M. M., Tan, A. B., & Leong, M. H. (2016). Bar Code‐Based Management to Enhance Efficiency of a Sterile Supply Unit in Singapore. AORN journal, 103(4), 407-413. 19. Fuhrer, P., &Guinard, D. (2006). Building a smart hospital using RFID technologies: use cases and implementation. Fribourg, Switzerland: Department of Informatics-University of Fribourg. 20. Scholl, J., Syed-Abdul, S., & Ahmed, L. A. (2011). A case study of an EMR system at a large hospital in India: challenges and strategies for successful adoption. Journal of biomedical informatics, 44(6), 958-967. 21. Vetter, T. R., Uhler, L. M., &Bozic, K. J. (2017). Value-based healthcare: Preoperative assessment and global optimization (PASS-GO): Improving value in total joint replacement care. Clinical Orthopaedics and Related Research®, 475(8), 1958-1962. 22. Kleinberg, S., &Hripcsak, G. (2011). A review of causal inference for biomedical informatics. Journal of biomedical informatics, 44(6), 1102-1112. 23. Holzinger, A., Kosec, P., Schwantzer, G., Debevc, M., Hofmann-Wellenhof, R., &Frühauf, J. (2011). Design and development of a mobile computer application to reengineer workflows in the hospital and the methodology to evaluate its effectiveness. Journal of biomedical informatics, 44(6), 968-977. 24. Xing, J., Burkom, H., &Tokars, J. (2011). Method selection and adaptation for distributed monitoring of infectious diseases for syndromic surveillance. Journal of biomedical informatics, 44(6), 1093-1101. 25. Chunning, Z., & Kumar, A. (2000). JIT application: process-oriented supply chain management in a health care system. In Management of Innovation and Technology, 2000. ICMIT 2000. Proceedings of the 2000 IEEE International Conference on (Vol. 2, pp. 788-791). IEEE. 26. AbuKhousa, E., Al-Jaroodi, J., Lazarova-Molnar, S., & Mohamed, N. (2014). Simulation and modeling efforts to support decision making in healthcare supply chain management. The Scientific World Journal, 2014. 27. Acheampong, P., Zhiwen, L., Antwi, H. A., Boateng, F., Akomeah, M. O., &Boadu, A. B. (2017). Engaging Constructive Modelling Concepts to Augment Supply Chain Management Decisions in Ghana's Health Sector. European Journal of Contemporary Research, 6(1). Authors: Amandeep, Sanjeev Kumar, Vikas Chauhan, Prem Kumar Paper Title: LTE-A Heterogeneous Networks Using Femtocells Abstract: For the improvement of coverage and services of quality, Femtocells play important role in heterogenous Networks in LTE-A networks. Femtocells are used to provide good indoor voice, increase network capacity and high data coverage in LTE-A. the problem of Cross-Tier interference is a large problem in Femtocells Networks. Cross-Tier interference is an interference between Femtocells base station and Microcell’s base station in a network structure. Throughput is increased while Cross-Tier interference can be decreased using Femtocell in any Networks. In this paper, we also show experiment results obtain by a simulation framework which shows how Femtocells can increase the throughput and reduce the interference.

Keywords: Heterogeneous Network, Experiment, Femtocells, LTE, Interference, Throughput, Pathloss, SINR.

References: 1. Yamamoto, T., & Konishi, S. (2013). “Impact of small cell deployments on mobility performance in LTE-Advanced systems”. In Personal, Indoor and Mobile Radio communications Workshops, IEEE 24th International Symposium, pp. 189-193, 2013. 45. 2. Bouras, C., Kokkinos, V., Kontodimas, K., & Papazois, A.. A simulation framework for LTE-A systems with femtocell overlays. In Proceedings of the 7th ACM workshop on Performance monitoring and measurement of heterogeneous wireless and wired networks, pp. 85-90, (2012). 243-246 3. Trestian, R., Vien, Q. T., Shah, P., & Mapp, G. (2015, October). Exploring energy consumption issues for multimedia streaming in LTE HetNet small cells. In Local Computer Networks (LCN), 2015 IEEE 40th Conference on (pp. 498-501). IEEE. 4. Kosta, C., Hunt, B., Quddus, A. U., & Tafazolli, R.. On interference avoidance through inter-cell interference coordination (ICIC) based on OFDMA mobile systems. IEEE Communications Surveys & Tutorials, 15(3), 973-995, (2013). 5. Stanze, O., & Weber, A. (2013). Heterogeneous networks with LTE‐Advanced technologies. Bell Labs Technical Journal, 18(1), 41-58. 6. http://www.3gpp.org/technologies/keywords-acronyms/98-lte. 7. http://www.3gpp.org/technologies/keywords-acronyms/97-lte-advanced. 8. Zhou, Hao, Yusheng Ji, Xiaoyan Wang, and Shigeki Yamada. "eICIC configuration algorithm with service scalability in heterogeneous cellular networks." IEEE/ACM Transactions on Networking (TON) 25, no. 1 (2017): 520-535. 9. Alexiou, A., Bouras, C., Kokkinos, V., Kontodimas, K., & Papazois, A. (2011, October). Interference behavior of integrated femto and macrocell environments. In Wireless Days (WD), 2011 IFIP (pp. 1-5). IEEE. 10. Claussen, Holger. "Performance of macro-and co-channel femtocells in a hierarchical cell structure." In Personal, Indoor and Mobile Radio Communications, 2007. PIMRC 2007. IEEE 18th International Symposium on, pp. 1-5. IEEE, 2007. 11. https://en.wikipedia.org/wiki/LTE_(telecommunication) 12. http://www.3glteinfo.com/lte-advanced-heterogeneous-networks/ 13. http://www.2cm.com.tw/technologyshow_content.asp?sn=0912230018 14. De La Roche, G., Valcarce, A., López-Pérez, D., & Zhang, J. “Access control mechanisms for femtocells”. IEEE Communications Magazine, 2010. 15. Slamnik, N., Okic, A., & Musovic, J. “Conceptual radio resource management approach in LTE heterogeneous networks using small cells number variation”. In Telecommunications (BIHTEL), XI International Symposium, pp. 1-5, IEEE, 2016. 16. Seidel, E., & Saad, E. (2010). LTE Home Node Bs and its enhancements in Release 9. Nomor Research, 1-5. Authors: K. Himaja, K. S. Ramesh, S.Koteswara Rao Paper Title: Analysis of Seismic Signal using Maximum Entropy Method Abstract: Seismogenic disturbances are unpredictable hard knocks and are inevitable in nature. Earthquakes are one of the major seismic disturbances that are generated due to the sudden movement of the tectonic plates resulting in great loss to humanity. During the earthquake, abnormal energy is suddenly emanated into the earth’s lithosphere thereby generating the seismic waves. Seismic signals thus generated travel through the earth layers and are highly combined with locally generated noise. The noise thus associated with seismic signals can be eliminated using FIR based band pass filter. In this paper an attempt is made to apply Maximum Entropy Method for deriving the frequency components of the seismic signals, for which the power spectrum of the seismic signals is analyzed.

Keywords: Adaptive signal processing, Applied statistics, Maximum Entropy Method, Seismology, Stochastic Signal Processing.

References: 1. Monson H. Hayes, “Statistical Digital Signal Processing and Modeling”, John Wiley & Sons, INC, New York, 1976. 2. Dr. N. Purnachandra Rao, “Earthquakes” Andhra Pradesh Akademi of Sciences (APAS) publishers. 3. Donelan. M, A. Babanin, E. Sanina, D. Chalikov, “A Comparison Of Methods For Estimating Directional Spectra Of Surface Waves”, 2015. 46. 4. Wail A. Mousa , Abdullatif A. Al-Shuhail, “Processing of Seisemic Reflection data using Matlab”, Synthesis Lectures On Signal Processing., Morgon & Claypool Publishers. 247-251 5. Sverre holm, “Spectral Moment Matching- A Rational For Maximum Entropy Analysis”, Elsevier, pp.479-482, 1983. 6. Miguel A. Lagunas-Hernandez, M. Eugenia Santamaria-Perez, Anibal R. Figueiras-vidal, “ARMA Model Maximum Entropy Power Spectral Estimation”, IEEE Transactions on Acoustics, Speech, And Signal Processing, Vol. ASSP-32, No. 5, pp.984-990,Oct.1984. 7. L.H. Feng and G.Y. Luo “Maximum Entropy Method And Seismic Frequency – Magnitude Relation”, Department of Geography, Zhejiang Normal University, Jinhua 321004, China 2008. 8. Edwin T.Janes, “On the Rationale of Max-Ent Method”, Proceedings of The IEEE, Vol.70, No.9, pp.939-952, Sep.1982. 9. Sverre holm and Jens M. Hovem, “Estimation of Scalar Ocean Wave Spectra by Maximum Entropy Method”, IEEE journal on Oceanic Engineering, Vol. OE-4, No.3, pp.76-83, Jul.1979. 10. B.V.K. Vijaya Kumar & S.K. Mullick “Power Spectrum Estimation Using Maximum Entropy Method”, IETE Journal of Research 2015. 11. Abdussalam Addeeb, Abdulmagid Omar and Charles Slivinsky, “Maximum Entropy Method for Estimating Seismic Wave Amplitude”, IEEE, pp.1041-1046, 1989. 12. Petre Stoica and Randolph Moses, “Spectral Analysis Of Signals”, Prentice Hall, Inc, 2005. 13. G. Manolakis and Vinay k. Ingle, “Statistical And Adaptive Signal Processing” McGraw-Hill, 2000. 14. Sverre Holm, “Spectral Moment Matching in the Maximum Entropy Spectral Analysis Method”, IEEE transactions on information theory, vol. it-29, no. 2, march 1983. 15. S. J. Johnsen And N. Andersen, “On Power Estimation In Maximum Entropy Spectral Analysis”, Geophysics, Vol. 13. No. 4, June 1978. 16. B.V.K. Vijaya Kumar & S.K. Mullick, “Power Spectrum Estimation Using Maximum Entropy Method”, IETE Journal of Research. 17. S. Haykin and S. Kc&r, “Prediction-error filtering and maximum entropy spectral estimation,” in Nonlinear Methods of Spectral Analysis, S.Haykin, Ed. New York: Springer-Verlag, 1979, pp. 9-72. 18. Abies (J G), “Notes on Maximum Entropy Spectral Analysis”.Astron Astrophys, Suppl. 15, 1974. 19. Akaike (H). “Power Spectrum Estimation through Autoregressive Model Fitting”. Ann. Inst. Star. Math. 21, 3; 1969; 407-419. 20. S.F. Gull and J. Skilling, “Maximum entropy method in image processing” IEEE Proceedings, Vol. 131, Pt. F, No. 6, OCTOBER 1984. Authors: Umit Isikdag Paper Title: An Evaluation of Barriers to E-Procurement in Turkish Construction Industry Abstract: What: E-procurement provides chances for enhancing the traditional procurement approaches of the construction industry. Both suppliers and buyers in the supply chain utilize e-procurement methods as these help in the processes through providing opportunities for better communication and coordination. E-procurement expands the marketplace for all parties, which take part in the process. With e-procurement, the buyer gains the strategic advantage of i.) reaching more and more suppliers and ii.) the products of lower cost, while the supplier gets the advantage of reaching new customers in the online markets. In contrast to the globalization of procurement in many of the production sectors, research indicates that the advancement of e-procurement in the construction industry is slow and mostly occurs at the national level. This current situation is mainly caused by the barriers to e- procurement that appear from both supplier and buyer sides. How: This paper explores the barriers to e- 47. procurement in relation to the Construction Industry based on the data gathered from Turkey. The study involves an extensive literature review and a web-based questionnaire survey and interviews to determine the key barriers to 252-259 e-procurement in the construction industry. 64 stakeholders including engineers, architects from the public and private organizations (such as contractors, sub-contractors), and the providers of e-procurement services in Turkey participated in the study. Why: The findings indicated that the construction business organizations still seem to have not benefited from most values of e-procurement. The results of the study indicated the lack of trust between the parties and inadequacy of legal infrastructure as the most critical barriers. Another key barrier appears as the fear of unauthorized access to the critical project information. Efforts towards enhancing the security such as implementation of blockchain technologies and development of the legal infrastructure supporting these technologies can a key step towards overcoming key barriers to e-procurement.

Keywords: Construction, e-Procurement, e-Commerce, Turkey, Barriers

References: 1. European Commission “ICT Uptake, Working Group 1. ICT Uptake Working Group draft Outline Report”, October. Retrieved March 2008 from http://ec.europa.eu/enterprise/ict/policy/taskforce/wg/wg1_report.pdf. 2. BERR “Supporting Innovation in Services, Department for Business”, Enterprise and Regulatory Reform, Crown copyright, URN 08/1126. 3. Eadie R, Perera S, Heaney G. “A cross-discipline comparison of rankings for e-procurement drivers and barriers within UK construction organizations”, Journal of Information Technology in Construction (ITcon), 15, 217-233. 4. Martin J. “E-Tendering about time too”, RICS paper http://www.rics.org/site/scripts/download_info.aspx?downloadID=254&fileID=264 5. McIntosh, G., Sloan, B. “The potential impact of electronic procurement and global sourcing within the UK construction industry.” In proceedings of the 17th ARCOM Annual Conference, 5-7. 6. Love, P.E.D., Irani, Z., Li,H., Cheng, E.W.L., Tse, R.Y.C. "An empirical analysis of the barriers to implementing e-commerce in small- medium sized construction contractors in the state of Victoria, Australia", Construction Innovation: Information, Process, Management, 1, 31 – 41. 7. Kong, C.W., Li, H., Love, P.E.D. "An e‐commerce system for construction material procurement", Construction Innovation, 1(1), 43-54 8. Tserng H.P., Lin P.H. “An accelerated subcontracting and procuring model for construction projects”, Automation in Construction, 11(1), 105–125. 9. Chao, L., Hua, G.B. “Process modelling of E-procurement in the Singapore construction industry”, In the Proceedings of Distributing Knowledge In Building, Arhus, Denmark.,2002 10. Li, H., Kong, C., Pang, Y., Shi, W., and Yu, L. "Internet-Based Geographical Information Systems System for E-Commerce Application in Construction Material Procurement." J. Constr. Eng. Manage., 10.1061/(ASCE)0733-9364 129:6(689), 689-697. 11. Wamelink, H., Teunissen, W. “E-Business in the construction industry: a search for practical applications using the Internet”. International Association for Automation and Robotics in Construction. available at http://www.iaarc.org/publications/fulltext/isarc2003- 93.pdf, 543-547. 12. Dzeng, R.-J., Lin, Y.-C., “Intelligent agents for supporting construction procurement negotiation”, Expert Systems with Applications,(27), 107–119. 13. Kong, S.C.W., Li, Heng, Liang., Y. Hung, T. Anumba, C., Chen,Z. “Web services enhanced interoperable construction products catalogue”, Automation in Construction, 14(3) , 343-352 14. Hadikusumo, B., Petchpong, S., & Charoenngam, C. “Construction material procurement using Internet-based agent system.” Automation in Construction, 14(6), 736-749. 15. Luu, D.T., Ng, S.T., Chen, S.E., Jefferies, M. "A strategy for evaluating a fuzzy case-based construction procurement selection system", Advances in Engineering Software, 37(3), 159-171. 16. Stephenson, P., Chia, P. P. “E-Procurement: An Assessment of UK Practice In. Construction”, In proceedings of the CCIM2006 Sustainable Development through Culture and Innovation, Dubai, UAE, 592-601. 17. Perera, S., Eadie, R., Heaney, G., Carlisle, J. “Methodology for Developing a Model for the Analysis of E-Procurement Capability Maturity of Construction Organisations”, In proceedings of the Joint International Conference on Construction Culture, Innovation, and Management (CCIM), British University in Dubai, 634-644 18. Eadie R., Perera S., Heaney G., Carlisle J. “Drivers and Barriers To Public Sector E-Procurement Within Northern Ireland’s Construction Industry”, Journal of Information Technology in Construction, Journal of Information Technology in Construction (ITcon), 12, 103-120. 19. Vitkauskaitė, E., Gatautis, R. “E-Procurement perspectives in construction sector SMEs”, Journal of Civil Engineering and Management, 14(4), 287–294. 20. Alarcón, L.F., Muturana, S., Schonherr, I. “Benefits of Using E-Marketplace in Construction Companies: A Case Study”, in: Construction Supply Chain Management Handbook, London: CRC Press, Taylor & Francis Group, 17.1 – 17.19. 21. Eadie, R., Perera, S., Heaney, G. “Identification of e-procurement drivers and barriers for UK construction organisations and ranking of these from the perspective of quantity surveyors”, Journal of Information Technology in Construction (ITcon), 15, 23-43. 22. Hashim N., Said I., Idris N. H. “Exploring e-Procurement value for construction companies in Malaysia”, Procedia Technology, Vol. 9, 2013, pp. 836−845. 23. Ibem E. O., Laryea S. “e-Procurement use in the South African construction industry”, Journal of Information Technology in Construction, Vol. 20, 2015, pp. 364−384. 24. Aduwo E. B., Ibem E. O., Tunji-Olayeni P., Uwakonye O. U., Ayo-Vaughan E. K. “Barriers to the uptake of e-Procurement in the Nigerian building industry”, International Journal of Applied Theoretical and Applied Information Technology, Vol. 89, No. 1, 2016, pp. 133−147. Authors: T. Charan Singh, K. Raghu Ram, B.V. Sanker Ram Transient Stability Analysis of Six Phase Transmission System with Integration of WPGS and Paper Title: STATCOM with Smart Grid Abstract: In recent times Transient stability analysis has become a major concern in the operation of power systems due to the rising stress on power system networks. These difficulties require assessment of a power system’s ability to with stand instability while maintaining the excellence of service. Many different techniques have been projected for transient stability analysis in power systems, especially for a multi machine system. This paper describes simulation of six phase multi-machine power system (MMPS) with wind power generator integration in dynamic operation. By the introduction of wind power generation system (WPGS) in multi-machine at weak bus in parallel with STATCOM can improve the generator load angle deviation during fault condition. The MMPS performance is analysed by placing six phase line between different buses. The replacement of transmission line can reduces the line impedances, which results in reduced angle distortion of machines and 48. improved stability .The proposed WPGS based MMPS phase angle and frequency variations are analyzed during symmetrical and asymmetrical fault conditions. The MATLAB/Simulation software is used to test the behavior of proposed system. 260-266

Keywords: Wind system, six phase transmission line, STATCOM, multi-machine system, stability.

References: 1. D. Basic, J. G. Zhu, and G. Boardman, “Transient performance study of a brushless doubly fed twin stator induction generator,” IEEE Trans. energy Convers., vol. 18, no. 3, pp. 400–408, 2003. 2. G. K. Singh, “Modeling and experimental analysis of a self-excited six-phase induction generator for stand-alone renewable energy generation,” Renew. energy, vol. 33, no. 7, pp. 1605–1621, 2008. 3. J. R. Stewart and D. D. Wilson, “High phase order transmission--a feasibility analysis part I--steady state considerations,” IEEE Trans. Power Appar. Syst., no. 6, pp. 2300–2307, 1978. 4. T. L. Landers, R. J. Richeda, E. Krizanskas, J. R. Stewart, and R. A. Brown, “High phase order economics: constructing a new transmission line,” IEEE Trans. Power Deliv., vol. 13, no. 4, pp. 1521–1526, 1998. 5. J. M. Arroyo and A. J. Conejo, “Optimal response of a power generator to energy, AGC, and reserve pool-based markets,” IEEE Trans. Power Syst., vol. 17, no. 2, pp. 404–410, 2002. 6. J. R. Stewart, E. Kallaur, and I. S. Grant, “Economics of EHV high phase order transmission,” IEEE Trans. power Appar. Syst., no. 11, pp. 3386–3392, 1984. 7. N. G. Hingorani, L. Gyugyi, and M. El-Hawary, Understanding FACTS: concepts and technology of flexible AC transmission systems, vol. 1. IEEE press New York, 2000. 8. G. Cai, Q. Sun, C. Liu, P. Li, and D. Yang, “A new control strategy to improve voltage stability of the power system containing large- scale wind power plants,” in Electric Utility Deregulation and Restructuring and Power Technologies (DRPT), 2011 4th International Conference on, 2011, pp. 1276–1281. 9. L. Wang and C.-T. Hsiung, “Dynamic stability improvement of an integrated grid-connected offshore wind farm and marine-current farm using a STATCOM,” IEEE Trans. power Syst., vol. 26, no. 2, pp. 690–698, 2011. 10. S. S. Venkata, W. C. Guyker, W. H. Booth, J. Kondragunta, N. K. Saini, and E. K. Stanek, “138-kV, six-phase transmission system: fault analysis,” IEEE Trans. Power Appar. Syst., no. 5, pp. 1203–1218, 1982. 11. A. P. Apostolov and R. G. Raffensperger, “Relay protection operation for faults on NYSEG’s six-phase transmission line,” IEEE Trans. Power Deliv., vol. 11, no. 1, pp. 191–196, 1996. 12. N. B. Bhatt, S. S. Venkata, W. C. Guyker, and W. H. Booth, “Six-phase (multi-phase) power transmission systems: fault analysis,” IEEE Trans. Power Appar. Syst., vol. 96, no. 3, pp. 758–767, 1977. 13. R. J. Vidmar, “On the use of atmospheric plasmas as electromagnetic reflectors,” IEEE Trans. Plasma Sci, vol. 21, no. 3, pp. 876–880, 1992. Authors: K Durga Prasad, D Vasumathi Privacy Preserving Data Analysis using Decision Tree learning Algorithm through Additive Paper Title: Homomorphic Encryption Abstract: Privacy preserving is an emerging concern in the field of data mining. The Randomization technique protects privacy with loss of accuracy. The secure multi-party computation increases the accuracy and conserves privacy but the computational complexity is more. The encryption of data using cryptography makes the data secure without loss of accuracy and reduces the communication complexity. The proposed technique is privacy preserving decision tree algorithm using cryptographic approach. The data miner collects frequencies and combined frequencies from the users and learns the classification rules from the decision tree. The data miner learns only frequencies of the sensitive data. The experimental result shows that proposed privacy preserving decision tree algorithm is computationally efficient and the accuracy is more than the randomization models. The communication complexity is less compared with the secure multi-party computation models.

Keywords: Cryptographic encryption, Data Analysis, Decision Tree and Privacy Preserving.

References: 1. Evfimievski A, “Randomization in privacy-preserving Data mining”. ACM Sigkdd Explorations Newsletter, vol.4, no. 2, pp43-48, 2002. 2. Oded Goldreich, “Secure Multi-Party Computation” 2002 with reference to better exposition provided in Chapter 7 of (Volume 2 of) Foundations of Cryptography. ISBN 0-521-83084-2, Published in the US in May 2004. 3. Lindell Y & Pinkas B, “Secure multiparty computation for privacy-preserving data mining”, Journal of Privacy and Confidentiality, vol. 1, no.1, pp.5 – 27,2009 4. Krishnamurty Muralidhar & Rathindra Sarathy, “Data Shuffling –A New Masking Approach for Numerical Data Management science”, 2006, Sci.52,658-670. DOI=http://dx.doi.org/10.1287/mnsc.1050.0503. 5. Kargupta H, Datta H, et. al. “On the privacy preserving properties of random data perturbation techniques”, 2003, In The Third IEEE International Conference on Data Mining. 6. A. C. Yao, "Protocols for secure computations" 23rd Annual Symposium on Foundations of Computer Science (sfcs 1982)(FOCS), vol. 00, no. , pp. 160-164, 1982. doi:10.1109/SFCS.1982.88 7. B Pinkas, ”Cryptographic techniques for privacy-preserving data mining” ACM SIGKDD, Volume 4 Issue 2, Pages 12-19, doi - 49. 10.1145/772862.772865,2002. 8. Vaidya J & Clifton C ”Privacy preserving naive Bayes classifier on vertically partitioned data”, SIAM International Conference on Data Mining,2004. 267-272 9. Craig Gentry, “Fully homomorphic encryption using ideal lattices” In Proceedings of the forty-first annual ACM symposium on Theory of computing. ACM, New York, NY, USA, 169-178. DOI: https://doi.org/10.1145/1536414.1536440, 2009. 10. Zhiqiang Yang & Sheng Zhong et al “Privacy-Preserving Classification of User Data without Loss of Accuracy”, PG - 92-102, Proceedings of the 2005 SIAM International Conference on Data Mining, 2005,doi - 10.1137/1.9781611972757.9. 11. Agarwal R & Srikant R, “Privacy preserving data mining” In Proc. of ACM SIGMOD Conference on Management of Data, ACM Press, pages 439-450,2000. 12. Du W & Zhan Z, “Using randomized response techniques for privacy-preserving data mining”, In Proc.of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining pages 505-510. ACM Press., 2003,doi>10.1145/956750.956810. 13. Zhan J “Using Homomorphic Encryption For Privacy-Preserving Collaborative Decision Tree Classification”, IEEE Symposium on Computational Intelligence and Data Mining, 2007. 14. Chen Tingting & Zhong Sheng, “Privacy-preserving backpropagation neural network learning”, IEEE Transactions, 20(10):1554–1564, DOI: 10.1109/TNN.2009.2026902,2009. 15. Louis J M Aslett & Esperanca M, et al “A review of homomorphic encryption and software tools for encrypted statistical machine learning”, arXiv:1508.06574, 2015b. , 2015. 16. Kaleli C & Polat H “Privacy-Preserving Naïve Bayesian Classifier–Based Recommendations on Distributed Data”, Computational Intelligent,Vol. 31, 2015. 17. Huai Mengdi, Huang Liusheng, et al “Privacy Preserving Naive Bayes Classification” In Proc. of International Conference Knowledge Science, Engineering and Management, Volume 9403, pages 627-638, 2015. 18. Agarwal D, and Agarwal C “On the design and quantification of privacy preserving data mining algorithms” In Proc. of the 20th ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems, ACM Press, pages 247-255, 2001,doi>10.1145/375551.375602. 19. Kantarcioglu M & Vaidya J “Architecture for privacy-preserving mining of client information”, In IEEE ICDM Workshop on Privacy, Security and Data Mining, pages 37-42, 2002. 20. Rebecca Wright and Zhiqiang Yang “Privacy-preserving Bayesian network structure computation on distributed heterogeneous data”, In Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining (KDD'04),ACM,NewYork, NY, USA,713-718,2014, DOI=http://dx.doi.org/10.1145/1014052.1014145. 21. Durga Prasad k, et al “Privacy-preserving Data Analysis over Naive Bayesian Classifier for Continuous and Discrete Data”(accepted paper), 2018. 22. Archer, D. W., Bogdanov, D., Lindell, Y., Kamm, L., Nielsen, K., Pagter, J. I., Wright, R. N. “ From Keys to Databases—Real-World Applications of Secure Multi-Party Computation.” The Computer Journal. doi:10.1093/comjnl/bxy090,2018. 23. Lindell, Yehuda & Pinkas, Benny.. “Secure Multiparty Computation for Privacy-Preserving Data Mining.” IACR Cryptology ePrint Archive. 2008. 197. 10.29012/jpc.v1i1.566., 2008. 24. Orlandi, C. “Is multiparty computation any good in practice?” 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). doi:10.1109/icassp.2011.5947691,2011. Authors: Karanam Deepak. Design and Analysis of High Speed and Low Power Reversible Vedic Multiplier Incorporating with Paper Title: QSDN Adder Abstract: This present work deals with a reversible Vedic type multiplier using the earliest Urdhva Tiryagbhyam sutras of Vedic type mathematics combine with the QSD adder (Quaternary Signed digit number adder). There are three activities be intrinsic into duplication halfway items age, fractional items decrease and expansion. Quick snake design in this way enormously upgrades the speed of the general procedure. A pass on free math errand be able to be cultivated use a top radix number formation, for instance, QSD adder. In QSD, each one number can be address by a digit as of - 3 to 3. Pass on complimentary development as well as distinctive exercises on incalculable, for instance, 64, 128, or more can be executed with consistent deferment and less multifaceted nature. The proposed multiplier configuration is contrasted and a reversible Vedic multiplier consolidates a QSD Quaternary Signed digit number adder viper among a transformation section for quaternary to paired change. The proposition demonstrates a most extreme speed enhancement.

Keywords: Arithmetic Multiplier, Quaternary Signed Digit adder [QSD], UrdhvaTiryagbhyam, Vedic type 50. Mathematics, Carry free addition, QSD, Redundancy.

References: 273-279 1. S. TahmasbiOskuii, P. G. Kjeldsberg, and O. Gustafsson, “Transition activity aware design of reduction-stages for parallel multipliers,” in Proc. 17th Great Lakes Symp.On VLSI, March 2007, pp. 120–125. 2. M. Perkowski, P. Kerntopf, A. Buller, M. Chrzanowska-Jeske, A. Mishchenko, X. Song,A. Al-Rabadi, L. Jozwiak, A. Coppola and B. Massey, “Regular realization of symmetric functions using reversible logic”, in Proceedings of EUROMICRO Symposium on Digital Systems Design (Euro-Micro’01),Warsaw, Poland, pp. 245–252, September 2001 3. Ayman A. Fayed, Magdy A. Bayoumi, "A Novel Architecture for Low Power Design of Parallel type of Multipliers," wvlsi, pp.0149, IEEE Computer Society Workshop on VLSI 2001. 4. C.-Y. Han, H.-J.Park, and L.-S. Kim. A low-power array multiplier using separated multiplication technique. IEEE Transactions on Circuits and Systems II: Analog and Digital Signal Processing. vol.48, pp. 866- 871 (2001) 5. S. Shah, A. Al-Khalili, and D. Al-Khalili. Comparison of 32-bit multipliers for various performance measures. Proceedings of the 12th IEEE International Conference on Microelectronics, (2000) 6. K. Thakre, S. S. Chiwande and S. D. Chafale, "Design of low power multiplier using reversible logic gate," 2014 International Conference on Green Computing Communication and Electrical Engineering (ICGCCEE), Coimbatore, 2014, pp. 1-6. 7. M.Anitha, A.Rajani, N.Pushpalatha, “Optimized multiplier using reversible logic gates: a vedic Mathematical approach”, (IJARCET) Volume 3 Issue 10, October 2014. 8. Gowthami P, RVS Satyanarayana, “Design of Digital Adder Using Reversible Logic”, IJERA, Vol. 6, Issue 2, (Part - 1), pp.53-57, February 2016. Authors: S.P. Sundar Singh Sivam, Ganesh Babu Loganathan, K. Saravanan Impact of Point Angle on Drill Product Quality and Other Responses When Drilling EN- 8: A Case Paper Title: Study of Ranking Algorithm Abstract: In the present work Drilling parameters has been advanced for EN-8 combination steel utilizing GRA (Grey Relational Examination). The parameters advanced are axle speed (SS - 3000, 3500 and 4000 rpm), feed rate (FR - 0.18, 0.20 and 0.22 mm/rev) and cemented Carbide twist drill of 14.5 mm width with Three flutes point angle (PA - 118,127 and 1350) And Lubrications Used Dry, Wet and Air on bases of surface harshness, Hole distance across, Thrust Force and Burr Size precision reactions. It is performed with the assistance of established carbide contort drills. On the bases of GRA alongside recognizable proof, huge commitment of parameters has been completed by utilizing ANOVA. Out of three factors considered point edge has huge impact on reactions as contrast with other parameters.

Keywords: Drilling, Lubrications, Ranking Algorithm

References: 1. Davim Paulo J., Conceic C.A.¸ & Antonio (2000). Optimal drilling of particulate metal matrix composites based on experimental and 51. numerical procedures, International Journal of Machine Tools & Manufacture, Vol.41,pp. 21–31. a. TosunNihat (2005). Determination of optimum parameters for multi-performance characteristics in drilling by using grey relational analysis, Int J Adv Manuf Technol, Vol.28, pp. 450–455. 280-282 2. S.P. Sundar Singh Sivam et al.,. “Orbital cold forming technology - combining high quality forming with cost effectiveness - A review”. Indian Journal of Science and Technology. Vol 9(38), October 2016, DOI: 10.17485/ijst/2016/v9i38/91426. 3. Sutherland, J. W., Kulur, V. N., King, N. C., 2000, An Experimental Investigation of Air Quality in Wet and Dry Turning, Annals of the CIRP, 49/1: 61-64. 4. S.P.Sundar Singh Sivam et al., “Frequently used Anisotropic Yield Criteria for Sheet Metal Applications: A Review”, Indian Journal of Science and Technology. Indian Journal of Science and Technology. Volume 9, Issue 47, December 2016. DOI: 10.17485/ijst/2015/v8i1/92107. 5. Daniel, C. M., Olson, W. W., Sutherland, J. W., 1997, Research Advances In Dry and Semi-dry Machining, SAE Technical Paper No. 970415 and SAE Transactions, Journal of Materials and Manufacturing, 106: 373-383. 6. S.P. Sundar Singh Sivam et al.,,An Experimental Investigation And Optimisation Of Ecological Machining Parameters On Aluminium 6063 In Its Annealed And Unannealed Form, Journal Of Chemical And Pharmaceutical Sciences. Page No Page (46 – 53), 2015. 7. König, W., 1999, Fertigungsverfahren I – Drehen, Fräsen, Bohren, Springer-Verlag, BerlinHeidelberg. 8. S.P.Sundar Singh Sivam et al., “Frequently used Anisotropic Yield Criteria for Sheet Metal Applications: A Review”, Indian Journal of Science and Technology. Indian Journal of Science and Technology. Volume 9, Issue 47, December 2016. DOI: 10.17485/ijst/2015/v8i1/92107. 9. P. Sundar Singh Sivam, et al., (2018). Comparison of Manufacturing Data Analysis For 5 & 3-Axis Vertical Machining Center for the Time and Tool Benefits of Industries. International Journal of Engineering & Technology, 7(4.5), 196-201. doi:http://dx.doi.org/10.14419/ijet.v7i4.5.20044. 10. P. Sundar Singh Sivam et al.,. (2018). Development of Vibrator Feeding Mechanism Using Two Sets of Rollers for the Separation of Ball Grading For Industry Benefits. International Journal of Engineering & Technology, 7(4.5), 202-206. doi:http://dx.doi.org/10.14419/ijet.v7i4.5.20045 11. S.P. Sundar Singh Sivam et al., “Investigation exploration outcome of Heat Treatment on Corrosion Resistance of AA 5083 in Marine Application”. International Journal of Chemical Sciences (ISSN 0972-768 X). Page No Page (15 – 22), 2015. 12. SIVAM, S. P. Sundar Singh et al.”Multi Response Optimization of Setting Input Variables for Getting Better Product Quality in Machining of Magnesium AM60 by Grey Relation Analysis and ANOVA." Periodica Polytechnica Mechanical Engineering, [S.l.], 2017. ISSN 1587-379X. https://doi.org/10.3311/PPme.11034. 13. S.P. Sundar Singh Sivam et al.,.” Analysis of residual stresses, thermal stresses, cutting forces and other output responses of face milling operation on ze41 magnesium alloy." International Journal of Modern Manufacturing Technologies, Pp. No 92-100. ISSN 2067–3604, Vol. X, No. 1 / 2018. 14. Sivam, S. P. S. S., et al., “The Grey Relational Analysis and Anova to Determine the Optimum Process Parameters for Friction Stir Welding of Ti and Mg Alloys”, Periodica Polytechnica Mechanical Engineering. doi: https://doi.org/10.3311/PPme.12117. 15. S. P. S. S. Sivam et al.,"Competitive study of engineering change process management in manufacturing industry using product life cycle management — A case study," 2017 International Conference on Inventive Computing and Informatics (ICICI), Coimbatore, 2017, pp. 76-81. doi: 10.1109/ICICI.2017.8365247 16. S. P. Sundar Singh Sivam et al., (2019) A study of cooling time, copper reduction and effects of alloying elements on the microstructure and mechanical properties of SG iron casting during machining, Australian Journal of Mechanical Engineering, DOI: 10.1080/14484846.2018.1560679 17. S.P. Sundar Singh Sivam et al., (2018) "THICKNESS DISTRIBUTION AND NUMERICAL MODELLING OF CONVENTIONAL SUPERPLASTIC FORMING IN AA2024 ALLOY", International Journal of Modern Manufacturing Technologies, ISSN 2067– 3604,76,85, Vol. X, No. 2 / 2018 Authors: Venkata Ramana N, Chandra Sekhar Kolli, Ravi Kumar T, P Nagesh Paper Title: Hybrid K-Mir Algorithm to Predict Type of Lung Cancer Among Stoicism Abstract: Health care is the maintenance of health via the prevention, diagnosis, and treatment of disease. The disease that persists over a long period of time is known as Chronic Disease. Chronic diseases may create additional activity restrictions. Common chronic conditions include lung disease, heart stroke, cancer, obesity, and diabetes. Chronic diseases usually show no symptoms and hence not diagnosed in advance. Hence it is necessary to predict the patient-specific chronic diseases in early stage for effective prevention. Machine learning being the vital component of Data Analytics that facilitates the medical domain for malignancy predictions. Patients suffering from misdiagnosed and undiagnosed chronic diseases can be easily recognized with the help of these hospital systems. These systems enable the doctors to take precautionary measures and thereby minimizing the chances of a patient being affected. A new hybrid K-MLR framework using K-means and Multiple Linear Regression has been proposed for diagnosing the type of lung cancer among the patients. As most of the real datasets are high-dimensional, this hybrid framework uses K-Means clustering algorithm that eliminates the noise from the image based dataset at the initial stage. Afterward to reduce the dimensionality it detects the features of nodules in 3D lung CT scans and partitions the data to form the clusters. Finally it reads the new patient data with malignant nodules to predict the type of associated cancer based on the intensity of the nodule features extracted from each CT scan report using Multiple Linear Regression Analysis. Clustering prior to classification makes the hybrid approach beneficial.

Keywords: Lung cancer, pulmonary nodules, CT scan, Prediction, K-means, and Regression

References: 1. Rubin, G. D. (2015). Lung nodule and cancer detection in CT screening. Journal of thoracic imaging, 30(2), 130. 52. 2. Wang H, Guo XH, Jia ZW, Li HK, Liang ZG, Li KC, He Q. Multilevel binomial logistic prediction model for malignant pulmonary nodules based on texture features of CT image. Eur J Radiol 2010;74:124-9. 3. Gibbs P, Turnbull LW. Textural analysis of contrast-enhanced MR images of the breast. Magn Reson Med 2003;50:92-8. 283-287 4. Cavouras D, Prassopoulos P, Pantelidis N. Image analysis methods for solitary pulmonary nodule characterization by computed tomography. Eur J Radiol 1992;14:169-72. 5. McNitt-Gray MF, Wyckoff N, Sayre JW, Goldin JG, Aberle DR. The effects of co-occurrence matrix based texture parameters on the classification of solitary pulmonary nodules imaged on computed tomography. Comput Med Imaging Graph 1999;23:339-48. 6. Dujardin M, Gibbs P, Turnbull LW. Texture analysis of 3T high resolution T2 weighted images in ovarian cystadenoma versus borderline tumor. Proc Intl Soc Magn Reson Med 2014;22:2218. Available online: http://cds.ismrm.org/protected/14MPresentations/abstracts/2218.pdf 7. Chae HD, Park CM, Park SJ, Lee SM, Kim KG, Goo JM. Computerized texture analysis of persistent part-solid ground-glass nodules: differentiation of preinvasive lesions from invasive pulmonary adenocarcinomas. Radiology 2014;273:285-93. 8. Ganeshan B, Abaleke S, Young RC, Chatwin CR, Miles KA. Texture analysis of non-small cell lung cancer on unenhanced computed tomography: initial evidence for a relationship with tumour glucose metabolism and stage. Cancer Imaging 2010;10:137-43. 9. Ganeshan B, Goh V, Mandeville HC, Ng QS, Hoskin PJ, Miles KA. Non-small cell lung cancer: histopathologic correlates for texture parameters at CT. Radiology 2013;266:326-36. 10. V.Krishnaiah “Diagnosis of Lung Cancer Prediction System Using Data Mining Classification Techniques” International Journal of Computer Science and Information Technologies, Vol. 4 (1), 39 – 45 www.ijcsit.Com ISSN: 0975-9646, 2013. 11. Zakaria Suliman zubi “Improves Treatment Programs of Lung Cancer using Data Mining Techniques” Journal of Software Engineering and Applications, 7, 69-77, February 2014. 12. K. Balachandran “Classifiers based Approach for PreDiagnosis of Lung Cancer Disease” International Journal of Computer Applications® (IJCA) (0975 – 8887), proceedings on National Conference on Emerging Trends in Information & Communication Technology (NCETICT 2013). 13. Anam Tariq, M. Usman Akram and M. Younus Javed, “Lung Nodule Detection in CT Images using Neuro Fuzzy Classifier”, Fourth International Workshop on Computational Intelligence in Medical Imaging (CIMI), pp:49-53, 2013. 14. Ada R. Wolfsen, William D. Odell, ProACTH: Use for early detection of lung cancer, The American Journal of Medicine, Volume 66, Issue 5, Pages 765–772, May 1979. 15. Dechang Chen “Developing Prognostic Systems of Cancer Patients by Ensemble Clustering” Hindawi publishing corporation, Journal of Biomedicine and Biotechnology Volume, Article Id 632786, 2009. 16. Vesal, S., Ravikumar, N., Ellman, S., & Maier, A. (2018). Comparative Analysis of Unsupervised Algorithms for Breast MRI Lesion Segmentation. In Bildverarbeitung für die Medizin 2018 (pp. 257-262). Springer Vieweg, Berlin, Heidelberg. 17. Gao, X., Chu, C., Li, Y., Lu, P., Wang, W., Liu, W., & Yu, L. (2015). The method and efficacy of support vector machine classifiers based on texture features and multi-resolution histogram from 18F-FDG PET-CT images for the evaluation of mediastinal lymph nodes in patients with lung cancer. European journal of radiology, 84(2), 312-317. 18. Soni Lanka., Madhavi M. R., Abusahmin, B.S., Puvvada, N., Lakshminarayana, V., (2017), "Predictive data mining techniques for management of high dimensional big-data". Journal of Industrial Pollution Control vol 33, pp 1430-1436. 19. Venkata Ramana N , Seravana Kumar P. V. M , Puvvada Nagesh .” Analytic architecture to overcome real time traffic control as an intelligent transportation system using big data”. International Journal of Engineering & Technology, 7 (2.18) (2018) 7-11 20. N. VenkataRamana , Puvvada Nagesh , Seravana Kumar P. V. M , U Vignesh “IoT Based Scientific design to conquer constant movement control as a canny transportation framework utilizing huge information available in Cloud Networks ”. Jour of Adv Research in Dynamical & Control Systems, Vol. 10, 07-Special Issue, 2018 21. Venkata Ramana N., Nagesh P., Lanka S., Karri R.R. (2019), "Big Data Analytics and IoT Gadgets for Tech Savvy Cities". In: Omar S., Haji Suhaili W., Phon-Amnuaisuk S. (eds) Computational Intelligence in Information Systems. CIIS 2018. Advances in Intelligent Systems and Computing, vol 888. pp 131-144, Springer Nature. 22. U. Vignesh, Sivakumar, N. Venkata Ramana “Survey and implementation on classification algorithms with approach on the environment”. International Journal of Engineering & Technology, 7 (2.33) (2018) 438-440 23. Soni Lanka., Madhavi M. R., Abusahmin, B.S., Puvvada, N., Lakshminarayana, V., (2017), "Predictive data mining techniques for management of high dimensional big-data". Journal of Industrial Pollution Control vol 33, pp 1430-1436. Authors: Sanjeev Kumar Gupta, R. C. Mehta, Piyush Singhal Experimental Evaluation and Empirical Formulation of Hydraulic Jump Characteristics in Sloping Paper Title: Prismatic Channel Abstract: Hydraulic jump is frequently used for dissipation excess energy downstream of hydraulic structure. This abundance energy, whenever left unchecked, will have unfavorable impact on the banks and downstream of the channel bed. In this paper hydraulic jump characteristics are experimentally evaluated and empirical correlations for depth ratio and relative height are produced in sloping channel by adopting the impact of both strategy Froude number and approaching Reynolds number and neglecting the frictional effect. The developed empirical correlations are validated using Gandhi (2014) data. The present correlation of jump characteristics gives better agreement with experimental data and can be used for preliminary design.

Keywords: hydraulic jump, Froude number, Reynolds number, energy dissipation etc

References: 1. W. H. Hager, “Energy Dissipators and Hydraulic Jump”, Kluwer Academic Publishers, London, 1992. 2. E.A. Elevatorski, “Hydraulic Energy Dissipator” McGraw Hill, New York, 1959. 3. W. H. Hager, and R. Bremen, “Classical hydraulic jump: Sequent depths”, J. Hydraul. Res., 27(5), 1989, pp. 565–585. 4. B. A.Bakhmeteff, and A. E Matzke, "The Hydraulic Jump in Terms of Dynamic Similarity, Transactions, ASCE, Vol. 101, Paper No. 1935, 1936, pp. 630-680. 5. J. N. Bradley, and A. J Peterka,"The hydraulic design of stilling basins," Journal of Hydr. Div., ASCE, 82(5), 1957, paper 1401. 53. 6. V. T. Chow, “Open-Channel Hydraulics” McGraw Hill, New York, 1959. 7. R. Silvester,. Hydraulic Jump in All Shape of Horizontal Channels, J. Hydraulic Division 90(1), 1964, pp:23–55. 8. N. Rajaratnam and K. Subramanya, “Profile of Hydraulic Jump”, Journal of Hydraulic Division, ASCE, Vol.94, No.3, 1968, pp. 663 – 288-292 673. 9. K. Herbrand, ”The Spatial Hydraulic Jump”, Journal of Hydraulic Research, Vol.11, No.3, 1973, pp. 205 – 218. 10. I. Ohtsu and Y. Yasuda, “Characteristics of Supercritical Flow below Sluice Gate”, Journal of Hydraulic Engineering, ASCE, Vol.120, No.3, 1994, pp. 332 – 346 11. 11. H. Chanson and T. Brattberg, “Experimental Study of the Air-Water Shear Flow in a Hydraulic Jump”, Department of Civil Engineering, the University of Queensland, Brisbane, Australia. International Journal of Multiphase Flow, Vol 26, No 4, 2000, pp.583- 607. 12. Noor Afzal and A. Bushra,” Structure of Turbulent Hydraulic Jump in a Trapezoidal Channel”, Journal of Hydraulic Research, Vol – 40, No – 2, 2002, pp. 168-174. 13. S. Kucukali, H.Chanson, “Turbulence measurements in the bubbly flow region of hydraulic jumps”, Experimental Thermal and Fluid Science Vol. 33, 2008, pp. 41–53. 14. M. Naseri and F. Othman, “Determination of the length of hydraulic jumps using artificial neural networks”, Advances in Engineering Software, Vol. 48, 2012, pp 27–31. 15. Gupta SK, Mehta RC, Dwivedi VK. Modeling of relative length and relative energy loss of free hydraulic jump in horizontal prismatic channel. Procedia Engineering. 2013 Jan 1; 51:529-37. 16. Gupta SK, Mehta RC, Dwivedi VK. Modeling of relative length and relative energy loss of hydraulic jump in sloping prismatic channels for environmental hazards control. 2nd Intern. InConf. on Climate Change & Sustainable Management of Natural Resources, CP–77 2010 Dec (pp. 05-07). 17. N. Y. Saad and E. M. Fattouh, Hydraulic characteristics of flow over weir with circular openings, Ain Shams Engineering Journal, Volume 8, Issue 4, 2016, pp 515-522. 18. S. Gandhi, “Characteristics of Hydraulic Jump”, International Journal of Mathematical, Computational, Physical, Electrical and Computer Engineering Vol:8, No:4, 2014, pp. 693-697. Authors: J Sirisha Devi, Siva Prasad Nandyala Paper Title: Electroencephalography and Physiological Signals for Emotion Analysis Abstract: A novel method for Electroencephalography (EEG) based emotion analysis using Gray Level Co- occurrence Matrix1 (GLCM) features contrast, correlation, energy, and homogeneity has been discussed with peripheral physiological signals. Emotions are classified using Linear Discriminant Analysis (LDA) and obtained an accuracy of 93.8. The proposed novel method discussed the effect of distances, and direction on GLCM features 54. for different emotions. This paper concluded that GLCM features are an effective measure to discriminate the emotions and give significant knowledge for each emotion. The proposed novel methodology can be used as a tool 293-297 for emotion analysis and it can also be useful for observing brain lobe variation globally.

Keywords: Electroencephalography, Gray Level Co-occurrence Matrix1, physiological signals, Linear Discriminant Analysis

References: 1. Moataz El Ayadi, Mohamed S Kamel, and Fakhri Karray. Survey on speech emotion recognition: Features, classification schemes, and databases. Pattern Recognition, 44(3):572–587, 2011. 2. Ira Cohen, Ashutosh Garg, Thomas S Huang, et al. “Emotion recognition from facial expressions using multilevel HMM”, in Neural information processing systems, volume 2. Citeseer, 2000. 3. Yedatore V Venkatesh, Ashraf A Kassim, Jun Yuan, and Tan Dat Nguyen on, “The simultaneous recognition of identity and expression from bu-3dfe datasets” Pattern recognition letters, 33(13):1785–1793, 2012 4. Bert Arnrich, Cornelia Setz, Roberto La Marca, Gerhard Tr¨oster, and Ulrike Ehlert. What does your chair know about your stress level? IEEE Transactions on Information Technology in Biomedicine, 14(2):207–214, 2010. 5. Wanhui Wen, Guangyuan Liu, Nanpu Cheng, Jie Wei, Pengchao Shangguan, and Wenjin Huang. Emotion recognition based on multi- variant correlation of physiological signals. IEEE Transactions on Affective Computing, 5(2):126–140, 2014. 6. M Tuceryan and AK Jain. Texture analysis. the handbook of pattern recognition and computer vision, river edge, 1998. 7. Sander Koelstra, Christian Muhl, Mohammad Soleymani, Jong-Seok Lee, Ashkan Yazdani, Touradj Ebrahimi, Thierry Pun, Anton Nijholt, and Ioannis Patras. Deap: A database for emotion analysis; using physiological signals. IEEE Transactions on Affective Computing, 3(1):18–31, 2012. 8. James A Russell. A circumplex model of affect. Journal of Personality and Social Psychology, 39(6):1161–1178, 1980. 9. Thea Andersen, Gintare Anisimovaite, Anders Christiansen, Mohamed Hussein, Carol Lund, Thomas Nielsen, Eoin Rafferty, Niels C Nilsson, Rolf Nordahl, and Stefania Serafin. A preliminary study of users’ experiences of meditation in virtual reality. In Virtual Reality (VR), 2017 IEEE, pages 343–344. IEEE, 2017 10. Zeynab Mohammadi, Javad Frounchi, and Mahmood Amiri. Wavelet-based emotion recognition system using EEG signal. Neural Computing and Applications, 28(8):1985–1990, 2017. 11. N Murali Krishna, J Sirisha Devi, Y Srinivas. A Novel Approach for Effective Emotion Recognition Using Double Truncated Gaussian Mixture Model and EEG.I.J. Intelligent Systems and Applications, 2017 12. N Murali Krishna, J Sirisha Devi, N Siva Prasad. Emotion Recognition Using Skew Gaussian Mixture Model for Brain–Computer Interaction. Soft Computing in Data Analytics, Advances in Intelligent Systems and Computing, 2019 Authors: P.Sakthi Shunmuga Sundaram, N.Hari Basker, L.Natrayan Paper Title: Smart Clothes with Bio-Sensors for ECG Monitoring Abstract: Aging society leads to more demands on health care system. The study shows that cardiovascular diseases are the most common and threatening diseases to the elderly. Moreover, more and more elderly live alone recently. Therefore, a total solution for elderly home care leads the way. In this study, we develop smart clothes to record three lead electrocardiography (ECG). Our system consists of (1) the conductive fiber clothes with four electrodes to acquire physiological signals, (2) a gateway to digitize, process and upload raw data to the server, and (3) the service server to analyze data and make a health profile. The system had been applied to the elderly community to evaluate performance. The experiment results show the average accuracy of ECG data is 86.82%. Thirty-five volunteers (age > 65, 15 male and 20 female) feel the smart clothes comfortable and easy to use than the traditional medical device.

Keywords: Smart Wearable Device; Smart Clothes; Long-Term Care; Electrocardiography; Bio-Sensor

References: 1. Anonymous, “Trends in aging–united states and worldwide,” MMWR Morb Mortal Wkly, vol. 52, no. 6, pp. 101–104, 2003. 55. 2. L. Natrayan and M. Senthil Kumar. Study on Squeeze Casting of Aluminum Matrix Composites-A Review. Advanced Manufacturing and Materials Science. Springer, Cham, 2018. 75-83. (https://doi.org/10.1007/978-3-319-76276-0_8.) 3. M. Senthil Kumar et. al, Experimental investigations on mechanical and microstructural properties of Al2O3/SiC reinforced hybrid 298-301 metal matrix composite, IOP Conference Series: Materials Science and Engineering, Volume 402, Number 1, PP 012123. (https://doi.org/10.1088/1757-899X/402/1/012123) 4. C. C. Lin, M. J. Chiu, C. C. Hsiao, R. G. Lee, and Y. S. Tsai, “A wireless healthcare service system for elderly with Dementia,” IEEE Trans. Inf. Technol. Biomed., vol. 10, no. 4, pp. 696–704, 2006. 5. L.Natrayan et al. Optimization of squeeze cast process parameters on mechanical properties of Al2O3/SiC reinforced hybrid metal matrix composites using taguchi technique. Mater. Res. Express; 5: 066516. (DOI: 10.1088/2053-1591/aac873,2018) 6. S.Yogeshwaran, R.Prabhu, Natrayan.L, Mechanical Properties Of Leaf Ashes Reinforced Aluminum Alloy Metal Matrix Composites, International Journal of Applied Engineering Research ISSN 0973-4562 Volume 10, Number 13, 2015. 7. S. Sneha and U. Varshney, “A wireless ECG monitoring system for pervasive healthcare,” Int. J. Electron. Healthcare, vol. 3, no. 1, pp. 32–50, 2007. 8. J. Muhlsteff, O. Such, R. Schmidt, M. Perkuhn, H. Reiter, J. Lauter, J. Thijs, G. Musch, and M. Harris, “Wearable approach for continuous ECG–and activity patient-monitoring,” in the Proceedings of the 26th Annu. Int. Conf.EMBC. 2004, pp. 2184–2187. 9. L.Natrayan et al. An experimental investigation on mechanical behaviour of SiCp reinforced Al 6061 MMC using squeeze casting process. Inter J Mech Prod Engi Res Develop., 7(6):663–668, 2017. 10. T. Pawar, N. S. Anantakrishnan, S. Chaudhuri and S. P. Duttagupta, “Impact analysis of body movement in ambulatory ECG,” in the Proceedings of the Engineering in Medicine and Biology Society. 2007, pp. 5453-5456. 11. M. S. Santhosh, R. Sasikumar, L. Natrayan, M. Senthil Kumar, V. Elango and M. Vanmathi. (2018). Investigation of mechanical and electrical properties of kevlar/E-glass and basalt/E-glass reinforced hybrid Composites. . Inter J Mech Prod Engi Res Develop., 8(3): 591-598. Authors: D. Lavanya, N.Thirupathi Rao, Debnath Bhattacharyya, Tai-Hoon Kim Paper Title: Generalized Detection of Colloid Cyst in Brain using MRI Scan/CT Scan Abstract: Brain is one of the most important organs in the human body. The working of this organ decides the human being work and his life to success. In order to lead the good life, one should have the brain and its related parts under good condition, i.e., not affected with any diseases or any serious problems. The presence of cyst in the 56. brain is one of the important issues to be considered and identification of such cyst in good time is very important for the health of a human being. If the cyst is not identified in appropriate times, the brain will be suffered with 302-306 serious issues and it may lead to the loss of the human being. Hence, in this article a new approach is taken to consideration for identification of the cyst in the brain through MRI/CT scan images. In the current work, a new approach of matrix method with the combination of monochrome images was considered for identification of the cyst presence with MRI/CT scan images. A new algorithm was also proposed to find the presence of cyst in the brain with more accurate performance. The performance of the current model was verified with two sets of scan images and the results are displayed in the result section.

Keywords: Neuroepithelial Cyst, Magnetic Resonance Images (MRI), Computed Tomography (CT), Fixed Threshold Method.

References: 1. Q. Javed and A. Dutta, “Third Ventricular Colloid Cyst and Organic Hypomania”, Progress in Neurology and Psychiatry, (2014), pp.18. 2. http://www.medicalnewstoday.com/articles/181727.php[Accessed 17.06.2018]. 3. http://www.abta.org/secure/resource-one-sheets/cysts.pdf[Accessed 18.06.2018]. 4. K. Sheikh, V. Sutar and S. Thigale, “Clustering based Segmentation Approach to Detect Brain Tumor from MRI Scan”, International Journal of Computer Applications (0975 – 8887), vol. 118, no. 8, (2015), pp.36-39. 5. E. E. Mohd, A. Muhd, M. Mohd, H. Z. Z. Htike and S. L. Win, “Brain Tumor Convergence and Services (IJITCS), vol. 4, no. 1, (2014), pp.1-11. 6. V. D. Dharmale and P. A. Tijare, “Segmentation and Canny Edge Method in MRI Brain Cyst Detection”, International Journal of Advanced Computer Research, vol. 3, no. 4, (2013), pp.289-293. 7. L. P. Bhaiya, S. Goswami and V. Pali, “Classification of MRI Brain Images using NeuroFuzzy Model”, International Journal of Engineering Inventions, Vol. 1, no. 4, (2012), pp.27-31. 8. M. Tariq, A. Khawajah and M. Hussain, “Image Processing with the specific focus on early tumor detection”, International Journal of Machine Learning and Computing, vol. 3, no. 5, (2013), pp. 404- 407. 9. C. Mamourian, L. D. Cromwell and R. E. Harbaugh, “Colloid Cyst of third Ventricle: Sometimes More Conspicuous on CT than MR”, AJNR Am J Neuroradiol, (1998), pp.875-878.[Accessed 02.07.2018]. 10. https://www.researchgate.net/publication/286816381_A_Comparative_Analysis_on_Edge 11. detection_ofColloid_Cyst_A_Medical_Imaging_Approach [Accessed 03.07.2018]. 12. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1550234/[Accessed 03.07.2018]. 13. https://en.wikipedia.org/wiki/Image_noise#Low_and_high-ISO_noise_examples [Accessed 05.07.2018]. 14. http://mstrzel.eletel.p.lodz.pl/mstrzel/pattern_rec/filtering.pdf[Accessed 06.07.2018]. 15. http://www.mecs-press.org/ijisa/ijisa-v5-n11/IJISA-V5-N11-3.pdf[Accessed 07.07.2018]. 16. V. Kshirsagar and J. Panchal, “Segmentation of Brain Tumor and Its Area Calculation”, International Journal of Advanced Research in Computer Science and Software Engineering, vol. 4, no. 5, (2014), pp. 528-529. 17. https://en.wikipedia.org/wiki/Cyst[Accessed 16.07.2018]. 18. Debapriya Hazra, Debnath Bhattacharyya, Hye-Jin Kim, “Detection of Colloid Cyst in Brain through Image Processing Techniques”, International Journal of Multimedia and Ubiquitous Engineering, Vol.11, No.9 (2016), Pp.343-354. Authors: Nagi Reddy. B, A. Pandian, O. Chandra Sekhar, M. Ramamoorty Paper Title: Performance and Dynamic Analysis of Single Switch AC-DC Buck-Boost Buck Converter Abstract: Dynamic analysis of proposed single switch ac-dc buck-boost buck converter is presented in this paper. The proposed converter is an integrated converter contains two inductors, one is at input side and other one is at output side. To achieve unity power factor at input terminals, the input inductor is designed for discontinuous mode (DCM). This condition will eliminate extra control technique for power factor correction (PFC). The output side inductor is operated in DCM to reduce the bus capacitor voltage, thereby reducing the capacitance size. A PI controller is designed to regulate the pulses for the converter. The proposed converter is designed in MATLAB software for 60V output voltage. The analysis has been done for three different cases (variable frequency, variable input and variable load) to verify the converter performance.

Keywords: Single switch, ac-dc converter, buck-boost, power factor correction (PFC), dynamic analysis.

References: 57. 1. M. Brkovic and S. Cuk, “Novel single stage ac-to-dc converters with magnetic amplifiers and high power factor,” in Proc. IEEE Appl. Power Electron. Conf., 1995, pp. 447–453. 2. Nagi Reddy. B, O. Chandra Sekhar, M. Ramamoorty, “Analysis and implementation of single-stage buck-boost buck converter for 307-313 battery charging applications”; Journal of Advanced Research in Dynamical and Control Systems (JARDCS), Vol. 10, No. 4, April 2018, pp 462-475. 3. M. T. Madigan, R.W. Erickson, and E. H. Ismail, “Integrated high quality rectifier-regulators,” IEEE Trans. Ind. Electron., vol. 46, no. 4, pp. 749–758, Aug. 1999. 4. R. Redl, L. Balogh, and N. O. Sokal, “A new family of single stage isolated power factor correctors with fast regulation of the output voltage,” in Proc. IEEE Power Electron. Spec. Conf., 1994, pp. 1137–1144. 5. M. M. Jovanovic, D. M. Tsang, and F. C. Lee, “Reduction of voltage stress in integrated high quality rectifier regulators by variable frequency control,” in Proc. IEEE Appl. Power Electron. Conf., 1994, pp. 569–575. 6. M. J. Willers, M. G. Egan, J. M. D. Murphy, and S. Daly, “A BIFRED converter with a wide load range,” in Proc. IEEE Int. Conf. IECON, 1994, 7. pp. 226–231. 8. Nagi Reddy. B, A. Pandian, O. Chandra Sekhar, M. Rammoorty, “Design of Non-isolated integrated type AC-DC converter with extended voltage gain and high power factor for Class-C&D applications”. International Journal of Recent Technology and Engineering (IJRTE), Vol. 7, No. 5, Jan 2019, pp 230-236. 9. Nagi Reddy. B, O. Chandra Sekhar, M. Ramamoorty, “Implementation of zero-current switch turn-ON based buck-boost buck type rectifier for low power applications”. International Journal of electronics – Taylor & Francis publication (Accepted for publication). Authors: Richa Gupta, Radhika Goel Paper Title: A Necessary and Sufficient Condition for the Existence of Asymmetrical Reversible VLCs Abstract: Affix-free codes are widely used in multimedia communications because of its error tolerance 58. capbility. Reversible Variable Length Code (RVLC) is a type of affix-free code. In literature, there are many construction algorithms available for RVLCs. But unlike Variable Length Codes (VLCs), RVLCs lack in the area 314-317 of its mathematical development in the form of lower bound or upper bound on average codeword length, bounds on existence, and related Theorems. Only few mathematicians have done some work on this. In 2014, Richa and Radhika have proposed and discussed the necessary and sufficient condition on the number of codewords for a particular (bit length vector) required for the existence of symmetrical RVLCs. This paper is an extension of the earlier published paper on the similar ground, but for asymmetrical RVLCs. This paper derives and discusses necessary and sufficient condition, on bit length vector (the number of codewords for a particular length), required for the existence of asymmetrical RVLCs over the given D-ary code alphabet.

Keywords: Affix-free codes, Symmetrical RVLC, asymmetrical RVLCs, mathematical bound on RVLC, bit length vector, Kraft inequality.

References: 1. D. Huffman, “A method for the Construction of Minimum Redundancy Codes”, Proceedings of IRE, 40, 1962, pp. 1098-1101. 2. ISO/IEC JTC1/SC29/WG11/N3908, “MPEG-4 video verification model,” Vers. 18.0, Jan. 2001. 3. ITU-T Recommendation H.263, “Video coding for low bit rate communication,” Annex D, Feb. 1998. 4. H. Wang, S. N. Koh, and W.W. Chang, “Application of reversible variable-length codes in robust speech coding,” IEEE Proc. Commun., vol. 152, no. 3, June 2005, pp. 272-276. 5. Y. Takishima, M. Wada, and H. Murakami, “Reversible variable length codes,” IEEE Trans. Commun., vol. 43, Feb.-Apr. 1995, pp. 158–162. 6. C. W. Tsai and J. L. Wu, "Modified symmetrical reversible variable-length code and its theoretical bounds," IEEE Trans. Inform. Theory, vol. 47, Sept. 2001, pp. 2543-2548. 7. W. H. Jeong and Y. S. Ho, “Design of Symmetrical Reversible Variable Length Codes from the Huffman Code,” Picture Coding Symposium, 2003, pp. 135-138. 8. R.Goel and R. Gupta. "Redesigning of the Construction of Symmetrical RVLCS Based On Graph Model.", International Journal of Information & Computation Technology, vol. 4, no. 11, 2014, pp. 1063-1068. 9. H. J. Yan, C. Y. Lin, L. Zhong , “On constructing symmetrical reversible variable-length codes independent of the Huffman code, ’’The National Key Laboratory on Integrated Service Networks, Xidian University, Xi’an 710071, China, accepted Feb. 22, 2006. 10. S. Golomb, “Run Length Encodings,” IEEE Transactions on Information Theory, vol. 12, no. 3, 1966, pp. 399-401. 11. Abedini, S. P. Khatri, and S. A. Savari, “A SAT-based scheme to determine optimal fix-free codes,” Proc. of the 2010 IEEE Data Compression Conference, Snowbird, Utah, March 2010, pp. 169-178. 12. S. M. Hossein, T. Yazdi and S. A. Savari, “On the Relationships among Optimal Symmetric Fix-Free Codes,” IEEE Data Compression Conference, 2013, pp. 391-400. 13. A. Savari, “On optimal reversible-variable-length codes,” Proc. Information Theory and Applications Workshop, La Jolla, CA, February 10, 2009, pp. 311-317. 14. K. Sayood, Introduction to data compression, New Delhi: Elsevier, 2011. 15. L.G. Kraft, A device for quantizing, grouping and coding amplitude modulated pulses, Master’s thesis, Dept. of Electrical Engineering, M.I.T., Cambridge, Mass., 1949. 16. Goel, R., and Gupta, R. Necessary and sufficient condition for the existence of symmetrical Reversible Variable Length Codes, based on Kraft's inequality. In IEEE Conference publication Recent Advances and Innovations in Engineering (ICRAIE), May, 2014, pp. 1-3. Authors: R A Veer, L C Siddanna Gowd Paper Title: A Novel Classification Approach for MIMO-OFDM Abstract: The expanding unpredictability of designing cellular networks recommends that machine learning (ML) can successfully enhance 5G advances. Machine learning has proven successful a performance that scales with the measure of accessible data. The absence of vast datasets restrains the twist of machine learning applications in remote interchanges. The transmission state is thought to be a component of the highlights of a channel situation like the obstruction and noise, the relative motion between the transmitter and the receiver and this capacity is acquired by the machine learning strategy. The preparation dataset is produced by recreations on the channel condition. The Jrip, J48 and Naïve Bayes are the three algorithms implemented in this research work. This research work test if machine learning methods can predict the transmission states with a high accuracy compared to conventional approaches.

Keywords: Machine Learning, Jrip, MIMO, J48, OFDM, CRC and Naïve Bayes.

59. References: 1. Omri and R. Bouallegue, New Transmission Scheme for MIMO-OFDM System, International Journal of Next-Generation Networks 318-320 (IJNGN) Vol.3, No.1, March 2011. 2. Sumitra N. Motade,; Anju V. Kulkarni. Channel Estimation and Data Detection Using Machine Learning for MIMO 5G Communication Systems in Fading Channel, Technologies, 2018, 6, 72. 3. https://pdfs.semanticscholar.org/d091/af5c2f1e693b5a66ccb76f93956c3199f152.pdf 4. Seppo Hämäläinen, Peter Slanina, Magnus Hartman, Antti Lappeteläinen, Harri Holma, and Oscar Salonaho. A novel interface between link and system level simulations. In Proceedings of the ACTS Mobile Telecommunications Summit, volume 97, pages599–604, 1997. 5. Joseph Mitola. Cognitive radio—an integrated agent architecture for software definedradio. 2000. 6. Charles Clancy, Joe Hecker, Erich Stuntebeck, and Tim O’Shea. Applications of machine learning to cognitive radio networks. IEEE Wireless Communications, 14(4), 2007. 7. Tobias Gruber, Sebastian Cammerer, Jakob Hoydis, and Stephan ten Brink. On deep Learning-based channel decoding. arXiv preprint arXiv:1701.07738, 2017. 8. Emre Telatar. Capacity of multi-antenna gaussian channels. European transactions on telecommunications, 10(6):585–595, 1999. 9. Gerard J Foschini. Layered space-time architecture for wireless communication in a fading environment when using multi-element antennas. Bell labs technical journal, 1 (2):41–59, 1996. 10. Vahid Tarokh, Hamid Jafarkhani, and A Robert Calderbank. Space-time block codes from orthogonal designs. IEEE Transactions on information theory, 45(5):1456–1467, 1999. Authors: Ramjeevan Singh Thakur Paper Title: Associative Analysis among Attribute of ILPD Medical Datasets Using ARM 60. Abstract: Early detection of liver disease plays a major role in efficient diagnosis the disease. It significantly increases the chance of effective treatment. The liver is one of the largest organs in the human body. It plays an 321-328 important role in digestion, as detoxifying chemicals in the digestion process. A dreadful fact of liver disease is that, the liver maintains a normal functionality even after partially damage. The major challenge in liver disease is to find the hidden patterns of liver disorder. The proposed approach analysis the patterns on the selected features using association rule mining (ARM) technique. The performance of the proposed approach is tested on the well- renowned ILPD dataset from the UCI repository. ILPD dataset consists of different clinical examination parameter like total bilirubin, direct bilirubin, SGPT, SGOT, alkphos, total protein, albumin etc. The proposed approach selected the essential features from ILPD and ARM is applied to find the association among attributes to detect pattern.

Keywords: Indian Liver Patient Datasets, Association rule mining, Liver Disorder, Associative Analysis.

References: 1. Ben-Cohen, E. Klang, A. Kerpel, E. Konen, M. M. Amitai, and H. Greenspan, "Fully convolutional network and sparsity-based dictionary learning for liver lesion detection in CT examinations," Neurocomputing, vol. 275, pp. 1585-1594, 2018. 2. R.-H. Lin and C.-L. Chuang, "A hybrid diagnosis model for determining the types of the liver disease," Computers in Biology and Medicine, vol. 40, no. 7, pp. 665-670, 2010. 3. M. Frid-Adar, I. Diamant, E. Klang, M. Amitai, J. Goldberger, and H. Greenspan, "GAN-based Synthetic Medical Image Augmentation for increased CNN Performance in Liver Lesion Classification," arXiv preprint arXiv:1803.01229, 2018. 4. Z. Janikow, "Fuzzy decision trees: issues and methods," IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), vol. 28, no. 1, pp. 1-14, 1998. 5. T. R. Baitharu and S. K. Pani, "Analysis of Data Mining Techniques for Healthcare Decision Support System Using Liver Disorder 6. Dataset," Procedia Computer Science, vol. 85, pp. 862-870, 2016. 7. Y. Kumar and G. Sahoo, "Prediction of different types of liver diseases using rule based classification model," Technology and Health Care, vol. 21, no. 5, pp. 417-432, 2013. 8. S. Dhamodharan, "Liver disease prediction using bayesian classification," in 4th National Conference on Advanced Computing, Applications & Technologies, 2014, pp. 1-3. 9. N. Nahar and F. Ara, "Liver disease prediction by using different Decision Tree techniques," International Journal of Data Mining & Knowledge Management Process (IJDKP), vol. 8, no. 2, 2018. 10. P. Rajeswari and G. S. Reena, "Analysis of liver disorder using data mining algorithm," Global journal of computer science and technology, vol. 10, no. 14, pp. 48-52, 2010. 11. Pathan, D. Mhaske, S. Jadhav, R. Bhondave, and K. Rajeswari, "Comparative Study of Different Classification Algorithms on ILPD Dataset to Predict Liver Disorder.", IJRASET, vol. 06, pp. 388-394, 2018P. 12. Thangaraju and R. Mehala, "Performance Analysis of PSO-KStar Classifier over Liver Diseases," International Journal of Advanced Research in Computer Engineering, vol. 04, no. 07, pp. 3132-3137, 2015. 13. R. Agrawal and R. Srikant, "Fast algorithms for mining association rules in large databases, In Proc. of the 20th VLDB Conference, 1994, pp. 487-499. 14. R. Srikant and R. Agrawal, "Mining generalized association rules," Future generation computer systems 13, no. 2-3, pp.161-180, 1997. 15. R. Srikant, "Fast algorithms for mining association rules and sequential patterns," PhD diss., University of Wisconsin, Madison, 1996. 16. R. V. Priya, A. Vadivel, and R. Thakur, "Frequent pattern mining using modified CP-tree for knowledge discovery," in International Conference on Advanced Data Mining and Applications, 2010, pp. 254-261: Springer. 17. Sabnis, N. Khare, R. Thakur, and K. Pardasani, "Karnaugh Map Model for Mining Association Relationships in Web Content Data: Hypertext," Data Mining and Knowledge Engineering, vol. 4, no. 11, pp. 579-587, 2012. 18. V. Tiwari and R. S. Thakur, "P²MS: a phase-wise pattern management system for pattern warehouse," International Journal of Data Mining, Modelling and Management, vol. 7, no. 4, pp. 331-350, 2015. 19. V. Tiwari and R. S. Thakur, "Towards important issues of pattern retrieval: pattern warehouse," International Journal of Data Science, vol. 2, no. 1, pp. 1-14, 2017. 20. V. Tiwari and R. Thakur, "A Level Wise Tree Based Approach for Ontology-Driven Association Rules Mining," Data Mining and Knowledge Engineering, vol. 4, no. 5, pp. 252-259, 2012. 21. S. Rajput, R. S. Thakur, and G. S. Thakur, "An Integrated Approach and Framework for Document Clustering Using Graph Based Association Rule Mining," in Proceedings of the Second International Conference on Soft Computing for Problem Solving (SocProS 2012), December 28-30, 2012, pp. 1421-1437: Springer. 22. J. Han, J. Pei, and M. Kamber, Data mining: concepts and techniques. Elsevier, 2011. 23. ILPD Dataset: https://archive.ics.uci.edu/ml/datasets/ILPD+(Indian+Liver+Patient+Dataset). Authors: Kulkarni Rashmi Manik, S Arulselvi, B Karthik Paper Title: Designing Network Interface Component for Peripheral IP cores in Networks-on-chip Abstract: The Network-Interface-Componant (NIC) is required for IP cores for interconnecting IPs to Routers in NoC. In implementation of NoC, Interface Component is very crucial for adapting IPs in NoC. NIC as a software component occupies processor’s considerable execution time. Processor can be relieved from this overload by introducing separate hardware as NIC. A hierarchical topology for NoC is considered in this research article. In hierarchical topology, each router can connect to eight nodes (IP) of same hierarchy and to a router in next hierarchy. Each node is connected to router port with NIC. The fixed address based routing is implemented in the NOC. The network packet switching based transactions among various nodes is assumed. The implementation of NIC design with options for different IPs (considering existing bus based interfaces) is attempted in this work.

61. Keywords: NoC, NIC, IPs, PE, ASIC and NS/CS. 329-336 References: 1. Brahim Attia, Abdelkrim Zitouni, Kholdoun Torki and Rached Tourki “A Low Latency and Power ASIC Design of ModularNetwork Interfaces for Network on Chip”, IJCSES International Journal of Computer Sciences and Engineering Systems, Vol. 5, No. 4, October 2011. 2. Masoud Daneshtalab, Masoumeh Ebrahimi, Juha Plosila, Hannu Tenhunen, “CARS: Congestion-Aware Request Scheduler forNetwork Interfaces in NoC-based Manycore Systems”. 3. Wang Jian, Yang Zhijia,“Design of network adapter compatible OCP for high-throughput NOC”, Applied Mechanics and Materials Vols. 313-314 pp 1341-1346, Trans Tech Publications, Switzerland, 25 March 2013. 4. Azad Fakhari, “Designing Customizable Network-on-Chip withsupport for Embedded Private Memory for Multi-Processor System-on- Chips”, Thesesand Dissertations,University of Arkansas, Fayetteville, May 2014 5. Masoumeh Ebrahimi, Masoud Daneshtalab, N P Sreejesh, Pasi Liljeberg, Hannu Tenhunen, “Efficient Network Interface Architecture forNetwork-on-Chips”, Department of Information Technology, University of Turku, Turku, Finland. 6. Leandro Fiorin, Mariagiovanna Sami, “Fault-Tolerant NetworkInterfaces for Networks-on-Chip”, IEEE Transactionson Dependableand Secure Computing, VOL. 11, NO. 1, Jan/Feb 2014. 7. Tung Nguyeny, Duy-Hieu Buiy, Hai-Phong Phany, Trong-Trinh Dangand Xuan-Tu Trany, “High-Performance Adaption of ARM Processorsinto Network-on-Chip Architectures”, ySIS Laboratory, VNU University of Engineering and Technology,Cau Giay, Hanoi, Vietnam. 8. Rachid Dafali, Jean-Philippe Diguetand Jean-Charles Creput “Self-Adaptive Network-on-Chip Interface”, Submittedto IEEE Embedded Systemsletters, Vol. X, No. X, Month Year. 9. Ahmed H.M. Soliman, E.M. Saad, M. El-Bablyand Hesham M. A. M. Keshk, “Designing a WISHBONE Protocol Network Adapter for an Asynchronous Network-on-Chip”,IJCS (International Journal of Computer Science), Issues, Vol. 8, Issue 4, No 2, University of Helwan, Cairo, Egypt 11795, Helwan, July 2011. 10. Vijaykumar R Urkude1, Dr. P. Sudhakara Rao, “Low Power 2-D Mesh Network-on-Chip Router using Clock Gating Techniques”,IOSR Journal of VLSI and Signal Processing (IOSR-JVSP) Volume 6, Issue 6, Ver. I, PP 85-91,Nov -Dec 2016. 11. Alexandros Daglis, Stanko Novakovi´c, Edouard Bugnion, Babak Falsafi, Boris Groty,”Manycore Network Interfaces for In-Memory Rack-Scale Computing” In Proceedings of the 42nd International Symposium on Computer Architecture (ISCA 2015), EcoCloud, EPFL yUniversity of Edinburgh 12. Marcelo Ruaro, Felipe B. Lazzarotto, César A. Marcon, Fernando G. Moraes “DMNI: A Specialized Network Interface for NoCbasedMPSoCs” PUCRS University, Computer Science Department, Porto Alegre, Brazil 13. Ruxandra Pop and Shashi Kumar, “A Survey of Techniques forMapping andScheduling Applications toNetwork on Chip Systems”, ISSN 1404 – 0018, Research Report 04:4, Embedded Systems Group, Department of Electronics and Computer Engineering, School of Engineering, Jönköping University, Jönköping, SWEDEN 14. Brahim Attia,Abdelkrim Zitouni and Rached Tourki, “Design andimplementation of network interfacecompatible OCP For packet based NOC”, International Conference on Design & Technology of Integrated Systems in Nanoscale Era, Faculty of Sciences of Monastir, Laboratory of Electronic and Micro-Electronic (LAB-IT06), Monastir, 5019, Tunisia, 2010. 15. Sujay Gejji & Tripti Kulkarni, “DesignofreconfigurableandmodularNOCinterfacewithadvancednetworking functionalities”,Department of E&C, PESIT,IRD India Bangalore, Karnataka. 16. Jens Spars, “Design of Networks-on-Chip for Real-TimeMulti-ProcessorSystems-on-Chip”, Department of Informatics and Mathematical ModellingTechnical University of Denmark. 17. Nauman Jalil, Adnan Qureshi, Furqan Khan, and Sohaib Ayyaz Qazi, “Routing Algorithms, Process Model for Quality ofServices (QoS) and Architectures forTwo-Dimensional4 x 4 Mesh Topology Network-on-Chip”, International Journal of Computer Theory and Engineering, Vol. 4, No. 1, February 2012. 18. Ryuya Okada, Prof. Abderazek Ben Abdallah “Design of Core Network Interface for Distributed Routing in OASISNoC”, ASL - Parallel Architecture Group, 2012. 19. Calin Ciordas, Kees Goossens, Twan Basten, Andrei Radulescu, Andre Boon, “Transaction Monitoring in Networks on Chip:The On- Chip Run-Time Perspective” 20. Design Methodology for Electronic Systems, Eindhoven University of Technology, Eindhoven, Embedded Systems Architectures on Silicon, Philips ResearchLaboratories, Prof. Holstlaan 4, NL-5656 AA Eindhoven. 21. Chenxin Zhang & Xiaodong Liu, “A presentation on Network-on-Chip(NoC)” 22. P. Gratz, C. Kim, R. McDonald, S.-W. Keckler, and D. Burger, “Implementation and evaluation of on-chip network architectures”, Proceedings of the International Conference on Computer Design, pages 477–484, October 2006. 23. Glovanni De Michel, Luca Benini, “Networks on chips”, Morgan Koufmann Publications 24. Masoud Oveis-Gharan, Gul N. Khan, “Statistically adaptive multi FIFO buffer architecture for Netwrok on chip, Microprocessor and Microsystems 39(2015). 25. Jason Cong, Michael Gill, Yuchen Hao, Glenn Reinman, Bo Yuan, “On chip interconnection network for Accelerator-Rich Architectures”. DAC’15. 26. Wan-Ting Su, Jih-Sheng Shen, Pao-Ann Hsiung, “Network on chip router design with buffer stealing”, 2011 IEEE. 27. Sudeep Pasricha, Nikil Dutt, Fadi J. Kurdahi, “Dynamically Re-configurable On-Chip Communication Architectures for Multi Use-Case Chip Multiprocessor Applications”, 2009 IEEE. 28. Zhonghai Lu, Ming Liu, Axel Jantsch, “Layered Switching for Network On Chips”, DAC 2007. Authors: G Jahnavi Chowdary , S. Palani Kumar Advance Control Scheme for Correction of Power Factor and Voltage Stability by Using Electric Paper Title: Spring Abstract: A novel smart technology has been introduced in the demand side management which can be used in real time i.e., electric spring. This electric spring provides voltage, power stability and found to be useful in maintaining the voltage supply in spite of the fluctuations caused by the intermediate nature of renewable energy sources and implemented in conjunction with non-critical loads and critical loads like electric heaters, refrigerators, laptops, building security systems. To get better power factor correction, voltage support, power balance in loads, using the properties of PLL through single phase d-q transformation scheme is developed. In order to improve power-factor and voltage stability, fuzzy control scheme is proposed in this paper. By using Fuzzification control scheme, power factor at loads, voltage stability of the system can be achieved. The integration of electric spring in sequence to non-critical loads forms a smart load. Thereby alteration of active power and reactive power is done automatically near non-critical loads. Simulation results are carried out for ES based on PLL control by using fuzzy logic controller and their results are analyzed. 62. Keywords: Fuzzification, Electric Spring, Critical loads, Non-critical loads, Voltage stability, Renewable energy sources, Power quality. 337-342

References: 1. M. Parvania and M. Fotuhi - Firuzabad, “Demand response scheduling by stochastic SCUC,” IEEE Trans. Smart Grid, vol.1,no.1, pp.89- 98,2010. 2. “Electric springs- A New Smart Grid Technology,” Shu Yuen (Ron) Hui, Fellow, IEEE, Chi Kwan Lee, Member, IEEE, and Felix F. Wu, Fellow, IEEE. 3. C. K. Lee, K. L. Cheng, and W. M. Ng, “Load characterization of electric spring,” in Proc. 2013 IEEE Energy Convers. Congr. Expo., Sep. 2013,pp. 4665–4670. 4. C. K. Lee, S. C. Tan, F. F. Wu, S. Y. R. Hui, and B. Chaudhuri, “Use of Hooke’s law for stabilizing future smart grid—The electric spring concept,” in Proc. IEEE Energy Convers. Congr. Expo., Sep. 2013, pp. 5253–5257. 5. C. K. Lee, B. Chaudhuri, and S. Y. Hui, “Hardware and control implementation of electric springs for stabilizing future smart grid with intermittent renewable energy sources,” IEEE J. Emerg. Sel. Topics Power Electron.,vol. 1, no. 1, pp. 18–27, Mar. 2013. 6. K. T. Mok, S. C. Tan, and S. Y. R. Hui, “Decoupled power angle and voltage control of electric springs,” IEEE Trans. Power Electron., vol. 31,no. 2, pp. 1216–1229, Feb. 2016 7. Q. Wang, M. Cheng, and Z. Chen, “Steady-state analysis of electric springs with a novel delta control,” IEEE trans. Power electron., vol.30,no.12, pp.7159-7169,dec 2015. 8. C. K. Lee and S. Y. Hui, “Reduction of energy storage requirements in future smart grid using electric springs,” IEEE Trans. Smart Grid, vol. 4, no. 3, pp. 1282–1288, Sep. 2013. 9. J. Soni and S. K. Panda, “Electric spring for voltage and power stability and power factor correction,” in Proc. 2015 9th Int. Conf. Power Electron.,Jun. 2015, pp. 2091–2097 10. J. Soni, K. R. Krishnanand, and S. K. Panda, “Load-side demand management in buildings using controlled electric springs,” in Proc. 40th Annu. Conf. IEEE Ind. Electron. Soc., Oct. 2014, pp. 5376–5381. 11. F. Xiao, L. Dong, L. Li, and X. Liao, “A frequency-fixed SOGI based PLL for single-phase grid-connected converters,” IEEE Trans. Power Electron.,vol. 32, 12. Electric Spring for Voltage and Power Stability and Power Factor Correction Jayantika Soni, Student Member, IEEE, and Sanjib Kumar Panda, Senior Member, IEEE no. 3, pp. 1713–1719, Mar. 2016. 13. S. R. Arya, B. Singh, A. Chandra, and K. Al-Haddad, “Power factor correction and zero voltage regulation in distribution system using DSTATCOM,” in Proc. 2012 IEEE Int. Conf. Power Electron., Drives,Energy Syst., Dec. 2012, pp. 1–6. Authors: M. Jagannath, C. Madan Mohan, Aswin Kumar, M.A. Aswathy, N. Nathiya Paper Title: Design and Testing of a Spirometer for Pulmonary Functional Analysis Abstract: Chronic Obstructive Pulmonary Disease (COPD) is considered as one of the greatest life-threatening syndromes worldwide, and it is estimated that over 600 million are afflicted with the disease. The objective of this study is to design and develop a spirometer which is functionally as well as cost effective. Authors have planned to keep the cost below 100$. The proposed spirometer has four main components – spirometer body, Circuitry, Computer and Software. The spirometer body includes a differential pressure sensor and a pilot tube through which the patient blows. The output is transmitted to the microcontroller. The analog to digital convertor within the microcontroller is employed for the conversion. Then the pressure difference output from the pressure sensor is converted into mass flow rate which is subsequently converted into volume. The microcontroller relays this data via a Universal Serial Bus (USB) connection to a computer which transmits this to the JavaScript based graphical user interface. This interface is used to display the flow and volume data in real-time. Then this experiment has proceeded further with this study by testing it on people. A spirometric test was conducted on 20 individuals of different ages, heights and gender. Their test results were tabulated and inferences on their breathing condition were drawn accordingly. The results show that lung capacity decreases with age. Although the current design is not able to meet clinical accuracy, with professional manufacturing, such a design could yield a device capable of meeting clinical accuracy without a significant increase in price.

Keywords: Chronic obstructive pulmonary disease; Microcontroller; Spirometer; Universal Serial Bus.

References: 1. https://www.nih.gov/news-events/news-releases/new-survey-suggests-growing-awareness-copd-nations-fourth-leading-killer Last accessed on January 2019. 63. 2. Á.A. Cruz, R. Stelmach and E.V. Ponte, “Asthma prevalence and severity in low‐resource communities,” Current Opinion in Allergy and Clinical Immunology, vol. 17, pp. 188–93, 2017. 343-347 3. V. Agarwal and N.C.S. Ramachandran, “Design and development of a low-cost spirometer with an embedded web server,” International Journal of Biomedical Engineering and Technology, vol. 1, no. 4, pp. 439-452, 2008. 4. R.O. Crapo, “Pulmonary Function Testing” in Baum’s Textbook of Pulmonary Diseases, 7th ed., Philadelphia: Lippincott Williams and Wilkins, 2004. 5. A.D. Gascoigne, P.A. Corris, J.H. Dark and G.J. Gibson, “The biphasic spirogram: a clue to unilateral narrowing of a mainstem bronchus,” Thorax, vol. 45, pp. 637-38, 1990. 6. G.T. Ferguson, P.L. Enright, A.S. Buist and M.W. Higgins, “Office spirometry for lung health assessment in adults,” Chest, vol. 117, pp. 1146-61, 2000. 7. C.W. Lin, D.H. Wang, H.C. Wang and H.D. Wu, “Prototype development of digital spirometer,” Proc. IEEE conference on Engineering in Medicine and Biology, vol. 20, no. 4, pp. 1786–1788, 1998. 8. G.P.K. Economou, P.D. Goumas and K. Spiropoulos, “A novel medical decision support system,” IEEE Control and Computing Journal, pp. 177–183, 1993. 9. W.G. Downing Jr., “Electronic measurements of pulmonary mechanics,” WESCON ‘95. Conference Record, November, pp.644–649, 1995. 10. J.L. Hankinson, J.R. Odencrantz and K.B. Fedan, “Spirometric reference values from a sample of the general U.S. population,” The American Journal of Respiratory and Critical Care Medicine, vol.159, pp. 179–187, 1999. 11. M.R. Miller, J. Hankinson, V. Brusasco, F. Burgos, R. Casaburi, A. Coates et al., “Standardisation of spirometry,” The European Respiratory Journal, vol.26, pp. 319-38, 2005. 12. W. Barud, S. Ostrowski, A. Wojnicz, J.A. Hanzlik, B. Samulak and J.J. Tomaszewski, “Evaluation of lung function in male population from vocational mining schools of the Lublin Coal Basin,” Annales Universitatis Mariae Curie-Sklodowska Mathematica, vol. 46, pp. 39-43, 1991. 13. Y. Tang, M.J. Turner, J.S. Yem and A.B. Baker, “Calibration of pneumotachographs using a calibrated syringe,” The Journal of Applied Physiology, vol. 95, pp. 571-76, 2003. 14. S. Stanojevic, A. Wade and J. Stocks, “Reference values for lung function: past, present and future,” The European Respiratory Journal, vol. 36, pp. 12–19, 2010. 15. F. Al-Ashkar, R. Mehra and P. J. Mazzone, “Interpreting pulmonary function tests: Recognize the pattern, and the diagnosis will follow,” Cleveland Clinic Journal of Medicine, vol. 70: 866–881, 2003. Authors: I V S Venugopal, D Lalitha Bhaskari, M N Seetaramanath Paper Title: A Progressive Classification Framework for Detecting SPAM emails and Identification of Authors 64. Abstract: Emails are the most popular form of communication in the space of cyber communications. In the recent past, many of the instances were observed, where the mode of communication were shifted to instance 348-359 communication methods such as instance messages or video-based services for interaction. Nevertheless, for a detailed communication, there is no replacement of email communications. A number of surveys have reported that the amount of emails exchanged daily ranges between 200 to 250 million every day including the personal, business or promotional emails. Considering such a massive space for information exchange, it is regardless to mention that this space becomes the target for information misuses. One of the biggest threat to the email collaboration is spam emails containing unsolicited information or many of the cases asking for critical information of the recipients. Most of the email service providers helps the users by incorporating a spam filtering process to prevent spamming in the email servers. Nonetheless, due to the critical nature of language used in communication makes the spam detection highly difficult. The fundamental strategies followed by most of the filters are to detect the spam emails based on specified key words. Regardless to mention, that in different domains of business or studies, some of the keywords carry different significance and cannot be blacklisted. Also, the inappropriate detection of the email as spam may lead to severe information loss. A good amount of research attempts is made in the recent past to build a framework for detection of spams as perfect as possible. However, due to the mentioned restriction the bottleneck still persists in between email filtration and detection of spam accuracy. Thus, this work proposes a novel automatic framework for detecting the spam emails on a wide range of domains. The obtained accuracy is significantly high for this framework due to the multiple layered approach adapted. The framework deploys classification of the emails in various domains and further applies the keyword- based filtration process with analysis of term frequency along with identification of the nature of the sender for confirmation of the process resulting into progressive classification in order to make the world of email communication highly secure and satisfiable.

Keywords: Spam filtering, Term Frequency, Term Relation, Domain Knowledge, Author identification, progressive classification

References: 1. R. Team, "Email statistics report 2015-2019", Mar. 2015. 2. J. D. Brutlag, C. Meek, "Challenges of the email domain for text classification", Proc. ICML, pp. 103-110, 2000. 3. W. W. Cohen, "Learning rules that classify e-mail", Proc. AAAI Spring Symp. Mach. Learn. Inf. Access, pp. 25, 1996. 4. E. Blanzieri, A. Bryl, "A survey of learning-based techniques of email spam filtering", Artif. Intell. Rev., vol. 29, pp. 63-92, Sep. 2008. 5. T. S. Guzella, W. M. Caminhas, "A review of machine learning approaches to spam filtering", Expert Syst. Appl., vol. 36, pp. 10206- 10222, Oct. 2009. 6. S. Abu-Nimeh, D. Nappa, X. Wang, S. Nair, "A comparison of machine learning techniques for phishing detection", Proc. Anti-Phishing Work Groups 2nd Annu. Ecrime Res. Summit, pp. 60-69, 2007. 7. A. Almomani, B. B. Gupta, S. Atawneh, A. Meulenberg, E. Almomani, "A survey of phishing email filtering techniques", IEEE Commun. Surveys Tuts., vol. 15, pp. 2070-2090, 4th Quart. 2013. 8. Y. W. Wang, Y. N. Liu, L. Z. Feng, X. D. Zhu, "Novel feature selection method based on harmony search for email classification", Knowl.-Based Syst., vol. 73, pp. 311-323, Jan. 2015. 9. M. R. Schmid, F. Iqbal, B. C. M. Fung, "E-mail authorship attribution using customized associative classification", Digit. Investigat., vol. 14, pp. S116-S126, Aug. 2015. 10. M. T. Banday, S. A. Sheikh, "Multilingual e-mail classification using Bayesian filtering and language translation", Proc. Int. Conf. Contemp. Comput. Informat., pp. 696-701, 2015. 11. M. Mohamad, A. Selamat, "An evaluation on the efficiency of hybrid feature selection in spam email classification", Proc. 2nd Int. Conf. Comput. Commun. Control Technol., pp. 227-231, 2015. 12. N. A. Novino, K. A. Sohn, T. S. Chung, "A graph model based author attribution technique for single-class e-mail classification", Proc. 14th IEEE/ACIS Int. Conf. Comput. Inf. Sci. (ICIS), pp. 191-196, Sep. 2015. 13. W. Li, W. Meng, Z. Tan, Y. Xiang, "Towards designing an email classification system using multi-view based semi-supervised learning", Proc. 13th IEEE Int. Conf. Trust Secur. Privacy Comput. Commun. (TrustCom), pp. 174-181, Sep. 2015. 14. W. Li, W. Meng, "An empirical study on email classification using supervised machine learning in real environments", Proc. IEEE Int. Conf. Commun. (ICC), pp. 7438-7443, Jun. 2015. 15. Z. J. Wang, Y. Liu, Z. J. Wang, D. L. Liu, X. B. Zhu, K. L. Xu, D. M. Fang, "E-mail filtration and classification based on variable weights of the Bayesian algorithm" in Applied Science Materials Science and Information Technologies in Industry, Zürich, Switzerland:Trans Tech Publications Ltd, vol. 513, pp. 2111-2114, 2014. 16. S. A. Saab, N. Mitri, M. Awad, "Ham or spam? A comparative study for some content-based classification algorithms for email filtering", Proc. (MELECON), pp. 439-443, 2014. 17. M. R. Islam, J. Abawajy, M. Warren, Multi-Tier Phishing Email Classification with an Impact of Classifier Rescheduling, New York, NY, USA:IEEE, 2009. 18. A. A. Akinyelu, A. O. Adewumi, "Classification of phishing email using random forest machine learning technique", J. Appl. Math., vol. 2014, pp. 1-6, Apr. 2014. 19. J. C. Gomez, M. F. Moens, "PCA document reconstruction for email classification", Comput. Statist. Data Anal., vol. 56, pp. 741-751, Sep. 2012. 20. N. Al Fe’ar, E. Al Turki, A. Al Zaid, M. Al Duwais, M. Al Sheddi, N. Al Khamees, E-Classifier: A Bi-Lingual Email Classification System, New York, NY, USA:IEEE, 2008. 21. E. K. Jamison, I. Gurevych, "Headerless quoteless but not hopeless? Using pairwise email classification to disentangle email threads", Proc. 9th Int. Conf. Recent Adv. Natural Lang. Process., pp. 327-335, 2013. 22. J. Ratkiewicz et al., "Detecting and Tracking Political Abuse in Social Media", Proc. 5th Int’l AAAI Conf. Weblogs and Social Media, 2011 23. P.-A. Chirita, J. Diederich, W. Nejdl, "Mailrank: Using Ranking for Spam Detection", Proc. 14th ACM Int’l Conf. Information and Knowledge Management, pp. 373-380, 2005 24. H. Yu et al., "Sybillimit: A Near-Optimal Social Network Defense against Sybil Attacks", IEEE/ACM Trans. Networking, vol. 18, no. 3, pp. 885-898, 2010. 25. J. Ratkiewicz et al., "Truthy: Mapping the Spread of Astroturf in Microblog Streams", Proc. 20th Int’l Conf. Comp. World Wide Web, pp. 249-252, 2011. 26. X. Hu et al., "Social Spammer Detection in Microblogging", Proc. 23rd Int’l Joint Conf. Artificial Intelligence, pp. 2633-2639, 2013. 27. Shivam Aggarwal,Vishal Kumar and S.D.Sudarshan,“Identification and Detection of Phishing Emails Using Natural Language Processing Techniques”, Proceedings of the 7th International Conference on Security of Information and Networks,2014. 28. A. Pandian and Mohamed Abdul Karim, “Detection of Fraudulent Emails by Authorship Extraction”,International Journal of Computer Applications (0975 – 8887), Volume 41– No.7, March 2012. 29. Hongming Che, Qinyun Liu and Lin Zou “A Content-Based Phishing Email Detection Method”, IEEE International Conference on Software Quality, Reliability and Security Companion (QRS-C),2017. 30. H. Alghamdi, "Can Phishing Education Enable Users To Recognize Phishing Attacks" in Dublin Institute of Technology, Dublin, Ireland, 2017. Authors: Kadali lakshmi, Anakapalli Suresh, Arshini Gubbala Development of FPGA Based Multi-Channel Temperature Controller using Thermistors for under Paper Title: Water Vehicles Abstract: Under water vehicles with electrical propulsion such as underwater autonomous vehicles are designed to propel with high energy batteries. These batteries are the main source of power to motor and other electronic subsystems. Temperature of the batteries is one of the critical parameter that gives the information about the health of the battery and whether the battery is able to deliver the required power to other subsystems. In case of any abnormality such as battery short circuit or other reasons, the temperature of the battery may shoot up to the alarm levels at various places of the battery and other sub sections near to the battery because the temperature is transferred from battery to the nearby shell and other subsystems. For this application, Multi-channel temperature controller is designed, verified and tested in the battery assembly. The proposed system can monitor and control up to the 32 temperature channels by integrating thermistors in the complete test set-up and it is designed in such a way that the battery is disconnected from the other subsystems in case of any abnormality or temperature is increased beyond the safety limit. In this paper, design, calibration and integration and testing of multi-channel Temperature controller using FPGA with thermistors is discussed and the system has internal memory and it can store the temperature at various channels in flash memory so that the system is well suited for not only self- controlled underwater vehicles but also thermal engine based systems. The system can also monitor and control the temperature in harsh environment even also in industrial applications. The system is designed in Spartan 3FPGA 65. using VHDL and verification of the design is done Xilinx chip-scope-pro. The front end Graphical User Interface (GUI) is designed for online monitoring, data downloading and processing using visual C++ and MATLAB. 360-364

Keywords: Multi-channel Intelligent temperature controller, FPGA based system, Thermistors, Battery controller with onboard systems, Battery monitoring system, Data Acquisition Systems, Graphical User Interface (GUI).

References: 1. Circuit Design with VHDL--- Volnei A.Pedroni . 2. FPGA Prototyping by VHDL Examples Xilinx SpartanTM-3 version----Pong P. Chu 3. Practical Data Acquisition for instrumentation & control system---- john park & steve mackay. 4. PI Daijun, ZHANG Haiyong and YE ianyang, "Design of High Speed Real-time Data Acquisition System Based on FPGA ", in Modem electronic technology, 2009, pp. 12-14.High Performance Octal UART XR16L788 data sheet, REV1.2.2, October 2005. 5. OMEGA Temperature Measurement Handbook, Omega Instruments, Inc.. 6. AD7655 16 bit Analog to Digital Converter,Data sheet, Analog Devices. 7. FDTI Chip FT245R USB FIFO IC Datasheet. 8. Xilinx Spartan-3E FPGAFamily Data Sheet. 9. Numonyx SLC NAND Flash Memories datasheet. 10. Jie Li, Qiao Jiang, Xi-ning Yu and Ying DU (2010), “Intelligent Temperature Detecting System”, 2010 International Conference on Intelligent System Design and Engineering Application, National Key Laboratory for Electronic Measurement, North University of China, Taiyuan, 030051, China 11. S. Thanee S. Somkuarnpanit , “FPGA Based multi protocol data acquisition system with High speed USB interface”. IMECS, March 10- 12, 2010. Authors: Vijayakumar R, NidhyaKumari R, Himani J, Rahul V, Varun V Paper Title: Fabrication of Low Cost Solar using Polypropylene (PPR) Pipes – An Investigation Abstract: The conversion of solar energy into thermal and electrical form is possible is possible by the using photovoltaic modules and solar collectors. Solar collector absorbs the direct solar radiation and converts it into thermal energy, which can be stored in the form of sensible heat/ latent heat and a combination of both diffused in the working fluids. Present work deals with the performance evaluation of solar power based air heater fabricated using polypropylene (PPR) pipes foiled with aluminium. The use of PPR polymer results in the reduction of total initial cost of fabrication. The working fluid, which was air, is introduced into the collector system made of polypropylene pipes and were the fluid (air) is heated by using solar energy. The outer surface of the PPR pipes were paint in black color to maximize the absorption of incoming solar radiation. The air absorbs the entrapped heat of the pipe and the heated air was comes out of the system. Further, to minimize heat losses from the front collector, glass is used as a top cover. The change in temperature of the fluid with respect to time was observed. The effectual inlet load of fluid (air) on the performance of solar heater was investigated by varying the mass flow 66. rate (MFR) of the fluid (air) 365-367 Keywords: air solar heater, PPR pipes, mass flow rate, efficiency

References: 1. R. K. Aharwal, K. Bhupendra Gandhi, J. S. Saini. "Heat transfer and friction characteristics of SAH ducts on absorber plate." International journal of heat and mass transfer, volume 52, no. 25, 2009, pp. 5970-5977. 2. S. Y.-Ali. "Study and optimization of the thermal performances fin absorber plates, with various glazing." Renewable Energy, Volume 30, no. 2, 2005, pp. 271-280. 3. Chabane, Foued, NoureddineMoummi, Said Benramache. "Experimental analysis on thermal performance of a solar air collector in a region of Biskra, Algeria." Journal of Power Technologies, Volume 93, no. 1, 2013, pp.52-58. 4. Chow, Tin Tai. "A review on photovoltaic/thermal hybrid solar technology." Applied energy, Volume 87, no. 2, 2010, pp. 365-379. 5. P. Dhiman, N. S. Thakur, A. Kumar, S. Singh. "An analytical model of a novel parallel flow packed bed SAH." Applied energy, Volume 88, 2011, pp. 2157-2167 6. Y. K. Durgesh, A. K. Rai, V. Sachan. "Experimental study of SAH." International Journal of Advanced Research in Engineering and Technology, volume 5, no. 5, 2014, pp.102–106 67. Authors: Dinokumar Kongkham, M.Sundararajan Paper Title: Minimized Interference in CRN using Conjunction Analysis and Resource Utilization over the Network Abstract: Subjective radio system is a main correspondence organize which makes a long range correspondence in a minimal effort and it is a quickest remote system. Usage of unused range band of essential client by optional client. Because of expanding more number of optional clients at that point naturally emerging shot for the impact of the two signs. The impedance is the principle issue in the intellectual radio system because of movement and various correspondence. To diminish obstruction numerous methodologies and calculations are proposed. Yet at the same time it stays unsolved. To decrease the obstruction besides we proposed a bar structure i.e. the signs of a similar group hubs or adjacent hubs join a flag and asset to frame a solid flag called shaft flag and after that correspondence will be continued. The bar will assist correspondence with being solid and limit the impedance with different bars. We can diminish impedance up to 7-8% of the current approach.

Keywords: Intellectual radio network(CRN), obstruction, bar flag; bunch of hubs.

References: 1. Sheng ; Wang ; Cai Qin ; Weidong Wang, “Interference Alignment assisted by D2D communication for the Downlink of MIMO Heterogeneous Networks,” EEE Access, ISSN: 2169-3536, 2018. 2. Solmaz Niknam ; Balasubramaniam Natarajan ; Reza Barazideh, “Interference Analysis for Finite-Area 5G mmWave Networks Considering Blockage Effect,” IEEE Access, ISSN: 2169-3536, 2018. 3. Longwei Wang ; Qilian Liang, “Partial Interference Alignment for Heterogeneous Cellular Networks,” IEEE Access, ISSN: 2169-3536, 2018. 4. Sina Maleki ; Juan Merlano Duncan ; Jevgenij Krivochiza ; Symeon Chatzinotas ; Björn Ottesten, “SDR Implementation of a Test bed for Real-Time Interference Detection With Signal Cancellation,” IEEE Access ( Volume: 6 ), Pp 20807 – 20821, 2018 5. Yunchao Song ; Chen Liu ;, “The Pre-coding Scheme Based on Domain Selective Interference Cancellation in 3D Massive MIMO,” EEE Communications Letters, pp. 1–1, 2018. 6. Cui ; Yu Chen ; Wei Ni ; Tao ; Ping Zhang, “Effective Capacity Analysis in Ultra-Dense Wireless Networks With Random Interference,” IEEE Access ( Volume: 6 ), pp. 19499 - 19508, 2018. 368-375 7. Yang ; Pei Liu ; Liang Li, “Interference Compensation for Smart Grid Communications: A Distributed Power Control Approach,” IEEE Access ( Volume: 6 ), pp. 18643 - 18654 , 2018. 8. Salama S. ; Wessam Mesbah ; Thomas Kaiser, “Artificial Noise-Based Physical-Layer Security in Interference Alignment Multi pair Two-Way Relaying Networks,” EEE Access, vol. 6, pp. 19073 - 19085, 2018. 9. Chao Dong ; Kai ; Lin, “An Ordered Successive Interference Cancellation Detector With Soft Detection Feedback in IDMA Transmission,” EEE Access ( Volume: 6 ), Pp. 8161 - 8172, 2018. 10. Christos ; Kai-Kit Wong, “Constructive Interference Based Secure Pre-coding: A New Dimension in Physical Layer Security,” EEE Transactions on Information Forensics and Security, vol. 13, issue. 9, pp. 2256 - 2268, 2018. 11. S. Haykin, “Cognitive radio: Brain-empowered wireless communications,” IEEE J. Sel. Areas Common., vol. 23, pp. 201-220, Feb. 2005. 12. Goldsmith, S., I. Maric, and S. Srinivasa, “Breaking spectrum gridlock with cognitive radios: an information theoretic perspective,” Proc. IEEE, vol. 97, no. 5, pp. 894-914, May 2009. 13. J. N. Laneman, D. N. C. Tse and G. W. Wornell, “Cooperative diversity in wireless networks: efficient protocols and outage behavior,” IEEE Trans. Info. Theory, vol. 50, no. 12, pp. 3062-3080, Dec. 2004. 14. X. Zhang, Z. Yan, Y. Gao, and W. Wang, “On the study of outage performance for cognitive relay networks (CRN) with the Nth best- relay selection in Rayleigh-fading channels,” IEEE Wireless Commun. Lett. vol. 2, no. 1, pp. 110-113, Feb. 2013. 15. M. Xia and S. A¨, “Cooperative AF relaying in spectrum-sharing systems: performance analysis under average interference power constraints and Nakagami-m fading,” IEEE Trans. Commun., vol. 60, no. 6, June 2012. 16. S. I. Husain, M.-S. Alouini, K. Qaraqe, and M. Hasna, “Reactive relay selection in underlay cognitive networks with fixed gain relays,” IEEE Int’l Conf. on Commun. (ICC’12), Canada, June 2012, pp. 1784-1788. 17. T. Q. Duong, V. N. Q. Bao, H. Tran, G. C. Alexandropoulos and H.-J. Zepernick, “Effect of primary network on performance of spectrum sharing AF relaying,” Electronics., 5th January 2012, vol. 48, no. 1. 18. X. Guan, W. Yang, and Y. Cai, “Outage performance of statistical CSI assisted cognitive relay with interference from primary user,” IEEE Commun. Lett. vol. 17, no. 7, pp. 1416-1419, July 2013. 19. P. Yang, Q. Zhang, L. J. Qin, “Outage performance of underlay cognitive opportunistic multi-relay networks in the presence of interference from primary user,” Wireless Pers. Commun. (2014) 74:343-358. 20. X. Wang, H. Zhang, T. A. Gulliver, W. Shi, “Outage performance of a proactive DF cognitive relay network with a maximum transmit power limit,” Journal of Information & Computational Science, 10:18 (2013), pp. 5927-5934, Dec. 2013 Authors: Dinokumar Kongkham, M Sundararajan Paper Title: Optimization Scheme with Energy Detector Model for Cognitive Radio Networks Abstract: Cognitive Radio (CR) is a promising technology in the wireless communication system for resolving the resource utilization problems and spectral clogging problems in the spectrum based applications. It aims to enhance spectrum sharing scheme in Multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) to enable with next generation systems. Efficient utilization of Spectrum sensing and computational complexity is still an unsolved issue in the ultra-wide band (UWB) radio spectrum. Generally, conventional methods include spectrum sensing to identify the primary users and spectrum usage, which helps to make data transmission possible from secondary users. However, they obtain poor throughput, higher transmission 68. power and longer sensing time. In order to resolve this issue, we propose novel hybrid access optimization scheme with energy detector model for achieving the significant compressive spectrum sensing in the MIMO-OFDM, 376-381 which is based on cognitive ratio network (CRN). The proposed method develops sparsity signal model with the help of orthogonal transform of Fractional Fourier Transformation (FRFT) for reducing the signal to noise ratio (SNR). Furthermore, modulated signals from secondary users are forwarded to DSP (Digital signal Processing). Hence, the proposed system achieves higher accuracy in detecting the false probability, energy detection, optimal sensing time, and higher throughput than efficient compressive sensing method.

Keywords: Spectrum Sensing, Novel Hybrid Access Optimization scheme, Energy detector, Sparsity Signal Model and Fractional Fourier Transformation.

References: 1. W. Lee and D.-H. Cho. (2013). “Channel selection and spectrum availability check scheme for cognitive radio systems considering user mobility,” IEEE Commun. Lett., vol. 17, no. 3, pp. 463–466, Mar. 2. J. Lee, J. G. Andrews, and D. Hong. (2015). “Spectrum-sharing transmission capacity with interference cancellation,” IEEE Trans. Commun., vol. 61, no. 1, pp. 76–86, Jan. 3. B.Rassouli and A. Olfat. (2012). “Periodic spectrum sensing parameters optimization in cognitive radio networks,” IET Commun., vol. 6, no. 18,pp. 3329–3338, Dec. 4. Q. Li, Z. Li, J. Shen, and R. Gao. (2015), “A novel spectrum sensing method in cognitive radio based on suprathreshold stochastic resonance,” in Proc.IEEE Int. Conf. Commun. (ICC), pp. 4426–4430. 5. Y. Huret al.. (2007). “A wideband analog multi-resolution spectrum sensing (MRSS) technique for cognitive radio (CR) systems,” in Proc. IEEEInt. Symp. Circuits Syst. (ISCAS), May, pp. 1–4. 6. T. Yucek and H. Arslan. (2010). “A survey of spectrum sensing algorithms for cognitive radio applications,” IEEE Commun. Surveys Tuts., vol. 11, no. 1, pp. 116–130. 7. Y. Pei, A. T. Hoang, and Y.-C. Liang. (2015). “Sensing-throughput tradeoff in cognitive radio networks: How frequently should spectrum sensing be carried out?” in Proc. IEEE 18th Int. Symp. Personal, Indoor MobileRadio Commun. (PIMRC), pp. 1–5. 8. S. Stotas and A. Nallanathan. (2012). “Overcoming the sensing-throughput tradeoff in cognitive radio networks,” in Proc. IEEE Int. Conf.Commun. (ICC), pp. 1–5. 9. Y.-C. Liang, Y. Zeng, E. C. Y. Peh, and A. T. Hoang. (2010). “Sensingthroughput tradeoff for cognitive radio networks,” IEEE Trans. WirelessCommun., vol. 7, no. 4, pp. 1326–1337. 10. A PHY/MAC Proposal for IEEE 802.22 WRAN Systems Part 1: The PHY, IEEE Standard 802, 2006. 11. H. Kim and K. G. Shin. (2013). “In-band spectrum sensing in cognitive radio networks: Energy detection or feature detection?” in Proc. 14th ACMInt. Conf. Mobile Comput. Netw.pp. 14–25. 12. S. Kyperountas, N. Correal, and Q. Shi. (2010). “A comparison of fusion rules for cooperative spectrum sensing in fading channels,” EMS Research, Motorola, Libertyville, IL, USA 13. H. Urkowitz. (2006). “Energy detection of unknown deterministic signals,” Proc. IEEE, vol. 55, no. 4, pp. 523–531. 14. H. V. Poor. (1988). An Introduction to Signal Detection and Estimation, vol. 1. New York, NY, USA: Springer-Verlag p. 559. 15. E. C. Y. Peh, Y.-C. Liang, Y. L. Guan, and Y. Zeng. (2015). “Optimization of cooperative sensing in cognitive radio networks: A sensing-throughput tradeoff view,” IEEE Trans. Veh. Technol., vol. 58, no. 9, pp. 5294–5299. 16. D. Cabric, S. M. Mishra, and R. W. Brodersen. (2004). “Implementation issues in spectrum sensing for cognitive radios,” in Proc. Conf. Rec. 38thAsilomar Conf. Signals, Syst. Comput., vol. 1. Nov. 2004, pp. 772–776. 17. W. H. Press, S. A. Teukolsky, W. T. Vetterling, and B. P. Flannery. (2009). “Numerical recipes source code CD-ROM,” in The Art of ScientificComputing, 3rd ed. Cambridge, U.K.: Cambridge Univ. Press. 18. L. N. de Castro and J. Timmis. (2006). Artificial Immune Systems: A New Computational Intelligence Approach. New York, NY, USA:Springer-Verlag. 19. J. Kennedy. (2012). “Particle swarm optimization,” in Encyclopedia of Machine Learning. New York, NY, USA: Springer-Verlag, pp. 760–766. 20. J. F. Kennedy, J. Kennedy, and R. C. Eberhart, Swarm Intelligence. San Mateo, CA, USA: Morgan Kaufmann, 2001. Authors: Altaf C, Shah Aqueel Ahmed Paper Title: Energy Efficient and Reliable Routing Protocol in Wireless Ad Hoc Network Abstract: In wireless ad hoc network, increased lifetime, reliability and energy efficiency is main concern. The significant techniques Reliable Minimum Energy Routing (RMER) and Reliable Minimum Energy Cost Routing (RMECR) are developed here to reach this concern. These protocols are compared with TMER (Traditional Minimum Energy Routing) and ETX (Expected Transmission Count) by energy utilization, battery energy of nodes remaining along with quality of links, also comparison has made here. With investigations made on Energy- Aware Routing in ad hoc networks, two techniques namely RMER and RMECR stated here can increase the operational lifetime of the network by means of reliable, energy-efficient routes. The RMECR is the new idea of wireless ad hoc networks in case of energy efficient routing algorithm. RMER technique is the point of reference in understanding Energy Efficiency of the RMECR algorithm determines the routes which are required low energy consumption while transmitting packets without considering the battery energy left of the nodes.

Keywords: Mobile Ad hoc network, Routing, Energy Efficiency, Reliability, RMER and RMECR.

References: 69. 1. D.J. and D.D Vergados and Pantazis NA, “Energy- Efficient Route Selection Strategies for Wireless Sensor Networks,” Mobile Networks and Applications, vol. 13, nos. 3-4,pp. 285-296, Aug. 2008. 382-385 2. H. Zhang, A.A, and P. Sinha, “Link Estimation and Routing in Sensor Network Backbones: Beacon-Based or Data-Driven?” IEEE Transaction on Mobile Computing, vol. 8(5), pp. 653-667, May 2009. 3. Z.J Qiao.C and Wang.X (2006) ‘On Accurate Energy Consumption Models for Wireless Ad Hoc Networks,’ IEEE Transactions on Wireless Communications. Vol. 5(11), pp. 3077-3086. 4. Verma, S. Kim, S. Choi, and S.-J. Lee, “Reliable, Low Overhead Link Quality Estimation for 802.11 Wireless Mesh Networks,” Proceeding. IEEE Fifth Annual Communication Society Conf. Sensor, Mesh and Ad Hoc Communications and Networks (SECON ’08), June 2008. 5. A.Misra and S.Banerjee(2002) ‘MRPC: Maximizing Network Lifetime for Reliable Routing in Wireless Environments,’ Proceeding. IEEE Wireless Communications and Networking Conference. (WCNC’02).Vol 7,No.6, pp. 800-806. 6. Mohanoor.A.B S.Radhakrishnan and V.Sarangan(2009) ‘Online Energy Aware Routing in Wireless Networks,’Ad Hoc Networks. Vol. 7, No. 5, pp. 918-931. 7. J.H Chang and L.Tassiulas ( 2004) ‘Maximum Lifetime Routing in Wireless Sensor Networks,’ IEEE/ACM Transactions on Networking.Vol. 12, No. 4, pp. 609-619. 8. Nishant G.S, R. Das, (1998 )’Energy-Aware On-Demand Routing For Mobile Ad Hoc Networks’, IEEE Transactions on Wireless communications, vol.6 , no.11,pp.1300-1313. 9. Canming J.Yi Shi and Thomas H.Y (2005) ‘Cherish every Joule: Maximizing throughput with an eye on network-wide energy consumption’ Proceedings. IEEE Wireless communications. Vol 3, No.5, pp.850-857. 10. K.K.H and K.G Shin.V “On Accurate Measurement of Link Quality in Multi-Hop Wireless Mesh Networks,” Proceeding. ACM Mobile Communications, pp. 38-49, 2006. Authors: AC. Priya Ranjani, M. Sridhar 70. Distributed Web Usage Mining Based Ecommender System in Big Data Analytics using Hybrid Firefly Paper Title: Algorithm Abstract: One of the fast upcoming data mining disciplines that deal with large, unstructured complex data is Big data analysis. Web usage mining is a primary area of research that has been focusing on the valuable information derived from web server logs. Not having any explicit ratings of the users, the large data volume and its sparse nature have been posing challenges to the techniques of collaborative filtering with respect to performance and scalability. Techniques like clustering are dependent on the discovery of offline patterns from the user transactions and are used to improve scalability in terms of collaborative filtering but at reduced cost and recommendation accuracy. To improve the situation, this work has been taken up on the basis of nature inspired, meta heuristic algorithms Firefly and Teaching Learning Based Optimization (FA-TLBO). This FA- TLBO was hybridized using the K-Means algorithm (FA-TLBO with K-Means) in order to obtain optimal cluster centres. There were numerical experiments which indicated the fact that novel FA-TLBO with K-means was more efficient compared to TLBO algorithm.

Keywords: Big Data Analysis, Web Usage Mining, Recommender System, Clustering, K-Means Algorithm, Firefly Algorithm (FA) and Teaching Learning Based Optimization (TLBO).

References: 1. J. Zakir, T.Seymour, and K.Berg, “Big Data Analytics,” Issues in Information Systems, 2015, .16(2), pp. 81-90. 2. V.Dagade, M.Lagali, S.Avadhani, and P. Kalekar, “Big Data Weather Analytics Using Hadoop,” in IJETCSE, 14(2), pp.847-851. 3. R.Ali, “Cluster Optimization for Improved Web Usage Mining”, in IJRITCC, 2015,3(11), pp.6394-6399. 4. M. Sajwan, K.Acharya, and S.Bhargava, “Swarm intelligence based optimization for web usage mining in recommender system,” 2014, IJCATR, 3(2), pp.119-124. 5. J.Vellingiri, S.Kaliraj, S.Satheeshkumar, and T.Parthiban . “A novel approach for user navigation pattern discovery and analysis for web usage mining,”. JCS, 2015, 11(2), pp.372-382. 6. Abbas, L.Zhang, and S.U.Khan, “A survey on context-aware recommender systems based on computational intelligence techniques,” In Computing, Springer, 97(7), pp.667-690. 7. M.Jafari, F.S.Sabzchi, and A.J.Irani, “Applying web usage mining techniques to design effective web recommendation systems: A case study”, Advances in Computer Science: an International Journal, 3(2), 2014, pp.78-90. 8. 8 .C.Shahabi and F.Banaei-Kashani, “Efficient and anonymous web-usage mining for web personalization”, INFORMS Journal on Computing, 2003, pp.123-147. 9. 9..A.G.Abdalla, T.M.Ahmed and M.E.Seliaman, “Web Usage Mining and the Challenge of Big Data: A Review of Emerging Tools and Techniques”, In Handbook of Research on Trends and Future Directions in Big Data and Web Intelligence, . IGI Global, 2015, (pp. 418- 447. 10. 10. S.P.Malarvizhi, and B.Sathiyabhama, “Frequent page sets from web log by enhanced weighted association rule mining”, Cluster Computing, 2016, 19(1), pp.269-277. 11. 11.E.Tuba, R.Jovanovic, R.C.Hrosik, A. Alihodzic and M.Tuba, “Web Intelligence Data Clustering by Bare Bone Fireworks Algorithm 386-393 Combined with K-Means”. In Proceedings of the 8th International Conference on Web Intelligence, Mining and Semantics, (2018, June) 12. (article 7). ACM 13. 12.R.Katarya and O.P.Verma, “An effective web page recommender system with fuzzy c-mean clustering. Multimedia Tools and Applications”, ,2017, 76(20), pp.21481-21496. 14. A.K.Tripathi, K.Sharma and M.Bala, “A Novel Clustering Method Using Enhanced Grey Wolf Optimizer and MapReduce. “,Big Data Research, Elsevier, 2018 15. Q.Lin, X.Wang, B.Hu, L.Ma, F. Chen, J.Li, and C.A.Coello Coello, “Multiobjective Personalized Recommendation Algorithm Using Extreme Point Guided Evolutionary Computation”, Hindawi Complexity, 2018. 16. 15.Y.Djenouri, Z.Habbas, D.Djenouri, and M.Comuzzi, “Diversification heuristics in bees swarm optimization for association rules mining,” In Pacific-Asia Conference on Knowledge Discovery and Data Mining “, Springer, 2017, pp. 68-78. 17. 16.K.E.Heraguemi, N.Kamel, and H.Drias, “Multi-swarm bat algorithm for association rule mining using multiple cooperative strategies,” Applied Intelligence, 2016, 45(4), pp.1021-1033. 18. 17.X.Wei, Y.Wang, Z.Li, Z., Zou, T., & Yang, G. “Mining users interest navigation patterns using improved ant colony optimization. Intelligent Automation & Soft Computing”, 2015, 21(3), pp.445-454. 19. A.Agarwal, and N.Nanavati, “Association rule mining using hybrid GA-PSO for multi-objective optimisation,” In Computational Intelligence and Computing Research (ICCIC), 2016 IEEE International Conference on pp. 1-7. 20. 19.J.Umarani, R.Sivaprakash, and G.Thangaraju, “Web Usage Mining Analysis for Big Data Applications in Government Sectors of India”, International Journal of Emerging Technology in Computer Science & Electronics (IJETCSE), 2016, 23 (5), pp.201-211. 21. 20.S.Burla, “High Dimensional Data Clustering Using Hybridized Teaching-Learning-Based Optimization”, Journal of Computer and Mathematical Sciences, 2013, 4(3), pp.135-201. 22. S.X.Yang, and X.He “Firefly algorithm: recent advances and applications”,2013, arXiv preprint arXiv: pp.1308.3898. 23. L.Zhang, L.Liu, S.X.Yang, andY. Dai, “A novel hybrid firefly algorithm for global optimization,” 2016, PloS one, 11(9), e0163230. 24. L.Zhou, and L.Li,(2018). “Improvement of the Firefly-based K-means Clustering Algorithm,” International Conference on Data Science,2018, pp.157-162. 25. R.V.Rao, V.J.Savsani, and D.P.Vakharia,, “Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems,” 2011, Computer-Aided Design, 43(3), pp.303-315. 26. 25.R.R.Kurada, K.K.Pavan, and A.A.Rao, “Automatic teaching–learning-based optimization: A novel clustering method for gene functional enrichments”, In Computational Intelligence Techniques for Comparative Genomics, Springer, Singapore, pp. 17-35. 27. P.K.Mummareddy, and S.C.Satapaty, “An hybrid approach for data clustering using K-means and teaching learning based optimization,” In Emerging ICT for Bridging the Future-Proceedings of the 49th Annual Convention of the Computer Society of India CSI , Springer, Cham, 2015, Vol. 2, pp. 165-171. 28. R.Singh, H.Chaudhary, and A.K.Singh, “A new hybrid teaching–learning particle swarm optimization algorithm for synthesis of linkages to generate path, In Sadhana, 2017, 42(11), pp.1851-1870. 29. R.Singh, H.Chaudhary, and A.K.Singh, “A new hybrid teachinglearningparticle swarm optimization algorithm for synthesis of linkages to generate path, In Sadhana, 2017, 42(11), pp.1851-1870. 30. S.Tuo, L.Yong, Y.Li, Y.Lin and Q.Lu, “HSTLBO: A hybrid algorithm based on Harmony Search and Teaching-Learning-Based Optimization for complex high-dimensional optimization problems”, PloS one, 2017, 12(4), e0175114. Authors: Srinivasa Rao Divvela, V Sucharitha Paper Title: Efficient Algorithm using Big Data for Frequent Itemsets Mining 71. Abstract: Future trends are being estimated with the help of tools in data mining which allows the making of decisions to be data driven and analyze them carefully with the corresponding tools. In various fields of mining the 394-396 most important practice of mining of data is the Associate-rule mining. Major issue in any of the techniques being the generation of the frequent data-item sets which has to be solved efficiently. Many techniques have been put forth for this only purpose of itemset generation like Apriori-algorithm, FP_Growth-algorithm, and many other solutions are being offered to solve the issue. Many outsets of the problem yet to be fully implemented such as large clusters solving and distribution along with parallelization (automatic) etc. Many of these issues can be solved with the implementation of Framework of MapReduce on Improved Apriori algorithm. Lessening of time due to parallel executions can be achieved with the help of this. This procedure considerably decreases the time of execution and also a significant rise in efficiency is observed.

Keywords: MapReduce, Improved Apriori, mining, Frequent data-item sets.

References: 1. YalingXun, Jifu Zhang and Xian Qin, “Fidoop: Parallel mining of frequent Itemsets using MapReduce”, IEEE Trans.onsys.man and cybernetics, Vol. 46,No.3, March 2016. 2. Sheelagole and Bharat Tidke, “Frequent Itemset Mining for BigData in social media using ClustBigFIM algorithm”, Intl Conf.on Pervasive Computing, IEEE 2015. 3. Marconi K, Lehmann H. Big Data and Health Analytics[M]. BocaRaton:CRC Press, 2014. 4. McKinsey&Company. The big-data revolution in US health care: Accelerating value and innovation [R]. http://www.mckinsey.com/industries/healthcare-systems-and-services/our-insights/the-big-data-revolution-in-us-health-care, 2013. 5. Zahra Farzanyar and Nick Cercone, “Efficient mining of frequent itemsets in social network data based on MapReduce framework”, Proceedings of the 2013 IEEE International Conference on Advances inSocial Networks Analysis and Mining. 6. M.-Y. Lin, P.-Y. Lee, and S.-C. Hsueh, “Apriori-based frequent itemset mining algorithms on MapReduce”, International Conference onUbiquitous Information Management and Communication, ACM, 2012. Authors: Alwyn Varghese, Anand. N, Prince Arulraj G Paper Title: Investigation on Impact Strength of Fiber Reinforced Concrete Subjected To Elevated Temperature Abstract: The effect of elevated temperature on impact strength of Fiber Reinforced Concretes (FRC) is investigated in this paper. Cylinder specimens are used with different types of fibers such as Aramid, Basalt, Carbon, Glass, Polypropylene and PVA. All the specimens were exposed to elevated temperature as per standard fire curve following ISO 834. After heating the specimens are cooled by natural air prior to impact strength test. The tests are conducted as per ACI committee 544. Test result reveals that addition of fiber enhances the impact strength of concrete specimens. Concrete with Carbon fiber and Basalt fiber exhibits better performance than concrete with other fibers. In unheated condition Carbon fiber shows 5.9 times increase in impact resistance with respect to reference specimen. For 90 minutes of heat exposure, all FRCs except concrete with Aramid fiber shows 2 times better impact resistance than that of reference specimen.

Keywords: Fiber Reinforced Concrete, Impact Strength, Elevated Temperature, Carbon Fiber, Basalt Fiber

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Impact or blast induced fire simulation of bi-directional PSC panel considering concrete confinement and spalling effect. Engineering Structures, 149, 113-130. 17. ACI Committee 544, (July 1978). Measurement of properties of fibre reinforced concrete. Journal, American Concrete Institute, Proc. Vol. 75, No. 7, pp. 283-90. 18. IS: 516–1959. Methods of Tests for Strength of Concrete. 19. ISO 1975 ‘‘Fire resistance tests-elements of building construction.’’ International Standard ISO 834, Geneva. 20. Saxena, R., et al. (2018). Impact resistance and energy absorption capacity of concrete containing plastic waste. Construction and Building Materials, 176, 415-421. 21. Mohammad hosseini H, et al. (2017). The impact resistance and mechanical properties of concrete reinforced with waste polypropylene carpet fibres. Construction and Building Materials. 143, 147-157. 22. Mastali M, et al. (2016). The impact resistance and mechanical properties of reinforced self-compacting concrete with recycled glass fibre reinforced polymers. Journal of Cleaner Production. 124, 312-324. 23. Guo YC, et al. (2014). Compressive behaviour of concrete structures incorporating recycled concrete aggregates, rubber crumb and reinforced with steel fibre, subjected to elevated temperatures. Journal of Cleaner Production. 72, 193-203. 24. Anand N and Prince Arulraj G. (2014). Effect of grade of concrete on the performance of self-compacting concrete beams subjected to elevated temperatures, Fire Technology. 50(5), 1269–1284. 25. Anand N, et al. (2014). Stress strain behavior of Normal compacting and Self compacting concrete under elevated temperatures, Journal of Structural Fire Engineering. 5 (1), 63–75. 26. Antony Godwin, et al. (2016). Influence of mineral admixtures on mechanical properties of self-compacting concrete under elevated temperature, Fire and Materials. 40(7), 940–958. 27. Purkiss, J. A. (1988). Toughness measurements on steel fibre concrete at elevated temperatures. International Journal of Cement Composites and Lightweight Concrete, 10(1), 39-47. 28. Alwyn Varghese, et al. (2018). Studies on Behaviour of Fire Affected Fiber Reinforced Concrete, International Journal of civil engineering and technology. 9(10), 1668–1675. Authors: Annamahesh A, Sunitha K Rangarajan S Paper Title: Study on Mechanical Behavior of Graphene Based Polymer Composites Abstract: Addition of Graphene in the matrix improves the mechanical properties, which makes it potentially good reinforcement in polymer composites. Graphene possess unique mechanical properties, which makes it attractive filler for producing multi-functional composites for a wide range of applications. It is an overview on the state of the art of graphene, including material synthesis and characterization. It helps in identifying its influence on the multi-functional and mechanical properties of the composites. Graphene was synthesized by a simple method (Hummer’s Method). Characterization is done by X-Ray Diffraction, SEM images for the prepared graphene. It is found that mechanical properties are improved tensile strength, flexural strength and heat distortion temperature of the glass epoxy laminated composite when the small amount of grapheme added to the epoxy matrix material.

Keywords: Epoxy Nano Composites, Graphene, Mechanical properties.

References: 1. Tapas kuilla, SambhuBhadra and Dahuyao, Recent advances in graphene based polymer composites, Polymer Science, 35,(10), 1350- 1375, 2010. 2. Yuchi Fan, Lianjun Wang and Jianlin, Preparation and Electrical properties of graphenenano sheet/Al2O3composites, Carbon Science, 48(10), 1743-1749, 2010. 3. Pandyaraj, V., Ravi Kumar, L., Chandramohan, D. Experimental investigation of mechanical properties of GFRP reinforced with coir and flax, International Journal of Mechanical Engineering and Technology,9(1034-1042), pp. 1034-1042. 4. Chandramohan.D and S.Rajesh, Increasing Combusting Resistance For Hybrid Composites, International Journal of Applied 73. Engineering Research,9(20), 6979-6985,2014. 5. Chandramohan.D et.al., Review On Tribological Performance Of Natural Fibre Reinforced Polymer Composites,Journal of Bio- and Tribo- Corrosion, Journal of Bio- and Tribo-Corrosion,4(4),55,2018. 403-406 6. Chandramohan, D and John Presin Kumar A. Experimental data on the properties of natural fiber particle reinforced polymer composite material, Data in Brief,13, pp. 460-468,2017. 7. Adams R.D and Singh M.M, The effect of immersion in sea water on the dynamic properties of fibre-reinforced flexibilised epoxy composites, Composite Structures, 31(2 ), 1995. 8. Jeffrey R.Potts and Todd.M.Alam, Thermomechanical properties of chemically modifiedgraphene/poly(methyl methacrylate) composites madeby in situ polymerization, Carbon Science, 49(10), s 2615-2623, 2011. 9. Chandramohan.D et.al., Progress of biomaterials in the field of orthopaedics, American Journal of Applied Sciences, 11 (4),623-630,2014. 10. Chandramohan, D., Marimuthu, K. Applications of natural fiber composites for replacement of orthopaedic alloys, Proceedings of the International Conference on Nanoscience, Engineering and Technology, 6167942, pp. 137-145,2011. 11. Chandramohan.D., and A.Senthilathiban. Effects of chemical treatment on jute fiber reinforced composites, International Journal of Applied Chemistry, 10 (1),153-162,2014. 12. Murali, B., Chandra Mohan, D., Nagoor Vali, S.K., Muthukumarasamy, S., Mohan, A. Mechanical behavior of chemically treated jute/polymer composites, Carbon - Science and Technology,6(1), pp. 330-335. 13. Murali, B., Chandra Mohan, D. Chemical treatment on hemp/polymer composites, Journal of Chemical and Pharmaceutical Research,6(9), pp. 419-423. 14. Chandramohan, D., Bharanichandar, J. Natural fiber reinforced polymer composites for automobile accessories, American Journal of Environmental Sciences,9(6), 494-504,2014. 15. Chandramohan, D.and Marimuthu, K., Natural fibre particle reinforced composite material for bone implant, European Journal of Scientific Research, Vol.54, No.3,384-406,2011. 16. Chandramohan, D. and Marimuthu, K., Characterization of natural fibers and their application in bone grafting substitutes, Acta of Bioengineering and Biomechanics, 13(1),77-84,2011. 17. Praveenkumar, R., Periyasamy, P., Mohanavel, V., Chandramohan, D. Microstructure and mechanical properties of MG/WC composites prepared by stir casting method, International Journal of Mechanical Engineering and Technology,9(10), pp. 1504-1511,2018. 18. Chandramohan.D and S.Rajesh, Increasing Combusting Resistance For Hybrid Composites, International Journal of Applied Engineering Research,9(20), 6979-6985,2014. Authors: Naveen Kumar K, Maheshwar Pratap Identifying Durability Failure Parts using 24 Months-In-Service Data: A Case-Based Empirical Study Paper Title: from an Automobile Manufacturer in India Abstract: This paper analyses the warranty claims data to identify faulty parts contributing to increasing failure using Weibull Analysis, in the automobile industry. Unlike studies in the past, this study uses 24 month service data to investigate the cause of failure due to faulty parts.Usually, the forecasting of the part failure is done 74. for the 3 months in service (MIS) data and the automobile manufacturers use this parameter to set Key Performance Indicators (KPI) for quality improvement among design engineers. The KPI set using 3MIS data is 407-411 used to determine 12 MIS and 24MIS KPIs. The period used in the development of KPIs affects the number of failed parts to be selected for improvement. As the monitoring period of countermeasure takes long durations, the repetitive failures added in data during the monitoring period, make the analysis complicated. Also, the seasonal pattern of failures cannot be addressed using 3MIS data. By increasing the analysis period to 24MIS, this paper finds evidence that increase in MIS leads to the identification of faulty parts that are causing repeated failures. The scope of the study extends towardsthe detection of new issues and towards monitoringthe effectiveness of existing countermeasures.This reduces warranty costs for the manufacturer and provides time to develop appropriate countermeasures along with increased monitoring period of failure parts leading to durability quality improvement.

Keywords: Warranty claims forecasting, Warranty Analysis,Weibull Analysis, Part Drability.

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A modeling framework for assessing the impact of new time/mileage warranty limits on the number and cost of automotive warranty claims. Reliability Engineering & System Safety, 88(2), 157-169. 14. Wu, J., McHenry, S., & Quandt, J. (2013). An application of Weibull analysis to determine failure rates in automotive components. In 23rd International Technical COnference on the Enhanced Safety of Vehicles (ESV) (pp. 13-0027). 15. Aldridge, D. S. (2006, January). Prediction of potential warranty exposure and life distribution based upon early warranty data. In Reliability and Maintainability Symposium, 2006. RAMS'06. Annual (pp. 159-164). IEEE. 16. Summit, R. A. (2012). Modelling component reliability using warranty data. ANZIAM Journal, 53, 437-450. 17. Lawless, J., Hu, J., & Cao, J. (1995). Methods for the estimation of failure distributions and rates from automobile warranty data. Lifetime Data Analysis, 1(3), 227-240. 18. Ciampi, A., Lawless, J. F., McKinney, S. M., &Singhal, K. (1988). Regression and recursive partition strategies in the analysis of medical survival data. Journal of clinical epidemiology, 41(8), 737-748. 19. Achcar, J. A., Brookmeyer, R., & Hunter, W. G. (1985). An application of Bayesian analysis to medical follow‐up data. Statistics in medicine, 4(4), 509-520. 20. Taghipour, S., Banjevic, D., &Jardine, A. K. (2011). Reliability analysis of maintenance data for complex medical devices. Quality and Reliability Engineering International, 27(1), 71-84. 21. Della Bona, A., Anusavice, K. J., &DeHoff, P. H. (2003). Weibull analysis and flexural strength of hot-pressed core and veneered ceramic structures. Dental Materials, 19(7), 662-669.Abernethy, Robert, The New Weibull Handbook, (3rd Edition), 1999 22. X. Yan, X. Ma and R. Zheng, ―Comparison of the Parameters Estimation Methods for 3-Parameter Weibull Distribution‖, Journal of Ningbo University(Natural Science & Engineering Edition, CHN, vol. 18, (2005) March, pp. 301–305. 23. Xie, M., Tang, Y., &Goh, T. N. (2002). A modified Weibull extension with bathtub-shaped failure rate function. Reliability Engineering & System Safety, 76(3), 279-285. 24. Gupta, R. C., Gupta, P. L., & Gupta, R. D. (1998). Modelling failure time data by Lehman alternatives. Communications in Statistics- Theory and methods, 27(4), 887-904. 25. Crowder, M. J., Kimber, A. C., Smith, R. L., & Sweeting, T. J. (1991). Statistical analysis of reliability dataChapman and Hall. New York. Authors: K. Ranjith Kumar, M. Surya Kalavathi Optimal Sizing of Grid Connected Hybrid PV/Wind/Battery Power System using Satin Bowerbird Paper Title: Optimization Abstract: Renewable energy sources are gaining more attention due to quick reduction of fossil fuels, global warming and energy crisis over the past few decades. Photovoltaic and Wind are the outstanding sources among the various offered renewable sources owing to the complementary nature of these sources. But the availability of the generated energy and the cost of the system are the two major limitations of these sources. Hybrid Power System (HPS) can alleviate the deviations in energy generated with the assistance of energy storage systems like batteries. On the other hand the cost of the energy needs to be minimized. Therefore, optimization of energy generation with storage system in light of investment cost and unpredictability alleviation is imposing to the monetary achievability of Hybrid Power System. This work presents a novel methodology based on Satin 75. Bower Bird optimization to obtain the optimal sizing and power management of hybrid photovoltaic/wind/battery power system. The HPS has been simulated using MATLAB using practical load and weather data of PV and wind system: which gives better performance under all operating conditions. 412-418

Keywords: Photovoltaic, Wind, Battery, Hybrid Power System, multi-objective optimization, and Satin Bower Bird

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Optimal hybrid renewable energy design in autonomous system using iterative-Pareto-fuzzy technique. Int J Electr Power Energy Syst 2015;64:242–9. 12. Jui-YL, Cheng C-L, Hui-C C. A mathematical technique for hybrid power system design with energy loss considerations. Energy Convers Manag 2014;82:301–7. 13. Sunanda S, Chandel SS. Review of software tools for hybrid renewable energy systems. Renew Sustain Energy Rev 2014;32:192–205. 14. Mellit A, Kalogirou SA, Drif M. Application of neural networks and genetic algorithms for sizing of photovoltaic systems. Renew Energy 2010;35:2881–93. 15. Rajkumar RK, Ramachandaramurthy VK, Yong BL, Chia DB. Techno-economical optimization of hybrid pv/wind/battery system using Neuro-Fuzzy. Energy 011;36:5148–53. 16. Luo Y, Shi L, Tu G. Optimal sizing and control strategy of isolated grid with wind power and energy storage system. Energy Convers Manag 2014;80:407–15. 17. Masoud S, Tarek YV. Multi-objective optimal design of hybrid renewable energy systems using PSO-simulation based approach. Renew Energy 2014;68:67–79. 18. Akbar M, Alireza A. Artificial bee swarm optimization for optimum sizing of a stand-alone PV/WT/FC hybrid system considering LPSP concept. Sol Energy 2014;107:227–35. 19. Y. V. P Kumar , R B Singu, Renewable energy based microgrid system sizing and energy management for green buildings. J. Mod. Power Syst. Clean Energy (2015) 3(1):1–13 20. Y. M. Atwa, E. F. El-Saadany, M. M. A. Salama, and R. Seethapathy, Optimal Renewable Resources Mix for Distribution System Energy Loss Minimization IEEE Transactions on power systems, vol. 25, no. 1, February 2010 360-370 21. M B. Shadmand, and R S. Balog, Multi-Objective Optimization and Design of Photovoltaic-Wind Hybrid System for Community Smart DC Microgrid, IEEE Transactions on Smart Grid 22. SH S.Moosavi, V K Bardsiri, Satin bowerbird optimizer: A new optimization algorithm to optimize ANFIS for software development effort estimation. Eng. Appl. Artif. Intell. 2017, 60, 1–15. 23. https://power.larc.nasa.gov/data-access-viewer/ Authors: S.P. Sundar Singh Sivam, Ganesh Babu Loganathan, K. Saravanan, S. RajendraKumar Outcome of the Coating Thickness on the Tool Act and Process Parameters When Dry Turning Ti–6Al– Paper Title: 4V Alloy: GRA Taguchi & ANOVA Abstract: In the primary days of Titanium Nitride tools, before coatings, tool manufacturers appreciated the tools would last elongate and scuffle cratering if they put a little bit of Titanium Nitride (TiN) in the combination when making the tool. This had the anticipated consequence, but the more TiN that was added, the feebler and more brittle the tool became. Then someone hit on the idea of applying a thin layer of TiN to the surface of the tool. This study results the Turning experiment conducted on the Ti–6Al–4V alloy of orthogonal array with Taughi grey relational analysis. Emphases on the optimization of turning process Constraints using the technique to get Min surface roughness (Ra), Roundness (s), Tool Wear and Cutting force in TIN with Different Coating Thickness by PVD Technique. A number of Turning experiments remained conducted mistreatment the L9 OA on All Gear Lathe. The experimentations remained achieved on Ti–6Al–4V alloy block of cutting tool of an CNMP120408-SM TN8025 of 12 mm diameter with cutting point 140 degrees, used throughout the experimental work beneath different Coating Thickness. Grey relational Analysis & ANOVA was used to work out the foremost important Cutting speed, feed rate, Depth of Cut and Different Coating Thickness of TIN with 50,100,150 μm by PVD Method which affecting the response.

76. Keywords: Ti–6al–4v, TIN Coatings, Grey Relation Taguchi method.

References: 419-423 1. https://www.productionmachining.com/articles/cutting-tool-coating-production 2. Tzeng, Y. F.; Chen, F. C. Multi objective process optimization for turning of tool steels. International Journal of Machining and Machinability of Materials. 1, 1(2006), pp. 76-93. DOI: 10.1504/IJMMM.2006.010659 3. Tosun, N. Determination of optimum parameters for multi-performance characteristics in Turning by using grey relational analysis. // International Journal of Advanced Manufacturing Technology. 28, 5-6(2006), pp. 450-455. DOI: 10.1007/s00170-004-2386-y 4. Chang, C. K.; Lu, H. S. Design optimization of cutting parameters for side milling operations with multiple performance characteristics. // International Journal of Advanced Manufacturing Technology. 32, 1-2(2007), pp. 18-26. DOI: 10.1007/s00170-005-0313-5 5. S.P. Sundar Singh Sivam, Mr. .Abburi Lakshman kumar, K. Sathiya Moorthy, RajendraKumar. “Investigation exploration outcome of Heat Treatment on Corrosion Resistance of AA 5083 in Marine Application”. International Journal of Chemical Sciences (ISSN 0972-768 X). Page No Page (15 – 22), 2015. 6. Hrelja, M.; Klancnik, S.; Irgolic, T.; Paulic, M.; Jurkovic, Z.; Balic, J.; Brezocnik, M. Particle swarm optimization approach for modelling a turning process. Advances in Production Engineering & Management. 9, 1(2014), pp. 21-30.DOI: 10.14743/apem2014.1.173 7. S.P. Sundar Singh Sivam, V.G Umasekar, Shubham Mishra, Avishek Mishra, Arpan Mondal. “Orbital cold forming technology - combining high quality forming with cost effectiveness - A review”. Indian Journal of Science and Technology. Vol 9(38), October 2016, DOI: 10.17485/ijst/2016/v9i38/91426. 8. Nian,C.Y., Yang,W.H.,Tarng, Y.S.,1999. Optimization of turning operations with multiple performance characteristics, Journal of Materials Processing Technology 95, 90–96. 9. Chang, C. K.; Lu, H. S. 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M., Mondal, B. and Ghosh, S., 2009.Optimisation of machining parameters for hard machining: grey relational theory approach and ANOVA, International Journal of Advanced Manufacturing Technology 45, 1068–1086. 14. Dewangan, S., Biswas, C. K., 2013. Optimization of machining parameters using grey relation analysis for EDM with impulse flushing, International Journal for Mechatronics and Manufacturing Systems 6, 144-158. 15. S.P. Sundar Singh Sivam, M.Gopal, S.Venkatasamy, Siddhartha Singh, “An Experimental Investigation And Optimisation Of Ecological Machining Parameters On Aluminium 6063 In Its Annealed And Unannealed Form”, Journal Of Chemical And Pharmaceutical Sciences. Page No Page (46 – 53), 2015. 16. Sivam, S.P.S.S., UmaSekar, V.G., Saravanan, K., RajendraKumar, S., Karthikeyan, P. and SathiyaMoorthy, K. (2016b) ‘Frequently used anisotropic yield criteria for sheet metal applications: a review’, Indian Journal of Science and Technology, December, Vol. 9, No. 47, DOI: 10.17485/ijst/2015/v8i1/92107. 17. S.P. Sundar Singh Sivam, Mrinal Deepak Ji Bhat, Shashank Natarajan, Nishant Chauhan.” Analysis of residual stresses, thermal stresses, cutting forces and other output responses of face milling operation on ze41 magnesium alloy." International Journal of Modern Manufacturing Technologies, Pp. No 92-100. ISSN 2067–3604, Vol. X, No. 1 / 2018. 18. Sivam, S. P. S. S., Saravanan, K., Pradeep, N., Moorthy, K. and Rajendrakumar, S. “The Grey Relational Analysis and Anova to Determine the Optimum Process Parameters for Friction Stir Welding of Ti and Mg Alloys”, Periodica Polytechnica Mechanical Engineering. doi: https://doi.org/10.3311/PPme.12117. 19. P. Sundar Singh Sivam, S., Saravanan, K., Pradeep, N., Rajendra Kumar, S., & Karuppiah, S. (2018). 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Sundar Singh Sivam, Durai Kumaran, Krishnaswamy Saravanan, Venugopal Guruswamy Umasekar, Sankarapandian Rajendrakumar, Karuppiah Sathiya Moorthy (2018) "THICKNESS DISTRIBUTION AND NUMERICAL MODELLING OF CONVENTIONAL SUPERPLASTIC FORMING IN AA2024 ALLOY", International Journal of Modern Manufacturing Technologies, ISSN 2067– 3604,76,85, Vol. X, No. 2 / 2018 23. S. P. S. S. Sivam, S. RajendraKumar, S. Karuppiah and A. Rajasekaran, "Competitive study of engineering change process management in manufacturing industry using product life cycle management — A case study," 2017 International Conference on Inventive Computing and Informatics (ICICI), Coimbatore, 2017, pp. 76-81. doi: 10.1109/ICICI.2017.8365247. Authors: Brijesh Kumar, Niraj Kumar Shukla, Sunil Kumar Sinha, Ajay Shekhar Pandey Autotransformer Connected 24 Pulse AC-DC Converter for Vector Controlled Induction Motor Drive: Paper Title: A Matlab Simulation Abstract: This paper deals with the steady state performance analysis of an autotransformer based 24- pulse ac- dc converter feeding variable frequency vector controlled squirrel cage induction motor drives at different mechanical load and constant reference speed. These variable frequency induction motor drives are generally operated in vector controlled mode due to their inherent advantages. There are three new elements which are added in the proposed model, first is three single phase autotransformers for phase shifting of 3-phase supply, second one is 24-pulse converter to eliminate the harmonics injected to the source and third one is interphase transformers to ensure the independent operation of the rectifier circuits. The feedback closed loop control system is used to control the speed of the induction motor, which has highly nonlinear torque-speed characteristics. This simulation is done to analyse the parameters of ac electric drive in terms of settling time, steady state error and overshoot. The simulation results show that the speed control performance reduces the steady state error and maximum overshoot under different load conditions.

Keywords: Vector Controlled Induction Motor, PWM Inverter, Autotransformer, Interphase Transformers, FOC.

77. References: 1. G. Seguier’ “Power electronic Converters: AC-DC Conversion,” McGraw Hill Book Company, New York, 1987. 2. B. K. Bose, “Modern Power Electronics and AC Drives”, Pearson Education, New Delhi,2001. 424-429 3. Dr. P. S. Bimbhra “Electrical machinery”, Khanna Publishers, Delhi. 4. J. Maslin, Sharon, G. F. Jones and Irwin “Electrical Induction Apparatus”, US Patent 2,307,527, Jan. 5, 1943. 5. D. A. Paice, “Power Electronic Converter Harmonics: Multipulse Methods for Clean Power”, IEEE Press, New York, 1996. 6. Mohamed E. El-Hawary “Principles of Electric Machines with Power Electronic applications,” Prentice- Hall.USA,1986. 7. D.A. Paice, “Wye connected 3-phase to 9-phase autotransformer with reduced winding currents,” U.S. Patent No. 6,191,968 B1, Feb. 20, 2001. 8. L. Chen and G. K. Horng, “A new passive 28-step current shaper for three- phase rectification,” IEEE Trans. on Industrial Electronics, Vol.47, No.6, Dec.2000, pp. 1212-1219. 9. Singh, G. Bhuvaneshwari and Vipin Garg, “A Twelve- Phase AC-DC Converter for Power Quality Improvements in Direct Torque Controlled Induction Motor Drives”, in Proc. Of Conf. IEEE- ICIEA 2006, May 2006, pp.257-263. 10. V. A. Boshnyaga, L.P. Kalinin and V.M. Postolaty, “Phase- Shifter”, US Patent 4,013,942, March 22, 1977. 11. Muhammad H. Rashid A Hand book of “Power Electronics, Circuits, Devices and Applications” Prentice-HallTM, New Delhi, 110017. 12. R. Krishnan, “Electric Motor Drives: Modeling, Analysis, and Control, “Prentice-Hall of India, New Delhi, 2003. 13. Gopal K. Dubey “Fundamentals of Electric Drives,”. Narosa Publishing house. New delhi,2005. 14. Ion Boldea and S. A. Nasar “Electric drives,” CRC Press, USA. 2006. 15. SINGH B., GARG V., BHUVANESWARI G.: ‘24-pulse ac–dc converter for harmonic mitigation, IET Power Electron., 2009, 2, (4), pp. 364–377 16. SINGH B., BHUVANESWARI G., GARG V.: ‘Polygon connected Autotransformer based 24-pulse converter for harmonic mitigation’.Pending Indian Patent, filed January 2006 17. Goran Rafajlovski and Krste Najdenkoski, “Trends in controlling high performance induction motor drives”, Republic of Macedonia. 18. Dal Y. Ohm, “Dynamic model of Induction Motors for vector control” Drivetech, Inc., Blacksburg, Virginia. 19. IEEE Standard 112-1991, "IEEE Standard Test Procedure for Polyphase Induction Motors and Generators", Institute of Electrical and Electronics Engineers, Inc. 20. B.L. Theraja and A.K. Theraja “A Text book of electrical technology”, Volume II S. Chand & Company LTD, New Delhi. Authors: Y. Srinivasa Rao, Mohammed Ali Hussain Adaptive Quality of Service Medium Access Control protocol for IEEE 802.11 based Mobile Ad hoc Paper Title: Network Abstract: Mobile ad hoc network is an infrastructure less wireless multi hop network with heterogeneous mobile nodes dispersed in wireless communication zone. MANET has different application in different fields, due to its distributed, adaptive and self-formation capabilities. Providing quality of service communication is one of the important considerable issue in MANET. One of the major facto to achieve the QoS communicates is efficient MAC protocol. This paper defines a adaptive – QoS MAC protocol (AQMP) for IEEE 802.11 based MANET. AQMP protocol improve the QoS based on majorly four considerations I). Prioritize the nodes based on their network load, II). Assignment of nodes for medium access, III). Prioritize the traffic based on their sensitivity, and IV). Assignment of MAC settings to prioritized traffic. Performance results indicates that proposed MAC protocol out perform in comparison with existing adaptive MAC protocols.

Keywords: MANET, QoS, MAC, Priority, access categoty and simulation.

References: 1. Mohammad, A. A. K., Mahmood, A. M., & Vemuru, S. “Providing Security Towards the MANETs Based on Chaotic Maps and Its Performance”, In Microelectronics, Electromagnetics and Telecommunications (pp. 145-152). Springer, (2019) 2. Rao, Y. Srinivasa, and Mohammed Ali Hussain. "Dynamic MAC Protocol to Enhancing the Quality of Real Time Traffic in MANET Using Network Load Adaptation." 1612-1617, (2018) 3. Neeraja, Y., V. Sumalatha, and Sd Muntaz Begum. "Comprehensive Survey of Medium Access Control Protocols for MANETs." International Journal of Emerging Trends & Technology in Computer Science 2, no. 3 (2013) 78. 4. Holt, Charles C. "Forecasting seasonals and trends by exponentially weighted moving averages." International journal of forecasting 20, no. 1: 5-10. (2004) 5. Draft, I. T. U. T. "recommendation and final draft international standard of joint video specification (ITU-T Rec. H. 264| ISO/IEC 14496-10 430-433 AVC)." Joint Video Team (JVT) of ISO/IEC MPEG and ITU-T VCEG, JVTG050 33, (2003) 6. Marwaha, S., Indulska, J. and Portmann, M., , December. Challenges and recent advances in QoS provisioning, signaling, routing and MAC protocols for MANETs. In Telecommunication Networks and Applications Conference, 2008. ATNAC 2008. Australasian (pp. 97-102), (2008) 7. Dhilip Kumar V, Vinoth Kumar V ,Kandar D, “Data Transmission Between Dedicated Short Range Communication and WiMAX for Efficient Vehicular Communication” Journal of Computational and Theoretical Nanoscience,Vol.15,No.8,pp.2649-2654, (2018) 8. Lee, Sunghee, and Kwangsue Chung. "The study of dynamic video frame mapping scheme for multimedia streaming over IEEE 802.11 e WLAN." International Journal of Multimedia and Ubiquitous Engineering 8, no. 1: 163-174, (2013) 9. Choi, Sunghyun, Javier Del Prado, and Stefan Mangold. "IEEE 802.11 e contention-based channel access (EDCF) performance evaluation." In Communications, 2003. ICC'03. IEEE International Conference on, vol. 2, pp. 1151-1156. IEEE, (2003). 10. Mangold, Stefan, Sunghyun Choi, Guido R. Hiertz, Ole Klein, and Bernhard Walke. "Analysis of IEEE 802.11 e for QoS support in wireless LANs." IEEE wireless communications 10, no. 6 (2003): 40-50. 11. Lucas, James M., and Michael S. Saccucci. "Exponentially weighted moving average control schemes: properties and enhancements." Technometrics 32, no. 1 : 1-12. (1990) 12. Benyassine, Adil, Eyal Shlomot, H-Y. Su, Dominique Massaloux, Claude Lamblin, and J-P. Petit. "ITU-T Recommendation G. 729 Annex B: a silence compression scheme for use with G. 729 optimized for V. 70 digital simultaneous voice and data applications." IEEE Communications Magazine 35, no. 9 : 64-73. (1997) 13. Schwarz, Heiko, Detlev Marpe, and Thomas Wiegand. "Overview of the scalable video coding extension of the H. 264/AVC standard." IEEE Transactions on circuits and systems for video technology 17, no. 9 : 1103-1120. (2007) 14. Issariyakul, Teerawat, and Ekram Hossain. "Introduction to Network Simulator 2 (NS2)." In Introduction to Network Simulator NS2, pp. 21-40. Springer, Boston, MA, 2012. 15. Rao, Y. Srinivasa, and Mohammed Ali Hussain. "Analytical Approach to Estimate the occurrence of bottleneck node in multi hop communication Network",IJRECE ,Vol.7,Issue1,ISSN:2393-9028, (2019) Authors: Anju Kalwar, Reema Ajmera, C.S. Lamba An Empirical Study in Small Firms for Web Application Development and Proposed New Parameters Paper Title: for Develop New Web Application Model Abstract: Over The last ten decades, the web application has imposed a great impact on the modern society. In companies and in other sectors of development many web development methodologies are being implemented on a daily basis for the development out of which some are being customized by the company itself . In this paper, I was surveyed many web development companies and fill the survey form using some parameters and find new parameters developing the new web application model.

Keywords: Web Application; Model; Empirical Study 79. References: 434-436 1. Fayad ME, Laitinen M, Ward RP. Thinking objectively: software engineering in the small. Communications of the ACM. 2000 Mar 1;43(3):115-8. 2. Hofer, C., 2002. Software development in Austria: results of an empirical study among small and very small enterprises. In Euromicro Conference, 2002. Proceedings. 28th (pp. 361-366). IEEE. 3. C. Y. Laporte, A. Renault, J. Desharnais, N.Habra, M. Abou El Fattah, and J. Bamba, In Proc. SWDC-REK, (2005), 153–163 4. Dangle, K.C., Larsen, P., Shaw, M. and Zelkowitz, M.V., 2005. Software process improvement in small organizations: a case study. IEEE software, 22(6), pp.68-75. 5. Ahmad, F., Baharom, F. and Husni, M., 2012. Investigating the Awareness of Applying the Important Web Application Development and Measurement Practices in Small Software Firms. arXiv preprint arXiv:1201.1967. 6. R KETTELERIJ, Faculty of Science, University of Amsterdam, www.science.uva.n, (2006). 7. Eldai, O.I., Ali, A.H.M.H. and Raviraja, S., 2008. Towards a new methodology for developing web-based systems. World Academy of Science, Engineering and Technology, 46, pp.190-195. 8. Mujumdar, A., Masiwal, G. and Chawan, P.M., 2012. Analysis of various software process models. International Journal of Engineering Research and Applications, 2(3), pp.2015-2021. Authors: S.P. Sundar Singh Sivam, Ganesh Babu Loganathan, K. Saravanan, S. RajendraKumar Multi-Response Enhancement of Drilling Process Parameters for AM 60 Magnesium Alloy as per the Paper Title: Quality Characteristics utilizing Taguchi-Ranking Algorithm and ANOVA Abstract: This investigation shows the improvement of Drilling parameters on AM-60 Mg alloy made with the help of Gravity Die Casting and with reactions upheld symmetrical cluster with Grey relational analysis - GRA. Which Focuses on the streamlining of Drilling constraints utilizing the system to get least surface Roughness (Ra), Tool Wear, Cutting Time, Power Requirement and Torque and Max MRR. Concentrates on the optimization of drilling constraints utilizing the procedure to get minimum surface roughness (Ra), Thrust Force, Burr size and Circularity Error. An amount of drilling experiments remained conducted mistreatment the L9 OA on CNC Machining Center. The trails remained achieved on Mg alloy block cutting tool of an ISO 460.1-1140- 034A0-XM GC3 of 12 mm diameter with Tool Angle 140 degrees, used throughout the experimental work beneath dry cutting conditions. This experimental study results like Ra, TF, CE, and BZ were analyzed. GRA & ANOVA was utilized to effort out the principal essential Spindle speed, feed rate, Titanium Coated for Drill Bits (TiN, TiAN, TiCN) with 0.020 in Coating Thickness manipulating the Reaction. The essential and collaboration effect of the data influences on the ordinary responses remain analyzed. The standard qualities and projected values are truly near.

Keywords: AM 60, Dry Drilling, Grey relational Analysis Taguchi method

References: 1. Davim JP (2003) Study of drilling metal-matrix composites based on the Taguchi Techniques. J Mater Process Technol 132:250– 254 2. Tosun G, Mehtap Muratoglu (2004) The drilling of Al/SiCp metal matrix composites. Part I: Microstructure, Compos Sci Tech 64: 209– 308 3. Tosun G, MehtapMuratoglu (2004) The drilling of Al/SiCp metal matrix composites. Part II: Work piece Surface integrity, Compos Sci Tech 64:1413–1418 4. Davim JP (2003) Design of optimization of cutting parameters for turning metal matrix composites based on the orthogonal arrays. J Mater Process Technol 132:340–344 5. Manna A, Bhattacharayya B (2003) A study of machinability of Al-SiC metal matrix Composites. J Mater Process Technol 140: 711–716 6. Mohan NS, Ramachandra A, Kulkarni SM (2005) Influence of Process parameters on cutting force and torque during drilling of glass-fiber polyester reinforced composites. Compos Struct 71:407– 413 7. Tosun N (2006) Determination of optimum parameters for multiperformance characteristics in drilling by using grey relational analysis. Int J Adv Manuf Technol 28:450–455 8. Lin CL, Lin JL, Ko TC (2002) Optimization of the EDM Process based on the orthogonal array with fuzzy logic and 9. Grey relational analysis method. Int J AdvManufTechnol 19: 271–277 10. Deng J (1989) Introduction to grey system. Grey Syst 1:1–24 80. 11. Jeyapaul R, Shahabudeen P, Krishnaiah K (2005) Quality management research by considering multi-response problems in the Taguchi method - a review. Int J AdvManufTechnol 26: 1331–1337 437-440 12. Multi response optimization of machining parameters of drilling Al/SiC metal matrix composite using grey relational analysis in the Taguchi method 2007, A. NoorulHaq &P. Marimuthu &R. Jeyapaul, Int J AdvManufTechnol (2008) 37:250–255, DOI 10.1007/s00170- 007-0981-4. 13. SIVAM, S. P. Sundar Singh et al.”Multi Response Optimization of Setting Input Variables for Getting Better Product Quality in Machining of Magnesium AM60 by Grey Relation Analysis and ANOVA." Periodica Polytechnica Mechanical Engineering, [S.l.], 2017. ISSN 1587- 379X. https://doi.org/10.3311/PPme.11034 14. Sivam, S.P.S.S et al.,, , An Experimental Investigation And Optimisation Of Ecological Machining Parameters On Aluminium 6063 In Its Annealed And Unannealed Form, Journal Of Chemical And Pharmaceutical Sciences. Page No Page (46 – 53), 2015. 15. Sivam, S.P.S.S et al.,, 2015, “Application of Forming Limit Diagram and Yield Surface Diagram to Study Anisotropic Mechanical Properties of Annealed and Unannealed SPRC 440E Steels”. Journal of Chemical and Pharmaceutical Sciences. ISSN: 0974-2115, Page No (15 – 22). 16. Sivam, S.P.S.S et al.,. (2016). Investigation exploration outcome of heat treatment on corrosion resistance of AA 5083 in marine application. Journal of Science and Technology. 14 : 453-460.14 (S2), 2016, ISSN 0972-768X. 17. Sivam, S.P.S.S., Umasekar, V.G., Mishra, A., Mishra, S. and Mondal, A. (2016) ‘Orbital cold forming technology – combining high quality forming with cost effectiveness – a review’, Indian Journal of Science and Technology, October, Vol. 9, No. 38, DOI: 10.17485/ijst/2016/ v9i38/91426. 18. Sivam, S.P.S.S et al.,. (2016) ‘Frequently used anisotropic yield criteria for sheet metal applications: a review’, Indian Journal of Science and Technology, December, Vol. 9, No. 47, DOI: 10.17485/ijst/2015/v8i1/92107. 19. S.P. Sundar Singh Sivam et al,.” Analysis of residual stresses, thermal stresses, cutting forces and other output responses of face milling operation on ze41 magnesium alloy." International Journal of Modern Manufacturing Technologies, Pp. No 92-100. ISSN 2067–3604, Vol. X, No. 1 / 2018. 20. Sivam, S. P. S. S et al., “The Grey Relational Analysis and Anova to Determine the Optimum Process Parameters for Friction Stir Welding of Ti and Mg Alloys”, Periodica Polytechnica Mechanical Engineering. doi: https://doi.org/10.3311/PPme.12117. 21. P. Sundar Singh Sivam et al, S., (2018). Comparison of Manufacturing Data Analysis For 5 & 3-Axis Vertical Machining Center for the Time and Tool Benefits of Industries. International Journal of Engineering & Technology, 7(4.5), 196-201. doi:http://dx.doi.org/10.14419/ijet.v7i4.5.20044. 22. P. Sundar Singh Sivam et al, (2018). Development of Vibrator Feeding Mechanism Using Two Sets of Rollers for the Separation of Ball Grading For Industry Benefits. International Journal of Engineering & Technology, 7(4.5), 202-206. doi:http://dx.doi.org/10.14419/ijet.v7i4.5.20045 23. S. P. Sundar Singh Sivam et al, (2019) A study of cooling time, copper reduction and effects of alloying elements on the microstructure and mechanical properties of SG iron casting during machining, Australian Journal of Mechanical Engineering, DOI: 10.1080/14484846.2018.1560679 24. S.P. Sundar Singh Sivam et al, (2018) "THICKNESS DISTRIBUTION AND NUMERICAL MODELLING OF CONVENTIONAL SUPERPLASTIC FORMING IN AA2024 ALLOY", International Journal of Modern Manufacturing Technologies, ISSN 2067– 3604,76,85, Vol. X, No. 2 / 2018 25. S. P. S. S. Sivam et al, "Competitive study of engineering change process management in manufacturing industry using product life cycle management — A case study," 2017 International Conference on Inventive Computing and Informatics (ICICI), Coimbatore, 2017, pp. 76- 81. doi: 10.1109/ICICI.2017.8365247. Authors: D. Krishna Madhuri Paper Title: A Machine Learning based Framework for Sentiment Classification: Indian Railways Case Study Abstract: Machine learning in the field of computer science is the application of Artificial Intelligence (AI) that helps in making systems intelligent. It focuses on producing algorithms that may lead to AI applications in the real world. As enterprises are producing huge amount of data, it became indispensable to have machine learning techniques in place for discovering business intelligence from data for strategic decision making. However, in the contemporary era, the traditional data may be deemed inadequate for decision making. The rationale behind this is that people of all walks of life are able to exchange ideas and opinions/sentiments over social media like Facebook and Twitter. In other words, there is social feedback exists in Online Social Networks (OSNs). Collection of social media data related to business and using machine learning algorithms to extract useful knowhow from such data bestows competitive edge to enterprises. The existing literature on sentiment analysis has plenty of methods for discovering sentiments. However, it is still an open problem to have optimizations. In this paper we proposed a framework for discovering sentiments from tweets of Indian Railways. This is a domain specific framework which leverages business intelligence through different classifiers such as C4.5, Naive Bayes, SVM and Random Forest. An evaluation procedure with measures like precision, recall, F-Measure and accuracy is provided. The empirical study with Indian Railways case study revealed that the proposed framework is useful in sentiment analysis and can be tailored to suit other domains as well. By considering the atweets of Indian Railways as a case study evaluation is made in terms of precision, recall and F-Measure.

Keywords: Sentiment classification, machine learning, C4.5, Naive Bayes, SVM, Random Forest

References: 1. Duyu Tang, Furu Wei, Nan Yang, Ming Zhou, Ting Liu and Bing Qin. (2014). Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification, p1555–1565. 2. Duyu Tang, Bing Qin and Ting Liu. (2015). Document Modeling with Gated Recurrent Neural Network for Sentiment Classification, p1422–1432. 3. Abinash Tripathy, Ankit Agrawal and Santanu Kumar Rath. (2016). Classification of sentiment reviews using n-gram machine learning approach. elsever, p117–126. 4. Xiang Zhang, Junbo Zhao and Yann LeCun. (2015). Character-level Convolutional Networks for Text Classification, p1-9. 5. Leona Yi-Fan Su, Michael A. Cacciatore, Xuan Liang, Dominique Brossard, Dietram A. Scheufele and Michael A. Xenos. (2016). Analyzing public sentiments online combining human- and computer-based content analysis. Information, Communication & Society, p1- 81. 24. 6. Lei Zhang, Riddhiman Ghosh, Mohamed Dekhil, Meichun Hsu and Bing Liu . (2011). Combining Lexicon-based and Learning-based Methods for Twitter Sentiment Analysis, p1-9. 441-445 7. Zhiyuan Chen, Nianzu Ma and Bing Liu. (2018). Lifelong Learning for Sentiment Classification, p1-8. 8. Navonil Majumder, Soujanya Poria, Alexander Gelbukh and Erik Cambria . (2017). Deep Learning-Based Document Modeling for Personality Detection from Text. iEEE iNTElliGENT SYSTEmS, p74-79. 9. Mehdi Allahyari. (2017). A Brief Survey of Text Mining Classification, Clustering and Extraction Techniques. KDD Bigdas, p1-13. 10. Maria Giatsoglou. (2017). Sentiment analysis leveraging emotions and word embeddings. elsever, p214–224. 11. Aytug˘ Onan and Serdar Korukog˘lu. (2017). A feature selection model based on genetic rank aggregation for text sentiment classification. Journal of Information Science. 43 (1), p25–38. 12. Yafeng Ren, Yue Zhang, Meishan Zhang and Donghong Ji. (2016). Context-Sensitive Twitter Sentiment Classification Using Neural Network, p1-7. 13. Soujanya Poria, Erik Cambria, Grégoire Winterstein and Guang-Bin Huang. (2014). Sentic patterns: Dependency-based rules for concept- level sentiment analysis. Elsever, 69, p45–63. 14. Shuhua Monica Liu and Jiun-Hung Chen. (2015). A multi-label classification based approach for sentiment classification. elsever . 42, p1083–1093. 15. Weiyuan Li and Hua Xu . (2013). Text-based emotion classification using emotion cause extraction. elsever, p1-8. 16. Xavier Glorot. (2011). Domain Adaptation for Large-Scale Sentiment Classification A Deep Learning Approach, p1-8. 17. Richard Socher, Alex Perelygin, Jean Y. Wu, Jason Chuang, Christopher D. Manning, Andrew Y. Ng and Christopher Potts. (2013). Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank, p1631–1642. 18. Alexander Pak and Patrick Paroubek. (2013). Twitter as a Corpus for Sentiment Analysis and Opinion Mining, p1320-1326. 19. Mike Thelwall, Kevan Buckley, Georgios Paltoglou and Di Cai . (2012). Sentiment Strength Detection in Short Informal Text. Journal of the American Society for Information Science and Technology. 61 (12), p2544–2558. 20. Andrew L. Maas, Raymond E. Daly, Peter T. Pham, Dan Huang, Andrew Y. Ng and Christopher Potts. (2011). Learning Word Vectors for Sentiment Analysis, p142–150. 21. Tetsuji Nakagawa. (2010). Dependency Tree-based Sentiment Classification using CRFs with Hidden Variables, p786–794. 22. Long Jiang, Mo Yu, Ming Zhou, Xiaohua Liu and Tiejun Zhao. (2011). Target-dependent Twitter Sentiment Classification, p151–160. 23. Xiaolong Wang. (2011). Topic Sentiment Analysis in Twitter: A Graph-based Hashtag Sentiment Classification Approach. ACM, p1-10. 24. G.Vinodhini and RM.Chandrasekaran. (2012). Sentiment Analysis and Opinion Mining: A Survey. International Journal of Advanced Research in Computer Science and Software Engineering. 2 (6), p1-11. 25. Efstratios Kontopoulos . (2013). Ontology-based sentiment analysis of twitter posts. elsever, p4065–4074. 26. Yan Dang, Yulei Zhang, and Hsinchun Chen. (2010). A Lexicon-Enhanced Method for Sentiment Classification: An Experiment on Online Product Reviews. IEEE, p1-8. 27. Xia Hu, Lei Tang, Jiliang Tang and Huan Liu. (2013). Exploiting Social Relations for Sentiment Analysis in Microblogging. ACM, p1- 10. 28. Morteza Babaie. (2011). Classification and Retrieval of Digital Pathology Scans: A New Dataset. IEEE, p1-10. 29. Soujanya Poriaa. (2017). Ensemble application of convolutional neural networks and multiple kernel learning for multimodal sentiment analysis. elsever, p217–230. Authors: P. Anusha, G. Kalpana, T. Vigneswaran Paper Title: FPGA Implementation of Logarithmic Multiplier 82. Abstract: logarithmic multiplier is the vital procedure mainly for DSP, image processing and 3-D graphic applications. Log multiplier converts the multiplication into addition; hence it will reduce the number of 446-449 computation steps to speed up the multiplication. In multiplication process, the reduction of partial products contributes most to the overall delay, power and area. Adder Compressors are employed to reduce the latency of this step. Analysis is done by coding the designs in HDL and synthesized with Xilinx ISE 14.7 using Virtex6 or spartan3 series of FPGA. Optimized architectures are synthesized using Encounter RTL Compiler Tool in Cadence and obtained the reports on power and area. The results indicate the better speed high performance and overall efficiency of logarithmic multiplication

Keywords: LNS (logarithmic number systems), Arithmetic circuit, multiplication, LUT, Mitchell.

References: 1. Bansal Y, Madhu C and Kaur P. (2014 ) High speed Vedic multiplier designs-A review on IEEE Recent Advances in Engineering and Computational Sciences (RAECS), (pp. 1-6). 2. M. Fonseca (2011) “Design of Pipelined Butterflies from Radix-2 FFT with Decimation in Time Algorithm using Efficient Adder Compressors,” in Circuits and Systems (LASCAS), IEEE Second Latin American Symposium on, feb. 2011, pp. 1-4 3. John N Mitchell.(1962) Computer multiplication and division using binary logarithms. IRE Transactions on Electronic Computers, (4):pp. 512-517. 4. V. Mahalingam, N. Rangantathan, (2006) Improving Accuracy in Mitchell’s Logarithmic Multiplication Using Operand Decomposition, IEEE Transactions on Computers, Vol. 55, No. 2, pp. 1523-1535 5. Ellaithy DM, El. Moursy MA, Ibrahim GH, Zaki A and Zekry (2017) A. Double Logarithmic Arithmetic Technique for Low-Power 3-D Graphics Applications. IEEE Transactions on Very Large Scale Integration (VLSI) Systems: pp. 2144-52. 6. Ioannis Kouretas, Charalambos Basetas and Vassilis Paliouras. (2014) Low-power logarithmic number system addition/subtraction and their impact on digital filters. IEEE transactions on computers, 62(11), pp. 2196-2209. 7. R. R. Selina, (2013) “VLSI implementation of piecewise approximated antilogarithmicconverter,” in Proc. Int. Conf. Commun. Signal Process. . (ICCSP), pp. 763–766. 8. Rabaey, J.M., Chandrakasan and Nikolic, B. (2002): ‘Digital integrated circuits’ (Prentice Hall). 9. K. Johansson, O. Gustafsson and L. Wanhammar, (2008) “Implementation of elementary functions for logarithmic number systems,” IET Comput. Digit.Tech., vol. 2, no. 4, pp. 295–304. 10. C.T. Kuo and T.B. Juang, (2012) “A lower error antilogarithmic converter using novel four-region piecewise-linear approximation,” in Proc. IEEE Circuits Syst. Conf., vol. 2. Dec., pp. 507 510. Authors: D. Helen Paper Title: An Energy-Efficient Routing using Fuzzy Model Based Clustering for Mobile Ad Hoc Network Abstract: Mobile Ad hoc NETwork (MANET) is an infrastructure-less, autonomous network, the nodes are connected through the wireless multi-hop links. The absence of infrastructure and dynamic environment demands to form a new set of routing protocol for MANET. Routing is a main issue in MANET due to its mobility and inadequate resource availability. Especially, energy-efficient routing is essential because every node is operated by exhausted battery power. Power failure of an individual node partitioned the entire network architecture. So, routing in MANET shall use the available battery energy in an effective way to enhance the network lifetime. The Fuzzy Model-based Clustering (FMC) algorithm recognizes the reliable and loop-free route between the nodes by choosing an optimal cluster head. The FMC uses the speed, residual energy and signal strength as factors in order to find the efficient cluster head. The nodes are implementing the fuzzy logic mechanism to estimate the node cost. The node with the highest cost is selected as cluster head. The cluster head achieves the data packet transmission. Hence, the FMC preserves the stable network by reducing the reselection of cluster head and minimizes the re-affiliation of all the nodes in the cluster. The FMC algorithm maintains the packet delivery ratio, average delay, energy consumption by 87.3%, 17.5 %, and 25.83% respectively, over the existing AODV and FCESRB protocols.

Keywords: autonomous, clustering, fuzzy logic, signal strength.

References: 1. Adebanjo Adekiigbe. A and Kamalrulnizam Abu Bakar. K (2013),” Using Fuzzy Logic to Improve Cluster Based Routing Protocol in 83. Mesh Client Networks”, International Journal of Innovative Computing, Vol.3, No.2, pp. 1-11. 2. Beongku An. B and Symeon Papavassiliou. S (2001),” A Mobility-Based Clustering Approach To Support Mobility Management And Multicast Routing In Mobile Ad-Hoc Wireless Networks”, International Journal of Network Management, Vol.11, No.6, pp. 387-395. 450-454 3. Deny J, Sundhararajan M (2016),” Performance assessment and comparisons of single and group mobility in MANET,Insdian Journal of Science and Technology Vol.9, No.21,pp.1–6. 4. Floriano De Rango.F, Francesca Guerriero.F and Peppino Fazio.P (2012), “Link-stability and energy aware routing protocol in distributed wireless networks”, IEEE Transaction Parallel Distributed System, Vol.23, No.4, pp.713-726. 5. Ghosekar. P, Katkar. G and Ghorpade. P (2010),” Mobile Ad Hoc Networking: Imperatives and Challenges”, International Journal of Computer Applications, Special Issue on “Mobile Ad-Hoc Networks”, pp. 153-158. 6. Hakan Bagci. H, Adnan Yazici.A (2010),”An Energy Aware Fuzzy unequal Clustering Algorithm for Wireless Sensor Networks, In Proceedings of IEEE World Congress on Computational Intelligence, Barcelona, Spain. 7. Heinzelman. W, Chandrakasan. A and Balakrishnan. H (2000),” Energy Efficient Communication Protocol for Wireless Microsensor Networks”, Proceeding of the 33rd annual Hawaii International Conference on System Sciences Vol.8, pp.1–10. 8. Helen D, Arivazhagan (2016),” An Intelligent Energy Efficient Routing Protocol for Mobile Ad-Hoc Network”, Indian Journal of Science and Technology, Vol 9. No.45,pp.1-5 9. Jeoren Hoebeke. J, Ingrid Moerman.I, Bart Dhoedt.B and Piet Demester.P (2004),” An Overview of Mobile ad hoc Networks: Applications &Challenges”, Journal of the Communications Network, Vol.3, No.3, pp.60-66. 10. Jiang. M, Li. J. and Tay. Y. C, (1999), “Cluster Based Routing Protocol (CBRP)”, IETF, Internet draft. 11. Larki.F.A, Seyed. J. M and Harounabadi .A (2014), “Increased Longevity of Wireless Ad hoc Network through Fuzzy System”, Decision Science Letters, Vo.3, No.3, pp. 1-9. 12. Muneer Bani Yassein.M, Naveen Hijazi.N,” Improvement On Cluster Based Routing Protocol Using Vice Cluster Head”, In Proceeding of 4th International Conference on Next Generation Mobile Application, Services And Technologies, pp.137-141. 13. Sahar Adabi.S,Sam Jabbehdari.S, Ali Rezaee,”Distributed Fuzzy Score Based Clustering Algorithm For Mobile Ad Hoc Network. In Proceeding of 3rd IEEE Asia-Pacific Services Computing Conference, pp.193-198. 14. Saleh Ali Al-Omari.K, Putra Sumari.P (2010), “ An overview of Mobile Ad hoc networks for existing protocols and applications”, International journal on applications of graph theory in wireless ad hoc networks and sensor networks, Vol.2,No.1, pp.87-110. 15. Shayesteh Tabatabaei.S,Mohammad Teshnehlab.M,syed Javad Mirabedini.S(2015),“Fuzzy-Based Routing Protocol to Increase Throughput in Mobile Ad Hoc Networks”, Wireless Personal communications, Vol.84,No.4 , pp. 2307–2325. 16. Shanthi HJ and Marie Anita E (2014),” Performance analysis of black hole attacks in geographical routing MANET”, International Journal of Engineering and Technology (IJET) vol.6,No.5,pp-2382-2387. Authors: R. Srinivasan, S. Poongavanam , R. Vettriselvan, J. Rengamani, Fabian Andrew James Network Optimization for Distribution of South Based OEM’s Passenger Vehicles to other Zones of Paper Title: India with Reduced Lead-Time Abstract: A survey conducted among top auto makers in India highlighted the fact that technology is widely sent to be a supply chain enable, reducing inventory levels and stocking, shortening lead times and fostering as sprit of collaboration with suppliers and dealers. IT Managers indicate lack of alignment between business goals and it implementation plans in majority of the companies. Although it found that there is a high awareness among Indian Tier-1 companies regarding lead time. The usage of productive enhancing tools such as data analytics, ERP, rivet care still at low levels specially among Tier-2 suppliers due to challenges such as cultural, financial, organizational and technological barriers to be overcome majority of the maimed at improving service levels. E-payment and clearance facilities and enhancing visibility leading to be after coordination and reducing on core activities, vendor base rationalization at all echelons of the supply chain. 84. Keywords: Lead time, Network Optimization, OEM, Passenger, Vehicle 455-458 References: 1. Rajasekar D (2017). A study on motivation level of employees in automobile industry, International journal of Mechanical engineering and technology, 8(12), pp744- 749. 2. Shameem A (2017). Innovative strategy for launch of new brand of cement, International journal of Mechanical engineering and technology,8(5), pp 411- 417. 3. Nishant Kaushik, Executive- Business Development Wallenius Wilhelmsen Logistics (India) Pvt. Ltd 4. Saranya.S Human Resource Manager Wallenius Wilhelmsen Logistics (India) Pvt. Ltd 5. Industry report 2014 6. Society of Indian Automobile Industry (SIAM) 7. State Transport Authority (Tamil nadu) 8. Vettriselvan R., Ruben Anto., & Jesu Rajan FSA (2018), Rural lighting for energy conservations and sustainable development, International Journal of Mechanical Engineering and Technology, 9(7):604-611. Authors: Arif Sari, Samson Oluwaseun Fadiya, Acheme Okolobia Odeh. Paper Title: A Rumor Algorithm Propagation Considering Block Omission in a Blockchain System Abstract: In this article we experimented the rumor spreading algorithm of data propagation in a blockchain system with specific focus on the block omission rate. The algorithm introduced here was modeled and simulated by a new class of extended Petri nets called “Elementary nets”. This type of nets is suitable for the representation of the functions of an information system. The descriptive and analytical power of the elementary net was employed in this article to model and perform simulation experiments to measure the omission rates of blocks propagated in the blockchain network using the rumor algorithm. The aim of the research is to model and simulate block data propagation in the blockchain system considering block omission. The modified rumor algorithm for the blockchain system was proposed in our Ph.D. thesis with the introduction of a switching module that regulate block dissemination in the model. The result of our research shows a steady decline in the block omission rates with increasing number of nodes. This is a very significant criteria in the implementation of a reliable and scalable block propagation scheme for the blockchain system.

Keywords: Blockchain, Block propagation, Elementary nets, Petri nets, Rumor Algorithm.

References: 1. Nakamoto, S. (2008). Bitcoin: A peer-to-peer electronic cash system. 2. Li, J. (2018). Data Transmission Scheme Considering Node Failure for Blockchain. Wireless Personal Communications, 1-16. 85. 3. Kostin, A., & Ilushechkina, L. (2010). Modeling and Simulation of Distributed Systems:(With CD-ROM). World Scientific Publishing Company. 4. Bahri, L., Carminati, B., & Ferrari, E. (2018). Decentralized privacy preserving services for online social networks. Online Social 459-467 Networks and Media, 6, 18-25. 5. Biswas, K., & Muthukkumarasamy, V. (2016, December). Securing smart cities using blockchain technology. In High Performance Computing and Communications; IEEE 14th International Conference on Smart City; IEEE 2nd International Conference on Data Science and Systems (HPCC/SmartCity/DSS), 2016 IEEE 18th International Conference on (pp. 1392-1393). IEEE. 6. Qin, B., Huang, J., Wang, Q., Luo, X., Liang, B., & Shi, W. (2017). Cecoin: A decentralized PKI mitigating MitM attacks. Future Generation Computer Systems. 7. Sagirlar, G., Carminati, B., Ferrari, E., Sheehan, J. D., & Ragnoli, E. (2018). Hybrid-IoT: Hybrid Blockchain Architecture for Internet of Things-PoW Sub-blockchains. arXiv preprint arXiv:1804.03903. 8. Feng, Q., He, D., Zeadally, S., Khan, M. K., & Kumar, N. (2018). A survey on privacy protection in blockchain system. Journal of Network and Computer Applications. 9. Karp, R., Schindelhauer, C., Shenker, S., & Vocking, B. (2000). Randomized rumor spreading. In Foundations of Computer Science, 2000. Proceedings. 41st Annual Symposium on (pp. 565-574). IEEE. 10. Danzi, P., Kalør, A. E., Stefanović, Č., & Popovski, P. (2017). Analysis of the Communication Traffic for Blockchain Synchronization of IoT Devices. arXiv preprint arXiv:1711.00540. 11. Mattila, J. (2016). The blockchain phenomenon. ):‘Book The Blockchain Phenomenon’(Berkeley Roundtable of the International Economy, 2016, edn.). 12. Kosba, A., Miller, A., Shi, E., Wen, Z., & Papamanthou, C. (2016, May). Hawk: The blockchain model of cryptography and privacy- preserving smart contracts. In Security and Privacy (SP), 2016 IEEE Symposium on (pp. 839-858). IEEE. 13. Zyskind, G., & Nathan, O. (2015, May). Decentralizing privacy: Using blockchain to protect personal data. In Security and Privacy Workshops (SPW), 2015 IEEE (pp. 180-184). IEEE. 14. Zheng, Z., Xie, S., Dai, H., Chen, X., & Wang, H. (2017, June). An overview of blockchain technology: Architecture, consensus, and future trends. In Big Data (BigData Congress), 2017 IEEE International Congress on (pp. 557-564). IEEE. 15. Cachin, C. (2016, July). Architecture of the Hyperledger blockchain fabric. In Workshop on Distributed Cryptocurrencies and Consensus Ledgers. 16. Xu, X., Weber, I., Staples, M., Zhu, L., Bosch, J., Bass, L., ... & Rimba, P. (2017, April). A taxonomy of blockchain-based systems for architecture design. In Software Architecture (ICSA), 2017 IEEE International Conference on (pp. 243-252). IEEE. 17. Iansiti, M., & Lakhani, K. R. (2017). The truth about blockchain. Harvard Business Review, 95(1), 118-127. 18. Yasaweerasinghelage, R., Staples, M., & Weber, I. (2017, April). Predicting latency of blockchain-based systems using architectural modelling and simulation. In Software Architecture (ICSA), 2017 IEEE International Conference on (pp. 253-256). IEEE. 19. Göbel, J., Keeler, H. P., Krzesinski, A. E., & Taylor, P. G. (2016). Bitcoin blockchain dynamics: The selfish-mine strategy in the presence of propagation delay. Performance Evaluation, 104, 23-41. 20. Lee, V., & Wei, H. (2016, June). Exploratory simulation models for fraudulent detection in Bitcoin system. In Industrial Electronics and Applications (ICIEA), 2016 IEEE 11th Conference on (pp. 1972-1977). IEEE. 21. Tosh, D. K., Shetty, S., Liang, X., Kamhoua, C. A., Kwiat, K. A., & Njilla, L. (2017, May). Security implications of blockchain cloud with analysis of block withholding attack. In Proceedings of the 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (pp. 458-467). IEEE Press. 22. Cachin, C., De Caro, A., Moreno-Sanchez, P., Tackmann, B., & Vukolic, M. (2017). The Transaction Graph for Modeling Blockchain Semantics. Cryptology ePrint Archive, Report 2017/1070. 23. Xiong, Z., Zhang, Y., Niyato, D., Wang, P., & Han, Z. (2017). When mobile blockchain meets edge computing: challenges and applications. arXiv preprint arXiv:1711.05938. Aitbek Kakimov, Aleksandr Mayorov, Nadir Ibragimov, Gulmira Zhumadilova, Alibek Muratbayev, Authors: Madina Jumazhanova, Zhunus Soltanbekov, Zhanibek Yessimbekov Paper Title: Design of Equipment for Probiotics Encapsulation Abstract: This paper describes the construction and operating principle of the probiotics encapsulation equipment. The capsules were obtained by drop-by-drop method with different concentration of alginate (0.5, 1.0 and 1.5%) and gelatin. The viscosity of gelling liquids was measured at different temperatures. The most optimal option is the composition of capsules containing 1% alginate and 1% gelatin, the solution should be used at a temperature of 30-50 ° C. Capsules made from this composition have a rounded shape, equal size, soft texture, stable for physical impact.

Keywords: encapsulation, probiotic, alginate, capsule, installation

86. References: 1. Bepeyeva, A., de Barros, J.M., Albadran, H., Kakimov, A.K., Kakimova, Z.K., Charalampopoulos, D, Khutoryanskiy, V.V., 2017. Encapsulation of Lactobacillus casei into calcium pectinate‐chitosan beads for enteric delivery. Journal of food science, 82(12), pp. 2954- 468-471 2959. 2. Kakimov, A., Kakimova, Z., Mirasheva, G., Bepeyeva, A., Toleubekova, S., Jumazhanova, M., Zhumadilova, G., Yessimbekov, Z., 2017. Amino acid composition of sour-milk drink with encapsulated probiotics. Annual Research and Review in Biology, 18(1), ARRB-36079. 3. Kakimov, A.K., Mayorov, A.A., Ibragimov, N.K., Zhumadilova, G.A., 2017. Capsule forming by drop-by-drop method. Proceeding of international conference “Kazakhstan-Kholod 2017”, Almaty, Kazakhstan, pp. 107-109 4. Burgain, J., Gaiani, C., Linder, M., Scher, J., 2011. Encapsulation of probiotic living cells: From laboratory scale to industrial applications. Journal of food engineering, 104(4), pp. 467-483. 5. Cook, M.T., Tzortzis, G., Charalampopoulos, D., Khutoryanskiy, V.V., 2012. Microencapsulation of probiotics for gastrointestinal delivery. Journal of Controlled Release, 162(1), pp. 56-67. 6. Paques, J.P., van der Linden, E., van Rijn, C.J., Sagis, L.M., 2014. Preparation methods of alginate nanoparticles. Advances in colloid and interface science, 209; pp. 163-171. 7. Vivek, K., 2013. Use of encapsulated probiotics in dairy based foods. International Journal of Food, Agriculture and Veterinary Sciences, 3(1), pp. 188-199. Authors: K. Selvakumar , G. Pattabirani A Clustered Fuzzy and Dynamically Well Organized Load Balancing Algorithm (CFDLB) for Network Paper Title: Life Time Enhancement in Wireless Sensor Networks Abstract: In recent past, wireless sensor networks have been exploited and tapped for their immense potential as they are ideal choices for real time wireless communication applications. Nodes which form the back bone of the wireless sensor networks (WSN) together with an efficient routing scheme define the overall efficiency of the WSN. In recent times, research on load balancing algorithms have been investigated as the nature of incoming traffic composed of packets of information is mostly stochastic and unpredictable in nature. Since the nodes are limited by their power provision in the form of batteries which cannot be frequently replaced, are prone to over utilization in transmitting all information through a single or selected nodes closest to the base station resulting in quick drain of power supply. Hence an intelligent and efficient method of load balancing mechanism is necessary to ensure that the work load is distributed in a more or less uniform manner resulting in ideal power saving. A clustered fuzzy engine model is proposed in this research article which is capable of sensing the input traffic 87. conditions and consequently invokes the fuzzy engine to decide upon an optimal cluster head among the set of available nodes to handle the incoming traffic. The proposed algorithm utilizes a rotational method of utilization of 472-479 cluster head (CH) to ensure that all member nodes are utilized in a uniform manner based on the incoming traffic. The proposed algorithm has been implemented, experimented and compared in performance with LEACH, DLBA and GLBA algorithms and the proposed hybrid approach outperforms the existing techniques in terms of average energy consumption and load distribution.

Keywords: Wireless sensor networks, Load balancing algorithms, soft computing, fuzzy inference engine, cluster head selection.

References:

1. Tang Yunjian, Shi Weiren, Yi Jun, Wang Yanxia (2011), “Dynamic Load-balancing Algorithm of WSN for Data Gathering

Application”, Computer Engineering and Applications, 47(6):122-126. 2. Han Zhang, Liang Li, Xin-fang Yan and Xiang Li, "A Load-balancing Clustering Algorithm of WSN for Data Gathering," 2011 2nd International Conference on Artificial Intelligence, Management Science and Electronic Commerce (AIMSEC), Dengleng, 2011, pp. 915-918. 3. Ozdemir S, “Secure load balancing via hierarchical data aggregation in heterogeneous sensor networks.” J. Inf. Sci. Eng., vol. 25, no. 6, pp. 1691–1705, 2009. 4. Eghbali A N and M. Dehghan (2007), “Load-balancing using multi-path directed diffusion in wireless sensor networks,” Mobile Ad-Hoc and Sensor Networks, 44–55. 5. Meenakshi Diwakar, Sushil Kumar (2012), “An energy efficient level based Clustering routing protocol for wireless Sensor networks” International Journal Of Advanced Smart Sensor Network Systems, 2(2):55-65. 6. Low C P, C. Fang, J. M. Ng and Y. H. Ang, "Load-Balanced Clustering Algorithms for Wireless Sensor Networks," 2007 IEEE International Conference on Communications, Glasgow, 2007, pp. 3485-3490. 7. Robin Gulerial and Ankit Kumar Jain (2013), “Geographic load balanced routing in wireless sensor network”, International journal of computer network and information security, 8:62 – 70. 8. Petrioli C, M. Nati, P. Casari, M. Zorzi and S. Basagni, "ALBA-R: Load-Balancing Geographic Routing Around Connectivity Holes in Wireless Sensor Networks," in IEEE Transactions on Parallel and Distributed Systems, vol. 25, no. 3, pp. 529-539, March 2014. 9. Younis O and S. Fahmy (2004), “HEED: A Hybrid, Energy- Efficient, Distributed Clustering Approach for Ad-hoc Sensor Networks,” IEEE Transactions on Mobile Computing, 3(4):366-379. 10. Sardor Q Hojiev and Dong Seong Kim (2015), “Dynamic load balancing algorithm based on users immigration in wireless LAN”, Journal of advances in computer networks, 114 – 118. 11. Bejerano Y, S.-J. Han, and L. Li, “Fairness and load balancing in wireless LANs using association control,” IEEE/ACM Transactions on Networking, pp. 560–573, 2007. 12. YSu Y, S. Zheng, S. Gamage and K. Li, "A Dynamic Load Balancing Routing Algorithm for Distributed Wireless Sensor Networks," 2007 International Conference on Wireless Communications, Networking and Mobile Computing, Shanghai, 2007, pp. 2625-2628. 13. Yan T., Bi Y., Sun L., Zhu H. (2005) Probability Based Dynamic Load-Balancing Tree Algorithm for Wireless Sensor Networks. In: Lu X., Zhao W. (eds) Networking and Mobile Computing. ICCNMC 2005. Lecture Notes in Computer Science, vol 3619. Springer, Berlin, Heidelberg 14. Ali Ghaffari and Vida Aghakhanloye Takanloo (2011), “QoS based routing protocol with load balancing for wireless multimedia sensor networks using genetic algorithm”, World applied sciences journal, 15(12): 1659 – 1666. 15. Arash Rahbari, Arash Ghorbannia Delavar (2016), “BCWSN: A dynamic load balancing algorithm for decrease in congestion cost in wireless sensor network”, Journal of mathematics and computer science, 16:18-25. 16. Ren Song Ku and Chia Yi (2015), “A load balancing routing algorithm for wireless sensor networks based on domain decomposition”, Ad Hoc networks, 30: 63 – 83. 17. Eslami M, J. Vahidi, M. Askarzadeh, Designing and Implementing a Distributed Genetic Algorithm for Optimizing Work Modes in Wireless Sensor Network, J. math. comput. sci., 11 (2014), 291-299. 18. Raha, Arnab & Naskar, M & Paul, Avishek & Chakraborty, Arpita & Karmakar, Anupam (2013) “A Genetic Algorithm Inspired Load Balancing Protocol for Congestion Control in Wireless Sensor Networks using Trust Based Routing Framework (GACCTR)”, International Journal of Computer Network and Information Security, 5: 9-20. 19. Castano F, A. Rossi, M. Sevaux, N. Velasco, On the use of multiple sinks to extend the lifetime in connected wireless sensor networks, Electron. Notes Discrete Math., 41 (2013), 77-84. 20. Modupe I A, O. O. Olugbara, A. Modupe, Minimizing Energy Consumption in Wireless Ad hoc Networks with Meta heuristics, Procedia Comput. Sci., 19 (2013), 106 - 115. 21. Mehmood A, Z. Lv, J. Lloret, and M. M. Umar, “ELDC: an artificial neural network based energy-efficient and robust routing scheme for pollution monitoring in WSNs,” IEEE Transactions on Emerging Topics in Computing, vol. 99, p. 1, 2017 22. Kacimi R, R. Dhaou, A. L. Beylot, Load balancing techniques for lifetime maximizing in wireless sensor networks, Ad Hoc Networks, vol 11,no8 (2013), 2172 - 2186. 23. E.Vishnupriya, T. Jayasankar and P. Maheswara Venkatesh ,SDAOR: Secure Data Transmission of Optimum Routing Protocol in Wireless Sensor Networks For Surveillance Applications, ARPN Journal of Engineering and Applied Sciences, 10- 16( 2015), 6917- 6931. 24. K.VinothKumar, T.Jayasankar, M.Prabhakaran and V. Srinivasan, Fuzzy Logic based Efficient Multipath Routing for Mobile Adhoc Networks, Appl. Math. Inf. Sci. vol 11, no.2, (2017), 449–455. Authors: Mohammed I. Alwanain Paper Title: Effects of User-Awareness on the Detection of Phishing Emails: A Case Study Abstract: In recent years, most of our daily services have been increasingly linked to the Internet, such as online banking and online shopping, thereby making our lives more comfortable and manageable, wherever we may be and at any time of day. However, this ubiquity of service also carries a critical security threat, which can cost Internet users dearly. Therefore, improving Internet users’ security awareness is a matter of high importance, especially in light of the significant growth of online services. This paper investigates the effects of security awareness and phishing knowledge on users’ ability to detect phishing emails and websites. In this approach, two experiments were conducted to evaluate the effects of security awareness. The results of these experiments revealed that phishing awareness has a significant positive effect on users’ ability to distinguish phishing emails and websites, thereby avoiding attacks.

88. Keywords: Anti-phishing countermeasures, online fraud, E-commerce security, online banking security, evaluation experiments 480-484 References: 1. B. B. Gupta, N. Arachchilage, and K. Psannis, “Defending against phishing attacks: taxonomy of methods, current issues and future directions,” Telecommun. Syst., vol. 67, no. 2, pp. 247–267, 2018. 2. A. K. Jain and B. B. Gupta, “Phishing detection: analysis of visual similarity based approaches,” Secur. Commun. Networks, vol. 2017, no. 5421046, 2017. 3. FBI, “Annual Internet Crime Report 2017,” 2017. [Online]. Available: https://www.fbi.gov/news/stories/2017-internet-crime-report- released-050718. 4. S. Ragan, “Senior executives blamed for a majority of undisclosed security incidents,” 2013. [Online]. Available: http://www.networkworld.com/article/2171678/data-center/senior-executives-blamed-for-a-majority-ofundisclosed-%0Dsecurity- incidents.html. 5. A. Alnajim, “A country based model towards phishing detection enhancement,” Int. J. Innov. Technol. Explor. Eng., vol. 5, no. 1, pp. 52– 57, 2015. 6. R. Dhamija, J. D. Tygar, and M. Hearst, “Why phishing works,” in the SIGCHI conference on Human Factors in computing systems, 2006, pp. 581–590. 7. “Symantec, Mitigating Online Fraud: Customer Confidence, Brand Protection, and Loss Minimization.,” 2004. [Online]. Available: http://www.antiphishing.org/sponsors_technical_papers/symantec_online_fraud.pdf. 8. IID, “eCrime Trends Report.” [Online]. Available: http://internetidentity.com/resources. 9. L. F. Cranor, S. Egelman, J. I. Hong, and Y. Zhang, “Phinding Phish: An Evaluation of Anti-Phishing Toolbars,” 2006. 10. A. Alnajim and M. Munro, “An evaluation of users’ tips effectiveness for Phishing websites detection,” in The third IEEE International Conference on Digital Information Management ICDIM, 2008, pp. 63–68. 11. S. Sheng, B. Magnien, A. Kumaraguru, Ponnurangam Acquisti, L. F. Cranor, and E. Hong, Jason and Nunge, “Anti-phishing phil: the design and evaluation of a game that teaches people not to fall for phish,” in The 3rd symposium on usable privacy and security SOUPS ’07, 2007, pp. 88 – 99. 12. P. Kumaraguru, Y. Rhee, A. Acquisti, L. F. Cranor, J. Hong, and E. Nunge, “Protecting people from phishing: the design and evaluation of an embedded training email system,” in The SIGCHI conference on Human factors in computing systems, 2007, pp. 905 – 914. 13. A. Alnajim and M. Munro, “An anti-phishing approach that uses training intervention for phishing websites detection,” in the 6th IEEE International Conference on Information Technology - New Generations (ITNG), 2009, pp. 405–410. 14. J. S. Downs, M. Holbrook, and L. F. Cranor, “Behavioral response to phishing risk,” in the anti-phishing working groups 2nd annual eCrime researchers summit, 2007, pp. 37 – 44. 15. T. N. Jagatic, N. A. Johnson, M. Jakobsson, and F. Menczer, “Social phishing,” Commun. ACM, vol. 50, no. 10, pp. 94–100, 2007. Authors: B.Murali Krishna, G.L.Madhumati, Habibulla Khan FPGA based Pseudo Random Sequence Generator using XOR/XNOR for Communication Paper Title: Cryptography and VLSI Testing Applications Abstract: Random number generators are most prominently used in the area of communication to provide security for information systems through pseudo random sequences. It also applicable for key generation in cryptography applications and signature analyzer to generate test patterns for Built-In-Self Test. In conventional method, random numbers are generated by a reference value i.e., seed value, using a XOR gate. The new proposed methods present a linear feedback shift register (LFSR) which generates an arbitrary number based on XOR, XNOR gates with and without seed value using multiplexer. Multiplexer is append to generate a random value at user defined state in runtime. Hardware complexity and power consumption is reduced by replacing the multiplexer with tristate buffers. Result analysis indicates that proposed LFSR with and without seed value gives a better performance, low power consumption and improves more randomness in runtime with Partial Reconfiguration (PR). Resource utilization for standard XOR based LFSR is compared with proposed LFSR using XOR and XNOR logic. Proposed method is designed in Verilog HDL, simulated with ISE Simulator, synthesized and implemented using Xilinx ISE, targeted for Spartan3E XC3S500E-FG320-4 and Virtex-5 XUPV5LX-110T architecture.

Keywords: LFSR, XOR, XNOR, Multiplexer, Xilinx, PR, FPGA.

References: 1. S. Ergun and S. Ozoguz, Truly Random Number Generators Based on a Non-autonomous Chaotic Oscillator, AEU-International Journal Electronics & Communications,Vol. 61, No. 4, 2007, pp. 235-242. 2. YilongLiao, XiangningFan Mathematical calculation of sequence length in LFSR- dithered MASH digital delta-sigma modulator with odd 89. initial condition, AEU - International Journal of Electronics and Communications Volume 82, December 2017, Pages 533-542. 3. MariosKalyvas, Kostas,Yiannopoulos, Thanassi,s Houbavlis, Hercules Avramopoulos Design Algorithm of All-Optical Linear Feedback 485-494 Shift Registers AEU - International Journal of Electronics and Communications Volume 57, Issue 5, 2003, Pages 328-332. 4. Efficient Parallel Architecture for Linear Feedback Shift Registers, J. Jung and H. Yoo and Y. Lee and I. C. Park, IEEE Transactions on Circuits and Systems II: Express Briefs, Nov 2015, volume 62, pp.1068-1072. 5. Test vector encoding using partial LFSR reseeding, C. V. Krishna and A. Jas and N. A. Touba, Proceedings International Test Conference, 2001, pp. 885-893. 6. The K-distribution of Generalized Feedback Shift Register Pseudorandom Numbers, Fushimi, M. and Tezuka, S.,Communications of the ACM, July 1983, volume 26, pp. 516--523. 7. MC-DS-CDMA pseudo-noise acquisition algorithm research using computer model, A. D. Zolotuev and F. G. Khisamov and M. V. Milovanov and D. M. Sobachkin, 23rd Telecommunications Forum Telfor (TELFOR), Nov2015, pp.329-332. 8. Low Complexity Wiener Filtering in CDMA Systems Using a Class of Pseudo-Noise Spreading Codes, R. Carvajal and K. Mahata and J. C. Aguero, IEEE Communications Letters, Nov 2012, volume 16, pp.1357-1360. 9. Design and analysis of linear feedback shift register(LFSR) using gate diffusion input(GDI), R. Sharma and B. Singh, 5th International Conference on Wireless Networks and Embedded Systems (WECON), Oct 2016,pp.1-5. 10. Multiple test set generation method for LFSR-based BIST, Youhua Shi, Zhe Zhang, Proceedings of the 2003 Asia and South Pacific Design Automation Conference, Nov 2003, pp. 863-868. 11. Low-Power Programmable PRPG With Test Compression Capabilities,M. Filipek and G. Mrugalski and N. Mukherjee and B. Nadeau- Dostie and J. Rajski and J. Solecki and J. Tyszer, IEEE Transactions on Very Large Scale Integration (VLSI) Systems, June 2015, volume 23, pp.1063-1076. 12. An Improved DCM-Based Tunable True Random Number Generator for Xilinx FPGA A. P. Johnson, R. S. Chakraborty and D. Mukhopadyay, in IEEE Transactions on Circuits and Systems II: Express Briefs, vol. 64, no. 4, pp. 452-456, April 2017. 13. Cellular Automata-Based Parallel Random Number Generators Using FPGAs David H. K. Hoe, Jonathan M. Comer, Juan C. Cerda, Chris D. Martinez, and Mukul V. ShirvaikarInternational Journal of Reconfigurable Computing Volume 2012, Article ID 219028, 13 pages 14. Design and Implementation of Multibit LFSR on FPGA to Generate Pseudorandom Sequence NumberDebarshi Datta, Bipa Datta, Himadri Sekhar Dutta2017 Devices for Integrated Circuit (DevIC), 23-24 March, 2017, Kalyani, India 15. Low Power Memory Built in Self Test Address Generator Using Clock Controlled Linear Feedback Shift Registers K. Murali Krishna, M. Sailaja Journal of Electronic Testing Issue 1/2014 Authors: D Ramamurthy, Mahesh P K Brain Tumor Segmentation based on Rough Set Theory for MR Images with Cellular Automata Paper Title: Approach Abstract: Prediction of brain tumour and analysis is very critical in medical image processing since the treatment 90. is based on radio surgery. Classifying the enhanced and necrotic cells is very essential in clinical radio surgeries, where in a radio oncology expert predicts the tumors manually for contrast enhanced T1-MR images. Prediction 495-499 best works with cellular automata (CA) iterative algorithm by deriving transition rules from the tumour properties with adaptive method. Rough set theory with attribute reduction algorithm is used for classifying the enhanced and necrotic cells. In this work a semi interactive prediction algorithm is used with CA and Rough set theory for incomplete data prediction in medical images. Semi interactive algorithms require less manual intervention with high computation speed.

Keywords: Brain tumor prediction, Rough Set Algorithm, Cellular automata, magnetic resonance imaging (MRI), radio surgery, enhanced cells, necrotic cells, reduct.

References: 1. Kailash Sinha, G.R.Sinha., “Efficient Segmentation Methods for Tumor Detection in MRI Images”, 2014 IEEE Student’s Conference on Electrical, Electronics and Computer Science. 2. Riddhi.S.Kapse , Dr. S.S. Salankar , Madhuri.Babar “Literature Survey on Detection of Brain Tumor from MRI Images”, IOSR Journal of Electronics and Communication Engineering (IOSR-JECE), e-ISSN: 2278-2834,p- ISSN: 2278-8735.Volume 10, Issue 1, Ver. II (Jan - Feb. 2015), PP 80-86. 3. Cuttmann” Automated Segmentation of Cerebral Ventricular Compartments”, , C.R.G, ISMRM (2003). 4. M. Prastawa, E. Bullitt, N. Moon, K. Leemput, and G. Gerig, “Automatic brain tumor segmentation by subject specific modification of atlas priors,” Acad. Radiol., vol. 10, pp. 1341–1348, 2003. 5. M. Prastawa, E. Bullitt, S. Ho, G. Gerig, A brain segmentation framework based on outliner detection, Medical Image Analysis, 8: 275- 283, 2004. 6. Rajiv Kumar, Arthanariee A. M,” A Comparative Study of Image Segmentation Using Edge-Based Approach”, World Academy of Science, Engineering and Technology International Journal of Mathematical and Computational Sciences Vol:7, No:3, 2013. 7. K. S. Angel Viji, Dr J. Jayakumari, “Modified Texture Based Region Growing Segmentation of MR Brain Images”, IEEE Conference on Information and Communication Technologies (ICT 2013) pp:691-695. 8. K. Jumaat, R. Mahmud and S. S. Yasiran “Region and boundary segmentation of microcalcifications using seed-based region growing and mathematical morphology”, Intemational Conference on Mathematics Education Research, Vol. 8, pp.634—639, 2010. 9. Y. Boykov and M.-P. Jolly, “Interactive graph cuts for optimal boundary and region segmentation of objects in n-d images”, in Proc. ICCV, 2001, pp. 105–112 10. Priyansh Sharma and Jenkin Suji, “A Review on Image Segmentation with its Clustering Techniques”, International Journal of Signal Processing, Image Processing and Pattern Recognition Vol.9, No.5 (2016), pp.209-218. 11. J.selvakumar, A.Lakshmi, T.Arivoli, “Brain Tumor Segmentation and Its Area Calculation in Brain MR Images using K-Mean Clustering and Fuzzy C-Mean Algorithm”, IEEE-International Conference On Advances In Engineering, Science And Management 12. S. R. Kannan, ”Segmentation of MRI Using New Unsupervised Fuzzy C-Means Algorithm” ICGST-GVIP Journal, Vol. 5, Issue 2, Jan.2005. 13. Yailé Caballero, Rafael Bello, Delia Alvarez, Maria M. Garcia, “Two new feature selection algorithms with Rough Sets Theory”,IFIP International Conference on Artificial Intelligence in Theory and Practice IFIP AI 2006: Artificial Intelligence in Theory and Practice pp 209-216. 14. Jianchao Han, Ricardo Sanchez, Xiaohua Hu, “Feature Selection Based on Relative Attribute Dependency: An Experimental Study ”, International Workshop on Rough Sets, Fuzzy Sets, Data Mining, and Granular-Soft Computing RSFDGrC 2005: Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing pp 214-223. 15. Wu, Y., Phol, K. Warfield, S.K., Andac Hamamci*, Nadir Kucuk, Kutlay Karaman, Kayihan Engin, and Gozde Unal “Tumor-Cut: Segmentation of Brain Tumors on Contrast Enhanced MR Images for Radiosurgery Applications”, IEEE Transactions On Medical Imaging, Vol. 31, No. 3, March 2012, pp:790-804. 16. Rachana Rana H.S, Bhdauria Annapuma Singh, “Brain Tumour Extraction from MRI Images Using Bounding-Box with Level Set Method”, IEEE 2013,pp: 319-324. Authors: K. P. Prasad Rao, P. Srinivasa Varma, RBR Prakash Paper Title: Five Phase DSTATCOM with Fuzzy Controller for Industrial and Domestic Applications Abstract: To attain good products from the industry, industry required the quality of power and efficiency of the systems like machines, lighting system and other equipments. This power quality problems are going to mitigate by the FACTS devices or controllers. The problems like voltage sag (Power Quality) because of the load increases suddenly, voltage swell because of load decreases suddenly may happened. In this paper, sag of voltage as a quality problem in power system arises, since of sudden amplified the load and it is going to mitigate with Static Synchronous Compensator (STATCOM).

Keywords: DSTATCOM, Ten Pulse VSC, Fuzzy Logic Controller.

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Bhimsingh, VipinGarg, Gurumoorthy Bhuvaneshwari, “A24-pulse AC-DC converter employinga pulsedoubling technique for vector- controlled induction motor drives.” 20. Bhim Singh,Ganjay Gairole, “Anautotransformer – based 36 – pulse controlled AC-DC converter,” IETE Journal of research, vol. 54,Issue 4, July-August 2008, pp. 255-263. 21. Atif Iqbal, Shaik Moinuddin, M.Rizwan Khan, Sk.MoinAhmed, and Haithen Abu-Rub, “A NovelThree-Phase to Five-Phase Transformation using a special transformer connection,” IEEE Transactions on power delivering, vol. 25, no. 3, JULY 2010, p. no: 1637 – 1644. 22. P.C.Krause, “Analysis of electric machinery,” Newyork: Mc. Graw Hill, 1986. 23. K.P.Prasad Rao,B. KrishnaVeni, D. RaviTeja, “Five LegInverter for Five Phase Supply,” International Journalof Engineering Trends and Technology- Volume3Issue2- 2012, pp. 144 – 152. 24. DuroBasic, Jain Guo Zhu, Gerard Boardman, “Transient performance study of brushlessdoubly fed twin stator generator,”IEEE Trans. Energy convers., vol. 8, no. 3, pp. 400-408, July 2003. 25. V.Krishna Kumar, V. Kamaraj, S. Jeevananthan, "Parrallel Fuzzy Logic ControllersforIndependentControl ofTwo PermanentMagnet Synchronous Motors fed by a Five Leg Inverter for Electric Vehicles", Journal of Electrical Engineering, Volume: 17/2017, Edition: 1. 26. K.P.Prasad Rao, P.Srinivasa Varma, "Five Phase DVR with Fuzzy Logic Controller," Journal of Advanced Research in Dynamical and Control System, Vol. 9. Sp- 18/ 2017. 27. K.P.Prasad Rao, P.SrinivasaVarma, "A NovelFive PhaseDSATCOM for Industrial Loads," International Journal of Engineering &Technology, 7 (1.8), 2018 56-61 Authors: N.V. Sarathbabu Goriparti, Ch. S. N. Murthy, M. Aruna Minimization of Specific Energy of a Belt Conveyor Drive System using Space Vector Modulated Direct Paper Title: Torque Control Abstract: The main aim of this paper is to model and minimize the specific energy of belt conveyor drive system under different operating conditions such as different loading conditions, different conveyor inclinations, different conveying lengths and lifts, and different capacities using a real time fabricated experimental setup. Further, it demonstrates the advantages of using a variable speed drives (VSDs) for energy savings. Conveyors are designed for transporting goods, ores, minerals, and other such products with maximum rated capacities for any operating sections. But, they operate at a relatively much lower capacity. This is due to the supply and demand side implications of the operating section, they will run at the same constant rated speed causing higher power losses. This behavior will highly degrades the energy efficiency of conveyor. In the present study, an attempt is made to improve the energy efficiency of the conveyor by minimizing the per unit energy consumption using the methodology having combined advantages of energy efficiency modeling with less friction coefficient as per DIN 22101 and superior speed control technique for conveyor kind of high torque loads called space vector based direct torque control. In this paper, specific energies are estimated for the same belt conveyor system, found that with the use of VSDs, specific energies are reduced an amount 10-12% depending upon the capacities at which they have run.

Keywords: Specific energy, Variable speed drives, Energy efficiency

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Casadei, G. Serra, and A. Tani, “The use of matrix converters in direct torque control of induction machines," IEEE transactions on industrial electronics, vol. 48, no. 6, pp. 1057-1064, 2001. 19. F. Tazerart, Z. Mokrani, D. Rekioua, and T. Rekioua, “Direct torque control implementation with losses minimization of induction motor for electric vehicle applications with high operating life of the battery," International Journal of Hydrogen Energy, vol. 40, no. 39, pp. 13827-13838, 2015. 20. E. Ozkop and H. Okumus, “Direct torque control of induction motor using space vector modulation (SVM-DTC)," 2008 12th International Middle-East Power System Conference, pp. 368-372, 2008. 21. C. Lascu and A. M. Trzynadlowski, “Combining the principles of sliding mode, direct torque control, and space-vector modulation in a high-performance sensorless ac drive," IEEE Transactions on industry applications, vol. 40, no. 1, pp. 170-177,2004. Authors: Gayathiri Kathiresan, Krishna Mohanta, Khanaa VelumailuAsari COMPACT: Classifying Stream Data Optimally Using a Modified Pruning and Controlled Tie- Paper Title: threshold Abstract: Big data mining become important in extracting the potential information from the continuously arriving stream data. By extracting knowledge, the data mining algorithms significantly compute feasible decisions for various applications. The Very Fast Decision Tree (VFDT) classifier is a widely applied incremental decision tree to make better decisions. The VFDT classifier processes the arrival of the new instances, without storing them and updates the existing tree structure. Most of the conventional incremental decision tree based algorithms exploit the hoeffding’s bound based on the user-defined tie-threshold to split the tree and to manage the tree growth. Even though the size of the tree tremendously increases when handling the fluctuated and imbalanced stream data, it suffers from the misclassification issue due to lack of capturing the optimal attributes over the incoming stream data and declines the classification accuracy and performance. In order to resolve these issues, this paper extends the VFDT, named as Classifying stream data optimally using a Modified Pruning technique And Controlled Tie- threshold (COMPACT). The COMPACT method includes two components, such as enhanced information gain measurement and tie-breaking threshold based pruning method. In order to improve the VFDT performance without affecting the imbalanced data stream handling, the enhanced information gain measurement effectively identifies an optimal number of attributes for a data stream. In order to avoid the information gain biasing, it utilizes the advantages of enhanced splitting metric in attribute reduction. Instead of randomly selecting the threshold, the tie-breaking threshold based pruning method determines the tie-breaking threshold using a number of breaking points. The tie-breaking threshold based pruning method ensures the optimal tree structure while handling the large-scale stream dataset. Finally, the COMPACT method is evaluated using the weather dataset to demonstrate the efficiency. The proposed method significantly outperforms the existing DTFA approach in terms of recall, Root Mean Square Error (RMSE) rate, and execution time.

Keywords: Big data, stream data, VFDT classifier, bias, information gain, threshold, pruning, imbalanced data, optimal attributes, and decision making.

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De Rosa, Rocco, and NicoloCesa-Bianchi, “Splitting with confidence in decision trees with application to stream mining,” In Proceedings of the IEEE International Joint Conference on Neural Networks (IJCNN), pp.1-8, 2015. 12. Zhao, Minyue, and Xiang Li, “An application of spatial decision tree for classification of air pollution index,” In Proceedings of the 19th IEEE International Conference on Geoinformatics, pp.1-6, 2011. 13. Ben-Haim, Yael, and Elad Tom-Tov, “A streaming parallel decision tree algorithm,” Journal of Machine Learning Research,Vol.11, pp.849-872, 2010. 14. Liang, Chunquan, Yang Zhang, Peng Shi, and Zhengguo Hu, “Learning accurate very fast decision trees from uncertain data streams,” International Journal of Systems Science,Vol.46, No.16, pp.3032-3050, 2015. 15. Yang, Hang, and Simon Fong, “Optimized very fast decision tree with balanced classification accuracy and compact tree size,” In IEEE3rd International Conference on Data Mining and Intelligent Information Technology Applications (ICMiA),pp.57-64, 2011. 16. Naidu, Ch SKVR, and T. Y. Ramakrushna, “Augmentation of very fast decision tree algorithm aimed at data mining,” IJRCCT, Vol.4, No. 9, pp.684-690, 2015. 17. Dong, Z. J., S. M. Luo, Tao Wen, F. Y. Zhang, and L. J. Li, “Random forest-based very fast decision tree algorithm for data stream,” Res. Paper,Vol.12, pp. 52-57, 2017. 18. Da Costa, Victor GuilhermeTurrisi, André Carlos Ponce de Leon Ferreira, and SylvioBarbon Junior, “Strict Very Fast Decision Tree: a memory conservative algorithm for data stream mining,” Pattern Recognition Letters, Vol.116, pp.22-28, 2018. 19. Minegishi, Tatsuya, Masayuki Ise, AyahikoNiimi, and Osamu Konishi, “Extension of Decision Tree Algorithm for Stream Data Mining Using Real Data,”2009. 20. Yang, Hang, and Simon Fong, “Incremental optimization mechanism for constructing a decision tree in data stream mining,” Mathematical Problems in Engineering, 2013. 21. Yang, Hang, and Simon Fong, “Moderated VFDT in-stream mining using adaptive tie threshold and incremental pruning,” In Springer International Conference on Data Warehousing and Knowledge Discovery, pp.471-483, 2011. 22. Xu, Wenhua, Zheng Qin, Hao Hu, and Nan Zhao, “Mining uncertain data streams using clustering feature decision trees,” In Springer Int. Conf. on Advanced Data Mining and Applications, pp.195-208, 2011. 23. Duda, Piotr, MaciejJaworski, Lena Pietruczuk, and LeszekRutkowski, “A novel application of hoeffding's inequality to decision trees construction for data streams,” In IEEE International Joint Conference on Neural Networks (IJCNN), pp.3324-3330, 2014. 24. Al-Kateb, Mohammed, and Byung Suk Lee, “Adaptive stratified reservoir sampling over heterogeneous data streams”, Information Systems, Vol.39, pp.199-216, 2014. Authors: B. Teertha Priyanka, K. Rekhamchala, CH. Jothi Naga Sindhura, P. Sai Amal Mohith, V. Saritha Analysis and Design of Compact Triple Band Notched Circular Monopole Antenna Using Mushroom Paper Title: EBG Structures and Compact Spiral Slotted EBG Structures Abstract: This work presents analysis and design of a low profile multi band notched UWB circular monopole antenna. Multi band rejection characteristics of the proposed antenna can be achieved by placing the EBG structures in the proximity of the feed line. To avoid the interference with narrow bands with frequency ranges from (3.2-4.0) GHz – WiMAX, (5.0-5.9) GHz – WLAN and (7.1-8.4) GHz – X-Band (both uplink and downlink), it is essential for any antenna operating in UWB to have band rejection features. The proposed antenna has the dimensions of 46x26x1.6 mm3. The simulation was carried out through HFSS.

Keywords: EBG structure, Multi Band-Notch, UWB antenna.

References: 1. NaveenJaglan, Binod K.Kanaujia, Samir D.Gupta, andShweta Srivastava “Triple Band NotchedUWB AntennaDesign Using Electromagnetic Band GapStructures” Progress In Electromagnetics Research C, Vol. 66, 139–147, 2016. 2. Son rinh-Van, ChienDao-Ngoc SchoolofElectronicsand Telecommunications,Hanoi University of Science and Technology, Hanoi,Vietnam “DualBand-Notched UWBAntenna based on ElectromagneticBand Gap Structures” REVJournal on Electronics and Communications, Vol. 1, No. 2, April – June, 2011. 3. F.Alizadeh,J.Nourinia, Ch. Ghobadi,and B. Mohammadi “A Dual Band Rejection UWB AntennaUsing EBG” 2017 IEEE 4thInternational Conference n Knowledge-Based Engineering and Innovation (KBEI) December 22nd, 2017. 4. Dinesh Sethi,Ajay Yadav R. K. Khanna “Dual Notched Ultra Wideband Microstrip Antenna With CSRR Slot and EBG structure” 94. International Journal of EngineeringResearch & Technology(IJERT) ISSN: 2278-0181 Vol.3 Issue 9, September, 2014. 5. Hao Liu andZiqiang Xu “Design of UWB MonopoleAntenna withDual NotchedBandsUsing OneModifiedElectromagnetic-Bandgap Structure”Hindawi PublishingCorporation The ScientificWorld Journal Volume 2013, Article ID 917965, 9 pages. 520-525 6. F. Mouhouche,A. Azrar,M. Dehmas and K.Djafri “Compact Dual-Band Reject UWBMonopoleAntenna using EBGStructures” The 5th InternationalConference onElectrical Engineering –Boumerdes (ICEE-B) October 29-31, 2017,Boumerdes,Algeria. 7. Abdel moumenKaabal, SaidaAhyoud andAdelAsselman “ANew Design of Star Antenna for Ultra Wide Band Applicationswith WLAN- Band-Notched Using EBG Structures” International Journal of Microwave and Optical Technology, vol.9, no.5, September 2014. 8. GauravK. Pandey, Hari S. Singh, Pradutt K. Bharti, and Manoj K. Meshram, “DESIGN OF WLAN BANDNOTCHED UWB MONOPOLEANTENNA WITHSTEPPED GEOMETRYUSING MODIFIED EBG STRUCTURE”,ProgressInElectromagnetics Research B, Vol. 50, 201–217, 2013. 9. F.Yang and Y.Rahmat-Samii, “Electromagnetic BandGap Structures in Antenna Engineering”, 1st edition,Cambridge University Press,2009. 10. AthiraKaladharan, RiaMaria George, “AWideband Bluetooth-UWB AntennawithTribandNotchedCharacteristics”,2016International Conference on EmergingTechnological Trends (ICETT) ,Oct – 2016. 11. SayedArif Ali,DeepakJhanwar ,Dhirendra Mathur,“Design of a CompactTriple Band-Notch Flower-Shaped Hexagonal Microstrip Patch Antenna”,InternationalConference on Information Technology (InCITe) Oct – 2016. 12. Vaishali.S.Varpe, Dr.R.P.Labade, “ACompact PrintedWide-SlotUWB Antenna with Band-Notched Characteristics”, InternationalConference on ComputingCommunication Controlandautomation (ICCUBEA), August 2016. 13. M. A. Abdalla,A. Al-Mohamadi, A. Mostafa “Dual Notching of UWB Antenna Using Double InversedU-ShapeCompact EBG Structure”, 10th InternationalCongress onAdvanced ElectromagneticMaterialsin Microwaves andOptics – Metamaterials, Greece,17-22 September 2016. 14. Zhang, S., & Pedersen,Gert Frølund. (2016). Mutual coupling reduction for UWBMIMO antennas witha widebandneutralization line IEEE Antenna andWireless Propagation Letters, 15,166–169. 15. Wang,J.-H., Yin, Y.-Z., Liu X.-L., & Wang, T. (2013).Trapezoid UWB antenna withdual bandnotchedcharacteristics forWiMAX/WLAN bands. Electronics Letters, 49(11), 685–686. Authors: S. M. K. CHAITANYA, P. RAJESH KUMAR Classification of Kidney Images using Particle Swarm Optimization Algorithm and Artificial Neural Paper Title: Networks Abstract: Ultrasound (US) imaging is used to provide the structural abnormalities like stones, infections and cysts for kidney diagnosis and also able to produce information about kidney functions. The main aim of this work is classifying the kidney images by using US according to relevant features selection. In this work, images of kidney are classified as abnormal images by pre-processing (i.e. grey-scale conversion), generate region-of- interest, extracting the features as multi-scale wavelet-based Gabor method, Particle Swarm algorithm (PSO) for 95. optimization and Artificial Neural Networks (ANN). The PSO-ANN method is simulated on the platform of MATLAB and these results are evaluated and contrasted. The results obtained through this method are better in 526-530 terms of accuracy, sensitivity and specificity.

Keywords: Artificial Neural Networks, Gabor feature extraction, Kidney diagnosis, Particle Swarm Optimization, Ultrasound images.

References: 1. F. Kanavati, T. Tong, K. Misawa, M. Fujiwara, K. Mori, D. Rueckert, and B. Glocker, “Supervoxel classification forests for estimating pairwise image correspondences.,” Pattern Recognition, vol. 63, pp. 561-569, 2017. 2. Eklund, P. Dufort, D. Forsberg and S. M. La Conte, “Medical image processing on the GPU – past, present and future,” Med. Image Anal., vol. 17, pp. 1073–1094, 2013. 3. O. Reiche, K. Häublein, M. Reichenbach, M. Schmid, F. Hannig, J. Teich and D. Fey, “Synthesis and optimization of image processing accelerators using domain knowledge,” J. Syst. Architect., vol. 61, pp. 646–658, 2015. 4. K. Sharma, N. D. Toussaint, G. J. Elder, R. Masterson, S. G. Holt, P. L. Robertson, and C. S. Rajapakse, “Magnetic resonance imaging based assessment of bone microstructure as a non-invasive alternative to histomorphometry in patients with chronic kidney disease,” Bone, 2018. 5. Razik, C. J. Das, and S. Sharma, "Angiomyolipoma of the Kidneys: Current Perspectives and Challenges in Diagnostic Imaging and Image-Guided Therapy," Current problems in diagnostic radiology 2018. 6. Świetlicka, “Trained stochastic model of biological neural network used in image processing task,” Appl. Math. Comput, vol. 267, pp. 716–726, 2015. 7. J. Tian, J. Xue, Y. Dai, J. Chen and J. Zheng, “A novel software platform for medical image processing and analyzing,” IEEE Trans. Inf. Technol. Biomed, vol. 12, pp. 800–812, 2008. 8. S. Gur and M. Top, “Regional clustering of medical imaging technologies,” Comput. Hum. Behav., vol. 61, pp. 333–343, 2016. 9. F. Rengier, M. F. Häfnerb, R. Unterhinninghofenc, R. Nawrotzkid, J. Kirsch, H.-U. Kauczor and F. L. Giesel, “Integration of interactive three-dimensional image post-processing software into undergraduate radiology education effectively improves diagnostic skills and visual-spatial ability,” Eur. J. Radiol, vol. 82, pp. 1366–1371, 2013. 10. L. Sajn and M. Kukar, “Image processing and machine learning for fully automated probabilistic evaluation of medical images,” Comput. Methods Prog. Biomed, vol. 104, pp. e75–e86, 2011. 11. F. Zhao, J. Zhao, W. Zhao, F. Qu and L. Sui, “Local region statistics combining multi-parameter intensity fitting module for medical image segmentation with intensity in homogeneity and complex composition,” Optics Laser Technol, vol. 82, pp. 17–27, 2016. 12. J. Serrat, F. Lumbreras, F. Blanco, M. Valiente, and M. López-Mesas, “myStone: A system for automatic kidney stone classification,” Expert Systems with Applications, vol. 89, pp. 41-51, 2017. 13. J. Verma, M. Nath, P. Tripathi, and K. K. Saini, “Analysis and identification of kidney stone using Kth nearest neighbour (KNN) and support vector machine (SVM) classification techniques,” Pattern Recognition and Image Analysis, vol. 27, no. 3, pp. 574-580, 2017. 14. M. B. Subramanya, V. Kumar, S. Mukherjee, and M. Saini, “SVM-based CAC system for B-mode kidney ultrasound images,” Journal of digital imaging, vol. 28, no. 4, pp. 448-458, 2015. 15. S. A. Tuncer, and A. Alkan, "A decision support system for detection of the renal cell cancer in the kidney." Measurement vol. 123, pp. 298-303, 2018. 16. K. D. Krishna, V. Akkala, R. Bharath, P. Rajalakshmi, A. M. Mohammed, S. N. Merchant, and U. B. Desai, “Computer aided abnormality detection for kidney on FPGA based IoT enabled portable ultrasound imaging system,” Irbm, vol. 37, no. 4, pp. 189-197, 2016. Authors: Dhana Lakshmi Potteti, Venkateswara Rao N Paper Title: Spectrum Sensing using Single Ring Law Abstract: The concept of cognitive radio is becoming increasing popular as it is a prominent solution for spectrum scarcity problem. A smart and wise usage of available spectral resources is an interesting feature of cognitive radio. In the terminology of cognitive radio, a primary (licensed) user and a secondary (unlicensed) user are usually heard. A secondary user transmits data only if the primary user does not use the alloted spectral resources. For sensing the presence or absence of the primary user, spectrum sensing is necessary. Conventionally many techniques such as energy detection (ED), eigenvalue based approaches have been designed for spectrum sensing. Recently large random matrix theory based analytics has shown that Single Ring Law (SRL) can be an effective solution for binary hypothesis testing problems. Hence, in this paper spectrum sensing for multiple antenna system is investigated using SRL based parameters. In Rayleigh and Nakagami fading channels, the SRL based detection is employed and has been found to be a consistent tool for spectrum sensing.

Keywords: spectrum sensing, single ring law, detection probability, opportunistic spectrum access, secondary user, energy detection 96. References: 1. Ngo,Hien Quoc, Erik G. Larsson, and Thomas L. Marzetta. Energy and spectral efficiency of very large multiuser MIMO 531-534 systems." IEEE Transactions on Communications 61 (4) (2013) 1436-1449. 2. Malik, Shahzad A.,Shah, M. A.,Dar,A. H., Haq, A., Khan, A. U., Javed, T., & Khan, S. A. "Comparative analysis of primary transmitter detection based spectrumsensing techniques in cognitive radio systems."Australianjournalofbasicandapplied sciences 4 (9) (2010) 4522-4531. 3. Urkowitz,Harry."Energydetectionofunknowndeterministic signals." Proceedingsof the IEEE 55.4 (1967) 523-531. 4. Zeng, Yonghong, andYing-Chang Liang."Spectrum-sensing algorithms for cognitive radio based on statistical covariances." IEEE transactions on Vehicular Techlogy 58 (4) (2009) 1804-1815. 5. Wang, Rui, and Meixia Tao. "Blindspectrumsensing byinformation theoreticcriteria." GlobalTelecommunicationsConference (GLOBECOM 2010), IEEE (2010) 1-5. 6. Ciuonzo, Domenico, Pierluigi Salvo Rossi,andSubhrakantiDey. "Massive MIMO channel-aware decision fusion." IEEE Transactions on Signal Processing 63 (3) (2015): 604-619. 7. Ding, Guoru, Xiqi Gao, Zhen Xue, Yongpeng Wu,andQingjiang Shi. "Massive MIMO for Distributed Detection with Transceiver Impairments." IEEE Transactions onVehicular Technology (2017). 8. Guionnet, Alice, Manjunath Krishnapur, and Ofer Zeitouni. "The single ring theorem." Annals of mathematics (2011): 1189-1217. 9. Pallaviram Sure,NarendraBabuC andChandraMohanBhuma, “Applicability of big data analytics to massive MIMO systems,” IEEE Annual India Conference (INDICON), IEEE (2016) 1-5. 10. Zhang, Changchun, and RobertC. Qiu. "Massive MIMOas abig data system: Random matrix models and testbed." IEEE Access 3 (2015) 837-851. Authors: Pravallika Injarapu, Harsha Sanka, Naga Sai Manasa, Vineeth Chowdary, Saritha Vanka Paper Title: Analysis and Design of a Low Profile Multiband Antenna for IOT Applications Abstract: Increased intervention of IOT in everyday applications created an open challenge to the researchers 97. regarding the usage of RFID technique. This requires the process of integrating IOT to various wireless standards. This work proposes, a very low profile planar circular patch united with Koch fractals and rectangles alternatively 535-539 on its circumference fed by a microstrip line and two more Koch fractals on either side of the circular patch which operates over the bands of 1.5GHz-1.65GHz, 1.92GHz-2.17GH, 2.56GHz-2.85GHz, 3.68GHz-4.0GHz, 4.73GHz- 4.94GHz, 5.36GHz-5.57GHz at SHF, UHF and Microwave frequency bands ,GSM 850 MHz, GSM 900 MHz, LTE-700 MHz, LTE-800MHz, TV broadcasting.

Keywords: GSM, Internet of Things (IOT), Long Term Evolution, RFID.

References: 1. VyshnaviDas S K, Dr. T.Shanmuganantha “DesignofMultiband Microstrip PatchAntenna forIOT Applications”, Proceedings of2017 IEEE InternationalConference onCircuits and 2. Kumud RanjanJha, GhanshyamMishra and Satish K.Sharma “An Systems (ICCS 2017).Octahedron ShapedPlanarAntenna for IOT Applications”, 2017 IEEE. 3. Vyshnavi Das S K,T Shanuganatham “Design of Triple StarfishShaped Microstrip PatchAntenna for IOT Applications”,Proceedingsof 2017 IEEE Internationa Conference onCircuits andSystems (ICCS 2017). 4. Duong Thi Thanh Tu,Nguyen TuanNgoc, ForestZhu, Diep N. Nguyen, ErykDutkiewicz “Quad-Band Antennafor GSM/WSN/WLAN /LTE -A Applicationin IOTDevices” , 201717th International Symposium on Communications and Information Technologies (ISCIT). 5. VikramN, KashwanK. R“Designof ISMBand RFID Reader Antenna for IOT Applications”, IEEE WiSPNET 2016conference. 6. Mehr-e-Munir “E-ShapePatchAntenna for4G, LTEand S-BandApplications”, proceedingsof 2018, 15thInternationalBhurban Conferenceon Applied Sciences andTechnology (IBCAST). 7. Praveen V. Naidu,Arvind Kumar, andVinay Kumar “AMiniaturized Triple Band ACS-fedMonopolePrinted AntennawithMeandered and Circular RingShapeResonatorsforWLAN/WiMAX Applications”, 2017 Progress in Electromagnetics Research Symposium Fall (PIERS — FALL), Singapore, 19–22 November. 8. MuhammadSajid Iqbal,Syed Awais Wahab Shah “Design of aCompact UWB Patch Antenna havingRectangular ParasiticElementsfor UWB andBluetooth Applications”, 2017IEEE. 9. D. Paret, “RFID at ultra andsuper high frequenciestheory and application,” Hoboken, NJ, USA, Wiley, 2009. 10. Daniel Zucchetto,Andrea Zanella, “Uncoordinated accessschemes for the IOT:approaches, regulations, and performance”,IEEE Communication Magazine, 2017. (In Press) 11. Ala Al-Fuqaha, Mohsen Guizani,MehdiMohammadiMohammed Aledhari and Moussa Ayyash, “Internet of Things: A Survey on EnablingTechnologies,Protocols, andApplications,” IEEE Communications Surveys &Tutorials, vol. 17,Issue. 4, fourth quarter 2015, pp. 2347-2376, June 2015. 12. S. Purushothaman, S. Ragahavan and SenthilKumar, “A design of compact metamaterial encumberedmonopoleantenna with defected groundstructurefornavigation (L/S-band) applications”,India Conference (INDICON), IEEE Annual, 2016. 13. C Elavarasi andT Shanmuganantham, “SRR loaded periwinkleflower shapedfractalantenna formultiband applications”,Microwave and Optical Technology Letters 59 (10), 2518-2525, 2017. 14. ChowYenDesmond Sim, Yuan KaiShih, and Ming Hsuan Chang, “Compact slot antenna for wirelesslocal network2.4/5.2/5.8GHz applications,” IETMicrowave,Antenna and Propagation, vol.9, issue.6, pp. 495-501, April 2015. Authors: G Aloy Anuja Mary, Sheeba Santosh Paper Title: Impact of Secondary user Density on Cognitive Radio Networks Abstract: Reasonableness plays a central movement for each structure blends intellectual radio systems (CRNs). Point of fact, CRNs gives a fit, free and dynamic specific condition performing specific activities, through which unlicensed clients get the favored viewpoint to utilize, understood run. This paper tends to the joint issues in CRNs, for example, helpful hand-off determination, range partaking in asset portion. In this paper, we propose a half and half enhancement method for effective asset designation (HOERA) in CRNs. The basic target is to open up execution of system as for the general structure compel by playing out a joint hand-off choice and asset portion among different transfers. In the first place, bunching is performed by an enhanced swarm streamlining (ISO) calculation that understands the challenges in extensive scale advancement issue specifically to partition arrange into gatherings. At that point, Stephanie-Mathisen basic leadership show used to figure the transfer hubs to apportioning the activity levels in the system. In addition, the asset designation is performed to accomplish most extreme utility expecting parallel power allotment. The outcomes demonstrates that the viability of proposed HOERA plot which enhances framework execution and less computational unpredictability for bigger systems.

Keywords: optimal resource allocation, clustering, relay selection, hybrid optimization technique

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Authors: Vani G, Bharathi Malakreddy A Paper Title: A Review on Identification & Analysis of Security Issues and Challenges of IoT based Healthcare Abstract: Healthcare applications are one of the major fields from the business user’s perspective and an important domain. Healthcare applications require a degree of authentication & authorization. An Authentication is a mechanism of authorizing a particular integrity in a communication system which assures the authenticity of the element in intercommunication. It is one of the fundamental objectives of the security. In this paper, we are focusing on a multi-factor authentication method for the IoT based healthcare systems. The survey will find the multi-factor authentication related work, different types of security attacks, risk, security gaps in healthcare systems. As a result, there will be a gap that could be further investigated so that more types of authentications are feasible. The conclusion of this paper is that by using a multi-factor authentication method, there are possibilities for proposing a secured authentication and authorized algorithm for IoT based healthcare system and overviewing of sensor destruction and different types of potential attacks on IoT devices based on IoT healthcare system

Keywords: Authentication, Authorization, Multi-factor, Role-Based Access Control, data confidentiality, confidentiality, sensitivity, security attack

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Liu et , “Cryptography”, in third IEEE International Conference, Pune, India August 2017, IEEE. Available online at 'ieeexplore.ieee.org/Xplore/home.jsp'. 22. Ometov, Aleksandr et al, “Multi-Factor Authentication - A Survey Cryptography”, vol. 2. 23. Jose Costa, “A Two Factor Authentication scheme”, DOI: 10.13140/RG.2.2.16228.99207, Jun 14, 2017 24. Kennedy. Eand Millard .C, “Data security & Multifactor authentication ' in “Computer Law & Security Review”, Volume: 32, Number 1, 2016, pp 91 - 110. 25. Camenisch J, Fischer S et.al, 'Privacy andidentity management for life', in 'Springer Science & Business Media', 2011. 26. Eldefrawy M.H; Alghathbar K et.al 'OTP based two factor authentication using mobile phones', in 8 International Conference on IT, April 2011, pp 327 - 331. 27. Bruce Ndibanje; Hoon-Jae Lee et.al “Security Analysis and Improvements of Authentication and Access Control in the IoT', in Volume 14, 2014, pp 14786 -14805. 28. Jose L; Hernández Ramos; Antonio J; Skarmeta et al, “Distributed Capability-Based Access Control for the IoT”, in Vol: 3, Number 3/4, 2013, pp.1-16. 29. Bruce Ndibanje , Hoon-Jae Lee, and Sang-Gon Lee, ‘Security Analysis and Improvements of Authentication and Access Control in the Internet of Things’, Sensors, Vol. 14, 2014, pp 14786-14805. 30. Muhammad, K.R.R.S.; Lee S: Lee Y.K. 'Biometric Based Distributed Key Management Approach for Wireless Body Area Network. Sensors”', in 2010, Vol: 10, pp. 3911-3933. 31. Sanaz Rahimi Moosavi et.al “SEA – A Secure & Efficient authentication and authorization architecture for IoT based Healthcare using Smart gateways”, in Procedia CS Journal, published in Elsevier in 2015, Vol.52, pp. 452-459 32. Vani.G, Bharathi Malakreddy.A, “Security challenges in Internet of Things in Healthcare Domain”, in September 2016 ,DOI No. IAECS IRAJ DOI 5592,pp.141-144. 33. Vani.G, Bharathi Malakreddy.A, “ Survey on Security challenges in IoT in Healthcare domain”, in ICNTET, 2018,ISBN- CFP18P34- PRT/978-1-5386-5629-7. Authors: Sk. Sadiya Shireen, B. Murali Krishna, K. Naga Lakshmi Prasanna, A. Poorna Chander Reddy Paper Title: FPGA Based RSA Authenticated Data Hiding in Image through Steganography Abstract: Now-a-days, information security has a vital role in different applications of digital communications like medical, military, commerce etc., to conceal the secret data from unauthorized access. Steganography is the most eminent technique for providing information security with the help of a carrier file. The communication carrier can be of various formats like text, image, video etc. Among all these, digital images are the most common format due to high capacity and frequency of availability. In image steganography, the secret data is embedded into an inconspicuous carrier i.e., digital image is used as cover image to conceal the secret message which is known as stego image. Cryptography techniques are used to strengthen the security for the stego image. In this paper, a zigzag method has proposed for concealing patient’s secret information with RSA cryptography algorithm in a RGB medical cover image. The medical cover image is implemented on Nexys 2 1200E FPGA (Field Programmable Gate Array)

Keywords: Cryptography, RSA Algorithm, Steganography, RGB medical cover image, Stego image, FPGA

References: 1. ‘Privacy , Confidentiality : and Electronic Medical Records Abstract The enchanced Goals of Informantional Security In Health Care’, 1996. 2. ‘Summary of the HIPAA Security Rule’, pp. 1–8, 2019. 3. M. E. Hellman, ‘M. E. Hellman, “An Overview of Public Key Cryptography,” IEEE Commun. Mag ., vol. 16, no. 6, May 2002, pp. 42– 100. 49.’, no. 6, pp. 42–49, 2002. 4. J. Gupta, ‘A Review on Steganography techniques and methods’, vol. 1, no. 1, pp. 1–4, 2015. 550-554 5. K. Joshi, P. Dhankhar, and R. Yadav, ‘A new image steganography method in spatial domain using XOR’, in 12th IEEE International Conference Electronics, Energy, Environment, Communication, Computer, Control: (E3-C3), INDICON 2015, 2016. 6. K. H. Jung and K. Y. Yoo, ‘Steganographic method based on interpolation and LSB substitution of digital images’, Multimed. Tools Appl., 2015. 7. F. Huang, Y. Zhong, and J. Huang, ‘Improved algorithm of edge adaptive image steganography based on LSB matching revisited algorithm’, Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 8389 LNCS, no. 2, pp. 19–31, 2014. 8. C. G. Tappe and A. V Deorankar, ‘An Improved Image Steganography Technique based on LSB’, Int.Res. J. Eng. Technol., pp. 2395–56, 2017. 9. J. Mielikainen, ‘LSB matching revisited’, IEEE Signal Process. Lett., vol. 13, no. 5, pp. 285–287, 2006. 10. V. M. Potdar and E. Chang, ‘Grey level modification steganography for secret communication’, Ind. Informatics, 2004. INDIN ’04. 2004 2nd IE, no. June, pp. 223–228, 2004. 11. D. George, ‘RSA Encryption System Using Encoded Multiplier and Vedic Mathematics’, pp. 19–22, 2013. 12. F. A. P. Petitcolas, R. J. Anderson, and M. G. Kuhn, ‘Information Hiding — A Survey’, vol. 87, no. July, pp. 1062–1078, 1999. 13. Y. P. Astuti, D. R. Ignatius, M. Setiadi, E. H. Rachmawanto, and C. A. Sari, ‘Simple and Secure Image Steganography using LSB and Triple 14. XOR Operation on MSB’, pp. 191–195, 2018. 15. M. Jain and S. K. Lenka, ‘Diagonal queue medical image steganography with Rabin cryptosystem’, Brain Informatics, vol. 3, no. 1, pp. 39–51, 2016. 16. S. Debnath, M. Kalita, and S. Majumder, ‘A review on hardware implementation of steganography’, Proc. 2nd Int. Conf. 2017 Devices Integr. Circuit, DevIC 2017, pp. 149–152, 2017. 17. K. Joshi and R.Yadav, ‘A new LSB-S image steganography method blend with cryptography for secure communication’, Proc. 2015 3rd Int. conf. image Inf. Process. ICIIP.2015,pp-86-90, 2016. Authors: Manjula Devarakonda Venkata, Sumalatha Lingamgunta Paper Title: Triple-Modality Breast Cancer Diagnosis and Analysis in middle aged women by Logistic Regression 101. Abstract: Breast cancer is found to be the foremost root cause of deaths associated with cancer in Asian women, 555-562 and in recent days, it has become common among women out running cervical cancer. This work intends to analyse, evaluate and compare the effectiveness of the existing breast cancer imaging schemes like Ultrasound, Mammography, and Magnetic resonance imaging techniques using Logistic regression, a statistical prediction machine learning tool for diagnosing breast cancer. Using the logistic regression tool, breast cancer factor values are obtained and tabulated to compare the suggested methods. The tabulated results validate that, MRI exhibits remarkably higher sensitivity values compared to other imaging techniques such as mammography and ultrasound imaging could be ineffective in patients with cancer history and fails to diagnose some mass in dense breast tissue.

Keywords: Breast Cancer, Mammogram, Magnetic Resonance Imaging, Ultrasound, Logistic regression.

References: 1. Chakraborti, K. L., Bahl, P., Sahoo, M., Ganguly, S. K., & Oberoi, C. (2005). Magentic resonance imaging of breast masses: Comparison with mammography. Indian Journal of Radiology and Imaging, 15(3), 381. 2. Yusuff, H., Mohamad, N., Ngah, U. K., & Yahaya, A. S. (2012). Breast cancer analysis using logistic regression. International Journal of Research and Reviews in Applied Sciences, 10(1), 14-22. 3. Prasad, N., & Houserkova, D. (2007). The role of various modalities in breast imaging. Biomedical Papers of the Medical Faculty of Palacky University in Olomouc, 151(2). 4. Kuhl, C. K., Schrading, S., Leutner, C. C., Morakkabati-Spitz, N., Wardelmann, E., Fimmers, R., & Schild, H. H. (2005). Mammography, breast ultrasound, and magnetic resonance imaging for surveillance of women at high familial risk for breast cancer. Journal of clinical oncology, 23(33), 8469-8476. 5. Reddy, D. H., & Mendelson, E. B. (2005). Incorporating new imaging models in breast cancer management. Current treatment options in oncology, 6(2), 135-145. 6. Kriege, M., Brekelmans, C. T., Boetes, C., Besnard, P. E., Zonderland, H. M., Obdeijn, I. M., ... & Muller, S. H. (2004). Efficacy of MRI and mammography for breast-cancer screening in women with a familial or genetic predisposition. New England Journal of Medicine, 351(5), 427-437. 7. Lewin, J. M., Hendrick, R. E., D’Orsi, C. J., Isaacs, P. K., Moss, L. J., Karellas, A., ... & Cutter, G. R. (2001). Comparison of full-field digital mammography with screen-film mammography for cancer detection: results of 4,945 paired examinations. Radiology, 218(3), 873-880. 8. Fischer, U., Baum, F., Obenauer, S., Luftner-Nagel, S., Von Heyden, D., Vosshenrich, R., & Grabbe, E. (2002). Comparative study in patients with microcalcifications: full-field digital mammography vs screen-film mammography. European radiology, 12(11), 2679- 2683. 9. Kolb, T. M., Lichy, J., & Newhouse, J. H. (2002). Comparison of the performance of screening mammography, physical examination, and breast US and evaluation of factors that influence them: an analysis of 27,825 patient evaluations. Radiology, 225(1), 165-175. 10. Liberman, L., Morris, E. A., Dershaw, D. D., Abramson, A. F., & Tan, L. K. (2003). MR imaging of the ipsilateral breast in women with percutaneously proven breast cancer. American Journal of Roentgenology, 180(4), 901-910. 11. Kristoffersen Wiberg, M., Aspelin, P., Perbeck, L., & Bone, B. (2002). Value of MR imaging in clinical evaluation of breast lesions. Acta Radiologica, 43(3), 275-281. 12. Berg, W. A., Gutierrez, L., NessAiver, M. S., Carter, W. B., Bhargavan, M., Lewis, R. S., & Ioffe, O. B. (2004). Diagnostic accuracy of mammography, clinical examination, US, and MR imaging in preoperative assessment of breast cancer. Radiology, 233(3), 830-849. 13. MARIBS Study Group. (2005). Screening with magnetic resonance imaging and mammography of a UK population at high familial risk of breast cancer: a prospective multicentre cohort study (MARIBS). The Lancet, 365(9473), 1769-1778. 14. Lee, C. H., Dershaw, D. D., Kopans, D., Evans, P., Monsees, B., Monticciolo, D., & Hendrick, E. (2010). Breast cancer screening with imaging: recommendations from the Society of Breast Imaging and the ACR on the use of mammography, breast MRI, breast ultrasound, and other technologies for the detection of clinically occult breast cancer. Journal of the American college of radiology, 7(1), 18-27. Authors: P Osman, PV Sridevi, K V S N Raju Paper Title: Dual Band Band-Pass Filters using Plasmonic Split-mode Ring Resonator Abstract: This article presents a two types of plasmonic split mode ring resonator band-pass filter (BPF) using metal-insulator-metal (MIM) waveguide. The two filters operate at two optical wavelengths in between O (1260- nm to1360-nm) and L (1565-nm to 1625-nm) bands. The designed split-modes ring resonators are designed using local resonance and notch perturbation split-modes respectively. A full wave simulation software tool has been used in the designing of the split-mode resonator band-pass filter. These filters are used in the designing of dualband high density photonic integrated circuits (PICs).

Keywords: (MIM) waveguide, (BPF), (1260-nm to1360-nm), (PICs),(1565-nm to 1625-nm) bands.

References: 1. W.Mu andJ. B.Ketterson, “Long-range surfaceplasmonpolaritons propagating on adielectric waveguide support,” vol.36, no. 23, pp. 102. 4713-4715, Dec. 2011. 2. P. Taylor,C. Li,D. Qi, J. Xin, andF. Hao, “Metal-insulator-metal plasmonicwaveguidefor low- distortionslow lightattelecom frequencies,”vol.61 no.8 , pp. 37-41, Nov. 2014. 563-565 3. J. H.Zhu,Q. J.Wang, P. Shum,and X. G. Huang, “A Simple Nanometeric PlasmonicNarrow-Band FilteStructure Basedon Metal - Insulator - Metal Waveguide,” vol 10, no. 6, pp.1371-1376, Nov. 2011. 4. G. Duan,P. Lang, L.Wang, L.Yu, and J Xiao, “Aband-pass plasmonic filter with dual-square ring resonator,” Mod. Phys. Lett. B, vol. 28, no. 23, pp.1450188(1-5),Sep. 2014. 5. G.Zheng, L.Xu, and Y. Liu,“Tunable plasmonic filter with circular metal-insulator-metal ring resonatorcontaining double narrow gaps,” Pramana J. Phys., vol. 86, no. 5, pp. 1091-1097, May 2016. 6. N.JankovicandN. Cselyuszka,“Multiple fano-likeMIMplasmonic structure basedon triangularresonatorfor refractiv index sensing,” Sensors (Switzerland), vol 18, no. 1,pp.287(1-10), Jan. 2018. 7. S. M. Grist et al., “Silicon photonic micro-disk resonators for label-free biosensing,” Opt. Express, vol. 21, no.7, pp. 7994-7998, Mar. 2013. 8. Jhonsonand Christy, “Optical Constants ofNoble Metals, ” Phys.Rev. Lett.,vol.6, no. 12, pp.4370-4379, Dec.1972. 9. A. Kamma, G. S.Reddy, P.Suggisetti, andJ. Mukherjee,“A Novel and Compact Ultra-Wide Band ( UWB Filter Using Modified Split Ring Resonato ( MSRR ),” vol. 8, pp. 69–71, 2014. 10. I. WolffandN.Knoppik, “Microstripring resonatorand dispersion measurementonmicrostrip lines,”Electron. Lett., vol. 7, pp. 779(1- 3),Nov. 1971. 103. Authors: N Saida Naik, A Sai Pallavi, L Srujana Paper Title: Improvement of Sag under Different Fault Conditions Abstract: The improvement of power flow in a distributed system can be achieved by the FACTS compensator that is D-STATCOM (DISTRIBUTION_STATIC_COMPENSATOR) also known as which is shunt connected, is explained in this paper. To reduced the Sag-in-voltage issues(power quality issue), a Distribution-STATCOM is used which is connected at PCC (Point of Common Coupling).The advantage of quick operation of Distribution- STATCOM makes the it more efficient and hence power flow is improved. Varied controllers are utilzed to operate the Distribution-STATCOM. To enhance the power flow, we are simulating and designing it with PI Controller. In distribution networks with linear balanced loads, their power flow can be increases at varied fault conditions such as L-L Faults (Line to Line),L-G Faults (Line to Ground), L-L-G Faults, L-L-L-G Faults. These faults are studied and simulated output waveforms are presented also calculating THD (Total Harmonic Distortion) with and without Distribution-STATCOM Compensator. The harmonics and Sag-in-voltages due to LG, LLG & LLLG faults in this proposed system are reduced and we can achieve enhanced power flow. The reduction of faults and trhe value of THD ( Total-Harmonic-Distortion) can be simulated and studied in MATLAB.

Keywords: DSTATCOM, PI, FAULTS, PWM 566-569

References: 1. Lakshman naik popavath, k.palanisamy “a dstatcom for enhancement of power quality in distribution systems” international journal of pure and applied mathematics volume 118 no. 18 2018, 4083-4094 issn: 1311-8080 (printed version); issn: 1314-3395 (on-line version). 2. Manpreetsingh, jaspreetkaur “role of dstatcom in distribution network under various fault conditions” international journal of advanced research in electrical,electronics and instrumentation engineering vol. 4, issue 7, july 2015 issn (print) : 2320 – 3765 , issn (online): 2278 – 8875. 3. Miss mallelaleelamounika ,mrk.v.kishore “ modelling and simulation of d-statcom for power quality problems using sinusoidal pulse width modulation (spwm)” ijcsiet--international journal of computer science information and engg., technologies issue5-volume2, issn 2277- 4408. 4. Kartikparmarpratikkabrawala prof. Pinkalj.patel “simulation and analysis of dstatcom” 2014 ijedr volume 2, issue 1 issn: 2321-9939. 5. Darjidhaval d. Patel sumit r. Prof.hardik h. Raval “improving voltage profile of distribution system using dstatcom” 2014 ijedr volume 2, issue 1 issn: 2321-9939. 6. Honey baby swaminathan.p j. Jayakumar “enhancing power quality issues in distribution system using d-statcom” international journal of recent research aspects issn: 2349-7688, vol. 4, issue 4, dec 2017, pp. 356-359. 7. Akshaybhargav, harsh sharma “control of total harmonic distortion in distribution network using compensation” international journal of science and research (ijsr) volume 5 issue 5, may 2016 issn (online): 2319-7064 Authors: T. Charan Singh, K. Raghu Ram, B.V. Sanker Ram Transient Stability Analysis of Six Phase Transmission System with Integration of WPGS and Paper Title: STATCOM with Smart Grid Abstract: In recent times Transient stability analysis has become a major concern in the operation of power systems due to the rising stress on power system networks. These difficulties require assessment of a power system’s ability to with stand instability while maintaining the excellence of service. Many different techniques have been projected for transient stability analysis in power systems, especially for a multi machine system. This paper describes simulation of six phase multi-machine power system (MMPS) with wind power generator integration in dynamic operation. By the introduction of wind power generation system (WPGS) in multi-machine at weak bus in parallel with STATCOM can improve the generator load angle deviation during fault condition. The MMPS performance is analysed by placing six phase line between different buses. The replacement of transmission line can reduces the line impedances, which results in reduced angle distortion of machines and improved stability .The proposed WPGS based MMPS phase angle and frequency variations are analyzed during symmetrical and asymmetrical fault conditions. The MATLAB/Simulation software is used to test the behavior of proposed system.

Keywords: Wind system, six phase transmission line, STATCOM, multi-machine system, stability.

104. References: 1. D. Basic, J. G. Zhu, and G. Boardman, “Transient performance study of a brushless doubly fed twin stator induction generator,” IEEE Trans. energy Convers., vol. 18, no. 3, pp. 400–408, 2003. 570-576 2. G. K. Singh, “Modeling and experimental analysis of a self-excited six-phase induction generator for stand-alone renewable energy generation,” Renew. energy, vol. 33, no. 7, pp. 1605–1621, 2008. 3. J. R. Stewart and D. D. Wilson, “High phase order transmission--a feasibility analysis part I--steady state considerations,” IEEE Trans. Power Appar. Syst., no. 6, pp. 2300–2307, 1978. 4. T. L. Landers, R. J. Richeda, E. Krizanskas, J. R. Stewart, and R. A. Brown, “High phase order economics: constructing a new transmission line,” IEEE Trans. Power Deliv., vol. 13, no. 4, pp. 1521–1526, 1998. 5. J. M. Arroyo and A. J. Conejo, “Optimal response of a power generator to energy, AGC, and reserve pool-based markets,” IEEE Trans. Power Syst., vol. 17, no. 2, pp. 404–410, 2002. 6. J. R. Stewart, E. Kallaur, and I. S. Grant, “Economics of EHV high phase order transmission,” IEEE Trans. power Appar. Syst., no. 11, pp. 3386–3392, 1984. 7. N. G. Hingorani, L. Gyugyi, and M. El-Hawary, Understanding FACTS: concepts and technology of flexible AC transmission systems, vol. 1. IEEE press New York, 2000. 8. G. Cai, Q. Sun, C. Liu, P. Li, and D. Yang, “A new control strategy to improve voltage stability of the power system containing large- scale wind power plants,” in Electric Utility Deregulation and Restructuring and Power Technologies (DRPT), 2011 4th International Conference on, 2011, pp. 1276–1281. 9. L. Wang and C.-T. Hsiung, “Dynamic stability improvement of an integrated grid-connected offshore wind farm and marine-current farm using a STATCOM,” IEEE Trans. power Syst., vol. 26, no. 2, pp. 690–698, 2011. 10. S. S. Venkata, W. C. Guyker, W. H. Booth, J. Kondragunta, N. K. Saini, and E. K. Stanek, “138-kV, six-phase transmission system: fault analysis,” IEEE Trans. Power Appar. Syst., no. 5, pp. 1203–1218, 1982. 11. A. P. Apostolov and R. G. Raffensperger, “Relay protection operation for faults on NYSEG’s six-phase transmission line,” IEEE Trans. Power Deliv., vol. 11, no. 1, pp. 191–196, 1996. 12. N. B. Bhatt, S. S. Venkata, W. C. Guyker, and W. H. Booth, “Six-phase (multi-phase) power transmission systems: fault analysis,” IEEE Trans. Power Appar. Syst., vol. 96, no. 3, pp. 758–767, 1977. 13. R. J. Vidmar, “On the use of atmospheric plasmas as electromagnetic reflectors,” IEEE Trans. Plasma Sci, vol. 21, no. 3, pp. 876–880, 1992. Authors: R. Umamaheswari, Ch. Sumanth Kumar Optimization of Training Sequence Based Sparse Channel Estimation for Mmwave Communications in Paper Title: 5G Abstract: In this paper to achieve higher data rates with high spectral efficiency and high accuracy we designed training sequence sparse channel estimation based on BAT, Cuckoo and Firefly algorithms. By using the above techniques we design a Training sequence channel estimation to reduce the bit error rate, mean square error and accurate recovery of data. The firefly optimization is the promising technique to reduce the bit error rate and to increase the signal to noise ratio to achieve high spectral efficiency Gbps.

Keywords: Quadrature Amplitude Modulation , Bit Error rate, Signal to Noise Ratio

References: 1. Ma, Xu, Fang Yang, Sicong Liu, Jian Song, and Zhu Han. "Design and optimization on training sequence for mmwave communications: A new approach for sparse channel estimation in massive MIMO." IEEE journal on selected areas in communications 35, no. 7 (2017): 1486-1497. 2. Rappaport, Theodore S., Yunchou Xing, George R. MacCartney, Andreas F. Molisch, Evangelos Mellios, and Jianhua Zhang. "Overview of Millimeter Wave Communications for Fifth-Generation (5G) Wireless Networks—With a Focus on Propagation Models." IEEE 105. Transactions on Antennas and Propagation 65, no. 12 (2017): 6213-6230. 3. González-Prelcic, Nuria, Anum Ali, Vutha Va, and Robert W. Heath. "Millimeter-Wave Communication with Out-of-Band Information." 577-581 IEEE Communications Magazine 55, no. 12 (2017): 140-146. 4. Niu, Yong, Yong Li, Depeng Jin, Li Su, and Athanasios V. Vasilakos. "A survey of millimeter wave (mmwave) communications for 5g: opportunities and challenges. arXiv preprint." arXiv preprint arXiv:1502.07228 (2015). 5. Niu, Y., Y. Li, D. Jin, L. Su, and A. Vasilakos. "A survey of milimeter wave (mmwave) communications for 5g: Opportunities and challenges." Computer Science-Networking and Internet Architecture (2015). 6. Schniter, Philip, and Akbar Sayeed. "Channel estimation and precoder design for millimeter-wave communications: The sparse way." In Signals, Systems and Computers, 2014 48th Asilomar Conference on, pp. 273-277. IEEE, 2014. 7. Xiao, Ming, Shahid Mumtaz, Yongming Huang, Linglong Dai, Yonghui Li, Michail Matthaiou, George K. Karagiannidis et al. "Millimeter wave communications for future mobile networks." IEEE Journal on Selected Areas in Communications 35, no. 9 (2017): 1909-1935. 8. Zhao, Lou, Derrick Wing Kwan Ng, and Jinhong Yuan. "Multi-user precoding and channel estimation for hybrid millimeter wave systems." IEEE Journal on Selected Areas in Communications 35, no. 7 (2017): 1576-1590. 9. Zhao, Lou, Derrick Wing Kwan Ng, and Jinhong Yuan. "Multiuser precoding and channel estimation for hybrid millimeter wave MIMO systems." In Communications (ICC), 2017 IEEE International Conference on, pp. 1-7. IEEE, 2017. 10. Arora, Sankalap, and Satvir Singh. "A conceptual comparison of firefly algorithm, bat algorithm and cuckoo search." In Control Computing Communication & Materials (ICCCCM), 2013 International Conference on, pp. 1-4. IEEE, 2013. 11. Fister Jr, Iztok, Xin-She Yang, Iztok Fister, Janez Brest, and Dušan Fister. "A brief review of nature-inspired algorithms for optimization." arXiv preprint arXiv:1307.4186 (2013). Authors: B. Prasanthi, N. Nagamalleswararao Optimal kernel based Neutrosophic Soft sets Clustering for Image Segmentation based on Pareto Paper Title: Optimal Algorithm Abstract: In bio-medical image processing, brain image segmentation is an aggressive concept in present days. Disorders of brain mainly requires accurate tissue extraction and classification of magnetic resonance (MR) medical brain images, which is very effective and important to detect different types of tumors, and necrotic tissue classification and segmentation. To handle brain image segmentation, mathematical tools like fuzzy sets, rough sets and soft sets are used to define uncertainty and vagueness of brain images. Accurate and effective segmentation and detection of tumor on brain image is still a challenging task in medical brain images with respect to reduction of noise, smoothness of image and accuracy for segmentation of medical brain images and other parameters. We propose and introduce a Novel Brain Segmentation approach based on neutrosophic soft sets is introduced to explore uncertainties relates to white, grey and cerebro spinal fluid matters for the detection tumor from MR brain image with respect to bias field estimation and co-relation based on decision making. Our proposed approach consist Pareto Optimization algorithm to support neutrosophic soft sets approximations for the optimal kernel parameters (like kernel functions). These approximations are free to define weight parameters and average, 106. median, weight filters and less complexity compared to existing algorithms. Our experimental results show effective performance of proposed approach with respect to segmentation accuracy, time and jacquard parameters 582-591 compared to existing algorithms.

Keywords: Segmentation of brain image, fuzzy c-means, Intuitionistic neutrosophic soft sets, rough sets, magnetic resonance.

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Rousseau,A non-local fuzzy segmentation method: Application to brain MRI,Pattern Recognition, vol. 44, pp. 1916-1927, 2011. 9. Z. X. Ji, Q. S. Sun, and D. S. Xia, A modified possibilistic fuzzy c-means clustering algorithm for bias field estimation and segmentation of brain MR image, Computerized Medical Imaging and Graphics, vol. 35, p. 383397, 2011. 10. Ahmed MN, Yamany S, Mohamed N, Farag A, Moriarty T. A modified fuzzy c-means algorithm for bias field estimation and segmentation of MRI data. IEEE Trans Med Imaging 2002;21(3):193-9. 11. N.R. Pal, K. Pal, J.M. Keller, J.C. Bezdek, A possibilistic fuzzy c-means clustering algorithm, IEEE Trans. Fuzzy Syst. 13 (4) (2005) 517- 530. 12. K.-S. Chuang, H.-L. Tzeng, S. Chen, J. Wu, T.-J. Chen, Fuzzy c-means clusteringwith spatial information for image segmentation, Comput. Med. ImagingGraph. 30 (1) (2006) 9-15. 13. W. Cai, S. Chen, D. Zhang, Fast and robust fuzzy c-means clustering algorithms incorporating local information for image segmentation, Pattern Recognit. 40(3) (2007) 825-838. 14. S. Krinidis, V. Chatzis, A robust fuzzy local information c-means clustering algorithm, IEEE Trans. Image Process. 19 (5) (2010) 1328- 1337. 15. C. Li, J.C. Gore, C. Davatzikos, Multiplicative intrinsic component optimization(MICO) for MRI bias field estimation and tissue segmentation, Magn. Reson.Imaging 32 (7) (2014) 913-923. 16. Chen S, Zhang D. Robust image segmentation using FCM with spatial constraint based on new kernel-induced distance measure. IEEE Trans Syst Man Cybern B 2004;34 (4):1907-16. 17. Souza, C.R. Kernel functions for machine learning applications. Creat. Commons Attrib. Noncommer. Share Alike 2010, 3, 29. 18. Lin, K.-P. A novel evolutionary kernel intuitionistic fuzzy C-means clustering algorithm. IEEE Trans. Fuzzy Syst. 2014, 22, 1074–1087. 19. Yang, M.S.; Tsai, H.S. A Gaussian kernel-based fuzzy c-means algorithm with a spatial bias correction.Pattern Recogn. Lett. 2008, 29, 1713–1725. 20. Zhang, D.-Q.; Chen, S.-C. Clustering incomplete data using kernel-based fuzzy C-means algorithm. Neural Process. Lett. 2003, 18, 155– 162. 21. Zhang, D.-Q.; Chen, S.-C. A novel kernelized fuzzy c-means algorithm with application in medical image segmentation. Artif. Intell. Med. 2004, 32, 37–50. 22. Gong, M.G.; Liang, Y.; Shi, J.; Ma, W.P.; Ma, J.J. Fuzzy C-Means Clustering With Local Information and Kernel Metric for Image Segmentation. IEEE Trans. Image Process. 2013, 22, 573–584. 23. Ji, Z.X.; Liu, J.Y.; Cao, G.; Sun, Q.S.; Chen, Q. Robust spatially constrained fuzzy c-means algorithm for brain MR image segmentation. Pattern Recogn. 2014, 47, 2454–2466. 24. Li, C.M.; Gore, J.C.; Davatzikos, C. Multiplicative intrinsic component optimization (MICO) for MRI bias field estimation and tissue segmentation. Magn. Reson. Imaging 2014, 32, 913–923. 25. Krinidis, S.; Chatzis, V. A Robust Fuzzy Local Information C-Means Clustering Algorithm. IEEE Trans. Image Process. 2010, 19, 1328– 1337. 26. Goldberg, D.E. Genetic Algorithms in Search, Optimization, and Machine Learning; Addison-Wesley: Boston, MA, USA, 1989. 27. Tao, J.; Wang, N. DNA computing based RNA genetic algorithm with applications in parameter estimation of chemical engineering processes. Comput. Chem. Eng. 2007, 31, 1602–1618. 28. Zang, W.; Zhang, W.; Zhang, W.; Liu, X. A Genetic Algorithm Using Triplet Nucleotide Encoding and DNA Reproduction Operations for Unconstrained Optimization Problems. Algorithms 2017, 10, 16. 29. Zang,W.; Ren, L.; Zhang,W.; Liu, X. Automatic Density Peaks Clustering Using DNA Genetic Algorithm Optimized Data Field and Gaussian Process. Int. J. Pattern Recogn. 2017, 31, 1750023. 30. Zang,W.; Jiang, Z.; Ren, L. Improved spectral clustering based on density combining DNA genetic algorithm. Int. J. Pattern Recogn. 2017, 31, 1750010. 31. Zang, W.; Sun, M.; Jiang, Z. A DNA genetic algorithm inspired by biological membrane structure. J. Comput. Theor. Nanosci. 2016, 13, 3763–3772 32. Elazab, A.; Wang, C.; Jia, F.; Wu, J.; Li, G.; Hu, Q. Segmentation of brain tissues from magnetic resonance images using adaptively regularized kernel-based fuzzy-means clustering. Comput. Math. Method Med 2015. 33. Gong, M.G.; Liang, Y.; Shi, J.; Ma, W.P.; Ma, J.J. Fuzzy C-Means Clustering With Local Information and Kernel Metric for Image Segmentation. IEEE Trans. Image Process. 2013, 22, 573–584. 34. Pillonetto, G.; Dinuzzo, F.; Chen, T.S.; De Nicolao, G.; Ljung, L. Kernel methods in system identification, machine learning and function estimation: A survey. Automatica 2014, 50, 657–682. 35. Zhang, L.;Wang, N. A modified DNA genetic algorithm for parameter estimation of the 2-Chlorophenol oxidation in supercritical water. Appl. Math. Model. 2013, 37, 1137–1146. 36. Gousias, I.S.; Edwards, A.D.; Rutherford, M.A.; Counsell, S.J.; Hajnal, J.V.; Rueckert, D.; Hammers, A. Magnetic resonance imaging of the newborn brain: Manual segmentation of labelled atlases in term-born and preterm infants. Neuroimage 2012, 62, 1499–1509. Authors: Sandeep Raskar, kamatchi Iyer Paper Title: Node mobility prediction in Wireless Adhoc Network Abstract: Generally, Link between any two nodes in the wireless network is a bottleneck in process of selecting a path in a network, or among or across multiple networks. Nodes are moving in the network unknowingly. So even if an algorithm selects the node during the path initialization process for the transmission of data among source and destination node, we cannot predict that node will be on the same position in the network when actual data transmission starts. There are many algorithm defined in selection of path in ad hoc network. This paper proposes new concept called Neural Network (NN) model. It predicts the node movements in ad-hoc network during data transmission. Algorithm can predicts the movement of a node in a network on the basis of information of previous movements of that node.

107. Keywords: Network Model; shortest path routing; Neural Network. 592-595 References: 1. S.Jayashri and M.Malathi, " Robust against route failure using power proficient reliable routing in MANET", Alexandria Engineering Journal, Available online, 2016. 2. Qingyang Song, ZhaolongNing, ShiqiangWang and AbbasJamalipour, " Link stabilityestimationbasedonlinkconnectivitychangesinmobile ad-hoc networks", JournalofNetworkandComputerApplications, vol. 35, pp. 2051–2058,2012. 3. Dr. K. Poulose Jacob, Preetha K G, and Dr. A Unnikrishnan, " An Effective Path Protection Method to Attain the Route Stability in MANET", vol. 2, no. 6, 2013, A International Journal of Advanced Research in Computer and Communication Engineering. 4. Shubhajeet Chatterjee and Swagatam Das, " Ant colony optimization based enhanced dynamic source routing algorithm for mobile Ad- hoc network", Information Sciences, vol. 295 pp. 67–90, 2015. 5. Alma A.M.Rahat, Richard M.Everson and Jonathan E.Fieldsend, " Evolutionary multi-path routing for network lifetime and robustness in wireless sensor networks", Ad Hoc Networks, vol. 52, pp. 130-145, 2016. 6. Mawloud Omar, SabrineHedjaz, SouhilaRebouh, KatiaAouchar, BournaneAbbache and AbdelkamelTari, " On-demand source routing with reduced packets protocol in mobile ad-hoc networks", AEU - International Journal of Electronics and Communications, vol. 69, no.10, pp.1429-1436, 2015. 7. Sudip Misra, Sanjay K. Dhurandher, Mohammad S. Obaidat,∗, Pushkar Gupta, Karan Verma and Prayag Narula, " An ant swarm-inspired energy-aware routing protocol for wireless ad-hoc networks", The Journal of Systems and Software, vol. 83 , pp. 2188–2199, 2010. 8. Huafeng Wu, JunWang, Raghavendra RaoAnanta, Vamsee ReddyKommareddy, RuiWang and PrasantMohapatra, " Prediction based opportunistic routing for maritime search and rescue wireless sensor network", Journal of Parallel and Distributed Computing, vol. 111, pp. 56-64, 2018. 9. Gyanappa A.Walikar and Rajashekar C.Biradar, " A survey on hybrid routing mechanisms in mobile ad hoc networks", Journal of Network and Computer Applications", vol. 77, pp. 48-63, 2017. 10. Haiying Shen and Lianyu Zhao, " ALERT: An Anonymous Location-Based Efficient Routing Protocol in MANETs", IEEE Transactions on Mobile Computing, vol. 12, no. 6, 2013 11. Ritu Sharma, " A Secure and Proficient Routing Protocol in Mobile Ad-hoc Networks using Genetic Mechanism", International Journal of Innovative Research in Computer and Communication Engineering, vol. 4, no.6, 2016. 12. K. Manjappa and R. M. Reddy Guddeti, "Mobility aware-termite: a novel bio inspired routing protocol for mobile ad-hoc networks," in IET Networks, vol. 2, no. 4, pp. 188-195, December 2013. 13. Somayeh Taheri, Salke Hartung and Dieter Hogrefe, " Anonymous group-based routing in MANETs", Journal of Information Security and Applications, vol. 22 no. C, pp. 87-98, 2015. 14. GurpreetSingh, NeerajKumar and AnilKumarVerma, " ANTALG:AnInnovativeACObasedRoutingAlgorithmforMANETs", Journal ofNetworkandComputerApplications, vol. 45, pp. 151–167, 2014. 15. ShahramJamali, LeilaRezaei and Sajjad JahanbakhshGudakahriz, " An Energy-efficient Routing Protocol for MANETs: a Particle Swarm Optimization Approach", Journal of Applied Research and Technology, vol. 11, no. 6, pp.803-812, 2013. 16. Hasan Abdulwahid, BinDai, BenxiongHuang and ZijingChen, " Scheduled-links multicast routing protocol in MANETs", Journal of Network and Computer Applications, vol. 63, pp. 56-67, 2016. 17. A.Amuthan, N.Sreenath, P.Boobalan and K.Muthuraj, " Dynamic multi-stage tandem queue modeling-based congestion adaptive routing for MANET", Alexandria Engineering Journal, Available online, 2017. 18. Malik N.Ahmed, Abdul HananAbdullah, HassanChizari and OmprakashKaiwartya, " F3TM: Flooding Factor based Trust Management Framework for secure data transmission in MANETs", Journal of King Saud University - Computer and Information Sciences, vol. 29, no. 3, pp. 269-280, July 2017. 19. ArindrajitPal, Jyoti PrakashSingh and Paramartha Dutta, " Path length prediction in MANET under AODV routing: Comparative analysis of ARIMA and MLP model", Egyptian Informatics Journal, vol.16, no. 1, pp. 103-111, 2015. 20. Saad M.Adam and RosilahHassan, " Delay aware Reactive Routing Protocols for QoS in MANETs: a Review", Journal of Applied Research and Technology, vol. 11, no. 6, pp. 844-850, 2013. 21. Vishal Sharma, Harsukhpreet Singh, Mandip Kaur and Vijay Banga, " Performance evaluation of reactive routing protocols in MANET networks using GSM based voice traffic applications", Optik, vol. 124, pp. 2013– 2016, 2013. 22. Paramjit Singh, Ajay K. Sharma and T.S. Kamal, " An adaptive neuro-fuzzy inference system modeling for VoIP basedIEEE 802.11g MANET", Optik, vol. 127, pp.122–126, 2016. 23. Pedro Garcıa Lopez , Raul Gracia Tinedo and Josep M.Banu´ s Alsina, " Moving routing protocols to the user space in MANET middleware", Journal of Network and Computer Applications, vol. 33, pp.588–602, 2010. 24. HaidarSafa, Marcel Karam and BassamMoussa, " PHAODV: Power aware heterogeneous routing protocol for MANETs", Journal of Network and Computer Applications, vol.46, pp. 60-71, 2014. 25. J. Sathiamoorthy, B. Ramakrishnan and Usha. M, " Design of a proficient hybrid protocol for efficient route discovery and secure data transmission in CEAACK MANETs", Journal of Information Security and Applications, vol. 36, pp. 43–58, 2017. 26. Marjan Kuchaki Rafsanjani and Hamideh Fatemidokht, " FBeeAdHoc: A secure routing protocol for BeeAdHoc based on fuzzy logic in MANETs", AEUE - International Journal of Electronics and Communications, vol. 69, no. 11, pp. 1613-1621, 2015. 27. Rajashekhar C.Biradar and SunilkumarS.Manvi, " Neighbor supportedreliablemultipathmulticastroutinginMANETs", Journal ofNetworkandComputerApplications, vol. 35, pp.1074–1085, 2012. 28. MEI Jing-qing and JI Hong, LI Yi, " Query routing mismatch alleviation architecture for P2P file lookup in MANETs", The Journal of China Universities of Posts and Telecommunications, vol. 18, no. 4, pp. 111–117, 2011. 29. Fahad TahaAL-Dhief, NaseerSabri, S.Fouad, N.M. AbdulLatiff, Musatafa Abbas, AbboodAlbader, " A review of forest fire surveillance technologies: Mobile ad-hoc network routing protocols perspective", Journal of King Saud University - Computer and Information Sciences, Available online,2017. 30. Yogeswaran Mohan, Sia Seng Chee, Donica Kan Pei Xin and Lee Poh Foong, " Artificial Neural Network for Classification of Depressive and Normal in EEG", 2016 IEEE EMBS Conference on Biomedical Engineering and Sciences (IECBES), 2016. Authors: Sampath S S, Prasanth Sreekumar, Chithirai Pon Selvan M Paper Title: Estimation of Power in High Altitude Freely Suspended Wind Turbine Abstract: Conventional wind turbines are restricted in its use due to certain limitations and challenges in its position. To use wind turbine efficiently and economically, it is required to overcome space requirements, noise, variation in air current and set up cost. This study attempts to design and fabricate suspended wind turbine to overcome the above stated hurdles. In this current work, the blades and the alternator are placed in the helium balloon housing which is adjourned in the atmospheric air and supported to the floor surface with tether. A tether made of conductive material is todiffuse the generated power from the airborne housing to the floor base. Blades are made of aluminium and it ensures low rotational inertia. The proposed suspended wind mill in this study is able to generate power output which is comparatively cheaper than conventional wind turbines and also work will be able to cater the needs of electric power to remote areas and farms. Entire setup is modelled in 3D software Creo and the simulation is carried out using ANSYS software. 108. Keywords: Alternator, finite element method, turbine blade, renewable energy, power 596-602

References: 1. M. Diehl, “Airborne wind energy concepts and its physical foundations,” Green Energy Technology, pp. 3–22, 2013. 2. L. Fagiano and T. Marks, “Design of a Small-Scale Prototype in Airborne Wind Energy,” IEEE/ASMETransactions on Mechatronics and is subject to IEEE, pp. 1–18, 2014. 3. Lorenzo Fagiano and Dario Piga , “Optimization of airborne wind energy generators”, International journal of robust and nonlinear control, September 2011 , pp. 2055–2083, 2009. 4. N. Bilaniuk, D. Ph, P. Eng and L. T. a Windpower, “Generic System Requirements for High Wind Turbines,” October, 2009. 5. C. L. Archer and K. Caldeira, “Global assessment of wind power,” Energies, vol. 2, pp. 307–319, 2009. 6. L. Fagiano, M. Milanese and D. Piga, “High-altitude wind power generation for renewable energy ” . 7. B. Lansdorp and M. Sc, “Comparison of high-altitude wind energy generation with ground based generator” China International Renewable Energy Equipment & Technology Exhibition and Conference, pp. 1–9, 2005. 8. A. Bolonkin, “Using of High Wind Energy ” , Smart Grid and Renewable Energy , vol. 2011, no. May, pp. 75–85, 2011. 9. C. L. Archer, “An Introduction to Meteorology for Airborne Wind Energy” Airborne Wind Energy, Green Energy and Technology, 2013, pp. 81–94. 10. T. Ezaki, “Effect of coning angle for single-wire-suspended down-wind turbine” pp. 1–6. 11. J.Helsen,F.Vanhollebeke,D.Vandepitte and W.Desmet,“Some trends in wind turbine upscaling.” 12. M. Haastrup, M. R. Hansen and M. K. Ebbesen,“Modeling of Wind Turbine Gearbox Mounting”, Modeling, Identiﬁcation and Control, vol. 32, no. 4, pp. 141–149, 2011. 13. W. T. Chong, A. Fazlizan, S. C. Poh, K. C. Pan, W. P. Hew and F. B. Hsiao, “The design , simulation and testing of an urban vertical axis wind turbine with the omni-direction-guide-vane q,” Appl. Energy, pp. 5–8, 2013. 14. H. A. Yanto, C. Lin, J. Hwang and S. Lin, “Modeling and control of household-size vertical axis wind turbine and electric power generation system,” PEDS, pp. 1301–1307. 15. S. S. Sampath, SawanShetty and ChithiraiPonSelvan M, “Estimation of power in low velocity vertical axis wind turbine” Frontiers of mechanical engineering, 2015, pp:1-8. 16. D. S. Parker, J. R. Sherwin and B. Hibbs, “Development of High Efficiency Air Conditioner Condenser Fans,” ASHRAE Trans., vol. 111, p. 511, 2005. Authors: Rudi Kurniawan Arief, Erry Yulian T. Adesta, Irfan Hilmy Paper Title: Hardware Improvement of FDM 3D Printer: Issue of Bed Leveling Failures Abstract: Rapid Prototyping is one of many technologies that trigger the Industrial Revolution 4.0. The open source system that applied to 3D printer system make the research development grow rapidly. Most favorable research topics are in the area of extrusion head, material and functional modification. But the difficulties in leveling the heated bed has created worst user experiment and cause some catastrophic failures to be happens. This paper reviewed the research conducted around improvement of the FDM printer’s hardware. The cause of most occur failures in FDM printing also discussed. To overcome the disturbing failure caused by the lack of levelness of the heated bed, a pine trees liked pin system is introduced.

Keywords: FDM, 3D Printer, Bed Leveling, Heated Bed, Printing Failures.

References: 1. V. Sharma and S. Singh, “Rapid Prototyping : Process advantage , comparison and application,” Int. J. Comput. Intell. Res., vol. 12, no. 1, pp. 55–61, 2016. 2. A. Kumar Singh and S. Chauhan, “Technique to Enhance FDM 3D Metal Printing,” Bonfring Int. J. Ind. Eng. Manag. Sci., vol. 6, no. 4, pp. 128–134, 2016. 3. R. Nagpal, R. Gupta, and V. Gupta, “A review on trends and development of rapid prototyping processes in industry,” A Rev. trends Dev. rapid Prototyp. Process. Ind., vol. 2, no. 4, pp. 224–228, 2017. 4. K. Kun, “Reconstruction and development of a 3D printer using FDM technology,” Procedia Eng., vol. 149, no. June, pp. 203–211, 2016. 5. S. K. Ueng, L. K. Chen, and S. Y. Jen, “A preview system for 3D printing,” Proc. 2017 IEEE Int. Conf. Appl. Syst. Innov. Appl. Syst. Innov. Mod. Technol. ICASI 2017, pp. 1508–1511, 2017. 6. E. Fang and S. Kumar, “The Trends and Challenges of 3D Printing,” in Encyclopedia of Information Science and Technology, Fourth Edition, 4th ed., no. August, M. Khosrow, Ed. PA: IGI Global, 2018, pp. 4382–4388. 7. S. P. Deshmukh et al., “Design and development of XYZ scanner for 3D printing,” 2017 Int. Conf. Nascent Technol. Eng. ICNTE 2017 - Proc., 2017. 8. B. M. Schmitt, C. F. Zirbes, C. Bonin, D. Lohmann, D. C. Lencina, and A. da C. Sabino Netto, “A Comparative Study of Cartesian and Delta 3D Printers on Producing PLA Parts,” Mater. Res., vol. 20, pp. 883–886, 2017. 9. R. Jerez-Mesa, J. A. Travieso-Rodriguez, X. Corbella, R. Busqué, and G. Gomez-Gras, “Finite element analysis of the thermal behavior of a RepRap 3D printer liquefier,” Mechatronics, vol. 36, pp. 119–126, 2016. 10. M. Teliskova, J. Torek, T. Cmorej, M. Kocisko, and J. Petrus, “Adjustments of RepRap type printer workbench,” 2017 4th Int. Conf. Ind. 109. Eng. Appl. ICIEA 2017, pp. 15–19, 2017. 11. A. Quatrano, M. C. De Simone, Z. B. Rivera, and D. Guida, “Development and implementation of a control system for a retrofitted CNC 603-614 machine by using Arduino,” FME Trans., vol. 45, no. 4, pp. 565–571, 2017. 12. K. Gunaydin, “Common FDM 3D Printing Defects,” Istanbul, 2018. 13. F. Sovaila, C. Sovaila, and N. Baroiu, “Universal Delta 3D Printer,” J. Ind. Des. Eng. Graph. 29, vol. 4, no. 4, pp. 33–37, 2016. 14. A. Ogulmus, A. Cakan, and M. Tınkır, “Modeling And Position Control Of Scara Type 3D Printer,” Int. J. Sci. Technol. Res., vol. 5, no. 12, pp. 140–143, 2016. 15. M. R. Pfeifer, “Rapid Prototyping Technologies for Manufacturing and Maintenance Activities,” Technol. Eng., vol. 14, no. 2, pp. 2–4, 2017. 16. H. W. Lim, Cassidy, T. and Cassidy, and T. Diane, “Application Research of 3D Printing Technology on Dress Forms,” Int. J. Eng. Technol., vol. 9, no. 1, pp. 78–83, 2017. 17. V. G. Surange and P. V Gharat, “3D Printing Process Using Fused Deposition Modelling (FDM),” Int. Res. J. Eng. Technol., vol. 3, no. 3, pp. 1403–1406, 2016. 18. G. I. J. Salentijn, P. E. Oomen, M. Grajewski, and E. Verpoorte, “Fused Deposition Modeling 3D Printing for (Bio)analytical Device Fabrication: Procedures, Materials, and Applications,” Anal. Chem., vol. 89, no. 13, pp. 7053–7061, 2017. 19. E. Soriano Heras, F. Blaya Haro, J. M. de Agustín del Burgo, M. Islán Marcos, and R. D’Amato, “Filament advance detection sensor for fused deposition modelling 3D printers,” Sensors (Switzerland), vol. 18, no. 5, 2018. 20. M. Stopka, R. Kohár, P. Weis, and J. Šteininger, “Concept of modular 3D printer construction,” IOP Conf. Ser. Mater. Sci. Eng., vol. 393, p. 012092, 2018. 21. A. Daniel and V. Christian, “Improvements to control system of a multi-extruder 3D printer using a controller duet card,” 2017 Congr. Int. Innov. y Tendencias en Ing. CONIITI 2017 - Conf. Proc., vol. 2018–Janua, pp. 1–6, 2018. 22. H. L. Oo, K. Z. Ye, and Y. H. Linn, “Modeling and Controlling of Temperature in 3D Printer ( FDM ),” IEEE, pp. 1738–1742, 2018. 23. A. Patil, B. Patil, R. Potwade, A. Shinde, and R. Shinde, “Design and Development of FDM Based Portable 3D Printer,” Int. J. Sci. Eng. Res., vol. 8, no. 3, pp. 116–120, 2017. 24. P. Zeleny and V. Ruzicka, “The Design of The 3D Printer for Use in Gastronomy,” MM Sci. J., vol. d, no. 1, pp. 1668–1673, 2017. 25. Y. Xie, Y. Tan, G. Ma, J. Zhang, and F. Zhang, “Design and Implementation of Chocolate 3D Printer,” DEStech Trans. Comput. Sci. Eng., no. itms, 2017. 26. M. Alimanova, A. Zholdygarayev, A. Tursynbekova, and D. Kozhamzharova, “Overview of a Low-cost Self-Made 3D Food Printer,” IEEE, vol. 0, 2017. 27. E. Acosta, “Laser Printhead Concept Design For a 3D Moving Platform,” California State University, 2017. 28. X. Chen, X. Liu, P. Childs, N. Brandon, and B. Wu, “A Low Cost Desktop Electrochemical Metal 3D Printer,” Adv. Mater. Technol., vol. 2, no. 10, 2017. 29. S. Han, Y. Xiao, T. Qi, Z. Li, and Q. Zeng, “Design and Analysis of Fused Deposition Modeling 3D Printer Nozzle for Color Mixing,” Adv. Mater. Sci. Eng., vol. 2017, 2017. 30. E. Koc, “Investigation of Heat Sink Geometry Effect On Cooling Performance For A FDM 3D Printer Liquefier,” Internaational Conf. Energy Therm. Eng., no. March 2018, pp. 569–573, 2017. 31. L. Maria and E. Piperi, “Extruder head thermal analysis for an open- source 3D printer,” 1st Int. Conf. Eng. Entrep. Proc., no. December, 2017. 32. J. Q. Oberhauser, “Design, Construction, Control, and Analysis of Linear Delta Robot,” Ohio University, 2016. 33. B. Hoy, “Design and Implementation of a Three- Dimensional Printer Using a Cylindrical Printing Process,” San Luis Obispo, 2016. 34. K. H. Lin, C. Y. Shen, J. L. Du, G. Y. Wang, H. M. Chen, and J. D. Tseng, “A design of constant temperature control system in 3D printer,” 2016 IEEE Int. Conf. Consum. Electron. ICCE-TW 2016, pp. 30–31, 2016. 35. C. T. Hsieh, “Development of an integrated system of 3D printer and laser carving,” Proc. Tech. Pap. - Int. Microsystems, Packag. Assem. Circuits Technol. Conf. IMPACT, no. 84, pp. 420–423, 2016. 36. G. Wang, L. Yao, W. Wang, J. Ou, C.-Y. Cheng, and H. Ishii, “xPrint: A Modularized Liquid Printer for Smart Materials Deposition,” Proc. 2016 CHI Conf. Hum. Factors Comput. Syst. - CHI ’16, pp. 5743–5752, 2016. 37. Z. Pilch, J. Domin, and A. Szlapa, “The impact of vibration of the 3D printer table on the quality of print,” 2015 Sel. Probl. Electr. Eng. Electron. WZEE 2015, 2015. 38. W. Gao, Y. Zhang, D. C. Nazzetta, K. Ramani, and R. J. Cipra, “RevoMaker: Enabling Multi-directional and Functionally-embedded 3D printing using a Rotational Cuboidal Platform,” Proc. 28th Annu. ACM Symp. User Interface Softw. Technol. - UIST ’15, pp. 437–446, 2015. 39. M. H. Ali, N. Mir-Nasiri, and W. L. Ko, “Multi-nozzle extrusion system for 3D printer and its control mechanism,” Int. J. Adv. Manuf. 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(Engineering Sci., vol. 50, no. 1, pp. 78–84, 2014. 45. S. E. Hudson, “Printing Teddy Bears: A Technique for 3D Printing of Soft Interactive Objects,” in CHI 2014, Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, 2014, pp. 459–468. 46. R. Bartoš, “Design of Heated Print Bed For FDM 3d Printer With F.E.M,” BRNO University of Technology, 2014. 47. David W. Eld, “Ultra Affordable Rapid Prototyping : Creation and Setup of an Experimental Fabrication Machine,” University of Idaho, 2014. 48. J. Dvoracek, “A Distribution of Temperature Field in the Fdm Printhead,” New Technol. Manuf., vol. 2, no. 1, pp. 1–5, 2011. 49. N. I. Jaksic, “What to do when 3D printers go wrong: Laboratory Experiences,” in 122nd ASEE Annual Conference & Exposition, 2015, no. June 2015, p. 26.1730.1-26.1730.11. 50. R. Song and C. Telenko, “Material Waste of Commercial FDM Printers under Realstic Conditions,” Proc. 27th Annu. Int. Solid Free. Fabr. Symp., no. 2015, pp. 1217–1229, 2016. 51. K. Kumar, S. C. Sai, S. Galla, and V. Sri, “Measurement of Surface Defects in 3D Printed Models,” 2016. Authors: P. Srinivasa Rao, P. Manoj Kumar, G. Tirupathi Naidu Paper Title: Study the Impact of Blast Load on G+7 Multistoried RCC Structure with Varied Distances Abstract: Nowadays, due to increased terrorist activity throughout the world, Blasts have been taking place irrespective of the location. In order to withstand such blasts, the structure should be designed such a way that the detailing and grade of concrete should be improvised. This current study includes the behaviour of G+7 multi storied structure subjected to 100 kg TNT explosion which is assumed to be taken place at 10 m, 20 m, 30 m and 40 m away from the structure. As per IS 4991:1968, Blast Pressures are calculated manually and executed in STAAD Pro tool. The results of Blast loads on structure is compared in its static condition and redesigned the structure to sustain the Blast loads.

Keywords: Blast, Tri Nitro Toluene, Blast Pressures, Explosion, STAAD Pro

References: 1. Aditya C, Snehal V. and Mevada V.,Comparative Study of Response of Structures Subjected to Blast and Earthquake loading. International Journal of Engineering Research and Applications,6(5),62-66, (2016). 2. Aditya Kumar singhand Saha P., Behaviour of Reinforced Concrete Beams under Different Kinds of Blast Loading, International Journal of Civil Engineering Research, 5(1),13-20, (2014). 110. 3. Amol B and Potnis S.C., Blast Analysis of Structures. International Journal of Engineering Research and Technology, 2(7), 2120-2126, (2013). 4. Aswin Vijay and Subha K. A Review on Blast Analysis of Reinforced Concrete Viaduct Pier, International Research Journal of 615-621 Engineering and Technology, 4(3), 986-991, (2013). 5. BhosaleS.D. and SuryawanshiY.R., Dynamic Behaviour of Frame Structure subjected to Blast loading. International Advanced Reasearch Journal in Science, Engineering and Technology, 3(8),245-249, (2016). 6. Deependra Nayakand Vinay Kumar singh.,Analysis the Behaviour of Composite Concrete Structure Subjected to Blast Load. International Journal for Scientific Research and Development, 5(7), 208-211, (2017). 7. Deshmukh C.M.and PiseC.P., Behaviour of RCC Structural Members for Blast Analysis. International Journal of Engineering Research and Application, 6(11), 48-53, (2016). 8. Meganadh M and Reshma T. Blast Analysis and blast resistant design of RCC residential building. International Journal of Civil Engineering and Technology, 8(3), 761 – 770, (2017). 9. Paresh Tank and Parikh K., Study of Blast Load on Industrial structure. International Journal of Advance Engineering and Reasearch Development, 2(2), 256-259, (2015). 10. Prajanaand Deepthishree Aithal., Parametric Study of Multi Storey Buildings for Blast Load. International Journal of Advance Research, Ideas and Innovations in Technology. 11. Suresh Kumar M.P., and Siva Kumar M., Blast Resistant Structure. International Journal for Scientific Research and Development, 3(8), 815-820, (2015). 12. Swami Gaikwad and Shirsath M.N., Study of Blast Analysis for Structural Building. International Research Journal of Engineering and Technology, 4(7), 987-99, (2017). 13. Zeynep Koccaz, and Faith Sutcu., Architectural and Stuctural Design for Blast Resistant Buildings. World Conference on Earth Quake engineering. 111. Authors: D Narasimha Rao, P Srinivasa Varma Paper Title: Fractional Order-PID Controlled Closed-loop MLI based DP-FC for Fourteen-Bus System Abstract: This work manages improvement of time response in fourteen- bus-framework utilizing MLI based Distributed Power Flow Controller (DP-FC)with PI and FOPID which is made out of a Distributed Power Flow, new gadget inside the group of FACTS. The DP-FC has a similar control capacity as the UP-FC, however with much lower cost and higher unwavering quality. This effort tends to one of the utilizations of the DP-FC to Compensate Voltage list in Transmissions Systems. Fourteen-bus-framework with ordinary VSI and with nine- level-MLI based DP-FC is mimicked and their outcomes are exhibited. Closed-loop-fourteen bus-framework With PI &FOPID-controllers are mimicked and the dynamic reaction shows that FOPID-controlled-DP-FC produces better reaction when-make-out with-PI-controlled-DP-FC.

Keywords: About four key words or phrases in alphabetical order, separated by commas.

References: 1. GudaPriyanka,-K.Jaghannath,-D.Kumara Swamy, “-A-facts-device: distributed power flow controller(DP-FC)”,international research journal of advanced engineering& science-ISSN-2455-9024, Volu-1, Issue-3, pp-95-102, 2016. 2. RashmiRaghav, AshrafRaza, Mohammad Asim,”Distributed power flow controller – an improvement of unified powe flow controller”, International-jour of Advance –Engi.& Research Develop, Volu-2,Issu- 5, May-2015. 3. MengluGao;AihongTang;YongHuang ; QiushiXu;Hongsheng Zhao ;XuZheng,“Research on the interaction between the series inverters of distributed power flow controller, 2017-Interna,Conf.on-Industrial Informatics Computing Technology, Intelligent Technology, Industrial Information Integration (ICI-ICII) Year:2017,Page- 313 – 316. 4. SandeepR. Gaigowal ;M.M.Renge, “Distributed power flow controller using single phase DSSC to realize active power flow control through transmission line”, 2016 Interna.Confe., on Computation o f Power, Energy Information & Commuincation(ICC-PEIC) - April- 2016. 5. GayathriReddy ; I.KumarSwamy,“Analyzing the performance of distributed power flow controller in transmission system”,2017 Intern Conf. on intelligent Computing & Control Systems(IC-ICCS)-2017 Page-1196 – 1199 6. G.MadhusudhanaRao ;V.AnweshaKumar ; B.V.SankerRam,“Design of a neural network based distributed power flow controller(DP-FC) for power system stability”, Inter. Conf, on Signal Processing, Communication, Power& Embedded System (SCO-PES) DOF 5-Oct-2016. 7. V.AnanthaLakshmi ; T.R.Jyothsna,“ Mitigation of voltage & current variations due to three phase fault in a single machine system using distributed power flow controller”, 2016 Intern ,Confe ,on Electrical, Electronics,& Optimization Technique- (IC-EEOT) DOC-: 5-Mar- 622-627 2016 8. MonikaSharma ; Annapurna Bhargava ;-Pinky Yadav,“Oscillation Damping with DP-FC Using Optimization Techniques”,-2016 Intern..,Conf on micro electronics & telecommunication engineering(ICM-ETE) 2016 , Page- 343 - 348 9. S.Vadivel, B.Baskaran, “Distributed power flow controller(DP-FC) to improve the power quality of thirty three bus radial system”, intern.,jour.., of engin..,inventions issn-2278-7461, Volu-5, Issue-10 (Nov-2016)-PP- 31-44 10. Ahmad Jamshidi, S.Masoud Barakati & M.Moradi Ghahderijani, “Impact of distribute power flow controller to improve power quality based on synchronous reference frame method”, IACSIT Intern..,-jour..,of Engi..,& Tech, Vol-4, No-5, Oct -2012. 11. K.S.Srikanth, “Design & implementation- of new DP-FC to control power quality” Int.- J.Chem.Sci.14(4), 2016, Pg 2066-2074 ISSN- 0972-768. 12. Kuldeep Sain, Aakash Saxena, MR-Farooqi,” Analysis of distributed power flow controller in power system network for improving power flow control”, Indonesian Journal of Electrical Engin..,& Computer Science Vol-2, No-3,Jun-2016, pp-510 - 521. 13. Meghana MC,Shruthi N, ThejarajuYB, VishnuSB, “Application of distributed power flow controller in power system to mitigate voltage sag&swell”, ISSN (PRINT) 2320 – 8945, Vol -4, Issue -3, 2016. 14. AnjuAntony, Geevarghese Kurian Mathew,“ A comparative study on power quality improvement in a hybrid system using DVR & STATCOM vs. distributed power flow controller (DP-FC)’, International Research Journal of Engg & Tech(IRJET) Volu-3 Issue: 09 - Sep-2016. 15. Gaurav V.Waghulde, Prasad D. Kulkarni, “Implementation of distributed power flow controller to improve power quality for 220kv transmission line”, International Journal of advanced research in electrical, electronics & instrumentation Engineering , Vol-4, Issue-7, July-2015 16. NikitaGupta, Vahadood Hasan, “Comparison of performance of distributed power flow controller(DP-FC)& unified power flow controller( UPFC)”, International Journal of Advance Engineering & Research Development Volu-2, Issue-4, Apr- 2015. 17. V.Sudheer Kumar1 , RajaReddy.Duvvuru, “Enhancement of distributed power flow controller during series converter failures”, International Journal of Innovative research in electrical &electronics instrumentation & control engineering ,vol-4,issu-11,nov-2016 18. Ch.RangaRao, N.HariCharan & K.RajeshBabu, “Modelling & simulation of DP-FC system for power quality improvement”, International Journal of Electrical & Electronics engineering research (IJEEER)-ISSN (P): 2250-155X; ISSN (E): 2278-943X Vol-5, Issu-3,Jun- 2015,Pg 61-66. 19. M. BinduSahithi , Y.Vishnu Murthulu , “power quality enhancement & mitigation of voltage sag using DP-FC”, International Journal of Engineering . Trends & Technology (IJETT) Vol-40 Num-5 Oct-2016. 20. P.Ramesh, M.DamodaraReddy, “ Optimal placement of distributed power flow controller for loss reduction using firefly& genetic algorithm”, IJRET:-International Journal of Research in Engineering & Technology Volume-04 Issue- 09 Sept-2015. Authors: Sudha Dukkipati, P Siva Shankar, A V G A Marthanda Paper Title: A Novel Approach for Power Factor Controller Design Abstract: Industries facing problem of low power factor (P.F) is very common and so are the efforts at improving it through various methods. In general shunt capacitor banks are used to improve the power factor and these banks are located very near and down the final transformer. Attempts will be to maintain the P.F as close to unity as possible. Switching operation of capacitors in a P.F control panel can be achieved by the micro-controller. But it may be very involved and difficult task to write and achieve a running and successful programme on micro- 112. controller for all the power factor values in demand from the same panel. In this work the design procedure of a programme usable in a micro-controller is shown. This is achieved in/and through a lab tool of simulation and can 628-631 be done also through similar tools. P.F control devices doing switching operation of capacitors can also be used. But they have associated complexity, extra cost, extra efforts at design and finally limitations imposed by the device in power handling. Hence, the present choice of avoiding P.F Controller may be better. The implicit instructions derived from the use of the lab tool implemented as required through the use of this programme can simplify and be extended easily to wide ranges of power handling leading to energy savings.

Keywords: Power Factor, Micro controller, Switched capacitor panel, Shunt capacitor banks, Lab tool simulation devices

References: 1. en.wikipedia.org/wiki/Power_factor_correction. 2. RamasamyNatarajan (2005). “Power System Capacitors.” Boca Raton, FL: Taylor & Francis. 3. T.J.E.MILLER, 1982. “Reactive Power Control in Electric Systems” 1982 by Jihn Wiley & Sons Inc. 4. IEEE Guide for the Protection of Shunt Capacitors Banks. 5. A.S pabla, “Electric Power Distribution” (Fourth edition) Tata McGraw-Hill Publishing Company Limited. 6. William D. Stevenson, Jr, “Elements of Power System Analysis” (Third edition) 1955, 1962, 1975 by McGraw-Hill, Inc. 7. R.S.ARORA. “Handbook of Electrical Engineering.” 2004. (Fourth edition), New Delhi. 8. http://www.electricaltechnology.org/2013/11/how-to-calculate-suitable-capacitor-size-for-power-factor-improvement.html 9. V.K Mehta and Rohit Mehta, “Principles of power system”, S. Chand & Company Ltd, Ramnagar, Newdelhi-110055, 4th Edition, Chapter, 6. 10. Stephen, J. C. (1999). “Electric Machinery and Power System Fundamentals.” 3rd.ed. United State of America: McGraw-Hill Companies, Inc. 11. Introduction to NI-LabVIEW 12. www.ni.com/nationalinstruments Authors: Robert Ambunda, Marion Sinclair Paper Title: Effect of Two-Lane Two-Way Rural Roadway Design Elements on Road Safety Abstract: Road design elements are some of the key factors influencing road user behaviour and road safety on roads worldwide. The study developed fatal crash predictive Negative Binomial Regression (NBR) models that identified statistically significant relationships between the combination of road design elements (radii and number of horizontal curves, hard shoulder widths, traffic operating speeds, road length and access control) selected for the study and the fatal crashes rates on the selected roads on the Namibian rural road network. The crash predictive NBR models developed indicated that the combination of the various selected road design elements had significant influence on the fatal crash rates, with various correlation magnitudes on roads with various lane widths. The study results brought to the fore the impact that interactions between selected road design elements has on existing road design and maintenance methods in Namibia. The NBR fatal crash models will assist transportation engineers in identifying hazardous road sections and implementing the appropriate remedial measures to reduce crash risk levels sustainably.

Keywords: Crash modelling; fatal crashes; negative binomial regression; road design elements; road safety

References: 1. Abele, L. and Møller, M. (2011) ‘The Relationship between Road Design and Driving Behavior’, RSS 2011: Road Safety and Simulation 2011 Conference, 2012, pp. 1–16. Available from: http://scholar.google.com/scholar?hl=en&btnG=Search&q=intitle:THE+RELATIONSHIP+BETWEEN+ROAD+DESIGN+AND+DRIVI NG+BEHAVIOR#1%5Cnhttp://scholar.google.com/scholar?hl=en&btnG=Search&q=intitle:The+Relationship+between+Road+Design+a nd+Driving+Behavior#1. (Accessed: 10 November 2017). 2. Ahmed, I. (2013) ‘Road Infrastructure and Road Safety’, Transport and Communications Bulletin for Asia and the Pacific, (83), pp. 19– 25. 3. Alsubeai, A. M. (2017) Evaluating the Interactive Effects of Traffic Volumes and Access Density on Crash Frequency. South Dakota State University. 4. Ben-Bassat, T. and Shinar, D. (2011) ‘Effect of shoulder width, guardrail and roadway geometry on driver perception and behavior’, Accident Analysis and Prevention. Elsevier Ltd, 43(6), pp. 2142–2152. Available from: doi: 10.1016/j.aap.2011.06.004. (Accessed: 24 113. July 2018). 5. Čičković, M. (2016) ‘Influence of Human Behaviour on Geometric Road Design’, Transportation Research Procedia, 14(0), pp. 4364– 632-637 4373. Available from: doi: 10.1016/j.trpro.2016.05.358. (Accessed: 12 May 2017). 6. Dehuri, A. N. (2013) Impacts of Roadway Condition , Traffic and Manmade Features on Road Safety, Asian Journal of Civil and Road Engineering. India National Institute of Technology. 7. Deller, J. (2013) ‘The influence of road design speed, posted speed limits and lane widths on speed selection: a literature synthesis’, in Australasian Transport Research Forum, p. 14p. Available from: http://www.atrf.info/papers/2013/index.aspx. (Accessed: 06 June 2017). 8. Dwikat, M. G. (2014) Modeling Relationship between Geometric Design Consistency and Road Safety for Two-Lane Rural Highways in the West Bank. An- Najah National University. 9. Easa, S. M., Dong, H. and Li, J. (2007) ‘Use of Satellite Imagery for Establishing Road Horizontal Alignments’, Journal of Surveying Engineering, 133(1), pp. 29–35. Available from: doi: 10.1061/(ASCE)0733-9453(2007)133:1(29). (Accessed: 19 May 2017). 10. Garber, N. J. and Hoel, L. a (2009) Traffic and Highway Engineering. Fourth, Cengage Learning. Fourth. Edited by H. Gowans. Toronto, Canada: Nelson Education Ltd. Available from: doi: 10.1061/(ASCE)HY.1943-7900.0000746 (Accessed: 11 August 2017). 11. Garcia, R. and Abreu, L. (2016) ‘Road safety in rural roads of two lanes’, RevistaIngenieria De Construccion, 31(1), pp. 54–60. 12. Gaudry, M. and Vernier, K. (2002) ‘Effects of road geometry and surface on speed and safety’, Accident Analysis & Prevention 34.3, 34(November 2002), pp. 357–365. 13. Hagenzieker, M. P., Commandeur, J. J. F. and Bijleveld, F. D. (2014) ‘The history of road safety research: A quantitative approach’, Transportation Research Part F: Traffic Psychology and Behaviour. Elsevier Ltd, 25(PART B), pp. 150–162. Available from: doi: 10.1016/j.trf.2013.10.004. (Accessed: 22 March 2018). 14. Iyinam, A., Iyinam, S. and Ergun, M. (1997) ‘Analysis of relationship between highway safety and road geometric design elements: Turkish case’, Faculty of Civil Engineering, Turkey. Available from: http://www.trafficforum.ethz.ch/vwt_2003/beitraege/vwt19proceedings_contribution_91.1-91.8.pdf. (Accessed: 09 November 2017). 15. Jaiswal, A. and Bhatore, A. (2016) ‘Effect on Road Safety by Roadway condition, traffic and manmade features’, International Journal of Latest Trends in Engineering and Technology, 7(3), pp. 41–48. Available from: doi: 10.21172/1.73.006. (Accessed: 14 July 2018). 16. Joanne, M. (2013) RAP Road Risk Mapping Manual : Design Specification. 17. Karlaftis, M. G. and Golias, I. (2002) ‘Effects of Road Geometry and Traffic Volumes on Rural Roadway Accident Rates’, Accident Analysis and Prevention, 34(3), pp. 357–365. Available from: doi: 10.1016/S0001-4575(01)00033-1. (Accessed: 21 November 2017).

18. Karlaftis, M. G. and Golias, I. (2009) ‘Impacts of Roadway Condition , Traffic and Manmade Features on Road Safety’, International Journal of Civil Engineering & Technology, 4(July), pp. 19–25. Available from: doi: 10.1061/(ASCE)HY.1943-7900.0000746. (Accessed: 17 September 2017). 19. Krug, E. and Sharma, G. (2009) ‘Risk factors for road traffic injuries’, AsociatiaVictimelorAccidentelor de Circulatie din Romania, 7(2), pp. 23–39. 20. Mohammed, H. (2013) ‘The Influence of Road Geometric Design Elements on Highway Safety’, International Journal of Civil Engineering & Technology, 4(4), pp. 146–162. 21. Othman, S. and Thomson, R. (2007) ‘Influence of Road Characteristics on Traffic Safety’, in ESV 20th Conference. Lyon, pp. 1–10. 22. Persia, L., Usami, D., De Simone, F. and Yannis, G. (2016) ‘Management of Road Infrastructure Safety’, Transportation Research Procedia. Elsevier B.V., 14, pp. 3436–3445. Available from: doi: 10.1016/j.trpro.2016.05.303. (Accessed: 13 June 2018). 23. Porter, R., Donnell, E. and Mason, J. (2012) ‘Geometric Design, Speed, and Safety’, Transportation Research Record: Journal of the Transportation Research Board, 2309, pp. 39–47. Available from: doi: 10.3141/2309-05. (Accessed: 15 June 2017). 24. Taylor, M. C., Lynam, D. A. and Baruya, A. (2000) ‘The effect of drivers’ speed on the frequency of road accidents’, Transportation Research Laboratory, p. 56. Available from: doi: 10.1051/ matecconf 2016 00 / 47 3 04. (Accessed: 16 November 2017). 25. Vayalamkuzhi, P. and Amirthalingam, V. (2016) ‘Influence of geometric design characteristics on safety under heterogeneous traffic flow’, Journal of Traffic and Transportation Engineering (English Edition). Elsevier Ltd, 3(6), pp. 559–570. Available from: doi: 10.1016/j.jtte.2016.05.006. (Accessed: 13 May 2018). 26. Woolley, J. E., Dyson, C. B., Taylor, M., Zito, R. and Stazic, B. (2002) ‘Impacts of Lower Speed Limits in South Australia’, IATSS Research. International Association of Traffic and Safety Sciences, 26(2), pp. 6–17. Available from: doi: 10.1016/S0386-1112(14)60038- 8. (Accessed: 26 July 2017). 27. Yingxue, Z. (2009) ‘Analysis of the Relation between Highway Horizontal Curve and Traffic Safety’, 2009 International Conference on Measuring Technology and Mechatronics Automation, ICMTMA 2009, 3, pp. 479–481. Available from: doi: 10.1109/ICMTMA.2009.511. (Accessed: 22 February 2017). Authors: Manmohan Shukla, B. K. Tripathi Paper Title: Inversion ofcomplex Neural Network Abstract: This paper presents a novel application of complex neural network which has been modeled by the implementation of gradient descent inversion algorithm in complex domain. The methods reported prior to this work were limited to real domain only. By the learning of function mapping in complex domain, the performance of neural network has been analyzed. An improved performance of complex neural network has resulted in the development of a Novel Complex Neuron Model.

Keywords: inversion, complex-valued neural network, gradient descent search and activation function.

References: 1. H. Leung and S. Haykin, “The Complex Backpropagation Algorithm”, IEEE Trans.On Signal Processing, Vol. 39, No. 9, September (1991). 2. G. M. Georgiou and C. Koutsougeras, “Complex Domain Backpropagation”, IEEE Trans. on Circuits and Systems-II : Analog and Digital Signal Processing, Vol 39, No. 5, May (1992). 3. N. Benvenuto and F. Piazza, “On the Complex Backpropagation Algorithm”, IEEE Trans. On Signal Processing, Vol. 40, No. 4, April 114. (1992). 4. T. Nitta, “A Back-propagation Algorithm for Neural Networks Based on 3D Vector Product”, Proc. of 1993 International Joint 638-641 Conference on Neural Networks. 5. S. Lee and R. M. Kil, “Inverse Mapping of Continuous Functions Using Local and Global Information”, IEEE Tran. on Neural Networks, Vol. 5, No. 3, May(1994). 6. T. Nitta, “A Quaternary Version of the Back-propagation Algorithm”, ICNN 1995. 7. S. Jankowski, A. Lozowski and J. Zurada, “Complex-Valued Multistate Neural Associative Memory”, IEEE Trans. on Neural Networks, Vol. 7, No. 6, November 1996. 8. C.You and D. Hong, “Adaptive Equalization Using the Complex Backpropagation Algorithm”, IEEE International Conference on Neural Networks, Vol. 4, Jun 1996 9. T. Nitta, “An Extension of the Back-Propagation Algorithm to Complex Numbers”, Neural Networks, Vol. 10, No. 8, 1997. 10. M. H. Hassoun, Fundamentals of Artificial Neural Networks, New Delhi, Prentice Hall of India, 1998. 11. C. A. Jensen, R. D. Reed, R. J. Marks, M. A. El-sharkawi, J. Jung, R. T. Miyamoto, G. M. Anderson and C. J. Eggen, “Inversion of Feedforward Neural Networks: Algorithms and Applications”, Proceedings of the IEEE, Vol. 87, No. 9, September(1999). 12. C. Lin and C. Li, “A sum-of-product neural network (SOPNN)”, Neurocomputing, 2000. 13. T. Nitta, “An Analysis of the Fundamental Structure of Complex-Valued Neurons”, Neural Processing Letters12, pp.239-246, 2000. 14. T. Nitta, “Generalization of the Complex-valued Neural Networks with the Orthogonal Decision Boundary”, KES 2002. 15. C. Igel and M. Husken, “Improving the Rprop Learning Algorithm”, Proc. of the Second International Symposium on Neural Computation, pp. 115-121, 2000. 16. M. Sinha, P. K. Kalra and K. Kumar, “Parameter estimation using compensatory neural networks”, Sadhana, Vol. 25, April 2000. Authors: K. Naga Lakshmi Prasanna, B. Murali Krishna, SK. Sadiya Shireen, A. Poorna Chander Reddy Paper Title: FPGA Based Convolutional Encoder for GSM-900 Architecture Abstract: This paper presents one of the most popular current techniques of enhancing the reliability, accuracy and security in data communication systems i.e., error-correcting codes such as convolutional codes. To correct and decode the errors that occur during data transmission on communication channels by introducing some redundancy in their encoding. In advanced wireless communication, reliability and accuracy are two main constraints of hand held devices such as mobile phones. Now a days, mobile phones uses wireless standards such as Code Division Multiple Access (CDMA), Global System for Mobile Communication (GSM) for communication purpose. Apart from above constraints quality of service and security are highly desirable. The 115. proposed architecture implemented for convolutional encoder GSM-900 by using XOR free approach methodology with a required constraint length (K=5) and a data transmission code rate (R=1/2) using Xilinx 14.7 642-650 ISE software. The convolutional encoder for GSM-900 architecture verified on Nexys2 1200E Field Programmable Gate Array (FPGA).

Keywords: Convolution Encoder, Error Control Codes, Field Programmable Devices (FPGA) and Global System for Mobile Communication (GSM), Linear Feedback Shift Register (LFSR) and XOR free approach.

References: 1. J. Dielissen, Eindhoven, N. Engin, S. Sawitzki, and K. van Berkel, “Multistandard FEC Decoders for wireless devices,” IEEE Trans. Circuits Syst. II, Exp. Briefs, vol. 55, no. 3, pp. 284–288, 2008. 2. G. Purohit, K. S. Raju, V. K. Chaubey, “A New XOR-Free Approach for implementation Convolutional Encoder” IEEE embedded systems letters, vol. 8, no. 1, March 2016. 3. J. Hagenauer, “Forward error correcting for CDMA systems,” in Proc .Int. Symp. Spread Spectr. Tech. Appl. Proc., Mainz, Sep. 1996, pp.566–569. 4. R. Pasko, P. Schaumont, V. Derudder, S.Vernalde, and D. Durackova, “A new algorithm for Elimination of common subexpressions,”IEEE Trans. Comput. Des. Integr. Circuits Syst., vol. 18, no. 1, pp. 58–68, Jan. 1999. C. [5] Huang, J. Li, and M. Chen, “Optimizing XOR-based codes,” U.S. Patent 8209577 B2, Jun. 26, 2012. 5. J. Viterbi, “Convolutional codes and their performance in communication systems,”IEEE Trans. Comm. Technol., vol. 19, no. 5, pp.751– 772, Oct. 1971. 6. Y.Yibin, K.Roy, and R.Drechsler, “Power consumption in XOR based circuits,” in Proc.ASP-DAC, pp. 299–302, Jan.1999. Huang, J. Li, and M. Chen, “Optimizing XOR-based codes,” U.S. Patent 8209577 B2, Jun. 26, 2012. 7. G. Purohit, K. S. Raju, V. K. Chaubey, “XOR-Free Implementation of Convolutional Encoder for Reconfigurable Hardware “Hindwai, vol. 8, no. 1, Jan 2016. 8. R. Mohan and P. P. Chakrabarti, “Factorizing FSM’s with modify and restore method,” IEEE Transactions on Circuits and Systems II: Analog and Digital Signal Processing, vol.44, No. 5, pp. 371–377, 1997. 9. R. J. McEliece and L. Onyszchuk, “The extended invariant factor algorithm with application to the Forney analysis of convolutional codes,” in Proceedings of the IEEE International Symposium on Information Theory, p. 142, San Antonio, Tex, USA, January 1993. 10. S.Devdas and A. R. Newton, “Decomposition and factorization of sequential finite State machines,” IEEE Transactions on Computer- Aided Design, vol. 8, no. 11, pp.1206–1217, 1989. 11. G. D. Forney Jr., “Convolutional codes I: algebraic structure,” IEEE Transactions on Information Theory, vol. 16, no. 6, pp. 720–738, 1970. 12. G.D. Forney Jr., “Convolutional codes. II. Maximum-likelihood decoding,” Information and Computation, vol. 25, pp. 222–266, 1974. 13. G.De Micheli, R. K. Brayton, and A. Sangiovanni-Vincentelli, “Optimal state assignment for finite state machines,” IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, vol. 4, no. 3, pp. 269–284, 1985. 14. M. J. Avedillo, J. M. Quintana, and J. L. Huertas, “State merging and state splitting via state assignment: a new FSM synthesis algorithm,” IEE Proceedings—Computers and Digital Techniques, vol. 141, no. 4, pp. 229–237, 1994. 15. P. Ashar, S. Devadas, and A. R. Newton, “Optimum and heuristic algorithms for an approach to finite state machine decomposition,” IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, vol. 10, no. 3, pp. 296–310,1991. Authors: M.V.S. Ram Prasad, B. Suribabu Naick, Zaamin Zainuddin Aarif Paper Title: Design and Implementation of High Speed 16-Bit Approximate Multiplier Abstract: A multiplier extensively impact on the postpone and strength intake of an arithmetic processor. The accurate results are not usually required in many packages, like records processing and virtual signal processing (DSP). Therefore, the layout of multipliers is in particular centered on speed and power consumption. These parameters are specially finished by way of approximate multipliers. In this paper a new 16 bit approximate multiplier is designed. The partial merchandise of the proposed multiplier are revised and re organized to introduce varying probability phrases. The complexities of the addition of those partial merchandise are reduced based at the possibility. Synthesis results show that the proposed multiplier achieves higher velocity and power consumption in comparison to the preceding precise multiplier

Keywords: Approximate computing, Compressors, multiplier 116. References: 1. J. Han and M. Orshansky."Approximate Computing: An Emerging Paradigm For Energy-Efficient Design." In approaches of the eighteenth 651-654 IEEE European Test Symposium, Avignon, France, May 2013, pp.1-6. 2. J. Liang, J. Han and F. Lombardi."New measurements for the unwavering best of envisioned and probabilistic adders."IEEE Trans. PCs. Vol.Sixty two, no.9, pp.1760-1771, Sept. 2013. 3. Momeni, J. Han, P. Montuschi and F. Lombardi."Structure and Analysis of Approximate Compressors for Multiplication." IEEE Trans. PCs, vol.64, no.4, pp. 984-994, Apr. 2015. 4. C.H. Lin and I.C. Lin. "High exactness estimated multiplier with blunder treatment." In Proc. ICCD'thirteen: In the 2013 IEEE thirty first International Conference on Computer Design (ICCD). Asheville, NC, USA, Oct. 2013, pp. 33-38. 5. K. Bhardwaj, P.S. Maneand J. Henkel. "Power-and territory talented Approximate Wallace Tree Multiplier for blunder versatile frameworks." In Sym. ISQED'14: In fifteenth International Symposium on Quality Electronic Design (ISQED), Santa Clara, CA, USA. Blemish. 2014, pp. 263-269. 6. S. Narayanamoorthy, H.A. Moghaddam, Z. Liu, T. Park, N.S. Kim, "Vitality Efficient Approximate Multiplication for Digital Signal Porcessing and Classificaiton Applications." IEEE Trans. Large Scale Integration Systems (VLSI), vol.23, no.6, pp.1180-1184, Jun. 2015. 7. M.S. Lau, K.V. Ling and Y.C. Chu. "Vitality conscious probabilistic multiplier: shape and exam." In Proceedings of the 2009 typical assembly on Compilers, engineering, and amalgamation for inserted frameworks, Grenoble, France, Oct. 2009, pp.281-290. Authors: Vikram Gupta, Sarvjit S. Bhatia Paper Title: Reliable Cloud Based Framework for the Implementation of ERP Abstract: Universally, technological innovations act as engine of growth for the developing economy. Technological revolutions act as an accelerator to enhance the economy worldwide. In the present scenario the technology has considerably impacted different aspects of life so that the business environment has changed thoroughly. Simply granting the access to the similar technologies utilized by the large business houses will flourish the business of small scale industries. As result flexibility, scalability, adaptability, availability, cost efficiency characteristics will be attained by adopting the innovation i.e. cloud based ERP in the organizations. 117. Adopting Cloud based ERP system offers highly scalable, reliable, on-demand services with agile management capabilities on an as-needed basis. The present work is based on the concepts of social sciences and latest trends of 655-661 information technology. In the framework, Diffusion of Innovation (DOI) theory and Technology-Organization- Environment (TOE) framework are synthesized. The framework examines and validates various social, technological, organizational and environmental factors that impact the cloud based ERP adoption. All these factors have significant impact on the adoption. The findings will propose practical recommendations to the successful adoption of cloud based ERP.

Keywords: Cloud Computing, DOI, Enterprise Resource Planning, IaaS, PaaS, SaaS, TOE.

References: 1. Yusuf Y., Gunasekaran A., "Enterprise information systems project implementation: a case study of ERP in Rolls-Royce", International Journal of Production Economics 87 (3) (2004) 251-266 2. I. Saini, A. Khanna and V. Kumar, "ERP Systems: Problems and Solution with Special Reference to Small & Medium Enterprises", International Journal of Research in IT & Management 2(2) (2012) 715–725. 3. Gupta, V., Bhatia, S., S. "Cloud Computing: An Operational Framework in Implementation of ERP", International Journal of Advanced Research in Computer Science and Software Engineering 7(2) (2017)164-169. 4. Rogers, E.M., "Diffusion of innovations (5th Ed.)", The Free Press, New York, 2003. 5. Saunders, M., Thornhill, A. & Lewis P. "Research methods for business students (5th Ed.)", Financial Times/ Prentice Hall, Harlow, 2009. 6. Zikmund, WG, Babin, BJ, Carr, JC & Griffin, M (2013). "Business research methods, 9th edn", South-Western, Cengage Learning, USA. 7. Williams B., Brown, T. & Onsman A., "Exploratory factor analysis: a five step guide for novices", Australasian Journal of Paramedicine 8(3) 1-13 (2010). 8. Hair J., Black W., Babin B., & Anderson R. "Multivariate Data Analysis: Global Edition (7th Ed.)", Pearson, Upper Saddle River, 2010. 9. Chin W.W., "Issues and Opinion on Structural Equation Modeling", MIS Quarterly 22(1) vii-xvi (1998). 10. George D., Mallery P., "IBM SPSS Statistics 23 Step by Step: A Simple Guide and Reference (14th Ed.)", (2016). Authors: S. Sandhya Rani, K. Kumar Naik Paper Title: Analysis of Circular Patch Antenna with Complementary Split Ring Resonator on Ground Plane Abstract: A complimentary split ring resonator (CSRR) defected ground structured Circular Patch Antenna is proposed for WiMAX applications. Rectangular slits are loaded on circular patch with CSRR on ground plane for better impedance matching and enhanced gain. The proposed antenna is designed using High Frequency Structural Simulator (HFSS) and Computer Simulation Technology (CST) simulation tools. The proposed rectangular slit loaded circular patch antenna resonates at 8.5GHz and 8.54 GHz frequencies for HFSS and CST simulator with return loss of -26.39dB and -33.8 dB respectively. The maximum gain is observed as 8.29dBi and 8.17dBi for both HFSS and CST simulators.

Keywords: Circular patch antenna, CSRR, WiMAX application.

References: 1. Chandra Bhan , Ajay Kumar Dwivedi, Brijesh Mishra and Anil Kumar, “ Quad Bands U-Shaped Slot Loaded Probe Fed Microstrip Patch Antenna,” Second International Conference on Advances in Computing and Communication Engineering , pp. 409 – 412, 2015. 2. Mohamed A. , Islam Md. Rafiqul , Sarah Yasmin and K. Badron, “ Design of a quintuple band microstrip patch antenna using multiple L-Slots,” 2016 International Conference on Computer and Communication Engineering (ICCCE), pp. 30-35, 2016. DOI: 10.1109/ICCCE.2016.20. 118. 3. Partha P. Shome, Taimoor Khan and Rabul H. Laskar, “ A state‐of‐art review on band‐notch characteristics in UWB antennas,” Int J RF Microw Comput Aided Eng. 2018. https://doi.org/10.1002/mmce.21518. 662-665 4. Naimur Rahman, Mohammad Tariqul Islam, Zulfiker Mahmud and Md Samsuzzaman, “ The broken-heart printed antenna for Ultra wideband applications: Design and characteristics analysis,” IEEE Antennas and Propagation Magazine, Vol. 60, Issue 6, pp. 45-51, 2018. 5. Budhadeb Maity, “ Design of dual band L-slot microstrip patch antenna for wireless communication,” International Conference on Computer Communication and Informatics (ICCCI), pp. 1 – 4, 2017. DOI: 10.1109/ICCCI.2017.8117769. 6. Gopinath Samanta, Debasis Mitra , Sekhar Ranjan and Bhadra Chaudhuri, “ Miniaturization of a patch antenna using circular reactive impedance substrate,” Int J RF Microw Comput Aided Eng. 2017 https://doi.org/10.1002/mmce.21126 7. Sk Nurul Islam, Mukesh Kumar, Gobinda Sen and Santanu Das, “ Design of a compact triple band antenna with independent frequency tuning for MIMO applications, “ Int J RF Microw Compt Aided Eng. 2018. https://doi.org/10.1002/mmce.21620. 8. L.S.Yang, L.Yang, Y.A.Zhu, Kuniaki Yoshitomi and HaruichiKanaya, “ Polarization reconfigurable slot antenna for 5.8 GHz wireless applications,” AEU-International Journal on Electronics and Communications, Volume 101, pp. 27-32, mar 2019. https://doi.org/10.1016/j.aeue.2019.01.022 9. Kwok Kan So, Kwai Man Luk and Chi Hou Chan, “ A High- Gain Circularly Polarized U- Slot Patch Antenna Array [Antenna Designers Notebook],” IEEE Antennas and Propagation Magazine, Vol. 60, Issue 5, pp. 147-153, 2018. 10. Som Pal Gangwar , Kapil Gangwar and Arun Kumar, “ A compact microstrip patch antenna with five circular slots for wideband applications,” 2018 3rd International Conference on Microwave and Photonics (ICMAP), pp. 1 – 2, 2018. 11. Daniel Colles and Dean Arakaki, “Multi-technique broadband microstrip patch antenna design2014,” IEEE Antennas and Propagation Society International Symposium (APSURSI) , pp. 1879 – 1880, 2014. 12. Mahrukh Khan and Deb Chatterjee, “ Analysis of reactive loading in a U-slot Microstrip patch using the theory of characteristics modes [Antenna Applications Corner],” IEEE Antennas and Propagation Magazine, Vol. 60, Issue 6, pp. 88- 97, 2018. Authors: Amit Verma, Manish Prateek Paper Title: T-NOT Gate : A Novel Circuit based on Ternary Logic Abstract: In this paper a novel circuit for the t-NOT gate is proposed using op-amp 741 IC and the basis AND and OR binary gate. Proposed circuit is based on the concept of ternary logic, where the term ternary means three logic that is 0, 1 and 2 instead of traditional two logic 0,1 (binary). As the ternary logic can be the altenate for the radix 2 that is binary logic to reduce the transition delay, enhance the processing speed, reduce the memory requirement, reduce circuit complexity and number of electronic components The truth table, symbol and state 119. transition diagram is also presented in the paper. Which show that t-NOT gate is a unary gate that takes single input and provide the next immediate logic in clockwise cyclic direction as the output as shown in the state 666-671 transition diagram mention in the paper. Further the standard working of op-amp 741 IC is discussed, the actual voltmeter reading for various input voltages greater than the upper limit +VCC is presented in tabular format. Simulation of the binary AND and OR gate is carried out using proteus to prove that the current binary AND, OR gate can be used as MIN and MAX gate for logic circuits of higher radix. Here the MIN and MAX logic means the gate giving the minimum and maximum voltage as the output voltage among the various input voltages.

Keywords: op-amp 741 IC, ternary, multi-valued logic.

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Enumerating fuzzy switching functions and free kleene algebras. Computers & mathematics with applications, 10(1):25–35, 1984. 1 14. Tsutomu Sasao. Ternary decision diagrams. survey. In Multiple-Valued Logic, 1997. Proceedings., 1997 27th International Symposium on, pages 241–250. IEEE, 1997. 1 15. Melvin A Breuer. A note on three-valued logic simulation. IEEE Transactions on Computers, 100(4):399–402, 1972. 1 16. Miron Abramovici, Melvin A Breuer, and Arthur D Friedman. Digital systems testing and testable design, volume 2. Computer science press New York, 1990. 1 17. Yukihiro Iguchi, Tsutomu Sasao, and Munehiro Matsuura. A method to evaluate logic functions in the presence of unknown inputs using lut cas-cades. In Multiple-Valued Logic, 2004. Proceedings. 34th International Symposium on, pages 302–308. IEEE, 2004. 1 18. Petr Hajek,´ Kamila Bendova,´ and Zdenekˇ Renc. The guha method and the three-valued logic. Kybernetika, 7(6):421–435, 1971. 1 19. Masao Mukaidono. Regular ternary logic functions? ternary logic func-tions suitable for treating ambiguity. IEEE transactions on computers, 20. (2):179–183, 1986. 1, 2 21. Sheng Lin, Yong-Bin Kim, and Fabrizio Lombardi. Cntfet-based design of ternary logic gates and arithmetic circuits. IEEE transactions on nanotechnology, 10(2):217–225, 2011. 1, 2 22. CR Mingoto. A quaternary half-adder using current-mode operation with bipolar transistors. In Multiple-Valued Logic, 2006. ISMVL 2006. 36th International Symposium on, pages 15–15. IEEE, 2006. 1 23. Alex Heung and HT Mouftah. Depletion/enhancement cmos for a lower power family of three-valued logic circuits. IEEE Journal of Solid-State Circuits, 20(2):609–616, 1985. 1, 2 24. Sheng Lin, Yong-Bin Kim, and Fabrizio Lombardi. A novel cntfet-based ternary logic gate design. In Circuits and Systems, 2009. MWSCAS’09. 52nd IEEE International Midwest Symposium on, pages 435–438. IEEE, 2009. 1 25. A Srivastava and K Venkatapathy. 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On the mathematical structure of the c-type fail safe logic. IEICE Trans. Electron., 52(12):812–819, 1969. 2 43. Michael Yoeli and Shlomo Rinon. Application of ternary algebra to the study of static hazards. Journal of the ACM (JACM), 11(1):84– 97, 1964. 2 Authors: Hee-Dong Park Design and Implementation of u-Health System to Support Interoperability with Legacy Non-Standard Paper Title: 120. Medical Devices Abstract: Although there have been worldwide studies on u-Health services to converge the latest IT 672-676 technologies into the medical field, there are still limitations to compatibility and interoperability with existing legacy non-standard medical devices. This paper proposes a u-Health system architecture to utilize legacy non- standard medical devices for standard u-Healthcare services by adding a codec and algorithms to IEEE 11073 agent. The proposed agent located between medical devices and mobile manager encodes non-standard data of legacy devices to IEEE 11073 standard data format and decodes vice versa using the developed codec and algorithms, which makes it possible to support data compatibility and interoperability between u-Health standard systems and non-standard systems. The proposed system is implemented by using Intel Edison board and android- based smartphone to verify performance and effectiveness of the proposed system. The implementation results show that legacy non-standard medical devices can be utilized for the u-Health standard services based on the IEEE 11073 PHD protocol by using the proposed system, which means that the proposed system can contribute to the growth and extension of u-Health services by solving the problem of service limitations caused by existing legacy non-standard devices.

Keywords: Agent, Interoperability, IEEE 11073, Legacy non-standard medical devices, Mobile manager, u- Health.

References: 1. The Institute of Electrical and Electronics Engineers, ISO/IEEE 11073-20601 Standard for Health Informatics-Personal Health Device Communication-Application Profile-optimized Exchange Protocol, ISO/IEEE 11073-20601, 2014. 2. The Institute of Electrical and Electronics Engineers, ISO/IEEE 11073-20702 Standard for Health Informatics-Point-of-care medical device communication—Part 20702: Medical devices communication profile for web services, ISO/IEEE 11073-20702, 2018. 3. Health Level 7 International, Available: http://www.hl7.org 4. The DICOM Standard PS3.1 2019a, Available: http://dicomstandard.org 5. ZigBee Alliance, ZigBee Wireless Sensor Applications for Health, Wellness and Fitness, 2009. Available: https://www.zigbee.org 6. Malcolm Clarke, Joost de Folter, Vivek Verma, Hulya Gokalp, “Interoperable End-to-End Remote Patient Monitoring Platform Based on IEEE 11073 PHD and ZigBee Health Care Profile,” IEEE Transactions on Biomedical Engineering, Vol. 65, Issue 5, 2018, pp. 1014-125. 7. T. H. Laine, C. Lee, H. Suk, “Mobile Gateway for Ubiquitous Health Care System Using ZigBee and Bluetooth," 2014 Eighth International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing, 2014, pp. 139-145. 8. M. Carke, "Developing a Standard for Personal Health Devices Based on 11073," Proceeding of IEEE EMBS Conference, pp. 6174-6176, 2007. 9. Egner, F. Moldoveanu, N. Goga, A. Moldoveanu, V. Asavei, and A. Morar, “Enhanced Communication Protocol for ISO/IEEE 11073- 20601,” Universitatea Politehnica Bucuresti Scientific Bulletin, Series C, Vol. 75, Issue 2, pp. 3-16, 2013. 10. J.C. Nam, W.K. Seo, J.S. Bea, and Y.Z. Cho, “Design and Development of Personal Healthcare System Based on IEEE 11073/HL7 Standards Using Smartphone,” The Journal of Korean Institute of Communications and Information Sciences, Vol. 36, No. 12, pp. 1556- 1564, 2011. 11. J.Y. Lee, Y.R. Jeong, H.D. Park, “Development of Open H/W-Based IEEE 11073 Agent and Manager for Non-Standard Health Devices,” Journal of Korea Multimedia Society, Vol. 19, No. 3, March 2016, pp. 595-602. 12. ISO/IEC 8824-1:2008 Information Technology Abstract Syntax Notation One: Specification of Basic Notation, 2008. 13. H.H. Do, J.M. In, and S.K. Lee, “Implementation of ASN.1 Converter for Applying ISO/IEEE 11073 MDER,” Korean Institute of Information Technology, Vol. 10, No. 4, pp. 19-30, 2012. 14. ISO/IEEE 11073-20101:2004-Health Informatics-Point-of-Care Medical Device Communication-Part 20101: Application Profiles Base Standard, 2004. Authors: Shaikh Afroz Fatima Muneeruddin, Fabiha Fathima, Sameera Iqbal Muhammad Iqbal Paper Title: Correlative Band Mapping for Multi Spectral Image Fusion Abstract: This paper presents the process of image fusion, based on spectral correlative property. In the process of image fusion, frequency domain image fusions are more effective in coding compared to spatial domain fusion. In the process of frequency based fusion coding, wavelet approach is used in various formats to obtain spectral resolution and a fusion mapping is derived to map these resolution information. Wherein, spectral based fusion approach has higher significance of image coding accuracy, the resolution code overhead is observed. It is seen that, for larger resolution information’s, the retrieved accuracy of fusions higher, however the processing coefficients are large in count. To overcome this processing overhead issue, a new approach based on correlative band mapping approach is proposed. The evaluation result for the proposed approach illustrates a higher accuracy in fusion region prediction and mapping accuracy.

Keywords: Image Fusion, Hierarchical Modeling, correlative band mapping approach, wavelet coding. 121. References: 677-685 1. M. Canaud, A.Nabavi, C.Becarie, D.Villegas and N-E El Faouzi,, “A realistic case study for comparison of data fusion and assimilation on an urban network – The Archipel Platform”, Transportation Research Procedia Vol.6, pp.28-49, Elsevier, 2015. 2. Zheng Youzhi, Qin Zheng, “Objective Image Fusion Quality Evaluation Using Structural Similarity”, Tsinghua Science and Technology, Vol.14, No.6, pp.703-709, IEEE, 2009. 3. Yifang Ban, and Alexander Jacob, “Object-Based Fusion of Multitemporal Multiangle ENVISAT ASAR and HJ-1B Multispectral Data for Urban Land-Cover Mapping”, IEEE Transactions on Geo-science and Remote Sensing, Vol. 51, No. 4, IEEE, 2013. 4. Yang Oua, Dai GuangZhia, “Color Edge Detection Based on Data Fusion Technology in Presence of Gaussian Noise”, Procedia Engineering, Vol.15, Pp.2439–2443, Elsevier, 2011. 5. Li Dapeng, “A novel method for multi-angle SAR image matching”, Chinese Journal of Aeronautics, Vol.28, pp. 240–249 Elsevier, 2015. 6. Changtao He, Quanxi Liu, Hongliang Li, Haixu Wang, “Multimodal medical image fusion based on IHS and PCA”, Procedia Engineering, Vol.7, pp.280–285 Elsevier, 2010. 7. Yong Yang, “A Novel DWT Based Multi-focus Image Fusion Method”, Procedia Engineering, Vol.24, pp.177–181, Elsevier, 24, 2011. 8. QifanWanga, ZhenhongJiaa, XizhongQina, JieYangb, YingjieHuc, “A New Technique for Multispectral and Panchromatic ImageFusion”, Vol.24, pp. 182–186, Elsevier, 2011. 9. LIU Fu, LI Jin, Huang Caiyun, “Image Fusion Algorithm Based on Simplified PCNN in Non-sub-sampled Contour let Transform Domain”, Vol.29, 1434–1438, Elsevier, 2012 10. Fengrui Chena, Fen Qina, GuangxiongPengb, Shiqiang Chena, “Fusion of Remote Sensing Images Using Improved ICA Mergers Based on Wavelet Decomposition”, Vol.29, Pp. 2938–2943, Elsevier, 2012. 11. Youdong Ding, Cai xi, Xiaocheng Wei, Jianfei Zhang, “A New Framework for Image Completion Based on ImageFusion Technology”, Vol.29, Pages 3826–3830, Elsevier, 2012. 12. Tao Wu, Xiao-Jun Wu, Xiao-Qing Luo, “A Study on Fusion of Different Resolution Images”, Vol.29, pp. 3980–3985, Elsevier, 2012. 13. LU Heli, QIN Yaochen, ZHANG Lijun, LU Chaojun, LU Fengxian, “A Case Study of Model-Based Satellite Image Fusion”, Vol.37, pp. 268–273, Elsevier, 2012. 14. C.T.Kavitha, C.Chellamuthu, R.Rajesh, “Medical image fusion using combined discrete wavelet and ripplet transforms”, Vol.38,pp. 813– 820, Elsevier, 2012. 15. C.T.Kavitha, C.Chellamuthu, R.Rajesh, “Multimodel medical image fusion using discrete ripplet transform and intersecting cortical model”, Vol.38, pp. 1409-1414, Elsevier, 2012. 16. Pierre Lassalle, JordiInglada, Julien Michel, “A Scalable Tile-Based Framework forRegion-Merging Segmentation”, IEEE Transactions on Geoscience and Remote Sensing, Vol.53, pp. 5473 – 5485, IEEE, 2015. 17. Bibo Lu, Hui Wang, Chunli Miao, “Medical Image Fusion with Adaptive Local Geometrical Structure and Wavelet Transform”, Vol.8, pp. 262–269, Elsevier, 2011. 18. Sourav Pramanik, Swagati kaPrusty, Debotosh Bhattacharjee , Piyush Kanti Bhunre, “A Region-to-Pixel Based Multi-sensor Image Fusion”, Vol.10, pp. 654–662, Elsevier, 2013. 19. Deng Minghuia,Zeng Qingshuanga and Zhang Lanying, “Research on Fusion of Infrared and Visible Images Based on Direction let Transform”, Vol.3, pp. 67–72, Elsevier, 2012. 20. A. AnoopSuraj, Mathew Francis, T.S. Kavya, T.M. Nirmal, “Discrete wavelet transform based image fusion and de-noising in FPGA”, Vol.1, pp. 72–81, Elsevier, 2014. 21. Shaikh Afroz Fatima Muneeruddin, “Visual Improvements in Color Image Processing Using Regularized Filtration”, American International Journal of Research in Science, Technology, Engineering & Mathematics, 10(3), March-May, 2015, pp. 277-283 22. Wentao Yao, Zhidong Deng, “A Robust Pedestrian Detection Approach Based on Shape let Feature and Haar Detector Ensembles”, Tsinghua Science and Technology, Vol.13 pp. 314-322, IEEE, 2012. Authors: K.Suresh Kumar, Y.Rajasree Rao, K.Manjunathachari Paper Title: Low Power Fault Free Coding Design for Cam Interface Abstract: In the design modeling of a CAM interface, the controlling and access operation defines the performance of memory interfacing. In a CAM application, data stored in the Memory units are mapped to a given query input and an output is developed as a match signal to which as decision is made. in the process of CAM operation, to obtain a faster matching and low power consumption, a new search approach and pattern alignment logic is defined. To improve the storage capacity of a CAM unit, a multi page interface is proposed. To the defined unit a new fault tolerance approach is integrated for a reliable, low power and fast processing CAM application.

Keywords: CAM unit, low power, fast search, high volume, fault tolerant.

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Lockwood, “Fast and scalable address matching for content filtering,” in Proc. Symp. Arch. for Netw. Commun. Syst. (ANCS), Oct. 2005, pp. 183–192. 12. E. C. Oh and P. D. Franzon, ‘‘Design considerations and benefits of three-dimensional ternary content addressable memory,’’ in Proc. IEEE Custom Integr. Circuits Conf., 2007,pp. 591–594. 13. D. Bhattacharya, A. Bhoj, and N. Jha,‘‘Design of efficient content addressable memories in high-performance FinFET technology,’’ IEEE Trans. Very Large Scale Integr. (VLSI) Syst., vol. 23, no. 5,pp. 963–967, May 2015. 14. A. McAuley and P. Francis, ‘‘Fast routingtable lookup using CAMs,’’ in Proc. IEEE12th Annu. Joint Conf. IEEE Comput.Commun. Soc., Netw.: Found. Future,1993, vol. 3, pp. 1382–1391. 15. F. Yu, R. Katz, and T. Lakshman,‘‘Gigabit rate packet pattern-matchingusing TCAM,’’ in Proc. 12th IEEEInt. Conf. Netw. Protocols, Oct. 2004,pp. 174–183. 16. K. Eshraghian, ‘‘Memristormos contentaddressable memory (MCAM): Hybridarchitecture for future high performancesearch engines,’’ IEEE Trans. Very LargeScale Integr. (VLSI) Syst., vol. 19, no. 8,pp. 1407–1417, Aug. 2011. 17. Q. Guo, X. Guo, Y. Bai, and E. Ipek,‘‘A resistive TCAM accelerator fordata-intensive computing,’’ in Proc. 44thAnnu. IEEE/ACM Int. Symp. Microarchitect.,2011, pp. 339–350. 18. K.Suresh Kumar, Y.Rajasree Rao, K. Manjunathachari, “Fast Map- Addressing for Content Addressable Memories using Register Reconfiguration”, 5th IEEE International Conference on Communication and Signal, 2016. 19. K.Suresh Kumar, Y.Rajasree Rao, K. Manjunathachari, “Low-Power Correlative Register Sequencing for Content Addressable Memory”, International Journal of Engineering Studies (IJES) Vol 8, 2016. 20. K.Suresh Kumar, Y.Rajasree Rao, K. Manjunathachari, “A Fault Tolerance Approach For Multi-Page Content Addressable Memory”, 4th IEEE International Conference on Innovations in Information, Embedded And Communication Systems(ICIIECS-2017), March 2017. 21. K.Suresh Kumar, Y.Rajasree Rao, K. Manjunathachari, “ Robust Fault Tolerance In Content Addressable Memory Interface”, IOSR Journal of VLSI And Signal Processing (IOSR-JVSP), Volume 7, Issue 3, Ver. I, May 2017. 23. K.Suresh Kumar, Y.Rajasree Rao, K. Manjunathachari, “Address Mapping in Content Addressable Memory Interface With a Low Power Approach”, International Journal of Engineering Research and Development(IJERD), Volume 13, Issue 12, Ver I, December, 2017 Authors: Gouse Baig Mohammad, U Ravi Babu RCAC: A Secure and Privacy Preserving RFID based Cloud-Assisted Access Control to IoT Integrated Paper Title: Smart Home Abstract: With the emergence of Internet of Things (IoT), smart and intelligent applications are being developed. One of the key enabling technologies of IoT is Radio Frequency Identification (RFID). RFID uniquely identifies all connected devices and things in an IoT use case like smart home which may be part of smart city use case in turn. Therefore IoT applications are implicitly made RFID critical. Thus ensuring security and privacy in RFID communications is indispensable for sustainable growth in such applications. With respect to smart home access control, there might be privacy attacks since RFID carries sensitive information of users. Cyber criminals may target to destroy critical digital infrastructure. RFID authentication is made large scale in IoT integrated applications. Therefore, it is essential to have cloud-assisted solution. With cloud integration, RFID authentication reaps benefits of cloud such as scalability, availability and fault tolerance at server side. Nevertheless, cloud is untrusted environment from user point of view and vulnerable to attacks. Therefore there is need for secure and privacy preserving RFID based authentication mechanism. Such system should be able to prevent both internal and external attacks. The mechanisms found in literature are using various schemes to implement security. However, consideration of probability of internal attacks solicits a new model for enhancing security in smart home use case. Towards this end, we proposed a secure and privacy preserving framework to safeguard interests of all stakeholders of the use case as far as security is concerned. The framework is known as RFID based Cloud- assisted Access Control (RCAC). It enables secure communications among parties involved in access control mechanism. It is lightweight, secure, privacy preserving and prevents external and internal attacks. Amazon EC2 is used as cloud platform to evaluate the framework. Experimental results are encouraging and RCAC shows performance improvement over the state of the art.

Keywords: Radio Frequency Identification, Internet of Things, RFID based authentication, cloud assisted RFID authentication

References: 1. Muhammad RaisulAlam, M. B. I. Reaz and M. A. Mohd Ali.(2012). A Review of Smart Homes – Past, Present, and Future. IEEE Transactions on Systems Man and Cybernetics Part C, p1-16. 2. Pardeep Kumar, AnBraeken, Andrei Gurtov, JariIinatti and Phuong Hoai Ha. (2017). Anonymous Secure Framework in Connected Smart Home Environments. IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY.12 (4), p968-979. 3. José L. Hernández · M. Victoria Moreno · Antonio J. Jara · Antonio F. Skarmeta. (2014). A soft computing based location-aware access control for smart buildings. 3, p1-16. 123. 4. Joseph Bugeja, Andreas Jacobsson and Paul Davidsson. (2016). On Privacy and Security Challenges in Smart Connected Homes . European Intelligence and Security Informatics Conference, p172-175. 5. Min Chen, Jiafu Wan, Sergio Gonz´alez, Xiaofei Liao and Victor C.M. Leung. (2014). A Survey of Recent Developments in Home 693-700 M2M Networks. IEEE COMMUNICATIONS SURVEYS & TUTORIALS.16 (1), p98-114. 6. ProsantaGope, Ruhul Amin, S.K. Hafizul Islam, Neeraj Kumar, Vinod Kumar Bhalla .(2017). Lightweight and privacy-preserving RFID authentication scheme for distributed IoT infrastructure with secure localization services for smart city environment. ELSEVIER, p1-10. 7. Kuan Zhang, Jianbing Ni, Kan Yang, Xiaohui Liang, JuRen, and Xuemin (Sherman) Shen. (2017). Security and Privacy in Smart City Applications: Challenges and Solutions. IEEE, p122-129. 8. JORDI MONGAY BATALLA, ATHANASIOS VASILAKOS AND MARIUSZ GAJEWSKI. (2017). Secure Smart Homes: Opportunities and Challenges. ACM Computing Surveys.50 (5), p1-32. 9. Christos Stergioua , Kostas E. Psannis, Byung-GyuKimb, Brij Gupta. (2018). Secure integration of IoT and Cloud Computing. ELSEVIER.78, P964–975. 10. Mung Chiang and Tao Zhang. (2016). Fog and IoT: An Overview of Research Opportunities. IEEE INTERNET OF THINGS JOURNAL.3 (6), P854-864. 11. Abdul Samada, PrashantMurdeshwar, ZohaibHameed. (2010). High-credibility RFID-based animal data recording system suitable for small-holding rural dairy farmers. ELSEVIER.73, P213–218. 12. Wei Xie1 , Lei Xie2 , Chen Zhang1 , Quan Zhang1 and Chaojing Tang. (2013). Cloud-based RFID Authentication . IEEE International Conference on RFID, p168-175. 13. Terence. K. L. Huia, R. Simon Sherratta , Daniel D´ıazS´anchez. (2015). Major Requirements for Building Smart Homes in Smart Cities based on Internet of Things Technologies, p1-20. 14. Parikshit N. Mahalle, BayuAnggorojati, Neeli R. Prasad and Ramjee Prasad. (2013). Identity Authentication and Capability Based Access Control (IACAC) for the Internet of Things. Journal of Cyber Security and Mobility. 1, p 309–348. 15. Dieter Uckelmann, Mark Harrison and Florian Michahelles. (2011). Architecting the Internet of Things, p1-378. 16. Dr. V. Bhuvaneswari and Dr. R Porkodi. (2014). The Internet of Things (IoT) Applications and Communication Enabling Technology Standards: An Overview. International Conference on Intelligent Computing Applications, p324-329. 17. PallaviSethi and Smruti R. Sarangi. (2017). Internet of Things: Architectures, Protocols, and Applications. Journal of Electrical and Computer Engineering, p1-26. 18. Benjamin Khoo. (2014). RFID - from Tracking to the Internet of Things: A Review of Developments. IEEE, p1-9. 19. Jing Liu and Yang Xiao and C. L. Philip Chen . (2012). Authentication and Access Control in the Internet of Things . 32nd International Conference on Distributed Computing Systems Workshops, p588-592. 20. Mian Ahmad Jan, Priyadarsi Nanda, Xiangjian He, Zhiyuan Tan and Ren Ping Liu. (2014). A Robust Authentication Scheme for Observing Resources in the Internet of Things Environment. IEEE, p1-8. 21. SRAVANI CHALLA1 , MOHAMMAD WAZID1 , ASHOK KUMAR DAS, NEERAJ KUMAR, ALAVALAPATI GOUTHAM REDDY3 , EUN-JUN YOON4 , AND KEE-YOUNG YOO. (2017). Secure Signature-Based Authenticated Key Establishment Scheme for Future IoT Applications. IEEE. Translations and content mining are permitted for academic research onl. 5, p3028-3043. 22. MuhamedTurkanovic, BoštjanBrumen, Marko Hölbl. (2014). A novel user authentication and key agreement scheme for heterogeneous ad hoc wireless sensor networks, based on the Internet of Things notion. ELSEVIER.20, p96–112. 23. Alessandra Rizzardi a , Sabrina Sicaria,n , Daniele Miorandi b , Alberto Coen-Porisini . (2016). AUPS: An Open Source AUthenticated Publish/Subscribe system for the Internet of Things. ELSEVIER.62, p29–41. 24. Son N. Han, Noel Crespi. (2017). Semantic service provisioning for smart objects: Integrating IoT applications into the web. ELSEVIER.76, p180–197. 25. Yudai Komori, Kazuya Sakai, Satoshi Fukumoto, Fast and Secure Tag Authentication in Large-Scale RFID Systems Using Skip Graphs, Computer Communications (2017), doi: 10.1016/j.comcom.2017.11.008. Authors: Vani Garikipati, N Naga Malleswara Rao Paper Title: Secured Cluster Based Distributed Fault Diagnosis Routing for MANET Abstract: Mobile Ad-hoc Network (MANET) has become very crucial for many industrial applications. It is dynamic in nature. Due to its mobility and resource constrainedness and dynamic topology, MANET is vulnerable to many attacks. Therefore it is indispensable to have secure and efficient communications in MANET. Towards this end, in this paper, a novel routing approach is proposed. It is known as cluster-based distributed fault diagnosis routing which is highly secure in nature. The proposed system model includes fault diagnosis and also secure key distribution. Keeping this in mind clusters are created in MANET appropriately. The cluster-based approach in MANET is capable of distributing the aggregated data. Data in each cluster is to be distributed to respective data center. A node in the cluster that has high energy resources is considered to be cluster head. The process of secure routing in the MANET is made by defining a procedure known as pseudonymity. The proposed model is implemented using NS2 simulations.

Keywords: Mobile Ad Hoc Network, Clustering, Diffie-Hellman key, pseudonym

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References: 1. Anilkumar. D.B, Dr. Anoop Kumar Elia , Computational analysis of Intake manifold design of a four cylinder diesel engine in Technical research organization,5(4), 2018. 2. Wolf Bauer and John B. Heywood, Oshin Avanessian and Derlon Chu , Flow Characteristics in Intake Port of Spark Ignition Engine Investigated by CFD and Transient Gas Temperature Measurement in SAE technical paper series 961997. 3. V. Bellenger , A. Tcharkhtchi , Ph. Castaing , Thermal and mechanical fatigue of a PA66/glass fibers composite material in ELSEVIER International Journal of Fatigue 28 (2006) pp.1348–1352. 4. M.A. Ceviz , Intake plenum volume and its influence on the engine performance, cyclic variability and emissions in ELSEVIER Energy Conversion and Management 48 (2007) pp.961–966 . 5. M.A. Ceviz , M. Akin , Design of a new SI engine intake manifold with variable length plenum in ELSEVIER Energy Conversion and Management 51 (2010) pp.2239–2244 . 6. Ryan Ilardo , Christopher B. Williams, Design and manufacture of a Formula SAE intake system using fused deposition modeling and fiber-reinforced composite materials in Rapid Prototyping Journal, 16(3), pp. 174 – 179. 7. Mohamed Ali Jemni, Gueorgui Kantchev, Mohamed Salah Abid , Influence of intake manifold design on in-cylinder flow and engine performances in a bus diesel engine converted to LPG gas fuelled, using CFD analyses and experimental investigations in ELSEVIER Energy 36 (2011) pp.2701-2715. 8. A.Manmadhachary, M.Santosh kumar , Y.Ravi kumar , Design&manufacturing of spiral intake manifold to improve Volument efficiency of injection diesel engine byAM process in 5th International Conference of Materials Processing and Characterization (ICMPC 2016) pp. 1084–1090. 9. M. Safari , M. Ghamari and A. Nasiritosi , Intake Manifold Optimization by Using 3-D CFD Analysis in SAE technical paper series 2003- 32-0073. 10. Robert M. Siewert, Roger B. Krieger, Mark S. Huebler, Prafulla C. Baruah and Bahram Khalighi and Markus Wesslau , Modifying an Intake Manifold to Improve Cylinder-to-Cylinder EGR Distribution in a DI Diesel Engine Using Combined CFD and Engine Experiments in SAE technical paper series 2001-01-3685. 11. Jianmin Xu , Flow analysis of engine intake manifold based on computational fluid dynamics in IOP Conf. Series: Journal of Physics: Conf. Series 916 (2017) 012043. 12. Jordan Lee, Lisa Roessler , Vibration Welded Composite Intake Manifolds Design Considerations and Material Selection Criteria in SAE technical paper series 970076. 13. Case Study On Plastic intake manifold in MATERIALS & DESIGN 13(6), 1992. 14. Ch. Indira Priyadarsini Flow analysis of intake manifold using CFD in International Journal of Engineering and Advanced Research Technology 2(1), 2016. 15. RepairPal Homepage http://repairpal.com/intake-manifold last accessed on 2017/09/18. 16. Yu, J., Vuorinen, V., Kaario, O., Sarjovaara, T., & Larmi, M.: Visualization and analysis of the characteristics of transitional under expanded jets. International Journal of Heat and Fluid Flow 44, 140-154 (2013). Authors: D. Rajasekar, J. Rengamani Paper Title: A Study on the Port Hinterland Connectivity of Chennai Port Sector Abstract: Port hinterland connectivity plays vital role for the growth of any seaport. The economic growth and trade in India depends on maritime transport which in turn depends on good port hinterland connectivity. Seaport hinterland connectivity contains various mode of transport like roadways,railways, airways, inland waterways and inland freight facilities for various cargos.The seaports are connected to inland freight facilities which act like transit place which connect both importers and exporters in the hinterland to seaports and facilitating regional and cross broader trade.The Major ports in India when compared with world class ports still lags behind in hinterland connectivity, this lead to port congestion and directly affects the port performance.The Public –Private model investment which is encouraged by the government showing better results in development of port hinterland connectivity. The Chennai port lacks good hinterland connectivity due to which the port faces lots of challenges. The study reiterates Port hinterland connectivity at present infrastructure development and challenges faced by Chennai port.

Keywords: Chennai Port, Congestion, Vessel, Ports, hinterland connectivity, Draft, Berth.

References: 1. Aronietis, R., Van de Voorde, E., Vanelslander, T. (2010). Port competitiveness determinants of selected European ports in the containerized cargo market. Paper presented at IAME2010. 2. Connie Chen (India spring board, April 2014 issue of Port strategy, 2014 Holman Fenwick Willan LLP. 126. 3. Consequences of Port Congestion on Logistics and Supply Chain in African Ports, by Dr. USMAN GIDADO (FCILT), Sea/Maritime Transport Modal Representative, CILT Nigeria. 712-717 4. D.Rajasekar and Dr. J. Rengamani, A Study on the Infrastructural Facilities of the Seaports in Chennai Cluster. International Journal of Civil Engineering and Technology, 8(11), 2017, pp. 591–599. 5. Dr.J.Rengamani and Dr.A.Shameem, A Study on the Civil Engineering Logistics Growth and Challenges in India. International Journal of Civil Engineering and Technology, 9(8), 2018, pp. 44-53. 6. Giuliano, G., and T. O’Brien. 2007. “Reducing Port-Related Truck Emissions: The Terminal Gate Appointment System At The Ports Of Los Angeles And Long Beach.” Transportation Research Part D 12(7), pp. 460–473. 7. Huynh, N., and C. M. Walton. 2008. “Robust Scheduling Of Truck Arrivals At Marine Container Terminals.” Journal of Transportation Engineering 134(8), pp. 347–353. 8. Huynh, N., F. Harder, D. Smith, S. Omar, and P. Quyen. 2011. “An Assessment of Truck Delays at Seaports Using Terminal Webcams.” TRB paper 2222. pg 54 -62. 9. Issue 7, pp. 523-527. (2007). 10. Muralidharan Balasubramaniam and Dr.J.Rengamani, Inevitability in the Growth and Development of Green Port Operations in the Seaports of Chennai Cluster, International Journal of Mechanical Engineering and Technology, 9(9), 2018, pp. 489–496. 11. Pallis, A.A., Vitsonis, T.K., and DeLangen, P.W. (2010) Port Economics, Policy, and Management: Review of an Emerging Research Field. Transport Reviews, 30(1), 115-161. 12. Port Congestion and Implications to Maritime Logistics, chapter 4, by Hilde Meersman, Eddy Van de Voorde and Thierry Vane/slander, 2012 by Emerald group Publishing Limited. 13. Roso V. “Evaluation of the dry port concept from an environmental perspective: A note.” 14. Transportation Research Part D: Transport and Environment, Elsevier B.V., Volume 12, 15. U.S. Container Port Congestion and Related International Supply Chain Issues: Causes, Consequences and Challenges, FMC Port Forums, 2015 16. Vacca, I., M. Bierlaire, and M. Salani. 2007. “Optimization at Container Terminals: Status, Trends and Perspectives.” In the 7th Swiss Transport Research Conference. Authors: Manas Kumar Yogi, L. Yamuna Enhancing Ability of User Personalization by Application of Rough Fuzzy Grouping Mechanism for Paper Title: Improved Web Intelligence 127. Abstract: In contemporary world, Web personalization tenders accurate means for the evolution of operations that have the enticing feature to satisfy compelling obligation of their end user. To perform that, developers of web 718-722 need to face an decisive trial regarding the disclosure of information of concern which the end users show while they reach out to various sites. Web Usage Mining is a functioning exploration region which regards the disclosure of helpful examples of run of the mill client practices by using utilization information. Grouping has been hugely applied for sake of classifying users having identical concerns. Rough fuzzy grouping proves to be an mechanism handy to deduce user sections from web use information accessible via server history files. It is well known that fuzzy grouping works on mechanism of distance-based metrics to judge the similarity among user choices. But the application of such techniques may propel to feeble outcomes by classifying user groups that do not include the meaningful knowledge assimilated . In this paper, we advocate an technique based on a rough fuzzy grouping algorithm armed with a rough fuzzy similarity metric to deduce user groups. For pertinence, we deploy the presented technique on users data extricated from server history files of a popular web site.

Keywords: rough ,fuzzy, similarity measures, grouping, personalization, user categorization.

References: 1. Abraham, A., Wang, X.: i-Miner: A Web Usage Mining Framework Using Hierarchi- cal Intelligent Systems. In: The IEEE Int. Conf. on Rough fuzzySystems, pp. 1129–1134. IEEE Press, Los Alamitos (2003) 2. Arotaritei, D., Mitra, S.: Web Mining: a survey in the rough fuzzyframework. Rough fuzzySets and System 148, 5–19 (2004) 3. K.C. Lee, J.S. Kim, N.H. Chung, and S.J. Kwon, “Fussy Cognitive Map Approach to Web-Mining Inference Amplification,” Expert System with Applications, vol. 22, pp. 197-211, 2002. 4. Y. Li and N. Zhong, “Ontoserver historyy-Based Web Mining Model: Representations of User Profiles,” Proc. IEEE/WIC Int’l Conf. Web Intelligence, pp. 96-103, 2003 5. N. Zhong, J. Liu, and Y.Y. Yao, “In Search of the Wisdom Web,” Computer, vol. 35, no. 11, pp. 27-31, Nov. 2002. 6. Z.Y. Lu, Y.Y. Yao, and N. Zhong, “Web Server history Mining,” Web Intelligence, pp. 174-194, 2003. 7. M. Perkowitz and O. Etzioni, “Adaptive Web Sites,” Comm. ACM, vol. 43, no. 8, pp. 152-158, 2002. 8. T.Y. Yan, M. Jacobsen, H. Garcia-Molina, and U. Dayal, “From User Access Patterns to Dynamic Hypertext Linking,” Proc. Fifth Int’ World Wide Web Conf., 1996. 9. Martin-Bautista, M.J., Vila, M.A., Escbar-Jeria, V.H.: In: IADIS European Conference Data Mining, pp. 73–76 (2008) Authors: Pooja G, S. Murali Krishna, V. Ravi Paper Title: Multi-Level Memristor Memory: Design and Performance Analysis Abstract: Memristor-based memories are one of the attractive candidates to replace present memory technologies due to its novel characteristics such as non-volatile storage, nanosize cell, compatibility with CMOS, low power dissipation, and multi-level cell (MLC) operation etc. However, the device needs to overcome the potential challenges such as process variations, non-deterministic nature of the operation, sneak path issues, nondestructive write and read operation. One of the most important characteristics of memristor memories is its ability to store multiple bits in one cell. In this paper, we design a low power, high-speed multi-level memristor based memories. Additionally, the performance analysis of the multi-level memristor memories has been performed under various memristor models and window functions.

Keywords: Memristor, Non-volatile memory,

References: 1. Chua L. Memristor-the missing circuit element. IEEE Trans Circuit Theory 1971;18:507–19. 2. Strukov DB, Snider GS, Stewart DR, Williams RS. The missing memristor found. Nature 2008;453:80. 3. Amdapurkar A, Naik DK, Ravi V. Design and Development of Memristor-based Combinational Circuits. Int J Recent Innov Trends Comput Commun 2016;4. 4. Chandni MD, Ravi V. Built in self test architecture using concurrent approach. Indian J Sci Technol 2016;9. 5. Sharma A, Ravi V. Built in self-test scheme for SRAM memories. Adv. Comput. Commun. Informatics (ICACCI), 2016 Int. Conf., IEEE; 128. 2016, p. 1266–70. 6. Chaitanya MK, Ravi V. Design and development of BIST architecture for characterization of S-RAM stability. Indian J Sci Technol 723-729 2016;9. 7. Rabbani P, Dehghani R, Shahpari N. A multilevel memristor–CMOS memory cell as a ReRAM. Microelectronics J 2015;46:1283–90. 8. Kvatinsky S, Ramadan M, Friedman EG, Kolodny A. VTEAM: A general model for voltage-controlled memristors. IEEE Trans Circuits Syst II Express Briefs 2015;62:786–90. 9. Wong H-SP, Lee H-Y, Yu S, Chen Y-S, Wu Y, Chen P-S, et al. Metal–oxide RRAM. Proc IEEE 2012;100:1951–70. 10. Strukov DB, Snider GS, Stewart DR, Williams RS. The missing memristor found. Nature 2009;459:1154–1154. doi:10.1038/nature08166. 11. Kvatinsky S, Talisveyberg K, Fliter D, Kolodny A, Weiser UC, Friedman EG. Models of memristors for SPICE simulations. Electr. Electron. Eng. Isr. (IEEEI), 2012 IEEE 27th Conv., IEEE; 2012, p. 1–5. 12. Radwan AG, Fouda ME. On the mathematical modeling of memristor, memcapacitor, and meminductor. vol. 26. Springer; 2015. 13. Biolek Z, Biolek D, Biolková V. SPICE model of memristor with nonlinear dopant drift. Radioengineering 2009;18:210–4. 14. Kvatinsky S, Friedman EG, Kolodny A, Member S, Weiser UC. TEAM : ThrEshold Adaptive Memristor Model 2013;60:211–21. 15. Zha J, Huang H, Liu Y. A novel window function for memristor model with application in programming analog circuits. IEEE Trans Circuits Syst II Express Briefs 2016;63:423–7. 16. Kvatinsky S, Talisveyberg K, Fliter D, Friedman EG, Kolodny A, Weiser UC. Verilog-A for Memristor Models. CCIT Tech Rep 2011;8. 17. Ravi V, Prabaharan SRS. Fault tolerant adaptive write schemes for improving endurance and reliability of memristor memories. AEU- International J Electron Commun 2018;94:392–406. 18. Ravi V, Prabaharan SRS. Weak Cell Detection Techniques for Memristor-Based Memories 2018:101–10. 19. Reddy MGSP, Ravi V. Nondestructive Read Circuit for Memristor-Based Memories. Nanoelectron. Mater. Devices, Springer; 2018, p. 123–31. 20. Ho Y, Huang GM, Member S, Li P, Member S. Dynamical Properties and Design Analysis for Nonvolatile memristor memories 2011;58:724–36. Authors: Harish Baraithiya, R. K. Pateriya 129. Paper Title: Classifiers Ensemble for Fake Review Detection Abstract: The growth of e-commerce businesses has attracted many consumers, because they offer a range of 730-736 products at competitive prices. One thing most purchasers rely on when they purchase online is for product reviews to conclude their decision. Many sellers use the decision to impact the review to hire the paid review authors. These paid review authors target the particular brand, store or product and write reviews to promote or demote them according to the requirements of their hired employees. In view of the effects of these fake reviews, a number of techniques to detect these fake reviews have already been proposed. Because of nature of the reviews it is difficult to classify them using single classifier. Hence in this paper, we proposed an ensemble classifier based approach to detect the fake reviews. The proposed ensemble classifier uses support vector machine (SVM), Naïve Byes classifier and k- nearest neighbor (KNN mutual) classifiers. The proposed technique is evaluated using Yelp and Ott. et al [10] datasets. The evaluation results show that the proposed classifier provides better classification accuracy on both datasets.

Keywords: Fake review detection, ensemble classifiers, Behavioral analysis, Opinion Spam.

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Dave, Kushal and Lawrence, Steve and Pennock, David M, Mining the peanut gallery: Opinion extraction and semantic classification of product reviews, Proceedings of the 12th international conference on World Wide Web, 519--528 (2003). 6. Duan, Huiying and Zirn, C{\"a}cilia, Can we identify manipulative behavior and the corresponding suspects on review websites using supervised learning?, Nordic Conference on Secure IT Systems, 215--230 (2012). 7. Feng, Song and Banerjee, Ritwik and Choi, Yejin, Syntactic stylometry for deception detection, Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics, 2, 171--175 (2012). 8. Yuan, Ling and Li, Dan and Wei, Shikang and Wang, Mingli, Research of Deceptive Review Detection Based on Target Product Identification and Metapath Feature Weight Calculation, Complexity, (2018). 9. Li, Luyang and Qin, Bing and Ren, Wenjing and Liu, Ting, Document representation and feature combination for deceptive spam review detection, Neurocomputing, 254, 33--41 (2017). 10. Ott, Myle and Choi, Yejin and Cardie, Claire and Hancock, Jeffrey T, Finding deceptive opinion spam by any stretch of the imagination, Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, 1, 309--319 (2011). 11. Khalifa, Malika Ben and Elouedi, Zied and Lef{\`e}vre, Eric, Fake Reviews Detection Under Belief Function Framework, International Conference on Advanced Intelligent Systems and Informatics, 395--404 (2018). 12. Mukherjee, Subhabrata and Dutta, Sourav and Weikum, Gerhard, Credible review detection with limited information using consistency features, Joint European Conference on Machine Learning and Knowledge Discovery in Databases, 195--213 (2016). 13. Chauhan, Shashank Kumar and Goel, Anupam and Goel, Prafull and Chauhan, Avishkar and Gurve, Mahendra K, 2017 4th International Conference on Signal Processing and Integrated Networks (SPIN), , 390--393 (2017). 14. Mukherjee, Arjun and Venkataraman, Vivek and Liu, Bing and Glance, Natalie S, What yelp fake review filter might be doing?, ICWSM, 409--418 (2013). 15. Rout, Jitendra Kumar and Dalmia, Anmol and Choo, Kim-Kwang Raymond and Bakshi, Sambit and Jena, Sanjay Kumar, Revisiting Semi-Supervised Learning for Online Deceptive Review Detection., IEEE Access, 5-1, 1319--1327 (2017). 16. Liu, Yuanchao and Pang, Bo, A Unified Framework for Detecting Author Spamicity by Modeling Review Deviation, Expert Systems with Applications, (2018). 17. Jindal, Nitin and Liu, Bing, Opinion spam and analysis, Proceedings of the 2008 international conference on web search and data mining, 219--230 (2008). 18. Mukherjee, Arjun and Kumar, Abhinav and Liu, Bing and Wang, Junhui and Hsu, Meichun and Castellanos, Malu and Ghosh, Riddhiman, Spotting opinion spammers using behavioral footprints, Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining, 632--640 (2013). 19. Wang, Guan and Xie, Sihong and Liu, Bing and Philip, S Yu, Review graph based online store review spammer detection, 2011 ieee 11th international conference on Data mining (icdm), 1242--1247 (2011). 20. Mukherjee, Arjun and Liu, Bing and Wang, Junhui and Glance, Natalie and Jindal, Nitin, Detecting group review spam, Proceedings of the 20th international conference companion on World wide web. 93--94 (2011). 21. Lim, Ee-Peng and Nguyen, Viet-An and Jindal, Nitin and Liu, Bing and Lauw, Hady Wirawan, Detecting product review spammers using rating behaviors, Proceedings of the 19th ACM international conference on Information and knowledge management, 939--948 (2010). 22. Mukherjee, Arjun and Liu, Bing and Glance, Natalie, Spotting fake reviewer groups in consumer reviews, Proceedings of the 21st international conference on World Wide Web, 191--200 (2012). 23. Jindal, Nitin and Liu, Bing and Lim, Ee-Peng, Finding unusual review patterns using unexpected rules, Proceedings of the 19th ACM international conference on Information and knowledge management, 1549--1552 (2010). 24. Mukherjee, Arjun and Liu, Bing, Improving gender classification of blog authors, Proceedings of the 2010 conference on Empirical Methods in natural Language Processing, 207--217 (2010). 25. Li, Jiwei and Ott, Myle and Cardie, Claire and Hovy, Eduard, Towards a general rule for identifying deceptive opinion spam, Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, 1, 1566--1576 (2014). 26. 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"A fast clustering-based feature subset selection algorithm for high-dimensional data." IEEE transactions on knowledge and data engineering 25, no. 1 (2013): 1-14. 32. Lin, Lin, Xue-Hai Yuan, and Zun-Quan Xia. "Multicriteria fuzzy decision-making methods based on intuitionistic fuzzy sets." Journal of computer and System Sciences 73, no. 1 (2007): 84-88. 33. Perera, L. P., J. P. Carvalho, and C. Guedes Soares. "Fuzzy logic based decision making system for collision avoidance of ocean navigation under critical collision conditions." Journal of marine science and technology 16, no. 1 (2011): 84-99. 34. Ishibuchi, Hisao, Ken Nozaki, Naohisa Yamamoto, and Hideo Tanaka. "Construction of fuzzy classification systems with rectangular fuzzy rules using genetic algorithms." Fuzzy sets and systems 65, no. 2-3 (1994): 237-253. 35. Zhou, Enwang, and Alireza Khotanzad. "Fuzzy classifier design using genetic algorithms." Pattern Recognition 40, no. 12 (2007): 3401- 3414. 36. 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In Information Retrieval & Knowledge Management,(CAMP), 2010 International Conference on, pp. 200-203. IEEE, 2010. 47. Etaiwi, Wael, Arafat Awajan, and Dima Suleiman. "Statistical Arabic Name Entity Recognition Approaches: A Survey." Procedia Computer Science 113 (2017): 57-64. 48. J. Han, and M. Kamber, “Data mining: concepts and techniques’” (2nd ed.). Morgan Kaufmann Publishers, 2006. 49. D. L. Olson, D. Delen, “Advanced Data Mining Techniques,” 2008. 50. Breiman, Leo. "Bagging predictors." Machine learning 24, no. 2 (1996): 123-140. 51. Bauer, Eric, and Ron Kohavi. "An empirical comparison of voting classification algorithms: Bagging, boosting, and variants." Machine learning 36, no. 1-2 (1999): 105-139. 52. Dietterich, Thomas G. "Ensemble methods in machine learning." In International workshop on multiple classifier systems, pp. 1-15. Springer, Berlin, Heidelberg, 2000. Authors: P. Devika, V.Prashanthi, G.Vijay Kanth, J Thirupathi Paper Title: RFID Based Theft Detection and Vehicle Monitoring System using Cloud Abstract: The rapid advancement in technology has become important to deploy various technology boosters in our daily life that fulfil our requirements by increasing security. Now a day’s thefts are making up their offence on committed areas like banks, street robbery, commercial robbery, jewellery, public vehicles etc. Theft is not just about losing property, sometimes, victims may get seriously injured. Generally when the vehicles get robbed, we file compliant or search CC footages for identification which is time consuming and inaccurate. In this paper we propose a system for theft detection and vehicle security. Our main aim is to implement IOT & RFID based theft detection and vehicle monitoring system using cloud. The RFID tag is used for authentication and raspberry pi is used as the micro controller .Whenever a vehicle theft takes place, the authorized person (owner) will receive a mail including the picture of the vehicle from the information that is stored in cloud.

Keywords: street robbery, commercial robbery, jewellery, public vehicles

130. References: 1. Manish Buhptani, Shahram Moradpour, "RFID Field Guide - Developing Radio Frequency Identification Systems", Prentice Hall, 2005, 737-739 pp 7-9, 16-225, 160, 231 2. Intelligent Traffic Management system, CCTV Capable of generating E-challan for Indore city (RLVD System) 3. An Introduction to RFID Technology (Radio Frequency Identification ) Communications and Network, 2010, August, 2, 3Communications and Network 4. Raspberry Pi:credit card-sized single-board computer- http://www.plusdigit.com/2014/10/25/ raspberry-picredit-card-sized-single-board- computer/ 5. RFID - http://ageqnies.com/html/rfid.html 6. RFID reader module with antenna - usb – uart- https://robokits.co.in/wireless-solutions/rfid/rfid - reader -module-with-antenna-usb-uart 7. “A Smart Information System for Counting People”, P. Devika, A. Manusha Reddy, G. Vijay Kanth, Chaitrali Dangare, Y. Indu and B. Padmaja, Journal of Advanced Research in Dynamical and Control Systems, Issue 11, 2018. 8. “Exploring M-Learning Benefits for Higher School Education”, Chaitrali S. Dangare, B. Anand Kumar, A. Manusha Reddy, Y. Indu and B. Padmaja, , Journal of Advanced Research in Dynamical and Control Systems, Issue 11, 2018. 9. “Secure data communication using isecLEACH protocol in WSN’s”, M.Anitha, P. Devika, A. Manusha Reddy, G. Vijay Kanth, ISSN 1314-3395, IJPAM,2018. 10. V.Prashanthi, D.Suresh Babu, C.V.Guru Rao, Network Coding aware Routing for Efficient Communication in Mobile Ad-hoc Networks, International Journal of Engineering & Technology(UAE), ISSN: 2227-524X. 7 (3) (2018) 1474-1481 Authors: MSV. Prasad, G. Chaitanya Eswara Naidu, B. Sandya Sri Paper Title: Assessing Investors’ Knowledge about Commodity Trading in India Abstract: The study involves investors’ knowledge of the commodity market. Management brokerage services can know whether investors understand the commodity market. Provide investors with further development advice 131. on organizational development and awareness rising. The goal is to study the level of knowledge, preferably demographics and factors that influence commodity investment. This study uses a descriptive research project. Use 740-748 self-contained questionnaires to collect respondents' data. The questionnaire includes factors that affect investors in the commodity market. In order to understand investors’ understanding of the commodity market, investors’ opinions were collected as the main data by conducting surveys of 500 individual respondents in Hyderabad. Other data is collected from books, magazines, etc. Data compilation and analysis use statistical tools. Chi-square tests and analysis of variance are also used to test hypotheses. Compared with other commodities, investment preferences are oil, silver, copper and gold. The main factors affecting commodity investment are online software, friends and brokerage services.

Keywords: brokerage services, hypotheses, factors affecting commodity investment

References: 1. Jena, Pratap Kumar and PhannidraGoyari. "Real Relationship Between Commodities, Stocks and Credit Cards in India: DCC Model Analysis". IUP Journal of Applied Finance 22, no. 1 (2016): 37. 2. Capil, Sheba and Kanval Nyan Kapil. "Merchandise Trade Advisor (ctas) for the Indian Commodity Market." International Journal of the Five (2010): 124-137. 3. Ftiti, Z., Kablan, S. &Guesmi, K. (2016). What can we learn about commodities and credit cycles? Evidence from African exporting countries. Energy Economics, 60, 313-324. 4. Han, L., Li, Z., & Yin, L. (2017). Impact of investor attention on future commodity markets. Newspapers on futures market. 5. Bring Bush (2017). Investor protection and information is an important pillar of agenda and post-crisis rule control - the way forward. Sector and economic outlook, 56 (1), 29-60. 6. Erb, c. B, and Harvey, CIM (2016). Misleading confusion about future investment in raw materials. Data from financial analyst 72 (4), 26-35. 7. Monga, O.P., Dawra, S., Monga, A. & Bansal, A.A.K. (2016). Investor Perspectives on Gold Investing: Some Reflections. International Journal of Engineering Engineering Business and Enterprise (IJEBEA), 17 (1), 05-09 8. Periyasamy, S. (2016). Impact of investor information programs on potential investors on the Stock Exchange of India. International Journal of Research, Information, and Governance Research 6 (2), 21-23. 9. Chen, Y. & Chang, Y. K. (2015). Investor structure and information efficiency of future commodity prices. Review of International Monetary Fund, 42, 358-367. 10. Mellios, C., Six, P. & Lai, A.N. (2016). Dynamic expectations and curbs of future commodity markets with stochastic comfort income. European Data on Operational Data 250 (2): 493-504. 11. Iqbal, S., Hussain, N., Latif, M & Aslam, S. (2013). Types of Investors and Irregularities in Financial Markets: Comparisons of individual and foreign investors, and their role in decision-making in investment. Journal of Scientific Research 17 (11), 1591-1596. Authors: Sapna Kumari. C, K. V. Prasad Paper Title: A Novel S-box Generation of AES using Elliptic Curve Cryptography (ECC) Abstract: In recent decades, the security of the data is playing a major role in communication systems due to more attackers between the channel media. The security level is depending on secret key, as per literature survey of ECC guide, higher the bits size of the keys, higher the security [19]. Therefore the generation of key with large size is the major challenging task. At present, Advanced Encryption Standard (AES) is a better cryptography system where the encryption and decryption can be performed with fixed key size of 128bits, 192 bits and 256 bits. The security level has been increased in AES due to the S-Box and it consists of 256 different values in the form of 16x16 matrixes, but to generate 256 values the Galois Field (GF) has been used. GF requires a lot of hardware resources with more number of arithmetic operations like multiplication, additions and inversions [20-21]. To overcome this issue, a novel S-Box generation using Elliptic Curve Cryptography (ECC )and BWMC methods are proposed. The ECC uses point addition and point doubling to generate 256 values without multiplication operations. After generation of the matrix, its values are encrypted and decrypted using bitwise matrix code (BWMC). The proposed work has been designed using Verilog HDL, simulated and validated on Vertex-5 FPGA development board. From the results obtained from novel S-Box and BWMC techniques there is an improvement in terms of delay i.e. 73.1% as compared with hamming codes and 69% improvement in speed as compared with MC’s[22].

Keywords: AES, BWMC, ECC, Point addition, point doubling, FPGA, Security system and S-Box.

132. References: 1. Arunkumar, Dr. S.S. Tyagi, ManishaRana, NehaAggarwal, PawanBhadana, ManavRachna (2011), A Comparative Study of Public Key Cryptosystem based on ECC and RSA. International Journal on Computer Science and Engineering (IJCSE), ISSN : 0975-3397 Vol. 3 749-765 No. 5 May 2011, 1904-1909. 2. O. Srinivasa Rao (2010), Efficient Mapping Methods For Elliptic Curve Cryptosystems. International Journal of Engineering Science and Technology, Vol. 2(8), 2010, ISSN: 0975-5462, 3651-3656 3. C. Wang, M. Daneshmand, M. Dohler, X. Mao, R. Q. Hu and H. Wang (2013), Guest Editorial - Special Issue on Internet of Things (IoT): Architecture, Protocols and Services”. IEEE Sensors Journal, vol. 13, issue. 10, pp. 3505–3510. 4. S. Raza, (2014) Secure communication for the Internet of Things - a comparison of link-layer security and IP sec for 6LoWPAN. Security and Communication Networks, vol. 7, no. 12, pp. 2654–2668. 5. Gonzalez G. Organero M, Kloos C (2008). Early in infrastructure of all Internet of Things in space for learning. 8th IEEE International Conference on Advance Learning Technologies, pp-381-383. 6. Yanbing Liu, Wenping Hu, Jiang Du (2011), Network Information Security Architecture Based on Internet of Things. ZTE Communication, Vol. 17(1):17-20 7. Riaz Naseer and Jeff Draper (2008), “Parallel Double Error Correcting Code Design to Mitigate Multi-Bit Upsets in SRAMs”. 978-1- 4244-2361-3/08, IEEE. 8. Juan Antonio Maestro, Pedro Reviriego, SanghyeonBaeg, Shijie Wen, Richard Wong (2013), Soft error tolerant Content Addressable Memories (CAMs) using error detection code and duplication. Microprocessors and Microsystems 37 (2013) 1103–1107, 2013 Elsevier B.V. http://dx.doi.org/10.1016/j.micpro.2013.10.003, 0141-9331. 9. Gustavo Neuberger, Fernanda Gusmao de lima, kastensmidt and Ricardo Reis (2005), An Automatic Technique for Optimizing Reed – Solomon Codes to Improve Fault Tolerance in Memories. IEEE Co-published by the IEEE CS and the IEEE CASS IEEE Design & Test of Computers, 0740-7475/05 10. Sanghyeon Baeg, Pedro Reviriego, Juan Antonio Maestro, Shijie and Richard Wong (2011), Analysis of a Multiple Cell Upset Failure Model for Memories. ACM Transactions on Design Automation of Electronic Systems, Vol. 16, No. 4, Article 45, 1084-4309 11. Sandeep M D and Rajashekhargouda C. Patil (2016), An Approach to Reduce Number of Redundant Bits used To Overcome Cell Upsets in Memory using Decimal Matrix Code. International Journal of Science, Engineering and Technology Research (IJSETR), Volume 5, Issue 5, 200-2010. 12. Costas Argyrides, StephaniaLoizidou and Dhiraj K. Pradhan (2008), Area Reliability Trade – Off in Improved Reed Muller Coding. SAMOS 2008, LNCS 5114, 2008 Springer – Verlag Berlin Heidelber, pp. 116-125. 13. Bertozzi, D., Et Al (2005).: Error Control Schemes For On-Chip Communication Links: The Energy-Reliability Tradeoff. IEEE Transactions On Computer-Aided Design Of Integrated Circuits And Systems, Vol. 24, No. 6. 14. Rossi, D., Metra, C(2003): Error correcting strategy for high speed and density reliable flash memories. IEEE Journal. Electronic Testing, Theory and applications, 19(5), 511–521. 15. Argyrides, C., Pradhan, D.K (2007): Improved Decoding Algorithm for High Reliable Reed Muller Coding. SAMOS 2008, LNCS 5114, pp. 116–125, 2008, (Eds.): SAMOS 2008, LNCS 5114, pp. 116–125. 16. R.Balamurugan, V.Kamalakannan, S.Tamilselvan and D.RahulGandh, (2014) “Enhancing Security in Text Messages Using Matrix based Mapping and ElGamal Method in Elliptic Curve Cryptography”. International Conference on Contemporary Computing and Informatics (IC3I 2014), pp 103-106. 17. Smyth N., McLoone M., McCanny J. V (2006). WLAN Security Processor. IEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications, Vol. 53, Issue 7, ISSN: 1057-7122, pp: 1506- 1520. 18. Bae D., Kim G., Kim J., Park S., Song O (2005). An Efficient Design of CCMP for Robust SecurityNetwork. ICISC 2005, Lecture Notes in Computer Science 3935, Springer-Berlin,2006, pp. 352-361. 19. Syed Mohsin Abbas (2014), "An Efficient Multiple Cell Upsets Tolerant Content-Addressable Memory", IEEE Transactions On Computers, VOL. 63, NO. 8. 20. Fu Minfeng 92010), Elliptic Curve Cryptosystem EIGamal Encryption and Transmission Scheme. International Conference on Computer Application and System Modeling (ICCASM 2010), 978-1-4244-7237-6/10, IEEE, 2010. Authors: K. Martin Sagayam, D. Narain Ponraj, Jenkin Winston, Yaspy J C, Esther Jeba D, Antony Clara Authentication of Biometric System using Fingerprint Recognition with Euclidean Distance and Neural Paper Title: Network Classifier Abstract: Nowadays, Fingerprint recognition is the one of the authentication used for security applications. It provides hope for the society in reliable authentic biometric systems. Fingerprint technology emerges in various sectors such as government, organizations, libraries, universities, banks etc. It is widely used for biometric systems other than Iris, Face, Hand, Voice and Signature because of its uniqueness and distinctness. Traditional methods are not effectively used for analyzing the texture feature of finger print than neural network classifier. Fingerprint recognition will be identified and classified with the help of Euclidean distance and NN classifier for better accuracy has proposed in this paper. It uses certain techniques in preprocessing the image such as Histogram equalization and Fast Fourier transform. The performance of the proposed approach has significant result than the existing techniques used in the finger print recognition system.

Keywords: Euclidean distance, Fingerprint recognition, NN classifier

References: 1. Ravi.J, K.B.Raja and Venugopal.K.R (2009). “Fingerprint Recognition using Minutia Score Matching”. International Journal of Engineering Science and Technology. 2. NeerajBharagava, AnchalKumawat, RituBharagava (2015). “Fingerprint Matching of Normalized Image based on Euclidean Distance”. International Journal of computer Application .Volume 120-No 24. 3. Subba Reddy Borra, G.Jagadeeswar Reddy and E.SreenivasaReddy(2016). “An Efficient Fingerprint Enhancement Technique Using Wave Atom Transform and MCS Algorithm”.Procedia Computer Science 89(2016) 785-793. 4. Anil K. Jain, JianjiangFeng, Karthik Nanda Kumar(2010). “Fingerprint Matching”.IEEE Computer Science. 5. Virginia Espinosa(2002). “Minutiae Detection Algorithm for Fingerprint Recognition”.IEEE AESS System Magazine. 133. 6. Iwasokun Gabriel Babatunde, Alese Boniface Kayoed, AkinyokunOluwole Charles, OlabodeOlatubosun(2012). “Fingerprint Image Enhancement-Segmentation to Thinning”. (IJACSA) International Journal of Advanced Computer Science and Applications. 7. P.Gnanasivam, S. Muttan, “An efficient Algorithm for fingerprint preprocessing and feature extraction”, Science direct (2010) 749-765 8. Feng Zhao, Xiaoou Tang, “Preprocessing and post processing for skeleton-based fingerprint minutiae extraction”, Science direct (2007) 9. Khaled Ahmed Nagaty, “Fingerprints classification using artificial neural networks: a combined structural and statistical approach, Elsevier (2001) 10. Chandana, “Fingerprint Recognition based on Minutiae Information”, International Journal of Computer Applications, Volume 120 – No.10, June 2015 11. Mouad.M.H.Ali, “Fingerprint Recognition for Person Identification and Verification Based on Minutiae Matching”, 2016 IEEE 6th International Conference on Advanced Computing 12. Lu Jiang, Chaochao Bai, “A Direct Fingerprint Minutiae Extraction Approach Based on Convolutional Neural Networks”, IEEE 2016 13. D. Ezhilmaran , “A Review Study on Fingerprint Image Enhancement Techniques”, International Journal of Computer Science & Engineering Technology, 2014 14. Morteza Zahedi, “Combining Gabor filter and FFT for fingerprint enhancement based on a regional adaption method and automatic segmentation”, springer (2015) 15. Manhua Liu, “Latent Fingerprint Enhancement via Multi-Scale Patch Based Sparse Representation”, IEEE(2014) 16. Mikel Galar, Joaquín Derrac. “A survey of fingerprint classification Part II: Experimental analysis and ensemble proposal”, Elsevier(2015) 17. Mikel Galar, Joaquín Derrac. “A survey of fingerprint classification Part I: Taxonomies on feature extraction methods and learning models”, Elsevier(2015) 18. Gabor A. Werner, “ Tuning an artificial neural network to increase the efficiency of a fingerprint matching algorithm”, IEEE(2016) 19. Asif Iqbal Khan, “Strategy to Extract Reliable Minutia Points for Fingerprint Recognition”, IEEE (2014) 20. Satishkumar Chavan, “Fingerprint Authentication using Gabor Filter based Matching Algorithm”, ICTSD (2015) 21. Atul S. CHAUDHARI,“Implementation of Minutiae Based Fingerprint Identification System Using Crossing Number Concept”, Informatica Economica vol.18, no.1/2014 22. Puja S. Prasad, B. Sunitha Devi, “A Survey of Fingerprint Recognition Systems and Their Applications”,Springer nature Singapore pvt. Limited, ICCCE, 2018 23. P. Pakutharivu, M. V. Srinath, “A Comprehensive Survey on Fingerprint Recognition Systems, Indian Journal of Science and Technology, 2015 Authors: K. Martin Sagayam, D. Narain Ponraj, Jenkin Winston, Yaspy J C, Esther Jeba D, Antony Clara Authentication of Biometric System using Fingerprint Recognition with Euclidean Distance and Neural Paper Title: 134. Network Classifier Abstract: Nowadays, Fingerprint recognition is the one of the authentication used for security applications. It 749-765 provides hope for the society in reliable authentic biometric systems. Fingerprint technology emerges in various sectors such as government, organizations, libraries, universities, banks etc. It is widely used for biometric systems other than Iris, Face, Hand, Voice and Signature because of its uniqueness and distinctness. Traditional methods are not effectively used for analyzing the texture feature of finger print than neural network classifier. Fingerprint recognition will be identified and classified with the help of Euclidean distance and NN classifier for better accuracy has proposed in this paper. It uses certain techniques in preprocessing the image such as Histogram equalization and Fast Fourier transform. The performance of the proposed approach has significant result than the existing techniques used in the finger print recognition system.

Keywords: Euclidean distance, Fingerprint recognition, NN classifier

References: 24. Ravi.J, K.B.Raja and Venugopal.K.R (2009). “Fingerprint Recognition using Minutia Score Matching”. International Journal of Engineering Science and Technology. 25. NeerajBharagava, AnchalKumawat, RituBharagava (2015). “Fingerprint Matching of Normalized Image based on Euclidean Distance”. International Journal of computer Application .Volume 120-No 24. 26. Subba Reddy Borra, G.Jagadeeswar Reddy and E.SreenivasaReddy(2016). “An Efficient Fingerprint Enhancement Technique Using Wave Atom Transform and MCS Algorithm”.Procedia Computer Science 89(2016) 785-793. 27. Anil K. Jain, JianjiangFeng, Karthik Nanda Kumar(2010). “Fingerprint Matching”.IEEE Computer Science. 28. Virginia Espinosa(2002). “Minutiae Detection Algorithm for Fingerprint Recognition”.IEEE AESS System Magazine. 29. Iwasokun Gabriel Babatunde, Alese Boniface Kayoed, AkinyokunOluwole Charles, OlabodeOlatubosun(2012). “Fingerprint Image Enhancement-Segmentation to Thinning”. (IJACSA) International Journal of Advanced Computer Science and Applications. 30. P.Gnanasivam, S. Muttan, “An efficient Algorithm for fingerprint preprocessing and feature extraction”, Science direct (2010) 31. Feng Zhao, Xiaoou Tang, “Preprocessing and post processing for skeleton-based fingerprint minutiae extraction”, Science direct (2007) 32. Khaled Ahmed Nagaty, “Fingerprints classification using artificial neural networks: a combined structural and statistical approach, Elsevier (2001) 33. Chandana, “Fingerprint Recognition based on Minutiae Information”, International Journal of Computer Applications, Volume 120 – No.10, June 2015 34. Mouad.M.H.Ali, “Fingerprint Recognition for Person Identification and Verification Based on Minutiae Matching”, 2016 IEEE 6th International Conference on Advanced Computing 35. Lu Jiang, Chaochao Bai, “A Direct Fingerprint Minutiae Extraction Approach Based on Convolutional Neural Networks”, IEEE 2016 36. D. Ezhilmaran , “A Review Study on Fingerprint Image Enhancement Techniques”, International Journal of Computer Science & Engineering Technology, 2014 37. Morteza Zahedi, “Combining Gabor filter and FFT for fingerprint enhancement based on a regional adaption method and automatic segmentation”, springer (2015) 38. Manhua Liu, “Latent Fingerprint Enhancement via Multi-Scale Patch Based Sparse Representation”, IEEE(2014) 39. Mikel Galar, Joaquín Derrac. “A survey of fingerprint classification Part II: Experimental analysis and ensemble proposal”, Elsevier(2015) 40. Mikel Galar, Joaquín Derrac. “A survey of fingerprint classification Part I: Taxonomies on feature extraction methods and learning models”, Elsevier(2015) 41. Gabor A. Werner, “ Tuning an artificial neural network to increase the efficiency of a fingerprint matching algorithm”, IEEE(2016) 42. Asif Iqbal Khan, “Strategy to Extract Reliable Minutia Points for Fingerprint Recognition”, IEEE (2014) 43. Satishkumar Chavan, “Fingerprint Authentication using Gabor Filter based Matching Algorithm”, ICTSD (2015) 44. Atul S. CHAUDHARI,“Implementation of Minutiae Based Fingerprint Identification System Using Crossing Number Concept”, Informatica Economica vol.18, no.1/2014 45. Puja S. Prasad, B. Sunitha Devi, “A Survey of Fingerprint Recognition Systems and Their Applications”,Springer nature Singapore pvt. Limited, ICCCE, 2018 46. P. Pakutharivu, M. V. Srinath, “A Comprehensive Survey on Fingerprint Recognition Systems, Indian Journal of Science and Technology, 2015