S. No

Page No.

1.

Authors:

Paper Title:

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. Peters, A. Panah, K. Truong, and R. Heath, “Relay Architectures for 3GPP LTE Advanced,”, EURASIP Journal on Wireless Communications and Networking, May 2009.
5. 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. Cho and Z. Haas, “On the Throughput Enhancement of the Downstream Channel in Cellular Radio Networks Through Multihop Relaying,” IEEE Journal on Selected Areas in Communications, pp. 1206–1219, Sept. 2004.
7. 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.
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. 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. 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. 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. 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

1-4

2.

Authors:

Paper Title:

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 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 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, 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. 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. 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. 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. 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. 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), 1613–1626
18. -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. 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.

5-8

3.

Authors:

Paper Title:

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 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 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. 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. 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. 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

9-12

4.

Authors:

Paper Title:

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.

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 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.

13-17

5.

Authors:

Paper Title:

Abstract: The analysis has been carried out on conformal circular patch antenna for MICS, ISM, WMTS applications. The proposed antenna is considered with flexible polyimide substrate material. The circular patch antenna with rectangular slits loaded has considered on top of the substrate and concentric circular ring slots has considered on the ground plane of substrate. The proposed antenna is tested in on-body and in-body characteristics of human tissue simulation model by considering the electrical properties. The proposed antenna operates at 0.41GHz (0.34 –0.52GHz), 1.24GHz (0.77–1.43 GHz) dual-bands for on-body mode with reflection coefficient of -14.2dB, -26.3dB. For in-body mode the antenna operates at 0.80GHz (0.65–0.92GHz), 1.42GHz (1.27–1.61GHz) with reflection coefficient of -21.1dB, -18.1dB respectively. Specific absorption rate (SAR), gain, radiation patterns are presented in the results.

Keywords: Dual-band, defected ground structure (DGS), industrial, scientific and medical (ISM), in-body, medical implant communication service (MICS), on-body, specific absorption rate (SAR), wireless medical telemetry services (WMTS).

References:

1. 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. 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. 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. 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. 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. 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. 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. 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. 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.

18-21

6.

Authors:

Paper Title:

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 the knowledge will transfer to the server for the longer term analysis and records.

Keywords: Raspberry Pi, Rfid Reader Module, GSM Module, Ph Sensor, CLR(Corrected lactometer Reading), IOT(Thing speak).

References:

1. 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. 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.

22-24

7.

Authors:

Paper Title:

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.

Keywords: Custom Map Reduce, Hadoop Distributed File System, Hadoop Framework, Security vulnerabilities.

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 Access5 (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 Data1 (2018): 33.
5. Dou, Zuochao, et al. "Robust insider attacks countermeasure for Hadoop: Design and implementation,." IEEE Systems Journal2 (2018): 1874-1885.
6. Cloud Security Overview https://www.cloudera.com/documentation/enterprise/5-12-x/PDF/cloudera-security.pdf

25-28

8.

Authors:

Paper Title:

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 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, 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, 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, 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, 7, 109–119.
21. Koster, J. R., Equatorial scintillation (1972), Planetary and Space Science, 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, 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, 350, pp. 47-56.

29-34

9.

Authors:

Paper Title:

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 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 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. 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.

35-40

10.

Authors:

Paper Title:

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." 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.
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): 364-374 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 João 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-Fernández, 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

41-48

11.

Authors:

Paper Title:

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 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.
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. 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.

49-51

12.

Authors:

Paper Title:

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

References:

1. Balanis, C. A., Antenna Theory: Analysis and Design, John Wileyand Sons, 1982
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.

52-56

13.

Authors:

Paper Title:

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 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. Brin, and L. Page , “The anatomy of a large-scale hypertextual web search engine,” Computer networks and ISDN systems, vol. 30, no. 1,pp.107-117,Apr. 1998.
2. 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.
3. 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.
4. Olston and M. Najork, “Web crawling,” Foundations and Trends in Information Retrieval, vol. 4,no. 3, pp.175-246, Feb. 2010.
5. 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.
6. Dmitriev, “Host-based seed selection algorithm for web crawlers,” US Patent App. 12/259,164, Oct. 2008.
7. 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.
8. 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.
9. Weisstein and W. E., “Website of the Simple Directed Graph – from Wolfram Math world,” 1996
10. M. Kleinberg, “Authoritative sources in a hyperlinked environment,” Journal of the ACM, vol.46,no.5, pp.604-632, Sep. 1999.
11. TORONTO.EDU, “Website of the Datasets for Experiments on Link Analysis Ranking Algorithms,” http://www.cs.toronto.edu/tsap/experiments/datasets/index.html, 1986.

57-63

14.

Authors:

Paper Title:

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.

Keywords: Nitrogen, PSA (pressure swing Adsorption), Aspen Plus®, Design Expert®.

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. R.Vinson,“ Air separation control technology“, Computers and Chemical Engineering, 30, 2006, pp. 1436-1446
3. Ivanova, R. Lewis,“ Producing Nitrogen via Pressure swing Adsorption“, Chemical Engineering Progress,108(6), 2012, pp. 38 -42..
4. 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. 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.

64-69

15.

Authors:

Paper Title:

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 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 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. 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. 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. 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. 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

70-74

16.

Authors:

Paper Title:

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

References:

1. Cua, K. O., McKone, K. E., & Schroeder, R. G. (2001). Relationships between implementation of TQM, JIT, and TPM and manufacturing 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.

75-87

17.

Authors:

Paper Title:

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. Marshall, “Companies Increasingly Trademark Hashtags,” Wall Street Journal, 30-Mar-2016.
2. 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. Marshall, “Companies Increasingly Trademark Hashtags,” Wall Street Journal, 30-Mar-2016.
5. 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. Yuzdepski, “Goodbye Stars, Hello Facebook Business Recommendations,” Vendasta Blog, 2018.
7. 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. -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. 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].
10. 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. Hu, I. Bose, N. S. Koh, and L. Liu, “Manipulation of online reviews: An analysis of ratings, readability, and sentiments,” Decis. Support Syst., vol. 52, no. 3, pp. 674–684, Feb. 2012.
12. 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. 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. 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. 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. 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. 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. 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. 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. Manning, P. Raghavan, and H. Schuetze, Introduction to Information Retrieval, 1st ed. England: Cambridge University Press, 2009.
24. Dempsey, “Porter2 Stemmer Documentation,” 2016.
25. 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. Plutchik, “A psychoevolutionary theory of emotions,” Soc. Sci. Inf., vol. 21, no. 4–5, pp. 529–553, Jul. 1982.
27. Plutchik, “The Nature of Emotions: Clinical Implications,” in Emotions and Psychopathology, Springer, Boston, MA, 1988, pp. 1–20.
28. 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.

88-92

18.

Authors:

Paper Title:

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 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 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. 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. 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. 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. 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.

93-97

19.

Authors:

Paper Title:

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

References:

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,
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. 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.

98-102

20.

Authors:

Paper Title:

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. K. Srivastava and I. P. Singh, “Hybrid epoxy nanocomposites: lightweight materials for structural applications,” Polym. J., vol. 44, no. 4, pp. 334–339, 2012.
2. 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.
3. 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. 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. K. Singh and K. Sharma, “Mechanical and Viscoelastic Properties of In-situ Amine Functionalized Multiple Layer Grpahene / epoxy Nanocomposites,” pp. 1–11, 2018.
6. 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. 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. Pecora, “Dynamic light scattering measurements of nanometer particles in liquids,” J. Nan. Part. Res., vol. 2, pp. 123–131, 2000.
9. Ross Hallett, “Particle size analysis by dynamic light scattering,” Food Res. Int., vol. 27, no. 2, pp. 195–198, 1994.
10. 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. Braun and V. Kestens, “RESEARCH PAPER A new certified reference material for size analysis of nanoparticles,” 2012.
12. 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. 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. 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. 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.

103-107

21.

Authors:

Paper Title:

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. 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.
2. 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. 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. 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. 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. 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. 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. 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. 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.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.

108-111

22.

Authors:

Paper Title:

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.

Keywords: Non-Local Mean Filter, Blockwise approach, Magnetic Resonance (MR) Image, Wavelet Transform and denoising.

References:

1. Gerig, R. Kikinis, O. K¨ubler, and F. Jolesz, “Nonlinear anisotropic filtering of MRI data,” IEEE Transactions on Medical Imaging, vol. 11, pp. 221–232, June 1992.
2. Nowak, “Wavelet-based rician noise removal for magnetic resonance imaging,” IEEE Transactions on Image Processing, vol. 8,pp. 1408–1419, October 1999.
3. Pizurica, W. Philips, I. Lemahieu, and M. Acheroy, “A versatile wavelet domain noise filtration technique for medical imaging,” IEEE Trans. Medical Imaging, vol. 22, pp. 323–331, 2003.
4. E. Alexander, R. Baumgartner, A. R. Summers, C. Windischberger, M. Klarhoefer, E. Moser, and R. L. Somorjai, “A wavelet-based method for improving signal-to-noise ratio and contrast in MR images.,” Magn Reson Imaging, vol. 18, pp. 169–180, February 2000.
5. Aja-Fernandez, M. Niethammer, M. Kubicki, M. E. Shenton, and C. F. Westin, “Restoration of dwi data using a rician lmmse estimator,” Medical Imaging, IEEE Transactions on, vol. 27, no. 10, pp. 1389–1403, 2008.
6. Coup´e, P. Yger, S. Prima, P. Hellier, C. Kervrann, and C. Barillot, “An Optimized Blockwise NonLocal Means Denoising Filter for 3-D Magnetic Resonance Images,” IEEE Transactions on Medical Imaging, vol. 27, pp. 425–441, April 2008.
7. Coup´e, P. Hellier, S. Prima, C. Kervrann, and C. Barillot, “3D wavelet subbands mixing for image denoising,” Journal of Biomedical Imaging, vol. 2008, no. 3, pp. 1–11, 2008.
8. Buades, B. Coll, and J.-M. Morel, “A review of image denoising algorithms, with a new one,” Multiscale Modeling and Simulation, vol. 4, no. 2, pp. 490–530, 2005.
9. Gopatoti, A., Gopathoti, K.K., Shanganthi, S.P., Nirmala, C. “Deep CNN based image denoising under different noise conditions”, Journal of Advanced Research in Dynamical and Control Systems,10(3) (2018), pp. 1094-1101.
10. Gopatoti, A., Ramadass, N. “Performance of adaptive subband thresholding technique in image denoising”, Journal of Advanced Research in Dynamical and Control Systems, Vol. 9, No. 12, (2017), pp.151-157.
11. Pruessmann KP, Weiger M, Scheidegger MB, Boesiger P. SENSE:sensitivity encoding for fast MRI. Magn Reson Med 1999;42: 952–962.
12. Griswold MA, Jakob PM, Heidemann RM, et al. Generalized autocalibrating partially parallel acquisitions (GRAPPA). Magn Reson Med 2002;47:1202–1210.
13. Gopatoti, A., Naik, M.C., Gopathoti, K.K.” Convolutional Neural Network based image denoising for better quality of images”, International Journal of Engineering and Technology(UAE), Vol.7, No.3.27, (2018), pp. 356-361.
14. Gopatoti, A., Veeranjaneyulu, G. and Naik, M.C. Impulse Noise Removal in Digital Images by using Image Fusion Technique. Journal of Advanced Research in Dynamical and Control Systems 10 (6) (2018).
15. Wang, A. Bovik, H. Sheikh, and E. 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.

112-118

23.

Authors:

Paper Title:

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.

References:

1. Wu L. et al., “SLA-based resource provisioning for hosted software-as-a-service applications in cloud computing environments,” IEEE 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. 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. 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.

119-123

24.

Authors:

Paper Title:

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 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.

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

References:

1. 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. 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. E-ard, C. Thianpong, P. E-ard, Turbulent heat transfer enhancement by counter/co-swirling flow in a tube fitted with twin twisted tapes, Experimental Thermal and Fluid Science 34 (2010) 53–62.
6. Smith E-ard, Pongjet Promvonge, Performance assessment in a heat exchanger tube with alternate CW and CCW twisted-tape inserts, International Journal of Heat and Mass Transfer 53 (2010) 1364–1372.
7. Khwanchit Wongcharee, Smith 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.
8. Pethkool, S. E-ard, S. Kwankaomeng, P. Promvonge, Turbulent heat transfer enhancement in a heat exchanger using helically corrugated tube, International Communications in Heat and Mass Transfer 38 (2011) 340–347.
9. Wongcharee, S. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. E-ard, P. Promvonge, Thermal characteristics in round tube fitted with serrated twisted tape, Appl. Therm. Eng. 30 (13) (2010) 1673–1682.
28. 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. 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. 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. 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.

124-130

25.

Authors:

Paper Title:

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.
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).
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, YushengJi, 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 integratedfemto 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.

131-134

26.

Authors:

Paper Title:

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

References:

1. Puttre M., "Capturing design data with digitizing systems", Mechanical Engineering 1994,116(1),62-65
2. Wohlers T., "The technology behind 3D digitizing", Computer Graphics World 1997,3(20), 47-54
3. Rogers DF, Adams JA, "Mathematical elements for computer graphics", Tata Mc Graw Hill, Second edition 2002
4. Tiller Wayne, Piegl L, "The NURBS Book", New York, Springer-Verlag 1995
5. De Boor C, "A practical guide to splines", New York, Springer-Verlag 1978
6. Bartels RH, Beaty JC and Barskey BA, "An introduction to splines for use computer graphics and geometric modeling", Morgan Kaufman 1987
7. Piegl L, "On NURBS: A survey", IEEE Trans. Computer Graphics and Applications 1991, 11(5)
8. Yamaguchi F, "Curves and surfaces in computer aided geometric design", New York, Springer Verlag 1988
9. Cohen FS, Wang JY, "Modeling image curves using invariant 3D object curve model- A path to 3D recognition and shape estimation from image contours", IEEE Trans. Pattern analysis and machine intelligence 1994, 16(1),13-21
10. Wang JY, Cohen FS, "3D object recognition and shape estimation from image contours using B-splines, shape invariant matching and neural network", IEEE Trans. Pattern Analysis and machine Intelligence 1994, 16(1), 13-21
11. Cohen FS, Huang Z, Yang Z, "Matching and identification of curves using B-splines curve representation", IEEE Trans. Image Processing 1995, 4(1), 1-10
12. Huang Z, Cohen FS, "Affine invariant moments and B-splines for object recognition from image curves", IEEE Trans. Image Processing 1996, 5(10), 1473-1480
13. Milroy M, Bradley C, Vickers G, Weir D, "G¹ continuity of the B-splines surface patches in reverse engineering", Computer Aided Design 1995, 27, 471-478
14. Krishnamurthy V, Levoy M, "Fitting smooth surfaces to dense polygon meshes" Proceeding SIGGRAPH, Computer Graphics Proc., Ann. Conf. Series 1996, 313-324
15. Andersson E, Andersson R, Boman M, Elmroth T, Dahlberg B, Johansson B, "Automatic construction of surfaces with prescribed shape", Computer Aided Design 1988, 317-324
16. Ma W, Kruth J, "Parameterization of randomly measured points for least squares fitting of B-splines curves and surfaces", Computer Aided Design 1995, 27, 663-675
17. Eck M, Hoppe H, "Automatic reconstruction of B-spline surface of arbitrary topology type", Proceeding SIGGRAPH’96, Comp. Graphics Proc., Ann. Conf. Series 1996, 325-334
18. Cohen FS, "Ordering and parameterizing scattered 3D data for B-spline surface approximation", IEEE Transactions on Pattern analysis and machine intelligence 2000, 22(6)
19. Kuo CC, Yau HT, "A Delaunay based region growing approach to surface reconstruction from unorganized points", Computer Aided Design 2005, 37,825-835
20. Woo H, Kang E, Wang S, Lee KH, "A new segmentation method for point cloud data", Int. J. of Machine Tools and manufacture 2002, 42, 167-178
21. NAG, "Fortran Library Manual Mark" 15 Numerical Algorithm Group Limited 1991, chapter F04
22. De Boor C, "A practical guide to splines" Springer 1978
23. Cox MG, "Linear algebra support modules for approximation and other software" Scientific software systems, Chapman and Hall 1990, 21-29

135-140

27.

Authors:

Paper Title:

Abstract: This paper presents the mathematical modeling of ten degree of freedom of manipulator. Workspace of each leg calculated by applying the method of Denavit_Hatretnberg notation scheme. Forward and inverse kinematics obtained for the manipulator of lower body of humanoid robot. Static forces on the joint calculated for the joint to hold the particular position of the lower body. Dynamics torque obtained by applying the principles of Lagrangian dynamics. A nonlinear feedback measured from the output end to control the movement of leg. A computed control torque approach has been used to avoid the oscillation of the system. Several experiment done of the mat lab to verify the analytical and simulation result.

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. 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. 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
7. 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. 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. 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

141-146

28.

Authors:

Paper Title:

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.

Keywords: Cryptography, Symmetricencryption, Asymmetric encryption, AES, RSA, Image slicer.

References:

1. Abdelali El Bouchti, Samir Bahsani, Trik Nahhal “Encryption As A Service For Data Healthcare Cloudsecurity. “
2. Perera, A. Zaslavsky, P. Christen, D. Georakopoulos,“Context Aware Computing For The Internet Of Things.”
3. Gubbi, R. Buyya, S. Marusic, And M. Palaniswami, “Internet Of Things (Iot): A Vision, Architectural Elements, And Future Directions.”
4. Qiang, G.Quan, B.Yu, L.Yang, “Research On Security Issues Of The Internet Of Things.”
5. Friedemann, And C. Floerkemeier. "From The Internet Of Computers To The Internet Of Things."
6. Challal, E. Natalizio, S.Sen, And A.Maria Vegni “Internet Of Things Security And Privacy: Design Methods And Optimization”, Add Hoc Network
7. Tawalbeh, M. Mowafi And W. Aljoby, "Use Of Elliptic Curve Cryptography For Multimedia Encryption," In Iet Information Security.
8. 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. 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

147-151

29.

Authors:

Paper Title:

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.

References:

1. Implementation and development of single feed design using multiple U slotted patch antenna for wireless applications Vikram Thakur, International Journal of Engineering Research & technology (IJERT).
2. 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. 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. E Collin, “Foundations for Microwave Engineering” ,IEEE Press 2nd Edition, 2002.
7. 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

152-155

30.

Authors:

Paper Title:

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 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 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.

References:

1. Caloffi, F. Rossi, and M. Russo, “What makes SMEs more likely to collaborate? Analysing the role of regional innovation policy”. European Planning Studies, vol. 23(7), 2015, pp. 1245-1264.
2. K. Klevorick, R. C. Levin, R. R. Nelson, and S. G. Winter, “On the sources and significance of inter industry differences in technological opportunities”, Research Policy, 24(2), 1995, pp. 185–205.
3. Spithoven, W. Vanhaverbeke, and N. Roijakkers, “Open innovation practices in SMEs and large enterprises”, Small Business Economics, vol. 41, 2013, pp. 537-562.
4. Bird, “Towards a theory of entrepreneurial competency”, Advances in Entrepreneurship, Firm Emergence and Growth, vol. 2, 1995, pp. 51-72.
5. Baden, C. Fuller, and M. S. Morgan, “Business models as models”, Long Range Planning, 43(2/3), 2010, pp. 156–171.
6. George, and M. Paul, IBM SPSS Statistics 23 Step by Step, New York, Routledge, 2016, pp. 231-260.
7. L. Brannon, and J. Wiklund, “An analysis of business models : Firm characteristics , innovation and performance”, Academy of entrepreneruship journal, vol. 22(1), 2016, pp.1–21.
8. M. Lambert, and M. C. Cooper, “Issues in supply chain management”, Ind Mark Manage, vol. 29(1), 2000, pp. 65–83.
9. S. Diana, “Exploratory or confirmatory Factor Analysis”, Proc. of SUGI 31, 2006, pp.200-23, San Francisco, California.
10. Autio, L. Dahlander, and L. Frederiksen, “Information exposure, opportunity evaluation, and entrepreneurial action: An investigation of an online user community”, Academy of Management Journal, vol. 56, 2013, pp. 1348–1371.
11. Zancul, “Product Life Cycle Management: PLM Systems Selection Based on Reference Models”, São Carlos: Escola de Engenharia de São Carlos, Universidade de São Paulo. 2009.
12. Devakumar, and G. Barani, “Marketing strategies for competitive advantage: structural equation modelling approach on agricultural sector industry in South India”, Int. J. Business Excellence, vol. 9(2), 2016, pp.225-239.
13. Hosseininia, and A. Ramezani, “Factors Influencing Sustainable Entrepreneurship in Small and Medium-Sized Enterprises in Iran: A Case Study of Food Industry” Sustainability, vol. 8, 1010, 2016, pp. 1-20.
14. Lay, M. Schroeter, and S. Biege, “Service based business concepts: a typology for business to business markets”, European Management Journal, vol. 27(6), 2009, pp. 442–455.
15. Singh, and B. Zhao, “Agricultural mechanization situation in Asia and the Pacific region”, Agricultural Mechanization in Asia, Africa, and Latin America, vol. 47(2), 2016, pp.15-25.
16. T. Valderrama, and M. Mendigorri, “Content validation of a measure of R and D effectiveness”, R and D Management, vol. 35(3), 2005, pp. 311-331.
17. Daire, C. Joseph and R. M. Michael “Structural Equation Modelling: Guidelines for Determining Model Fit”, Electron Journal of Business Research Methods, vol. 6(1), 2008, pp. 53-60.
18. J. P. Schoemaker, S. Heaton, and D. Teece, “Innovation, dynamic capabilities, and leadership”, California Management Review, 61(1), 2018, pp. 15-42.
19. M. Morris, W. J. Webb, J. Fu, and S. Singhal, “A competency-based perspective on entrepreneurship education: Conceptual and Empirical Insights”, Journal of Small Business Management, vol. 51(3), 2013, pp. 352-369.
20. Volberda, and A. Lewin, “Co-evolutionary dynamics within and between firms: From evolution to co-evolution”, Journal of Management Studies, vol. 40, 2003, pp. 2111-2136.
21. Mani, and P. K. Sahoo, “Present status and future prospects of agricultural machinery research in India” Agricultural Mechanization in Asia, Africa and Latin America, vol. 47(2), 2016, pp.36-40.
22. D. Thompson, and I. C. MacMillan, “Business models: creating new markets and societal wealth”, Long Range Planning, vol. 43(2/3), 2010, pp. 291–307.
23. F. Hair, R. E. Anderson, and R. L. Tatham, Multivariate data analysis, New Jersey, Prentice Hall, 2006.
24. F. Hair, R. E. Anderson, R. L. Tatham, and W. C. Black, Multivariate Data Analysis, Prentice- Hall, Upper Saddle River: New Jersey, 1998.
25. Koschatzky, and T. Stahlecker, “A new challenge for regional policy-making in Europe? Chances and risks of the merger between cohesion and innovation policy”, European Planning Studies, vol. 18(1), 2010, pp. 1-19.
26. Laursen, and A. Salter, “Open for innovation: The role of openness in explaining innovative performance among UK manufacturing firms”, Strategic Management Journal, vol. 27(2), 2006, pp. 131–150.
27. Muff., and T. Dylick, “An organisational roadmap of business sustainability”, April 30, 2014, Available: SSRN: https://ssrn.com/abstract=2442211 or http://dx.doi.org/10.2139/ssrn.2442211
28. J. Cronbach, “Estimate alpha and the internal structure of tests”, Psychometrika, vol. 16(3), 1951, pp. 297–334.
29. T. Hu, and P. M. Bentler, “Cutoff Criteria for Fit Indexes in Covariance Structure Analysis: Conventional Criteria versus New Alternatives”, Structural Equation Model, vol. 6(1), 1999, pp. 1-55.
30. D. Gomez, R. Baxter, R. Roy, and M. Kalta. “Trough-Life Integration Using PLM. Proceeding of the 19th CIRP Design”, Conference–Competitive Design, Cranfield University, Cranfield, 2009.
31. E. Porter, and M. R. Kramer, “Creating shared value”, Harvard Business Review January/February: 63–77, 2011.
32. Spence, and H. L. Essoussi, “SME brand building and management: an exploratory study”, European Journal of Marketing, vol. 44(7/8), 2010, pp. 1037-1054.
33. Taylor, and A. Murphy, “SMEs and e-business”, Journal of Small Business and Enterprise Development, vol. 11(3), 2004, pp. 280 – 289.
34. Bendary, and E. A. Minyawi, “Entrepreneurial competencies effect on Small and Medium Enterprises performance through the mediation effect of psychological contracting of outsourcing”, International Journal of Business and Economic Development, vol. 3(2), 2015, pp. 23-38.
35. Anderson, and M. L. Tushman, “Technological discontinuities and dominant designs: A cyclical model of technological change”, Administrative Science Quarterly, vol. 35, 1990, pp. 604–633.
36. C. Grindley, and D. J. Teece, “Managing intellectual capital: Licensing and cross-licensing in semiconductors and electronics” California Management Review, vol. 39(2), 1997, pp. 8–41.
37. S. G. D. Oliveira, D. D. Silva, L. F. D. Silva, M. D. S. Lopes, and A. L. Helleno, “Factors that influence product life cycle management to develop greener products in the mechanical industry”, International Journal of Production Research, vol. 54(15), 2016, pp. 4547-4567.
38. Skarzynski, and J. R. Latre, “Lessons to jumpstart disruptive innovation”, Strategy & Leadership, 39(1), 2011, pp. 5-10.
39. Amit, and C. Zott, “Value creation in E-Business”, Strategic Management Journal, vol. 22, 2001, pp. 493-520.
40. G. McGrath, “Business models: a discovery driven approach”, Long Range Planning, vol. 43(2), 2010, pp. 247–261.
41. K. Huet, and D. J. Freyman, “What sustainable entrepreneurship looks like: An exploratory study from a student perspective”, In Social Entrepreneurship Leveraging Economic, Political, and Cultural Dimensions, Springer: Berlin, Germany, 2011, pp. 155–178.
42. Katila, and G. Ahuja, “Something old, something new: A longitudinal study of search behavior and new product introduction” Academy of Management Journal, vol. 45(6), 2002, pp. 1183–1194.
43. R. Nelson, and S. G. Winter, An evolutionary theory of economic change. Cambridge, MA: Harvard University Press, 1982.
44. A. Kumara, and C. Sahasranam, “Entrepreneurial characteristics among business management students: an empirical study”, The Icfaian Journal of Management Research, vol. 8(6), 2009, pp. 7-29.
45. Evans et. al., “Business model innovation for sustainability: towards a unified perspective for creation of sustainable business models”, Business Strategy and the Environment, vol. 26, 2017, pp. 597-608.
46. Grodal, A. Gotsopoulos, and F. F. Suarez, “The co evolution of technologies and categories during industry emergence”, Academy of Management Review, vol. 40(3), 2015, pp. 423-445.
47. M. Giarratana, M. Mariani, and I. Weller, “Rewards for patents and inventor”, Behaviours in Industrial Research and Development, vol. 61(1), 2018, pp. 264-292.
48. M. Tsai, C. M. Tsai, and C. C. Chang, “The Direct and Indirect Factors on Affecting Organizational Sustainability” Journal of Management and Sustainability, vol. 3(4), 2013, pp. 67-76.
49. Mitchelmore, and J. Rowley, “Entrepreneurial competencies : A Literature Review and Development Agenda”, International journal of entrepreneurial behaviour and research, vol. 16(2), 2010, pp. 92-111.
50. S. Webber, “Leadership and trust facilitating cross-functional team success”, Journal of Management Development, vol. 21(3), 2002, pp. 201 – 214.
51. Schaltegger and M. Wagner, “Sustainable entrepreneurship and sustainability innovation: categories and interactions”, Business Strategy and the Environment, vol. 20(4), 2011, pp. 222–237.
52. Schaltegger, L. F. Freund, and G. E. Hansen, “Business models for sustainability: A co-evolutionary analysis of sustainable entrepreneurship, innovation, and transformation”, Organization and Environment, 2016, pp. 1-26.
53. Shadfar, and I. Malekmohammadi, “Application of Structural Equation Modelling (SEM) in restructuring state intervention strategies toward paddy production development”, International Journal of Academic Research in Business and Social Sciences, vol. 3(12), 2013, pp. 576-618.
54. Tehseen, and T. Ramayah, “Entrepreneurial competencies and SMEs business success: The contingent role of external integration”, Mediterranean Journal of Social Sciences, vol. 6(1), 2015, pp. 50-60.
55. Krasnicka, and T. Ingram, “Effects of innovation and their measurement in small and medium sized enterprises”, International Journal of Contemporary Management, vol. 12(3), 2013, pp. 50-65.
56. Lichtenthaler, and H. Ernst, “Developing reputation to overcome the imperfections in the markets for knowledge”, Research Policy, vol. 36(1), 2007, pp. 37–55.
57. Stubbs, and C. Cocklin, “Conceptualizing a ‘sustainability business model”, Organization Environment, vol. 21(2), 2008, pp. 103–127.
58. W. Fan, and E. 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.

156-164

31.

Authors:

Paper Title:

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.
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 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

165-168

32.

Authors:

Paper Title:

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. 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. 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. 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. 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. 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.
6. 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. Baviskar and S. Patil, "Improvement of data object's membership by using Fuzzy K-Means clustering approach", In Proc. of International Conference on In Computation of Power, Energy Information and Communication, IEEE, pp. 139-147, 2016.
8. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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.

169-173

33.

Authors:

Paper Title:

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 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 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. 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. 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.

174-178

34.

Authors:

Paper Title:

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. Han, J. Pei, and M. Kamber, Data mining: concepts and techniques. Elsevier, 2011.
2. 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. 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. Nagaraj and A. Sridhar, "NeuroSVM: A Graphical User Interface for Identification of Liver Patients," arXiv preprint arXiv:1502.05534, 2015.
5. Hopkins. (11/05/2018). Liver: Anatomy and Functions.
6. S. A. Benjamin Wedro. (11/05/2018). Liver Disease Facts.
7. -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. S. P. B. a. N. B. V. Bendi Venkata Ramana. Machine Learning Repository [Online].
9. 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. 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. 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. 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. Hammad and S. AbouRizk, "Knowledge discovery in data: A case study," Journal of Computer and Communications, vol. 2, no. 05, p. 1, 2014.
14. B. Priya, P. L. Juliet, and P. Tamilselvi, "Performance Analysis of Liver Disease Prediction Using Machine Learning Algorithms," 2018.
15. 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. 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. 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. Cortes and V. Vapnik, "Support-vector networks," Machine learning, vol. 20, no. 3, pp. 273-297, 1995.
20. Saxena. (2017, 11/05/2018). SVM CLASSIFIER, INTRODUCTION TO SUPPORT VECTOR MACHINE ALGORITHM.
21. Ray. (2015 11/05/2018). Understanding Support Vector Machine algorithm.
22. Saxena. (2016, 11/05/2018). KNN CLASSIFIER, INTRODUCTION TO K-NEAREST NEIGHBOR ALGORITHM.
23. 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. Patel and G. S. Thakur, "Improved Fuzzy-Optimally Weighted Nearest Neighbor Strategy to Classify Imbalanced Data," 2017.
25. 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. 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. 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. Zheng, Y. Cai, and Y. Li, "Oversampling method for imbalanced classification," Computing and Informatics, vol. 34, no. 5, pp. 1017-1037, 2016.
29. 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. Brownlee. (2016, 11/05/2018). Confusion Matrix in Machine Learning.

179-186

35.

Authors:

Paper Title:

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. 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. 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 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
5. -K Toh, Maximum Battery Life Routing to Support Ubiquitous Mobile Computing in Wireless Ad hoc Networks‖, , 2001 ,IEEE.
6. L. Flood and M.C. Jackson , "Creative problem solving". John Wiley and Sons, Chichester, (1991), ISBN 0-471-93052-0.
7. Siddesh Gundagatti Karibasappa , 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. Battat and H. Kheddouci, “HMAN: Hierarchical Monitoring for Ad Hoc Network,” in IEEE/IFIP EUC, 2011.
10. 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. Ramachandran, E. Belding-Royer, and K. Almeroth, “DAMON: A Distributed Architecture for Monitoring Multi-hop Mobile Networks,” in IEEE SECON, 2004.
12. Riggio, M. Gerola, D. Miorandi, A. Zanardi, and F. Jan, “A Distributed Network Monitoring Framework for Wireless Networks,” in IFIP/IEEE IM, 2011.
13. Graffi, D. Stingl, J. Rueckert, A. Kovacevic, and R. Steinmetz, “Monitoring and Management of Structured Peer-to-Peer Systems,” in IEEE P2P, 2009.
14. 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. van de Bovenkamp, F. Kuipers, and P. Van Mieghem, “Gossip-based Counting in Dynamic Networks,” in IFIP NETWORKING, 2012.
16. 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.

187-191

36.

Authors:

Paper Title:

Abstract: The article presents a detail overview of different classification techniques for colon cancer prediction 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. 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. 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. Evaluation of Classification Models in Machine Learning. Theory and Applications of Mathematics & Computer Science. 2017; 7(1):39 – 46.
16. Reena S. G., Rajeswari P. A Survey of Human Cancer Classification using Micro Array Data. International Journal of Computer Technology and Applications. 2011; 2 (5):1523-1533.
17. Siang T. C., Soon T.W., Kasim S., Mohamad M. S., Howe C. W., Deris S.,Zakaria Z., Shah A.Z., Ibrahim Z. A review of cancer classification software for gene expression data. International Journal of Bio-Science and Bio-Technology.2015;7(4):89-108.
18. Tarek S., Elwahab R. A., Shoman M. Gene expression-based cancer classification. Egyptian Informatics Journal.2016; 18:151-159.
19. Trevino V., Falciani F.GALGO:An R package for Genetic Algorithm Searches. Bioinformatics.2006.
20. Torrente A., Lukk M., Xue V., Parkinson H., RungJ., Brazma A. Identification of Cancer Related Genes Using a Comprehensive Map of Human Gene Expression. PLOS ONE. 2016;11(6):1-20. 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. 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.

192-199

37.

Authors:

Paper Title:

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 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-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 geneticIRACST-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.

200-204

38.

Authors:

Paper Title:

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 and it was found to be economical and energy efficient source to utilize translucent concrete in rooms or buildings.

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 &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

205-207

39.

Authors:

Paper Title:

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. 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. 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 Int. Conf. Control Syst. Comput. Eng., pp. 546–550, 2011.
5. Kumar and I. I. T. Bombay, “Cell Phone / Tower Radiation Hazards & Solutions,” no. July, 2012.
6. M. Dawoud, “High Frequency Radiation and Human Exposure,” no. October, pp. 1–7, 2003.
7. 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. Kanaya, “Multi-Band Miniaturized Slot Antenna with Multi-Band Impedance Matching Circuit,” vol. 0, pp. 551–554, 2014.
9. Dixon, “Radio Frequency Energy Harvesting,” pp. 2–3, 2014.
10. 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. 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. 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. 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. Emery, “Cockcroft-Walton Voltage Multiplier,” pp. 1–8, 2013.
15. M. Waghamare and R. P. Argelwar, “High Voltage Generation by using Cockcroft-Walton Multiplier,” vol. 4, no. 2, pp. 256–259, 2015.
16. Thakare, S. B. Urkude, and R. P. Argelwar, “Analysis of Cockcroft - Walton Voltage Multiplier,” vol. 5, no. 3, pp. 3–5, 2015.
17. 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. 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. 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. 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.

208-211

40.

Authors:

Paper Title:

Abstract: Software Testing Effort (STE) plays a big role in code development method that highly contributes 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 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 re-engineering (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.Yogesh Kumar, Rahl Rishi, “Dragon
11. fly 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.

212-218

41.

Authors:

Paper Title:

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:

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 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.

219-225

42.

Authors:

Paper Title:

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:

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).
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.

226-229

43.

Authors:

Paper Title:

Abstract: In data analysis, signal processing plays a prominent role since the received sunspot data continuously 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 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. POSTALCLOGLU, K. ERKAN, E.D. BOLAT. Comparison of Kalman filter and wavelet filter for denoising.
6. 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. 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. 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. 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. 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. 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.

230-236

44.

Authors:

Paper Title:

Abstract: The objective 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 CSSD and 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.

Keywords: Value Stream Mapping, Program Evaluation and Review Technique, Optimized Workflow.

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).

237-242

45.

Authors:

Paper Title:

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 24^th International Symposium, pp. 189-193, 2013.
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).
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.

243-246

46.

Authors:

Paper Title:

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. N. Purnachandra Rao, “Earthquakes” Andhra Pradesh Akademi of Sciences (APAS) publishers.
3. M, A. Babanin, E. Sanina, D. Chalikov, “A Comparison Of Methods For Estimating Directional Spectra Of Surface Waves”, 2015.
4. Wail A. Mousa , Abdullatif A. Al-Shuhail, “Processing of Seisemic Reflection data using Matlab”, Synthesis Lectures On Signal Processing., Morgon & Claypool Publishers.
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. 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. 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. 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. J. Johnsen And N. Andersen, “On Power Estimation In Maximum Entropy Spectral Analysis”, Geophysics, Vol. 13. No. 4, June 1978.
16. V.K. Vijaya Kumar & S.K. Mullick, “Power Spectrum Estimation Using Maximum Entropy Method”, IETE Journal of Research.
17. 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, 15, 1974.
19. Akaike (H). “Power Spectrum Estimation through Autoregressive Model Fitting”. 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.

247-251

47.

Authors:

Paper Title:

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-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 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.

252-259

48.

Authors:

Paper Title:

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.

References:

1. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. J. Vidmar, “On the use of atmospheric plasmas as electromagnetic reflectors,” IEEE Trans. Plasma Sci, vol. 21, no. 3, pp. 876–880, 1992.

260-266

49.

Authors:

Paper Title:

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. 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 - 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.
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.

267-272

50.

Authors:

Paper Title:

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], Urdhva Tiryagbhyam, Vedic type Mathematics, Carry free addition, QSD, Redundancy.

References:

1. 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. 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. -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. 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. 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. 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.

273-279

51.

Authors:

Paper Title:

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 numerical procedures, International Journal of Machine Tools & Manufacture, Vol.41,pp. 21–31.
2. TosunNihat (2005). Determination of optimum parameters for multi-performance characteristics in drilling by using grey relational analysis, Int J Adv Manuf Technol, 28, pp. 450–455.
3. 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.
4. 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.
5. 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.
6. 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.
7. 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.
8. König, W., 1999, Fertigungsverfahren I – Drehen, Fräsen, Bohren, Springer-Verlag, BerlinHeidelberg.
9. 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.
10. 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.
11. 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
12. 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.
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. 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.
15. 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.
16. 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
17. 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
18. 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

280-282

52.

Authors:

Paper Title:

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.
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.
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. 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. 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. 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. 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.

283-287

53.

Authors:

Paper Title:

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. H. Hager, “Energy Dissipators and Hydraulic Jump”, Kluwer Academic Publishers, London, 1992.
2. A. Elevatorski, “Hydraulic Energy Dissipator” McGraw Hill, New York, 1959.
3. H. Hager, and R. Bremen, “Classical hydraulic jump: Sequent depths”, J. Hydraul. Res., 27(5), 1989, pp. 565–585.
4. 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. N. Bradley, and A. J Peterka,"The hydraulic design of stilling basins," Journal of Hydr. Div., ASCE, 82(5), 1957, paper 1401.
6. T. Chow, “Open-Channel Hydraulics” McGraw Hill, New York, 1959.
7. Silvester,. Hydraulic Jump in All Shape of Horizontal Channels, J. Hydraulic Division 90(1), 1964, pp:23–55.
8. Rajaratnam and K. Subramanya, “Profile of Hydraulic Jump”, Journal of Hydraulic Division, ASCE, Vol.94, No.3, 1968, pp. 663 – 673.
9. Herbrand, ”The Spatial Hydraulic Jump”, Journal of Hydraulic Research, Vol.11, No.3, 1973, pp. 205 – 218.
10. 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. 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. 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. 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. 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. Gandhi, “Characteristics of Hydraulic Jump”, International Journal of Mathematical, Computational, Physical, Electrical and Computer Engineering Vol:8, No:4, 2014, pp. 693-697.

288-292

54.

Authors:

Paper Title:

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 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 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

293-297

55.

Authors:

Paper Title:

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,
2. 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. Senthil Kumar et. al, Experimental investigations on mechanical and microstructural properties of Al₂O₃/SiC reinforced hybrid 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. 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. 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. 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. 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. 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. 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. 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.

298-301

56.

Authors:

Paper Title:

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 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 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. 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. 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. 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. 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. 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. 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. 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. 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.

302-306

57.

Authors:

Paper Title:

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:

1. 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 battery charging applications”; Journal of Advanced Research in Dynamical and Control Systems (JARDCS), Vol. 10, No. 4, April 2018, pp 462-475.
3. 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. 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. 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. 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. 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), 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).

307-313

58.

Authors:

Paper Title:

Abstract: Affix-free codes are widely used in multimedia communications because of its error tolerance 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 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. 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. 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. Takishima, M. Wada, and H. Murakami, “Reversible variable length codes,” IEEE Trans. Commun., vol. 43, Feb.-Apr. 1995, pp. 158–162.
6. 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. 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. 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. 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. 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. 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. Savari, “On optimal reversible-variable-length codes,” Proc. Information Theory and Applications Workshop, La Jolla, CA, February 10, 2009, pp. 311-317.
14. Sayood, Introduction to data compression, New Delhi: Elsevier, 2011.
15. 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.

314-317

59.

Authors:

Paper Title:

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.

References:

1. Omri and R. Bouallegue, New Transmission Scheme for MIMO-OFDM System, International Journal of Next-Generation Networks (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.

318-320

60.

Authors:

Paper Title:

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 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. -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. 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. 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. R. Baitharu and S. K. Pani, "Analysis of Data Mining Techniques for Healthcare Decision Support System Using Liver Disorder Dataset," Procedia Computer Science, vol. 85, pp. 862-870, 2016.
6. 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.
7. Dhamodharan, "Liver disease prediction using bayesian classification," in 4th National Conference on Advanced Computing, Applications & Technologies, 2014, pp. 1-3.
8. 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.
9. 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.
10. 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.
11. 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.
12. Agrawal and R. Srikant, "Fast algorithms for mining association rules in large databases, In Proc. of the 20th VLDB Conference, 1994, pp. 487-499.
13. Srikant and R. Agrawal, "Mining generalized association rules," Future generation computer systems13, no. 2-3, pp.161-180, 1997.
14. Srikant, "Fast algorithms for mining association rules and sequential patterns," PhD diss., University of Wisconsin, Madison, 1996.
15. 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.
16. 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.
17. 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.
18. 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.
19. 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.
20. 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.
21. Han, J. Pei, and M. Kamber, Data mining: concepts and techniques. Elsevier, 2011.
22. ILPD Dataset: https://archive.ics.uci.edu/ml/datasets/ILPD+(Indian+Liver+Patient+Dataset).

321-328

61.

Authors:

Paper Title:

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.

Keywords: NoC, NIC, IPs, PE, ASIC and NS/CS.

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
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
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 Y
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, 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, Holstlaan 4, NL-5656 AA Eindhoven.
21. Chenxin Zhang & Xiaodong Liu, “A presentation on Network-on-Chip(NoC)”
22. 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.

329-336

62.

Authors:

Paper Title:

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.

Keywords: Fuzzification, Electric Spring, Critical loads, Non-critical loads, Voltage stability, Renewable energy sources, Power quality.

References:

1. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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 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.

337-342

63.

Authors:

Paper Title:

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.
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.
3. 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. O. Crapo, “Pulmonary Function Testing” in Baum’s Textbook of Pulmonary Diseases, 7th ed., Philadelphia: Lippincott Williams and Wilkins, 2004.
5. 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. 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. 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. 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. G. Downing Jr., “Electronic measurements of pulmonary mechanics,” WESCON ‘95. Conference Record, November, pp.644–649, 1995.
10. 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. 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. 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. 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. 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.

343-347

64.

Authors:

Paper Title:

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 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. Team, "Email statistics report 2015-2019", Mar. 2015.
2. D. Brutlag, C. Meek, "Challenges of the email domain for text classification", Proc. ICML, pp. 103-110, 2000.
3. W. Cohen, "Learning rules that classify e-mail", Proc. AAAI Spring Symp. Mach. Learn. Inf. Access, pp. 25, 1996.
4. Blanzieri, A. Bryl, "A survey of learning-based techniques of email spam filtering", Artif. Intell. Rev., vol. 29, pp. 63-92, Sep. 2008.
5. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. C. Gomez, M. F. Moens, "PCA document reconstruction for email classification", Comput. Statist. Data Anal., vol. 56, pp. 741-751, Sep. 2012.
20. 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. 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. Ratkiewicz et al., "Detecting and Tracking Political Abuse in Social Media", Proc. 5th Int’l AAAI Conf. Weblogs and Social Media, 2011
23. -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. 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. 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. 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. 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. Alghamdi, "Can Phishing Education Enable Users To Recognize Phishing Attacks" in Dublin Institute of Technology, Dublin, Ireland, 2017.

348-359

65.

Authors:

Paper Title:

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 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.

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. Thanee S. Somkuarnpanit , “FPGA Based multi protocol data acquisition system with High speed USB interface”. IMECS, March 10-12, 2010.

360-364

66.

Authors:

Paper Title:

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 rate (MFR) of the fluid (air).

Keywords: Air Solar Heater, Ppr Pipes, Mass Flow Rate, Efficiency.

References:

1. 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. 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. 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. 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

365-367

67.

Authors:

Paper Title:

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.
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. 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. 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. 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. 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. 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. 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. 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. 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. 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

368-375

68.

Authors:

Paper Title:

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 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, 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. 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. 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. 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. 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. 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. 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. 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. 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. -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. 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. 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. Urkowitz. (2006). “Energy detection of unknown deterministic signals,” Proc. IEEE, vol. 55, no. 4, pp. 523–531.
14. V. Poor. (1988). An Introduction to Signal Detection and Estimation, vol. 1. New York, NY, USA: Springer-Verlag p. 559.
15. 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. 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. 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. N. de Castro and J. Timmis. (2006). Artificial Immune Systems: A New Computational Intelligence Approach. New York, NY, USA:Springer-Verlag.
19. Kennedy. (2012). “Particle swarm optimization,” in Encyclopedia of Machine Learning. New York, NY, USA: Springer-Verlag, pp. 760–766.
20. F. Kennedy, J. Kennedy, and R. C. Eberhart, Swarm Intelligence. San Mateo, CA, USA: Morgan Kaufmann, 2001.

376-381

69.

Authors:

Paper Title:

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:

1. 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.
2. 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. 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, 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. 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. 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. 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.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.

382-385

70.

Authors:

Paper Title:

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. Zakir, T.Seymour, and K.Berg, “Big Data Analytics,” Issues in Information Systems, 2015, .16(2), pp. 81-90.
2. Dagade, M.Lagali, S.Avadhani, and P. Kalekar, “Big Data Weather Analytics Using Hadoop,” in IJETCSE, 14(2), pp.847-851.
3. Ali, “Cluster Optimization for Improved Web Usage Mining”, in IJRITCC, 2015,3(11), pp.6394-6399.
4. 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. 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. 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. .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. 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. E.Tuba, R.Jovanovic, R.C.Hrosik, A. Alihodzic and M.Tuba, “Web Intelligence Data Clustering by Bare Bone Fireworks Algorithm Combined with K-Means”. In Proceedings of the 8th International Conference on Web Intelligence, Mining and Semantics, (2018, June)
12. (article 7). ACM
13. 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. K.Tripathi, K.Sharma and M.Bala, “A Novel Clustering Method Using Enhanced Grey Wolf Optimizer and MapReduce. “,Big Data Research, Elsevier, 2018
15. 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. 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. 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. 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. 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. 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. 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. X.Yang, and X.He “Firefly algorithm: recent advances and applications”,2013, arXiv preprint arXiv: pp.1308.3898.
23. Zhang, L.Liu, S.X.Yang, andY. Dai, “A novel hybrid firefly algorithm for global optimization,” 2016, PloS one, 11(9), e0163230.
24. Zhou, and L.Li,(2018). “Improvement of the Firefly-based K-means Clustering Algorithm,” International Conference on Data Science,2018, pp.157-162.
25. 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. 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. 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. 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. 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. 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.

386-393

71.

Authors:

Paper Title:

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 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. -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.

394-396

72.

Authors:

Paper Title:

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

References:

1. Ramakrishna, G., & Sundararajan, T. (2005). Impact strength of a few natural fibre reinforced cement mortar slabs: a comparative study. Cement and concrete composites, 27(5), 547-553.
2. Gopalaratnam, V. S. et al,. (1984). A modified instrumented Charpy test for cement-based composites. Experimental mechanics, 24(2), 102-111.
3. Hibbert, A. P., & Hannant, D. J. (1978, April). The design of an instrumented impact test machine for fibre concretes. In Testing and Test Method of Fibre Cement Composites’, Proceedings of RILEM Symposium (The Construction Press, Sheffield, 1978)(pp. 107-120).
4. Schrader, E. K. (1981, March). Impact resistance and test procedure for concrete. In Journal Proceedings(Vol. 78, No. 2, pp. 141-146).
5. Cantwell, W. J., & Morton, J. (1991). The impact resistance of composite materials—a review. composites, 22(5), 347-362.
6. Gopalaratnam, V. S., & Shah, S. P. (1985). Strength, Deformation and Fracture Toughness of Fiber Cement Composites at Different Rates of Flexural Loading. In Unknown Host Publication Title. Swedish Cement & Concrete Research Inst.
7. Swamy RN, Jojagha AH. (1982 Nov) Impact resistance of steel fibre reinforced lightweight concrete. International Journal of Cement Composites and Lightweight Concrete. 1;4(4), 209-20.
8. Banthia, N. et al. (1998). Impact resistance of fiber reinforced concrete at subnorma temperatures. Cement and Concrete Composites, 20(5), 393-404.
9. Muguruma, H., & Watanabe, F. (1990). Ductility improvement of high-strength concrete columns with lateral confinement. Special Publication, 121, 47-60.
10. Ho, J. C. M., et al. (2010). Effectiveness of adding confinement for ductility improvement of high-strength concrete columns. Engineering Structures, 32(3), 714-725.
11. Balendran, R. V. et al. (2002). Influence of steel fibres on strength and ductility of normal and lightweight high strength concrete. Building and environment, 37(12), 1361-1367.
12. Nili, M., & Afroughsabet, V. (2010). The effects of silica fume and polypropylene fibers on the impact resistance and mechanical properties of concrete. Construction and Building Materials, 24(6), 927-933.
13. Fraternali, F. et al. (2011). Experimental study of the thermo-mechanical properties of recycled PET fiber-reinforced concrete. Composite Structures, 93(9), 2368-2374.
14. Ruan, Z. et al. (2015). Numerical investigation into dynamic responses of RC columns subjected for fire and blast. Journal of Loss Prevention in the Process Industries, 34, 10-21.
15. Ngo, T. et al. (2007). Blast loading and blast effects on structures–an overview. Electronic Journal of Structural Engineering, 7(S1), 76-91.
16. Choi, S. J., et al. (2017). 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.

397-402

73.

Authors:

Paper Title:

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,
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. D and S.Rajesh, Increasing Combusting Resistance For Hybrid Composites, International Journal of Applied Engineering Research,9(20), 6979-6985,2014.
5. 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.
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. 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. 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. D and S.Rajesh, Increasing Combusting Resistance For Hybrid Composites, International Journal of Applied Engineering Research,9(20), 6979-6985,2014.

403-406

74.

Authors:

Paper Title:

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 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 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.

References:

1. Rinne, H. (2008). The Weibull distribution: a handbook. Chapman and Hall/CRC.
2. J. Mitchell, ―The effect of the threshold stress on the determination of the Weibull parameters in probabilistic failure analysis‖, Engineer Fracture Mechanics, vol. 70, (2003) pp. 2559-2567.
3. Brombacher, A. C., Sander, P. C., Sonnemans, P. J., & Rouvroye, J. L. (2005). Managing product reliability in business processes ‘under pressure’. Reliability Engineering & System Safety, 88(2), 137-146.
4. Aarset, M. V. (1987). How to identify a bathtub hazard rate. IEEE Transactions on Reliability, 36(1), 106-108.
5. Kunitz, H. (1989). A new class of bathtub-shaped hazard rates and its application in a comparison of two test-statistics. IEEE Transactions on Reliability, 38(3), 351-354.
6. Jagtap, M. M., & Teli, S. N. WARRANTY PROCESS FLOW ANALYSIS IN AUTOMOTIVE INDUSTRY.
7. Lipson, C., SHETH, N., & SHELDON, D. (1966, July). Reliability and Maintainability in Industry and the Universities. In Symposium on Deep Submergence Propulsion and Marine Systems (p. 2598).
8. Huang, G., Lin, W., & Niu, Q. (2016). Risk Analysis Model of Automobile Defect Based on Weibull. International Journal of Hybrid Information Technology, 9(1), 353-366.
9. Kleyner, A., & Sandborn, P. (2005). A warranty forecasting model based on piecewise statistical distributions and stochastic simulation. Reliability Engineering & System Safety, 88(3), 207-214.
10. Guida, M., & Pulcini, G. (2002). Automotive reliability inference based on past data and technical knowledge. Reliability Engineering & System Safety, 76(2), 129-137.
11. Wu, S. (2012). Warranty data analysis: a review. Quality and Reliability Engineering International, 28(8), 795-805
12. Davis, T. (1999). A simple method for estimating the joint failure time and failure mileage distribution from automobile warranty data. Ford Technical Journal, 2(6), 1-11.
13. Rai, B., & Singh, N. (2005). 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. 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 data Chapman and Hall. New York.

407-411

75.

Authors:

Paper Title:

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 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.

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

References:

1. https://www.bloomberg.com
2. Jonathan W K, Brain. T and Robert. S, “A system design approach for unattended solar energy harvesting supply,” IEEE Transactions on Power Electronics., vol. 24, issue.4, pp. 952–962, April 2009.
3. Mohammed B.H and Robert S, “Optimization of photovoltaic-wind hybrid system for apartment complexes and other community living environments by minimizing excess capacity,” 2012 38th IEEE Photovoltaic Specialists Conference 2012, pp. 531–536.
4. Dhaker A. Andre M, and Gerard. C, “Eco-design optimization of an autonomous hybrid wind/photovoltaic system with battery storage,” IET Renewable Power Generation, vol. 6, issue.5, pp. 358–371, sept 2012.
5. Bogdan S B., Ziyad : ‘Methodology for optimally sizing the combination of a battery bank and PV array in wind/PV Hybrid system’, IEEE Transactions on Energy Conversion., Vol.11, issue.2, pp. 367–375, June1996
6. Riad C., Saifur R, ‘Unit sizing and control of hybrid wind-solar power systems’, IEEE Transactions on Energy Conversion, Vol.12, issue.1, pp. 79–85,1997
7. W D Kellogg, Hashem N, Giri V, Victor G, ‘Generation unit sizing and cost analysis for stand-alone wind, photovoltaic, and hybrid wind/PV systems’, IEEE Trans. Energy Convers., 1998, 13, (1), pp. 70–75
8. Siad D, Gilles N, Belhamel M, Mourad H, M., Alain L.: ‘Design and techno-economical optimization for hybrid PV/Wind system under various meteorological conditions’, Appl. Energy, 2008, 85, (10), pp. 968–987
9. Jun-h S, Zhi-d Z,Xin.-j Z., Guang.-y C.: ‘Robust design and optimization for autonomous PV-wind hybrid power systems’, J. Zhejiang Univ. SCIENCEA, 2008, 9, (3), pp. 401–409
10. Hongxing Y, Z Wei., Lou, C.: ‘Optimal design and techno-economic analysis of a hybrid solar-wind power generation system’, Appl. Energy, 2009, 86, (2), pp. 163–169
11. RNSR Mukhtaruddin, Hasimah HR, Mohammad YH, Jasrul JBJ. 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 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. 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/

412-418

76.

Authors:

Paper Title:

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.

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

References:

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. 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. 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. 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
10. P.Sundar Singh Sivam, V.G.UmaSekar, K.Saravanan, S RajendraKumar, P.Karthikeyan, K.SathiyaMoorthy, “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.
11. Gupta, M., Kumar, S.,2013.Multi-objective optimization of cutting parameters in turning using grey relational analysis, International Journal of Industrial Engineering Computations 4, 547-558.
12. Fung, C. P., 2003.Manufacturing process optimization for wear property of fiber-reinforced polybutylene terephthalate composites with grey relational analysis, Wear 254, 298–306.
13. Gopalsamy, B. 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. 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. 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. Sundar Singh Sivam, S., Saravanan, K., Pradeep, N., Rajendra Kumar, S., & Karuppiah, 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.
20. Sundar Singh Sivam, S., Saravanan, K., Pradeep, N., Rajendra Kumar, S., Mathur, S., Dingankar, U., & Arora, A. (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
21. P. Sundar Singh Sivam, A. Rajasekaran, S. RajendraKumar, K. SathiyaMoorthy & M. Gopal (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
22. P. 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. 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.

419-423

77.

Authors:

Paper Title:

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.

References:

1. Seguier’ “Power electronic Converters: AC-DC Conversion,” McGraw Hill Book Company, New York, 1987.
2. K. Bose, “Modern Power Electronics and AC Drives”, Pearson Education, New Delhi,2001.
3. P. S. Bimbhra “Electrical machinery”, Khanna Publishers, Delhi.
4. Maslin, Sharon, G. F. Jones and Irwin “Electrical Induction Apparatus”, US Patent 2,307,527, Jan. 5, 1943.
5. 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. 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. 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. 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. 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. L. Theraja and A.K. Theraja “A Text book of electrical technology”, Volume II S. Chand & Company LTD, New Delhi.

424-429

78.

Authors:

Paper Title:

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)
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 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)

430-433

79.

Authors:

Paper Title:

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.

References:

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. 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.

434-436

80.

Authors:

Paper Title:

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
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
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. 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. 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. 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. 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. 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. 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.

437-440

81.

Authors:

Paper Title:

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, p1-24.
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.
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. 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.

441-445

82.

Authors:

Paper Title:

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 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. 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. 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: 2144-52.
6. Ioannis Kouretas, Charalambos Basetas and Vassilis Paliouras. (2014) Low-power logarithmic number system addi- tion/subtraction and their impact on digital filters. IEEE transactions on computers, 62(11), 2196-2209.
7. 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. 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. 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.

446-449

83.

Authors:

Paper Title:

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 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.
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. 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. W, Chandrakasan. A and Balakrishnan. H (2000),” Energy Efficient Communication Protocol for Wireless Microsensor Networks”, Proceeding of the 33^rd 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. M, Li. J. and Tay. Y. C, (1999), “Cluster Based Routing Protocol (CBRP)”, IETF, Internet draft.
11. 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 4^th 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 3^rd 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, 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.
17. Vennila, D.Arivazhagan, N.Manickasankar(2004),International Journal of Computer Science and Mobile Computing, Vol.3 Issue.5, May-2014, pg. 239-244.
18. K and Varadhan . K (2011), “The NS Manual”, The VINT project, UC Berkeley, LBL, USC/ISI, and Xerox PARC, Available at: http://www.isi.edu/nsnam/ns/doc/ 2011.
19. O and Fahmy. S (2004), “HEED: A Hybrid, Energy-Efficient, Distributed Clustering Approach For Ad Hoc”, In Proceeding of IEEE INFOCOM, an extended version IEEE Transaction and Mobile Computing, Vol.3, No.4, pp.366-379.
20. Yu. J, Qi. Y, Wang. G, and Gu. X (2012),” A cluster-based routing protocol for wireless sensor networks with non-uniform node distribution”, International Journal of Electronics Communication, Vol.66, No.1, pp. 54-61.

450-454