International Journal of Recent Technology and Engineering

ISSN : 2277 - 3878 Website: www.ijrte.org Volume-7 Issue-4S2, December 2018 Published by: Blue Eyes Intelligence Engineering and Sciences Publication

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Editorial Members Dr. Uma Shanker Professor, Department of Mathematics, Muzafferpur Institute of Technology, Muzafferpur(Bihar), India

Dr. Rama Shanker Professor & Head, Department of Statistics, Eritrea Institute of Technology, Asmara, Eritrea

Dr. Vinita Kumar Department of Physics, Dr. D. Ram D A V Public School, Danapur, Patna(Bihar), India

Dr. Brijesh Singh Senior Yoga Expert and Head, Department of Yoga, Samutakarsha Academy of Yoga, Music & Holistic Living, Prahladnagar, Ahmedabad (Gujarat), India.

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

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

S. Volume-7 Issue-4S2, December 2018, ISSN: 2277-3878 (Online) Page No Published By: Blue Eyes Intelligence Engineering & Sciences Publication No.

Authors: K. Vidhya, M. Kasiselvanathan Paper Title: Smart Helmet and Bike System Abstract: Nowadays most of the countries are enforcing their citizen to wear helmet while riding bike and not to ride bike when the person is under the influence of alcohol, but still rules are being violated. In order to overcome this problem, “Accident Detection, Theft and Drive protection using Intelligent Safety Helmet” is developed. It consists of an intelligent system embedded into the helmet and the vehicle. Helmet unit ensures that the rider is wearing helmet and not under the influence of alcohol throughout the ride. It communicates with vehicle unit to switch off ignition system of bike if above condition is not met. Vehicle unit checks and intimates accident through geometric coordinates via Short Message Service. By using geometric coordinates, location of the in juried rider can be traced using simple Global Positioning System tracking application. Also, this system provides theft protection as helmet is also essential along with key to start bike.

Keywords: ATMEL Microcontroller, Arduino, GSM, GPS.

1. References: 1. Amitava das and SoumitraGoswami [2015],”Design And Implementation of Intelligent Helmet to Prevent Bike Accident In India". 2. E.D.Dekiaris, A.Spadoni and S.I.Nikolaou,May 2009,"New safety and comfort in powered two wheelers". 1-4 3. Harish Chandra Mohandas, Raja Kumar Mahapatra and Jyotirmayee Module (2014)”, Anti-Theft Mechanism System with Accidental Avoidance and Cabin Safety System for Automobiles”, International Refereed Journal of Engineering and Science (IRJES), Vol. 3, No. 4. 4. M.Pieve,F.Tesauri and A.Spadoni,May 2009,"Mitigation accident risk in powered two wheelers:Improving effectiveness of human machine interface collision avoidance system in two wheelers" 5. Orace, V.S.Reinhardt and S.A Vanghn, “Integrated disease Surveillance Project –Project Implementation plan”, 2004. 6. Ping Li, RamyMeziane, Martin J.-D. Otis, Hassan Ezzaidi, September 2015” A Smart Safety Helmet using IMU and EEG sensors for worker fatigue detection” REPARTI Center, University of Quebec at Chicoutimi Chicoutimi, Canada Email: [email protected] Philippe Cardou REPARTI Center, Laval University Quebec, Canada 7. Prof.PratikshaBhuta,KaranDesai,ArchitaKeni, “Alcohol Detection and vehicle controlling",International journal of Engineering Trends and application, 2015. 8. Prof.Sudarsan .K and Kumaraguru . P (2014), “Helmet for Road Hazard Warning with Wireless Bike Authentication and Traffic Adaptive Mp3 Playback”, International Journal of Science and Research (IJSR), Vol. 3, No. 3, ISSN. 9. Prof.Sudarshanraju,Manjesh, January 2015 ”Two Wheelers By Smart Helmet And Four Wheelers by Vehicular Communication”, International Journal Of Engineering Research, Hindupur. 10. Vijay J, Saritha B, PriyadharshiniB, Deepeka S and Laxmi R (2011), “Drunken Drive Protection System”, International Journal of Scientific & Engineering Research, Vol. 2, No. 12, ISSN: 2229-5518. Authors: Wan Rosli, WI, Siti Nur Haffizah, R. Nurraihana, H. Ant oxidative and Scavenging Properties of Polyphenolic Rich-Fraction of Cornett’s (Young Zea Paper Title: mays) Abstract: Background: Cornlettes has the potential to be used as functional pharma-nutritional ingredient in food products because of the occurrence of various bioactive compounds in it. Objective: This study was conducted to determine antioxidant and scavenging activities of phenolic rich-fractions of cornlettes extracts. Methods: DPPH radical scavenging, Ferric reducing antioxidant power and Phosphomolybdenum assay were used to determine anti- oxidative and scavenging properties of cornlettes fractions. Results and Discussion: The ethyl acetate fraction was possessed higher antioxidant activity in each antioxidant assay tested. In DPPH assay, the IC50 value of DPPH scavenging activity of the ethyl acetate, hexane, water and crude were 0.28 mg/mL, 0.26 mg/mL, 2.10 mg/mL and 1.85 mg/mL, respectively. The percentages of DPPH reduction in ethyl acetate and hexane possessed more effective anti-oxidative capacity as compared to water and crude fractions. For FRAP assay, the ethyl acetate fraction significantly exhibited the highest reducing power activity of antioxidants compared to other fractions. The IC50 value of FRAP activity of the ethyl acetate, hexane, crude and water were 0.40 mg/mL, 0.82 mg/mL, 2.24 mg/mL and 1.58 mg/mL, respectively. In addition, phosphomolybdate assay was also shown that ethyl acetate fraction had the highest total antioxidant capacity than other fractions. However, at low concentration (0.05-0.2 mg/mL), each 2. fraction was not significant to each other. Concusion: The ethyl acetate fraction was possessed higher antioxidant and scavenging capacities followed by hexane, crude and water fractions in each antioxidant assay tested. 5-8 Keywords: Antioxidant Activity, Cornlettes, Ethyl Acetate, Hexane, Phenolic Fraction,

References: 1. Ayaz, M., Junaid, M., Ahmed, J., Ullah, F., Sadiq, A., Ahmad, S. & Imran, M. (2014). Phenolic contents, antioxidant and anticholinesterase potentials of crude extract, subsequent fractions and crude saponins from Polygonum hydropiper L. BMC Complementary and Alternative Medicine. 14(1): 145-148. 2. Brand-Williams, W., Cuvelier, M.E. & Berset, C. (1995) Use of a free radical method to evaluate antioxidant activity. Food Sci Technol. 28: 25–30. 3. Chan, S.W., C.Y. Lee, C.F. Yap, W.M. Wan Aida, C.W. & Ho, C. (2009). Optimisation of extraction conditions for phenolic compounds from limau purut (Citrus hystrix) peels. International Food Research Journal. 16(2): 203-213. 4. Deciga-Campos, M., Rivero-Cruz, I., Arriaga-Alba, M., Castaneda-Corral, G., Angeles-Lopez, G.E., Navarrete, A. & Mata, R. (2007). Acute toxicity and mutagenic activity of Mexican plants used in traditional. Journal of Ethnopharmacology, 110: 334-342 5. Duh, P.D., Tu, Y.Y. & Yen, G.C. (1999). Antioxidant activity of the aqueous extract of harn jyur (Chrysanthemum morifolium Ramat). Lebensm. Wiss Technol. 32: 269-277 6. Hismath, I., Wan Aida, W.M. & Ho, C.W. (2011). Optimization of extraction conditions for phenolic compounds from neem (Azadirachta indica) leaves. International Food Research Journal. 18(3): 931-939 7. Kaur, R., Arora, S. & Singh, B. (2008). Antioxidant activity of the phenol rich fractions of leaves of Chukrasia tabularis A. Juss. Bioresour. Technol. 99(16): 7692-7698. 8. Manach, C., Scalbert, A., Morand, C., Remes, C. & Jimenez, L. (2004). Polyphenols: Food sources and bioavailability. American Journal of Clinical Nutrition. 79: 727-747 9. Muhammad, H.S. and Muhammad, S. (2005). The use of Lawsonia inermis linn. (henna) in the management of burn wound infections. African Journal of Biotechnology. 4(9): 934-937. 10. Nurhanan, A.R., Wan Rosli, W.I & Mohsin S.S.J (2012). Total polyphenol content and free radical scavenging activity of cornsilk (Zea mays hairs).Sains Malaysiana 41(10): 1217–1221 11. Oyaizu M. (1986). Studies on products of browning reaction: Antioxidative activity of products of browning reaction prepared from glucosamine. Jpn J Nutr. 44: 307–315. 12. Prieto, P., Pineda, M. & Aguilar, M. (1999). Spectrophotometric quantitation of antioxidant capacity through the formation of a phosphomolybdenum complex: Specific application to the determination of vitamin E. Anal Biochem. 269(2): 337-41 13. Ravisankar, N., Sivaraj, C., Seeni, S., Joseph, J. & Raaman, N. (2014). Antioxidant activites and phytochemical analysis of methanol extract of leaves of Hypericum hookerianum. Int J Pharm Pharm Sci. 6(4): 456-460 11. Reidah, A.I.M.M. (2013). Characterization of phenolic compounds in highly-consumed vegetable matrices by using advanced analytical techniques.(PhD thesis, University of Granada). Authors: A. Vinoth, S. Saravanakumar Region based Minutiae Mass Measure for Efficient Finger Print Forgery Detection in Health Care Paper Title: System Abstract: The modern security system has used various biometrics in authenticating the human. Among them, the finger print has been used as the key in major systems. Even though, the finger prints are unique and cannot be modified, there are intrusions which are performed by fake finger prints prepared by malicious entities. Various medical organizations maintain records of different patients which has more sensitive data which has to be secured from illegal access. Even the finger prints has been used as key there are malformed users who can try to intrude the system and steal information. So detection the forged finger prints becomes more essential. Number of approaches available for the detection of forged prints, they does not produce efficient results in forgery detection. Towards the problem of forgery detection, an efficient Region Based Minutiae Mass Measure (RMMM) approach is presented towards support the security of health care systems. The user has been validated with general information and the finger print has been captured through the capturing device. The method first enhances the input finger print image by applying gabor filter to remove the noise. Then the noise removed image has been improved for its quality by sharpening the edges of ridges present in the image. Then the image has been split into number of regions and for each sectional image, the method extracts various minutiae features like ridge island, number of ridge dots, ridge ends, ridge enclosures, and ridge bifurcation. Using the features extracted, the method estimates the Minutiae mass value for each sectional image. The same has been performed in the input test image and based on the minutiae mass value, the forged print has been detected. The method has produced efficient results on forged finger print detection and improves the classification accuracy.

Keywords: Finger Print, Authentication Systems, Minutiae, MMM, Forgery Detection, Health Care Systems.

References: 1. R.Josphineleela et.al , A New Approach Of Altered Fingerprints Detection On The Altered And Normal Fingerprint Database, Indian Journal of Computer Science and Engineering (IJCSE), Vol. 3 No.6 Dec 2012-Jan 2013 2. Soweon Yoon Altered Fingerprints: Analysis and Detection, IEEE Transaction on software engineering, 34(3):451-64 · July 2011 3. Maciej Szymkowski ; Khalid Saeed , A novel approach to fingerprint identification using method of sectorization, IEEE conference on , 3. Biometrics and Kansei Engineering (ICBAKE), 2017. 4. Rudolf Haraksim ; Alexandre Anthonioz ; Altered fingerprint detection – algorithm performance evaluation, IEEE Conferences on 9-14 Biometrics and Forensics (IWBF), 2016 5. Serena Papi ; Matteo Ferrara ; On the Generation of Synthetic Fingerprint Alterations, Biometrics Special Interest Group (BIOSIG), 2016 6. A.Vinoth et. Al, An Analysis of Altered Fingerprint Detection, Recognition and Verification, , International Journal of Computer Science and Mobile Computing, Vol.5 Issue.1, January- 2016, pg. 178-182 7. S. Selvarani ; S. Jebapriya ; R. Smeeta Mary, Automatic Identification and Detection of Altered Fingerprints , IEEE International conference on Intelligent Computing Applications (ICICA), 2014. 8. Ctirad Sousedik ; Christoph Busch, Presentation attack detection methods for fingerprint recognition systems: a survey, IET Biometrics ( Volume: 3, Issue: 4, 12 2014 ) 9. K.latha, Manikandan, Critical Analysis and Detection of Altered Fingerprints, International Journal of Innovative Research in Science, Engineering and Technology Volume 3, Special Issue 3, March 2014 10. Asraful Syifaa’ Ahmad, Rohayanti Hassan, and Razib M. Othman, An investigation of fake fingerprint detection approaches, AIP Conference Proceedings, Volume 1891, Issue 1, 2017. 11. Z. Xia, R. Lv, Y. Zhu, P. Ji, H. Sun, and Y. Q. Shi, “Fingerprint liveness detection using gradient-based texture features,” Signal, Image Video Process., vol. 11, pp. 1–8, 2016. 12. A. Hadid, N. Evans, S. Marcel, and J. Fierrez, “Biometrics Systems Under Spoofing Attack: An evaluation methodology and lessons learned,” IEEE Signal Process. Mag., vol. 32, no. 5, pp. 20–30, 2015. 13. E. Park, W. Kim, Q. Li, H. Kim, and J. Kim, “Fingerprint liveness detection using CNN features of random sample patches: Liveness detection using CNN features,” Lect. Notes Informatics (LNI), Proc. - Ser. Gesellschaft fur Inform., vol. P-260, 2016. 14. G. Arunalatha and M. Ezhilarasan, “Fingerprint Spoof Detection Using Quality Features,” Int. J. Secur. Its Appl., vol. 9, no. 10, pp. 83–94, 2015. 15. J. Galbally, F. Alonso-Fernandez, J. Fierrez, and J. Ortega-Garcia, “A high performance fingerprint liveness detection method based on quality related features,” Futur. Gener. Comput. Syst., vol. 28, no. 1, pp. 311–321, 2012. 16. A.Vinoth and S.Saravanakumar, Accuracy Fingerprint Matching For Altered Fingerprint Using Divide And Conquer And Minutiae Matching Mechanism, ARPN Journal of Engineering and Applied Sciences, VOL. 11, NO. 21, 2016. 17. Alyssa Iacona,Health Care Information Technology: Securing the Electronic Health Record with Biometric Technology, A journal of undergraduate student research, vol.15, issue 4, 2018. 18. Daniel Matthew L Storisteanu, Toby L Norman, Alexandra Grigore and Tristram L Norman, Biometric Fingerprint System to Enable Rapid and Accurate Identification of Beneficiaries,Global health science and practices, vol 3, issue 1, 2015. 19. Abdul, Wadood, Alzamil, Abdullah,Fingerprint and Iris Template Protection for Health Information System Access and Security, Journal of Medical Imaging and Health Informatics, Volume 7, Number 6, 2018. 20. Eliezer Ofori Odei-Lartey, The application of a biometric identification technique for linking community and hospital data in rural Ghana, Global health action, 2016. Authors: Ahmed Ali Khan, Mohd Khairo Anuar Mohd Ariffin, Shamsuddin Sulaiman, Faizal Mustapha Paper Title: Factors Influencing Project Selection for SMEs Abstract: in this current study the authors will elaborate on the factors that influence project selection for small and medium enterprises (SMEs). There are a variety of tangible and intangible factors which impact on the decision- making, and lead organizations to choose the right project. This study focuses on 11 factors, i.e. cultural, process, knowledge of business, knowledge of work, education, experience, risk awareness, governance, selection of players, preconceptions and timeframe. The influence of these factors was determined by answering five questions which are, RQ1: Do contributors have a vision about the best practice? RQ2: What is the variance among the current practice and the best practice? RQ3: How does this vision match with what is the best practice? RQ4: Are there important factors to achieve ideal project selection? RQ5: Will these set of questions be set as a base for the decision for the achievement of ideal project selection? These factors are tested through a comprehensive questionnaire which was distributed to SME organizations. About 12 companies responded with 166 participants in total. The results were then analysed using SPSS and the outcome is in the form of a correlation for the above mentioned factors. It was found that those factors significantly influenced the manager in their project selection. Moreover, each factor has several correlations with others; however, these are different in terms of the correlation values. As the result, the authors ware able to classify the factors accordingly with relation to the correlation value level. 4. Keywords: Project, Influencing Factors, Risk Analysis, Risk Awareness, 15-18 References: 1. Ngoc Se (2010) ''Project Management'' Supplier Conference Bachelor’s Thesis Business Management. (p. 1- 2). 2. Project Management Institute Inc. (2013) '' A Guide to the Project Management Body of Knowledge (PMBOK® Guide) – Fifth Edition'' (P. 3). 3. Project Management Institute (2004) ''A Guide to the Project Management Body of Knowledge: PMBOK® Guide, 3rd Edition'' (p. 8-10, 37). 4. Roger Atkinson (1999) '' Project management: cost, time and quality, two best guesses and a phenomenon, its time to accept other success criteria'' Bournemouth University, (p. 337-338). 5. William Shakespeare (2000) '' Basic Skills for Project Managers'' Chapter 2. (p. 15-18) 6. BA. Isaac Tetteh (2014) '' Use of Project Management Methods'' Master Thesis. Masaryk University (p. 21). 7. Idisemi Apulu (2012) ''Developing a Framework for Successful Adoption and Effective Utilisation of ICT by SMEs in Developing Countries: a Case Study of Nigeria'' (p. 4, 10-11). 8. Meridith Levinson (2008) ''Project Management: The 14 Most Common Mistakes IT Departments Make''. http://www.cio.com/article/2434788/p roject-management/project- management--the-14-most-common- mistakes-it-departments-make.html 9. JenniferLonoff Schiff (2012) ''12 CommonProjectManagement Mistakes--and How to Avoid Them''. Business and Technology writer and a contributor toCIO.com. http://www.cio.com/article/2391872/p roject-management/12- 10. common- project-management-mistakes--and- how-to-avoid-them.html 11. Sanjay D. Beley and Pravada S. Bhatarkar (2013) '' The Role of Information Technology in Small and Medium Sized Business'' International Journal of Scientific and Research Publications, 2 February 2013 (p. 1). Authors: K. Bhuvaneshwari, D. Akila, P. Rajesh Paper Title: Robust Visual Object Tracking Via Fast Gabor Approximation Abstract: Visual Object Tracking is the process of finding a moving object (or multiple objects) over time using a camera. It has a variety of uses, some of which are: human-computer interaction, security and surveillance, video communication and compression, traffic control. Video tracking can be a time consuming process due to the huge amount of data in video.The main aim of object tracking is to estimate the states of the target in image sequences.Visual object tracking is challenging due to image variations caused by various factors, such as object deformation, scalechange, illumination change and occlusion.To overcome these challenges, windowing technique is applied in the proposed work.It is used to remove the noise in the image and gives the exact image. Experimental results are done for various sequences in the video and it is analysed the windowing technique is robust to partial occlusions and variations of illumination and pose, resistent to nearby distracters.Also,it performs favorably against several state-of-the-art algorithms.

Keywords: (or multiple objects) over time using a camera. To overcome these challenges.

5. References: 1. Zhenyu He, Senior Member, IEEE, Xin Li, Xinge You, Senior Member, IEEE, Dacheng Tao, Fellow, IEEE,and Yuan Yan Tang, Fellow,” Connected Component Model for Multi-ObjectTracking”,VOL. 11, NO. 4, MAY 2016. 19-22 2. Jiatong Li, Chenwei Deng, Senior Member, IEEE, Richard Da Xu,Dacheng Tao, Fellow, IEEE, and BaojunZhao,”Robust Object Tracking With Discrete Graph BasedMultiple Experts”. 3. Min Li, Zhaoxiang Zhang, Kaiqi Huang and Tieniu Tan,“ROBUST VISUAL TRACKING BASED ON SIMPLIFIED BIOLOGICALLY INSPIRED FEATURES”,National Laboratory of Pattern Recognition,Institute of Automation, Chinese Academy of Sciences. 4. Wei ZhongDalian University of Technology ,Huchuan Lu, Ming-Hsuan Yang,” . Robust Object Tracking via Sparsity-based Collaborative Model”. 5. T. Serre, L. Wolf, and T. Poggio, “Object recognition withfeatures inspired by visual cortex,” Proc. CVPR’05, 2005. 6. Y. Huang, K. Huang, L. Wang, D. Tao, T. Tan, and X. Li, “Enhancedbiologically inspired model,” Proc. CVPR’08, 2008. 7. J. Mutch and D. G. Lowe, “Object class recognition and localizationusing sparse features with limited receptive fields,” Int.J. Comput.Vis., vol. 80, no. 1, pp. 45–57, 2008. 8. Niblack, R. Barber, W. Equitz, M. Fickner, E. Glasman, D. Petkovic,and P. Yanker, “The QBIC project: Querying images by content using color, texture and shape,” in Proc. SPIE—Storage and Retrieval for Imageand Video Databases, 1993, pp. 173–187. 9. B. Schiele and J. Crowley, “Recognition without correspondence usingmultidimensional receptive field histograms,” Int. J. Comput. Vis., vol. 36,no. 1, pp. 31–50, Jan. 2000. 10. B. Leibe and B. Schiele, “Interleaved object categorization and segmentation,”in Proc. Brit. Mach. Vis. Conf., 2003, pp. 1–10. 11. H. Murase and S. K. Nayar, “Visual learning and recognition of 3-dobjects from appearance,” Int. J. Comput. Vis., vol. 14, no. 1, pp. 5– 24,Jan. 1995. 12. M. Turk and A. P. Pentland, “Eigenfaces for recognition,” J. Cognitive Neurosci., vol. 3, no. 1, pp. 71–96, Winter 1991. 13. C. Harris and M. Stephens, “A combined corner and edge detector,” inProc. 4th Alvey Vis. Conf., 1988, pp. 147–151. 14. T. Tuytelaars and L. V. Gool, “Matching widely separated views based onaffine invariant regions,” Int. J. Comput. Vis., vol. 59, no. 1, pp. 61–85,Aug. 2004. 15. K. Mikolajczyk and C. Schmid, “Scale and affine invariant interest pointdetectors,” Int. J. Comput. Vis., vol. 60, no. 1, pp. 63–86, Oct. 2004. 16. J. Matas, O. Chum,M. Urban, and T. Pajdla, “Robust wide-baseline stereofrom maximally stable extremal regions,” Image Vis. Comput., vol. 22,no. 10, pp. 761–767, Sep. 2004. 17. GAOCHENG LIU1,2, SHUAI LIU 1,2, KHAN MUHAMMAD 3, (Student Member, IEEE),“Object Tracking in Vary Lighting Conditions for Fog Based Intelligent Surveillance of Public Spaces”. date of publication May 10, 2018. 18. Raphael Angulu, Jules-Raymond Tapamo, AderemiAdewumi,” Human Age Estimation Using Multi-FrequencyBiologically Inspired Features (MF-BIF)”,Conference Paper, 2017. Adi Azriff, Cherian Johny, S.M. Abdul Khader, Raghuvir Pai B, M. Zuber, K.A. Ahmed, Zanuldin Authors: Ahmad Numerical Study of Haemodynamics in Abdominal Aorta with Renal Branches Using Fluid – Paper Title: Structure Interaction under Rest and Exercise Conditions Abstract: Computational simulations studying the complex interaction of blood flow through elastic arteries has demonstrated the haemodynamics of cardiovascular diseases such as atherosclerosis. The aim of present study is to investigate the hemodynamic behavior in 3D models of an idealistic abdominal aorta with renal branches based on (Computed Tomography) CT image. A new technique is used to develop the idealistic model from the single slice. 3D abdominal aorta model with renal branches is generated using ANSYS Design modeler and numerical analysis is performed using FSI solver in ANSYS-17. The blood flow is assumed to be incompressible, homogenous and Newtonian, while artery wall is assumed to behave linearly elastic. The two-way sequentially coupled transient FSI analysis is performed using FSI solver for three pulse cycles. The investigation is focused on haemodynamic parameters such as flow velocity, Wall Shear Stress (WSS), pressure contours, arterial wall deformation and von- Mises stress are studied at the bifurcation and critical zones. The flow variables are monitored throughout pulsatile flow subjected to both resting and exercise cases which is indicated through results obtained. This preliminary study shall be useful to carry out FSI simulation in patient specific cases.

Keywords: Renal Artery, ANSYS FSI, Exercise and Resting Condition, Normal and High Blood Pressure.

References: 1. Andrea S. Les, Shawn C. Shadden, C. Alberto Figueroa and Charles A. Taylor, 2010. Quantification of Hemodynamics in Abdominal Aortic Aneurysms During Rest and Exercise using Magnetic Resonance Imaging and Computational Fluid Dynamics. Annals of Biomedical Engineering, 38(4):1288–1313. 2. Albert Scott, Robert S. Balaban and Jenn Stroud Rossmann, 2014. Influence of the renal artery ostium flow diverter on hemodynamics and atherogenesis. Journal of Biomechanics, 47(7):1594–1602. 3. Amirhossein Arzani, Andrea S. Les and Shawn C. Shadden, 2014. Effect of exercise on patient specific abdominal aortic aneurysm flow 6. topology and mixing. International Journal of Numerical Methods in Biomedical Engineering, 30(2): 280–295. 4. ANSYS Release 17.0 Documentation, 2016. ANSYS Company, Pittsburgh, PA. 5. Charles A. Taylor, Thomas J. R. Hughes and Christopher K. Zarins, 1998. Finite Element Modeling of Three-Dimensional Pulsatile Flow in the Abdominal Aorta: Relevance to Atherosclerosis. Annals of Biomedical Engineering, 26: 975–987. 23-27 6. Charles A. Taylor, Thomas J.R. Hughes and Christopher K. Zarins, 1999. Effect of exercise on hemodynamic conditions in the abdominal aorta. Journal of Vascular Surgery, 29(6):1077-1089. 7. D. Lee and J.Y. Chen, 2002. Numerical simulation of steady flow fields in a model of abdominal aorta with its peripheral branches. Journal of Biomechanics, 35:1115–1122. 8. Ga-Young Suh, Andrea S and Charles A. Taylor, 2011. Hemodynamic Changes Quantified in Abdominal Aortic Aneurysms with Increasing Exercise Intensity Using MR Exercise Imaging and Image-Based Computational Fluid Dynamics. Annals of Biomedical Engineering, 39(8):2186–2202. 9. George C. Kagadis, Eugene D. Skouras, Dimitris Karnabatidis and George C. Nikiforidis, 2008. Computational representation and hemodynamic characterization of in vivo acquired severe stenotic renal artery geometries using turbulence modeling. Medical Engineering & Physics, 30:647–660. 10. Ian Marshall, Shunzhi Zhao, Peter Hoskins and X Yun Xu, 2004. MRI and CFD studies of pulsatile flow in healthy and Stenosed carotid bifurcation models. Journal of Biomechanics, 37:679–687. 11. James E. Moore, Chengpei Xub and David N. Ku. 1994. Fluid wall shear stress measurement in a model of human abdominal aorta: oscillatory behavior and relationship to atherosclerosis. Atherosclerosis, 10:225-240. 12. Khader, S. M. A. et al., 2013. Study of the influence of Normal and High Blood pressure on normal and stenosed Carotid Bifurcation using Fluid-Structure Interaction. Applied Mechanics and Materials, 315: 982–986. 13. Liang Fuyou, Yamaguchi Ryuhei and Hao Liu, 2006. Fluid Dynamics in Normal and Stenosed Human Renal arteries: an Experimental and Computational Study. Journal of Biomechanical Science and Engineering, 1 (1): 171-182. 14. Mortazavinia Z, Arabi S and Mehdizadeh A. R, 2014. Numerical Investigation of Angulation Effects in Stenosed Renal Arteries. Journal of Biomedical Physics and Engineering, 4(1):1-8. 15. Raghuvir Pai, et al., 2016. Fluid-Structure Interaction Study of Stenotic Flow in Subject Specific Carotid Bifurcation—A Case Study. Journal of Medical Imaging and Health Informatics, 6:1494–1499 16. Tang T Beverly, Cheng P Christoper and Charles A Taylor, 2006. Abdominal Aortic Haemodynamic in young healthy adults at rest and during lower limb exercise: qualification using image-based computer modelling. American Journal of Heart Circulation Physiology, 291: H668-676. 17. Weisheng Zhang, Yi Qian and Mengsu Zeng, 2014. Haemodynamic analysis of renal artery stenosis using computational fluid dynamics technology based on unenhanced steady-state free precession magnetic resonance angiography: preliminary results. International Journal of Cardiovascular Imaging, 30:367–375. 18. Y. Fung, 1984. Biodynamics-Circulation. Springer Verlag, New York Inc. Authors: B. Mahalakshmi, G. Suseendran Paper Title: ZIKA Virus: A Secure System USINF NBN Classifier for Predicting and Preventing ZIKA in Cloud Abstract: One of the upcoming mosquitoes borne disease is Zika Virus which is spreading rapidly around the 7. world. The traditional ways of detecting Zika Virus are not much effective. To overcome a proposed method of cloud based system is developed with the combination of cloud computing mobile phones, fog computing finally IoT for 28-32 sensing the mosquitoes. Here fog computing acts as a bond between cloud and user for reducing the latency level and increasing the processing speed. A NBN- Naïve Bayesian Network algorithm is to find the infected patient and uninfected one, mosquitoes breeding sites and dense site by mapping it in GPS and finding the location. The cloud based system provides high accuracy in predicting the result using NBN classifier and also shows the risk prone site location to the government health sector. It provides the user a better communication with the health sector, to avoid the outbreak of ZVD and, the complete cloud system gives better high accuracy for prediction and preventing.

Keywords: Zika Virus, Fog Computing, NBN classifier, Cloud Computing, Data protection.

References: 1. Sarmiento-Ospina A, Vsquez-Serna H, Jimenez-Canizales CE, Villamil-Gmez , Rodriguez-Morales AJ. Zika virus associated deaths in Colombia. Lancet Infect Dis. 2016;16:523-524. 2. World Health Organization, Zika outbreak: WHO’s global emergency response plan. 2016. 3. Musso D, Roche C, Robin E, Nhan T, Teissier A, Cao-Lormeau VM. Potentialsexual transmission of Zika virus. Emerg Infect Dis. 2015;21:359- 361. 4. Paixao , Barreto F, Teixeira GM, Costa CM, Rodrigues L. History, epidemiology, and clinical manifestations of Zika: A systematic review.Am J Public Health. 2016;106:606-612. 5. Nishiura H, Mizumoto K, Rock KS, Yasuda Y, Kinoshita R, Miyamatsu Y. A theoretical estimate of the risk of microcephaly during pregnancy with Zika virus infection. Epidemics. 2016;15:66-70. 6. Petersen E,Wilson ME, Touch S, et al. Rapid spread of Zika virus in the Americas - Implications for public health preparedness for mass gatherings at the 2016 Brazil Olympic Games. Int J Infect Dis. 2016;44:11-15. 7. Lopez-Barbosa N, Gamarra JD, Osma JF. The future point-of-care detection of disease and its data capture and handling. Anal Bioanal Chem. 2016;408:2827-2837. 8. Quwaider M, Jararweh Y. A cloud supported model for efficient community health awareness. Pervasive Mob Comput. 2016;28:35-50. 9. Mamun KAA, Alhussein M, Sailunaz K, Islam MS. Cloud based framework for Parkinsons disease diagnosis and monitoring system for remote healthcare applications. Future Gener Comput Syst. 2017;66: 36-47. 10. Zhang Z, Wang H, Wang C, Fang H. Cluster-based epidemic control through -based body area networks. IEEE Trans Parallel Distrib Syst. 2015;26:681-690. 11. Sareen S, Sood SK, Gupta SK. Towards the design of a secure data outsourcing using fragmentation and secret sharing scheme. Information Security Journal: A Global Perspective. 2016;25:39-53. 12. Mahalakshmi, B., and G. Suseendran. "Effectuation of Secure Authorized Deduplication in Hybrid Cloud." Indian Journal of Science and Technology 9.25 (2016). 13. Hadavi, A. M., & Jalili, R. (2010). Secure data outsourcing based on threshold secret sharing; towards a more practical solution. In Proceedings of VLDB PhD workshop, Singapore. 14. Han, J., Susilo, W., & Mu, W. (2013). Identity-based data storage in cloud computing. Future Generation Computer Systems, 29(3), 673– 681. doi:10.1016/j.future.2012.07.010 15. Sareen S, Sood SK, Gupta SK. A cloud-based seizure alert system for epileptic patients that uses higher-order statistics. IEEE Comp Sci Eng. 2016;18:56-67. Authors: Zainudin, W.N.R.A., Wan Abdullah, W.M.Z., Ramli, N.A. A Preliminary Study On Electricity Affordability and Willingness to Pay (WTP) On Maximum Paper Title: Demand (MD) Charge Among Residential Electricity Customers in Malaysia Abstract: Implementation of maximum demand charge is a possible solution to better reflect the cost of generating and delivering electricity. However, this implementation leads to additional charge in the electricity bill and would effect on the electricity affordability among the residential customers. As a preliminary study on the issue related to electricity affordability and willingness to pay for maximum demand charge, this paper uses survey data collected from 411 residential electricity customers in Malaysia and descriptive analysis. Findings from this study indicates most of the respondents do not face electricity unaffordability problem and seem to be willing to pay for the maximum demand charge.

Keywords: Maximum Demand Charge, Willingness to Pay, Electricity Affordability,

References: 1. Energy Commission, Peninsular Malaysia Electricity Supply Industry Outlook 2013, Malaysia, 2013. 2. Hau, L. C., and Lim, Y. S., “A real-time active peak demand reduction for battery energy storage with limited capacity”. Journal of Communications. Vol. 11, (9), pp. 841-847, 2016. 3. Tenaga Nasional Berhad (TNB). https://www.tnb.com.my/commercial-industrial/maximum-demand/. Accessed: 10 April 2017. 4. Xu, H., and Li, B., “Reducing electricity demand charge for data centers with partial execution”. In Proceedings of the 5th international 8. conference on Future energy systems. pp. 51-61, 2014. 5. Prosser, R. D., and Shao, V., U.S. Patent Application No. 13/423,958, 2012. 33-37 6. Baidoo, I., Al-Hassan, R. A., Asuming-Brempong, S., Osei-Akoto, I., and Asante, F. A., Willingness to pay for improved water for farming in the Upper East Region of Ghana, 2013. 7. Padi, A., Addor, J. A., and Nunfam, V. F., An econometric model of factors influencing households’ willingness to pay for improved solid waste management service within the Sekondi–Takoradi metropolis in the western region of Ghana, 2015. 8. Vondolia, G. K., Do Ghanaian farmers have preferences for the national biodiversity strategy? A case study of farmers living around the Kakum National Park in the Central Region, 2009. 9. Carlsson, F., and Martinsson, P. “Willingness to pay among Swedish households to avoid power outages: a random parameter Tobit model approach”. The Energy Journal. pp. 75-89, 2007. 10. Goett, A. A., Hudson, K., and Train, K. E., “Customers' choice among retail energy suppliers: The willingness-to-pay for service attributes”. The Energy Journal. pp. 1-28, 2000. 11. Quartey, D. J., “The demand for energy and economic welfare in Ghana”. International Conference on Energy and People: Futures, Complexity and Challenges, Lady Margaret Hall, University of Oxford, Oxford. pp. 20-21, 2011. 12. Abdullah, S., and Mariel, P., “Choice experiment study on the willingness to pay to improve electricity services”. Energy Policy. Vol. 38, (8): pp. 4570-4581, 2010. 13. Abdullah, S., and Jeanty, P. W., “Willingness to pay for renewable energy: Evidence from a contingent valuation survey in Kenya”. Renewable and Sustainable Energy Reviews. Vol. 15, (6): pp. 2974-2983, 2011. 14. Panayides, P., “Coefficient alpha: interpret with caution”. Europe’s Journal of Psychology. Vol. 9, (4): pp. 687-696, 2013. 15. Ranasinghe, A., Study on Requirements of Prospective Electricity Consumers and Fuel (electricity) Poverty & Affordability, 2011. 16. DeCicco, J., Munoz, D. H., and Neidert, L., University of Michigan Energy Survey. Institute for Social Research, 2014. 17. Zikmund, W. G., “Sample designs and sampling procedures”. Business research methods. Vol. 7: pp. 368-400, 2003. 18. Creswell, J. W., Research design: Qualitative & quantitative approaches. Sage Publications, Inc., 1994. Authors: T. Nathiya, G. Suseendran An Effective Way of Cloud Intrusion Detection System Using Decision tree, Support Vector Machine Paper Title: and Naïve Bayes Algorithm Abstract: Cloud computing is a vast area, use the resources with cost-effectively. The service provider is to share the resources anywhere at any time. But the network is the most vital to accessing data in the cloud. The cloud malicious takes advantages while using the cloud network. Intrusion Detection System (IDS) is monitoring the network and notifies attacks. In Intrusion Detection System, anomaly technique is most important. Whenever Virtual Machine is created, IDS track the known and unknown data’s. If any unknown data found, Intrusion Detection System detects the data using anomaly classification algorithm and send the report to admin. This paper proposes we are using support vector machine (SVM), Naive Bayes, and decision tree (J48) algorithms for predicting unwanted data’s. In these algorithms are help us to overcome the high false alarm rate. Our proposed work implemented part using the WEKA tool to give a statistical report, which gives a better outcome in little calculation time.

Keywords: SVM, Naive Bayes, Decision Tree (J48), NSL-KDD dataset, H-IDS.

References: 1. . Kumar, “A Survey on Intrusion Detection Systems for Cloud Computing Environment,” International Journal of Computer Applications, vol. 109, no. 1, pp. 6–15, 2015. 2. K. Arjunan and C. N. Modi, “An enhanced intrusion detection framework for securing network layer of cloud computing,” ISEA Asia Security and Privacy Conference 2017, ISEASP 2017, 2017. 3. B. Mahalakshmi and G. Suseendran, “Effectuation of Secure Authorized Deduplication in Hybrid Cloud,” Indian Journal of Science and Technology, vol. 9, no. 25, Jul. 2016. 4. T. Nathiya, “Reducing DDOS Attack Techniques in Cloud Computing Network Technology,” International Journal of Innovative Research in Applied Sciences and Engineering (IJIRASE), vol. 1, no. 1, pp. 23–29, 2017. 5. R. K. Bathla, G. Suseendran, and Shallu, “Research analysis of big data and cloud computing with emerging impact of testing,” International Journal of Engineering and Technology(UAE), vol. 7, no. 3.27 Special Issue 27, pp. 239–243, 2018. 6. R. Staudemeyer and C. Omlin, “Extracting salient features for network intrusion detection using machine learning methods,” South African Computer Journal, vol. 52, no. July, pp. 82–96, 2014. 7. S. G. Kene and D. P. Theng, “A Review on Intrusion Detection Techniques for cloud computing and Security Challenges,” IEEE sponsored 2nd International Conference on Electronics and Communication Systems (ICECS), pp. 227–232, 2015. 9. 8. N. Modi, “An Efficient Security Framework to Detect Intrusions at Virtual Network Layer of Cloud Computing,” 19th international ICIN conference- Innovations in clouds, and Network, pp. 133–140, 2016. 38-43 9. T. Nathiya and G. Suseendran, An Effective Hybrid Intrusion Detection System for Use in Security Monitoring in the Virtual Network Layer of Cloud Computing Technology,Data Management, Analytics and Innovation, Advances in Intelligent Systems and Computing,839, pp.483-496,2019. Doi: 10.1007/978-981-13-1274-8_3. 10. Hasan, M. Nasser, B. Pal, and S. Ahmad, “Support Vector Machine and Random Forest Modeling for Intrusion Detection System ( IDS ),” Journal of Intelligent Learning Systems and Applications, vol. 6, no. February, pp. 45–52, 2014. 11. Farnaaz and M. A. Jabbar, “Random Forest Modeling for Network Intrusion Detection System,” Procedia Computer Science, vol. 89, pp. 213–217, 2016. 12. G. V. Nadiammai and M. Hemalatha, “Effective approach toward Intrusion Detection System using data mining techniques,” Egyptian Informatics Journal, vol. 15, no. 1, pp. 37–50, 2014. 13. Jing, Y. Bi, and H. Deng, “An innovative two-stage fuzzy kNN-DST classifier for unknown intrusion detection,” International Arab Journal of Information Technology, vol. 13, no. 4, pp. 359–366, 2016. 14. Rouhi, F. Keynia, and M. Amiri, “Improving the Intrusion Detection Systems’ Performance by Correlation as a Sample Selection Method,” Journal of Computer Sciences and Applications, vol. 1, no. 3, pp. 33–38, 2013. 15. O. Osanaiye, H. Cai, K. K. R. Choo, A. Dehghantanha, Z. Xu, and M. Dlodlo, “Ensemble-based multi-filter feature selection method for DDoS detection in cloud computing,” Eurasip Journal on Wireless Communications and Networking, vol. 2016, no. 1, 2016. 16. Ö. Cepheli, S. Büyükçorak, and G. Karabulut Kurt, “Hybrid Intrusion Detection System for DDoS Attacks,” Journal of Electrical and Computer Engineering, vol. 2016, 2016. 17. N. M. Turab, A. Abu, and T. Shadi, “C Loud Computing Challenges and Solutions,” International Journal of Computer Networks & Communications (IJCNC), vol. 5, no. 5, pp. 209–216, 2013. 18. N. Hubballi and V. Suryanarayanan, “False alarm minimization techniques in signature-based intrusion detection systems: A survey,” Computer Communications, vol. 49, pp. 1–17, 2014. 19. K. Chai, H. T. Hn, and H. L. Cheiu, “Naive-Bayes Classification Algorithm,” Bayesian Online Classifiers for Text Classification and Filtering, pp. 97–104, 2002. 20. H. Chauhan and A. Chauhan, “Implementation of decision tree algorithm c4.5 1,” International journal of scientific and research publications, vol. 3, no. 10, pp. 4–6, 2013. 21. O. Catak and M. E. Balaban, “CloudSVM: Training an SVM classifier in cloud computing systems,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 7719 LNCS, no. January, pp. 57–68, 2013. 22. M. S. Revathi, “A Detailed Analysis on NSL-KDD Dataset Using Various Machine Learning Techniques for Intrusion Detection,” International Jornal of Engineering Research and Technology, vol. 2, no. 12, pp. 1848–1853, 2013. 23. L. Dhanabal and S. P. Shantharajah, “A Study on NSL-KDD Dataset for Intrusion Detection System Based on Classification Algorithms,” International Journal of Advanced Research in Computer and Communication Engineering, vol. 4, no. 6, pp. 446–452, 2015. Authors: Wan Abdullah, W.M.Z., Zainudin, W.N.R.A., Mohamad Ishak, W.W. 10. The Scale Validation of Public Participation of Renewable Energy (RE) Development in Malaysia: An Paper Title: Exploratory Factor Analysis (EFA) Abstract: Renewable Energy studies are gaining more interest by involved parties such as energy producer as well as research community. However, there are still limited number of studies in particular concerning instrument to measure public participation towards Renewable Energy (RE) development in Malaysia. The instrument for assessing public participation seem to require some extent of revalidation. Thus, the purpose of this paper is to validate the factor structure of public participation instrument that measured by willingness to pay level and it determinants. The questions of this developed scale were derived from previous published quantitative study. Validity and reliability of the instruments were assessed with Exploratory Factor Analysis (EFA) and Cronbach’Alpha respectively. Four hundred and five respondents had participated in this study. Results from this study revealed that the originally validated form of every factor structure, i.e. Willingness to pay (WTP), Awareness on RE (ARE), Knowledge on RE (KRE), Willingness to adopt RE technology (WTA) and Attitude towards RE usage (AURE) and Environmental concern (EC) had performed as per previous literatures within this research context. Furthermore, all the revised factors had a good reliability with the Cronbach’s Alpha value well above 0.70 of threshold. Therefore, a total of 45-items revised in public participation toward RE measurement might be better suited for use within the selected population. This information provides preliminary evidence that the public participation towards RE instrument is a reliable and a valid measure that can be used to provide the data regarding Renewable Energy (RE) development in Malaysia. However, in order to cross validate, in future study can be conducted by engaging other location to cater different public opinion.

Keywords: Renewable Energy (RE), Willingness to Pay (WTP), Exploratory Factor Analysis (EFA) And Measurement Scale,

References: 1. A. Biswas, "A Study of Consumers ’ Willingness to Pay for Green Products", vol. 4, no. 3, pp. 211–215, 2016. 2. S. Mustapa, L. Y. P. L. Y. Peng, and A. Hashim, "Issues and challenges of renewable energy development: A Malaysian experience", Energy and Sustainable Development: Issues and Strategies (ESD2010), Proceedings of the International Conference on, pp. 1–6, 2010. 3. M. H. M. Ashnani, A. Johari, H. Hashim, and E. Hasani, "A source of renewable energy in Malaysia, why biodiesel?", Renewable and Sustainable Energy Reviews, vol. 35, pp. 244–257, 2014. 4. A. Biswas and M. Roy, "Green products: An exploratory study on the consumer behaviour in emerging economies of the East", Journal of Cleaner Production, vol. 87, no. 1, pp. 463–468, 2015. 5. T. Li and Z. Meshkova, "Examining the impact of rich media on consumer willingness to pay in online stores", Electronic Commerce 44-48 Research and Applications, vol. 12, no. 6, pp. 449–461, 2013. 6. C. Donaldson, "Eliciting patients â€TM values by use of ` willingness to pay ’: letting the theory drive the method", Health Expectation, no. 4, pp. 180–188, 2000. 7. Scottish Executive, "Attitudes and Knowledge of Renewable Energy amongst the General Public", 2003. 8. I. Morgil, N. Secken, A. S. Yucel, O. O. Oskay, S. Yavuz, and E. Ural, "Developing a renewable energy awareness scale for pre-service chemistry teachers", Turkish Online Journal of Distance Education, vol. 7, no. 1, pp. 57–67, 2006. 9. R. Bord, R. O’Connor, and A. Fisher, "In what sense does the public need to understand global climate change?", Public Understanding, vol. 9, no. 2000, pp. 205–218, 2000. 10. C. L. Kimberlin and A. G. Winterstein, "Validity and reliability of measurement instruments used in research", American Journal of Health- System Pharmacy, vol. 65, pp. 2276–84, 2008. 11. J. E. Bartlett, J. W. Kotrlik, and C. C. Higgins, "Organizational Research: Determining Appropriate Sample Size in Survey Research", Information Technology, Learning, and Performance Journal, vol. 19, no. 1, pp. 43–50, 2001. 12. A. Field, Discovering Statistics Using SPSS, vol. 81, no. 1. London, 2013. 13. H.-K. Bang, A. E. Ellinger, J. Hadjimarcou, and P. A. Traichal, "Consumer concern, knowledge, belief, and attitude toward renewable energy: An application of the reasoned action theory", Psychology and Marketing, vol. 17, no. 6, pp. 449–468, 2000. 14. M. S. Aini and M. Goh Mang ling, "Factors Affecting the Willingness to Pay for Renewable Energy amongst Eastern Malaysian Households : A Case Study", Social Science and Humanities, vol. 21, no. 1, pp. 147–164, 2013. 15. T. E. Curry, "Public Awareness of Carbon Capture and Storage: A Survey of Attitudes toward Climate Change Mitigation", no. 1999, p. 94, 2004. 16. N. Fransson and T. Garling, "Environmental Concern : Conceptual Definitions , Measurement Methods , And Research Findings", Journal of Environmental Psychology, vol. 19, pp. 369–382, 1999. 17. A. M. Salleh, "The Economic Valuation of Solar Water Heating Systems and The Determinants of Its Adoption by Libyan Households", 2015. 18. D. H. Carlson, "Environmental Concern on South Africa: The Development of A Measurement Scale", 2004. 19. E. Ferguson and T. Cox, "Exploratory Factor Analysis: A Users’ Guide", International Journal of Selection and Assessment, vol. 1, no. 2, pp. 84–94, 1993. 20. M. R. Matthews-Ewald, A. Posada, M. Wiesner, and N. Olvera, "An exploratory factor analysis of the Parenting strategies for Eating and physical Activity Scale (PEAS) for use in Hispanic mothers of adolescent and preadolescent daughters with overweight", Eating Behaviors, vol. 19, pp. 193–199, 2015. 21. H. F. Kaiser, "The Application of Electronic Computers To Factor Analysis", Educational And Psychological Measurement, Vol. xx, No. 1, Pp. 141–151, 1960. 22. R. B. Cattell, "The Scree Test For The Number Of Factors", Multivariate Behavioral Research, vol. 3171, no. April, 1966. 23. Z. Awang, A Handbook on SEM for Academicians and Practitioners: The Step by Step Practical Guides for the Beginners. Bandar Baru Bangi.: MPWS Rich Resources., 2014. 24. R. Kardooni, S. B. Yusoff, and F. B. Kari, "Renewable energy technology acceptance in Peninsular Malaysia", Energy Policy, vol. 88, pp. 1– 10, 2015. Authors: Saidhbi Sheik, Thirupathi Rao Komati Paper Title: A Way to Secure the Data in Cloud Data Storage by Using Cloud Mechanism Abstract: Cloud computing significantly plays a role in the aspect of effective resource utilization and service consumption. Irrespective of the type of clouds (ex. Private, public, hybrid or inter-cloud), every service providers concentrates on the data residing in cloud servers. Each and every moment, the researchers and scholars are 11. proposing multiplicity of security algorithms to secure cloud data during the transactions. Most of the cloud data secure algorithms are focusing on the way to secure to cloud data in a single direction by using cryptographic 49-53 algorithms. In this research paper focuses on a new direction to combine the features of data compression with the cloud data in order to secure the cloud data storage.

Keywords: Cloud, data, storage and compression.

References: 1. “Securing and Managing Data for Cloud Storage applications using High Throughput Compression (HTC) “, International Journal of Advanced Scientific and Technical Research , Issue 5 volume 3, May-June ,2015 , ISSN 2249-9954. 2. “Developing Secure Cloud Storage System by Applying AES and RSA cryptography algorithms with Role based Access Control Model”, International Journal of Computer Applications (ISSN: 0975-8887), Volume 118-No 12, May 2015. 3. “Secure User Data in Cloud Computing Using Encryption Algorithms” by RachnaArora, AnshuParashar , International Journal of Engineering Research and Applications , ISSN: 2248-9622, Vol-3, Issue 4, Jul-Aug 2013,pp.1922-1926. 4. “Data Security Using Compression and Cryptography Techniques” by Ruchita Sharma and SwarnalataBollavarapu, International Journal of Computer Applications (ISSN: 0975-8887), Volume 117-No 14, May 2015. 5. “Data Security Algorithms for Cloud Storage System using Cryptographic Method” byPrakash G L, Dr. Manish Prateek, and Dr. Inder Singh, International Journal of Scientific & Engineering Research, Volume 5, Issue 3, March -2014, ISSN 2229-5518. 6. “New mechanism for Cloud Computing Storage Security” by AlmokhtarAit El Mrabti, NajimAmmari, AnasAbou El Kalam, AbdellahAitOuahman OSCARS Laboratory, National School of Applied Sciences, CadiAyyad University ,arrakesh, Morocco,Mina Montfort ,ARTIMIA, 75 Street Guy Mˆoquet, 92240 Malakoff, France, (IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 7, No. 7, 2016. 7. C:\Documents and Settings\DELL\Local Settings\temp\IM\LZSS (LZ77) Discussion and Implementation.mht. 8. D.A. Huffman, "A Method for the Construction of Minimum Redundancy Codes", Proceedings of the I.R.E., September 1952, pp 1098- 1102. 9. T. Bell, J. Cleary, and I. Witten, “Data compression using adaptive coding and partial string matching,” IEEE Transactions on Communications, Vol. 32 (4), p. 396-402, 1984. 10. Moffat, Implementing the PPM data compression scheme, IEEE Transactions on Communications, Vol. 38 (11), pp. 1917- 1921,November1990. 11. Ziv, J., & Lempel, A. “A Universal Algorithm for Sequential Data Compression,” IEEE Transactions on Information Theory, 23(3), pp.337- 343, May 1977. 12. Ziv, J., & Lempel, A. “Compression of individual sequences via variable-rate coding,” IEEE Trans. Inform. Theory, 24(5), 530-536, September 1978. Authors: Imma Widyawati Agustin, Christia Meidiana Paper Title: Accident Model of Car in Urban Area of Surabaya Abstract: The amount of private transportation more than mass transportation is one of the factors causing high traffic density. The interest of the Indonesian population to use mass transportation is still low, consequently many still choose to use private transportation. This mode of personal transportation is not effective because the load factor is low that is about 1 to 5 people per vehicle on the roads. The main purpose of the research was to create accident model of car in urban area. The research used generalized linear model (GLM) method. The results showed that the car drivers in Surabaya are often involved in accidents are male and aged in the range of 15 to 25 years. Geometric characteristic of roads in Surabaya City are wide of lane: 7.4 to 28.0, number of lanes: 2 - 6, shoulder width: 0 to 3 m, speed: 19.55 to 55.88 km per hour, vehicle volume: 1845,83 pcu per hour to 12594,03 pcu per hour. Model car accidents in several segments of Surabaya that formed: McA = 0.00225FLOW1, 030.EXP (.0034 Speed).

Keywords: Accident-Model, Driver, Urban-Area, Generalized-Linear-Model,

References: 12. 1. Ambarwati, Lasmini., Sulistio, Harnen., Negara, Hendika, G., Hariadi, Z., 2010. Characteristic and Accident Probability on Private Car in Urban Area. Rekayasa Sipil Journal :Vol. 4, No. 2, pp. 124-135. 54-56 2. Bolla, Margareth E., Tri Mardiyati W.Sir, Christofel N. Bara., 2014. Pemodelan Kecelakaan Sepeda Motor pada Ruas Jalan di Kota Atambua. Kupang :FST UNDANA, Kupang. 3. Mark, W, Hoglund., 2017. Safety-Oriented Bicycling and Traffic Accident involvement. IATSS Research. 4. Masayoshi, Tanishita., Bert, Van, Wee., 2017. Impact of Vehicle Speeds and Changes in Mean Speeds on per Vehicle-kilometer Traffic Accident Rates in Japan. IATSS Research 41, pp. 107-112. 5. Paraskevi, Michalaki., Mohammed, A, Quddus., David, Pitfield., Andrew, Huetson., 2015. Exploring the Factor Affecting Motorway Accident Severity in England Using the Generalised Ordered Logistic Regression Model. Journal of Safety Research 55, pp. 89-97. 6. Petr, Pokorny., Jerome, Drescher., Kelly, Pitera., Thomas, Jonsson., 2017. Accidents Between Freight Vehicles and Bicycles, with a Focus on Urban Area. Transportation Research Procedia 25, pp. 999-1007. 7. Rui, Garrido., Ana, Bastos., Ana, de Almeida., Jose, Paulo, Elvas., 2014. Prediction of Road Accident Severity Using The Ordered Probit Model. Transportation Research Procedia 3, pp. 214-223. 8. Taylor, M., Kennedy, J.V., and Baruya, A., 2002. The Relationship Between Speed and Accidents on Rural Single-Carriageway Roads. Report TRL 511. Crowthorne, UK 9. Wiranto, Eddi., Setyawan, Ary., Sumarsono, A., 2014. Evaluasi Tingkat Kerawanan Kecelakaan pada Ruas Jalan Boyolali-Ampil km. 29+000 – 34+000. Matriks Teknik Sipil. 10. Yannis, George., Theofilatos, Athanasios., Pispiringos, George., 2017. Investigation of Road Accident Severity per Vehicle Type. Transportation Research Procedia 25, pp. 2076-2083. Authors: R. Renuga, k. Reshma Paper Title: Tweets Mining for Classification and Rapid Response for Pessimistic Ones Abstract: is virtual network of communities/groups where people can create and share media /opinions/ideas on various things/ objects/ persons/ topics related to entertainment, sports, politics, science, travel etc. Twitter is the most popular micro-blogging site, which provides access to the data for non-commercial and research purpose. Mining topics in Twitter is increasingly attracting more attention. Our aim is to automate the process of 13. responding to pessimistic tweets in feedback given to electronic products. In this way customer will be satisfied by quick response from company and retailer will get some time to work on the customer problem. So Customer- 57-60 Retailer relationship can be improved. In this paper we present how Open source social media intelligence (OSSMInt) can be applied on twitter tweets to extract necessary information from the feedback that has been tweeted by customers of a particular product and quick response to those customers who are dissatisfied with product. Here we concentrate on negative feedback and satisfy the customer temporarily by allowing the retailer to work on the product. We use necessary algorithms, tools and techniques to get the data from twitter, classify it and reply the customer with quick response who has given negative feedback. This classification can be shared with a data scientist for further procedure to get a solution to the problem escalated by the customer. This works as a connecting bridge between a customer and retailer.

Keywords: This classification can be shared with a data scientist for further procedure to get a solution to the problem escalated by the customer.

References: 1. Swati Agarwal., Ashish Sureka.: Investigating the Role of Twitter in E-Governance by Extracting Information on Citizen Complaints and Grievances Reports. 2. Agarwal, S., Mittal, N., Sureka, A.: Potholes and bad road conditions-mining Twitter to extract information on killer roads. In: The ACM India Joint International Conference (CoDS- COMAD), India. ACM (2018, under-review) 3. Agarwal, S., Sureka, A.: Using KNN and SVM based one- class classifier for detecting online radicalization on Twitter. In: Natarajan, R., Barua, G., Patra, M.R. (eds.) ICDCIT 2015. LNCS, vol. 8956, pp. 431-442. Springer, Cham (2015). 4. Agarwal, S., Sureka, A.: Investigating the potential of aggregated tweets as surro- gate data for forecasting civil protests. In: Pro- ceedings of the 3rd IKDD Conference on Data Science, p. 8. ACM (2016) 5. Mittal, N., Agarwal, S., Sureka, A.: Got a complaint?- Keep calm and tweet it!.In: Li, J., Li, X., Wang, S., Li, J., Sheng, Q.Z. (eds.) ADMA 2016. LNCS (LNAI), vol. 10086, pp. 619-635. Springer, Cham (2016). 6. Agarwal, A., Xie, B., Vovsha, I., Rambow, O., Passonneau, R. (2011). Sentiment analysis of Twitter data. In Proc. WLSM-11s. Davidov, D., Tsur, O., Rappoport, A. (2010). Enhanced Sentiment Learning Using Twitter Hashtags and Smileys. ICCL-10. 7. De Smedt, T., Daelemans, W. (2012). Pattern for Python. Journal of Machine Learning Research, 13, 2063-2067. 8. Fellbaum, C. (1998).WordNet: An Electronic Lexical Database. Cambridge, MA: MIT Press. 9. C.J. Hutto.,Eric Gilbert.: VADER: A Parsimonious Rule-based Model for Sentiment Analysis of Social Media Text. Authors: Septiana Hariya Paper Title: How to Optimize Service Quality of City Bus in Bandung City Abstract: The rapid growth of the population affects the increase in the number of private vehicles, but the increase is not matched by the ability of public transport in providing good services related to security, safety, comfort, affordability, and equality. This study aims to evaluate the service performance of the city bus so that it could be seen improvements of the performance of urban bus service in Bandung. The method of analysis used in this research was Importance Performance Analysis (IPA). Based on the IPA performance of urban bus service still needs to be improved. There are several service attributes that become the main priority in improving the performance of the city bus service that is security, safety, comfort, affordability, and equality both for bus stop and bus.

Keywords: Sevice-Quality, City-Bus, IPA (Importance Performance Analysis),

References: 1. Arraningrum, Wuri, 2013. Peningkatan Kualitas Pelayanan Pegawai dengan Menggunakan Integrasi Metode Importance Performance Analysis (IPA)-Quality Function Dployment (QFD) Jejaring Administrasi Publik. Retrive 12 December 2017, from http://journal.unair.ac.id/Download:fullpapers-dmpf8207b57fefull.pdf 2. Basuki, Imam dan Susanto, Benidiktus. 2014. Kajian Penerapan Angkutan Umum Perkotaan Tanpa Bayar. Procedings The 17 th FSTPT International Symposium Vol.2 No.1, Jember University, 22-24 August 2014. 14. 3. Daniels M.J., Harmon. K, Laurlyn K., Vese Jr.Rodney., Park, Minkyung, Brayley. E, Russell. 2018. Tourism Management 64. Spatial dynamics of tour bus transport within urban destinations. pp.129-141 61-64 4. Dwiryanti, Aprisia Esty dan Ratnasari, Anita. 2013. Analisis Kinerja Pelayanan Bus Rapid Transit (BRT) Koridor II Terboyo –Sisemut (Studi Kasus: Rute Terboyo – Sisemut Kota Semarang). Semarang: Universitas Diponegoro. (Jurnal Teknik PWK Volume 2 Nomor 3 tahun 2013) 5. Eudy, L., Caton, M., Post, M., 2014. Transit Investments for Greenhouse Gas and Energy Reduction Program: Second Assessment Report. Technical Report No. 0064, Federal Transit Administration. 6. Hariyani, S. 2017. School bus’s level of service in Malang City. IOP Conf. Series: Earth and Environmental Science 70 012023. 7. Herdiana, Sony. 2012. Evaluasi Kinerja Pelayanan Angkutan Bus Damri Kota Bandung Berdasarkan Persepsi Pengguna dan Pengelola. Bandung: Institut Teknologi Nasional. (Reka Loka Jurnal Online Institut Teknologi Nasional) 8. Mahmoud, M., Garnett, R., Ferguson, M., Kanaroglou, P., 2016. Electric buses: a review of alternative powertrains. Renew. Sustain. Energy Rev. 62, pp. 673–684. 9. Ratanavaraha, V., Jomnonkwao, S., Khampirat, B., Watthanaklang, D., & Iamtrakul, P. 2016. The complex relationship between school policy, service quality, satisfaction, and loyalty for educational tour bus services: A multilevel modeling approach. Transport Policy, 45, pp. 116-126. 10. Regulation of the Minister of Transportation of the Republic of Indonesia No. PM. 10 Year 2012 on Minimum Service Standards of Road- Based Mass Transportation 11. Sevilla, C.G., Jesus A.O., Twila G.P., Bella P.R., Gabriel G.U. 1993. Research Methods, Rex Printing Co. Inc., Quezon City. 12. Thompson, K., and Schofield, P. 2007. An investigation of the relationship between public transport performance and destination satisfaction. Journal of Transport Geography, 15, pp. 136-144. 13. Wang.Y, Huang. Y, Xu. J, Barclay.N. 2017. Optimal recharging scheduling for urban electric buses: A case study in Davis. Transportation Research Part E 100 pp. 115–132. Authors: R. Balakrishna, R. Anandan Paper Title: Early Diagnosis of Chronic and Acute Pancreatitis using Modern Soft Computing Techniques Abstract: The analysis of pancreatic cancer at an incipient stage is very crucial for raising the endurance of the patients. The infection of pancreas is called as Pancreatitis, either be acute (sudden and severe) or chronic (ongoing). Across the globe, Pancreatic cancer is the fourth most cause of cancer related to death and the most challenging 15. aspect of pancreatic cancer is diagnosing at an incipient stage. The pancreas is a gland that disguises digestive 65-69 enzymes as well as important hormones and the most common causes of chronic pancreatitis is heavy consumption of alcohol, followed by gallstones. Current work presents the creation of datasets which comprises of pancreas images and it is segregated by using Big Data analytics tools like Hadoop, Next the segregated images are preprocessed for removal of noises or any other disturbance which occurs in the images. The preprocessing is done by Wiener’s filter and the PSNR, MSE and SNR values are noted. Next the preprocessed image is segmented, In order to find the region of interest and the segmentation is performed by machine learning algorithm called Support Vector Machine (SVM).Finally we need to extract the features of the images in the region of interest identified in the segmentation process. The promising results indicate that pancreatic cancer can be diagnosed with high accuracy.

Keywords: Pancreas, Preprocessing, Pancreatitis, Segmentation, Extraction, Prediction and Incipient.

References: 1. Shijin Kumar P.S and Dharun V.S, “Extraction of Texture Features using GLCM and Shape Features using Connected Regions”, International Journal of Engineering and Technology (IJET), Vol 8 No 6 Dec 2016-Jan 2017. 2. Balakrishna and R.Anandan, “Soft Computing Analysis for Detection of Pancreatic Cancer Using MATLAB”, International Journal of Pure and Applied Mathematics (IJPAM), Volume 119 No. 18 2018, 379-392. 3. Padmasheela keshvan, Dr.R.Anandan, R.Balakrishna, “Segmentation Of Pancreatic Tumor Using Region Based Active Contour”, Journal of Advanced Research in Dynamical and Control Systems (JARDCS), Spl. Issue (04), June 2017. 4. Shijin Kumar P S and Dharun V S, “Hybrid Brain MRI Segmentation Algorithm Based on K-means Clustering and Texture Pattern Matrix”, International Journal of Applied Engineering Research. Volume 11,No. 6 (2016). 5. Wolz, R., Chu, C., Misawa, K., Fujiwara, M., Mori, K. and Rueckert, D. “Automated abdominal multi-organ segmentation with subject- specific atlas generation”, IEEE transactions on medical imaging 32 (9) (2013) 1723-1730. 6. Wolfgang CL, Herman JM, Laheru DA, Klein AP, Erdek MA, Fishman EK, Hruban RH, "Recent progress in pancreatic cancer CA”, A Cancer Journal for Clinicians 63 (5), September 2013. 7. Hiremath P, Shivashankar S. “Wavelet basedfeatures for texture classification”, Graphics,Vision and Image Processing Journal;6:55- 8.2006. 8. Strumia M, Schmidt FR, Anastasopoulos C, Granziera C, Krueger G, Brox T, “White Matter MS-Lesion Segmentation using a Geometric Brain Model”, IEEE Trans Med Imaging. 2016 Jul;35(7):1636-46. 9. C.Bhuvaneswari,P.Aruna,D.Loganathan,” Classification of Lung Diseases by Image Processing Techniques Using Computed Tomography Images”, International Journal of Advanced Computer Research, Volume-4 Number-1 Issue-14 March-2014. 10. S.A. Sherman ; H.T. Lynch ; V.R. Haynatzka ; R.E.Brand ; G.R.Haynatzki ; S.A.Sherman ; H.T.Lynch ; V.R.Haynatzka ;R.E.Brand ; G.R. Haynatzki, “A Comparison of Statistical Approaches for Genetic Anticipation with Application to Pancreatic Cancer”, IEEE Conferences, 2007. 11. Xavier Bresson ; Jean-philippe Thiran “Image Segmentation Model using Active Contour and Image Decomposition” IEEE Conferences, 2006. Authors: Yernazarova A., Kaiyrmanova G., Zhubanova A. Paper Title: Microorganisms in Oil Reservoirs of West Kazakhstan Abstract: Nowadays, the oil and gas branch of Kazakhstan is the largest, fastest growing area, where development of oil and gas is steadily increasing. The efficiency of oil extraction of from oil-bearing strata by modern, industrial by mastered methods of development in all oil-producing countries for today is considered to be unsatisfactory, despite of the fact that the consumption of petroleum products in the world is growing every year . The average ultimate of oil recovery in different countries and regions is from 25 to 40%.One of the most effective method for enhanced oil recovery is the microbial enhanced oil recovery (MEOR). The use of microbiological approaches in the development of the ways of increasing oil production requires a thorough screening of active microbial strains with a high target activity among a large diversity of microorganisms' species of a natural microflora objects of the environment on the territory of the deposit. The aim of this work was the isolation microbial strains resistant to extreme conditions in oil-producing regions, for further constructing the microbial consortium for using in biotechnology development that leads to the oil recovery enhancement.

16. Keywords: Microbial Enhanced Oil Recovery, Microorganisms, 70-72 References: 1. Al-Bahry SN, Al-Wahaibi YM, Elshafie , Al-Bemani AS, Joshi SJ, Al-Makhmari HS, Al-Sulaimani HS. Biosurfactant production by Bacillus subtilis B20 using date molasses and its possible application in enhanced oil recovery. International Biodeterioration and Biodegradation. 2013; 81:141–146. 2. Al-Bahry SN, Elshafie AE, Al-Wahaibi YM, Al-Bemani AS, Joshi SJ, Al-Maaini RA, Al- Alawi WJ, Sugai Y, Al-Mandhari M. Microbial consortia in Oman oil fields: a possible use in enhanced oil recovery. Journal of Microbiology and Biotechnology. 2013; 23(1): 106–117. 3. Eden B, Laycock P, Fielder M. Oilfield Reservoir Souring. HSE Books; 1993. 90 p. 4. Jimoh IA. Microbial enhanced oil recovery. PhD thesis. Luma Print; 2012. 6700 Esbjerg. 5. Lien T, Madsen M, Rainey FA, Birkeland NK. Petrotoga mobilis sp. nov., from a North Sea oil production well. International Journal of Systematic Bacteriology. 1998; 48:1007– 1013. 6. Miroshnikov V, Kurbanbayev MI, Tolokonskyi S. EOR at fields in Kazakhstan. In: Proceeding of International Symposium on Theory and Practice of Application of Enhanced Oil Recovery Methods, ; 2011. p. 34–41 (in Russian). 7. Nazina TN, Pavlov NK, Tatarkin Y, Shestakov NM, Babich TL, Sokolov DS, Ivoylov VS, Turov TP, Hisametdinov MR, Ibatullin RR, Belyaev SS, Ivanov MV. The development of biotechnology increase the degree of oil recovery from carbonate oil reservoirs on the territory of the Republic of Tatarstan. Electronic Scientific Journal Georesources. Geoenergetika. Geopolitics. 2012;2(6):1–6 (in Russian). Authors: R. Gayathri, Pradeep Kumar, PonMary Pushpa Latha, J. Paper Title: Design of Air Storage Tank and Run Time Calculation for Supersonic Blow Down Wind Tunnel Abstract: The probed analysis report pertaining to the effectual design of the air storage tank, together with experimental findings incorporated into the piping element and pipe bends, brings to light concerning the efficient theory of thick –walled cylindrical vessels, with hemi-spherical heads, on meticulous consideration of parameters ranging from mechanical properties to economic feasibility. Mild steel was identified to be the apt material for the 17. construction of the tank and together by aiding in product design tasks.Moreover, electroplating and passivation 73-77 processes were carried out for providing strength to the selected material. Passing ahead of the limitations imposed by this design, calculated results end up by satisfying the boundary conditions. Finally, after estimating the suitable material for pipes and bends, the product design triumphantly surges ahead to be wholesome in efficiency.

Keywords: Supersonic wind tunnel, runtime, blow down, storage tank and piping

References: 1. Bhavin Bharath, Design and Fabrication of a Supersonic wind tunnel, International Journal of Engineering and Applied Sciences, Volume-2, Issue-5, pp.103-107( 2015). 2. Alan Pope and Kennith L.Goin, “High speed wind tunnel testing”, John Wiley&Sons, Inc., New York,1978. 3. Gitin M Maitra and LV Prasad, “Hand Book of Mechanical Design”, Tata McGraw-Hill, 1985 4. Bansal R K, “Strength of Materials”, Lakshmi Publishing co, New Delhi, 2007. 5. Ramamurtham S, ”Strength of Materials”, Dhanpat Rai Publishing co, New Delhi, 2008. 6. Rajput R K, ” Strength of Materials”,2006. 7. Anderson J. D., Modern Compressible Flow with Historical Perspective, McGraw- Hill Inc., New York, 1990 8. Joji matsumoto ,”Design and testing of a subscale supersonic Aero propulsion wind tunnel”, Presented to the Faculty of the Graduate School of The University of Texas at Arlington (2000). 9. Design of a supersonic nozzle by A. McCABE Ph.D. the Mechanics of fluid Department, University of Manchester, Aeronautical Research Council Reports and Memoranda. 10. High speed wind tunnel and test systems design hand book, Lockheed Martin Misiles And Fire Control, Publication number Aer-Eir-13552- E. 11. H. W. Liepmann, A. Roshiko, ELEMENTS OF GASDYNAMICS California Institute of technology, John Wiley & sons, New York 1957. Authors: Seungkyung Park, Seungmo Kim, Dongho Shin, Geum-Su Yeom Paper Title: Experimental Study of Electric Field Assisted Bioaerosol Collector Abstract: Analysis of airborne biological particles essentially requires an efficient sample collection method. In this paper, we have designed and tested a small scale bioaerosol collector based on cyclone and external electric field. The efficiency of the system was evaluated by model particles, potassium chloride. Several parameters including cyclone geometry, flow rate, and applied voltage have been experimentally tested and the effects on the collection efficiency were studied. As a result, the presented bioaerosol collector showed a collection efficiency of approximately 65% for 0.3 m sized particles at the flow rate of 400 lpm and 25 kV voltage. Demonstrating the potential for high efficiency bioaerosol collector.

Keywords: Bio-Aerosol, Electrokinetic, Cyclone, Filtration.

18. References: 1. HEI. State of Global Air 2017. Special Report. 2017. 78-81 2. WHO. Air quality guidelines for particulate matter, ozone, nitrogen dioxide and sulfur dioxide. 2005. 3. W.Bergman. Further Development of the Cleanable Steel HEPA Filter, Cost/Benefit Analysis, and Comparison with Competing Technologies. 1996. 4. Abraham G. HEPA Filter Replacement Experience in a Biological Laboratory. Journal of the American Biological Safety Association. 1999:134-42. 5. Sung B-J. Fine-Particle Collection Using an Electrostatic Precipitator Equipped With an Electrostatic Flocking Filter as the Collecting Electrode. Plasma Process Polym. 2006:661-7. 6. Barth W. Design and Layout of the Cyclone Separator on the Basis of New Investigations. BWK. 1956. 7. Iozia DL, and Leith, D. The Logistic Function and Cyclone Fractional Efficiency. Aerosol Sci Technol. 1990. 8. Plucinski J. Collection of aerosol particles in a cyclone with an external electic field. Aerosol Sci. 1989;20:6. 9. Peyrous R. The Effect Of Relative Humidity On Ozone Production By Corona Discharge In Oxygen Or Air - A Numerical Simulation - Part II : Air. OZONE SCIENCE & ENGINEERING. 1990;12:41-64. 10. Park S. Performance Prediction Model for Designing a Multiple Electro-cyclone System. 2017;14:325-331. Authors: S. Pramothini, Y.V.V.S. Sai Pavan, N. Harini Paper Title: Securing Images with Fingerprint Data using Steganography and Blockchain Abstract: Innovation of technology and the availability of fast Internet makes information distribution over the world easy and economical. This has attracted many adversaries who work for stealing and tampering the privacy and the works of legitimate users. Steganography and digital watermarking techniques have been continuously adopted in research for enhancing the security of data, specifically images. Recent research has shown the ability of blockchain to play an integral role in storage and distribution of medical images in a more secure fashion. With the aim of verifying the suitability of blockchains to offer an efficient and autonomous mechanism for securing images. A scheme that uses steganography to ensure confidentiality and blockchains to ensure non-repudiation is presented in this paper. Detailed experimentation brought out the ability of the proposed scheme in terms of enhancing the security compared to existing schemes in the literature.

Keywords: Blockchain, Non-repudiation, Hash, Steganography, Fingerprint 19. References: 82-85 1. S. Kaur, S. Bansal and R. K. Bansal, "Steganography and classification of image steganography techniques," 2014 International Conference on Computing for Sustainable Global Development (INDIACom), New Delhi, 2014, pp. 870-875. A. A. J. Altaay, S. B. Sahib and M. Zamani, "An Introduction to Image Steganography Techniques," 2012 International Conference on Advanced Computer Science Applications and Technologies (ACSAT), Kuala Lumpur, 2012, pp. 122-126. 2. Tulyakov Sergey, Farooq Faisal, Govindaraju Venu, “Symmetric Hash Functions for Fingerprint Minutiae”, Springer Berlin Heidelberg ,Berlin, Heidelberg. 3. R. Cappelli, M. Ferrara and D. Maltoni, "Fingerprint Indexing Based on Minutia Cylinder-Code," in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 33, no. 5, pp. 1051-1057, May 2011. 4. Dr T.R Padmanabhan, N.Harini and Dr.C.K.Shyamala, “Cryptography and security”, WileyIndia, FirstEdition, 2011 5. Cox, Ingemar & Miller, Matthew & Bloom, Jeffrey & Fridrich, Jessica & Kalker, Ton, “Digital Watermarking and Steganography”, 6. Yafeng Zhou and W. W. Y. Ng, "A study of influence between digital watermarking and steganography," 2013 International Conference on Wavelet Analysis and Pattern Recognition, Tianjin, 2013, pp. 49-55 7. doi: 10.1109/ICWAPR.2013.6599291 8. L. S. Sankar, Sindhu, M., and Sethumadhavan, M., “Survey of consensus protocols on blockchain applications”, in 2017 4th International Conference on Advanced Computing and Communication Systems, ICACCS 2017, 2017 9. Z. Zheng, S. Xie, H. Dai, X. Chen and H. Wang, "An Overview of Blockchain Technology: Architecture, Consensus, and Future Trends," 2017 IEEE International Congress on Big Data (BigData Congress), Honolulu, HI, 2017, pp. 557-564 10. Rose Leaden, “A Snapshot of Online Image Theft”, Entrepreneur India (2018) , Berify, https://www.entrepreneur.com/article/309876 11. Andrea Feustel, “Image Theft Ranking”, Copy Track (2017), https://www.copytrack.com/image-theft-ranking 12. Sergey Tulyakov, Faisal Farooq, Praveer Mansukhani, Venu Govindaraju, “Symmetric hash functions for secure fingerprint biometric systems”,Pattern Recognition Letters, Volume 28, Issue 16, 2007, Pages 2427-2436, ISSN 0167-8655 13. S. Anjana and Amritha, P. P., “A Novel Method for Secure Image Steganography”, in Artificial Intelligence and Evolutionary Algorithms in Engineering Systems: Proceedings of ICAEES 2014, Volume 1, P. L. Suresh, Dash, S. Subhransu, and Panigrahi, K. Bijaya New Delhi: Springer India, 2015, pp. 151–158 14. Abira Dasgupta ,Rajesh kumar tiwari ,Arup paul, “Digital watermarking and Steganography Techniques:A Technical overview”, International Journal of Computer Engineering and Applications, Special Edition, www.ijcea.com ISSN 2321-3469 15. H. G. Do and W. K. Ng, "Blockchain-Based System for Secure Data Storage with Private Keyword Search," 2017 IEEE World Congress on Services (SERVICES), Honolulu, HI, 2017, pp. 90-93. doi: 10.1109/SERVICES.2017.23 16. W. Pourmajidi and A. Miranskyy, "Logchain: Blockchain-Assisted Log Storage," 2018 IEEE 11th International Conference on Cloud Computing (CLOUD), San Francisco, CA, 2018, pp. 978-982. doi: 10.1109/CLOUD.2018.00150 Authors: Jestin, J., Mohd Zaid Othman, Ahmad Mujahid Ahmad Zaidi Investigation of Trinitrotoluene (TNT) Equivalent Ratio of Spherical PE4 Charge Detonated on Non- Paper Title: Rigid Ground Surface Based on Peak Incident Overpressure Values Abstract: Peak incident overpressure is a parameter which is commonly used to estimate explosive performance. However, the peak incident overpressure of an explosive differs from one another depending on various factors. Therefore, it is convenient to equate the overpressure of an explosive in question to the equivalent overpressure of a standard material such as trinitrotoluene (TNT). TNT equivalent ratio for a number of high explosives can be found in literature except for plastic explosive (PE4) which is often of limited references. In this paper, an investigation of TNT equivalent ratio of spherical PE4 charge detonated on non-rigid ground surface at standoff distances of 1 m to 3.1 m was carried out using numerical simulation software and validated with experimental results. Results showed that TNT equivalent ratio 1.37 appeared to be reasonably well for spherical PE4 charge detonations on non-rigid ground surface at a standoff distance of 1 m whereas 1.19 was appropriate for standoff distances of 2.6 m and 3.1 m. This indicates that TNT equivalent ratio based on peak incident overpressure varies with distance and therefore two values of TNT equivalent ratio are proposed in this case study.

Keywords: TNT Equivalent Ratio, Peak Incident Overpressure, Plastic Explosive and AUTODYN3D,

20. References: 1. Ngo, T., Mendis, P., Gupta, A., & Ramsay, J. (2007). Blast Loading and Blast Effects on Structures An Overview. Electronic Journal of Structural Engineering, 7, 76–91. https://doi.org/no DOI 86-89 2. Smith, P. D., & Hetherington, J. G. (1994). Blast and Ballistic Loading of Structures. (P. D. Smith & J. G. Hetherington, Eds.), Butterworth- Heinemann Ltd (1st ed.). Butterworth-Heinemann Ltd. 3. Kinney, G.F. & Graham, K.J. (1985). Explosive Shocks in Air. 2nd Edition, Springer Verlag. 4. Kingery, C. N. and Bulmash, G. (1984). Airblast Parameters from TNT Spherical Air Burst and Hemispherical Surface Burst. Technical Report ARBRL-TR-02555, U.S. Army Ballistic Research Laboratory, Aberdeen Proving Ground, MD. 5. Formby, S. A. and Wharton, R. K. (1996). Blast Characteristics and TNT Equivalence Values for Some Commercial Explosives Detonated at Ground Level. Journal of Hazardous Materials, 50, 83–198. 6. Wharton, R. K, Formby, S. A. and Merrifield, R. (2000). Airblast TNT Equivalence for a Range of Commercial Blasting Explosives. Journal of Hazardous Materials, 79, 31–39. 7. Rigby, S. E. and Sielicki, P. W. (2014). An Investigation of TNT Equivalence of Hemispherical PE4 Charges. Engineering Transactions, 62 (4), 423–435 8. Weckert, S. and Anderson, C. (2006). A Preliminary Comparison between TNT and PE4 Landmines. Australian Government, Department of Defence. 9. Jestin J., Faisal Ali., Mohd Zaid Othman, Ahmad Mujahid Ahmad Zaidi, Hapsa Husen (2016). Performance of small scale hexagonal portable soil-filled barrier subjected to blast load. Electronic Journal of Geotechnical Engineering, 21 (5), 1809-1817. 10. Laine, L. & Sandvik, A. (2001). Derivation of Mechanical Properties for Sand. Proceedings of 4th Asia-Pacific Conference on Shock & Impact Loads on Structures, 361-368. 11. Hyde, D. W. (1991). Conventional Weapons Program (ConWep), U.S Army Waterways Experimental Station, Vicksburg, MS, US Authors: S. Shalini, A.M. Aswini Priyadharssini, M. Saranyaa, R. Sushmi, P. Dhinesh Kumar Paper Title: Efficient Implementation of Unmanned Ground and Ariel Vehicle with Nano-Quadcopter Abstract: Technologies have developed far aside in the Defence field. To make more efficient, we have designed a new modern efficient SPY BOT which plays a major role in spying and as a support. This robot is small and easy to transport. The intervention troop uses a camera to capture the data such as images and videos . The intention is to reduce human victims in terrorist attacks. Thus by designing a RF based spy robot which has a wireless camera and Nano-quad, which can spy enemies secretly and can enter in restricted areas too.

Keywords: RF (Radio Frequency), GPS (Global Positioning System), PIR (Passive Infrared), UDM (Ultrasonic Distance Meter), quad copter, live-session, spy robot. 21. References: 90-92 1. www.circuitstoday.com “Mobile Operated Spy Robot” 2. www.retron.com “Wireless Spy Camera Robots” 3. Microchip “PIC16F87X Data Sheet 40-pin8-bit CMOS FLASH Microcontroller”, ISO 9001/QS-9000, Microchip Technology Incorporated, USA, 2001. 4. Microchip, “PIC 16F627A/628A/648A Data Sheet”, ISO/TS 16949:2002, Microchip Technology Incorporated, USA, 2005. 5. D. Ibrahim, “Microcontroller Based Applied Digital Control”, ISBN: 0-470-86335-8, John Wiley and Sons, Ltd, England, 2006. 6. J.Iovine, “PIC Robotics: A Beginner’s Guide to Robotics Projects Using the PIC Micro”, McGraw-Hill, 2004 7. Wilmshurst, “Designing Embedded Systems with PIC Micro controllers”, ISBN-10: 0-7506-6755-9, Elsevier, 2007. 8. P. Robert, “Introduction to Gear Design” , Continuing Education and Development , Course No:M03-016,2012. 9. Ad Hoc, Project Report, 2D1426 Robotics and Autonomous Systems. 10. http://en.wikipedia.org/wiki/Charge-coupled device 11. http://en.wikipedia.org/wiki/DC_ Motor 12. http://www.campuscomponent.com “L298N Motor Driver” 13. Robert L Boylestad and Louis Nashelsky , “Electronic Device and Circuit Theory” , 8th edition 2006. 14. Mr.Lokesh Mehta, Mr.Pawan Sharma “Spy Night Vision Robot with Moving Wireless Video Camera”. International journal of research in engineering technology and management (IJRETM), 2014 15. The 8051 microcontroller and embedded system using assembly and C , second edition (ISBN: 9780131194021) by Mazidi Muhammad Ali (2008) 16. Dhiraj Singh Patel “Mobile Operated Spy Robot “International journal of emerging technology and advanced engineering (IJETAE), 2013 17. KalyaneeN.Kapadnisetal.Int.journal of engineering research and applications, ISSN: 22489622.2014; 4(4):06-09p 18. Mr.Lokesh Mehta, Mr.Pawan Sharma, International journal of research in engineering technology and management, ISSN 2347-7539. 19. Wai Mo MoKhaing, KyawThiha, International Journal of Science, engineering and technology research (IJSETR), 2014; 3(7) 20. KunjGudhka, Aishwarya Kadam, Devika Kale, et al. International journal of electrical and electronics. Authors: Byung Gyoo Kang, Yazhong Zhang, Ruoyu Jin, Craig Matthew Hancock, Bo Li, Llewellyn Tang Paper Title: An Investigation into the Application of 3-D Printing to Construction Projects in Ningbo, China Abstract: 3-D printing brings huge potentials to construction projects such as time, cost, quality and safety. Objectives of construction projects can be enhanced and achieved with superior performances. This research has investigated the perceptions of construction engineers towards the benefits and obstacles of 3 –D printing in construction projects. The questionnaire survey was conducted in Ningbo, China. The survey participants include building engineers, architects, civil engineers, project managers, MEP engineers and consulting engineers. A total of 100 replies were received. The respondents show very positive perceptions towards the benefits of 3-D printing. ‘Reduced material wastage’, ‘reduced equipment usage’ and ‘improved safety in construction site’ are the top three benefits. The obstacles are related to technical competence, materials and size/cost of 3-D printer. Outcomes of the survey are discussed in detail with future researches, especially the integration between 3-D printing and Building Information Modelling (BIM).

Keywords: 3-D printing, Construction, Benefits, Obstacles.

References: 22. 1. Anjuma, T., Dongrea, P., Misbaha, F., and Nanyama, V. N. 2017. Purview of 3-DP in the Indian Built Environment Sector, Creative Construction Conference 2017, 19-22 June 2017, Primosten, Croatia, 228 – 235. 2. Archdaily 2 Sep. 2014. Chinese Company Showcases Ten 3-D-Printed Houses. https://www.archdaily.com/543518/chinese-company- 93-95 showcases-ten-3-D-printed-houses/ [Accessed 23 Apr. 2018] 3. Archdaily 26 Jan 2015 Chinese Company Constructs the World's Tallest 3-D Printed Building. https://www.archdaily.com/591331/chinese- company-creates-the-world-s-tallest-3-D-printed-building [Accessed 24 Apr 2018] 4. Bogue, R. 2013. 3-D printing: the dawn of a new era in manufacturing? Assembly Automation. 33(4): 307-311. 5. Bradshaw, S., Bowyer, A. and Haufe, P. 2010. The intellectual property implications of low-cost 3D printing. ScriptEd 7 (1):5–31. 6. Eastman, C., Teicholz, P., Sacks R. and Liston, K. 2011. BIM Handbook: A Guide to Building Information Modeling for Owners, Managers, Designers, Engineers and Contractors. John Wiley & Sons. 7. Lim, S., Buswell, R. A., Le, T. T., Austin, S. A., Gibb, A. G. F. and Thorpe, T. 2012. Developments in construction-scale additive manufacturing processes. Automation in Construction. 21: 262-268. 8. Nematollahia, B., Xiab, M. and Sanjayanc, J. 2017. Current Progress of 3-D Concrete Printing Technologies. 34th International Symposium on Automation and Robotics in Construction. 9. R.A. Buswell, Soar, R.C., Gibb, A.G.F. and Thorpe A. 2006 Freeform construction: mega-scale rapid manufacturing for construction, Automation in Construction. 16: 224–231. 10. The Telegraph 6 Jun 2016 China makes the world's first functional 3-D-printed building in . https://www.telegraph.co.uk/sponsored/china-watch/technology/12210678/china- makes-3-D-printed-building-dubai.html [Accessed 25 Apr 2018] 11. Wua, P., Wang, J., and Wang, X. 2016. A critical review of the use of 3-D printing in the construction industry. Automation in Construction. 68: 21–31. Authors: K. Sivakumar A.S. Prakaash Paper Title: Sequence Classifier Methods on Numerous Item set Mining by SVM in Operation Dataset Abstract: The adoption tree of the number of item sets includes the most recapitulate form of method unreservedly or apparently. As for the example, Support Vector Machine (SVM) system can be used to find out the approximate tree in the span of fashion with the accompany of the age group. Constant systems, definite or indefinite, helps to evaluate the estimation of the tree of item sets. In the sequence to explore the classification tree in the wide shape with the collaboration aspirant based generation SVM system can be executed. On the basis of classification tree is formed upon the joining, the execution tree would be used for the garnishing of the candidate. Systems like SVM take the help of vertical relationships among the prognosis databases in order to avoid the re-doing of the computation of the work done for the shorter designs comprising different dimensions. With the usage of maximum and closed designs of quick styles of the prospecting methods much better performances can also be attained. Full of 23. effectiveness-based development for quick figures prospecting systems are also analyzed in this blog. This paper most importantly reviewed about the quick designs of prospecting systems. 96-100

Keywords: In the sequence to explore the classification tree in the wide shape with the collaboration aspirant based generation SVM system can be executed

References: 1. Rich Caruana and Alexandru Niculescu-Mizil (2006), An Empirical Comparison of Supervised Learning Algorithm, 23rd International Conference on Machine Learning, Pittsburgh. 2. S.Bhatia, P. Prakash and G.N. Pillai, SVM based Decision Support System for Heart Disease Classification with Integercoded Genetic Algorithm to select critical features, Proceedings of the World Congress on Engineering and Computer Science, San Francisco, USA, pp.34- 38, 2008. 3. S.Ghumbre, C. Patil, and A.Ghatol, Heart disease diagnosis using support vector machine,Proceedings of the International Conference on Computer Science and Information Technology (ICCSIT '11), Pattaya, Thailand, 2011. 4. Priyanka AnandraoPatil, R. V. Mane,” Prediction of Students Performance Using Frequent Pattern Tree,” Sixth International Conference on Computational Intelligence and Communication Networks,2014 5. Mustafa Agaoglu, "Predicting Instructor Performance Using Data Mining Techniques in Higher Education," IEEE Access , Volume: 4 ,2016. 6. P Deepa Shenoy, Srinivasa K G, Venugopal K R and L M Patnaik, “Dynamic Association Rule Mining using Genetic Algorithms,” Intelligent Data Analysis, vol. 9, no. 5, pp. 439–453, 2005. 7. P Deepa Shenoy, Srinivasa K G, Venugopal K R and L M Patnaik,“Evolutionary Approach for Mining Association Rules on Dynamic Databases,”7th Pacific-Asia Conference on Knowledge Discovery and Data Mining(PAKDD)2003, Seoul, South Korea, pp. 325–336, 2003. 8. S. J. Rizvi and J. R. Haritsa, “Maintaining Data Privacy in Association Rule Mining,” Proceedings of the 28th International Conference on Very Large Data Bases, pp. 682–693, 2002. 9. S. M. Darwish, M. M. Madbouly, and M. A. ElHakeem, “A Database Sanitizing Algorithm for Hiding Sensitive Multi-level Association Rule mining,” International Journal of Computer and Communication Engineering, vol. 3, no. 4, pp. 285– 293, 2014. 10. J. Vaidya and C. Clifton, “Privacy Preserving Association Rule Mining in Vertically Partitioned Data,” Proceedings of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 639–644, 2002. 11. J. Vaidya and C. Clifton, “Secure Set Intersection Cardinality with Application to Association Rule Mining,” Journal of Computer Security, vol. 13, no. 4, pp. 593–622, 2005. 12. Kaur, P., Kaushal, S Security concerns in cloud computing. In: Accepted For International Conference on High Performance Architecture And Grid Computing-2011. Chitkara University, Rajpura (2011) Byung Gyoo Kang, Zhen Yan, Ruoyu Jin, Craig Matthew Hancock, Llewellyn Tang, Georgios Authors: Kapogiannis Paper Title: Current Practice of Building Information Modelling in the Ningbo Construction Industry Abstract: Building Information Modelling (BIM) is a revolution in the construction industry. Shanghai Disneyland, Phoenix Media Centre in Beijing, Shanghai Tower are good examples of BIM projects in China. However, BIM maturity levels for medium and large size cities need to be identified in depth. This research has investigated the level of BIM maturity in Ningbo, China. A quantitative questionnaire survey was conducted and 112 replies were received from construction engineers. The maturity of BIM in Ningbo has been identified as a transit from level one to level two, i.e. from lonely BIM to federated model. However, the engineers’ BIM competence level in Ningbo is still at the infant stage. Further, lack of BIM industry standard has been identified as the most significant risk. To overcome these problems, the industry level BIM education/training programme and BIM standards need to be developed and provided.

24. Keywords: BIM, Maturity, Construction. 101-104 References: 1. Autodesk. What is BIM? https://www.autodesk.co.uk/solutions/building-information-modeling/overview [Accessed 24 Mar 2018] 2. British Standards Institution (BSI) http://bim-level2.org/en/about/ [Accessed 20 Mar 2018] 3. Darius Migilinskas, Vladimir Popov, et.al.(2013) The Benefits, Obstacles and Problems of Practical Bim Implementation, Procedia Engineering, Vol. 57, pp. 767-774. 4. Eastman, C., Teicholz, P., Sacks R. and Liston, K. 2011. BIM Handbook: A Guide to Building Information Modeling for Owners, Managers, Designers, Engineers and Contractors. John Wiley & Sons. 5. MHURC (Ministry of Housing and Urban-Rural Construction of China). 2012. The Report of the Basic Situation of China Construction Development. China Construction Daily. Beijing. 6. National Building Specifications (NBS). BIM dimensions - 3D, 4D, 5D, 6D BIM explained. https://www.thenbs.com/knowledge/bim- dimensions-3d-4d-5d-6d-bim-explained. [Accessed 29 Mar 2018] 7. National Building Specifications (NBS). BIM Levels explained. https://www.thenbs.com/about-nbs [Accessed 24 Mar 2018] 8. Royal Institution of Chartered Surveyors (RICS). What is BIM? http://www.rics.org/uk/knowledge/glossary/bim-intro/ [Accessed 24 Mar 2018] Authors: Saish Bhende, Kutub Thakur, Jason Teseng, Md Liakat Ali, Nan Wang Paper Title: Character Recognition Using Hidden Markov Models Abstract: The need for the character recognition in today’s real time world has motivated us to do study on the process of HMM’s character recognition. This technique is the finest and accurate one to get a word or a character or a line to be recognized almost at 100 percent success rate. This paper shows the different steps to be followed during the iterations for the recognition. The paper basically reflects the introduction to the procedure to the prediction to the typical errors to the success rate of the method.

Keywords: This technique is the finest and accurate one to get a word or a character or a line to be recognized almost at 100 percent success rate.

References: 25. 1. J. Rocha & T. Pavlidis: A Shape Analysis Model with Applications to a Character Recognition System" IEEE Trans. Pattern Machine Intell., Vol. 16, No. 4, 1994 2. L. Wang & T. Pavlidis: Direct Gray-Scale Extraction of Features for Character Recognition" IEEE Trans. Pattern Machine Intell., Vol. 15, 105-110 No. 10, 1993 3. F. R. Chen, L. D. Wilcox & D. S. Bloomberg: Detecting and Locating Partially Specified Keywords in Scanned Images using Hidden Markov Models" Proc. Sec. Int. Conf. Doc. Anal. Recog., pp. 133{138, 1993. 4. M-Y. Chen, A. Kundu& J. Zhou: OnLine Handwritten Word Recognition Using a Hidden Markov Model Type Stochastic Network" IEEE Trans. Pattern Machine Intell., Vol 16, No. 5, pp 481-496, 1994. A. B. Bose & S. Kuo: Connected and Degraded Text Recognition using Hidden Markov Model." Pattern Recognition, Vol 27, No. 10, pp. 1345-1363, 1994. 5. O. E. Agazzi& S-S. Kuo: Hidden Markov Model based Optical Character Recognition in the Presence of Deterministic Transformations" Pattern Recognition, Vol 26, No. 12, pp. 1813-1826, 1993. 6. L. R. Rabiner and S. E. Levinson: A Speaker-Independent, Syntax-directed, Con-nected Word Recognition System Based on Hidden Markov Models and Level Building." IEEE Transaction on Acoustics, Speech and Signal Processing, vol ASSP-33, No 3, p. 561-573. 7. L. R. Rabiner: A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition." Proceeding of the IEEE, vol 77, No 2. 8. G. Konheim: Cryptography: A Primer.", John Wiley, New York, 1982. 26. Authors: Wan Rosli, WI, Siti Nur Haffizah, R. Nurraihana, H. Antioxidative and Scavenging Properties of Polyphenolic Rich-Fraction of Cornlettes (Young Zea Paper Title: mays) Abstract: Background: Cornlettes has the potential to be used as functional pharma-nutritional ingredient in food products because of the occurrence of various bioactive compounds in it. Objective: This study was conducted to determine antioxidant and scavenging activities of phenolic rich-fractions of cornlettes extracts. Methods: DPPH radical scavenging, Ferric reducing antioxidant power and Phosphomolybdenum assay were used to determine anti- oxidative and scavenging properties of cornlettes fractions. Results and Discussion: The ethyl acetate fraction was possessed higher antioxidant activity in each antioxidant assay tested. In DPPH assay, the IC50 value of DPPH scavenging activity of the ethyl acetate, hexane, water and crude were 0.28 mg/mL, 0.26 mg/mL, 2.10 mg/mL and 1.85 mg/mL, respectively. The percentages of DPPH reduction in ethyl acetate and hexane possessed more effective anti-oxidative capacity as compared to water and crude fractions. For FRAP assay, the ethyl acetate fraction significantly exhibited the highest reducing power activity of antioxidants compared to other fractions. The IC50 value of FRAP activity of the ethyl acetate, hexane, crude and water were 0.40 mg/mL, 0.82 mg/mL, 2.24 mg/mL and 1.58 mg/mL, respectively. In addition, phosphomolybdate assay was also shown that ethyl acetate fraction had the highest total antioxidant capacity than other fractions. However, at low concentration (0.05-0.2 mg/mL), each fraction was not significant to each other. Concusion: The ethyl acetate fraction was possessed higher antioxidant and scavenging capacities followed by hexane, crude and water fractions in each antioxidant assay tested.

Keywords: Antioxidant Activity, Cornlettes, Ethyl Acetate, Hexane, Phenolic Fraction.

References: 1. Ayaz, M., Junaid, M., Ahmed, J., Ullah, F., Sadiq, A., Ahmad, S. & Imran, M. (2014). Phenolic contents, antioxidant and anticholinesterase potentials of crude extract, subsequent fractions and crude saponins from Polygonum hydropiper L. BMC Complementary and Alternative Medicine. 14(1): 145-148. 2. Brand-Williams, W., Cuvelier, M.E. & Berset, C. (1995) Use of a free radical method to evaluate antioxidant activity. Food Sci Technol. 28: 111-114 25–30. 3. Chan, S.W., C.Y. Lee, C.F. Yap, W.M. Wan Aida, C.W. & Ho, C. (2009). Optimisation of extraction conditions for phenolic compounds from limau purut (Citrus hystrix) peels. International Food Research Journal. 16(2): 203-213. 4. Deciga-Campos, M., Rivero-Cruz, I., Arriaga-Alba, M., Castaneda-Corral, G., Angeles-Lopez, G.E., Navarrete, A. & Mata, R. (2007). Acute toxicity and mutagenic activity of Mexican plants used in traditional. Journal of Ethnopharmacology, 110: 334-342 5. Duh, P.D., Tu, Y.Y. & Yen, G.C. (1999). Antioxidant activity of the aqueous extract of harn jyur (Chrysanthemum morifolium Ramat). Lebensm. Wiss Technol. 32: 269-277 6. Hismath, I., Wan Aida, W.M. & Ho, C.W. (2011). Optimization of extraction conditions for phenolic compounds from neem (Azadirachta indica) leaves. International Food Research Journal. 18(3): 931-939 7. Kaur, R., Arora, S. & Singh, B. (2008). Antioxidant activity of the phenol rich fractions of leaves of Chukrasia tabularis A. Juss. Bioresour. Technol. 99(16): 7692-7698. 8. Manach, C., Scalbert, A., Morand, C., Remes, C. & Jimenez, L. (2004). Polyphenols: Food sources and bioavailability. American Journal of Clinical Nutrition. 79: 727-747 9. Muhammad, H.S. and Muhammad, S. (2005). The use of Lawsonia inermis linn. (henna) in the management of burn wound infections. African Journal of Biotechnology. 4(9): 934-937. 10. Nurhanan, A.R., Wan Rosli, W.I & Mohsin S.S.J (2012). Total polyphenol content and free radical scavenging activity of cornsilk (Zea mays hairs).Sains Malaysiana 41(10): 1217–1221 11. Oyaizu M. (1986). Studies on products of browning reaction: Antioxidative activity of products of browning reaction prepared from glucosamine. Jpn J Nutr. 44: 307–315. 12. Prieto, P., Pineda, M. & Aguilar, M. (1999). Spectrophotometric quantitation of antioxidant capacity through the formation of a phosphomolybdenum complex: Specific application to the determination of vitamin E. Anal Biochem. 269(2): 337-41 13. Ravisankar, N., Sivaraj, C., Seeni, S., Joseph, J. & Raaman, N. (2014). Antioxidant activites and phytochemical analysis of methanol extract of leaves of Hypericum hookerianum. Int J Pharm Pharm Sci. 6(4): 456-460 14. Reidah, A.I.M.M. (2013). Characterization of phenolic compounds in highly-consumed vegetable matrices by using advanced analytical techniques.(PhD thesis, University of Granada). Authors: Aman Kumar Mishra, P. Vijaykumar Paper Title: Analysis of Spectrum Occupancy using Naïve Bayesian Classifier Abstract: This work evaluates spectrum occupancy in cognitive (CRN) based on naïve Bayesian classifier (NBC).It considers OFDM based users as primary users(PU) and 64-QAM based user as secondary user(SU). The motivation for this work is the classification problem in spectrum sensing, wherein it becomes important for secondary users (SUs) to sense free channel and use it for its own transmission/reception purpose given PU or SU are not present for effective utilization of spectrum that eventually leads to increased network throughput. Data were collected as constellation points of OFDM and 64-QAM at transmission power of -10dBm (0.1mw). Our proposed evaluation can be applied to D2D communication in next-generation heterogeneous network, where devices are considered as SUs and cellular based users are considered as PU. The complete architecture can be considered as decentralized network, where devices (SU) can use channel upon confirming the channel not being occupied by any 27. other PU or SUs, this is believed to increase throughput of SUs. NBC is considered because it considers all features independents and gives good model for classification in future. 115-117

Keywords: The complete architecture can be considered as decentralized network, where devices (SU) can use channel upon confirming the channel not being occupied by any other PU or SUs, this is believed to increase throughput of SUs.

References: 1. Y. Xu, P. Cheng, Z.chen, Y. Li, B. Vucetic,” Mobile Collaborative Spectrum Sensing for Heterogeneous Networks: A Bayesian Machine Learning Approach”, IEEE Transcation on Signal Processing, 2018. 2. Kulin, M., Kazaz, T., Moerman, I. and De Poorter, E., 2018. End-to-end learning from spectrum data: A deep learning approach for wireless signal identification in spectrum monitoring applications. IEEE Access, 6, pp.18484-18501. 3. D. D Testa, M. Danieletto, M. Zorzi, "A Machine Learning based ETA estimator for WiFi transmission," IEEE Transactions on Wireless Communication, vol. 16, pp. 7011-7024, 2017. 4. J. Chen, Y. Deng, J. Jia, M. Dohler, A. Nallanathan,"Cross Layer QoE optimization for D2D communication in CR enabled Hetrogenous ,"IEEE Transcation on Cognitive Communication and Networking, Sept 2018. 5. F. Azmat, Y. Chen,N. Stocks,“Analysis of Spectrum Occupancy Using Machine Learning Algorithms," IEEE Transcations on Vechicular Technology, 2015. 6. X. Li, J. Fang, W. Cheng, H. Duan, Z. Chen, H. Li, “Intelligent Power Control for Spectrum Sharing in Cognitive Radios,” IEEE Access, 2018. Authors: Faieza Abdul Aziz, Faid Abdullah, Lai Lai Win Paper Title: Using Marker Based Augmented Reality for Training in Automotive Industry Abstract: Currently, Augmented Reality (AR) is a rapid growing research topic in many different training fields. The user can interact with information overlay onto real world as well as computer model generated and displayed within it. This study develop an AR application for training system in automotive industry and evaluate its effectiveness. The application was developed using Unity 3D and Vuforia and installed in Samsung Galaxy A Tablet. For the experiment, 10 students from Institut Latihan Perindustrian Kuala Lumpur were selected and divided randomly into two groups; paper based group and AR based group. Participants changed the task at the end of each condition and answer survey at the end of the experiment. The experiment shows reduction of 10.27 % and 42.86 % in Task Completion Time and error counts using AR based instruction. The survey analysis also shows that most of the participant preferred AR to be used in performing maintenance task. This is an ongoing project where Virtual Reality application for engine assembly, disassembly and performance will be evaluated to enhance learning method in tertiary education. The engine system will be designed by using Maya 2018 and will import to Unity game engine. From this experiment, the VR application will be evaluated and analysed in order to evaluate the effectiveness of VR implementation.

Keywords: Augmented Reality, Maintenance Instruction, Unity 3D, Vuforia.

References: 1. Sherman R. and Craig B. (2003). Understanding Virtual Reality: Interface, Application and Design, pp 18. San Francisco, USA: Morgan Kaufmann 28. 2. Riccardo Palmarini, John Ahmet Erkoyuncu, Rajkumar Roy, Hosein Torabmostaedi, A Systematic Review Of Augmented Reality Applications In Maintenance, In Robotics and Computer-Integrated Manufacturing, Volume 49, 2017, Pages 215-228. 118-121 3. Haritos T. and Macchiarella, 1994 “A Mobile Application of Augmented Reality for Aerospace Maintenance,” Proc. 24th Digital Avionics Systems Conf. (DASC 05), vol. 1, IEEE Press, pp. 5.B.3-1– 5.B.3-9. 4. Wagner D., Schmalstieg D., Billinghurst M. (2006) Handheld AR for Collaborative Edutainment. In: Pan Z., Cheok A., Haller M., Lau R.W.H., Saito H., Liang R. (eds) Advances in Artificial Reality and Tele-Existence. Lecture Notes in Computer Science, vol 4282. Springer, Berlin, Heidelberg. 5. M. Hincapié, A. Caponio, H. Rios, E. González Mendívil , An introduction to augmented reality with applications in aeronautical maintenance, in: Int. Conf. Trans- parent Opt. Networks, 2011, pp. 1–4 6. Barnes, Kassandra; Marateo, Raymond C.; and Ferris, S. Pixy (2007) "Teaching and Learning with the Net Generation," Innovate: Journal of Online Education: Vol. 3: Iss. 4, ArticleAvailableat:http://nsuworks.nova.edu/innovate/vol3/iss4/1 7. Azuma R., A Survey Of Augmented Reality, Preasence Teleoperators Virtual Environ. 6 (4) (1997) 355–385. 8. Siltanen S., Theory and Applications of Marker-Based Augmented Reality, 2012. 9. Xiao C. and Lifeng Z., "Implementation Of Mobile Augmented Reality Based On Vuforia And Rawajali," 2014 IEEE 5th International Conference on Software Engineering and Service Science, Beijing, 2014, pp. 912-915. 10. Sanna , Manuri F., Lamberti F., Member S., Paravati G., Pezzolla P., Using Hand- Held Devices To Support Augmented Reality-Based Maintenance And Assembly Tasks, in: IEEE Int. Conf. Consum. Electron. Using, 2015, pp. 178–179. 11. Westerfield G., Mitrovic A., Billinghurst M., Intelligent Augmented Reality Training For Motherboard Assembly, Int. J. Artif. Intell. Educ. 25 (1) (2015) 157–172. 12. Wang X., Ong S.K., Nee A.Y.C., Real-Virtual Components Interaction For Assembly Simulation And Planning, Rob. Comput. Integr. Manuf. 41 (2016) 102–114. 13. Billinghurst, M., and Dünser, A. (2012). Augmented Reality in the Classroom. Computer, 45(7), 56–63. 14. Rohidatun, M. W., Faieza, A. A., Rosnah, M. Y., Nor Hayati, S., and Rahinah, I. (2016). 15. Development of Virtual Reality (VR) System with Haptic Controller and Augmented Reality (AR) System to Enhance Learning and Training Experience. International Journal of Applied Engineering Research, 11(16), 8806-8809. Authors: P. Vijaya Kumar, Kshema Maria George, Akhil Krishnan Nair, M. Sangeetha Paper Title: Palm Vein Image Classification using Neural Network Abstract: Image classification is the process of identifying and deciding what the image is by analysing the numerical properties of the different features of image and then organizing these data into categories. Image classification consists of training and testing. This is implemented using Raspberry Pi and an IR Camera module. And the classification is done using artificial intelligence. The various steps related to this are pre-processing of image, detecting ROI, extraction of features, neural network etc. Image classification is core for computer vision and has numerous practical applications. Two of these are baggage scanning and palm vein recognition. The technique of image processing and artificial intelligence can be used to scan the objects in a baggage and to indicate whether the 29. object is dangerous or not. Palm vein recognition is a very useful and reliable tool for biometrics. Its advantage is that it can detect whether a person is dead or alive and since it is hidden inside the skin it is almost impossible to be 122-124 imitated and won’t be affected by various skin problems as in the case of fingerprint recognition technique. Thus the results from our experiment can be used for wide variety of applications.

Keywords: Palm vein, Pre-processing, PNN, CNN.

References: 1. Zhang, H. and Hu, D., 2010, May. A palm vein recognition system. In Intelligent Computation Technology and Automation (ICICTA), 2010 International Conference on (Vol. 1, pp. 285-288). IEEE. 2. Perwira, D.Y., Agung, B.T. and Sulistiyo, M.D., 2014, November. Personal palm vein identification using principal component analysis and probabilistic neural network. In Information Technology Systems and Innovation (ICITSI), 2014 International Conference on (pp. 99-104). IEEE. 3. Fronitasari, D. and Gunawan, D., 2017, July. Palm vein recognition by using modified of local binary pattern (LBP) for extraction feature. In Quality in Research (QiR): International Symposium on Electrical and Computer Engineering, 2017 15th International Conference on (pp. 18-22). IEEE. 4. Wang, M., Zheng, S., Li, X. and Qin, X., 2014, April. A new image denoising method based on Gaussian filter. In Information Science, Electronics and Electrical Engineering (ISEEE), 2014 International Conference on (Vol. 1, pp. 163-167). IEEE 5. Chaudhary, C. and Patil, M.K., 2013. Review of image enhancement techniques using histogram equalization. International Journal of Application or Innovation in Engineering and Management (IJAIEM), 2(5). 6. Reza, A.M., 2004. Realization of the contrast limited adaptive histogram equalization (CLAHE) for real-time image enhancement. Journal of VLSI signal processing systems for signal, image and video technology, 38(1), pp.35-44. 7. Hoshyar, A.N. and Sulaiman, R., 2010, June. Review on finger vein authentication system by applying neural network. In Information Technology (ITSim), 2010 International Symposium in (Vol. 2, pp. 1020-1023). IEEE. 8. Manocha, M. and Kaur, P., 2013. Palm vein recognition for human identification using NN. International Journal of Innovative Technology and Exploring Engineering, 3(7), pp.140-143. 9. Chen, E-Liang, et al. "An automatic diagnostic system for CT liver image classification." IEEE Transactions on Biomedical Engineering 45.6 (1998): 783-794. 10. Mao, K.Z., Tan, K.C. and Ser, W., 2000. Probabilistic neural-network structure determination for pattern classification. IEEE Transactions on neural networks, 11(4), pp.1009-1016. 11. Wu, S.G., Bao, F.S., Xu, E.Y., Wang, Y.X., Chang, Y.F. and Xiang, Q.L., 2007, December. A leaf recognition algorithm for plant classification using probabilistic neural network. In Signal Processing and Information Technology, 2007 IEEE International Symposium on (pp. 11-16). IEEE. 12. Yan, Xuekui, et al. "Palm vein recognition based on multi-sampling and feature-level fusion." Neurocomputing 151 (2015): 798-807. 13. Mirmohamadsadeghi, L. and Drygajlo, A., 2014. Palm vein recognition with local texture patterns. Iet Biometrics, 3(4), pp.198-206. 14. Ma, X., Jing, X., Huang, H., Cui, Y. and Mu, J., 2016. Palm vein recognition scheme based on an adaptive Gabor filter. Iet Biometrics, 6(5), pp.325-333. 15. amak, W., Trabelsi, R.B., Damak, M.A. and Sellami, D., 2018. Dynamic ROI extraction method for hand vein images. IET Computer Vision. Volume: 12, Issue: 5, 8 2018.pp.no. 586 – 595 16. Yuehang Wang ; Zhengxiong Li ; Tri Vu ; Nikhila Nyayapathi ; Kwang W. Oh ; Wenyao Xu ; Jun Xia,A Robust and Secure Palm Vessel Biometric Sensing System Based on Photoacoustics,IEEE Sensors Journal Year: 2018, Volume: 18, Issue: 14,Pages: 5993 – 6000. Authors: S. M. Shafie, Z. Othman, N. Hami Paper Title: A Life Cycle Assessment for Garden Waste Management in Northern Region of Malaysia Abstract: recently, the production of garden waste shows an increasing pattern throughout the year in Malaysia. Garden waste consists of grass clippings, tree cuttings, small branches, leaves, and woody debris. This paper studied the garden waste management in Kedah and focused on the environmental impact. The life cycle assessment comprises a collection of garden waste, transportation to a collection center, and transportation to a landfill. The aim of the study was to analyze emissions of garden waste generated in 2015, which is 6,240 tonnes. The result indicated the environmental impact of current garden waste management practice in Kedah. It denoted that this impact could be reduced by using garden waste for power generation. Hopefully, this paper could help stakeholders to consider the potential of garden waste in cutting down the environmental impact through utilizing it for power generation.

Keywords: Life cycle assessment, Garden waste, Northern region, Malaysia.

References: 1. Akhtar, M., Hannan, M. A., Begum, R. A., Basri, H., & Scavino, E. (2017). Backtracking search algorithm in CVRP models for efficient solid waste collection and route optimization. Waste Management, 61, 117-128. 2. Aleluia, J., & Ferrão, P. (2016). Characterization of urban waste management practices in developing Asian countries: A new analytical 30. framework based on waste characteristics and urban dimension. Waste Management, 58, 415-429. 3. Andersen, J. K., Boldrin, A., Samuelsson, J., Christensen, T. H., & Scheutz, C. (2010). Quantification of greenhouse gas emissions from 125-128 windrow composting of garden waste. Journal of Environmental Quality, 1-4. 4. Billa, L., Pradhan, B., & Yakuup, A. (2014). GIS routing and modelling of residential waste collection for operational management and cost optimization. Pertanika J. Sci. & Technol, 22(1), 193-211. 5. Boldrin, A. (2009). Environmental assessment of garden waste management. PhD, Technical University of Denmark, Denmark. 6. L.A. Guerrero, L.A., Maas, G., & Hogland, W. (2013). Solid waste management challenges for cities in developing countries. Waste Management, 33, 220-232. 7. Kalanatarifard, A., & Yang, G. S. (2012). Identification of the municipal solid waste characteristics and potential of plastic recovery at Bakri Landfill, Muar, Malaysia. Journal of Sustainable Development, 5(7), 1913-9071. 8. Kathirvale, S., Yunus, M. N. M., Sopian, K., & Samsuddin, A. H. (2004). Energy potential from municipal solid waste in Malaysia. Renewable Energy, 29(4), 559-567. 9. Oldfield, T. L., White, E., & Holden, N. M. (2018). The implications of stakeholder perspective for LCA of wasted food and green waste. Journal of Cleaner Production, 170, 1554-1564. 10. Sanaz Saheri, Masoud Aghajani Mir, Noor Ezlin Ahmad Basri, Rawsan Ara Begum, & Noor Zalina Mahmood. (2009). Solid waste management by considering composting potential in Malaysia toward a green country. Journal of Social Sciences and Humanities, 4(1), 48- 55. 11. Yadav, P., & Samadder, S. R. (2018). A critical review of the life cycle assessment studies on solid waste management in Asian countries. Journal of Cleaner Production, 185, 492-515. 12. Zakareta, M. M., & Shafie, S. M. (2016). Garden waste management: Process and cost. Paper presented at the Symposium on Technology Management & Logistics (STML–Go Green) 2016 UUM, Sintok. Authors: N. Arivazhagan and V. Govindharajan An Improved Decision Tree based Mammogram Image Classification of Breast Cancer for Decision Paper Title: Support Systems Abstract: Decision support system is used in medical field to enable the physician to make quick and efficient 31. diagnosis of diseases using information and communication technology. Quick retrieval of image sample for comparison and classification is one of the important criteria for the design of such a system. Fast decision and 129-131 accuracy of the classification is always trade off. The fast decision and retrieval usually suffer from a problem of less accuracy. Here in this paper a hybrid mechanism based image retrieval cum classification is proposed which will make quick decision with guaranteed level of accuracy. The proposed system is tested on mammogram medical images for finding early detection of cancer at various stages .The computational complexity analysis result on large image data base system proves the proposed scheme can be implementable in a typical practical decision support system.

Keywords: Mamogram Images, Decision support systems, Early cancer detection.

References: 1. Guimond and G. Subsol, “Automatic MRI database exploration and applications,” Int. J. Pattern Recognit. Artif. Intell., vol. 11, pp. 1345– 1365, 1997. 2. Jinman Kim, Weidong Cai, Member, Dagan Feng, Hao Wu, “A New Way for Multidimensional Medical Data Management: Volume of Interest (VOI)-Based Retrieval of Medical Images With Visual and Functional Features”, IEEE Transactions On Information Technology In Biomedicine, Vol. 10, No. 3, July pp. 598-608, 2006. 3. S. Minoshima, K. L. Berger, K. S. Lee, and M. A. Mintun, “An automated method for rotational correction and centering of three- dimensional functional brain images,” J. Nucl. Med., vol. 33, pp. 1579–1585, 1992. 4. S. Minoshima, R. A. Koeppe, M. A. Mintun, K. L. Berger, S. F. Taylor, K. A. Frey, and D. E. Kuhl, “Automated detection of the intercommissural line for stereotactic localization of functional brain images,” J. Nucl. Med., vol. 34, pp. 322–329, 1993. 5. S. Minoshima, R. A. Koeppe, K. A. Frey, and D. E. Kuhl, “Anatomic standardization: Linear scaling and nonlinear warping of functional brain images,” J. Nucl. Med., vol. 35, pp. 1528–1537, 1994. 6. Konstantinos P. Exarchos, Yorgos Goletsis, Dimitrios I. Fotiadis, “Multiparametric Decision Support System for the Prediction of Oral Cancer Reoccurrence”, IEEE Transactions on Information Technology In Biomedicine, Vol. 16, No. 6, pp.1127-1135, November 2012. 7. M. Hall, “Correlation-based feature selection for discrete and numeric class machine learning,” in Proc. 17th Int. Conf. Mach. Learning, 2000, pp. 359–366. 8. R. Kohavi and G. John, “Wrappers for feature subset selection,” Artif. Intell., vol. 97, pp. 273–324, 1997 9. https://www.breastcancer.org/symptoms/understand_bc/statistics 10. S. Y. Semenov, A. E. Bulyshev, A. Abubakar, V. G. Posukh, Y. E. Sizov, A. E. Souvorov, P. M. van den Berg, and T. C. Williams, “Microwave-tomographic imaging of the high dielectriccontrast objects using different image-reconstruction approaches,” IEEE Trans. Microwave Theory Tech., vol. 53, no. 7, pp. 2284–2294, Jul. 2005. 11. Émilie Niaf, Rémi Flamary, Olivier Rouvière, Carole Lartizien, and Stéphane Canu, “Kernel-Based Learning from Both Qualitative and Quantitative Labels: Application to Prostate Cancer Diagnosis Based on Multiparametric MR Imaging”, IEEE Transactions On Image Processing, vol. 23, no. 3, march 2014. 12. S. Poplack, P. Carney, and J. Weiss, “Screening mammography: Costs and use of screening-related Services1,” Radiology, vol. 234, no. 1, pp. 79–85, 2005. 13. Nishant Uniyal, Hani Eskandari, Purang Abolmaesumi, Samira Sojoudi, Paula Gordon, Linda Warren, Robert N. Rohling, Septimiu E. Salcudean, and Mehdi Morad, “Ultrasound RF Time Series for Classification of Breast Lesions”, IEEE Transactions On Medical Imaging, vol. 34, no. 2, pp. 652-662, 2015. 14. Tomasz M. Grzegorczyk*, Paul M. Meaney, Peter A. Kaufman, Roberta M. diFlorio-Alexander, Keith D. Paulsen, “Fast 3-D Tomographic Microwave Imaging for Breast Cancer Detection”, IEEE Transactions on Medical Imaging, Vol. 31, No. 8, August 2012 15. M. N. Gurcan, B. Sahiner, N. Petrick, H. P. Chan, E. A. Kazerooni, P. N. Cascade, and L. Hadjiiski, “Lung nodule detection on thoracic computed tomography images: Preliminary evaluation of a computer-aided diagnosis system,” Med. Phys., vol. 29, no. 11, pp. 2552–2558, 2002. 16. J. Tang, R. M. Rangayyan, J. Xu, I. E. Naqa, and Y. Yang, “Computeraided detection and diagnosis of breast cancer with mammography: Recent advances,” IEEE Trans. Inf. Technol. Biomed., vol. 13, no. 2, pp. 236-251, Mar. 2009. 17. K. Ganesan, U. Acharya, C. K. Chua, L. C. Min, K. Abraham, and K. Ng, “Computer-aided breast cancer detection using mammograms: A review,” IEEE Rev. Biomed. Eng., vol. 6, pp. 77–98, Mar. 2013. 18. M. F. Ganji and M. S. Abadeh, “A fuzzy classification system based on Ant Colony Optimization for diabetes disease diagnosis,” Expert Syst. Appl., vol. 38, no. 12, pp. 14650–14659, 2011. 19. Linqi Song, William Hsu, Member, Jie Xu, Mihaela van der Schaar, “Using Contextual Learning to Improve Diagnostic Accuracy: Application in Breast Cancer Screening”, IEEE Journal of Biomedical And Health Informatics, Vol. 20, No. 3, May 2016 20. G. Subash Kumar, V. Nagarajan, “Local curve pattern for content-based image retrieval”, Pattern Analysis and Applications, Accepted: 9 July 2018. 21. RD Rajagopal, S Murugan, K Kottursamy, V Raju, ‘Cluster based effective prediction approach for improving the curable rate of lymphatic filariasis affected patients’, springer cluster computing, pp.1-9, 2018. Authors: Yury V. Belousov, Fedor V. Rekach, Svetlana L. Shambina Paper Title: Modelling of the Tools’ Power Interaction during Mechanical Machining by Cutting Abstract: The article deals with modelling of force interaction between tool and workpiece during machining. The dependences were obtained for determination of the workpiece material’s deformation under the cutting wedge, as well as the value of the rear corner of the instrument.

Keywords: Cutting Efforts, Rear Corner of the Instrument, Elastic Displacements, Relative Deformation,

References: 1. Shambina S.L. Anisotropic composite materials and peculiarities of calculations for constructions made of these materials// Structural 32. mechanics of engineering constructions and buildings. – (2005), No. 1. – P. 113-118. 2. Shambina S.L., Rekach F.V., Belousov Y.V. On new modification of some strength criteria for anisotropic materials // Key Engineering 132-134 Materials. Vol. 724. 2016. P. 53-57. Smart Materials Technologies. 3. Belousov . V. Calculation of permissible stresses for the evaluation of contact strength of cylindrical gears under load// structural mechanics of engineering constructions and buildings.- 2015, № 6.- P. 29-32. 4. Belousov Yu. V. Analysis of the stress state of a circular intermittent weld of t-joints // Construction mechanics of engineering structures and structures.- 2015, № 3.- P. 54-58. 5. Belousov Yu. V. Determination of residual stresses during etching of the cantileverfixed sample // Construction mechanics of engineering structures and structures.− 2016, № 1.- P. 55-60. 6. Deithard T. Einteilige wdlzfrdzer vertreiben kosten aus der scrienfertigung//.WB Werkstatt und Betried. 2015. №3. – p. 30-32. 7. Hoffman N.P., Stolz U. Ontransient growth of wear pattern properties// Wear. 2010. V. 268. №7-8. – p. 886-892. 8. Zubchaninov V.G. Basic theory of elasticity and plasticity: Textbook for mechanical engineering specialties of high schools. - Moscow: Higher School (1990), p. 368 p. Authors: V. Robin Rohit, Ramaraj Eswarathevar 33. Paper Title: Service Differentiation for Achieving Fairness in Multi-Traffic Class in MANET Abstract: Multi-access for multi-mode terminals is possible in heterogeneous networks environment that becomes a 135-140 critical issue for transmission in parallel with meeting user Quality of Service (QoS) performance requirements and throughput maximization. Without considering the service type, the existing optimal resource allocation schemes may allocate the scarce radio resources inefficiently. To overcome these issues, in this paper, we propose service differentiation for achieving fairness in multi-traffic class in MANET. This proposed methodology includes three stages of solution. In the first stage, we assign priority through a well-defined structure. In the second stage, we use utility function to have more differentiation. In the last stage, we use piggy backing for data packet classification. As the proposed solution determines the priority based services, it becomes easy to assign a work with a good working scenario. A good differentiation is created among the user. The third part is able to solve data packet generation according to the space available.

Keywords: Priority assignment, utility function, piggy backing.

References: 1. Wei Liu and Yuguang Fang, “Courtesy Piggybacking: Supporting Differentiated Services in Multihop Mobile Ad Hoc Networks”, IEEE Transactions on Mobile Computing, 3.4, pp. 380-393, 2004. 2. Hannah Monisha. J and RhymendUthariaraj .V, “User Profile Based Proportional Share Scheduling and MAC Protocol for MANETs”, International Journal of Distributed and Parallel Systems (IJDPS) Vol.3, No.1, January 2012. 3. Mohammad AminulHaq, Mitsuji Matsumoto, Jacir Luiz Bordim, MasakatsuKosuga and Shinsuke Tanaka, “Admission Control and Service Differentiation Based QoS Provisioning for Mobile Ad hoc Network”, IEEE Vehicular Technology Conference- VTC -Spring , vol. 4, pp. 2712-2718 , 2004. 4. Yuming Jiang, Chen-KhongTham and Chi-Chung Ko, “A Probabilistic Priority Scheduling Discipline for Multi-Service Networks”, Proceedings of Sixth IEEE Symposium on Computers and Communications, 2001. 5. Mieso K. Denko, “A Reputation-Based Service Differentiation Scheme for Mobile Ad Hoc Networks”, IEEE International Conference on Wireless and Mobile Computing, Networking and Communications, WiMob'2005, Vol.3, 2005. 6. Xiaobo Zhou, Yu Cai, Ganesh K. Godavari and C. Edward Chow, “An Adaptive Process Allocation Strategy for Proportional Responsiveness Differentiation on Web Servers”, Proceedings of IEEE International Conference on Web Services, 2004. 7. Amrit Kumar and Aura Ganz, “Provisioning Link Layer Proportional Service Differentiation in Wireless Ad Hoc Networks with MIMO links”, Proceedings of 16th International Conference on Computer Communications and Networks ICCCN 2007. 8. Lijun Chen, Steven H. Low and John C. Doyle, “Random Access Game and Medium Access Control Design”, IEEE/ACM Transactions on Networking, Vol. 18, No. 4, August 2010. 9. Jie Miao, Zheng Hu, Canru Wang, RongrongLian and Hui Tian, “Optimal Resource Allocation for Multi-Access in Heterogeneous Wireless Networks”, IEEE 75th Vehicular Technology Conference (VTC Spring), 2012. 10. Li Yang, “A Service Priority Differentiated Quality of Service Routing Protocol for MANET: Mobile Ad hoc Network”, Information Technology Journal, 2013.. 11. Hannah Monisha. J and RhymendUthariaraj .V, “User Profile Based Proportional Share Scheduling and MAC Protocol for MANETs”, International Journal of Distributed and Parallel Systems (IJDPS) Vol.3, No.1, January 2012. 12. Robin Rohit and Dr. Ramaraj, “Cross-layer Based Adaptive Reservation Technique for Mobile Ad-hoc Networks”, Proceedings of International Conference on Advances in Computing, Communication and Information Science ACCIS-‘14, India, Elsevier : Science and Technology, June 26-28, isbn – 9789351072478, pp 35 – 46, 2014 13. Network Simulator: http:///www.isi.edu/nsnam/ns Authors: Yeon Taek OH Paper Title: Study of Driving Torque through Dynamic Analysis of Robot Abstract: these days, the interests on the low-cost handling robots are increasing because it is important to get down the unit cost of production to get the price competitiveness. The robot joint with simple mechanism is more suitable to implement the low-cost robot system as well known. The moving parts of robot have to be designed for simple and robust. But the dynamic characteristics analysis is induced by the robot links because they drive in high acceleration and deceleration. In this reason, the dynamic characteristics analysis of the high-speed robot is very important in the design process. In this paper, the study on robot driving torque analysis of an articulated robot has been done and the re-search results will be introduced.

Keywords: Composite; High Speed Robot; Dynamic Analysis; Robot Driving Torque; Simulation Analysis; Actuator Module,

References: 1. Spong, M. W. and Vidyasagar, M., “Robot Dynamics and Control”, John Wiley & Sons, 1989. 34. 2. Richard, P. Paul., “Robot Manipulator: Mathematic, Programming, And Control”, MIT Press, Cambridge, MA, 1982. 3. Craig, J. J., “Introduction to Robotics: Mechanic & Control,” Addison- Wesley, Reading, MA, 1985. 4. Siciliano, B. and Khatib, O., “Springer Handbook of Robotics,” Oussama, Springer, 2008. 141-145 5. Boer, C. R. and Molinari, T. L., “Parallel Kinematic Machines, in: Smith, K. S., (Eds),” Springer, 1999. 6. Tsai, L., Robot Analysis: The Mechanics of Serial and Parallel Manipulators, John Wiley & Sons, 1999. 7. Stewart, D., “A platform with Six Degree of Freedom”, Proc. Inst. Mech. Eng., London, vol.180, no.15, pp.371-386, 1965. 8. Chanhun Park, Hun Min Do, Taeyong Choi and Byungin Kim, “Study on the structural analysis of small size industrial high speed parallel robot”, J. Korea Soc. Precision Eng., Vol. 30, No. 9, pp. 923-930, 2013. 9. Hyung-Sik Choi, Jong-Rae Cho, Jae-Gwan Hur, Chi-Kwang Chun, “Structural analysis of the light robot manipulator capable of handling heavy payload”, Journal of Korean society of Marine Engineering, Vol. 2, No. 2, pp. 318-324, 2010. 10. H. Asada, “Dynamic analysis and design of robot manipulators using inertia ellipsoids”, Robotics and Automation. Proceedings. 1984 IEEE International Conference on, Atlanta, 1984. 11. Haihong Li and Zhiyong Yang, “Dynamic Analysis of a Parallel Pick-and-Place Robot With Flexible Links”, ASME 2008 9th Biennial Conference on Engineering Systems Design and Analysis, 2008. 12. Junzhi Yu, Y. F. Li, Younghui Hu and Long Wang, “ Dynamic Analysis and Control Synthesis of a Link-Based Dolphin-Like Robot Capable of Three-Dimensional Movements”, Intelligent Robotics and Automation, Vol. 23, No. 23, 2009. 13. Ziqiang Zhang, Yongjie Zhao and Gang Cheng, “Inverse rigid-body dynamic analysis for a 3UP S-P RU parallel robot”, Advances in Mechanical Engineering, Vol. 9, No. 2, 2017. 14. N. Prabhu and M. Dev Anand, “Solution for Dynamic Analysis of SCORBOT- Vu Plus Industrial Robot Manipulator”, Journal of the Association of Engineers, Vol. 84, No. 3, 2014. 35. Authors: L. Sivagami, J. Martin Leo Manickam Paper Title: Cluster based Time Synchronization Algorithm for Mobile Underwater Sensor Networks (UWSN) Abstract: Underwater Sensor Network (UWSN) performs the functionality of data transmission through wireless mobile nodes in the aquatic environment. But, due to the harsh and uneven nature of the aquatic environment, the operation of the UWSN is effected badly as no time synchronization between nodes, In this paper, we propose to develop a Cluster based Time Synchronization Algorithm for Mobile UWSN. This algorithm aims at synchronizing the time between the network nodes by performing inter cluster as well as intra cluster synchronization. In this way, once the nodes are time synchronized, the network operation will be carried out smoothly, in turn consuming lesser energy to perform the network functionality.

Keywords: UWSN, Cluster, Synchronization, Mobile, Underwater

References: 1. Ming Xu, Guangzhong Liu,Daqi Zhu and Huafeng Wu,(2014), A Cluster-Based Secure Synchronization Protocol for Underwater Wireless Sensor Networks,Hindawi Publishing Corporation, International Journal of Distributed Sensor Networks,Volume 2014, Article ID 398610,pp:1-13. 2. Anil C.B., Vani Sreekumar and Manjari Joshi,(2014), A SURVEY ON VARIOUS TIME SYNCHRONIZATION TECHNIQUES IN UNDERWATER SENSOR NETWORKS,INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING & TECHNOLOGY (IJCET),Volume 5, Issue 12, pp. 116-122. 3. Vidya.R, Ambika.G.N, P Haripriya and Poojashree N.S, (2016), Innovative Routing and Time Synchronization in Underwater Sensor 146-152 networks, International Journal of Scientific & Engineering Research, Volume 7, Issue 1. 4. Jun Liu, Zhaohui Wang, Zheng Peng, Michael Zuba, Jun-Hong Cui and Shengli Zhou,(2011), TSMU: A Time Synchronization Scheme for Mobile Underwater Sensor Networks, IEEE Globecom. 5. Affan A. Syed and John Heidemann,(2006), Time Synchronization for High Latency Acoustic Networks, IEEE,Infocom. 6. Zhengbao Li, Zhongwen Guo, Feng Hong and Lu Hong,(2013), E2DTS: An energy efficiency distributed time synchronization algorithm for underwater acoustic mobile sensor networks, Ad Hoc Networks,Vol-11,pp:1372–1380. 7. Jun Liu,Zhong Zhou,Zheng Peng,Jun-Hong Cui and Lance Fiondella,(2013), Mobi-Sync: Efficient Time Synchronization for Mobile Underwater Sensor Networks, IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS, VOL. 24, NO. 2. 8. Ying Guo and Yutao Liu,(2013), Time Synchronization for Mobile Underwater Sensor Networks,JOURNAL OF NETWORKS, VOL. 8, NO. 1. 9. Oriol Pallares, Pierre‐Jean Bouvet and Joaquin del Rio,(2015), Hybrid time synchronization for Underwater Sensor Networks,ACTA IMEKO,Volume 4, Number 3,pp:30 ‐ 35. 10. P.G.Kulurkar and Yogadhar Pandey (2013), TIME-SYNCHRONIZATION IN UNDERWATER SENSOR NETWORKS, International Journal of Computer Technology and Electronics Communication,Vol-2,Issue-1. 11. Jinwang Yi,Diba Mirza,Curt Schurgers and Ryan Kastner (2013), Joint Time Synchronization and Tracking for Mobile Underwater Systems,ACM,WUWNet. 12. L. Sivagami and J. Martin Leo Manickam,(2016), Cluster-Based MAC Protocol for Collision Avoidance and TDMA Scheduling in Underwater Wireless Sensor Networks, The Computer Journal Advance Access. 13. Network Simulator: http:///www.isi.edu/nsnam/ns Authors: Christia Meidiana, Istiq Dhany Nurfitriya, Kartika Eka Sari Paper Title: Multi-criteria Evaluation for Determination of Anaerobic Di-gester Location in Rural Area Abstract: Multi-criteria analysis is used in this study to support the decision making of choosing the suitable location for anaerobic digester (AD). Land availability is the main factor in the area of study influencing the interest of farmers to utilize manure for biogas production indicated by low manure utilization rate which is only 22%. The rest is disposed of in ditches and streams threatening the environment. Analytical Hierarchy Process (AHP) comprising four criteria, land suitability, land availability, farmer’s capability, and safety requirement are used to assess the most appropriate AD scale in the study area. The most suitable AD scale has the highest total value. Seven experts in rural biogas management are asked for their opinion about the most appropriate AD scale for processing the manure waste. The result of AHP shows that land availability is the main factor as it has the highest value (77.03%), while farmer’s capability, land suita-bility and safety requirement has less value which is 0.1977, 0.0165 and 0.0155 respectively. Furthermore, household scale AD with ca-pacity from 4 m3 – 12 m3 is the most suitable AD scale for the location. However, not all capacities of AD can be necessarily constructed because of land availability factor. Overlay technique is used to identify the location and type of AD. Overlaid of three variables i.e. interest, land availability and cows ownership comes to the result that there are 155 out of 167 farmers interested in constructing AD. There are 75 farmers who meet the land availability criteria, while 80 farmers have no adequate land area for constructing the AD. However, only 64 individual ADs can be constructed when all variables (interest, 36. land availability and cow ownership) are considered. Other farmers who have interest should be construct either the individual AD under the stall (67 farmers) or communal AD (24 farmers). 153-157

Keywords: Analytical Hierarchy Process, Spatial Cluster Analysis, Rural Area, Biogas,

References: 1. REN21 (2012). Renewables 2012. Global status report. Renewable Energy Policy Network for the 21st Century (REN21). Paris: REN21 Secretariat. 2. Al Garni, H., Kassem, A., Awasthi, A., Komljenovic, D., Al-Haddad , K., (2016). A Multi Criteria Decision Making Approach for Evaluating Renewable Power Generation Sources in Saudi Arabia. Sustainable Energy Technologies and Assessments. 16: 137–150. 3. Taleghani G, Kia AS (2005). Technical-economical Anal-ysis of the Saveh Biogas Power Plant. Renewable Energy. 30(3): 441 – 446. 4. Ma J, Scott NR, DeGloria S, Lembo AJL. (2005). Siting Analysis of Farm-based Centralized Anaerobic Digester Systems for Distributed Generation using GIS. Biomass and Bioenergy. 28(6): 591 – 600. 5. Silva, S., Luı´s A¸ ada-Almeida, Dias, LC. (2014). Biogas Plants Site Selection. Integrating Multi Criteria Decision Aid methods and GIS techniques: A case study in a Portu-guese region. Biomass and Bioenergy. 71. 58 – 68. 6. Peter NW, Drake L , Johnny M. (2014). Economic Viabil-ity of Biogas Energy Production from Family-sized Di-gesters in Uganda. Biomass and Bioenergy . 70. 26 – 39 7. Meidiana, C., Rafsanjani, A. (2015). The Spatial-economic Approach for Determining Biogas Management in Rural Area. International Journal of Applied Engineering Re-search. 10 (95). 31-35 8. Owen, Anthony D. (2006). Renewable energy: Externality Costs as Market Barriers. Energy Policy, 34 (5): 632-642. 9. Pegels, A. (2010). Renewable Energy in South Africa: Po-tentials, Barriers and Options for Support. Energy Policy; 38 (9):4945-4954 Cheng- Dar, Y., Grant Gwo-Liang, Y. (2007). Decision Support System for Exploiting Local Renewable Energy Sources: a Case Study of the Chigu Ar-ea of Southwestern Taiwan. Energy Policy, 35: 383–394. 10. Butchholz, T., Rametsteiner, E., Volk, T.A., Luzadis, V.A. (2009). Multi-criteria Analysis for Bioenergy Systems As-sessments. Energy Policy, 37: 484–495. 11. Perpi˜na, C., Martinez-Llariob, JC., Perez-Navarroa Angel. (2013). Multi-criteria Assessment in GIS Environments for Siting Biomass Plants. Land Use Policy, 31: 326– 335 12. Franco, C., Bojesen, M., Hougaard, Jens Leth., Nielsen, K. (2015). A Fuzzy Approach to a Multiple Criteria and Geo-graphical Information System for Decision Support on Suitable Locations for Biogas Plants. Applied Energy, 140: 304–315 13. ESRI 2012 (2011). ArcGIS Desktop: Release 10.1. Red-lands: Environmental Systems Research Institute. 14. Mateo, JRSC. (2012). Multi-criteria Analysis in the Re-newable Energy Industry. Santander: Springer Science & Business Media; 15. Saaty TL, Vargas LG. (2012). Models, Methods, Concepts and Applications of the Analytic Hierarchy Process. 2nd ed. New York: Springer; 16. Peter NW, Drake L , Johnny M. (2014). Economic Viabil-ity of Biogas Energy Production from Family-sized Di-gesters in Uganda. Biomass and Bioenergy . 70. 26 – 39 Authors: P.S. Sujith Kumar, A. Ramesh Babu Cross-Layer based Congestion Detection and Dynamic Proxy Acknowledgment Scheme for TCP in Paper Title: MANET Abstract: In Mobile Ad Hoc Networks, the existing transmission technique does not guarantee connectivity and congestion free transmission. Also, the static proxy nodes are not suitable during the transmission and quick as well as accurate MAC layer contention detection technique is required. Hence in this paper, we propose a cross-layer based congestion detection and adaptive proxy acknowledgement scheme for TCP in MANET. In this scheme, the underlying MANET routing protocol selects the proxy nodes along the source and destination based on link availability and link-layer transmission queue length metrics parameters. The local congestion is detected by verifying missing TCP sequence. And the end to end congestion is detected based on the frame transmission efficiency. By simulation results, we show that the proposed technique guarantees connectivity and congestion free transmission.

Keywords: Cross-layer, Congestion, MANET, Proxy.

37. References: 1. Ammar Mohammed Al-Jubari, Mohamed Othman, Borhanuddin Mohd Ali and Nor Asilah Wati Abdul Hamid, "TCP performance in multi- hop wireless ad hoc networks: challenges and solution", EURASIP Journal on Wireless Communications and Networking 2011, 2011:198. 158-165 2. Rachana Buch, Ashish Kumar Srivastava and Nitul Dutta, "An Augmentation Of TCP For Competency Enlargement In MANET", International Journal Of Current Engineering And Scientific Research (Ijcesr),Volume-3, Issue-2, 2016. 3. Prakash B. Khelage and Uttam D. Kolekar, "Improving TCP Throughput Using Modified Packet Reordering Technique (MPRT) Over MANETs", (IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 5, No. 8, 2014. 4. Huaguang He, Taoshen Li, Luting Feng and Jin , "Frame Transmission Efficiency-Based Cross-Layer Congestion Notification Scheme in Wireless Ad Hoc Networks", Sensors 2017, 17, 1637; doi:10.3390/s17071637. 5. Wesam A. Almobaideen, Njoud O. Al-maitah, "TCP Karak: A New TCP AIMD Algorithm Based on Duplicated Acknowledgements for MANET", Int. J. Communications, Network and System Sciences, 2014, 7, 396-407. 6. May Zin Oo, Mazliza Othman and Timothy O’Farrell, "A Proxy Acknowledgement Mechanism for TCP Variants in Mobile Ad Hoc Networks", KICS, 2014. 7. D.Sunitha, A. Nagaraju and G.Narsimha, "Cross-layer based Smart Acknowledgment Scheme for TCP in MANETs", International Journal of Computer Science and Network Security, VOL.16 No.7, July 2016. 8. K.Tarun kumar, Ch.Satyendra and D.Sunitha, "A Proposal to use Cross-Layer based TCP Protocol for Congestion Control in Multi hop Mobile Ad hoc Networks", Futuristic Areas in Computer Engineering and Technology (FACET-13),2013. 9. Faisal Nawab, Kamran Jamshaidy, Basem Shihaday, and Pin-Han Hoz, "MAC-layer Protocol for TCP Fairness in Wireless Mesh Networks", IEEE, 2012. Authors: Faridah Kormin, Muhammad Khan, Nor Shafawati Mohd Shafie, Azhari Hamid Nour, Statistical Mixture Design: Study of Solvent Performance in Temperature Controlled Microwave Paper Title: Assisted Extraction System on Antioxidant Properties of Nephrolepis biserrata (Schott.) Sw. frond Extract Abstract: Statistical simplex-centroid design mixtures of water, acetone, acetonitrile, ethanol was used to extract Nephrolepis biserrata (Schott.) SW. frond (NBF) using temperature controlled microwave extraction system (TCMES). The effects of different solvents and their mixtures on its antioxidant properties were studied. The result shows, the quality of extracted material is found to depend on the solvent proportions according to special cubic model of TPC, TFC and IC50. The binary and quaternary solvents are found to explain the superior extraction results as compared to single solvent for TPC, TFC and IC50.

38. Keywords: Mixture Design, Nephrolepis Biserrata (Schott.) Sw, TPC, TFC, IC50.

166-172 References: 1. E.D. Neas, and M.J. Collins, “Microwave heating. Theoretical concepts and equipment design, In Kingston, H.M. and Jassie, L.B. (Ed). I Introduction to microwave sample preparation. Theory and practice”. Ame. Chem. Soc. Washington, D.C., Chap. 2, pp. 7-32, 1988. 2. L. Perreux, ”A Tentative Rationalization of Microwave Effects In Organic Synthesis According to The Reaction Medium, and Mechanistic Considerations”, Tetrahedron, Vol. 57, pp 9199-9223, 2001. 3. V. Mandal, Y. Mohan, and S. Hemalatha, “Microwave Assisted Extraction - An Innovative and Promising Extraction Tool for Medicinal Plant Research”, Pharma. Review. Vol. 1, pp. 7-18, 2007. 4. B. Kaufmann, and P. Christen, “Recent extraction techniques for natural products: microwave-assisted extraction and pressurized solvent extraction”, Phytochem. anal, Vol. 13, pp. 105-113, 2002. 5. H.M. Kingston, and S. J. Haswell, “Microwave Enhanced Chemistry: Fundamentals Sample Preparation and Applications, American Chemical Society. 1997. 6. K. Ganzler, A. Salgo, and K. Valko, “Microwave extraction, a novel sample preparation method for chromatography. J.Chrom. Vol. 371, pp. 299–306, 1986. 7. H. Christensen, “Uses of ferns in two indigenous communities in Sarawak, Malaysia”, In R.J. Johns (ed.): Holttum Memorial Volume, Roy. Bot. Gard., Kew (U.K.), pp 177-192, 1997. 8. K. Siems, F. Weigt, and E. Wollenweber, “Drimanes from the epicuticular wax of the fern Nephrolepis biserrata”, J. Phytochem. Vol. 33, pp. 89-97. 1996. 9. D. Marinova, F. Ribarova, and M. Atanassova, “Total Phenolics And Total Flavonoids In Bulgaria Fruits And Vegetables”, J. Uni. Chem. Tech. and Metal. Vol. 40, pp. 255-260, 2005. 10. Braca, De. Nunziatina, L. Tommasi , D.B. Lorenzo, P. Cosimo, P. Matteo, and M. Ivano, ”Antioxidant Principles from Bauhinia tarapotensis”. J. Nat. Pro.Vol. 64, pp.892–895, 2001. 11. W.G. Cochran, and G.M. Cox, “Experimental designs”2nd ed. New York, John Wiley & Sons, pp.611, 1992. 12. D.C. Mothgomery, “Design and analysis of experiments”. 5thed, New York: John Wiley and Sons, pp.170-210, 2001. 13. Y. Yang, and F. Zhang, “Ultrasound-assisted extraction of rutin and quercetin from Euonymus alatus (Thunb.) Sieb”, Ultra. Sonochem., Vol. 15, pp. 308-313, 2008. 14. S.J. Kim, H.N, Murthy, E.J. Hahn, H.L. Lee, and K.Y. Paek, “Parameters affecting the extraction of ginsenosides from the adventitious roots of ginseng (Panax ginseng C.A. Meyer). Sep. Pur. Tech., Vol. 56, pp. 401-406, 2007. 15. H. Nawaz, J. Shi, G.S. Mittal, and Y. Kakuda, “Extraction of polyphenols from grape seeds and concentration by ultrafiltration” Sep. Pur. Tech., Vol. 48, pp. 176-181, 2006. 16. N. Turkmen, F. Sari, and Y.S. Velioglu, “Effects of extraction solvents on concentration and antioxidant activity of black and black mate tea polyphenols determined by ferrous tartrate and Folin–Ciocalteu methods” Food Chem. Vol. 99, pp. 835-841, 2006. 17. J.M. Oespchuck, “A history of microwave-heating applications”, IEEE Trans, Microwave Theory Tech., Vol. 32, pp. 1200–1224, 1984. 18. C.R. Strauss, and R.W. Trainor, “Developments in microwave-assisted organic chemistry”, Aust. J. Chem.,Vol. 48, pp.1665-1692, 1995. 19. S. Frere, V. Thiéry, and T. Besson, “Microwave acceleration of the Pechmann reaction on graphite:montmorillonite K10: application to the preparation of4-substituted 7-aminocoumarins”, Tetrahedron Lett.,Vol. 42, pp. 2791-2794, 2001. 20. R.M. Kelly, and N.A. Rowson, “Microwave Reduction of Oxidized Ilmenite Concentrates”, Minerals Eng., Vol. 8, pp. 1427-1438, November, 1995. 21. X. Liao, “Dielectric properties and their application in microwave-assisted organic chemical reactions”, PhD Thesis. McGill University, Canada, 2002. 22. H. Yilmaz, “Excess Properties of Alcohol, Water Systems at 298.15 K”, Turk. J. Phys. Vol. 26, pp. 243-246, 2002. 23. F. Kormin, N.H. Abdurahman, R.M. Yunus, M. Rivai, “Study the Heating Mechanisms of Temperature Controlled Microwave Closed System (TCMCS),” Intl. J. Eng. Science Inno. Tech, Vol. 2, pp. 417-429, September, 2013. 24. R. Yazdanparast. A. Ardestani. “In Vitro Antioxidant and Free Radical Scavenging Activity of Cyperus Rotundus”. J. Med. Food. 10(4): 667- 674, 2007. 25. M. .F. A. Ghafar, K. N. Prasad, K.K. Weng and A. Ismail. “ Flavonoid, hesperidine, total phenolic contents and antioxidant activities from Citrus species”African Journal of Biotechnology. Vol. 9(3), pp. 326-330, 2010 . 26. M.R. Criado, S.P. Torre, I.R. Pereiro, and R.C. Torrijos, “Optimization of a microwave-assisted derivatization–extraction procedure for the determination of chlorophenols in ash samples”. J. Chrom. A, Vol.1024, pp. 155–163, 2004. 27. N. Lay-Keow, and H. Michel, “Effects of moisture content in cigar tobacco on nicotine extraction similarity between Soxhlet and focused open-vessel microwave-assisted techniques” J. Chrom. A, Vol.11, pp. 213–219,2003 Authors: P. Rama Mohan Rao, S. Karthik Paper Title: Investigation on Flexural Behaviour of Beam with Bamboo as Main Rebars Abstract: This study mainly focusing on to reduce the cost of materials which are utilized for construction purpose particularly steel. Steel is the material which is used for all types of reinforcement in column, beam and slab. The main disadvantage of this material that it easily corrodes when it interacts with moisture and due to this effect, its strength is also greatly reduced and it leads to durability problem in buildings. For reducing this effect, we utilize the bamboo rebars as a reinforcement in the place of steel for not only to increase durability property but also to enhance the utilization of low-cost and efficient materials for construction purpose. The bamboo can be replaced in structural member with certain percentages such as 25 %, 50 %, 75 % and 100% as a main rebars. The bamboo rebars were placed in both tension and compression zone on 0.7 m beams. The beams are tested under loading frame and test results are obtained. In loading frame, two-point loads were given in one- third of position on both ends in beam. From the test result, it was compared with conventional beam in flexural strength, deflection and their crack pattern and it shows that 25 % bamboo replacement beam obtain greater strength than other percentage of bamboo beams. From our research, we recommend that bamboo can also be utilized in members instead of steel in structural members.

Keywords: Bamboo reinforcement beams, crack pattern, deflection and flexural strength. 39. References: 173-177 1. Usha Rani, Martina Jenifer, (2017) “Investigation On The Flexural Behaviour Of Bamboo Reinforced Concrete Beams”, International Journal of Research in Science and Technology, Vol. No. 7, Issue No. I. 2. Masakazu TeraiAnd Koichi Minami, “Research and Development on Bamboo Reinforced Concrete Structure”. 3. Moroz ,Lissel , Hagel, (2014) “Performance of bamboo reinforced concrete masonry shear walls” Construction and Building Materials 61 PP 125–137. 4. Adom-Asamoah Mark, Afrifa Owusu Russell, (2011) “A comparative study of Bamboo reinforced concrete beams using different stirrup materials for rural construction” International Journal Of Civil And Structural Engineering, Volume 2, No 1, Pp 407- 423. 5. Adekunle, Adewuyi Adegboyega, Otukoya Oluwole, Olaniyi Oladipupo, Olafusi, (2015) ‘Comparative Studies of Steel, Bamboo and Rattan as Reinforcing Bars in Concrete: Tensile and Flexural Characteristics’, Open Journal of Civil Engineering, Vol.5, No.02, pp.228-238. 6. Jigar K. Sevalia, Nirav B. Siddhpura, Chetan S. Agrawal, Deep B. Shah, Jai V. Kapadia, (2013) “Study on Bamboo as Reinforcement in Cement Concrete”, International Journal of Engineering Research and Applications (IJERA) Vol. 3, Issue 2, pp.1181-1190 7. KuteWakchaure,(2014) “Performance Evaluation for Enhancement of Some of the Engineering Properties of Bamboo as Reinforcement in Concrete”, J. Inst. Eng. India Ser. 94(4):235–242 8. Jennifer Gottrona, Kent, A. Harries and Qing FengXu, (2014) ‘Creep Behavior of Bamboo’, Construction and Building Material, Vol.66, No.05, pp.79-88. 9. IS: 6874-2008 Method of tests for bamboo (CED 9: Timber and Timber Stores). 10. IS: 13311(Part 2) - 1992 Methods of testing for Non-Destructive testing on concrete. 11. IS: 383- 1970 Specifications for coarse and fine aggregate from natural sources of concrete. 12. IS: 10262 – 2009, Concrete Mix Proportioning – Guidelines 13. IS: 2386 (Part 1)-1963 Methods of test for aggregates for concrete: Part 1 Determination of particle size and shape. 14. IS: 2386(Part 3)-1963 Methods of test for aggregates for concrete: Part 3 Determination of specific gravity, density, voids, absorption and bulking. 15. IS: 2386(Part 4)-1963 Methods of test for aggregates for concrete: Part 4 Determination of attrition, abrasion, crushing, and impact value of aggregate. 16. Agarwal, A., Nanda, B., & Maity, D. (2014). Experimental investigation on chemically treated bamboo reinforced concrete beams and columns. Computers and Chemical Engineering, 71, 610–617. Authors: A.F. Gorospe, E.A. Barez, J.K. Coloso, J.C. Enzon, F. J. Tan, F.A.A Uy Assessment for the Design Elevation of the Calumpang Bridge in Batangas, Philippines at Different Paper Title: Rainfall Return Periods and the Typhoon Rammasun Flood Event with the Introduction of LiDAR Data Abstract: On July 2014, Typhoon Rammasun destroyed the Calumpang Bridge over the Calumpang River in Batangas, Philippines. While re-habilitation efforts for the bridge have finished, the validity of its elevation still needs to be studied. In order to create an elevation design that prevents flooding from reaching the bridge, a study was conducted using HEC-RAS, HEC-HMS, GIS software, and LiDAR data to model the hydraulic response and generate indundation maps of the Calumpang River during different rainfall return periods. It was found that: (1) only the downstream areas of the floodplain are significantly affected by flooding events from Typhoon Rammasun and at 15, 25, 50, and 100 year return periods, (2) water surface elevation only reached up to 4.45 meters during the typhoon, and (3) the 9-meter high deck of the rehabilitated Calumpang Bridge is safe from water reaching over the 40. deck.

Keywords: Bridge, Elevation, Hydrologic Modelling, Lidar, Inundation Maps, 178-181

References: 1. C. Hung and and W. Yau. “Behavior of scoured bridge piers subjected to flood-induced loads,” Engineering Structures, vol. 80, pp. 241-250, December 2014. 2. G. Lee and E. Sternberg. “A new system for preventing bridge collapses,” Issues in Science and Technology, vol. 24, no. 3, pp. 31-36, June 2008. 3. V. Meesuk, Z. Vojinovic, A. Mynett, and A. Abdullah. “Urban flood modelling combining top-view LiDAR data with ground-view SfM observations,” Advanced in Water Resources, vol. 75, pp. 105-117, January 2015. 4. The Mapúa Phil-LiDAR 1 Project, Mapúa Institute of Technology, Manila, Philippines, 2014. 5. Introduction to LiDAR DEM, UP Disaster Risk and Expo-sure Assessment for Mitigation, Quezon City, Philippines, 2014. 6. Department of Public Works and Highways, Philippines. Authors: K. Jayaram, C. Arun Paper Title: Performance Analysis of Low Power Superimposing OFDM System Architecture Abstract: Modern communication required high speed of data transmission and reception. Orthogonal Frequency Division (OFDM) offers high data rate for communication in wireless medium. The multi users can be transmitted using OFDM system in parallel way of transmission and reception approach. The main limitation of multiuser OFDM system is that it can able to transmit the data of unicast and multicast separately. In this paper, superimposing OFDM architecture is proposed to overcome such limitations in conventional methods. The performance of the proposed superimposing OFDM architecture is analyzed in terms of power consumption with state of arts.

Keywords: OFDM, encoder, unicast, multicast, power consumption.

References: 1. Q. Wang, Z. Wang and L. Dai, "Multiuser MIMO-OFDM for Visible Light Communications," in IEEE Photonics Journal, vol. 7, no. 6, pp. 41. 1-11, Dec. 2015. 2. Abhinav Johri, Dr. Farooq Husain, Ekta Agarwal, “Improving fitness factor of multi-user MIMO-OFDM based Visible light communication 182-184 system by using genetic algorithms”, International Research Journal of Engineering and Technology, Volume: 04 Issue: 05, May -2017. 3. Rajbir Kaur, Charanjit Kaur, “Investigation on Channel Estimation techniques for MIMO- OFDM System for QAM/QPSK Modulation”, International Journal of Computational Engineering Research (ijceronline.com) Vol. 2 Issue. 5, Sep 2012, pp. 1419-1424. 4. D. Kalaivani and S. Karthikeyen, “VLSI Implementation of Area-Efficient and Low Power OFDM Transmitter and Receiver”, Indian Journal of Science and Technology, Vol 8, no 18, pp. 657-667, 2015. 5. Isael Diaz, Siyu Tan, Yun Miao, Leif Wilhelmsson, Ove Edfors and Viktor Öwall, "A 350μW Sign-Bit architecture for multi-parameter estimation during OFDM acquisition in 65nm CMOS", IEEE International Symposium on Circuits and Systems (ISCAS), Lisbon , pp. 2984- 2987, 2015. 6. M. Ismail, I. Ahmed and J. Coon, “Low Power Decoding of LDPC Codes,” Hindawi Publishing Corporation ISRN Sensor Networks, Vol 2013, pp.1-10, Article ID 650740, 2013. 7. H. Zhang, C. Li; S. Chen, X. Tan, N. Yan and H. Min, "A Low Power OFDM-Based Wake-Up Mechanism for IoE Applications," IEEE Transactions on Circuits and Systems II: Express Briefs , Vol.5, no 99, pp.1-10, 2017. 8. E. Bedeer and M. J. Hossain, "Performance of Low-Complexity Uniform Power Loading OFDM Systems With Reduced Feedback Over Rayleigh Fading Channels," in IEEE Transactions on Wireless Communications, Vol 15, no 6, pp. 3783-3795, 2016. 9. Tran MinhHai, Saotome Rie, Taisaku Suzuki and Tomohisa Wada, “An Acoustic OFDM System with Symbol-by-Symbol Doppler Compensation for Underwater Communication,” The Scientific World Journal, Vol 2016, Article ID 7528353, pp.1-11, 2016. Authors: Nor Hana Adam, Mohd Suffian Yusoff, Hamidi Abdul Aziz Fractionation of Organic Content in Mature Leachate from Semi-Aerobic Landfill by Means Physico- Paper Title: Chemical Method Abstract: Chemical oxygen demand(COD) was the main characteristic in analyzed the quality of wastewater and leachate. Soluble COD(SCOD) and particulate COD(PCOD) previously determined by physico-chemical methods 42. such as i) coagulation-flocculation ii) filtration. These meth-ods widely used in wastewater treatment. However, research on COD fraction for leachate is limited. This study aims to analyzed and de-termine the efficient method for 185-187 PCOD and SCOD in mature leachate from semi-aerobic landfill. Three methods were applied in this re-search to compare the significant features of the three methods. Method 1(M1) and Method 3(M3) were applied from previous studies on COD fraction in wastewater. Modified method with optimum dosage of zinc sulphate known as Method 2(M2) was investigated in evaluate the correlation between COD particulate and coagulant. The result showed that PCOD in M2 is dominant (38.54%) due to the optimum dosage of coagulant while PCOD for M1 and M3 showed the results (M1=27.00%; M2=27.58%), respectively. In conclusion, the results of this study validate that M1, M2 and M3 in determine PCOD in wastewater and leachate. Effect of optimum dosage in M2 was consid-ered as new finding. Further research on COD fractions of leachate should be investigated to identify the appropriate treatment for mature leachate.

Keywords: Leachate; COD Fraction; Soluble COD; Particulate COD,

References: 1. Foo, K.Y., et al., Batch adsorption of semi-aerobic landfill leachate by granular activated carbon prepared by microwave heating. Chemical Engineering Journal, 2013. 22: p. 259-264. 2. Aziz, S.Q., et al., Leachate characterization in semi-aerobic and anaerobic sanitary landfills: A comparative study. Journal of Environmental Management 2010. 9: p. 2608-2614. 3. Salem, Z., et al., Evaluation of landfill leachate pollution and treatment. Desalination 2008. 220(1-3): p. 108–114. 4. Klučáková, M., Conductometric study of the dissociation behavior of humic and fulvic acids. Reactive and Functional Polymers 2018. 128:p 24-28. 5. Huang, T., et al., Beneficiation and influencing factors of coal-series kaolin for the reduction of COD. Applied Clay Science 2017. 138:p 34- 39. 6. Ziyang, L., et al.., Natural attenuation and characterization of contaminants composition in landfill leachate under different disposing ages. Science of the Total Environment, 2009. 407(10): p. 3385-3391. 7. Sun, Y., et. al., Characteristics of water quality of municipal wastewater treatment plants in China: implications for resources utilization and management.. Journal of Cleaner Production 2016. 131:p. 1-9. 8. Wang, H., et. al., Removal of humic substances from reverse osmosis (RO) and nanofiltration (NF) concentrated leachate using continuously ozone generation-reaction treatment equipment. Waste Management 2016. 56: p. 271-279. 9. Fan, H-J., et. al., Characteristics of landfill leachates in central Taiwan. Science of the Total Environment 2016. 361: p. 25-37. 10. Long, Y., et. al., Effective removal of contaminants in landfill leachate membrane concentrates by coagulation. Chemosphere 2017. 167:p. 512-519. 11. Hu, Z., et al., Evaluation of a rapid physical–chemical method for the determination of extant soluble COD. Water Research 2002. 36: p. 617- 624. 12. Yusoff, M.S., et al., Floc behavior and removal mechanisms of cross-linked Durio zibethinus seed starch as a natural flocculant for landfill leachate coagulation-flocculation treatment. Waste Management 2018. 74: p. 362-372. 13. Xiaoli, C., et al., Spectroscopic studies of the effect of aerobic conditions on the chemical characteristics of humic acid in landfill leachate and its implication for the environment. Chemosphere 2013. 91: p. 1058-1063. 14. Verma, M and Kumar, R.N., Can coagulation—flocculation be an effective pre-treatment option for landfill leachate and municipal wastewater co-treatment?. Perspectives in Science 2016. 8: p. 492-494. Authors: B. Rajasekaran, C. Arun Detection of Malicious Nodes in Wireless Sensor Networks based on Features Using Neural Network Paper Title: Computing Approach Abstract: The detection of malicious or hidden nodes in Wireless Sensor Network (WSN) is important for improving the performance of the WSN. In this paper, distance metric and probabilistic features are extracted from each individual in WSN with respect to its surrounding nodes. These individual extracted features are given to the input of the classification algorithm. This paper uses feed forward back propagation neural networks for training and testing the individual nodes using the extracted node features. The concert of this hidden node identification in WSN using metric and probabilistic features based classification algorithm analyzed energy consumption, throughput and delay.

Keywords: Malicious, nodes, metric, features, neural networks.

References: 43. 1. Preethi M , Rashmi Purad, Kavya D S, Chandrakala H ,” A Technique for Malicious Node Detection for Adaptive Data Fusion in Wireless Sensor Networks”, International Journal of Scientific and Research Publications, Volume 8, Issue 6, June 2018. 2. M. Singh, G. Mehta, C. Vaid and P. Oberoi, "Detection of Malicious Node in Wireless Sensor Network Based on Data Mining," 2012 188-192 International Conference on Computing Sciences, Phagwara, 2012, pp. 291-294. 3. Atassi, N. Sayegh, I. Elhajj, A. Chehab and A. Kayssi, "Malicious Node Detection in Wireless Sensor Networks," 2013 27th International Conference on Advanced Information Networking and Applications Workshops, Barcelona, 2013, pp. 456-461. 4. P. Padmaja and G. V. Marutheswar, "Detection of Malicious Node in Wireless Sensor Network," 2017 IEEE 7th International Advance Computing Conference (IACC), Hyderabad, 2017, pp. 193-198. 5. W. R. Pires, T. H. de Paula Figueiredo, H. C. Wong and A. A. F. Loureiro, "Malicious node detection in wireless sensor networks," 18th International Parallel and Distributed Processing Symposium, 2004. Proceedings., Santa Fe, NM, USA, 2004, pp. 24-27. 6. Roy Sandip et al., "Secure data aggregation in wireless sensor networks: Filtering out the attacker's impact", IEEE Transactions on Information Forensics and Security, vol. 9, no. 4, pp. 681-694, 2014. 7. K Pradeepa, WR Anne, S Duraisamy, "Design and implementation issues of clustering in Wireless Sensor Networks", International Journal of Computer Applications, vol. 47, no. 11, pp. 23, 2012. 8. R Azarderskhsh, A Reyhani, "Secure clustering and symmetric key establishment in heterogeneous wireless sensor networks", Eurasip Journal on Wireless Communications and Networking Article ID: 893592, pp. 1-12, 2011. 9. K. Kalpakis, K. Dasgupta, P. Namjoshi, "Efficient algorithms for maximum lifetime data gathering and aggregation in wireless sensor networks", Computer Networks, vol. 42, no. 6, pp. 697-716, August 2003. Authors: S. Nurulain, M.S. Najib, MH Alsibai, MR Mohamed, M. R. Salim, H. Manap Paper Title: A Potential Development of Haze Detection System Using an Open-Path Optical Method Abstract: this paper describes a potential development of haze measurement system by measuring visibility of a 44. medium instead of measuring con-centration of particulate matter in air. This measurement system is using an open path optical method to measure visibility of a few series of known-VLT thin film. VLT or visible light transmission 193-195 rate plays an important role in order to determine the visibility of a medium and can be an alternative method to determine the haze index. Current instrument to measure haze is very big in size and cost too expensive relatively. Therefore an alternative method using a laser pointer as a light source and a spectrometer as a detector is introduced. The result shows that the measurement system is capable to measure a few series of known-VLT thin film within the visible light region (535-540 nm). It shows that this measurement system is capable to measure the visibility of haze and the response time is less than 1 s is reported.

Keywords: Optical Sensor, Haze Detection, Open-Path Method,

References: 1. BBC news, “What causes South East Asia's haze?”, Oct 2015, from http://www.bbc.com/news/world-asia-34265922 2. Department of Environment (DOE), “Air Pollutant Index (API)”, Official Portal of Ministry of Natural Resources & Environment Malaysia Last update: 4 Oct 2017, from https://www.doe.gov.my/portalv1/en/info-umum/english-air-pollutant-index-api/100. 3. Mingsheng Li, Lin Jia, Fengying Zhang, Maogui Hu, Yu Shi, Xi Chen, "Characteristics of haze weather in Chongqing, China and its determinants analysis based on automatic monitoring stations", Atmospheric Pollution Research, Vol. 7, Issue 4, July 2016, pp. 638-646. 4. Sam-Quarcoo Dotse, Lalit Dagar, Mohammad Iskandar Petra, Liyanage C. De Silva, " Influence of Southeast Asian Haze episodes on high PM10 concentrations across Brunei Darussalam", Environmental Pollution, Volume 219, December 2016, pp. 337-352. 5. De Lin Show, Su-Chin Chang, " Atmospheric impacts of Indonesian fire emissions: Assessing Remote Sensing Data and Air Quality During 2013 Malaysian Haze", Procedia Environmental Sciences, Volume 36, 2016, pp. 176-179. 6. Xie, X.S., Tao, S.C., Zhou, X.J., “Measuring visibility using digital photography”. Chinese Science Bulletin. 44, 1999, pp. 1130-1134. 7. O’Loingsigh, T., McTainsh, G.H., Tapper, N.J., Shinkfield, P., “Lost in code: acritical analysis of using meteorological data for wind erosion monitoring”, Aeolian Res. 2, 2010, pp. 49-57. 8. Mazuina Mohamad, Hadi Manap, "An Overview of Optical Fibre Sensors for Medical Applications", International Journal of Engineering Technology and Sciences IJETS, Vol. 1, No. 1, June 2014. 9. H. Manap, G Dooly, S O'Keeffe, E Lewis, "Ammonia detection in the UV region using an sensor", Sensors, 2009 IEEE, pp.140- 145. 10. H. Xia, W Liu, Y Zhang, R Kan, M Wang, "An approach of open-path gas sensor based on tunable diode laser absorption spectroscopy", Chinese Optics Letters Vol. 6, Issue 6, 2008, pp. 437-440. Authors: N. Asha, Prasanna Mani Paper Title: Knowledge-based Acceptance Test driven agile Approach for Quality Software Development Abstract: Agile approaches in DevOps context evolved SDLC to focus much on iteratively, communication and interactivity between different project roles. In this service-oriented world, much attention has to be given to customer specifications by providing continuous applications delivery with reduced time-to-market. Testing and the Quality Assurance (QA) activities take a central role in ensuring the accomplishment of users’ acceptance criteria and the quality of the delivered software. Instead of contractual approach the agile approach is used to emphasis on taming the Requirements Engineering, Testing and Quality Assurance activities. This increased importance of testing methodology manifests the software developing companies to advance further on testing approaches, preventing defects during the development process. This paper presents a testing methodology to apply Acceptance Test Driven Development (ATDD) techniques while developing DEVOps projects, termed Acceptance Test Simple Testing (ACT-ST) methodology. ACT-ST approach is very evident, supported by the open source framework that generates test cases using the syntactic structure of Gherkin language from ATDD scenario specifications extracted from the user stories quoted with acceptance criteria. ACT-ST approach promotes continuous Metric-based Quality Check, structured User Stories with acceptance criteria for test case generation, agile test reporting, Knowledge Repository and Functional Knowledge Documentation for governance of Quality Management System (QMS).

Keywords: Software testing, ATDD, Quality Management System, DevOps.

References: 1. “Accounting for UX work with user stories in Agile Projects” authors Hoa Loranger and Page Laubheimer, NN/g Nielsen Norman Group, 12 March 2017, https://www.nngroup.com/articles/ux-user-stories/ (article). 45. 2. “Agile software development methodologies and how to apply them”, author Monjurul Habib, 30th December 2013 (article). 3. “An approach for iterative and requirements-based quality assurance in DevOps” author Albert Tort, The magazine for RE professionals 196-202 from IREB, Issue 2016 -03 4. A.S.Syed Fiaz, N.Asha, D.Sumathi and A.S.Syed Navaz. 2016. Data Visualization: Enhancing Big Data More Adaptable and Valuable. International Journal of Applied Engineering Research. 11(4): 2801-2804. 5. A.S.Syed Navaz, P.Jayalakshmi, N.Asha. 2015. Optimization of Real-Time Video Over 3G Wireless Networks” September. International Journal of Applied Engineering Research. 10(18): 39724-39730. 6. A.S.Syed Navaz & Dr.G.M.Kadhar Nawaz. 2016. Flow Based Layer Selection Algorithm for Data Collection in Tree Structure Wireless SensorNetworks. International Journal of Applied Engineering Research. 11(5): 3359-3363. 7. A.S.Syed Navaz and Dr. G.M. Kadhar Nawaz. 2016. Layer Orient Time Domain Density Estimation Technique Based Channel Assignment in Tree Structure Wireless Sensor Networks for Fast Data Collection. International Journal of Engineering and Technology. 8(3): 1506-1512. 8. A.S.Syed Navaz, N.Asha & D.Sumathi “Energy Efficient Consumption for Quality Based Sleep Scheduling in Wireless Sensor Networks” March - 2017, ARPN Journal of Engineering and Applied Sciences, Vol No - 12, Issue No - 5, pp.–1494-1498. 9. Boehm, Barry. "A survey of agile development methodologies." Laurie Williams (2007). 10. Carrera, Álvaro, Carlos A. Iglesias, and Mercedes Garijo. "Beast methodology: An agile testing methodology for multi-agent systems based on behaviour driven development." Information Systems Frontiers 16.2 (2014): 169-182. 11. Cohn, M: User Stories Applied: For Agile Software Development. Addison Wesley (2004) 12. Fitzgerald, Brian, et al. "Scaling agile methods to regulated environments: An industry case study." Software Engineering (ICSE), 2013 35th International Conference on. IEEE, 2013. 13. FUADI, Ashar. "Introducing tcframe: A Simple and Robust Test Cases Generation Framework." Olympiads in Informatics (2015): 57. 14. Hammond, Susan, and David Umphress. "Test driven development: the state of the practice." Proceedings of the 50th Annual Southeast Regional Conference. ACM, 2012. 15. Hong, Weiyin, et al. "User acceptance of agile information systems: a model and empirical test." Journal of Management Information Systems 28.1 (2011): 235-272. 16. Hong, Weiyin, et al. "User acceptance of agile information systems: a model and empirical test." Journal of Management Information Systems 28.1 (2011): 235-272. 17. Janus, André, et al. "The 3c approach for agile quality assurance." Proceedings of the 3rd International Workshop on Emerging Trends in Software Metrics. IEEE Press, 2012. 18. Martin, R., &Melnik, G. (2008). Tests and requirements, requirements and tests: a m¨obius strip. IEEE Software, 25(1), 54–59. 19. Mugridge, R., & Cunningham, W. (2005). Fit for developing software: framework for integrated tests. Upper Saddle River, NJ:Prentice Hall. 20. N. Asha, et al. “Customer segregation in banking organisation using knowledge management.” IJPT Vol. 8 Issue No.3 (Sep-2016): 17645- 17649 21. Nuutila, Esko, and Eljas Soisalon-Soininen. "On finding the strongly connected components in a directed graph." Information Processing Letters 49.1 (1994): 9-14. 22. Pohl, Klaus. Requirements engineering: fundamentals, principles, and techniques. Springer Publishing Company, Incorporated, 2010. 23. Tort, Albert, Antoni Olivé, and Maria-Ribera Sancho. "An approach to test-driven development of conceptual schemas." Data & Knowledge Engineering 70.12 (2011): 1088- 1111. 24. Tort, A.: The Recovery Approach. Available at: https://re-magazine.ireb.org/issues/2015-1-ruling-complexity/the-recover-approach/ 25. TestLink Open Source Test Management. Available at: https://testlink.org 26. Wynne, M.: The cucumber book: behaviour-driven development for testers and developers. Pragmatic Bookshelf (2012). Authors: Budari, N.M., Ali, M.F., Ku Hamid, K.H., Khalil, K.A., Zainal, S., Musa, M. Evaluation of Power Effect on Disruption of Escherichia Coli Wild Type Cells in Flow Cell Ultrasound Paper Title: Treatment Abstract: Flow cell ultrasound treatment used non-chemical action and was conducted with room temperature and ambient conditions of pressure for disruption of cells, allowing the consideration of ultrasound as a clean technology. The 30 kHz flow cell ultrasound that was demonstrated technically at 55 ml/min and 35 % power increments had higher performances, with (i) 94.59 % model organism disruption and (ii) the lowest cost of treatment at 0.0579 kWh/liter of electric energy per order and equally to 2.90 USD/m3. Furthermore, there was a statistically significant difference by one-way anova between flow rates and power effect in flow cell ultrasound (p < 0.05), where Tukey Honest Sig-nificant Difference (HSD) post hoc analysis revealed that the increase of flow rate from 40 to 70 ml/min was generating increases of power from 0.00336 to 0.00409 kW. Thus, flow cell ultrasound is an efficient sustainable technology but the economic costs need to be carefully balanced with the need for sustainable treatment for future.

Keywords: Flow Cell Ultrasound, Escherichia Coli, Power, Electric Energy per Order, Monetary Cost,

References: 1. R. A. Al-Juboori and T. F. Yusaf, “Improving the performance of ultrasonic horn reactor for deactivating microorganisms in water,” IOP Conf. Ser. Mater. Sci. Eng., vol. 36, pp. 1–13, Sep. 2012. 2. W. Q. Betancourt and J. B. Rose, “Drinking water treatment processes for removal of Cryptosporidium and Giardia.,” Vet. Parasitol., vol. 126, no. 1–2, pp. 219–34, Dec. 2004. 3. M. R. Doosti, R. Kargar, and M. H. Sayadi, “Water treatment using ultrasonic assistance : A review,” in International Academy of Ecology and Environmental Sciences, 2012, vol. 2, no. 2, pp. 96–110. 4. B. Tansel, “New Technologies for Water and Wastewater Treatment: A Survey of Recent Patents,” Recent Patents Chem. Eng., vol. 1, no. 1, pp. 17–26, Jan. 2008. 5. N. M. Budari, M. F. Ali, K. H. Ku Hamid, K. A. Khalil, S. Zainal, and M. Musa, “Flow Cell Ultrasound Treatment : The Influence of Sonication Media and Temperature on the Disruption of Escherichia coli wild Type Cells,” Adv. Sci. Lett. J., p. In Press, 2017. 6. S. N. Hussain, N. de Las Heras, H. M. a Asghar, N. W. Brown, and E. P. L. Roberts, “Disinfection of water by adsorption combined with electrochemical treatment.,” Water Res., vol. 54, pp. 170–8, May 2014. 7. C. Yanmin, “Visible-light-driven Photocatalytic Disinfection of Bacteria by the Natural Sphalerite,” The Chinese University of Hong Kong, 46. 2011. 8. G. Andaluri, E. V Rokhina, and R. P. S. Suri, “Evaluation of relative importance of ultrasound reactor parameters for the removal of estrogen hormones in water.,” Ultrason. Sonochem., vol. 19, pp. 953–958, 2012. 203-206 9. O. Ayyildiz, “Ultrasonic and Air Stripping Removal of CCl4 and TCA from Water: Investigating Synergistic Effects,” Illinois Institute of Technology, Chicago, 2003. 10. J. H. Gibson, D. Hai, N. Yong, R. R. Farnood, and P. Seto, “A literature review of ultrasound technology and its application in wastewater disinfection,” Water Qual. Res. J. Can., vol. 43, no. 1, pp. 23–35, 2008. 11. N. Gera and S. Doores, “Kinetics and mechanism of bacterial inactivation by ultrasound waves and sonoprotective effect of milk components,” J. Food Sci., vol. 76, no. 2, pp. 111–119, 2011. 12. M. Villamiel and P. De Jong, “Inactivation of Pseudomonas fluorescens and Streptococcus thermophilus in Trypticase Soy Broth and total bacteria in milk by continuous-flow ultrasonic treatment and conventional heating,” J. Food Eng., vol. 45, pp. 171–179, 2000. 13. N. M. Budari, M. F. Ali, K. H. Ku Hamid, M. Musa, K. A. Khalil, and N. F. Khairuddin, “Escherichia coli Wild Type Cells Disruption by Ultrasound Treatment for Bacterial Disinfection,” in The International Civil and Infrastructure Engineering Conference (InCIEC 2015), 2016, pp. 21–31. 14. S. Z. Salleh and J. S. Roberts, “Ultrasound pasteurization : The effects of temperature , soluble solids , organic acids and pH on the inactivation of Escherichia coli ATCC 25922,” Ultrason. Sonochem., vol. 14, pp. 323–329, 2007. 15. M. L. Moncada Reyes, “Influence of Low Sonication Intensities at Different Temperatures on Acid Tolerance, Bile Tolerance, Protease Activity, and Growth of Yogurt Culture Bacteria Lactobacillus Delbrueckii ssp. Bulgaricus and Streptococcus Salivarius ssp. Thermophilus,” Louisiana State University, 2011. 16. B. Zhou, “Investigation on factors influencing ultrasound-assisted surface decontamination of fresh-cut vegetables,” University of Illinois Urbana, 2010. 17. N. N. Mahamuni and Y. G. Adewuyi, “Advanced oxidation processes (AOPs) involving ultrasound for waste water treatment: a review with emphasis on cost estimation.,” Ultrason. Sonochem., vol. 17, no. 6, pp. 990–1003, Aug. 2010. 18. N. M. Budari, M. F. Ali, and J. Kassim, “Physical Contaminants Removal from Continuous Process of Water Filtration by a New Low-Cost Palm Shell Charcoal,” Adv. Mater. Res., vol. 911, pp. 397–404, Mar. 2014. 19. R. H. Jawale and P. R. Gogate, “Combined treatment approaches based on ultrasound for removal of triazophos from wastewater,” Ultrason. Sonochem., vol. 40, pp. 89–96, 2018. 20. M. Sivakumar and a B. Pandit, “Ultrasound enhanced degradation of Rhodamine B: optimization with power density.,” Ultrason. Sonochem., vol. 8, no. 3, pp. 233–240, 2001. 21. T. Yusaf and R. A. Al-Juboori, “Alternative methods of microorganism disruption for agricultural applications,” Appl. Energy, vol. 114, pp. 909–923, Feb. 2014. 22. E. Gunerken, E. D’Hondt, M. H. . Eppink, L. Garcia-Gonzalez, K. Elst, and R. . Wijffels, “Cell disruption for microalgae biorefineries,” Biotechnol. Adv., vol. 33, no. 2, pp. 243–260, 2015. Authors: M. Senthil Vadivu, M.N. Vijayalakshmi An Efficient Multi-Feature Best Decision Based Forest Fire Detection (MF-BD-FFD) From Still Paper Title: 47. Images Abstract: Fire attack in forest makes major degradation in the forest environment and ecosystems. Forest fire 207-212 detection in the early stage can prevent major causes due to fire attack. A novel digital image processing based on multi-feature best decision-based forest fire detection (MF-BD-FFD)is proposed in this work. To increase the sensitivity of detection, color and texture feature with hybrid decision making algorithms such as artificial neural networks (ANN), Support Vector machine (SVM), k- nearest classifier (KNN) is used and optimized output will be selected. By using proposed method, the accuracy of the system is increased with a factor of 5% when compared to the conventional technique.

Keywords: Forest Fire Detection (FFD), Artificial Neural Networks (ANN), Support Vector Machine (SVM), k- nearest Classifier (KNN), Digital Image Processing (DIP)

References: 1. Jonas, Peter Navratil, Vanessa Keuck, Keith Peterson, and Florian Siegert ,Monitoring _re and selective logging activities in tropical peat swamp forests,IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 5, no. 6 (2012): 1811-1820. 2. Benjamin, Sam G., B. Radhakrishnan, T. G. Nidhin, and L. Padma Suresh. Extraction of _re region from forest fire images using colour rules and texture analysis., Emerging Technological Trends (ICETT), International Conference on, pp. 1-7. IEEE, 2016. 3. Saber, Eli S., and A. Murat Tekalp. Integration of color, edge, shape, and texture features for automatic region-based image annotation and retrieval., Journal of Electronic Imaging 7, no. 3 (1998): 684-701. 4. Celik, Turgay, Hasan Demirel, Huseyin Ozkaramanli, and Mustafa Uyguroglu. Fire detection using statistical color model in video sequences., Journal of Visual Communication and Image Representation 18, no. 2 (2007): 176-185. 5. Hu, Zhongwen, Qingquan Li, Qian Zhang, and Guofeng Wu. Representation of block-based image features in a multi-scale framework for built-up area detection.,Remote Sensing 8, no. 2 (2016): 155. 6. Celik, Turgay. ,Fast and e_cient method for _re detection using imageprocessing., ETRI journal 32, no. 6 (2010): 881-890. 7. Shao, Jing, GuanxiangWang, andWei Guo. An image-based _re detection method using color analysis., In Computer Science and Information Pro-cessing (CSIP), 2012 International Conference on, pp. 1008-1011. IEEE,2012 8. Prema, C. Emmy, S. S. Vinsley, and S. Suresh. ,E_cient ame detection based on static and dynamic texture analysis in forest fire detection.,FireTechnology 54.1 (2018): 255-288. 9. Benjamin, Sam G., B. Radhakrishnan, T. G. Nidhin, and L. PadmaSuresh. Extraction of _re region from forest _re images using color rulesand texture analysis.,Emerging Technological Trends (ICETT), International Conference on, pp. 1-7. IEEE, 2016. 10. Chou, Kuang-Pen, Mukesh Prasad, Deepak Gupta, Sharmi Sankar, Ting-Wei Xu, Suresh Sundaram, Chin-Teng Lin, and Wen-Chieh Lin. Block-based feature extraction model for early fire detection., Computational Intelligence (SSCI), 2017 IEEE Symposium Series on, pp. 1-8. IEEE, 2017. 11. Gope, Hira Lal, Machbah Uddin, Shohag Barman, Dilshad Islam, and Mohammad Khairul Islam ,Fire Detection in Still Image Using ColorModel.,IEEE Journal of Selected Topics in Applied Earth Observationsand Remote Sensing, 5, no. 6 (2012): 1811-1820. 12. Tarabalka, Yuliya, Mathieu Fauvel, Jocelyn Chanussot, and J_on AtliBenediktsson SVM-and MRF-based method for accurate classi_cation ofhyperspectral images., IEEE Geoscience and Remote Sensing Letters 7,no. 4 (2010): 736-740. 13. Saber, Eli S., and A. Murat Tekalp. Integration of color, edge, shape,and texture features for automatic region-based image annotation and retrieval.,Journal of Electronic Imaging 7, no. 3 (1998): 684-701. 14. Celik, Turgay, Hasan Demirel, Huseyin Ozkaramanli, and MustafaUyguroglu. Fire detection using statistical color model in video sequences.,Journal of Visual Communication and Image Representation 18, no. 2(2007): 176-185. 15. Tom Toulouse, Lucile Rossi, Antoine Campana, Turgay Celik, MoulayAkhlouf. Computer vision for wild_re research: An evolving image datasetfor processing and analysis., Fire Safety Journal 92 (2017): 188-194. Authors: Mohd Aidil Adhha Abdullah, Lai Zi Hui, Mazidah Mamat Effect of Twice-Functionalized Montmorillonite to the Morphological Properties of PMMA/MMT Paper Title: Nanoco mposites Abstract: Sodium montmorillonite (MMT) was modified to org anophilic montmorillonite (OMMT) by exchanging Na+ ion in MMT with trihexyltetradecylphosphonium (THTDP) ion by cation-exchange method. The OMMT was then underwent second modification involving silane coupling agent of aminopropyltrimethoxylsilane (APTMS) to produce twice-functionalized clay (grafted-OMMT). The resulting OMMT and grafted-OMMT were characterized by Fourier transform infrared (FT-IR), nitrogen adsorption-desorption and X-ray diffraction (XRD) analyses. Addition of grafted-OMMT to PMMA matrix resulted in stronger polymer-clay interaction by forming exfo-liated and intercalated types nanocomposites compared to OMMT which formed conventional type composite.

Keywords: Organo-Montmorillonite, Twice-Functionalized Montmorillonite, Modified Clay,

48. References: 1. Tunç, S. & Duman, O. 2010. Preparation and characterization of biodegradable methylcellulose/montmorillonite nanocomposite 213-216 films. Applied Clay Science. 48(3):414-42. 2. Pinnavaia, T.J. & Beall, G.W. (Eds.), 2000. Polymer-Clay Nanocomposites. John Wiley& Sons Ltd, Chichester, England. 3. Olad, A. 2011. Polymer/Clay Nanocomposites. In Advances in diverse industrial applications of nanocomposites.113-138. 4. Abdullah, M.A.A., Mamat, M., Rusli, S. A., Kasim, A.A. & Sudin, N.H. 2015.Synthesis and Characterization of Thermally Stable Organo- Montmorillonitefor Polymer Composite. Journal of Applied Science Research. 11(24):34-38. 5. Angaji, M.T., Rafiee, R., Hemmati, M., Abdollahi, M., Razavi Aghjeh, M.K. 2014.Parametric Studies on the Grafting of Poly(Methyl Methacrylate) onto Organophilic Montmorillonite Using Silylated Clay Platelets. Journal of Macromolecular Science Part B. 53(6): 957- 974. 6. Shanmugharaj, A. M., Rhee, K. Y. & Ryu, S.H. 2006. Influence of Dispersing Medium on Grafting of Aminopropyltriethoxysilane in Swelling Clay Materials. Journal of Colloid Interface Science. 298 (2): 854-859. 7. Di Gianni, A., Amerio, E., Monticelli, O. & Bongiovanni, R. 2008. Preparation of Polymer/Clay Mineral Nanocomposites Via Dispersion of Silylated Montmorillonite in a Uv Curable Epoxy Matrix. Applied Clay Science.42: 116-124. 8. Piscitelli, F., Posocco, P., Toth, R., Fermeglia, M., Pricl, S., Mensitieri, G. & Lavorgna, M. 2010. Sodium montmorillonite silylation: Unexpected effect of the aminosilane chain length. Journal of Colloid Interface Science. 351: 108–115. 9. Mansoori, Y., Atghia, S.V., Sanaei, S.S., Zamanloo, M.R. & Imanzadeh, G. 2010. PMMA-clay nanocomposite materials: free- radically grafting of PMMA onto organophilic montmorillonite (20A). Macromolecular Research. 18(12):1174-1181. 10. Silva, T. F., Soares, B., Ferreira, G. S.C. & Livi, S. 2014. Silylated montmorillonite as nanofillers for plasticized PVC nanocomposites: Effect of the plasticizer. Applied Clay Science. 99: 93-99. 11. Mansoori, Y., Atghia, S.V., Zamanloo, M.R., Imanzadeh, G. & Sirousazar, M. 2010. Polymer clay nanocomposites: free-radical grafting of polyacrylamide onto organophilic montmorillonite. European Polymer Journal. 46(9):1844-1853. 12. Valtchev, V., Mintova, S. & Tsapatsis, M.(Eds.). 2011. Ordered Porous Solids: RecentAdvances and Prospects, Elsevier. 13. Yuan, P., Southon, P.D., Liu, Z., Green, M.E.R., Hook, J.M., Antill, S.J. & Kepert,C.J. 2008. Functionalization of halloysite clay nanotubes by grafting with 3aminopropyltriethoxysilane. Journal of Physical Chemistry C. 112(40): 15742-15751. 14. Jin, J., Fu, L., Yang, H. & Ouyang, J. 2015. Carbon hybridized halloysite nanotubes for high performance hydrogen storage capacities. Sciencetific Report. 5:124-129. 15. Peixoto, A. F., Fernandes, A. C., Pereira, C., Pires, J., & Freire, C. 2016. Physicochemical characterization of organosilylated halloysite claynanotubes. Microporous and Mesoporous Materials. 219: 145-154. 16. He, H., Tao, Q., Zhu, J., Yuan, P., Shen, W. & Yang, S. 2013. Silylation of Clay Mineral Surfaces. Applied Clay Science.71: 15-20. 17. Silva, A.A., Dahmouche, K. & Soares, B. G. 2011. Nanostructure and Dynamic Mechanical Properties of Silane-Functionalized Montmorillonite/ Epoxy Nanocomposites. Applied Clay Science. 54(2): 151-158. 16. Bee, S.-L., Abdullah, M. A.A., Mamat, M., Bee, S.-T., Sin, L. T., Hui, D. & Rahmat, A.R. 2017. Characterization of Silylated Modified Clay Nanoparticles and its Functionality in PMMA. Composites Part B Engineering. 110: 83-95. Lalithamani Nithyanandham, Aniruddha Madduri, Sainadh Makineni, Sanjay Bhargav Patibandla, Authors: M. Srinandhini Paper Title: Streamlined Slide Generation by Performing Extractive Text Summarization Abstract: Power Point Presentations have long been used by people to display information in an easy and eye- catching manner. Many organizations use the PowerPoint Presentations for discussing their projects with stakeholders, or for their project reviews. Many students use the Presentations to display their ideas of designs and how they implement it. This involves the user to analyse the content on which the presentation is to be made, shrink the content into smaller parts, and finally creating slides manually.There are many tools which are able to generate slides, and which automate the process of doing that. Generation of slides must involve some content, and this work aims to involve content given by the user varying from a normal text file to a web-page, and make available to the user the slides, based on the content.Our work involves automating the process by summarizing the entire text document given as an input, and generating PowerPoint slides using the aforementioned summary.

Keywords: Extractive summary, Textmining, Text Ranking, Text Summarization.

References: 1. Vishal Gupta and Gurupreet Singh Lehel," A survey of text summarization extractive techniques",journal of emerging technologies in web intelligence,volume.2, no.3, August 2010. 2. Razieh Abbasi-ghalehtaki, Hassan Khotanlou and Mansour Esmaeilpour," A combinational method of fuzzy, particle swarm optimization and cellular learning automata for text summarization", journal swarm and evolutionary computation, volume 30, October 31,2016. 3. Rasim M. Alguliyev, Nijat R. Isazade,"An unsupervised approach to generating generic summaries of documents ", journal applied soft computing, volume 34 issue C, September 2015. 4. Changjian Fang,Dejun Mu,Zhenghong Deng,zhing Wu," Word-sentence co-ranking for automatic extractive text summarization "Journal Expert Systems with Applications : An international journal volume 72, issue c, April 2017. 5. Gunes Erkhan and Dragomir R. Radev,"Lexrank: graph-based lexical centralityassalienceintext summarization",journal of Artificial Intelligence research 22 (2004). 6. Hongyan Jing," Sentence reduction for automatic text summarization", proceedings of the sixth conference on applied natural language processing, association for computational linguistics Stroudsburg, 2000. 49. 7. Lawrence H Reeve, Hyoli Han and Ari D.Brooks," The use of domain-specific concepts in biomedical text summarization", Information Processing and management, Volume 43, Issue 6, 2007. 217-222 8. Yue Hu and Xiaojun Wan," PPSGen: learning-basedpresentationslides generation for academic papers", Volume:27, issue 4, April 1,2015. 9. Ning Zhong, Yuefeng Li, and Sheng-Tang Wu," effective pattern discovery for text mining " IEEE transactions on knowledge and data engineering, volume.24, No.1, January 2012. 10. Ganesan, Kavita, ChengXiangZhai, and Jiawei Han,"Opinosis: A graphbased approach to abstractive summarization of highly redundant opinions.", Volume 2, Published 2010. 11. Massih-Reza Amini, Patrick Gallinari,” Self-supervised learning for automatic text summarization by text-span extraction”,23rd BCS European annual colloquium on information retrieval, 2001. 12. Priya Ganguly, Prachi M. Joshi,” IPPTGen-Intelligent PPT Generator”, 2016 International conference on computing, analytics and security trends (CAST) college of engineering, Pune, India. December 19-21, 2016. 13. Anusha Bagalkotkar, Ashesh Khandelwal, Shivam Pandey, Sowmya Kamath S,” A novel technique for efficient text document summarization as a service”, 2013 Third International Conference on Advances in Computing and Communications. 14. Mehdi Allahyari, Seyedamin Pouriyeh, Mehdi Assefi, Saeid Safaei, Elizabeth D. Trippe, Juan B. Gutierrez, Krys Kochut, “Text summarization techniques: A brief survey”, arxiv, July 2017, USA. 15. Chaudhary M., Kumar H. (2016), “A Framework to Rank Nodes in Social Media Graph Based on Sentiment-Related Parameters”, In: Satapathy S., Joshi A., Modi N., Pathak N.(eds) proceedings of international conference on ICT for sustainable development. Advances in intelligent systems and computing, Vol 409. Springer, Singapore. 16. S.A.Babara, Pallavi D.Patil, “Improving performance of text summarization”, volume 46, pages 354-363,2015. 17. Yadav C.S., Sharan A., Kumar R., Biswas P. (2016) A New approach for single text document summarization. In: Satapathy S., Raju K., Mandal J., Bhateja V. (eds) Proceedings of the second international conference on computer and communication technologies. Advances in intelligent systems and computing, Vol 380. Springer, New Delhi. 18. Changjian Fang, Dejun Mu, Zhenghong Deng,Zhiang Wu“ Word-sentence co-ranking for automatic extractive text summarization.”, Volume 72,15 April 2017, Pages 189-195. 19. Rasmita Rautray, Rakesh Chandra Balabantaray, “Bio-inspired approaches for extractive document summarization: A comparative study”, volume 3, issue 3, July 2017, Pages 119-130. 20. Farshad Kiyoumars, “Evaluation of automatic text summarizations based on human summaries”, volume 192, 24 June 2015, pages 83-91. 21. Tian Wang, Yang Li, Yonghong Chen, Hui Tian, Yiqiao Cai, Weijia Jia, Baowei Wang,"Fog-based evaluation approach for trustworthy communication in Sensor-cloud system", communications letters IEEE, vol. 21, pp. 2532-2535, 2017, ISSN 1089-7798. 22. Rafael Ferreira, Luciano de Souza Cabral," Assessing sentence scoring techniques for extractive text summarization", volume 40, issue 14, 15 October 2013, pages 5755-5764. 23. Simone Teufel and Marc Moens, "Summarizing scientific articles: experiments with relevance and rhetorical status", volume 28, issue 4, December,2002 ,p.409-445. 24. [24] -Han Hua, Yen-Liang Chenb, Hui-Ling Chou," Opinion mining from online hotel reviews – A text summarization approach ", Volume 53, Issue 2, March 2017, pages 436-449. 25. Francesco Ronzano, Horacio Saggion, "Knowledgeextraction and modeling from scientific publications", part of the lecture notes in computer science book series (LNCS, volume 9792). 26. M. V. K. Kiran, R. E. Vinodhini, R. Archanaa and K. Vimalkumar, "User specific product recommendation and rating system by performing sentiment analysis on product reviews," 2017 4th International Conference on Advanced Computing and Communication Systems (ICACCS), Coimbatore, 2017, pp. 1-5. 27. Hans Moen, Laura-Maria Peltonen, Juho Heimonen, Antti Airola, Tapio Pahikkala, Tapio Salakoski, Sanna Salanteräc, “12”, Artificial Intelligence in Medicine, Volume 67, February 2016, Pages 25-37. 28. Ailin Li, Tao Jiang, Qingshuai Wang, Hongzhi Yu,” The Mixture of Textrank and Lexrank Techniques of Single Document Automatic Summarization Research in Tibetan”, 8th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC), 27- 28 August,2016. 29. Hannah M.E., Geetha T.V., Mukherjee S. (2011) Automatic Extractive Text Summarization Based on Fuzzy Logic: A Sentence Oriented Approach. In: Panigrahi B.K., Suganthan P.N., Das S., Satapathy S.C. (eds) Swarm, Evolutionary, and Memetic Computing. SEMCCO 2011. Lecture Notes in Computer Science, vol 7076. Springer, Berlin, Heidelberg. 30. Rada Mihalcea, Paul Tarau,”TextRank: Bringing Order Into Texts”, Conference on Empirical Methods, in Natural Language Processing, 2004. Mazidah Mamat, Mohd Aidil Adhha Abdullah, Adila Mohd Jaafar, Rosmadila Abd Rahman, Siti Authors: Syakirah Jamal Safuan Synthesis of Nickel/Aluminium-Layered Double Hydroxide As Potential Adsorbent for Methyl Paper Title: Orange and Crystal Violet Dyes Abstract: in this study, nickel/aluminium-layered double hydroxide (NAL) was synthesized to be used as adsorbent to remove dyes in water. Two types of dyes which are anionic and cationic dyes, respectively, were chosen, namely methyl orange (MO), and crystal violet (CV). NAL was synthesized via co-precipitation method and characterized by using powder X-ray diffractometer (PXRD) and Fourier transform infra-red spectrophotometer (FTIR). The removal of MO and CV dyes were conducted using different dosages of NAL. As the amount of NAL increases, the removal percentage of both dyes increased. NAL was able to remove up to 99.9% of anionic dye (MO). However, only 31.5% of cationic dye (CV) was successfully removed from water after being in contact with NAL for 24 hours.

Keywords: Layered Double Hydroxide, Dye Removal, Adsorption, Methyl Orange; Crystal Violet,

References: 1. Bi, B., et al., Heteropoly blue-intercalated layered double hydroxides for cationic dye removal from aqueous media. Applied Clay Science, 2011. 54: p. 242-247. 2. Rafatullah, M., et al., Adsorption of methylene blue on low-cost adsorbents: A review. Journal of Hazardous Materials, 2010. p. 177: 70-80. 3. Ertaş, M., et al., Removal of methylene blue from aqueous solution using cotton stalk, cotton waste and cotton dust. Journal of Hazardous 50. Materials, 2010. 183: p. 421-427. 4. Vaccari, A., Preparation and catalytic properties of cationic and anionic clays. Catalysis Today, 1998. 41: p. 53 – 71. 5. Villegas, J. C., et al., New layered double hydroxide containing intercalated manganese oxide species: Synthesis and characterization. Inorganic Chemistry, 2003. 42(18): p. 5621-5631. 223-226 6. Hongo, T., et al., Synthesis and adsorption properties of nanosized Mg-Al layered double hydroxides with Cl−, NO3− or SO42− as interlayer anion. Materials Science-Poland, 2011. 29(2): p. 86 – 91. 7. Mamat, M., et al., Behavior of layered double hydroxides having different divalet transition metal groups. Applied Mechanics and Materials, 2014. 563: p. 94 – 101. 8. Li, K.W., et al., The pH effects on the formation of Ni/Al nitrate form layered double hydroxides (LDHs) by chemical precipitation and hydrothermal method. Materials Chemistry and Physics, 2010. 121: p. 223 – 229. 9. Abdolmohammad-Zadeh, H., et al., Nickel-aluminium layered double hydroxide as a nanosorbent for selective solid-phase extraction and spectrofluorometric determination of salicyclic acid in pharmaceutical and biological samples. Talanta, 2011. 84: p. 368-373. 10. Gullipali, CH. S., et al., Batch study, equilibrium and kinetics of adsorption of selenium using rice husk ash (RHA). Journal of Engineering Science and Technology, 2011. 6(5): p. 586-605. 11. As’ari, R., et al., Kinetic study of palm oil adsorption onto acetylation treated oil palm mesocarp fiber. Journal of Applied Sciences Research, 2015. 11(24): p. 22-26. 12. Mary, P.P., et al., Adsorption of copper (II) ions from aqueous solution on carbons from morinda citrifolia bark. World Journal of Pharmaceutical Research, 2015. 4(5): p. 1246-1253. 13. Ansari, R. and Mosayebzadeh, Z., Removal of Eosin Y, an Anionic dye, from aqueous solutions using Conducting Electroactive Polymers. Iranian Polymer Journal, 2010. 19(7): p. 541 – 551. 14. Li, Q., et al., A comparative study on the properties, mechanisms and process design for the adsorption of non-ionic or anionic dyes onto cationic-polymer / bentonite. Journal of Environmental Management, 2010. 91: p. 1601-1611. 15. Mittal, A., et al., Batch and Bulk Removal of a Triarylmethane Dye, Fast Green FCF, from Wastewater by Adsorption Over Waste Materials. Journal of Hazardous Materials, 2009. 163: p. 568 – 577. Authors: D.S. Gayathri , Nagarajan Munusamy Paper Title: Classifying Alzheimer’s Disease Using Adaptive Neuro Fuzzy Inference System Abstract: Alzheimer's Disease (AD) may be a sort of dementia disease which is unpredictable with diagnose by understanding the clinical perception alone. Identifying Alzheimer’sdiseaseby scanning the brain using Magnetic Resonance Imaging (MRI) information is a Fundamental concern in the neurosciences. Universal evaluation of functional scan images regularly depends on Manual reorientation, visual reading and Furthermore, semi quantitative examination from certain specific segments of the cerebrum.This paper suggested the Adaptive Neuro methodology 51. for robotized multiclass analysis of Dementia with the higher order reasoning about MRI Image of a human Brain. The Process begins with the pre-processing the MRI Images by disposing the noises present in them, like labels and 227-233 X- Ray marks by using Tracking Algorithm. Feature Extraction process, eliminates the high frequency components using Discrete Wavelet Transform (DWT). Thus derived coefficients makeuse of primary couple of DWT coefficients for the preparation of classification in the means of Normal, Mild cognitive Influence; Alzheimer’s disease using Adaptive Neuro Fuzzy Algorithm (ANFIS). The testresult consequence demonstrates that the proposed technique execution posses a better result than by comparing with different order methodologies.

Keywords: ANFIS, MRI Image, Alzheimer’s Disease, DWT.

References: 1. E. M. Ali, A. F. Seddik. M. H. Haggag, "Using Data Mining Techniques for children Alzheimerclassification based on MRI", International Journal of Computer applications , Pp. 36-42, Vol. 131 , No. 2 , 2015. 2. E. M. Ali, A. F. Seddik. M. H. Haggag, "Classification of Hydrocephalus using TAN", International Journal od Advanced Research in Computer Science and Software Engineering, Pp. 90-97, Vol. 5 , Issue. 11, 2015. 3. Rajkumar, "A Multi- Stage Hybrid , CAD Approach for MRI Brain Alzheimer Recognition and Classification", The IIOAB Journal School of Computing Science and Engineering VIT University, 2016. 4. Olfa Ben Ahmed, Jenny Benois-Pineau, Michèle Allard, Chokri Ben-Amar, Gwénalle Catheline. “Classification of Alzheimer’s disease subjects from MRI using hippocampal visual features”, Multimedia Tools and Applications, Springer Verlag, pp.35, 2014. 5. Geetha C, Pugazhenthi D, “Classification of alzheimer's disease subjects from MRI using fuzzy neural network with feature extraction using discrete wavelet transform.”, Computational Life Sciences and Smarter Technological Advancement, 2018. 6. Pies RW. Alzheimer's Redux: “A preliminary take on the new diagnostic criteria”, Psychiatric Times, p.24-30, 2012. 7. Ashburner J, Friston KJ. “Voxel-Based Morphometry-the methods”, Neuroimage; p: 805-821, 2000. 8. Wolz R, Julkunen V, Koikkalainen J, Niskanen E, Zhang DP, Rueckert D, Soininen H, Lötjönen J; “Alzheimer's Disease Neuroimaging Initiative. Multi-method analysis of MRI images in early diagnostics of Alzheimer's disease”, P:6, 2011. 9. Mangin JF, Rivière D, Cachia A, Papadopoulos-Orfanos D, Collins DL, Evans AC, Régis J. Object-based strategy for morphometry of the cerebral cortex. Inf Process MedImaging 2003; 18: 160-171. 10. Toews M, Wells W, Collins DL, Arbel T. “Feature-Based Morphometry: Discovering Group-related Anatomical Patterns”, Neuro Image, p: 49, 2010. 11. Chupin M, Gerardin E, Cuingnet R. “Fully automatic hippocampus segmentation and classification in Alzheimer’s disease and mild cognitive impairment applied on data from ADNI”. Hippocampus; 19: P:579-587, 2009. 12. Gerardin E, Chetelat G, Chupin M. “Multidimensional classification of hippocampal shape features discriminates Alzheimers disease and mild cognitive impairment from normal aging”. Neuro Image; Vol: 47, P : 1476-1486, 2009. 13. Luis Javier Herrera, Ignacio Rojas, H. Pomares, A. Guillén, O. Valenzuela, O. Baños, “Classification of MRI images for Alzheimer’s disease detection”, IEEE BioMedical Communication, 2013. 14. Sakthivel K, Jayanthiladevi A, Kavitha C. “Automatic detection of lung cancer nodules by employing intelligent fuzzy cmeans and support vector machine”. Biomed Res, P: 27, 2016. 15. Agarwal M, Mostafa J. “Image retrieval for Alzheimer disease detection. In: Proceedings of the First MICCAI international conference on Medical Content-Based Retrieval for Clinical Decision Support”, Springer-Verlag, Berlin, Heidelberg, 2010. 16. Kamil A., Rustamov S., Clements M.A., Mustafayev E. “Adaptive Neuro-Fuzzy Inference System for Classification of Texts”, Studies in Fuzziness and Soft Computing, vol 361. Springer, 2018. 17. Karaboga, D. & Kaya, E. “Adaptive network based fuzzy inference system (ANFIS) training approaches: a comprehensive survey”, Artificial Intelligence Review, Springer, 2018. 16. http://adni.loni.usc.edu/ Authors: Ida Bagus Made Putra Jandhana, Teuku Yuri M. Zagloel, Rahmat Nurcahyo Paper Title: Measuring Industrial Resiliency by Using Data Envelopment Analysis Approach Abstract: Having several economic crises that affect industrial sector performance in the past decades, decision makers should consider to utilize an application that enables them to measure industrial resiliency more precisely. This research contributes not only a framework for the devel-opment of resilience measurement application, but also several theories for the concept building blocks, such as performance measurement management, and resilience engineering in real world environment. The data evaluated in this paper was taken from the metal industry sector from Statistics Indonesia. The sectoral efficiency was measured by using Data Envelopment Analysis in series. The result shows that there were sectoral efficiency drops when currency exchange shocks occur. This research is a continuation of previously published paper on performance measurement in the industrial sector. Finally, this paper contributes an alternative performance measurement method in indus-trial sector based on resilience concept. Moreover,this research demonstrates how applicable the concept of Resilience Engineering is and its method of measurement..

Keywords: Resilience, Measurement, Data Envelopment Analysis, Sector, Industrial,

References: 1. Liu, L., & Jiang, Z. (2016). Influence of technological innovation capabilities on product competitiveness. Industrial Management & Data Systems, 116(5), 883-902. 52. 2. Choon Tan, K., Kannan, V. R., Handfield, R. B., & Ghosh, S. (2000). Quality, manufacturing strategy, and global competition: An empirical analysis.Benchmarking: An International Journal, 7(3), 174-182. 234-238 3. Zagloel, Y., & Jandhana, I. B. M. P. (2016). Literature Review of Industrial Competitiveness Index: Research Gap. 4. Agus, A. A., Isa, M., Farid, M. F., &Permono, S. P. (2015). An assessment of SME competitiveness in Indonesia. Journal of Competitiveness, 7(2). 5. Azadeh, A., Salehi, V., Ashjari, B., &Saberi, M. (2014). Performance evaluation of integrated resilience engineering factors by data envelopment analysis: The case of a petrochemical plant. Process Safety and Environmental Protection, 92(3), 231-241. 6. Hollnagel, E., Woods, D. D., & Leveson, N. (2007). Resilience engineering: Concepts and precepts. Ashgate Publishing, Ltd.. 7. Taticchi, P., Tonelli, F., & Cagnazzo, L. (2010). Performance measurement and management: a literature review and a research agenda. Measuring business excellence, 14(1), 4-18. 8. Righi, A. W., Saurin, T. A., & Wachs, P. (2015). A systematic literature review of resilience engineering: Research areas and a research agenda proposal. Reliability Engineering & System Safety, 141, 142-152. 9. Nurcahyo, R., &Wibowo, A. D. (2015). Manufacturing Capability, Manufacturing Strategy and Performance of Indonesia Automotive Component Manufacturer. Procedia CIRP, 26, 653-657. 10. Neely, A., Gregory, M., & Platts, K. (2005). Performance measurement system design: A literature review and research agenda. International journal of operations & production management, 25(12), 1228-1263. 11. Bititci, U. S., Turner, U., & Begemann, C. (2000). Dynamics of performance measurement systems. International Journal of Operations & Production Management, 20(6), 692-704. 12. Rogers, G. F. C. (1983). The Nature of Engineering a Philosophy of Technology. Palgrave Macmillan. 13. Fowler, J. W., & Rose, O. (2004). Grand challenges in modeling and simulation of complex manufacturing systems. Simulation, 80(9), 469- 476. 14. Chapman, C. B., & Cooper, D. F. (1983). Risk engineering: basic controlled interval and memory models. Journal of the Operational Research Society, 51-60. 15. Dey, P. K., Kinch, J., & Ogunlana, S. O. (2007). Managing risk in application development projects: A case study. Industrial Management & Data Systems, 107(2), 284. 16. Dinh, L. T. T., et al. (2012). "Resilience engineering of industrial processes: Principles and contributing factors." Journal of Loss Prevention in the Process Industries25(2): 233-241. 17. Wreathall, J. (2006). Developing models for measuring resilience. John Wreathall & Co., Inc., Dublin, Ohio. 18. Rose, A., & Krausmann, E. (2013). An economic framework for the development of a resilience index for business recovery. International Journal of Disaster Risk Reduction, 5, 73-83. 19. Pindyck, Robert S and Daniel L. Rubienfield. (2005). Microeconomics Sixth Edition. Pearson Prentice Hall, New Jersey 20. IMD, I. (2012). World Competitiveness Yearbook. International Institute for Management Development, Lausanne. 21. Schwabb, Klaus (2013). Global Economic Report. World Economic Forum. Geneva. Switzerland. 22. Allen, Karen Lynn. (2016). Efficiency Is Not the Enemy of Resiliency. http://www.resilience.org/stories/2016-03-07 23. Howell, Lee. (2013). Resilience: What It is and Why It’s Needed. www.pwc.com. 24. Holling, C.S. (1996) Engineering Resilience versus Ecological Resilience, in: P.C. Schulze (Ed.) Engineering Within Ecological Constraints, pp. 31 – 44 (Washington, DC, National Academy Press). 25. Reig-Martı́nez, E., &Picazo-Tadeo, A. J. (2004). Analysing farming systems with Data Envelopment Analysis: citrus farming in Spain. Agricultural Systems, 82(1), 17-30. 26. Blancard, S., &Hoarau, J. F. (2013). A new sustainable human development indicator for small island developing states: A reappraisal from data envelopment analysis. Economic Modelling, 30, 623-635. 27. United Nations. (2017). Resilience and Resource Efficiency in Cities. United Nations Environment Programs. 28. Wen, M. (2015). Uncertain data envelopment analysis. Springer. 29. Charnes, A., Cooper, W. W., Lewin, A. Y., Morey, R. C., & Rousseau, J. (1984). Sensitivity and stability analysis in DEA. Annals of Operations Research, 2(1), 139-156. 30. Statistics Indonesia (2017), https://www.bps.go.id/ 31. Negara, S. D., & Adam, L. (2012). Foreign direct investment and firms' productivity level: lesson learned from Indonesia. ASEAN Economic Bulletin, 29(2), 116-127. Authors: N. Geetha Lakshmi, D. Shanmuga Priyaa Handling of Indeterminacy for Trust Aware Energy Consumption Using Adaptive Intuitionistic Fuzzy Paper Title: Environment in Wireless Sensor Networks Abstract: In Wireless Sensor Network Environment (WSN), the most critical parameter of sensor nodes is the optimal usage of life time. An efficient WSN protocol needs to conserve energy as the main objective of maximizing the network lifetime. Further, secure topology construction is also included in this work, because trust value is considered as a vital factor which affects the behavior of nodes. The incompletion or inconsistency in gathering information of sensor nodes is not well-handled in most existing techniques for the selection of cluster head, taking into account trust Value, Residual Energy, Shortest Path (distance), and Number of Neighbor Nodes. This paper has devised a two-stage optimized energy consumption scheme termed AIFMDMCS. This work elects cluster heads under the condition of indeterminacy in selection criteria with the aid of Intuitionistic fuzzy Logic based decision making. These cluster heads are responsible for collecting and integrating the data received from cluster nodes. The integrated data packets are transferred to the base station using Intuitionistic fuzzy inference engine for improved load balancing, in case of high traffic and presence of collision detection. The simulation results demonstrate that this approach is more effective in protracting network lifespan, because in WSN, it finds the optimal shortest route, and, during vagueness while electing both cluster heads, the degree of indeterminacy is considered.

Keywords: Wireless Sensor Networks, Energy Consumption, trust aware, Cluster Head Selection, Intuitionistic Fuzzy Logic, Uncertainty, indeterminacy

References: 1. Dilip Kumar and Trilok C, Distributed Cluster Head Election (DCHE) Scheme for Improving Lifetime of Heterogeneous Sensor Networks, Tamkang Journal of Science and Engineering, Vol. 13, No. 3, pp. 337-348, 2010 53. 2. Babar Nazir and Halabi Hasbullah, Mobile Nodes based Clustering Protocol for Lifetime Optimization in Wireless Sensor Network, In: International Conference on Intelligent and Information Technology, pp. 615-620, 2010. 3. Ben Alla Siad and EZZATI Abdellah, Improved and Balanced LEACH for Heterogeneous Wireless Sensor Networks, Vol. 02, No. 239-247 08, 2633-2640, 2010. 4. Md. Golam Rashed and M. Hasnat Kabir, WEP: an Energy Efficient Protocol for Cluster Based Heterogeneous Wireless Sensor Networ, International Journal of Distributed and Parallel Systems (IJDPS), Vol.2, No.2, pp 54-60, March 2011 5. Xiaojiang Du and Fenging Lin, Designing Efficient Routing Protocol for Heterogeneous Sensor Network, IEEE, Performance, Computing and Communication conference, 8 (1), 125-13, 2005. 6. Jung-Hwan Kim and Chauhdary Sajjad Hussain, PRODUCE: A Probability-Driven Unequal Clustering Mechanism for Wireless Sensor Networks, IEEE, 22nd International Conference on Advanced Information Networking and Applications – Workshops, WAINA, March 2008. 7. Sanjeev Kumar Gupta, Neeraj Jain and Poonam Sinha, Node Degree Based Clustering for WSN, International Journal of Computer Applications (IJCA), Volume 40 - Number 16. 2012. 8. O. D. Incel, A. Ghosh, B. Krishnamachari, and K. Chintalapudi, False data collection in tree-based wireless sensor networks,” IEEE Trans.Mobile Comput., vol. 11, no. 1, pp. 86–99, Jan. 2012. 9. X. Xu, X. Y. Li, X. Mao, S. Tang, and S. Wang, “A delay-efficient algorithm for data aggregation in multihop wireless sensor networks,”IEEE Trans. Parallel Distrib. Syst., vol. 22, no. 1, pp. 163–175, Jan.2011. 10. G. S. Kasbekar, Y. Bejerano, and S. Sarkar, “Lifetime and coverage guarantees through distributed coordinate-free sensor activation,”IEEE/ACM Trans. Netw., vol. 19, no. 2, pp. 470–483, Apr. 2011. 11. Sk Kajal Arelin Imon,Adnan Khan,Mario Di Francesco and Sajal K.Das, Energy efficient Randomized switching for maximizing lifetime in the tree based wireless sensor networks, IEEE/ACM Trans.Netw.,Oct.2015. 12. Suparna Biswas, Jayita Saha, Tanumoy Nag, A Novel Cluster Head Selection Algorithm for Energy-Efficient Routing in Wireless Sensor Network, 2016 IEEE 6th International Conference on Advanced Computing (IACC), 27-28 Feb. 2016 13. Shahab Tayeb, Miresmaeil Mirnabibaboli and Shahram Latifi, Cluster Head Energy Optimization in Wireless Sensor Networks, Software Networking, Vol: 2016 Issue: 1, Published In: January 2017 14. Sree Vidya R. C., Nagaraja , G. S. Cluster Based Sleep Scheduling Mechanism For Wireless Sensor Networks, Journal of Theoretical And Applied Information Technology, Vol.95. No 8, pp 1737-1744, 30th April 2017. 15. B. Muthusenthil, H. Kim, SHRP - Secure Hybrid Routing Protocol over Hierarchical Wireless Sensor Networks, International Journal of Computers Communications & Control, 12(6), 854-870, December 2017. 16. Wan Isni Sofiah Wan Din, Saadiah Yahya, Rozita Jailani, Mohd Nasir Taib, Ahmad Ihsan Mohd Yassin and Razulaimi Razali, Fuzzy Logic for Cluster Head Selection in Wireless Sensor Network, International Conference on Advanced Science, Engineering and Technology (ICASET, AIP Conference Proceedings 1774, 050006 (2016) 2015 17. Guihai Chen · Chengfa Li, An unequal cluster-based routing protocol in wireless sensor networks, Springer Science Business Media, LLC, 2007. 18. R.S. Marin-Perianu and J. Scholten, Cluster-based service discovery for heterogeneous wireless sensor networks, International Journal of Parallel, Emergent and Distributed Systems, 2007. 19. Chong Wang and Jiakang LiuAn Improved LEACH Protocol for Application Specific Wireless Sensor Networks, IEEE: WiCOM 09 Proceedings of the 5th International Conference on Wireless Communication Networking and Mobile Computing, 2009. 20. D. Kumar, 2009. Energy Efficient Heterogeneous Clustered Scheme for Wireless Sensor Networks, In Elsevier Computer Communications. 21. Elbhiri Brahim and Saadane Rachid, Stochastic and Balanced Distributed Energy-Efficient Clustering (SBDEEC) for heterogeneous wireless sensor networks, Signal Processing and communications group UPC, 2009. 22. Kavitha B, Karthikeyan S, Sheeba Maybell P, Emerging Intuitionistic Fuzzy Classifiers for Intrusion Detection System, Journal of Advances In Information Technology, vol. 2, no. 2, pp 99-108, 2011. 23. Intuitionistic Fuzzy Sets, Krassimir T. Atanassov, Fuzzy Sets and Systems, North-Holland, Volume 20 (1986), pages 87-96, ISSN 0165- 0114 24. Intuitionistic Fuzzy Sets, Krassimir T. Atanassov, Series Studies in Fuzziness and Soft Computing, Volume 35, Springer Physica-Verlag, 1999, ISBN 3-7908-1228-5 25. Xu Z, Interactive Intuitionistic Fuzzy Multi-Attribute Decision Making. In: Intuitionistic Preference Modeling and Interactive Decision Making. Studies in Fuzziness and Soft Computing, vol 280. Springer, Berlin, Heidelberg, PP 195-223, 2014 26. Zeshui Xua, Meimei Xia, Identifying and eliminating dominated alternatives in multi-attribute decision making with intuitionistic fuzzy information, Applied Soft Computing 12 (2012) 1451–1456 27. Husna Jamal Abdul Nasir1, Ku Ruhana Ku-Mahamud Wireless Sensor Network: A Bibliographical Survey, Indian Journal of Science and Technology, Vol 9(38), DOI: 10.17485/ijst/2016/v9i38/91416, October 2016 28. Singh SK, Singh MP, Singh DK. Energy-efficient homogeneous clustering algorithm for wireless sensor network. International Journal of Wireless & Mobile Networks (IJWMN). 2010 Aug; 2(3):49-61. 29. Katiyar V, Chand N, Soni S. Clustering algorithms for heterogeneous wireless sensor network: A survey. International Journal of Applied Engineering Research. 2010 Apr; 1(2):273-8 Authors: Nur Hazwani Hussin, Muhammad Mokhzaini Azizan, Azuwa Ali, Mahmoud A. M. Albreem Comparison Performance Based on Distance of Energy Encryption in Medium Field for Wireless Paper Title: Power Transfer System Abstract: Energy encryption is one of medium for the security of wireless power transfer (WPT). Generally, the encryption technique is most im-portant thing are used to protect energy transmission channels from an unauthorized receiver. Besides, the encryption technique in medium field of WPT system also can be used to transmit the data securely. In the research on the effects of security key for secure of energy trans-fer to authorized receiver is important of the encryption techniques of WPT. Furthermore, chaos theory are proposed to the energy encryp-tion scheme of WPT system. In chaos theory, to chaotically regulate the switching frequency the logistic map are applied to proposed securi-ty key. In addition, the power and distance performance are effect on the characteristics of chaos theory. Accordingly, this paper explore mainly adequate distance based on mobile charging application. Energy encryption in medium field for WPT system are focusing on dis-tance performance of this research. Meanwhile, this research is deal on the comparison of performance in distance based on mobile charging application.

Keywords: Chaos Theory, Energy Encryption, Mobile Charging Application, Security, Wireless Power Transfer,

References: 1. K. Huang and V. Lau, “Enabling wireless power transfer in cellular networks: Architecture, modeling and deployment,” IEEE Transactions on Wireless Communications, vol. 13, no. 2, pp. 902–912, February 2014. 54. 2. L. Xie, Y. Shi, Y. Hou, and A. Lou, “Wireless power transfer and applications to sensor networks,” IEEE Wireless Communications, vol. 20, no. 4, pp. 140–145, August 2013 3. X. Mou and H. Sun, “Wireless power transfer: Survey and roadmap,” IEEE Vehicular Technolology Conference, vol. 2015, pp. 1–13, 2015. 248-252 4. J. Hirai, K. Tae-Woong, and A. Kawamura, “Wireless transmission of power and information and information for cableless linear motor drive,” IEEE Transactions on Power Electronic, vol. 15, pp. 21–27, 2000. 5. O. H. Stielau and G. a Covic, “Design of loosely coupled inductive power transfer systems,” International Conference on Power System Technology Proceedings, vol. 1, pp. 85–90, 2000. 6. W. Chwei-Sen, O. H. Stielau, and G. A. Covic, “Design considerations for a contactless electric vehicle battery charger,” IEEE Transaction on Industrial Electronics, vol. 52, pp. 1308–1314, 2005. 7. A. Woojin, J. Sungkwan, L. Wonkyum, K. Sangsik, P. Junseok, S. Jaegue, K. Hongseok, and K. Kyoungchoul, “Design of coupled resonators for wireless power transfer to mobile devices using magnetic field shaping,” IEEE International Symposium on Electromagnatic Compatibility, pp. 772–776, 2012. 8. Agbinya, Johnson I., "Wireless power transfer," River Publishers, vol. 45, 2015. 9. R. A. Bercich, D. R. Duffy, and P. P. Irazoqui, “Far-field RF powering of implantable devices: Safety considerations,” IEEE Transactions on Biomedical Engineering, vol. 60, pp. 2107–2112, 2013. 10. T. P. Duong and J.-W. Lee, “Experimental Results of High-Efficiency Resonant Coupling Wireless Power Transfer Using a Variable Coupling Method,” IEEE Microwave and Wireless Components Letters, vol. 21, pp. 442–444, 2011. 11. K. T. Chau and Z. Wang, "Chaos in Electric Drive System," John Wiley Publishers, 2001. 12. S. J. Gerssen-Gondelach and A. P. C. Faaij, “Performance of batteries for electric vehicles on short and longer term,” Journal Power Sources, vol. 212, pp. 111–129, 2012. 13. C. Sanghoon, K. Yong-Hae, S.-Y. Kang, L. Myung-Lae, L. Jong-Moo, and T. Zyung, “Circuit-model-based analysis of a wireless energy transfer system via coupled magnetic resonances,” IEEE Transactions Industrial Electronics, vol. 58, pp. 2906–2914, 2011. 14. M. Galizzi, M. Caldara, V. Re, and A. Vitali, “A novel Qi-standard compliant full-bridge wireless power charger for low power devices,” pp. 44–47, 2013. Authors: G. Lakshmy, B. Seetha Devi, B. Ramesh Paper Title: The Blooming Prospects of Probiotic Products in India Abstract: The history of fermented human food is centuries old. The documentation of Indian foods can be 55. traced to a period before 3,000 B.C. The presence of bacteria known as probiotics is behind the enhanced flavor and medicinal properties of fermented foods. An increase in the life style disorders due to the junk food and fast food 253-258 culture has created problems among the people all over the world and this in turn has led to the increased dependency on wonder drugs and switching over to many probiotic health products. Adoption of a Probiotic culture in a country like India to prevent various diseases rather than seeking cure for them through the production of Probiotic foods at the household level is quite inevitable.

Keywords: Probiotics, Prebiotics, Probiotic functional foods, Probiotic Beverages, Bio-preservatives, Human Probiotics, Animal Probiotics, Probiotic-infused juices, Nutraceuticals, Yogurt, Kefir, Sauerkraut, Tempeh, Kimchi, Liquid Probiotics, Life style disorders, Functional Food, Intestinal flora

References: 1. Anita Kumari (2014), Ph.D. Thesis, Department of Biotechnology, Himachal Pradesh University, Shimla. 2. S Parvez, K.A. Malik, S. Ah Kang, H.‐Y. Kim (2006), https://doi.org/10.1111/j.1365-2672.2006.02963.x 3. Nangia T, Setia V, Kochhar GK, Kaur K, Bansal R and Sharma R, Probiotics (2014) Journal of Periodontal Medicine & Clinical Practice Vol- I, Issue - 2, May - Aug 2014, pp.144-151. 4. Edward R. Farnworth (2008), The Journal of Nutrition, Volume 138, Issue 6, 1 June 2008, pp. 1250–1254 5. Ashwani Kumar & Dinesh Kumar(2015), Development of antioxidant rich fruit supplemented Probiotic Yogurts using free and microencapsulated Lactobacillus rhamnosus culture, Journal of Food Science and Technology ISSN 0022-1155 Journal of Food Science Technology, DOI 10.1007/s13197-015-1997-7. 6. Singh Avinash (2015), Development of technology to manufacture Probiotic Herbal Yogurt, Sam Higginbottom Institute of Agriculture, Technology and Sciences, http://hdl.handle.net/10603/176217 7. CR SOCCOL et al. (2010): The Potential of Probiotics, Food Technol. Biotechnol. 48 (4), pp. 413–434 8. Tamang JP and Samuel D. Dietary culture and antiquity of fermented foods and beverages. In: Tamang JP and Kailasapathy, K editors. Fermented foods and beverages of the world. Boca Raton, FL (USA): CRC Press, Taylor & Francis Group; 2010. p. 1e40. 9. Prakash O. Food and drinks in ancient India. Delhi (India): Munshi Ram Monoharlal Publication; 1961. 10. Anon., “India Probiotic Market Forecast and Opportunities, 2019” Tech Sci Research, Sept., 2014 11. Bonifait L, Chandad F, Grenier D. Probiotics for oral health: Myth or reality? J Can Dent Assoc. 2009;75:585–90., [PubMed] 12. Gupta V, Garg R. Probiotics. Indian J Med Microbiol. 2009; 27:202–9., [PubMed] 13. Bruno G. Knoxville, Tennessee: Huntington College of Health Sciences; 2009. Smart Supplementation: Probiotics; pp. 1–5. 14. Ahrné S, Nobaek S, Jeppsson B, Adlerberth I, Wold AE and, Molin G. The normal Lactobacillus flora of healthy human rectal and oral mucosa. Journal of Applied Microbiology. 1998;85:88–94, [PubMed] 15. https://www.yakult.co.in/science_research_lcs_publications.php 16. https://www.statista.com/topics/2157/internet-usage-in-india/ 460 Million mobile internet users with 15% active on social media. Fakroul Ridzuan Hashim, Ja’afar Adnan, Anis Shahida Niza Mokhtar, Amir Firdaus Rashidi Nik Authors: Ghazali Nik Daud Paper Title: Motion Artifact Cancellation from ECG Signals using NLMS based Adaptive Filters Abstract: in the research, the normalized LMS adaptive filters is improved and proposed to reduce the motion artifact (MA) noise from ECG signals. The simulation result gives by the improved versions of adaptive filter (NLMS, PNLMS and IPNLMS) show superior performance when compared to other technique such as wavelet and empirical mode decomposition. Among adaptive filter, the PNLMS adaptive filter give the best performance among NLMS and IPNLMS adaptive filters.

Keywords: ECG Signal, Adaptive Filter, Proportionate, Improved Proportionate, μ-law,

References: 1. M. Z. U. Rahman, R. A. Shaik and D. V. R. K. Reddy, “An Efficient Noise Cancellation Technique to remove noise from the ECG Signal using Normalized signed Regressor LMS Algorithm,” IEEE International Conference on Bioinformatics and Biomedicine. 2. N. V. Thakor and Y. S. Zhu, “Applications of Adaptive Filtering to ECG Analysis: Noise Cancellation and Arrhythmia Detection,” IEEE Transactions on Biomedical Engineering, vol. 38(8), pp. 785-794, 1991. 3. D. G. E. Robertson, G. E. Caldwell, J. Hamill, G. Kamen and S. N. Whittlesey, “Research Methods in Biomechanics”. Champaign, Illinois, Human Kinetics Publisher, 2004. 4. D. A. Tong, K. A. Bartels and K. S. Honeyager, “Adaptive Reduction of Motion Artifact in The Electrocardiogram,” Proceedings of Second 56. Joint EMBS/BMES Conference Houston, 2002. 5. S. Ohta, Y. Kajikawa and Y. Nomura, “Acoustic Echo Cancellation using Sub-Adaptive Filter,” IEEE International Conference on Acoustics, Speech and Signal Processing, 2007. ICASSP 2007. 259-261 6. H. Deng and M. Doroslovacki, “Improving Convergence of the PNLMS Algorithm for Sparse Impulse Respose Identification,” IEEE Signal Processing Letters, Vol 12(3), 2005. 7. B. B. Nair, V. P. Mohandas, N. R. Sakthivel, S. Nagendran, A. Nareash, R. Nishanth, S. Ramkumar and D. Manoj Kumar, “Application of Hybrid Adaptive Filters for Stock Market Prediction,” International Conference on Communication and Computational Intelligent (INCOCCI), 2010. 8. S. Selvan, and R. Srinivasan, “Removal of Ocular Artifacts from EEG using an Efficient neural network Based Adaptive Filtering Technique”, IEEE Signal Processing Letters, Vol. 6(12), 1999. 9. S. Mehrkanoon, M. Moghavveyemi and H. Fariborzi, “Real Time Ocular and Facial Muscle Artifacts Removal from EEG Signals using LMS Adaptive Algorithm”, International Conference on Intelligent and Advance Systems, 2007. 10. E. Braunwald, “Heart Disease: A Textbook of Cardiovascular Medicine”, Fifth Edition, Philadelphia: WB Saunders Co, 1997. ISBN: 0721656668. 11. Raya, M. A. D. and L. G. Sison, Adaptive noise cancelling of motion artifact in stress ECG signals using accelerometer. in Engineering in Medicine and Biology, 2002. 24th Annual Conference and the Annual Fall Meeting of the Biomedical Engineering Society EMBS/BMES Conference, Proceedings of the Second Joint. 2002. 12. S. Haykin, Adaptive Filter Theory.,Prentice Hall, 2002. 13. D. L. Duttweiler, “Proportionate Normalized Least Mean Squares Adaptation in Echo Cancelers”, IEEE Transactions on Speech and Audio Processing, Vol. 8(5), pp. 508-518, 2000. 14. J. Benesty and S. L. Gay, “An Improved PNLMS Algorithm”, IEEE International Conference on Acoustic, Speech and Signal Processing, Vol. 2, pp 1881-1884, 2002. Authors: S. Jawahar, P. Sumathi Fast and Accurate Identification of Short Tandem Repeats (STRs) Using Hash Function in DNA Paper Title: 57. Sequences Abstract: The main challenge in bioinformatics is the size and complexity of input datasets. Tandem repeats 262-266 detection is important function in biology and medicine for phylogenic studies and diagnosing various diseases. Short Tandem Repeats (STRs) plays an important role in human genetic disease and for various regulatory mechanism and evolution. The mutation rate is higher in STR which leads to more biological research in this area. In our study at least two adjacent nucleotide patterns are considered as tandem repeats. The Short Tandem Repeats (STRs) is identified and investigated for diseases related mutation in human. The proposed algorithm Short Tandem Repeat using Hashing (STRH) uses hash table for fast storing and easy retrieval of values. The hash function generally hashes a longer string into much shorter string with fixed length. The analysis of STRH is made using five genes, HUMTH01, CSF1 Receptor, FIBRA, TPOX and VWF. Mostly the STRs in the five genes are tetranucleotide and contains perfect tandem repeat. The proposed STRH algorithm identifies more number of tandem repeats than the traditional algorithms.

Keywords: Bioinformatics, tandem repeat, hash function, short tandem repeat (STRs), tetra nucleotide

References: 1. Carter, J. Lawrence, and Mark N. Wegman. "Universal classes of hash functions. Proceedings of the ninth annual ACM symposium on Theory of computing. ACM, 1977. 2. Dietzfelbinger, Martin, et al. "Dynamic perfect hashing: Upper and lower bounds." SIAM Journal on Computing 23.4 (1994): 738-761. 3. International Human Genome Sequencing Consortium Initial sequencing and analysis of the human genome. Nature. 2001;409:860–921 4. Subramanian S. Genome-wide analysis of microsatellite repeats in humans: their abundance and density in specific genomic regions. Genome Biol. 2003;4:R13 5. S. Leclercq, E. Rivals, and P. Jarne, “Detecting microsatellites within genomes: significant variation among algorithms,” BMC Bioinformatics, vol. 8, article 125, 2007. 6. K. G. Lim, C. K. Kwoh, L. Y. Hsu, and A. Wirawan, “Review of tandem repeat search tools: a systematic approach to evaluating algorithmic performance,” Briefings in Bioinformatics, vol. 14, no. 1, Article ID bbs023, pp. 67–81, 2013. 7. National Center for Biotechnology Information (www.ncbi.nlm.nih.gov) Authors: Hanis Mohd Yusoff*, Soraya Shafawati Mohamad Tahier, Ku Halim Ku Bulat Paper Title: Preliminary Studies of the Effect of Recognition Layer’s Length in Electrochemical DNA Sensor Abstract: DNA-based electrochemical sensing can promise a simple, accurate and inexpensive for disease diagnosis. One of the main parts in this type of DNA sensor is linker or recognition layer. This study focuses on preliminary approach towards the effect of the linker in DNA sensor. A theoretical approach has been carried out to four different lengths of linker which involve Schiff base molecules with different R group attached ro the molecule (3,6,9, and 12 carbon chain). This study has been carried out using density functional theory (DFT) bu using GAUSSIAN 09 software package with the standard 6-31G9(d,p) basis set for all the calculations. Structure drawing was done with GaussView 5.0. Energy, dipole moment, HOMO, LUMO, hardness ( ), softness ( ) and energy gap were calculated. Results showed that the length of the linker does not play a role in this study. The best results obtained was from 6 carbon length as it has the highest value of dipole moment which is 4.1622 and the lowest energy gap of 3.13 eV among the other chain. This theoretical result will be compared with experimental results in the near future.

Keywords: DNA Sensor, Linker in Electrochemical DNA Sensor, Density Functional Theory, 58. References: 267-269 1. Karamunge K. G and Vibhute Y. B. 2013. Synthesis and Antimicrobial Activity of Some New Schiff Bases. Journal of Physics: Conference Series. 423: 1-6. 2. Sashikala S and Syed Shafi S. 2014. Synthesis and Characterization of Chitosan Schiff bAse Derivatives. Der Pharmacia Lettre. 6(2): 90-97. 3. Nahid S., Zeinab G and Saba H. 2012. Binding Studies a New Water-Soluble Iron(III) Schiff Base Complex to DNA Using Multispectroscopic Methods. Bioinorganic Chemistry and Applications. 2012: 1-9. 4. Hassan K., Majid R., Misagh M-M., Koray S., Nefise D and Huseyin U. 2015. Synthesis and Characterization of Co(II), Ni(II), Cu(II) and Zn(II) Complexes with a New Homopiperazine Macrocyclic Schiff Base Ligand. Inroganica Chimica Acta. 432: 243-249. 5. Gregory D. T., Michael G. H and Jacqueline K. B. 2003. Electrochemical DNA Sensors. Nature Biotechnology. 10: 1192-1199. 6. Jiao W., Anqi S., Xian F., Xiaowei H and Yuzhong Z. 2015. An Ultrasensitive Supersandwich Electrochemical DNA Biosensor based on Gold Nanoparticles Decorated Reduced Graphene Oxide. Analytical Biochemistry. 469: 71-75. 7. Roy D., Todd K. and John M. 2009. Gauss View. Version 5. Semichem Inc. Shawnee Mission. 8. Thalamuthu S., Annaraj B and Neelakantan M. A. 2014. A Systematic Investigation on Biological Activities of a Novel Double Zwitterionic Schiff Base Cu(II) Complex. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy. 118: 120-129. 9. Aljahdali M and Ahmad A. E-S. 2013. Synthesis, Characterization, Molecular Modelling and Biological Activity of Mixed Ligand Complexes of Cu(II), Ni(II), and Co(II) based on 1,10-phenanthroline and Novel Thiosemicarbazone. 407: 58-68. 10. Salihovic M., Huseinovic S., Spirtovic-H. S., Osmanovic A., Dedic A., Asimovic Z and Zavrsnik D. 2014. DFT Studies and Biological Activity of Some Methyxanthines. Bulletin of Chemists and Technologists of Bosnia and Herzegovina. 42: 31-36. Authors: G. Simi Margarat, S. Sivasubramanian Paper Title: Moving Basket Ball Detection and Tracking System by different Approaches Abstract: This paper presents the detail investigation on different methods used for the basket ball detection and tracking. The object detection and tracking with high performance ratio for video object detection and tracking is achieved in the methods investigated. But most of the methods suffer from computational complexity. The reduction of complexity can happen at any stages of the ball tracking like preprocessing, segmentation, feature extraction, 59. background subtraction and hole filling. The methods investigated in this paper are trajectory based ball-detection and tracking method, region growing algorithm, PSO algorithm and Mean Shift algorithm with HSV color space and 270-278 texture features. Detail investigation on the approaches, implementation issues and future trends are presented.

Keywords: Basket ball detection, Tracking, Trajectory, Particle swarm optimization, Mean shift algorithm, segmentation

References: 1. D’Orazio T, Ancona N, Cicirelli G and Nitti M, “A ball detection algorithm for real soccer image sequences”, in Proc. of 16th International Conference on Pattern Recognition, vol. 1, pp. 210–213, 2002. 2. Yu X, Leong H W, Xu C and Tian Q, “Trajectory-based ball detection and tracking in broadcast soccer video”, IEEE Trans. Multimed., vol. 8, no. 6, pp. 1164–1178 ,2006. 3. Chen H T, Chen H S, Hsiao M H, Tsai W J and Lee S Y, “A trajectory-based ball tracking framework with visual enrichment for broadcast baseball videos”, J. Inf. Sci. Eng. 24, pp. 143–157, 2008. 4. Chen H T, Tien M C, Chen Y W, Tsai W J and Lee S Y, “Physics-based ball tracking and 3D trajectory reconstruction with applications to shooting location estimation in basketball video”, Elsevier Sci. J. Vis. Commun. Image Represent. 20, pp. 204–216, 2009. 5. Sugandi B, Kim H S, Tan J K and Ishikawa S, “Tracking of moving object by using low resolution images”, in Proc. Of International Conference on Innovative Computing, Information and Control, pp. 408–411, 2007. 6. Nepal S, Srinivasan U and Reynolds G, “Automatic detection of ‘goal’ segments in basketball videos”, in Proc. of the 9th ACM International Conference on Multimedia, pp. 261, 2001. 7. Shum H and Komura T, “A spatiotemporal approach to extract the 3D trajectory of the baseball from a single view video sequence”, in IEEE International Conference on Multimedia and Expo, pp. 1583–1586, 2004. 8. Liang D, Liu Y, Huang Q and Gao W, “A scheme for ball detection and tracking in broadcast soccer video”, Springer Berlin, vol. 3767, pp. 864–875, 2005. 9. Chen W and Zhang Y J, “Tracking ball and players with applications to highlight ranking of broadcasting table tennis video”, in Proc. of IMACS Multi conference on Computational Engineering in Systems Applications, pp. 1896–1903, 2006. 10. Choi K, Park B, Lee S and Seo O, “Tracking the ball and players from multiple football videos”, Int. J. Inf. Acquis. World Scientific Publishing Company, vol. 1, no. 8, pp. 1-8, 2006. 11. Pollard and Anton, "Detecting and tracking all moving objects in wide-area aerial video", In Computer Vision and Pattern Recognition Workshops (CVPRW), IEEE Computer Society Conference, pp. 15-22, 2012. 12. Roomi and Karpaga, "Detection and tracking of moving objects by fuzzy textures",Computing, Communications and Networking Technologies (ICCCNT), 2013 Fourth International Conference on Tiruchengode, pp. 1-5, 2013. 13. Kinjal and Darshak, “A Survey on Moving Object Detection and Tracking in Video Surveillance System”, International journal of soft computing and Engineering, vol. 2, no. 3, pp. 44-48, 2012. 14. Sharmila Sujatha G and Prof. Valli Kumari V, "An Efficient Motion Based Video Object Detection and Tracking System", International Conference on Innovative Trends in Electronics Communications and Applications" (ICIECA15), vol. 1, no. 13, pp. 85-97, 2015. 15. Kennedy J and Eberhart R, “Particle swarm optimization”, In Proc. of IEEE Int. Conf. on Neural Networks, vol. 4, pp. 1942–1948, 1995. 16. Zivkovic Z and van der Heijden F, “Efficient adaptive density estimation per image pixel for the task of background subtraction”, Pattern Recogn. Lett., vol. 27, no. 7, pp. 773–780, 2006. 17. Kwolek B, “Object tracking via multi-region covariance and particle swarm optimization”, 11th IEEE Int. Conf. on Advanced Video and Signal Based Surveillance (AVSS), pp. 418–423, 2009. 18. Perˇse M, Kristan M, Kovaˇciˇc S, Vuˇckoviˇc G and Perˇs J, “A trajectory-based analysis of coordinated team activity in a basketball game Computer Vision and Image Understanding”, Computer Vision Based Analysis in Sport Environments, vol. 113, no. 5, pp. 612 – 621, 2009. 19. Ning J, Zhang L and Zhang D et al, “Robust mean shift tracking with corrected background-weighted histogram”, IET Comput. Vis., vol. 6, no. 1, pp. 62–69, 2012. 20. Yi Wu, Jongwoo Lim and Ming-Hsuan Yang, “Object Tracking Benchmark”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 37, no. 9, pp. 1834–1848, 2015. 21. Vojir T, Noskova J and Matas J, “ Robust scale-adaptive mean-shift for tracking”, Pattern Recognition Letters, vol. 49, pp. 250–258, 2014. 22. Feng F, Wu X J and Xu T, “Object tracking with kernel correlation filters based on mean shift”, 2017 International Smart Cities Conference, 2017. 23. Pawel Lenik, Tomasz Krzeszowski, Krzysztof Przednowek and Justyna Lenik, “The analysis of basketball free throw trajectory using PSO algorithm”, 3rd Int. Congress on Sport Sciences Research and Technology Support, pp. 1-7, 2015. 24. Sharmila Sujatha G and Prof. Valli Kumari V, “An Innovative Moving Object Detection And Tracking System By Using Modified Region Growing Algorithm”, Signal & Image Processing : An International Journal (SIPIJ), vol. 7, no. 2, pp. 39-55, 2016. 25. Jinhang Liu and Xian Zhong, “An object tracking method based on Mean Shift algorithm with HSV color space and texture features”, Springer, pp. 1-12, 2018. 26. Bodhisattwa Chakraborty and Sukadev Meher, “A real-time trajectory-based ball detection-and-tracking framework for basketball video”, Springer, vol. 42, no. 2, pp. 156-170, 2013. Authors: Gil-Oulbé Mathieu, Svetlana L. Shambina, Dau Tyekolo, Ismel Taha Farhan Functionally Graded Segment of an Epitrochoidal Shells Structures– the Geometric Nonlinear Stress- Paper Title: Strain Analysis Abstract: The geometric nonlinear stress-strain state of functionally graded Epitrochoidal Shells under pressure and thermal environment is investigat-ed in this work. Material properties are taken as temperature dependent. Finite element solutions are obtained using the commercially soft-ware ANSYS. The effect of different geometry and material property parameters on the stress-strain state of functionally graded Epitro-choidal Shells under pressure and thermal environment is demonstrated. Finally, the change of the stresses, displacements, rotations and stains were investigated and presented.

Keywords: Functionally Graded Material, Cyclic Shells, Functionally Graded Epitrochoidal Shell, Mechanical and Thermal Material Properties, Conserva-Tive, Non-Conservative, Block Lanczos Method, Spectral Transformations.

60. References: 1. Mihailescu M., Horvath I., 1977. Velaroidal shells for covering universal industrial halls, Acta Techn. Acad. Sci. Hung.,85(1-2): 135-145. 2. Reddy, J.N., 2004. An Introduction to Nonlinear Finite Element Analysis, Publisher: Oxford University Press | ISBN: 019852529X | edition 279-282 2004 |Scan PDF |463 pages |101.9 mb. 3. Gil-oulbé Mathieu. The stress-strain ana ysis of epitrochoidal shells. – PhD thesis – Moscow – 1997. 4. Krivoshapko, S.N., Ivanov, V.N., 2015. Encyclopedia of Analytical Surfaces, DOI 10.1007/978-3-319-11773-7. Springer International Publishing Switzerland. 5. Krivoshapko, S.N., Mamieva, I.A., 2012. Outstanding space structures of the last 20 years, Journal Montazhn. i spetsial. raboti v stroitelstve, 12: 8-14. 6. Kim, Nam-Ho, 2015. Introduction to nonlinear finite element analysis, DOI 10.1007/978-1-4419-1746-1. Springer New York Heidelberg Dordrecht London © Springer Science+Business media New York. 7. Hughes, T.J.R., The finite element method, Prentice Hall, Englewood Cliffs, NJ, 1987 8. Parlett, B., The symmetric eigenvalue problem, Prentice-Hall, Englewood Cliffs, NJ, 1980. 9. Papadrakakis, M., Solving large –scale problems in mechanics, John Wiley & Sons Ltd., 1993. 10. Fialko, S.Yu., High-performance iterative and sparse direct solvers in Robot software for static and dynamic analysis of large-scale structures. Proceedings of the second European conference on computational mechanics, Poland, June 26- 29, 2001, 18 p. 11. Simon, H., The Lanczos algorithm with partial reorthogonalization, Math. Comp., 42, pp. 115-136, 1984. 12. Grimes, R.G. Lewis, J.G., Simon, H.D., A shifted block Lanczos algorithm for solving sparse symmetric generalized eigenproblems, SIAM J. Matrix Anal. Appl, V.15, 1: pp. 1- 45, 1994. Authors: A. Udhayakumar Paper Title: Enhancing Job Scheduling in Cloud Using Widespread Primary Algorithm Abstract: Cloud computing is a developing innovation in appropriated computing which encourages pay per show according to client request and prerequisite. Cloud comprise of an accumulation of virtual machine which incorporates both computational and storeroom. The primary point of cloud computing is to give productive access to remote and geologically appropriated assets. Cloud is creating step by step and faces numerous difficulties, one of them is scheduling. Scheduling alludes to an arrangement of strategies to control the request of work to be performed by a PC framework. A decent scheduler adjusts its scheduling technique as per the changing condition and the sort of undertaking. In this exploration paper we displayed a Widespread Primary algorithm for productive execution of assignment and examination with FCFS and Round Robin Scheduling. Algorithm ought to be tried in cloud Sim toolbox and result demonstrates that it gives better execution contrasted with other customary scheduling algorithm.

Keywords: Virtual Machine, Job Scheduling, Cloud Computing.

References: 1. Burya R Raman, R. Calheiros, R.N.(2009) “Modeling and Simulation of Scalable Cloud Environment and the Cloud Sim Toolkit: Challenges and Opportunities’’, IEEE publication 2009,pp1-11 2. Dr. Sudha Sadhasivam, R. Jayarani, Dr. N. Nagaveni, R. Vasanth Ram “Design and Implementation of an efficient Twolevel Scheduler for Cloud Computing Environment” In Proceedings of International Conference on Advances in Recent Technologies in Communication and Computing, 2009 3. G. Guo-Ning and H. Ting-Lei, “Genetic Simulated Annealing Algorithm for Task Scheduling based on Cloud Computing Environment,” In Proceedings of International Conference on Intelligent Computing and Integrated Systems, 2010, pp. 60-63 4. Rajkumar Rajavel , Mala T “Achieving Service Level Agreement in Cloud Environment using Job Prioritization in Hierarchical 61. Scheduling” Proceeding of International Conference on Information System Design and Intelligent Application,2012 , vol 132, pp 547- 554 283-286 5. Q. Cao, W. Gong and Z. Wei, “An Optimized Algorithm for Task Scheduling Based On Activity Based Costing in Cloud Computing,” In Proceedings of Third International Conference on Bioinformatics and Biomedical Engineering, 2009, pp. 1-3 6. Y. Yang, Kelvin, J. chen, X. Lin, D.Yuan and H. Jin, “An Algorithm in Swin DeW-C for Scheduling Transaction Intensive Cost Constrained Cloud Workflow,” In Proceedings of Fourth IEEE International Conference on eScience, 2008, pp. 374-375 7. Jasmin James, Dr. Bhupendra Verma “Efficient Vm Load Balancin Algorithim For A Cloud Computing Environment ” In Proceeding of International Journal on Computer Science and Engineering (IJCSE) Vol. 4 No. 09, Sep 2012 . 8. Medhat A. Tawfeek, Ashraf El-Sisi, Arabi E. keshk, Fawzy A. Torkey “Cloud Task Scheduling Based on Ant Colony Optimization” In Proceeding of IEEE International Conference on Computer Engineering & Systems (ICCES), 2013 9. Monica Gahlawat, Priyanka Sharma (2013) “ Analysis and Performance Assessment of CPU Scheduling Algorithm in Cloud Sim” International Journal of Applied Information System(IJAIS)- 1SSN: 2249-0868 Foundation of Computer Science FCS, New York, USA Volume5- No 9, July 2013 10. Pawar, C. S., & Wagh, R. B. (2012). “Priority Based Dynamic resource allocation in Cloud computing”, International Symposium on Cloud and Services Computing, IEEE, 2012 pp 1-6. 11. Raghavendra Achar_, P. Santhi Thilagam, Shwetha D_, Pooja H_, Roshni_ and Andrea “Optimal Scheduling of Computational Task inCloud using Virtual Machine Tree” In Proceeding of Third International Conference on Emerging Applications of Information Technology (EAIT), IEEE Publication, 2012 12. Gemma Reig, Javier Alonso and Jordi Guitart, “Prediction ofJob Resource Requirements for Deadline Schedulers to Manage High-Level SLAs on the Cloud”, 9th IEEE International Symposium on Network Computing and Applications, 2010 13. Jinhua Hu, Jianha Gu, Guofei Sun, Tianhai Zhao, NPU HPC Center Xi’an, China “A Scheduling Strategy on Load Balancing of Virtual Machine Resources in Cloud Computing Environment”, IEEE 2010. 14. Suraj Pandey, Department of Computer Science and Software Engineering, the University of Melbourne, Australia, “Scheduling and Management of Data Intensive Application Workflows in Grid and Cloud Computing Environments” Dec 2010 15. Ashutosh Ingole, Sumit Chavan, Utkarsh Pawde. “An optimized algorithm for task scheduling based on activity based costing in cloud computing” (NCICT) 2011, Proceedings published in International Journal of Computer Applications® (IJCA) 16. Zhong, H., Tao, K. and Zhang, X. 2010 “ An Approach to Optimize Resource Scheduling Algorithm for Open-Source Cloud Systems” The Fifth Annual China Grid Conference. IEEE Computer Society, 978-0-7695-4106-8. Authors: Mohammad Faseehuddin, Jahariah Sampe Paper Title: Design of Improved Current Differencing Buffered Amplifier for Analog Signal Processing Abstract: this paper presents an improved design of current differencing buffered amplifier (CDBA) capable of performing both current mode and voltage mode operations. The improvised flipped voltage follower based current input stage of CDBA offers very low input resistance of only 12.31Ω for considerable frequency range. The voltage follower stage also performs almost rail to rail and exhibits an extremely low output resistance of 3.55 Ω. The circuit is designed in 0.18 µm TSMC technology and the performance of the proposed circuit is examined using H-Spice. The current transfer bandwidth is found to be 67.9 MHz while the Voltage follower bandwidth is 161MHz. The circuit operates at a reasonably low supply voltage of±0.6V while dissipating 0.63mW of power.

62. Keywords: CDBA, Current Differencing Unit, Voltage Buffer, 287-290 References: 1. K. C. Smith and A. Sedra, “CC The current conveyor;A new circuit building block,” Proc. IEEE, vol. 56, no. 8, pp. 1368–1369, 1968. 2. Sedra and K. Smith, “A second-generation current conveyor and its applications,” IEEE Trans. Circuit Theory, vol. 17, no. 1, pp. 132–134, 1970. 3. P. Eloranta and C. Toumazou, Current Conveyors, vol. 4. 2004. 4. M. Faseehuddi, J. Sampe, and M. S. Islam, “Designing Ultra Low Voltage Low Power Active Analog Blocks for Filter Applications Utilizing the Body Terminal of MOSFET: A Review,” Asian J. Sci. Res., vol. 9, no. 3, pp. 106–121, 2016. 5. M. Y. Yasin and B. Gopal, “High Frequency Oscillator Design Using a Single 45 nm CMOS Current Controlled Current Conveyor (CCCII+) with Minimum Passive Components,” Circuits Syst., vol. 2, no. 2, pp. 53–59, 2011. 6. Fabre, O. Saaid, F. Wiest, and C. Boucheron, “High frequency applications based on a new current controlled conveyor,” IEEE Trans. Circuits Syst. I Fundam. Theory Appl., vol. 43, no. 2, pp. 82–91, 1996. 7. S. Ozoguz, A. Toker, and C. Acar, “Current-mode continuous-time fully-integrated universal filter using CDBAs,” Electron. Lett., vol. 35, no. 2, pp. 97–98, 1999. 8. D. Biolek, “CDTA–building block for current-mode analog signal processing,” Proc. ECCTD, vol. 3, pp. 397–400, 2003. 9. Multipliers, “A Modified CFOA and Its Applications to Simulated,” vol. 55, no. 1, pp. 266–275, 2008. 10. J. K. Pathak, A. K. Singh, and R. Senani, “New Voltage Mode Universal Filters Using Only Two CDBAs,” vol. 2013, 2013. 11. Özcan, S., A. Toker, C. Acar, H. Kuntman, and O. Çiçekoģlu. "Single resistance-controlled sinusoidal oscillators employing current differencing buffered amplifier." Microelectronics Journal 31, no. 3 (2000): 169-174. 12. LAHIRI and A. CHOWDHURY, “Four Quadrant Analog Multiplier Using Dual-Current-Controlled Current Differencing Buffered Amplifier,” J. Circuits, Syst. Comput., vol. 20, no. 2, pp. 223–231, 2011. 13. Cakir, S. Minaei, and O. Cicekoglu, “Low voltage low power CMOS current differencing buffered amplifier,” Analog Integr. Circuits Signal Process., vol. 62, no. 2, pp. 237–244, 2009. 14. R. G. Carvajal, J. Ramírez-Angulo, A. J. López-Martín, A. Torralba, J. A. G. Galán, A. Carlosena, and F. M. Chavero, “The flipped voltage follower: A useful cell for low-voltage low-power circuit design,” IEEE Trans. Circuits Syst. I Regul. Pap., vol. 52, no. 7, pp. 1276–1291, 2005. 15. Padilla-cantoya and P. M. Furth, “High performance voltage follower with very low output resistance for WTA applications,” no. 3, pp. 1–7. Authors: V. Sathyavathy, D. Shanmuga Priyaa Paper Title: Software Testing Techniques with Artificial Intelligence in Iot Applications Abstract: The main goal and objective of Internet of things are control, management and co-ordination of various fields in a comfortable, effective and secure way. Another important emerging technology is Artificial Intelligence for developing automatic systems that learn from environment, can perceive the environment and make decision making using test case based reasoning. In various domains or areas of knowledge-based, vision ability, learning capability, decision making capability and analytical reasoning, the Artificial Intelligence (AI) provides a better solution for almost all automatic systems. Ihis paper discusses software testing types for home automation systems and how these system can utilize the Artificial Intelligence techniques for test case generation so as to increase its effectiveness, powerfulness etc.

Keywords: IoT, Testing Techniques, Test case, Artificial Intelligence

63. References: 1. Sandeep Kumar and Mohammed Abdul Qadeer, “Application of AI in Home Automation”, IACSIT International Journal of Engineering and 291-293 Technology, Vol. 4, No. 6, December 2012. 2. Miroslav Bures, Tomas Cerny, Internet of Things: Current Challenges in the Quality Assurance and Testing Methods, publication 9th iCatse Conference on Information Science and Applications 3. Bindia Tarika, Review on Software Testing Techniques, International Journal on Recent and Innovation Trends in Computing and Communication ISSN: 2321-8169 Volume: 2 Issue: 1 4. Jogi John, Mangesh Wanjari, Performance Based Evaluation of New Software Testing Using Artificial Neural Network International Journal of Science and Research (IJSR) ISSN (Online): 2319-7064 5. Artificial Intelligence for Health and Health Care, December 2017, Dolores Derrington, JSR-17-Task-002 6. Berndt, D.J., Fisher, J., Johnson, L., Pinglikar, J., and Watkins, A., “Breeding Software Test Cases with Genetic Algorithms,” In Proceedings of the Thirty-Sixth Hawaii International Conference on System Sciences (HICSS-36), Hawaii, January 2003. 7. Mark Last, Shay Eyal1, and Abraham Kandel, “Effective Black-Box Testing with Genetic Algorithms,” IBM conference. 8. Rajesh Shanmugasundaram, “IoT basics and Testing focus”, September 2015,HCL Technologies. 9. Roni Stern and Meir Kalech, “Integrating Artificial Intelligence in Software Testing”, Software symphosium, September 2013. 10. Manjunatha GuruLingiah kukkuru,” Testing IoT applications, Infosys Limited 2017. Authors: N. Yotha, A. Klamnoi, T. Botmart Paper Title: Global Synchronization in Arrays of Coupled Neural Networks with Uncertainties and Mixed Delays Abstract: this paper deal with the problem of global synchronization in arrays of coupled neural networks with uncertainties and mixed delays. By construction of a suitable Lyapunov-Krasovskii’s functional (LKF), Kronecker product properties and utilization of Wirtinger's inequality, novel delay-dependent criteria for the robust synchronization of the networks are established in terms of linear matrix inequalities (LMIs) which can be easily solved by various effective optimization algorithms. A numerical example is given to illustrate the effectiveness of the proposed method.

Keywords: Synchronization, Neural Networks, Time- Varying Delay, Leakage Delay. 64. References: 294-298 1. Gu, K., Kharitonov, V.L. and Chen, J., (2003). Stability of time-delay system, Boston: Birkhauser. 2. Park, M.J., Kwon, O.M., Park, Ju H., Lee, S.M. and Cha, E.J. (2012). Synchronization criteria for coupled neural networks with interval time-varying delays and leakage delay, Appl. Math. Comput., Vol. 218, pp. 6762-6775. 3. Seuret, A. and Gouaisbaut, F. (2013). Wirtinger-based integral inequality: application to time-delay systems. Automatica. Vol. 49, pp. 2860- 2866. 4. Zhang, X.M. and Han, Q.L. (2014). Global asymptotically stability analysis for delayed neural networks using a matrix-based quadratic convex approach. Neural Netw. Vol. 54, pp. 57-69. 5. Du, Y. and Xu, R., (2014). Robust synchronization of an array of neural networks with hybrid coupling and mixed time delays, ISA Transactions, Vol. 53, pp. 1015-1023. 6. Cichocki, A. and Unbehauen, R. (1993). Neural networks for optimization and signal processing, Wiley, Hoboken, NJ. 7. Kamil, I. A. (2012). Self-synchronization in chaotic systems, Journal of Engineering and Applied Sciences, Vol. 7, pp. 411-417. Authors: Pritam Limbaji Sale, Spoorti J Jainar, B.G. Nagaraja A Comparison of Features for Multilingual Speaker Identification - A Review and Some Experimental Paper Title: Results 65. Abstract: Countries like India, Canada, Malaysia, etc. are multilingual in nature. People in multilingual countries have habituated to use several languages. Due to the increased number of multilingual speaker identification system 299-304 applications, the interest in the area has grown notably in recent years. The accuracy of speaker recognition system is severely degraded if training and testing speech languages are different. In speaker recognition area, researchers have made many attempts to tackle language mismatch issues. Choosing a suitable feature extraction method for obtaining appropriate information using speech signal is an essential task. This paper reports a concise experimental review of ten feature extraction techniques for the multilingual scenario. The monolingual, crosslingual and multilingual speaker identification studies are carried out using randomly selected 50 speakers from the IITG multi-variability speaker recognition (IITG-MV) database. Comparative results indicate that subband centroid frequency coefficients (SCFC), linear frequency cepstral coefficients (LFCC) and multitaper Mel frequency cepstral coefficients (MFCC) features are considerably more useful in all the speaker identification. Further, concluding any relation to speaker identification performance in the language mismatch environment is identification as the distribution of speakers in different languages is non-uniform.

Keywords: Speaker identification, monolingual, cross lingual, multilingual, LPCC, MFCC, IMFCC, LFCC, RFCC, multitaper MFCC, SCFC, SSFC, GMM-UBM.

References: 1. J. P. Campbell, Speaker recognition: A tutorial, Proc. IEEE, vol. 85, no. 9, pp. 1437-1462, 1997. 2. T. Kinnunen and H. Li, An Overview of text-independent speaker recognition?: From features to super vectors, Speech Commun., vol. 1, no. 1, pp. 12-40, 2009. 3. P. H. Arjun, Speaker recognition in Indian languages: A feature based approach, Ph.D. dissertation, Indian Institute of Technology Kharagpur, Dept. of Electrical Engg., Kharagpur, India, Jul. 2005. 4. B. G. Nagaraja, Multilingual speaker identification, Ph.D. dissertation, Visvesvaraya Technological University, Belagavi, India, Oct. 2014. 5. H. Lei and E. Lopez, Mel, linear, and antimel frequency cepstral coefficients in broad phonetic regions for telephone speaker recognition, Interspeech, pp. 2323-2326, 2009. 6. Y. Wang and B. Lawlor, Speaker recognition based on MFCC and BP neural networks, 28th Irish Signals Syst. Conf., pp. 1-4, 2017. 7. N. Wang and L. Wang, Robust speaker recognition based on multi-stream features, IEEE International Conference on Consumer Electronics- China (ICCE-China), pp. 1-4, 2016. 8. X. Shi, H. Yang, and P. Zhou, Robust speaker recognition based on improved GFCC, 2nd IEEE International Conference on Computer and Communications, no. 4, pp. 1927-1931, 2016. 9. U. Bhattacharjee and K. Sarmah, A multilingual speech database for speaker recognition, IEEE Int. Conf. Signal Process. Comput. Control. ISPCC 2012, pp. 1-5, 2012. 10. S. Sarkar, K. S. Rao, D. Nandi, and S. B. S. Kumar, Multilingual speaker recognition on Indian languages, Proc. Annu. IEEE India Conf. INDICON 2013, pp. 1-5, 2013. 11. H. S. Jayanna, Limited data speaker recognition Ph.D. dissertation, Indian Institute of Technology Guwahati, Dept. of of Electronics & Communication Engg., Mar. 2009. 12. S. Chakroborty, A. Roy, and G. Saha, Improved closed set text-independent speaker identification by combining MFCC with evidence from flipped filter banks, Int. J. Signal Process., vol. 4, no. 2, pp. 114-121, 2007. 13. T. Kinnunen, R. Saeidi, F. Sedlak, K.A. Lee, J. Sandberg, M. Hansson-Sandsten and H. Li, Low-variance multitaper MFCC features: A case study in robust speaker verification, IEEE Trans. Audio, Speech Lang. Process., vol. 20, no. 7, pp. 1990-2001, 2012. 14. M. J. Alam, T. Kinnunen, P. Kenny, P. Ouellet, and D. O'Shaughnessy, Multitaper MFCC and PLP features for speaker verification using i- vectors, Speech Commun., vol. 55, no. 2, pp. 237-251, 2013. 15. T. Kinnunen, R. Saeidi, J. Sandberg, and M. Hansson-sandsten, What else is new than the Hamming window? Robust MFCCs for speaker recognition via multitapering, Proc. Interspeech, vol. 20, no. 7, pp. 2734-2737, 2010. 16. D. J. Thomson, Spectrum estimation and harmonic analysis, Proc. IEEE, vol. 70, no. 9, pp. 1055-1096, 1982. 17. M. Hansson and G. Salomonsson, A multiple window method for estimation of peaked spectra, IEEE Trans. Signal Process., vol. 45, no. 3, pp. 778{781, 1997. 18. K. S. Riedel and A. Sidorenko, Minimum bias multiple taper spectral estimation, IEEE Trans. Signal Process., vol. 43, no. 1, pp. 188-195, 1995. 19. Md. Sahidullah and G. Saha, A novel windowing technique for efficient computation of MFCC for speaker recognition, IEEE signal processing letters, vol. 20, no. 2, pp. 149-152, 2013. 20. X. Zhou, D. Garcia-Romero, R. Duraiswami, C. Espy-Wilson, and S. Shamma, Linear versus mel frequency cepstral coefficients for speaker recognition, Proc. IEEE Work. Autom. Speech Recognit. Understanding, ASRU 2011, pp. 559-564, 2011. 21. Md. Sahidullah, TomiKinnunen, and CemalHanili, A comparison of features for synthetic speech detection, Proc. Interspeech, pp. 2087- 2091, 2015. 22. T. Hasan, S. O. Sadjadi, G. Liu, N. Shokouhi, H. Boril, and J. H. L. Hansen, CRSS systems for 2012 NIST speaker recognition evaluation, Proc. ICASSP, IEEE Int. Conf. Acoust. Speech Signal Process., pp. 6783-6787, 2013. 23. P. Rajan, T. Kinnunen, C. Hanilci, J. Pohjalainen, and P. Alku, Using group delay functions from all-pole models for speaker recognition, Proc. Annu. Conf. Int. Speech Commun. Assoc. INTERSPEECH, pp. 2489-2493, 2013. 24. T. Thiruvaran, E. Ambikairajah, and J. Epps, Group delay features for speaker recognition, Proc. 6th Int. Conf. Information, Commun. Signal Process. ICICS, vol. 2, no. 2, pp. 1-5, 2007. 25. K. KuldipPaliwal, Spectral subband centroid features for speech recognition, Proc. IEEE International Conference on Acoustics, Speech and Signal Processing, vol. 2, pp. 617-620. 1998. 26. N. P. H. Thian, C. Sanderson, and S. Bengio, Spectral subband centroids as complementary features for speaker authentication, Proc. Biometric Authentication, pp. 631-639, 2004. 27. J. Kua, Investigation of spectral centroid magnitude and frequency for speaker recognition, Proc. Odyssey, pp. 34-39, 2010. 28. E. Scheirer and M. Slaney, Construction and evaluation of a robust multi feature speech/music discriminator, Proc. IEEE Int. Conf. Acoust. Speech, Signal Process., vol. 2, pp. 2-5, 1997. 29. B. C. Haris, G. Pradhan, A. Misra, S. Shukla, R. Sinha, and S. R. M. Prasanna, Multi-variability speech database for robust speaker recognition, Proc. Natl. Conf. Commun., pp. 1-5, 2011. 30. G. Pradhan and S. R. M. Prasanna, Significance of vowel onset point information for speaker verification," Int. J. Comput. Comm. Tech., vol. 2, no. 6, pp. 56-61, 2011. 31. J. P. Campbell Jr, Testing with the YOHO CD-ROM voice verification corpus, Proc. Int. Conf. Acoust. Speech, Signal Process. ICASSP-95, vol. 1, pp. 341-344, 1995.Geoffrey Durou, Multilingual text-independent speaker identification, Proc. MIST'99 Workshop, pp. 115-118, 1999. Authors: Akira Nakamura, Tomoshige Kudo, Keita Nishioka Paper Title: Development of Visualization System of Knowledge Necessary for Solving Mathematical Questions Abstract: we have been developing the visualization system of mathematical knowledge structure based on the 66. hyperlink structure of mathematical e-learning website and expanding the knowledge structure to the field of STEM. We applied this technology to mathematical exercises which are incorporated into our mathematical e-learning 305-309 website and developed a new visualization system which shows knowledge necessary for solving mathematical questions by using network graphs. This system support learners who don’t have enough knowledge to solve questions by indicating what knowledge is necessary. The learner can study basic knowledge by just clicking hyperlinks on the knowledge structure map. We also developed print materials combined with the new system via QR code. This visualization system is a good tool for self-adaptive learning.

Keywords: E-Learning; Network Graph; Hyperlink; Knowledge Structure; Mathematical Question.

References: 1. S. Negash and M. V. Wilcox, “E-learning classifications: differences and similarities,” Handbook of Distance Learning for Real-Time and Asynchronous Information Technology Education, IGI Global, pp. 1-23, 2008 2. T. M. Duffy and D. H. Jonassen, “Constructivism: new implications for educational technology,” Educational Technology, vol. 31, no. 5, pp. 7-12, May 1991. 3. Y. Atif, R. Benlamri, and J. Berri, “Learning objects based framework for self-adaptive learning,” Education and Information Technologies, vol. 8, no. 4, pp. 345-368, 2003. 4. A. Nakamura, “Self-adaptive e-learning website for mathematics,” International Journal of Information and Education Technology, vol. 6, no. 12, pp. 961-965, 2016, DOI: 10.7763/IJIET.2016.V6.825. 5. A. Nakamura, “Graph drawing of knowledge structure of mathematics,” The SIJ Transactions on Computer Science Engineering & its Applications (CSEA), vol. 2, no. 4, pp. 161-165, June 2014. 6. A. Nakamura, “Hierarchy construction of mathematical knowledge,” Lecture Notes on Information Theory, vol. 2, no. 2, pp. 203-207, June 2014. DOI: 10.12720/lnit.2.2.203-207. 7. A. Nakamura, “Graph drawing of knowledge structure of mathematics combined with knowledge level,” Proceedings of INTED2015 Conference (9th International Technology, Education and Development Conference), 2-4 March 2015, Madrid, Spain, pp. 2576-2579. 8. A. Nakamura, T. Kudo, and K. Nishioka, “Development of the visualizing system of knowledge structure based on STEM e-learning website,” Proceedings of the 9th International Conference on Language, Innovation, Culture & Education 2018, 24-25 February 2018, Bangkok, Thailand, pp. 55-61. 9. A. Nakamura, “The development of math learning materials: integrating print materials and web materials utilizing a mobile phone,” Proceedings of IADIS International Conference Mobile Learning 2010, 19-21 March 2010, Porto, Portugal, pp. 393-396. 10. A. Nakamura, “Math learning materials combining print materials and web based training,” proceedings of 14th International Conference on Interactive Collaborative Learning (ICL2011) ̶ 11th International Conference Virtual University (vu'11), 21-23 September 2011, Piestany, Slovakia, pp. 214 - 218, doi:10.1109/ICL.2011.6059578. 11. A. Nakamura, T. Kudo, and K. Nishioka. “The concept of self-adaptive integrated web based learning environment for STEM,” Proceedings of The Fifth International Conference on E-Learning and E-Technologies in Education (ICEEE2016). 6-8 September 2016, Kuala Lumpur, Malasyia, pp. 50-54. 12. K. Nishioka, T. Kudo, and A. Nakamura, “Learning support website of physics with emphasis on connection with mathematics,” Proceedings of The 9th International Multi-Conference on Complexity, Informatics and Cybernetics 2018, 13-16 March 2018, Florida, USA, pp. 155-157. 13. KIT Mathematics Navigation (2004-)..Available at http://w3e.kanazawa-it.ac.jp/math/ 14. KIT Physics Navigation (2016-)....Available at http://w3e.kanazawa-it.ac.jp/math/physics/ 15. KIT Engineering Navigation (2017-)....Available at http://w3e.kanazawa-it.ac.jp/math/engineering/ 16. A. Nakamura, “Log analysis of mobile user behavior for a public-facing math e-learning site,” GSFT International Journal of Education, vol. 1 no. 2, pp. 38-42, November, 2013. Authors: Yogesh Kumaran, Chandrashekar M. Patil A Brief Review of the Detection of Diabetic Retinopathy in Human Eyes Using Pre-Processing & Paper Title: Segmentation Techniques Abstract: In this research article, a brief insight into the detection of DR in human eyes using different types of preprocessing & segmentation techniques is being presented. There are a number of methods of segmenting the blood vessels that are present in the retina & once the retinal nerve fibres are segmented, one can detect whether the eyes are affected with diabetic retinopathy or not. In fact, this detection depends on the area of the RNFL network. If the total area of the nerve fibre is less, then it is affected with diabetic retinopathy (DR)& if the area of the nerve network is more, then the eyes are not affected with the diabetic retinopathy and hence it is normal. It is a well- known fact that diabetics assumes a vital job in the health of the human beings & affects each and every organ. One such organ in the human eye. This DR will give rise tovision loss in the human eye as the optic nerve is connected to the brain. The retinal fundus images are commonly used for detecting & analyzing of disease in disease affected images. Raw retinal fundus images are difficult to process by machine learning algos. Hence, a survey is being given here in this very context. This is a review paper / survey paper in which any researcher who reads this paper, he / she can get some idea about the disease in the human eye, how it gets affected, symptoms, etc... In fact to say, the paper 67. can be thought of as an introductory paper about the diabetic retinopathy& its background. Various research analyzers have chipped away at this theme of the topic till now. To start with, 100’s of research papers were collected 310-320 from various sources, studied @ length & breadth and a brief review of the eye disease issues was being made & presented here in a nutshell. In the sense, the recent works done by various authors across the globe is being presented here in this context so that this review article serves as the base for any researcher who is working in the field of ophthalmology could define their ownnew research problem. One of the important organ of the human being is the eye. It has to be noted that if the eyes are not there, then the whole world would be dark & the human life even though it is existing will be a waste. Different types of the diseases occurs in the eyes. One of the deadliest disease which occurs in the eyes is the DR. This disease is the second largest disease which is occurring amongst the human beings as per the WHO – United Nations survey. Hence, utmost importance has to be given to the eye care. This disease occurs due the reduction of the nerve area in the retina. If the area of the RNFL decreases, then the optic nerve which is connecting to the brain gets damage, leading to the loss of vision. In this paper, a mere introduction is given to the diabetic retinopathy disease. Hence, anexhaustive review is given w.r.t. the said disease, which is the topic of research taken by us as a part of the Ph.D. programme.

Keywords: Segmentation, Retina, Nerve Fibre, Artificial Neural Networks, Detection, Blood Vessel, Diabetic Retinopathy, Data Sets, Histogram, Enhancement, Feature Extraction, Pre-processing, Simulation, Image Processing, Matlab.

References: 1. R. GeethaRamani, L. Bala Subramanian, “Retinal blood vessel segmentation employing image processing and data mining techniques for computerized retinal image analysis”, Jour. of Biocybernetics & Biomed.Engg., Vol. 36, No. 1, pp pp. 102-118, 2016. 2. S. Tang, T. Lin, J. Yang, J. Fan, “Retinal Vessel Segmentation using Supervised Classification based on Multi-scale vessel filtering and Gabor Wavelet”, Jour. of Med. Imag.&Health Info.,Vol. 5, pp 1571-1574, 2015. 3. A.J. Abdallah, P.E. Undrill, M.J. Cree, J.A. Olson, K.C. McHardy, P.F. Sharp, J.V. Forrester, “A comparison of computer based classification methods applied to the detection of microaneurysms in ophthalmic fluorescein angiograms”, Computer Biological Medicine, Vol. 28, No. 3, pp 225–238, 1998. 4. U. Akram M. Khan, “Multilayered thresholding- based blood vessel segmentation for screening of diabetic retinopathy” Jour. of Engg. Comp., Vol. 29, No. 2, pp 165–73, 2013. 5. C. Aslani, “The Human Eye: Structure and Function”, Sinauer Associates, Sunder-land, MA, 1999. 6. L. Zhang, M. Fisher, W. Wang, “Retinal vessel segmentation using multi-scale textons derived from keypoints”,Jour. of Comp. Med. Imaging &Grap., Vol. 45, pp 47-56, 2015. 7. C. Hassana, J.F. Boyce, H.L. Cook, T.H. Williamson, “Automated localization of the optic disc, fovea & retinal blood vessels from digital color fundus images”, British Journal of Ophthal., Vol. 83, No. 8, pp. 902–910, 1999. 8. C. Sinthanayothin, J.F. Boyce, T.H. Williamson, H.L. Cook, E. Mensah, S. Lal, D.Usher, “Automated detection of diabetic retinopathy on digital fundus images”, Diabet. Med. Jour., Vol. 19, No. 2, pp. 105–112, 2002. 9. K.A. Vermeer, F.M. Vos, H.G. Lemij, A.M. Vossepoel, “Model based method for retinal blood vessel detection”, Comps. in Bio.& Med., Vol. 34, No. 3, pp. 209–219, 2004. 10. S. Chaudhuri, S. Chateterjee, N. Katz, M. Nelson, M. Goldbaum, “Detection of blood vessels in retinal images using two-dimensional matched filters”, IEEE Trans. in Med. Imaging, Vol. 8, No 3, pp, 263–269, 1989. 11. T. Chanwimaluang, G. Fan, “An efficient algorithm for extraction of anatomical structures in retinal images”, Proc. of ICIP, pp. 1193–1196, 2003. 12. Hoover, V. Kouznetsova, M. Goldbaum, “Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response”, IEEE Trans. in Med. Imaging, Vol. 19, No. 3, pp. 203–211, 2000. 13. M.E. Martinez-Perez, A.D. Hughes, A.V. Stanton, S.A. Thom, A.A. Bharath, K.H.Parker, “Segmentation of retinal blood vessels based on the second directional derivative & region growing”, Proc. of ICIP, pp. 173–176, 1999. 14. M.E. Martinez-Perez, A.D. Hughes, A.V. Stanton, S.A. Thom, A.A. Bharath, K.H.Parker, “Scale-space analysis for the characterization of retinal blood vessels”, Med. Image Compg.& Comp. Assisted Intervention, Taylor and A. Colchester, (Eds.), Springer: New York, Lecture Notes Computer Science, 16794, pp 90–97, 1999. 15. Y. Wang, S.C. Lee, “A fast method for automated detection of blood vessels in retinal images”, IEEE Comp. Soc.,Proc. of Asilomar Conf., pp.1700–1704, 1998. 16. X. Jiang, D. Mojon, “Adaptive local thresholding by verification based multi-threshold probing with application to vessel detection in retinal images”, IEEE Trans. on Patt. Ana. Mac.Intelli., Vol. 254, No. 1, pp. 131–137, 2003. 17. F. Zana, J.C. Klein, “Segmentation of vessel-like patterns using mathematical morphology & curvature evaluation”, IEEE Trans. on Med.Imag., Vol. 11, No. 7, pp. 1111–1119, 2001. 18. M. Niemeijer, J. Staal, B. Van Ginneken, M. Loog, M.D. Abràmoff, “Comparative study of retinal vessel segmentation methods on a new publicly available database”, M. Fitzpatrick, M. Sonka (Eds.), Proc. of SPIE Med. Image., 5370, 648–656, 2004. 19. J. Staal, M.D. Abramoff, M. Niemeijer, M.A. Viergever, B. van Ginneken, “Ridge-based vessel segmentation in color images of the retina”, IEEE Trans. on Med.Imag., Vol. 23, No. 4 , pp. 501–509, 2004. 20. L. Zhou, M.S. Rzeszotarski, L.J. Singerman, J.M. Chokreff, “The detection and quantification of retinopathy using digital angiograms”, IEEE Trans. on Med.Imag., Vol. 13, No. 4, pp. 619–626, 1994. 21. Y. Tolias, S.M. Panas, “A fuzzy vessel tracking algorithm for retinal images based on fuzzy clustering”, IEEE Trans. on Med. Imag., Vol. 17, No. 2, pp. 263–273, 1998. 22. Can, H. Shen, J.N. Turner, H.L. Tanenbaum, B. Roysam, “Rapid automated tracing and feature extractionfrom retinal fundus images using direct exploratory algorithms”, IEEE Trans. on IT in Biomed., Vol. 3, No. 2, pp. 125–138, 1999. 23. O. Chutatape, L. Zheng, S.M. Krishnan, “Retinal blood vessel detection and tracking by matched Gaussian and Kalman filters”, Proc. 20thAnnual Int. Conf. IEEE Engg.& Med. Bio., pp. 3144–3149, 1998. 24. V.S. Lee, R.M. Kingsley, E.T. Lee, “The diagnosis of diabetic retinopathy, Ophthalmoscopy vs. fundus photography”, Jour. of Ophthalmology, Vol. 100, pp. 1504–1512, 1993. 25. Research Section, “Digital Retinal Image for Vessel Extraction (DRIVE) Database”, Univ. Med. Center Utrecht, Image Sci. Inst., Utrecht, The Netherlands, 2000. 26. D.L. Toulson, J.F. Boyce, “Segmentation of MR images using neural nets”, IEEE Trans. on Image Proc. in Medicine, pp. 284–292, 1991. 27. S.C. Lo, M.T. Freedman, J.S. Lin, “Automatic lung nodule detection using profile matching and backpropagation neural network techniques”, Jour. of Digital Imaging, Vol. 6, pp. 48-54, 1993. 28. Kulwinder S. Mann &Sukhpreet Kaur, “Segmentation of retinal blood vessels using artificial neural networks for early detection of diabetic retinopathy”, Published by American Inst. of Physics, Proc. of the AIP Conf., Vol. 1836, Issue 1, Article id020026, Jun. 2017. 29. M. Garcia, C.I. Sanchez, M.I. Lopez, D. Abasolo, R. Hornero, “Neural network based detection of hard exudates in retinal images”, Comp. Methods Programs Biomed., Vol. 93, pp. 9–19, 2009. 30. A.M. Mendonc¸ Aurelio Campilho, “Segmentation of retinal blood vessels by combining the detection of centerlines& morphological reconstruction”, IEEE Trans. on Med. Imaging, Vol. 25, No. 9, pp. 1200–1213, 2006. 31. M. Larsen, J. Godt, N. Larsen, H. Lund-Andersen, A.K. Sjolie, E. Agardh, H.Kalm, M. Grunkin, D.R. Owens, “Automated detection of fundus photographic red lesions in diabetic retinopathy”, Investigative Ophthalmology &d Vision Sci., Vol. 44, No. 2, pp. 761–766, 2003. 32. Hoover M. Goldbaum, “Locating the optic nerve in a retinal image using the fuzzy convergence of the blood vessels”, IEEE Trans. on Med. Imag., Vol. 22, No. 8, pp. 951–958, 2003. 33. Madhura Jagannath Paranjpe, M.N. Kakatkar, “Review of methods for diabetic retinopathy detection and severity classification”, IJRET – Int. Journ. of Res. in Engg. & Tech., eISSN: 2319-1163, pISSN: 2321-7308, Vol. 3, Issue 3, pp. 619-624, Mar. 2014. 34. Adarsh. P and D. Jeyakumari, “Multiclass SVM-Based Automated Diagnosis of Diabetic Retinopathy”, Int. Conf. on Comm.& Signal Proc., India, April 3-5, 2013. 35. Acharya U.R., Lim C.M., Ng. E.Y.K., Chee C. and Tamura T., “Computer based detection of diabetes retinopathy stages using digital fundus images”, J. Eng. Med, Vol. 223, Vol. H5, pp. 545–553, 2009. 36. Acharya U.R., Chua K.C., Ng. E.Y.K., Wei W. and Chee C., “Application of higher order spectra for the identification of diabetes retinopathy stages”, J. Med. Systems, Vol. 32, No. 6, pp. 481-488, 2008. 37. C. Sinthanayothin J.F. Boyce T.H. Williamson, H.L. Cook E., Mensah S. Lal & D. Usher, “Automated detection of diabetic retinopathy on digital fundus images”, Jour. of Diabet. Med. Wiley On-line Library, Vol. 19, pp. 105–112, 7 Mar. 2002. 38. Upendra Kumar, “Significant Enhancement of Segmentation Efficiency of Retinal Images Using Texture-Based Gabor Filter Approach Followed by Optimization Algorithm”, Management Association Information Resources, Ophthalmology Journal - Breakthroughs in Research and Practice, IGI Global, Edited book, pp. 53-68, 2018. 39. Gehad Hassan, “A Review of Vessel Segmentation Methodologies and Algorithms: Comprehensive Review”, Management Association Information Resources, Ophthalmology Journal - Breakthroughs in Research and Practice, IGI Global, Edited book, Chap. 1, pp. 1-16, IGI Global, Edited book, 2018. 40. Javeria Amin, Muhammad Sharif, and Mussarat Yasmin, “A Review on Recent Developments for Detection of Diabetic Retinopathy”, Hindawi’sScientifica, Vol. 2016, Article ID 6838976, 20 pages, 2016. 41. JasemAlmotiri, Khaled Elleithy and AbdelrahmanElleithy, “Retinal Vessels Segmentation Techniques and Algorithms: A Survey”, Appl. Sci. Review, Vol. 8, Issue 155, 2018. 42. M. J. Fowler, “Microvascular and Macrovascular Complications of Diabetes”, Clinical Diabetes, Vol. 26, No. 2, pp. 77-82, 2008. 43. R. Klein, B.E. Klein, S.E.Moss, T. Y.Wong, “Retinal vessel caliber and microvascular and macrovascular disease in type 2 diabetes: XXI: the Wisconsin Epidemiologic Study of Diabetic Retinopathy,” Ophthalmology, Vol. 114, No. 10, pp. 1884-1892, 2007. 44. S.H. Rezatofighi, A. Roodaki, and H. Ahmadi Noubari. “An enhanced segmentation of blood vessels in retinal images using contourlet”,IEEE Engg.in Medicine & Biology Society, pp. 3530-3533, 2008. 45. P. Feng, Y. Pan, B. Wei, W. Jin, and D. Mi, “Enhancing retinal image by the Contourlet transform”,Pattern Recogn. Lett., Vol. 28, pp. 516- 522, 2007. 46. Z. HongQing, “Segmentation of blood vessels in retinal images using 2-D entropies of gray level-gradient co-occurrence matrix”,IEEE, pp.509-512, 2004. 47. S.Mukhopadhyay, B.Chanda, “Hue preserving color image enhancement using multi-scale morphology”,Indian Conf. on Comp. Vision, Graphics & Image Proc., 2002. 48. S.G. Mallat, “A theory for multi-resolution signal decomposition the wavelet representation”, IEEE Trans. Pattern Anal. Mac.Intelli.,Vol. 11, No. 7, pp. 674-689, 1989. 49. M. Al-Rawi, M. Qutaishat, M. Arrar, “An improved matched filter for blood vessel detection of digital retinal images”,Springer, Computers in Biology and Medicine, Vol. 37, pp. 262 – 267, 2007. 50. M. G Mendonça and A. Campilho, “Segmentation of retinal blood vessels by combining the detection of centerlines and morphological reconstruction”,IEEE Trans. Med. Imag, Vol. 147, pp. 1200-1213, 2006. 51. H.R. Tavakolli, H.R. Pourreza, “An Enhanced Retinal Vessel Detection Algorithm”,SpringerInnovations and Adv. Techniques in Systems, Computing Sci. & Software Engg., pp. 6-8, 2008. 52. Seyed Mohsen Zabihi, Morteza Delgir and Hamid Reza Pourreza, “Retinal Vessel Segmentation Using Color Image Morphology and Local Binary Patterns”, 2010 6th Iranian Conf. on Machine Vision & Image Proc., Isfahan, Iran, 27-28 Oct. 2010. Muhammad Kashfi Shabdin, Mohd Azizi Abdul Rahman, Saiful Amri Mazlan, Siti Aishah Abdul Authors: Aziz, Irfan Bahiuddin Effect of Graphite Reinforcement on the Resistivity Property of Magnetorheological Elastomer Paper Title: (MRE) Abstract: Resistivity property of graphite based MRE is demonstrated in the present research. In this paper, a study on the force sensitive property by using a composite of graphite (Gr) and magnetorheological elastomer (MRE) is suggested where graphite powder was used as a conductive element in the composite. The carbonyl iron particles and graphite powder were mixed in the silicon rubber to produce the Gr-MRE composite. The resistivity properties are then identified from a designated test rig that delivers different magnitude of the applied force on the sample. Results showed that the resistivity of the material decreased as the applied force increase from 700 to 1000g, where the magnitude of the resistance decreases approximately 79 to 66%. By having higher magnetic flux density in the material, the stiffness of the Gr–MRE could possibly be controlled depending on the magnitude of the force.

Keywords: Magnetorheological Elastomer, Graphite, Resistivity Property.

References: 1. Bossis, G., Abbo, C., Cutillas, S., Lacis, S., & Métayer, C. (2001). Electroactive and Electrostructured Elastomers. International Journal of Modern Physics B, 15(06n07), 564–573. 2. Carlson, J. D., & Jolly, M. R. (2000). MR fluid, foam and elastomer devices. Mechatronics, 10(4), 555–569. 3. Dang, a., Ooi, L., Fales, J., & Stroeve, P. (2000). Yield Stress Measurements of Magnetorheological Fluids in Tubes. Industrial & Engineering Chemistry Research, 39, 2269–2274. 4. de Vicente, J., Klingenberg, D. J., & Hidalgo-Alvarez, R. (2011). Magnetorheological fluids: a review. Soft Matter, 7(8), 3701. 5. Fuchs, A., Zhang, Q., Elkins, J., Gordaninejad, F., & Evrensel, C. (2007). Development and Characterization of Magnetorheological Elastomers. Journal of Applied Polymer Science, 105, 2497–2508. 6. Jolly, M. R., Carlson, J. D., & Muñoz, B. C. (1999). A model of the behavior of magnetorheological materials. Smart Materials and 68. Structures, 5(5), 607–614. 7. Ju, B., Tang, R., Zhang, D., Yang, B., Yu, M., & Liao, C. (2015). Temperature-dependent dynamic mechanical properties of magnetorheological elastomers under magnetic field. Journal of Magnetism and Magnetic Materials, 374, 283–288. 321-323 8. Kaleta, J., Królewicz, M., & Lewandowski, D. (2011). Magnetomechanical properties of anisotropic and isotropic magnetorheological composites with thermoplastic elastomer matrices. Smart Materials and Structures, 20(8), 085006. 9. Li, W. H., Du, H., Chen, G., Yeo, S. H., & Guo, N. Q. (2002). Nonlinear rheological behavior of magnetorheological fluids: step-strain experiments. Smart Materials and Structures, 11(2), 209–217. 10. Li, W., Kostidis, K., Zhang, X., & Zhou, Y. (2009). Development of a Force Sensor Working with MR Elastomers, 233–238. 11. Park, B. J., Fang, F. F., & Choi, H. J. (2010). Magnetorheology: materials and application. Soft Matter, 6(21), 5246. 12. Sorokin, V. V., Ecker, E., Stepanov, G. V., Shamonin, M., Monkman, G. J., Kramarenko, E. Y., & Khokhlov, A. R. (2014). Experimental study of the magnetic field enhanced Payne effect in magnetorheological elastomers. Soft Matter, 10(43), 8765–8776. 13. Tian, T. F., Li, W. H., Alici, G., Du, H., & Deng, Y. M. (2011). Microstructure and magnetorheology of graphite-based MR elastomers. Rheologica Acta, 50(9–10), 825–836. 14. Yang, Z., Qin, C., Rao, Z., Ta, N., & Gong, X. (2014). Design and analyses of axial semi-active dynamic vibration absorbers based on magnetorheological elastomers. Journal of Intelligent Material Systems and Structures, 25(17), 2199–2207. 15. Yoon, J.-H., Yang, I.-H., Jeong, U.-C., Chung, K.-H., Lee, J.-Y., & Oh, J.-E. (2013). Investigation on Variable Shear Modulus of Magnetorheological Elastomer Based on Natural Rubber due to Change of Fabrication Design. Engineering, 53(5), 992–1000. 16. A. Nakamura, “Self-adaptive e-learning website for mathematics,” International Journal of Information and Education Technology, vol. 6, no. 12, pp. 961-965, 2016, DOI: 10.7763/IJIET.2016.V6.825. 17. A. Nakamura, “Graph drawing of knowledge structure of mathematics,” The SIJ Transactions on Computer Science Engineering & its Applications (CSEA), vol. 2, no. 4, pp. 161-165, June 2014. 18. A. Nakamura, “Hierarchy construction of mathematical knowledge,” Lecture Notes on Information Theory, vol. 2, no. 2, pp. 203-207, June 2014. DOI: 10.12720/lnit.2.2.203-207. 19. A. Nakamura, “Graph drawing of knowledge structure of mathematics combined with knowledge level,” Proceedings of INTED2015 Conference (9th International Technology, Education and Development Conference), 2-4 March 2015, Madrid, Spain, pp. 2576-2579. 20. A. Nakamura, T. Kudo, and K. Nishioka, “Development of the visualizing system of knowledge structure based on STEM e-learning website,” Proceedings of the 9th International Conference on Language, Innovation, Culture & Education 2018, 24-25 February 2018, Bangkok, Thailand, pp. 55-61. 21. A. Nakamura, “The development of math learning materials: integrating print materials and web materials utilizing a mobile phone,” Proceedings of IADIS International Conference Mobile Learning 2010, 19-21 March 2010, Porto, Portugal, pp. 393-396. 22. A. Nakamura, “Math learning materials combining print materials and web based training,” proceedings of 14th International Conference on Interactive Collaborative Learning (ICL2011) ̶ 11th International Conference Virtual University (vu'11), 21-23 September 2011, Piestany, Slovakia, pp. 214 - 218, doi:10.1109/ICL.2011.6059578. 23. A. Nakamura, T. Kudo, and K. Nishioka. “The concept of self-adaptive integrated web based learning environment for STEM,” Proceedings of The Fifth International Conference on E-Learning and E-Technologies in Education (ICEEE2016). 6-8 September 2016, Kuala Lumpur, Malasyia, pp. 50-54. 24. K. Nishioka, T. Kudo, and A. Nakamura, “Learning support website of physics with emphasis on connection with mathematics,” Proceedings of The 9th International Multi-Conference on Complexity, Informatics and Cybernetics 2018, 13-16 March 2018, Florida, USA, pp. 155-157. 25. KIT Mathematics Navigation (2004-)..Available at http://w3e.kanazawa-it.ac.jp/math/ 26. KIT Physics Navigation (2016-)....Available at http://w3e.kanazawa-it.ac.jp/math/physics/ 27. KIT Engineering Navigation (2017-)....Available at http://w3e.kanazawa-it.ac.jp/math/engineering/ 28. A. Nakamura, “Log analysis of mobile user behavior for a public-facing math e-learning site,” GSFT International Journal of Education, vol. 1 no. 2, pp. 38-42, November, 2013. Authors: G. Subhashini, V. Neelambary Paper Title: Securing and Transmitting Quantum Data on Wireless Sensor Network Abstract: Signcryption is a cryptographic technique for simultaneously performing both digital signature and data encryption. It is an effective technique for protecting the confidentiality and unforgeability of communications in Internet of Things (IoT) systems, especially when a number of generated cipher texts can be aggregated into a compact form. This paper focus on device capture attacks those are commonly threatening the implementations of signcryption on unattended devices by enabling an attacker to extract the cryptographic key from a captured device. The proposed obfuscator can protect signcryption programs from key-extraction attacks by transforming the programs into unintelligible obfuscated programs. The scheme’s security features with respect to obfuscation, confidentiality, and unforgeability have been theoretically proved. Moreover, in comparison with other (non- obfuscatable) aggregate signcryption schemes, the scheme’s computational efficiency is positioned at a medium level while the communication cost is also relatively small, with extra unique security features benefiting from obfuscation. Experiments on different devices indicated that the proposed scheme performs reasonably well as expected. The scheme is widely applicable for various scenarios of IoT, where information is sent from unattended leaf nodes to a sink point.

Keywords: Signcryption; Internet of Things (Iot); Aggregate Signcryption; Sink Point

References: 1. Yang Shi, Member, IEEE, Jingxuan Han, Xiaoping Wang, JiayaoGao, and Hongfei Fan, Member, IEEE” An Obfuscatable Aggregatable 69. Signcryption Scheme for Unattended Devices in IoT Systems” VOL. 4,AUGUST 2017 2. Y. Ren, V. I. Zadorozhny, V. A. Oleshchuk, and F. Y. Li, “A novelapproach to environmental management in unattended wireless sensor 324-328 networks,” IEEE Trans. Mobile Comput., vol. 13, no. 7, pp. 1409–1423, Jul. 2015. 3. P. Tague, M. Y. Li, and R. Poovendran, “Mitigation of control channel jamming under node capture attacks,” IEEE Trans. Mobile Comput.,vol. 8, no. 9, pp. 1221–1234, Sep. 2015. 4. L. Shen, J. Ma, X. Liu, F. Wei, and M. Miao, “A secure and efficient ID based aggregate signature scheme orwireless sensor networks,” IEEEInternet Things J., to be published, Nov 2014. 5. R. Cheng, B. Zhang, and F. G. Zhang, “Trust management for secure obfuscation of encrypted verifiable encrypted signatures,” in Proc. Provable Security, Xi’an,China, 2014, pp. 188–203. 6. X.-Y. Ren, Z.-H. Qi, and Y. Geng, “Provably secure aggregate signcryption scheme,” ETRI J., vol. 34, no. 3, pp. 421–428, 2012. 7. D. H. Yum and P. J. Lee, “Exact formulae for resilience in random key pre distribution schemes,” IEEE Trans. Wireless Commun., vol. 11, no. 5, pp. 1638–1642, May 2012. 8. A. Newell, H. Yao, A. Ryker, T. Ho, and C. Nita-Rotaru, “Node-capture resilient key establishment in sensor networks: Design space and new protocols,” ACM Comput. Surveys, vol. 47, no. 2, pp. 1–34, 2014. 9. C. Lin, G. Wu, C. Yu, and L. Yao, “Maximizing destructiveness of node capture attack in wireless sensor networks,” J. Supercomput., vol. 71, no. 8, pp. 3181–3212, 2015. 55 10. S. Goldwasser and G. N. Rothblum, “On best-possible obfuscation,”J. Cryptol., vol. 27, no. 3, pp. 480–505, 2014. 11. K. Xing and X. Cheng, “From time domain to space domain: Detecting replica attacks in mobile ad hoc networks,” in Proc. IEEE Infocom, San Diego, CA, USA, 2010, pp. 1–9. 12. [12] J. M. Bahi, C. Guyeux, M. Hakem, and A. Makhoul, “Epidemiological approach for data survivability in unattended wireless sensor networks,” J. Netw.Comput. Appl., vol. 46, pp. 374–383, Nov. 2014. 13. Y. Ren, V. I. Zadorozhny, V. A. Oleshchuk, and F. Y. Li, “A novel approach to trust management in unattended wireless sensor networks,” IEEE Trans. Mobile Computing., vol. 13, no. 7, pp. 1409–1423, Jul. 2014. 14. B. Barak et al., “On the (Im)possibility of obfuscating programs,” J. ACM, vol. 59, no. 2, Apr. 2012, Art. no. 6. 15. B. Barak et al., “On the (Im)possibility of obfuscating programs,” in Proc. Adv. Cryptol. (CRYPTO), Santa Barbara, CA, USA, 2001. Authors: Mohar Kassim, Sharulfadly Rustam, Rahmat Sholihin Mokhtar Paper Title: Mobile Software Application for Measuring Cardiovascular Endurance Fitness for Cadets Officers Abstract: the purpose of this study is to build portable application software to determine the level of cardiovascular fitness for cadet students of the National Defence University of Malaysia (UPNM). Fitness in the context of this study refers to physical fitness, specifically the cardiovascular endurance level test battery in the form 70. of a 2.4 km run test for UPNM cadet students. This run test will be conducted to measure, test, and evaluate the 329-332 performance of UPNM cadet students. All the run test results can be recorded electronically inside the portable software and will later be able to show the level of cardiovascular fitness of every cadet student according to age and gender. This software can also calculate the body mass index (BMI). Normative survey method will be used in this study through the analysis of the 2.4 km run test results. The run test scores will be classified in interval and ratio scales. Based on the findings of this study, portable application software will have produced. The software will be able to directly assist the Military Training Academy (ALK), Malaysian Armed Forces (ATM), and other relevant agencies in determining the level of cardiovascular fitness among their staff. The test can be done electronically and on portable mode. The next step to be taken is to have this application patented.

Keywords: Development, Software, Application, Portable, Fitness Norms, Cardiovascular Endurance,

References: 1. UPNM. Latar Belakang : UPNM. Retrieved November 14, 2012, from Universiti Pertahanan Nasional Malaysia 2. Website: http://www.upnm.edu.my/index.php?req=7 3. Dove-Edwin, F. H. The fitness parameters if 14-17 year old children in Sierra Lione. 2009. Michigan : ProQuest Dissertations & Theses (PQDT). 4. Penney, D., & Clarke . Inclusion in Sport Education. In P. Dawn, Sport Education in Physical Education: Research Based Practice. Loughborough: Taylor & Francis, 2005. pp.41-54 5. Amalina Farhi Ahmad Fadzlah @ Muhammad Faizi Musa. Pembangunan Aplikasi Perisian Pintar Mudah Alih Menggunakan Kaedah Logik Kabur Dalam Arena Sains Sukan Golf. Proceeding of The International Conference on Artificial Intelligence in Computer Science & ICT (AICS2013) 25-26/11/13 Langkawi, Malaysia. 2013. 62-70. 6. Johnson , B. L., & Nelson , J. K. Practical Norms for 7. Evaluation in Physical Education.1986. Michigan: Burgess Publishing Company. 8. Ahmad, H. Pengukuran Kecergasan Motor. 2004 9. Tanjung Malim: Quantum Books. 10. Bird, S. The role of fitness testing & selecting and using fitness test. 2014, July 28. Sportsheet. 11. Gregory, G. H., & Charles, D. Laboratory Manual for Exercise Physiology. 2012. Champaign, IL: Human Kinetics. 12. Cooper Institute for Aerobics Research. FITNESSGRAM test administration manual (4th ed.). 2007 Champains IL: Human Kinetics. 13. ACSM. ACSM’s fitness book. 1999. United States of 14. America : United states of America : Human Kinetic 15. John , A. G., Joseph, A. N., & Campagna , P. D. (1999). The Prediction of V˙ O2max: A Comparison of 7 Indirect Tests of Aerobic Power. Journal of Strength and Conditioning Research, 1999. 346-252. 16. MINDEF. Ministry of Defense, Singapore. 2015 January 11. 17. Retrieved from Mindef: ons/eBooks/More_eBooks/MySonTheNSMan_whatParentsShldKnowabtNS_29may08.pdf 18. Cooper, K. H. The Aerobic Program for Total Well-Being. 1982 New York: Bantam Books. 19. Kassim,.M. (2008). A Qualitative Study of the Relationship Between the Knowledge and Behaviour of Coaches in two Football Academies in Malaysia. 2008. Unpublished doctoral thesis, Loughborough University, UK. Authors: J. Santhosh, G. Arulkumaran, P. Balamurugan Paper Title: Improved Energy Intrusion Detection System using Fuzzy System in Wireless Sensor Network Abstract: Wireless Sensor Network (WSN) are predominantly used in collecting information from remote regions. As the data is transmitted in unsecured wireless medium, it is more susceptible for attacks. The distribution of the nodes in WSN eases the task for the attackers to alter the node characteristics and behaviour. This malicious node is a severe threat in terms of data security and resource management to the entire WSN, in which it is deployed. The proposed Co-Active Adaptive Neuro Fuzzy Inference System (CANFIS) is an energy efficient malicious node detection method that detects the presence of malicious nodes in WSNs by considering the space metrics and heuristic features of nodes. The proposed method performance is investigated in provisos of its packet delivery ratio, detection rate and energy consumption, which shows a remarkable progress over the other state of art methods.

Keywords: Wireless Sensor Networks, Malicious nodes, energy efficient, Cluster head

References: 1. Kuhn, F, Moscibroda, T&Wattenhofer, R 2004, ‘Initializing Newly Deployed Ad Hoc and Sensor Networks’, Proceedings of ACM MOBICOM,pp. 260–274. 2. Xiao, XY, Peng, WC, Hung, CC & Lee, WC 2007, ‘Using Sensor Ranks for In-Network Detection of Faulty Readings in WSNs,’ International Workshop Data Engineering for Wireless and Mobile Access, Beijing, pp. 1-8. 3. Atakli, IM, Hu, H, Chen, Y, Ku, WS&Su, Z, ‘Malicious Node Detection in WSNs Using Weighted Trust Evaluation,’ Proceedings of Spring Simulation Multi-Conference, Ottawa, 14-17 April 2008, pp. 836-843. 71. 4. Loo, CE, Ng, MY, Leckie, C &Palaniswami, M 2006, ‘Intrusion detection for routing attacks in sensor networks’, Int. J. Dist. Sens. Netw. Vol.2, pp. 313–332. 333-338 5. Lu, C, Blum, B, Abdelzaher, T, Stankovic, J & He, T 2002, ‘RAP: A Real-Time Communication Architecture for Large-Scale WSNs’, IEEE Real-Time Applications Symposium. 6. Mamun, MSI &Sultanul Kabir, AFM 2010, ‘Hierarchical Design Based Intrusion Detection System For Wireless Ad Hoc Sensor Network’, International Journal of Network Security & Its Applications (IJNSA), vol. 2, no. 3. 7. Mao, Y 2010, ‘A Semantic-based Intrusion Detection Framework for WSN’, Networked Computing (INC), 6th International Conference, Gyeongju, Korea (South). 8. Momani, M &Challa, S 2010, ‘Survey of Trust Models in Different Network Domain,’ International Journal Ad Hoc, Sensor & Ubiquitous Computing, vol. 1, no. 3, pp. 1-19. 9. Muktikanta Sa & Amiya Kumar Rath 2011, ‘A Simple Agent Based Model for Detecting Abnormal Event Patterns in Distributed Wireless Sensor Networks’, Proceedings of the 2011 International Conference on Communication, Computing & Security, ACM, pp. 67-70. 10. Neenu George &Parani, TK 2014, ‘Detection of Node Clones in WSN Using Detection Protocols,’ International Journal of Engineering Trends and Technology (IJETT), vol. 8, no. 6. 11. Nidhi Lal, Shishupal Kumar, Aditya Saxena, Vijay KM &Chaurasiya 2015, ‘Detection of Malicious Node Behaviour via I-Watchdog Protocol in Mobile with DSDV Routing Scheme’, Procedia Computer Science, vol. 49, pp. 264-273. 12. Onat, I & Miri, A 2005, ‘A Real-Time Node-Based Traffic Anomaly Detection Algorithm for Wireless Sensor Networks’, In Proceedings of Systems Communications 2005 (ICW/ICHSN/ICMCS/SENET 2005), Montreal, QC, Canada, 14–17 August 2005. 13. Patel Nakul 2013, ‘A Survey on Malicious Node Detection in WSNs,’ International Journal of Science and Research (IJSR), vol. 2, no. 1. 14. Perrig, A, Szewczyk, R, Tygar, J, Wen, V & Culler, D 2002, ‘SPINS: Security Protocols for Sensor Networks’, ACM Journal of Wireless Networks. 15. Rajasegarar, S, Leckie, C &Palaniswami, M 2008, ‘Anomaly Detection in WSNs,’ IEEE Wireless Communications, vol. 15, no. 4, pp. 34- 40. 16. Rejina Parvin, J &Vasanthanayaki, C 2015, ‘Particle Swarm Optimization-Based Clustering by Preventing Residual Nodes in WSNs’, IEEE Sensors Journal, vol. 15, no. 8, pp. 4264–4274. 17. Renyong Wu, Xue Deng, Rongxing Lu &Xuemin (Sherman) Shen 2015, ‘Trust-Based Anomaly Detection in Emerging Sensor Networks’, International Journal of Distributed Sensor Networks, vol. 2015, no. 363569, pp. 1-14. 18. Seo Hyun Oh, Chan O. Hong & Yoon-Hwa Choi 2012, ‘A Malicious and Malfunctioning Node Detection Scheme for WSNs,’ WSN, vol. 4, pp. 84-90. 19. Seong-Lyun Kim &Seong-Lyun Kim 2011, ‘Optimal Detection of Spatial Opportunity in Wireless Networks,’ IEEE Communications Letters, vol. 15, no. 4. 20. Somasundara, Kansal, A, Jea, D, Estrin, D & Srivastava, M 2006, ‘Controllably mobile infrastructure for low energy embedded networks’, IEEE Transactions on Mobile Computing, vol. 5, no. 8, pp. 958–973. 21. Song, X, Chen, G & Li, X 2010, ‘A Weak Hidden Markov Model Based Intrusion Detection Method for Wireless Sensor Networks’, International Conference on Intelligent Computing and Integrated Systems (ICISS), pp. 887-889. 22. Sudip Misra, Venkata Krishna, P & Kiran Isaac Abraham 2010, ‘Energy Efficient Learning Solution for Intrusion Detection in WSNs’, Proceedings of the 2nd international Conference on Communication systems and Networks. 23. Sung-Jib Yim& Yoon-Hwa Choi 2012, ‘Neighbour-Based Malicious Node Detection in WSNs’, WSN, vol. 4, pp. 219-225. 24. Yan, KQ, Wang, SC &Liu, CW 2009, ‘A Hybrid Intrusion Detection System of Cluster-based WSNs’, Proceedings of the International MultiConference of Engineers and Computer Scientists 2009, Hong Kong, vol. IIMECS 25. Ju, L, Li, H, Liu, Y, Xue, W, Li, K & Chi, Z 2010, ‘An Improved Detection Scheme Based on Weighted Trust Evaluation for WSNs,’ Proceedings of the 5th International Conference on Ubiquitous Information Technology and Applications, Sanya, pp. 1-6. 26. Fenye Bao, Mostefa Fatima Zohra, MekkakiaMaazaZoulikha&Khelifa Said 2012, ‘Techniques Of Detection Of The Hidden Node In ,’ Proceedings of the World Congress on Engineering Vol II WCE 2011, London, U.K. 27. Gopal, R, Parthasarathy, V & Mani, A 2013, ‘Techniques to identify & eliminate malicious nodes in cooperative wireless networks’, International Conference on Computer Communication & Informatics, Coimbatore, India. Authors: K.R. Asha, K. Suresh A Real Time Cloud Based Energy Efficient Mechanism for Radiation Affected WSN to In-crease Paper Title: Network Life Time and Mobility Abstract: Due to the huge advancement of technology in wireless sensor network, WSN is used in large applications such as security, surveillance, health care, environment monitoring, and object tracking. In these kind of applications sensor nodes are detecting physical phenomena’s like temperature, pressure, and humidity. Detected information's are finally forwarded to sink node or base station node. In this communication pattern, cooperation of each node in the network enable the hop by hop communication which reduces the energy consumption as well as time delay. Also due to some accidental events such as huge electro-magnetic waves and radiation will effects the sensors. This leads to the failure of certain nodes and that will affects all the nodes communication. Therefore, proposed mechanisms will tackle these kind of accidental events to avoid communication failure and also gives a technique for increasing energy efficiency of nodes to increase the lifetime of network. Data sensed by sensor will be collected, processed and sent through cluster heads to the base station in an efficient way. From the base stations collected data will be upload to cloud for user accessibility using cloud services. Simulation results show better performance concern to energy efficiency and the lifetime of the network.

Keywords: Acoustic communication, Cloud Computing, Data reduction, RF communication, Wireless sensor Network.

References: 1. I. F. Akyildiz, S. Weilian, Y. Sankarasubramaniam, and E. Cayirci, “A survey on sensor networks,” Commun. Mag., vol. 40, no. 8, pp. 102–114, Nov. 2002. 2. I. F. Akyildiz, W. Su, Y. Sankarasubramaniam, and E. Cayirci, “Wire less sensor networks: A survey,” Comput. Netw., vol. 38, no. 4, pp. 393–422, 2002. 3. Pushpendu Kar, Sudip Misra,"Reliable and Efficient Data Acquisition In Wireless Sensor Network in the Presence of Transfaulty Nodes”, IEEE Transactions On Network And Service Management 1932- 4537(C) 2015 72. 4. J.-H. Jeon, H.-J. Byun, and J.-T. Lim, “Joint contention and sleep control for lifetime maximization in wireless sensor networks,” IEEE Commun. Lett., vol. 17, no. 2, pp. 269–272, Feb. 2013. 339-345 5. K. T. Phan, R. Fan, H. Jiang, S. A. Vorobyov, and C. Tellambura, “Network lifetime maximization with node admission in wireless. multimedia sensor networks,” IEEE Trans. Veh. Technol., vol. 58, no. 7, pp. 3640–3646, Sep. 2009 6. F. Shan, W. Liang, J. Luo, and X. Shen, “Network lifetime maximization for time-sensitive data gathering in wireless sensor networks,” Comput. Netw., vol. 57, no. 5, pp. 1063–1077, 2013. 7. M. Tahir and R. Farrell, “Optimal communication-computation tradeoff for wireless multimedia sensor network lifetime maximization,” in Proc. IEEE Wireless Commun. Netw. Conf. (WCNC), Buda pest, Hungary, Apr. 2009, pp. 1–6. 8. H. Wang, N. Agoulmine, M. Ma, and Y. Jin, “Network lifetime optimization in wireless sensor networks,” IEEE J. Sel. Areas Commun., vol. 28, no. 7, pp. 1127–1137, Sep. 2010. 9. S. Zhao, L. Tan, and J. Li, “A distributed energy efficient multicast routing algorithm for WANETs,” Int. J. Sensor Netw., vol. 2, nos. 1–2, pp. 62–67, 2007. 10. S. S. McClure et al., “Radiation effects in micro-electromechanical systems (MEMS): RF relays,” IEEE Trans. Nucl. Sci., vol. 49, no. 6, pp. 3197–3202, Dec. 2002. 11. H. R. Shea, “Radiation sensitivity of microelectro mechanical system devices,” J. Micro/Nanolithogr. MEMS MOEMS, vol. 8, no. 3, pp. 1– 11, Jul. 2009. 12. W. Dargie and C. Poellabauer, Fundametals of Wireless Sensor Net works, X. Shen and Y. Pan, Eds. Hoboken, NJ, USA: Wiley, 2010. 13. T. Vladimirova et al., “Characterising wireless sensor motes for space applications,” in Proc.2nd NASA/ESA Conf. Adapt.Hardware Syst., Edinburgh, U.K., Aug. 2007, pp. 43–50. 14. I. F. Akyildiz, D. Pompili, T. Melodia, “Underwater Acoustic Sensor Networks: Research Challenges," Elsevier's Journal of Ad Hoc Networks, Vol. 3, Issue 3, pp. 257-279. 15. G. Dini, M. Pelagatti, and I. M. Savino, “An algorithm for reconnecting wireless sensor network partitions,” in Proc. 5th EurConf. Wireless Sensor Netw., 2008, pp. 253–267. 16. F. Senel, M. F. Younis, and K. Akkaya, “Bio-inspired relay node placement heuristics for repairing damaged wireless sensor networks,” IEEE Trans. Veh. Technol., vol. 60, no. 4, pp. 1835–1848, May 2011. 17. Liansheng Tan ,Mou Wu, ”Data Reduction in Wireless Sensor Networks: A Herachical LMS Predication Approach”, IEEE Sensors Journal,vol.16 No. 6, March 15,2016. 18. C. Alippi, G. Anastasi, M. D. Francesco, and M. Roveri, “An adaptive sampling algorithm for effective energy management in wireless sensor networks with energy-hungry sensors,” IEEE Trans. InstrumMeas., vol. 59, no. 2, pp. 335–344, Feb. 2010. 19. J.Praiseline Karunya,T.Aruna ,“Performance Analysis of Energy- Aware Sensor Node Design in Wireless Sensor Networks”, International Journal of Electrical, Electronics and , ISSN: 2320-2084 Volume.2, Issue-2, Feb.2014. 20. Younis, O. and Fahmy, S., HEED: a hybrid, energy-efficient, distributed clustering approach for ad hoc sensor networks. IEEE Transactions on mobile computing, 3(4), pp.366-379, 2004 21. Mao Y, Chengfa L., “EECS: an energy efficient clustering scheme in wireless sensor networks,” IEEE international performance computing and communication conference; p. 535–40, 2005. 22. Shigei, N., Miyajima, H., Morishita, H. and Maeda, M.,” Centralized and distributed clustering methods for energy efficient wireless sensor networks,”. In Proceedings of the International Multi Conference of Engineers and Computer Scientists, Vol. 1, pp. 18-20, March, 2009. Authors: A.S. Yahya, W. Hashim, A.F. Ismail, S.H. Sulaiman, M.F. Sulaiman Paper Title: CubeSat: LEO Mission Control Unit Potential Communication Concept for AMR System Abstract: The dramatic drop in equipment pricing, better services and efficiency of small satellite candidate is an interesting communication technology alternative for the deployment of Smart Grid (SG). The use of commercial- off-the-shelf (COTS) components and the ongoing miniaturiza-tion of several technologies have already led to scattered instance of mission with promising scientific value. In this paper, the potential of Low Earth Orbit (LEO) Mission Control Unit (MCU) for small class satellite payloads is presented with the goal of providing utilities in- dustry to low cost and efficiency system towards enhancement of the services provided. Many commercial companies have emerged to cater this objective, however most of them provide an expensive LEO satellites solution. Therefore, this paper proposes a low cost and compact platform to substitute the conventional one. The proposed solution in this paper is targeting Automatic Meter Reading (AMR) system spe-cifically at rural area meter. In particular, this research is intended mainly for the researchers to start an exploration of new technology to be implemented in near future. Advanced development and assessment of CubeSats for SG will make it possible to be used as an alternative communication link.

Keywords: CubeSat; AMR System, Satellite Communication, Smart Grid; LEO Satellite.

73. References: 1. A.S.Yahya et al., 2017. Development of mission control unit prototype for small class payloads. Journal of , Electronic and Computer. 346-349 2. Erik Kulu, 2017. Nanosatellite database. Retrieved 12 April 2017, from http://www.nanosats.eu 3. Greg Richardson et al., 2015. Small satellite trends 2009-2013. 29th Annual AIAA/USU Conference on Small Satellites. 4. A.R.Aslan et al., 2013. Development of a LEO communication Cubesat. 6th International Conference on Recent Advances in Space Technologies (RAST). 637-641. 5. Yik Kuan Hiew et al., 2013. Spectrum band for smart grid implementation in Malaysia. IEEE Student Conference on Research and Development (SCOReD). 6. Tarek Khalifa et al., 2011. A s urvey of communication protocols for automatic meter reading applications. IEEE Communication Surveys and Tutorial, VOL 1, NO 2. 7. Rosila Senan, 2008. Charting the roadmap for AMR. Retrieved 24 April 2017, from http://www.metering.com/charting-the-roadmap-for-amr 8. AMR/AMI Infrastructure. Retrieved 8 May 2017, from https://www.digi.com/pdf/wp_amrami.pdf 9. Chelsea Hawkins, Allen Berthold, 2015. Consideration for adopting AMI and AMR: A comprehensive guide for water utilities. Texas Water Resources Institute Educational Material. 10. Christian Bergan, 2011. Demystifying satellite for the smart grid: Four common misconceptions. Retrieved 23 April 2017, from http://www.idirect.net/ 11. Edwin Phala, 1999. Automatic Meter reading with satellite technology, Retrieved 3 May 2017, from http://www.metering.com/wp- content/uploads/Edwin%20Phala.pdf (1) 12. Jos Heyman, 2009. FOCUS: Cubesat – A Costing + Pricing Challenge by SatMagazine, Retrieved 4 May 2017, from http://www.satmagazine.com/story.php?number=602922274 13. Bob Gohn and Clint Wheelock, 2010. Smart grid network technologies and the role of satellite communications. Research report – Pike Research LLC, Boulder, CO. Authors: Katikireddy Srinivas, K.V.D. Kiran Paper Title: Computational Approach to Overcome Overlapping of Clusters by Fuzzy k-Means Abstract: Of all the clustering algorithms, the frequently employed methods of partitioning algorithms include k-means, medoids and certain modifications. For K-means, a centroid represents the mean or median point of a group and for K-medoids, wherein a medoid represents the most central point of a data group. We present a hybrid method with both algorithms; k-medoids and k-means to cluster a dataset of thyroid disease drugs and the program is run to generate clusters centred on k-means and k-medoids, followed by enhancing the outcome by implementingfuzzy k- means. Clusterability was carried out by Hopkins statistic and cluster validity by Nbclust resulted in k=3. Both the methods resulted in clusters with negative silhouettes, however, hybrid clustering algorithm resulted in partial overlapping of data points, hence fuzzy k-means algorithm was applied on sub-set of dataset. Finally, of all the six fuzzy algorithms studied, fkm algorithm displayed superior separation of clusters with well-defined data points.

Keywords: k-means, k-medoids, fuzzy k-means, clustering, thyroid disease 74.

References: 350-355 1. Hastie T, Tibshirani R, Friedman J. Unsupervised learning. In The elements of statistical learning 2009 (pp. 485-585).Springer New York 2. J. B. MacQueen (1967): "Some Methods for classification and Analysis of Multivariate Observations, Proceedings of 5-th Berkeley Symposium on Mathematical Statistics and Probability", Berkeley, University of California Press, 1:281-297 3. Kaufman, L. and Rousseeuw, P.J. (1987), Clustering by means of Medoids, in Statistical Data Analysis Based on the L1 - Norm and Related Methods, edited by Y. Dodge, North- Holland, 405–416 4. Hartigan, J. A.; Wong, M. A. (1979), "Algorithm AS 136: A K-Means Clustering Algorithm". Journal of the Royal Statistical Society, Series C 28 (1): 100–108 5. Milligan GW, Cooper MC. An examination of procedures for determining the number of clusters in a data set. Psychometrika. 1985 Jun 27;50(2):159-79 6. Fraley C, Raftery AE. How many clusters? Which clustering method? Answers via model-based cluster analysis.The computer journal. 1998 Jan 1;41(8):578-88 7. MacQueen, J. (1967) Some methods for classification and analysis of multivariate observations. In Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, eds L. M. Le Cam & J. Neyman, 1, pp. 281–297. Berkeley, CA: University of California Press. 8. Halkidi M, Batistakis Y, Vazirgiannis M. On clustering validation techniques.Journal of intelligent information systems. 2001 Dec 1;17(2):107-45 9. http://www.malacards.org/ 10. Nolwenn Le Meur and Robert Gentleman. Analyzing Biological Data Using R: Methods for Graphs and Networks.Chapter 19 11. Huber W, Carey VJ, Long L, Falcon S, Gentleman R. (2007) Graphs in molecular biology. BMC Bioinformatics, 8(6):S8 12. Hopkins, Brian; Skellam, John Gordon (1954)."A new method for determining the type of distribution of plant individuals".Annals of Botany. Annals Botany Co. 18 (2): 213–227 13. Banerjee, A. (2004). "Validating clusters using the Hopkins statistic". IEEE International Conference on Fuzzy Systems: 149–153 14. J. C. Bezdek, Pattern Recognition with Fuzzy Objective Function Algorithms (Plenum Press, New York, 1981 15. MacQueen, J. (1967) Some methods for classification and analysis of multivariate observations. In Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, eds L. M. Le Cam & J. Neyman, 1, pp. 281–297. Berkeley, CA: University of California Press. 16. Kaufman, L. and Rousseeuw, P.J. (1990). Finding Groups in Data: An Introduction to Cluster Analysis.Wiley, New York 17. Theodoridis S, Koutroubas K (2008).Pattern Recognition.4th edition. Academic Press. 18. Li R., Mukaidono M., 1995. A maximum entropy approach to fuzzy clustering. Proceedings of the Fourth IEEE Conference on Fuzzy Systems (FUZZ-IEEE/IFES ’95), pp. 2227-2232 19. Dave’ R.N., 1991. Characterization and detection of noise in clustering. Pattern Recognition Letters, 12, 657-664 20. Gustafson E.E., Kessel W.C., 1978. Fuzzy clustering with a fuzzy covariance matrix. Proceedings of the IEEE Conference on Decision and Control, pp. 761-766 21. Ferraro M.B., Giordani P., 2013. A new fuzzy clustering algorithm with entropy regularization. Proceedings of the meeting on Classification and Data Analysis (CLADAG). Authors: A.S. Sathish, R. Indradevi, Sreeram Gangineni Paper Title: A Service Quality and its Influence on Customer Satisfaction in a Multi-Speciality Hospital Abstract: Objective: The objectives of this paper is to assess the quality of services offered by multi-speciality hospital suffice patient satisfaction. Also to bring out the various factors that creates patient satisfaction. Further to evaluate the aspects relating to expectation, perception and satisfaction of the services provided by the hospital. Purpose: The purpose of this paper is to examine service quality in multi-speciality hospital in a town where fast mushrooming of private hospitals are at large in South India, India. Specifically, this study examines the five dimensions of SERVQUAL instrumentation (reliability, assurance, tangibles, empathy, and responsiveness) with respect to customer satisfaction of the services offered by the hospital. A comprehensive service quality measurement scale (SERVQUAL) is empirically evaluated for its potential usefulness in a Multispecialty Hospital.Findings: The patients showed positive response on the service quality variables and do have high agreement levels of the dimensions of SERVQUAL.Service quality has emerged as the highest expected aspect by the patients at the hospital.Research limitations: The research scope registered only patients experiences, (respondents) and experiences observed at the time of study. Practical Implications: The study provides a new understanding of SERVQUAL dimensions in the context of a multi-speciality hospital in a place where these services are provided and offered with a differentiation. Thus provides an understanding of these dimensions and its role in making the organisation stand out among the intense competition and sustain in the long run.

Keywords: Service Quality, Patients, Customer Satisfaction, and multi-speciality hospital. Paper type: Research paper.

References: 75. 1. Frost and Sullivan. Health Care Industry in India [Internet] Department of Industrial Policy and Promotion (DIPP), RNCOS Reports, Media Reports, Press Information Bureau (PIB), Union Budget 2017-18 [Citied 2018 Jun]. Available from: http://guides.library.uwa.edu.au/c.php?g=324981&p=2178452 356-359 2. Weitzman, B.C. In: Kovner, A.R. Health care delivery in the United States, Berlin: Springer (1995) (5th ed.). 3. SaadAndaleeb S. Determinants of customer satisfaction with hospitals: a managerial model. International Journal of Health Care Quality Assurance. 1998 Nov 1;11(6):181-7. 4. Craig TJ, Perlin JB, Fleming BB. Self-reported performance improvement strategies of highly successful Veterans Health Administration facilities. American Journal of Medical Quality. 2007 Nov;22(6):438-44. 5. Parasuraman A, Zeithaml VA, Berry LL. A conceptual model of service quality and its implications for future research. the Journal of Marketing. 1985 Oct 1:41-50. 6. Parasuraman A, Zeithaml VA, Berry LL. Servqual: A multiple-item scale for measuring consumer perc. Journal of retailing. 1988 Apr 1;64(1):12. 7. Graham Saunders S. Measuring and applying the PAKSERV service quality construct: Evidence from a South African cultural context. Managing Service Quality: An International Journal. 2008 Sep 5;18(5):442-56. 8. Rohini R, Mahadevappa B. Service quality in Bangalore hospitals-an empirical study. Journal of services Research. 2006 Apr 1;6(1):59. 9. Pakdil F, Harwood TN. Patient satisfaction in a preoperative assessment clinic: an analysis using SERVQUAL dimensions. Total Quality Management & Business Excellence. 2005 Jan 1;16(1):15-30. 10. Rose R, Uli J, Abdul M, Looi Ng K. Hospital service quality: a managerial challenge. International Journal of Health Care Quality Assurance. 2004 May 1;17(3):146-59. 11. Bamidele AR, Hoque ME, Van der Heever H. Patient satisfaction with the quality of care in a primary health care setting in Botswana. South African Family Practice. 2011;53(2). 12. Purcărea VL, Gheorghe IR, Petrescu CM. The assessment of perceived service quality of public health care services in Romania using the SERVQUAL scale. Procedia Economics and Finance. 2013 Jan 1;6:573-85. 13. DK, Wilton PC. Models of consumer satisfaction formation: An extension. Journal of marketing research. 1988 May 1:204-12. 14. Zairi M. Managing customer satisfaction: a best practice perspective. The TQM Magazine. 2000 Dec 1;12(6):389-94. 15. Taylor SA, Cronin Jr JJ. Modeling patient satisfaction and service quality. Journal of health care marketing. 1994 Mar 1;14(1). 16. McAlexander JH, Kaldenberg DO, Koenig HF. Service quality measurement. Journal of health care marketing. 1994 Sep 1;14(3):34-40. 17. Hair Jr JF, Hult GT, Ringle C, Sarstedt M. A primer on partial least squares structural equation modeling (PLS-SEM). Sage Publications; 2016 Feb 29. Authors: B. Gomathy, G. Shanmugavadivel Paper Title: Viable Routing In Multi Hop Crn Based On High Mobility and Path Stability 76. Abstract: Fast topology changes in high mobility cognitive radio networks (CRNs), increases the routing scheme complexity. Based on reflection of node capacity and stability of the path we suggest a new CRN routing scheme in 360-363 this article. To describe and gauge interface strength, execution in light of the development of exceedingly versatile airborne nodes [e.g., unmanned aerial vehicles (UAVs)] realistic mobility model is presented first. Next we discuss about CRN topology administration conspire in view of a gathering model that reflects radio connection accessibility, and the cluster heads (CHs) are chosen in view of the hub degree level, the ordinary number of bounces, and channel changing from part center points to the CH. In the perspective of the discrete particle swarm optimization algorithm and node compression concept propose two new basic control channel (CCC) determination designs. From the CH select the gateway and inter cluster control channels, by consider the total throughput and average delay between two CHs while the transmission of control information. At last we proposed based another steering plan in view of the capacity of node that firmly coordinates with the channel task. Our recreation comes about demonstrate that our suggested CCC determination plot has maximum throughput and little transmission time. Differentiated and other pervasive CRN coordinating techniques, we proposed that directing plan achieves cut down ordinary end-to-end put off and higher bundle transport extent for high-smallness CRN applications, (for example, airborne observation).

Keywords: Routing scheme, Cognitive radio networks (CRNs), routing scheme, cluster head, high- mobility, multihop cluster, path stability.

References: 1. P. J. Kolodzy, "Range arrangement team report," Fed. Commun. Comm.Washington, DC, Rep. Et docket 02-135, Nov. 2002. 2. H. Mama, L. Zheng, X. Mama, and Y. Luo, "Range a mindful directing for multihop intellectual radio systems with a solitary handset," in Proc. IEEE CrownCom, 2008, pp. 1– 6. 3. A. M. James, "A connection quality-mindful chart display for intellectual radio system steering topology the board," M.S. proposal, Rochester Inst. Technol.,Rochester, NY, 2007. 4. H. Khalife, S. Ahuja, N. Malouch, and M. Krunz, "Probabilistic way determination in entrepreneurial psychological radio systems," in Proc. IEEE Globecom, 2008, pp. 1– 5. 5. M. Dish, R. Huang, and Y. Tooth, "Cost structure for artful multihop steering in subjective radio systems," in Proc. IEEE MILCOM, 2008,pp. 1– 7. 6. I. F. Akyildiz, W. Lee, M. C. Vuran, and S. Mohanty, "People to come/dynamic range get to/psychological radio remote systems: Asurvey,"Comput. Netw., vol. 50, no. 13, pp. 2127–2159, Sep. 2006. 7. G. Zhu, I. F. Akyildiz, and G. Kuo, “STOD: A spectrum-tree based ondemand routing protocol for multi-hop cognitive radio networks,” in Proc.IEEEGlobecom, 2008, pp. 1–5. 8. G. Cheng, W. Liu, Y. Liu, and W. Cheng, “Spectrum aware on-demand routing in cognitive radio networks,” in Proc. IEEE DySPAN, 2007,pp. 571–574. 9. M. Gerharz, C. de Waal, M. Straight to the point, and P. Martini, "Connection steadiness in versatile remote specially appointed systems," in Proc. IEEE LCN, 2002, pp. 30– 39. 10. T. Camp, J. Boleng, and V. Davies, “A survey of mobility models for ad hoc network research,” Wireless Commun. Mobile Comput., vol. 2, no. 5,pp. 483–502, Aug. 2002. Authors: Syed Ali Fathima S J, Sumathi V P, Sumanth S Data analytics in football sport to identify gaps for the improvement of quality opportunities Paper Title: throughout world-wide teams Abstract: Football is a widely known sport. Billions watch and play the game around the world. Data Analytics has assumed a huge role in the world of Football. It has transformed how people approach games, team formation, player selection etc. Data analytics has enabled teams from around the world to understand their game better and perform better. Data analytics is also used to predict the outcomes of games enabling people to make educated guesses while betting. There is no doubt that Football is worldwide sport. However, there are so many teams worldwide who haven’t improved when compared to some of the others. Few teams don’t even manage to make into the main tournaments like FIFA. Some countries lack funding and some teams don’t have the exposure to standard equipment, coaching opportunities etc. It is very important for a Football enthusiast to know that the game keeps evolving towards a point where there are more quality teams around the world. It is very important for data analytics to move into this direction of finding answers to the question “What can be done to provide quality opportunities to the teams worldwide?”. The present paper discusses exactly that and looks to provide an answer to that very question.

Keywords: Data analytics, Football, Pandas, Players, Python, Sports, Statistics.

References: 77. 1. M. Andrews, and P. Harrington, “Off Pitch: Football’s Financial Integrity Weaknesses, and How to Strengthen Them”, Faculty Research Working Paper Series, Harvard Kennedy School, 2016, Paper No. 311, [Online] Available: https://research.hks.harvard.edu/publications/workingpapers/Index.aspx 364-368 2. Davenport, T. H, “Analytics in sports: The new science of winning”, International Institute for Analytics, 2014, no. 2, pp.1-28. 3. Gangal, A., Talnikar, A., Dalvi, A., Zope, V., and Kulkarni, A., “Analysis and Prediction of Football Statistics using Data Mining Techniques”.,International Journal of Computer Applications, 2015, 132(5), pp. 8-11. 4. Horne, J. D., and Manzenreiter, W., “Accounting for mega-events: forecast and actual impacts of the 2002 Football World Cup Finals on the host countries Japan/Korea.” International review for the sociology of sport, 2004, 39(2),pp. 187-203. 5. Rein, R., and Memmert, D., “Big data and tactical analysis in elite soccer: future challenges and opportunities for sports science”, SpringerPlus, 2016, 5(1) pp. 1410. 6. Sarmento, H., Marcelino, R., Anguera, M. T., CampaniÇo, J., Matos, N., and LeitÃo, J. C., “Match analysis in football: a systematic review.”, Journal of sports sciences, 2014, 32(20), pp. 1831-1843. 7. Christopher Carling, Craig Wright, Lee John Nelson, Paul S Bradley. “Comment on ‘Performance analysis in football: A critical review and implications for future research”. Journal of Sports Sciences, 2014, 32:1, pp. 2-7. 8. http://nielsensports.com/global-interest-football/, Last Accessed 09/05/2018. 9. http://www.fifa.com/about-fifa/who-we-are/the-game/index.htm, Last Accessed 10/05/2018. 10. https://datafloq.com/read/how-big-data-is-changing-the-world-of-football/1796, Last Accessed 11/05/2018. 11. https://thesetpieces.com/features/football-and-data-the-future-of-analytics/, Last Accessed 11/05/2018. 12. https://www.footballhistory.org/, Last Accessed 12/05/2018. 13. https://www.theguardian.com/football/2014/mar/09/prmier-league-football-clubs-computer-analysts-managers-data-winning, Last Accessed 12/05/2018. 14. https://www.independent.co.uk/sport/football/premier-league/transfer-window-football-betting-analytics-moneyball-a7934181.html, Last Accessed 12/05/2018. 15. Manjula Sanjay, Shreyas Srinivasan and Rahul Kulkarni, Data mining technique for Best 11, International Journal of Conceptions on Information Technology and Computing Vol. 4, Issue. 2, 2016. 16. http://www.ai-man.ir/en/post/international-football-results-from-1872-to-2017-en/, Last Accessed 20/06/2018. 17. Sumathi, V P, Kousalya, K, Vanitha, V, Cynthia, J, “Crowd estimation at a social event using call data records”, 2018, Int. J. Business Information Systems, Vol 28, Issue. 2, pp 446-461. Authors: Kirubakaran. S, Arunkumar. M, Prakash S.P Paper Title: P2P Based Fast Replica Algorithm for Semantic Overlay Networks Abstract: Nowadays the Overlay networks are emerged as a prevailing and bendable technique. This paper aims to optimize the throughput parameter of overlay networks. A network based on similar content is created and called as Semantic overlay Network. To duplicate the contented within the semantic system, FastReplica methods are used to condense the contented relocate time. This file are down loaded through various network in parallel manner. The vibrant main schemes are obtained to the main end users of the domain SON at the similar time, alluring dissimilar portion of that file from dissimilar nodes and saturated in local point. The loads are animatedly collective in the middle of all the peers. while considering a peer-to-peer (P2P) architecture queries are propagated to Overlay Broker (OB). The OB in turn returns the IP address of the semantic group requested by the node. Then the node queries the semantic members for the file. All the connections are maintained by nodes. For instance, “Jazz” archive of the main node will connect to additional similar nodes. All the nodes of Semantic Overlay Network (SON) are organized by themselves to the OB. The uncertainty are in retreat to the suitable SONs, which increasing the probability of matching files and dipping the look for the dissimilar nodes on the main content

Keywords: Replica algorithm, overlay networks, IP address, P2P

78. References: 1. Amutharajj, J., & Radhakrishnann, S. (2007, February). The Dominating Set Theory Based Semantic Overlay Networks for Efficient Content Distribution. In Signal Processing, Communications and Networking, 2007. ICSCN'07. International Conference on (pp. 228-232). 369-372 IEEE. 2. J. Byerss, J. Considinen, M. Mitzenmacher, and S. Rost, “ The Informed content delivery across adaptive overlay networks,” Proc. ACM SIGCOMM, Pittsburgh, PA, Aug. 2004, pp. 47–60. 3. Arturoo Crespoo, Hectorr Garciaa Molina, “Semantic Overlay Networks for P2P Systems,” Google Technologies Inc., Stanford University January 2003. 4. Thorstenn Strufee / Dietrich Reschke, “Efficient Content Distribution in semi-decentralized Peer-to-Peer-Networks” Proceedings of 8th Netties Conference, Technische Universität Ilmenau, September 30th to October 2nd 2002. 5. Kirkk L. Johnsons, John F. Carr, Markk S. Day, M. Franss Kaashoek, “Certain Measured Performance of Content Distribution Networks” Technical Report, SightPath, Inc. 135 Beaver Street, Waltham, MA 02452, USA, 2000. 6. Michael O. “Rabinhood ,The Efficient dispersal of information for security, load balancing, and fault tolerance,” Journal of the ACM (JACM), v.36 n.2, p.335-348, April 1989. 7. L. Cherkasova, J. Lee, “FastReplica: Efficient Large File Distribution within Content Delivery Networks”, Proc. of the 4th SENIX Symposium on Internet Technologies, March, 2002. 8. Pablo Rodriques and Ernst W. Biersack, “Dynamic Parallel Access to Replicated Content in the Internet” , IEEE / ACM Transactions on Networking, vol. 10 No.4, Aug 2002. 9. J. Byers, M .Luby, M. Mitzenmacher, A. Rege., “A Digital Fountain approach to reliable distribution of bulk data”, Proc. of ACM SIGCOMM, 1998 10. Project JXTA:Technical Specification Sun Microsystems, Inc. April 25, 2001.“Survey of Content Delivery Networks (CDNs)”, web source. 11. Mohammadi Mallil, Chadii Barakatt, Walid Dabbous, “An Efficient Approach for Content Delivery in Overlay Networks”, Projet Planet, INRIA-Sophia Antipolis, France, 2005. Authors: M. Parimala, D. Arivuoli, S. Krithika Paper Title: Separation Axioms in Ideal Minimal Spaces Abstract: The purpose of this article is to study few separation axioms in ideal minimal spaces. The separation axioms under mIαg-closed sets namely, mIαg-T0-spaces and mIαg-T1-spaces were studied. Comparison of these spaces with some existing spaces were established. Necessary and sufficient conditions of mIαg-T0 and mIαg-T1 spaces are also proved.2010 Mathematics Subject Classification. 54A05, 54D10,54D25,54C05

Keywords: and Phrases : m-T0-spaces, m-T1-spaces, mIαg-T0 spaces, mIαg- T1 spaces 79. References: 373-375 1. D.Arivuoli, M.Parimala, On generalised closed sets in ideal minimal spaces, Asia 2. Life Sciences, Supplement 14. No.1 , March 2017,85-92. 3. K.Kuratowski, Topology, Vol. I. Academic Press, New york, 1996. 4. H.Maki,J.Umehara,T.Noiri. Every Topological space in pre T(1/2). Mem. Fac. Sci. 5. Kochi Univ. Ser. A Math.,1996,17:33-42. 6. O.B.Ozbakiri and E.D.Yildirim,On some closed sets in ideal minimal spaces, Acta Math. Hungar.,125(3)(2009),227-235. 7. M.Parimala, D.Arivuoli, Submaximality in term of ideal minimal spaces. (communicated) 8. Takashi NOIRI and Valeriu POPA, On m-D-Separation Axioms. Univ. Fen. Fak. Mat. Dergirsi,61-62(2002-2003),15-28. Authors: L. Sudha, P. Thangaraj Paper Title: Enhancing Energy Exertion Using Multi Hop Data Aggregation in Wireless Sensor Network Abstract: The Ad Hoc On-Demand Distance Vector (AODV), Hybrid Energy Efficient Distributed (HEED) and multi-hop LEACH protocols are discussed. In multi-hop clustering, the sensor is selected as CH according to the two parameters maximum envelop position reckoning of the sensor nodes. The multi-hop LEACH is to choose SNs as 80. CHs by alternation, The towering energy indulgence in exchange the information among the BS is extend to all 376-381 sensor Nodes in the set of networks. By means of this in sequence every node will prefer the presence cluster head (CH) based on the negligible packet failure. Mat lab simulations demonstrate that taking into consideration packet failure in choosing the most excellent communication path has a significant contact on plummeting the energy utilization of the complex as well as greater than ever network throughput.

Keywords: Energy Exertion, Routing Protocols, Packet Failure, Wireless Sensor Networks.

References: 1. Patil, N. S., &Patil, P. R. (2010, December). Data aggregation in wireless sensor network. In IEEE international conference on computational intelligence and computing research (Vol. 6). 2. Abhilash, L. N., Goenka, D., & Kumar, C. (2014, February). Dynamic data aggregation for energy optimization in multi-hop Wireless Sensor Networks. In Advance Computing Conference (IACC), 2014 IEEE International (pp. 143-148). IEEE. 3. Sony, C. T., Sangeetha, C. P., &Suriyakala, C. D. (2015, April). Multi-hop LEACH protocol with modified cluster head selection and TDMA schedule for wireless sensor networks. In Communication Technologies (GCCT), 2015 Global Conference on (pp. 539-543). IEEE. 4. Gill, R. K., Chawla, P., & Sachdeva, M. (2014). Study of LEACH routing protocol for Wireless Sensor Networks. In International Conference on Communication, Computing & Systems (ICCCS-2014). 5. Yassen, Muneer Bani, ShadiAljawaerneh, and Reema Abdulraziq. "Secure low energy adaptive clustering hierarchal based on internet of things for wireless sensor network (WSN): Survey." In Engineering & MIS (ICEMIS), International Conference on, pp. 1-9. IEEE, 2016. Authors: L. Latha, M. Kaviya Paper Title: A Real Time System for Two Ways Communication of Hearing and Speech Impaired People Abstract: Generally Sign language uses hand gestures for communication; it is used by the hearing and the speech impaired people to interact with others. But it is very difficult for the normal people to understand it, so this paper proposes a real time system for better communication with normal people and disabled people. The gestures shown by the impaired people will be captured and the corresponding voice output is produced as one way and the voice input by normal people is taken and the periodic gesture will be displayed to them as another.This system uses RASPBERRY PI kit as the hardware, where a Pi camera, LCD display, Speaker and Microphone will be attached along with it. First the image acquisition is carried where it captures the input image and then image pre-processing is done to extract the foreground image from the background, then feature extraction is carried out to extract the necessary details. The extracted image is matched with the dataset and the corresponding voice output is generated for that gesture. Likewise, a microphone is used to capture the speech input of the normal people, then it is pre- processed to remove the extra noise in the speech signal and feature extraction is carried out to identify the necessary details and finally extracted voice is matched with the dataset and the corresponding hand gestured image will be displayed in LCD display. By using this method the communication gap between the impaired and normal people get reduced.

Keywords: Feature extraction, pre-processing, matching

81. References: 1. Real Time Two Way Communication Approach for Hearing Impaired and Dumb Person Based on Image Processing”, Shweta,S. Shinde, 382-385 Rajesh M.Autee ,Vitthal K.Bhosale in IEEE International Conference on Computational Intelligence and Computing Research 2016. 2. A Portable Tool for Deaf and Hearing Impaired People “,R. A. D. K. Rupasinghe, D. C. R. Ailapperuma, P.M.B.N.E. De Silva and A. K. G. Siriwardana, B.H. Sudantha in 2014. 3. “Hand Gesture Recognition and Voice Conversion System Using Sign Language Transcription System” , Vajjarapu Lavanya, Akulapravin, M.S., Madhan Mohan in national Journal of electronics & communication technology in 2014. 4. ”Real Time Indian Sign Language Recognition System to aid Deaf-dumb People”, P. Subha Rajam ,Dr. G. Balakrishnan in 978-1-61284- 307-0/11/$26.00 ©2011 IEEE. 5. Assisting System for Deaf and Mute Using Arduino Lilypad and Accelerometer”, Rakesh N. S Rachamadugu Saicharan, Praveen Kumar, Harish N. G, Aneeta, S.Antony in International Journal of Advance Research, Ideas and Innovations in Technology in 2017. 6. ”Real-time Dynamic Hand Gesture Recognition using Hidden Markov Models”, M.M.Gharasuie , H.Seyedarabi in 978-1-4673-6184- 2/13/$31.00 ©2013 IEEE. 7. ”Embedded Image Capturing System Using Raspberry Pi System“, G.Senthilkumar1, K.Gopalakrishnan2 , V. Sathish Kumar in International Journal of Emerging Trends & Technology in Computer Science (IJETTCS) in 2014. 8. https://en.wikipedia.org/wiki/Raspberry_Pi“, Wikipedia about raspberry pi. 9. “Hand Gesture Recognition Algorithm for Smart Cities based on Wireless Sensor”, Thittaporn Ganokratanaa, Suree Pumrin in 2017. 10. “Counting Aid for Dumb and Illiterate People using Image Processing Approach” 11. Sarthak Panda, Sasmita Mahakud, and Mihir Narayan Mohanty in 2016. 12. On improving the Performance of Multimodal biometric authentication through Ant colony optimization, L.Latha, S.Thangasamy - WSEAS transactions on information science and applications, Issue 12, Volume 8, December 2011. 13. “A survey on various communication systems for hearing and speech impaired people”, M.kaviya, L.Latha, International Journal of Advanced Research in Science and Engineering (IJARSE), in January 2018. Authors: Kirubakaran R, Francis Jency X,Aswini D Paper Title: Mobile Learning for Education in India - A Feasibility Study Abstract: The increasing growth and implementation of mobile technologies has made social changes in several areas such as Communication, Entertainment, Technology and so on. This development led to the introduction of mobile phones and its use in education is remarkable. Since India is one of the fastest growing markets for mobile phone services, this paper presents the prospects and challenges involved in implementing mobile learning in India. The aim of this paper is to describe the current state of mobile learning, the benefits, barriers, and challenges that 82. would support teaching and learning. This paper analyses the different study carried out based on mobile learning technologies in different countries and brought in suggestion of mobile learning and proposed the theoretical 386-389 significance of mobile learning in education.

Keywords: Mobile learning, M-Learning, Educational technology

References: 1. Ying Yang, Dan Tian and Ling Wu, "Influence analysis of mobile learning research on modern distance education," 2016 2nd IEEE International Conference on Computer and Communications (ICCC), , 2016, pp. 883-886. 2. Alhajri, Rana, “Prospects and Challenges of Mobile Learning Implementation: A Case Study”, Journal of Information Technology & Software Engineering, 2016. 3. A. Lu, Q. Chen, Y. Zhang and T. Chang, "Investigating the Determinants of Mobile Learning Acceptance in Higher Education Based on UTAUT," 2016 International Computer Symposium (ICS), Chiayi, 2016, pp. 651-655. 4. S. Paturusi, Y. Chisaki, T. Usagawa and A. Lumenta, "A study of students' acceptance toward mobile learning in higher education institution in Indonesia," 2015 International Conference on Information & Communication Technology and Systems (ICTS), Surabaya, 2015, pp. 193- 196. 5. Murphy, A., Farley, H., Johnson, C., Lane, M., Carter, B. Hafeez-Baig, A. Midgley, W., Dekeyser, S., Rees, S., Mitchell, M., Doyle, J. and Koronios, A. (2013). Listening to the student voice: How are students really using mobile technologies for learning? In: 30th Australasian Society for Computers in Learning in Tertiary Education Conference (ASCILITE 2013): Electric Dreams, 1-4 Dec 2013. 6. M. Al-Emran and K. Shaalan, "Learners and educators attitudes towards mobile learning in higher education: State of the art," 2015 International Conference on Advances in Computing, Communications and Informatics (ICACCI), Kochi, 2015, pp. 907-913. 7. D. Sapargaliyev, "Development of mobile learning in higher education of Russia," Proceedings of 2012 International Conference on Interactive Mobile and Computer Aided Learning (IMCL), Amman, 2012, pp. 48-51. 8. A. Alowayr and R. McCrindle, "User interfaces for mobile learning in higher education in Saudi Arabia," 2016 International Conference on Information Society (i-Society), Dublin, 2016, pp. 178-179. 9. G. Tuparov, A. A. A. Alsabri, D. Tuparova and G. Tuparov, "Students' readiness for mobile learning in Republic of Yemen ??? A pilot study," 2015 International Conference on Interactive Mobile Communication Technologies and Learning (IMCL), Thessaloniki, 2015, pp. 190-194. 10. Berque, D. A., Prey, J., & Reed, R. H. (Eds.). (2006). The impact of tablet PCs and pen-based technology on education: Vignettes,evaluations, and future directions. Purdue University Press. 11. Berque, D., Bonebright, T., Dart, J., Koch, Z., & O’Banion, S. (2007). Using DyKnow Software to support group work: A mixed- method evaluation. JC Prey, RH Reed, & DA Berque, The Impact of Tablet PCs and Pen-Based Technology on Education: Beyond the Tipping Point, (pp. 11-20). 12. Perkins, S., & Saltsman, G. (2010). Mobile learning at Abilene ChristianUniversity: Successes, challenges, and results from year one. Journal of the Research Center for Educational Technology, 6(1), (pp. 47-54). 13. Jaradat, R. M. (2014). Students' Attitudes and Perceptions towards using m-learning for French Language Learning: A case study on Princess Nora University. Int. J. Learn. Man. Sys, 2(1), (pp. 33-44). 14. Kim, S. H., Mims, C., & Holmes, K. P. (2006). An introduction to current trends and benefits of mobile wireless technology use in higher education. AACE Journal, 14(1), (pp. 77-100). 15. Uzunboylu, H., Cavus, N., & Ercag, E. (2009). Using mobile learning to increase environmental awareness. Computers & Education, 52(2), (pp. 381-389). 16. Hall Jr, O. P., & Smith, D. M. (2011). Assessing the role of mobile learning systems in graduate management education. In Hybrid Learning, (pp. 279-288). Springer Berlin Heidelberg. 17. Fong, W. W. (2013). The Trends in Mobile Learning. In Hybrid Learning and Continuing Education (pp. 301-312). Springer Berlin Heidelberg. 18. Gikas, J., & Grant, M. M. (2013). Mobile computing devices in higher education: Student perspectives on learning with cellphones, & social media. The Internet and Higher Education, 19, (pp. 18-26). 19. Kinash S, Brand J, Mathie T (2012) Challenging mobile learning discourse through research: Students perceptions of Blackboard Mobile Learn and iPads. Australian Journal of Educational Technology 28: 639-655. 20. Nassuora, A. B. (2012). Students acceptance of mobile learning for higher education in Saudi Arabia. American Academic & Scholarly Research Journal, 4(2), (pp. 24-30). 21. Traxler J (2007) Defining, discussing and evaluating mobile learning: The moving finger writes and having writ... The International Review of Research in Open and Distance Learning 8: 9-24.. 22. Kam, M., Agarwal, A., Kumar, A., Lal, S., Mathur, A., Tewari, A., & Canny, J. (2008, February), “Designing e-learning games for rural children in India: a format for balancing learning with fun”, In Proceedings of the 7th ACM conference on Designing interactive systems (pp. 58-67) 23. Kumar, A., Reddy, P., & Kam, M. (2011). “SMART: Speech-enabled Mobile Assisted Reading Technology for Word Comprehension”, Lecture Notes in Computer Science Artificial Intelligence in Education, 497-499 24. Quinn C (2000) mLearning: Mobile, Wireless, In-Your-Pocket Learning. LiNE Zine 25. Biswajit Boity, “Mobile Learning in India: Scope, Impact and Implications, Hardvard University, December 2015 26. R. Kirubakaran, C. M. Prathibhan and C. Karthika, "A cloud based model for deduplication of large data," 2015 IEEE International Conference on Engineering and Technology (ICETECH), Coimbatore, 2015, pp. 1-4 27. Kirubakaran R, Bharathipriya C, Francis Jency X and Mano Prathibhan C, " Statistics based load evaluation in web applications using fuzzy logic and dynamic track bots", International Journal of Pure andf Applied Mathematics, 2018, Vol 118, No .8 pp. 435-444 28. http://trai.gov.in/release-publication/reports/telecom-subscriptions-reports Authors: Thanabal V, Saravanan D Paper Title: A Study on The Quality Characteristics of Yarns Made from Coir Fibres Abstract: The spinning system employed for the spinning of coir yarn from coir fibers follows the principle of open end spinning. Coir yarn produced by manual or mechanical means is of two ply structure. The single yarn produced by the mechanized process is a an open end type of yarn where two single yarn from the adjacent spinning heads are converged together and then twisted together before winding it on a bobbin. The single yarn produced is of core yarn type and the core component is generally a mono filament polyester fiber, with coir fibers wrapped over the filament to form a sheath. Fiber length distribution, packing fraction and breaking strength of both single and two ply 83. coir yarn were tested and analyzed. 390-393 Keywords: Coir fiber, Core spun yarn, Fiber length distribution, Packing fraction, Sheath fibers, Wrapper

References: 1. Akila Rajan, and T. Emila Abraham..Coir Fiber Process and Opportunities. Journal of Natural Fibers, Vol 3(4) 2007, pp.1-11 2. Kundan Meshram,S.K. Mittal, P.K.Jain, P.K. Agarwal. Application of Coir Geotextile in Rural Roads Construction on BC Soil Subgrade. International Journal of Engineering and Innovative Technology.3 (4) 2013 pp. 264 - 268 3. Majid Ali, Coconut fibre: A versatile material and its applications in engineering. Journal of Civil Engineering and Construction Technology. 2011,2(9) pp. 189-197. 4. R.I. Meenatchisunderam.1980. Retting of coir – A review. Ceylon Cocon.Plrs, 1975 – 1980.7. pp. 20-28 Authors: A Jayaraman, N Sathyakumar, S B Prasath 84. Cost Comparison of Roof Truss Angle Section and Channel Section Purlins by Working Stress and Paper Title: Limit State Method Abstract: Industrial structural design to be adequate, usually four main aims of this study – value, protection, cost- effective and stylishness must be fulfilled. This paper reported that, the performance and reasonable of industrial structures by evaluation of LSM and WSM. This paper presents a study on performance and reasonable of purlins of roof trusses by LSM and WSM. The conjectural data are designed using IS –code of IS 875-1975 (part III), IS 800 – 2007 using LSM, IS 800- 1984 using WSM and the section properties of the specimens are obtained using steel table. The research project aims to provide which method is cost-effective, more load transport capability and high flexural strength. The angle section less economical compare with channel section section. The cost of channel section is 54.31 % and 60.12% is less than the angle section in similar organization load in WSM and LSM.

Keywords: Working stress method, limit state method, roof trusses, purlins industrial structures,

References: 394-399 1. Anbuchezian .A, Dr. Baskar.G (2013) “Experimental study on cold formed steel purlin sections” Engineering Science and Technology: An International Journal (ESTIJ), ISSN: 2250-3498, Vol.3, No.2, April 2013 276. 2. Sagar D. Wankhade, P Pajgade S (June 2014) “ Design & Comparison of VariousTypes of Industrial Buildings” International Refereed Journal of Engineering and Science (IRJES) ISSN (Online) 2319-183X, (Print) 2319-1821 Volume 3, Issue 6 (June 2014), PP.13-29 3. Govindasamy.P, Sreevidya .V, Dr Jayagopal L.S “Comparative Study on Cold Form Purlins for Distortional Buckling Behaviour” international journal of engineering sciences & research Technology ISSN: 2277-9655 Volume 6, Issue 4(20170. 4. S. K. Duggal” Limit State Design of Steel Structures” McGraw Hill Education (28 May2010) 5. N. Subramanian “Design of Steel Structures based on Limit state Method of design as Per standardised code IS - 800: 2007. 6. S.S Bhavikatti “Design of steel structure by limit state method as per IS - 800: 2007” 7. IS - 875: 1975 Specification for dead load ,live load and wind load calculation Structural Steel Section. 8. IS - 800 Code of Practice for General Construction in Steel, Bureau of Indian Standards, New Delhi, 2007. 9. Steel tables in SI Units . Authors: R. Bharani Kumar, A. Loganayaki Analysis of Z-Source Matrix Converter with Different PWM Control Schemes for Wind Energy Paper Title: Conversion Systems Abstract: This paper concentrates on evaluating performance of three phase z-source matrix converter (ZSM). The proposed converter is used in permanent magnet synchronous generator (PMSG) based 2.5 kW wind energy system. The proposed WES output supply frequency changes with respect to the change in wind velocity. The main objective of this ZSM 16nverter is to maintain constant frequency and constant load voltage when WES frequency changes with 16nd speed. It also reduces the power loss during low wind velocity and also due to total harmonic distortions (THD). In this work, the ZSM converter performance parameters like output voltage fi16uency, voltage magnitude, THD level are analyzed for various wind velocities and switching frequencies. The Sinusoidal pulse width modulation (SPWM), and modified carrier PWM schemes are used to control the matrix converter and results are to compare the evaluated parameters of the ZSM converter.

Keywords: z-source matrix converter (ZSM), permanent magnet synchronous generator (PMSG), total harmonic distortions (THD). 85. References: 400-402 1. T. Shanker and R. K. Singh, "Wind energy conversion system: A review," 2012 Students Conference on Engineering and Systems, Allahabad, Uttar Pradesh, 2012, pp. 1-6. doi: 10.1109/SCES.2012.6199044. 2. N. Ramesh Babu, P. Arulmozhivarman, “Wind Energy Conversion Systems – A Technical Review”, Journal of Engineering Science and Technology, Vol. 8, No. 4, pp. 493 – 507, 2013. 3. Thiringer.T.; “Power Quality Measurements Performed on a Low-Voltage Grid Equipped with Two Wind Turbines", Energy Conversions, IEEE Transactions on, Vol.11, Issue 3, September 1996,Pages :601-606. 4. M. Venturini, "A New Sine Wave in Conversion Technique Which Eliminates Reactive Elements,” in Proceedings of Powercon 7, pp. E3/1- E3/15, 1980. 5. M. Kazerani, Dynamic Matrix Converter Theory Development and Application, Ph.D. thesis, Department of Electrical Eng., McGill University, Canada, 1995. 6. H. Nikkhajoei, A. Tabesh, and R. Iravani, "Dynamic model of a matrix converter for controller design and system studies", IEEE Trans. on Power Delivery, Vol. 21, Issue 2, Apr. 2006, pp.744 - 754. 7. P.W. Wheeler, J. Rodriguez, J. C. Clare, L. Empringham, A. Weinstein, "Matrix converters: a technology review, "IEEE Trans. on Industrial Electronics vol. 49, Issue 2, pp. 276 – 288 April 2002. 10. Bharani Kumar, Ramasamy., Nirmal Kumar, A. and Maheswari "Modeling and Simulation of Wind Turbine Driven Axial type PM generator with Z Source Inverter", Australian Journal of Electrical and Electronics Engineering, Vol.9 No.1, pp.27-41, 2012. Authors: Harikumar Rajaguru, Vigneshkumar Arunachalam Correlation Dimension Based Performance Analysis of Alcoholic EEG data with PCA and PSO Paper Title: Classifiers Abstract: Excessive drinking Alcohol is a serious problem in the world as it causes a lot of health issues. It causes heavy damage in the human brain and problems like lack of memory and concentration accompanied by impaired decision making takes place. The electrical activity of human brain is identified by, Electroencephalography (EEG) 86. signals. An EEG signal depicts multiple patterns for various neurological disorders. Hence, it is widely preferred one in clinical diagnosis. A chronic alcoholic patient’s EEG and the Correlation Dimension (CD) features are analyzed. 403-406 The obtained CD features are classified with the Principle Component Analysis (PCA) and Particle Swarm Optimization (PSO) Classifiers. The bench mark parameters such as Good Value(GV), AUC, Specificity and Sensitivity are compared in both optimization. The PSO Classifier out performed PCA Classifier with higher AUC of 97.92% when compared with PCA’s AUC of 96.85%.

Keywords: EEG, Correlation Dimension, Particle Swarm Optimization (PSO), Principle Component Analysis (PCA)

References: 1. Y.Zou, D.Miao, D.Wang, “Research on Sample Entropy of Alcoholic and Normal People”, Chinese Journal of Biomedical Engineering, vol. 29, pp. 939-942, 2010 2. M.O.Berman and K. Marinkovi, “Alcohol: Effects on neurobehavioral functions and the brain," Neuropsychology Review, vol. 17, pp. 239- 257, 2007. 3. D. Wu, Z. H. Chen, R. F. Feng, G. Y. Li, and T. Luan, "Study on human brain after consuming alcohol based on EEG signal," in Proc. 2010 3rd IEEE International Conference on Computer Science and Information Technology, vol. 5, 2010 4. H.Rajaguru, S.K. Prabhakar, ‘Softmax Discriminant Classifier for Detection of Risk Levels in Alcoholic EEG Signals’, IEEE Proceedings of the International Conference on Computing Methodologies and Communication (ICCMC 2017), Erode, India 5. R.Harikumar, P.Sunil Kumar, “An Exhaustive Analysis of Code Converters as Pre-Classifiers and Kmeans, SVD, PCA, , MEM, PSO, HPSO and MRE as Post Classifiers for Classification of Epilepsy from EEG Signals”, Journal of Chemical and Pharmaceutical Sciences, Vol.9(3), pp:818-822, May 2016 6. Sunil Kumar Prabhakar, Harikumar Rajaguru, “Analysis of Centre Tendency Mode Chaotic Modeling for Electroencephalography Signals obtained from an Epileptic Patient”, Advanced Studies in Theoretical Physics Vol. 9, March 2015, no. 4, pp 171 - 177. 7. R.Harikumar, T.Vijayakumar, Comprehensive Analysis of Hierarchical Aggregation Functions Decision Trees, SVD, K-means Clustering, PCA and Rule Based AI Optimization in the Classification of Fuzzy based Epilepsy Risk Levels from EEG Signals, International Journal of Computer Information Systems and Industrial Management Applications (IJCISIM), Vol 5, 2012, pp260-268. 8. J.Kennedy and R.Eberhart , Particle Swarm Optimization, Proc. IEEE International Conf. on Neural Networks, Perth , Vol. 4, pp 1942- 1948,1995. Authors: R. Maheshprabhu, M.S Hema, G. Prema Arokia Mary Paper Title: Information System for Performance Improvement of Small and Medium Scale Enterprises Abstract: Indian small and medium scale enterprises (SMEs) have wide scope to improve the efficiency of operations by incorporating Information Technology (IT) tools. Applications of Information technologies in SMEs are comparatively less due to additional investment and low retention of skilled employees. This work is to develop a software tool for automating the process of documentation and report generation in quality management system. Proposed tool will perform ISO 9001:2008 documentation and generate reports. The generated reports will assist decision making for improving the overall performance of the ISO certified SMEs. The developed tool is implemented in a medium scale enterprise and the performance of quality management is compared. Proposed tool doesn’t require huge investment as it was developed using open source software. Majority of the departments witnessed 10% of performance improvement when compared to earlier scenario.

Keywords: Quality management system, Small and medium scale enterprise, IT tool, Open source software, ISO9001:2008

References: 1. Li, L, Su, Q & Chen X, 2011, ‘Ensuring supply chain quality performance through applying the SCOR model’, International Journal of Production Research, vol. 49, no.1, pp. 33-57 2. Mjema, EAM, Victor, MAM &Mwinuka, MSM 2005, ‘Analysis of roles of IT on quality managemen’, The TQM Magazine, vol.17, no. 4, pp. 364-75. 3. Rodrı´guez, CS &Lorente, ARM 2011, ‘Effect of IT and quality management on performance’, Industrial Management & Data Systems, vol. 111, no. 6, pp. 830-848. 4. Moriarty, JP 2011 ‘A theory of benchmarking’, Benchmarking: An International Journal, vol. 18, no. 4, pp. 588-612. 5. Al-Rawahi, AMS & Bashir, HA 2011, ‘On the implementation of ISO 9001:2000: a comparative investigation’, The TQM Journal, vol. 23, no. 6, pp. 673-687. 6. Parthiban, P&Goh, M 2011, ‘An integrated model for performance management of manufacturing units’, Benchmarking: An International 87. Journal, vol. 18, no. 2, pp. 261-281. 7. Gomes, CF&Yasin, MM 2011, ‘A systematic benchmarking perspective on performance management of global small to medium-sized 407-411 organizations. An implementation-based approach’, Benchmarking: An International Journal, vol. 18, no. 4, pp. 543-562. 8. Ahmad Azarnik, Mojtaba Alizadeh, Jafar Shayan and Sasan Karamizadeh, 2012, ‘Associated Risks of Cloud Computing for SMEs’,’ Open International Journal of Informatics (OIJI)’. Vol. 1, 2012. 9. Al-Rawahi, AMS & Bashir, HA 2011, ‘On the implementation of ISO 9001:2000: a comparative investigation’, The TQM Journal, vol. 23, no. 6, pp. 673-687. 10. Aldowaisan, TA & Youssef, AS 2006, ‘An ISO 9001:2000-based framework for realizing quality in small businesses’, , vol.34, pp. 231 – 235. 11. Davenport T. H., Barth, P., & Bea n, R. (2013). How “Big Data is different”, MIT Sloan Management Review, 54(1). 12. Davenport T. H., & Patil, D. J. (2012). ‘Data Scientist’, Harvard Business Review, 90, 70 – 76 13. Doruk Sena , Melike Ozturkb , Ozalp Vayvayc, 28-30 October 2016,’ An Overview of Big Data for Growth in SMEs’, 12th International Strategic Management Conference, ISMC 2016, 28-30 October 2016, Antalya, Turkey. 14. Dr. P.Balaram Babu, 2014,’ Impact Of Cloud Computing On Small And Medium Enterprises In India’, International interdisciplinary research journal, issn 2347-6915,giirj, vol.2 (1). 15. Dr. U. Jayalakshmi Srikumar,2013, ‘Cloud Computing and SMES In India-Opportunities and Challenges’, International Journal of Current Research, Vol. 5, Issue, 08, pp.2379-2383, August, 2013. 16. Gamal Abdulnaser Alkawsi, Ahmad Kamil Mahmood and Yahia Mohamed Baashar, 2015,’ Factors Influencing The Adoption Of Cloud Computing IN Sme: A Systematic:Review’ ,‘International Symposium on Mathematical Sciences and Computing Research (ISMSC)’, 978- 1-4799-7896-0/15. 17. Kimia Ghaffari, Mohammad Soltani Delgosha and Neda Abdolvand, 2014, ‘Towards Cloud Computing: A Swot Analysis on Its Adoption In SMEs’, International Journal of Information Technology Convergence and Services (IJITCS) Vol.4, No.2, April 2014. 18. King, J., & Magoulas, R. (2014). 2014 Data Science Salary Survey Tools, Trends, What Pays (and What Doesn’t) for Data Professionals. 2014 Data Science Salary Survey Tools,Trends, What Pays (and What Doesn’t) for Data Professionals. Sebastopol, CA, USA. 19. Retrieved from http://www.oreilly.com/data/free/2014-data-science-salary-survey.csp 20. Maheshprabhu, R&Sakthivel, M, 2014, ‘A decision support system to improve the effectiveness of quality management in a medium scale enterprise’, International Journal of Applied Engineering Research’, vol. 23, pp. 22059-22074. 21. Maheshprabhu, R&Sakthivel, M, 2015, ‘Design and implementation of an information system to support quality management in small and medium scale enterprises’, ‘Research Journal of Applied Sciences, Engineering and Technology ‘, 11(10): 1151-1158, 2015. 22. Mpho Mohlameane and Nkqubela Ruxwana, 2014, ‘The Awareness of Cloud Computing: A Case Study of South African SMEs’,International Journal of Trade, Economics and Finance, Vol. 5, No. 1. 23. Ogbuokiri, B.O.(MSc.)1, Udanor C.N. (PhD)2, Agu, M.N. (PhD),2015,’ Implementing Bigdata analytics for small and medium enterprise (SME) regional growth’, IOSR Journal of Computer Engineering (IOSR-JCE) e-ISSN: 2278-0661, p-ISSN: 2278-8727, Volume 17, Issue 6, Ver. IV (Nov – Dec. 2015), PP 35-43, University of Nigeria, Nsukka, Enugu state. 24. Reena Singh and Ripuranjan Sinha, 2016,’ Big data security and privacy issues in SMEs, Suresh Gyan Vihar University International Journal of Environment, Science and Technology Volume 2, Issue 1, 2016, pp.31-35 ISSN:2394-9570, Suresh Gyan Vihar University, Jaipur. 25. Sangita G.Patil , Dr. P.T.Chaudhari ,2014,’ Problems of Small Scale Industries in India’, International Journal of Engineering and Management Research, Volume-4, Issue-2, April-2014, ISSN No.: 2250-0758, pp . 19-21. 26. Sanjay Pal 2013, ‘Empowering MSMEs through Cluster Development Programme’, The Management Accountant, Volume 48 No 5, pp. 528-535. 27. Sudha Venkatesh, Krishnaveni Muthiah,2012’ SMEs in India: Importance and Contribution’, Asian Journal of Management Research, Volume 2 Issue 2, 2012. 28. Xiaolong Jin, Benjamin W. Wah, Xueqi Cheng and Yuanzhuo Wang,’Significance and Challenges of Big Data Research’, ‘Big Data Research’. 2(2), pp.59–64 Authors: S. Kanagaraj, M.S. Hema, M. Nageswara Gupta Paper Title: Environmental Risk Factors and Parkinson’s Disease – A Study Report Abstract: Parkinson’s Disease (PD) also known as Parkinson’s is the painful and dangerous disease that occurs when the nerve cells or neurons in the brain die or become enervated. The risk of Parkinson’s takes place due to environmental risk factors such as age, gender, head injury, area of residence, occupation, pesticide exposure, herbicide exposure, exposure to metals, solvents and polychlorinated biphenyls (PCBs) and genetic predisposition. The current research is to study the people of TamilNadu state with list of questionnaires to find them experiencing various symptoms in their routine life. In this article the study is carried out to identify environmental factors and their origins for Parkinsonism. The outcome of this paper is to discriminate healthy people from those with PD based on environmental risks and other factors that affects the ageing people.

Keywords: Parkinson’s Disease, Neurons, Environmental risk factors, Questionnaires, Smart phone, Mobile android application, PD questionnaire, Spiral test, Tapping speed.

References: 1. Abdulraheem M Alshehri, “Parkinson’s disease: an overview of diagnosis and ongoing management” International Journal of Pharmaceutical Research & Allied Sciences, 2017, 6(2):163-170 ISSN: 2277-3657 2. Neelam Chhillar, Neeraj Kumar Singh, B. D. Banerjee, Kiran Bala, Md Mustafa, Deepika Sharma, and Mitrabasu Chhillar, “Organochlorine Pesticide Levels and Risk of Parkinson’s Disease in North Indian Population” Hindawi Publishing Corporation, ISRN Neurology, Volume 2013, Article ID 371034, 6 pages 3. C. A. Davie “A review of Parkinson’s disease”, British Medical Bulletin 2008; 86: 109–127 4. J Jankovic, “Parkinson’s disease: clinical features and diagnosis”, J Neurol Neurosurg Psychiatry 2008;79:368–376 5. Ayla Fil, Roberto Cano-de-la-Cuerda, Elena Muñoz-Hellín, Lidia Vela, María Ramiro-González,César Fernández-de-las-Peñas, “ Pain in Parkinson disease: A review of the literature”, Parkinsonism and Related Disorders 19 (2013) 285-294 88. 6. J. Michael Ellis, Matthew J. Fell, “Current approaches to the treatment of Parkinson’s Disease”, Bioorganic & Medicinal Chemistry Letters 27 (2017) 4247–4255 412-415 7. William Dauer and Serge Przedborski, “Parkinson’s Disease: Review Mechanisms and Models”, Neuron, Vol. 39, 889–909, September 11, 2003 8. Ming-Tsung Tseng, Chin-Hsien Lin, “Pain in early-stage Parkinson’s disease: Implications from clinical features to pathophysiology mechanisms”, Journal of the Formosan Medical Association (2017) 116, 571-581 9. Amy Reevea, Eve Simcoxa, Doug Turnbulla, “Ageing and Parkinson’s disease: Why is advancing age the biggest risk factor?”, Ageing Research Reviews 14 (2014) 19–30 10. F D Dick, G De Palma, A Ahmadi, N W Scott, G J Prescott, J Bennett, S Semple, S Dick, C Counsell, P Mozzoni, N Haites, S Bezzina Wettinger, A Mutti, M Otelea, A Seaton, P So¨derkvist, A Felice, on behalf of the Geoparkinson study group, “Environmental risk factors for Parkinson’s disease and parkinsonism: the Geoparkinson study”, Occup Environ Med 2007;64:666–672 11. Peter Hagell, Carita Nygren, “The 39 item Parkinson’s disease questionnaire (PDQ-39) revisited: implications for evidence based medicine”, J Neurol Neurosurg Psychiatry 2007;78:1191–1198 12. National Institutes of Health U.S. Department of Health and Human Services “Parkinson’s Disease and Environmental Factors”, February 2014 13. Sang-Myung Cheona, Lilian Chanb. Daniel Kam Yin Chanb, Jae Woo Kima, “Genetics of Parkinson’s Disease - A Clinical Perspective”, Journal of Movement Disorders 2012;5:33-41 14. Letizia Polito, Antonio Greco, and Davide Seripa,” Genetic Profile, Environmental Exposure, and Their Interaction in Parkinson’s Disease”, Hindawi Publishing Corporation Parkinson’s Disease Volume 2016, Article ID 6465793, 9 pages 15. S-E Soh, JL McGinley, JJ Watts, R Iansek, ME Morris,” Rural living and health-related quality of life in Australians with Parkinson's disease”, Rural and Remote Health 12: 2158. (Online) 2012 16. Harvey Checkoway, Karen Powers, Terri Smith-Weller, Gary M. Franklin, W. T. Longstreth, and Phillip D. Swanson, “Parkinson’s Disease Risks Associated with Cigarette Smoking, Alcohol Consumption, and Caffeine Intake”, American Journal of Epidemiology, Vol. 155, No. 8 17. Annett Blochberger, DipClinPharm, MRPharmS, and Shelley Jones, DipClinPharm, MRPharmS, “Parkinson’s disease clinical features and diagnosis”, Clinical Pharmacist, Vol 3 December 2011 18. N.Rajathi, S.Kanagaraj, R.Brahmanambika, K.Manjubarkavi,2016, ‘Early Detection of Dengue using Machine Learning Algorithms’, Global Conference on Advances in Science, Technology and Management (GCASTM-2017) Authors: Sowmiya K S,Saranya.K, Sumathi.V.P Paper Title: Exploratory Data Analysis of Drug Consumption Abstract: Drug utilization is the consumption of the drug in all the state or country. In the rushing world, technology development is peaked. Human beings are fully addicted with technology, intake of food and that leads to 89. easy attack on diseases results. To cure the disease, intake of medicines at over dosage of chemical compounds leads to lot of side effects. In previous analysis, it tells about the maximum and minimum utilization of drugs and increase 416-421 in number of enrols and the mean number of prescription can be found. In addition to that, amount spent for drugs for Medicaid enrols has been increased in the last decade dramatically Exploratory data analysis (EDA) is an approach for analyzing data sets to summarize their main characteristics, often with visual methods. The methodologies used for this analysis are statistical analysis - collecting, exploring and presenting large amounts of data in a graphical view-pie chart, bar, line graph can be plotted. This analysis speaks the maximum and minimum utilization of drug with limited dosage in a particular state, comparison of the drug with maximum utilization in every quarter can be analysed with the count of prescriptions given in the data set. Maximum drug used in particular time period and the survey of disease is shown.

Keywords: Drug consumption, Cost,National drug code,FFSU

References: 1. The paper “Trends and Current Drug Utilization Patterns of Medicaid Beneficiaries” by Terry R. Lied, Ph.D., Julio Gonzalez, M.P.H., Wendy Taparanskas, Ph.D., and Tejas Shukla, M.S. in 2006. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4194949/ 2. The paper “Generic Drug Cost Containment in Medicaid: Lessons from Five State MAC Programs” by Richard G. Abramson, M.D., Catherine A. Harrington, Pharm. D., Ph.D., Raad Missmar, Susan P. Li, and Daniel N. Mendelson, M.P.P. in 2004.https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4194860/ 3. The paper “Medicaid Prescription Drug Spending in the 1990s: A Decade of Change” by David K. Baugh, M.A., Penelope L. Pine, Steve Blackwell, Ph.D., J.D., R.Ph., and Gary Ciborowski, M.A.in 2004 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4194863/ 4. The paper “Comparison of drug utilization patterns in observational data: antiepileptic drugs in pediatric patients” by Florence T Bourgeois, MD, MPH Karen L Olson, PhD., Annapurna Poduri, MD and Kenneth D Mandl, MD, MPH in 2015 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4573831/ 5. The paper “Changes in Drug Utilization during a Gap in Insurance Coverage: An Examination of the Medicare Part D Coverage Gap” by Jennifer M.Polinski, William H. Shrank, Haiden A. Huskamp, Robert J. Glynn, Joshua N. Liberman, Sebastian Schneeweiss in 2011 http://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1001075 6. https://www.doctorshealthpress.com/food-andnutrition-articles/alternative-remedies-food-andnutrition-articles/bronchospasm-treatment/ Authors: Jayaraman, A, N Sathyakumar, V Senthilkumar Seismic Performance of Cold Formed Steel and Conventional Steel of Industrial Structures Using Paper Title: Splice Connections Abstract: In this project it is proposed to carry out the design of industrial roof truss of conventional steel as well as the cold formed steel structures using splice connections. Splices are therefore most often used when the structural element is required in longer length and also resistant the seismic force in structural elements. Industrial roof truss is designed manually for both conventional and cold formed steel as per codal provisions. Experimental Investigation is finally done for the Splice Connections with a specimen. The project is all about how the section is deduced and economical while using splice connections. Experimental and theoretical results indicate the significant reduction in member and deflection with incremental increase of ultimate load carrying capacity. Finally a comparative study will be done based on seismic performance, cost and weight of steel member for cold formed section & conventional steel section with and without splice connection.

Keywords: Splice Connection, Howe Type roof truss, Limit state method, ConventionalSteel, Cold Formed Steel

References: 1. M F Wonlf and KF Chunlf “Experimental investigation of cold-formed steel Beam- Column sub-frames: Pilot study” Fifteenth International Speciality Conference on Cold-Formed Steel Structures St. Louis, Missouri U.S.A., October 19-20, 90. 2000. 2. [ K.K. Sangle et al “Seismic Analysis of High Rise Steel Frame Building with and Without Bracing”15WCEE Lisboa 2012. 3. R. B. Kulkarni and S.P.Deshamukh “Experimental Study of Bolted Connections Using Light Gauge Rectangular Hollow 422-429 Section, Normal Concrete andGeopolymer Concrete in filled only at the Joints” International Journal of Sciences: Basic and Applied Research (IJSBAR) ISSN 2307 -4531, Vol.2 Issue 02, April-2013. 4. A Jayaraman (etal) “Design of economical of roof trusses & Purlin (Comparison of limit state And working stress method)” International Journal of Research in Engineering and Technology” ISSN: 2319-1163 Vol. 3 Issue 10, October-2014. 5. [ AJayaraman (etal) “behaviour and design of light gauge cold formed steel flexural Members (comparison of channel and built up channel section)” Indian Journal of Scientific Engineering and Technology Research” ISSN: 3941-0181 Vol. 3 Issue 19, September -2014. 6. ShahForamAshokbhai , Mr.Kaushal R. Thakkar and Mr.Paresh N. Nimodiya “ Comparative Study of Hot Rolled Steel Sections and Cold Formed Steel Sections for Industrial Shed” International Journal of Engineering Research & Technology (IJERT)” ISSN: 2278-0181 Vol. 6 Issue 04, April-2017. 7. S. K. Duggal” Limit State Design of Steel Structures”McGraw Hill Education (28 May2010) 8. N. Subramanian “Design of Steel Structures based on Limit state Method of design as Per standardised code IS - 800: 2007. 9. S.S Bhavikatti “Design of steel structure by limit state method as per IS - 800: 2007” 10. IS - 801:1975 Code of Practice for Use of Cold Formed Light Gauge Steel Structural Members in General Building Construction. 11. IS - 811: 1987 Specification for Cold Formed Light Gauge Structural Steel Section. 12. IS - 800 Code of Practice for General Construction in Steel, Bureau of Indian Standards, New Delhi, 2007. 13. IS - 1893 -2002 “Criteria for Earthquake Resistance and Construction of Buildings” Bureau of Indian Standards, New Delhi. Authors: K. SrinivasRao, PreetaSharan, Anil Tiwari Paper Title: Detection of liver Cancer using Lab-On-Chip Based Optical Biosensor by Nano Cavity Sensing Hole Abstract: The paper includes the Nano cavity Implementation of the biosensor in the two stages of the cancer including the primary and the secondary. The key challenge in the work is to provide the accuracy with the sensors 91. and the results by communicating the medical reports to the doctors or patient. By imposing the requirement to the modern development in challenging the medical aspects with the Nano Plasmon execution. The Photonic Crystal 430-434 model simulation is done in the 2D model of Holes-In-Slab with change in the refractive index in selected sensing hole. The refractive index material of the normal cancer by combining the refractive index of the primary and secondary stage in order to know the tissue is cancerous or non-cancerous. Six different sensing holes are considered with six different RI Sensitivity and Quality factors for different sensing holes for different cancerous tissues are investigated. The overall quality factor obtained is 123400 µm/RIU for 1550 nm of wavelength. High quality factor is obtained from the H4 holes in the slab of nearly of 2250 and sensitivity of 0.95nm/RIU.

Keywords: Quality factor, Biosensors, Refractive Index, Photonic Crystal, Early stage Diagnosis.

References: 1. Nina Skievesen, AmolieTeu, Martin Kristensen, Jorgen Kjems, Lars H. Frandsen and peter I. Borel, (2007)“Photonic crystal waveguide biosensor”, Optic Express, Vol. 15,Issue 6, pp. 3169-3176. 2. HakanInan, MuhammetPoyraz, FatihInci, Mark A. Lifson, Murat Baday, Brian T. Cunningham, andUtkanDemirci, 2017 July 26“Photonic crystals: emerging biosensors and their promise for point-of-care applications”.Royal society of chemistry,, 46(2): 366–388. 3. ManpreetChabra, Chetan Selwal,12-13 July 2014 Design of a photonic crystal biosensor using DNA filled microcavity and ring cavity coupled with waveguide, IEEE Explorer . 4. NargesMalmir,KiazandFazihi, A highly-sensitive label-free biosensor based on two dimensional photonic crystals with negative refraction,Journal of Modern Optics. 5. A.A.RifatG. AmouzadMahdirajiY.G.SheeMd. JubayerShawonF.R. MahamdAdikan,2016, A Novel Photonic Crystal Fiber Biosensor Using Surface Plasmon Resonance, Science Direct Procedia Engineering. Volume 140, Pages 1-7. 6. Kelsey I. MacConaghy, Christopher I. Geary, Joel L. Kaar, and Mark P. Stoykovich, April 24, 2014,Photonic crystal Kinase Biosensor, Journal of American Chemical Society. 136 (19), pp 6896–6899. 7. Bailin Zhang, Juan Manuel Tamez-Vela, Steven Solis, Gilbert Bustamante, Ralph Peterson, ShafiqurRahman, Andres Morales, Liang Tang, Jing Yong Ye, “Detection of Myoglobin with an Open-Cavity-Based Label-Free Photonic Crystal Biosensor”, Journal of Medical Engineering, Volume 2013, Article ID 808056, 7 pages. 8. Wei Zhang, Seok-min Kim, Nikhil Ganesh, Ian D. Block, Patrick C. Mathias, Hsin-Yu Wu, and Brian T. Cunninghama), (2010 “Deposited nanorod films for photonic crystal biosensor applications”, Journal of Vacuum Science & Technology A 28, 996). 9. KeigoAono, Shoma Aki, Kenji Sueyoshi, Hideaki Hisamoto and Tatsuro Endo.”5 July 2016Development of optical biosensor based on photonic crystal made of TiO2 using liquid phase deposition”. The Japan Society of Applied Physics. 10. WeiliChen Kenneth D. Long Jonas Kurniawan Margaret Hung Hojeong Yu Brendan A. Harley Brian T. Cunningham,22 August 2015 “Planar Photonic Crystal Biosensor for Quantitative Label‐Free Cell Attachment Microscopy” Advanced Optical Materials. Authors: Nagaraj S, Dr. Rajashree V Biradar An approach to sense Carbon Monoxide by MQ-7 sensors and to increase lifetime of WSN using Paper Title: MMBS protocol Abstract: A portable base station approach has been implemented in this study to lessen the vitality utilization of bunch heads by encasing them with the Monitoring Mobile Base Station (MMBS), while on other hand a fluffy rationale is connected to deal with the movement of the base station. The proposed system is useful in sensing the Carbon Monoxide (CO) air pol-lutant using the MQ-7 Sensors. Each bunch head is appointed with a critical degree by the fluffy framework in view of info parameters, for example vitality, closeness to base station, and group size. At that point it advances the base station towards the bunch head with the high Critical Degree value, so it can substantially spare most of its strength.

Keywords: Monitoring Mobile Base Station (MMBS), Cluster Head, Fuzzy Logic, LEACH-C, Sensor Nodes, Energy.

References: 1. W. R. Heinzelman, A. Chandrakasan, and H. Balakrishnan,Energyefficient for Wireless Microsensor Networks., in IEEE Computer Society Proceedings of the Thirty Third Hawaii International Conference on System Sciences (HICSS '00), Washington, DC: IEEE, 2000, pp. 1-10. 2. W. R. Heinzelman, A. Chandrakasan and H. Balakrishnan,“An application-specific protocol architecture for wireless microsensor networks,” in IEEE Transactions on Wireless Communications, 1(4), pp. 660 – 670, Oct. 2002. 3. I. Gupta, D. Riordan and S. Sampalli, "Cluster -head Election using Fuzzy Logic for Wireless Sensor Networks", Communication Networks and Services Research Conference, pp.255-260,May 2005. 4. Jong-Myoung Kim, Seon-Ho Park, Young-Ju Han, TaiMyoung Chung, “CHEF: Cluster Head Election mechanism using Fuzzy logic in 92. Wireless Sensor Networks” ICACT, PP. 654-659, Feb. 2008. 5. AbhijeetAlkesh, Ashutosh Kumar Singh, N.Purohit, “A moving base station strategy using fuzzy logic for life time enhancement in wireless sensor network”, in proc. of International Conference on Communication Systems and Network Technologies, 2011, pp 198202. 435-440 6. HodaTaheri et.al.“An energy-aware distributed clustering protocol in wireless sensor networks using fuzzy logic”, Ad hoc Networks 10 (2012), pp. 1469-1481. 7. Tripti Sharma, Brijesh Kumar, “F-MCHEL: Fuzzy Based Master Cluster Head Election Leach Protocol in Wireless Sensor Network”, International Journal of Computer Science and , Volume 3, Issue 10, October 2012, pp. 8-13. 8. Z.W.Siew, C.F.Liau, A.Kiring, M.S. Arifianto, K.T.K. Teo, “Fuzzy Logic Based Cluster Head Election for Wireless Sensor Network”, in Proc. of 3rd CUTSE International Conference, Malaysia, 2011, pp. 301-306. 9. Vibha Nehra, Raju Pal, Ajay K Sharma, “Fuzzy-based Leader Selection for Topology Controlled PEGASIS Protocol for Lifetime Enhancement in Wireless Sensor Network”, International Journal of Computers & Technology, Volume 4, No. 3, March-April, 2013, pp.755-764. 10. Ran, Huazhong Zhang, Shulan Gong, “Improving on LEACH Protocol of Wireless Sensor Networks Using Fuzzy Logic”, Journal of Information & Computational Science 7, (3) (2010), pp. 767–775. 11. Hironori Ando, Leonard Barolli, ArjanDurresi, FatosXhafa, and Akio Koyama, "An Intelligent Fuzzy-based Cluster Head Selection System for WSNs and Its Performance Evaluation for D3N Parameter", 2010 International Conference on Broadband, Wireless Computing, Communication and Applications, 2010, pp. 648-653. 12. ZohreArabi, "HERF: A Hybrid Energy Efficient Routing using a Fuzzy Method in Wireless Sensor Networks", International Conference on Intelligent and Advanced Systems (ICIAS), 2010, pp.1-6. 13. E.H. Mamdani, S. Assilian, “An experiment in linguistic synthesis with a fuzzy logic controller”, International Journal of Man– Machine Studies 7 (1) (1975), pp. 1–13. 14. Kemal Akkaya, Mohamed Younis, “A survey on routing protocols for wireless sensor networks”, Ad Hoc Networks 3 (2005), pp. 325– 349. 15. PadmalayaNayak, D. Anurag, V.V.N.A Bhargavi, “ Fuzzy based method Super Cluster Head election for Wireless Sensor Network with Mobile Base Station (FM-SCHM)”, in Proc. of 2nd International Conference on Advanced Computing Methodologies”, GRIET, Hyderabad, 2013, pp. 422-427. 16. Y.Wang, F. Wu, and Y. Tseng, “ Algorithms and Applications for Mobile Sensor Networks,” Wireless Comm. Mobile Computing, Vol. 12, 2012, pp. 7-21. 17. M.J. Handy, M. Haase, and D. Timmermann, “Low energy adaptive clustering hierarchy with deterministic cluster-head selection,” in Proc. of Int. Workshop Mobile Wireless Commun. Netw., 2002, pp. 368–372. 18. PadmalayaNayak, D. Anurag ” A Fuzzy Logic based clustering algorithm for WSN to extend the Network Lifetime” Accepted for Publication in IEEE SENSOR JOURNAL, DOI: 10.1109/JSEN.2015.2472970 19. O. Castillo, P. Melin, Soft Computing and Fractal Theory for Intelligent Manufacturing (Springer, Heidelberg, 2003) 20. O. Castillo, P. Melin, Type-2 Fuzzy Logic: Theory and Applications (Springer, Heidelberg,(2008) 21. N.N. Karnik, J.M. Mendel, An introduction to type-2 fuzzy logic systems, Technical Report, University of Southern California ,1998 Authors: A.S.M.Touhidul Islam, Shahryar Sorooshian, Shariman Bin Mustafa Paper Title: What Lean Is Really About: Malaysian Automotive Perspective Abstract: With the tremendous advancement of lean, it has evolved a lot. Many authors have tried to express true nature of lean in various ways. But still it remains incomplete and ambiguous that it is possible to create a versatile but easily understandable and acceptably meaningful definition. Specially, lean is recommended to be practiced and followed by adapting it to the current operational and cultural status and future requirements of the specific industry. So, the author has interviewed 16 lean experts from different key functional areas in a renowned Malaysian automotive industry, and based on the analysis of qualitative data and information received from them, proposed a new definition of lean. With clear fundamental understanding, the journey of lean is expected to be more effective, efficient and sustainable to maximize productivity and quality with lower cost and time spent in any organization.

Keywords: Definition of Lean, Malaysian Automotive, Experts’ Interview, Pareto Analysis.

References: 1. Alves, A.C., Dinis-Carvalho, J. and Sousa, R.M. (2012), “Lean production as promoter of thinkers to achieve companies’ agility”, The Learning Organization, Vol. 19 No. 3, pp. 219-237. 2. Amin, A. N. M., Mahmood, W. H. W., Kamat, S. R., & Abdullah, I. (2018). Conceptual Framework of Lean Ergonomics for Assembly Process: PDCA Approach. Journal of Engineering and Science Research, 2(1), 51-62. 3. Anvari, Alireza, Yusof Ismail, and Seyed Mohammad Hossein Hojjati. 2011. A study on total quality management and lean manufacturing: Through lean thinking approach. World Applied Sciences Journal 12: 1585–96. 4. Czabke, J. (2007). Lean thinking in the secondary wood products industry: challenges and benefits. Unpublished Master thesis, Oregon State University, Oregon State. 5. De Treville, S. and Antonakis, J. (2006), “Could lean production job design be intrinsically motivating? Contextual, configurational, and levels-of-analysis issues”, Journal of Operations Management, Vol. 24 No. 2, pp. 99-123. 6. Fliedner, Gene, and Karl Majeske. 2010. Sustainability: The new lean frontier. Production and Inventory Management Journal 46: 6–13. 7. Hallgren, M. and Olhager, J. (2009), “Lean and agile manufacturing; external and internal drivers and performance outcomes”, International Journal of Operations & Production Management, Vol. 29 No. 10, pp. 976-999. 8. Howell, G.A. (1999), “What is lean construction-1999?”, Proceedings Seventh Annual Conference of the International Group for Lean Construction, University of California, Berkeley, CA, July 26-28. 9. Hopp, W.P. and Spearman, M.L. (2004), “To pull or not to pull: what is the question?”, Manufacturing and Service Operations Management, Vol. 6 No. 2, pp. 133-148. 10. Karlsson, C., & Åhlström, P. (1996). Assessing changes towards lean production. International Journal of Operations & Production Management, 16(2), 24-41. 11. Krafcik, J.F. (1988), “Triumph of the lean production system”, Sloan Management Review, Vol. 30 No. 1, pp. 41-52. 12. Liker, J.K. and Wu, Y.C. (2000), “Japanese automakers, US suppliers and supply-chain superiority”, Sloan Management Review, Vol. 42 No. 93. 1, pp. 81-93. 13. MIT (2000), “Transitioning to a lean enterprise: a guide for leaders”, 1/2/3, available at: http://lean.mit.edu/Products/TTL/TTL-vol1.pdf 441-447 (accessed December 3, 2012). 14. Modi, D. B., & Thakkar, H. (2014). Lean thinking: reduction of waste, lead time, cost through lean manufacturing tools and technique. International Journal of Emerging Technology and Advanced Engineering, 4(3), 339-334. 15. Naylor, J.B., Naim, M.M. and Berry, D. (1999), “Leagility: integrating the lean and agilemanufacturing paradigms in the total supply chain”, International Journal of Production Economics, Vol. 62 No. 1, pp. 107-118. 16. Pettersen, J. (2009). Defining lean production: some conceptual and practical issues. The TQM Journal, 21(2), 127-142. Retrieved from https://en.oxforddictionaries.com/definition/lean 17. Rothstein, J.S. (2004), “Creating lean industrial relations: general motors in Silao, Mexico”, Competition and Change, Vol. 8 No. 3, pp. 203- 221. 18. Sahari, M. M. (2015). Malaysia Automotive Industry Review & Insight 2014 / 2015 [PDF file] 19. Scherrer-Rathje, M., Boyle, T. A., & Deflorin, P. (2009). Lean, take two! Reflections from the second attempt at lean implementation. Business horizons, 52(1), 79-88. 20. Seth, D. and Gupta, V. (2005), “Application of value stream mapping for lean operations and cycle time reduction: an Indian case study”, Production Planning & Control, Vol. 16 No. 1, pp. 44-59. 21. Shah, R. and Ward, P.T. (2007), “Defining and developing measures of lean production”, Journal of Operations Management, Vol. 25 No. 1, pp. 785-805. 22. Shah, R., & Ward, P. T. (2003). Lean manufacturing: context, practice bundles, and performance. Journal of operations management, 21(2), 129-149. 23. Singh, R. (1998), “Lean manufacturing: changing paradigms in product manufacturing, design & supply”, The Third International Conference on Quality Management, available at: www.qmconf.com/Docs/singh98.pdf (accessed January 20, 2012). 24. Simpson, D.F. and Power, D.J. (2005), “Use the supply relationship to develop lean and green suppliers”, Supply Chain Management: An International Journal, Vol. 10 No. 1, pp. 60- 68. 25. SME Corp. 2010. SME Annual Report 2009/10. Retrieved from http://www.smecorp.gov.my/index.php/my/sme-annual-report/book/2/Array 26. Storch, R.L. and Lim, S. (1999), “Improving flow to achieve lean manufacturing in shipbuilding”, Production Planning & Control, Vol. 10 No. 2, pp. 127-137. 27. Sultana, M., & Ibrahim, K. A. (2014). Challenges and Opportunities for Malaysian Automotive Industry. American International Journal of Contemporary Research, 4(9), 175-182. 28. “Sustainable of Manufacturing with Lean Production System and Automotive Supplier Excellence Programme” [PDF file]. 29. Taj, S. and Morosan, C. (2011), “The impact of lean operations on the Chinese manufacturing performance”, Journal of Manufacturing Technology Management, Vol. 22 No. 2, pp. 223- 240. 30. Trienekens, J. H., & Omta, S. W. F. (Eds.). (2002). Paradoxes in Food Chains and Networks: Proceedings of the Fifth International Conference on Chain and Network Management in Agribusiness and the Food Industry (Noordwijk, 6-8 June 2002). Wageningen Academic Pub. 31. Womack, J. P., Womack, J. P., Jones, D. T., & Roos, D. (1990). Machine that changed the world. Simon and Schuster. 32. Womack, J.P. and Jones, D.T. (1994), “From lean production to the lean enterprise”, Harvard Business Review, Vol. 72 No. 2, pp. 93-103. 33. Worley, J. (2004), “The role of socio-cultural factors in a lean manufacturing implementation”, unpublished master thesis, Oregon State University, Corvallis, OH. Authors: S.V.G.Reddy, K.Thammi Reddy, V.ValliKumari Paper Title: Optimization of Deep Learning using various Optimizers, Loss functions and Dropout Abstract: Deep Learning is gaining lot of prominence due to its break through results in various fields like Computer Vision, Natural Language Processing, Time Series Analysis, Health Care etc. Earlier, the Deep Learning was implemented using the batch and stochastic gradient descent algorithms and some optimizers which lead to very less performance of the models. But today, lot of work is going on for the enhancement of the performance of Deep Learning using various optimization techniques. So, in this context, It is proposed to build a Deep Learning model using various Optimizers (Adagrad, RmsProp, Adam), Loss functions (mean squared error, binary cross entropy) and Dropout concept for the Convolutional neural networks and Recurrent neural networks and verify the performance such as Accuracy and Loss of the model. The proposed model has achieved maximum Accuracy when Adam optimizer and mean squared error loss function are applied on convolutional neural networks and the model is run with minimum Loss when the same Adam optimizer and mean squared error loss function are applied on Recurrent neural networks. While performing the Regularization of the model, the maximum Accuracy is achieved when the Dropout with a minimum fraction ‘p’ of nodes is applied on convolutional neural networks and the model has run with minimum Loss when the same dropout value is applied on Recurrent neural networks

Keywords: Deep Learning, Convolutional Neural Networks, CNN, Recurrent Neural Networks, RNN, Computer Vision, Natural language processing, Time Series Analysis.

References: 1. B. Zhou, A. Khosla, A. Lapedriza, A. Oliva, And A. Torralba, “Learning Deep Features For Discriminative Localization,” In Proceedings Of The Ieee Conference On Computer Vision And Pattern Recognition , 2016, Pp. 2921–2929 2. Https://Www.Superdatascience.Com/Deeplearning/ 3. https://bhatsnotes.com/2016/12/23/artificial-intelligence-t-hub/ 4. M. D. Zeiler And R. Fergus, “Visualizing And Understanding Convolutional Networks,” In European Conference On Computer Vision 94. Springer, 2014, Pp. 818–833 5. A.Negi, C.Bhagvati, B.Krishna, An OCR system for Telugu, IEEE Proceedings of Sixth International conference on Document Analysis and 448-455 Recognition, DOI: 10.1109/ICDAR.2001.953958 6. Ruben Tolosana, Ruben Vera-Rodriguez, Julian Fierrez, (Member, Ieee), And Javier Ortega-Garcia, (Fellow, Ieee), “ Exploring Recurrent Neural Networks For On-Line Handwritten Signature Biometrics”, Ieee Access, Volume 6, P 5128-5138, 2018, 7. A.Graves, A. R. Mohamed, and G. Hinton, ‘‘Towards end-to-end speech recognition with recurrent neural networks,’’ in Proc. Int. Conf. Mach. Learn. , vol. 14. 2014, pp. 1764–1772. 8. S.Hochreiter, Y. Bengio, P. Frasconi, and J. Schmidhuber, ‘‘Gradient flow in recurrent nets: The difficulty of learning long-term dependencies,’’ in A Field Guide to Dynamical Recurrent Networks, S. C. Kremer and J. F. Kolen, Eds. 2001. 9. S. Hochreiter and J. Schmidhuber, ‘‘Long short-term memory,’’ Neural Comput. , vol. 9, no. 8, pp. 1735–1780, 1997. 10. Sebastian Ruder, “An Overview Of Gradient Descent Algorithms”, Cornell University Library, Arxiv: 1609.04747[Cs. Lg] 11. AnirbanSarkar, AdityaChattopadhyay, PrantikHowlader, V. Balasubramanian, Grad-Cam++: “Generalized Gradient-Based Visual Explanations For Deep Convolutional Networks”, Proceedings Of Ieee Winter Conference On Applications Of Computer Vision (Wacv'18), Mar 2018. [Arxiv] 12. R. R. Selvaraju, A. Das, R. Vedantam, M. Cogswell, D. Parikh, And D. Batra, “Grad-Cam: Why Did You Say That? Visual Explanations From Deep Networks Via Gradient-Based Localization,” ArxivPreprint Arxiv:1610.02391 , 2016. 13. AsmelashTekaHadgu; Aastha Nigam ; Ernesto Diaz-Aviles, “Large-scale learning with Adagrad on Spark, 2015 IEEE International Conference on Big Data (Big Data), DOI: 10.1109/BigData.2015.7364091 14. Mahesh Chandra Mukkamala, Matthias Hein , “Variants of RmsProp and Adagrad with Logarithmic Regret Bounds”, Proceedings of the International Conference on Machine Learning, Sydney, Australia, PMLR 70, 2017, arXiv:1706.05507v2[cs.LG] 15. Zijun Zhang, “Improved Adam Optimizer for Deep Neural Networks”, 978-1-5386-2542-2/18/ ©2018 IEEE 16. KatarzynaJanocha, Wojciech Marian Czarnecki, “On Loss Functions for Deep Neural Networks in Classification”, Theoretical foundations of machine learning, Vol. 25 (2016): 49–59 doi: 10.4467/20838476SI.16.004.6185 17. NitishSrivastava, Geoffrey Hinton, Alex Krizhevsky, IlyaSutskever, RuslanSalakhutdinov, “Dropout: A Simple Way to Prevent Neural Networks from Overfitting”, Journal of Machine Learning Research 15 (2014), 1929-1958 18. Vu Pham ; ThéodoreBluche ; Christopher Kermorvant ; JérômeLouradour, “Dropout Improves Recurrent Neural Networks for Handwriting Recognition”, 2014 IEEE 14th International Conference on Frontiers in Handwriting Recognition, DOI: 10.1109/ICFHR.2014.55 19. Irwan Bello, BarretZoph, vijayvasudevan, QuocV.Le, “Neural optimizer search with Reinforcement learning”, Proceedings of the 34th International Conference on Machine Learning , Sydney, Australia, PMLR 70, 2017. Copyright 2017 by the author(s). 20. Mohaksrivatsava, s.pallavi, srijita Chandra, G.Geetha, “ Comparison of optimizers implemented in generative adversarial network(GAN)”, International journal of Pure and Applied mathematics, vol. 119, no 12, 2018.