Special Issue No. – 7, February, 2020

Abstract:

The impact of good driving and rode safety plays a major role in automobile sector. Though autonomous driving and modern driving techniques are improving worldwide, the study of driver behavior and characteristics become indispensable. The research on driving science has taken long strides since its inception.  Driving behavior analysis requires more valid attributes and the evaluation process requires better prediction models .The role of Artificial intelligence and machine learning in driver-behavior prediction have given new dimension to extract valuable results. This paper deploys a novel scheme to predict the driver behavior using advanced machine learning technique.

Keywords:

Driver behavior,Drowsiness detection,Machine learning,Traffic accident analysis,

Refference:

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III. B.Shi,L. Xu, J.Hu, Y.Tang, H.Jiang, W. Meng, H. Liu, “Evaluating driving styles by normalizing driving behavior based on personalized driver modeling” , IEEE Transactions on Systems, Man, and Cybernetics: Systems,Volume : 45,Issue :12, pp.1502-1508, 2015
IV. D.Mitrovic, “Reliable method for driving events recognition” ,IEEE transactions on intelligent transportation systems” Volume : 6,Issue :2, pp.198-205,2005
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VII. G.J.asim AL-Anizy, M.J.Nordin, M.M. Razooq, “Automatic driver drowsiness detection using haar algorithm and support vector machine techniques” , Asian Journal of Applied Sciences, Volume :8,Issue :2,pp.149-157, 2015
VIII. G.Meiring, H.Myburgh, “A review of intelligent driving style analysis systems and related artificial intelligence algorithms” , Sensors, Volume :15,Issue: 12, pp.30653-30682 ,2015
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XII. J.Zhang, Z. Wu, F. Li, C. Xie, T. Ren, J.Chen, L. Liu, “ A deep learning framework for driving behavior identification on in-vehicle CAN-BUS sensor data” , Sensors, Volume : 19,Issue : 6, p.1356, 2019
XIII. K.Dwivedi, K. Biswaranjan, A. Sethi, “ Drowsy driver detection using representation learning” , IEEE International Advance Computing Conference (IACC), pp. 995-999,2014
XIV. L.M.Bergasa, D.Almería, J.Almazán, J.J.Yebes, R.Arroyo, , “Drivesafe: An app for alerting inattentive drivers and scoring driving behaviors” ,IEEE Intelligent Vehicles symposium proceedings, pp. 240-245, 2014
XV. L.Wei, S.C. Mukhopadhyay,R. Jidin,C.P.Chen, , “Multi-source information fusion for drowsy driving detection based on wireless sensor networks”,Seventh International Conference on Sensing Technology (ICST) ,pp. 850-857, 2013
XVI. M.Chong,A.Abraham,M.Paprzycki, “Traffic accident analysis using machine learning paradigms” , Informatica, Volume :29,Issue :1, 2005
XVII. M.Ngxande, J.R. Tapamo, M.Burke, , “Driver drowsiness detection using behavioral measures and machine learning techniques: A review of state-of-art techniques” , Pattern Recognition Association of South Africa and Robotics and Mechatronics (PRASA-RobMech) ,pp. 156-161, 2017
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Abstract:

In recent years several classification techniques have been proposed which are classified into supervised and unsupervised classifications. In unsupervised classification, fuzzy clustering analysis is a most common technique since it never needs training data for fuzzy clustering algorithm. Nevertheless, different clustering algorithms have different initial conditions to generate different partitions and use different parameters in order to produce different results. Thus, the partitions generated by fuzzy clustering algorithm are in need to validate. Many cluster validity indices have been proposed in the last three decades for validating type-1 fuzzy based FCM algorithm. Recently many type-2 fuzzy based applications were presented due to its extract degree of fuzziness. But its computational complexity is very high, so interval type-2 fuzzy system is widely used in many applications. After the updation of cluster centriods in type-2 fuzzy based FCM algorithm, the   type-2 fuzzy membership function is taken as unreliability of type-1 membership function. Therefore there is a need for a new method to validate the cluster validity index for interval type-2 fuzzy system based applications. In this paper, we have presented a new approach of validating the 14 cluster validity indices and performed extensive comparison of the mentioned indices in conjunction with various interval type-2 fuzzy c-means clustering algorithms. For experimental analysis we have taken the number of widely used datasets and Berkely image database. 

Keywords:

Cluster validity indices,IT2FCM,Extended IT2FCM,IT2FCMα,

Refference:

I. A.Vadivel, ShamikSural, A.K. Majumdar, “An Integrated Color and Intensity Co-occurrence Matrix,” Pattern Recognition Letter 28, pp.974-983, 2007.
II. Cheul Hwang and Frank Chung-Hoon Rhee, “Uncertain Fuzzy Clustering: Interval Type-2 Fuzzy Approach to C-Means,” IEEE Transactions on Fuzzy Systems,Vol.15,No.1, February 2007.
III. D.W. Kim, K.H. Lee, D. Lee, “On cluster validity index for estimation of the optimal number of fuzzy clusters”, Pattern Recognition 37 pp.2009–2025, 2004.
IV. Dongrui Wu, “A Brief Tutorial on Interval Type-2 Fuzzy Sets and Systems”, July 22, 2010
V. Du Y., Zhang Y., Ling F., Wang Q., Li W., Li X., “Water Bodies’ Mapping from Sentinel-2 Imagery with Modified Normalized Difference Water Index at 10-m Spatial Resolution Produced by Sharpening the SWIR Band.”, Remote Sens. 2016, 8, 354.
VI. DzungDinh Nguyen, Long Thanh Ngo, “GMKIT2-FCM: A Genetic-based improved Multiple Kernel Interval Type-2 FUzzy C-means clustering”, Cybernetics (CYBCONF), 2013 IEEE International Conference, 2013
VII. DzungDinh Nguyen, Long Thanh Ngo, “Multiple kernel interval type-2 fuzzy c-means clustering”, 2013 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE),2013.
VIII. E. Backer, A.K. Jain, “A clustering performance measure based on fuzzy set decomposition”, IEEE Trans. Patten Anal. Mach. Intell. 3 (1), pp. 66–74, 1981.
IX. E. Rubio; O. Castillo; P. Melin, “A new Interval Type-2 Fuzzy Possibilistic C-Means clustering algorithm”, 2015 Annual Conference of the North American Fuzzy Information Processing Society (NAFIPS) held jointly with 2015 5th World Conference on Soft Computing (WConSC), Pages: 1 – 5, 2015.
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XI. Elid Rubio, Oscar Castillo, Fevrier Valdez, Patricia Melin, Claudia,I. Gonzalez, and Gabriela Martinez, “An Extension of the Fuzzy Possibilistic Clustering Algorithm Using Type-2 Fuzzy Logic Techniques”, Advances in Fuzzy Systems, Volume 2017, Article ID 7094046, 23 pages
XII. Fangfang Zhang, XuezhongQiana, “A New Validity Index for Fuzzy Clustering”, Journal of Computational Information Systems 8: 14, pp. 5875–5883, 2012.
XIII. Feng Zhao, YileiChen,Hanqiang Liu ,Jiulun Fan, “Alternate PSO-Based Adaptive Interval Type-2 Intuitionistic Fuzzy C-Means Clustering Algorithm for Color Image Segmentation”, IEEE Access, May 29, 2019.
XIV. Ha Dai Duong, DzungDinh Nguyen, Long Thanh Ngo, Dao ThanhTinh, “An Improvement of Type-2 Fuzzy Clustering Algorithm for Visual Fire Detection”, International Journal of Computer Information Systems and Industrial Management Applications, Volume 5, pp. 235-242, 2013.
XV. Hung Quoc Truong, Long ThanhNgo,Long Pham, “Interval Type-2 Fuzzy Possibilistic C-Means Clustering Based on Granular Gravitational Forces and Particle Swarm Optimization”, JCAII,Vol.23,No.3PP 529-601May,2019.
XVI. I. Gath, A.B. Geva, “Unsupervised optimal fuzzy clustering,” IEEE Trans. Pattern Anal. Mach. Intell., 11(7), pp. 773-781,1989.
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XXIV. JifaGuo and HongyuanHuo ,”An Enhanced IT2FCM,Algorithm Integrating Spectral Indices and Spatial Information for Multi-Spectral Remote Sensing Image Clustering”, Remote Sens. 2017, 9(9), 960
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XXXV. Nguyen, D.D.; Ngo, L.T.; Pham, L.T.; Pedrycz, W, “Towards hybrid clustering approach to data classification: Multiple kernels based interval-valued Fuzzy C-Means algorithms”, Fuzzy Sets Syst. 2015, 279, 17–39.

XXXVI. O.Mema Devi, ShahinAra Begum, “A new cluster validity index for type-2 fuzzy c-means algorithm, Advances in Computing, Communications and Informatics (ICACCI)”, 2013 International Conference, 2013.
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XXXVIII. P.Murugeswari,Dr.D.Manimegalai,”Color Textured image segmentation using ICICM-Interval type-2 fuzzy c-means clustering hybrid approach, “ Engineering Journal, vol.16, issue 5,2012.
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XLI. Satpathy, Sambit,SwapanDebbarma, Aditya S. Sengupta, and Bidyut K. Bhattacaryya. “Design a FPGA, fuzzy based, insolent method for prediction of multi-diseases in rural area.” Journal of Intelligent & Fuzzy Systems, 2019, pp 1-8.
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XLIX. Zhi Liu; ShuqiongXu; Yun Zhang; Chun Lung Philip Chen, “A Multiple-Feature and Multiple-Kernel Scene Segmentation Algorithm for Humanoid Robot”, IEEE Transactions on Cybernetics, Pages: 2232 – 2240, Volume: 44, Issue: 11, 2014.

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Abstract:

In the real time scenario, image encryption has been carried out early to the compression for maintaining the safety of the image. In this paper, a highly efficient image Encryption-Then-Compression (ETC) system has been designed, where the lossless compression is taken into account. The proposed image encryption method is operated with image encryption AES and RSA algorithm with Set Partition in Hierarchical tree(SPIHT) which shows logically high security compression technique. The ETC method  is proved  to be more simpler and efficient method while analyzing the parameters like Compression Ratio (CR), Peak Signal to Noise Ratio (PSNR).

Keywords:

Compression,Encrypted Image,Decrypted Image,Decompression,

Refference:

I. C.Priya , T.Kesavamurthy & M.UmaPriya , An Efficient Lossless Medical Image compression using Hybrid Algorithm, Advanced Materials Research,No.984,pp. 1276-1281,2014.
II. Hussain, N., Boles, W and Boyd, C.,A review of medical image water-marking requirements for teleradiology, J. Digital Imag., Vol.26,No .2,pp 326–343, 2013.
III. Jablon, D. ,Strong password only authenticated key exchange, computer communication review, ACM SIGCOMM Comput. Commun. Rev.,Vol. 26,No.5,pp.5-26,1997.
IV. Janaki.R and Dr.Tamilarasi.A, “Still Image Compression by Combining EZW Encoding with Huffman Encoder” IJCA, VOL.1, NO.7, 2011.
V. .Jinlei Zhang, Houqiang Li and Chang Wen Chen, “Distributed Lossless Coding Techniques for Hyperspectral Images” IEEE Journal of Selected Topics in Signal Processing, Vol.1,No2,pp.2-5, 2015.
VI. Prior, F., Ingeholm, M.L., Levine B.A.andTrabox, L., Potential Impact of HITECH Security Regulations on Medical Imaging, in Proc. Eur. Molecular Biol. Conf., , pp. 2157-2160 , 2009.
VII. Ramya,C. and Subha Rani ,S.,Contrast Enhancement for Bio-Medical Image Sequences ,International J. of Computer and Electronics Engineering, pp.121-124,2012.
VIII. RichaGoyal and SouravGarg, “Lossless Image Compression using Data Folding followed by Arithmetic Coding” IOSR Journal of Computer Engineering, e-ISSN: 2278-0661, p-ISSN:2278-8727, VOL.17, No. 2,2015.
IX. Zhou “Designing an Efficient Image Encryption-Then-Compression System via Prediction Error Clustering and Random Permutation” IEEE Transactions on IFS, VOL.9, NO.1, 2014.

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Abstract:

Recently customers are exposed to large variety of products and information on Internet, there is a necessity to filter, prioritize and personalize appropriate information to increase e-commerce demand. Using Recommender system, Business to Consumer (B2C) relationship can be benefitted and optimal, product selection is generated by solving voluminous data dynamically .In this work, a collaborative filtering is proposed to achieve top N recommendation about products to the consumers for purchase. In this work, the proposed recommender system focuses on obtaining similar group of customers using novel method. Personalized customer product recommendation is obtained by using classification and clustering algorithms. Good product evaluation is done using metrics like root mean square error (RMSE), mean square error (MSE). Recommender system has proved to enhance quality of decision making procedure and it gives a great impact on people’s decision making. This work gives a recommender system which increases the value of e-commerce websites and worthiness in encountering best products for customers. 

Keywords:

Recommender system,Collaborative filtering,decision making,Business-Consumer,

Refference:

I. Ansari, M. H., Moradi, M., “CodERS: a hybrid recommender system for an E-learning system. 2nd Int Conf of Signal Processing and Intelligent Systems, IEEE, Dec 2016, pp. 1-5
II. Arvind, B. V., Swaminathan, J., “An improvised filtering based intelligent recommendation technique for web personalization”, In 2012 Annual IEEE India Conf IEEE, Dec 2012, pp. 1194-119
III. Bai, T., Zhao, W. X., He, “Characterizing and predicting early reviewers for effective product marketing on e-commerce websites”, IEEE Trans on Knowledge and Data Engineering, 30(12), , 2018, 2271-2284.
IV. Cai, Y., & Zhu, D. “Trustworthy and profit: A new value-based neighbor selection method in recommender systems under shilling attacks”, Decision Support Sys, 124, 2019, 113112.
V. Devi, M. K., Samy, “Probabilistic neural network approach to alleviate sparsity and cold start problems in collaborative recommender systems” In 2010 IEEE Int Conf on Computational Intelligence and Computing Research (pp. 1-4). IEEE, Dec 2012, pp. 1-4
VI. Isinkaye, F. O., Folajimi, Y. O., “Recommendation systems: Principles, methods and evaluation”, Egyptian Informatics Journal, 16(3), 2015, 261-273.
VII. Iwahama, K., Hijikata, Y., “Content-based filtering system for music data. In 2004 IntSym on Applications and Internet Workshops. IEEE, Jun 2004, pp. 480-487
VIII. Katarya, R., Verma, O. P. “An effective collaborative movie recommender system with cuckoo search. Egyptian Informatics Journal, 18(2), 2017, 105-112
IX. Kompan, M., “Content-based news recommendation. In International conference on electronic commerce and web technologies” Springer, Sep 2010, pp. 61-72
X. Li, H., “Content-based filtering recommendation algorithm using HMM”. In 2012 Fourth Int. Conf on Comp and Info Sciences. IEEE., Aug 2012, pp. 275-277
XI. Li, Y., Wang, H., “A study on content-based video recommendation. In 2017 IEEE International Conference on Image Processing IEEE, Sep 2017, pp. 4581-4585
XII. Mahmoud, D. S., & John, “Enhanced content-based filtering algorithm using Artificial Bee Colony optimisation. In 2015 SAI Intelligent Systems Conf, IEEE, Nov 2015, pp.115-163
XIII. Mathew, P., Kuriakose, B., “Book Recommendation System through content based and collaborative filtering method. In 2016 Int Conf on Data Mining and Advanced Computing (pp. 47-52). IEEE, Mar 2016, pp. 47-52
XIV. Meryem, G., Douzi, K., “Toward an E-orientation platform: Using hybrid recommendation systems” In 2016 11th International Conference on Intelligent Systems: Theories and Applications, IEEE, Oct 2016, pp. 1-6
XV. Nilashi, M., Ibrahim, O., “A recommender system based on collaborative filtering using ontology and dimensionality reduction techniques. Expert Systems with Applications, 2012,507-520
XVI. Pal, A., Parhi, P., “An improved content based collaborative filtering algorithm for movie recommendations”, In 2017 Tenth Int Conf on Contemporary Computing, IEEE, Aug 2017, pp. 1-3
XVII. Portugal, I., Alencar, P., “The use of machine learning algorithms in recommender systems: A systematic review”, Expert Systems with Applications, 97, 2018, 205-227.
XVIII. Pujahari, A., Padmanabhan, V. “Group Recommender Systems: Combining user-user and item-item Collaborative filtering techniques.” Int Conf on Information Technology, IEEE, Dec 2015, pp. 148-152
XIX. Satpathy S, Prakash, M, DebbarmaSwapana, SenguptaAditya S.C, BhattacaryyaBidyut K.D, “Design a FPGA, fuzzy based, insolent method for prediction of multi-diseases in rural area”, Journal of Intelligent & Fuzzy Systems, ISSN 1064-1246, vol. Pre-press, pp. 1-8, 2019.
XX. Soliman, T. H. A., Mohamed, S. A. E. M., “Developing a mobile location-based collaborative Recommender System for GIS applications”, 10th Int Conf on Computer Engineering & Systems, IEEE, Dec 2015, pp. 267-273
XXI. Turnip, R., Nurjanah, “Hybrid recommender system for learning material using content-based filtering and collaborative filtering with good learners’ rating. In 2017 IEEE Conf on e-Learning, e-Management and e-Services, IEEE, Nov 2017, pp. 61-66
XXII. Yadav, S., Nagpal, S. “An Improved Collaborative Filtering Based Recommender System using Bat Algorithm”, Procedia computer science, 132, 2018, pp.1795-1803
XXIII. Yu, C., Tang, Q., “A Recommender System for Ordering Platform Based on an Improved Collaborative Filtering Algorithm” Int Conf on Audio, Language and Image Processing, IEEE, Jul 2018, pp. 298-302
XXIV. Zhang, F., Gong, T., “Fast algorithms to evaluate collaborative filtering recommender systems. Knowledge-Based Systems, 96, 2016, 96-103

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Abstract:

Wireless sensor networks (WSNs) comes under a kind of ad-hoc networks where network nodes hold sensors on board and sense diverse factors around deployed region. WSN turns to be extremely popular owing to its diverse applications nature comprising cyber-physical systems, disaster relief, precision agriculture, rescue operation, healthcare and object tracking in terrestrial environment to examine physical factors of space applications, human and so on. Enormous applications utilize sensor nodes based location information as inherent features. This information is more essential to recognize spatial co-ordinates of data origination. Extensively, localization approaches are categorized into range free and range based approaches. In this busy world, it is difficult for the parents to control child movements in crowded places.  There is a substantial risk that the child may get lost in the crowd. This has motivated to propose a solution to track the child movement in the crowded area like shopping mall, theatre, Play station etc. This proposed system helps to track the location of the child in In-Door environment using range-based localization technique.  RSSI is a parameter used to estimate the child location and communicate its position to parents.  Child and parents are considered as nodes, child would be wearing ZigBee  and GSM modules which periodically sends signals to the parent node. If the child gets moved away from parent, then parent receive the accurate location of the child and track the children within a range. With the use of distance measurement, position of children will be computed, and location of child is informed to parents.

Keywords:

ZigBee,Cyber-Physical Systems,RSSI,Parent node,

Refference:

I. Alrajeh,“Localization Techniques in Wireless Sensor Networks”, International Journal of istributed Sensor Networks, 2013

II. Azzedineboukerche, horacio A. B. F. “Localization systems for wireless sensor networks”, IEEE 2007

III. Benoit Latr´e , Bart Braem,“A Survey on Wireless Body Area Networks”,Springer, 2011

IV. B. P. Sreeja , G. Saratha Devi , “Encrypting Text Messages Using DNA Cryptographic Model” Bioscience Biotechnology Research Communications , Vol 12 No (1) March 2019

V. Bogdan Antonescu, “Wireless Body Area Networks: Challenges, Trends and Emerging Technologies”, IEEE conference 2013

VI. C.BharathiPriya, S.Sivakumar, A Survey on LocalizationTechniques in Wireless Sensor Networks ,International Journalof Engineering and Technology,Volume7,,Page:125-129

VII. Elnahrawy, Eiman, Li, “The Limits of Localization using Signal Strength”, IEEE SECON, pp.406—414, 2004

VIII. He, T., Huang, C., “Range-free localizationschemes for large scale sensor networks”, IEEE 2003.

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X. JanireLarranaga, LeireMuguira, , “An Environment Adaptive ZigBee-based Indoor Positioning Algorithm” Int conf on Indoor Positioning and Indoor Navigation, 2012

XI. Jennifer Yick, Dipak Ghosal, “Wireless sensor network survey”, ELSEVIER 2008

XII. Jiuqiang Xu, Wei Liu, “Distance Measurement Model Based on RSSI in WSN”, Scientific research, 2011

XIII. K. Benkič, M. Malajner, “Using RSSI value for distance estimation in Wireless sensor networks based on ZigBee”, IEEE, 2008

XIV. LinqingGui ,Thierry Val, “Improvement of range-free localization technology by a novel DV-hop protocol in wireless sensor networks”, Elsevier, Adhoc Networks,2016

XV. N Bulusu, J Heidemann, “GPS-less Low Cost Outdoor Localization For Very Small Devices”, IEEE Personal Communications, Vol 7. No.5, pp. 27-34, Oct 2000

XVI. Niculescu, “Ad hoc positioning system (APS)”, In IEEE GLOBECOM. (2001),2926–2931

XVII. Nidhi Patel, Hiren Kathiriya, “Wireless Sensor Network using Zigbee”, Int J. of Research in Engineering and Technology, 2013

XVIII. Obaid ur Rehman, Nadeem Javaid, “Performance Study of Localization Techniques in Wireless Body Area Sensor Networks”, IEEE, 2012

XIX. Ondrej S, Zdenek B, Petr F, “ZigBee Technology and Device Design”, Int conf on Networking, Systems and Mobile Communications and Learning Technologies, IEEE, April 2006

XX. Satpathy S, Prakash, M, Debbarma Swapana, Sengupta Aditya S.C, BhattacaryyaBidyut K.D, “Design a FPGA, fuzzy based, insolent method for prediction of multi-diseases in rural area”, Journal of Intelligent & Fuzzy Systems, ISSN 1064-1246, vol. Pre-press, pp. 1-8, 2019.

XXI. Rim Negraa, ImenJemilia, “Wireless Body Area Networks: Applications and technologies”, ELSEVIER, 2016

XXII. S Tomic,, M Beko, “Distributed algorithm for target localization in wireless sensor networks using RSS and AoA measurements”, Elsevier,2017

XXIII. Shengnan Gai, Eui-Jung Jung, “Localization Algorithm Based on Zigbee Wireless Sensor Network with Application to an Active Shopping Cart”, IEEE/RSJ Int conf on Intelligent Robots and Systems, 2014.

XXIV. Tashnim J.S. Chowdhurya, “Advances on localization techniques for wireless sensor networks”, ELSEVIER, 2016

XXV. Xin Hu, LianglunCheng,“A Zigbee-based localization Algorithm For Indoor Environments” IEEE, 2011

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Abstract:

The subset of Artificial Intelligence (AI) is Machine Learning.  Machine Learning (ML) has a rapid growth in all fields of research such as medical, bio-surveillance, robotics and all other industrial applications. Improvements in accuracy and efficiency of ML techniques in bio-informatics have steadily increased for solving problems in medicine. The aim of this paper is to give brief note about applications of ML in bio-informatics and science research. Bioinformatics involves the interaction of biology, computer science and statistics. In bioinformatics, Data were extracted, analyzed and classified for the prediction of various diseases. This process is time consuming and expensive. To reduce the cost and time, traditional techniques for extracting and analyzing the data were replaced by machine learning techniques.

Keywords:

Refference:

I. Aerts, S., Van Loo, P., Moreau, Y., & De Moor, B. (2004). A genetic algorithm for the detection of new cis-regulatory modules in sets of coregulated genes. Bioinformatics, 20(12), 1974-1976.
II. Alom, M. Z., Taha, T. M., Yakopcic, C., Westberg, S., Sidike, P., Nasrin, M. S., …&Asari, V. K. (2019). A state-of-the-art survey on deep learning theory and architectures. Electronics, 8(3), 292.
III. Alpaydin, E. (2009). Introduction to machine learning. MIT press.
IV. Bhaskar, H., Hoyle, D. C., & Singh, S. (2006). Machine learning in bioinformatics: A brief survey and recommendations for practitioners. Computers in biology and medicine, 36(10), 1104-1125.
V. Bockhorst, J., Craven, M., Page, D., Shavlik, J., &Glasner, J. (2003). A Bayesian network approach to operon prediction. Bioinformatics, 19(10), 1227-1235.
VI. Buskirk, T. D., Kirchner, A., Eck, A., &Signorino, C. S. (2018). An introduction to machine learning methods for survey researchers. Survey Practice, 11(1), 2718.
VII. Das, K., &Behera, R. N. (2017). A survey on machine learning: concept, algorithms and applications. International Journal of Innovative Research in Computer and Communication Engineering, 5(2), 1301-1309.
VIII. Ferrucci, D., Brown, E., Chu-Carroll, J., Fan, J., Gondek, D., Kalyanpur, A. A., …&Schlaefer, N. (2010). Building Watson: An overview of the DeepQA project. AI magazine, 31(3), 59-79.
IX. Gentleman, R., Carey, V., Huber, W., Irizarry, R., &Dudoit, S. (Eds.). (2006). Bioinformatics and computational biology solutions using R and Bioconductor. Springer Science & Business Media.
X. Inza, I., Calvo, B., Armañanzas, R., Bengoetxea, E., Larrañaga, P., & Lozano, J. A. (2010). Machine learning: an indispensable tool in bioinformatics. In Bioinformatics methods in clinical research (pp. 25-48). Humana Press.
XI. Kaur, S., & Jindal, S. (2016). A survey on machine learning algorithms. Int J Innovative Res AdvEng (IJIRAE), 3(11), 2349-2763.
XII. Larranaga, P., Calvo, B., Santana, R., Bielza, C., Galdiano, J., Inza, I., …& Robles, V. (2006). Machine learning in bioinformatics. Briefings in bioinformatics, 7(1), 86-112.
XIII. Mathé, C., Sagot, M. F., Schiex, T., &Rouzé, P. (2002). Current methods of gene prediction, their strengths and weaknesses. Nucleic acids research, 30(19), 4103-4117.
XIV. Manyika, J. (2011). Big data: The next frontier for innovation, competition, and productivity.
http://www.mckinsey.com/Insights/MGI/Research/Technology_and_Innovation/Big_data_The_next_frontier_for_innovation.
XV. Mitra, S., Datta, S., Perkins, T., &Michailidis, G. (2008). Introduction to machine learning and bioinformatics. Chapman and Hall/CRC.
XVI. Parmigiani, G., Garrett, E. S., Irizarry, R. A., &Zeger, S. L. (2003). The analysis of gene expression data: an overview of methods and software. In The analysis of gene expression data (pp. 1-45). Springer, New York, NY.
XVII. Sapp, C. E. (2017). Preparing and architecting for machine learning. Gartner Technical Professional Advice, 1-37.
XVIII. Witten, I. H., Frank, E., Hall, M. A., & Pal, C. J. (2016). Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann.

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Abstract:

In a higher education system, student faces a difficulty in choosing a right course from the large pool of courses in the institution. The course recommender framework provides necessary guidance to student network to choose a course in their scholarly schedule. This paper explores the potential of Educational data mining for course selection recommendation by predicting student’s course selection which involves analysing admission data of student in the institution. The proposed framework was designed as agent based recommender system to improve the efficiency of recommendations. There are three agents in this model, Pattern discovery agent generates the course selection pattern which is filtered by filtering agent. The recommendation agent provides recommendation. The proposed model was analyzed and tested using admission data collected from the higher educational institution. More specifically the model is applied on 10000 student admission data in the distance learning programme. The model is evaluated by three experiments, the experimental results indicates that this recommender system can more accurate predictions of course selections.

Keywords:

Data mining,classification mining,collaborative recommendation,course selection,EDM,

Refference:

I. Amer Al-Badarenah, Jamal Alsakran, “An Automated Recommender System for Course Selection” (IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 7, No. 3,pages 163-175, 2016

II. Cesar Vivalardi, Javier Bravo, L. S. A. O. “Recommendation in higher education using data mining techniques”. Master’s thesis, Universidad Autonoma de Madrid,2009

III. E K Subramanian, Ramachandran “Student Career Guidance System for Recommendation of Relevant Course Selection” International Journal of Recent Technology and Engineering (IJRTE) ISSN: 2277-3878, Volume-7, Issue-6S4, April 2019
IV. Gerasimos (Jerry) Spanakis, Samira Elatia, “Course Recommender System based on Graduating Attributes” conference paper Research gate 2017
V. Goga, M., Kuyoro, S., and Goga, N. “A recommender for improving the student academic performance”. Procedia-Social and Behavioral Sciences”,180:1481–1488.2015
VI. Ipperciel, D. and ElAtia, S. “Assessing graduate attributes: Building a criteria-based competency model”. International Journal of Higher Education, 3(3):p27., 2014
VII. JinjiaoLin, HailaPu,yibinLi,JianLian “Intelligent recommendation system for course selection in smart Education” “ International Conference on Identification, Information and Knowledge in the Internet of Things”,2017
VIII. Kiratijuta Bhumichitr, Songsak Channarukul Recommender Systems for University Elective Course Recommendation, IEEE explore,2017

IX. Ko Kang Chu, Maiga Chang, Y.-T. H. “Designing a course recommendation system on web based on the students’ course selection records. Master’s thesis, Chung-Yuan Christian University,pages493-4962009
X. S. Jothilakshmi&, Dr. M. hangaraj “a survey on mining algorithms and techniques used in target marketing of higher educational institutions” International Journal of Computer Engineering and Applications, Volume XII, Issue VII, ISSN 2321-3469 .2018
XI. Surabhi Dwivedi, Dr. Kumari Roshini ,Recommender system for Big Data in Education VS 5th National Conference on E-Learning & E-Learning Technologies (ELELTECH), 2017

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Abstract:

MANET is an emerging technology that allows the users to interact without physical infrastructure irrespective of geographical location.  In particular, energy efficient routing is the most important design for network operation due to the effect of increased data rates in wireless networks. The security aspects are to be considered for an efficient routing. The main challenge and research area in MANET is a route path identification, intrusion detection and energy consumption. Energy maintenance is the most important issue to be handled in order to avoid the excess usage of resources by mobile nodes which lead to route path breakup. Due to the lack of central server and infrastructure in MANET, security problems are to be addressed in order to preserve the network from attackers. In this work, techniques are proposed to handle energy efficient routing in the clustered environment while maintaining trustworthiness and security under emergency rescue conditions. Nodes are simulated using NS 2 and performance parameters are compared with existing algorithms.

Keywords:

MANET,Security,Authentication,Routing,Energy efficiency,Clustering,

Refference:

I. AmolBhosle&YogadharPandey 2013, ‘Applying Security to Data Using Symmetric Encryption in MANET’, International Journal of Emerging Technology and Advanced Engineering, vol. 3, no. 1, pp. 426-430.

II. AshishBagwari, Pankaj Joshi, VikasRathi&Vikram Singh Soni 2011, ‘Routing Protocol Behavior with Multiple Cluster Head Gateway in Mobile Ad hoc Network’, International Journal of Ad hoc, Sensor & Ubiquitous Computing (IJASUC), vol. 2, no. 4, pp. 133-142.

III. Elhadi M Shakshuki Nan Jang &Tarek R Sheltami 2013, ‘EAACK: A Secure Intrusion Detection System for MANETs’, IEEE Transactions on Industrial Electronics, vol. 60, no. 3, pp. 1089-1098.

IV. Fan-Hsun Tseng, Li-Der Chou & Han-Chieh Chao, 2011, ‘A survey of black hole attacks in wireless Mobile Ad hoc Networks’, Humancentric Computing and Information Sciences, Springer, vol. 1, no. 4, pp. 1-16.

V. Floriano De Rango, Francesca Guerriero&Peppino Fazio, 2012, ‘Link-Stability and Energy Aware Routing Protocol in Distributed Wireless Networks’, IEEE Transactions on Parallel and Distributed Systems, vol. 23, no. 4, pp. 713-726.

VI. Ha Dang &Hongyi Wu, 2010, ‘Clustering and Cluster-Based Routing Protocol for Delay-Tolerant Mobile Networks’, IEEE Transactions on Wireless Communications, vol. 9, no. 6, pp. 1874-1881.

VII. HaiyingShen&Lianyu Zhao 2013, ‘ALERT: An Anonymous Location-Based Efficient Routing Protocol in MANETs’, IEEE Transactions on Mobile Computing, vol. 12, no. 6, pp. 1079-1093.

VIII. Iftikhar Ahmad, HumairaJabeen& Faisal Riaz, 2013, ‘Improved Quality of Service Protocol for Real Time Traffic in MANET’, International Journal of Computer Networks and Communications (IJCNC), vol. 5, no. 4, pp. 75-86.
JavadVazifehdan, Venkatesha Prasad, R &IgnasNiemegeers, 2014, ‘Energy-Efficient Reliable Routing Considering Residual Energy in Wireless Ad Hoc Networks’, IEEE Transactions on Mobile Computing, vol. 13, no. 2, pp. 434-447.

IX. Jinhua Zhu &Xin Wang 2011, ‘Model and Protocol for Energy- Efficient Routing over Mobile Ad hoc Networks’, IEEE Transactions on Mobile Computing, vol. 10, no. 11, pp. 1546-1557.

X. Kamal Kumar Chauhan&Amit Kumar Singh Sanger, 2012, ‘Securing Mobile Ad hoc Networks: Key Management and Routing’, International Journal on AdHoc Networking Systems, vol. 2, no. 2, pp. 65-75.

XI. Karunakaran, S &Thangaraj, P 2011, ‘A cluster Based Service Discovery Protocol for Mobile Ad hoc Networks’, American Journal of Scientific Research, no. 11, pp. 179-190.

XII. Keshav Kumar Tiwari& Sanjay Agrawal 2013, ‘A Secure Reputation- Based Clustering Algorithm for Cluster based energy optimized Mobile Ad hoc Network’, International Journal of Advanced Research in Computer Science and Software Engineering, vol. 3, no. 5, pp. 232- 236.

XIII. MadhukarRao, G, NadeemBaig, M, Fareed Baba, Md&Kanthi Kumar K, 2011, Energy Efficient Reliable Routing Protocol for Mobile Ad hoc Networks’, IEEE International Conference on Electronics Computer Technology, pp. 296-299.

XIV. MenakaSivakumar 2018, “Secured Routing Deterrent to Internal Attacks for Mobile AD HOC Networks”, Journal of Engineering Science and Technology Review 11 (1) 1 – 9.

XV. MenakaSivakumar 2018, “Secured Routing Deterrent to Internal Attacks for Mobile AD HOC Networks” Journal of Engineering Science and Technology Review 11 (1) 1 – 9.

XVI. Ming Li, Pan Li, Xiaoxia Huang, Yuguang Fang &SavoGlisic, 2015, ‘Energy Consumption Optimization for Multihop Cognitive Cellular Networks’, IEEE Transactions on Mobile Computing, vol. 14, no. 2, pp. 358-372.

XVII. Mohamed, MEA Mahmoud &Xuemin (Sherman) Shen 2013, ‘A Secure Payment Scheme with Low Communication and Processing Overhead for Multihop Wireless Networks’, IEEE Transactions on Parallel and Distributed Systems, vol. 24, no. 2, pp. 209-224.

XVIII. Pradip De, Yonghe Liu &Sajal K Das, 2010, ‘Energy-Efficient Reprogramming of a Swarm of Mobile Sensors’, IEEE Transactions on Mobile Computing, vol. 9, no. 5, pp. 703-718.

XIX. Seyed Amin Hosseini Seno, Tat Chee Wan &RahmatBudiarto 2011, ‘Energy Efficient Cluster based Routing protocol for MANETs’, International Conference on Computer Engineering and Applications, IPCSIT, vol. 2, pp. 380-384.

XX. Shengrong Bu, Richard Yu, F, Xiaoping Liu, P & Helen Tang 2011, ‘Structural Results for Combined Continuous User Authentication and Intrusion Detection in High Security Mobile Ad hoc Networks’, IEEE Transactions on Wireless Communications, vol. 10, no. 9, pp. 3064- 3073.

XXI. Sourav Bhattacharya, HenrikBlunck, MikkelBaunKjærgaard&PetteriNurmi 2015, ‘Robust and Energy-Efficient Trajectory Tracking for Mobile Devices’, IEEE Transactions on Mobile Computing, vol. 14, no. 2, pp. 430-443.

XXII. SurendranSubbaraj&PrakashSavarimuthu 2014, ‘Eigen Trust-based non- cooperative game model assisting ACO look-ahead secure routing against selfishness’, EURASIP Journal on Wireless Communications and Networking, vol. 78, no. 1, pp. 1-120.

XXIII. Tao Shu& Marwan Krunz 2010, ‘Coverage-Time Optimization for Clustered Wireless Sensor Networks: A Power-Balancing Approach’, IEEE/ACM Transactions on Networking, vol. 18, no. 1, pp. 202-215.

XXIV. YuvarajKumbharey, SuweshShukla&SushilChaturvedi 2013, ‘Renovated Cluster Based Routing Protocol for MANET’, International Journal of Advanced Computer Research, vol. 3, no. 1, pp. 206-211.

XXV. Zhongyuan Qin, Xinshuai Zhang, KerongFeng, Qunfang Zhang &Jie Huang 2015, ‘An efficient key management scheme based on ECC and AVL Tree for large scale Wireless Sensor Networks’, Hindawi Publishing Corporation, International Journal of Distributed Sensor Networks, vol. 2015, pp. 1-7.

XXVI. Ziane Sara &MekkiRachida 2015, ‘Energy-Efficient Inter-Domain Routing Protocol for MANETs’, Elsevier, Procedia Computer Science, vol. 52, pp. 1059-1064.

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Abstract:

Authentication protocol is a very important protocol for Communication protocols like NFC(Near Field Communication). Its working recurrence is 13.56 MHz with the transmission speed go from 106 Kbps to 424 Kbps. In light of the common idea of remote communication frameworks, there are a couple of sorts of security vulnerabilities. Beginning late, a pseudonym based NFC convention (PBNFC) convention has been proposed to withstand the security traps found in the prohibitive security protection security custom. Regardless, this undertaking encourage analyses PBNFCP and exhibits that in any case it fails to keep the ensured security properties, for instance, pantomime assaults against a foe, who is a poisonous selected customer having a significant name relating private key.The proposed SEAP is repeated for the conventional security attestation utilizing the extensively perceived AVISPA (Automated Validation of Internet Security Protocols and Applications). SEAP is secure and effective when contrasted with the related existing verification conventions for NFC applications.

Keywords:

Short range communication,life time based,Restrictive Security,AVISPA,

Refference:

I. Gartner, “Market Insight: The Outlook on Mobile Payment,” Market Analysis and Statistics, May 2010.

II. Juniper Research, “NFC Mobile Payments & Retail Marketing-Business Models & Forecasts 2012-2017,” May 2012.

III. Kaarthik K, Sivaranjani S, “A Novel PDA Technique with Flying Capacitor for Buck Boost Converter”,IJITEE, ISSN: 2278-3075, Volume-8 Issue-5S March, 2019.

IV. R. Want, “Near field communication,”IEEE Pervasive Comput., vol.10, no.3, pp. 4 – 7, July. 2011.

V. S. Sivaranjani,V. Ashok and P.Vinoth Kumar, “Data Scheduling for an Enhanced Cognitive Radio System in Healthcare Environment”, Bioscience Biotechnology Research Communications, Issue Vol 11 No 2, 2018,pp-147-157.

VI. Sivaranjani S, Kaarthik K,”IOT based Intelligent parking system at airport, International Journal of Recent Technology and Engineering”, Volume-7, Issue-6S4, April 2019,pp-513-516.

VII. V. Coskun, B.Ozdenizci, and K. Ok, “A survey on near field communication (NFC) technology,” Wireless Pers. Commun., vol. 71, no. 3, pp. 2259-2294, Aug. 2013.

VIII. V. Odelu, A. K. Das, and A. Goswami, “A secure biometrics-based multi-server authentication protocol using smart cards,” IEEE Trans. Inf.Forensics Security, vol. 10, no. 9, pp. 1953-1966, Jun. 2015.

IX. V. Patil, N. Varma, S. Vinchurkar, and B. Patil, “NFC based health monitoring and controlling system,” in Proc. IEEE Global Conferenceon Wireless Computing and Networking, Lonavala, India, pp. 133-137, Dec. 2014.

X. W. Lumpkins and M. Joyce, “Near-Field Communication: It Pays: Mobile payment systems explained and explored,” IEEE Consume.Electron. Mag., vol.4, no.2, pp.49-53, Apr. 2015.

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Abstract:

The notion of the paper has to investigate  new set named as nano g*b-closed sets(Ng*b) in nano topological spaces(NTS). Some importantresults of Ng*b-closed sets are analysed. Also, we examine  the relationship of Ng*b-closed sets with other sets in NTS.

Keywords:

Nanog*b-closed,nanobclosure,

Refference:

I. A. Dhanis Arul Mary and I. Arockiarani, Onnanogb-closed sets in nano topological spaces, International Journal of Mathematical Archieve, 6(2), (2015), 54-58.

II. A. Dhanis Arul Mary, I. Arockiarani, On Semi pre-closed sets in nano topological spaces, Mathematical Sciences Int. Research Journal, 3(2)(2014), ISSN 2278- 8697.

III. C.R. Parvathy and , S. Praveena, On nano generalized pre regular closed sets in nano topological spaces, IOSR Journal of Mathematics (IOSR-JM), 13(2)(2017), 56-60.

IV. D. Andrijevic, On b-open sets, Mat. Vesink, 48 (1996), 59 – 64.

V. D. Vidhya and R. Parimelazhagan , g*b-closed sets in topological spaces,Int.J. Contemp. Math. Sciences, 7(2012), 1305-1312.

VI. D. Vidhya and R. Parimelazhagan , g*b-Continuous Maps and Pasting Lemma in Topological spaces, Int. J. Math. Analysis, 6(2012), 2307-2315.

VII. I. L. Reilly and Vamanamurthy, On -sets in topological spaces, Tamkang J. Math., 16(1985), 7-11.

VIII. K. Bhuvaneshwari and K. MythiliGnanapriya, Nano generalized closed sets, International Journal of Scientific and Research Publications, 4(5)(2014), 1-3.

IX. K. Bhuvaneswari and K. MythiliGnanapriya,Onnanogeneralised pre closed sets and nano pre generalised closed sets in nano topological spaces, International Journal of Innovative Research in Science, Engineering and Technology, 3(10)(2014), 16825-16829.

X. M. LellisThivagar and Carmel Richard, Note on Nano topological spaces – communicated.

XI. M. LellisThivagar and Carmel Richard, On nano forms of weakly open sets, International Journal of Mathe-matics and statistics Invention, 1(1),(2013),31-37.

XII. M. Parimala , C. Indirani. C and S. Jafari, On Nano b-open sets in Nano topological spaces, Jordan Journal of Mathematics and Statistics,9(3), (2016), 173-184.

XIII. N. Levine, Generalized closed sets in topology, Rend. Circ. Mat. Palermo (2) 19 (1970), 89-96.

XIV. N. Nagaveni and M.Bhuvaneswari, On nano weakly generalized closed sets, International Journal of Pure and Applied Mathematics, 106(7),(2016),129-137.

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