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Increase in storing the Electronic Health Records (EHR) of patients has developed a large scale. Healthcare data analytics is rapidly emerging with huge potential for organizations to provide healthcare by reducing the costs and improving healthcare decisions. Analytics help in gaining the information to improve decision making by using advanced data mining tools. A healthcare information and management system uses big data analytics for operational excellence. As Electronic Healthcare records are unstructured in nature, big data adoption is gaining importance in processing and visualizing the data. Big data utilizes Hadoop framework to process the large data sets in distributing computing environment. This paper discusses the survey on the analyzation of EHR by using Big Data Analytics.

Health care, Electronic Health Record, Big Data Analytics

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[10] Jaeyong Bae, Jason M. Hockenberry, Kimberly J. Rask, Edmund R. Becker, “Evidence that electronic health records can promote physician counseling for healthy behaviors”, Health Care Manage Rev, 42(3), pp. 258-268, 2017.
[11] Claudia Leonardi, Neal R. Simonsen, Qingzhao Yu, Chi Park, Richard A. Scribner, “Street Connectivity and Obesity Risk: Evidence From Electronic Health Records”, American Journal of Preventive Medicine, 52(1S1):pp. S40–S47, 2017.
[12] Benjamin A Goldstein, Ann Marie Navar, Michael J Pencina, John PA Ioannidis, “Opportunities and challenges in developing risk prediction models with electronic health records data: a systematic review”, Journal of the American Medical Informatics Association, pp.1-11, 2016.
[13] Martin C. Gulliford, Judith Charlton, Toby Prevost, Helen Booth, Alison Fildes, Mark Ashworth, Peter Little johns, Marcus Reddy, Omar Khan, Caroline Rudisill, “Costs and Outcomes of Increasing Access to Bariatric Surgery: Cohort Study and Cost-Effectiveness Analysis Using Electronic Health Records”, Value in Health, pp. 85-92, 2017.
[14] Yih-Ing Hser, Larissa J. Mooney, Andrew J. Saxon, Karen Miotto, Douglas S. Bell, David Huang, “Chronic pain among patients with opioid use disorder: Results from electronic health records data”, Journal of Substance Abuse Treatment, pp. 26–30, 2017.
[15] Juan D Chaparro, David C Classen, Melissa Danforth, David C Stockwell, Christopher A Longhurst, “National trends in safety performance of electronic health record systems in children’s hospitals”, Journal of American Medical Informatics Association, pp. 1–8, 2016.
[16] Julian Wolfson, David M. Vock, Sunayan Bandyopadhyay, Thomas Kottke, Gabriela Vazquez-Benitez, Paul Johnson, Gediminas Adomavicius, Patrick J. O’Connor, “Use and Customization of Risk Scores for Predicting Cardiovascular Events Using Electronic Health Record Data”, Journal of American Heart Association, pp. 1-18, 2017.
[17] Matthew F. Daley, Douglas A. Newton, Lynn DeBar, Sophia R. Newcomer, Lisa Pieper, Joseph A. Boscarino, Sengwee Toh, Pamala Pawloski, James D. Nordin, Cynthia Nakasato, Lisa J. Herrinton, Regina Bussing, “Matthew F. Daley, Douglas A. Newton, Lynn DeBar, Sophia R. Newcomer, Lisa Pieper, Joseph A. Boscarino, Sengwee Toh, Pamala Pawloski, James D. Nordin, Cynthia Nakasato, Lisa J. Herrinton, Regina Bussing, “Accuracy of Electronic Health Record–Derived Data for the Identification of Incident ADHD”, Journal of Attention Disorders, pp. 1-10, 2014.
[18] Martin R. Cowie, Juuso I. Blomster, Lesley H. Curtis, Sylvie Duclaux, Ian Ford, Fleur Fritz, Samantha Goldman, Salim Janmohamed, Jo¨rg Kreuzer, Mark Leenay, Alexander Michel, Seleen Ong, Jill P. Pell, Mary Ross Southworth, Wendy Gattis Stough, Martin Thoenes, Faiez Zannad, Andrew Zalewski, “Electronic health records to facilitate clinical research”, Clin Res Cardiol, pp. 1-9, 2016.
[19] Stefan Koudstaa, Mar Pujades-Rodriguez, Spiros Denaxas, Johannes M.I.H. Gho, Anoop D. Shah, Ning Yu, Riyaz S. Patel, Chris P. Gale, Arno W. Hoes, John G. Cleland, Folkert W. Asselbergs, and Harry Hemingway, “Prognostic burden of heart failure recorded in primary care, acute hospital admissions, or both: a population-based linked electronic health record cohort study in 2.1 million people”, European Journal of Heart Failure, pp. 1-9, 2016.
[20] Yichuan Wang, LeeAnn Kung, William Yu Chung Wang, Casey G. Cegielski, “An Integrated Big Data Analytics-Enabled Transformation Model: Application to Health Care”, Information & Management, pp. 1-50,2018.
[21] Hemingway H, Feder G S, Fitzpatrick N K, Denaxas S, Shah A D & Timmis A D, “Using nationwide `big data` from linked electronic health records to help improve outcomes in cardiovascular diseases: 33 studies using methods from epidemiology, informatics, economics and social science in the ClinicAl disease research using LInked Bespoke studies and Electronic health Records (CALIBER) programme”, Programme Grants for Applied Research, Volume: 5, Issue 4, 2017.
[22] Christian R. Macedonia, Clark T. Johnson, Indika Rajapakse, “Advanced Research and Data Methods in Women’s Health- Big Data Analytics, Adaptive Studies, and the Road Ahead”, Clinical Expert Series, Obstetrics & Gynecology, pp. 249-264, Volume 129, Number 2, 2017.
[23] Scott M. Sutherland, Stuart L. Goldstein, Sean M. Bagshaw, “Leveraging Big Data and Electronic Health Records to Enhance Novel Approaches to Acute Kidney Injury Research and Care”, Blood Purification, Issue 44, pp. 68-76, 2017.
[24] Vera Ehrenstein, Henrik Nielsen, Alma B Pedersen, Søren P Johnsen, and Lars Pedersen, “Clinical epidemiology in the era of big data: new opportunities, familiar challenges”, Clinical Epidemiology, 9: pp. 245–250, 2017.

S. Thirumurugam, P. Thambidurai, "A Survey on Electronic Health Records and Big Data Analytics for Healthcare", International Journal of Computer Sciences and Engineering, Vol.06, Issue.02, pp.100-107, 2018.

Cloud computing is expected to trigger high demand for Traffic Redundancy Elimination(TRE) solutions as the amount of data exchanged between the cloud and its users is expected to dramatically increase. We present PACK (Predictive ACKs),a novel end-to-end TRE system, designed for cloud computing customers .The cloud environment redefines the TRE system requirements, making proprietary middle-box solutions inadequate .To reduce bandwidth cost ,Cloud –Based Traffic redundancy elimination(TRE) system should make use of sophisticated use of cloud resources, so that the additional cost of TRE computation and storage can be optimized .This TRE technique uses Predictive. ACK’s(PACK), designed for cloud computing customers. It gives a methodology to reduce the cloud bandwidth by making use of predictions for the future data, thereby eliminating redundant data. It is a receiver driven TRE technique, that allows the receiver to use newly received chunks to identify previously received chunk chains , that can be used to send predictions for the subsequent data.. This technique does not require the sender to continuously maintain the receiver’s status, unlike traditional approach. Predictive ACK’s is suitable in pervasive computation environment. It is transparent to all TCP based application and network devices .The main advantage of PACK is that it can offload cloud –server TRE effort to end client, thereby minimizing the processing cost induced by the TRE algorithm.

Traffic redundancy elimination, cloud computing, predictions

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P.Joseph Charles, S.Thulasi Bharathi, Angel Preethi, "Traffic Redundancy and Elimination Approach to Reduce Cloud Bandwidth and Costs", International Journal of Computer Sciences and Engineering, Vol.06, Issue.02, pp.108-111, 2018.

Big Data has gained much attention from the academia and the IT industry. In the digital and computing world, information is generated and collected at a rate that rapidly exceeds the boundary range. Currently, over 2 billion people worldwide are connected to the Internet, and over 5 billion individuals own mobile phones. By 2020, 50 billion devices are expected to be connected to the Internet. In the recent times the amount of data are generated and stored by various industries are rapidly increasing on the internet thus data scientists are facing a lot of challenges for maintaining a huge amount of data as the fast growing industries require the significant information for enhancing the business and for predictive analysis of the information. This paper focuses on the various states of art studies towards Big Data Analytics techniques.

Big Data Analytics, Hadoop, Map Reduce, Data Center, Hadoop Distributed File System (HDFS)

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[12] Anitya Kumar Gupta, Srishti Gupta, "Security Issues in Big Data with Cloud Computing", International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.6, pp.27-32, 2017
[13] S. Sathyamoorthy, "Data Mining and Information Security in Big Data", International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.3, pp.86-91, 2017
[14] Z. Ren, X. Xu, M. Zhou, J. Wan, and W. Shi. Workload analysis, implications and optimization on a production hadoop cluster: A case study on taobao. IEEE Transactions on Services Computing, 7(2):307{321, 2014
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[17] V.K. Gujare, P. Malviya, "Big Data Clustering Using Data Mining Technique", International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.2, pp.9-13, 2017
[18] J. Zhan, L. Wang, X. Li, W. Shi, C. Weng, W. Zhang, and X. Zang. Cost-aware cooperative resource provisioning for heterogeneous workloads in data centers. IEEE Transactions on Computers, 62(11):2155-2168, 2013.

S. Chitra, P. Srivaramanga, "An Efficient Survey on Predictive Analytics in Big Data", International Journal of Computer Sciences and Engineering, Vol.06, Issue.02, pp.112-117, 2018.

In recent years, images play an important role in day to day life and with the advent of increasing multimedia content in the internet, manipulating information from the images in image database is necessary and useful task. Valuable informations are hidden in images and it reveal useful information to the user. Now a days, image mining is one of the research area for many researchers. It is a new focus to data mining and also utilizes the algorithms in data mining and image processing. Image mining is the process of searching and identifying the pattern present in the images, extracting useful information that is not explicitly present in the images. The goal of image mining is to mine knowledge from large image database. Various techniques has been used to discover knowledge from image database. This paper provides a review of various techniques used for discovery of knowledge from images using image mining techniques.

Image Mining,Retrieval,Classification,Clustering,Indexing

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[9] Dr.Rina, ShailjaAgnihotri, “A Review of Image Mining Concepts, Techniques and Challenges” , International Journal of Innovative Research in Computer and Communication Engineering , Vol. 4, Issue 9, September 2016.
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D. Angayarkanni, L.Jayasimman, "Image Mining Techniques – A Review", International Journal of Computer Sciences and Engineering, Vol.06, Issue.02, pp.118-122, 2018.

Data Storage in the new era of storing data anywhere and everywhere has paved way to find use and access the available data in an efficient manner hence it is no longer an warehousing issue. Moreover for various cloud applications invariably providing sufficient space for the available data in an user friendly manner is a very important task of Cloud Storage. However studies are on, on the issue of Data Storage as to make it more competitive and user friendly nowadays.

Compression, Decompression, Cloud, Storage, Techniques

[1] A.P. Mohod, A. Pankar, A Mohite, A. Dondalkar, A. Kadhe, Manish Naik, "Digital Health Record and Prescription", International Journal of Scientific Research in Computer Science and Engineering, Vol.6, Issue.1, pp.60-62, 2018.
[2] Anitha H M, P. Jayarekha , "Security Challenges of Virtualization in Cloud Environment", International Journal of Scientific Research in Computer Science and Engineering, Vol.6, Issue.1, pp.37-43, 2018.
[3] Sakshi kathuria, "A Survey on Security Provided by Multi-Clouds in Cloud Computing", International Journal of Scientific Research in Network Security and Communication, Vol.6, Issue.1, pp.23-27, 2018.
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[5] Naresh Kumar, Ishu Devi, "Various Chunking and Deduduplication Techniques in Big Data", International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.3, pp.129-131, 2017
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[7] B. Sowmya, K. Madhavi, "Secured Cloud Storage via Attribute-based Encryption", International Journal of Computer Sciences and Engineering, Vol.5, Issue.7, pp.96-100, 2017.
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G. Uma, L. Jayasimman, "Cloud Storage Optimization using Compression Technique", International Journal of Computer Sciences and Engineering, Vol.06, Issue.02, pp.123-125, 2018.

The usages of cloud computing application in libraries is a relatively new way where can have lot of applications in school and higher education in India. Libraries are moving towards cloud computing implementation to use its services and features to make their infrastructures user friendly and fast services. This article discusses the new proposed model for Campus Based Community Cloud (CBCC) in jusuit college libraries in tamil nadu and tells the best practices for the implementation of cloud computing in the library environment to get the maximum benefit of it and reduce the operational cost. Load balancing techniques uses at high user satisfaction and usage of library resource ratio by guaranteeing a proficient and reasonable allocation of each computing resource.

Load balancing, capital cost, operational cost, Higher education library, Library as a service (Laas). Campus Based Community Cloud (CBCC)

[1] N. Gosavi, S. S. Shinde and B. Dhakulkar, “Use of Cloud Computing is Library And Information Science Field,” International Journal of Digital Library Services, vol. 2, no. 3, pp. 51-106, 2012.
[2] Fan Bingsi, Cloud Computing and Libraries: Defense for Research on the Cloud Computing, Library and Information Service, 2009.
[3] Hu Guangxia, Research on Information Service Model of University Library in Digital era, Tianjin Polytechnic University, 2007.
[4] Huang Fuyan, Research on the Development of Library Information Service Models in the Information Culture Environment, OJ Xiangtan University, 2008.
[5] Judith Hurwitz , Robin Bloor , Marcia Kaufman and Dr. FernHalper , Cloud Computing For Dummies , Wiley Publishing , 2009.
[6] B. P. Rima, E. Choi, and I. Lumb, "A Taxonomy and Survey of Cloud Computing Systems", Proceedings of 5th IEEE International Joint Conference on INC, IMS and IDC, Seoul, Korea, pp.44-5l, August 2009.
[7] Nidhi Jain Kansal, Inderveer Chana, "Cloud Load Balancing Techniques: A Step Towards Green Computing", IJCSI, Vol. 9, Issue, January 2012.

R. Justin Kennedy, L. Jayasimman, "A Proposed Model for Campus Based Community Cloud(CBCC) for Higher Education in Jusuit College Libraries in TN using Load Balancing Techniques", International Journal of Computer Sciences and Engineering, Vol.06, Issue.02, pp.126-129, 2018.

World Wide Web is a huge warehouse of web pages and links. It offers large quantity of data for the Internet users. The growth of web is incredible as around one million pages are added per day. Users’ accesses are recorded in weblogs. Web usage mining is a variety of mining technique in logs. Because of the outstanding usage, the log files are growing at a faster rate and the size is suitable very large. This leads to the complexity for mining the practice log according to the needs. This provides a vast field for the researchers to supply their proposal to develop a better mining technique. In this paper, we analyse and study Markov model and allKth Markov model in Web prediction. We propose a new customized Markov model to ease the issue of scalability in the number of paths. In adding, we there a new two-tier prediction structure that creates an example classifier EC, based on the training examples and the generated classifiers. We show that such framework can advance the prediction time without compromising prediction accuracy. We have used standard benchmark data sets to analyze, compare, and demonstrate the effectiveness of our techniques using variations of Markov models and relationship rule mining. Our experiments demonstrate the effectiveness of our modified Markov model in reducing the number of paths without compromising accuracy. Additionally, the results support our investigation conclusions that accuracy improves with higher orders of all-Kth model

Big Data Sentiment Analysis

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S.Karthikeyan, P.Srivaramangai, "Sentiment Analysis Methods on Web Predication", International Journal of Computer Sciences and Engineering, Vol.06, Issue.02, pp.130-134, 2018.

Nowadays huge amount of images are produced with the rapid development of digital imaging technology in various fields such as medical, astronomy, weather forecasting, photography, satellite imaging etc. So maintaining images in the large databases, extracting useful information from the images and retrieval similar images are the emerging research area in current scenario. A point of interest in an image is called feature that transform pictorial information into alpha numeric data. That feature can be used for solving many problems such as reducing the dimension of the image, classifying the images, indexing the images in the image database, automatic data analysis and retrieval of images from the database, etc. One of the main tasks in imaging technology is to extract useful and important features from the images. This paper presents a study on various low level feature extraction techniques used in image mining system that is used for various applications of imaging technology.

Image Mining, Feature extraction, color, Texture, Shape, Image retrieval

[1] AboliW.Hole, Prabhakar L.Ramteke, “Design and Implementation of Content Based Image Retrieval Using Data Mining and Image Processing Techniques”, International Journal of Advance Research in Computer Science and Management Studies, March 2015.
[2] AnkitaTripathi, Hitesh Jangir “A Study on Image Mining Methods and Techniques “, International Journal of Innovative Research in Computer and Communication Engineering April 2016.
[3] K.Saraswathi, Dr.V.GaneshBabu,”A Review on Image Mining Techniques”,International Journal of Scientific & Engineering Research, May-2017.
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[10] AnkitaTripathi,Shivam Pandey, Hitesh Jangir, ”An improved and efficient image mining technique for classification of textual images using low-level image features”, IEEE International Conference on Inventive Computation Technologies, 2016.
[11] FatemehAlamdar, Mohammad Reza Keyvanpour “A New Color Feature Extraction Method Based on Quad Histogram” , 3rd International Conference on Environmental Science and Information Application Technology (ESIAT 2011)
[12] JeyanthiPrabhu and JawaharSenthil Ku ,“Wavelet Based Content Based Image Retrieval Using Color And Texture Feature Extraction By Gray Level Concurrence Matrix And Color Concurrence Matrix “, Journal of Computer Science Volume10(1),2014 Science Publications
[13] Alphonsa Thomas, SreekumarK,”A Survey on Image Feature Descriptors-Color, Shape and Texture, International Journal of Computer Science and Information Technologies, 2014.
[14] SumanKhokhar , SatyaVerma , “Content Based Image Retrieval with Multi-Feature Classification by Back-propagation Neural Network”, International Journal of Computer Applications Technology and Research, 2017.
[15] BhagyashriAmrutkar, Lokesh Singh “Efficient Content Based Image Retrieval Using Combination Of Dominant-Color, Shape And Texture Features And K-Means Clustering” International Journal Of Engineering And Computer Science ,January 2016
[16] S.KannanDr.V.MohanDr.N.Anbazhagan ”Image Clustering and Retrieval using Image Mining Techniques“ IEEE International Conference on Computational Intelligence and Computing Research 2010
[17] ParagDhonde, Prof. C. M. Raut “Precise & Proficient Image Mining Using Hierarchical KMeans Algorithm“, International Journal of and Research Publications, January 2015.
[18] Rahul Mehta, Nishchol Mishra, Sanjeev Sharma, “Color - Texture based Image Retrieval System”, International Journal of Computer Applications, June 2011.
[19] ManimalaSingham and K.Hemachandran “Content Based Image Retrieval using Color and Texture”, An International Journal of Signal & Image Processing, February 2012.
[20] J. C. Kavitha; A Suruliandi “Texture and color feature extraction for classification of melanoma using SVM”, International Conference on Computing Technologies and Intelligent Data Engineering (ICCTIDE), 2016
[21] Shijin Kumar P.S, Dharun V.S “Extraction of Texture Features using GLCM and Shape Features using Connected Regions”, International Journal of Engineering and Technology (IJET) 2016
[22] Jigisha M. Patel, Nikunj C. Gamit “A Review on Feature Extraction Techniques in Content Based Image Retrieval”, IEEE WiSPNET 2016 conference
[23] Aman Chadha ,SushmitMallik ,RavdeepJohar “Comparative Study and Optimization of Feature-Extraction Techniques for Content based Image Retrieval“,International Journal of Computer Applications, August 2012.
[24] G.Nagarajan, R.I.Minu, B Muthukumar , V.Vedanarayanan &S.D.Sundarsingh “Hybrid Genetic Algorithm for Medical Image Feature Extraction and selection”, International Conference on Computational Modelling and Security (CMS 2016) Published by Elsevier
[25] Shijin Kumar P.S, Dharun V.S “ Extraction of Texture Features using GLCM and Shape Features using Connected Regions” International Journal of Engineering and Technology (IJET),2017
[26] Sushant Shrikant Hiwale, “Quick Interactive Image Search in Huge Databases Using content-Based Image Retrieval”, IEEE Sponsored 2nd International Conference on Innovations in Information Embedded and Communication Systems(ICIIECS), 2015.
[27] BalvantTarulatha,Ms. NamrataShroff,Prof. M B Chaudhary “VIBGYOR Indexing Technique For Image Mining”, IEEE International Conference on , Data Mining and Advanced Computing (SAPIENCE),2016.
[28] Muhammad Imran,Rathiah Hashim,Noor Eliza Abd Khalid ,”Color Histogram and First Order Statistics for Content Based Image Retrieval”, International conference on Recent Advances on Soft Computing and Data Mining 2014.
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D. Angayarkanni, L. Jayasimman, "Feature Extraction Techniques in Image Mining System –A Survey", International Journal of Computer Sciences and Engineering, Vol.06, Issue.02, pp.135-139, 2018.

Live VM migration help attain both cloud-wide load balancing and operational consolidation while the migrating VMs remain accessible toward users. To avoid period of high-load for the complicated resources, IaaS-cloud operators assign specific time windows for such migrations to occur in an orderly manner. Moreover, provider normally relies on share-nothing architectures to get scalability. In this paper, we focus on the immediate scheduling of live VM migrations in large share-nothing IaaS clouds, such that migration are complete on time and without adversely affecting agreed-upon SLAs. We offer a scalable, distributed network of brokers that oversees the progress of all on-going migration operations within the context of a provider. Brokers make use of an fundamental exceptional purpose file system, termedMigrateFS, that is capable of both replicating and keeping in sync virtual disks while the hypervisor live-migrates VMs (i.e., RAM and CPU state). By restrictive the resources consumed during migration, brokers implement policies to reduce SLA violations while seeking to complete all migration tasks on time.

Distributed Systems, Cloud Computing, IaaS Clouds, Virtual Machine Migration

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L.Jayasimman, B.Geetha Dhanalakshmi, "Resource Provisioning for Ensuring QoS in Virtualized Environments", International Journal of Computer Sciences and Engineering, Vol.06, Issue.02, pp.140-146, 2018.

There is a burgeoning need to consider new ways of providing early education services for young and often newly diagnosed children with Autism Spectrum Disorder (ASD) and their families. Such children do not respond naturally to direct curricular delivery, typically utilized in inclusive classrooms that predominate public education, but instead, need an educational model incorporating intra and interpersonal development skills. Also, there is an essential need for the facility to keep track of and addressing uneven progress in specific areas; characteristic of learners with ASD. In this paper, ranking feature selection techniques like Information Gain, Chi-Square, Gain Ratio, ReliefF are used for pre-processing the ASD dataset.

Autism Spectrum Disorder

[1] Eman Ahmed Zaky, “Autism Spectrum Disorder (ASD); The Past, The Present, and The Future”, Journal of Child & Adolescent Behavior, pp.1-4, 2017.
[2] Bappaditya Adak and Santoshi Halder, “Systematic Review on Prevalence for Autism Spectrum Disorder with Respect to Gender and Socio-Economic Status”, Journal of Mental Disorders J and Treatment, Volume 3, Issue 1, pp.1-9, 2017.
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[4] Milos Radovic, Mohamed Ghalwash, Nenad Filipovic, and Zoran Obradovic, “Minimum redundancy maximum relevance feature selection approach for temporal gene expression data”, BMC Bioinformatics, 18:9, pp.1-14, 2017.
[5] Pream Sudha V, “Feature Selection Techniques for the Classification of Leaf Diseases in Turmeric”, International Journal of Computer Trends and Technology (IJCTT), Volume 43 Number 3, pp.138-142, January 2017.
[6] Pream Sudha V, “Feature Selection Techniques for the Classification of Leaf Diseases in Turmeric”, International Journal of Computer Trends and Technology (IJCTT), Volume 43 Number 3, pp.138-142, January 2017.
[7] Omer Faruk ERTUGRUL, Mehmet Emin TAGLUK, “A fast feature selection approach based on extreme learning machine and coefficient of variation”, Turkish Journal of Electrical Engineering & Computer Sciences, pp. 3409 – 3420, 2017.
[8] B. S. Harish, M. B. Revanasiddappa, “A Comprehensive Survey on various Feature Selection Methods to Categorize Text Documents”, International Journal of Computer Applications, Volume 164 - No.8, pp.1-7, 2017.
[9] Szidonia Lefkovtis, Laszlo Lefkovits, “Gabor Feature Selection based on Information Gain”, 10th International Conference Interdisciplinary in Engineering, pp. 892-898, 2017.
[10] R.P.L. Durgabai, “Feature Selection using ReliefF Algorithm”, International Journal of Advanced Research in Computer and Communication Engineering, Vol. 3, Issue 10, pp. 8215-8218, October 2014.

S. Padmapriya, S. Murugan, "Comparative Study on the Feature Selection Techniques for Autism Spectrum Disorder", International Journal of Computer Sciences and Engineering, Vol.06, Issue.02, pp.147-154, 2018.