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In Agriculture sector where the government and their supporting agencies need to make numerable decisions based on the adverse weather factors and the reports submitted to them. One of the essential issue is the crop insurance based on weather factors. Data mining and analytics techniques are necessary approaches for accomplishing practical and effective solutions for this problem. In addition to adverse weather conditions, variability in crop yields, input levels and damage statistics for a pre-identified crop or variety of crops information which are more relevant for farmers to make use of critical farming decisions. This paper focuses on the analysis of categories of data in agriculture and provides the bigdata approach for crop insurance data base on the background of insurance industry reform combined with the analytics technologies and to create the awareness of crop insurance scheme through online self-service weather insurance for farmers .Big Data involves the Multi criteria decisions involving spatially identifying the affected areas,Weather data, Farmer interviews, dry and wet management areas, Expert opinion of local extension officers, historical weather data, website data would help insurance companies to select the crop insurance products.

Bigdata , Crop insurance, Agriculture, Data mining.

[1] Enhancing technology use in agriculture insurance, National Institute for Transforming India (Niti) Aayog Government of India December, 2016
[2] S.V.R.K. Prabhakar, J.J. Pereira, J.M. Pulhin, G.S. Rao,H. Scheyvens and J. Cummins, “Effectiveness of Insurance for Disaster Risk Reduction and Climate Change Adaptation: Challenges and Opportunities”, Institute for Global Environmental Strategies, Hayama, Japan, 2015.
[3]kentaro kuwata,faizan Mahmood,Ryosuke Shibasaki,”Weather Index for Crop Insurnace to mitigate Basis Risk”,2015,IEEE
[4]Prof.Mayura nagar,mukesh kumar,”Big Data analytics in agriculture and distribution channel”,2017 IEEE
[5]https://www.newstatesman.com/environment/2013/10/agriculture-companies-are-turning-big-data-profit-climate-change
[6]WEATHER INDEX INSURANCE FOR AGRICULTURE: Guidance for Development Practitioners,World bank,November 2011
[7] Weather Index-based Insurance in Agricultural Development A Technical Guide, 2011 by the International Fund for Agricultural Development (IFAD)
[8] David Mäder-Soyka, Fang-Yu Liang, Mangesh Niranjan Patankar, Sophia Van ,”Big data approaches to crop insurance in Asia”, 17 August 2016
[9] S. Sinha and N. K. Tripathi, “Assessment of crop insurance international practices, policies and technologies as risk mitigation tools in India and Thailand,” Int. J. Adv. Reserach, vol. 2, no. 9, pp. 769–788, 2014.

K.P. Mangani, R. Kousalya, "Big Data Approach for Weather Based Crop Insurance", International Journal of Computer Sciences and Engineering, Vol.06, Issue.08, pp.1-4, 2018.

This paper describes how big data technologies are converging to offer a cost-effective delivery model social media based big data analytics. Social Media is a powerful technology to perform massive-scale and complex computing. It eliminates the need to maintain expensive computing hardware, dedicated space, and software. Massive growth in the scale of data or big data generated through social media has been observed. Addressing big data is a challenging and time demanding task that requires a large computational infrastructure to ensure successful data processing and analysis. In this paper the relationship between big data and social media, the classification of big data and the scope of big data analytics are discussed.

Big Data, Social Media, Techniques, Big Data Analytics, Clustering

[1] D.Aruna Kumari , Dr.K.Rajasekhar rao, M.suman “ Privacy preserving distributed data mining using steganography “In Procc. Of CNSA-2010, Springer Libyary
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[3] Agrawal, R. & Srikant, R.(2000). Privacy Preserving Data Mining. In Proc. of ACM SIGMOD Conference on Management of Data (SIGMOD’00), Dallas, TX.
[4] Alexandre Evfimievski, Tyrone Grandison Privacy Preserving Data Mining. IBM Almaden Research Center 650 Harry Road, San Jose, California 95120, USA
[5] Agarwal Charu C., Yu Philip S., Privacy Preserving Data Mining: Models and Algorithms, New York, Springer, 2008.
[6] Oliveira S.R.M, Zaiane Osmar R., A Privacy-Preserving Clustering Approach Toward Secure and Effective Data Analysis for Business Collaboration, In Proceedings of the International Workshop on
Privacy and Security Aspects of Data Mining in conjunction with ICDM 2004, Brighton, UK, November 2004.
[7] Flavius L. Gorgônio and José Alfredo F. Costa“Privacy-Preserving Clustering on Distributed Databases:A Review and Some Contributions
[8] D.Aruna Kumari, Dr.K.rajasekhar rao,M.Suman “Privacy preserving distributed data mining: a new approach for detecting network traffic using steganography” in international journal of systems and technology(IJST) june 2011.
[9] Binit kumar Sinha “Privacy preserving, and C. S. Yang, A Fast VQ Codebook Generation Algorithm via Pattern Reduction, Pattern Recognition Letters, vol. 30, pp. 653{660, 2009}
[10] C. W. Tsai, C. Y. Lee, M. C. Chiang Kurt Thearling, Information about data mining and analytic technologies http://www.thearling.com/
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[13] Vassilios S. Verykios, Elisa Bertino, Igor Nai Fovino State-of-the-art in Privacy Preserving Data Mining in SIGMOD Record, Vol. 33, No. 1, March 2004.
[14] Quantization: A Review”, IEEE Transactions on Communications, Vol. 36, No. 8, August 1988.
[15] Berger T, “Rate Distortion Theory”, Englewood Cliffs, Prentice-Hall,NJ, 1971.
[16] A.Gersho and V.Cuperman, “Vector Quantization: APattern Matching Technique for Speech Coding”, IEEE Communications, Mag., pp 15-21, 1983.
[17] "Privacy Preserving Data Mining - IBM Research: Almaden: San Jose
[18] D.Aruna Kumari, Dr.K.Rajasekhara rao, M.suman “Privacy Preserving Clustering in DDM using Cryptography”in TJ-RJCSE-IJ-06.

K. Yemunarane, D. Hemavathi, "An Investigation on Social Media Issues Using Big Data Analytics", International Journal of Computer Sciences and Engineering, Vol.06, Issue.08, pp.5-8, 2018.

Nowadays, Big data is growing vey faster in the world. Big data is the large volume of data that consists of both structured and unstructured on a day-to-day basis. But it`s not the amount of data. Big data is the data which includes sensor data, biometric data, Geo-spatial, Healthcare, power grid, transport, search engine and in Social networks. Hadoop process large amounts of data, in parallel, clusters of commodity hardware in a reliable and fault-tolerant manner. In this paper we review the Image processing using Map reduce technique with the help of HIPI (the image processing Tool).

Hadoop, Map reduce, Big data, Image Processing, HIPI

[1] https://zephoria.com/top-15-valuable-facebook-statistics
[2]Hadoop map reduce framework.http://hadoop.apache.org/mapreduce/.
[3]Customizing input file formats for image processing in hadoop. Arizona State University. Online at:
http://hpc. asu. edu/node/97.
[4]DEAN, J.,AND GHEMAWAT,S.2008. Mapreduce: Simplified data processing on large clusters. Communications of the ACM 51, 1,107–113.
[5] PDF: HIPI: A Hadoop Image Processing Interface for Image-based Map Reduce Tasks. Available from: https://www.researchgate.net/publication/266464321_HIPI_A_Hadoop_Image_Processing_Interface_for_Image-based_MapReduce_Tasks [accessed Jul 18 2018].
[6] http://hipi.cs.virginia.edu/
[7] https://github.com/uvagfx/hipi
[8] http://hipi.cs.virginia.edu/gettingstarted.html
[9] http://hipi.cs.virginia.edu/examples.html

P.S. Vijayalakshmi, K. Gomathy, C. Kumuthini, "HIPI: A REVIEW ON HADOOP MAP REDUCE FRAMEWORK USING IMAGE PROCESSING IN BIGDATA", International Journal of Computer Sciences and Engineering, Vol.06, Issue.08, pp.9-14, 2018.

This paper focuses the art of big data in medical field general background of big data is discussed first and then its related areas, such as cloud computing, data centers, internet of things and Hadoop. The value chain four phases are discussed here that is data generation, data acquisition, data storage and data analysis. For each phase here we are introducing general background technical challenges and review the latest advantages. Big data is a concept which defines the difference between itself and “massive data” or “very big data”. Three v s of big data are volume, velocity and variety which are defined by Doung Laney in 2001.

Big data analytics, information management, literature review, health care, data driven application

[1]. W. Raghupathi, V. Raghupathi, "Big data analytics in healthcare: promise and potential", Health Information Science and Systems, vol. 2, pp. 1-10, 2014. WHO. Mobile phones help people with diabetes to manage fasting and feasting during Ramadan. Features. 2014.
[2]. David Houlding, MSc, CISSP. Health Information at Risk: Successful Strategies for Healthcare Security and Privacy. Healthcare IT Program Of ce Intel Corporation, white paper. 2011.
[3]. Giangregorio LM, Leslie WD, Lix LM, Johansson H, Oden A, McCloskey E, et al. FRAX underestimates fracture risk in patients with diabetes. J Bone Miner Res. 2012;27(2):301–8.
[4]. Cruz JA, Wishart DS. Applications of machine learning in cancer prediction and prognosis. Cancer Inform. 2007;2:59–77.
[5]. Witten IH (Ian H., Frank E, Hall MA (Mark A, Pal CJ. Data mining: practical machine learning tools and techniques. 621 p.
[6]. Maglogiannis IG. Emerging artificial intelligence applications in computer engineering: real word AI systems with applications in eHealth, HCI, information retrieval and pervasive technologies. IOS Press; 2007. 407 p.
[7]. Kurth T, Walker AM, Glynn RJ, Chan KA, Gaziano JM, Berger K, et al. Results of multivariable logistic regression, propensity matching, propensity adjustment, and propensity-based weighting under conditions of nonuniform effect. Am J Epidemiol. 2005;163(3):262–70.
[8]. Nemes S, Jonasson JM, Genell A, Steineck G. Bias in odds ratios by logistic regression modelling and sample size. BMC Med Res Methodol. 2009;9(1):56.
[9]. Larsen K, Merlo J. Appropriate assessment of neighborhood effects on individual health: integrating random and fixed effects in multilevel logistic regression. Am J Epidemiol 2005;161(1):81–88.
[10]. Vittinghoff E, McCulloch CE. Relaxing the rule of ten events per variable in logistic and Cox regression. Am J Epidemiol. 2007;165(6):710–8.

Subhadra K., N Kavitha, "Big Data Analytics for Health Care Applications Using Cloud Computing- A Study", International Journal of Computer Sciences and Engineering, Vol.06, Issue.08, pp.15-17, 2018.

Nowadays, people doing agriculture are gradually decreasing in all over the world. When agriculture turned out to be smart, people can get better result than traditional technologies followed early. Farmers using modern technologies can control their cost, maintenance and monitoring performance of their filed. IoT and cloud can be combined together and can be applied in various domains of agriculture like temperature detection, moisture sensing, controlling and monitoring irrigation and so on. Traditional methods of farming have lot of drawbacks like wasting of water resources, unaware of seeds sowing, etc. Precision agriculture can give more accurate and better result with modern technologies. In this paper, we presented some typical applications of IoT and Cloud in agriculture field and Security threads that causes obstacles in implementation of smart farming. This survey helps for the better understanding of different technologies and to build sustainable smart agriculture.

The IoT,Cloud Computing,Big Data,Data Mining,Smart Farming, Precision Farming

[1] Amogh Jayaraj Rau et al,” The IoT Based Smart Irrigation System and Nutrient Detection with Disease Analysis”, 2017 IEEE Region 10 Symposium (TENSYMP).
[2] Mahammad Shareef Mekala,”A Novel Technology for Smart Agriculture Based on The IoT with Cloud Computing”, International conference on I-SMAC (The IoT in Social, Mobile, Analytics and Cloud),(I-SMAC 2017).
[3] S.Rajeswari,” A Smart Agricultural Model by Integrating The IoT,Mobile and Cloud-based Big Data Analytics” 2017 International Conference on Intelligent Computing and Control (I2C2)
[4] Thomas Truong,” An The IoT Environmental Data Collection System for Fungal Detection in Crop Fields”, 2017, IEEE 30th Canadian Conference on Electrical and Computer Engineering (CCECE).
[5] https://dzone.com/articles/the-future-of-smart-farming-with-The IoT-and-open-sour.
[6] Manual Diaz, Cristian Martin, Bartolome Rubio,"State-of -the -art, challenges and open issues in the integration of internet of things and cloud computing", journal of network and computer application 67,2016.
[7] Zhitao Guhan, Ging Li,Jun Wu, "Achieving efficient and secure data acquisition for cloud supported Internet of things in Smart Grid" , IEEE Journal of Internet of Things.
[8] Radadiya B.L, Thakkar R.G, Thumar V.M and Chaudhari,”Cloud computing and agriculture”, International journal of agriculture Science, ISSN: 0975-3710&E-ISSN: 0975-9107, Volume 8, Issue 22, 2016.
[9] Mohit kumar Navinay, Rahul Gedam, “A Review Paper on Internet of things based Application Smart Agricultural System”, International Journal of Latest Engineering and Management Research (IJLEMR), ISSN: 2455-4847 www.ijlemr.com,Volume 02 - Issue 04, April 2017, PP. 69-71
[10] Hemlata Channe, Sukhesh Kothari , Dipali Kadam, “ Multidisciplinary Model for Smart Agriculture using Internet-of-Things (The IoT), Sensors, Cloud-Computing, Mobile-Computing & Big-Data Analysis” , Int.J.Computer Technology & Applications,Vol 6 (3),374-382.
[11] Sukhpal Singh Gill, Inderveer Chana, Rajkumar Buyya,”The IoT Based Agriculture as a Cloud and Big Data Service:The Beginning of Digital India” Journal of Organizational and End User Computing Volume 29 • Issue 4 • October-December 2017.
[12] Major Singh Goraya1, Harjinder Kaur,” Cloud Computing in Agriculture”, HCTL Open International Journal of Technology Innovations and Research (IJTIR),http://ijtir.hctl.org,Volume 16, July 2015,e-ISSN: 2321-1814, ISBN (Print): 978-1-943730-43-8.
[13] THE IOT Vaishali S, Suraj S, Vignesh G, Dhivya S and Udhayakumar S,”Mobile Integrated Smart Irrigation Management and Monitoring System Using The IoT”, International Conference on Communication and Signal Processing, April 6-8, 2017, India.
[14] Carolus cambra, Sandra sendra,Jaime L loret,Laura Garcia,” An The IoT service oriented system for agriculture monitoring” IEEE ICC 2017 SAC Symposium Internet of Things Track.
[15] Alesso Botta,Walter de Donato, Valerio Persico,Antonio Pescape,"On the Integration of Cloud computing and Internet Of Things", International conference on future Internet of Things and cloud,2014.
[16] Dr.S Umamaheswaari,”Communication Protocol for the Internet of Things,Chapter 6,Dr.G.R.D college of science.

Mahalakshmi. P, S. Umamaheswari, "The IoT and Cloud Technologies Based Smart Farming and its Applications", International Journal of Computer Sciences and Engineering, Vol.06, Issue.08, pp.18-22, 2018.

The World Wide Web has extremely huge amount of information and it facilitates the user to search for data by moving from one document to another. Web mining is the application of Data mining and it is the procedure of discovering and extracting fruitful information from extremely large web data. The web is rapidly began to modernize and enlarged. In such case web mining is becoming a challenging task. It has to handle different communities, different external interfaces etc. In this paper we are focusing on web mining process and one of its type, web usage mining. This paper covers the basic concept of web mining and detailed description of web usage mining.

Data mining, web mining, web content mining, web structure mining, web usage mining

[1]. https://cs.wmich.edu/~yang/teach/cs595/han/ch01
[2]. https://searchcrm.techtarget.com/definition/Web-mining
[3]. http://cyberartsweb.org/cpace/ht/lanman/wsm1.htm
[4]. https://pdfs.semanticscholar.org/5b6d/a3ba6338326facfab93d53927cc300953547

Ramajayam G., Soundharya V., Likitha M.S., "A Survey on Webmining and Web Usuageminig", International Journal of Computer Sciences and Engineering, Vol.06, Issue.08, pp.23-26, 2018.

Stress is actually survival response when our body gets any outside force or event. The human body is designed to experience stress and react to it. Stress can be positive, keeping us alert, motivated and ready to avoid danger. Sometimes the stress becomes negative when a person faces continues challenges without any relaxation, mental tension caused by demanding, conflicts with others. Stress that continues without relief, a hormone called coristol is released into blood stream suppressing the functioning of immune, digestive and reproductive system. Because of this it is important to practice stress management in order to keep our body healthy. The purpose of this study is to find out the level of stress among various categories of people like school going students, working people and people under medical treatment using data mining techniques. Data mining is a process used in many areas to turn raw data into useful information. It is the process of discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems.

Machine learning,Coristol, discovering patterns,Data mining

[1] Lazarus, R. S. (1966), “Psychological Stress and the Coping Process” New York, Toronto, London: McGraw-Hill Book Co.
[2] Lazarus, R.S. & Cohen, J.B (1977),”Environmental Stress”, In I. Altman and J.F.Wohlwill (eds), Human Behavior and Environment.(Vol 2) New York: Plenum.
[3] Dr. Anil Kumar, Professor & Meenakshi Yadav, Haryana School of Business, Guru Jambheshwar University of Science &Technology, Hisar, “ Journal of Management Research ,ISSN 2347-4270, Vol 3 Issue 1, October 2014
[4] T.-S. Lim, W.-Y. LOH, and W. Cohen. A comparison of prediction accuracy, complexity, and training time of thirty-three old and new classification algorithms. Machine Learning, 39, 2000.
[5] S. Michie,” Causes and management of stress at work. Occupational and Environmental Medicine” , 59(1):67, 2002.
[6] Prof. Dr. Wolfgang Karl Hardle, “Time series Data Mining Methods: A Review”, Berlin, March 25, 2015.
[7] Vemuri Swathi, M.Sudhir Reddy,”Stress Among working Women: Literature Review”, IJCEM International Journal of Computational Engineering & Management, Vol. 19 Issue 4, July 2016 ISSN (Online): 2230-7893.
[8] V.R.Kavitha, “Activity based Mental Stress Detection and Analysis”, International Journal of Computer Applications (0975 – 8887) Volume 152 – No.10, October 2016.
[9] Kanta Devi, "Level of stress among working and non-working women in chandigarh", International Journal of Science & Enignieering Research, Volume 7, Issue 4, April-2016, ISSN 2229-5518 .
[10] D.Umanandhini,G.Kalpana,”Survey on Stress Types Using Data Mining Algorithms”, International Journal of Innovative Research in Advanced Engineering (IJIRAE) ISSN: 2349-2163, Issue 04, Volume 4 (April 2017).
[11] Mr. Sudhir M. Gorade1, Prof. Ankit Deo2,Prof. Preetesh Purohit,” Early Identification of Diseases Based on Responsible Attribute Using Data Mining”, International Research Journal of Engineering and Technology (IRJET), Volume: 04 Issue: 07 | July -2017.
[12] J.S.Kanchana, R.Surya, H.Thaqneem Fathima, R.Sandhiya,”Stress Detection Using Classification Algorithm”,(IJERT) International Journal of Engineering Research & Technolgies,Vol.7 Issue 04, April 2018.
[13] Siobhan Hugh-Jones, Sally rose, Gina Z.Koutsopoulou,Ruth Simms-Ellis,” Mindfulness (2018) 9:474–487.

K. Yemunarane, A. Hema, "A Survey on Stress Detection using Data Mining Techniques", International Journal of Computer Sciences and Engineering, Vol.06, Issue.08, pp.27-29, 2018.

The thriving medical applications of Data mining in the field of medicine and public health led to the popularity of its use in KDD (Knowledge Discovery in Data Mining.). Disease diagnosis is one of the applications in the medical field. Data Mining tools are establishing the successful result in ADHD. This survey paper reveals Attention Deficit Hyper Active Disorder (ADHD) is a pattern of behaviour that affects approximately 3 to 5% of school going population. This paper surveys on implementation methods by using well known Data Mining techniques. Data Mining provides the methodology and technology to transform these mounds of data into useful information for decision making. The aim of this survey is to predict ADHD problems using Data Mining techniques like classification, Clustering, AI Neural networks, Bayesian Classifiers and Decision Trees. To implement these classification techniques different sources and methods of data collection, data set, data distribution and normalization are required .Therefore this paper aims to understand about Mining and its importance in Psychology.

KDD,ADHD,DataNormalization,Datamining,Classification

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[5]Julie m. David Statistical machine learning techniques For the prediction of learning disabilities In school-age children (Final Thesis) [4] Kipli, Kuryati, Abbas Z. Kouzani, and Isredza Rahmi A. Hamid. "Investigating machine learning techniques for detection of depression using structural MRI volumetric features." International journal of bioscience, biochemistry and bioinformatics 3.5 (2013): 444-448.
[6]Pang-Ning, T., Michael, S., Vipin, K: Introduction to Data Mining,Low price edn. Pearson Education, Inc., London, 2008.
[7]Crealock Carol, Kronick Doree: Children and Young People with Specific Learning Disabilities, Guides for Special Education, No. 9, UNESCO, 1993
[8]Li Lin, Longbing Cao, Jiaqi Wang, Chengqi Zhang, “The Applications of Genetic Algorithms in Stock Market Data Mining Optimisation”, Proceedings of Fifth International Conference on Data Mining, Text Mining and their Business Applications,pp- 593-604,sept 2005.
[9]Dabek, Filip, and Jesus J. Caban. "A Neural Network Based Model for Predicting Psychological Conditions." Brain Informatics and Health. Springer International Publishing, 2015. 252-261.
[10] Tawseef Ayoub Shaikh.,”A Prototype of Parkinson’s and primary tumor disease prediction using data mining techniques”, International Journal of Engineering Science Invention, vol 3,Issue:4, April 2014.
[11]”Prediction of Mental Health Problems Among Children Using Machine Learning Techniques”.Ms. Sumathi M.R., Research Scholar, Dept. of Computer Science, Bharathiar University, Coimbatore, India.Dr. B. Poorna, Principal, S.S.S. Jain College, T.Nagar, Chennai, India. (IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 7, No. 1, 2016.
[12] An ADHD Diagnostic Approach Based on Binary-Coded Genetic Algorithm and Extreme Learning Machine Vasily Sachnev* School of Information, Communication and Electronics Engineering, The Catholic University of Korea, Bucheon, Korea bassvasys@hotmail.com Sundaram Suresh School of Computer Engineering, Nanyang Technological University, Singapore ssundaram@ntu.edu.sg. Journal of Computing Science and Engineering, Vol. 10, No. 4, December 2016, pp. 111-117
[13]Performance Analysis of Machine Learning Techniques to Predict Mental Health Disorders in Children Anjume S1*, Amandeep K1, Aijaz Ah M2, Kulsum F3 Department of Computer Science and Engineering, Desh Bhagat University, Punjab, India1 Department of Mathematics, Higher Education J&K, India2 Data Storage Engineer, CSC Pvt. Ltd, Hyderabad, India3 . International Journal of Innovative Research in Computer and Communication Engineering (An ISO 3297: 2007 Certified Organization) Vol. 5, Issue 5, May 2017
[14] Amos Fleischmann, Erez C.Miller Online Narratives by Adults with ADHD who were Diagnosed in Adulthood.

M. Lalithambigai, A. Hema, "A Survey on ADHD using Data Mining Techniques", International Journal of Computer Sciences and Engineering, Vol.06, Issue.08, pp.30-33, 2018.

Cloud computing is information technology (IT) model that allows the ubiquitous access to shared pools of configurable system resources and higher-level services provisioned with lesser effort. Cloud computing provides number of advantages in information technology. Cloud storage is an essential service of cloud computing that are prevalent as it present low-cost and on-demand utilization of large storage and processing resources. Security is an essential barrier to comprehensive prevalence of cloud computing. Security is mainly prioritized portion for any cloud computing environment during data storage and data transaction. Cloud computing approach is linked with users sensitive data stored both at client’s end and cloud servers. For improving the security level, authentication techniques are used for allowing the authenticated user to access the data. But, complexity of the security is high when decentralization of data over the wide area of network. In addition, existing methods failed to improve the authentication accuracy and data confidentiality. Our main objective of the work is to improve the security level during the data storage and data transaction by using cryptosystem techniques.

Cloud computing, ubiquitous, security, data confidentiality, data transaction, data storage

[1] Hui Tian, Yuxiang Chen, Chin-Chen Chang, Hong Jiang, Yongfeng Huang, Yonghong Chen and Jin Liu “Dynamic-Hash-Table Based Public Auditing for Secure Cloud Storage”, IEEE Transactions on Services Computing, Volume 10, Issue 5, September-October 2017, Pages 701 – 714
[2] Geeta Sharma and Sheetal Kalra, “Identity based secure authentication scheme based on quantum key distribution for cloud computing”, Peer-to-Peer Networking and Applications, Springer, Volume 11, Issue 2, March 2018, Pages 220–234
[3] Joseph K. Liu, Kaitai Liang, Willy Susilo, Jianghua Liu and Yang Xiang, “Two-Factor Data Security Protection Mechanism for Cloud Storage System”, IEEE Transactions on Computers, Volume 65, Issue 6, June 2016, Pages 1992 – 2004
[4] Sheren A. El-Booz, Gamal Attiya and Nawal El-Fishawy, “A secure cloud storage system combining time-based one-time password and automatic blocker protocol”, EURASIP Journal on Information Security, Springer, Volume 13, December 2016, Pages 1-13
[5] Willy Susilo, Peng Jiang, Fuchun Guo, Guomin Yang, Yong Yu and Yi Mu, “EACSIP: Extendable Access Control System with Integrity Protection for Enhancing Collaboration in the Cloud”, IEEE Transactions on Information Forensics and Security, Volume 12, Issue 12, December 2017, Pages 3110 – 3122
[6] Ruhul Amin, Neeraj Kumar, G.P. Biswas, R. Iqbal and Victor Chang, “A Light Weight Authentication Protocol for IoT-enabled Devices in Distributed Cloud Computing Environment”, Future Generation Computer Systems, Elsevier, Volume 78, Part 3, January 2018, Pages 1005-1019
[7] Yong Yu, Man Ho Au Giuseppe Ateniese, Xinyi Huang, Willy Susilo, Yuanshun Dai, and Geyong Min, “Identity-based Remote Data Integrity Checking with Perfect Data Privacy Preserving for Cloud Storage”, IEEE Transactions on Information Forensics and Security, Volume 12, Issue 4, April 2017, Pages 767 – 778
[8] Nesrine Kaaniche and Maryline Laurent, “Data Security and Privacy preservation in Cloud Storage Environments based on Cryptographic Mechanisms”, Computer Communications, Elsevier, Volume 111, 1 October 2017, Pages 120-141
[9] Joseph K. Liu, Man Ho Au, Xinyi Huang, Rongxing Lu and Jin Li, “Fine-grained Two-factor Access Control for Web-based Cloud Computing Services”, IEEE Transactions on Information Forensics and Security, Volume 11, Issue 3, March 2016, Pages 484 – 497
[10] Mazhar Ali, Saif U. R. Malik and Samee U. Khan, “DaSCE: Data Security for Cloud Environment with Semi-Trusted Third Party”, IEEE Transactions on Cloud Computing, Volume 5, Issue 4, October-December 2017, Pages 642 – 655

Kavitha K, Saravanan V, "SURVIVAL STUDY ON DATA STORAGE AND TRANSACTION SECURITY USING AUTHENTICATION TECHNIQUES", International Journal of Computer Sciences and Engineering, Vol.06, Issue.08, pp.34-39, 2018.

Identification of overlapping clusters in complex data has been remaining as the problem to tackle. To the best knowledge, no evolutionary and unsupervised clustering approach is able to detect it successfully. Most of the existing evolutionary clustering techniques fail to detect complex/spiral shaped clusters. This research adopts an optimization method called Ant Colony Optimization (ACO) with the existing algorithm called Evolutionary Multi-objective Clustering (EMC) for overlapping clusters detection. This work resolves the problem of overlapping clusters by enhancing the multi-objective evolutionary clustering approach with Genetic Algorithm (GA) with variable length chromosome & local search for feature selection. Combined with Evolutionary Multiobjective Clustering, Ant Colony Optimization (ACOEMCOC) approach succeeds in obtaining non-dominated and near-optimal clustering solutions in terms of different cluster quality measures like purity, and index etc., and classification performance.

Evolutionary Multiobjective Clustering (EMC), EMCOC, FEMCOC, Genetic Algorithm (GA), Ant Colony Optimization (ACO) algorithm

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S. Punithavathy, "An Ant Colony Optimization based Evolutionary Multi-objective Clustering for Overlapping Clusters Detection (ACOEMCOC)", International Journal of Computer Sciences and Engineering, Vol.06, Issue.08, pp.40-48, 2018.