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This paper proposes a novel approach for image caption generation. Being able to describe the content of an image in natural language sentences is a challenging task, but it could have great impact because great amount of resources is required to meet the demands of vast availability of image dataset. The growing importance of image captioning is commensurate with requirement of image based searching, image understanding for visual impaired person etc. In this paper, we develop a model based on deep recurrent neural network that generates brief statement to describe the given image. Our models use a convolutional neural network (CNN) to extract features from an image. We used ranking objective to pay attention to subtle difference between the similar images to generate discriminatory captions. MS COCO dataset is used, nearly half of the dataset for training and one fourth of dataset for each validation and testing. For every image five captions are provided to train the model Our model consistently outperforms other models with on ranking objective. We evaluated our model based on BLEU, METEOR and CIDEr scores.

Visual Geometry Group, Long Short Term Memory, Ranking Objective, Image Captioning

[1] Andrej Karpathy and Li Fei-Fei, “Deep visual-semantic alignments for generating image descriptions”. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 3128–3137, 2015.
[2] Kyunghyun Cho, Bart Van Merrienboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio, “Learning phrase representations using rnn encoderdecoder for statistical machine translation”, arXiv preprint arXiv:1406.1078, 2014.
[3] Ryan Kiros, Ruslan Salakhutdinov, and Rich Zemel, “Multimodal neural language models”. In Proceedings of the 31st International Conference on Machine Learning (ICML-14), pages 595–603, 2014
[4] Junhua Mao, Wei Xu, Yi Yang, Jiang Wang, Zhiheng Huang, and Alan Yuille, “Deep captioning with multimodal recurrent neural networks (m-rnn)”, arXiv preprint arXiv:1412.6632, 2014.
[5] Ryan Kiros, Ruslan Salakhutdinov, and Richard S Zemel, “Unifying visual-semantic embeddings with multimodal neural language models”, arXiv preprint arXiv:1411.2539, 2014.
[6] Oriol Vinyals, Alexander Toshev, Samy Bengio, and Dumitru Erhan, “Show and tell: A neural image caption generator”. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 3156–3164, 2015.
[7] Jia Deng,Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei, “Imagenet: A large-scale hierarchical image database”. In Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on, pages 248–255. IEEE, 2009.
[8] Karen Simonyan and Andrew Zisserman, “Very deep convolutional networks for large-scale image recognition”, arXiv preprint arXiv:1409.1556, 2014.
[9] Hochreiter, Sepp, and Jrgen Schmidhuber, “Long Short-Term Memory”, Neural Computation 9.8 (1997): 1735-780. Web. 23 Apr. 2016
[10] Lin, Tsung-Yi, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollr, and C. Lawrence Zitnick, “Microsoft COCO: Common Objects in Context” Computer Vision ECCV 2014 Lecture Notes in Computer Science (2014): 740-55. Web. 27 May 2016
[11] Papineni, Kishore, Salim Roukos, ToddWard, Wei-Jing Zhu, Bleu: a method for automatic evaluation of machine translation” Proceedings of the 40th Annual Meeting on Association for Computation Linguistics (ACL): 311-318 (2002). Web. 24 May 2016
[12] Karpathy, Andrej, and Li Fei-Fei, “Deep Visual-semantic Alignments for Generating Image Descriptions” 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2015). Web. 29 May 2016
[13] Chen, Xinlei and C. Lawrence Zitnick, “Learning a Recurrent Visual Representation for Image Caption Generation”, CoRR abs/1411.5654 (2014). Web. 19 May 2016
[14] Donahue, Jeff, Lisa Anne Hendricks, Sergio Guadarrama, Marcus Rohrbach, Subhashini Venugopalan, Trevor Darrell, and Kate Saenko, “Long-term Recurrent Convolutional Networks for Visual Recognition and Description”, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2015). Web. 20 Apr. 2016

Geetika, Tulsi Jain, "Discriminatory Image Caption Generation Based on Recurrent Neural Networks and Ranking Objective," International Journal of Computer Sciences and Engineering, Vol.5, Issue.10, pp.260-265, 2017.

The problem of computational efficiency in adaptive algorithms, which is current and pressing, can be solved through their implementation in parallel frameworks, like CUDA, OpenCL, etc. The approach taken to parallelize any complex operation requires its separation into several distinct and independent sub-operations. We employed the same procedure to parallelize the BP (or Backpropagation) network algorithm. The function breakdown of the BP network involved breaking its overall operation into Feed-forward and Back-Propagate sub-operations, which was further divided into smaller independent execution groups. We applied parallel constructs on those independent execution groups and used the MNIST dataset to compare the algorithm’s performance with respect to the sequential algorithm. Comparing their performances, we found that the efficiency of the algorithm depended on the size of the BP network. In the large network with massive number of weight connections, we saw a significant improvement in the convergence time. This makes our algorithm preferable in feedforward networks having large number of hidden layers, neurons and weight connections.

Backpropagation, Supervised Learning, CUDA, parallel

[1] G. S. Almasi, A. Gottlieb, “Highly Parallel Computing”, Benjamin-Cummings Publishing Co., Inc., USA, 1989.
[2] D. E. Rumelhart, G. E. Hinton, R. J. Williams, “Learning representations by back-propagating errors,” Nature, vol. 323, no. 6088, pp. 533–536, 1986.
[3] C. Li, C. Yu, “Performance evaluation of public non-profit hospitals using a BP artificial neural network: The case of Hubei province in china,” International Journal of Environmental Research and Public Health, Aug 2013.
[4] Y. Li, Y. Fu, H. Li, and S. W. Zhang, “The improved training algorithm of back propagation neural network with self-adaptive learning rate,” in 2009 International Conference on Computational Intelligence and Natural Computing, pp. 73–76, 2009.
[5] J. Zhu, P. Sutton, “FPGA implementations of neural networks–a survey of a decade of progress,” Field Programmable Logic and Application, pp. 1062-1066, 2003.
[6] I. B. D. Steinkraus, P.Y. Simard, “Using GPUs for machine learning algorithms,” Document Analysis and Recognition, pp. 1115-1120, 2009.
[7] C. M. Bishop, “Pattern Recognition and Machine Learning”, Springer-Verlag New York, USA, 2006.
[8] Y. Yuan, “Step-sizes for the gradient method,” AMS/IP Studies in Advanced Mathematics, 1999.
[9] R. A. Jacobs, “Increased rate of convergence through learning rate adaptation,” Neural Networks, vol. 1, 1988.
[10] M. J. Flynn, “Some computer organizations and their effectiveness,” IEEE Transactions on Computers, vol. C-21, no. 9, pp. 948–960, 1972.
[11] E. Kussul, T. Baidyk, “Improved method of handwritten digit recognition tested on MNIST database,” Image and Vision Computing, vol. 22, no. 12, pp. 971 – 981, 2004.
[12] J. D. Owens, M. Houston, D. Luebke, S. Green, J. E. Stone, J. C.Phillips, “GPU computing,” Proceedings of the IEEE, vol. 96, no. 5, pp. 879–899, May 2008.
[13] U. Ray, T.K. Hazra, U.K. Ray, "Matrix Multiplication using Strassen’s Algorithm on CPU & GPU", International Journal of Computer Sciences and Engineering, Vol.4, Issue.10, pp.98-105, 2016.

K. Devkota, P. Bhattarai, "Parallel Implementation of Gradient Descent Algorithm for Backpropagation Networks," International Journal of Computer Sciences and Engineering, Vol.5, Issue.10, pp.266-272, 2017.

The mobile ad-hoc networks are the mobile wireless networks which have no fixed infrastructure and routers, each node act as a router such that the end to end quality of service (QoS) is unpredictable or the single node. The end to end quality of service metrics is not changeable or fixed when the mobile networks have seen in the whole formed by combining several different nodes. Logical time (coherence time) is the time taken to send the information or a file to all the nodes is max and the end to end quality of service metrics is constant. The spreading period is the area covered or extended over a wide area over a period of time, it’s the time duration to send the information or file to the mobile network of the individual nodes. If found that the logical time is more than the spreading period the quality of service metrics is followed a particular path. The objective of this paper is to calculate or measure the end to end quality of service of each node in a mobile network and describe how energy map is constructed in a mobile wireless network.

QoS, Logical Time, Spreading period, QoS metrics

[1] Min Kyoung Park, Member, IEEE and Volkan Rodoplu, Member IEEE “Energy Maps for Mobile Wireless networks coherence Time Versues Spreading Period” in International Communications Conference (ICC) accepted Jan 5, 2009.
[2] D. Niculescu and B. Nath, “Trajectory based forwarding and its application” in Proc. ACM/IEEE MOBICOM, 2003.
[3] M.K. Park and V.Rodoplu, “Energy maps for large-scale, mobile wireless networks” in Proc. International Communications Conference (ICC), Jun. 2007, pp. 3136-3141.
[4] S. Chakrabarthi and A. Mishra, “QoS issues in ad hoc wireless networks” IEEE Commnications Magazine, pp. 142-148, Feb. 2001.
[5] A. Iwata,C.-C Chian, G. Pei, M. Gerla and T.-W. Chen, “Scalable Routing Strategies for ad hoc wireless networks” IEEE Trans. J. Selected Areas Commun, Vol.17, No. 8, pp. 1369-1379, Aug. 1999.
[6] C. Zhu and M. S. Corson, “QoS routing for mobile ad hoc networks” in Proc. IEEE INFOCOM 2002, Aug. 2002, pp. 1488-1505.
[7] S. Subramanian and S. Shakkottai, “Geographic Routing with limited information in sensor networks” in Proc 4th Int. Conf. Information Processing in Sensor Networks, Apr. 2005.
[8] P.Pal, R.Pal Observing of Mobility Model against Reactive Routing Protocols in MANETs of throughput, Average end-to-end delay, packet delivery ratio” in International Journal of Computer Sciences and Engineering (IJCSE) Vol.5, Issue.1, pp. 43-52, Jan-2017.

Bura Vijay Kumar, Srinivas Aluvala, K. Sangameshwar, "Energy Mapping Approach for QoS in MANETs," International Journal of Computer Sciences and Engineering, Vol.5, Issue.10, pp.273-275, 2017.

Google Suite is a package of integrated cloud based services that provide an organization a novel way to work together online. The advantages are not just limited to personalized business Gmail and instant chat messaging but extend to real time document collaboration, social media, video conferencing, increased cloud storage capacity, eDiscovery, log analysis, audit reports, enhanced security controls & data loss prevention. It is a set of applications that can prove invaluable to the organisation as it facilitates communication & information sharing for quick decision making and follow ups. With this, G Suite has emerged as a competitor to MS Office 365 which offers similar package benefits to users. Still, the innovative model of G Suite proves more promising when it comes to versatility, cost efficiency and feasibility.

Google, Suite, Cloud, Storage, Collaboration, Gmail

[1] Mahapatra. C., & Mahapatra. T., “Review: Synthesizing Information Technology with Human Capital Management – A Success Story With Future Prospects”, ZIJMR, Vol.3, Issue.4, 2013.
[2] Herrick. D. R., “Google this!: Using Google apps for collaboration and productivit”, In Proceedings of the 37th annual ACM SIGUCCS fall conference (2009): communication and collaboration, pp.55-64.
[3] Nakayama. M., & Taylor. C, “The Effects of Perceived Functionality and Usability on Privacy and Security Concerns about Adopting Cloud Application Adoption”, In Proceedings of the Conference on Information Systems Applied Research, pp. 1508, 2016.
[4] Holton. B, “A Review of the Audio Tutorial for the Google Suite of Products by Mystic Access”.
[5] Roda. E. M., & Luiz. S. S., “Google´ S Drive Possibilities For Classroom And Distance Teaching”. Bienvenidos, pp. 261-268,2016.
[6] Bray. M., “Going Google: privacy considerations in a connected world. Knowledge Quest”, Vol.44, Issue.4, pp.36, 2016
[7] Introducing G Suite (formerly Google Apps for Work), a set of Intelligent apps for business by Google Cloud. https://gsuite.google.com. Accessed on 28.03.2017
[8] Skendžić. A., & Kovačić. B, “Office suites in the cloud: Microsoft Office 365 versus Google Apps”, In International Convention on Information and Communication Technology, Electronics and Microelectronics, 2012.
[9] Barney. D., “FACE-OFF: Google Apps vs. Office 365. Redmond: the Independent Voice of the Microsoft IT Community”, Vol.18, Issue.3, 2012.
[10] Raja. S., “Google’s Race Against Apple And Microsoft: Enter The Chromebook. The Market Mogul”, 2016.
[11] Mykytenko. P. V., “Using Cloud Computing In Solving The Problems Of Logic. Information Technologies And Learning Tools”, Vol.57, Issue.1, pp.104-114, 2017.
[12] Wray. C, “Staying in the Know: Tools You Can Use to Keep Up With Your Subject Area. Collection Management”, Vol.41, Issue.3, pp.182-186,2016.
[13] Waters. I., Greve, D., & Strant, L., “Microsoft Office 365–Exchange Online Implementation and Migration. Packt Publishing Ltd”.
[14] Franks. P. C., “Integration of Enterprise Content Management and Software as a Service. In Security in the Private Clou”, CRC Press, pp. 17-35, 2016.
[15] Arichi Arzare, Suneha Chaudhari, Sayalee Desai and Sonali Jadhav, "User-Defined Classification for Email System using Back Propagation Algorithm", International Journal of Computer Sciences and Engineering, Vol.3, Issue.4, pp.1-5, 2015.

Tania Mahapatra, Meenu Chopra, Cosmena Mahapatra, "Google Suite: An Integrated IT application for Improved Business Efficiency," International Journal of Computer Sciences and Engineering, Vol.5, Issue.10, pp.276-279, 2017.

For most of the people, web interaction is a very common phase to acquire information. It is possible that in a combined environment, more than one person may try to obtain similar information in one domain. One person may like to solve a problem using an unfamiliar Apache Tomcat which he had studied by another person before. Connecting and then sharing with that persons will be more beneficial to get there learned knowledge. Fine-grained knowledge sharing is proposed for this combined environment. The system is proposed to classify the surfed data into clusters and summarize the details in fine grained details. For any system the efficiency depends upon the surfing. The framework of proposed work includes: (1) Data which is surfed, clustered into tasks. (2)Then task is mined in fine grained output. To get proper result,the search method is applied to the output (mined results).The concept of Data Mining in fine grained knowledge is combined with the information gathering and classification to produce efficient data searching technique in agriculture system.

Fine-grain, Cluster, Web-mine

[1] Boser, B. E., I. Guyon, and V. Vapnik (1992), “A training algorithm for optimal margin classifiers”, . In Proceedings of the Fifth Annual Workshop on Computational Learning
[2] V. Vapnik, The Nature of Statistical Learning Theory, NY: Springer-Verlag. 1995.
[3] Meijuan Gao, Jingwen Tian, Shiru Zhou, “Research of Web Classification Mining Based on Classify Support Vector Machine “, SECS International Colloquium on Computing, Communication, Control, and Management -2009.
[4] Wang, P., Hu, J., Zeng, H. J., & Chen, Z. (2009). “Using Wikipedia knowledge to improve text classification”, Knowledge and Information Systems, 9-13.
[5] Wei Wu & Zhengdong Lu Hang Li ,”Learning Bilinear Model for Matching Queries and Documents”,
[6] Krishi-Mitra: “Expert System for Farmers” ,IJCSMC, Vol. 4, Issue. 4, April 2015, pg.893 899
[7] Cambazoglu B. B., Aykanat C.:” Performance of query processing implementations in ranking-based text retrieval systems using inverted indices”. International Journal of Food and Agricultural Economics ISSN 2147-8988 Vol. 1 No.1 pp. 63-74.
[8] Federal Committee on Statistical Methodology (1980), Statistical Policy Working Paper 5: Report on Exact and Statistical Matching Techniques Washington, DC: Office Federal Statistical Policy and Standards, U.S. Department of Commerce. “Organizational History of the Department of Agriculture & Cooperation. Retrieved” 5 July 2012.
[9] Z. Pawlak, “Rough sets and intelligent data analysis”, Information Sciences, 147 (2002) 1 12.
[10] K.I. Kim, K. Jung, S.H. Park, H.J. Kim, “Support vector machines for texture classification”, IEEE Transactions on Pattern Analysis and Machine Intelligenc 24(11) (2002) 15421550.
[11] Latha Parthiban, “A Novel Face Recognition Algorithm with Support Vector Machine Classifier”, International Journal of Mathemetics and Scientific Computing”, Vol. 1,no. 1,2011.
[12] R.Muralidharan , and Dr.C.Chandrasekar, “Object Recognition Using Support Vector Machine Augmented by RST Invariants”IJCSI International Journal of Computer Science Issues, Vol. 8, Issue 5, No 3, September 2011.
[13] Mohd. Aquib Ansari, Diksha Kurchaniya, Manish Dixit, Punit Kumar Johari, "An Effective Approach to an Image Retrieval using SVM Classifier", International Journal of Computer Sciences and Engineering, Vol.5, Issue.6, pp.62-72, 2017.
[14] R. Baker and K. Yacef, “The State of Educational Data Mining in 2009: A Review and Future Visions’, J. Educational Data Mining, vol. 1, no. 1, pp. 3-17, 2009.
[15] J. Kaur, S.S. Sehra, S.K. Sehra, "A Systematic Literature Review of Sentiment Analysis Techniques", International Journal of Computer Sciences and Engineering, Vol.5, Issue.4, pp.22-28, 2017.
[16] Uma Aggarwal, Gaurav Aggarwal , "Sentiment Analysis on Demonetization using SVM", International Journal of Computer Sciences and Engineering, Vol.5, Issue.6, pp.183-187, 2017.
[17] Binder, J., Murphy, K., & Russell, S. (1997b). Space-efficient inference in dynamic probabilistic networks. In Proceedings of the Fifteenth International Joint Conference on Artificial Intelligence (IJCAI-97) Nagoya, Japan. Morgan Kauf-mann.
[18] A. M. Turing, Intelligent Machinery, in Machine Intelligence, B. Meltzer and D. Michie, Eds. Edinburgh: Ed-inburgh University Press, 1969, vol. 5, National Physical Laboratory Report (1948).
[19] Chih-Wei Hsu, Chih-Chung Chang, Chih-Jen Lin, A Practical Guide to Support Vector Classification.
[20] Ziyu Guan, Shengqi Yang, Huan Sun, Mudhakar Srivatsa,Xifeng Yan,Fine-Grained Knowledge Sharing in Collaborative Environments , IEEE Transactions on Knowledge and Data Engineering,2015.
[21] Pradipsinh K. Chavda and Jitendra S.Dhobi , proposed the A Survey of Model Used for Web Users Browsing Behavior Prediction in Computer Engineering and Intelligent Systems, Vol.6, No.3, 2015.
[22] Cong Wang , Kui Ren , Wenjing Lou and Shucheng Yu proposed the Achieving secure, scalable and fine-grained data access control in cloud computin.,INFOCOM, 2010 preceedings IEEE.
[23] Iftikhar, N, Integration, Aggregation, and Exchange of Farming Device Data: A high level perspective, Application of digital information and web Technologies, 2009. ICADIWT09.
[24] Rakesh. S.Shirsath, Vaibhav A.Desale, Amol. D.Potgantwar, "Big Data Analytical Architecture for Real-Time Applications", International Journal of Scientific Research in Network Security and Communication, Vol.5, Issue.4, pp.1-8, 2017.
[25] Pramod.G. Patil, Sneha.A. Khaire, "Fine-Grained Knowledge In Agriculture System", International Journal of Computer Sciences and Engineering, Vol.5, Issue.10, pp.287-292, 2017.

Pramod.G. Patil, Sneha.A. Khaire, "Fine-Grained Knowledge In Agriculture System," International Journal of Computer Sciences and Engineering, Vol.5, Issue.10, pp.280-284, 2017.

An Authentication for the digital transaction is a most vital problem. To secure any transactions through digital, mobile, internet, online system is very important. Nowadays India is gradually moving towards digital transactions. To make sure the confidential transactions are very important. The textual password for the Authentication is the most common method, here user need to memorize their passwords during their transaction, somewhere it is vulnerable to social engineering, cyber-attacks, and shoulder surfing. Regarding this, some graphical passwords are introduced to avoid the textual passwords. To address this problem, we can implement an application called color code system with OTP. It is a new technique based on the graphical password, in which here we use an OTP and color coding to solve the security problem. Here once the colors are exhibited with a specific rating, after this, some passwords are generated with some evaluation of colors, further the same colors are brought forth in a random path., we can insert the passwords according to the previous colors and ratings for user authentication which is called color code authentication, by this technique we can overcome the attacks like shoulder surfing.

Authentication, Transaction, Color, Ratings, Shoulder Surfing

[1]. X. Suo, Y. Zhu and G. Owen, “Graphical Passwords: A Survey”. In Proc. ACSAC`05. Vol5 Feb 2015, 2277128x
[2]. G. E. Blonder. Graphical passwords. United States Patent 5559961, 1996.
[3]. Rami El Sawda, & Habib Hamam RGB Coloured Image Encryption Processes Using Several Colored Keys Images”
[4]. A. F. Syukri, E. Okamoto, and M. Mambo, "A User Identification System Using Signature Written with Mouse," in Third Australasian Conference on Information Security and Privacy (ACISP), pp. 403-441.
[5]. M Sreelatha , M Sshashi , M Anirudh , MD Sulthan Ahamer, V Manoj Kumar .”Authentication Schemes for Session Passwords using Color and Images”,International Journal of Network Security &Its Applications, Vol.3,No.3,May 2011.
[6]. Nirmala.B, Manigandan.T, Kumanan.K.”Authentication Schemes For Session PasswordsUsing Text And Colors”,IJEDR, Vol5, Issue 2, 2017.
[7]. Z.Zheng, X.Liu, L.Yin, Z.Liu “A hybrid password authentication scheme based on color and text” Journal of computers-May 2010.
[8]. S.Balaji, Lakshmi.A, V.Revanth, M.Saragini, V.Venkateswara Reddy “Authentication Techniques For Engendering Session Passwords With Colors And Text” Advances in Information Technology and Management Vol. 1, No. 2, 2012.
[9]. Rohit Jagtap, Vaibhav Ahirrao, Vinayak Kadam, Nilesh Aher “Authentication schemes for session password using color and special characters” International Journal of Innovations & Advancement in Computer Science, April 2014.
[10]. Rachita Dubey, Jijo S.Nair, "A Review on Secured One Time Password (OTP) Based Authentication & Validation System", International Journal of Computer Sciences and Engineering, Vol.5, Issue.6, pp.232-236, 2017.
[11]. Salim Istyaq, Lovish Agrawal , "A New Technique For User Authentication Using Numeric One Time Password Scheme", International Journal of Computer Sciences and Engineering, Vol.4, Issue.5, pp.163-165, 2016.
[12]. Dr.D.S. Rao et. al. ,” One Time Password”, Securty Journal of Computer Technology and Applications, Sept-Oct 2011. Vol(2)5, ISSN:2229-6093

Shivamurthaiah M, Sitesh Kumar Sinha, Praveen Kumar K Manasa L R, "An Authentication for digital transaction with OTP using Color Code Systems (CCS)," International Journal of Computer Sciences and Engineering, Vol.5, Issue.10, pp.285-287, 2017.

Hearing deficit individuals face challenges that cannot be avoided but they can overcome it with the powerful tool of education. It is a tool that is undeterred to the barriers of communication and thus it promises them a normal life of equality and independence. This paper is an attempt to propose the android application with modules for students, volunteers and donors catering to various aspects of learning to become a one stop solution for the hearing impaired. The underlined features of the application are text-to-speech conversion, a pool of tests with detailed progress report for students were the study resources are monitored by volunteer module. Additionally donation module records donors donating phones, books and money.

Android application, text-to-speech, Progress report

[1] s. b. a.sujith kumar, "an android app for specially able people," international journal of advanced reaserch in computer science and software engineering, vol. 4, no. 9, pp. 244-248, 2014.
[2] m. t. k. miralkumar surati, "comprehensive regarding hearing impairing using smart android phone," international journal of advanced reaserch in computer and communication engineering, vol. 4, no. 7, p. online, july 2015.
[3] m. m. p. r. apeksha khilari, "android based aid for deaf," international journal of technical reaserch and application, vol. 5, no. 3, p. online, 2016.

Aditya Vadhavkar, Abhishek Sengupta, Rahul Jadhav, Rishabh Patil, Vaishnavi Patil, "Unmute: an Android app for Deaf and Hard Hearing Students," International Journal of Computer Sciences and Engineering, Vol.5, Issue.10, pp.288-291, 2017.

Today, maximum of the organizations have to deal with big quantities of records, that is hastily growing. In order to address these explosively growing amounts of information, one has so that it will extract, examine, and process information time to time. Clustering has for this reason been identified keeping in view this example and it is considered as an essential device used to analyze huge statistics. Technological progress, specifically inside the regions of finance and enterprise informatics, poses a big task for big scale records clustering. To deal with this issue, researchers have provided you with parallel clustering algorithms that are primarily based on parallel programming fashions. MapReduce is one of the most typically used frameworks used for this motive and it has received high consciousness thanks to its flexibility, fault tolerance and programming ease. However, the overall performance has trouble for iterative packages. This paper gives an in depth evaluation of iterative frameworks which could help MapReduce for overcoming boundaries for iterative algorithms.

clustering, framework and Map reduces

[1]. Kang U, Tong H, Sun J, Lin C-Y, Faloutsos C. GBASE, in Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining – KDD’11, San Diego,CA,USA,2011 Aug, pp. 1091.
[2]. “YouTube statistic.” [Online]. Available:http://www.youtube.com/yt/press/statistics.html. [last accessed June 22, 2014],Dare Accessed:22/6/2014.
[3]. Kim W. Parallel clustering algorithms: survey, CSC 8530 parallel algorithms, 2009, pp.1-32.
[4]. Aiyer A, Bautin M, Chen GJ, Damania P, Khemani P, Muthukkaruppan K, Vaidya M. Storage infrastructure behind facebook messages using HBase at scale. IEEE Data Engineering 2012, 35(2), pp.4–13.
[5]. Bu Y, Howe B, Balazinska M, Ernst MD. HaLoop: efficient iterative data processing on large clusters, in 36thInternational Conference on Very Large Data Bases, 2010 Sep, 3(1-2),pp.285-96.
[6]. Page L, Sergey Brin RM, Winograd T. The PageRank citation ranking: bringing order to the web, 1998 Jan, pp.1-17..
[7]. Mohebi A, Aghabozorgi S, Ying Wah T, Herawan T, Yahyapour R. Iterative big data clustering algorithms: a review, Software Practicle Experience, 2016,46,pp. 107–29.
[8]. Zikopoulos P, Parasuraman K, Deutsch T, Giles DC. Harness the Power of Big Data the IBM Big Data Platform [Kindle Edition], 1st edn. McGraw-Hill Osborne Media: New York, 2012 Sep.
[9]. Assunção MD, Calheiros RN, Bianchi S, Netto MAS, Buyya R. Big Data computing and clouds: Trends and future directions, Journal of Parallel and Distributed Computing 2014,75(13),pp.156–75.
[10]. Riccomini C. Samza: Real-time stream processing at LinkedIn, 2013. Retrieved July 5, 2014, from http://www. infoq.com/presentations/samza-linkedin, Date ACCESSED: 5/7/2014.
[11]. Murthy A. Tez: accelerating processing of data stored in HDFS, 2013. [Online]. Available: http://hortonworks.com/blog/introducing-tez-faster-hadoop-processing/,Date Accessed: 20/2/2013.
[12]. Dean J, Ghemawat S. MapReduce: simplified data processing on large clusters, Communications of the ACM 2008, 51(1),pp.1–13.
[13]. Dhillon IS, Modha DS. A data-clustering algorithm on distributed memory multiprocessors, in Proceeding Revised Papers from Large-Scale Parallel Data Mining, Workshop on Large-Scale Parallel KDD Systems, SIGKDD, 1999,pp.245–60.
[14]. Forman G, Zhang B. Distributed data clustering can be efficient and exact, ACM SIGKDD Explorations Newsletter 2000, 2(2),pp.34–38.
[15]. Kang U, Papalexakis E, Harpale A, Faloutsos C. GigaTensor, in Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining – KDD’12, 2012,pp.1-316.
[16]. Kang U, Tsourakakis CE, Faloutsos C. PEGASUS: mining peta-scale graphs, Knowledge and Information Systems 2010,27(2),pp.303–25.
[17]. White T. Hadoop—The Definitive Guide: Storage and Analysis at Internet Scale, 3rd edn. O’Reilly R (ed.). Ireland, 2012 Jan, pp.1-647.
[18]. Snir M, Otto S, Huss-Lederman S, Walker D, Dongarra J. MPI–the Complete Reference, Second edn, the MPI Core, MIT Press: Cambridge, MA, USA, 1998 Sep,pp.1-350.
[19]. Stonebraker M, Abadi D, DeWitt DJ, Madden S, Paulson E, Pavlo A, Rasin A. MapReduce and parallel DBMSs,Communications of the ACM 2010, 53(1),pp.1-64.
[20]. Pavlo A, Paulson E, Rasin A, Abadi DJ, DeWitt DJ, Madden S, Stonebraker M. A comparison of approaches to largescale data analysis, In Proceedings of theACMSIGMODInternational Conference on Management of Data (SIGMOD), 2009, pp.165–78.
[21]. Ekanayake J, Li H, Zhang B, Gunarathne T, Bae S-H, Qiu J, Fox G. Twister, in Proceedings of the 19th ACM International Symposium on High Performance Distributed Computing - HPDC’10,Chicago, 2010 Jun, pp. 810-18.
[22]. Malewicz G, Austern MH, Bik AJ, Dehnert JC, Horn I, Leiser N, Czajkowski G. Pregel, in Proceedings of the 2010 international conference on Management of data - SIGMOD’10, 2010,pp.1- 135.
[23]. Valiant LG. A bridging model for parallel computation. Communications of the ACM 1990, 33(8),pp.103–11.
[24]. Condie T, Conway N, Alvaro P, Hellerstein JM, Elmeleegy K, Sears R. MapReduce online, in NSDI’10 Proceedings of the 7th USENIX conference on Networked systems design and implementation Berkley,,2010,21,pp.1-15.
[25]. Zaharia M, Chowdhury M, Franklin MJ, Shenker S, Stoica I. Spark: cluster computing with working sets, in HotCloud’10 Proceedings of the 2nd USENIX conference on Hot topics in cloud computing, 2010, pp.1-10.
[26]. Yu Y, Isard M, Fetterly D, Budiu M, Erlingsson Ú, Gunda PK, Currey J. DryadLINQ: a system for general-purpose distributed data-parallel computing using a high-level language, in OSDI’08 Proceedings of the 8th USENIX conference on Operating systems design and implementation, Berkeley,USA,CA,2008, pp. 1–14.
[27]. Chambers C, Raniwala A, Perry F, Adams S, Henry RR, Bradshaw R, Weizenbaum N. FlumeJava, ACM SIGPLAN Notices 2010, 45(6),pp.1-363.
[28]. Thulasiraman K, Swamy MNS. Graphs: Theory and Algorithms. John Wiley & Sons, Inc.: New York, 1992.
[29]. Logothetis D, Olston C, Reed B, Webb KC, Yocum K. Stateful bulk processing for incremental analytics, in Proceedings of the 1st ACM symposium on Cloud computing - SoCC’10, 2010 Jun, pp.1-12 ..
[30]. Zhang Y, Gao Q, Gao L, Wang C. PrIter: a distributed framework for prioritizing iterative computations. IEEE Transactions on Parallel and Distributed Systems 2013, 24(9),pp.1884–93.
[31]. Hellerstein JM, Haas PJ, Wang HJ. Online aggregation, Proceedings of the 1997 ACM SIGMOD International Conference on Management of Data – SIGMOD’97, 1997, 26(2),pp.171–82.
[32]. Wu X, Kumar V, Ross Quinlan J, Ghosh J, Yang Q, Motoda H, McLachlan GJ, Ng A, Liu B, Yu PS, Zhou Z-H, Steinbach M, Hand DJ, Steinberg D, Top 10 algorithms in data mining, Knowledge and Information Systems 2007, 14(1),pp.1–37.
[33]. Zhao W, He Q, Ma H. Parallel K-Means Clustering Based on, 2009, pp.674–79.
[34]. Zhou P, Ye W, Lei J. Large-scale data sets clustering based on MapReduce and Hadoop, The Journal of ComputerInformation Systems 2011, 16(7),pp.5956–63.
[35]. Nguyen CD, Nguyen DT, Pham V. LNCS 7975 - Parallel two-phase K-means, 2013,7975, pp.224–31.
[36]. Pham DT, Dimov SS, Nguyen CD. An incremental K-means algorithm, in Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 2004, pp.783–95.
[37]. Bahmani B, Moseley B, Vattani A, Kumar R, Vassilvitskii S. Scalable k-means++. Proceedings of the VLDB Endowment 2012, 5(7),pp.622–33.
[38]. Arthur D, Vassilvitskii S. k-means++: the advantages of careful seeding, in SODA’07 Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms, 2007,pp. 1027–35.
[39]. Li C, Zhang Y, Jiao M, Yu G. Mux-Kmeans : Multiplex Kmeans for clustering large-scale data set categories and subject descriptors, in Proceedings of the 5th ACM workshop on Scientific cloud computing - ScienceCloud’14, 2014, pp. 25–32.
[40]. Aljarah I, Ludwig SA. Parallel particle swarm optimization clustering algorithm based on MapReduce methodology, in 2012 Fourth World Congress on Nature and Biologically Inspired Computing (NaBIC), Mexico City,2012 Nov,pp. 104–11.
[41]. Kennedy J, Eberhart R. Particle swarm optimization. Proceedings of ICNN’95 - International Conference on Neural Networks, 1995,4,pp.1942–48.
[42]. Sun Z, Fox G, Gu W, Li Z. A parallel clustering method combined information bottleneck theory and centroid-based clustering, Journal of Supercomputing 2014, 69(1),pp.452–67.
[43]. Tishby N, Pereira FC, Bialek W. The information bottleneck method, Apr. 2000,pp.1-16.
[44]. Satish Narayana Srirama PJ, Vainikko E. Adapting scientific computing problems to clouds using MapReduce, Future Generation Computer Systems 2012, 8(1),pp.184–92.
[45]. Kaufman L, Rousseeuw P. Finding groups in data: an introduction to cluster analysis, in Wiley Interscience, 1990, pp.1-5.
[46]. Martin Ester XX, Kriegel H-P, Jörg S. A density-based algorithm for discovering clusters in large spatial databases with noise, in 2nd International Conference on Knowledge Discovery and Data Mining, Portland, 1996,pp. 226–31.
[47]. Li L, Xi Y. Research on clustering algorithm and its parallelization strategy, International Conference on Computational and Information Sciences Chengudu,China, 2011, pp.325–28.
[48]. Kim Y, Shim K, Kim M-S, Sup Lee J. DBCURE-MR: an efficient density-based clustering algorithm for large data using MapReduce.,Information Systems 2014, 42,pp.15–35.
[49]. Ankerst M, Breunig MM, Kriegel H-P, Sander Journal of OPTICS, ACM SIGMOD Record, 1999, 28(2),pp.49–60.
[50]. Zhao W, Martha V, Xu X. PSCAN: A Parallel Structural Clustering Algorithm for Big Networks in MapReduce, in 2013 IEEE 27th International Conference on Advanced Information Networking and Applications (AINA), Barcelona,2013, pp.862–69.
[51]. NandiniRaghavan U, Albert R, Kumara S. Near linear time algorithm to detect community structures in large-scale networks, Physical Review 2007 Sep, 76(3),pp.1.
[52]. Sun T, Shu C, Li F, Yu H, Ma L, Fang Y. An efficient hierarchical clustering method for large datasets with MapReduce, in 2009 International Conference on Parallel and Distributed Computing, Applications and Technologies,Higashi Hiroshima, 2009 Dec,pp. 494–99.
[53]. Jin WLC, Patwary MMA, Agrawal A, Hendrix W. DiSC: A distributed single-linkage hierarchical clustering algorithm using MapReduce, in Proceedings of the 4thInternational SC Workshop on Data Intensive Computing in the Clouds, 2013,pp.1-10.
[54]. Cormen TH, Leiserson CE, Rivest RL, Stein C. Introduction to Algorithms, 2nd edn, McGraw-Hill Higher Education: New York, USA, 2001,pp.1-640.
[55]. Parsons L, Haque E, Liu H. Subspace clustering for high dimensional data, ACM SIGKDD Explorations Newsletter 2004, 6(1),pp. 90–105.
[56]. R. Murugesh, I. Meenatchi, "A Study Using PI on: Sorting Structured Big Data In Distributed Environment Using Apache Hadoop MapReduce", International Journal of Computer Sciences and Engineering, Vol.2, Issue.8, pp.35-38, 2014.
[57]. Fries ST, Wels S. Projected clustering for huge data sets in MapReduce | Chair of Computer Science 9, in International Conference on Extending Database Technology (EDBT 2014), Athens, Greece, 2014,pp. 49–60.
[58]. Moise G, Sander J, Ester M. P3C: a robust projected clustering algorithm, in Sixth International Conference on Data Mining (ICDM’06), Hong Kong,2006 Dec, pp.414–25.
[59]. Hyndman RJ. The problem with Sturges’ rule for constructing histograms, no. 1995 Jul,pp. 1–2.
[60]. Elgohary A, Farahat AK, Kamel MS, Karray F. Embed and conquer: scalable embeddings for kernel k-means on MapReduce, in Appears in Proceedings of the SIAM International Conference on Data Mining (SDM), 2014, 2013,pp. 1–18.
[61]. Über die praktische Auflösung von linearen. “Integralgleichungen mit Anwendungen auf Randwertaufgaben”, Acta Mathematica, 1930, 54(1),pp.185–204.
[62]. Rahul R. Ghuleand Sachin N. Deshmukh, "Comparative Study on Speculative Execution Strategy to Improve MapReduce Performance", International Journal of Computer Sciences and Engineering, Vol.3, Issue.3, pp.197-200, 2015.
[63]. Chen W-Y, Sunnyvale Y, Song H, Bai C-JL, Chang EY. Parallel spectral clustering in distributed systems, IEEE Transactions on Pattern Analysis and Machine Intelligence 2011, 33(3),pp.568–86.
[64]. Maschho K, Sorensen D. A portable implementation of ARPACK for distributed memory parallel architectures, In Proceeding of Copper Mountain Conference on Iterative Methods, 1996,pp.1-8.
[65]. Papadimitriou S, Sun J. DisCo: distributed co-clustering with Map-Reduce: A Case Study Towards Petabyte-Scale End-to-End Mining, in 2008 Eighth IEEE International Conference on Data Mining, Pisa,2008 Dec,pp.512–21.
[66]. Su S, Cheng X, Gao L, Yin J. Co-Cluster D: a distributed framework for data co-clustering with sequential updates, in International Conference on Data Mining (ICDM), 2013 IEEE 13th,Dallas TX, 2013, pp.1193–98.
[67]. M. Shankar Lingam, A. M. Sudhakara, "A Brief Account of Iterative Big Data Clustering Algorithms", International Journal of Computer Sciences and Engineering, Vol.5, Issue.10, pp.300-309, 2017.
[68]. Dhillon IS. Co-clustering documents and words using bipartite spectral graph partitioning, in Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining - KDD’01, 2001,pp. 269–74.
[69]. P. Dadheech, D. Goyal, S. Srivastava, "Performance Improvement of Heterogeneous Hadoop Clusters Using MapReduce For Big Data", International Journal of Computer Sciences and Engineering, Vol.5, Issue.8, pp.211-214, 2017.
[70]. Zaharia M, Chowdhury M, Das T, Dave A, Ma J, McCauley M, Franklin MJ, Shenker S, Stoica I. Resilient distributed datasets: a fault-tolerant abstraction for in-memory cluster computing, in Proceeding NSDI’12 Proceedings of the 9th USENIX conference on Networked Systems Design and Implementation, 2012,2,pp.1-14.

M. Shankar Lingam, A. M. Sudhakara, "A Brief Account of Iterative Big Data Clustering Algorithms," International Journal of Computer Sciences and Engineering, Vol.5, Issue.10, pp.292-301, 2017.

Algorithms in data science are all the rage today with data scientists. With the right algorithm businesses can get measurable value from the huge volume of data collected. There are several types of algorithms- Regression, Clustering, Decision Tree etc. For this paper we will focus on clustering algorithms which are widely used in sorting and classifying big data. The way data is classified is critical to analysts studying the data to provide insights to business decisions. Every large data set can use clustering algorithms to process a variety of data to produce great results. Algorithms are used in image and data processing, calculations, and automated reasoning. The aim of this paper is to touch upon big data analytics, other authors views on this topic in our literature review section, define cluster analysis, present different clustering methodologies with its advantages and disadvantages, a comparison of different clustering algorithms, and wrap up with findings and discussion from our review papers.

Clustering, data mining, big data analytics

[1] O.A. Abbas, “Comparisons Between Data Clustering Algorithms”, International Arab Journal of Information Technology (IAJIT), Vol.5, Issue.3 pp.321-325, 2008.
[2] R. Agrawal, and J. Agrawal, “Analysis of Clustering Algorithm of Weka Tool on Air Pollution Dataset”, International Journal of Computer Applications, Vol.168, No. 13, 2017.
[3] S. Äyrämö, and T. Kärkkäinen, “Introduction to partitioning-based clustering methods with a robust example. Reports of the Department of Mathematical Information Technology”, Software engineering and computational intelligence. University of Jyväskylä, Finland, Series C, 1/2006.
[4] M. Dave and H. Gianey, “Different clustering algorithms for Big Data analytics: A review”, In System Modeling & Advancement in Research Trends (SMART), IEEE International Conference (pp. 328-333). 2016
[5] J.R. Fernandez and E.M. El-Sheikh, “CluSandra: A framework and algorithm for data stream cluster analysis”, International Journal of Advanced Computer Science and Applications, Vol.2, No.11, pp. 87–99, 2011.
[6] 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.
[7] M. Halkidi and I. Koutsopoulos, “Online clustering of distributed streaming data using belief propagation techniques”, In Mobile Data Management (MDM), 2011 12th IEEE International Conference on (Vol. 1, pp. 216-225), 2011
[8] AR. PonPeriasamy, E. Thenmozhi, “A Brief survey of Data Mining Techniques Applied to Agricultural Data”, International Journal of Computer Sciences and Engineering, Vol.5, Issue.4, 2017
[9] J.F. Laloux, N.A. Le-Khac, and M.T. Kechadi, “Efficient distributed approach for density-based clustering”, In Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE), 2011 20th IEEE International Workshops on (pp. 145-150). 2011
[10] W.K. Liao, Y. Liu, and A. Choudhary, “A grid-based clustering algorithm using adaptive mesh refinement”, In 7th Workshop on Mining Scientific and Engineering Datasets of SIAM International Conference on Data Mining (Vol. 22, pp. 61-69), 2004.
[11] S.L. Nimmagadda and H. Dreher, “Petro-data cluster mining-knowledge building analysis of complex petroleum systems”, In Industrial Technology, 2009. ICIT 2009. IEEE International Conference on (pp. 1-8), 2009.
[12] P. Sharma, “Comparative Analysis of Various Clustering Algorithms Using WEKA”, International Research Journal of Engineering and Technology (IRJET), Vol.2, Issue.04, 2015.
[13] P. Singh, and A. Surya, “Performance Analysis of clustering algorithms in data mining in WEKA”, International Journal of Advances in Engineering & Technology, Vol.6, Issue.6, pp.1866-1873, 2015.
[14] Ruchi Jayaswal, Jaimala Jha , Ravi Devesh , "An Effective Method of Image Mining using K-Medoid Clustering Technique", International Journal of Computer Sciences and Engineering, Vol.5, Issue.6, pp.206-214, 2017.
[15] P.N. Tan, M. Steinbach and V. Kumar, “Data mining cluster analysis: basic concepts and algorithms”, “Introduction to data mining”, Pearson Education, India. pp. 487–569, 2013.
[16] I. Triguero, J. Maillo, J.Luengo, S. García and F. Herrera, “From Big data to Smart Data with the K-Nearest Neighbours algorithm”, In Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData), 2016 IEEE International Conference on (pp. 859-864), 2016.

B. Ramesh, K. Nandhini, "Clustering Algorithms – A Literature Review," International Journal of Computer Sciences and Engineering, Vol.5, Issue.10, pp.302-306, 2017.

Data security has a vital issue in cloud enlisting condition; it transforms into a noteworthy issue due to the data which is secured diversely completed the cloud. Data protection and security are the two essential parts of customer`s stress in cloud information innovation. Different techniques as for these points of view are grabbing thought over the cloud preparing conditions and are investigated in both endeavors and scholastics. Data protection and security protection are transforming into the most enormous points of view for the future update and headway of cloud figuring innovation in the field of business and government parts. Thusly, in this paper, the cloud figuring security techniques are reviewed and its troubles as for data protection are discussed. The rule purpose of this proposed work is to update the data protection and security for the trustworthy cloud condition. This close research examination of the ebb and flow cloud security approach as for the data protection and security techniques utilized as a part of the cloud enrolling. It will be significant to enhance the security of data stockpiling in a cloud space.

Cloud Computing, Security Issues and challenges, Cloud Architecture, Data Privacy

[1] Nitesh Jain, Pradeep Sharma, "A Security Key Management Model for Cloud Environment", International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.1, pp.45-48, 2017.
[2] V.K. Saxena, S. Pushkar, "Privacy Preserving using Encryption Proxy in Data Security", International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.2, pp.36-41, 2017.
[3] Rakesh Prasad Sarang and Rajesh Kumar Bunkar, "Study of Services and Privacy Usage in Cloud Computing", International Journal of Scientific Research in Computer Science and Engineering, Vol.1, Issue.6, pp.7-12, 2013.
[4] Abhinay B.Angadi, Akshata B.Angadi, Karuna C.Gull, "Security Issues with Possible Solutions in Cloud Computing-A Survey",International Journal of Advanced Research in Computer Engineering and Technology (IJARCET), Vol.2, Issue 2, 2013.
[5] Rachna Jain, Sushila Madan and Bindu Garg, "Analyzing Various Existing Security Techniques to Secure Data Access in Cloud Environment", International Journal of Computer Sciences and Engineering, Vol.3, Issue.1, pp.130-135, 2015.
[6] Yinqian Zhang,Ari Juels, Michael K. Reiter, "CrossVM side channels and their utilization to remove private keys",ACM meeting on Computer and interchanges security,PP. 305-316,2012.
[7] BhruguSevak, "Security against Side Channel Attack in Cloud Computing",International Journal of Engineering and Advanced Technology (IJEAT), Vol-2, Issue-2, 2012.
[8] Aye Thu, "Incorporated Intrusion Detection and Prevention System with Honeypot on Cloud Computing Environment", International Journal of Computer Applications,Vol. 67– No.4, 2013.
[9] R.Piplode, P. Sharma and U.K. Singh, "Study of Threats, Risk and Challenges in Cloud Computing", International Journal of Scientific Research in Computer Science and Engineering, Vol.1, Issue.1, pp.26-30, 2013.
[10]Mitchell Cochran,Paul D. Witman, "Administration And Service Level Agreement Issues In A Cloud Computing Environment",Journal of Information Technology Management, Vol. XXII, Number 2, 2011.
[11] Grispos, G., Glisson, W.B., and Storer, T., "Cloud Security Challenges: Investigating Policies, Standards, and Guidelines in a Fortune 500 Organization", 21st European Conference on Information Systems, 5-8, 2013. [12]Bijayalaxmi Purohit,PawanPrakash Singh, "Information spillage examination on cloud computing", International Journal of Engineering Research and Applications, Vol. 3, Issue 3, 2013.
[13]V. Shobana, M. Shanmugasundaram, "Information Leakage Detection Using Cloud Computing",I nternational Journal of Emerging Technology and Advanced Engineering, Vol.3, Special Issue 1, 2013.
[14]Manas M N, Nagalakshmi C K, Shobha G, "Cloud Computing Security Issues And Methods to Overcome",International Journal of Advanced Research in Computer and Communication Engineering,Vol. 3, Issue 4, 2014.
[15] Tomoyoshi Takebayashi, Hiroshi Tsuda, TakayukiHasebe, RyusukeMasuoka, "Information Loss Prevention Technologies", FUJITSU Sci. Tech, vol.46, No.1, PP 47-55, 2010.

N. Chandramouli, B. Manjula, "A Study on Data Security and Privacy Protection Issues in Cloud Computing," International Journal of Computer Sciences and Engineering, Vol.5, Issue.10, pp.307-313, 2017.