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First part of this thesis which was needed to be developed before moving onto the next levels is Cloud Computing. The client section was a simple application for user which was made on Socket Programming in Java. Application connection is to be made with Server which is a LAN based server on which data is to be uploaded and downloaded. Since backbone network (client and server) is developed, then it needs to be secured with a particular algorithm or technique. The algorithm studied is known as RSA key oriented algorithm which offers dynamic security on client and server level during communication. The other technique for advancing the level of scalability and improvement along the network layer we used is NTRU encryption. Since this algorithm encrypt data of every type ensures the originality of particular data and adds the advancement in throughput level. These two algorithms are implemented to run on single server on a different core thread thus making the idle cores of sever in use for both the algorithms. It is reflecting that the parallelism encryption done on single content during the data storage on network marginally increase the speed of execution and decryption and encryption timings have been increased. Other part of this thesis also deals with the approaches followed previously which are the part this thesis.

RSA, LOSSY, SAAS

[1] Ranjit Ranjan, Dr. A.S Baghel, Sushil Kumar “Improvement of NTRU cryptosystem” International Journal of Advanced Research in Computer Science Vol-2, Issue-9, Page no-79-84, September 2012.
[2] Subedari Mithila, P. Pradeep Kumar, “Data Security through Confidentiality in Cloud Computing Environment”, Subedari Mithila et al, / (IJCSIT) International Journal of Computer Science and Information Technologies, Vol-2, Issue-5, Page no-1836-1840, 2011.
[3] Gurkamal Bhullar and Navneet Kaur “Concurrency and security control with NTRU” International Journal of Innovative Research in Computer Science and Communication Engineering Vol-2, Issue 3, March 2014.
[4] Dr. C. Sunil Kumar, J. Seetha, S.R Vinotha “Security implications of Distributed parallel cloud database management system models” International Journal of Software Computing and Software Engineering, Vol-2, Issue-11,Page no-20-28, 2012.
[5] Shashi Mehrotra Seth and Rajan Mishra, “Comparative Analysis of Encryption Algorithm for Data Communication”, International Journal of Computer Science and Technology Vol-4, Issue-8, Page no-348-354, August 2014.
[6] YashPal Mote and Shekhar Gaikward, “Superioer Security Data Encryption Algorithm”, International Journal of Computer Scienece, Vol- 6,Page no-171-181 , July 2012.
[7] Parsi Kalpana and Sudha Singaraju, ”Data security in cloud computing using RSA algorithm, International Journal of Research in Computer and Communication Technology, Vol-1, Issue-4, Page no-143-146, September 2012.

Deepali and Namita Kakkar, "Hybrid Parallel Multithreading Encryption," International Journal of Computer Sciences and Engineering, Vol.3, Issue.6, pp.50-52, 2015.

Now a days these are many E-Resources to learn. By using this resources E-Learning is very effective learning things in present days. The Data Mining applications are powerful tools in various domains. By applying these Data mining applications on E-Learning system, it is a good idea to improve the capabilities of E-Learning system. The main advantage of Data mining and E-Learning interaction it is very useful to learn many advanced technologies very easy by using plying Data mining technologies very easily by using applying Data mining technologies to E-Learning System.

EDM, KDD, E-Learning, Educational Data, Online courses

[1]. Jaiwei Han Micheline Kamber, “ Data Mining concepts and Techniques”. 2012.
[2]. Abeer Badr EI Din Ahmed, Ibrahim Sayed Elaraby “Data Mining: A prediction for Student’s Performance Using Classificatio Method.” World Journal of Computer Applicationsand Technology 2(2): 43-47, 2014.
[3]. Aarti Sharma, ahul Sharma, Vivek Kr. Sharma, Vishal Shrivatava, “Application of Data Mining – A Survey Paper”, International Journal of Computer Science and Information Technologies. Vol. 5(2), 2014, 2023-2025.
[4]. S.Laxmi Prabha, “Educational Data Mining Applications”, Operational Research and Applications: An International Journal(ORAJ), Vol. 1. No.1, August-2014
[5]. Yousf A.ALMazroui,” A survey of Data Mining In the context of E-Learning”, International Journal of Information Technology & Computer Science (IJITCS), Volume 7: No:3Issue on January / Febraury, 2013.
[6]. Margo Hanna,” Data mining in the e-learning domain”, Volume 21-Number 1, 2004-29-34.

S. Nagaparameshwara Chary, "A Study On E-Learning by Applying Data Mining Techniques," International Journal of Computer Sciences and Engineering, Vol.3, Issue.6, pp.53-55, 2015.

Haptic technology, haptics, or kinesthetic communication, is tactile feedback technology which recreates the sense of touch by applying forces, vibrations, or motions to the user. This mechanical stimulation can be used to assist in the creation of virtual objects in a computer simulation, to control such virtual objects, and to enhance the remote control of machines and devices (telerobotics). It has been described as "doing for the sense of touch what computer graphics does for vision". Haptic devices may incorporate tactile sensors that measure forces exerted by the user on the interface.

Tactile;Haptic Sensors;Mobile Device; Touch Synopsis

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D.Bullarao, P.Khatija Khanam and P.Nageswara Rao, "Tactile Robot Vibrating Detection Using Haptic Wireless Sensors," International Journal of Computer Sciences and Engineering, Vol.3, Issue.6, pp.56-60, 2015.

Recently, probabilistic graph have more interest in the data mining. After some result it is found that correlations may exist among adjacent edges in various probabilistic graphs. As one of the basic mining techniques, graph clustering is widely used. Different Clustering methods are used. But, when correlations are considered, it becomes more challenging to efficiently cluster probabilistic graphs. Here, we define the problem of clustering correlated probabilistic graphs and its techniques. To solve the challenging problem the PEEDR and the DPTC clustering algorithm are defined for each of the proposed algorithms, with some several pruning techniques and Different Similarity measures.

Clustering; Correlated; Probabilistic Graph; Graph Clustering; Pruning

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[11] G. Cormode and A. McGregor, “Approximation Algorithms for Clustering Uncertain Data,” Proc. 27th ACM SIGMOD-SIGACTSIGART Symp.Principles of Database Systems (PODS), pp. 191-200, 2008.
[12] S. Gu¨nnemann, H. Kremer, and T. Seidl, “Subspace Clustering for Uncertain Data,” Proc. SIAM Int’l Conf. Data Mining (SDM),pp. 385-396, 2010
[13] S. Guha and K. Munagala, “Exceeding Expectations and Clustering Uncertain Data,” Proc. 28th ACM SIGMOD-SIGACT-SIGARTSymp. Principles of Database Systems (PODS), pp. 269-278, 2009.
[14] Yu Gu,Chunpeng Gao, Gao Cong, and Ge Yu, "Effective and Efficicient Clustering Methods for correlated probabulistic graph" IEEE Transactions On Knowledge And Data Engineering, Vol. 26, No. 5, May 2014.

Sawant Ashlesha G., "Correlated Probabilistic Graph with Clustering," International Journal of Computer Sciences and Engineering, Vol.3, Issue.6, pp.61-64, 2015.

The use of computer-based and online education systems has made new data available that can describe the temporal and process-level progression of learning. Many scholars are interested in improving e-learning in order to provide easy access to educational materials. There is, however, the need to incorporate the ability to classify learners into these learning systems. Learner classification is used adaptively to provide relevant information for the various categories of learners. The modern ubiquity of computer use and internet access has dramatically impacted many facets of education, including engineering education. There has been a rapid rise over the last decade in the use of computer-based or online formats either to facilitate (e.g. online distribution of materials) or conduct higher-education courses, and enrollment among all students in at least one online course is now at 32%. Many universities now include online programs, and online publisher resources have also grown correspondingly. There is also a need for learning to continue, whether learners are on- or off-line. In many parts of the world, especially in the eve loping world, most people do not have reliable continuous internet connections. We tested an Adaptive e-earning Model prototype that implements an adaptive presentation of course content under conditions of intermittent internet connections This prototype was tested in February 2011 on undergraduate students studying a database systems course. This study found out that it is possible to have models that can adapt to characteristics such as the learner’s level of knowledge and that it is possible for learners to be able to study under both on- and off-line modes through adaptation.

E-learning, Internet, Adaptation, Agent-based

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Kranthi Kiran G, Umashankar Rao E, Md Shabbeer, "Agent-based flexible e-learning paradigm," International Journal of Computer Sciences and Engineering, Vol.3, Issue.6, pp.65-75, 2015.

Vehicles have become one of the most common commodities that the masses have adopted during these modern times. Tracking all such vehicles and regulating their inflow and outflow at any institution/organization is still a labor intensive task. With the development of technology, every day we come across one or the other situation where technology is skillfully replacing all such labor orientated tasks with faultless automated systems which are in most situations cheaper and more efficient. The proposed system in this paper elucidates a similar elegant automation solution for the mentioned situation. This system has the ability to register and deregister a vehicle from a database, identifying it based on its number plate which is fed dynamically to the system. Further this proposed technology makes use of a fast optical character recognition system to map the dynamically obtained characters from the license plate by the system to alphanumeric characters used by standard license plates across the country and eventually registers/deregisters users to automate the tedious gatekeeping process, as we know it today.

Vehicle management system; License plate recognition; Digital Image Processing; Optical Character Recognition; Machine vision; Machine Learning; Automation Systems; Vehicle Parking Automation Systems

[1] Ahmad and Mohammad, 2009,” Efficient Farsi License Plate Recognition”,Information, Communications and Signal Processing, 2009, ISBN:978-1-4244-4657-5, Pages (1-5),8-10 Dec 2009.
[2] Shaohong Wu, “A Novel Accurately Automatic License Plate Localization Method” ICEES, ISBN: 978-1-4577-0576-2, Pages (155-160),10-12July 2011.
[3] Maini R, Aggarwal H, “Study and comparison of various image edge detection techniques”, International Journal of Image Processing, Volume (03), Issue (01), Pages (1-11), 15 March 2009.
[4] V. Koval, V. Turchenko, V. Kochan, A. Sachenko, G. Markowsky”, Smart License Plate Recognition System Based on Image Processing Using Neural Network”,Pages (123-127)8-10 September 2003, Lviv, Ukraine.
[5] Hao Chen, Jisheng Ren, Huachun Tan, Jianqun Wang, “ A novel method for license plate localization”,Image and Graphics, 2007. ICIG 2007, Pages (604-609)22-24 August 2007.
[6] SerkanOzbay, Ergun Ercelebi, “Automatic vehicle identification by plate recognition”, International Science Index, Volume:1, No.:9, Pages (222-225), 2007.
[7] Yungang Zhang, Changshui Zhang, “A New algorithm for character segmentation of license plate”, Proc. Of IEEE Intelligent Vehicles Symposium, Pages(106-109), 2003
[8] Tran DurDuan, Duong AnhDuc, Tran Le, Hong Du, “Combining Hough transform and contour algorithm for detecting vehicle license plate”, Proc. Of International Symposium Intelligent Multimedia, Video and Speech Processing,2004, Pages(747-750).
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Aditya Rawat and Suvigya Awasthi, "Dynamic Vehicle Management System Using Fast Optical Character Recognition Technique," International Journal of Computer Sciences and Engineering, Vol.3, Issue.6, pp.76-81, 2015.

In Data Mining Research, Association rule mining plays a significant role in data mining. This paper presents the review of Association rule mining. The analysis of research survey would give the instruction concerning somewhat has been done previously in the same area, what is the present tendency and what are the other related areas. Big data is the word for a set of data sets which are enormous and convoluted, it holds structured and unstructured both varieties of data. Data comes from everywhere, sensors used to amass climate information, posts to social media sites, digital pictures and videos etc. This data is known as big data. Useful data can elicit from this big data with the help of data mining. In this paper, the association rule of data mining and advanced big data mining algorithms are scrutinized.

Association rule, Apriori Algorithm Big data mining, Data mining

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[2] Khurana K and Sharma S, ―A comparative analysis of association rule mining algorithms, International Journal of Scientific and Research Publications, Volume 3, Issue 5, pp 38-45, May 2013.
[3] Borgelt, C. “Efficient Implementations of Apriori and Eclat”. Workshop of frequent item set mining implementations (FIMI 2003, Melbourne, FL, USA).
[4] Hunyadi, D.”Performance comparison of Apriori and FP-Growth Algorithms in Generating Association Rules”.Proceedings of the European Computing Conference ISBN: 978-960-474-297-4.
[5] Thieme, S.L. “Algorithmic Features of Eclat”. FIMI, Volume 126 of CEUR Workshop Proceedings, CEUR- WS.org, 2004.
[6] Ms. Dhamdhere Jyoti L., Prof. Deshpande Kiran B. "An Effective Algorithm for Frequent Itemset Mining on Hadoop.", International Journal of Science, Engineering and Technology Research (IJSETR), Volume 3, Issue 8, August 2014.
[7] Agrawal, R., Shafer, J.C., "Parallel mining of association rules.", IEEE Transactions on Knowledge and Data Engineering, Volume.8, no.6, pp 962-969, Dec 1996.
[8] A. Swami, T. Imielienski, R. Agrawal," Mining Association Rules between Sets of Items in Large databases.", ACM Press, pp 207–216, July 1993
[9] Brain.s Motwani R,Ullman.J.D and S. Tsur,"Dynamic itemsets counting and implication rules for market basket analysis.", ACM-SIGMOD ,pages 255-264, May 1997.
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Ahilandeeswari. G, DR.R.Manicka Chezian, "A Comparative analysis of Association rule excavating in Big Data Mining Algorithms," International Journal of Computer Sciences and Engineering, Vol.3, Issue.6, pp.82-88, 2015.

Text search is a classical problem in Computer Science, which reside in many data-intensive applications. For this problem, suffix arrays are the most widely known and used data structures, which enabling fast searches for phrases, terms, substrings and regular expressions in large texts. Potential application domains of this method includes large-scale search services, such as Web search engines, plagiarism checker where it is necessary to efficiently process intensive traffic streams of on-line queries. Suffix array is an effective way to construct the index of the full text i.e. sorted array of all suffix of string which is important for different kind of applications, perhaps most notably string matching, string discovery and block-sorting data compression. This paper elucidates intensive research toward efficient construction of suffix arrays with algorithms striving not only to be fast, but also “lightweight” (in the idea that they use small working memory).

Suffix sorting, Suffix array, fm index, trie structure

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[4] Ahmed Abdelhadi, A. H. Kandil1 and Mohamed Abouelhoda. “Cloud-based Parallel Suffix Array Construction based on MPI ” Middle East Conference on Biomedical Engineering (MECBME) ,(2014).
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Nagendra Singh, "Role of Suffix Array in String Matching: A Comparative Analysis," International Journal of Computer Sciences and Engineering, Vol.3, Issue.6, pp.89-93, 2015.

The process of pattern recognition pose quiets a lot of challenges especially in recognizing hand-written scripts of different languages in India, in spite of several advancement in technologies pertaining to optical character recognition (OCR). Handwriting continues to persist as means of documenting information for day today life especially in rural areas. There exist a need to develop handwritten character recognition system for its applications in post offices, bank cheque processing, handwritten document processing etc,. In this paper a handwritten Kannada alphabets recognition using neuro-genetic hybrid system is proposed which makes use of wavelet transform coefficients as feature vectors. Subset of these feature vectors is selected using genetic algorithm and is given to neural network for classification. Higher degree of accuracy in results has been obtained with the implementation of this approach on a comprehensive database compared to conventional systems.

Pattern Recognition, OCR, Wavelets Transformation, Kannada alphabets, Genrtic algorithms, Neural Networks

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Sreedharamurthy S K and H.R.Sudarshana Reddy, "Feature Subset Selection Using Genetic Algorithms for Handwritten Kannada Alphabets Recognition," International Journal of Computer Sciences and Engineering, Vol.3, Issue.6, pp.94-99, 2015.

Recent advances in signal processing have made possible the use of brain waves or EEG signals for user recognition and also for communication between human and computers. Electroencephalography (EEG) is sensitive to electrical field generated by the electric currents in the brain, and EEG recordings are acquired with portable and relatively inexpensive devices when compare to the other brain imaging techniques. EEG signals are representative signals containing the information about state of human brain. EEG signals are sometimes uses for clinical applications for medical diagnostics. The shape of the wave may contain useful information about the state of the brain. It has been known that different regions of the brain are activated according to the associated mental status, for example, emotional status, cognitive status, etc. Since the difference in activities of the brain causes the difference in characteristics of EEG, it has been attempted to investigate the brain activity through analyzing EEG.

Electroencephalography (EEG), brain rhythms, biometrics, Brain Computer Interfacing (BCI), Feature Extraction, Auto-regression, Classification

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Bhagyashri D. Dangewar, and R. V. Pujeri, "A Survey on Cognitive Biometrics: EEG based approach to user recognition," International Journal of Computer Sciences and Engineering, Vol.3, Issue.6, pp.100-103, 2015.