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In this study, the problem of building Level-based topologies for Wireless Sensor Networks with several sinks is considered. The optimization relies on different levels of decision: choosing which sensors are masters and balancing the load among sinks, in order to prolong the network life time and improve its scalability. In this paper we present an enhancement to the GRPW algorithm for wireless sensor networks. Performance of GRPW algorithm depends heavily on single sink position , we propose a protocol based on Multiple Static Sinks, we modified the existing sink location privacy protection scheme by dividing nodes in the network containing multiple sink into different levels in which real packets are forwarded to sink belong to corresponding logical levels and the intermediate node generating fake packets and sending it to fake sinks. Using OMNET++ simulation and the MiXiM framework, it is shown that proposed protocol significantly improves the robustness and adapts to rapid topological changes with multiple sinks, while efficiently reducing the communication overhead and the energy consumption .

Wireless Sensor Network (WSN), Routing, Multiple Sink, Localization, Geographic Routing

[1] M. Hammoudeh, �Putting the lab on the map: A wireless sensor network system for border security and surveillance,� in Proceedings of the International Conference on Internet of Things and Cloud Computing, ser. ICC �16. New York, NY, USA: ACM, 2016, pp. 4:1� 4:1. [Online]. Available: http://doi.acm.org/10.1145/2896387.2900338 .
[2] R. Jurdak, P. Sommer, B. Kusy, N. Kottege, C. Crossman, A. Mckeown, and D. Westcott, �Camazotz: Multimodal activity-based gps sampling,� in Proceedings of the 12th International Conference on Information Processing in Sensor Networks, ser. IPSN �13. New York, NY, USA: ACM, 2013, pp. 67�78. [Online]. Available: http://doi.acm.org/10.1145/2461381.2461393
[3] Sharma, Shamneesh, Dinesh Kumar, and Keshav Kishore. "Wireless Sensor Networks-A Review on Topologies and Node Architecture." International Journal of Computer Sciences and Engineering 1.2 (2013): 19-25.
[4] Nathiya, R., and S. G. Santhi. "Energy Efficient Routing with Mobile Collector in Wireless Sensor Networks (WSNs)." International Journal of Computer Sciences and Engineering 2 (2014): 36-43.
[5] M. Krol, E. Schiller, F. Rousseau, and A. Duda, �Weave:� Efficient geographical routing in large-scale networks,� in Proceedings of the 2016 International Conference on Embedded Wireless Systems and Networks, ser. EWSN �16. USA: Junction Publishing, 2016, pp. 89�100. [Online]. Available: http://dl.acm.org/citation.cfm?id=2893711.2893726
[6] L. Gavrilovska, S. Krco, V. Milutinovic, I. Stojmenovic, and R. Trobec, Application and Multidisciplinary Aspects of Wireless Sensor Networks: Concepts, Integration, and Case Studies, 1st ed. New York, NY, USA: Springer-Verlag New York, Inc., 2010.
[7] K. Andrea and R. Simon, �Design and evaluation of an rpl-based multi-sink routing protocol for low-power and lossy networks,� in Proceedings of the 18th ACM International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems, ser. MSWiM �15. New York, NY, USA: ACM, 2015, pp. 141�150. [Online]. Available: http://doi.acm.org/10.1145/2811587.2811614
[8] M. Hammoudeh, �Applying wireless sensor networks to solve real-world problems,� in Proceedings of the International Conference on Intelligent Information Processing, Security and Advanced Communication, ser. IPAC �15. New York, NY, USA: ACM, 2015, pp. 1:1�1:1. [Online]. Available: http://doi.acm.org/10.1145/2816839.2816935
[9] R. Lent and J. Barria, �Sensor-aided routing for mobile ad hoc networks,� in Proceedings of the 2009 International Conference on Wireless Communications and Mobile Computing: Connecting the World Wirelessly, ser. IWCMC �09. New York, NY, USA: ACM, 2009, pp. 371�375. [Online]. Available: http://doi.acm.org/10.1145/1582379.1582461
[10] Singh, Umesh Kumar, et al. "An Overview and Study of Security Issues & Challenges in Mobile Ad-hoc Networks (MANET)." International Journal of Computer Science and Information Security 9.4 (2011): 106.
[11] B. Karp and H. T. Kung, �GPSR: greedy perimeter stateless routing for wireless networks,� in Proceedings of the 6th annual international conference on Mobile computing and networking, ser. MobiCom �00. New York, NY, USA: ACM, 2000, pp. 243�254. [Online]. Available: http://dx.doi.org/10.1145/345910.345953
[12] A. Rao, S. Ratnasamy, C. Papadimitriou, S. Shenker, and I. Stoica, �Geographic routing without location information,� in Proceedings of the 9th annual international conference on Mobile computing and networking, ser. MobiCom �03. New York, NY, USA: ACM, 2003, pp. 96�108. [Online]. Available: http://doi.acm.org/10.1145/938985.938996
[13] F. Kuhn, R. Wattenhofer, Y. Zhang, and A. Zollinger, �Geometric ad-hoc routing: of theory and practice,� in Proceedings of the twentysecond annual symposium on Principles of distributed computing, ser. PODC �03. New York, NY, USA: ACM, 2003, pp. 63�72. [Online]. Available: http://doi.acm.org/10.1145/872035.872044
[14] C. Saad, A. Benslimane, and J.-C. Konig, �AT-Dist: A Distributed� Method for Localization with High Accuracy in Sensor Networks,� International journal Studia Informatica Universalis, Special Issue on �Wireless Ad Hoc and Sensor Networks�, vol. 6, no. 1, p. N/A, 2008. [Online]. Available: http://hal-lirmm.ccsd.cnrs.fr/lirmm-00270283
[15] C. T. Kone, M. David, and F. Lepage, �Multi-channel clustering algorithm for improving performance of large-scale wireless multi-sink sensor networks,� in Proceedings of the 6th International Wireless Communications and Mobile Computing Conference, ser. IWCMC �10. New York, NY, USA: ACM, 2010, pp. 691�695. [Online]. Available: http://doi.acm.org/10.1145/1815396.1815555
[16] J. Son, J. Pak, and K. Han, �In-network processing for wireless sensor networks with multiple sinks and sources,� in Proceedings of the 3rd International Conference on Mobile Technology, Applications & Systems, ser. Mobility �06. New York, NY, USA: ACM, 2006. [Online]. Available: http://doi.acm.org/10.1145/1292331.1292391
[17] A. Arya, A. Malik, and S. Kumar, �A routing protocol for detecting holes in wireless sensor networks with multiple sinks,� in Proceedings of the Third International Symposium on Women in Computing and Informatics, ser. WCI �15. New York, NY, USA: ACM, 2015, pp. 103� 108. [Online]. Available: http://doi.acm.org/10.1145/2791405.2791480
[18] B. Carballido Villaverde, S. Rea, and D. Pesch, �Inrout - a qos aware route selection algorithm for industrial wireless sensor networks,� Ad Hoc Netw., vol. 10, no. 3, pp. 458�478, May 2012. [Online]. Available: http://dx.doi.org/10.1016/j.adhoc.2011.07.015
[19] M. N. Moghadam, H. Taheri, and M. Karrari, �Multi-class multipath routing protocol for low power wireless networks with heuristic optimal load distribution,� Wirel. Pers. Commun., vol. 82, no. 2, pp. 861�881, May 2015. [Online]. Available: http://dx.doi.org/10.1007/s11277-014- 2257-2
[20] S. Sons, �Under the sink: Fast network routing, meet userspace,� Linux J., vol. 2016, no. 262, Feb. 2016. [Online]. Available: http://dl.acm.org/citation.cfm?id=2903207.2903213
[21] L. Yao, L. Kang, P. Shang, and G. Wu, �Protecting the sink location privacy in wireless sensor networks,� Personal Ubiquitous Comput., vol. 17, no. 5, pp. 883�893, Jun. 2013. [Online]. Available: http://dx.doi.org/10.1007/s00779-012-0539-9
[22] A. I. Erzin, V. V. Zalyubovskiy, and H. Choo, �Maximizing lifetime for a sensor network,� in Proceedings of the 2Nd International Conference on Ubiquitous Information Management and Communication, ser. ICUIMC �08. New York, NY, USA: ACM, 2008, pp. 436�440. [Online]. Available: http://doi.acm.org/10.1145/1352793.1352884
[23] O. Ozkan, M. Ermis, and I. Bekmezci, �A hybrid matheuristic approach for designing reliable wireless multimedia sensor networks,� in Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation, ser. GECCO Companion �15. New York, NY, USA: ACM, 2015, pp. 875�882. [Online]. Available: http://doi.acm.org/10.1145/2739482.2768431
[24] Y. Sabri and N. E. Kamoun, �Article: Geographic routing with logical levels forwarding for wireless sensor network,� International Journal of Computer Applications, vol. 51, no. 11, pp. 1�8, August 2012, full text available.
[25] Y.SABRI and N.ElKAMOUN, � A Distributed Method for Localization in Large-Scale Sensor Networks based on Graham�s scan ,� Journal of Selected Areas in Telecommunications (JSAT). [Online]. Available: http://www.cyberjournals.com/Papers/Jan2012/04.pdf
[26] Y. Sabri and N. E. Kamoun, �Article: A distributed method to localization for mobile sensor networks based on the convex hull,� International Journal of Advanced Computer Science and Applications(IJACSA), vol. 3, no. 10, August 2012. [Online]. Available:http://thesai.org/Publications/ViewPaper?Volume=3Issue=10Code=IJACSASerialNo=6sth

Y. SABRI, "Geographical Routing Problem of Wireless Sensor Networks with Multiple Mobile Sinks," International Journal of Computer Sciences and Engineering, Vol.4, Issue.10, pp.76-83, 2016.

Now a day�s wireless sensor networks are the main source of communication, so it is an important area of research. The main issues of wireless sensor networks are energy consumption and communication consistency. In this research paper the energy is reducing by minimizing the distance between sensor nodes and increases the accuracy of message transfer. An adaptive uninterrupted communication algorithm is proposed to maintain the consistency of network by using wireless mesh network topology. Mesh topology is used as it is a less expensive network than traditional network, it is extremely adaptive and expandable and it also supports high demand. UV-rays are used to data transmission in wireless mesh network since UV-links can give faster communication rate. In this research paper dijkstra�s algorithm is used to find out the shortest path between source and destination nodes, so that as the distance between the sensors nodes are minimised as a result less energy is used in wireless mesh network communication (E d2).

wireless sensor network (WSN), dijkstra�s algorithm, AUICA algorithm, sensor node, UV-rays, topology management, mesh wireless network

[1] et al. Chanchal Yadav et al., �Security Techniques in Wireless Sensor Networks: A Survey�, International Journal of Advanced Research in Computer and Communication Engineering Vol. 4, Issue 4, April 2015.
[2] et al. Vijay S. Kale and Rohit D. Kulkarni, �An Overview on Wireless Sensor Networks Technology and Simulation Software�s�, International Journal of Advanced Research in Computer and Communication Engineering Vol. 5, Issue 5, May 2016.
[3] et al. Sanjeev Kumar Gupta and Poonam Sinha, �Overview of Wireless Sensor Network: A Survey�, International Journal of Advanced Research in Computer and Communication Engineering Vol. 3, Issue 1, January 2014.
[4] et al. M. Vahabi, M. F. A. Rasid, R. S. A. R. Abdullah, and M. H. F. Ghazvini, �Adaptive Data Collection Algorithm for Wireless Sensor Networks�, IJCSNS International Journal of Computer Science and Network Security, VOL.8 No.6, June 2008.
[5] et al. S.S. Gautam, Shivlal Mewada, P Sharma, "Classification of Efficient Symmetric Key Cryptography Algorithms", (IJCSIS) International Journal of Computer Science and Information Security, vol. 14, no. 2, Feb 2016.
[6] et al. Shivlal, Pradeep Sharma, S.S Gautam, �Exploration of Efficient Symmetric AES Algorithm�, IEEE 2016 Symposium on Colossal Data Analysis and Networking (CDAN), pp(1-5), Mar, 2016. Doi: 10.1109/CDAN.2016.7570921,
[7] et al. Ricardo O. Rodr�guez Cruz and Carlos L. Santos V�zquez, �Ultraviolet Communication�, http://facultad.bayamon.inter.edu/ cgonzalezr/ELEN4618 /Paper_spring_09/Ultraviolet %20Comm.%20Paper.pdf,
[8] et al. Sharma, Shamneesh, Dinesh Kumar, and Keshav Kishore. "Wireless Sensor Networks-A Review on Topologies and Node Architecture." International Journal of Computer Sciences and Engineering 1.2 (2013): 19-25.
[9] et al. Jolly Soparia and Nirav Bhatt, �A Survey on Comparative Study of Wireless Sensor Network Topologies�, International Journal of Computer Applications (0975 � 8887) Volume 87 � No.1, February 2014.
[10] et al. Kukade, Madhavi S., and Kapil N. Hande. "Analysis of Uniform Distribution of Storage Nodes in Wireless Sensor Network." International Journal of Computer Sciences and Engineering, (2014): 43-47.
[11] et al. Zhengyuan Xu, Gang Chen, Feras Abou-Galala and Michael Leonardi, Research Gate �Experimental performance evaluation of non-line-of-sight ultraviolet communication systems�, The International Society for Optical Engineering, DOI: 10.1117/12.735183, October 2016.
[12] et al. Ambreen and Haque Nawaz, �Wireless Sensor Network Through Shortest Path Route�, International Journal of Emerging Technology and Advanced Engineering, ISSN 2250-2459, , Volume 3, Issue 2, February 2013.

A. Kumar, R. Vaid, "Adaptive Uninterrupted Communication Algorithm for Wireless Mesh Network by Using UV-Rays and Dijkstra�s Algorithm," International Journal of Computer Sciences and Engineering, Vol.4, Issue.10, pp.84-91, 2016.

Stock market analysis is the process of analyzing and monitoring stocks so it is also a process of calculating the future trends on the basis of historical trends. This whole concept is volatile, as the stock prices having the tendency to rise and fall. However, we know that there is a defined pattern in insight of any sequenced event therefore we can extract some hidden pattern thorough analysis. In this paper we have developed a decision support model to classify and predict the stock market by data mining techniques like classification and prediction. In this way we have developed some decision rules as model to increase the probability of right decision so that an investor can took profit in the stock investment. in this study we analyze the historical price data of the specific industry group Named Infosys Pvt. Ltd. to make sure that the investors is moving with right decision in order to increase the possibility of profit in their investments. Therefore the main task is to predict and classify the stock prices of Infosys Company on the basis of past prices.

Classification, Data Mining, Prediction, Stock market

[1] Jan Ivar Larsen, � Predicting Stock Prices Using Technical Analysis and Machine Learning � thesis submitted in Norwegian University of Science and Technology, June 2010.
[2] Bodie−Kane−Marcus, Book-�equilibrium in capital markets, Chapter 12; market efficiency� The McGraw−Hill Companies, 2001.
[3] Carol Hargreaves, Yi Hao, �Prediction of Stock Performance Using Analytical Techniques� Journal Of Emerging Technologies In Web Intelligence, Vol-5, Issue-2, May 2013.
[4] Jan Ivar Larsen, �Predicting Stock Prices Using Technical Analysis and Machine Learning�, Norwegian University of Science and Technology, June 2010.
[5] K. Senthamarai Kannan, P. Sailapathi Sekar, M.Mohamed Sathik and P. Arumugam, �Financial Stock Market Forecast using Data Mining Techniques�, Intertnational multiconference of engineers and computer scientists ,Vol-1 Issue-1, pp.17-19, 2010.
[6] Debashish Das and Mohammad Shorif Uddin, �Data Mining And Neural Network Techniques In Stock Market Prediction�- A Methodological Review, International Journal of Artificial Intelligence & Applications (IJAIA),Vol-4,Issue-1, January 2013.
[7] Harish R Pawar, Prasad G Gaikwad, Umesh G Bombale, Dipak D Jagtap and Santosh Durugkar, "Intelligence Stock Forecasting Using Neural Network", International Journal of Computer Sciences and Engineering, Volume-02, Issue-04, pp.103-106, Apr -2014,.
[8] Parth Mody, Advait Marathe, Viral Parekh & Siddhesh Owalekar �An Optimized Approach To Analyze Stock Market� International Journal of Computer Sciences and Engineering, vol-3,Issue-4, pp.19-24,2014.
[9] Ehsan Hajizadeh, Hamed Davari Ardakani and Jamal Shahrabi- �Application of data mining techniques in stock markets� ,A survey; Journal of Economics and International Finance,Vol- 2,Issue-7, pp.109-118, July 2010.
[10] Qasem A. Radaideh, Adel, Eman Alnagi, �Predicting stock prices using data mining techniques� - the international Arab conference on information technology (acit�2013),2013.
[11] Livermore, J. L., �How to trade in stocks, the Livermore formula for combining time element and price� New York, NY: Duel.1940
[12] Stock Market Dataset Available from Yahoo finance India (web-https://in.finance.yahoo.com).
[13]Aditya Joshi, Nidhi Pandey, (Professor) Rashmi Chawla, Pratik Patil-�Use of Data Mining Techniques to Improve the Effectiveness of Sales and Marketing� International Journal of Computer Science and Mobile Computing , Vol-4,Issue-4,pp.81-87,2015.

S. Gour , "Developing Decision Model by Mining Historical Prices Data of Infosys for Stock Market Prediction," International Journal of Computer Sciences and Engineering, Vol.4, Issue.10, pp.92-97, 2016.

In this paper we have successfully implemented Matrix Multiplication using Strassen`s Algorithm on a NVIDIA GPU using CUDA. We have used the multiple cores of the GPU to reduce the computation time drastically. We have also compared the time taken by matrix multiplication using Strassen`s algorithm on both CPU and GPU. We have found that the GPU implementation was much faster, but only when the recursion was performed till a certain limit. Beyond that limit, the computation took much more time than expected. Also, we found that implementing Matrix Multiplication using Strassen`s algorithm on the CPU yielded some very positive results. By conducting experiments, we came to the conclusion that the recursion limit can be comparatively smaller for matrix multiplication using Strassen`s algorithm on CPU than for matrix multiplication using Strassen`s algorithm on GPU.

GPU, CUDA, Matrix Multiplication, Strassen�s Algorithm, Cache, Speedup

1] Francois Le Gall, �Powers of Tensors and Fast Matrix Multiplication,� Cornell University Library, arXiv:1401.7714 [cs.DS], 2014.
[2] Wikipedia, Strassen Algorithm, https://en.wikipedia.org/wiki/Strassen_algorithm.
[3] Junjie Li, Sanjay Ranka, Sartaj Sahni, �Strassen�s Matrix Multiplication on GPUs,� 2011 IEEE 17th International Conference on Parallel and Distributed Systems(ICPADS), pp. 157-164, 2011.
[4] C. P. Patidar and Meena Sharma, "Histogram Computations on GPUs Kernel using Global and Shared Memory Atomics", ISROSET-International Journal of Scientific Research in Computer Science and Engineering, Volume-01, Issue-04, Page No (1-6), Aug 2013
[5] Pujianto Yugopuspito, Sutrisno, Robertus Hudi, �Breaking through memory limitation in GPU parallel processing using Strassen Algorithm,� 2013 International Conference on Computer, Control, Informatics and Its Applications(IC3INA), pp. 201-205, 2013.
[6] Ayaz ul Hasan Khan, Mayez Al-Mouhamed, Allam Fatayer, �Optimizing strassen matrix multiply on GPUs�, 2015 16th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD), pp. 1-6, 2015.
[7] V. Strassen, �Gaussian elimination is not optimal,� Numerische Mathematik, Vol. 13, No. 4, pp. 354-356, August 1969.
[8] Thomas H. Cormen, Charles E. Leiserson, Ronald L. Livest, Clifford Stein, Introduction to Algorithms, Second Edition. MIT Press and McGraw-Hill, 2001. Chapter 28: Section 28.2: Strassen�s algorithm for matrix multiplication, pp. 735-741.
[9] CUDA C Programming Guide, https://docs.nvidia.com/cuda/cuda-c-programming-guide
[10] John Nickolls, �GPU parallel computing architecture and CUDA programming model,� 2007 IEEE Hot chips 19 Symposium (HCS), pp. 1-12, 2007.
[11] Fazlul Kader Murshed Nawaz, Arnab Chattopadhyay, Kirthan G J, Girish D Mane, Rohith N Savanth, �Comparison of Open MP and CUDA�, International Journal of Computer Science and Engineering E-ISSN: 2347-2693, Vol.2, Issue-12, pp.38-41, 2014.

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.

There are typical applications where only periodic patrol inspections are sufficient instead of continuous monitoring like in traditional coverage. This periodic monitoring is termed as sweep coverage. In the sweep coverage scenario deployment of static sensor nodes may partially solve the purpose but it suffers from poor efficiency and unnecessary extra overhead. Moreover static sensor network suffers from static sink neighborhood problem as in static sensor network all sensing data from the sensors are relayed to the sink node (base station) through multi hop. As a result, the sensors near to the sink node become the bottleneck since they have to relay the data of other nodes. Once they die, the sink disconnects from the rest of the network while the rest of sensors are still fully operational with sufficient residual energy. To overcome this problem in our work, we proposed Mobile Sink Wireless Sensor Network (MSWSN). We assume that the given region is Rectangular and our aim is to do Sweep Coverage for Boundary of the Region. In Wireless sensor network Sensor node has fixed communication range ( let D be the communication range then Sensor node will cover all the points which lie within D distance from it in all directions ) and therefore to guarantee the coverage of Boundary Mobile sink will not traverse whole of the boundary but visit certain points in the Boundary known as points to Visit ( P1, P2, ----- Pn ). Points to Visit ( P1, P2, ----- Pn ) are to be chosen in such a way that every boundary point lie within the communication range of Mobile sink from at least one points to Visit ( P1, P2, ----- Pn ) and Mobile Sink must visit every edge of the boundary during traversal. Keeping above coverage conditions in mind our main objective is to choose points to Visit ( P1, P2, ----- Pn ) in such a way that the overall length of closed path travelled by the Mobile sink to collect the data is minimum.

Sweep coverage problem; Area sweep coverage; Point sweep coverage; convex hull algorithm; Tessellation

[1] Gurbax kaur, Ritesh Sharma, "Point Sweep Coverage in Wireless Sensor Networks Using Convex Hull Algorithm", International Journal of Computer Sciences and Engineering, Volume-04, Issue-08, Page No (23-27), Aug -2016
[2] Gurbax Kaur, Ritesh Sharma, �A Review on Sweep Coverage in Wireless Sensor Networks�, International Journal of Computer Sciences and Engineering, Volume-4, Issue-6, Page No (113-117), June 2016
[3] Barun Gorian , Partha Sarthi Mandal,�Approximation algorithm for sweep coverage in wireless sensor networks � ,Journal of Parallel and Distributed Computing , Volume-74, Issue-8, Page No(2699-2707), Aug 2014.
[4] Mo Li, Wei-Fang Cheng, Kebin Liu, Yunhao Liu,Xiang-Yang Li, Xiangke Liao, �Sweep coverage with mobile sensors�, Transaction on Mobile Computing , Volume-01, Issue-11, Page No (1534�1545), Nov 2011.
[5] Barun Gorian ,Partha Sarthi Mandal, �Point and area sweep coverage in wireless sensor networks�,11th International Symposium and Workshops on Modeling and Optimization in Mobile, Ad Hoc and Wireless Networks (WiOpt), Tsukuba Science City, pp (140-145) ,May 13th-17th ,2013,ISBN : 978-1-61284-824-2.
[6] Nasimkhazan,Ali Braumandinia and Nima Ghazanfari Motlagh,�Node Placement and coverage in Asymmetric area�, International Journal of Advanced Research in Computer Science and Software Engineering, Volume-2, Issue-11, Page No (278-282), Nov 2012.
[7] R.A. Jarvis, �on the identification of the convex hull of a finite set of points in the plane�, Information Processing Letters, Volume-2, Issue-1, Page No (18-21), Dec 1973.
[8] Novella Bartolini, Tiziana Calamoneri, Emanuele G. Fusco, Annalisa Massini, Simone Silvestri , � Push & pull: autonomous deployment of mobile sensors for a complete coverage�, Wireless Netw. , Volume-16, Issue-3, Page No (607�625), Jan 2009.
[9] Adrian Dumitrescu, MinghuiJiang, �Sweeping an oval to a vanishing point�, Elsevier: Discrete applied mathematics, Volume-159, Issue-14, Page No (1436-1442), Aug 2011.
[10] Edoardo S. Biagioni, Galen Sasaki,�Wireless sensor placement for Reliable and Efiicient data collection�,IEEE 36th Annual Hawaii International Conference on System Sciences, 2003. Proceedings of the - Big Island, HI, USA, January 6th-9th, 2003, ISBN: 0-7695-1874-5.
[11] Santosh kumar, Ten H .Lai, Anish Arora,�Barrrier coverage with wireless sensors�,Mobicom�05 Proceedings of the 11th annual international conference on Mobile computing and networking,Org by-ACM, NY, USA, pp(284-298) , August 28 , 2006 ,ISBN: 1-59593-020-5.
[12] Xiaojiang Du,Fengjing Lin, �Improving sensor network performance by deploying mobile sensors�,IEEE international performance, computing, and communications conference , Phoenix, AZ, USA ,pp(67-71),April 7th � 9th ,2005, ISBN: 0-7803-8991-3.
[13] Hung-Chi Chu, Wei-Kai Wang, Yong-Hsun Lai, �Sweep coverage mechanism for wireless sensor networks with approximate patrol times�, Ubiquitous Intelligence & Computing and 7th International Conference on Autonomic & Trusted Computing (UIC/ATC), Xian, Shaanxi, pp( 82�87) ,October 26th-29th,2010,ISBN: 978-0-7695-4272-0.
[14] Yi Ning Chen, Ko-Jui Lin ; Chang Wu Yu,�Dynamis coverage techniques in mobile wireless sensor networks� ,Fifth International Conference on Ubiquitous and Future Networks (ICUFN), Da Nang, pp(12-17), July 2nd -5th ,2013, ISSN:2165-8528.
[15] Min Xi, Kui Wu, Yong Qi, Jizhong Zhao, Yunhao Liu, Mo Li, �Run to potential:sweep coverage in wireless sensor networks� ,IEEE: International Conference on Parallel Processing (ICPP-2009),Sponsored by-IACC, Vienna , Austria , pp ( 50�57) ,September 22nd-25th , 2009 ,ISBN: 978-0-7695-3802-0.
[16] Gautam K. Das, Sandip Das, Subhas C. Nandy, Bhabani P. Sinha, �Efficient algorithm for placing a given number of base stations to cover a convex region�, Journal of. Parallel Distributed Computing,Volume- 66,Issue-11,Page No (1353 � 1358),July 2006.
[17] Junzhao Du,Yawei Li,Hui Liu and Kewai Sha, � On sweep coverage with minimum mobile sensors�, 16th international conference on Parallel and Distributed System, Shanghai, pp(283-290). December 8th-10th, 2010, ISBN: 978-1-4244-9727-0.
[18] A. Bykat, �Convex Hull Of A Finite Set Of Points In Two Dimensions�, Information Processing Letter, Volume-7, Issue-6, Page No (296-298), Oct 1978.

R. Sharma, G. Kaur, "Sweep Coverage for Boundary of Rectangular Region Using Geometric Approach," International Journal of Computer Sciences and Engineering, Vol.4, Issue.10, pp.106-111, 2016.

In speech recognition, a speaker dependent isolated word recognition system is used for small vocabulary in different applications for voice control systems. Dynamic Time Warping (DTW) algorithm is used for pattern matching when two sequences of unequal size are available. When test data and reference data or sequences are available of unequal in nature with time domain then existing DTW algorithm takes time more, while proposed solution will give the efficient algorithm which reduces the computation time without degradation of accuracy and efficiency.

Dynamic time warping, speech recognition, speaker dependent

[1] Titus Felix FURTUNA, Dynamic Programming Algorithms in Speech Recognition, Revista Informatica Economica nr. 2(46), 2008, pp 94- 99.
[2] B. H. Jaung and L.R. Rabiner , Automatic Speech Recognition � A Brief History of The Technology, Elsevier Encyclopedia of Language and Linguistics, Second Edition, 2005.
[3] Rubita Sudirman, Sh.-hussain Salleh, Ting Chee Ming, Local DTW Coefficients and pitch feature for back-propagation NN digit recognition. Proceedings of the IASTED International Conference on Networks and Communication Systems 2006. pp-201-206.
[4] Ghazi Al- Naymat, Sanjay Chawala, Javid Taheri, Sparse DTW A Novel Approach to Speed up Dynamic Time Warping, Proc. of 8th Australasian Data Mining Conference (AusDM�09), pp 117- 127.
[5] Tiberius Zaharia, Svetlana Segarceanu, Marius Cotescu, Alexandru Spataru, � Quantized Dynamic Time Warping (DTW) Algorithm� , IEEE, 2010, pp 91-94.
[6] L.R. Rabiner and R. W. Schafer, Thery and Applications of Digital Speech Processing, Prentice Hall Inc., 2011.
[7] L.R. Rabiner and R. W. Schafer, Introduction to Digital Speech Processing, Foundations and Trends in Signal Processing, Vol. 1, Nos. 1-2, NOW Publishers, Boston, pp. 1-200, 2007.
[8] S. Dusan and L.R. Rabiner. Can Automatic Speech Recognition Learn More From Human Speech Perception, Trens in Speech Technology, Proc. of the third conference on Speech Technology and Human Computer Dialogue. pp. 21-36, May 2005.
[9] Eiji Mizutani, The Dynamic Time Warping Algorithms. Lecture Note for Mechanical Engineering Seminar, Tokyo Metropolitan University. 2006. pp 1-11.
[10] Rubita Sudirman, Sh.-hussain Salleh. Ting Chee Ming, NN Speech Recognition Utilizing Aligned DTW Local Distance Scores, ICMT-192. pp 1-5.
[11] Sakoe, H. & S. Chiba.(1978) Dynamic programming algorithm optimization for spoken word recognition. IEEE, Trans. Acoustics, Speech and Signal Proc., Vol. ASSP-26. pp 43-49.
[12] Somya Adwan, Hamzah Arof, A Novel Double Stage Dynamic Time Warping Algorithm for Image Template Matching, Proceeding of the 6th IMT-GT Conference on Mathematics, Statistics and its Applications (ICMSA 2010), University Tunku Abdul Rahman , Kaula Lumpur Malaysia. Pp. 667-676.

M. Yadav, A. Aalam, "Five Stage Dynamic Time Warping Algorithm for Speaker Dependent Isolated Word Recognition in Speech," International Journal of Computer Sciences and Engineering, Vol.4, Issue.10, pp.112-115, 2016.

Cognitive radio have been considered as the able-bodied approach with the goal of enhancing spectral efficiency. The prime aim of cognitive radio is spectrum sensing. Spectrum sensing senses the presence of white spaces, but in fading environment unlicensed users are unable to discover the presence of licensed user in the band and this leads to interference. Therefore the aim of this paper is to increase SNR in two noisy environments, AWGN and Rayleigh Fading Environment using matched filter process and to increase lifetime of cognitive radio network using genetic algorithm.

Primary Users, Secondary Users, Matched Filter, Genetic Algorithm

[1] J. Mitola and G. Q. Maguire,� Cognitive radio: making software radios more personal,� IEEE Personal Communications, 6(4): pp.13-18, 1999.
[2] Feijing Bao, Huifang Chen, Lei Xie, �Analysis of Primary User Emulation Attack with Motional Secondary Users in Cognitive Radio Networks,� IEEE.
[3] T. Yucek and H. Arslan, �A survey of spectrum sensing algorithms for cognitive radio applications,� IEEE Communications Surveys and Tutorials, vol. 11, no. 1, pp. 116-130, 2009.
[4] S.Tamilarasan, P.Kumar, "A Survey on Dynamic Resource Allocation in Cognitive Radio Networks", International Journal of Computer Sciences and Engineering, Volume-04, Issue-07, Page No (86-93), Jul -2016
[5] Zhaoyu Gao, Haojin Zhu, Yao Liu, Muyuan Li, and Zhanfu Cao, �Location Privacy In Database-Driven Cognitive Radio Networks: Attacks And Countermeasures,� IEEE 2013.
[6] Krikidis, Ioannis, et al. "Cognitive legacy networks via cooperative diversity."IEEE Communications Letters 13.2 (2009): 106-108
[7] Akyildiz, Ian F., et al. "A survey on spectrum management in cognitive radio networks." IEEE Communications magazine 46.4 (2008): 40-48.
[8] Loong Lee, Wasan Kadhim Saad, Ayman Abd El-Saleh, Mahamod Ismail, �Improved Detection Performance of Cognitive Radio Networks in AWGN and Rayleigh Fading Environments.� Journal of Applied Research and Technology.
[9] T.Nilavazhaki, G.Sathish Kumar, "Spectrum Sensing Cognitive Radio For Opportunistic Access Environments Based Trust Management", International Journal of Computer Sciences and Engineering, Volume-04, Issue-04, Page No (298-301), Apr -2016

S. Khanam, A. Kaur, "Enhanced Detection of Cognitive Radio Under Noisy Channels," International Journal of Computer Sciences and Engineering, Vol.4, Issue.10, pp.116-119, 2016.

These days many current data mining tasks are accomplished successfully only in discovery of Association rule. It appeals more attention in frequent pattern mining because of its wide applicability. Many researchers successfully presented several efficient algorithms with its performances in the area of rule generation. This paper mainly assembles a theoretical survey of the existing algorithms. Here author provides the considered Association rule mining algorithms by beginning an overview of some of the latest research works done on this area. Finally, discusses and concludes the merits and limitation.

Data Mining; Association rule; frequent pattern; algorithm

[1] Yun H, Ha D, Hwang B, Ryu KH, �Mining association rules on significant rare data using relative support�, J Syst Softw, No. 67, Pp. (181� 191), 2003.
[2] HUANG Liusheng, CHEN Huaping, WANG Xun and CHEN Guoliang, �A Fast Algorithm for Mining Association Rules�, J. Comput. Sci. & Technol., Vol.15, No.6, Pp.(619 � 624), 2000.
[3] Nidhi Sethi and Pradeep Sharma, "Mining Frequent Pattern from Large Dynamic Database Using Compacting Data Sets", ISROSET-International Journal of Scientific Research in Computer Science and Engineering, Volume-01, Issue-03, Page No (31-34), Jun 2013
[4] Pradeep Sharma and Vijay Kumar Verma, "Data Dependencies Mining In Database by Removing Equivalent Attributes", ISROSET-International Journal of Scientific Research in Computer Science and Engineering, Volume-01, Issue-04, Page No (7-11), Aug 2013
[5] Padmanabhan B, Tuzhilin A, �A belief-driven method for discovering unexpected patterns�, In: Proceedings of the 4th international confercence on knowledge discovery and data mining, 1998.
[6] Chidanand Apt�, �Data Mining: An Industrial Research Perspective�, Ieee Computational Science & Engineering, CSE in Industry,1997, Pp 6- 9, 1997.
[7] Hussain F, Liu H, Suzuki E, Lu H, �Exception rule mining with a relative interestingness measure� Lect Notes Comput Sci, 1805,Pp.(86�97), 2000.
[8] Liu H, Lu H, Feng L, Hussain F, �Efficient search of reliable exceptions�, Lect Notes Comput Sci., 1574, Pp. (194�204), 1997.
[9] LIU Jun-qiang, PAN Yun-heAn, �Efficient algorithm for mining closed itemsets�, Vol.5, No. 1, Pp.(8 � 15), 2004.
[10] ZHOU Haofeng, ZHU Jianqiu, ZHU Yangyong and SHI Baile, �AR Miner : A Data Mining Tool Based on Association Rules�, J. Comput. Sci. & Techno], Vol.17, No.5, Pp.(594 � 602), 2002.
[11] Assaf Schuster, Ran Wolff, Dan Trock, �A high-performance distributed algorithm formining association rules Knowledge and Information Systems�, No. 7, Pp. (458�475), 2005.
[12] St�phane Lallich� Beno�t Vaillant� Philippe LencaA, �Probabilistic Framework Towards the Parameterization of Association Rule Interestingness Measures Methodol Comput Appl Probab�, No. 9, Pp.(447�463), 2007.
[13] Agrawal R, Imielinski T, Swami A, �Mining association rules between sets of items in large databases�, In: Proceedings of the 1993 ACM SIGMOD international conference on management of data, Washington, DC, ACM Press, New York, Pp (207�216), 1993.
[14] V. Palanisamy and A. Kumarkombaiya, "Designing a Knowledge Discovery of Clustering Techniques in Pharmaceutical Compounds", International Journal of Computer Sciences and Engineering, Volume-03, Issue-04, Page No (58-63), Apr -2015.
[15] Finn �rup Nielsen, Lars Kai Hansen, and Daniela Balslev, �Mining for Associations Between Text and Brain Activation in a Functional Neuroimaging Database Neuroinformatics�, Pp. (369�380), 2004.
[16] Varsha Kavi and Divyesh Joshi , "A Survey on Enhancing Data Processing of Positive and Negative Association Rule Mining", International Journal of Computer Sciences and Engineering, Volume-02, Issue-03, Page No (139-143), Mar -2014, E-ISSN: 2347-2693
[17] Srikant R, Agrawal R, �Mining quantitative association rules in large relational tables�, In: Proceedings of the 1996 ACM SIGMOD international conference on management of data, 1996, Pp (1�12), 1996.
[18] Brin S, Motwani R, Silverstein C, �Beyond market basket: generalizing association rules to correlations�, In: Proceedings of the 1997 ACM SIGMOD international conference on management of data, Pp (265�276), 1997.
[19] Padmanabhan B, Tuzhilin A, �Unexpectedness as a measure of interestingness in knowledge discovery. Decis Support Syst, vol. 27(3), Pp. (303�318), 1998.
[20] Padmanabhan B, Tuzhilin A, �Small is beautiful: discovering the minimal set of unexpected patterns�, In: Proceedings of the 6th ACM SIGKDD international conference on knowledge discovery and data mining, Pp. (54�63), 2000.
[21] Zhou L, Yau S, �Efficient association rule mining among both frequent and infrequent items�, Comput Math Appl, Vol. 54, Pp. (737�749), 2007.
[22] Wu X, Zhang C, Zhang S, �Efficient mining of both positive and negative association rule�, ACM Trans Inf Syst 22(3), Pp.(381�405), 2004.
[23] Yuan X, Buckles B, Yuan Z, Zhang J, �Mining negative association rules� In: Proceedings of the seventh international symposium on computers and communications, Pp. (623�629), 2002.
[24] Hong TP, Kuo CS, Chi SC, �Mining association rules from quantitative data�, Intell Data Anal, Vol. 3(5), Pp. (363�376), 1999.
[25] Zhang Tiejun, Yang Junrui and Wang Xiuqin, �An Algorithm for Mining Frequent Closed Itemsets�, Proceedings of 2008 3rd International Conference on Intelligent System and Knowledge Engineering, Pp. (240 � 245), 2008.
[26] Peng Gong, Chi Yang, Hui Li, and Weili Koul, �The Application of Improved Association Rules Data Mining Algorithm Apriori in CRM�, IEEE, Pp. (1 � 5), 2007.
[27] N.Balajiraja, �Mining Association Rule of Frequent Itemsets Measures for an Educational Environment�, International journal of Computer science and Engineering, vol 4 issue 6, Pp.8 � 17, 2016.
[28] Aruna J. Chamatkar and P.K. Butey , "Comparison on Different Data Mining Algorithms", International Journal of Computer Sciences and Engineering, Volume-02, Issue-10, Page No (54-58), Oct -2014.
[29] N.Balajiraja and G.Balakrishnan, �A Model of Algorithmic Approach to Itemsets Using Association Rules�, IACSIT, published in IEEE, 2011.
[30] Monali Dey and Siddharth Swarup Rautaray, "Disease Predication of Cardio- Vascular Diseases, Diabetes and Malignancy in Lungs Based on Data Mining Classification Techniques", International Journal of Computer Sciences and Engineering, Volume-02, Issue-04, Page No (82-98), Apr -2014
[31] Mohnish Patel, Aasif Hasan and Sushil Kumar, "A Survey: Preventing Discovering Association Rules For Large Data Base", ISROSET-International Journal of Scientific Research in Computer Science and Engineering, Volume-01, Issue-03, Page No (35-38), Jun 2013
[32] Mohammed M. Mazid, A.B.M. Shawkat Ali, Kevin S. Tickle, �Finding a Unique Association Rule Mining Algorithm Based on Data Characteristics�, 5th International Conference on Electrical and Computer Engineering, ICECE, IEEE, Pp. 9902 � 908), 2008.
[33] LI Pingxiang CHEN Jiangping BIAN FulingA, �Developed Algorithm of Apriori Based on Association Analysis�, Geo-spatial Information Science (Quarterly), vol. 7, Issue 2, Pp. (108-112), 2004.
[34] WEI Yong-qing , YANG Ren-hua , LIU Pei-yu , An Improved Apriori Algorithm for Association Rules of Mining, IEEE, Pp. (942 � 946), 2009.
[35] Huan Wu, Zhigang Lu, Lin Pan, Rongsheng Xu and Wenbao Jiang, �An Improved Apriori-based Algorithm for Association Rules Mining�, Sixth International Conference on Fuzzy Systems and Knowledge Discovery, IEEE, Pp. 51- 55), 2009.
[36] Parvinder S. Sandhu, Dalvinder S. Dhaliwal, S. N. Panda and Atul Bisht, �An Improvement in Apriori algorithm Using Profit And Quantity�, Second International Conference on Computer and Network Technology, IEEE, Pp. (3 - 7), 2010.
[37] Hamid Mohamadlou, Reza Ghodsi, Jafar Razmi, Abbas Keramati, �A method for mining association rules in quantitative and fuzzy data�, IEEE, Pp. (453 � 458), 2009.
[38] Wei-Min Ma � Ke Wang � Zhu-Ping Liu, �Mining potentially more interesting association rules with fuzzy interest measure�, Springer-Verlag, Pp. (1- 10), 2010.
[39] Yue S, Tsang E, Yeung D, Shi D, �Mining fuzzy association rules with weighted items�, In: Proceedings of the IEEE international conference on systems, man and cybernetics, Pp (1906�1911), 2000.
[40] Au WH, Chan KCC, �Mining changes in association rules: a fuzzy approach�, Fuzzy Sets Syst 149, Pp.(87�104), 2005.
[41] Hu YC, Chen RS, Tzeng GH, �Discovering fuzzy association rules using fuzzy partition methods�, Knowl Based Syst, Vol.16, Pp.(137� 147), 2003.
[42] N.Balajiraja and G.Balakrishnan, �Discovering of Frequent Itemsets an Improved Algorithm of Graph and Clustering Based Association Rule Mining (GCBARM)�, European Journal of Scientific Research vol 94 issue 2, Pp. (226-235), 2012.
[43] N.Balajiraja and G.Balakrishnan, �Multilevel Value Fuzzy Cluster Based Association Rules Mining (MFCBARM)�, Information, An International Journal Information, vol 16, 2013.

D.S. Kumar, N. Jayaveeran, "A Survey on Association Rule Mining Algorithms for Frequent Itemsets," International Journal of Computer Sciences and Engineering, Vol.4, Issue.10, pp.120-125, 2016.

Cloud computing is the buzz word, the corporate world pronounces every now and then. All the software companies are looking for a where about to store their data at a low cost and reduce the problem of selecting and updating a suitable infrastructure. The mushroom growth of startups and their prosperity and share in the software trends has also mounted the need of cloud computing. On the other hand, several other problems have taken birth like the believability of the cloud and the security concerns of it [1].The cloud users are stranded in a situation either to believe cloud or to wait for some more time for transiting to cloud .This position paper gives a thorough analysis of the cloud security and the stand the cloud computing has taken currently.

Cloud security alliance; Data loss; denial of service attack; Server Reboot

[1] R. K. L. Ko, "Cloud computing in plain English," ACM Crossroads, vol. 16 (3), pp. 5-6, 2010.
[2] https://cloudsecurityalliance.org
[3] https://cloudsecurityalliance.org/research/vulnerabilities
[4] https://cloudsecurityalliance.org/download/cloud-computing-vulnerability-incidents-a-statistical-overview
[5] https://cloudsecurityalliance.org/topthreats/csathreats.v1.0.pdf‎
[6] http://www.symantec.com/connect/blogs/avoiding-hidden-costs-cloud-0, Created: 15 Jan 2013, Updated: 15 Jan 2013 ,Tom Powledge.
[7] https://cloudsleuth.net/global-provider-view
[8] Shu-Min Chuang; Kuo-En Chang; Yao-Ting Sung, "The cost effective structure for designing hybrid cloud based enterprise E-learning platform," Cloud Computing and Intelligence Systems (CCIS), 2011 IEEE International Conference on , vol., no., pp.523,525, 15-17 Sept. 2011
[9] http://www.compuware.com
[10] http://www.compuware.com/en_us/about/techfail.html
[11] https://cloudsleuth.net/cdn-performance-analyzer
[12] https://csrc.nist.gov/publications/nistpubs/800-144/SP800-144.pdf‎
[13] Shaheen Ayyub and Devshree Roy, "Cloud Computing Characteristics and Security Issues", International Journal of Computer Sciences and Engineering, Volume-01, Issue-04, Page No (18-22), Dec -2013,
[14] Reddy, Varun K., and Jagadeeshwar E. Rao. "A Survey on Security in Cloud Using Homographic and Disk Encryption Methods." International Journal of Computer Sciences and Engineering 2 (2014).
[15] Putri, N.R., Mganga, M.C. 2011. Enhancing Information Security in Cloud qe Computing Services using SLA Based Metrics. Master�s thesis: Blekinge Institute of Technology.[Online].
[16] Mewada, Shivlal, Umesh Kumar Singh, and Pradeep Sharma. "Security Based Model for Cloud Computing." Int. Journal of Computer Networks and Wireless Communications (IJCNWC) 1.1 (2011): 13-19.
[17] Mewada, Shivlal, Umesh Kumar Singh, and Pradeep Sharma. "Security Enhancement in Cloud Computing (CC)." International Journal of Scientific Research in Computer Science and Engineering 1.01 (2013): 31-37.
[18] K. Erkan, �Evaluating IT Security Performance with Quantifiable Metrics�, Institutionen f�or Data- och Systemvetenskap, KTH.
[19] E. Chew, M. Swanson, K. Stine et al., "Performance Measurement Guide forInformation Security," NIST Special Publication 800-55 Revision 1, National Institute of Standards and Technology & U.S. Department of Commerce, 2008.
[20] http://www.pcisecuritystandards.org/pdfs/PCI_DSS_v2_Cloud_Guidelines.pdf
[21] Niketan Jivane, Supriya Jivane, S.Rajkumar and K.Marimuthu, �Enhancement of an algorithm to extract text-lines from images for blind and visually impaired persons through parallel approach, International Journal of Computer Sciences and Engineering 4(9), pp. 25-32, 2016.
[22] Xiong Li, Jianwei Niu, Marimuthu Karuppiah, Saru Kumari, Fan Wu(2016), Secure and Efficient Two-factors User Authentication Scheme with User Anonymity for Network Based E-Health Care Applications., Journal of Medical Systems, DOI: 10.1007/s10916-016-0629-8.
[23] Marimuthu Karuppiah, Saru Kumari, Xiong Li, Fan Wu, Muhammad Khurram Khan, R Saravanan, Sayantani Basu,(2016), A Dynamic ID-based Generic Framework for Anonymous Authentication Scheme for Roaming Service in Global Mobility Networks, Wireless Personal Communication, DOI: 10.1007/s11277-016-3672-3.
[24] Saru Kumari, Marimuthu Karuppiah, Xiong Li, Fan Wu, Ashok Kumar Das, Vanga Odelu (2016), A Secure Trust-Extended Authentication Mechanism for VANETs, Security and Communication Networks, DOI: 10.1002/sec.1602.
[25] Marimuthu Karuppiah, Saru Kumari, Ashok Kumar Das, Xiong Li, Fan Wu, Sayantani Basu (2016), A Secure Lightweight Authentication Scheme with User Anonymity for Roaming Service in Ubiquitous Networks, Security and Communication Networks, DOI: 10.1002/sec.1598.
[26] Marimuthu Karuppiah(2016), Remote User Authentication Scheme using Smart card: A Review, International Journal of Internet Protocol Technology, 9(2/3): 107�120.
[27] Fan Wu, Lili Xu, Saru Kumari, Xiong Li, Ashok Kumar Das, Muhammad Khurram Khan, Marimuthu Karuppiah, Renuka Baliyan (2016), A novel and provably secure authentication and key agreement scheme with user anonymity for global mobility networks, Security and Communication Networks, DOI: 10.1002/sec.1558.
[28] Swapnil Rajesh Telrandhe and Deepak Kapgate, "Authentication Model on Cloud Computing", International Journal of Computer Sciences and Engineering, Volume-02, Issue-10, Page No (33-37), Oct -2014,
[29] S. Mewada, P. Sharma and S. S. Gautam, "Exploration of efficient symmetric algorithms," 2016 3rd International Conference on Computing for Sustainable Global Development (INDIACom), New Delhi, India, 2016, pp. 663-666
[30] Karuppiah, Marimuthu, and R. Saravanan. "A secure remote user mutual authentication scheme using smart cards." Journal of information security and applications 19.4 (2014): 282-294.

B. Balamurugan, K. Marimuthu, S. Rajkumar, M. Alenezi, R. Niranchana, "Is Cloud Secure?," International Journal of Computer Sciences and Engineering, Vol.4, Issue.10, pp.126-129, 2016.

Management Information System (MIS) gives information for the activities in an organization. The main purpose of this research is, MIS provides accurate and faster information provide to facilitate the decision-making process and enable the institute planning, control, and operational functionalities to be carried out absolutely. Management Information System (MIS) is basically concerned with processing data into detail information and is then forwarded to the various Departments in an institute for appropriate decision-making. MIS is a subset of the overall planning and control activities covering the application of student technologies, and functionality of the institute. The information system is the m process to ensure that information is available to the administrator in the form they want it and when they need it.

Management Information Systems (MIS), Information Technology, Decision Making , MIS In An Organization, Transactional Processing Systems, Expert Systems, Job Marketability, IT job trends

[1]. Bowker, N., & Tuffin, K. (2002). Users with disabilities` social and economic development through online access. In M. Boumedine (Ed.), Proceedings of the IASTED International Conference on Information and Knowledge Sharing (pp. 122�127). Anaheim, CA: ACTA Press.
[2]. Durie, M. (2003). Ngākāhuipou: Launching Māori futures. Wellington, New Zealand: Huia Publishers.
[3]. Hazledine, T., &Quiggan, J. (2006). Public policy in Australia and New Zealand: The new global context. Australian Journal of Political Science, 41(2), 131�143.
[4]. Palladio�s Italian villas (2005) Available at: http://www.boglewood.com/palladio/
[5]. British Red Cross(2008)Opengardens. Available : http://www.redcross.org.uk/index.asp?id=39992
[6]. The Republic of PemberleyAllusions to books and authors in Jane Austen`s writings. Available at: http://www.pemberley.com/janeinfo/litallus.html

S. Mahajan, P. Khare, S. Kadare, M. Bhandare, "Design and Develop Information Management System for Technical Institute," International Journal of Computer Sciences and Engineering, Vol.4, Issue.10, pp.130-132, 2016.