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Clusters that are formed on the basis of density are very helpful and easy to understand. Also, they do not limit to their shapes. Basically, there are two types of density based approaches. First one is density based connectivity which concentrates on Density and Connectivity and another is Density function which is a total mathematical function. In this paper, a study of the three most popular density based clustering algorithms - DBSCAN, DENCLUE, and DBCLASD is presented and finally a comparison is provided between the same.

Clustering, Density based clustering, DBSCAN, DENCLUE, DBCLASD

[1] Avita Katal, Mohammad Wazid, and RH Goudar. Big data: Issues, challenges, tools and good practices. In Contemporary Computing (IC3), 2013 Sixth International Conference on, pages 404{409. IEEE, 2013.
[2] Amandeep Kaur Mann and Navneet Kaur, “Review Paper on Clustering Techniques”, Global Journal of Computer Science and Technology, Software and Data Engineering (0975-4350), Volume 13 Issue 5 Version 1.0 Year 2013.
[3] Khan, Kamran, et al. "DBSCAN: Past, present and future." Applications of Digital Information and Web Technologies (ICADIWT), 2014 Fifth International Conference on the. IEEE, 2014.
[4] Pavel Berkhin, “Survey of Clustering Data Mining Techniques”, Accrue Software, Inc.
[5] Nagpal, Pooja Batra, and Priyanka Ahlawat Mann. "Comparative study of density based clustering algorithms." International Journal of Computer Applications 27.11 (2011): 421-435.
[6] Xu, Xiaowei, et al. "A distribution-based clustering algorithm for mining in large spatial databases." Data Engineering, 1998. Proceedings., 14th International Conference on. IEEE, 1998.
[7] Vivek S Ware, Bharathi H N, “Study of Density based Algorithms”, International Journal of Computer Applications (0975 – 8887), Volume 69– No.26, May
2013.
[8] XU, X., ESTER, M., KRIEGEL, H.-P., and SANDER, J.1998. A distribution-based clustering algorithm for mining in large spatial databases. In Proceedings of the 14th ICDE, 324-331, Orlando, FL.
[10]A. K. Jain, M. N. Murty and P. J. Flynn, Data clustering: a review, CM, 31 (1999), pp. 264–323.

Harsh Shah, Karan Napanda and Lynette D’mello, "Density Based Clustering Algorithms," International Journal of Computer Sciences and Engineering, Vol.3, Issue.11, pp.54-57, 2015.

Sign Language is the language used by the deaf and dumb people to communicate. However, this language is rarely learnt by the general public. So it becomes difficult for these people to communicate with the general masses. Various such methods and techniques have been developed for the American Sign Language. This paper proposes an interpretation technique for the Indian Sign Language which is equally complex in nature and uses various parts of the body to convey messages such as hand orientations, palm movement, fingertips, etc. Our proposed technique will be able to take in a live video stream consisting of gestures and convert it into an equivalent sentence in English. The solution offered consists of steps like frame extraction, segmentation and refining of images, feature extraction, and training of neural network. The various methods will have different accuracy and efficiency levels and thus training of the network to perfectly guess each sign is of utmost importance.

Neural Networks, Video Processing, Indian Sign Language

[1] David M. Perlmutter, “The Language of Deaf”, The New York Review of Books, March 1991.
[2] Geetha M and Manjusha U. C, "A Vision Based Recognition of Indian Sign Language Alphabets and Numerals Using B-Spline Approximation", International Journal on Computer Science and Engineering (IJCSE), March 2012.
[3] Aradhana Kar and Pinaki Sankar Chatterjee, “A Video-based Approach for Translating Sign Language to Simple Sentence in English”, Proc. of Int. Conf. on Advances in Computer Science, AETACS, 2013.
[4] Adithya V, Vinod P.R and Usha Gopalakrishnan, “Artificial Neural Network Based Method for Indian Sign Language Recognition”, Proceedings of 2013 IEEE Conference on Information and Communication Technologies (ICT), 2013.
[5] Paulraj M P, Sazali Yaacob, Mohd Shuhanaz Zanar Azalan and Rajkumar Palaniappan, “A Phoneme Based Sign Language Recognition System using 2D Moment Invariant Interleaving feature and Neural Network”, IEEE Student Conference on Research and Development, 2011.
[6] J. Yang, W. Lu and A. Waibel, “Skin-color modeling and adaptation”, ACCV98, 1998.
[7] Yona Falinie bte Abdul Gaus, Farrah Wong and Kenneth teo, "Malaysian Sign Language Recognition Using Neural Network", Proceedings of 2009 Conference on Research and development (SCOReD), 2009.
[8] Amit Kumar Mandal and Dilip Kumar Baruah, “Image Segmentation Using Local Thresholding And Ycbcr Color Space”, Int. Journal of Engineering Research and Applications, Vol. 3, Issue 6, Nov-Dec 2013.
[9] Amanpreet Kaur and B.V Kranthi, “Comparison between YCbCr Color Space and CIELab Color Space for Skin Color Segmentation”, International Journal of Applied Information Systems (IJAIS) – ISSN : 2249-0868 Volume 3, No.4, July 2012.
[10] William K.Pratt, "Digital Image Processing-PIKS Scientific Inside lh ed ", A Wiley-Interscience publication.
[11] Khadidja Sadeddine, Fatma Zohra Chelali and Rachida Djeradi, “Sign Language Recognition using PCA, Wavelet and Neural Network”, Control, Engineering & Information Technology (CEIT), 2015

Juilee Rege, Ankita Naikdalal, Kaustubh Nagar and Ruhina Karani, "Interpretation of Indian Sign Language through Video Streaming," International Journal of Computer Sciences and Engineering, Vol.3, Issue.11, pp.58-62, 2015.

Speaker confirmation is feasible method of controlling access to computer and communication networks. Speakers resonance is different due to physiological differences such as vocal tract size, larynx size and other voice produce organs, and speaking manner differences such as accent and often used words. The task of automatic speaker identification is to identify the underlying speaker or confirm the claimed speaker from a sound recording, by exploiting these differences. This paper introduce the important concepts of speaker confirmation for security system.

Gaussian Mixture Model, Finite Impulse Response, Artificial Neural Network, Gaussian

[1] R.Mukherje, I.Tanmoy, and R.Sankar, "Text dependent speaker recognition using shifted MFCC". Southeast on, 2013, Proceedings of IEEE, Orlando, FL, USA,Vol.9, pp.1-4.
[2] Wu Ju, “Speaker Recognition System Based on Mfcc and Schmm”. Symposium on ICT and Energy Efficiency and workshop on Information Theory and Security, 2005, Dublin Ireland, pp. 88 – 92.
[3] D.A. Reynolds, “Speaker Identification and Verification Using Gaussian Mixture Speaker Models”, Speech Communication, Vol.17,1995, No. 1-2, pp. 91-108.
[4] W.Junqin, and Y. Junjun, “An Improved Arithmetic of Mfcc in Speech Recognitions System”. Electronics, Communications and Control (ICECC), International Conference on. IEEE, 2011, Zhejiang China, pp .719-722.
[5] U. Shrawankar, and V.M. Thakare, “Techniques for Feature Extraction In Speech Recognition System: A Comparative Study.”International Journal Of Computer Applications In Engineering, Technology And Sciences, Vol. 2, No.5,2010, pp. 412-418.
[6] H.Hermansky, “Perceptual Linear Predictive (PLP) Analysis of Speech”. Speech Technology Laboratory, Division of Panasonic Technologies,Vol.87,No.4, 1990,pp. 1738-1752.
[7] N. Wang, and P.C. Ching, “Robust Speaker Recognition Using Denoised Vocal Source and Vocal Tract Features speaker verification”, IEEE Transaction on Audio Speech and Language processing, Vol. 19, No. 1 ,2011, pp. 196-205.
[8] M.I. Faraj, and J. Bigun,"Synergy of lip-motion and acoustic features in biometric speech and speaker recognition".Computers, IEEE Transactions on computers Vol.56, No.9,2007, pp. 1169-1175.
[9] M.S. Sinith, A.Salim, K. Gowri Shankar ,S. Narayanan, and V. Soman, "A novel method for Text-Independent speaker identification using MFCC and GMM".Audio Language and Image Processing (ICALIP), International Conference on. IEEE,Shanghai, 2010,Vol.5, pp.292-296.
[10] A.Solomon off,. "Channel compensation for SVM speaker recognition". Odyssey.Vol. 4,2004, pp.57-62.
[11] R. Collobert, and S.Bengio, "SVM Torch: Support vector machines for large-scale regression problems". The Journal of Machine Learning Research, No .1,2001, pp. 143-160.
[12] D.E.Sturim, and D.A. Reynolds, "Speaker Adaptive Cohort Selection for Tnorm in Text-Independent Speaker Verification."ICASSP,No.1,USA ,2005, pp.741-744.
[13] G.S.V.S.Sivaram, Thomas, and H.Hermansky, “Mixture of Auto-Associative Neural Networks for Speaker Verification”. INTERSPEECH, Baltimore, USA,2011, pp. 2381-2384.
[14] S.Gfroerer, “Auditory instrumental forensic speaker recognition”. Proceedings of Eurospeech,Geneva, 2003,pp. 705–708.
[15] H.R.Bolt, and F.S.Cooper, “Identification of a Speaker by Speech Spectrograms”, American Association for the Advancement in Science, Science, Vol. 166, 1969.pp. 338–344.
[16] D.Charlet, D.Jouvet, and O.Collin, “An Alternative Normalization Scheme in HMM-based Text-dependent Speaker Verification”, Speech Communication, Vol. 31,2000, pp. 113-20.
[17] T.Dutta, “Dynamic Time Warping Based Approach to Text-Dependent Speaker Identification Using Spectrograms,” Congress on Image and Signal Processing, Vol. 2, No.8 ,2008, pp. 354-60.

Sukhandeep Kaur and Kanwalvir Singh Dhindsa, "Neural Network Based Speaker Verification using GFCC," International Journal of Computer Sciences and Engineering, Vol.3, Issue.11, pp.63-65, 2015.

With the unremitting advancement of internet and IT, tremendous growth of data has been observed. Data creation occurring at very fast pace, referred as big data, is a trending term these days. Big Data has been the topic of fascination for Computer Science fanatic around the world, and has gained even more prominence in the last few years. This paper scrutinizes the comparison of Hadoop Map Reduce and the newly introduced Apache Spark – both of which are framework for analyzing big data. Although both of these resources are based on the idea of Big Data, their performance varies significantly based on the application under consideration. In this paper two frameworks are being compared along with providing the performance comparison using word count algorithm. In this paper, various datasets has been analyzed over Hadoop Map Reduce and Apache Spark environment for word count algorithm. The system that comes out to be better is further used to analyze the research dataset of a university.

Big Data, Hadoop, HDFS, Map Reduce, Apache Spark

[1] Jacob,J.P., Basu A,“ Performance analysis of hadoop mapreduce on eucalyptus private cloud” , International Journal of Computer Applications , Vol.17, 2013.
[2] Guanghui, X., Feng, X., Hongxu, M. ,.“ Deploying and Researching Hadoop in Virtual Machines”, Proceeding of the IEEE,International Conference on Automation and Logistics,Zhengzhou, China, 2012.
[3] Ezhilvathani, A., Raja, K.,“Implementation of Parallel Apriori Algorithm on Hadoop Cluster”, IJCSMC, Vol. 2, 2013 pp.513 – 516.
[4] Zaharia,M., Chowdhury, M., Franklin J, Shenker, S., Stoica, I., " Resilient distributed datasets: A fault-tolerant abstraction for in-memory cluster computing". Technical Report UCB/EECS-2011-82, EECS Department, UC Berkeley, 2011.
[5] Peng, W.,, Yan, Q., Hua, Y. “Analysis and Study on the Performance of Query based on NoSQL Database”, Computer modelling & new technologies , 2014, pp.153-159 .
[6] Wang, L., Tao, J., Ranjan, R., Marten, H., Streit, A., Chen, J., Chen, D.,. “G-Hadoop: MapReduce across distributed data centers for data-intensive computing” , Parallel and Distributed Processing Symposium Workshops and Phd Forum ,IEEE 26th International , 2012, pp.2004-2011.
[7] Rao,B.T., Sridevi N.V.,Reddy V.K., Reddy L.S.S.“Performance Issues of Heterogeneous Hadoop Clusters in Cloud Computing”, Global Journal of Computer Science and Technology ,2011,Vol.11, Issue 8.
[8] Pradeepa, A., Thanamani, A.S. “ Hadoop file system and fundamental concept of mapreduce interior and closure rough set approximations”, International Journal of Advanced Research in Computer and Communication Engineering ,Vol. 2, Issue 10, 2013.
[9] Lee, C., Hseieha, K., Hsieha, S., Hsia, H.“ A Dynamic Data Placement Strategy for Hadoop in Heterogeneous Environments," Big Data Research, Vol. 1, 2014, pp.14–22.
[10] “Hadoop in Action” by Chuck Lam.
[11] White, Tom, 2011.“Hadoop the definitive guide” O’ Reilly media, Inc., CA.
[12] SBPU University Research Dataset: http://www.unipune.ac.in/dept/mental_moral_and_social_science/politics_and_public_administration/ppa_webfiles/pdf/new11/Link_Archives_PhDThesisList2011.pdf
[13] Apache Spark, http://spark.apache.org/
[14] Amp Lab web page : https:// amplab.cs.berkeley.edu/projects/spark- lightning-fast-cluster-computing
[15] http://www.gutenberg.org/ebooks/2600

Mantripatjit Kaur and Gurleen Kaur Dhaliwal, "Performance Comparison of Map Reduce and Apache Spark on Hadoop for Big Data Analysis," International Journal of Computer Sciences and Engineering, Vol.3, Issue.11, pp.66-69, 2015.

We are increasingly relying on web, and performing important transactions online through it. At the same time, quantity and impact of security vulnerabilities in such applications has grown as well. This work presents a survey of web security research which is the emerging domain that implements various detection prevention techniques for hinder content injection attacks on web applications. This paper provides a classification of the research areas on the content injection attacks. In this paper, we analyze important aspects in content injection attacks. In addition, this paper presents a survey of various security mechanisms adopted by web browsers to defend content injection attacks. The goals of this survey paper are two-fold: i) Serve as a guideline for researchers, who are new to web security and want to contribute to this research area, and ii) Provides further research directions required into content injection attack prevention.

XSS(Cross-site scripting), SQLI(Structural Query Language Injection),Content Injection,SQL queries

[1] G. Buehrer, B.W. Weide, and P.A.G. Sivilotti. “Using parse tree validation to prevent sql injection attacks”. In Proceedings of the 5th International Workshop on Software Engineering and Middleware, 2005.
[2] CGIsecurity. The cross-site scripting (xss) faq. http://www.cgisecurity.com/xss-faq.html.
[3] S. Crites, F. Hsu, and H. Chen. Omash: “Enabling secure web mashups via object abstractions”. In Proceedings of the International Conference on Computer and Communications Security (CCS), 2008.
[4] Xinshu Dong, Kailas Patil, Xuhui Liu, Jian Mao, and Zhenkai Liang. “An entensible security framework in web browsers”. Technical Report TR-SEC-2012-01, Systems Security Group, School of Computing, National University of Singapore, 2012.
[5] Xinshu Dong,Kailas Patil, Jian Mao, and Zenkai Liang.
“A comprehensive client-side behavior model for diagnosing attacks in ajax applications”. In proceedings of the 18th International Conference on Engineering of Complex Computer systems (ICECSS), 2013.
[6] Dennis Fisher. Persistent XSS bug on twitter exploited by worm http://threatpost.com/en us/blogs/persistent-xss-bug-twitter-being-exploited-092110
[7] W.G.J.Halfond and A. Orso. “Amnesia: analysis and monitoring for neutralizing sql-injection attacks”. In Proceedings of the 20th IEEE/ACM International Conference on Automated Software Engineering, 2005.
[8] W.G.J. Halfond and A. Orso. “Combining static analysis and runtime monitoring to counter sql-injection attacks”. In Proceed-ings of the Third International Workshop on Dynamic Analysis, 2005.
[9] W.G.J. Halfond, A. Orso, and P. Manolios. “Using positive tainting and syntax-aware evaluation to counter sql-injection at-tacks”. In Proceedings of the 14th ACM SIGSOFT International Symposium on Foundations of Software Engineering, 2006.
[10] Mark Hofman. Sql injection attack happening atm. isc.sans.org/diary/SQL+Injection+Attack+happening+ATM/12127.
[11] Collin Jackson, Andrew Bortz, Dan Boneh, and John C. Mitchell. “Protecting browser state from web privacy attacks”. In Proceedings of the International Conference on World Wide Web (WWW), 2006.
[12] Patil Kailas, Dong Xinshu, Li Xiaolei, Liang Zhenkai, and Jiang Xuxian. “Towards fine-grained access control in javascript contexts”. In Proceedings of the International Conference on Distributed Computing Systems, 2011.
[13] Ziqing Mao, Ninghui Li, and Ian Molloy. “Defeating cross-site request forgery attacks with browser-enforced authenticity protection”. In Financial Cryptography and Data Security, 13th International Conference, 2009.
[14] Leo A. Meyerovich and Benjamin Livshits. “ConScript: Specify-ing and enforcing fine-grained security policies for javascript in the browser”. In Proceedings of the IEEE Symposium on Security and Privacy (IEEE S & P), 2010.
[15] Mozilla Same origin policy for javascript. https://developer.mozilla.org/En/Same_origin_policy_for_javascript.
[16] The clickjacking meets xss: a state of art. http://www.milw0rm.com/papers/265, 2008.
[17] Anh Nguyen-tuong, Salvatore Guarnieri, Doug Greene, Jeff Shirley, and David Evans. “Automatically hardening web appli-cations using precise tainting”. In Proceeding of the 20th IFIP International Information Security Conference, 2005.
[18] National Institute of standards and technology. National vulnerability database (nvd) http://web.nvd.nist.gov/view/vuln/search
[19] Kailas Patil Ensuring session integrity in the browser environment http://scholarbank.nus.edu.sg/bitstream/
handle/10635/49161/ThesisHT080141L.pdf?sequence=1, 2013.
[20] Kailas Patil, Tanvi Vyas, Fredrik Braun, and Mark Goodwin. “Usercsp- user specified content security policies”. SOUPS’13 POSTER, 2013.
[21] Tadeusz Pietraszek, Chris V, and En Berghe. “Defending against injection attacks through context-sensitive string evaluation”. In Proceeding of the Recent Advances in Intrusion Detection, 2005.
[22] Cristian Pinzn, Javier Bajo Juan F. De Paz, lvaro Herrero, and Emilio Corchado. “Aiida-sql: An adaptive intelligent intrusion detector agent for detecting sql injection attacks”. In Proceedings of the 10th International Conference on Hybrid Intelligent systems 2010.
[23] OWASP-The open web application security project. OWASP top ten project. https://www.owasp.org/index.php/Top_10_2013-Top_10
[24] Charles Reis, John Dunagan, Helen J. Wang, Opher Dubrovsky, and Saher Esmeir. “Browsershield: Vulnerability-driven filtering of dynamic html”. In Proceedings of the Symposium on Oper-ating Systems Design and Implementation (OSDI), 2006.
[25] RSnake. Xss(cross site scripting) cheat sheet esp: for filter evasion. http://ha.ckers.org/xss.html.
[26] Jesse Ruderman. Signed scripts in mozilla. http://www.mozilla.org/projects/security/components/signed-scripts.html.
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[30] Stephen Thomas, Laurie Williams, and Tao Xie. “On auto-mated prepared statement generation to remove sql injection vulnerabilities”. In Proceedings of the Elsevier Journal on the Information and Software Technology, 2009.
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[32] Wikipedia Cross site scripting. https://en.wikipedia.org/wiki/Cross-site_scripting
[33] Wikipedia SQL injection https://en.wikipedia.org/wiki/SQL_injection
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[35] xssed.com. Myspace.com hit by a permanent xss. http://www. xssed.com/news/83/Myspace.com hit by a Permanent XSS/.
[36] xssed.com. New orkut xss worm by brazilian web secu-rity group. http://www.xssed.com/news/77/New Orkut XSS worm by Brazilian web security group/.
[37] K.S.Wagh, Vishal Jotshi, Harshal Dalvi, Manish Kamble. “Reversed proxy based XSS filtering”. In Proceeding of the International Journal on Computer Science and Engineering (IJCSE). Vol -3, Issue-5,Page No(175-180) May 2015.
[38] Jyotsnamayee Upadhyaya, Namita Panda, Arup Abhinna Acharya. “Attack Generation and Vulnerability Discovery in Penetration Testing using Sql Injection”. In Proceeding of the International Journal on Computer Science and Engineering (IJCSE). Vol -2, Issue-3,Page No(167-173) March 2014.

Sandeep D Sukhdeve and Hemlata Channe, "A Survey on Content Injection Attacks," International Journal of Computer Sciences and Engineering, Vol.3, Issue.11, pp.70-74, 2015.

The foremost problem found in the multimedia devices of today is of maintaining the battery life .The battery capacity of a device depends on the intensity of an image. Reducing the intensity increases the battery life of these devices. In this paper an image enrichment algorithm is proposed that enhance an image at low backlight. The just noticeable difference theory (JND) is applied and an image is decayed into two layers namely, the HVS response layer and the background luminance layer. The algorithm is further unmitigated to enhance dim backlight videos. By applying this algorithm videos can be played even at low battery level. Various algorithms can be then used to compress the output image.

luminance,backlight,HVS

[1] Akhil Paulose ,Lintu Liz Thomas,Rahul R Pilla i,Chitra Chandran, “ An Improved Framework for Backlight Scaled Images and Videos”,IJETT,volume 22 number 10 April 2015.
[2] Rajshree S. Dubey et. al.” Image Mining using Content Based Image Retrieval System” International Journal on Computer Science and Engineering Vol. 02, No. 07, 2010, 2353-2356
[3] Tai-Hsiang Huang, Kuang-Tsu Shih, Su-Ling yeh and Homer h.Chen,Fellow, “Enhancement of Backlight-scaled Images,” IEEE transactions on image processing, vol. 22, no.12, December 2013.
[4] H.Wilson,”A transducer function for threshold and suprathreshold human vision,” Biol.Cybern,vol.38,no.3,pp 171-178,october 1980.
[5] Y.Rao and L.Chen, “A survey of video enhancement techniques,”J.Inf.Multimedia Signal Process.,vol.3,no 1,pp 71-79,Jan.2012.
[6] T.H Kim, K.S. Choi and S.J.Ko,”Backlight power reduction using efficient image compensation for mobile devices,”IEEE trans.Consum.Electron.,vol.56,no.3,pp.1972-1978,Aug.2010.

Akhil Paulose, Basil.K.Eldose, Jafer.P, and Chithra S.S, "An Innovative Approach to Extend the Battery Life of Digital Devices," International Journal of Computer Sciences and Engineering, Vol.3, Issue.11, pp.75-79, 2015.

Through this paper we have representation of the IT strategies that were in effect from previous decade; these all strategies got frequent changes as demanded by time to give more effective, impressive and accurate services to the state’s people from various government organizations of states. These organization of state are putting their effort positively to place state on growing track through which state could achieve goal of smarter state of India that could make its name in whole world by their more positive effort with new IT models by which all cities and villages of state would be smarter by their effective, secure and paper less working technique with new IT concepts, Madhya Pradesh (M.P.) are the state that are enlighten itself by their more impressive and positive effort to achieve that great goal with new IT strategies. These all strategies help state to free it from crime and unemployment. These all also help to provide smart education, smart health care system, smart electricity supply etc.

IT Stategies; New State Government Polices(NSGP); State Education System (SES); Smart Health Care System(SHCS)

[1]. Developing a Rural Market e-hub The case study of e-Choupal experience of ITC.
[2]. Karwariya.Sateesh, Goyal Sandip, Land use and Land Cover mapping using digital classification technique in Tikamgarh district, Madhya Pradesh, India using Remote Sensing, international journal of geomatrics and geosciences Volume 2, No 2, 2011
[3]. Arijit Ghosh, Initiatives in ICT for rural development, Global Media Journal – Indian Edition/ISSN 2249-5835 Winter Issue / December 2011 Vol. 2/No.2
[4]. Directorate of Integrated Child Development Services Madhya Pradesh
[5]. Department of Telecommunications Ministry of Communications & Information Technology Government of India New Delhi Annual Report 2013-2014
[6]. ICT Vision Strategy and Concept Note for MP Police
[7]. MP tourism policy.
[8]. Enabling rural India with information and communication technology initiative, CASE STUDY: INDIA
[9]. Madhya Pradesh industry promotion policy.
[10]. Chaurasia, A. (2009), Notes on Poverty in Madhya Pradesh, Background paper prepared for Madhya Pradesh Poverty Reduction Strategy Paper, coordinated by Indira Gandhi Institute of Development Research, Mumbai.
[11]. Medium Term Health Sector Strategy Madhya Pradesh, Department of Public Health and Family Welfare Government of Madhya Pradesh
[12]. Final Report working group on rural roads in the 12th five year plan, Government of India lanning Commission Ministry of Rural Development October 2011 (revised on 23rd Jan, 2012)
[13]. Department of Technical Education and Skill Development, Technical Education and Skill Development Policy-2012 (As amended on 26 September 2014)
[14]. Annual Report 2009-10, Ministry of rural development Government of India,
[15]. Energy Statistics 2014 (Twenty First Issue) central statics office ministry of statistics and program implementation government of India New Delhi.
[16]. http://www.mponline.gov.in/Portal/Content/mpprofile.aspx
[17]. http://www.mp.gov.in/html/htmlpages/geninfo.html
[18]. http://www.mp.nic.in/MP-Population2011Final2014.pdf
[19]. http://dolr.nic.in/dolr/downloads/spsp/Madhya%20Pradesh_SPSP.pdf
[20]. http://www.relief.mp.gov.in/

Pratibha Sharma, "IT Strategies in Government Policies:A Case Study of M.P.(Part-I)," International Journal of Computer Sciences and Engineering, Vol.3, Issue.11, pp.80-88, 2015.

Functional Magnetic Resonance Imaging (fMRI) is a non-invasive technique for assessing tumor detection. The need to differentiate between normal and abnormal tissues and determine type of abnormality before biopsy or surgery motivated development and application of fMRI. There are several technical reasons that make the brain easier than other organs to be examined with fMRI. This paper presents our proposed methods and results for the analysis of the brain spectra of patients with three tumor types. After extracting features from fMRI data using wavelet and wavelet packets, artificial neural networks are used to determine the abnormalities in the Tumor and the type. The proposed methods like clinical and simulated fMRI data and biopsy results. The fMRI analysis results were correct 97% of the time when classifying the spectra of the clinical fMRI data into normal tissue, tumor. and radiation necrosis.

Magnetic Resonance Spectroscopic Imaging, Wavelet, Wavelet Packets, Artificial Neural Networks, Tumor, Necrosis

[1] Bonavita S, Di Salle F, Tedeschi D., Proton MRS in neurological disorders, European Journal of Radiology, No.31, 1999, pp.30-125.
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[5] Weber-Fahr W, Ende G, Braus D, Bachert P, Soher J, Henn F, Buchel C., A Fully Automated Method for Tissue Segmentation and CSF-Correction of Proton FMRI Metabolites Corroborates Abnormal Hippocampal NAA in Schizophrenia, NeuroImage, Vol.16, 2002, pp.49–60.
[6] Axelson D, Bakken I, Gribbestad I, Ehrnholm B, Nilsen G, Aasly J., Applications of Neural Network Analyses to In Vivo 1H Magnetic Resonance Spectroscopy of Parkinson Disease Patients, Journal of Magnetic Resonance Imaging, Vol.16, 2002, pp.13–20.

G Vijay Kumar and G V Raju, "A Real-Time Approach to Brain Tumor Detection Implementing Wavelets and ANN," International Journal of Computer Sciences and Engineering, Vol.3, Issue.11, pp.89-93, 2015.

As virtualization proceeds to gotten to be increasingly well known in enterprise and organizational networks, operators and administrators are turning to live rearrange of virtual machines fat that point a pick up the purpose of work notice adjusting and management. However, the security of live virtual machine rearrange has yet to be analyzed. This paper looks at this poorly explored range and endeavors to empirically illustrate the significance of securing the migration process. We start by defining and investigating three classes of dangers to virtual machine migration: control plane, data plane, and rearrange module threats. We at that point show how a malignant party utilizing these assault frame meets expectations can exploit the latest versions of the well-known Xen and VMware virtual machine monitors and present an instrument to automate the control of a visitor working system’s memory amid a live virtual machine migration. Utilizing this experience, we talk about frame meets expectations to ad-dress the deficiencies in virtualization programming and se-cure the live rearrange process.

Xen, VMWare, Virtual Machine, Live Migration

[1] Chanchio, K. ; Dept. of Comput. Sci., Thammasat Univ., Patumtani, Thailand ; Thaenkaew, P., “Time-Bound, Thread-Based Live Migration of Virtual Machines”, Published in: Cluster, Cloud and Grid Computing (CCGrid), 2014 14th IEEE/ACM International Symposium on Date of Conference: 26-29 May 2014 Page(s): 364 – 373.
[2] Anala, M.R. ; Dept. of Comput. Sci. & Eng., RVCE, Bangalore, India ; Shetty, J. ; Shobha, G., “A framework for secure live migration of virtual machines”, Published in: Advances in Computing, Communications and Informatics (ICACCI), 2013 International Conference on Date of Conference: 22-25 Aug. 2013 Page(s): 243 – 248.
[3] Shah, S.A.R. ; Korea Univ. of Sci. & Technol., Daejeon, South Korea ; Jaikar, A.H. ; Seo-Young Noh, “A performance analysis of precopy, postcopy and hybrid live VM migration algorithms in scientific cloud computing environment”, Published in: High Performance Computing & Simulation (HPCS), 2015 International Conference on Date of Conference: 20-24 July 2015 Page(s): 229 – 236.
[4] Deshpande, U. ; Binghamton Univ., Binghamton, NY, USA ; Keahey, K., “Traffic-Sensitive Live Migration of Virtual Machines”, Published in: Cluster, Cloud and Grid Computing (CCGrid), 2015 15th IEEE/ACM International Symposium on Date of Conference: 4-7 May 2015 Page(s): 51 – 60.
[5] Kejiang Ye ; Coll. of Comput. Sci., Zhejiang Univ., Hangzhou, China ; Xiaohong Jiang ; Ran Ma ; Fengxi Yan, “VC-Migration: Live Migration of Virtual Clusters in the Cloud”, Published in: Grid Computing (GRID), 2012 ACM/IEEE 13th International Conference on Date of Conference:20-23 Sept. 2012 Page(s): 209 – 218.
[6] Kejiang Ye ; Coll. of Comput. Sci., Zhejiang Univ., Hangzhou, China ; Xiaohong Jiang ; Dawei Huang ; Jianhai Chen, “Live Migration of Multiple Virtual Machines with Resource Reservation in Cloud Computing Environments”, Published in: Cloud Computing (CLOUD), 2011 IEEE International Conference on Date of Conference: 4-9 July 2011 Page(s): 267 – 274.
[7] Jiao Zhang ; Dept. of Comput. Sci. & Technol., Tsinghua Univ., Beijing, China ; Fengyuan Ren ; Chuang Lin, “Delay guaranteed live migration of Virtual Machines”, Published in: INFOCOM, 2014 Proceedings IEEE Date of Conference: April 27 2014-May 2 2014 Page(s): 574 – 582.
[8] Strunk, A. ; Dept. of Comput. Networks, Tech. Univ. of Dresden, Dresden, Germany, “A Lightweight Model for Estimating Energy Cost of Live Migration of Virtual Machines”, Published in: Cloud Computing (CLOUD), 2013 IEEE Sixth International Conference on Date of Conference: June 28 2013-July 3 2013 Page(s): 510 – 517.
[9] Sarker, T.K. ; Sch. of Electr. Eng. & Comput. Sci., Queensland Univ. of Technol., Brisbane, QLD, Australia ; Maolin Tang, “Performance-driven live migration of multiple virtual machines in datacenters”, Published in: Granular Computing (GrC), 2013 IEEE International Conference on Date of Conference: 13-15 Dec. 2013 Page(s): 253 – 258.
[10] Koto, A. ; Keio Univ., Yokohama, Japan ; Kono, K. ; Yamada, H., “A Guideline for Selecting Live Migration Policies and Implementations in Clouds”, Published in: Cloud Computing Technology and Science (CloudCom), 2014 IEEE 6th International Conference on Date of Conference: 15-18 Dec. 2014 Page(s): 226 – 233.
[11] Sisu Xi ; Dept. of Comput. Sci. & Eng., Washington Univ. in St. Louis, St. Louis, MO, USA ; Wilson, J. ; Chenyang Lu ; Gill, C., “RT-Xen: Towards real-time hypervisor scheduling in Xen”, Published in: Embedded Software (EMSOFT), 2011 Proceedings of the International Conference on Date of Conference: 9-14 Oct. 2011 Page(s): 39 – 48.
[12] Yun Chan Cho ; SungKyunKwan Univ., Suwon ; Jae Wook Jeon, “Sharing data between processes running on different domains in para-virtualized xen”, Published in: Control, Automation and Systems, 2007. ICCAS '07. International Conference on Date of Conference: 17-20 Oct. 2007 Page(s): 1255 – 1260.
[13] Liu Fagui ; Sch. of Comput. Sci. & Eng., South China Univ. of Technol., Guangzhou, China ; Zhang Hao ; Zhou Haiyan, “A Xen-Based Secure Virtual Disk Access-Control Method”, Published in: Multimedia Information Networking and Security (MINES), 2010 International Conference on Date of Conference: 4-6 Nov. 2010 Page(s): 375 – 378.
[14] Li Sun ; Sch. of Math. & Geospatial Sci., RMIT Univ., Melbourne, VIC ; Ebringer, T. ; Boztas, S., “An automatic anti-anti-VMware technique applicable for multi-stage packed malware”, Published in: Malicious and Unwanted Software, 2008. MALWARE 2008. 3rd International Conference on Date of Conference: 7-8 Oct. 2008 Page(s): 17 – 23.
[15] Nikzad, A. ; Eng. & Comput. Sci, Concordia Univ., Montreal, QC, Canada ; Khendek, F. ; Toeroe, M., “OpenSAF and VMware from the perspective of high availability”, Published in: Network and Service Management (CNSM), 2013 9th International Conference on Date of Conference: 14-18 Oct. 2013 Page(s): 324 - 331

D. Ragupathi and S.Sivaranjani, "Performance Enhanced Live Migration of Virtual Machines in the Cloud," International Journal of Computer Sciences and Engineering, Vol.3, Issue.11, pp.94-99, 2015.

Social system is a place where individuals exchange and offer information related to the current events all over the world .This particular behavior of customers made us center on this logic that preparing these substance might commercial us to the extractives the current subject of interest between the users. Applying information bunching procedure like Text-Frequency-based approach over these content might leads us up to the mark in any case there will be some chance of false positives. We propose a likelihood model that can catch both ordinary specifying behavior the other hand of a customer and too the recurrence of customers occurring in their mentions. It too lives up to expectations great indeed the substance of the messages are non-printed information. The test show that the proposed mention-peculiarity based approaches can identify new points at slightest as early as text-peculiarity based approaches, and in some cases much former at the point when the subject is poorly distinguished by the printed substance in the posts.

Change Point Detection, Anomaly scores, Mentions

[1] Jun Geng ; Dept. of Electr. & Comput. Eng., Worcester Polytech. Inst., Worcester, MA, USA ; Lifeng Lai, “Bayesian quickest change point detection and localization in sensor networks”, Published in: Global Conference on Signal and Information Processing (GlobalSIP), 2013 IEEE Date of Conference: 3-5 Dec. 2013 Page(s): 871 – 874.
[2] Fukushima, Y. ; Grad. Sch. of Natural Sci. & Technol., Okayama Univ., Okayama ; Murase, T. ; Fujimaki, R. ; Hirose, S., “Accuracy improvement of multi-stage change-point detection scheme by weighting alerts based on false-positive rate”, Published in: Communications Quality and Reliability, 2009. CQR 2009. IEEE International Workshop Technical Committee on Date of Conference: 12-14 May 2009 Page(s): 1 – 6.
[3] Kyong Joo Oh ; Graduate Sch. of Manage., Korea Adv. Inst. of Sci. & Technol., Seoul, South Korea ; Ingoo Han, “An intelligent clustering forecasting system based on change-point detection and artificial neural networks: application to financial economics”, Published in: System Sciences, 2001. Proceedings of the 34th Annual Hawaii International Conference on Date of Conference: 6-6 Jan. 2001.
[4] Ide, T. ; IBM Res., Yamato ; Papadimitriou, S. ; Vlachos, M., “Computing Correlation Anomaly Scores Using Stochastic Nearest Neighbors”, Published in: Data Mining, 2007. ICDM 2007. Seventh IEEE International Conference on Date of Conference: 28-31 Oct. 2007 Page(s): 523 – 528.
[5] Mishne, G. ; Electr. Eng. Dept., Technion - Israel Inst. of Technol., Haifa, Israel ; Cohen, I., “Multiscale anomaly detection using diffusion maps and saliency score”, Published in: Acoustics, Speech and Signal Processing (ICASSP), 2014 IEEE International Conference on Date of Conference: 4-9 May 2014 Page(s): 2823 – 2827.
[6] Yun Wang ; Cannady, J., “Develop a composite risk score to detect anomaly intrusion”, Published in: SoutheastCon, 2005. Proceedings. IEEE Date of Conference: 8-10 April 2005 Page(s): 445 – 449.
[7] Hailun Lin ; Key Lab. of Network Data Sci. & Technol., Inst. of Comput. Technol., Beijing, China ; Yantao Jia ; Yuanzhuo Wang ; Xiaolong Jin, “Populating knowledge base with collective entity mentions: A graph-based approach”, Published in: Advances in Social Networks Analysis and Mining (ASONAM), 2014 IEEE/ACM International Conference on Date of Conference: 17-20 Aug. 2014 Page(s): 604 – 611.
[8] Yanping Chen ; Dept. of Comput. Sci. & Technol., Xi'an Jiaotong Univ., Xi'an, China ; Qinghua Zheng ; Ping Chen, “A Boundary Assembling Method for Chinese Entity-Mention Recognition”, Published in: Intelligent Systems, IEEE (Volume:30 , Issue: 6 ) Page(s): 50 – 58.
[9] Zitouni, I. ; IBM T. J. Watson Res. Center, Yorktown Heights, NY ; Xiaoqiang Luo ; Florian, R., “A Cascaded Approach to Mention Detection and Chaining in Arabic”, Published in: Audio, Speech, and Language Processing, IEEE Transactions on (Volume:17 , Issue: 5 ) Page(s): 935 – 944 Date of Publication : July 2009.
[10] Ekbal, A. ; Dept. of Comput. Sci. & Eng., Indian Inst. of Technol. Patna, Patna, India ; Saha, S. ; Ravi, K., “Mention detection and classification in bio-chemical domain using Conditional Random Field”, Published in: Emerging Applications of Information Technology (EAIT), 2012 Third International Conference on Date of Conference: Nov. 30 2012-Dec. 1 2012 Page(s): 335 – 338.

C. Thangamalar and D.Gayathri, "Mining Unindustrialized Topics Based on User Mention," International Journal of Computer Sciences and Engineering, Vol.3, Issue.11, pp.100-104, 2015.