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Human hands are delicate instruments. Hand gestures and finger gestures are excellent ways of emphasizing what we say, but on the other hand they can also reveal our true intentions. In this paper introduced a continuous Indian sign language recognition system, wherever each the hands are used for playacting any gesture. Recognizing a sign language gestures from continuous gestures could be a terribly difficult analysis issue. This paper solve the problem using gradient based key frame extraction technique. These key frames are useful for splitting continuous language gestures into sequence of signs further as for removing uninformative frames. After splitting of gestures every sign has been treated as associate degree isolated gesture. Then features of pre-processed gestures are extracted using orientation histogram (OH) with principal component analysis (PCA) is applied for reducing dimension of features obtained after OH. Experiments are performed on our own continuous ISL dataset which is created using EOS camera in PG Research Laboratory (SPPU, Pune). Probes are tested exploitation varied forms of classifiers like, Manhattan distance, Correlation, Manhattan distance, City block distance, Euclidian distance etc. Comparative analysis of our projected theme is performed with varied forms of distance classifiers. From this analysis we tend to found that the results obtained from Correlation and Euclidian distance offers higher accuracy then alternative classifiers.

Gesture Recognition, Orientation histogram (OH); Correlation; Indian sign language (ISL); Principal component analysis (PCA);

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[9] Ramesh Kagalkar and Dr. Nagaraja H.N, �New Methodology for Translation of Static Sign Symbol to Words in Kannada Language�,� International Journal of Computer Applications(IJCA), Volume 121, No.20,PageNo. 25-30, July-2015.
[10] Ramesh M. Kagalkar, Dr. Nagaraja H.N and Dr. S.V Gumaste�,A Novel Technical Approach for Implementing Static Hand Gesture Recognition�, International Journal of Advanced Research in Computer and Communication Engineering (IJARCCE), Volume 4, Issue 7, July 2015.
[11] Ramesh M. Kagalkar and Dr. S.V Gumaste, �Automatic Graph Based Clustering for Image Searching and Retrieval from Database�, CiiT International Journal of Software Engineering and Technology, Volume 8, No 2, 2016.
[12] Ramesh M. Kagalkar and Dr. S.V Gumaste, �Review Paper: Detail Study for Sign Language Recognition Techniques�, CiiT International Journal of Digital Image Processing, Volume 8, No 3, 2016.
[13] Rashmi B. Hiremath and Ramesh Kagalkar, �A Methodology for Sign Language Video Analysis and Translation into Text in Hindi Language�,CiiT International Journal of Fuzzy System, Volume 8, No 5, 2016.
[14] Rashmi B. Hiremath and Ramesh Kagalkar, �Methodology for Sign Language Video Interpretation in Hindi Text Language�, International Journal of Innovative Research in Computer and Communication Engineering (IJIRCCE), Volume. 4, Issue 5, May 2016.
[15] Amitkumar and Ramesh Kagalkar, �Sign Language Recognition for Deaf User�, Internal Journal for Research in Applied Science and Engineering Technology (IJRASET), Volume 2, Issue 12, December 2014.
[16] Amit kumar and Ramesh Kagalkar, �Advanced Marathi Sign Language Recognition using Computer Vision�, International Journal of Computer Applications (IJCA), Volume 118, No. 13, May 2015.
[17] Amit kumar and Ramesh Kagalkar, �Methodology for Translation of Sign Language into Textual Version in Marathi�, CiiT, International Journal of Digital Image Processing, Volume 07, No.08, Aug 2015.
[18] Mrunmayee and Ramesh Kagalkar, �A Review On Conversion of Image To Text as Well as Speech using Edge Detection and Image Segmentation�, International Journal of Science and Research (IJSR), Volume 3, Issue 11, November 2014.
[19] Mrunmayee Patil and Ramesh Kagalkar, �An Automatic Approach for Translating Simple Images into Text Descriptions and Speech for Visually Impaired People�, International Journal of Computer Applications (IJCA), Volume 118, No. 3, May 2015.
[20] Kaveri Kamble and Ramesh Kagalkar, �A Review: Translation of Text to Speech Conversion for Hindi Language�, International Journal of Science and Research (IJSR), Volume 3, Issue 11, November 2014.
[21] Kaveri Kamble and Ramesh Kagalkar, �Audio Visual Speech Synthesis and Speech Recognition for Hindi Language� ,International Journal of Computer Science and Information Technologies (IJCSIT), CiiT International Journal of Data Mining Knowledge Engineering, Volume 6, Issue 2, April 2015.
[22] Kaveri Kamble and Ramesh Kagalkar � A Novel Approach for Hindi Text Description to Speech and Expressive Speech Synthesis� International Journal of Applied Information Systems (IJAIS), Volume 8, No.7, May 2015.
[23] Shivaji Chaudhari and Ramesh Kagalkar �A Review of Automatic Speaker Recognition and Identifying Speaker Emotion Using Voice Signal�, International Journal of Science and Research (IJSR), Volume 3, Issue 11, November 2014.
[24] Shivaji Chaudhari and Ramesh Kagalkar, �Automatic Speaker Age Estimation and Gender Dependent Emotion Recognition�, International Journal of Computer Applications (IJCA), Volume 117, No. 17, May 2015.
[25] Shivaji J. Chaudhari and Ramesh M Kagalkar, �A Methodology for Efficient Gender Dependent Speaker Age and Emotion Identification System�, International Journal of Advanced Research in Computer and Communication Engineering (IJARCCE), Volume 4, Issue 7, July 2015.
[26] Ajay R. Kadam and Ramesh Kagalkar, �Audio Scenarios Detection Technique�, International Journal of Computer Applications (IJCA), Volume 120, No. 16, June 2015.
[27] Ajay R. Kadam and Ramesh Kagalkar, �Predictive Sound Recognition System�, International Journal of Advance Research in Computer Science and Management Studies (IJARCSMS), Volume 2, Issue 11.
[28] Swati Sargule and Ramesh M Kagalkar, �Hindi Language Document Summarization using Context Based Indexing Model�, CiiT International Journal of Data Mining Knowledge Engineering, Volume 08, No. 01, Jan 2016.
[29] Swati Sargule and Ramesh M Kagalkar, �Methodology of Context Centered Term Indexing Style Intended For Hindi Language Document Summarization�, CiiT International Journal of Software Engineering, Volume 8, No 5, 2016.
[30] Vandana D. Edke and Ramesh M. Kagalkar, �Video Object Description of Short Videos in Hindi Text Language�, International Journal of Computational Intelligence Research, Volume 12, Number 2 (2016), pp. 103-116 � Research India Publications.

R.M. Kagalkar, S.V Gumaste, "Gradient Based Key Frame Extraction for Continuous Indian Sign Language Gesture Recognition and Sentence Formation in Kannada Language: A Comparative Study of Classifiers," International Journal of Computer Sciences and Engineering, Vol.4, Issue.9, pp.1-11, 2016.

Dynamic feature evaluation and concept evaluation is major challenging task in the field of stream data classification. The continuity of data induced a new feature during classification process, but the classification process is predefined task for assigning data into class. Stream data comes into multiple feature sub-set format into infinite length. The infinite length not decided the how many class are assigned. Genetic algorithm is well known population based method. The performance of genetic algorithm is better than other optimization technique such as POS and ANT colony optimization. The dynamic nature of genetic algorithm maintains the dynamic feature evaluation. The optimization process goes through multiple stages in terms of selection of feature and optimization of feature. The optimized feature reduces the unclassified region of class during classification. The proposed method for stream data classification is MMCM-GA is implemented in MATLAB 7.8.0. And test the validation process used some reputed data set from UCI machine learning prosperity. These data are corpus, forest and finally used glass dataset. Our empirical evaluation of result shows better feature evaluation and minimization of error rate in comprehension of MCM stream data classification

Stream Data Classification, POS, Ensemble, Optimal Feature, Genetic Algorithm (GA)

[1] Dayong Wang, Pengcheng Wu, Peilin Zhao, Yue Wu, Chunyan Miao, Steven C.H. Hoi �High-dimensional Data Stream Classification via Sparse Online Learning� IEEE International Conference on Data Mining, 2014. Pp 1007-1012.
[2] Mohammad M. Masud,Jing Gao, Latifur Khan, , Jiawei Han and Bhavani Thurai singham �Classification and Novel Class Detection in Concept-Drifting Data Streams under Time Constraints� IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, VOL. 23, NO. 6, JUNE 2011. Pp 859-874.
[3] Ge Song, Yunming Ye �A New Ensemble Method for Multi-label Data Stream Classification in Non-stationary Environment� 2014 International Joint Conference on Neural Networks July 6-11, 2014, Beijing, China, Pp 1776-1783.
[4] Rashmi Dutta Baruah, PlamenAngelov, Diganta Baruah �Dynamically Evolving Fuzzy Classifier for Real-time Classification of Data Streams� IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) , July 6-11, 2014, Beijing, China , Pp 383-389.
[5] Justin Ma, Alex Kulesza, Mark Dredze, Koby Crammer, Lawrence K. Saul, Fernando Pereira �Exploiting Feature Covariance in High-Dimensional Online Learning� 2009, Pp 493-500.
[6] Mohammad M. Masud , Qing Chen , Latifur Khan, Charu Aggarwal ,Jing Gao, Jiawei Han and Bhavani Thurai singham �Addressing Concept-Evolution in Concept-Drifting Data Streams� 2009, Pp 124-130
[7] Mohammad M. Masud, Tahseen M. Al-Khateeb, Latifur Khan,Charu Aggarwal, Jing Gao Jiawei Hanand Bhavani Thuraisingham�Detecting Recurring and Novel Classes in Concept-Drifting Data Streams� 2012. Pp 897-902.
[8] Mohammad M. Masud, Qing Chen, Jing Gao, Latifur Khan, Jiawei Han, and Bhavani Thuraisingham �Classification and Novel Class Detection of Data Streams in a Dynamic Feature Space� J.L. Balcazar et al. (Eds.): ECML PKDD 2010, Pp. 337�352.
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[10] Clay Woolam, Mohammad M. Masud, and Latifur Khan �Lacking Labels in the Stream: Classifying Evolving Stream Data with Few Labels� 2009, LNAI 5722. Pp. 552�562.
[11] Li Su, Hong-yanLiu, Zhen-Hui Song, �A New Classification Algorithm for Data Stream� , I.J.Modern Education and Computer Science, 2011. Pp 32-39
[12] Manjeet Kaur, Manoj agnihotri �A Hybrid technique using Genetic algorithm and ANT colony optimization for improving in cloud datacenter�. IJCSE, Volume 04, Issue-08.

G. Pandey, N. Mishra, "Optimal Feature Selection in Stream Data Classification Using Improved Ensemble Classifier for High Dimension Data," International Journal of Computer Sciences and Engineering, Vol.4, Issue.9, pp.12-18, 2016.

In the proposed system a social-network-aided efficient P2P live streaming is performed, To prove that system outperforms other representative systems in terms of overhead, video streaming efficiency and server load reduction, and the effectiveness of the system two strategies. Reducing the cost of system in structure maintenance and node communication. The files can be moved from a centralized files to a decentralize server to distribute load and solve shutdown problems. Also cloud can be used for faster availability of files. Identity of peers in a private network remains hidden behind their global endpoint, P2P applications cannot run between two peers in separate private networks.

Peer-to-peer (P2P) P2P networks, social networks, Distributed video-on-demand (VoD)

[1] F. Picconi and L. Massoulie, "Is there a future for mesh-based live video streaming?," in Proc. P2P, 2008, pp. 289-298.
[2] N. Magharei and R. Rejaie, "PRIME: Peer-to-peer receiver-driven mesh-based streaming," in Proc. IEEE INFOCOM, 2007, pp. 1415-1423.
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[5] F. Wang, Y. Xiong, and J. Liu, "mTreebone: A hybrid tree/mesh overlay for application layer live video multicast," in Proc. ICDCS, 2007, p. 49.
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[8] Shivlal Mewada, Umesh Kumar Singh and Pradeep Kumar Sharma, "A Novel Security Based Model for Wireless Mesh Networks", International Journal of Scientific Research in Network Security and Communication, Volume-01, Issue-01, pp (11-15), Mar -Apr 2013.
[9] Ramesh Kagalkar and Dr. Nagaraja H.N, �New Methodology for Translation of Static Sign Symbol to Words in Kannada Language�,� International Journal of Computer Applications(IJCA), Volume 121, No.20,PageNo. 25-30, July-2015.
[10] Ramesh M. Kagalkar, Dr. Nagaraja H.N and Dr. S.V Gumaste�,A Novel Technical Approach for Implementing Static Hand Gesture Recognition�, International Journal of Advanced Research in Computer and Communication Engineering (IJARCCE), Volume 4, Issue 7, July 2015.
[11] Ramesh M. Kagalkar and Dr. S.V Gumaste, �Automatic Graph Based Clustering for Image Searching and Retrieval from Database�, CiiT International Journal of Software Engineering and Technology, Volume 8, No 2, 2016.
[12] Ramesh M. Kagalkar and Dr. S.V Gumaste, �Review Paper: Detail Study for Sign Language Recognition Techniques�, CiiT International Journal of Digital Image Processing, Volume 8, No 3, 2016.
[13] Rashmi B. Hiremath and Ramesh Kagalkar, �A Methodology for Sign Language Video Analysis and Translation into Text in Hindi Language�,CiiT International Journal of Fuzzy System, Volume 8, No 5, 2016.
[14] Rashmi B. Hiremath and Ramesh Kagalkar, �Methodology for Sign Language Video Interpretation in Hindi Text Language�, International Journal of Innovative Research in Computer and Communication Engineering (IJIRCCE), Volume. 4, Issue 5, May 2016.
[15] Amitkumar and Ramesh Kagalkar, �Sign Language Recognition for Deaf User�, Internal Journal for Research in Applied Science and Engineering Technology (IJRASET), Volume 2, Issue 12, December 2014.
[16] Amit kumar and Ramesh Kagalkar, �Advanced Marathi Sign Language Recognition using Computer Vision�, International Journal of Computer Applications (IJCA), Volume 118, No. 13, May 2015.
[17] Amit kumar and Ramesh Kagalkar, �Methodology for Translation of Sign Language into Textual Version in Marathi�, CiiT, International Journal of Digital Image Processing, Volume 07, No.08, Aug 2015.
[18] Mrunmayee and Ramesh Kagalkar, �A Review On Conversion of Image To Text as Well as Speech using Edge Detection and Image Segmentation�, International Journal of Science and Research (IJSR), Volume 3, Issue 11, November 2014.
[19] Mrunmayee Patil and Ramesh Kagalkar, �An Automatic Approach for Translating Simple Images into Text Descriptions and Speech for Visually Impaired People�, International Journal of Computer Applications (IJCA), Volume 118, No. 3, May 2015.
[20] Kaveri Kamble and Ramesh Kagalkar, �A Review: Translation of Text to Speech Conversion for Hindi Language�, International Journal of Science and Research (IJSR), Volume 3, Issue 11, November 2014.
[21] Kaveri Kamble and Ramesh Kagalkar, �Audio Visual Speech Synthesis and Speech Recognition for Hindi Language� ,International Journal of Computer Science and Information Technologies (IJCSIT), CiiT International Journal of Data Mining Knowledge Engineering, Volume 6, Issue 2, April 2015.
[22] Kaveri Kamble and Ramesh Kagalkar � A Novel Approach for Hindi Text Description to Speech and Expressive Speech Synthesis� International Journal of Applied Information Systems (IJAIS), Volume 8, No.7, May 2015.
[23] Shivaji Chaudhari and Ramesh Kagalkar �A Review of Automatic Speaker Recognition and Identifying Speaker Emotion Using Voice Signal�, International Journal of Science and Research (IJSR), Volume 3, Issue 11, November 2014.
[24] Shivaji Chaudhari and Ramesh Kagalkar, �Automatic Speaker Age Estimation and Gender Dependent Emotion Recognition�, International Journal of Computer Applications (IJCA), Volume 117, No. 17, May 2015.
[25] Shivaji J. Chaudhari and Ramesh M Kagalkar, �A Methodology for Efficient Gender Dependent Speaker Age and Emotion Identification System�, International Journal of Advanced Research in Computer and Communication Engineering (IJARCCE), Volume 4, Issue 7, July 2015.
[26] Ajay R. Kadam and Ramesh Kagalkar, �Audio Scenarios Detection Technique�, International Journal of Computer Applications (IJCA), Volume 120, No. 16, June 2015.
[27] Ajay R. Kadam and Ramesh Kagalkar, �Predictive Sound Recognition System�, International Journal of Advance Research in Computer Science and Management Studies (IJARCSMS), Volume 2, Issue 11.
[28] Swati Sargule and Ramesh M Kagalkar, �Hindi Language Document Summarization using Context Based Indexing Model�, CiiT International Journal of Data Mining Knowledge Engineering, Volume 08, No. 01, Jan 2016.
[29] Swati Sargule and Ramesh M Kagalkar, �Methodology of Context Centered Term Indexing Style Intended For Hindi Language Document Summarization�, CiiT International Journal of Software Engineering, Volume 8, No 5, 2016.
[30] Rachana Palaskar, Shweta Pandey, Ashwini Telang, Akshada Wagh and Ramesh Kagalkar,� An Automatic Monitoring and Swing the Baby Cradle for Infant Care�, International Journal of Advanced Research in Computer and Communication Engineering (IJARCCE), Volume 4, Issue 12, December 2015.
[31] Ramesh M Kagalkar, Kajal Chavan, Asmita Jadhav, Ravina Patil and Asmita Rawool, �Self-Educating Tool Kit for Kids� CiiT Software Engineering and Technology, Volume 8, No 1, 2016.
[32] Vandana D. Edke and Ramesh M. Kagalkar, �Video Object Description of Short Videos in Hindi Text Language�, International Journal of Computational Intelligence Research, Volume 12, Number 2 (2016), pp. 103-116 � Research India Publications.

V. Waghchaure, V. Chapte, "Video on demand for Peer to Peer network," International Journal of Computer Sciences and Engineering, Vol.4, Issue.9, pp.19-24, 2016.

Applications with Text Extraction form image or videos are boon for people with blindness to assist them in their day to day life. The emergence of high resolution cameras on mobile devices can be used to extract text-line. The literature presents that there are a number of algorithms that have been proposed for the extraction of text-lines from images. The Computation cost of these methods is very high and failed to address the challenging problem in text extraction due to the scale and orientation of the characters. In this paper, we propose a method to enhance an algorithm for quick retrieval of text from image documents using Parallel Matlab. Finally, it demonstrates the improvement in extracting text lines from the existing algorithm through results.

Connected component based algorithm; Contrast segmentation algorithm; Text Recognition; Edge Detection; Texture based methods; Text Localization; Precision and Recall; Stroke width transforms (SWT); Color Polarity computation; Adaptive thresholding; Modified Dam point labeling; Inward Filling.

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N. Jivane, S. Jivane, S. Rajkumar, 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, Vol.4, Issue.9, pp.25-32, 2016.

A Signed Graph H = (G, σ) where G is a graph G = (V, E) and σ is a sign function σ =(+, -). When talk about a signed graph, the main focus is on its balance. In this paper we propose a dynamic programming algorithm based on depth-first search graph traversing which detects the balance in the signed graph. The proposed algorithm traverses every edge of the input signed graph at most once. The implementation of the proposed algorithm will use two linear lists (arrays), one of length |V| and the other of length (|V|+1)/2. Hence the algorithm will work much efficiently with respect to time complexity and space complexity.

Signed Graph, linear lists, dynamic programming, time complexity, space complexity, balance.

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[6] Arvind Srinivasan, �Local Balancing influences global structure in Social Networks�, PNAS, Vol. 108, No. 5, Pg. No: 1751 � 1752, 2011.
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S.K. Reddy Avula, P.S.K. Reddy, "An Algorithm with Optimal Time and Space Efficiency to Detect Balance of a Signed Graph," International Journal of Computer Sciences and Engineering, Vol.4, Issue.9, pp.33-35, 2016.

The Document summarization provides summary of document in a very short time. Existing systems for document summarization have work carried on English text summarization. Such systems do not consider the context of the word to produce summary. Previously implemented document summarization models generally use the similarity among sentences in the original document to extract the most relevant sentences. The documents along with the sentences are generally indexed using standard term indexing computation methods, which do not take into account the context related to document. System takes Hindi document as input. That document undergoes through the algorithm and final output is produced as summary of input Hindi document by considering the context of the word. The Bernoulli Model of Randomness technique is used to check the probability of co-occurrences of two terms in large corpus. The methodology used contains lexical association, sentences indexing, word indexing.

Document Summarization, Lexical Association, Context Indexing

[1] P. Goyal, L. Behera, and T. M. McGinnity, �A Context-Based Word Indexing Model for Document Summarization�, IEEE Trans. on Knowledge and Data Engineering, vol. 25, no. 8, August 2013.
[2] Q. Cao and S. Fujita, "Cost-effective replication schemes for query load balancing in DHT-based peer-to-peer P. Goyal, L. Behera, and T. M. McGinnity, �A Context-Based Word Indexing Model for Document Summarization�, IEEE Trans. on Knowledge and Data Engineering, vol. 25, no. 8, August 2013.
[3] X. Wan and J. Xiao, �Exploiting Neighborhood Knowledge for Single Document Summarization and Keyphrase Extraction,� ACM Trans. Information Systems, vol. 28, pp. 8:1-8:34, http://doi.acm.org/10.1145/1740592.1740596, June 2010.
[4] L.L. Bando, F. Scholer, and A. Turpin, �Constructing Query- Biased Summaries: A Comparison of Human and System Generated Snippets,� Proc. Third Symp. Information Interaction in Context, pp. 195-204, http://doi.acm.org/10.1145/1840784.1840813, 2010.
[5] X. Wan, �Towards a Unified Approach to Simultaneous Single- Document and Multi-Document Summarizations,� Proc. 23rd Int�l Conf. Computational Linguistics, pp. 1137-1145,http://portal.acm.org/citation.cfm?id=1873781.1873909, 2010.
[6] X. Wan, �An Exploration of Document Impact on Graph-Based Multi-Document Summarization,� Proc. Conf. Empirical Methods in Natural Language Processing, pp. 755-762, http://portal.acm.org/citation.cfm?id=1613715.1613811, 2008.
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[8] C. Shen and T. Li, �Multi-Document Summarization via the Minimum Dominating Set,� Proc. 23rd Int�l Conf. Computational Linguistics, pp. 984-992, http://portal.acm.org/citation.cfm?id=1873781.1873892, 2010.
[9] X. Wan and J. Yang, �Multi-Document Summarization Using Cluster-Based Link Analysis,� Proc. 31st Ann. Int�l ACM SIGIR Conf. Research and Development in Information Retrieval, pp. 299-306,http://doi.acm.org/10.1145/1390334.1390386, 2008.
[10] D. Wang, T. Li, S. Zhu, and C. Ding, �Multi-Document Summarization via Sentence-Level Semantic Analysis and Symmetric Matrix Factorization,� Proc. 31st Ann. Int�l ACM SIGIR Conf. Research and Development in Information Retrieval, pp. 307-314,http://doi.acm.org/10.1145/1390334.1390387, 2008.
[11] S. Harabagiu and F. Lacatusu, �Using Topic Themes for Multi- Document Summarization,� ACM Trans. Information Systems, vol. 28, pp. 13:1-13:47, http://doi.acm.org/10.1145/1777432.1777436, July 2010.
[12] R. Varadarajan, V. Hristidis, and T. Li, �Beyond Single-Page Web Search Results,� IEEE Trans. Knowledge and Data Eng., vol. 20,no. 3, pp. 411-424, Mar. 2008.
[13] E. Lloret, A. Balahur, M. Palomar, and A. Montoyo, �Towards Building a Competitive Opinion Summarization System: Challenges and Keys,� Proc. Human Language Technologies: The 2009 Ann. Conference of the North Am. Ch. Assoc. for Computational Linguistics, Companion Vol. : Student Research Workshop and Doctoral Consortium, pp. 72-77,http://portal.acm.org/citation.cfm?id=1620932.1620945, 2009.
[14] J.G. Conrad, J.L. Leidner, F. Schilder, and R. Kondadadi, �Query-Based Opinion Summarization for Legal Blog Entries,� Proc. 12th Int�l Conf. Artificial Intelligence and Law, pp. 167-176, http://doi.acm.org/10.1145/1568234.1568253, 2009.
[15] H. Nishikawa, T. Hasegawa, Y. Matsuo, and G. Kikui, �Opinion Summarization with Integer Linear Programming Formulation for Sentence Extraction and Ordering,� Proc. 23rd Int�l Conf. Computational Linguistics: Posters, pp. 910-918, http://portal.acm.org/citation.cfm?id=1944566.1944671, 2010.
[16] C.C. Chen and M.C. Chen, �TSCAN: A Content Anatomy Approach to Temporal Topic Summarization,� IEEE Trans.Knowledge and Data Eng., vol. 24, no. 1, pp. 170-183, Jan. 2012.
[17] B. Andreopoulos, D. Alexopoulou, and M. Schroeder, �Word Sense Disambiguation in Biomedical Ontologies with Term Co-Occurrence Analysis and Document Clustering,� Int�l J. Data Mining and Bioinformatics, vol. 2, pp. 193-215, http://portal.acm.org/citation.cfm?id=1413934.1413935, Sept. 2008.
[18] P. Goyal, L. Behera, and T. McGinnity, �Query Representation Through Lexical Assoc. for Information Retrieval,� IEEE Trans.Knowledge and Data Eng., vol. 24, no. 12, pp. 2260-2273, Dec. 2011.
[19] D.E. Losada and L. Azzopardi, �Assessing Multivariate Bernoulli Models for Information Retrieval,� ACM Trans. Information Systems, vol. 26, pp. 17:1-17:46, http://doi.acm.org/10.1145/1361684.1361690, June 2008.
[20] J. Clarke and M. Lapata, �Discourse Constraints for DocumentCompression,� Computational Linguistics, vol. 36, pp. 411-441, 2010.
[21] Y. Ouyang, W. Li, Q. Lu, and R. Zhang, �A Study on Position Information in Document Summarization,� Proc. 23rd Int�l Conf. Computational Linguistics: Posters, pp. 919-927, http://portal.acm.org/citation.cfm?id=1944566.1944672, 2010.
[22] M. Karimzadehgan and C. Zhai, �Estimation of Statistical Translation Models Based on Mutual Information for Ad Hoc Information Retrieval,� Proc. 33rd Int�l ACM SIGIR Conf. Research and Development in Information Retrieval, pp. 323-330, http://doi.acm.org/10.1145/1835449.1835505, 2010.
[23] Swati Sargule, Ramesh M Kagalkar �Hindi Language Document Summarization using Context Based Indexing Model�, CiiT International Journal of Data Mining Knowledge Engineering, Vol.08 No. 01, Jan Issue 2016.
[24] Ramesh M. Kagalkar, Dr. Nagaraj H.N and Dr. S.V Gumaste, �A Novel Technical Approach for Implementing Static Hand Gesture Recognition�, International Journal of Advanced Research in Computer and Communication Engineering ISSN (Online) 2278-1021 ISSN (Print) 2319-5940 Vol. 4, Issue 7, July 2015.
[25] Amit kumar and Ramesh Kagalkar �Advanced Marathi Sign Language Recognition using Computer Vision� , International Journal of Computer Applications (0975 � 8887) Volume 118 � No. 13, May 2015.
[26] Amit kumar and Ramesh Kagalkar �Sign Language Recognition for Deaf User�, Internal Journal for Research in Applied Science and Engineering Technology, Volume 2 Issue XII, December 2014.
[27] Ramesh. M. Kagalkar and Dr. S. V. Gumaste �Automatic Graph Based Clustering for Image Searching and Retrieval from Database�, CiiT Software Engineering and Technology, Vol 8, No 2, 2016.
[28] Ramesh. M. Kagalkar and Dr. S. V. Gumaste �Review Paper: Detail Study for Sign Language Recognition Techniques�, CiiT Digital Image Processing, Vol 8, No 3, 2016.
[29] Ramesh M Kagalkar, Kajal Chavan, Asmita Jadhav, Ravina Patil and Asmita Rawool,� Self-Educating Tool Kit for Kids� CiiT Software Engineering and Technology, Vol 8, No 1, 2016.
[30] Vasundhara Kadam and Ramesh Kagalkar,� Review on Textual Description of Image Contents�, International Journal of Computer Applications (0975 � 8887) National Conference on Advances in Computing (NCAC 2015)
[31] Mrunmayee Patil and Ramesh Kagalkar �An Automatic Approach for Translating Simple Images into Text Descriptions and Speech for Visually Impaired People�, International Journal of Computer Applications (0975 � 8887) Volume 118 � No. 3, May 2015.
[32] Kaveri Kamble and Ramesh Kagalkar, �Audio Visual Speech Synthesis and Speech Recognition for Hindi Language�, International Journal of Computer Science and Information Technologies (IJCSIT) ISSN (Online): 0975-9646, Vol. 6 Issue 2, April 2015.
[33] Shivaji Chaudhari and Ramesh Kagalkar �A Review of Automatic Speaker recognition and Identifying Speaker Emotion Using Voice Signal� International Journal of Science and Research (IJSR), Volume 3, Issue 11 November 2014.
[34] Ajay R. Kadam and Ramesh Kagalkar, �Audio Scenarios Detection Technique�, International Journal of Computer Applications (IJCA), Volume 120 June 2015, Edition ISBN: 973-93-80887-55-4.
[35] Ramesh.M.Kagalkar and P.N.Girija� Neural Network Based Document Image Analysis for Text, Image Localization Using Wavelet Decomposition and Mathematical Morphology� International Journal on Computer Science and Information Technology (IJCEIT) Volume 16, No 21, ISSN 0974-2034, Jan-Feb 2010.
[36] Ramesh.M.Kagalkar, Mrityunjaya .V. Latte and Basavaraj.M.Kagalkar �An Improvement In Stopping Force Level Set Based Image Segmentation� International Journal on Computer Science and Information Technology(IJCEIT) ISSN 0974-2034, Volume 24, Issue No 01, June � August 2010.
[37] Vrushali K Gaikwad and Ramesh Kagalkar �Security and Verification of Data in Multi-Cloud Storage with Provable Data Possession�, International Journal of Computer Applications (0975 � 8887) Volume 117 � No. 5, May 2015.
[38] Swati Sargule, Ramesh M Kagalkar �Methodology of Context Centered Term Indexing Style Intended For Hindi Language Document Summarization�, CiiT International Journal of Software Engineering and Technology, Vol.08 No. 05, June Issue 2016.

S. Sargule, R.M. Kagalkar, "Strategy for Hindi Text Summarization using Content Based Indexing Approach," International Journal of Computer Sciences and Engineering, Vol.4, Issue.9, pp.36-42, 2016.

Cloud computing is a sound area for the field of research. In the current scenario of advanced technology the client and server architecture is been shifting from distributed or cluster to cloud architecture. The main part of this research relies on robust architecture which deals with Cloud Storage as a Service (SAAS) and comparative security measures of improvement and modifications [1]. Cloud computing is the internet based technology which providing the computing resources in the form of services over the internet. But security is the main issue occurred in the cloud computing because of that growth of cloud computing is less.By using cryptography algorithms, we improve the data security in cloud computing. The cryptography algorithms are used for purpose of secure transmission of private or secret message. There are several number of cryptography algorithms but here we integrate the new technique with RSA algorithm using Homomorphic Encryption and performing the arithmetic process on RSA which improving the security level without compromising the security of existing technique [2]. In this paper our main work to ensure the security of data, so we proposed a method by implementing RSA algorithm using Homomorphic Encryption.

Cloud Computing; Encryption; Decryption; RSA; Homomorphic Technique

[1] Yamuna, V. and Priya, A. (2015), �efficient and secure storage in cloud computing RSA and DES function� International Journal of Innovation Research in Computer and Communication Engineering, ISSN 2320-9801, Vol. 3, issue 7, July 2015.
[2] Saveetha, P and Arumugam, S. (2015), �Study on Improvement in RSA Algorithm and its Implementation�, IJCCT, Vol. 3, Issue 6, 7 august 2012, pp 61-65.
[3] Jirwan, N.; Singh, A. and Vijay, S. (2013), �Review and Analysis of Cryptography Techniques�, International Journal of Scientific & Engineering Research, Vol. 4, March 2013.
[4] Yang, P.; Gui, X.; Yao, J.; Lin, J and Tian, F. (2013), �ICDM: An Encryption that Supports Unlimited Times Homomorphic Arithmetic Operations on Encrypted Data�, IEEE 16th International Conference on Computational Science and Engineering, 2013, pp 1220-1225
[5] Shivlal Mewada, Sharma Pradeep, Gautam S.S. (2016), �Classification of Efficient Symmetric Key Cryptography Algorithms�, International Journal of Computer Science and Information Security (IJCSIS) USA, Vol. 14, No. 2, pp (105-110), Feb 2016
[6] Kahte, A. (2013), �Cryptography and Network Security�, 2nd Edition, Biswarup Nil Kundu, 09 August, 2013.
[7] Gupta, P. and Gupta, S. (2012), �Mobile Cloud Computing: The Future of Cloud�, IJAREEIE, Vol. 1, September 2012, pp 134-145.
[8] Prasad, M.R.; Gyani, J and Murti, P.R.K. (2012), �Mobile Cloud Computing: Implications and Challenges�, Journal of Information Engineering and Applications, Vol. 2, 7, 2012, pp 7-15.
[9] Shivlal Mewada, Sharma Pradeep, Gautam S.S. (2016), �Exploration of Efficient Symmetric Algorithms�, 3rd IEEE International Conference on �Computing for Sustainable Global Development�, 16th -18th March, 2016, ISBN 978-93-80544-20-5
[10] Thambiraja, E.; Ramesh, G and Umarani, R. (2012), �A Survey on Various Most Common Encryption Techniques�, IJARCSSE, Vol. 2, July 2012, pp 226-233.
[11] Mewada, Shivlal, Pradeep Sharma, and S. S. Gautam(2016). "Exploration of efficient symmetric AES algorithm." In Colossal Data Analysis and Networking (CDAN), Symposium on, pp. 1-5. IEEE, 2016.
[12] Tebaa, M and Hajji, S.E. (2012), �Homomorphic Encryption Method Applied to Cloud Computing�, IEEE, June 2012, pp 86-89.
[13] Ranjit Ranjan, Dr. A.S Baghel, Sushil Kumar �improvement of NTRU cryptosystem� international journal of advanced research in computer science volume 2, issue September 2012.
[14] Parsi, K. (2012), �data security in cloud computing using RSA algorithm�, International Journal of Research in Computer and Communication technology, ISSN 2278-5841, Vol. 1, Issue 4, September 2012.
[15] Brenner, M.; Wiebelitz, J.; Vonvoigt, G and Smith, M. (2011), �Secret Program Execution in the Cloud Applying Homomorphic Encryption�, IEEE 5th International Conference on Digital Ecosystems and technologies (IEEE DEST), June 2011, pp 114-119.

Shiwali, G.K. Bhalla, "Providing Arithmetic Operations on RSA using Homomorphic Technique," International Journal of Computer Sciences and Engineering, Vol.4, Issue.9, pp.43-47, 2016.

ALU is very essential unit of any embedded processors. Very basic operations like addition, subtraction, multiplication and division are part of ALU unit. In literature, we have many algorithms to perform addition, subtraction and multiplication but less on division algorithm. The division algorithm performs, either by addition or subtraction, based on the signs of the divisor and partial remainder. Floating point division is considered as a high latency operation. Division algorithms have been developed to reduce latency and to improve the computational efficiency, hardware cost, area and power of processors. This paper presents different division algorithms such as Digit Recurrence Algorithm restoring, non-restoring and SRT Division (Sweeney, Robertson, and Tocher), Multiplicative Algorithm, Approximation Algorithms, CORDIC Algorithm and Continued Product Algorithm. This paper intended to compare various techniques used and their features relevant for various applications.

Division; SRT; Non Restoring; Restoring; FPGA; CORDIC; DSP

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[2] Inwook Kong, "Modified Improved Algorithms and Hardware Designs for Division by Convergence," Doctoral dissertation. University of Texas at Austin, 2009.
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[9] Jagannath Samanta, Mousam Halder, Bishnu Prasad De, �Performance Analysis of High Speed Low Power Carry Look-Ahead Adder Using Different Logic Styles�, International Journal of Soft Computing and Engineering (IJSCE), ISBN:2231-2307, Volume-02, Issue-06, Page No (330�336), 2013.
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[13] L. Montalvo, K. Parhi, and A. Guyot, �A Radix-10 Digit-Recurrence Division Unit: Algorithm and Architecture�, IEEE Transaction on Computers, Vol-56, No.6, Page No (727�739), June 2007.
[14] I. Rust and T. Noll, �A digit-set-interleaved radix-8 division/square root kernel for double precision floating point�, IEEE International Symposium on System on Chip (SoC), Page No (150�153), Nov 2010.
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[17] S. F. Oberman, �Floating Point Division and Square Root Algorithms and Implementation in the AMD-K7 Microprocessor�, in 14th IEEE Symposium on Computer Arithmetic, Page No (106�115), 1999.
[18] M. J. Schulte, D. Tan, and C. L. Lemonds, �Floating-Point Division Algorithms for an x86 Microprocessor with a Rectangular Multiplier,� in Proc. Int`l. Conf. on Computer Design (ICCD), Page No (304�310), 2007.
[19] W. Gallagher and E. Swartzlander, �Fault-Tolerance Newton-Raphson and Goldschmidt Dividers using Time Shared TMR�, IEEE Transaction on Computers, Vol-49, No. 6, Page No (588�595), June 2000.
[20] L. K. Wang and M. Schulte, �Processing Unit Having Decimal Floating-Point Divider Using Newton-Raphson Iteration�, Patent US 7,467,174 B2, Dec 16, 2008.
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[22] K. Maharatna, S. Banerjee, E. Grass, M. Krstic, and A. Troya, �Modified Virtually Scaling-Free Adaptive CORDIC Rotator Algorithm and Architecture�, IEEE Trans. on Circuits and Systems Floating-Point Division Operator based on CORDIC Algorithm 87 tems for Video Technology, Vol-15, No. 11, Page No (1463�1474), Nov 2005.
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R.S.Hongal, D.J. Anita , "Comparative Study of Different Division Algorithms for Fixed and Floating Point Arithmetic Unit for Embedded Applications," International Journal of Computer Sciences and Engineering, Vol.4, Issue.9, pp.48-54, 2016.

While the amount of information on the web reliably develops, it offers come to be an expanding number of confused as to internet web indexes to find data that satisfy clients` specific individual needs. Modified hunt can be certain techniques to help seek through tweaking postings in the event that you have not at all like data desire. Decent customization criteria will rely on upon internet corpus and inexhaustible individual single profiles. Interestingly, for the reason that internet corpus is really resting for the server, re-positioning for the customer side is really transmission capacity engaged as it needs a colossal number associated with postings went to the customer sooner than re-positioning. Almost all individualized search products and functions on the web like bing, www and Google Customized Search put into act your second move toward to adjust effects for the server through analyzing collected confidential information, electron search histories and personalized interests. In this paper a technique named user preference hierarchy is proposed. Experimental results show that the proposed technique gives better results than existing technique such as personal web search.

User History, Privacy, Websearch, User Profile, Web Browser

[1] Y. Zhu, L. Xiong, and C. Verdery, �Anonymizing User Profiles for Personalized Web Search,� Proc. 19th Int�l Conf. World Wide Web (WWW), pp.(1225-1226), 2010.
[2] J. Castellı�-Roca, A. Viejo, and J. Herrera- Joancomartı�, �Preserving User�s Privacy in Web Search Engines,� Computer Comm.,vol.32, no. 13/14, pp. 1541-1551,2009.
[3] Viejo and J.Castell a-Roca, �Using Social Networks to Distort Users� Profiles Generated by Web Search�, Computer Networks, vol. 54, no. 9, pp. 1343-1357, 2010.
[4] K.Ramanathan, J.Giraudi and A.Gupta, :Greating Hierararchcal User Profiles Using Wikipedia�, HP Labs, 2008.
[5] J. Teevan, S.T. Dumais, and D.J. Liebling, �To Personalize or Notto Personalize: Modeling Queries with Variation in User Intent, � Proc. 31st Ann. International ACM SIGIR Conf. Research and Development in Information Retrieval , pp 163-170, 2008.
[6] Rajneesh Shrivastava, Shivlal Mewada and Pradeep Sharma, "An Approach to Give First Rank for Website and Webpage Through SEO", International Journal of Computer Sciences and Engineering, Volume-02, Issue-06, pp (13-17), Jun -2014,
[7] G.Chen, H. Bai, L. Shou, and Y.Gao, �Ups: A Utility-Theoretic Approach to Privacy in Online Services,� J. Artificial Intelligence Research, vol. 39, pp. 633-662, 2010.
[8] J>S.Breese, D.Heckerman, and C.M.Kadie, �Empirical Analysis of Predictive Algorithms for Collaborative Filtering�, Proceedings of 14th Conference Uncertainity in Artificial Intelligence (UAI), pp.43-52, 1998.
[9] P.A. Chitra, W. Nejdl, R.Paiu and C. Kohlchutter, �Using ODP Metadat to Personalize Search�, Proceedings of 28th Ann. Int�l ACM SIGIR Conf. Research and Development Infromation retrieval, 2005.
[10] A.Pretschner and S. gauch, �ontology-Based Personalized Search and Browsing,� Proc. IEEE 11th Int�l Conf. Tools with Artificial Intelligence (ICTAI�99), 1999.
[11] Samanjeet Kaur and Sukhwinder Sharma,�Performing Efficient Phishing Web Page Detection�,International Journal of Computer Sciences and Engineering(IJCSE), pp 52-56, Vol.3, Issue 07, July 2015.

M. Karanam , "Avoiding Unnecessary Exposure of User Profile in Web Search," International Journal of Computer Sciences and Engineering, Vol.4, Issue.9, pp.55-58, 2016.

In order to find a geographical location in the Globe, we usually follow the geographical map. By a similar analogy, a Web-page from the World Wide Web (WWW), we usually use a Web search engine. Web crawler design is an important job to collect Web search engine resources from WWW. Millions of searches are done every minute around the Globe. A better Web search engine resource leads to achieve a better performance of the Web search engine. WWW is a huge resource of information. However this information is often spread throughout the internet via many Web servers and hosts. Every day people are publishing their Web pages in the Internet, as a result the traffic overhead increases exponentially. In order to produce a more accurate result, I have been motivated to follow a heuristic approach to design a Web crawler, which produces the best optimized search result in minimal time. This paper has built an approach to generate the best result in by Cuckoo Searching so that time will be least. I have divided my approach in two parts. First part is implementation of the crawler, which includes �what to search for�, �from where to search� and even filters the unwanted data. Second part proposed a string matching algorithm for producing the search result.

Cuckoo search; DNS; meta-heuristic; optimization; pattern recognition; web crawling;

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[2] Hu K., Wong W.S.: �A Probabilistic Model for Intelligent Web Crawlers�, 27th Annual International Computer Software and Applications Conference.
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[16] Shivlal Mewada, Sharma Pradeep, Gautam S.S., �Classification of Efficient Symmetric Key Cryptography Algorithms�, International Journal of Computer Science and Information Security (IJCSIS) USA, Vol. 14, No. 2, pp (105-110), Feb 2016
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J. Dewanjee, "Heuristic Approach for Designing a Focused Web Crawler using Cuckoo Search," International Journal of Computer Sciences and Engineering, Vol.4, Issue.9, pp.59-63, 2016.