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Content-based image retrieval (CBIR) is a method which uses visual contents to seek images from large size image databases according to the choice of the users. Human intervention in the text based image retrieval makes the system cumbersome, labor intensive and time consuming. Hence, there is a need to design the algorithms to retrieve the desired images from the database without human intervention, to enable for fast, accurate and reliable retrieval of the desired images. The challenge of the CBIR system is to identify the suitable features of images to retrieve image from image database. The algorithm presented in this paper uses color, texture and shape features to form the feature vector of training images and test images. These feature vectors and the k-NN classifier is used to search the test image in the database of training images. A database of 2732 fruit images from six different classes is used to test the proposed algorithm. The higher recognition accuracy achieved for the proposed algorithm is 98.43%.

CBIR, k-NN, Color, RGB, Texture, Entropy, Shape, Eccentricity

[1] S. K. Chang, and A. Hsu, "Image information systems: where do we go from here?" IEEE Trans. On Knowledge and Data Engineering, Volume – 05, Issue - 05, Page No. (431-442), 1992.
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[3] Ritendra Datta, Dhiraj Joshi, Jia Li and James Z., Wang, “Image Retrieval : Ideas, Influences and Trends of the New Age”, ACM Computing Surveys, Volume – 40, Issue - 02, Article 5, 2008.
[4] W. Niblack, R. Barber, “The QBIC project: Querying Images by Content using Color, Texture and Shape”, Storage and Retrieval for Image and Video Databases I, 1908, SPIE Proceedings Series, Feb. 1993.
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[8] Flickner, Sawhney, Niblack, Ashley, Huang, Dom, Gorkani, Hafner, Lee, Petkovic, Steele, Yanker, “Query by Image and Video Content: The QBIC System”, IEEE RFC 2460, Volume – 28, Issue – 09, Page No. (23-32), 1995.
[9] Coggins, J. M., “A Framework for Texture Analysis Based on Spatial Filtering,” Ph.D. Thesis, Computer Science Department, Michigan State University, East Lansing, Michigan, 1982.
[10] Tamura, H., S. Mori, and Y. Yamawaki, “Textural Features Corresponding to Visual Perception,” IEEE Transactions on Systems, Man, and Cybernetics, SMC-8, Page No. (460-473), 1978.
[11] Sklansky, J., “Image Segmentation and Feature Extraction,” IEEE Transactions on Systems, Man, and Cybernetics, SMC-8, Page No. (237-247), 1978.
[12] Haralick, R.M., “Statistical and Structural Approaches to Texture,” Proceedings of the IEEE, Volume - 67, Page No. (786-804), 1979.

Rajivkumar Mente and B V Dhandra, "Image Recognition using Visual Features," International Journal of Computer Sciences and Engineering, Vol.2, Issue.10, pp.1-4, 2014.

Word is used to convey or extract meaning of particular information. If data that is meaning associated with word is misinterpreted then it will lead to incorrect data. To avoid this problem these is need to resolve meaning of given word correctly. This task can be performed with the help of repository of ambiguous word WordNet2.1 which gives meaning and POS of given word. Now with the help of some other parameter this data could be utilized. That parameter is nothing but context around given word.

Decision List, Decision Tree, Naïve Bayes, supervised learning approaches, WSD, WordNet, and Senseval-3

[1] Approaches for Word Sense Disambiguation – A Survey, Pranjal Protim Borah, Gitimoni Talukdar, Arup Baruah, International Journal of Recent Technology and Engineering (IJRTE), ISSN:2277-3878, Volume-3, Issue-1, March2014.
[2] Miller, G. et al., 1993, Introduction to WordNet: An On-line Lexical Database, ftp://ftp.cogsci.princeton.edu/pub/wordnet/5papers.pdf, Princeton University.
[3] Ted Pedersen, A Decision Tree of Bigrams is an Accurate Predictor of Word Sense, department of computer science, university of Minnesota Duluth, Duluth, MN 55812 USA, 2004.
[4] Boshra F. Zopon AL_Bayaty, Shashank Joshi, Conceptualisation of Knowledge Discovery from Web Search, Bharati Vidyapeeth University, International Journal of Scientific & Engineering Research, Volume 5, Issue 2, February-2014, pages 1246- 1248.
[5] http://www.e-quran.com/language/english.
[6] http://www.senseval.org/senseval3.
[7] http://wordnet.princeton.edu.
[8] Boshra F. Zopon AL_Bayaty, Shashank Joshi, Empirical Implementation Naive Bayes Classifier for WSD Using WordNet., Bharati Vidyapeeth University, international journal of computer engineering & technology (IJCET), ISSN 0976 – 6367(Print), ISSN 0976 – 6375(Online), Volume 5, Issue 8, August (2014), pp. 25-31,© IAEME: ww.iaeme.com/IJCET.asp, Journal Impact Factor (2014): 8.5328 (Calculated by GISI), www.jifactor.com.
[9] Boshra F. Zopon AL_Bayaty, Shashank Joshi, Empirical Implementation Decision Tree Classifier to WSD Problem, International Conference on Emerging Trends Science and Cutting Edge Technology (ICETSCET), YMCA, 28,Sep, 2014.
[10] Boshra F. Zopon AL_Bayaty, Shashank Joshi, Sense Identification for Ambiguous Word Using Decision List” in International Journal of Advance Research in Science & Engineering (ISSN 2319-8354), Volume 03, Issue 10, October 2014.
[11] David Yarowsky, Hierarchical Decision Lists for Word Sense Disambiguation, Computers and the Humanities 34: 197-186, 2000, Kluwer Academic Publishers. Printed in the Netherlands, 2000.
[12] Nitin Indurkhya and Fred J. Damerau “HANDBOOK OF NATURAL LANGUAGE PROCESSING” SECOND EDITION. Chapman & Hall/CRC, USA, 2010.
[13] A Combative Study of Support Vector Machines Applied to he Supervised Word Sense Disambiguation Problem in the Medical Domain, Mahesh Joshi, Ted Pedersen and Richard Maclin, Department of Computer Science, University of Minnesota, Duluth, MN 55812, USA.
[14] Oi Yee Kwong, Psycholinguistics, Lexicography, and Word Sense Disambiguation, Department of Chinese, Translation and Linguistics, copyright 2012 by Oi Yee Kwong, 26th Pacific Asia Conference on Languge, Information and Computation pages 408-417, 2012.
[15] Learning Rules for Large Vocabulary Word Sense Disambiguation, Georgios Paliouras, Vangelis Karkaletsis, Constantine D. Spyropoulos, Institute of Informatics & Telecommunications, NCSR “Demokritos” Aghia Paraskevi Attikis, Athens, 15310, Greece.
[16] Daniel Jurafsky and James H. Martin, Naïve Bayes Classifier Approach to Word Sense Disambiguation, chapter 20, Computational Lexical Semantics, Sections 1 to 2, University of Groningen, 2009.
[17] Mahesh Joshi, MS, Serguei Pakhomov, PhD, […], and Christopher G. Chute, MD, DrpH. A Comparative Study of Supervised Learning as Applied to Acronym Expansion in Clinical Reports.
[18] Navigli, R. 2009.Word sense disambiguation: A survey. ACM Compute. Survey. 41, 2, Article 10 (February 2009), 69 pages DOI = 10.1145/1459352.1459355.

Boshra F. Zopon AL_Bayaty and Shashank Joshi, "The best performance method to Solve WSD Problem: Comparative Study," International Journal of Computer Sciences and Engineering, Vol.2, Issue.10, pp.5-8, 2014.

Cloud computing is one of the latest computing technology where the data’s applications and the IT services are provided with the help of Internet. The Grid computing, distributed computing and virtualization are the concepts that helped to build the cloud computing. Cloud computing provides various computing resources to the cloud users based on pay-per-usage basis. Resource scheduling and Job/Task scheduling are the most important considerations in the cloud and in which, the jobs submitted gets allocated by the resources efficiently. The goal of the cloud computing service provider, is to use the efficient resources to obtain the maximum profit and this results the task scheduling a core and one of the challenging issues in the cloud environment. The Task scheduling is an NP-hard optimization problem and where the meta-heuristic algorithms are proposed to provide an optimal solution. The scheduling strategy of a good task scheduler should get adopted based on the changing environments and the type of task scheduling used.

Cloud computing,Task Scheduling,Resource allocation,Quality-of-Service,Load Balancing

[1]. M. Gokilavani, S. Selvi and C. Udhayakumar, “A Survey on Resource Allocation and Task Scheduling Algorithms in Cloud Environment”, International Journal of Engineering and Innovative Technology (IJEIT), Volume 3, Issue 4, ISSN: 2277-3754, October 2013.
[2]. Sunny Kumar, Shivani Khurana, “Analysis of different Scheduling Algorithms under Cloud Computing”,International Journal of Computer Science and Information Technologies (IJCSIT), Vol. 5, Issue 2, ISSN:0975-9646, 2592-2595,2014.

[3]. Monika Choudhary, Sateesh Kumar Peddoju, “A Dynamic Optimization Algorithm for Task Scheduling in Cloud Environment”, International Journal of Engineering Research and Applications (IJERA), Vol. 2, Issue 3, May-Jun 2012, pp. 2564-2568.
[4]. “Cloud computing resources”, http://www.cloud9s.net/cloudcomputingresources.html
[5]. http://www.thecloudcomputing.org/2011
[6]. http://salsahpc.indiana.edu/CloudCom2010/papers.html
[7]. http://www.itworld.com/internet/69141/5-coolcloud-computing-research-projects
[8]. http://en.wikipedia.org/wiki/Cloud_computing
[9]. http://www.thecloudcomputing.org/2012/history.html
[10]. http://www.whereisdoc.co
[11]. Torry Harris' “Cloud Computing – An Overview”
[12]. Maheswari. R and S. Selvi, “A Survey on Scheduling Algorithms in Cloud Computing”, International Journal of Engineering Research & Technology (IJERT), Vol. 2 Issue 10, ISSN: 2278-0181, October – 2013.
[13]. Sujit Tilak, and Prof. Dipti Patil, “A Survey of Various Scheduling Algorithms in Cloud Environment”, International Journal of Engineering Inventions (IJEI), Volume 1, Issue 2, ISSN: 2278-7461, September 2012, PP: 36-39
[14]. Saeed Parsa and Reza Entezari-Maleki, ”RASA: A New Task Scheduling Algorithm in Grid Environment”, in World Applied Sciences Journal 7 (Special Issue of Computer & IT): 152-160, 2009, Berry M. W., Dumais, S. T., O’Brien G. W. Using linear algebra for intelligent information retrieval, SIAM Review, 1995, 37, pp. 573-595.
[15]. Nithiapidary Muthuvelu, Junyang Liu, Nay Lin Soe, Srikumar Venugopal, Anthony Sulistio and Rajkumar Buyya, “A Dynamic Job Grouping-Based Scheduling for Deploying Applications with Fine-Grained Tasks on Global Grids”, in Australasian Workshop on Grid Computing and e-Research (AusGrid2005), Newcastle, Australia., Conferences in Research and Practice in Information Technology, Vol. 44.
[16]. Meng Xu, Lizhen Cui, Haiyang Wang, Yanbing Bi, “A Multiple QoS Constrained Scheduling Strategy of Multiple Workflows for Cloud Computing”,in 2009, IEEE International Symposium on Parallel and Distributed Processing.
[17]. Dr. Amit Agarwal and Saloni Jain, “Efficient Optimal Algorithm of Task Scheduling in Cloud Computing Environment”, International Journal of Computer Trends and Technology (IJCTT), Volume 9 Issue7, March 2014.
[18]. Mrs. S. Selvarani, Dr. G. Sudha Sadhasivam, “Improved Cost - Based Algorithm For Task Scheduling In Cloud Computing”, IEEE, 2010.
[19]. Fei Teng, “Resource allocation and scheduling models for cloud computing”, Paris, 2011.
[20]. Emeakaroha, V.C., Brandic, I., Maurer, M. And Breskovic, I., “SLA-Aware Application Deployment and Resource Allocation in Clouds”, IEEE, 2011.
[21]. Daniel, D., Lovesum, S.P.J., “A novel approach for scheduling service request in cloud with trust monitor”, IEEE, 2011.
[22]. Boloor, K., Chirkova, R., Salo, T., Viniotis, Y., "Heuristic-Based Request Scheduling Subject to a Percentile Response Time SLA in a Distributed Cloud", IEEE, 2011.
[23]. Mehdi, N.A., Mamat, A. Amer, A., Abdul-Mehdi, Z.T., "Minimum Completion Time for Power-Aware
a. Scheduling in Cloud Computing", IEEE, 2012.
[24]. Luna Mingyi Zhang, Keqin Li, Yan-Qing Zhang, "Green Task Scheduling Algorithms with Speeds Optimization on Heterogeneous Cloud Servers", IEEE, 2011.
[25]. Xin Lu, Zilong GU, “A load-adaptive cloud resource scheduling model based on ant colony algorithm”, IEEE, 2011.
[26]. Gao Ming and Hao Li, "An Improved Algorithm Based on Max-Min for Cloud Task Scheduling", Yunnam University, China, 2011.
[27]. Ching-Hsien Hsu, Tai-Lung Chen, "Adaptive Scheduling Based on Quality of Service in Heterogeneous Environments", IEEE, 2010.
[28]. Shuo Liu, Gang Quan, Shangping Ren, "On-Line Scheduling of Real-Time Services for Cloud Computing", IEEE, 2010.
[29]. J. Yu and R. Buyya, “Workflow Scheduling Algorithms for Grid Computing”, Technical Report, GRIDS-TR-2007-10, Grid Computing and Distributed Systems Laboratory, The University of Melbourne, Australia, May 2007.
[30]. Anju Bala, Dr. Inderveer Chana, "A Survey of Various Workflow Scheduling Algorithms in Cloud Environment", 2nd National Conference on Information and Communication Technology (NCICT), 2011, Proceedings published in International Journal of Computer Applications® (IJCA).

R.Nallakumar, Dr.N.Sengottaiyan and S.Nithya, "A Survey of Task Scheduling Methods in Cloud Computing," International Journal of Computer Sciences and Engineering, Vol.2, Issue.10, pp.9-13, 2014.

Internet is the global system which is increasing day by day with a faster rate. With the increasing internet, social networking increases and people started to share information through different kinds of social media. In recent years several efforts were devoted for mining opinions and sentiments automatically from natural language in social media messages, news and commercial product reviews. This task involves a deep understanding of the explicit and implicit information, conveyed by the language. Most of these approaches refer to annotated corpora. The use of Opinion mining is to identify and extract the information, which is in the subjective form from the internet. This can be done with the help of data, required for processing. The methods used are natural language processing, text analysis. Sentiments are also extracted from the feedbacks. Feedback is important for selling or purchasing any product. While shopping whenever someone wants to choose any product, the opinion of others will always help him/her to choose the best product. But it is very difficult for customer to read thousands of reviews at a time and it also creates confusion. So some data mining techniques must be applied to solve these problems. Sentiment analysis also helps in identifying the attitude of the person. In our work, we present a system which develops a corpus for opinion and sentiment analysis. We will take the product reviews and classify them as positive, negative and objective. The system will further classify the positive and negative sentiments into emotions using Plutchik’s wheel of emotions and makes a dictionary. It uses fuzzy logic approach for prediction and generates output.

Sentiments, Sentiment Classification, Opinion mining, Corpora for sentiment analysis.

[1] Cristina Bosco, Viviana Patti and Andrea Bolioli, “Developing corpora for sentiment analysis and opinion mining: the case of irony and Senti-TUT”, IEEE Intelligent Systems, 2013.
[2] Amit Pimpalkar, Tejashree Wandhe, M. Swati Rao and Minal Kene “Review of Online Product using Rule Based and Fuzzy Logic with Smiley’s”, International Journal of computing and technology (IJCAT), Volume 1, Issue 1, February 2014.
[3] Rathawut Lertsuksakda, Ponrudee Netisopakul and Kitsuchart Pasupa “Thai Sentiment Terms Construction using the Hourglass of Emotions”, 6th International Conference on Knowledge and Smart Technology (KST), 2014.
[4] Aditi Gupta, Karthik Sondhi, Nishit Shivhre and Raunaq Kumar, “Sentiment Analysis for Social Media”, International Journal of Advanced Research in Computer Science and Software Engineering, Volume 3, Issue 7, July 2013.
[5] A. Mudinas, D. Zhang, and M. Levene, “Combining lexicon and learning based approaches for concept-level sentiment analysis”, Proceedings of the First International Workshop on Issues of Sentiment Discovery and Opinion Mining, no.5, pp. 1-8, 2012.
[6] Hemalatha, Dr. G. P Saradhi Varma and Dr. A. Govardhan, “Sentiment Analysis Tool using Machine Learning Algorithms”, International Journal of Emerging Trends & Technology in Computer Science (IJETTCS), Volume 2, Issue 2, March – April 2013.
[7] Aurangzeb Khan, Baharum Baharudin and Khairullah Khan, “Sentiment Classification Using Sentence-level Lexical Based Semantic Orientation of Online Reviews”, Trends in Applied Sciences Research, Vol. 6, pp. 1141-1157, July 2011.
[8] Jalaj S. Modha, Gayatri S. Pandi, and Sandip J. Modha, “Automatic Sentiment Analysis for Unstructured Data”, International Journal of Advanced Research in Computer Science and Software Engineering, Volume 3, Issue 12, December 2013.
[9] G. Vinodhini and RM. Chandrasekaran, “Sentiment Analysis and Opinion Mining: A Survey”, International Journal of Advanced Research in Computer Science and Software Engineering, Volume 2, Issue 6, June 2012.
[10] Liu Lizhen, Song Wei, Wang Hanshi, Li Chuchu and Lu Jingli, “A Novel Feature-based Method for Sentiment Analysis of Chinese Product Reviews”, in proceedings of ICT Management, China Communication, pp. 154-164, March 2014.
[11] Subhabrata Mukherjee and Pushpak Bhattacharyya, “Feature Specific Sentiment Analysis for Product Reviews”, Dept. of Computer Science and Engineering, IIT Bombay, 2011.

Dhanashri Chafale and Amit Pimpalkar, "Review on Developing Corpora for Sentiment Analysis Using Plutchik’s Wheel of Emotions with Fuzzy Logic," International Journal of Computer Sciences and Engineering, Vol.2, Issue.10, pp.14-18, 2014.

Reversibility plays a fundamental role when computations with minimal energy dissipation are considered. In recent years, reversible logic has emerged as one of the most important approaches for power optimization with its application in low power CMOS, optical information processing, quantum computing and nanotechnology. This research proposes a new implementation of adder in reversible logic. The design reduces the number of gate operations compared to the existing adder reversible logic implementations. So, this design gives rise to an implementation with a reduced area and delay. We can use it to construct more complex systems in nanotechnology.

Adder, Decimal Arithmetic, Reversible logic, Garbage output, HNG gate

[1] R. Landauer, "Irreversibility and Heat Generation in the Computational Process", IBM Journal of Research Development, 5, 1961, 183-191.
[2] Bennett, C., "Logical Reversibility of Computation," IBM Journal of Research and Development, 17, 1973, 525-532.
[3] Hafiz Md. Hasan Babu and A. R. Chowdhury, "Design of a Reversible Binary Coded Decimal Adder by Using Reversible 4-bit Parallel Adder", VLSI Design 2005, pp-255-260, Kolkata, India, Jan 2005.
[4] Himanshu. Thapliyal, S. Kotiyal and M.B Srinivas, "Novel BCD Adders and their Reversible Logic Implementation for IEEE 754r Format", VLSI Design 2006, Hyderabad, India, Jan 4-7, 2006, pp. 387-392.
[5] R. James, T. K. Shahana, K. P. Jacob and S. Sasi,"Improved Reversible Logic Implementation of Decimal Adder", IEEE 11th VDAT Symposium Aug 8-11, 2007.
[6] Md. M. H. Azad Khan, "Design of Full-adder With Reversible Gates", InternationalConference on Computer and Information Technology, Bangladesh, 2002, pp. 515-519.
[7] R. Feynman, "Quantum Mechanical Computers", Optical News, 1985, pp. 11-20.
[8] H. Thapliyal and M.B Srinivas, "A Novel Reversible TSG Gate and Its Application forDesigning Reversible Carry Look-Ahead and Other Adder Architectures", Tenth Asia-Pacific Computer Systems Architecture Conference, Singapore, Oct 24 - 26, 2005
[9] Rekha K.james,Shahana T.K,T.Poulose
Jacob,Sreela Sasi “A new look at Reversible logic implementation of Decimal adder”,IEEE 1- 4244-1368-0/07.
[10] Jagadguru Swami Sri Bharati Krishna Tirthaji Maharaja, Vedic Mathematics: Sixteen Simple Mathematical Formulae from the Veda, Delhi (1965).
[11] Rakshith Saligram and Rakshith T.R. "Novel Code Converter Employing Reversible Logic", International Journal of

P. Gopi Krishna, Dr.K.V.Subrahmanyam and S Chandra Sekhar, "Design of High Speed Low Power Multiplier Using Reversible Logic A Vedic Mathematical Approach," International Journal of Computer Sciences and Engineering, Vol.2, Issue.10, pp.19-25, 2014.

The large usage of smart phones and GPS enabled devices, which provides location based services, the necessity of outsourcing spatial data has grown rapidly over the past some years. Nevertheless a challenging problem remains in the database outsourcing paradigm is that the authentication of the query results at the client side. In this data owner delegates management of its database to the third party instant of directly served the request of clients. Ensuring spatial query integrity is critical because third party service provider is not always trustworthy. We propose an efficient road network optimized path finding technique using ant colony algorithm which utilizes the network Voronoi diagram and neighbors to prove the integrity of query results. Unlike previous work that consider only one data owner party but we are considering multi data owner party. This experiment will run on Google Android mobile devices.

Spatial database outsourcing, location-based service, service provider, voronoi diagram, spatial query

[1] Yinan Jing, Ling Hu, Wei-Shinn Ku and Cyrus Shahabi “Authentication of k Nearest Neighbor Query on Road Networks”, IEEE transactions, vol. 26, no. 6, June 2014.
[2] Preeti Tiwari, Dr. Swati V. Chande “Optimization of Distributed Database Queries Using Hybrids of Ant Colony Optimization Algorithm” International Journal of Advanced Research in Computer Science and Software Engineering 3(6), pp. 609-614 June - 2013.
[3] Xuefeng Liu, Yuqing Zhang, Member, IEEE, Boyang Wang, and Jingbo Yan “Mona: Secure Multi-Owner Data Sharing for Dynamic Groups in the Cloud” IEEE transactions on parallel and distributed systems, vol. 24, no. 6, June 2013.
[4] Krzysztof Jankowski and Pierre Laurent, “Packed AES-GCM Algorithm Suitable for AES/PCLMULQDQ Instructions” IEEE transactions on computers, vol. 60, no. 1, January 2011.
[5] H. Samet, J. Sankaranarayanan, and H. Alborzi, “Scalable network distance browsing in spatial databases”, SIGMOD, New York, NY, USA, pp. 43–54, 2008.
[6] K. C. K. Lee, W.-C. Lee, B. Zheng, and Y. Tian, “ROAD: A new spatial object search framework for road networks,” IEEE Transactions., vol. 24, no. 3, pp. 547–560, Mar. 2012.
[7] E. Mykletun, M. Narasimha, and G. Tsudik, “Authentication and integrity in outsourced databases,” TOS, vol. 2, no. 2, pp. 107–138, May 2006.
[8] H. Pang, A. Jain, K. Ramamritham and K.-L. Tan, “Verifying completeness of relational query results in data publishes”, SIGMOD Conference Baltimore, MD, USA, pp. 407–418, 2005.
[9] Majid Khan and M. N. A. Khan “ Exploring Query Optimization Techniques in Relational Databases” International Journal of Database Theory and Application Vol. 6, No. 3, June, 2013.
[10] Lei Zhang, Qianhong Wu, Agusti Solanas, Member, IEEE, and Josep Domingo-Ferrer, Senior Member, IEEE “A Scalable Robust Authentication Protocol for Secure Vehicular Communications” IEEE transactions on vehicular technology, vol. 59, no. 4, may 2010.

Gitali Rakshak and Amit Pimpalkar, "A Review on an optimized path finding on road network using Ant colony algorithm," International Journal of Computer Sciences and Engineering, Vol.2, Issue.10, pp.26-29, 2014.

Multiplication may be a for the most part used mathematical process, considerably in signal process and scientific applications. Multiplication having hardware challenge, and therefore the main criterion of upper speed, lower cost, and fewer VLSI space, the most apprehension in customary multiplication, typically realized by K no of cycles with shifting and adding, is to hurry up the underlying multi-operand addition of partial merchandise. during this paper we have a tendency to studied the changed Booth encryption (MBE) technique that has been introduced to scale back the quantity of PP rows, still keeping each straightforward and quick enough the generation method of every row.

Modified Booth Encoding, higher speed, lower cost, and less VLSI area

[1] R. Landauer, “Irreversibility and Heat Generation in the Computational Process”, IBM Journal of Research and Development, 5, pp. 183-191, 1961.

[2] C.H. Bennett, “Logical Reversibility of Computation”, IBM J.Research and Development, pp. 525-532, November 1973.
[3] T. Toffoli., “Reversible Computing”, Tech memo MIT/LCS/TM-151, MIT Lab for Computer Science (1980).
[4] E. Fredkin and T. Toffoli, “Conservative logic,” Int’l J. Theoretical Physics, Vol. 21, pp.219–253, 1982.

[5] R. Feynman, “Quantum Mechanical Computers,” Optics News, Vol.11, pp. 11–20, 1985.
[6] B. Parhami; “Fault Tolerant Reversible Circuits” Proc. 40th Asilomar Conf. Signals, Systems, and Computers, Pacific Grove, CA,Oct.2006.
[7] A. Peres, “Reversible Logic and Quantum Computers”, Physical review A, 32:3266- 3276, 1985.
[8] W. N. N. Hung, X. Song, G. Yang, J. Yang and M. Perkowski, “Quantum Logic Synthesis by Symbolic Reachability Analysis”, Proc. 41st annual .

T. Kavitha and B. Karunaiah , "An Improved Booth’s Recoding for Optimal Fault-Tolerant Reversible Multiplier," International Journal of Computer Sciences and Engineering, Vol.2, Issue.10, pp.30-32, 2014.

Cloud computing is an internet-based computing, where a set of resources and services such as applications, Storage and servers are delivered to computers and devices through the Internet. It incorporates large open distributed system, virtualization and internet delivery of services, dynamic provision of reconfigurable resources and on – demand operations. Cloud Computing is continuously growing and showing consistent growth in the field of computing. The major challenging task in cloud computing is the security and privacy issues caused by the outsourcing of infrastructure, sensitive data and critical applications and its multi- tenancy nature. The security for Cloud Computing is emerging area for research work and this paper discusses various types of authentication methods and multi-factor user authentication.

Cloud Computing, Security Issues, Cloud Computing Layers, Architecture diagram of AMOCC

[1] P. Barham et al., Xen and the Art of Virtualization, Proc. ACM Symposium on Operating Systems, Bolton Landing, NY, October 19–22, 2003.
[2] M.N. Bennani and D.A. Menasc´e, Resource Allocation for Autonomic Data Centers Using Analytic Performance Models, Proc. 2005 IEEE International Conference on Autonomic Computing, Seattle, WA, June 13-16, 2005
[3] R.J. Creasy, Digital Certificate Authentication, IBM J. Research and Development, Sept. 1981, pp. 483–490.
[4] R. Figueiredo, P.A. Dinda, and J. Fortes, Cloud services, IEEE Internet Computing, May 2005, Vol. 38, No. 5.
[5] R.P. Goldberg, Survey of Virtual Machine Research for Authentication, IEEE Computer, June 1974, pp.34–44
[6] G.J. Popek and R.P. Goldberg, Formal Requirements for Cloud Architectures, Comm. ACM, July 1975, pp. 412–421.
[7] D.A. Menasc´e, V.A.F. Almeida, and L.W. Dowdy, Performance by Design: Computer Capacity Planning by Example, Prentice Hall, Upper Saddle River, 2004.
[8] M. Rosenblum and T. Garfinkel, Virtual Machine Monitors: Current Technology and Future Trends, IEEE Internet Computing, May 2005, Vol. 38, No. 5.
[9] R. Uhlig et. al., Microsoft Cloud Technology, IEEE Internet Computing, May 2005, Vol. 38, No. 5.
[10] A. Whitaker, R.S. Cox, M. Shaw, and S.D. Gribble,-Rethinking the Design of Virtual Machine Monitors, IEEE Internet Computing, May 2005, Vol. 38, No. 5

Swapnil Rajesh Telrandhe and Deepak Kapgate, "Authentication Model on Cloud Computing," International Journal of Computer Sciences and Engineering, Vol.2, Issue.10, pp.33-37, 2014.

The focus of this Paper is the actual implementation of Network Router and verifies the functionality of the five port router for network on chip using the latest verification methodologies, Hardware Verification Languages and EDA tools and qualifies the Design for Synthesis an implementation. This Router design contains Four output ports and one input port, it is packet based Protocol. This Design consists Registers, Fsm and FIFO’s.For larger networks, where a direct-mapped approach is not feasible due to FPGA resource limitations, a virtualized time multiplexed approach was used. Compared to the provided software reference implementation, our direct-mapped approach achieves three orders of magnitude speedup, while our virtualized time multiplexed approach achieves one to two orders of magnitude speedup, depending on the network and router configuration.

FIFO, Fsm, Network-On-Chip, Register blocks, Simulation, Router

[1] “D. Chiou, “MEMOCODE 2011 Hardware/Software CoDesign Contest”, https://ramp.ece.utexas.edu/redmine/ Attachments/ DesignContest.pdf
[2] Blue spec Inc, http://www.bluespec.com
[3] Xilinx, “ML605 Hardware User Guide”,
http://www.xilinx.com/support/documentation /boards and kits/ug534.pdf
[4] Xilinx, “LogiCORE IP Processor Local Bus (PLB) v4.6”,
http://www.xilinx.com/support/documentation/ip documentation/plb v46.pdf
[5] “Application Note: Using the Router Interface to Communicate Motorola, ANN91/D Rev. 1, 01/2001.
[6] Cisco Router OSPF: Design& Implementation Guide, Publisher: McGraw-Hill
[7] “Nortel Secure Router 4134”, Nortel Pvt.Ltd.

K.Shiney, .K.V.Subrahmanyam and S Chandra Sekhar, "High Throughput Compact Delay Insensitive Asynchronous NOC Router," International Journal of Computer Sciences and Engineering, Vol.2, Issue.10, pp.38-40, 2014.

Image retrieval is one of the most interesting andfastest growing research areas in the field of digital image processing as well as for the information retrieval from web contents. In mostContent-Based Image Retrieval (CBIR) systems, an image isrepresented by a set of different level of visual features, by which can manage large databases. Most of the popular database removes the high-level semantic information.Here we this paper an novel approach named content based image retrieval using two tire architecture, to maintaining and reducing the exists gap between high-level and low-level features, where SVM classification is used in first layer after feature generation, therefore proceed it output as input into the second layer, where the resultant images again classified and will produce more accurate result while retrieval. And finally most similar images will retrieved according to the user specified query image.

Digital Image Processing, SVM, Fuzzy, CBIR, KNN, Semantic gap, colour feature

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Vinay Lowanshi and Shweta Shrivastava, "Two Tier Architecture for Content Based Image Retrieval Using Modified SVM and knn-GA," International Journal of Computer Sciences and Engineering, Vol.2, Issue.10, pp.41-45, 2014.