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Document summarization is the process of reducing size of text document and that retains the most important content of the original document into the reduced document(Summary).In recent year there are huge work has been done in document summarization. There are various techniques available for document summarization but most of the techniques used similarity of sentences to extract sentence, in the document summarization a context of the document are important, so our current method used term indexing model to gives index to document as well as sentences in that document. In this proposed system we used context based document indexing based on vector space model. This document indexing model works with document frequency (DF) and term frequency (TF).DF and TF model gives document indexing weight which is used for document summarization. We compare our system with traditional term based indexing model and will prove that our system gives better result than this system.

Vector space model, Document frequency, Term Frequency, Document context

[1] 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.
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[3] 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.
[4] 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.
[5] 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.
[6] Q.L. Israel, H. Han, and I.-Y. Song, “Focused Multi-Document Summarization: Human Summarization Activity vs. Automated Systems Techniques,” J. Computing Sciences in Colleges, vol. 25, pp. 10-20, http://portal.acm.org/citation.cfm?id=1747137. 1747140, May 2010.
[7] 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.
[8] 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.
[9] 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.
[10] 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.
[11] H. Daume´ III and D. Marcu, “Bayesian Query-Focused Summarization,” Proc. 21st Int’l Conf. Computational Linguistics and the 44th Ann. meeting of the Assoc. for Computational Linguistics, pp. 305-312, http://dx.doi.org/10.3115/1220175.1220214, 2006.
[12] D.M. Dunlavy, D.P. O’Leary, J.M. Conroy, and J.D. Schlesinger, “QCS: A System for Querying, Clustering and Summarizing Documents,” Information Processing and Management, vol.43, pp.1588-1605, http://portal.acm.org/citation.cfm?id=1284916.
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[13] 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.
[14] L.-W. Ku, L.-Y. Lee, T.-H. Wu, and H.-H. Chen, “Major Topic Detection and Its Application to Opinion Summarization,” Proc. 28th Ann. Int’l ACM SIGIR Conf. Research and Development in Information Retrieval, pp. 627-628, http://doi.acm.org/10.1145/ 1076034.1076161, 2005.
[15] 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.
[16] 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.

Vaibhav A. Chavan and Santosh R. Durugkar , "Information Retrieval System Using Vector Space Model for Document Summarization," International Journal of Computer Sciences and Engineering, Vol.2, Issue.10, pp.46-50, 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

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P.Ashok Shiva Kumar, .K.V.Subrahmanyam and S Chandra Sekhar , "Radix-4 And 8 Both Encoder Multi Modules Multiplier," International Journal of Computer Sciences and Engineering, Vol.2, Issue.10, pp.51-53, 2014.

Data mining an interdisciplinary research area spanning several disciplines such as machine learning, database system, expert system, intelligent information systems and statistic. Data mining has evolved into an active and important area of research because of previously unknown and interesting knowledge from very large real-world database. Many aspects of data mining have been investigated in several related fields. A unique but important aspect of the problem lies in the significance of needs to extend their studies to include the nature of the contents of the real world database. In this paper we are going to compare the three different algorithms which are commonly used in data mining. These three algorithms are CHARM Algorithm, Top K Rules mining and CM SPAM Algorithm.

Data Mining, CHARM algorithm, K rule mining, CM SPAM Algorithm

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Aruna J. Chamatkar and P.K. Butey , "Comparison on Different Data Mining Algorithms," International Journal of Computer Sciences and Engineering, Vol.2, Issue.10, pp.54-58, 2014.

This paper provides the information about Face Recognition Technology which gives the much more security in the field of multimedia and information technology. To provide the protection to the data we keep the password but as we know hackers can break the password, for that we keep password as our face. Thus for accessing some network or PC by an unauthorized person is virtually impossible and it helps to protect our data. It also provides the user friendliness in human interaction with computer as there is no such physical touch. In this image is captured and stored into database in compress form. Its benefits show in retrieval and in matching. Like the applications of teleconferencing and video call, face recognition is more efficient. Most of the cameras have this application of face recognition which detects the human face and shows appropriate square box on face. In this paper there is an introductory part of this technology. This shows the generic framework and variants that are frequently use by the face recognizer. Some well known face recognition algorithms, such as PCA, Eigenfaces, will also be explained in this paper.

PCA Technique, Data Flow Diagram, Principal Components Analysis (PCA)

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Divesh N. Agrawal and Deepak Kapgate, "Face Recognition Using PCA Technique," International Journal of Computer Sciences and Engineering, Vol.2, Issue.10, pp.59-61, 2014.

Lab Tracer is a remote desktop technology that serves as the future of computer applications. The main objective is to develop an application that can control a computer remotely. Remote desktop technology makes it possible to view another computer’s desktop on our computer. The person sitting on the server can view and control the client’s computer. With fast, reliable, easy-to-use pc from remote control software, it helps us to save hours of running up and down the stairs between computers. The basic idea behind this work is to capture the screenshot of the client and send to the server. In response, mouse events and key events are captured from the server and exchanged those events between the Server and the Client via network.

Lab tracer, Remote desktop technology, Remote desktop administration, Remote server, Remote client, Remote access, Remote Method Invocation

[1] Shubhra Saggar, “Controlling Remote Desktop”, proceedings of the 2nd National Conference INDIAcom-2008, Computing for Nation Development, February 08-09, 2008.
[2] Harsh Mittal1, Manoj Jain, Latha Banda, “Monitoring Local Area Network Using Remote Method Invocation”, International Journal of Computer Science and Mobile Computing (IJCSMC), Vol. 2, and Issue. 5, pg.50 – 55, May 2013,
[3] Mark P. Sullivan, “Secure Remote Network Administration and Power Management”, Thesis, June 2004.
[4] Janaka Wijekoon, Malitha Wijesundara,, Thilok Dassanayaka, Dineth Samarathunga, Rasanga Dissanayaka, Deshani Perera, ”The Advanced Remote PC Management Suite”, International Conference on Industrial and Information Systems (ICIIS), Pg. 410-413, 2011
[5] Anis Ismail, Mohammad Hajjar, Haissam Hajjar, “Remote Administration Tools: A Comparative Study”, Journal of Theoretical and Applied Information Technology (JATIT), 2005 – 2008.
[6] Shi-hai Huang, Chuang Lin, An'an Luo, Zhen Chen, Xin Jiang, Kai Wang, Hui Zhang, Xue-hai Peng, “Proxy-based Security Audit System for Remote Desktop Access”, 2009 IEEE
[7] Pan-Lung Tsai, Chin-Laung Lei, “Towards Ubiquitous Computing via Secure Desktop Service”, 2001 IEEE, Pg. 187-190.
[8] Timothy C. Lethbridge and Robert Laganière, “Object-Oriented Software Engineering: Practical Software Development using UML and Java”, Second Edition, McGraw Hill, ISBN 0-07-710908-2, 2001
[9] Teamviewer 9-overview
URL: http://www.teamviewer.com/en/index.aspx/.
[10] Remote desktop control
URL: http://www.remote-desktop control.com/

Midhya Mohan E, Filma Mathew, Glemin George, Harikrishnan V, Vaneza Benny, Bineesh M, "Lab Tracer- The Remote Desktop Technology," International Journal of Computer Sciences and Engineering, Vol.2, Issue.10, pp.62-66, 2014.

Portfolio construction is enabled through the multi objective optimization. The nature of the problem invites the construction through multi objective optimization. Genetic algorithm and the particle swarm optimization is used for the above purpose. The results obtained are compared against the classical Markowitz model. The data from the Nifty from March 2010 to October 2010 has been used. The Stocks from various sectors are used to build the portfolio. The proposed work is promising and the results obtained are outperforming. Comparing on both the algorithms PSO based multi objective optimization serves better than Genetic algorithms based on the results obtained.

Portfolio Optimization; MOPSO; MOGA

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B. UmaDevi, D. Sundar and DR. P. Alli, "Enhancement of the portfolio determination using Multi- Objective Optimization," International Journal of Computer Sciences and Engineering, Vol.2, Issue.10, pp.67-75, 2014.

In our day-today life we come across a lot of situations where coding is needed. Coding is a technique that reduces the space to store the data. It also makes it easier for future access. During coding, the data in one form is coded to another form which may be legible and sometimes not. This happens because the form of representing the data gets changed while coding. The existing coding formats like barcode have been successful because of its application in several areas like health care and hospital settings, library services and coding of products by its manufacturer. There have been a lot advancement in sensor Technology which paves way to an efficient coding system. Color sensor Technology helps us to replace the existing Bar code system. In this paper, a new approach for coding is introduced by using color codes in place of bar codes. Data compression and encoding are done here to make color codes. Bar code scanner cannot read the code if it is crumpled or distorted and also the length increases the scanning time also increases accordingly. The color fading can be reduced by using colors of different intensities.

Coding, Data Compression, Encoding, Color Code, Bar code, Color Sensor Technology

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[6] What is a Barcode scanner www.densowave.com/en/adcd/fundamental/barcode/scanner/
[7] How Barcode Scanner work, www.carolinabarcode.com/how-barcode-scanners-work/
[8] Barcode Scanners: How Do They Work, www.waspbarcode.com/buzz/how-barcode-scanners-work/
[9] CCD Barcode Scanner, www.barcodesinc.com/cats/barcode-scanners/ccd/

Neena George P, Silpa Johnson, Arif P.A, Naveen Pavitran, Diana Davis, "Color Code –The Replacement for Bar Code," International Journal of Computer Sciences and Engineering, Vol.2, Issue.10, pp.76-79, 2014.