iosr-JCE   Volume-11 ~ Issue-5

Abstract: Mobile Ad hoc Network is collection wireless mobile nodes which dynamically form a temporary network] =without the use of existing network infrastructure centralized administration. Due to dynamic property of mobiles nodes in MANET, They require good routing protocols. This paper analyzes the effect of random based mobility models on the performance of Proactive Routing Protocol (DSDV- Destination Sequence Distance Vector) and Reactive Routing Protocol (AODV- on Demand Distance Vector Routing Protocol, and (OLSR-Optimized Link State Routing Protocol). Performance Analysis is done with respect to receive rate and packet received for varying node densities.

[1]. Robinpreet Kaur & Mritunjay Kumar Rai "A Novel Review on Routing Protocols in MANETs", Undergraduate Academic Research Journal (UARJ), ISSN : 2278 – 1129, Volume-1, Issue-1, 2012
[2]. Boukerche, "Performance Evaluation of Routing Protocols for Ad Hoc Wireless Networks," Mobile Networks and Applications, pp. 9, 333-342, Kluwer Academic Publishers, 2004.
[3]. Shrikant Upadhyay " comparison and performance analysis of reactive type DSR, AODV and proactive type mobile ad-hoc network, using NS-2 simulator"Journal of Engineering and Computer Innovations Vol.2 (10), pp 36-47, March 2012.
[4]. D. Mahmood,N. javaid , U. Qasim , Z. A. Khan "Routing Load of Route Calculation and Route Maintenance in Wireless Proactive Routing Protocols" ,seventh International Conference on Broadband, wireless Computing, communication and applications,2012.
[5]. Savita Gandhi SMIEEE1, Nirbhay Chaubey MIEEE2, Naren Tada3, Srushti Trivedi3, "Scenario-based Performance Comparison of Reactive, Proactive & Hybrid Protocols in MANET", International Conference on Computer Communication and Informatics , 2012.
[6]. Md. Arafatur Rahman and Farhat Anwar, Jannatul Naeem and Md. Sharif Minhazul Abedin "A Simulation Based Performance Comparison of Routing Protocol on Mobile Ad-hoc Network (Proactive, Reactive and Hybrid)", International Conference on Computer and Communication Engineering,2012.
[7]. Nadin Qasim, Fatin Said, Hamid Aghvami, "Mobile Ad Hoc Networking Protocols Evaluation through Simulation for Quality of Service", IAENG International Journal of Computer Science,2009.
[8]. Rashmi Rohankar, Rinkoo Bhatia, Vineet Shrivastava , Deepak Kumar Sharma, "Performance Analysis of Various Routing Protocols (Proactive and Reactive) for Random Mobility Models of Adhoc Network", 1st Int‟l Conf. on Recent Advances in Information Technology, 2012.
[9]. Mehran Abolhasan , Tadeusz Wysocki , Eryk Dutkiewicz "A review of routing protocols for mobile ad hoc networks"

Abstract: In the past few years, we have seen a rapid expansion in the field of mobile computing due to the proliferation of inexpensive, widely available wireless devices A mobile ad hoc network (MANET) consists of mobile wireless nodes in which the communication between nodes is carried out without any centralized control. MANET is a self organized and self configurable network where the mobile nodes move arbitrarily. The mobile nodes can receive and forward packets as a router. In this paper provides insight into the potential applications of ad hoc networks and discusses the technological challenges that protocol designers and network developers are faced with. In this paper also present the impact of comparison of different routing protocol in term of different parameters.

[1]. Jeroen Hoebeke, Ingrid Moerman, Bart Dhoedt and Piet Demeester, "An Overview of Mobile Ad Hoc Networks: Applications and Challenges
[2]. Chander Diwaker and Anju Gill "Comparative Analysis of Routing in MANET" International Journal of Advanced Research in Computer Science and Software Engineering , july 2012.
[3]. Sankalp Bahadur Singh , Bharat Pesswani "A Performance Analysis Between AODV & DSR Routing Protocol" IJCTA | July-August 2012
[4]. Anju yadav, "Simulation Study and Implementation on Routing Protocols in MANET" International Journal of Computer Science & Management Studies, 2012
[5]. P. Misra, "Routing Protocols for Ad Hoc Mobile Wireless Networks", http://www.cis.ohio-state.edu/~jain/cis788-
[6]. Shweta, Vinit Kumar, Anish Mittal, Dr. Pankaj Gupta, Deepak Goel"Analysis of Routing in Mobile Ad-hoc Networks", International Journal of Computer Science & Management Studies, 2012
[7]. A.K. Gupta, Dr. H. Sadawarti and Dr. A. K. Verma, "Performance analysis of AODV, DSR & TORA Routing Protocols" in proceeding of IACSIT International Journal of Engineering and Technology, Vol.2, No.2, April 2010
[8]. Swati Saxena, Birbal Saran, Mr. Vijendra Singh, "SIMULATION AND PERFORMANCE ANALYSIS OF DSDV, OLSR, DSR AND ZRP ROUTING PROTOCOLS IN MANETs" , International Journal of Computer Trends and Technology-2012
[9]. C. Siva Ram Murthy and B.S Manoj, "Mobile Ad Hoc Networks- Architectures & Protocols", Pearson Education, New Delhi, 2004
[10]. Chlamtac, I., Conti, M., and Liu, J. J.-N. Mobile ad hoc networking: imperatives and challenges. Ad Hoc Networks, 1(1), 2003, pp. 13–6.

Abstract: TheSession Initiation Protocol (SIP), is an application-layer control (signaling) protocol for creating, modifying, and terminating sessions with one or more participants. SIP is developed by SIP working group specified by the Internet Engineering Task Force (IETF), peer-to-peer communication protocol to establish, manipulate, and tear down communication sessions at application layer. These sessions include Internet telephone calls, multimedia distribution, and multimedia conferences. SIP invitations used to create sessions, carry session descriptions that allow participants to agree on a set of compatible media types. The Automatic Callback feature sends a priority ring on phone, indicating the previously busy or unanswered extension you dialed is now available. Callback feature allows to receive Callback notification when a called party line becomes available. Callback provides the ability of the system to suspend the call completion service if the user, who originated Callback, is currently busy and receives call-back notification when the called party becomes available. When the originating user then becomes available, the call completion service resumes for that user. Keywords-Automatic Callback (ACB), Internet Engineering Task Force (IETF),Public Switched Telephone Network (PSTN), Session Initiation Protocol (SIP), Voice over Internet Protocol (VoIP)

[1] SIP: Session Initiation Protocol, by J. Rosenberg, H. Schulzrinne, G. Camarillo, A. Johnston, J. Peterson, R. Sparks, M. Handley, and E. Schooler, RFC 3261, June 2002.

[2] M. Handley, H. Schulzrinne, E. Schooler, J. Rosenberg, "SIP: Session Initiation Protocol", RFC 2543, XYZ; March 1999

[3] Session Initiation Protocol, KK Tan and HL Goh .P.13101314 Volume 2, IEEE International conference on Information Technology, 11:th14: th of December 2002
[4] IETF SIP Working Group; [Online]. Available: http://www.ietf.org/html.charters/sip-charter.html [5] SIP Communication for Dummies by Lawrence Miller, CISSP, and Peter H. Gregory, CISA, CISSP Foreword by Alan B. Johnston
[6] Cisco Unified Communications Manager Features and Services Guide, Release 8.5(1), [Online].Available:http://www.cisco.com/en/US/docs/voice_ip_comm/cucm/admin/8_5_1/ccmfeat/fscallbk.html

[7] "SIP: Session Initiation Protocol Service Examples", by S. Donovan, RFC 5359, October 2008

[8] Internet Communications using SIP: Delivering VOIP and Multimedia Services with Session Initiation Protocol, Second edition by Henry Sinnreich, Alan B. Johnston, Published by Wiley Publishing, Inc .
[9] A.Roach, "Event Notification in SIP", Internet Draft ;[Online]. Available: <draft-roach-sip-subcribe-notify-00.txt>, XYZ; March 2000.Work in progress.

[10] "Session Initiation Protocol (SIP)-Specific Event Notification" by A.Roach, RFC 3265, June 2002

Abstract: EEG signals can be used to solve many real life problems. But it would only be possible if the EEG signals corresponding to different cognitive tasks could be distinguished from one another. Here, Statistical analysis techniques are used to distinguish these cognitive tasks recorded in the form of EEG signals. The results show that there lies significance difference among EEG signals corresponding to different cognitive tasks performed.

Keywords: EEG, Intelligence, Psychometric Tests, Statistical Techniques, Kruskal-Wallis Test.

[1] Aruna Tyagi,Manoj Duhan and Dinesh Bhatia, " Effect of mobile phone radition on brain activity gvm vs cdma" (IJSTM Vol. 2, Issue 1, April 2011

[2] M. Teplan "Fundamental of EEG measurement" Measurement science review, Volume 2, Section 2, 2002

[3] Wolfgang Klimesch "EEG alpha and theta oscillations reflect cognitive and memory performance: a review and analysis" I Brain Research Review, 1999, 169–195.

[4] D posthuma, M.C.Neale, D.I. Boomsma, and E.J.C. Geus. "Are smarter Brains running faster? Heritability of Alpha Peak frequency IQ, and their correlation", Journal of Behavior Genetics, Nov 2001,Vol.31, No.6.

[5] Ricardo Vigário*, Jaakko Särelä, Veikko Jousmäki, Matti Hämäläinen, and Erkki Oja "Independent Component Approach to the Analysis of EEG and MEG Recordings" IEEE transaction on biomedical engineering , vol. 47, No. 5, May 2000 ,589

[6] Shah Aqueel Ahmed1, D. Elizabath Rani and Syed Abdul Sattar1,"Alpha Activity in EEG and Intelligence" (IJAIT) Vol. 2, No.1, February 2012

[7] Tien-Wen Lee ,Yu-Te Wu , Younger W.-Y. Yu , Hung-Chi Wu, Tai-Jui Chen" A smarter brain is associated with stronger neural interaction in healthy young females: A resting EEG coherence study", Intelligence, 40(2012),38-48.

[8] Xingyuan Wang, Juan Meng, Guilin Tan and Lixian Zou,"Research on the relation of EEG signal chaos characteristics with high-level intelligence activity of human brain", Nonlinear Biomedical Physics 2010, 4(2).

[9] Tongran Liu, Jiannong Shi, Daheng Zhao4, Jie Yang"The relationship between EEG band power, cognitive processing and intelligence in school-age children" ,Psychology Science Quarterly, 2008, 50(2), pp. 259-268.

[10] Zipora Libman "Integrating Real-Life Data Analysis in Teaching Descriptive Statistics: A Constructivist Approach" Journal of Statistics Education Volume 18, Number 1 (2010)

Abstract: The number of Android based smart phones is growing rapidly. They are increasingly used for security critical private and business applications, such as online banking or to access corporate networks. This makes them a very valuable target for an adversary. Up to date, significant or large scale attacks have failed, but attacks are becoming more sophisticated and successful. Thus, security is of paramount importance for both private and corporate users. In this paper, we give an overview of the current state of the art of Android security and present our extensible automated exploit execution framework. First, we provide a summary of the Android platform, current attack techniques, and publicly known exploits. Then, we introduce our extensible exploit execution framework which is capable of performing automated vulnerability tests of Android smart phones. It incorporates currently known exploits, but can be easily extended to integrate future exploits. Finally, we discuss how malware can propagate to Android smart phones today and in the future, and which possible threats arise. For example, Device to device infections are possible if physical access is given.

[1] Ben Elgin, "Google buys android for its mobile arsenal," http://www.businessweek.com/ technology/content/aug2005/tc20050817_0949_tc024.htm, August 2005.
[2] John Cox, "Why google's gphone won't kill apple's iphone," http://www.networkworld.com/ news/2007/100807-google-gphone-iphone.html, October 2007

[3] Mike Cleron, "Androidology: Architecture overview," http://www.youtube.com/watch?v=Mm6Ju0xhUW8, November 2007.

[4] Josh Aas, "Understanding the linux 2.6.8.1 cpu scheduler," Retrieved from http://www.angelfire.com/folk/citeseer/linux_scheduler.pdf, February 2005.

[5] Charles Manning and Wookey, YAFFS Specifications, Aleph One, Bottisham, UK, version 0.3 edition, February 2002, Retrieved from http://www.yaffs.net/yaffs-spec.

[6] Google, Mountain View, CA, Radio Layer Interface, March 2009, See path '/development/pdk/docs/telephony.html' in Android source tree.

[7] Ryan Gardner, "Multi-touch proof-of-concept," htttp://ryebrye.com/files/multitouch_archive.zip, November 2008, With instructions at http://www.ryebrye.com/blog/2008/11/30/g1-multitouch-proof-of-concept-soure-code-posted/.

[8] Open Handset Alliance, "Android overview," http://www.openhandsetalliance.com/

[9] Gartner, "Mobile Device Sales Q3 2011," November 2011. http://www.gartner.com/it/page.jsp?id=1848514.

[10] Android Security Team, "Android Security Overview," December 2011. http://source.android.com/tech/security/index.html.

Abstract: Handwriting processing is a domain in great expansion. The interest devoted to this field is not explained only by the exciting challenges involved, but also the huge benefits that a system, designed in the context of a commercial application, could bring Two classes of recognition systems are usually distinguished: online systems for which handwriting data are captured during the writing process, which makes available the information on the ordering of the strokes, and offline systems for which recognition takes place on a static image captured once the writing process is over. The field of personal computing has begun to make a transition from the desktop to handheld devices, thereby requiring input paradigms that are more suited for single hand entry than a keyboard. Online handwriting recognition allows for such input modalities. On-line handwritten scripts are usually dealt with pen tip traces from pen-down to pen-up positions.

Keywords: Training, pre-processing, recognition, strokes, segmentation.

[1] Yoshida, K, 1982 C&C System Research Laboratories Nippon Electric Co., Ltd

[2] Zafar, M.F. Informatics Complex, Islamabad Mohamad, D. ; Anwar, M.M. Multitopic Conference, 2006. INMIC '06. IEEE.

[3] Chiang, C.C. CompEuro '91. Advanced Computer Technology, Reliable Systems and Applications. 5th Annual European Computer Conference. Proceedings. 1991

[4] Liangwen Zhang Intelligent Computing and Intelligent Systems (ICIS), 2010 IEEE International Conference Nankai Univ., Tianjin, China.

Abstract: This paper focus on various Formal method and Formal specification languages and choosing the appropriate one for a particular problem. It describes how a particular specification language is used for a specific problem. A comparison of different formal specification languages is done taking various parameters.

Keywords: Formal method, Formal specification language, schema.

[1]. Nix, C.J. and Collins, B.P. ―The use of Software Engineering, including the Z notation, in development of CICS.‖1988

[2]. Spivey, J.M.. The Z Notation: A reference manual. Prentice- Hall, 1989.

[3]. Warmer, J., and A. Kleppe. Object Constraint Language, Addis ion- Wesley 1998.

[4]. C. B. Jones, Systematic Software Development using VDM, 2nd ed. Englewood Cliffs, PH, NJ:, 19190.

[5]. M. Jackson, ―Description is our business,‖ in VDM ‗91: Formal Soft.Dev. Methods. New York: Springer-Verlag, LNCS-551, pp. 1-9.

[6]. Software Development using Specification and Description Language- Real time, Sharath Gopalappa.

[7]. J. Guttag and J. Horning, Larch: Languages and Tools for Formal Specification, Springer-Verlag; 1993.

[8]. Cooke, D., and Gates, A., ―Language for the Specification of Software,‖ Journal of System Software, 1996, pp 269-307.

Abstract: In recent years, image processing acts as an objective for the evaluation of images. Texture is a primary feature which presents considerable information for image classification, and is an imperative content utilized in content-based image retrieval (CBIR) system. To employ texture-based image database retrieval, texture segmentation strategies are required to segment textured regions from arbitrary images in the database. Texture segmentation has been accepted as a complex crisis in image analysis. The main objective of image segmentation is cluster pixeling the regions equivalent to individual surfaces, objects, or ordinary parts of objects and to simplify and/or change the representation of an image into something that is more meaningful and easier to analyze. To enhance the texture image segmentation, in this work, we are going to implement a multitude regional texture extraction process for image segmentation. At first, natural and aerial images are extracted from the dataset and present region based segmentation process. Then a multitude regional texture extraction is proposed by implementing a local threshold values. An extraction of regions are accomplished by the respective dimensions. The algorithm provides a less natural metrics awareness in a minimum user interaction environment. The shape and size of the growing regions depend on look up table entries. The experimental evaluation is conducted with training samples of natural and aerial images to show the performance of multitude textural extraction for more efficient image segmentation with sharp demarcation of edge portions along with intensity levels.

Key Words: Image Segmentation, Texture Extraction, Region Growing, Natural and Aerial Images

[1] T.-Y. Law and P. A. Heng, (2000) "Automated extraction of bronchus from 3-D CT images of lung based on genetic algorithm and 3-D region growing", Proc. SPIE 3979, Medical Imaging 2000: Image Processing, 906-916.

[2] R. Susomboon, D. S. Raicu, and J. D. Furst, (2006)"Pixel-Based Texture Classification of Tissues in Computed Tomography", CTI Research Symposium, Chicago, April 2006.

[3] J. E. Koss, F. D. Newman, T. K. Johnson, D. L. Kirch, (1999) "Abdominal organ segmentation using texture transform and a Hopfield neural network", IEEE Trans. Medical Imaging, Vol.18, 640-648. [4] R. Adams, L Bischof, (1994) "Seeded region growing". IEEE Transaction Pattern Analysis Machine Intelligency 16, 641-647.

[5] N. A. Mat-Isa, M. Y. Mashor & N. H. Othman, (2005) "Seeded Region Growing Features Extraction Algorithm; Its Potential Use in Improving Screening for Cervical Cancer". International Journal of the Computer, the Internet and Management (ISSN No: 0858-7027) Vol. 13. No. 1 January-April

[6] Y. Tuduki, K. Murase, M. Izumida, H. Miki, K. Kikuchi, K. Murakami & J. Ikezoe (2000). "Automated Seeded Region Growing Algorithm for Extraction of Cerebral Blood Vessels from Magnetic Resonance Angiographic Data" Proceedings of The 22nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society 1756-1759

[7] P. A. Venkatachalam, U. K.Ngah, A. F. M. Hani& A. Y. M. Shakaff, (2002). "Seed Based Region Growing Technique in Breast Cancer Detection and Embedded Expert System". Proceedings of International Conference on Artificial Intelligence in Engineering and Technology 464-469

[8] V. A. Kovalev, F. Kruggel, H.-J Gertz, and D.Y. von Cramon. (2001) "Three-dimensional texture analysis of MRI brain datasets" IEEE Trans. on Medical Imaging, 20(5): 424-433. [9] S. A. Karkanis, et al., (1999) "Detecting abnormalities in colonoscopic images by texture descriptors and neural networks," Proc. of the Workshop Machine Learning in Med. App., 59-62.

[10] A. Madabhushi, M. Feldman, D. Metaxas, D. Chute, and J. Tomaszewski. (2003) "A novel stochastic combination of 3D texture features for automated segmentation of prostatic adenocarcinoma from high resolution MRI." Medical Image Computing and Computer-Assisted Intervention, volume 2878 of Lecture Notes in 8 J. Wu et al. / J. Biomedical Science and Engineering 2 (2009) 1-8 Computer Science, pp. 581-591. Springer-Verlag

Abstract: In today's digital world invisible ink and paper have been replaced by much more versatile and practical covers for hiding messages – digital documents, images, video, and audio files. As long as an electronic document contains irrelevant or redundant information, it can be used as a "cover" to hide secret messages. Cryptography is the study of encoding and decoding secret messages where as Steganography is the art and science of writing hidden messages in such a way that no one apart from the sender and intended recipient even realizes there is a hidden message. In this paper we implement Hill Cipher algorithm, for hiding our text behind the cover image and decrypt the cover image to get original text. The highlight of this paper is that, the key is encrypted and scrambled within the cover image.

Keywords : Encryption, Decryption, Hill Cipher, Steganography, Symmetric Key Encryption.

[1] Bibhudendra Acharya, Saroj Kumar Panigrahy and Debasish Jena. Image encryption using self invertible key matrix of Hill cipher algorithm. Ist International Conference on Advances in Computing. Chikhli, India. 21-22 February 2008.

[2] Bibhudendra Acharya, Girija Sankar Rath, Sarat Kumar Patra, Saroj Kumar Panigrahy. 2007. Novel Methods of Generating Self-Invertible Matrix for Hill Cipher Algorithm, International Journal of Security, Vol 1, Issue 1, 2007.

[3] S.K.Muttoo1, Deepika Aggarwal, Bhavya Ahuja. A Secure Image Encryption Algorithm Based on Hill Cipher System, Buletin Teknik Elektro dan Informatika (Bulletin of Electrical Engineering and Informatics) Vol.1, No.1, March 2012, pp. 51~60.

[4] W. Stallings, "Cryptography and Network Security", 4th edition, Prentice Hall, 2005

[5] Amogh Mahapatra Rrajballav Dash .Data encryption and decryption by using hill cipher technique and self repetitive matrix. Department of Electronics & Instrumentation Engineering National Institute of Technology Rourkela 2007

Abstract: This paper proposed a Weather Early Warning system for minimizing human life loss in natural disasters like tsunami, cyclone, tornado etc. The proposed system is a value added service which can be provided by telecomm operators to their subscribers. The proposed system alerts the subscribers through phone call and SMS depending on the weather condition of their locations. Keywords - Weather early warning, natural disaster pre-caution, value added service, weather api, weather server.

[1] Chanuka Wattegama, ICT for Disaster Management, Asia-Pacific Development Information Programme, 2007.
[2] Dr. Mashury Wahab, Integrated Data Communications and Processing For Early Warning System, The Euro-Southeast Asia Cooperation Forum on ICT research, October 6-7, 2008.
[3] Olaf Neussner, Allen Molen and Thomas Fischer, Using Geoinformation Technology for the Establishment of a Local Flood Early Warning System, Second International Conference of Geoinformation Technology for Natural Disaster Management and Rehablitation, December 01-02, 2008.
[4] Joko Windarto, Flood Early Warning System Develop at Garang River Semarang using Information Technology base on SMS and Web, International Journal Of Geomatics and Geosciences, Volume 1, No 1, 2010.
[5] Shanghai Meteorological Bureau, CMA, Overview of Shanghai Multi-hazard Early Warning system and the role of Meteorological Services, Shanghai 200030, China, 2010.
[6] Brendan Williams, Adequacy of forecasts and early warning systems particularly as they related to the flooding events in Toowoomba, and the Lockyer and Brisbane Valley, Queensland Floods Commission of Inquiry, March 2011.
[7] P. Vanderkimpen, I. Rocabado1, J. Cools, M. El-Sammany and A. Abdelkhalek, FlaFloM – an early warning system for flash floods in Egypt.
[8] Kerry Plowright, Global Multi-Hazard Early Warning System, The Australian Early Warning Network (EWN).
[9] Chai Mui Fatt, Asmadi bin Abdul Wahab, Norhadizah binti Mohd Khalid, Nasrul Hakim bin Hashim, Muhammad Nazri bin Noordin and Mohd Rosaidi bin Che Abas, Tsunami Databases for The National Tsunami Early Warning Centre of Malaysia: Toward The Implementation Plan of Regional Tsunami Watch Providers (Rtwp), Malaysian Meteorological Department.
[10] Rosita Galang and Rosmadinor Mohamad, Utilizing Satellite and Radar Images using Remote Sensing and GIS for Weather Forecasting, Jabatan Meteorology Malaysia.

Abstract: Social web applications like Facebook, YouTube, Delicious, Twitter and so many others have gained popularity among masses due to its versatility and potential of accommodating cultural perspectives in Social web paradigm. Social bookmarking systems facilitateusers to store, manageand share tagged web documents through folk classification system. These social toolsallow its users to associate free chosen keywords (tags) with documents for future considerations. Social tags reflect not only human cognition on contents the document contains inbut also used as index-terms in social searching. However search results associated with query-tags are randomly ordered either by popularity, interestingness or reverse chronological order with most recent bookmarks on top of search results, which limits the effectiveness of information searching in social bookmarking systems. A lot of research works have already been published to tickle the problem by exploiting different features of folksonomy structure. This survey provides a brief review of state-of-the-art, challenges and solutions towards recommending and ranking tagged web documents in Social Bookmarking Systems (SBS).

Keywords–Information Searching, Social bookmarking, Social Search, Ranking, bookmarks, Web 2.0

[1]. A. Hotho, R. Jaschke, C. Schmitz, G. Stumme, Information Retrieval in Folksonomies: Searching and Ranking. Proc. of the 3rd European Semantic Web conference. Montrnegro. , 2007,411-426.
[2]. Http://www.delicious.com/about.
[3]. S. Brin, L. Page, (1998). The Anatomy of a Large-Scale Hyper-textual Web Search Web Search Engine. Proceeding of the 7th International Conference on World Wide Web. Brisbane, Australia. 107-117.
[4]. J. Kleinberg, (1999). Authoritative Sources in Hyperlinked Environments. Journal of the ACM. 604-32.
[5]. G. Jeh and J. Widom. SimRank: a measure of structural-context similarity. In KDD'02: Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining, pages 538-543. ACM Press, 2002
[6]. R.Lempel, S.Moran, The stochastic approach for link-structure analysys (SALSA) and the TKC effect,
[7]. Proceedings of the 9th International World Wide web Conference, 2000
[8]. Thomas Vander Wal, (2007), Folksonomy Coinage and Definition, 604-611.
[9]. A. Hotho, R. Jaschke, C. Schmitz and G. Stumme, (2006), Bibsonomy: A Social Bookmark and Publication Sharing System. Proceeding of the First Conceptual Sturctures Tool Interoperability Workshop, pages 87-102.
[10]. A. Mathes (2004), Folksonomies - cooperative classification and communication through shared metadata, Graduate School of Library and Information Science

Abstract:Healthcare industry is a type of industry, where the data is very large and sensitive. The data is required to be handled very carefully without any mismanagement. There are various data mining techniques that have been used in healthcare industry but the research that has to be done now is on the performance of the various classification techniques. So that amongst the all, the best on can be chosen. In this paper, we aim to consider the accuracy percentage, sensitivity percentage and specificity percentage to provide a result.

Keywords: accuracy, classification, data mining, healthcare industry, sensitivity, specificity.

[1] M. S. Sapna and D. A. Tamilarasi, "Fuzzy Relational Equation in Preventing Neuropathy Diabetic", Internati- onal Journal of Recent Trends in Engineering, Vol. 2, No. 4, 2009, p. 126.
[2] Jeatrakul and K. W. Wong, "Comparing the Perform-ance of Different Neural Networks for Binary Classifica-tion Problems," The 8th International Symposium on Na- tural Language Processing, Bangkok, 20-22 October 2009, pp. 111-115. doi:10.1109/SNLP.2009.5340935
[3] S. W. Purnami, A. Embong, J. M. Zain and S. P. Rahayu, "A New Smooth Support Vector Machine and Its Appli- cations in Diabetes Disease Diagnosis," Journal of Com-puter Science, Vol. 5, No. 12, pp. 1006-1011.
[4] R. Radha and S. P. Rajagopalan, "Fuzzy Logic Approach for Diagnosis of Diabetes," Information Technology Jour- nal, Vol. 6, No. 1, pp. 96-102. doi:10.3923/itj.2007.96.102
[5] UCI Machine Learning Repository. http://www.ics.uci.edu/mlearn/MLRepository.html
[6] P. Werbos, "Beyond Regression: New Tools for Predi- ction and Analysis in the Behavioral Sciences," Ph.D. Thesis, Harvard University, Cambridge, 1974
[7] G. H. John and P. Langley, "Estimating Continuous Dis-tributions in Bayesian Classifiers," Proceedings of the 11th Conference on Uncertainty in Artificial Intelligence, San Francisco, 1995, pp. 338-345.
[8] J. Quinlan, "C4.5: Programs for Machine Learning," Mo- rgan Kaufmann, San Mateo, 1993
[9] I.H. Witten and E. Frank, "Data Mining: Practical Ma- chine Learning Tools and Techniques," 2nd Edition, Mor- gan Kaufmann, San Francisco, 2005.

Abstract: Under The National Mission on Education through Information and Communication Technology (NMEICT) a lot of e-learning material, applications and other useful information have been made available on its web portal. MHRD has also taken initiatives for the dissemination of this knowledge by distributing Aakash Tablets to the students at free of cost. While all these developments have enabled students to access the contents and use some applications available on the NMEICT Sakshat web portal, there is still a dearth for real computing applications/ facilities i.e. those which can enable them to run C/ Java/Matlab programs on their mobile devices or Notebooks. So, the desideratum of thispaperis to scrutinize and develop the cloud based application for C program creation, compilation and execution on lean mobile computing devices. This application accredit various users to access the various education material on the web and also assist them to performcomputation on computing machines. Setup a server and deploy the application on the cloud to test it across the range of different devices. So,with the help of this android application user can do their work anytime and anywhere. In this paper implementation of SAAS is done by using java as an front end.

Keywords: SAAS, Cloud Computing, Android, Java, C Compiler

[1] David Chappell, a Short Introduction to Cloud Platform; An Enterprise - Oriented Overview (2008) [2] Introduction to Cloud Computing architecture White Paper 1st Edition, June 200 [3] Prime Minister's official residence: i-Japan strategy 2015, (in Japanese), [4] http://www.kantei.go.jp/jp/singi/it2/kongo/digital/dai9/9siryou2.pdf Ministry of Internal Affairs and Communications
[5] Windows Azure, http://msdn.microsoft.com/jajp/azure/cc994380.aspx
[6] Google Apps ,http://www.google.com/a/help/intl/ja/admins/customers.html
[7] Amazon Elastic Compute Cloud (Amazon EC2) http://aws.amazon.com/ec2/#pricing [8] M.V. Luis, R. M. Luis, C. Juan, L. Maik. [9] A Break in the Clouds: Towards a Cloud Definition. Computer Communication Review, vol.39, pp.50-55, 2009
[10] OL.Googlehttp://developer.android.com/guide/topics/fundamentals.html

Abstract: Color Image segmentation plays a crucial role in many medical imaging applications. In this paper we present a novel algorithm for computer aided molecular modeling in MATLAB R2012, a real time simulation environment. This manuscript presents a novel algorithm for color image segmentation of Haematoxilin and Eosin images of colon cancer. With help of this algorithm we can classify the differences between benign and malignant tumor cells.

Keywords: - Color segmentation, Hierarchical Clustering, Real Time Simulation Environment, Molecular Modeling.

[1] Venkata Subbaiah Kotakadi, Gaddam Susmila Aparna, Prashanth B. U. V. and Sai D. V. R. Gopal. Article: Computer Aided Molecular modeling approach of H & E (Haemotoxylin & Eosin) images of Colon Cancer. International Journal of Computer Applications 44(9):5-8, April 2012. Published by Foundation of Computer Science, New York, USA.

[2] Otsu Nobuyuki, "A Threshold Selection Method from gray level Histograms", vol. SMC-9, no.1, January 1979.

[3] G.Deng, L. W. Cahill and G.R. Tobin, "The Study of Logarithmic Image Processing Model and Its Application to Image Enhancement".

[4] Joongho Chang, Gunhee Han, Hose M. Valverde, Norman C. Grisworld, J. Francisco Duque-Carillo, Edgar anchez-Sinencio,( 1997) "Cork Quality Classification System using a Unified Image Processing and Fuzzy-Neural Network Methodology", vol. 8, no. 4, July. [5] Crabb D.P., Edgar D.F., Fitzke F.W., Mcnaught A.I. & Wynn H.P. (1995). New Approach to Estimating Variability in Visual-Field Data Using An Image-Processing Technique. British Journal of Ophthalmology 79:213-217.

[6] R.C Gonzalez and R.E Woods , " Digital Image Processing using MATLAB", Addison Wesley/ Pearson education,2nd Education, 2002.