iosr-JCE   Volume-17 ~ Issue-1 ~ Jan-Feb 2015

Abstract: Cancer research is rudimentary research which is done to identify causes and develop strategies for prevention, diagnosis, treatment and cure. An optimized solution for the better treatment of cancer and toxicity minimization on the cancer patient is performed by identifying the exact type of tumor. A clear cancer classification analysis system is required to get a clear picture on the insight of a problem. A systematic approach to analyze global gene expression is followed for identifying exact problem area. Molecular diagnostics provide a promising option of systematic human cancer classification. But these types of tests are not mostly applied because characteristics molecular markers have yet to be identified for most solid tumors. Recently, DNA micro-array based tumor gene expression profiles have been used for cancer diagnosis. In the proposed system, gene expressions are taken from multiple sources and an ontological store is created. Ant colony optimization technique is used to analyze the cluster of data with attribute match association rule for detecting cancer using the acquired knowledge.

Keywords: Gene expression, cancer cells, ontological store;

[1]. G.-M. Elizabeth and P. Giovani, "Clustering and classification for gene expression data analysis". Johns Hopkins Univ.,Dept. Of Biostatist Working Paper 70. [2]. E. Shay,(2003,Jan.). "Microarray cluster analysis and applications". Available: http://www.science.co.il/enuka/Essays/Microarray-Review.pdf.
[3]. D. Jiang, C. Tang, and A.Zhang, "Cluster analysis for gene expression data: A Survey", IEEE Trans. Knowl. Data Eng., vol.16, no.11, pp. 1370-1386, Nov.2004.
[4]. D.A. Roff and R. Preziosi, "The estimation of the genetic correlation: The use of the jack knife," Heredity, vol. 73, pp.544-548, 1994.
[5]. T. Scharl and F. Leisch, "Jack knife distances for clustering time course gene expression data," in proc. ASA biometrics, p.8, 2006.
[6]. N. Pasquier, C. Pasquier, L. Brisson, and M. Collard, "Mining gene expression data using domain knowledge," Int. J. Softw. Informat, vol. 2, pp. 215-231, 2008.

Abstract: Distributed Systems is the study of geographically distant processors, connected to one another through intermediate devices such as routers and/or switches. Simulation provides an insight into the behavior of these distant apart systems. The internal details like designing, configuring and maintaining a reliable distributed system through protocols that include routing, switching, etc is well understood by this paper. The paper describes how the design of a distributed system be built on a Network Designer while using leased line, serial ports and Ethernet technologies. Further, these designs have been simulated on a Network Simulator with the use of routing protocols. For this scheme to be implemented successfully different equipment like routers, switches, Ethernet and serial connection, PCs, etc are used and techniques like IP addressing schemes and RIP are implemented. Further, the developed system is decomposed to form a Fault tree and its reliability is evaluated with the help of Fault tree Analysis.

Keywords: Router, Switches, Ethernet, Serial, IP Address, Routing Information Protocol, Fault Tree Analysis.

[1]. Casavant T. L. & M. Singhal, ―Readings in Distributed Computing Systems,‖ Llos Alamitos, CA: IEEE Computer Society Press. 1994.
[2]. Loy D. , Dietrich D. & H. J. Schweinzer , ―Open Control Networks, Boston,‖ MA: Kluwer Academic Publishers, 2001.
[3]. N. Lopez-Benitez, Dependability Modeling and Analysis of Distributed Programs, IEEE Trans. Software Engineering, Vol. 20 No 5, pp. 345- 352, May 1994.
[4]. A. Kumar, S. Rai and D. P. Agrawal, On Computer Communication Network Reliability Under Program Execution Constraints, IEEE Trans. On selected areas in Communications, Vol. 6, No 8, pp. 1393-1400, October 1988.
[5]. V. K. P. Kumar, S. Hariri, and C. S. Raghavendra, Distributed Program Reliability Analysis, IEEE Trans. On Software Engineering, Vol. SE-12, No 1, pp. 42-50, Jan 1986.
[6]. M. S Lin and D. J. Chen, General Reduction Methods for the Reliability Analysis of Distributed Computing Systems, The Computer Journal, Vol. 36, No 7, 1993.
[7]. Min X., Yuan-shun Dai, & Kim-leng Poh, ―Computer System Reliability, Models and Analysis,‖ Kluwer Academic Publishers, New York, 2004.

Abstract: Opinion mining, a trending research area where customers feels that opinions of others are always important for making decisions while purchasing the products. Here the problem is to collect those opinions and preprocess them. Thus, the customer reviews have been collected and after that Ontology is constructed to structure the information available in unstructured text reviews and to exploit semantic relation between them. Then we extract the features of the product and their opinions further polarities (positive, negative or neutral) of different features of certain digital products are identified. Opinions of different features of productivity customer are classified and summarized by an enhanced opinion mining technique. The performance of the system is evaluated by metrics such as precisions, recall and F-measure. This information provided to users will be more helpful to make decisions before buying a product. Keywords: ontology,opinion mining,feature extraction,pos tagging,polarity identification

[1] EfstratiosKontopoulos,ChristosBerberidis,Theologos Dergiades, Nick Bassiliades, " Ontology-based sentiment analysis of twitter posts,"Expert Systems with Applications 40, Elsevier (2013) 4065–4074.

[2] Abd. Samad Hasan Basaria,Burairah Hussina,I. Gede Pramudya Anantaa, Junta Zeniarjab, " Opinion Mining of Movie Review using Hybrid Method of Support Vector Machine and Particle Swarm Optimization", Elsevier, Procedia Engineering 53 ( 2013 ) 453 – 462.

[3] Li Chen, Luole Qi, Feng Wang, " Comparison of feature-level learning methods for mining online consumer reviews", Elsevier, Expert Systems with Applications 39 (2012) 9588–9601.

Abstract: Policy based management is an administrative approach that simplify the management of a given endeavor by establishing policies to deal with situation that are likely to occur. Most of the social network and mobile application in today's world define a very flexible policy that are used by user, easily which allows hacker to intrude in such social network and access user's private information, hence there is a need of strong policy for a social network application. The proposed approach verifies and analyzes the existing similar application and arrives at new policies by collaborating with the previous one to enforce security to the application and modification can be done with key generated by admin on permission by member. A text mining methodology proves to be simpler and stronger as more information about the application is not leaked out, it requires prior permission provided by user to track application information, thus policy admin forms an effective rule based system.

Keywords: Intruder, Policy based management, social networks, text mining.

[1]. Weili Han, Zheran Fang, Laurence Tianruo Yang, Gang Pan, and Zhaohui Wu, "Collaborative Policy Administration" IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS, VOL. 25, NO. 2, FEBRUARY 2014
[2]. Supriya V. Pawar, L. J. Sankpal," Collaborative Policy Administration in Online Social Networks "International Journal on Recent and Innovation Trends in Computing and Communication, Volume: 2 Issue: 5
[3]. Jerome H. Saltzer, And Michael D.Schroeder," The Protection of Information in Computer Systems"
[4]. Vibha Verma, Mr. Avinash Dhole, "Analysis of Comparison Between Single Encryption( Advance Encryption Scheme(AES)) and Multicrypt Encryption Scheme
[5]. Gorrell P. Cheek, Mohamed Shehab "Policy-by-Example for Online Social Networks" SACMATO 12 JUNE 20-22, 2012 NEWARK, NEW JERSY,USA,ACM.
[6]. Kathy Wain Yee Au, Yi Fan Zhou, Zhen Huang and David Lie " PScout: Analyzing the Android Permission Specification " CCS‟12.

Abstract: This paper presents an efficient maze solving algorithm. IEEE has launched a competition named "Micro mouse" where an autonomous robot or mice solves an unknown maze. The mouse find its way from the starting position to the central area of the maze without any intervention. To solve the maze, the mice implements one of many different searching algorithms such as the DFS, flood fill, BFS, modified flood fill. Several algorithms which originate from graph theory (GT) and non-graph theory (NGT) are currently being used to program the robot or mice. To compare the algorithms efficiency, they are simulated artificially and a comprehensive study is done by interpreting the statistics of interest.

[1]. Sadik A. M. J, Farid H. M. A. B., Rashid T. U., Syed A., Dhali M.A. "Performance analysis of micro mouse algorithms," Proc. 25th International Technical Conference on Circuits/Systems, Computers And Communications (ITC-CSCC), Pattaya, Thailand on 4-7 July, 2010; PID 0242, pp. 544–547.
[2]. "Micro mouse 2010 Competition Rules." IEEE Region 2 Student Activities Conference 2010 Web Page. Web. 21 Nov. 2009.[http://www.temple.edu/students/ieee/SAC/com petitions.html.]I . S. Jacobs and C. P. Bean, "Fine particles, thin films and exchange anisotropy," in Magnetism, vol. III, G. T. Rado and H. Suhl, Eds. New York: Academic, 1963, pp. 271–350.
[3]. Manoj Sharma,"Algorithms for Micro-mouse", Proc. 2009 International Conference on Future Computer and Communication. DOI 10.1109/ICFCC.2009.38.

Abstract:This paper has discusses the relevance of a quality TVE training as a tool for self-reliance. It identified skill and knowledge as the engine room for economic growth and sustainable livelihood only if effort could be geared towards quality TVET with respect to the goals of technical and vocational education. This study was guided by four research questions. The instrument used for the study was Quality Technical, Vocational Education and Training: Questionnaire (QTVET). Four factors were identified for attaining quality TVET for the production of needed workforce that will be involved in harnessing the available resources for pre and final production of goods and services for human use. Through this, gainful occupation will bring about self-reliance. Therefore, the following recommendations were made base on the result and findings; that the federal and state government must prioritize adequate time planning and review of TVET curriculumand that qualified staff must be where are they to be employed base on merit and competence.

Keynotes: Quality, Technical, Vocational Education and Training, Self-Reliance, Sustainable and Livelihood.

[1]. Abu.D (2010).Quality assurance in TVET. Abu Dhabi Centre for Technical and Technical Education and Training.Retrieved 6th May, 2014.
[2]. Adebayo.O; Oyenike.A.andAdesoji.O.(n.d) TVET programmes in Nigeria- Academia.edu.www.academia.edu/53894/68/Towards-QU-Retrieved 15th June 2014.
[3]. Ayonmike.C.S et al (2013) Towards Quality Technical Vocational Education and Training (TVET) programmes in Nigeria: Challenges and Improvement Strategies.NVETA 2013Conference. Las Vegas, Nevada, Georgia U.S.A.
[4]. Bewaji.J.I (2013) A Depressing story on the Disconnect Between Nigeria Tertiary Institutions and the Reality of Life after Graduation. www.risenetworks.org. Retrieved 7th May, 2014.

Abstract: This paper is deals with Parallel Distributed system. Hadoop has become a central platform to store big data through its Hadoop Distributed File System (HDFS) as well as to run analytics on this stored big data using its MapReduce component. Map Reduce programming model have shown great value in processing huge amount of data. Map Reduce is a common framework for data-intensive distributed computing of batch jobs. Hadoop Distributed File System (HDFS) is a Java-based file system that provides scalable and reliable data storage that is designed to span large clusters of commodity servers. In all Hadoop implementations, the default FIFO scheduler is available where jobs are scheduled in FIFO order with support for other priority based schedulers also. During this paper, we are going to study a Hadoop framework, HDFS design and Map reduce Programming model. And also various schedulers possible with Hadoop and provided some behavior of the current scheduling schemes in Hadoop on a locally deployed cluster is described.

Keywords: Hadoop, Map-Reduce, Big Data Analytics, Scheduling Algorithms

[1]. Apache Hadoop. http://hadoop.apache.org
[2]. J. Dean and S. Ghemawat. Mapreduce: Simplified data processing on large clusters. OSDI '04, pages 137–150, 2004
[3]. Sanjay Ghemawat, Howard Gobioff, and Shun-Tak Leung.The Google file system. In 19th Symposium on Operating Systems Principles, pages 29–43, Lake George, New York, 2003.
[4]. Hadoop Distributed File System, http://hadoop.apache.org/hdfs
[5]. Hadoop's Fair Scheduler http://hadoop.apache.org/common/docs/r0.20.2/fair_schedu ler.html
[6]. Hadoop's Capacity Scheduler: http://hadoop.apache.org/core/docs/current/capacity_sched uler.html

Abstract: This paper discusses a propose cloud infrastructure that combines On-Demand allocation of resources with improved utilization, opportunistic provisioning of cycles from idle cloud nodes to other processes It provides fault tolerance while running on inexpensive commodity hardware, and it delivers high aggregate performance to a large number of clients.Because for cloud computing to avail all the demanded services to the cloud consumers is very difficult. It is a major issue to meet cloud consumer's requirements. Hence On-Demand cloud infrastructure using map reduce configuration with improved CPU utilization and storage utilization is proposed using Google File System by using Map-Reduce. Hence all cloud nodes which remains idle are all in use and also improvement in security challenges and achieves load balancing and fast processing of large data in less amount of time. Here we compare the FTP and GFS for file uploading and file downloading; and enhance the CPU utilization and storage utilization and fault tolerance,..

[1]. Sanjay Ghemawat, Howard Gobioff and Shun-Tak Leung, "The Google File System" ACM SIGOPS Operating Systems Review, Volume 37, Issue 5, December 2003..
[2]. Shah, M.A., et.al.,"Privacy-preserving audit and extraction of digital contents", Cryptology ePrint Archive, Report 2008/186 (2008).
[3]. Juels, A., Kaliski Jr., et al.,"proofs ofretrievability for large files",pp. 584–597. ACM Press, New York (2007).
[4]. Sean Quinlan, Kirk McKusick "GFS-Evolution and Fast-Forward" Communications"
[5]. of the ACM, Vol 53, March 2010.
[6]. Divyakant Agrawal et al., " Big Data and Cloud Computing: Current State and Future Opportunities" , EDBT, pp 22-24, March 2011.

Abstract: Software Reliability Growth Model (SRGM) is used to assess software reliability quantitatively for tracking and measuring the growth of reliability. The potentiality of SRGM is judged by its capability to fit the software failure data. In this paper we propose Burr type III software reliability growth model based on Non Homogeneous Poisson Process (NHPP) with time domain data. The Maximum Likelihood (ML) estimation method is used for finding unknown parameters in the model on ungrouped data. How good does a mathematical model fit to the data is also being calculated. To assess the performance of the considered SRGM, we have carried out the parameter estimation on real software failure data sets. We also present an analysis of goodness of fit and reliability for given failure data sets.

Keywords: Burr type III, Goodness of fit, NHPP, ML estimation, Software Reliability, Time domain data.

[1]. Musa J.D, Software Reliability Engineering MCGraw-Hill, 1998.
[2]. Lyu, M.R., (1996). "Handbook of Software Reliability Engineering", McGraw-Hill, New York.
[3]. Musa, J.D., Iannino, A., Okumoto, k., 1987. "Software Reliability: Measurement Prediction Application" McGraw -Hill, New
York.
[4]. Wood. A (1996), "Predicting Software Reliability", IEEE Computer, 2253-2264.

Abstract: Web itself contains a large amount of documents available in electronic form. The available documents are in various forms and the information in them is not in organized form. The lack of organization of materials in the WWW motivates people to automatically manage the huge amount of information. Text-mining refers generally to the process of extracting interesting and non-trivial information and knowledge from unstructured text. Text mining framework contains Information Retrieval, Information Extraction, Information Mining and Interpretation. During Information Retrieval, so many web documents are retrieved. In that how we can find out similar documents among retrieved? This paper deals with the challenging issues and similarity measures for web document clustering .

Key words: Text Mining, Information Retrieval,Framework,Information Extraction,Similarity,Clustering

[1]. Wael H. Gomaa , Aly A. Fahmy " Short Answer Grading Using String Similarity And Corpus-Based Similarity",International Journal of Advanced Computer Science and Applications, Vol. 3, No. 11, 2012
[2]. Wael H. Gomaa , Aly A. Fahmy , " A Survey of Text Similarity Approaches ", International Journal of Computer Applications (0975 – 8887) Volume 68– No.13, April 2013
[3]. Kalaivendhan.K, Sumathi.P , "An Efficient Clustering Method To Find Similarity Between The Documents " ,International Journal of Innovative Research in Computer and Communication Engineering, Vol.2, Special Issue 1, March 2014
[4]. Mark.Dixon(1997), "An overview of Document Mining Technology",http://www.geocities.com/Research Triangle /Thinktank1997/mark/writing/dix 97-dm.ps
[5]. Anna Huang, "Similarity measures for Text document" ,Proceedings of the New Zealand CS Research Student Conference , April 2008, New Zealand.

Abstract: In the modern era of mobile and cloud computing, people are becoming more and more dependent on digital devices. In order to execute any application, a certain amount of storage space is mandatory which is to be used by the application as its own warehouse to store its data.While designing any storage architecture, we have data as the centre of attraction around which whole of our application design revolves.Cloud storage is a hot topic nowadays as the data storage capacity rates are increasing manifold's every year and has thus become a reality that all data centers and organizations should consider. This huge amount of data, thus poses a challenge for the construction of a good well defined, fault prone back end storage.This paper representsthe different available architectures that are used in storage technology foundation. Beginning with a conceptual overview of the SNIA reference model for cloud storage,the key concepts of cloud and other technologies which form a base for cloud storage are discussed. Followed by this, the three standard architectures related to cloud storage are discussed which are basicallyStorage Area Network (SAN), Direct Attached Storage (DAS) and Network Attached Storage(NAS). The paper concludes by pinpointing the future research and open challenges related to cloud storage. Keywords: Mobile Computing, Storage architecture, Cloud Storage, SNIA.

[1]. EMC, and EMC Education Services. Information Storage and Management: Storing, Managing, and Protecting Digital Information. LibreDigital, 2010.
[2]. Armbrust, M., Fox, A., Griffith, R., Joseph, A. D., Katz, R., Konwinski, A., ...&Zaharia, M. (2010). A view of cloud computing. Communications of the ACM,53(4), 50-58.
[3]. Chunhua, ZHOU Ke WANG Hua LI. "Cloud Storage Technology and Its Application [J]." ZTE Communications 4 (2010): 013.
[4]. Rimal, B. P., Choi, E., &Lumb, I. (2009, August). A taxonomy and survey of cloud computing systems. In INC, IMS and IDC, 2009. NCM'09. Fifth International Joint Conference on (pp. 44-51). Ieee.
[5]. Wu, Jiyi, et al. "Recent Advances in Cloud Storage." Proceedings of the Third International Symposium on Computer Science and Computational Technology (ISCSCT'10). 2010.
[6]. Meyer, Dutch T., et al. "Fast and cautious evolution of cloud storage."Proceedings of the 2nd USENIX conference on Hot topics in storage and file systems. USENIX Association, 2010.

Abstract: At the present time, hundreds of millions of active users all around the world are using online social network, such as Facebook, Twitter, Tumblr and LinkedIn. This service turned out to be one of the most well-liked and accepted services on the Internet. With the quick development of information technology and networking, the users became able to share many things on the web such as pictures, videos, their daily activities, attended events and even their location. Nonetheless, the majorities of social networks have weak user to user authentication method, which is based on some basic information like displayed name, photo. These weaknesses make it effortless to misuse user's information and do identity cloning attack to form fake profile.

[1]. Nielsen, Social Networks and Blogs, 4th Most Popular Online Activity, Nielsen Online Report, 2009.
[2]. Boyd, D and Ellison, NB, Social Network Sites: Definition, History, and Scholarship, Journal of Computer-Mediated Communication, 13, 2 (2007).
[3]. Stolen Facebook Accounts for Sale, http://tinyurl.com/25cngas,2010.
[4]. Personal communication with the Manager of User Support and the Product Manager of the Core and Community Management teams in Tuenti, 2011.
[5]. Fake Accounts in Facebook - How to Counter it, http://tinyurl.com/5w6un9u, 2010.

Abstract: Internet Protocol version 6 (IPv6) is the latest version of the Internet Protocol (IP). IPv6 is intended to replace IPv4, which is still widely used, in order to deal with the problem of IPv4 address exhaustion. In addition to evaluating the performance of IPv6, it is important to consider the interoperability between IPv4 and IPv6 networks, in addition to the migration process from IPv4 to IPv6. One way for IPv4 users to access IPv6 users/hosts is by encapsulating IPv6 packets within IPv4, in effect using IPv4 as a link layer for IPv6. This is known as tunnelling. The aim of this paper is to compare and evaluate the performance of IPv4, IPv6 and tunnelling (6to4) using OPNET 17.5. A computer simulation shows the theoretical comparison in terms of delay, throughput and packet loss.

Keywords: Automatic tunnelling, Delay, IPv4, IPv6, Manual tunnelling, OPNET, Packet loss, Throughput

[1]. Ghaida A.Y ALgadi & Amin B. A Mustafa comparison Throughput performance comparison between IPv4 and using Op-net simulator, IOSR Journal of Engineering (IOSR. JEN) volume 4, issue 08, august 2014.
[2]. Nousyba Hasab Elrasoul Abu Algasim & Amin B. A Mustafa IPv4 To IPv6 migration International Journal of engineering and technology research (IJETR(. & Communications (IJCNC) Issue 11, Volume2 , November 2014 .
[3]. Ghaida A.Y ALgadi & Amin B. A Mustafa , Evaluation and Comparisons of Migration Techniques From IPv4 To IPv6 Using GNS3 Simulator, IOSR Journal of Engineering (IOSR. JEN) volume 4, issue 08, august 2014 .
[4]. Nousyba Hasab Elrasoul Abu Algasim & Amin B. A Mustafa MPLS Vs IP routing and its Impact on QoS parameters , International Journal of engineering and technology research (IJETR(. & Communications (IJCNC) Issue 11, Volume2 , November 2014
[5]. Mutasim abdel Gaffar Mohamer & Amin B. A Mustafa performance Analysis of Mobile IPv6 Based on OPNET model , International Journal of Advanced research (IJAR ) Issue 11, Volume2 , November 2014 .

Abstract: The Internet Protocol Version 6 (IPv6) has gained popularity with companies, organizations and Internet service providers (ISPs) due to its enhancements over IPv4 such as (IPv6 128bit compared to of ipv4(32 bit). When migrating from IPv4 to IPv6, one should be careful about interruptions of service. In this paper, three mechanisms that can be employed to provide a smooth migration process. Results are verified with the Optimized Network Engineering Tool (OPNET) version 17.5 network simulation tool.

Keywords: Dual Stack, Tunnelling, NAT-PT OPNET 17.5 modeller delay , packet loss, throughput

[1]. Ioan Raicu, Sherali Zeadally. "Evaluating IPv4 to IPv6 Transition Mechanisms" , IEEE International Conference on Telecommunications 2003, ICT'2003, Volume 2, Feb 2003, pp 1091 - 1098.
[2]. J. L. Shah, J. Parvez. "Performance Evaluation of Applications inManual 6in4 Tunneling and Native IPv6/IPv4 Environments" , 2014 International Conference on Control, Instrumentation, Communication and Computational Technologies (ICCICCT), pp 782 - 786.
[3]. Ghaida A.Y ALgadi & Amin B. A Mustafa comparison Throughput performance comparison between IPv4 and using Op-net simulator, IOSR Journal of Engineering (IOSR. JEN) volume 4, issue 08, august 2014.
[4]. Nousyba Hasab Elrasoul Abu Algasim & Amin B. A Mustafa IPv4 To IPv6 migration International Journal of engineering and technology research (IJETR).& Communications (IJCNC) Issue 11, Volume2 , November 2014 .
[5]. Nousyba Hasab Elrasoul Abu Algasim & Amin B. A Mustafa MPLS Vs IP routing and its Impact on QoS parameters , International Journal of engineering and technology research (IJETR(. & Communications (IJCNC) Issue 11, Volume2 , November 2014 .

Abstract: In today's world most of us depend on Social Media to communicate, express our feelings and share information with our friends. Social Media is the medium where now a day's people feel free to express their emotions. Social Media collects the data in structured and unstructured, formal and informal data as users do not care about the spellings and accurate grammatical construction of a sentence while communicating with each other using different social networking websites ( Facebook, Twitter, LinkedIn and YouTube). Gathered data contains sentiments and opinion of users which will be processed using data mining techniques and analyzed for achieving the meaningful information from it. Using Social media data we can classify the type of users by analysis of their posted data on the social web sites. Machine learning algorithms are used for text classification which will extract meaningful data from these websites. Here, in this paper we will discuss the different types of classifiers and their advantages and disadvantages.

Keywords: Social Media Data, text classification, sentiment analysis, machine learning, classifiers

[1]. K. Nirmala, S. Satheesh kumar and Dr. J. Vellingiri "A Survey on Text categorization in Online Social Networks" International Journal of Emerging Technology and Advanced Engineering Volume 3, Issue 9, September 2013.
[2]. Vandana Korde, C Namrata Mahender "TEXT CLASSIFICATION AND CLASSIFIERS: A SURVEY" International Journal of Artificial Intelligence & Applications (IJAIA), Vol.3, No.2, March 2012.
[3]. Rizwana Irfan, Christine K. King, Daniel Grages, Sam Ewen, Samee U. Khan, Sajjada. Madani, Joanna Kolodziej, Lizhe Wang, Dan Chen, Amma R Rayes, Nikolaos Tziritas, Cheng - Zhong Xu, Albert Y. Zomaya, Ahmed Saeed Alzahrani, And Hongxiang Li "A Survey on Text Mining in Social Networks, " The Knowledge Engineering Review, United Kingdom, (2004) pp.1-24.
[4]. Susan Dumais John Platt David Heckerman, "Inductive Learning Algorithms and Representations for Text Categorization", Published by ACM, 1998.
[5]. Michael Pazzani, Daniel Billsus "Learning and Revising User Profiles: The Identification of Interesting Web Sites", Machine Learning, pp. 313–331, 1997.