Volume-3 , Issue-2 , Apr 2015, ISSN 2320-7639 (Online) Go Back

• Open Access   Article

  Evaluation of Stemming and Stop Word Techniques on Text Classification Problem

  Dharmendra Sharma and Suresh Jain

  Research Paper | Isroset-Conference (IJSRCSE)

  Vol.3 , Issue.2 , pp.1-4, Mar-2015

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  Abstract
  Now-a-days a huge amount of information is available over the internet in electronic format. This large amount of data can be analyzed to maximize the benefits, for intelligent decision making. Text categorization is an important and extensively studied problem in machine learning. The basic phases in text categorization include preprocessing features, extracting relevant features against the features in a database, and finally categorizing a set of documents into predefined categories. Most of the researches in text categorization are focusing more on the development of algorithms for optimization of preprocessing technique for text categorization. In this paper we are summarizing the impact of stop word and stemming onto feature selection.

  Key-Words / Index Term
  Machine Learning, Stemming, Feature Selection

  References
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  [3] T. G. Kolda, D. P. O'Leary, "A semidiscrete matrix decomposition for latent semantic indexing information retrieval", Journal ACM Transactions on Information Systems (TOIS) TOIS Homepage archive vol.16(4), pp. 322-346, Oct. 1998.
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  [6] Xue, X. and Zhou, Z. (2009) Distributional Features for Text Categorization, IEEE Transactions on Knowledge and Data Engineering,Vol. 21, No. 3, Pp. 428-442.
  
  [7] Porter, M. (1980) An algorithm for suffix stripping, Program, Vol. 14, No. 3, Pp. 130–137.
  [8] Karbasi, S. and Boughanem, M. (2006) Document length normalization using effective level of term frequency in large collections, Advances in Information Retrieval, Lecture Notes in Computer Science, Springer Berlin / Heidelberg, Vol. 3936/2006, Pp.72-83.
  [9] Diao, Q. and Diao, H. (2000) Three Term Weighting and Classification Algorithms in Text Automatic Classification, The Fourth International Conference on High-Performance Computing in theAsia-Pacific Region,Vol. 2, P.629.
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  [11]Sharma Dharmendra, jain suresh, “Content sharing in information storage and retrieval system using tree representation of documents”,IEEE ,International conference on IT industry, business and government,CSIBIG2014 page 1-4,2014
  

  Citation
  Dharmendra Sharma and Suresh Jain, "Evaluation of Stemming and Stop Word Techniques on Text Classification Problem," International Journal of Scientific Research in Computer Science and Engineering, Vol.3, Issue.2, pp.1-4, 2015

• Open Access   Article

  Relevance Multipath Forwarding in Intended Wireless Mesh Networks

  Neeraj Paliwal

  Research Paper | Isroset-Conference (IJSRCSE)

  Vol.3 , Issue.2 , pp.5-10, Mar-2015

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  The concept of Wireless Mesh Networks (WMNs) is getting huge response in the last years as a cutting in the cost as an alternative to traditional wired access networks. In the framework of WMNs assets are basically insufficient, which have lead to the scheme of dynamic routing in order to completely utilize the network facility. We fall out as a substitute in favor of extrication routing from forwarding. In this proposal a uniform load-balancing scheme is proposed in which it forwards incoming packets along with several already established paths in order to reduce a definite congestion function. In this scheme we consider a picky but very typical scenario where, a deliberate WMN where all links do not get in the way (interfere) with each other. We use an easy and adaptable congestion function, in that function the sum of the average queue length in excess of all network nodes interfaces. This scheme presents a method to learn this function through measurements and numerous simulations to demonstrate the structure, while match up to our scheme with IEEE 802.11s standard.

  Key-Words / Index Term
  Wireless Mesh Networks, Traffic Engineering, Load-Balancing

  References
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  Citation
  Neeraj Paliwal , "Relevance Multipath Forwarding in Intended Wireless Mesh Networks," International Journal of Scientific Research in Computer Science and Engineering, Vol.3, Issue.2, pp.5-10, 2015

• Open Access   Article

  Recent Methodologies for Improving and Evaluating Academic Performance

  Ajay Varma and Y. S. Chouhan

  Review Paper | Isroset-Conference (IJSRCSE)

  Vol.3 , Issue.2 , pp.10-16, Mar-2015

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  In the real world, predicting the performance of the students is a challenging task. Many of the well known technical colleges are successful as they have meritorious students and faculty with them and a foolproof system working for them to grow continuously. The primary goal of Data mining in practice tends to be Prediction and Description. For educational institutions, the success of creation of human capital is the subject of a continuous analysis. To date, higher educational organizations are placed in a very high competitive environment and to remain competitive, organizations need better assessment, evaluation, planning, and decision making. As such, classification modeling for academic performance for the graduates could provide some insight to the university in order to take necessary information for improving the students’ academic performance. Hence, the aim of this study is to provide the review of different data mining techniques that have been used in educational field with regard to evaluation of students’ academic performance. Academic Data Mining used many techniques such as Decision Trees, Neural Networks, Naïve Bayes, K- Nearest neighbor, and many others. Using these techniques many kinds of knowledge can be discovered such as association rules, classifications and clustering. The discovered knowledge can be used for prediction and analysis purposes of student patterns.

  Key-Words / Index Term
  Classification, Data Mining, Bayesian Network, Neural Network

  References
  [1]. Al-Radaideh, Q., Al-Shawakfa, E. and Al-Najjar, M. (2006) ‘MiningStudent Data Using Decision Trees’, The 2006 International Arab Conference on Information Technology (ACIT'2006) – Conference Proceedings.
  [2]. Ayesha, S. , Mustafa, T. , Sattar, A. and Khan, I. (2010) ‘Data Mining Model for Higher Education System’, European Journal of Scientific Research, vol. 43, no. 1, pp. 24-29.
  [3]. Baradwaj, B. and Pal, S. (2011) ‘Mining Educational Data to Analyze Student s’ Performance’, International Journal of Advanced Computer Science and Applications, vol. 2, no. 6, pp. 63-69.
  [4]. Chandra, E. and Nandhini, K. (2010) ‘Knowledge Mining from Student Data’, European Journal of Scientific Research, vol. 47, no. 1, pp. 156-163.
  [5]. El-Halees, A. (2008) ‘Mining Students Data to Analyze Learning Behavior: A Case Study’, The 2008 international Arab Conference of Information Technology (ACIT2008) – Conference Proceedings, University of Sfax, Tunisia, Dec 15- 18.
  [6]. Romero, C. and Ventura, S. (2007) ‘Educational data Mining: A Survey from 1995 to 2005’, Expert Systems with Applications (33), pp. 135-146.
  [7]. Shannaq, B. , Rafael, Y. and Alexandro, V. (2010) ‘Student Relationship in Higher Education Using Data Mining Techniques’, Global Journal of Computer Science and Technology, vol. 10, no. 11, pp. 54-59.
  [8]. S. T. Hijazi, and R. S. M. M. Naqvi, “Factors affecting student_sperformance: A Case of Private Colleges”, Bangladesh e-Journal ofSociology, Vol. 3, No. 1, 2006.
  [9]. Acid, S., Campos, L. M. D. &Castellano, J. G. (2005). Learning Bayesian Network classifiers:searching in a space of partially directed acyclic graphs, Machine Learning 59, 213–235.
  [10]. Bharadwaj, B. K. & Pal, S. (2011). Data mining: a prediction for performance improvement using classification. International Journal of Computer Science and Information Security (IJCSIS), 9(4), 136-140.
  [11]. Bezdek, J.C. (2011). Pattern Recognition with Fuzzy Objective Function Algorithms.Plenum Press, 2(3), 196-202.
  [12]. Bresfelean, V.P.; , "Analysis and Predictions on Students' Behavior Using Decision Trees in Weka Envi-ronment," Information Technology Interfaces, 2007. ITI 2007. 29th International Conference on , pp.51-56.
  [13]. Cheng, I.; Rodriguez, S.; Basu, A.; , "Multimedia and games incorporating student modeling for education," Technology for Education, 2009. T4E '09. International Workshop on , pp.91-94.
  [14]. Chenn-Jung Huang; Ming-Chou Liu; San-Shine Chu; Chin-Lun Cheng; , "Application of machine learning techniques to Web-based intelligent learning diagnosis system," Hybrid Intelligent Systems, 2004. HIS '04. Fourth International Conference on , pp. 242- 247.
  [15]. Dunn, J.C.(2010). A fuzzy relative of the ISODATA process and its use in detectingcompact well-separated clusters.Journal of Cybernetics. 3, 32–57.
  [16]. Colace, F.; De Santo, M.; , "Ontology for E-Learning: A Bayesian Approach," Education, IEEE Trans-actions on , vol.53, no.2, pp.223-233.
  [17]. Eschrich, S., Ke, Jingwei, Hall, L.O., &Goldgof, D.B. (2003).Fast accurate fuzzy clustering through data reduction.IEEE Transaction on Fuzzy Systems.11 (2), 262–270.
  [18]. Dunn, J.C.(2010). A fuzzy relative of the ISODATA process and its use in detectingcompact well-separated clusters.Journal of Cybernetics. 3, 32–57.
  [19]. Sierra, B., Serrano, N., Larrañaga, P., Plasencia, E. J., Inza, I., Jiménez, J. J., Revuelta, P. &Mora, M. L. (2001).Using Bayesian Networks in the construction of a bi-level multi- classifier, Arti ficial Intelligence in Medicine 22, 233–248.
  

  Citation
  Ajay Varma and Y. S. Chouhan, "Recent Methodologies for Improving and Evaluating Academic Performance," International Journal of Scientific Research in Computer Science and Engineering, Vol.3, Issue.2, pp.10-16, 2015

• Open Access   Article

  E-Ticketing Through E-Wallet Scheme

  Amit Kumar Nilosey and Rajesh Kharche

  Technical Paper | Isroset-Conference (IJSRCSE)

  Vol.3 , Issue.2 , pp.17-20, Mar-2015

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  Abstract
  Through this paper we are evaluating e-ticket system and specially discussing e-wallet scheme, hassle- free ticket booking which was launched by Indian Railway Catering and Tourism Corporation . E-wallet is a user friendly scheme aimed at making purchase of e-ticket easier. The e-wallet is a rolling deposit scheme wherein the customers will have an account with the IRCTC and deposit money to be used in future for booking e-tickets from its website (www.irctc.co.in).

  Key-Words / Index Term
  E-Ticketing, IRCTC, E-Wallet

  References
  [1]. http://www.thefreedictionary.com/e-ticket
  [2]. http://archive.indianexpress.com/news/save-time-use-e-ticketing-system/435501/
  [3]. http://en.wikipedia.org/wiki/Indian_Railway_Catering_and_Tourism_Corporation
  [4]. http://www.selfgrowth.com/articles/an-introduction-to-irctc-indian-railway-catering-and-tourism-corporation
  [5]. http://www.irctc.com/Company_Profile.html
  [6]. http://news.biharprabha.com/2013/08/irctc-website-gets-4x-faster-books-7200-tickets-per-minute/
  [7]. http://www.infoqueenbee.com/2014/03/will-irctcs-e-wallet-scheme-for-online.html
  [8]. http://edpl.in/index.php/page/view_page/15-Rolling-Deposit-Scheme---RDS
  [9]. L. Z. Wang, “Railway E-Ticketing System Introduction,” Modern Communication, No. 9, 2006, pp. 11-12.
  

  Citation
  Amit Kumar Nilosey and Rajesh Kharche, "E-Ticketing Through E-Wallet Scheme," International Journal of Scientific Research in Computer Science and Engineering, Vol.3, Issue.2, pp.17-20, 2015

• Open Access   Article

  Web Mining Using Cloud Computing Technology

  Rajesh Shah and Suresh Jain

  Review Paper | Isroset-Conference (IJSRCSE)

  Vol.3 , Issue.2 , pp.21-25, Mar-2015

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  Abstract
  In this paper we present the web mining using Cloud Computing Technology. Web mining includes how to extract the useful information from the web and gain knowledge using data mining techniques. Here so many resources and techniques are available i.e. web content mining, web structure mining, web usage mining and access through the web servers. Web mining techniques (specially web usage mining techniques) and applications are much needed in cloud computing. The implementation of these techniques through cloud computing will allow users to retrieve relevant and meaningful data from virtually integrated data warehouse which reduces cost and infrastructure.

  Key-Words / Index Term
  Data mining, Web Mining, Data Warehouse, Knowledge Discovery, Cloud Mining, Web Content Mining, Web Structure Mining, Web Usage Mining

  References
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  [4] Yongjian Fu et al , ”A Framework for Personal Web Usage Mining”.
  [5] Rajni Pamnani, Pramila Chawan Department of computer technology, VJTI University, Mumbai “Web Usage Mining:
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  page 12.
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  Citation
  Rajesh Shah and Suresh Jain, "Web Mining Using Cloud Computing Technology," International Journal of Scientific Research in Computer Science and Engineering, Vol.3, Issue.2, pp.21-25, 2015