Abstract

This Research paper focused on Classification accuracy based on Users' Preferences from the Web Learning System. This comparative study considers various classification algorithms like j48, Random Tree, Random Forest, CART and Naive Bayes in the Web Learning System. It also focuses on Artificial Neural Network (ANN) algorithms. The classification accuracy is identified by user's requirements based on the cognitive input. In this research Neural Network approach like MLP, PMLP, GO PMLP and PSO PMLP algorithms are proposed and validated. These algorithms classify the user preferences of the Web Learning System. As the User Preferences have many potential applications, mining on the User Preferences of the Web Learning System users was contemplated. Based on the response of the current users, a decision tree induction algorithm is used to predict the requirements of future users.

References

• Marian Simko, Michal Barla, Maria Bielikova, "ALEF: A Framework for Adaptive Web-based Learning 2. 0'', International Scientific Conferences on The World Computer Congress, pp. 1-12, 2010.
• Joseph A. Konstan Ricardo Conejo José L. Marzo Nuria Oliver (Eds. ) "User Modeling, Adaption, and Personalization 19th International Conference", Springer , UMAP 2011 Girona, Spain, July 11-15, 2011
• Shaharuddin Md Salleh, Zaidatun Tasir, Nurbiha A Shukor "Web-Based Simulation Learning Framework to Enhance Students' Critical Thinking Skills", International Educational Technology Conference on Procedia - Social and Behavioral Sciences, Volume 64, pp. 372-381, 2012.
• Mustafa, K. O. C. " Individual learner differences in web-based learning environments: From cognitive, affective and social cultural perspectives", Turkish Online Journal of Distance Education-TOJDE, Volume 6, Issue 4, pp. 1257-1261, 2005.
• Abdullah H. Wahbeh, Qasem A. Al-Radaideh, Mohammed N. Al-Kabi, Emad M. Al-Shawakfa, "A Comparison Study betweenmData Mining Tools over some Classification Methods" International Journal of Advanced Computer Science and Applications, Special Issue on Artificial Intelligence, pp. 18-26, 2011.
• Hong jun Lu, Rudy Setiono, Huan Liu, "Effective Data Mining Using Neural Networks", IEEE transactions on knowledge and data engineering, Volume 8, Number 6, pp. 957-961, 1996.
• G. Saravana kumar, P. K. Kalra, "Optimization by Neural Networks", Quarterly Scientific Magazine of IIT Kanpur, Volume 6, Number 3, pp. 87-91, 2004.
• D. Montana, "Introduction to the Special Issue: Evolutionary Algorithms for Scheduling", Evolutionary Computation, Volume 6, Number 1, 1998.
• Cristobal Romero, Sebastian Ventura , Carlos De Castro , Wendy Hall , Muan Hong Ng , Hong Ng "Using Genetic Algorithms for Data Mining in Web based Educational Hypermedia Systems", Proceedings of AH2002 workshop Adaptive Systems for Web based education, pp. 137-142, 2002.
• Qinghai Bai, "Analysis of Particle Swarm Optimization Algorithm", Computer Science and Informatics, Volume 3, Issue 1, pp 180-184, 2010.
• Maria-Iuliana DASCALU, "Application of Particle Swarm Optimization to Formative E-Assessment in Project Management", Informatica Economica, volume 15, Number 1, pp 48-59, 2011.
• Mona Alkhattabi, Daniel Neagu, Andrea Cullen, "Information Quality Framework for e-Learning Systems", Knowledge Management & E-Learning: An International Journal, Volume 2, Number 4, pp. 340-362, 2013.
• Biplab Kanti Das, Saurabh Pal, "A framework of Intelligent Tutorial System to incorporate adaptive learning and assess the relative performance of adaptive learning system over general classroom learning", International Journal of Multimedia and Ubiquitous Engineering, Volume 6, Number 1, pp. 43-54, 2011.
• Gwo-Jen Hwang, Peng-Yeng Yin, Tzu-Ting Wang, Judy C. R. Tseng, Gwo-Haur Hwang "An enhanced genetic approach to optimizing auto-reply accuracy of an e-learning system" Computers and Education, Volume 51, pp. 337-353, 2008.
• Kuok King Kuok, Sobri Harun, Siti Mariyam Shamsuddin, Po-Chan Chiu, "Evaluation of Daily Rainfall-Runoff Model Using Multilayer Perceptron and Particle Swarm Optimization Feed forward Neural Networks", Journal of Environmental Hydrology, Volume 18, pp. 1-16, 2010.
• Neeraj Bhargava, Atif Aziz, Rajiv Arya "Selection Criteria for Data Mining Software: A Study", International Journal of Computer Science Issues, Vol. 10, Issue 3, No 2, May 2013, ISSN (Print): 1694-0814 | ISSN (Online): 1694-0784
• L. Jayasimman, "A Framework to Enhance Classification Accuracy for Web Learning System", International Journal of Computer Science and Telecommunications, UK, Volume 5, Issue 11, November 2014, ISSN 2047-3338.
• L. Jayasimman, E. George Dharma Prakash Raj, "A Soft Computing Approach for User Preference in Web Based Learning", International Journal of Computer Applications,. Volume 61– No. 21, January 2013.