Implications of System Identification Techniques on ANFIS E-learners Activities Models - A Comparative Study

  • Rafiu Mope Isiaka Department of Computer Science, College of Information and Communication Technology, Kwara State University, Malete, Ilorin, Nigeria
  • Elijah Olusayo Omidiora Department of Computer Science and Engineering, Ladoke Akintola University of Technology, Ogbomoso, Nigeria
  • Olabiyisi O. Olabiyisi Department of Computer Science and Engineering, Ladoke Akintola University of Technology, Ogbomoso, Nigeria
  • Oladotun O. Okediran Department of Computer Science and Engineering, Ladoke Akintola University of Technology, Ogbomoso, Nigeria
  • Ronke Seyi Babatunde Department of Computer Science, College of Information and Communication Technology, Kwara State University, Malete, Ilorin, Nigeria
Keywords: Neuro Fuzzy Model, E-learners Activities, System Identification Technique, Dataset Normalization

Abstract

Efficient e-learners activities model is essential for real time identifications and adaptive responses. Determining the most effective Neuro- Fuzzy model amidst plethora of techniques for structure and parameter identifications is a challenge.  This paper illustrates the implication of system identification techniques on the performance of Adaptive Network based Fuzzy Inference System (ANFIS) E-learners Activities models. Expert knowledge and Historical data were used to formulate the system and their performances were compared. Similarly, comparison was made between membership functions. The models were simulated in MATLAB editor. The efficiency of the model was determined using both classification uncertainty metrics and confusion matrix–based metrics. The classification uncertainty metrics considered are Mean Absolute Error (MAE) and Root Mean-Squared Error (RMSE). The confusion matrix-based metrics used are Accuracy, Precision and Recall. It was discovered that the model based on Experts Knowledge outperform those based on Historical Data. The performances of the Membershp Functions at the identification of the Historical data respectively are Sigmoid, Gaussian, Triangular and G-Bells.

Author Biographies

Rafiu Mope Isiaka, Department of Computer Science, College of Information and Communication Technology, Kwara State University, Malete, Ilorin, Nigeria
Lecturer I
Elijah Olusayo Omidiora, Department of Computer Science and Engineering, Ladoke Akintola University of Technology, Ogbomoso, Nigeria
Professor
Olabiyisi O. Olabiyisi, Department of Computer Science and Engineering, Ladoke Akintola University of Technology, Ogbomoso, Nigeria

Professor


Oladotun O. Okediran, Department of Computer Science and Engineering, Ladoke Akintola University of Technology, Ogbomoso, Nigeria
Senior Lecturer
Ronke Seyi Babatunde, Department of Computer Science, College of Information and Communication Technology, Kwara State University, Malete, Ilorin, Nigeria
Lecturer II

References

(1) Ahmed, A., Jun, S., Rami, A., and Jun, Y., Modeling and simulation of an adaptive neuro-fuzzy inference system (ANFIS) for mobile learning. IEEE Transactions and Learning Technologies, 2012. 5(3): p. 226 – 237.

(2) Babuska, R., and Verbruggen, H.B., Fuzzy set methods for local modeling and identification. In Multi model Approaches to Modeling and Control, Yaylor & Francis. 1997. P. 75 – 100.

(3) Bodyanskiy Y., and Dolotov A., Methods and Instruments of Artificial Intelligence. Rzeszow-Sofia, Bulgaria: ITHE, 2010. p. 17–24.

(4) Ebru, A., and Parvinder, S.S., A soft computing approach for modeling of severity of faults in software systems. International Journal of Physical Sciences, 2010. 5(2): p. 74-85.

(5) Hoogendoorn, R.G., Van Arem, B., and Hoogendoorn, S.P., A neeurofuzzy apprach to modeling longitudinal driving behaviour and driving task complexity. International Journal of Vehicular Technology. 2013. P. 1-12.

(6) Isiaka, R.M., Omidiora, E.O, Olabiyisi, S.O, and Okediran, O.O., Mamdani fuzzy model for learning activities evaluation. International Journal of Applied Information Systems (IJAIS). Foundation of Computer Science FCS. 2014. New York, USA. 7(3): p. 1-8

(7) Jang, J.R., Sun , C.T., and Mizutani, E., Neuro-fuzzy and soft computing: A computational approach to learning and machine intelligence. 1997. USA: Prentice-Hall,Inc.

(8) Jones, M.T., Artificial intelligence: A systems approach. Infinity Science Press LLC, 2008. New Delhi

(9) Lan, T.H., Lo, E.W., Wu, M.S., Hu, T.M., Chou, P., Lan, T.Y., and Chiu, H.J., Performance of a neuro-fuzzy model in predicting weight changes of chronic schizophrenic patients exposed to antipsychotics.Molecular Psychiatry, 2008. 13: p. 1129-1137.

(10) Nikam, S.R., Nikumbh P.J., and Kulkarni, S.P., Fuzzy logic and neuro-Fuzzy modelling. Recent Trends in Computing. International Journal of Computer Applications (IJCA), 2012. 22: p. 22-31.

(11) Omidiora, E.O, Olabiyisi, S.O, Okediran, O.O., and Isiaka, R.M., Learner activities evaluation model: A neuro-fuzzy approach. International Journal of e-Education, e-Business, e-Management and e-Learning. 2013. 3(5): p. 421-424.

(12) Sevarac, Z., Neuro fuzzy reasoner for student modeling. Advanced Learning Technologies, 2006. IEEE Sixth International Conference. P. 740-744.

(13) Sun, Z., and Finnie, G., Experience management in knowledge management. Information Technology Papers. 2005. Paper 103. Retrieved on 23/04/2010 from http://epublications.bond.edu.au/ infotech_pubs/103.

(14) Yuanyuam, C., Limin, J., and Zundong, Z., Mamdani model based adaptive neuro fuzzy inference system and its application. World Academy of Science, Engineering and Technology. 2009. 3: p. 743- 750.

(15) Zadeh, L.A., Outline of a new approach to the analysis of complex systems and decision processes. IEEE Trans. on systems, Man and Cybernetics, 1999. 3 (1): p. 28-44.

Published
2016-03-03
How to Cite
Isiaka, R. M., Omidiora, E. O., Olabiyisi, O. O., Okediran, O. O., & Babatunde, R. S. (2016). Implications of System Identification Techniques on ANFIS E-learners Activities Models - A Comparative Study. Transactions on Machine Learning and Artificial Intelligence, 4(1), 15. https://doi.org/10.14738/tmlai.41.1799