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Which type of Expert System – Rule Base, Fuzzy or Neural is Most Suited for Evaluating Motivational Strategies on Human Resources :- An Analytical Case Study.
Viral Nagori, Bhushan Trivedi
Pages - 249 - 254     |    Revised - 15-09-2012     |    Published - 25-10-2012
Volume - 3   Issue - 5    |    Publication Date - October 2012  Table of Contents
Expert System, Neural Network, Motivational Strategies
The scope of expert systems in different areas and different domains are increasing. We are working on development of the expert system for evaluating motivational strategy on human resources. From the literature review, we found that mainly there are three approaches used for development of the expert system: Rule base, Fuzzy and Neural network. In the first half of the case study, we explored the pros and cons of each approach and provided the comparison of applicability of which approach is most suited and when. In the second half of the case study, we explored the feasibility of the approach for our domain area of motivational strategy on human resources. At the end, we found that Neural Network approach is the most suited for our domain because of the flexibility, adaptability to the changing environment and generalisation.
CITED BY (4)  
1 Abidin, S. A. Z., & Suhaimi, N. S. (2019). Decision Support System for Diagnosing Mobile Phone Failure using Rule-Based Technique.
2 Nagori, V., & Trivedi, B. (2016, September). Feasibility assessment of neural network based expert system prototype for evaluating motivational strategies. In 2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI) (pp. 1852-1857). IEEE.
3 Nagori, V. (2016, August). Techno-innovative solution in the form of neural expert system to address the problem of high attrition rate. In Proceedings of the International Conference on Advances in Information Communication Technology & Computing (pp. 1-4).
4 Hawi, R., Okeyo, G., & Kimwele, M. (2015). Techniques for Smart Traffic Control: An In-depth. International Journal of Computer Applications Technology and Research, 4(7), 566-573.
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Associate Professor Viral Nagori
GLS institute of Computer technology - India
Dr. Bhushan Trivedi
GLS institute of Computer technology - India

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