IDEAS home Printed from https://ideas.repec.org/a/aml/intbrm/v3y2012i5p249-254.html
   My bibliography  Save this article

Which type of Expert System – Rule Base, Fuzzy or Neural is Most Suited for Evaluating Motivational Strategies on Human Resources :- An Analytical Case Study

Author

Listed:
  • Viral Nagori

    (GLS institute of Computer technology, India)

  • Bhushan Trivedi

    (GLS institute of Computer technology, India)

Abstract

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.

Suggested Citation

  • Viral Nagori & Bhushan Trivedi, 2012. "Which type of Expert System – Rule Base, Fuzzy or Neural is Most Suited for Evaluating Motivational Strategies on Human Resources :- An Analytical Case Study," International Journal of Business Research and Management (IJBRM), Computer Science Journals (CSC Journals), vol. 3(5), pages 249-254, October.
  • Handle: RePEc:aml:intbrm:v:3:y:2012:i:5:p:249-254
    as

    Download full text from publisher

    File URL: https://www.cscjournals.org/manuscript/Journals/IJBRM/Volume3/Issue5/IJBRM-113.pdf
    Download Restriction: no

    File URL: https://www.cscjournals.org/library/manuscriptinfo.php?mc=IJBRM-113
    Download Restriction: no
    ---><---

    More about this item

    Keywords

    Expert System; Neural Network; Motivational Strategies;
    All these keywords.

    JEL classification:

    • M0 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - General

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:aml:intbrm:v:3:y:2012:i:5:p:249-254. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Nabeel Tahir (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.