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A Multi-Stage Fuzzy Model for Assessing Applicants for Faculty Positions in Universities

Author

Listed:
  • Raghda Hraiz

    (PSUT, Amman, Jordan)

  • Mariam Khader

    (SUT, Amman, Jordan)

  • Adnan Shaout

    (The University of Michigan, Dearborn, USA)

Abstract

Assessing applicants for faculty positions in universities involves many issues. Each issue may involve a judgment based on uncertain or imprecise data. The uncertainty in data may exist in the interpretation made by the evaluator. This issue might lead to improper decision making. Modeling such a system using fuzzy logic will provide a more efficient model for handling imprecision. This article presents a fuzzy system for modeling the assessment of applicants for employment at academic universities. This system will utilize a multi-stage fuzzy model for measuring and evaluating the applicants. Utilizing fuzzy logic for applicants' evaluation will help administrators in choosing the best candidates for faculty positions. The fuzzy system was developed using jFuzzyLogic Java library. The reliability of the proposed system was proved by evaluating real-world case studies to prove its effectiveness to mimic human judgment. Moreover, the developed system has been evaluated by comparing it with a traditional mathematical method to prove the credibility and fairness of the proposed fuzzy system.

Suggested Citation

  • Raghda Hraiz & Mariam Khader & Adnan Shaout, 2019. "A Multi-Stage Fuzzy Model for Assessing Applicants for Faculty Positions in Universities," International Journal of Intelligent Information Technologies (IJIIT), IGI Global, vol. 15(1), pages 1-33, January.
    • Handle: RePEc:igg:jiit00:v:15:y:2019:i:1:p:1-33
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    References listed on IDEAS

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    1. A. Anitha & Debi Prasanna Acharjya, 2016. "Customer Choice of Super Markets using Fuzzy Rough Set on Two Universal Sets and Radial Basis Function Neural Network," International Journal of Intelligent Information Technologies (IJIIT), IGI Global, vol. 12(3), pages 20-37, July.
    2. S. Sasirekha & S. Swamynathan, 2017. "Fuzzy Rule Based Environment Monitoring System for Weather Controlled Laboratories using Arduino," International Journal of Intelligent Information Technologies (IJIIT), IGI Global, vol. 13(1), pages 50-66, January.
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    Cited by:

    1. Nan Jing & Zhao Wu & Shanshan Lyu & Vijayan Sugumaran, 2021. "Information credibility evaluation in online professional social network using tree augmented naïve Bayes classifier," Electronic Commerce Research, Springer, vol. 21(2), pages 645-669, June.

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