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Hraiz R, Khader M, Shaout A. A Multi-Stage Fuzzy Model for Assessing Applicants for Faculty Positions in Universities. INTERNATIONAL JOURNAL OF INTELLIGENT INFORMATION TECHNOLOGIES 2019. [DOI: 10.4018/ijiit.2019010103] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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.
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Herawan T, Hassim YMM, Ghazali R. Functional Link Neural Network with Modified Artificial Bee Colony for Data Classification. INTERNATIONAL JOURNAL OF INTELLIGENT INFORMATION TECHNOLOGIES 2017. [DOI: 10.4018/ijiit.2017070101] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Functional Link Neural Network (FLNN) has emerged as an important tool for solving non-linear classification problem and has been successfully applied in many engineering and scientific problems. The FLNN structure is much more modest than ordinary feed forward network like the Multilayer Perceptron (MLP) due to its flat network architecture which employs less tuneable weights for training. However, the standard Backpropagation (BP) learning uses for FLNN training prone to get trap in local minima which affect the FLNN classification performance. To recover the BP-learning drawback, this paper proposes an Artificial Bee Colony (ABC) optimization with modification on bee foraging behaviour (mABC) as an alternative learning scheme for FLNN. This is motivated by good exploration and exploitation capabilities of searching optimal weight parameters exhibit by ABC algorithm. The result of the classification accuracy made by FLNN with mABC (FLNN-mABC) is compared with the original FLNN architecture with standard Backpropagation (BP) (FLNN-BP) and standard ABC algorithm (FLNN-ABC). The FLNN-mABC algorithm provides better learning scheme for the FLNN network with average overall improvement of 4.29% as compared to FLNN-BP and FLNN-ABC.
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Affiliation(s)
- Tutut Herawan
- Technology University of Yogyakarta, Yogyakarta, Indonesia
| | - Yana Mazwin Mohmad Hassim
- Tun Hussein Onn University of Malaysia, Faculty of Computer Science and Information Technology, Batu Pahat, Malaysia
| | - Rozaida Ghazali
- Tun Hussein Onn University of Malaysia, Faculty of Computer Science and Information Technology, Batu Pahat, Malaysia
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