A Novel Metaheuristic Approach to Optimization of Neuro-Fuzzy System for Students' Performance Prediction

Kashif Hussain, Noreen Talpur, Muhammad Umar Aftab, Zakria

Research output: Contribution to journalArticlepeer-review


Data mining is being increasingly leveraged in educational settings for achieving various different outcomes including students' learning patterns, course and teaching outcome assessment, and students' expected achievement prediction. Utilizing data collected from daily curricular and non-curricular activities, machine learning techniques have benefited administrators in making efficient decisions. Based on students' behavioral information, this research proposes student performance prediction model using fuzzy-based neural network (FNN) trained by a novel metaheuristic approach. Because original gradient-based learning method associated with FNN limits its performance, this research employs Henry Gas Solubility Optimization (HGSO) algorithm for tuning FNN parameters. The empirical analysis suggests superiority of results produced by the proposed approach as compared with the FNN trained by the competitive methods.
Original languageEnglish
Pages (from-to)1-9
Number of pages9
JournalJournal of Soft Computing and Data Mining
Issue number1
Publication statusPublished - 4 Mar 2020

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