EVALUATION OF STUDENT ACADEMIC PERFORMANCE USING ADAPTIVE NEURO-FUZZY APPROACH
DOI:
https://doi.org/10.51406/jnset.v11i1.1414Keywords:
Adaptive, Artificial Neural Network, Fuzzy, PerformanceAbstract
The Fusion of Artificial Neural Networks (ANN) and Fuzzy Inference System (FIS) has attracted a growing interest of researchers in various scientific and engineering areas. Due to the growing need for adaptive intelligent systems to solve real world problems. ANN learns by adjusting the interconnections between layers. FIS is a popular computing framework based on the concept of fuzzy set theory and fuzzy if-then rules. The advantages of the combination of ANN and FIS are apparent. The developed method uses a fuzzy system to support neural networks to enhance some of its characteristics like flexibility, speed and adaptability which is called the Adaptive Neuro-fuzzy inference system (ANFIS). Evaluating and assessing the student academic performance is not an easy task, especially when it involves many attributes or factors. Moreover, the knowledge of the human experts is acquired to determine the criteria of students’ academic performance and the decisions about their level of assimilation but most of the information is incomplete and vague. To overcome the problem, this work evaluates the student’s academic performance based on ANFIS tools which was implemented on MATLAB 7.6.0 (R2008a). The method produces crisp numerical outcomes that evaluate the student’s academic performance. The student performance after the training of the two inputs was at the average for semester1 and semester 2.
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