Abstract
The main goal of this study is to address the ongoing problem of low academic performance in higher education by using machine learning techniques. We use a dataset from a higher education institution that includes various information available at student enrollment, such as academic history, demographics, and socio-economic factors. To address this issue, we introduce a new method that combines the Slime Mould Algorithm (SMA) for efficient feature selection with a Forest-Optimized Neural Network (FO-NN) Classifier. Our method aims to identify students at risk of academic failure early. Using the SMA, we simplify the feature selection process, identifying important attributes for accurate predictions. The Forest Optimization technique improves the classification process by optimizing the neural network model. The experimental results of this study show that our proposed method is effective, with significant improvements in feature selection accuracy and notable enhancements in the predictive performance of the neural network classifier. By selecting a subset of relevant features, our approach deals with high-dimensional datasets and greatly improves the quality and interpretability of predictive models. The innovative combination of the SMA and the FO-NN classifier increases accuracy, interpretability, and the ability to generalize in predicting student performance. This work contributes to a more effective strategy for reducing academic underachievement in higher education.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
More From: International Journal of ADVANCED AND APPLIED SCIENCES
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.