Neutrosophic Deep Learning for Student Performance Prediction: A Novel Approach with Uncertainty Integration and Ethical Considerations | ||
| مجلة العلوم التجارية والبيئية | ||
| Volume 3, Issue 2, October 2024, Pages 252-274 PDF (1.4 M) | ||
| Document Type: المقالات الأصلية | ||
| DOI: 10.21608/jcese.2024.279883.1057 | ||
| Authors | ||
| A. A. Salama* ; Ahmed Mohamed Shitaya; Mohamed El Syed Wahed; Saied Helemy Abd El khalek; Amr Ismail | ||
| Math and Computer Science, Faculty of Science, Port Said University, Egypt | ||
| Abstract | ||
| This paper presents a methodology for predicting student performance using neutrosophic sets and deep learning techniques. The proposed approach involves feature selection and representation using neutrosophic sets, followed by model development using a suitable deep learning architecture. Uncertainty integration is achieved by incorporating neutrosophic values during training using specialized activation functions and modified loss functions. The model's performance is evaluated using appropriate metrics, and interpretation techniques are employed to understand the decision-making processes. Ethical considerations regarding student data collection and usage are also addressed. The proposed methodology offers a novel approach to student performance prediction that considers uncertainty and provides insights into the decision-making process, which can help educators, identify areas for improvement and provide targeted interventions. Keywords: Neutrosophic sets, Deep Learning, Student Performance Prediction, Uncertainty Integration, Feature Representation Keywords: Neutrosophic sets, Deep Learning, Student Performance Prediction, Uncertainty Integration, Feature Representation Keywords: Neutrosophic sets, Deep Learning, Student Performance Prediction, Uncertainty Integration, Feature Representation | ||
| Keywords | ||
| Neutrosophic sets; Deep Learning; Student Performance Prediction; Uncertainty Integration; Feature Representation | ||
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