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June 30, 2025Journal of Wireless Mobile Networks Ubiquitous Computing and Dependable Applications

Deep SVM-Driven Predictive Analytics for Improved Decision-Making in E-Learning

K R Shwetha D. Sharmila Banu

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Key Points

The proposed KSVM model achieved an impressive accuracy of 91.4%, outperforming existing models in predicting student performance.
Integration of residual deep belief networks for feature extraction alongside kernel support vector machines enhances data interpretation.
Conventional grading methods may not accurately reflect student skills, necessitating innovative approaches in online learning.
This predictive analytics framework combines diverse data sources, fostering improved assessments of student capabilities.

Implication

This research demonstrates improved decision-making in online learning environments using deep svm and predictive analytics.

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Cite This Study

Shwetha et al. (2025) studied this question.

synapsesocial.com/papers/68c1c9dd54b1d3bfb60f2e0c https://doi.org/10.58346/jowua.2025.i2.034

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