ARTICLE
A Case Study on Hyperparameter Optimization Methods in Scholarship Eligibility Prediction Using Neural Networks
- Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering (LNICST) , 173 : 91-108
Lien de l'article :
https://doi.org/10.1007/978-3-032-38682-3_6
Discipline :
Informatique
Auteur(s) :
Aminata Bagre, Moustapha Bikienga, Tclesphore B. Tiendrebeogo
Auteur(s) tagués :
BIKIENGA Moustapha
Renseignée par : BIKIENGA Moustapha
Résumé
Predicting scholarship eligibility is a key issue for higher education establishments wishing to ensure the fair and efficient allocation of grants. This prediction relies on the exploitation of academic and social data integrated into Decision Support Systems (DSS).
Mots-clés
hyperparameter optimization, Bayesian optimization, artificial neural networks, decision support system, student performance data