International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
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Volume 184 - Issue 27 |
Published: Sep 2022 |
Authors: Anietie Ekong, Abasiama Silas, Saviour Inyang |
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Anietie Ekong, Abasiama Silas, Saviour Inyang . A Machine Learning Approach for Prediction of Students’ Admissibility for Post-Secondary Education using Artificial Neural Network. International Journal of Computer Applications. 184, 27 (Sep 2022), 44-49. DOI=10.5120/ijca2022922340
@article{ 10.5120/ijca2022922340, author = { Anietie Ekong,Abasiama Silas,Saviour Inyang }, title = { A Machine Learning Approach for Prediction of Students’ Admissibility for Post-Secondary Education using Artificial Neural Network }, journal = { International Journal of Computer Applications }, year = { 2022 }, volume = { 184 }, number = { 27 }, pages = { 44-49 }, doi = { 10.5120/ijca2022922340 }, publisher = { Foundation of Computer Science (FCS), NY, USA } }
%0 Journal Article %D 2022 %A Anietie Ekong %A Abasiama Silas %A Saviour Inyang %T A Machine Learning Approach for Prediction of Students’ Admissibility for Post-Secondary Education using Artificial Neural Network%T %J International Journal of Computer Applications %V 184 %N 27 %P 44-49 %R 10.5120/ijca2022922340 %I Foundation of Computer Science (FCS), NY, USA
Student admission’s process is a method of selecting qualified candidates for admission. Challenges such as facility constraints and insufficient ability to meet the continuously rising needs of post-secondary education. There is still an absorption capacity problem in some parts of the world as the growing number of students applying for admission for post-secondary education far surpasses the rate of expansion and this makes the selection process to be a daunting tasks. In this study, Artificial Neural network (ANN) was adopted for the determination of admissibility of candidates for post-secondary education based on (O’level Results, CGPA (Cumulative Grade Point Average), Departmental Rank (DPR) etc. Results indicated effective prediction based the performance analysis using the Confusion Matrix and AUC -ROC and gave a 99% accuracy on the dataset.