CLOUD-EDGE CONTINUUM FRAMEWORK FOR ADMISSION DATA MANAGEMENT USING DEEP LEARNING MODEL

Abdullah M. Alashjaee, Mohammed Aljebreen, Hessa Alfraihi, Siwar Ben Haj Hassine, Omar Alghushairy, Bandar M. Alghamdi and Fouad Shoie Alallah
Additional contact information
Abdullah M. Alashjaee: Department of Computer Sciences, Faculty of Computing and Information Technology, Northern Border University, Rafha 91911, Kingdom of Saudi Arabia
Mohammed Aljebreen: ��Department of Computer Science, Community College King Saud University, P. O. Box 28095, Riyadh 11437, Saudi Arabia
Hessa Alfraihi: ��Department of Information Systems, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P. O. Box 84428, Riyadh 11671, Saudi Arabia
Siwar Ben Haj Hassine: �Department of Computer Science, Applied College at Mahayil King Khalid University, Muhayel Aseer 62529, Saudi Arabia
Omar Alghushairy: �Department of Information Systems and Technology, College of Computer Science and Engineering, University of Jeddah, Jeddah 21589, Saudi Arabia
Bandar M. Alghamdi: ��Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University Jeddah 21589, Saudi Arabia
Fouad Shoie Alallah: *Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University Jeddah, Saudi Arabia

FRACTALS (fractals), 2024, vol. 32, issue 09n10, 1-16

Abstract: The surge in applications necessitates a more intelligent and automated Higher Education Admission Management System (HEAMS). This research proposes a novel Deep Learning (DL)-based HEAMS utilizing a Cloud-Edge Architecture. The first step collects applicant data like transcripts, test scores, essays, and recommendations. Edge devices perform initial cleaning and preprocessing on these data to ensure quality and privacy. These preprocessed data using normalization and feature extraction using the Latent Dirichlet Allocation (LDA) are then transferred to the cloud where DL models, such as Convolutional Neural Networks (CNNs) for essays or Recurrent Neural Networks (RNNs) for transcripts, are trained. These models learn complex patterns from historical labeled data (admitted/not admitted) to predict an applicant’s success probability. During application evaluation, new data are fed through the trained models on the edge, generating probabilities for predefined classifications — high-potential, moderate, or low-potential. The cloud receives these probabilities and combines them with predefined admission criteria like minimum GPA. This combined analysis leads to a final classification using Novel Lite Convolutional Neural Network with Hybrid Leader-based Optimization (Lite CNN-HLO) for each applicant — admitted, waitlisted, or rejected and admission management system by refining admission decisions for admitted, waitlisted, and rejected applicants based on institutional priorities and constraints. The system not only generates classifications but also provides detailed model score breakdowns for transparency. This Cloud-Edge HEAMS offers improved efficiency, reduced workload for admissions staff, and potentially fairer decisions by mitigating bias through data-driven analysis.

Keywords: Higher Education Admission Management System (HEAMS); Latent Dirichlet Allocation (LDA); Lite Convolutional Neural Network with Hybrid Leader-based Optimization (Lite CNN-HLO); Transcripts; Test Scores; Essays; Recommendations (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1142/S0218348X25400110

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