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

被引:0
作者
Alashjaee, Abdullah M. [1 ]
Aljebreen, Mohammed [2 ]
Alfraihi, Hessa [3 ]
Hassine, Siwar Ben Haj [4 ]
Alghushairy, Omar [5 ]
Alghamdi, Bandar M. [6 ]
Alallah, Fouad Shoie [7 ]
机构
[1] Northern Border Univ, Fac Comp & Informat Technol, Dept Comp Sci, Rafha 91911, Saudi Arabia
[2] King Saud Univ, Community Coll, Dept Comp Sci, POB 28095, Riyadh 11437, Saudi Arabia
[3] Princess Nourah Bint Abdulrahman Univ, Coll Comp & Informat Sci, Dept Informat Syst, POB 84428, Riyadh 11671, Saudi Arabia
[4] Mahayil King Khalid Univ, Appl Coll, Dept Comp Sci, Muhayel Aseer 62529, Saudi Arabia
[5] Univ Jeddah, Coll Comp Sci & Engn, Dept Informat Syst & Technol, Jeddah 21589, Saudi Arabia
[6] King Abdulaziz Univ, Fac Comp & Informat Technol, Dept Informat Technol, Jeddah 21589, Saudi Arabia
[7] King Abdulaziz Univ, Fac Comp & Informat Technol, Dept Informat Syst, Jeddah, Saudi Arabia
关键词
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; STUDENT PERFORMANCE; EXPERIENCE;
D O I
10.1142/S0218348X25400110
中图分类号
O1 [数学];
学科分类号
0701 ; 070101 ;
摘要
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.
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页数:16
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