الفهرس 
1. Chapter (1) : Introduction & Related WorksOnly 14 pages are availabe for public view
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Abstract
The coordination and admission systems in traditional colleges are crucial for education decision-makers, helping select students who can perform well. This thesis addresses the challenge of creating intelligent information systems to select the best candidates while respecting admission quotas. The proposed solution involves two components: First, a secure and reliable system using historical enrollment data and smart data analytics, particularly deep neural networks, to assess student proficiency and select top candidates. Second, it considers various criteria such as physical, health, and academic fitness. Advanced predictive analytics help forecast student behavior throughout their university enrollment, streamlining the admission process.