جامعة ديالى

This project presents an integrated electronic system designed to automate and manage faculty promotions and annual allowances, developed utilizing ASP.NET Core and SQL Server. The core innovation lies in the deployment of a dual-layered artificial intelligence architecture. The first layer focuses on Intelligent Document Processing (IDP), where the system ingests uploaded letters of appreciation in PDF or image formats to verify document authenticity, extract the issuing authority, retrieve official metadata including document numbers and dates, and automatically identify the target employees. The second layer employs a rule-based inference engine that synthesizes these extracted inputs with institutional service records to proactively evaluate eligibility and predict promotion timelines with absolute precision. By enforcing strict programmatic constraints, the system eliminates administrative errors, prevents the duplication of appreciation rewards, and secures data privacy through a robust authorization matrix, offering a scalable framework for digital governance in higher education.

This project presents an intelligent load balancing and congestion control framework for 4G/LTE networks using reinforcement learning to optimize network performance under high-density traffic conditions. Implemented in the ns-3 simulator, the proposed system employs a mode-aware controller that dynamically switches between normal, load-balancing, and emergency modes based on real-time network KPIs. A TD3-based reinforcement learning agent optimizes Cell Individual Offset (CIO) values to improve user distribution, reduce congestion, and enhance throughput and packet delivery. The framework is evaluated using an Arbaeen pilgrimage scenario, demonstrating superior performance compared with conventional rule-based load balancing approaches while maintaining adaptive and reliable network operation.