A Multi-Faceted Leave Management Ecosystem Employing AI-Driven Semantic Categorization and Probabilistic Algorithms with Dynamic Schedule Reallocation (KEY IJP************578)

• A. Akhil ,N. Harshika,P. Uday Vardhan,C. Vaishak,B. Varshitha

Abstract

The project offers an intelligent and multifaceted Leave Management System designed for educational institutions. It integrates advanced automation and Artificial Intelligence (AI). Higher authorities can efficiently prioritize requests by using the system's semantic analysis to classify leave descriptions according to their textual content. The system provides a data-driven approach to decision-making by utilizing probabilistic scoring algorithms to predict the probability of leave acceptance based on historical and contextual data. Faculty members submit leave requests through the Leave Management System's dynamic, automated workflow and user-friendly interface. The technology ensures a smooth continuation of academic activities by dynamically reassigning the faculty's scheduled classes to other available staff members upon approval. An advanced scheduling system that assesses faculty availability and allocates classes to individuals with overlapping free hours makes the reassignment process easier. By providing faculty members with real-time emails and notifications about the approval or rejection of their leave requests, the technology further improves communication efficiency. In order to guarantee accuracy and transparency in leave monitoring, the system additionally keeps track of each faculty member's most recent leave total. This creative method preserves academic institutionseffectiveness while minimizing manual involvement, allocating resources optimally, and lowering administrative workload. The suggested System transforms conventional leave management procedures by combining AI-based semantic analysis, predictive modeling, and dynamic scheduling. This offers a scalable and effective answer to present issues in educational institutions.