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@article{179601, author = {Aayush Bhaskarwar and Akshat Dhanuka and Ziyaad Parkar and S.P. Shintre}, title = {Multitenant Leave System with Adaptive Leave Optimization and NLP Integration}, journal = {International Journal of Innovative Research in Technology}, year = {2025}, volume = {11}, number = {12}, pages = {8663-8672}, issn = {2349-6002}, url = {https://ijirt.org/article?manuscript=179601}, abstract = {Efficient leave management is crucial for maintaining productivity and meeting project deadlines, especially in organizations with interdependent teams. This paper presents the implementation of a Multitenant Leave Management System that integrates AI-driven modules for dynamic leave processing. Developed using React.js, Django, and PostgreSQL, the system automates leave request handling while minimizing workflow disruptions. A GPT-powered chatbot is integrated using Natural Language Processing (NLP) to interpret employee queries such as "Who is on leave today?" or "When can I apply for sick leave?" and convert them into real-time SQL queries for accurate data retrieval. To enhance efficiency, the system also features two custom algorithms—ADAPTIVE LEAVE OPTIMIZATION and PROACTIVE LEAVE RECOMMENDATION ALGORITHM which analyze historical leave usage and workload patterns to adjust leave policies and recommend conflict-free leave periods. This paper outlines the system architecture, module-wise implementation, NLP pipeline, and the analytical backend. The results demonstrate how the system supports intelligent decision-making and improves HR responsiveness, offering a scalable solution for modern organizations.}, keywords = {leave management system, chatbot integration, NLP, GPT API, SQL query generation, Django, React.js, PostgreSQL, ADAPTIVE LEAVE OPTIMIZATION, PROACTIVE LEAVE RECOMMENDATION ALGORITHM, HR automation, team collaboration, data-driven recommendations, workforce planning.}, month = {May}, }
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