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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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