Multitenant Leave System with Adaptive Leave Optimization and NLP Integration

  • Unique Paper ID: 179601
  • Volume: 11
  • Issue: 12
  • PageNo: 8663-8672
  • 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.

Copyright & License

Copyright © 2025 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

BibTeX

@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},
        }

Cite This Article

  • ISSN: 2349-6002
  • Volume: 11
  • Issue: 12
  • PageNo: 8663-8672

Multitenant Leave System with Adaptive Leave Optimization and NLP Integration

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