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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM63901
Volume:
10 |
5 |
May 2026
Pages:
1-3
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Master's of Computer Applications
Bapuji Institute Of f Engineering And Technology
Master's of Computer Applications
Bapuji Institute Of f Engineering And Technology
As one of the world’s largest rail networks, Indian Railways handles an incredible volume of daily inquiries regarding bookings, schedules, and live updates. However, relying on traditional phone lines and static websites often results in long wait times and a frustrating experience for travelers. To bridge this gap, we are proposing an **intelligent, AI-powered Chatbot** designed to transform how passengers interact with the railway system. By using Natural Language Processing (NLP) and Machine Learning (ML), this virtual assistant can understand conversational language and provide instant, personalized help.
KEYWORDS
This project successfully designed, implemented, and tested a robust, intelligent Chatbot system for the Indian Railways leveraging modern NLP and ML technologies. The core objective of improving passenger service efficiency and quality was achieved, as evidenced by the high self-service rate and rapid response times. By moving away from rigid, rule-based systems, the proposed solution demonstrated superior ability to understand and process natural human language, offering a scalable and personalized conversational experience. The system's modular architecture, encompassing a state-of-the-art ML pipeline for intent and entity recognition, proved highly reliable in the production environment
Bender, E. M., & Koller, A. (2017). Climbing Mount Pnini: Characterizing the production of referential descriptions in generative models. Proceedings of the 15th European Workshop on Natural Language Generation. F. Chollet (2017). Python deep learning. A Manning Publication. J.Devlin,M.W.Chang,K. Lee, & K. Toutanova (2019). BERT: Deep Bidirectional Transformer Language Understanding Pre-training. NAACL 2019 Conference Proceedings. Gunning, D. (2019). XAI: Explainable AI. Defense Advanced Research Projects Agency.
Publication Date
5/25/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
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