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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM62600
Volume:
10 |
5 |
May 2026
Pages:
1-4
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.

computer Engineering
Matoshri Collage of Engineering and Research Center

computer Engineering
Matoshri Collage of Engineering and Research Center

computer Engineering
Matoshri Collage of Engineering and Research Center

computer Engineering
Matoshri Collage of Engineering and Research Center

computer Engineering
Matoshri Collage of Engineering and Research Center
This paper presents an intelligent AI-based system designed to improve safety, communication, and navigation during large-scale events such as the Kumbh Mela. The proposed system integrates machine learning, natural language processing, and voice-based interaction to address challenges like misinformation, crowd congestion, and language barriers. It includes modules for fake news detection, risk-aware route guidance, multilingual voice assistance, and secure user authentication. The system provides real-time responses and supports decision-making for both users and authorities. The proposed solution aims to enhance user experience, reduce risks, and ensure efficient event management through a centralized digital platform.
KEYWORDS
The proposed system demonstrates how Artificial Intelligence can be effectively used to improve safety, communication, and navigation in large-scale events. By integrating modules such as fake news detection, route risk prediction, multilingual voice assistance, and secure authentication, the system provides a centralized and efficient platform for both users and administrators. The system reduces dependency on manual processes, enhances decision-making, and ensures better accessibility for users with different language backgrounds. It also offers quick and reliable responses, making it suitable for real-time environments. Overall, the project provides a scalable and user-friendly solution that can be applied to similar mass gathering events in the future. With further enhancements and real-time integration, the system has the potential to become a powerful tool for smart event management and public safety.
[1] K. Shu, A. Sliva, S. Wang, J. Tang, and H. Liu, “Fake News Detection on Social Media: A Data Mining Perspective,” ACM SIGKDD Explorations, 2017. [2] J. Devlin, M. Chang, K. Lee, and K. Toutanova, “BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding,” NAACL-HLT, 2019. [3] F. Pedregosa et al., “Scikit-learn: Machine Learning in Python,” Journal of Machine Learning Research, 2011. [4] World Health Organization, “Public Health for Mass Gatherings: Key Considerations,” WHO Press, 2020. [5] A. Paul and D. Singh, “Conversational Agents for Public Services using Dialogflow and Rasa,” International Journal of Artificial Intelligence, 2021. [6] R. Kumar and M. Joshi, “Voice-Based Interfaces for Low-Literacy Users in India,” ICTD Conference, 2021. [7] Python Software Foundation, “Python Documentation,” Available: https://www.python.org [8] SQLite Documentation, “SQLite Database Engine,” Available: https://www.sqlite.org [9] Google Developers, “Text-to-Speech (gTTS) Documentation,” Available: https://pypi.org/project/gTTS/ [10] Amazon Textract, Amazon Web Services,
Publication Date
5/6/2026
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.
Funding Information
No funding information available for this research.
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