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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM65438

AI-Powered Women Safety and Emergency Alert System Using Artificial Intelligence and Real-Time Emergency Communication

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM65438

Volume:

10 |

6 |

Jun 2026

Pages:

1-5

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM65438

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.

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Authors

SIA

Shaziya Ismuddin Ansari

Department of Artificial Intelligence and Data Science

Anjuman-I-Islam Abdul Razak Kalsekar Polytechnical Campus

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Mentors

PAK

Prof. Ali Karim

Department of Artificial Intelligence and Data Science

Anjuman-I-Islam Abdul Razak Kalsekar Polytechnical Campus

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Abstract

Women safety has become a major concern in modern society due to the increasing number of harassment, assault, and emergency incidents. Traditional safety applications mainly depend on manual actions and often fail to provide immediate assistance during critical situations. This research presents an AI-Powered Women Safety and Emergency Alert System designed to improve personal security through intelligent monitoring and rapid emergency communication. The proposed system integrates user authentication, emergency contact management, one-click SOS activation, real-time location sharing, voice-based emergency triggering, danger keyword detection using Natural Language Processing (NLP), and an AI-powered chatbot. The system continuously analyzes user inputs and emergency situations to generate alerts and notify registered contacts instantly. Flask is used as the backend framework, while HTML, CSS, and JavaScript provide the user interface. MongoDB is utilized for secure data storage and management. The developed system aims to reduce emergency response time and provide users with a reliable platform for seeking immediate assistance. Experimental evaluation demonstrates that the proposed solution effectively identifies emergency situations and successfully delivers alerts with location information. The integration of Artificial Intelligence enhances the overall efficiency, reliability, and usability of the safety system.

KEYWORDS

Women Safety Artificial Intelligence Emergency Alert System Natural Language Processing Flask Location Sharing SOS Alert.

Cite This Article

Shaziya Ismuddin Ansari, Prof. Ali Karim, "AI-Powered Women Safety and Emergency Alert System Using Artificial Intelligence and Real-Time Emergency Communication," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 6, pp. 1-5, Jun 2026. https://doi.org/10.55041/IJSREM65438

Full Article/Conclusion

The AI-Powered Women Safety and Emergency Alert System was developed to provide a smart, reliable, and efficient solution for improving personal safety during emergency situations. The system integrates multiple technologies, including Artificial Intelligence, Natural Language Processing, location tracking, emergency alert generation, audio evidence recording, and notification management, within a single platform.       The proposed solution overcomes several limitations of traditional safety applications by incorporating intelligent threat detection and automated emergency response mechanisms. Features such as SOS alert generation, real-time location sharing, emergency contact management, and AI-based danger keyword detection contribute to faster communication and improved emergency handling.        The system is designed to be user-friendly, secure, and scalable. By combining modern web technologies with AI-driven safety features, the platform enhances user confidence and provides additional protection during critical situations. The results demonstrate that the proposed system can serve as an effective tool for strengthening women’s safety and supporting emergency response activities.        Future enhancements may include integration with law enforcement agencies, advanced machine learning models, wearable device support, and real-time video evidence collection to further improve system capabilities and user protection.

References

1. A. R. Al-Ali, I. A. Zualkernan, and F. Aloul, “A Mobile GPRS-Sensors Array for Air Pollution Monitoring,” IEEE Sensors Journal, vol. 10, no. 10, pp. 1666–1671, 2010. 2. S. Priya, P. Devi, and R. Rajkumar, “Smart Women Safety System Using IoT and GPS,” International Journal of Engineering Research and Technology (IJERT), vol. 8, no. 6, pp. 1–5, 2019. 3. M. S. Hossain and G. Muhammad, “Cloud-Assisted Industrial Internet of Things (IIoT) – Enabled Framework for Health Monitoring,” Future Generation Computer Systems, vol. 66, pp. 132–139, 2017. 4. A. K. Jain and B. B. Gupta, “Machine Learning and Artificial Intelligence for Security Applications,” Journal of Information Security and Applications, vol. 58, 2021. 5. T. Mikolov, I. Sutskever, K. Chen, G. Corrado, and J. Dean, “Distributed Representations of Words and Phrases and Their Compositionality,” Advances in Neural Information Processing Systems (NIPS), 2013. 6. S. Bird, E. Klein, and E. Loper, Natural Language Processing with Python. Sebastopol, CA, USA: O’Reilly Media, 2009. 7. Flask Documentation, “Flask Web Development Framework,” Available: https://flask.palletsprojects.com/ 8. SQLite Documentation, “SQLite Database Engine,” Available: https://www.sqlite.org/ 9. Google Developers, “Geolocation API Documentation,” Available: https://developers.google.com/maps/documentation 10. Python Software Foundation, “Python Programming Language Documentation,” Available: https://docs.python.org/3/

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Publication Date

6/24/2026

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Review Rounds: 2

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