Copyright © 2026 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.

@article{188609,
        author = {K P Basavaraja and K Priya and Kumbara Ravi and Lakshmi V and Vinutha Prashanth},
        title = {Women Safety Analytics – Protecting Women from safety threats},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {7},
        pages = {4014-4020},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=188609},
        abstract = {Women’s safety has become a major concern due to rising threats such as harassment, stalking, and violence. This paper presents Women Safety Analytics, an intelligent safety application integrating real-time location sharing, emergency SOS alerts, nearby police/hospital detection, and AI-based risk analysis. The system uses smartphone sensors and geolocation services to detect unsafe conditions and provide quick access to emergency contacts. The proposed model enhances personal safety by combining usability, predictive analytics, and emergency response automation. Experimental results demonstrate an efficient response time and user-friendly interface capable of supporting critical safety situations.},
        keywords = {Women safety, Mobile application, Emergency alert system, Machine learning, Location analytics.},
        month = {December},
        }