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{170893,
        author = {Sneha Wadne and Y.R.Kalshetty and Pratibha Sindkhed and Vaibhavi Jadhav and Pooja More and Nandini Sherla},
        title = {Women Safety Analytics – Protecting Women from safety   threats},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {7},
        pages = {2323-2326},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=170893},
        abstract = {Women’s safety remains a critical concern globally, with increasing incidents of harassment, assault, and violence in public and private spaces. Despite various safety measures, women continue to face significant threats, particularly in high-risk areas or during specific times. This project, "Women Safety Analytics – Protecting Women from Safety Threats," aims to leverage data analytics to enhance safety and empower women through predictive insights. By analyzing crime data, social media trends, and geographical patterns, the project identifies high-risk areas and times prone to safety threats, enabling the development of targeted interventions. Machine learning algorithms and geospatial mapping techniques are employed to forecast safety risks, while mobile technologies and wearable devices are explored to offer real-time alerts and emergency responses. Additionally, sentiment analysis from social media and survey data provides a deeper understanding of women’s safety concerns. The findings aim to inform law enforcement strategies, enhance public awareness, and promote the use of technology in personal safety solutions. Ultimately, this project seeks to contribute to a safer environment for women by providing actionable, data-driven insights and technological innovations to mitigate safety threats.},
        keywords = {Women’s Safety, Data Analytics, Crime Analysis, Predictive Analytics, Machine Learning, Geospatial Mapping, Crime Hotspots, Safety Threats, Public Safety, Sentiment Analysis, Social Media Analytics, Mobile Safety Apps, Wearable Technology, Emergency Alerts, Risk Prediction, Gender-based Violence, Real-time Safety, Public Awareness, Machine Learning in Safety, Safety Interventions, Technology for Women’s Safety, Crime Prevention.},
        month = {December},
        }