Women's crimes in India are a serious social issue, and new-age solutions to their detection and prevention are essential. The article introduces an analytical model using modern data and geography tools to identify and map cases of violence against women in India. It uses several different types of sources such as police reports, social media and demographic statistics, to provide a thorough picture of the situation of gender-based violence in India. This model is used to look for early-warning signs of gender-based violence by analyzing many different sources of unstructured text. Using geospatial analysis makes it possible to build a predictive model that lets users spot high-risk areas more easily. To conclude the article, we highlight the results of using the model in real law enforcement, public safety and policy issues and discuss its usefulness. The outcomes let us see the how and when of these crimes, making it possible to direct resources and create focused ways to prevent them. This study is necessary to find solutions for women’s crime in India. This study highlights how using advanced analysis helps create effective, data-driven ways to keep women safe.
Zhuang Y, Almeida M, Morabito M, Ding W. Crime Hot Spot Forecasting: A Recurrent Model with Spatial and Temporal Information. 2017 IEEE International Conference on Big Knowledge (ICBK). IEEE; 2017. p. 143–50.
2.
Das P, Das AK. Crime analysis against women from online newspaper reports and an approach to apply it in dynamic environment. 2017 International Conference on Big Data Analytics and Computational Intelligence (ICBDAC). IEEE; 2017. p. 312–7.
3.
Verma M, Banerjee N. A Review of Sustainable Development and Women’s Empowerment. International Journal of SDG’s Prospects and Breakthroughs. 2024;(4):13–7.
4.
Kumar S, Naik V. Geospatial Analysis of Crime Against Women. 2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT). IEEE; 2023. p. 1–7.
5.
Muller H, Romano L. An exploratory study of the relationship between population density and crime rates in Urban Areas. Progression Journal of Human Demography and Anthropology. 2024;(4):28–33.
6.
Nguyen TT, Hatua A, Sung AH. Building a Learning Machine Classifier with Inadequate Data for Crime Prediction. Journal of Advances in Information Technology. 2017;141–7.
7.
Bhuvaneshwari M, Ramesh K. Impact of Emotional Intelligence on Leadership Effectiveness: A Study of Women Leaders in the Service Sector. Indian Journal of Information Sources and Services. 2025;15(1):289–98.
8.
Shama N. A machine learning approach to predict crime using time and location data (Doctoral dissertation. 2017;
9.
Anitha G, Dhivya R. Denial of Service Attack in Cyber Crime Security. International Journal of Advances in Engineering and Emerging Technology. 2019;(4):1–3.
10.
Tabedzki C, Thirumalaiswamy A, Van Vliet P, Agarwal S, Sun S. Yo home to Bel-Air: predicting crime on the streets of Philadelphia. 2018;
11.
Ramasamy L. Challenges and Opportunities of Women Participating in the Informal Sector in Malaysia: A Case on Women Street Vendors in Penang. International Academic Journal of Science and Engineering. 2018;05(02):11–23.
12.
Kanlanfeyi S, Kishore K. Using machine learning and recurrent neural networks for efficient crime detection and prevention. International Journal of Emerging Technologies and Innovative Research. 2019;(6):395–402.
13.
adevi D, hini N, thra P, anya S. Pothole Detection Using Deep Learning. International Academic Journal of Innovative Research. 2022;9(2):01–14.
14.
Nair S, Gopi E. Deep learning techniques for crime hotspot detection. 2019;13–29.
15.
Sandoval JIZ, Garcés E, Fuertes W. Ransomware Detection with Machine Learning: Techniques, Challenges, and Future Directions - A Systematic Review. Journal of Internet Services and Information Security. 2025;15(1):271–87.
16.
Anbarasu V, Pd S. Analysis and Prediction of Crime against Women Using Machine Learning Techniques. Annals of the Romanian Society for Cell Biology. 2021;(6):5183–8.
17.
Mohammed Hussain V, Abdul Azeez Khan DrA, Sathick DrJ, Raj DrA, Haja Alaudeen DrA. Machine Learning Based Vehicle Traffic Patterns Prediction Model (ML-VTPM) With Mobile Crowd Sensing for Transportation System. Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications. 2025;16(1):1–25.
18.
Jeyaboopathiraja J, Maria Priscilla G. A Thorough Analysis of Machine Learning and Deep Learning Methods for Crime Data Analysis. Lecture Notes in Networks and Systems. Springer Nature Singapore; 2021. p. 795–812.
19.
Saeed RM, Abdulmohsin HA. A study on predicting crime rates through machine learning and data mining using text. Journal of Intelligent Systems. 2023;32(1).
20.
Zhuang Y, Almeida M, Morabito M, Ding W. Crime Hot Spot Forecasting: A Recurrent Model with Spatial and Temporal Information. 2017 IEEE International Conference on Big Knowledge (ICBK). IEEE; 2017. p. 143–50.
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