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@article{178427,
        author = {C. Maneesh and M. Murugesh and S. Muthukumar and R. Harish and T. Arachelvi},
        title = {ENHANCED CRIME HOTSPOT PREDICTION AND VISUALIZATION FOR WOMEN'S SAFETY THROUGH DEEP LEARNING},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {3473-3479},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=178427},
        abstract = {The identification of crime hotspots—geographic places with a notably greater prevalence of criminal activity than nearby locations—is essential to preserving public safety and security. Hotspots where crimes like sexual harassment, assault, domestic violence, stalking, and human trafficking disproportionately affect women are especially worrisome. Better resource allocation, strategic policing, and community awareness are made possible by knowing and anticipating these hotspots. The goal of this project is to create a predictive system that analyzes extensive crime datasets, including variables like the type of crime, frequency, time of occurrence, and exact geographic location, using a Deep Explainable Decision Tree model. The technology can predict regions with a higher probability of crimes against women by analyzing and learning from past crime trends. Additionally, the explainable nature of the model improves interpretability and trust, enabling stakeholders to comprehend the elements that contribute to the formation of hotspots. By integrating Google Maps, the anticipated hotspots are shown, providing a user-friendly and interactive platform for citizens, urban planners, and authorities to plan preventive measures, monitor risk areas in real time, and ultimately help create a safer environment for women.},
        keywords = {Crime Hotspot Prediction, Women's Safety, Deep Explainable Decision Tree, Crime Data Analysis, Google Maps Visualization},
        month = {May},
        }