Computer Science
An Intelligent Framework for AI-Based Crime Prediction and Real-Time Emergency Response in Smart Policing Systems
The increasing complexity of urban environments and rising crime rates demand intelligent, data-driven solutions for effective law enforcement. This paper presents an AI-driven Crime Management System that integrates machine learning techniques — Random Forest for crime classification and K-Means clustering for hotspot detection — to analyze historical and real-time crime data. A real-time SOS emergency module powered by Socket.IO enables instant communication between citizens and law enforcement with live location tracking. Additionally, the system incorporates automated FIR processing with risk-based prioritization. Experimental evaluation demonstrates significant improvements in response time (from 8–10 min to 2–3 min), crime prediction accuracy (≈90%), and resource allocation efficiency, providing a scalable and intelligent framework for smart policing.