An Intelligent Framework for AI-Based Crime Prediction and Real-Time Emergency Response in Smart Policing Systems
A Smart Policing Platform Using Machine Learning and Real-Time Geolocation
Abstract
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.
How to cite this paper
Omkar Rathod, Sumiksha kamble, Aryan kamble, Siddharth Pandit. "An Intelligent Framework for AI-Based Crime Prediction and Real-Time Emergency Response in Smart Policing Systems." PaperNova (2026). https://www.papernova.online/papers/an-intelligent-framework-for-ai-based-crime-prediction-and-real-time-emergency-r-2z805