Predicting the Unpredictable: Machine Learning for Crime Pattern Recognition and Forensic Analytics
Keywords:
Machine Learning, Crime Pattern Recognition, Crime Prediction, Forensic Analytics, Predictive PolicingAbstract
Machine Learning (ML) is a game-changing technology for crime pattern recognition and forensic analytics, as it allows for data-driven analysis of massive and complex crime data sets. This review addresses the use of ML for crime pattern prediction, crime hotspot detection, offender behaviour analysis, serial crime linkage, and digital forensic investigations. It discusses the fundamental concepts of data preprocessing, feature engineering, major ML categories, and commonly used algorithms for crime prediction. The review also discusses the practical implications, model performance measures, technical hurdles, ethical concerns, and, importantly, the implications for the Indian criminal justice system. The current trends in AI, like Explainable Artificial Intelligence (XAI), federated learning, Graph Neural Networks (GNNs), multimodal data fusion, and human-AI collaborative investigation, are also discussed. In general, the use of ML holds great promise for improving intelligence-led policing and evidence-based forensic investigations, but needs to be implemented with transparency, fairness and legal accountability.
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