Advancements and Challenges in Crime Prediction: A Review of Machine Learning and Deep Learning Approaches
Crime prediction using machine learning and deep learning has emerged as a promising field to enhance public safety and optimize law enforcement resources. This study explores the application of advanced computational techniques to predict criminal activities by analyzing various factors, including historical crime data, socio-economic indicators, and spatial-temporal patterns. Machine learning models such as decision trees, random forests, and support vector machines, along with deep learning architectures like recurrent neural networks (RNNs) and convolutional neural networks (CNNs), are employed to identify patterns and forecast future crime hotspots. The integration of these models enables the extraction of meaningful insights from large datasets, leading to more accurate and timely predictions. The research demonstrates that deep learning models, particularly those incorporating spatial and temporal data, outperform traditional machine learning approaches in terms of prediction accuracy and robustness. Key challenges addressed include data preprocessing, feature selection, model training, and validation. Furthermore, the study emphasizes the ethical considerations and the necessity of ensuring privacy and mitigating biases in predictive policing systems. Overall, the findings highlight the potential of combining machine learning and deep learning techniques to revolutionize crime prediction, offering law enforcement agencies a powerful tool to anticipate and prevent criminal activities. Future work aims to refine these models and explore their applicability in real-world scenarios, contributing to safer communities and more efficient policing strategies.
Keywords: crime prediction, machine learning, deep learning, predictive policing, spatial-temporal analysis, public safety.
