Abstract: As crime rates continue to rise, law enforcement agencies face the challenge of dealing with increased demand for their services. However, with the use of intelligence, there is an opportunity to enhance prediction and prevention efforts. A study has introduced an approach that optimizes neural networks for predicting crime trends over time and space. The methodology involves utilizing Recurrent Neural Networks to analyse behaviour patterns, Deep Convolutional Neural Networks for understanding traits, and Cross Deep Learning for integrating facial recognition capabilities. The model is trained on crime data from a specific location, along with various spatial, temporal, and demographic factors. Moreover, extracting insights from these datasets through data mining is critical. Thorough testing is conducted to improve the accuracy of identifying behaviours, enabling targeted crime prevention strategies. The advanced models have demonstrated success in forecasting crime hotspots with accuracy, which can help in allocating resources effectively. By implementing surveillance and analysis in cities, not only are security measures enhanced, but the burden on law enforcement is also reduced. This allows them to focus on critical tasks. This approach underscores the potential of optimized deep learning algorithms and neural networks in predicting activities based on data analysis. These AI-driven tools offer intelligence to support resource allocation as police departments adopt cutting-edge practices. The proposed framework can be expanded to regions to address emerging security challenges. In conclusion, this groundbreaking approach highlights the importance of using advanced technologies in crime prevention, enabling law enforcement agencies to serve communities more effectively.

Key words: crime detection, data mining, deep learning, hybrid deep learning (HDL), convolutional neural network (CNN), recurrent neural network (RNN)