Advanced Machine Learning Techniques for Crime Prediction in Smart Heterogeneous Policing Networks
The proposed chapter suggest a highly complex machine learning crime prediction model on an intelligent heterogeneous policing network, which will have to consider dynamic environment, multimodal, and real-time decision-making. A deep learning (CNN LSTM) hybrid architecture that is founded on graph neural networks and reinforcement learning is created. With the assistance of multimodal fusion strategy and the security and privacy preservation measures that are inherent in the framework, the spatial, temporal, behavioral and environmental data are calculated. The proposed model is more predictive since it has an accuracy of 96.8, and AUC-ROC of 0.98, which is better than the traditional and standalone deep learning models. The adaptive learning and multimodal data can be much more useful regarding context awareness, power and responsiveness in the intricate police situations. The framework will also help in the real-time crime prediction and the best distribution of resources and as such can be applied in smart city policing systems.