Deep Neural Network Framework for the Identification of Smoking Events in 6G and Cloud-Enabled Smart Cities

This study introduces a deep neural network framework for the identification of smoking events, designed to integrate seamlessly with the burgeoning infrastructure of 6G and cloud-enabled smart cities. We developed a model that not only learns from high-dimensional data but also adapts to the dynamic urban landscapes. Our model underwent rigorous training and validation phases, achieving a classification accuracy of 71%, with balanced precision and recall across both smoking and nonsmoking classes. The results, encapsulated in a comprehensive confusion matrix, highlight the model's potential in real-time monitoring and public health policy enforcement. This paper details the model architecture, training process, and evaluation metrics, underscoring the significant role of deep learning in enhancing urban surveillance systems for future smart cities.