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Smart city article details
| Title | Deep Neural Network Framework For The Identification Of Smoking Events In 6G And Cloud-Enabled Smart Cities |
|---|---|
| ID_Doc | 18004 |
| Authors | Gupta B.B.; Chui K.T.; Gaurav A.; Wu J. |
| Year | 2024 |
| Published | Proceedings - 2024 IEEE/ACM 24th International Symposium on Cluster, Cloud and Internet Computing Workshops, CCGridW 2024 |
| DOI | http://dx.doi.org/10.1109/CCGridW63211.2024.00034 |
| Abstract | 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 non-smoking 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. |
| Author Keywords | Cloud Computing; Deep Learning Detection; Public Health Informatics; Smart City Surveillance; Smoker Behavior Analysis |
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