<![CDATA[Smoking remains a significant public health challenge globally, contributing to a wide range of detrimental health outcomes including cardiovascular diseases, cancer, and respiratory disorders. Despite concerted efforts to curb smoking rates through policy interventions, effective monitoring and enforcement remain complex and resource-intensive tasks for health authorities and organizations. Innovative approaches leveraging advanced technologies such as visual detection systems powered by deep learning offer promising solutions to enhance smoking behavior detection and monitoring. Integrating the Google Cloud Vision API enables real-time identification of smoking indicators and discrimination from complex visual backgrounds. This capability not only supports proactive health monitoring but also strengthens the enforcement of public health policies aimed at reducing smoking prevalence. The research methodology utilizes a dataset of 600 images sourced from the Kaggle platform, encompassing diverse scenarios to optimize model training. Techniques such as image segmentation, feature extraction, and machine learning-based classification are employed to achieve high levels of precision and recall in identifying smokers and cigarette smoke. Despite the advantages of scalability, robust infrastructure, and high availability facilitated by cloud computing, the study acknowledges challenges such as bandwidth constraints and security risks associated with handling sensitive health data. Nevertheless, technological innovations in visual detection systems and cloud services are underscored as pivotal in mitigating the health impacts of smoking and advancing public health initiatives.]]>
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