Machine learning for public wellness: optimizing hygiene practices and pollution monitoring in smart cities

Introduction. Public health in urban areas is of paramount importance, particularly in the context of smart cities where technology plays a vital role. The integration of sophisticated infrastructure and data-driven systems in smart cities has the potential to significantly enhance public health outcomes. This improvement hinges on optimizing various factors, especially in the realms of hygiene standards and pollution monitoring. The ability to adhere to stringent hygiene procedures and closely monitor pollutants is essential for mitigating health risks in densely populated urban environments. As metropolitan areas become increasingly complex, there is a pressing need to prioritize the optimization of these processes. Materials and Methods. To address the challenges associated with public health optimization in smart cities, this study introduces Optimized Public Wellness using Machine Learning (OPWML). OPWML employs advanced machine learning techniques to augment hygiene protocols and pollution surveillance in smart urban areas. The proposed approach incorporates real-time validation, enhanced data-collecting efficiency, intelligent intervention impact, and increased throughput. The methodology aims to streamline processes and overcome the limitations of current approaches, providing more precise and prompt outcomes. Results. Simulation findings demonstrate the superior performance of OPWML compared to other methods. The average estimate accuracy achieved by OPWML is 86.76%, showcasing its efficacy in delivering accurate results. Real-time validation latency is notably low at 12.99 ms, indicating the system’s responsiveness. With a data collection efficiency of 22.96 GB/hour, OPWML demonstrates its ability to efficiently gather relevant data. The smart intervention impact of 33.20% underscores the system’s effectiveness in implementing intelligent interventions. Additionally, the throughput of 314.67 kbps signifies the high processing capacity of OPWML. Limitations. While OPWML exhibits promising results, it is essential to acknowledge certain limitations in this study. The simulation-based nature of the findings may not fully capture real-world complexities. Additionally, the generalizability of the results to diverse urban contexts requires further investigation. Limitations such as data privacy concerns and potential technological barriers should also be considered when implementing OPWML in practical settings. Conclusion. In conclusion, Optimized Public Wellness using Machine Learning (OPWML) emerges as a powerful tool for transforming public health processes in smart cities. The study highlights OPWML’s capacity to significantly enhance hygiene protocols and pollution surveillance, ensuring a healthier and environmentally sustainable urban setting. While acknowledging certain study limitations, the overall outcomes emphasize the potential of OPWML in revolutionizing public health practices and contributing to the well-being of urban populations in the era of smart cities.