The spread of COVID-19 worldwide continues despite multidimensional efforts to curtail its spread and provide treatment. Efforts to contain the COVID-19 pandemic have triggered partial or full lockdowns across the globe. This paper presents a novel framework that intelligently combines machine learning models and the Internet of Things (IoT) technology specifically to combat COVID-19 in smart cities. The purpose of the study is to promote the interoperability of machine learning algorithms with IoT technology by interacting with a population and its environment to curtail the COVID-19 pandemic. Furthermore, the study also investigates and discusses some solution frameworks, which can generate, capture, store, and analyze data using machine learning algorithms. These algorithms can detect, prevent, and trace the spread of COVID-19 and provide a better understanding of the disease in smart cities. Similarly, the study outlined case studies on the application of machine learning to help fight against COVID-19 in hospitals worldwide. The framework proposed in the study is a comprehensive presentation on the major components needed to integrate the machine learning approach with other AI-based solutions. Finally, the machine learning framework presented in this study has the potential to help national healthcare systems in curtailing the COVID-19 pandemic in smart cities. In addition, the proposed framework is poised as a pointer for generating research interests that would yield outcomes capable of been integrated to form an improved framework.

中文翻译：

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一种基于智能城市的新型框架，探讨机器学习在抗击 COVID-19 中的应用前景


尽管采取了多方面措施来遏制其传播并提供治疗，但 COVID-19 在全球范围内的传播仍在继续。遏制新冠肺炎 (COVID-19) 大流行的努力已引发全球部分或全面封锁。本文提出了一种新颖的框架，该框架智能地结合了机器学习模型和物联网 (IoT) 技术，专门用于在智慧城市中对抗 COVID-19。该研究的目的是通过与人群及其环境互动来促进机器学习算法与物联网技术的互操作性，以遏制 COVID-19 大流行。此外，该研究还调查和讨论了一些解决方案框架，这些框架可以使用机器学习算法生成、捕获、存储和分析数据。这些算法可以检测、预防和追踪 COVID-19 的传播，并更好地了解智慧城市中的疾病。同样，该研究还概述了应用机器学习帮助世界各地医院对抗 COVID-19 的案例研究。研究中提出的框架全面介绍了将机器学习方法与其他基于人工智能的解决方案集成所需的主要组件。最后，本研究中提出的机器学习框架有可能帮助国家医疗保健系统遏制智慧城市中的 COVID-19 大流行。此外，拟议的框架有望成为产生研究兴趣的指针，这些研究兴趣将产生能够整合形成改进框架的成果。