| Title |
RETRACTED: A Novel Smart City-Based Framework on Perspectives for Application of Machine Learning in Combating COVID-19 (Retracted Article) |
| ID_Doc |
42544 |
| Authors |
Ezugwu, AE; Hashem, IAT; Oyelade, ON; Almutari, M; Al-Garadi, MA; Abdullahi, IN; Otegbeye, O; Shukla, AK; Chiroma, H |
| Title |
RETRACTED: A Novel Smart City-Based Framework on Perspectives for Application of Machine Learning in Combating COVID-19 (Retracted Article) |
| Year |
2021 |
| Published |
|
| Abstract |
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. |
| PDF |
https://doi.org/10.1155/2021/5546790
|
