[Retracted] A Novel Smart City‐Based Framework on Perspectives for Application of Machine Learning in Combating COVID‐19

  1. Absalom E. Ezugwu
  2. Ibrahim Abaker Targio Hashem
  3. Olaide N. Oyelade
  4. Mubarak Almutari
  5. Mohammed A. Al-Garadi
  6. Idris Nasir Abdullahi
  7. Olumuyiwa Otegbeye
  8. Amit K. Shukla
  9. Haruna Chiroma

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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.

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  1. Version published to 10.1155/2021/5546790
  2. ScreenIT

    SciScore for 10.1101/2020.05.18.20105577: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    No key resources detected.

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    About SciScore

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  3. Version published to 10.1101/2020.05.18.20105577 on medRxiv