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    Review of 'Cmbatting COVID-19: Artificial Intelligence Technologies & Challenges'

    Cmbatting COVID-19: Artificial Intelligence Technologies & ChallengesCrossref
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        Rated 4 of 5.
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        Rated 4 of 5.
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        Rated 4 of 5.
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        Rated 4 of 5.
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    Cmbatting COVID-19: Artificial Intelligence Technologies & Challenges

    AI works proficiently to emulate human intellect. It may also play an important role in understanding and recommending the creation of a COVID-19 vaccine. This outcome-driven technology is utilized for effective screening, assessing, forecasting, and tracking of present and potential future patients. Traditional network designs are unable to cope calmly with the impact of COVID-19 due to massive network data traffic and resource optimization requirements. As indicated by the growing amount of restorative clinical data, artificial intelligence (AI) has the potential to successfully boost the upper limit of the medical and health network. We discuss the primary uses of artificial intelligence technology in the process of suppressing the coronavirus from three main perspectives: prediction, symptom detection, and development, based on an extensive literature study. Furthermore, the advancement of next-generation network (NGN) technologies based on machine learning (ML) has given limitless opportunities for the formation of novel medical approaches. We have also discussed the challenges related to AI technologies in combatting COVID-19. The devastating epidemic of the Novel Coronavirus (Covid-19) has highlighted the importance of accurate prediction mathematical models. We have also discussed different mathematical models, their predictive capabilities, drawbacks, and practical validity.

      Review information

      This work has been published open access under Creative Commons Attribution License CC BY 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Conditions, terms of use and publishing policy can be found at www.scienceopen.com.

      Applied mathematics,Medicine,Artificial intelligence
      Gaussian models,Mathematical modeling,Neural Networks,COVID-19,NLP,Epidemic prevention and control,SVM
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      Review text

      References [32] and [34] not cited.

      Overall a good article it is. So can be considered for publication.


      Thanks for the review.

      Reference [32]: It is referred to in reference 8.

      Reference [34]: It is referred to in reference 1  


      2022-08-07 23:23 UTC

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