Machine Learning Approach Using KPCA-SVMs for Predicting COVID-19
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Date
2022-07-22
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Publisher
Springer Nature Switzerland AG 2022
Abstract
The world has met numerous epidemics in the past decades. Lately, a deadly
sickness identified as COVID-19 has surfaced from China [1, 2]. Inimitable public
health adversity is triggered by severe acute respiratory syndrome coronavirus 2
(SARS-CoV-2) [2–4]. The World Health Organization (WHO) termed the new
epidemic as COVID-19. It is acknowledged as a Public Health Emergency of
International Concern since the beginning of 2020. It was considered an epidemic
around the first quarter of 2020 [5–7], as Americans joined forces with investigative
institutions and scientific concerns for global artificial intelligence (AI) investigators’
activities in evolving groundbreaking machine learning measures that will
help tackle COVID-19 linked surveys [3]. COVID-19 is a novel solitary intelligent
ribonucleic acid (RNA) germ comprising of a huge pathological genomic sequence.
It alters advances fast with no specific limitation for evaluating or investigating
techniques or suitable medications. Secluding infected individuals by quarantining
is the utmost way of safeguarding the universe from more escalation of the deadly
COVID-19 [8].
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