Machine Learning Approach Using KPCA-SVMs for Predicting COVID-19

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dc.contributor.authorAkeem Femi Kadri
dc.contributor.authorMicheal Olaolu Arowolo
dc.contributor.authorSanjay Misra
dc.date.accessioned2025-04-07T12:37:06Z
dc.date.available2025-04-07T12:37:06Z
dc.date.issued2022-07-22
dc.description.abstractThe 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].
dc.identifier.citation28
dc.identifier.urihttps://kwasuspace.kwasu.edu.ng/handle/123456789/4893
dc.publisherSpringer Nature Switzerland AG 2022
dc.titleMachine Learning Approach Using KPCA-SVMs for Predicting COVID-19
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