About: The Number of Confirmed Cases of Covid-19 by using Machine Learning: Methods and Challenges

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An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	The Number of Confirmed Cases of Covid-19 by using Machine Learning: Methods and Challenges
Creator	Ahmad, Amir Barukab, Mohammed Gagan Kumar, · Garhwal, Sunita Malebary, Jameel Omar, · Ray, Kumar Santosh, · Sharaf, ·
source	Medline; PMC
abstract	Covid-19 is one of the biggest health challenges that the world has ever faced. Public health policy makers need the reliable prediction of the confirmed cases in future to plan medical facilities. Machine learning methods learn from the historical data and make predictions about the events. Machine learning methods have been used to predict the number of confirmed cases of Covid-19. In this paper, we present a detailed review of these research papers. We present a taxonomy that groups them in four categories. We further present the challenges in this field. We provide suggestions to the machine learning practitioners to improve the performance of machine learning methods for the prediction of confirmed cases of Covid-19.
has issue date	2020-08-04(xsd:dateTime)
bibo:doi	10.1007/s11831-020-09472-8
bibo:pmid	32837183
has license	no-cc
sha1sum (hex)	ea4c1a13f7211579a7bc57af7a34cdf70f64e999
schema:url	https://doi.org/10.1007/s11831-020-09472-8
resource representing a document's title	The Number of Confirmed Cases of Covid-19 by using Machine Learning: Methods and Challenges
has PubMed Central identifier	PMC7399353
has PubMed identifier	32837183
schema:publication	Arch Comput Methods Eng
resource representing a document's body	covid:ea4c1a13f7211579a7bc57af7a34cdf70f64e999#body_text
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