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Please use this identifier to cite or link to this item: https://hdl.handle.net/10923/23642
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dc.contributor.authorDORN, MARCIO-
dc.contributor.authorGRISCI, BRUNO IOCHINS-
dc.contributor.authorNARLOCH, PEDRO HENRIQUE-
dc.contributor.authorFELTES, BRUNO CÉSAR-
dc.contributor.authorAVILA, EDUARDO-
dc.contributor.authorKAHMANN, ALESSANDRO-
dc.contributor.authorClarice Sampaio Alho-
dc.date.accessioned2022-12-14T17:48:10Z-
dc.date.available2022-12-14T17:48:10Z-
dc.date.issued2021-
dc.identifier.issn2376-5992-
dc.identifier.urihttps://hdl.handle.net/10923/23642-
dc.language.isoen-
dc.relation.ispartofPEERJ COMPUTER SCIENCE-
dc.rightsopenAccess-
dc.titleComparison of machine learning techniques to handle imbalanced COVID-19 CBC datasets-
dc.typeArticle-
dc.date.updated2022-12-14T17:48:08Z-
dc.identifier.doiDOI:10.7717/peerj-cs.670-
dc.jtitlePEERJ COMPUTER SCIENCE-
dc.volume7-
dc.spagee670-
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