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A comprehensive comparison study of traditional classifiers and deep neural networks for forest fire detection

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dc.contributor.authorAkyol, K
dc.date.accessioned2023-05-20T23:46:00Z
dc.date.available2023-05-20T23:46:00Z
dc.date.issued2023.01.01
dc.identifier.doi10.1007/s10586-023-04003-z
dc.identifier.eissn1573-7543
dc.identifier.endpage
dc.identifier.issn1386-7857
dc.identifier.issue
dc.identifier.startpage
dc.identifier.urihttps://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=dspace_ku&SrcAuth=WosAPI&KeyUT=WOS:000984317400001&DestLinkType=FullRecord&DestApp=WOS
dc.identifier.urihttps://hdl.handle.net/20.500.12597/15539
dc.identifier.volume
dc.identifier.wosWOS:000984317400001
dc.relation.ispartofCLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS
dc.subjectForest fires
dc.subjectFire detection
dc.subjectDeep features
dc.subjectDeep neural networks
dc.titleA comprehensive comparison study of traditional classifiers and deep neural networks for forest fire detection
dc.typeArticle
dspace.entity.typeWos
relation.isPublicationOfWos209df678-aa0c-4664-abc3-0908ddfd6446
relation.isPublicationOfWos.latestForDiscovery209df678-aa0c-4664-abc3-0908ddfd6446

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