A machine learning approach to dental fluorosis classification

dc.contributor.author	Yetis, Aysegul Demir
dc.contributor.author	Yesilnacar, Mehmet Irfan
dc.contributor.author	Atas, Musa
dc.date.accessioned	16/12/21 12:06
dc.date.available	16/12/21 12:06
dc.date.issued	2021
dc.identifier.issn	1866-7511
dc.identifier.uri	http://dspace.beu.edu.tr:8080/xmlui/handle/20.500.12643/9908
dc.identifier.uri	https://doi.org/10.1007/s12517-020-06342-2
dc.description.abstract	Fluoride in groundwater has been found to pose a severe public health threat in two villages (Karata and Sarm) of western Sanliurfa in the southeastern Anatolia region of Turkey, where many cases of fluorosis, which detrimentally affects the teeth and bon
dc.description.sponsorship	Scientific and Technological Research Council of Turkey (TUBITAK)Turkiye Bilimsel ve Teknolojik Arastirma Kurumu (TUBITAK) [110Y234]; Scientific Research Projects Committee of Harran University (HUBAK)Harran University [17190]
dc.language.iso	English
dc.publisher	Sprınger Heıdelberg
dc.source	Arabıan Journal Of Geoscıences
dc.title	A machine learning approach to dental fluorosis classification
dc.type	Article
dc.identifier.issue	2
dc.identifier.doi	10.1007/s12517-020-06342-2
dc.identifier.wos	WOS:000609602400026
dc.identifier.volume	14

Files in this item

Files	Size	Format	View
There are no files associated with this item.