Novel machine learning techniques based hybrid models (LR-KNN-ANN and SVM) in prediction of dental fluorosis in groundwater

dc.contributor.author	Atas, Musa
dc.contributor.author	Yesilnacar, Mehmet Irfan
dc.contributor.author	Yetis, Aysegul Demir
dc.date.accessioned	16/12/21 12:06
dc.date.available	16/12/21 12:06
dc.identifier.issn	0269-4042
dc.identifier.uri	http://dspace.beu.edu.tr:8080/xmlui/handle/20.500.12643/9803
dc.identifier.uri	https://doi.org/10.1007/s10653-021-01148-x
dc.description.abstract	Studies have shown that excessive intake of fluoride into human body from drinking water may cause fluorosis adversely affects teeth and bones. Fluoride in water is mostly of geological origin and the amounts depend highly on many factors such as availabi
dc.description.sponsorship	TUBITAK (the Scientific and Technological Research Council of Turkey)Turkiye Bilimsel ve Teknolojik Arastirma Kurumu (TUBITAK); Scientific Research Projects Committee of Harran University (HU BAK)Harran University
dc.language.iso	English
dc.publisher	Sprınger
dc.source	Envıronmental Geochemıstry And Health
dc.title	Novel machine learning techniques based hybrid models (LR-KNN-ANN and SVM) in prediction of dental fluorosis in groundwater
dc.type	Article; Early Access
dc.identifier.doi	10.1007/s10653-021-01148-x
dc.identifier.wos	WOS:000714867300001

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