The classification of diseased trees by using kNN and MLP classification models according to the satellite imagery

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dc.contributor.authorUnlersen, Muhammed Fahri
dc.contributor.authorSabanci, Kadir
dc.date.accessioned2020-03-26T19:09:27Z
dc.date.available2020-03-26T19:09:27Z
dc.date.issued2016
dc.departmentSelçuk Üniversitesi, Mühendislik Fakültesi, Elektirik ve Elektronik Mühendisliği Bölümüen_US
dc.description.abstractIn this study, the Japanese Oak and Pine Wilt in forested areas of Japan was classified into two group as diseased trees and all other land cover area according to the 6 attributes in the spectral data set of the forest. The Wilt Data Set which was obtained from UCI machine learning repository database was used. Weka (Waikato Environment for Knowledge Analysis) software was used for classification of areas in the forests. The classification success rates and error values were calculated and presented for classification data mining algorithms just as Multilayer Perceptron (MLP) and k-Nearest Neighbor (kNN). In MLP neural networks the classification performance for various numbers of neurons in the hidden layer was presented. The highest success rate was obtained as 86.4% when the number of neurons in the hidden layer was 10. The classification performance of kNN method was calculated for various counts of neighborhood. The highest success rate was obtained as 72% when the count of neighborhood number was 2en_US
dc.identifier.citationUnlersen, M. F., Sabanci, K. (2016). The Classification of Diseased Trees by Using kNN and MLP Classification Models According to the Satellite Imagery. International Journal of Intelligent Systems and Applications in Engineering, 4(2), 25-28.
dc.identifier.endpage28en_US
dc.identifier.issn2147-6799en_US
dc.identifier.issn2147-6799en_US
dc.identifier.issue2en_US
dc.identifier.startpage25en_US
dc.identifier.urihttp://www.trdizin.gov.tr/publication/paper/detail/TWpFeU9UUXpNdz09
dc.identifier.urihttps://hdl.handle.net/20.500.12395/33030
dc.identifier.volume4en_US
dc.indekslendigikaynakTR-Dizinen_US
dc.institutionauthorUnlersen, Muhammed Fahri
dc.language.isoenen_US
dc.relation.ispartofInternational Journal of Intelligent Systems and Applications in Engineeringen_US
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectk - Nearest Neighboren_US
dc.subjectMultilayer Perceptron Neural Networken_US
dc.subjectWeka
dc.subjectClassification
dc.subjectRemote Sensing
dc.titleThe classification of diseased trees by using kNN and MLP classification models according to the satellite imageryen_US
dc.typeArticleen_US

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