Classification of the weather images with the proposed hybrid model using deep learning, SVM classifier, and mRMR feature selection methods
Küçük Resim Yok
Tarih
2022
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
As in many fields, the use of artificial intelligence methods in the classification of weather images will be very useful. In this study, a data set consisting of five classes such as cloudy, foggy, rainy, shine, and sunrise was used. A hybrid model has been developed to classify the images in the dataset. First of all, the features of the images in the dataset are obtained by using MobilenetV2, Densenet201, and Efficientnetb0 architectures, which are the most popular Convolutional Neural Network (CNN) architectures. These features are combined and optimized so that these optimized features are classified in the Support Vector Machine (SVM) classifier, one of the most popular classifier methods in machine learning. As a result, the developed hybrid model has outperformed the existing pre-trained architectures in the study. In addition, it has been proven that classification by concatenating the features obtained with CNN architectures is a successful method.
Açıklama
Anahtar Kelimeler
Deep Learning, Feature Extraction, mRMR, SVM, Classification
Kaynak
GEOCARTO INTERNATIONAL
WoS Q Değeri
Q2
Scopus Q Değeri
Cilt
Sayı
Künye
Yildirim, M., Çinar, A., & Cengil, E. (2022). Classification of the weather images with the proposed hybrid model using Deep Learning, SVM classifier, and mRMR feature selection methods. Geocarto International, (just-accepted), 1-11.
