Development of CNN architecture for Honey Bees Disease Condition

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dc.authoridHanbay, Davut/0000-0003-2271-7865
dc.authoridKarci, Ali/0000-0002-8489-8617
dc.authoridFIRAT, Huseyin/0000-0002-1257-8518
dc.authoridUZEN, Huseyin/0000-0002-0998-2130
dc.authoridYeroglu, Celaleddin/0000-0002-6106-2374
dc.authorwosidHanbay, Davut/AAG-8511-2019
dc.authorwosidKarci, Ali/AAG-5337-2019
dc.authorwosidFIRAT, Huseyin/ABB-7417-2021
dc.authorwosidUZEN, Huseyin/CZK-0841-2022
dc.contributor.authorUzen, Huseyin
dc.contributor.authorYeroglu, Celaleddin
dc.contributor.authorHanbay, Davut
dc.date.accessioned2024-08-04T20:46:55Z
dc.date.available2024-08-04T20:46:55Z
dc.date.issued2019
dc.departmentİnönü Üniversitesien_US
dc.descriptionInternational Conference on Artificial Intelligence and Data Processing (IDAP) -- SEP 21-22, 2019 -- Inonu Univ, Malatya, TURKEYen_US
dc.description.abstractHoney bees are one of the most important pollinators for a wide range of products in the food chain. Today, with the help of developing technology, observing bees healthy controls is a very important field of study. In this study, the images taken in the natural environment of the bees were processed with Convolutional Neural Network (CNN) architecture and the health status of the bees were classified The results obtained were promising for the studies to be carried out in this area. In addition, the structure of the CNN architectures was studied and the CNN architectures with different types and number of layers were compared with each other. As a result of the comparison, using the ideal number of convolution layers instead of using a great number of convolution layers for CNN architectures, increases the success. In addition, the use of normalization layers that serve as supporters in CNN architectures has been found to be very important for increasing success. In this study, 5 different CNN architectures were developed and the classification results obtained with these architectures were analyzed Among the architectures developed, KM_1 network architecture has achieved the best results with a success rate of 92,42.en_US
dc.description.sponsorshipIEEE Turkey Sect,Anatolian Sci,Inonu Univ, Comp Sci Dept,Inonu Univ, Muhendisli Fakultesien_US
dc.identifier.doi10.1109/idap.2019.8875886
dc.identifier.scopus2-s2.0-85074892626en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttps://doi.org/10.1109/idap.2019.8875886
dc.identifier.urihttps://hdl.handle.net/11616/99043
dc.identifier.wosWOS:000591781100018en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isotren_US
dc.publisherIeeeen_US
dc.relation.ispartof2019 International Conference on Artificial Intelligence and Data Processing (Idap 2019)en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectBee Image Dataseten_US
dc.subjectCNN Architecturesen_US
dc.subjectImage Processingen_US
dc.titleDevelopment of CNN architecture for Honey Bees Disease Conditionen_US
dc.typeConference Objecten_US

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