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- MakaleAçık Erişim2023Scopus SJRQ4Deep Learning Based Classification for Hoverflies (Diptera: Syrphidae)
Utku, Anıl; Ayaz, Zafer; Ciftci, Derya; Ali Akcayol, M.
Syrphidae is essential in pollinating many flowering plants and cereals and is a family with high species diversity in the order Diptera. These family species are also used in biodiversity and conservation studies. This study proposes an image-based CNN model for easy, fast, and accurate identification of Syrphidae species. Seven hundred twenty-seven hoverfly images were used to train and test the developed deep-learning model. Four hundred seventy-nine of these images were allocated to the training set and two hundred forty-eight to the test dataset. There are a total of 15 species in the dataset. With the CNN-based deep learning model developed in this study, accuracy 0.96, precision 0.97, recall 0.96, and f-measure 0.96 values were obtained for the dataset. The experimental results showed that the proposed CNN-based deep learning model had a high success rate in distinguishing the Syrphidae species.
AtıfScopus3WoS3OpenAlex3TR Dizin1
İncele - BildiriAçık Erişim2017Facial emotion recognition on a dataset using Convolutional Neural Network
Tümen, Vedat; Söylemez, Ömer Faruk; Ergen, Burhan
Nowadays, deep learning is a technique that takes place in many computer vision related applications and studies. While it is put in the practice mostly on content based image retrieval, there is still room for improvement by employing it in diverse computer vision applications. In this study, we aimed to build a Convolutional Neural Network (CNN) based Facial Expression Recognition System (FER), in order to automatically classify expressions presented in Facial Expression Recognition (FER2013) database. Our presented CNN achieved % 57.1 success rate on FER2013 database.
AtıfScopus68OpenAlex83İncele