Flower Pollination Algorithm-Optimized Deep CNN Features for Almond(Prunus dulcis) Classification
Yazarlar (3)
Arş. Gör. Mustafa Yurdakul Kirikkale Üniversitesi, Türkiye
Dr. Öğr. Üyesi İrfan Atabaş Kirikkale Üniversitesi, Türkiye
Prof. Dr. Şakir TAŞDEMİR Sinop Üniversitesi, Türkiye
Bildiri Türü Tebliğ/Bildiri Bildiri Dili İngilizce
Bildiri Alt Türü Tam Metin Olarak Yayınlanan Tebliğ (Uluslararası Kongre/Sempozyum)
Bildiri Niteliği Alanında Hakemli Uluslararası Kongre/Sempozyum
DOI Numarası 10.1109/ESIC60604.2024.10481555
Kongre Adı 2024 International Conference on Emerging Systems and Intelligent Computing (ESIC)
Kongre Tarihi 09-02-2024 / 10-02-2024
Basıldığı Ülke Basıldığı Şehir
Bildiri Linki https://ieeexplore.ieee.org/xpl/conhome/10481518/proceeding
UAK Araştırma Alanları
Yapay Zeka
Özet
Almond is a nut rich in essential nutrients. In addition to being a food, it is also used in cosmetics and the pharmaceutical industry. The market value of almonds is determined according to the quality of the almonds. Manually determining the quality of almonds by humans is a prone to error, time-consuming, and tiring process. In this study, For this reasons, well-known twelve pre-trained CNNs were used to classify almonds as normal and damaged. Then, the most successful model was used as a feature extractor, and the features were classified with various machine learning algorithms. In addition to all these, features were selected by using the FPA algorithm, and the classification process was carried out. Experimental results showed that the use of CNNs as feature extractors and classification with machine learning algorithms can provide better results than the classical softmax structure. In addition, the proposed …
Anahtar Kelimeler
Almond | Artificial Bee Colony | Classification | CNN | Feature Extraction | Flower Pollination Optimization
BM Sürdürülebilir Kalkınma Amaçları
Atıf Sayıları
Scopus 6
Google Scholar 11
Flower Pollination Algorithm-Optimized Deep CNN Features for Almond(Prunus dulcis) Classification

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