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Öğe Almond (Prunus dulcis) varieties classification with genetic designed lightweight CNN architecture(Springer, 2024) Yurdakul, Mustafa; Atabaş, İrfan; Taşdemir, ŞakirAlmond (Prunus dulcis) is a nutritious food with a rich content. In addition to consuming as food, it is also used for various purposes in sectors such as medicine, cosmetics and bioenergy. With all these usages, almond has become a globally demanded product. Accurately determining almond variety is crucial for quality assessment and market value. Convolutional Neural Network (CNN) has a great performance in image classification. In this study, a public dataset containing images of four different almond varieties was created. Five well-known and light-weight CNN models (DenseNet121, EfficientNetB0, MobileNet, MobileNet V2, NASNetMobile) were used to classify almond images. Additionally, a model called 'Genetic CNN', which has its hyperparameters determined by Genetic Algorithm, was proposed. Among the well-known and light-weight CNN models, NASNetMobile achieved the most successful result with an accuracy rate of 99.20%, precision of 99.21%, recall of 99.20% and f1-score of 99.19%. Genetic CNN outperformed well-known models with an accuracy rate of 99.55%, precision of 99.56%, recall of 99.55% and f1-score of 99.55%. Furthermore, the Genetic CNN model has a relatively small size and low test time in comparison to other models, with a parameter count of only 1.1 million. Genetic CNN is suitable for embedded and mobile systems and can be used in real-life solutions.Öğe Chestnut(Castanea Sativa) Varieties Classification with Harris Hawks Optimization based Selected Features and SVM(Institute of Electrical and Electronics Engineers Inc., 2024) Yurdakul, Mustafa; Atabaş, İrfan; Taşdemir, ŞakirChestnut(Castanea sativa) is a nutritious food with a hard outer shell. It is also used in different sectors for various purposes. Chestnut is a commercial product that is in demand worldwide due to its multi-purpose use. In order to determine the market value of chestnuts, it is necessary to classify it according to its types. With classical methods, people classify it manually. However, this method is tiring and error prone. In this study, for classifying chestnut varieties, features were extracted from chestnut images using various feature extraction methods. The extracted features were combined and classified with Linear, Poly and Radial Basis Function(RBF) kernels of Support Vector Machine(SVM). The combined handcrafted features and RBF kernel achieved an accuracy of 94.28%, precision of 93.83%, recall of 93.98%, F1-Score of 93.84%, and AUC of 99.25%. Furthermore, the most relevant features were selected using Arithmetic Optimization, Harris Hawks and Sooty Tern algorithms. The Harris Hawks Optimization selected features and RBF kernel showed the best classification performance with an accuracy of 95.84%, precision of 95.56%, recall of 95.51%, F1-score of 95.46% and AUC of 99.45%. © 2024 IEEE.Öğe Design of a Tracking Welding Robot Automation System for Manufacturing of Steam and Heating Boilers(2018) Ersöz, Süleyman; Türker, Ahmet Kürşad; Aktepe, Adnan; Atabaş, İrfan; Kokoç, MeldaFor satisfying customers companies want to respond to customer requests on time. At the same time, they expect production process to be completed with low cost and low loss. For this reason, the importance of mechanization and automation in production sector has increased. As a result, companies have begun to give more importance to robotic systems, which are the basic components of automation systems. Despite the likelihood of mistakes caused by physiological and mental states of humans, these systems can perform operations precisely without any variability. In this study, an application was carried out for the automation of welding process of industrial type boilers in different sizes and features. For products of which standard measurements or welding operations are difficult to perform manually, a robotic system was proposed in which measurement and welding operations can be performed automatically. In addition, operators are prevented from exposure to gas and light via the proposed system which enables a safer working condition.Öğe Dikey bir levhada doğal ısı taşınımının robot kol kullanılarak incelenmesi(Kırıkkale Üniversitesi, 2006) Atabaş, İrfan; Uzun, Doç. İbrahimÖZETD KEY B R LEVHADA DOĞAL ISI TAŞINIMININ ROBOT KOLKULLANILARAK NCELENMESATABAŞ rfanKırıkkale ÜniversitesiFen Bilimleri EnstitüsüMakine Anabilim Dalı, Yüksek Lisans TeziDanışman: Doç. Dr. brahim UZUNHaziran 2006, 74 sayfaDikey ve sabit yüzey sıcaklığındaki bir plakada doğal ısı taşınımıbüyüklüklerinin deneysel olarak ölçülmesi yapılmıştır. Sabit sıcaklıkta biryüzey elde etmek için kaynama sıcaklığındaki bir akışkan deposu kullanılmışve depo sıcaklığının değişmemesi için kaynama sıcaklığına ayarlanmış birtermostat bağlantılı ısıtıcı kullanılmıştır. Yüzeyin istenilen noktasınakonumlandırılabilen dört kol serbestlik derecesine sahip ve kartezyenkoordinatlarda üç boyutlu çalışan robot kol tasarımı yapılmıştır. Robot kolunuhareket ettiren adım motorlarından biri bir ray üzerinde robot kolun (x)yünündeki hareketini, diğer üç adım motoru ise robot kolun (y-z) düzlemindehareket etmesini sağlamaktadır. Bu plaka yüzeyinde istenilen noktaya(x,y,z)konumlandırılabilen robot kolunun en uç noktasına yerleştirilen hız veyasıcaklık algılayıcı büyüklüğü okuyarak sisteme bağlı bilgisayaraaktarmaktadır. Bilgisayarda çalışan bir program ve bu programın ara yüzü ilekontrol edilebilen robot kolu istenilen noktadaki sıcaklık veya hız değerleriniokumaktadır. Okunan sıcaklık değerleri bir bütünlük içerisindedeğerlendirilerek dikey doğrultuda sıcaklık dağılımları grafiksel olarak eldeedilmiştir. Elde edilen deneysel sonuçlar ile analitik olarak hesaplananbüyüklükler karşılaştırmalı olarak tablo ve grafiklerle verilmiştir.Anahtar Kelimeler: Robot kol, dikey levha, sıcaklık algılayıcılar, doğaltaşınım.iiÖğe Flower Pollination Algorithm-Optimized Deep CNN Features for Almond(Prunus dulcis) Classification(Institute of Electrical and Electronics Engineers Inc., 2024) Yurdakul, Mustafa; Atabaş, İrfan; Taşdemir, ŞakirAlmond 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 FPA-based feature extraction increases the classification performance. © 2024 IEEE.
