Browsing by Author "Karakose, Ercan"

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    Classification of apple images using support vector machines and deep residual networks
    (SPRINGER, 2023) Adige, Sevim; Kurban, Rifat; Durmus, Ali; Karakose, Ercan; 0000-0002-0277-2210; AGÜ, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü; Kurban, Rifat
    One of the most important problems for farmers who produce large amounts of apples is the classification of the apples accordingtotheir typesinashorttimewithouthandlingthem. Supportvectormachines(SVM) anddeepresidualnetworks (ResNet-50) are machine learning methods that are able to solve general classification situations. In this study, the classification of apple varieties according to their genus is made using machine learning algorithms. A database is created by capturing 120 images from six different apple species. Bag of visual words (BoVW) treat image features as words representing a sparse vector of occurrences over the vocabulary. BoVW features are classified using SVM. On the other hand, ResNet-50 is a convolutional neural network that is 50 layers deep with embedded feature extraction layers. The pretrained ResNet-50 architecture is retrained for apple classification using transfer learning. In the experiments, ourdataset is divided into three cases: Case 1: 40% train, 60% test; Case 2: 60% train, 40% test; and Case 3: 80% train, 20% test. As a result, the linear, Gaussian, and polynomial kernel functions used in the BoVW? SVM algorithm achieved 88%, 92%, and 96% accuracy in Case 3, respectively. In the ResNet-50 classification, the root-mean-square propagation (rmsprop), adaptive moment estimation (adam), and stochastic gradient descent with momentum (sgdm) training algorithms achieved 86%, 89%, and 90% accuracy, respectively, in the set of Case 3.
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    An optimal concentric circular antenna array design using atomic orbital search for communication systems
    (Walter de Gruyter GmbH, 2024) Durmus, Ali; Yildirim, Zafer; Kurban, Rifat; Karakose, Ercan; 0000-0002-0277-2210; AGÜ, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü; Kurban, Rifat
    In this study, optimum radiation patterns of Concentric Circular Antenna Arrays (CCAAs) are obtained by using the Atomic Orbital Search (AOS) algorithm for communication spectrum. Communication systems stands as a nascent technological innovation poised to revolutionize the landscape of wireless communication systems. It distinguishes itself through its hallmark features, notably an exceptionally high data transmission rate, expanded network capacity, minimal latency, and a commendable quality of service. The most important issue in wireless communication is a precision antenna array design. The success of this design depends on suppressing the maximum sidelobe levels (MSLs) values of the antenna in the far-field radiation region as much as possible. The AOS, which is a rapid and flexible search algorithm, is a novel physics-based algorithm. The amplitudes and inter-element spacing of CCAAs are optimally determined by utilizing AOS to the reduction of the MSLs. In this study, CCAAs with three and four rings are considered. The number of elements of these CCAAs has been determined as 4-6-8, 8-10-12 and 6-12-18-24. The radiation patterns obtained with AOS are compared with the results available in the literature and it is seen that the results of the AOS method are better.