Bilgisayar Mühendisliği Bölümü Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/203

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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.
A Comparative Analysis of Convolutional Neural Network Architectures for Binary Image Classification: A Case Study in Skin Cancer Detection
(Giresun Üniversitesi, 2024) Korkut, Şerife Gül; Kocabaş, Hatice; Kurban, Rifat; 0009-0007-1398-0924; 0009-0003-1760-0555; 0000-0002-0277-2210; AGÜ, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü; Korkut, Şerife Gül; Kocabaş, Hatice; Kurban, Rifat
In this study, a comprehensive comparative analysis of Convolutional Neural Network (CNN) architectures for binary image classification is presented with a particular focus on the benefits of transfer learning. The performance and accuracy of prominent CNN models, including MobileNetV3, VGG19, ResNet50, and EfficientNetB0, in classifying skin cancer from binary images are evaluated. Using a pre-trained approach, the impact of transfer learning on the effectiveness of these architectures and identify their strengths and weaknesses within the context of binary image classification are investigated. This paper aims to provide valuable insights for selecting the optimal CNN architecture and leveraging transfer learning to achieve superior performance in binary image classification applications, particularly those related to medical image analysis.
Gaussian of Differences: A Simple and Efficient General Image Fusion Method
(MDPI, 2023) Kurban, Rifat; 0000-0002-0277-2210; AGÜ, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü; Kurban, Rifat
The separate analysis of images obtained from a single source using different camera settings or spectral bands, whether from one or more than one sensor, is quite difficult. To solve this problem, a single image containing all of the distinctive pieces of information in each source image is generally created by combining the images, a process called image fusion. In this paper, a simple and efficient, pixel-based image fusion method is proposed that relies on weighting the edge information associated with each pixel of all of the source images proportional to the distance from their neighbors by employing a Gaussian filter. The proposed method, Gaussian of differences (GD), was evaluated using multi-modal medical images, multi-sensor visible and infrared images, multi-focus images, and multi-exposure images, and was compared to existing state-of-the-art fusion methods by utilizing objective fusion quality metrics. The parameters of the GD method are further enhanced by employing the pattern search (PS) algorithm, resulting in an adaptive optimization strategy. Extensive experiments illustrated that the proposed GD fusion method ranked better on average than others in terms of objective quality metrics and CPU time consumption.
Investigation of the performance and properties of ZnO/GO double-layer supercapacitor
(ELSEVIER, 2024) Büyükkürkçü, Handan; Durmuş, Ali; Çolak, Hakan; Kurban, Rifat; Şahmetlioğlu, Ertuğrul; Karaköse, Ercan; 0000-0002-0277-2210; AGÜ, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü; Kurban, Rifat
Composite electrode material was formed by mixing reduced graphene oxide (rGO) and zinc oxide (ZnO) compound, using the Hummers and green synthesis methods, respectively. Of rGO powder, 10 g was mixed with 10%, 20% and 30% ZnO, and composite electrodes were obtained by using 10% binder. The energy storage performance and structural characteristics of the supercapacitor were evaluated by analyzing the capacitance values of the synthesized electrodes. The structural characterization of ZnO/rGO composites was performed using X-ray diffraction and field-emission scanning electron microscopy. The electrochemical properties of the ZnO/GO electrodes were analyzed by cyclic voltammetry, electrochemical impedance and galvanostatic charge–discharge tests. The specific capacitance value of electrodes increased as zinc content increased in the ZnO/rGO composite material used to produce electrodes. The maximum specific capacitance values were measured at 5 mV/s scanning rate as 194.23 (rGO), 366.81 (10% ZnO), 383.18 (20% ZnO) and 410.48 F/g (30% ZnO). In conclusion, the use of composite material formed by the combination of ZnO nanoparticles obtained by green synthesis method from orange peel and graphene oxide increased the electrochemical efficiency of the supercapacitor.
Investigation of the structural and magnetic properties of rapidly solidified Nd–Fe–B–Ce alloys
(SPRINGER LINK, 2024) Aytekin, Orkun; Kurban, Rifat; Durmuş, Ali; Çolak, Hakan; Karaköse, Ercan; 0000-0002-0277-2210; AGÜ, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü; Kurban, Rifat
This study introduces the frst literature report of rapidly solidifed Nd–Fe–B– Ce alloys fabricated using the melt-spinning technique at varying disc rotation speeds. The resulting alloy images are then analyzed using various image processing techniques, and their structural and magnetic characteristics are described. The alloys are characterized using a variety of methods, including x-ray difraction (XRD), feld-emission scanning electron microscopy (FE-SEM), energy-dispersive x-ray spectroscopy (EDX), diferential thermal analysis (DTA), vibrating sample magnetometry (VSM), and Vickers microhardness tests. By using XRD, the tetragonal hard magnetic Nd2Fe14B phase is detected in the Nd30Fe65B0.9Ce5 alloy. The FE-SEM microstructure analysis shows that the grain structure of the ingot alloy is indistinct, and the tetragonal symmetric structure begins to appear at disc rotation speeds of 20 m/s and 40 m/s. The analysis of FE-SEM images using histogram analysis, the image segmentation technique, and VSM method reveals that the coercivity values of the sample produced at the 80 m/s solidifcation speed increased by approximately 34% when compared to the ingot alloy.
Multi-focus image fusion by using swarm and physics based metaheuristic algorithms: a comparative study with archimedes, atomic orbital search, equilibrium, particle swarm, artificial bee colony and jellyfish search optimizers
(SPRINGER, 2023) Çakıroğlu, Fatma; Kurban, Rifat; Durmuş, Ali; Karaköse, Ercan; 0000-0002-0277-2210; AGÜ, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü; Kurban, Rifat
The lenses focus only on the objects at a specific distance when an image is captured, the objects at other distances look blurred. This is referred to as the limited depth of field problem, and several attempts exist to solve this problem. Multi-focus image fusion is one of the most used methods when solving this problem. A clear image of the whole scene is obtained by fusing at least two different images obtained with different focuses. Block-based methods are one of the most used methods for multi-focus fusion at the pixel-level. The size of the block to be used is an important factor for determining the performance of the fusion. Thus, the block size must be optimized. In this study, the comparison between the swarm-based and physics-based algorithms is made to determine the optimal block size. The comparison has been made among the following optimization methods which are, namely, Archimedes Optimization Algorithm (AOA), Atomic Orbital Search (AOS) and Equilibrium Optimizer (EO) from the physics-based algorithms and Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC) and Jellyfish Search Algorithm (JSA) from swarm-based algorithms. The swarm-based ABC and JSA algorithms have shown a better performance when compared to physics-based methods. Moreover, meta-heuristic algorithms, in general, are more adaptive compared to the traditional fusion methods.
MULTILEVEL THRESHOLDING FOR BRAIN MR IMAGE SEGMENTATION USING SWARM-BASED OPTIMIZATION ALGORITHMS
(Kahramanmaraş Sütçü İmam Üniversitesi, 2024) Toprak, Ahmet Nusret; Şahin, Ömür; Kurban, Rifat; 0000-0002-0277-2210; AGÜ, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü; Kurban, Rifat
Image segmentation, the process of dividing an image into various sets of pixels called segments, is an essential technique in image processing. Image segmentation reduces the complexity of the image and makes it easier to analyze by dividing the image into segments. One of the simplest yet powerful ways of image segmentation is multilevel thresholding, in which pixels are segmented into multiple regions according to their intensities. This study aims to explore and compare the performance of the well-known swarm-based optimization algorithms on the multilevel thresholding-based image segmentation task using brain MR images. Seven swarm-based optimization algorithms: Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), Gray Wolf Optimizer (GWO), MothFlame Optimization (MFO), Ant Lion Optimization (ALO), Whale Optimization (WOA), and Jellyfish Search Optimizer (JS) algorithms are compared by applying to brain MR images to determine threshold levels. In the experiments carried out with mentioned algorithms, minimum cross-entropy, and between-class variance objective functions were employed. Extensive experiments show that JS, ABC, and PSO algorithms outperform others.
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.

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