Scopus İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395
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Conference Object Citation - Scopus: 2Re-Exploring the Kayseri Culture Route by Using Deep Learning for Cultural Heritage Image Classification Cultural Heritage Image Classification by Using Deep Learning: Kayseri Culture Route(Association for Computing Machinery, 2024-05-25) Kevseroğlu, Ozlem; Kurban, RifatThe categorization of images captured during the documentation of architectural structures is a crucial aspect of preserving cultural heritage in digital form. Dealing with a large volume of images makes this categorization process laborious and time-consuming, often leading to errors. Introducing automatic techniques to aid in sorting would streamline this process, enhancing the efficiency of digital documentation. Proper classification of these images facilitates improved organization and more effective searches using specific terms, thereby aiding in the analysis and interpretation of the heritage asset. This study primarily focuses on applying deep learning techniques, specifically SqueezeNet convolutional neural networks (CNNs), for classifying images of architectural heritage. The effectiveness of training these networks from scratch versus fine-tuning pre-existing models is examined. In this study, we concentrate on identifying significant elements within images of buildings with architectural heritage significance of Kayseri Culture Route. Since no suitable datasets for network training were found, a new dataset was created. Transfer learning enables the use of pre-trained convolutional neural networks to specific image classification tasks. In the experiments, 99.8% of classification accuracy have been achieved by using SqueezeNet, suggesting that the implementation of the technique can substantially enhance the digital documentation of architectural heritage. © 2024 Elsevier B.V., All rights reserved.Article Citation - WoS: 3Citation - Scopus: 5Multi-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, 2023-09-07) Cakiroglu, Fatma; Kurban, Rifat; Durmus, Ali; Karakose, ErcanThe 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.Article Citation - WoS: 15Citation - Scopus: 17Investigation of the Performance and Properties of ZnO/GO Double-Layer Supercapacitor(Pergamon-Elsevier Science Ltd, 2024-08) Buyukkurkcu, Handan; Durmus, Ali; Colak, Hakan; Kurban, Rifat; Sahmetlioglu, Ertugrul; Karakose, ErcanComposite 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.Article Citation - WoS: 2Citation - Scopus: 2Investigation of the Structural and Magnetic Properties of Rapidly Solidified Nd-Fe Alloys(Springer, 2024-07) Aytekin, Orkun; Kurban, Rifat; Durmus, Ali; Colak, Hakan; Karakose, ErcanThis study introduces the first literature report of rapidly solidified 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 diffraction (XRD), field-emission scanning electron microscopy (FE-SEM), energy-dispersive x-ray spectroscopy (EDX), differential 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 solidification speed increased by approximately 34% when compared to the ingot alloy.Article Citation - WoS: 16Citation - Scopus: 26Gaussian of Differences: A Simple and Efficient General Image Fusion Method(MDPI, 2023-08-15) Kurban, RifatThe 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.Article Citation - WoS: 20Citation - Scopus: 31Classification of Apple Images Using Support Vector Machines and Deep Residual Networks(Springer London Ltd, 2023-02-21) Adige, Sevim; Kurban, Rifat; Durmus, Ali; Karakose, ErcanOne of the most important problems for farmers who produce large amounts of apples is the classification of the apples according to their types in a short time without handling them. Support vector machines (SVM) and deep residual networks (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 pre-trained ResNet-50 architecture is retrained for apple classification using transfer learning. In the experiments, our dataset 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.