Scopus İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395
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Article Fine-Tuning Large Language Models for Turkish Flutter Code Generation(Sakarya University, 2025-12-29) Uluırmak, Buğra Alperen; Kurban, RifatThe rapid advancement of large language models (LLMs) for code generation has largely centered on English programming queries. This paper focuses on a low-resource language scenario, specifically Turkish, in the context of Flutter mobile app development. Two representative LLMs (a 4B-parameter multilingual model and a 3B code-specialized model) on a new Turkish question-and-answer dataset for Flutter/Dart are fine-tuned in this study. Fine-tuning with parameter-efficient techniques yields dramatic improvements in code generation quality: Bilingual Evaluation Understudy (BLEU), Recall-Oriented Understudy for Gisting Evaluation (ROUGE-L), Metric for Evaluation of Translation with Explicit Ordering (METEOR), Bidirectional Encoder Representations from Transformers Score (BERTScore), and CodeBLEU scores show significant increases. The rate of correct solutions increased from ~30–70% (for base models) to 80–90% after fine-tuning. The performance trade-offs between models are analyzed, revealing that the multilingual model slightly outperforms the code-focused model in accuracy after fine-tuning. However, the code-focused model demonstrates faster inference speeds. These results demonstrate that even with very limited non-English training data, customizing LLMs can bridge the gap in code generation, enabling high-quality assistance for Turkish developers comparable to that for English. The dataset was released on GitHub to facilitate further research in multilingual code generation.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: 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.