WoS İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/394
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Conference Object Citation - WoS: 2Citation - Scopus: 2Fine Tuning DeepSeek and Llama Large Language Models with LoRA(IEEE, 2025-06-25) Uluirmak, Bugra Alperen; Kurban, RifatIn this paper, Low-Rank Adaptation (LoRA) finetuning of two different large language models (DeepSeek R1 Distill 8B and Llama3.1 8B) was performed using the Turkish dataset. Training was performed on Google Colab using A100 40 GB GPU, while the testing phase was carried out on Runpod using L4 24 GB GPU. The 64.6 thousand row dataset was transformed into question-answer pairs from the fields of agriculture, education, law and sustainability. In the testing phase, 40 test questions were asked for each model via Ollama web UI and the results were supported with graphs and detailed tables. It was observed that the performance of the existing language models improved with the fine-tuning method.Article Citation - WoS: 6Citation - Scopus: 5An Optimal Concentric Circular Antenna Array Design Using Atomic Orbital Search for Communication Systems(Walter de Gruyter Gmbh, 2024-05-06) Durmus, Ali; Yildirim, Zafer; Kurban, Rifat; Karakose, ErcanIn 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.Conference Object Citation - Scopus: 1A Comprehensive Investigation into Strip Steel Defect Detection Using Traditional Machine Learning and Deep Learning Models(IEEE, 2025-05-23) Erkantarci, Betul; Kurban, Rifat; Bakal, Mehmet Gokhan; Kose, AbdulkadirThe steel manufacturing sector places great importance on guaranteeing the quality of strip steel products, which has led to a thorough investigation of defect detection approaches. This work conducts a comparative analysis of traditional machine learning and deep learning models to determine their efficacy in detecting defects in strip steel. Our analysis is based on a dataset that includes a variety of images of strip steel surfaces showing different types of defects. In this work, we adopt image preprocessing techniques to improve the quality of input images prior to the application of classification methods. We employ traditional ML algorithms including Support Vector Machine and Random Forest, and deep learning model AlexNet Convolutional Neural Networks for effective defect classification. Consequently, we present comparative evaluations that highlight the strengths and weaknesses of each approach, considering accuracy scores.