WoS İndeksli Yayınlar Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/394

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Now showing 1 - 10 of 37
  • Conference Object
    High Performance and Resource Efficient Low Density Parity Check Decoder Design
    (IEEE, 2025-06-25) Unal, Burak
    Low Density Parity Check (LDPC) codes have gained popularity in communication systems due to their capacity-approaching error correction performance. In this study, a highperformance LDPC decoding algorithm with extremely low resource usage is proposed. Among the hard decision class of LDPC decoders, Gallager B (GaB) provides high-performance hardware due to its computational simplicity. However, GaB suffers from poor error-correction performance. In this study, a new intrinsic computation technique for GaB called Intrinsic Gallager B (IGaB) is introduced to improve error correction performance. Our simulation results show that the IGaB algorithm provides better error correction performance compared with GaB. GaB and IGaB algorithms are implemented on Field Programmable Gate Array (FPGA) to compare hardware performance.
  • Conference Object
    Citation - WoS: 2
    Citation - Scopus: 2
    Fine Tuning DeepSeek and Llama Large Language Models with LoRA
    (IEEE, 2025-06-25) Uluirmak, Bugra Alperen; Kurban, Rifat
    In 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
    Tax Compliance Behaviour and Lab Experiments: A Literature Review
    (Maliye Bakanligi, 2021) Demirtas, Burak Kagan
    The purpose of this article is to conduct a literature review of the papers based on laboratory experiments to analyze tax evasion behaviors of individuals. Although experimental studies in economics have become more and more important day by day, there are almost no publications on experimental economics in the Turkish literature. The studies are examined especially in terms of experimental designs because this study also aims to increase awareness about laboratory experiments. This review also discusses the criticism of laboratory experiments and concludes that the results obtained from laboratory experiments are important and it would be beneficial to support them with field experiments.
  • Article
    Citation - WoS: 2
    Reconsidering the Changing Higher Education System from Sociocultural and Spatial Perspectives
    (Deomed Publ, Istanbul, 2020-07-27) Ayten, Asim Mustafa; Gover, Ibrahim Hakan
    Education and research are vital for social development and progress. The changing sociocultural structures and new needs have resulted in some important functional changes in higher education systems with a deep impact on universities serving these needs at the highest level. Besides experiencing these functional changes, the universities today have become spaces of socialization with their social, cultural and sports facilities, replacing their traditional spatial role of offering education only. The local dynamics changing with globalization have now reshaped the global and local roles of universities, highlighting the added value they provide to the society. Sociocultural changes trigger all these functional and structural changes in universities. Therefore, sociocultural factors and their importance should not be ignored in a changing higher education system. In this study, the impact of sociocultural factors with their related spatial structures on world higher education system will be analyzed within their historical contexts, and some suggestions for future universities will be offered considering the current changes. In the first part of the study, the changes in societies and universities will be presented within the historical context. In the second part, the spatial forms and structures of universities will be discussed. In the third part, the catalytic effects of the specific sociocultural factors will be highlighted and elaborated on. Finally, some suggestions will be made for the universities of the future in the light of the current situation and the data available.
  • Conference Object
    Citation - WoS: 1
    Citation - Scopus: 1
    Prediction of Type 2 Diabetes Using Metagenomic Data and Identification of Taxonomic Biomarkers
    (IEEE, 2024-05-15) Temiz, Mustafa; Kuzudisli, Cihan; Yousef, Malik; Bakir-Gungor, Burcu
    Nowadays, different molecular levels of -omics data on diseases are generated and analyzing these data with machine learning methods is one of the popular research topics. Among these data, the use of metagenomic data to facilitate the diagnosis, detection and treatment of diseases is increasing day by day. Type 2 diabetes (T2D) is a chronic disease characterized by insulin resistance and progressive dysfunction of pancreatic beta cells. While the number of people with diabetes is increasing by around 8% annually, the cost of treating the disease is rising by 18% per year. Therefore, the number of studies on the diagnosis, development and progression of T2D is increasing over time. The aim of this study is to achieve higher machine learning performance by using fewer metagenomic features and to achieve better classification performance by reducing computational costs. In this study, we compare the performance of three different methods using T2D-related metagenomic data. First, the MetaPhlAn tool is used to calculate the taxonomic species and their relative abundances in each sample. The SVM-RCE, RCE-IFE and microBiomeGSM tools used in this study are methods that perform classification by grouping and scoring features and are known to work well on complex datasets. In this study, the best results were obtained with the RCE-IFE tool with an AUC of 0.72 with an average of 125 features information. In addition, key taxonomic species identified by these tools as associated with T2D are presented in comparison to the literature.
  • Conference Object
    Lifetime Analysis of Underwater Wireless Networks Concerning Privacy With Energy Harvesting and Compressive Sensing
    (IEEE, 2019-04) Uyan, O. Gokhan; Gungor, V. Cagri
    Underwater sensor networks (UWSN) are a division of classical wireless sensor networks (WSN), which are designed to accomplish both military and civil operations, such as invasion detection and underwater life monitoring. Underwater sensor nodes operate using the energy provided by integrated limited batteries, and it is a serious challenge to replace the battery under the water especially in harsh conditions with a high number of sensor nodes. Here, energy efficiency confronts as a very important issue. Besides energy efficiency, data privacy is another essential topic since UWSN typically generate delicate sensing data. UWSN can be vulnerable to silent positioning and listening, which is injecting similar adversary nodes into close locations to the network to sniff transmitted data. In this paper, we discuss the usage of compressive sensing (CS) and energy harvesting (EH) to improve the lifetime of the network whilst we suggest a novel encryption decision method to maintain privacy of UWSN. We also deploy a Mixed Integer Programming (MIP) model to optimize the encryption decision cases which leads to an improved network lifetime.
  • Conference Object
    Investigation of Hepatocellular Carcinoma Molecular Mechanisms via in Silico Analyses
    (IEEE, 2020-09) Dogan, Refika Sultan; Saka, Samed; Gungor, Burcu Bakir
    Hepatocellular carcinoma (HCC) is the most common cause of cancer-related death in the world. The molecular changes in the organism during the development of HCC are not fully understood. The aim of the present study is to contribute to the identification of critical genes and pathways associated with HCC via integrating various bioinformatics methods. In this study, experiments were conducted on gene expression data of 14 HCC tissues and non-cancerous control tissues. A total of 1229 genes, which show a statistically significant change between the groups, were identified. Among these, 681 genes were upregulated and 548 genes were downregulated. Via mapping the detected genes into protein protein interaction networks, active subnetwork search, subnetwork topological analysis and functional enrichment analyses were carried out. The interactions between the molecular pathways affected by HCC were also presented.
  • Conference Object
    Citation - WoS: 2
    Credit Card Fraud Detection With Machine Learning Methods
    (IEEE, 2019-09) Goy, Gokhan; Gezer, Cengiz; Gungor, Vehbi Cagri
    With the increase in credit card usage of people, the credit card transactions increase dramatically. It is difficult to identify fraudulent transactions among the vast amount of credit card transactions. Although credit card fraud is limited in number of transactions, it causes serious problems in terms of financial losses for individuals and organizations. Even though large number of studies has been conducted to solve this problem, there is no generally accepted solution. In this paper, a publicly available data set is used. The unbalance problem of the data set was solved by using hybrid sampling methods together. On this data set, comparative performance evaluations have been conducted. Different from other studies, the Area Under the Curve (AUC) metric, which expresses the success in such data sets, has also been used in addition to standard performance metrics. Since it is also important to quickly detect credit card fraud transactions; the running time of different methods is also presented as another performance metric.
  • Conference Object
    Citation - WoS: 1
    Analysis of Battery-Powered Sensor Node Lifetime for Smart Grid Applications
    (IEEE, 2016) Eris, Cigdem; Gungor, V. Cagri; Boluk, Pinar Sarisaray
    Wireless Sensor Networks (WSNs) enable smart grids where sensor nodes monitor and control the important parameters of power grid components. However, energy-aware communication protocols should be developed to extend network lifetime of WSNs in smart grid environments. In this study, the lifetime of wireless sensor nodes has been analyzed for various smart grid environments, such as 500 kV substation, main power control room, and underground network transformer vaults. In addition, the effects of different operation modes of sensor nodes on node lifetime have been reviewed.
  • Article
    3D Sampling of K-Space With Non-Cartesian Trajectories in MR Imaging
    (Gazi Univ, Fac Engineering Architecture, 2025-02-03) Dundar, Mehmet Sait; Gumus, Kazim Z.; Yilmaz, Bulent
    This study presents an innovative approach to 3D k-space sampling in MR imaging using non-Cartesian concentric shell trajectories. The method involves 32 concentric shells of varying radii, allowing for rapid data acquisition through undersampling techniques. Simulations using IDEA software demonstrate that this approach can fill the k-space in less than one second, a significant time reduction compared to traditional FLASH sequences that can take 3-4 minutes. The concentric shell model enhances imaging efficiency by minimizing artifacts and ensuring uniform k-space filling, leading to higher resolution and faster scans. This technique shows promise for clinical applications, particularly in dynamic imaging scenarios such as acute stroke and pediatric radiology, where speed and precision are critical. As illustrated in Figure A, the concentric shell trajectories enable uniform k-space filling, significantly reducing scan times and improving image quality. These results are based on the simulations conducted with IDEA software.