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WoS İndeksli Yayınlar Koleksiyonu

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

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Department: search.filters.department.Abdullah Gül ÜniversitesiScopus Q: search.filters.scopusQuartile.N/A

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Now showing 1 - 9 of 9
  • Article
    Analysis of Power-Law Fin-Type Problems Using Physics Informed Neural Networks
    (Sciendo, 2025-12-01) Gocer, M.; Coskun, S. B.; Atay, M. T.
    This study aims to model the temperature distribution in a single fin subjected to steady one-dimensional heat conduction with nonlinear thermal behavior. For the modeling and solution of the problem, the Physics-Informed Neural Networks (PINNs) architecture was used. The temperature-dependent heat conduction problem and the nonlinear boundary conditions of this problem were formulated with a differential equation. With the help of the PINN architecture, the loss function was minimized in order to reduce the difference between the true value and the predicted value. During this minimization process, the PINN architecture was forced to be consistent with the physical laws. The results obtained after training the PINN architecture exhibit successful performance in terms of accuracy and reliability when compared with the results in the literature. These findings highlight the potential of PINNs as a powerful alternative to conventional methods for solving complex nonlinear heat conduction problems.
  • Conference Object
    Shooting a Water Slug Into an Air Column with and without Vent
    (Amer Soc Mechanical Engineers, 2025-07-20) Bozkus, Zafer; Dincer, Ali Ersin; Tijsseling, Arris S.; van de Ven, Fons
    Compressed air is used to shoot a single water slug into an upward sloping pipe with elbow and orifice at its upper end. The experiment concerns a 12 m long pipe of 0.1 m diameter connected to a 0.5 m3 air vessel. The 10 to 50 kg heavy slugs are initially at rest in the lower part of the system. Because the upper end is closed by a flange with orifice, the water slug is expected not to hit the upstream elbow. It causes - like a piston - a fast compression of the air column ahead of it. Sometimes the slug bounces back and forth, which results in a pressure oscillation of serious amplitude. Numerical simulations based on an elementary mathematical model are normally used to interpret the pressure measurements, not all of which are fully understood. Lessons learned are summarised, and suggestions for improved experiments and enhanced simulations are given. The research is of importance, for example, for steam lines where liquid condensates may collect in lower parts after power failure. Start-up of the system will then lead to rapid slug acceleration and potentially damaging impact on elbows, orifices, and machinery.
  • Conference Object
    Security Through Digital Twin-Based Intrusion Detection: A Swat Dataset Analysis
    (IEEE, 2023-10-18) Bozdal, Mehmet
    Digital twin, as a virtual replica of physical entity, offer valuable insights into Industrial Control System (ICS) behavior and characteristics. Leveraging the convergence of digital twins and cybersecurity, this research explores its role in securing critical infrastructure, using the Secure Water Treatment (SWaT) system as a case study. Existing intrusion detection systems (IDS) for SWaT encounter challenges related to requiring huge amounts of a dataset for training, being unable to adopt high data dimensionality, and adaptability to emerging threats. To address these issues, a hybrid digital twin model is proposed, combining physics-based models and data-driven approaches. This model facilitates precise attack localization and explainable IDS outcomes. The method exhibits promising capabilities for enhancing critical infrastructure security and adapting to evolving cyber threats. Experimental results demonstrate the ability to detect eight out of nine attack types.
  • Article
    Citation - WoS: 2
    Citation - Scopus: 2
    Numerical Analysis and Experimental Comparison of Stress and Stiffness Parameters of Steel Reinforced Geopolymer Concrete Columns
    (Elsevier Sci Ltd, 2026-01) Ozbayrak, Ahmet; Kucukgoncu, Hurmet; Aslanbay, Huseyin Hilmi; Aslanbay, Yuksel Gul; Altun, Fatih
    Despite extensive research, Geopolymer concrete (GPC) lacks reinforced concrete construction and design specifications. Developing such specifications requires comprehensive studies to promote the use of GPC, which is known for its superior performance and environmental benefits compared to ordinary Portland cement concrete (OPC). This study numerically investigated and compared the behavior and strength of fly ash-based geopolymer-reinforced concrete columns with the experimental results. Comparisons with OPC were made based on existing specifications. Herein, FEM analyses were conducted on 16 GPC and 4 OPC columns under eccentric axial compressive loads. Parameters such as eccentricity, reinforcement ratio, curing method, and activation solution ratios were varied. According to average numerical results, the GPC columns have 7% more moment capacity and 30% more curvature values than OPC. Moreover, GPC columns absorbed more energy than OPC columns. Also, GPC columns have higher axial load and bending moment carrying capacities than OPC for numerical results. Error analysis between FEM and experimental data revealed a strong correlation, with MAPE values of 8.88% (axial load) and 7.20% (moment) for GPC columns, confirming the reliability of the numerical model. ACI 318 and Eurocode 2 specifications were deemed applicable for GPC columns, provided axial loads are limited per TEC 2018.
  • Conference Object
    Enhancing Complex Disease Group Scoring with Mirgedinet: A Multi-Algorithm Machine Learning Framework Based on the GSM Approach
    (IEEE, 2025-06-25) Qumsiyeh, Emma; Bakir-Gungor, Burcu; Yousef, Malik
    Integrating biological prior knowledge for disease gene associations has shown significant promise in discovering new biomarkers with potential translational applications. This work investigates the application of a multi-algorithm machine learning framework based on the Grouping-Scoring-Modeling (G-S-M) approach for improving the prediction of complex diseases. The study identifies the primary gene and miRNA interactions in various complex diseases with the help of miRGediNET, which is a machine-learning based tool that integrates data from three biological databases. Traditional methods have only focused on independence between features; the G-S-M method focuses on aggregating genes based on biological interactions, pinpointing the scoring of gene groups for a disease, and modeling its predictive capability using advanced machine learning algorithms. In this research paper, seven algorithms, including Support Vector Machine, Decision Tree, and CatBoost, were applied to eight datasets extracted from the GEO database. This framework proved very robust in ranking gene clusters, thus predicting critical biomarkers while doing 100-fold randomized cross-validation within the evaluation. The results indicate this approach's high potential for refining disease and supporting research for choosing the best algorithm that can provide biological insights and computational advances.
  • 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
    Exploring Microbiome Signatures in Autism Spectrum Disorder via Grouping-Scoring Based Machine Learning
    (IEEE, 2025-06-25) Temiz, Mustafa; Ersoz, Nur Sebnem; Yousef, Malik; Bakir-Gungor, Burcu
    The rapid increase in omic data production increased the importance of machine learning (ML) methods to analze these data. In particular, the use of metagenomic data in the diagnosis, prognosis and treatment of diseases is becoming widespread. Autism Spectrum Disorder (ASD) is a neurodevelopmental disease that occurs in early childhood and continues lifelong. The aim of this study is to increase ML performance, reduce computational costs and achieve successful classification performance using a small number of metagenomic features. In addition, disease prediction is performed; ASD associated biomarkers are determined using the microBiomeGSM on metagenomic data. Classification is performed at three different taxonomic levels (genus, family and order) using the relative abundance values of species. The best performance metric (0.95 AUC) was obtained at the order taxonomic level using an average of 416 features with microBiomeGSM. The identified ASD-related taxonomic species are presented.
  • Conference Object
    Citation - WoS: 3
    Citation - Scopus: 4
    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.
  • Other
    Barriers in Sustainable Lean Supply Chain Management: Implementation in SMEs
    (Ege Univ, Fac. Economics & Admin. Sciences, 2025-02-04) Kazancoglu, Yigit; Takcı, Ebru; Ada, Erhan
    As the world undergoes significant transformations in various domains, including technology, energy supply and communication, the idea of sustainability has become a significant issue. This study investigates the barriers to Sustainable Lean Supply Chain (SLSC) management within Small and Medium-Sized Enterprises (SMEs) and explores the structural interrelationships among these barriers. A comprehensive literature review was carried out to recognize critical elements relevant to the research topic, resulting in the identification of fifteen specific elements that account for 85% of the barriers in SLSC management. The DEMATEL method was used to evaluate the significance and influence levels of these factors. Furthermore, structured in-depth interviews were conducted with ten experts representing sectors that constitute 85% of the SMEs operating in Kayseri Organized Industrial Zone (OIZ), Turkey, including metal products, furniture, plastic packaging, construction materials, textiles and food. The findings reveal that strategies represent the most significant barrier to SLSC management in SMEs. The barriers were analyzed in two dimensions: influencing and influenced factors. The primary influencing factor identified was laws, standards, regulations, and legislation while the most significant influenced factor was found supply and suppliers. The study concludes with findings and actionable recommendations for practitioners and decision-makers.