WoS İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/394
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Conference Object Security Through Digital Twin-Based Intrusion Detection: A Swat Dataset Analysis(IEEE, 2023-10-18) Bozdal, MehmetDigital 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.Conference Object Citation - WoS: 1Citation - Scopus: 1Oscillator Phase Noise Impact on Monostatic/Bistatic Space-Borne Sub-THz ISAR(IEEE, 2025-05-21) Bekari, Ali; Gashinova, Marina; Bekar, Muge; Martorellai, Marco; Antonioni, Michail; Bekar, Ali; Martorella, Marco; Antoniou, MichailThis study develops an oscillator phase noise model and analyzes its effects on the performance of spaceborne monostatic and bistatic Inverse Synthetic Aperture Radar (B-ISAR) systems operating at the sub-THz band. The B-ISAR study is of current importance as it can provide a basis for distributed space-based ISAR to enable persistent co-operative space domain awareness (Co-SDA).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, MalikIntegrating 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, BurakLow 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, BurcuThe 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: 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.Conference Object The Investigation of Rayleigh Backscattered Signal Statistics in a Φ-OTDR System Incorporating Optical Pre-Amplifier(IEEE, 2020) Uyar, Faruk; Kartaloglu, Tolga; Ozbay, Ekmel; Ozdur, IbrahimWe theoretically model and experimentally demonstrate the photon statistics of Rayleigh backscattered signal in a phi-OTDR based distributed fiber optical sensor in the presence of amplified spontaneous emission noise caused by optical pre-amplifier. (C) 2020 The Author(s)Conference Object Symmetric Electret-Based Vibration Energy Harvesters With Curved-Beam Hinges(IEEE, 2023-05-28) Hah, DooyoungBroadband power spectral characteristics are desirable in vibration energy harvesters, and it can be achieved by employing curved-beam hinges, which exhibit force-displacement nonlinearity. Via numerical analysis by using stochastic differential equations and colored-noise inputs, this study shows that a symmetric configuration of the curved-beam hinges in electret-based harvesters can produce higher (up to 8% more) power outputs than an asymmetric one. It also presents that the harvesters with curved-beam hinges can produce higher (up to 4.4 times) power outputs than those with straight hinges when the vibration magnitude is 0.05g.Conference Object Structural Integrity Analysis of a Two-Pole Synchronous Reluctance Machine With Non-Circular Shaft(IEEE, 2023-06-14) Tekgun, Didem; Tekgun, Burak; Alan, IrfanThis paper investigates the structural strength of a 6-inch diameter, two-pole, 4 kW line start synchronous reluctance machine (LS-SynRM) designed with a new non-circular shaft structure that serves as a pump motor. Flux paths on the rotor are widened while narrowing down the shaft of the motor on the q- axis to improve the motor efficiency. By using this method, a wider path is created for the flux in the d-axis. As a result, the inductance in the d-axis, the ratio of inductance between the d-axis and q-axis (referred to as saliency ratio), and the difference in inductance between the d-axis and q-axis are all amplified. To evaluate the structural strength of the machine, a series of analyses are conducted, including modal, harmonic, and static examination on the rotor using ANSYS Structural. The findings indicate that to prevent redundant deformations and undesirable vibrations because of resonance, the maximum safe limit for shaft size reduction is determined as 8 mm.Conference Object Citation - WoS: 1Sliding Mode Control of a Switched Reluctance Motor Drive With Four-Switch Bi-Directional Dc-Dc Converter for Torque Ripple Minimization(IEEE, 2020-09) Ates, Ertugrul; Tekgun, Burak; Ablay, GunyazIn this paper, a method to drive switched reluctance motors (SRM) with a modular four-switch bi-directional DC-DC converter and an H-bridge is proposed. The DC-DC converter operates as a buck or a boost converter with constant frequency to control each phase current while the H-bridge inverter switches only twice in a period to adjust the polarity of the phase voltage. Sliding mode control is designed to have fast and robust current control in the DC-DC converter. The sliding surface equation which is derived for all operation modes including buck and boost modes in motoring and regenerating conditions is defined with the estimated inductor current. The proposed drive system eliminates the bulk DC-capacitors and allows one to adjust the bus voltage individually for all phases. Moreover, the proposed system topology works with only one high-frequency switching device in the DC-DC conversion stage rather than two in conventional drives which provides a simpler current control and reduced switching losses.