Scopus İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395
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Conference Object Metabolomics Data Analysis to Discover Chronic Granulomatous Disease-Associated Biomarkers Utilizing G-S-M Machine Learning Model via Grouping Metabolites According to Ion Type(Institute of Electrical and Electronics Engineers Inc., 2024-10-16) Ersöz, Nur Sebnem; Bakir-Güngör, Burcu; Yousef, MalikChronic Granulomatous Disease (CGD) is a rare, inherited immunodeficiency disorder characterized by white blood cells unable to effectively kill certain bacteria and fungi. This defect results in the formation of clusters of immune cells called granulomas that form at sites of infection or inflammation. Therefore, identification of disease-related biomarkers is a critical step in advancing precision medicine and improving diagnostic accuracy. In this study, we applied a G-S-M machine learning approach to metabolomics data to uncover CGD-Associated biomarkers. We obtained a metabolomics dataset from Gene Expression Omnibus with GSE220260 accession number. Data includes 85 samples (16 healthy controls and 69 CGD samples) with comprehensive metabolic profiles obtained using liquid chromatography-mass spectrometry analysis. Dataset includes metabolite names with their ion type and formula. In order to identify CGD related metabolites and their ion types, G-S-M was used as a grouping function when performing machine learning oriented metabolomics data analysis. We have performed the G-S-M approach by grouping metabolites according to their ion type. In the training part of the G-S-M approach, metabolites annotated with selected ion types have been utilized to perform a two-class classification task which generates an important set of ion type output. We also compared the performance results of the G-S-M machine learning model with traditional feature selection methods; XGB, SKB, IG, FCBF, MRMR, CMIM with random forest classifier. 100 times Monte-Carlo Cross Validation was used in our experiments. It was observed that G-S-M, XGB, SKB and FCBF methods similarly provided the best performances. In this study, besides its performance, G-S-M method used groups based on ion types unlike TFS, and then identified relevant Chronic Granulomatous Disease-associated metabolites. © 2024 Elsevier B.V., All rights reserved.Article Comprehensive Optimization of Shot Peening Intensity Using a Hybrid Model With AI-Based Techniques via Almen Tests(Walter de Gruyter Gmbh, 2025-06-03) Karaveli, Kadir Kaan; Bal, BurakShot peening is a crucial surface treatment technique that significantly improves the mechanical properties of metallic components, particularly their fatigue resistance and ability to withstand corrosion cracking. This study aims to optimize the shot peening process for aviation applications by evaluating and comparing various mathematical modeling and optimization techniques. Seven mathematical models were analyzed using a neuro-regression method (NRM), among which the second-order trigonometric non-linear (SOTN) model exhibited the highest reliability, achieving R2 values of 0.93 and 0.90 for training and testing datasets, respectively. To improve the model's robustness, four optimization algorithms - differential evolution (DE), simulated annealing (SA), Nelder-Mead (NM), and random search (RS) - were applied to the SOTN model. Although each technique offered valuable insights, performance fluctuations across different intensity ranges necessitated the development of a hybrid optimization model that combines the strengths of all four methods. The hybrid model achieved a mean error of approximately 2.69 %, outperforming individual approaches and demonstrating strong potential for reliable shot peening optimization across a wide range of target intensities. These findings provide a comprehensive methodology for AI-based optimization of surface treatment processes in engineering applications.Conference Object A Horizontal Magnetic Tweezer for Single Molecule Micromanipulations(Institute of Electrical and Electronics Engineers Inc., 2018-10) Ablay, Günyaz; Böyük, Mustafa; Eroǧlu, Yakup; İçöz, KutayMagnetic tweezers are able to manipulate cells or biomolecules for various applications and measurements. In this work, a horizontal magnetic tweezer is designed, modeled and controlled for single molecule manipulations. A method is presented for dynamic modeling of horizontal magnetic tweezers. A linear control method is designed to ensure a wide range of operation conditions with zero steady-state error. The horizontal magnetic tweezer is able to generate a wide range of piconewton scale forces on a superparamagnetic microparticle for single molecule separation, and biosensor developments. © 2019 Elsevier B.V., All rights reserved.