Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7148
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Article Interpretable Structural Modeling of MR Images Using Q-Bezier Curves: A Geometry-Aware Paradigm Beyond Deep Learning(Elsevier Science Inc, 2026) Ozger, Faruk; Onan, Aytug; Turhan, Nezihe; Ozger, Zeynep OdemisMagnetic resonance (MR) imaging plays a critical role in diagnostic workflows, yet its reliability is frequently compromised by scanner-dependent bias, contrast variability, and intensity drift. Although deep learning methods achieve high performance, they generally require extensive supervision and demonstrate limited robustness across diverse clinical settings. To address these challenges, we propose a transparent, geometry-aware framework for annotation-free MR enhancement based on q-B & eacute;zier curves. This model incorporates an adaptive deformation parameter q(x) that modulates local curvature, facilitating flexible adaptation to complex anatomical boundaries. The framework comprises three principal mechanisms: (i) adaptive q(x) for local responsiveness, (ii) monotone q-Bezier tone curves for intensity standardization, and (iii) Tikhonov-regularized optimization for smooth mapping. As a result, the operator remains interpretable, operates in linear time, and provides explicit control over smoothness. The proposed approach was validated across five public cohorts (BraTS, ACDC, PROMISE12, fastMRI, IXI), demonstrating significant improvements in image fidelity (SSIM, CNR, NIQE) and downstream segmentation accuracy (Dice, HD95) relative to variational filters and state-of-theart foundation models. Additionally, cross-vendor experiments confirm its robustness without the need for retraining. Collectively, these findings establish q-Bezier modeling as a principled, lightweight, and clinically interpretable alternative that complements deep learning by providing a geometry-aware pathway to robust MR representation.Article Semi-Synthetic Sapogenin Derivatives Inhibit Inflammation-Induced Tumorigenic Signaling Alterations in Prostate Carcinogenesis(Elsevier Science Inc, 2026) Debelec-Butuner, Bilge; Ozturk, Mert Burak; Tag, Ozgur; Akgun, Ismail Hakki; Bedir, ErdalProstatic inflammation plays a pivotal role in prostate cancer development and progression via altering key cellular mechanisms, including proliferation, metastasis, and angiogenesis. Therefore, the use of antiinflammatory drugs could provide a valid contribution to PCa prevention and treatment. In our research, we explored semi-synthetic derivatives of cycloastragenol (CA) and astragenol (AG) to assess their potential to inhibit inflammation-mediated tumorigenic signaling. Building on our previous findings, which demonstrated their inhibitory activity on NFxB, we discovered that these molecules also suppress inflammation-induced cell proliferation and migration through distinct mechanisms. They effectively alleviated inflammation by reducing levels of ROS, NO, and VEGF expression. Furthermore, these molecules partially restored the expression of AR and the tumor suppressor NKX3.1, both of which are critical in prostate tumorigenesis within an inflammatory microenvironment. They also reversed inflammation-induced activation of Akt and (3-catenin signaling, suggesting their potential to inhibit inflammation-related prostate tumorigenesis. Our study further demonstrated that these molecules exhibited dose-dependent effects on inducing cell cycle arrest and apoptosis, as evidenced by increased p21 and decreased BCL-2 protein levels, leading to activated cell death and suppressed cellular migration. In conclusion, these semi-synthetic sapogenol derivatives demonstrate significant potential as antiinflammatory and anticancer agents, offering a promising approach for targeting prostatic inflammation and inflammation-driven prostate carcinogenesis.Article Citation - WoS: 1Citation - Scopus: 1Automating Software Size Measurement With Language Models: Insights From Industrial Case Studies(Elsevier Science Inc, 2026) Unlu, Huseyin; Tenekeci, Samet; Kennouche, Dhia Eddine; Demirors, OnurObjective software size measurement is critical for accurate effort estimation, yet many organizations avoid it due to high costs, required expertise, and time-consuming manual effort. This often leads to vague predictions, poor planning, and project overruns. To address this challenge, we investigate the use of pre-trained language models - BERT and SE-BERT - to automate size measurement based on textual requirements using COSMIC and MicroM methods. We constructed one heterogeneous dataset and two industrial datasets, each manually measured by experienced analysts. Models were evaluated in three settings: (i) generic model evaluation, where the models are trained and tested on heterogeneous data, (ii) internal evaluation, where the models are trained and tested on organization-specific data, and (iii) external evaluation, where generic models were tested on organization-specific data. Results show that organization-specific models significantly outperform generic models, indicating that aligning training data with the target organization's requirement style is critical for accuracy. SE-BERT, a domain-adapted variant of BERT, improves performance, particularly in low-resource settings. These findings highlight the practical potential of tailoring training data for broader adoption and cost-effective software size measurement in industrial contexts.Article An Alternative Software Benchmarking Dataset: Effort Estimation With Machine Learning(Elsevier Science Inc, 2026) Yurum, Ozan Rasit; Unlu, Huseyin; Demirors, OnurEffort estimation plays a vital role in software project planning, as accurate estimates of required human resources are essential for success. Traditional estimation models often depend on historical size and effort data, yet organizations frequently struggle to access reliable effort records. Public benchmarking datasets like ISBSG offer useful data but may lack coverage or involve licensing fees. To address this issue, we previously introduced a free, extendable benchmarking dataset that integrates functional size and effort data extracted from 18 studies. In this study, we examine the effectiveness of our dataset for predictive effort estimation and compare it with the widely used ISBSG dataset. Our analysis includes 337 records from our dataset and 732 ISBSG projects, focusing on those with COSMIC size data. We first developed and compared models using linear regression and nine machine learning algorithms - Bayesian Ridge, Ridge Regression, Decision Tree, Random Forest, XGBoost, LightGBM, k-Nearest Neighbors, Multi-Layer Perceptron, and Support Vector Regression. Then, we selected the best-performing models and applied them to an unseen evaluation dataset to assess their generalization performance. The results show that machine learning performance varies based on evaluation method and dataset characteristics. Despite having fewer records, our dataset enabled more accurate predictions than ISBSG in most cases, highlighting its potential for effort estimation. This study demonstrates the viability of our dataset for building predictive models and supports the use of machine learning in improving estimation accuracy. Expanding this dataset could offer a valuable, open-access resource for organizations seeking effective and lowcost estimation solutions.