Scopus İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12416/8651
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Article A User-Centric Domain-Adaptive Quality Model for Benchmarking Generative AI Systems(Institute of Electrical and Electronics Engineers Inc., 2026) Esirik, Buse Erol; Gokalp, EbruGenerative AI (GenAI) systems operate across diverse application domains where quality priorities shift dynamically in response to user expectations and contextual requirements. This variability calls for a comprehensive quality model that enables stakeholder-driven weight recalibration to support product evaluation and selection. However, existing approaches do not simultaneously account for GenAIspecific attributes, user-centric quality priorities, and domain-adaptive evaluation mechanisms. To bridge this gap, this study proposes the User-Centric Generative AI Quality Model (UC-GAIQM), a domainadaptive framework in which Analytic Hierarchy Process (AHP) weights can be recalibrated to reflect quality priorities across different application scenarios and user profiles. The proposed model was developed through a mixed-methods, three-phase research design. In the first phase, a Systematic Literature Review (SLR) and Multivocal Literature Review (MLR) established the theoretical foundation. In the second phase, a quantitative survey of active GenAI users (n = 111) validated eight quality dimensions through exploratory and confirmatory factor analysis (alpha = 0.94, KMO = 0.88, CFI = 0.943). In the third phase, a three-round expert-driven Delphi study confirmed the structural validity of the model (Kendall's W = 0.84), and an AHP study demonstrated the weight recalibration mechanism. UC-GAIQM comprises eight quality dimensions and thirty sub-dimensions aligned with key ISO/IEC standards, the NIST AI Risk Management Framework, and the EU AI Act. The results demonstrate that the proposed model facilitates dynamic, context-sensitive evaluation of GenAI products by enabling quality priority adaptation across application domains.Article Solution Approaches for the Dynamic Naval Air Defense Planning Problem(Institute of Electrical and Electronics Engineers Inc., 2026) Arslan, C.; Karasakal, O.; Kirca, Ö.The naval air defense planning (NADP) problem entails the defense of a naval fleet against aerial threats. This complex and dynamic problem requires real-time decision-making and adaptation to evolving warfare environment. While our previous work addressed the static NADP problem by proposing a mathematical model and heuristic solutions for sensor allocation, engagement scheduling, and ship routing, this study extends to the dynamic NADP problem. Unlike the static version, which assumes complete knowledge of future threats, the dynamic NADP problem requires continuous updates and real-time adjustments to decisions as new threats emerge and situational parameters change. We present modifications in the mathematical formulation, which is based on a mixed-integer nonlinear programming (MINLP) model, alongside a comprehensive simulation structure. We employ heuristic solution approaches that utilize a combination of a genetic algorithm, construction of an engagement graph to solve the shortest path problem, and dynamic programming (DP) techniques. Computational experiments are conducted to evaluate the effectiveness of these methods in addressing the dynamic NADP problem. The study also explores machine learning models for threat prioritization, offering innovative solutions to the challenges posed by dynamic naval air defense scenarios. © 2013 IEEE.Article Citation - WoS: 1Citation - Scopus: 1Optical Wireless Communication in Atmosphere and Underwater: Statistical Models, Improvement Techniques, and Recent Applications(Institute of Electrical and Electronics Engineers Inc., 2026) Ata, Y.; Al-Sallami, F.M.; Gökçe, M.C.; Vegni, A.M.; Rajbhandari, S.; Baykal, Y.Optical Wireless Communication Systems (OWCSs) are becoming more popular each day, especially after numerous mobile applications are being employed within the concept of Internet of Things (IoT). OWCSs are largely used in both terrestrial and non-terrestrial environments, like underwater, air, and space scenarios. Due to the large applicability of OWCS, it represents one of the main candidate technologies for the future 6G wireless communication systems. Naturally, this market trend forces the system designers to reach the best performance in their designs, as well as optimize the cost. In this survey paper, we intend to provide information to the researchers working in this field on the statistical models adopted in OWCS, the methods and techniques used to improve their performances, mainly in outdoor environment like air, space, and underwater. In this respect, the background on theoretical aspects of OWCS, together with their benefits, limitations and challenges are presented. Performance improvement techniques employed in OWCSs, such as power increase, partial coherence, beamforming, aperture averaging, spatial diversity, and intelligent reflecting surfaces, are also introduced. Finally, we discuss the open challenges that researchers are still facing, together with future directions on next steps for a large-scale adoption of OWCS. © 1998-2012 IEEE.Article Comprehensive Analysis of Data Augmentation Methods in Classification for an Imbalanced Epilepsy Dataset(Institute of Electrical and Electronics Engineers Inc., 2026) Calis, A.G.; Ergezer, H.Imbalanced class distribution reduces the generalizability of classifiers in EEG-based epilepsy detection. This study examines the impact of the synthetic minority oversampling technique (SMOTE) and its variants on imbalanced electroencephalography (EEG) data, utilizing an end-to-end data processing pipeline. Band-limited filtering is applied as pre-processing, and then the training data is gradually oversampled by 20% increments in four scenes. Experiments are conducted on coarse-k-nearest neighbor (Coarse-KNN), bagged trees, and artificial neural network (ANN) classifiers, and evaluation is performed using accuracy, precision, recall, F1 score, and Matthew’s correlation coefficient (MCC) metrics. In Scene #4, where the inter-class imbalance is eliminated, Borderline-SMOTE yielded the highest and most consistent results (F1 Score = 0.903–0.937, MCC = 0.830–0.894). Safe level-SMOTE (SL-SMOTE) and SMOTE/Geometric-SMOTE(G-SMOTE) produced second-ranked results. The findings demonstrate that appropriate variant selection provides consistent gains even across classifiers, making Borderline-SMOTE the recommended approach for imbalanced EEG classification. Furthermore, in the detailed analysis of ensemble sampling limits, SMOTE-based combined approaches (e.g., SL + G SMOTE) also produced consistent results. Basic descriptive statistics (mode, median, variance, and kurtosis) of the synthetic samples were found to be comparable to those of the real data, providing additional evidence of distributional consistency. © 2013 IEEE.Conference Object The Implementation of a Successive Cancellation Polar Decoder on Xilinx System Generator(Institute of Electrical and Electronics Engineers Inc., 2017) Arli, A.Ç.; Colak, A.; Gazi, O.Polar coding is the first kind of the capacity achieving codes which are defined for binary-input discrete memoryless channels initially. Parallel processing property of the FPGA allows to decode faster with a margin of complexity. Xilinx System Generator as a practical tool to construct decoding designs in shorter time is a fact. In this study, FPGA implementation of decoding polar codes through Xilinx System Generator is shown. © 2023 Elsevier B.V., All rights reserved.Conference Object Covariance Features for Trajectory Analysis(Institute of Electrical and Electronics Engineers Inc., 2016) Karadeniz, T.; Maras, H.H.In this work, we aimed to demonstrate that covariance estimation methods can be used for trajectory classification. We have shown that, features obtained via shrunk covariance estimation are suitable for describing trajectories. We have arrived to the conclusion that, when compared to Dynamic Time Warping, the explained technique is faster and may yield more accurate results. © 2017 Elsevier B.V., All rights reserved.Conference Object Citation - WoS: 1Citation - Scopus: 1Dengesiz Epilepsi Veri Seti İçin Sınıflandırmada Farklı SMOTE Yöntemlerinin Etkileri(Institute of Electrical and Electronics Engineers Inc., 2025) Calis, Ahmet Gokay; Ergezer, HalitIn this study, the effects of different SMOTE methods on machine learning algorithms for the imbalanced epilepsy dataset were investigated. After filtering, the imbalanced dataset was balanced with 5 different SMOTE methods and classified with various machine learning algorithms. Coarse-K-Nearest Neighbor, Bagged Trees, and Artificial Neural Networks models were evaluated in epilepsy detection. The performance of these different models was compared with Matthews Correlation Coefficient (MCC) and F1 Score metrics. The results showed that the Borderline-SMOTE algorithm had the highest F1 Score and MCC values among all machine learning algorithms. © 2025 Elsevier B.V., All rights reserved.Conference Object AviBERT: Transformer Tabanlı Hava Aracı Metni Sınıflandırma(Institute of Electrical and Electronics Engineers Inc., 2025) Unal, Muhammed Cihat; Yurtalan, Gokhan; Karatas, Yahya Bahadir; Karamanlioglu, Alper; Demirel, BerkanIn recent years, transformer-based models pre-trained on extensive corpora have played a critical role in the advancement of Natural Language Processing methodologies. Particularly, methods based on BERT have demonstrated remarkable performance across various tasks by offering robust capabilities in deeply understanding texts semantically. However, despite these advancements, there is a notable scarcity of studies applying these technologies in the aviation sector. This paper develops a multi-class classification model for aviation-specific texts using variants of BERT. The study encompasses the processes of collecting web content related to aircraft, labeling and model training. The details of the dataset are explained and the outcomes of the study are assessed based on the macro F1-score and accuracy of different models. © 2025 Elsevier B.V., All rights reserved.Conference Object Model Enhancement for UAV Stealth in X-Band(Institute of Electrical and Electronics Engineers Inc., 2025) Unalir, Dizdar; Yalcinkaya, Bengisu; Aydin, ElifWith the rapid advancement of technology, radar detection techniques continue to evolve, challenging the effectiveness of traditional unmanned aerial vehicles (UAVs) stealth techniques. As the usage of UAVs in military applications expands, the need for effective radar cross section reduction (RCSR) methods to enhance their stealth capabilities has grown significantly. In this study, we propose an enhancement of a previously developed Low-RCS UAV model, focusing on RCSR with shaping technique in the X-band. For the identification and optimization of the UAV model’s highly reflective components, a detailed simulative analysis of the RCS was performed using CST Studio Suite Environment. The modifications are applied to the body and leg components to minimize radar reflections. Simulation results demonstrated that the proposed enhancements significantly reduced RCS values compared to the original Low-RCS UAV model. A total of 13 dBsm reduction in RCS was observed compared to the traditional UAV models. Comparative analysis for different frequencies in X-Band and various aspect angles confirmed the effectiveness of the improved design, validating its potential for stealth applications. The findings can contribute to the research in UAV stealth technology and provide insights into future low-visibility UAV designs. © 2025 Elsevier B.V., All rights reserved.Conference Object Unified Lf-Norm Robust Fitting for Linear Models(Institute of Electrical and Electronics Engineers Inc., 2025) Nar, Fatih; Saran, Murat; Saran, Ayse Nurdan; Sen, BahaIn statistical learning, accurately estimating model parameters is crucial for reliable predictions. Managing residuals, the differences between observed and predicted values, is a key challenge. In regression, the residual penalty choice strongly affects model performance. The L<inf>2</inf>-norm penalty aligns with the least-squares approach, while the L<inf>1</inf>-norm provides robust fitting by minimizing the influence of outliers. To generalize models, the weights can be regularized using either the L<inf>2</inf>-norm or L<inf>1</inf>-norm, corresponding to Ridge and LASSO regularization, respectively. Many methods have been developed to penalize residuals and model weights, resulting in diverse cost functions optimized by specific numerical solvers. In this study, we propose the smooth L<inf>f</inf>-norm, a quasi-norm, as a unified framework for penalizing both residuals and model weights in linear models. Our efficient and robust numerical minimization scheme ensures fast and accurate fitting by minimizing our novel cost function. © 2025 Elsevier B.V., All rights reserved.
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