Scopus İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12416/8651
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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 Quantum Implementation of S-Boxes: A Memory Optimized Approach(Institute of Electrical and Electronics Engineers Inc., 2025) Tilki, Ozcan; Saran, A.N.; Cildiroglu, H.O.; Yayla, O.Substitution boxes (S-boxes) serve as fundamental non-linear components in symmetric cryptography, and their quantum circuit implementation is critical for quantum security. This work addresses the dual challenges of quantum circuit depth optimization and computational intractability in S-box synthesis. We introduce memory-optimized data structures, a pointer-efficient RandomAccessSet and a dynamic devector, that reduce memory overhead by 12 times per element, thereby mitigating the computational complexity associated with Pauli representation. Our enhanced Meet-in-the-Middle framework achieves exhaustive depth optimization for standardized S-boxes, demonstrating up to 8.5% depth reduction over DORCIS baselines at equivalent T-depth. The approach scales to 5-8-bit primitives, establishing memory efficiency as an independent resource dimension in quantum circuit synthesis. Comparative analysis under varied cost parameters provides new insights for resource-efficient cryptographic implementations on quantum hardware. © 2025 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 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.Conference Object Topic-Aware Multi-Class Classification for Financial Complaints: Comparing BERTopic With Classical Machine Learning Algorithms(Institute of Electrical and Electronics Engineers Inc., 2025) Uguz, Sezer; Kumbasar, Mert; Tokdemir, GulIn today's digital world, customers can utilize a variety of communication channels, such as business emails, consumer forms, feedback platforms, and dedicated complaint websites, to communicate their complaints. This study compares the performance of the supervised Bidirectional Encoder Representations from Transformers for Topic Modeling (BERTopic) with traditional machine learning algorithms, including Random Forest (RF), Support Vector Machines (SVM), Logistic Regression (LR), Naive Bayes (NB), K-nearest Neighbors (KNN), and eXtreme Gradient Boosting (XGBoost), for multi-class classification of financial customer complaints. The dataset consists of 16,715 balanced training data and 3,808 test data across five different categories, with the financial complaint data. Experimental results demonstrate that traditional machine learning models, particularly XGBoost, SVM, and LR, achieved the highest classification performance with accuracy rates close to 88%. BERTopic showed a competitive performance with an accuracy of 82.48%. The results suggest that while BERTopic offers interpretability advantages through topic modeling techniques, traditional algorithms provide higher accuracy. This study highlights the promising potential for future financial text analysis and customer complaint classification using hybrid methods, which could lead to more detailed, topic-aware classification approaches. © 2025 IEEE.Conference Object Citation - Scopus: 2An Application of Data Mining and Knowledge Discovery Process in the Field of Natural Gas Exploration(Institute of Electrical and Electronics Engineers Inc., 2014) Acar, M.A.; Tolun, M.R.; Elbaşi, E.Natural gas exploration has been shifting significantly in the last decades with the progression of new ingenious technologies. However, such technologies generates large amount of data sets and handling of them create problems such as interpretation of data. To solve such problems Data Mining techniques could be used. This work includes the application procedure of Data Mining and Knowledge Discovery to Natural Gas Well Log data using a set of algorithms and a decent Data Mining tool. And the success of each algorithm in terms of the amount of useful rules is compared.
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