Scopus İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12416/8651
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Article Citation - Scopus: 3A New Parallel Multi-Objective Harris Hawk Algorithm for Predicting the Mortality of COVID-19 Patients(PeerJ Inc., 2023) Dokeroglu, TanselHarris' Hawk Optimization (HHO) is a novel metaheuristic inspired by the collective hunting behaviors of hawks. This technique employs the flight patterns of hawks to produce (near)-optimal solutions, enhanced with feature selection, for challenging classification problems. In this study, we propose a new parallel multi-objective HHO algorithm for predicting the mortality risk of COVID-19 patients based on their symptoms. There are two objectives in this optimization problem: to reduce the number of features while increasing the accuracy of the predictions. We conduct comprehensive experiments on a recent real-world COVID-19 dataset from Kaggle. An augmented version of the COVID-19 dataset is also generated and experimentally shown to improve the quality of the solutions. Significant improvements are observed compared to existing state-of-the-art metaheuristic wrapper algorithms. We report better classification results with feature selection than when using the entire set of features. During experiments, a 98.15% prediction accuracy with a 45% reduction is achieved in the number of features. We successfully obtained new best solutions for this COVID-19 dataset.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.Article Stylometric Analysis of Sustainable Central Bank Communications: Revealing Authorial Signatures in Monetary Policy Statements(MDPI, 2025) Emekci, Hakan; Ozkan, IbrahimSustainable economic development requires transparent and consistent institutional communication from monetary authorities to maintain long-term financial stability and public trust. This study investigates the latent authorial structure and stylistic heterogeneity of central bank communications by applying stylometric analysis and unsupervised machine learning to official announcements of the Central Bank of the Republic of Turkey (CBRT). Using a dataset of 557 press releases from 2006 to 2017, we extract a range of linguistic features at both sentence and document levels-including sentence length, punctuation density, word length, and type-token ratios. These features are reduced using Principal Component Analysis (PCA) and clustered via Hierarchical Clustering on Principal Components (HCPC), revealing three distinct authorial groups within the CBRT's communications. The robustness of these clusters is validated using multidimensional scaling (MDS) on character-level and word-level n-gram distances. The analysis finds consistent stylistic differences between clusters, with implications for authorship attribution, tone variation, and communication strategy. Notably, sentiment analysis indicates that one authorial cluster tends to exhibit more negative tonal features, suggesting potential bias or divergence in internal communication style. These findings challenge the conventional assumption of institutional homogeneity and highlight the presence of distinct communicative voices within the central bank. Furthermore, the results suggest that stylistic variation-though often subtle-may convey unintended policy signals to markets, especially in contexts where linguistic shifts are closely scrutinized. This research contributes to the emerging intersection of natural language processing, monetary economics, and institutional transparency. It demonstrates the efficacy of stylometric techniques in revealing the hidden structure of policy discourse and suggests that linguistic analytics can offer valuable insights into the internal dynamics, credibility, and effectiveness of monetary authorities. These findings contribute to sustainable financial governance by demonstrating how AI-driven analysis can enhance institutional transparency, promote consistent policy communication, and support long-term economic stability-key pillars of sustainable development.Article Detection and Classification of Femoral Neck Fractures From Plain Pelvic X-Rays Using Deep Learning and Machine Learning Methods(Turkish Assoc Trauma Emergency Surgery, 2025) Sevinc, Huseyin Fatih; Ureten, Kemal; Karadeniz, Talha; Gultekin, Gokhan KorayBackground: Femoral neck fractures are a serious health concern, particularly among the elderly. The aim of this study is to diagnose and classify femoral neck fractures from plain pelvic X-rays using deep learning and machine learning algorithms, and to compare the performance of these methods. Methods: The study was conducted on a total of 598 plain pelvic X-ray images, including 296 patients with femoral neck fractures and 302 individuals without femoral neck fractures. Initially, transfer learning was applied using pre-trained deep learning models: VGG-16, ResNet-50, and MobileNetv2. Results: The pre-trained VGG-16 network demonstrated slightly better performance than ResNet-50 and MobileNetV2 for detecting and classifying femoral neck fractures. Using the VGG-16 model, the following results were obtained: 95.6% accuracy, 95.5% sensitivity, 93.3% specificity, 95.7% precision, 95.5% F1 Score, a Cohen's kappa of 0.91, and the Receiver Operating Characteristic (ROC) curve of 0.99. Subsequently, features extracted from the convolution layers of VGG-16 were classified using common machine learning algorithms. Among these, the k-nearest neighbor (k-NN) algorithm outperformed the others and exceeded the accuracy of the VGG-16 model by 1%. Conclusion: Successful results were obtained using deep learning and machine learning methods for the detection and classification of femoral neck fractures. The model can be further improved through multi-center studies. The proposed model may be especially useful for physicians working in emergency departments and for those not having sufficient experience in evaluating plain pelvic radiographs.Article Citation - WoS: 2Citation - Scopus: 2Machine Learning-Driven Approach for Reducing Tool Wear in Die-Sinking Electrical Discharge Machining(Springer Heidelberg, 2025) Cogun, Can; Ayli, EceThis study examines the use of machine learning (ML) techniques to optimize the basic machining parameters and protrusion dimensions that affect tool shape degeneration in die-sinking electric discharge machining (EDM). The primary objective is to decrease errors and enhance prediction and optimization effectiveness. This study introduces a completely novel tool geometry model aimed at minimizing tool shape degeneration, which, to our knowledge, has not been previously documented in the literature. Additionally, this research represents the first instance of employing ML techniques to generate data for addressing this specific type of problem, further advancing the field of die-sinking EDM. The pivotal machining parameters include discharge current, pulse time and machining depth. Three ML approaches are implemented in this investigation: Artificial Neural Network (ANN), Adaptive-Network-Based Fuzzy Inference System (ANFIS), and Support Vector Machine (SVM). In comparison with experimental outcomes, the ANN technique exhibited superior predictive ability with an coefficient of determination (R2) of 0.99985 and an Mean Relative Error (MRE) of 0.854%. Four distinct EDM machining scenarios are presented and machining parameters and protrusion dimensions are optimized using the ANN technique to decrease tool shape degeneration. Optimizing the machining parameters and diagonal dimensions of the protrusion substantially reduced tool shape degeneration. This research demonstrates the effectiveness of ANN in optimizing machining parameters and improving tool performance in die-sinking EDM. A significant reduction in total wear area of 66.7% was achieved with a considerably lower time cost through the optimized ANN network. While the study demonstrates promising results, its reliance on specific datasets for training may limit the generalizability of the model to broader machining scenarios.Article Citation - WoS: 3Citation - Scopus: 4Predicting Stability Factors for Rotational Failures in Earth Slopes and Embankments Using Artificial Intelligence Techniques(de Gruyter Poland Sp Z O O, 2024) Cemiloglu, Ahmed; Cao, Yingying; Sabonchi, Arkan K. S.; Nanehkaran, Yaser A.This study focuses on slope stability analysis, a critical process for understanding the conditions, durability, mass properties, and failure mechanisms of slopes. The research specifically addresses rotational-type failure, the primary instability mechanism affecting earth slopes. Identifying and understanding key factors such as slope height, slope angle, density, cohesion, friction, water pore pressure, and tensile cracks are essential for effective stabilization strategies. The objective of this study is to develop accurate predictive models for slope stability analysis using advanced intelligent techniques, including data mining mapping and complex decision tree regression (DTR). The models were validated using performance metrics such as mean absolute error (MAE), mean squared error (MSE), root mean square error (RMSE), and the coefficient of determination (R-2). Additionally, overall accuracy was assessed using a confusion matrix. The predictive model was tested on a dataset of 120 slope cases, achieving an accuracy of approximately 91.07% with DTR. The error rates for the training set were MAE = 0.1242, MSE = 0.1722, and RMSE = 0.1098, demonstrating the model's capability to effectively analyze and predict slope stability in earth slopes and embankments. The study concludes that these intelligent techniques offer a reliable approach for stability analysis, contributing to safer and more efficient slope management.Conference Object Citation - WoS: 14Citation - Scopus: 20A Machine Learning Study To Enhance Project Cost Forecasting(Elsevier, 2022) Narbaev, Timur; Hazir, Oncu; Inan, TolgaIn project management it is critical to obtain accurate cost forecasts using effective methods. This study presents a Machine Learning model based on Long-Short Term Memory to forecast the project cost. The model uses the seven-dimensional feature vector, including schedule and cost performance factors and their moving averages as a predictor. Based on the cost variation patterns from the training phase, we validate the model using three hundred experiments in the testing phase. Overall, the proposed model produces more accurate cost estimates when compared to the traditional Earned Value Management index-based model. Copyright (C) 2022 The Authors.Article Citation - WoS: 87Citation - Scopus: 97Application of Machine Learning Techniques for the Estimation of the Safety Factor in Slope Stability Analysis(Mdpi, 2022) Nanehkaran, Yaser Ahangari; Pusatli, Tolga; Jin Chengyong; Chen, Junde; Cemiloglu, Ahmed; Azarafza, Mohammad; Derakhshani, Reza; Ahangari Nanehkaran, Yaser; Chengyong, JinSlope stability is the most important stage in the stabilization process for different scale slopes, and it is dictated by the factor of safety (FS). The FS is a relationship between the geotechnical characteristics and the slope behavior under various loading conditions. Thus, the application of an accurate procedure to estimate the FS can lead to a fast and precise decision during the stabilization process. In this regard, using computational models that can be operated accurately is strongly needed. The performance of five different machine learning models to predict the slope safety factors was investigated in this study, which included multilayer perceptron (MLP), support vector machines (SVM), k-nearest neighbors (k-NN), decision tree (DT), and random forest (RF). The main objective of this article is to evaluate and optimize the various machine learning-based predictive models regarding FS calculations, which play a key role in conducting appropriate stabilization methods and stabilizing the slopes. As input to the predictive models, geo-engineering index parameters, such as slope height (H), total slope angle (beta), dry density (gamma(d)), cohesion (c), and internal friction angle (phi), which were estimated for 70 slopes in the South Pars region (southwest of Iran), were considered to predict the FS properly. To prepare the training and testing data sets from the main database, the primary set was randomly divided and applied to all predictive models. The predicted FS results were obtained for testing (30% of the primary data set) and training (70% of the primary data set) for all MLP, SVM, k-NN, DT, and RF models. The models were verified by using a confusion matrix and errors table to conclude the accuracy evaluation indexes (i.e., accuracy, precision, recall, and f1-score), mean squared error (MSE), mean absolute error (MAE), and root mean square error (RMSE). According to the results of this study, the MLP model had the highest evaluation with a precision of 0.938 and an accuracy of 0.90. In addition, the estimated error rate for the MLP model was MAE = 0.103367, MSE = 0.102566, and RMSE = 0.098470.Article Citation - WoS: 238Citation - Scopus: 308A Comprehensive Survey on Recent Metaheuristics for Feature Selection(Elsevier, 2022) Dokeroglu, Tansel; Deniz, Ayca; Kiziloz, Hakan EzgiFeature selection has become an indispensable machine learning process for data preprocessing due to the ever-increasing sizes in actual data. There have been many solution methods proposed for feature selection since the 1970s. For the last two decades, we have witnessed the superiority of metaheuristic feature selection algorithms, and tens of new ones are being proposed every year. This survey focuses on the most outstanding recent metaheuristic feature selection algorithms of the last two decades in terms of their performance in exploration/exploitation operators, selection methods, transfer functions, fitness value evaluations, and parameter setting techniques. Current challenges of the metaheuristic feature selection algorithms and possible future research topics are examined and brought to the attention of the researchers as well.Article Citation - WoS: 3Citation - Scopus: 4Towards an Earthquake-Resistant Architectural Design With the Image Classification Method(Taylor & Francis Ltd, 2024) Akan, Asli Er; Bingol, Kaan; Ormecioglu, Hilal Tugba; Er, Arzu; Ormecioglu, Tevfik Oguz; Er Akan, AslıArchitectural design is an interdisciplinary process which involves multiple stages that are interconnected. In this process, it is common for major decisions to be changed during the final stage, the analysis of the structural system. After making substantial corrections, the architect has to revisit the early stages, the preliminary project. This back-and-forth process can result in significant losses in time and cost. The proposed Irregularity Control Assistant (IC-Assistant) aims to provide architects with feedback on the conformity of structural system decisions to the irregularities defined in the Turkish Building Earthquake Code (TBEC-2018), using image processing methods at the early stages of the design process. The IC-Assistant was preliminarily created to evaluate the torsional irregularity of plan organization using deep learning methods. In this study, the results of the IC-Assistant were verified by structural analysis with the Prota-Structure program. The novelty of this study is the use of the image-classification method in earthquake-resistant architectural design. Up to this point, the method has been mainly used in facial recognition systems. This method minimizes time, human error, and cost losses and includes awareness of load bearing and earthquake resistance as inputs in the early stages of architectural design.