WoS İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/394
Browse
5 results
Search Results
Article Citation - WoS: 5Citation - Scopus: 4Investigating Strain Rate Effects on Damage Mechanisms in Hybrid Laminated Composites Using Acoustic Emission(Elsevier Sci Ltd, 2025-12) Gulsen, Abdulkadir; Kolukisa, Burak; Etcil, Mustafa; Caliskan, Umut; Zafar, Hafiz Muhammad Numan; Demirbas, Munise Didem; Bakir-Gungor, BurcuHybrid composites, which combine distinct fiber types such as carbon, basalt, and aramid, provide a synergistic balance of strength, stiffness, impact resistance, and energy dissipation, making them appealing for critical applications in aerospace, automotive, and other high-performance industries. Monitoring damage progression in these composites is vital for ensuring structural integrity and preventing catastrophic failures. Acoustic emission (AE) serves as a powerful, noninvasive technique for real-time structural health monitoring, capturing the transient stress waves generated when damage events occur. This study utilizes AE to examine the influence of strain rate on damage modes in carbon/basalt/aramid hybrid composites under three-point bending. An unsupervised feature selection based on Laplacian scores is employed to identify the most relevant AE features with damage modes, while SHapley Additive Explanations (SHAP) are used to evaluate the correlation between AE features and strain rates. The correlation analysis results indicate that peak frequency (PF) serves as a key indicator, demonstrating significant shifts at higher strain rates. Gaussian Mixture Model (GMM) clustering is used to analyze hybrid composites by examining clustered AE signals based on selected features identified through Laplacian scores, with Silhouette scores employed to determine the optimal number of clusters. This study highlights the role of AE in understanding fiber interactions and damage evolution, offering valuable insights into the mechanical performance and optimization of carbon/basalt/aramid hybrid composite structures.Article Citation - WoS: 40Citation - Scopus: 65Ensemble Feature Selection and Classification Methods for Machine Learning-Based Coronary Artery Disease Diagnosis(Elsevier, 2023-03) Kolukisa, Burak; Bakir-Gungor, BurcuCoronary artery disease (CAD) is a condition in which the heart is not fed sufficiently as a result of the accumulation of fatty matter. As reported by the World Health Organization, around 32% of the total deaths in the world are caused by CAD, and it is estimated that approximately 23.6 million people will die from this disease in 2030. CAD develops over time, and the diagnosis of this disease is difficult until a blockage or a heart attack occurs. In order to bypass the side effects and high costs of the current methods, researchers have proposed to diagnose CADs with computer-aided systems, which analyze some physical and biochemical values at a lower cost. In this study, for the CAD diagnosis, (i) seven different computational feature selection (FS) methods, one domain knowledge-based FS method, and different classification algorithms have been evaluated; (ii) an exhaustive ensemble FS method and a probabilistic ensemble FS method have been proposed. The proposed approach is tested on three publicly available CAD data sets using six different classification algorithms and four different variants of voting algorithms. The performance metrics have been comparatively evaluated with numerous combinations of classifiers and FS methods. The multi-layer perceptron classifier obtained satisfactory results on three data sets. Performance evaluations show that the proposed approach resulted in 91.78%, 85.55%, and 85.47% accuracy for the Z-Alizadeh Sani, Statlog, and Cleveland data sets, respectively.Article Citation - WoS: 6Citation - Scopus: 11Deep Learning Approaches for Vehicle Type Classification With 3D Magnetic Sensor(Elsevier, 2022-11) Kolukisa, Burak; Yildirim, Veli Can; Elmas, Bahadir; Ayyildiz, Cem; Gungor, Vehbi CagriIn the Intelligent Transportation Systems, it is crucial to determine the type of vehicles to improve traffic management, increase human comfort, and enable future development of transport infrastructures. This paper presents a deep learning-based vehicle type classification approach for intermediate road traffic. Specifically, a low-cost, easy-to-install, battery-operated 3-D traffic sensor is designed and developed. In addition, a total of 376 vehicle samples are collected, and the vehicles are identified into three different classes according to their structures: light, medium, and heavy. Firstly, an oversampling method is applied to increase the number of samples in the training set. Then, the signals are converted into time series for LSTM and GRU and 2-D images for transfer learning models. Finally, soft voting is proposed using the LSTM, GRU, and VGG16, which is the best transfer learning method for vehicle type classification. The developed system is portable, power-limited, battery-operated, and reliable. Comparative performance results show that the soft voting ensemble method using a deep learning classifier improves the accuracy and f-measure performances by 92.92% and 93.42%, respectively. Additionally, the battery lifetime of the developed magnetic sensor node can work for up to 2 years.Article Citation - WoS: 25Citation - Scopus: 41An Efficient Network Intrusion Detection Approach Based on Logistic Regression Model and Parallel Artificial Bee Colony Algorithm(Elsevier, 2024-04) Kolukisa, Burak; Dedeturk, Bilge Kagan; Hacilar, Hilal; Gungor, Vehbi CagriIn recent years, the widespread use of the Internet has created many issues, especially in the area of cybersecurity. It is critical to detect intrusions in network traffic, and researchers have developed network intrusion and anomaly detection systems to cope with high numbers of attacks and attack variations. In particular, machine learning and meta-heuristic methods have been widely used for network intrusion detection systems (NIDS). However, existing studies on these systems usually suffer from low performance results such as accuracy, F1-measure, false positive rate, and false negative rate, and generally do not use automatic parameter tuning techniques. To address these challenges, this study proposes a novel approach based on a logistic regression model trained using a parallel artificial bee colony (LR-ABC) algorithm with a hyper-parameter optimization technique. The performance of the proposed model is evaluated against state -of-the-art machine learning and deep learning models on two publicly available NIDS datasets. Comparative performance evaluations show that the proposed method achieved satisfactory results with accuracy of 88.25% on the UNSW-NB15 dataset and 90.11% on the NSL-KDD dataset, and F1-measures of 88.26% and 90.15%, respectively. These findings demonstrate the efficacy of the proposed LR-ABC model in enhancing the accuracy and reliability, while providing a scalable solution to adapt to the dynamic and evolving landscape of cybersecurity threats.Article Citation - WoS: 4Citation - Scopus: 5A Deep Neural Network Approach With Hyper-Parameter Optimization for Vehicle Type Classification Using 3D Magnetic Sensor(Elsevier, 2023-03) Kolukisa, Burak; Yildirim, Veli Can; Ayyildiz, Cem; Gungor, Vehbi CagriThe identification of vehicle types plays a critical role in Intelligent Transportation Systems. In this study, battery-operated, easy-to-install, low-cost 3-D magnetic traffic sensors have been developed for vehicle type classification problems. In addition, a new machine learning approach based on deep neural networks (DNN) with hyper-parameter optimization using feature selection and extraction methods has been proposed for vehicle type classification. A dataset is collected from the field, and vehicles are classified into three different classes, i.e., light: motorcycles, medium: passenger cars, and heavy: buses, based on vehicle structures and sizes. The proposed system is portable, energy-efficient, and reliable. The performance results show that the proposed method, which is based on a DNN classifier, has an accuracy of 91.15%, an f-measure of 91.50%, and a battery life of up to 2 years.