WoS İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/394
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Article Citation - WoS: 10Citation - Scopus: 8Ensemble Feature Selection for Clustering Damage Modes in Carbon Fiber-Reinforced Polymer Sandwich Composites Using Acoustic Emission(Wiley-VCH Verlag GmbH, 2024-07-15) Gulsen, Abdulkadir; Kolukisa, Burak; Caliskan, Umut; Bakir-Gungor, Burcu; Gungor, Vehbi CagriAcoustic emission (AE) serves as a noninvasive technique for real-time structural health monitoring, capturing the stress waves produced by the formation and growth of cracks within a material. This study presents a novel ensemble feature selection methodology to rank features highly relevant with damage modes in AE signals gathered from edgewise compression tests on honeycomb-core carbon fiber-reinforced polymer. Two distinct features, amplitude and peak frequency, are selected for labeling the AE signals. An ensemble-supervised feature selection method ranks feature importance according to these labels. Using the ranking list, unsupervised clustering models are then applied to identify damage modes. The comparative results reveal a robust correlation between the damage modes and the features of counts and energy when amplitude is selected. Similarly, when peak frequency is chosen, a significant association is observed between the damage modes and the features of partial powers 1 and 2. These findings demonstrate that, in addition to the commonly used features, other features, such as partial powers, exhibit a correlation with damage modes. This article presents a novel ensemble feature selection methodology to rank features relevant to damage modes on acoustic emission signals in carbon fiber-reinforced polymer sandwich composites. Subsequently, ranked features are utilized in unsupervised clustering models to identify damage modes. The comparative results demonstrate that, along with common features, other features, like partial powers, have a robust correlation with damage modes.image (c) 2024 WILEY-VCH GmbHArticle Citation - Scopus: 1Breast Cancer Detection Using a New Parallel Hybrid Logistic Regression Model Trained by Particle Swarm Optimization and Clonal Selection Algorithms(Wiley, 2025-04-29) Etcil, Mustafa; Dedeturk, Bilge Kagan; Kolukisa, Burak; Bakir-Gungor, Burcu; Gungor, Vehbi CagriBreast cancer is one of the most widespread kinds of cancer, especially in women, and it has a high mortality rate. With the help of technology, it is possible to develop a computer-aided method for the diagnosis of breast cancer, which is crucial for effective treatment. Recent breast cancer diagnosis studies utilizing numerous machine learning models were efficient and innovative. However, it has been observed that they may have problems such as long training times and low accuracy rates. To this end, in this study, we present a new classifier that utilizes a hybrid of the clonal selection algorithm (CSA) and the particle swarm optimization (PSO) algorithm for the training of the logistic regression (LR) model, which is named CSA-PSO-LR. The proposed method is evaluated using two publicly accessible breast cancer datasets, that is, the Wisconsin Diagnostic Breast Cancer (WDBC) database and the Wisconsin Breast Cancer Database (WBCD), with 10-fold cross-validation and Bayesian hyperparameter optimization techniques. Additionally, a CPU parallelization method is applied, which substantially shortens the training time of the model. The efficacy of the CSA-PSO-LR classifier is compared with state-of-the-art machine learning algorithms and related studies in the literature. Performance analysis indicates that the proposed method achieves 98.75% accuracy and 98.27% F1-score on the WDBC dataset, and 97.94% accuracy and 97.35% F1-score on the WBCD dataset. These results demonstrate the potential of the proposed method as an effective approach for improving breast cancer diagnosis.