Scopus İndeksli Yayınlar Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395

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Author: search.filters.author.Gulsen, AbdulkadirScopus Q: search.filters.scopusQuartile.Q1

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  • Article
    Citation - Scopus: 2
    Machine Learning Models With Hyperparameter Optimization for Voice Pathology Classification on Saarbrücken Voice Database
    (Elsevier Inc., 2025-01) Gulsen, Pervin; Gulsen, Abdulkadir; Alçı, Mustafa
    Early diagnosis and referral are crucial in the treatment of voice disorders. Contemporary investigations have indicated the efficacy of voice pathology detection systems in significantly contributing to the evaluation of voice disorders, facilitating early diagnosis of such pathologies. These systems leverage machine learning methodologies, widely applied across diverse domains, and exhibit particular potential in the realm of voice pathology classification. However, machine learning models and performance metrics employed in these studies vary significantly, making it challenging to determine the optimal model for voice pathology classification. In this study, healthy and pathological voices were classified with state-of-the-art machine learning models, and the performance results of the models were compared. The voice samples employed in our research were sourced from the Saarbrücken Voice Database, a reputable German database. Feature extraction from voice signals was conducted using the Mel Frequency Cepstral Coefficients method. To assess and enhance the models’ performance adequately, we employed hyperparameter optimization and implemented a 10-fold cross-validation approach. The outcomes revealed that the support vector machine model exhibited the highest accuracy, achieving 99.19% and 99.50% accuracies in the classification of male and female voice pathologies, respectively. © 2025 Elsevier B.V., All rights reserved.
  • Article
    Citation - WoS: 5
    Citation - Scopus: 4
    Investigating 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, Burcu
    Hybrid 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.