Scopus İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395
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Article Citation - Scopus: 4RCE-IFE: Recursive Cluster Elimination with Intra-Cluster Feature Elimination(PeerJ Inc., 2025-02-07) Kuzudisli, Cihan; Bakir-Gungor, Burcu; Qaqish, Bahjat; Yousef, MalikArticle Citation - Scopus: 7Network Anomaly Detection Using Deep Autoencoder and Parallel Artificial Bee Colony Algorithm-Trained Neural Network(PeerJ Inc., 2024-10-08) Dedeturk, Bilge Kagan; Bakir-Gungor, Burcu; Hacılar, Hilal; Gungor, Vehbi CagriArticle Follow-up of Health-Related Physical Fitness Elements in Mild Intellectual Disability for Three Years: A Sex Comparison(PeerJ Inc., 2026-03-04) Bozdağ, Berkan; Sönmez, Hüseyin Gazi; Prieto-González, Pablo; Karahan, Mustafa; Canli, Umut; Ergin, Murat; Koçak, Çalık VeliChildren with mild intellectual disability (MID) have significant limitations in both intellectual functioning and cognitive, social, and motor skill behaviors. Understanding the development of physical fitness in boys and girls with MID, and identifying sex-related differences can help devise interventional programs to improve physical fitness in these groups. The aim of this study was to compare sex differences in the time-dependent changes in health-related physical fitness components in individuals with MID. A longitudinal design was employed over three years. A total of 111 individuals with MID (46 girls and 65 boys) aged between 10 and 14 years (mean age 11.97 +/- 1.39 years) participated in the study. The physical fitness levels of the participants were assessed using the Brockport Physical Fitness Test (BPFT) battery. The tests included body composition (body height, body mass, and body mass index), aerobic endurance (15 m Progressive Aerobic Cardiovascular Endurance Run (PACER) test), and musculoskeletal function (dominant handgrip strength, back-saver sit-and-reach, and trunk lift). The results revealed that, over time, the longitudinal developmental trajectories for body mass, body height, aerobic endurance, and dominant handgrip strength were more favorable for boys. However, the longitudinal development curves for body mass index (BMI), trunk lift, and flexibility were similar for both boys and girls. The findings of this study provide valuable evidence for developing targeted physical activity programs for individuals with MID, and demonstrate the need for programs aimed at increasing aerobic endurance and muscle strength in girls with MID.Article Citation - Scopus: 5CSA-DE-LR Enhancing Cardiovascular Disease Diagnosis With a Novel Hybrid Machine Learning Approach(PeerJ Inc., 2024-07-18) Dedeturk, Beyhan Adanur; Dedeturk, Bilge Kagan; Bakir-Güngör, BurcuCardiovascular diseases (CVD) are a leading cause of mortality globally, necessitating the development of efficient diagnostic tools. Machine learning (ML) and metaheuristic algorithms have become prevalent in addressing these challenges, providing promising solutions in medical diagnostics. However, traditional ML approaches often need to be improved in feature selection and optimization, leading to suboptimal performance in complex diagnostic tasks. To overcome these limitations, this study introduces a new hybrid method called CSA-DE-LR, which combines the clonal selection algorithm (CSA) and differential evolution (DE) with logistic regression. This integration is designed to optimize logistic regression weights efficiently for the accurate classification of CVD. The methodology employs three optimization strategies based on the F1 score, the Matthews correlation coefficient (MCC), and the mean absolute error (MAE). Extensive evaluations on benchmark datasets, namely Cleveland and Statlog, reveal that CSA-DELR outperforms state-of-the-art ML methods. In addition, generalization is evaluated using the Breast Cancer Wisconsin Original (WBCO) and Breast Cancer Wisconsin Diagnostic (WBCD) datasets. Significantly, the proposed model demonstrates superior efficacy compared to previous research studies in this domain. This study’s findings highlight the potential of hybrid machine learning approaches for improving diagnostic accuracy, offering a significant advancement in the fields of medical data analysis and CVD diagnosis. © 2024 Elsevier B.V., All rights reserved.