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Predicting bullying victimization among adolescents using the risk and protective factor framework: a large-scale machine learning approach.
BMC Public Health
January 2025
Statistics, Brigham Young University, Provo, 84602, Utah, USA.
Background: Bullying, encompassing physical, psychological, social, or educational harm, affects approximately 1 in 20 United States teens aged 12-18. The prevalence and impact of bullying, including online bullying, necessitate a deeper understanding of risk and protective factors to enhance prevention efforts. This study investigated the key risk and protective factors most highly associated with adolescent bullying victimization.
View Article and Find Full Text PDFAge group classification based on optical measurement of brain pulsation using machine learning.
Sci Rep
January 2025
Research Unit of Health Sciences and Technology, University of Oulu, Oulu, Finland.
Optical techniques, such as functional near-infrared spectroscopy (fNIRS), contain high potential for the development of non-invasive wearable systems for evaluating cerebral vascular condition in aging, due to their portability and ability to monitor real-time changes in cerebral hemodynamics. In this study, thirty-six healthy adults were measured by single channel fNIRS to explore differences between two age groups using machine learning (ML). The subjects, measured during functional magnetic resonance imaging (fMRI) at Oulu University Hospital, were divided into young (age ≤ 32) and elderly (age ≥ 57) groups.
View Article and Find Full Text PDFA machine learning model the prediction of athlete engagement based on cohesion, passion and mental toughness.
Sci Rep
January 2025
College of Physical Education and Health Sciences, Zhejiang Normal University, Jinhua, 321004, China.
Athlete engagement is influenced by several factors, including cohesion, passion and mental toughness. Machine learning methods are frequently employed to construct predictive models as a result of their high efficiency. In order to comprehend the effects of cohesion, passion and mental toughness on athlete engagement, this study utilizes the relevant methods of machine learning to construct a prediction model, so as to find the intrinsic connection between them.
View Article and Find Full Text PDFInfluences of age and sex on the diagnostic accuracy of human epididymis secretory protein 4 for malignant pleural effusion.
Sci Rep
January 2025
Department of Laboratory Medicine, the Affiliated Hospital of Inner Mongolia Medical University, Hohhot, China.
Previous studies have suggested that the presence of human epididymal protein 4 (HE4) in pleural fluid can be used to diagnose malignant pleural effusion (MPE) with moderate accuracy. However, the factors that affect the diagnostic accuracy of HE4 remain unknown. This study aimed to examine how age and sex influence the diagnostic accuracy of HE4.
View Article and Find Full Text PDFEnhancing lane detection in autonomous vehicles with multi-armed bandit ensemble learning.
Sci Rep
January 2025
School of Computer Science Engineering and Information Systems, Vellore Institute of Technology, Vellore, India.
This study introduces a novel ensemble learning technique namely Multi-Armed Bandit Ensemble (MAB-Ensemble), designed for lane detection in road images intended for autonomous vehicles. The foundation of the proposed MAB-Ensemble technique is inspired in terms of Multi-Armed bandit optimization to facilitate efficient model selection for lane segmentation. The benchmarking dataset namely TuSimple is used for training, validating and testing the proposed and existing lane detection techniques.
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