The Purpose of this study is to propose a new integrative framework for athletic performance prediction based on state-of-the-art machine learning analysis and biometric data biometric scanning. By merging physiological signals i.e., Heart rate varia...
Existing research on human-automated vehicle (AV) interactions has largely focused on auditory explanations, with less attention to how voice characteristics shape user trust. This paper explores the influence of gender similarity between users and A...
BACKGROUND: In light of the heightened expectations surrounding the development of foreign language professionals in the age of artificial intelligence and the pursuit of academic excellence in Asian culture, Chinese English majors are faced with tre...
OBJECTIVE: This study aims to create a reliable framework for grading esophageal cancer. The framework combines feature extraction, deep learning with attention mechanisms, and radiomics to ensure accuracy, interpretability, and practical use in tumo...
BACKGROUND: Oral mucosal lesions are widespread globally, have a high prevalence in clinical practice, and significantly impact patients' quality of life. However, their pathogenesis remains unclear. Recent evidences suggested that hematological para...
BACKGROUND: Post-operative moderate-to-severe mitral regurgitation (MR) following transcatheter aortic valve replacement (TAVR) is associated with poor outcomes, yet the factors contributing to this complication are not well understood. This study ai...
Patients with intracerebral hemorrhage (ICH) are highly susceptible to sepsis. This study evaluates the efficacy of machine learning (ML) models in predicting sepsis risk in intensive care units (ICUs) patients with ICH. We conducted a retrospective ...
The present work examined children's attribution of psychological properties to inanimate agents in two experiments. In Study 1, an Interview Task and the Theory of Mind Scale (ToM Scale) were administered to 4-year-olds with either a human or a huma...
While continuous glucose monitoring (CGM) has revolutionized metabolic health management, widespread adoption remains limited by cost constraints and usage burden, often resulting in interrupted monitoring periods. We propose a deep learning framewor...