Probabilistic load forecasting has gained widespread attention for its effectiveness in
providing scientific power dispatch plans in recent years. Most existing works adopted …

The integration of deep learning techniques in pediatric neuroimaging has shown significant
promise in advancing various aspects of the field. This paper provides a comprehensive …

Objective. Attention-deficit/hyperactivity disorder (ADHD) is the most common
neurodevelopmental disorder in adolescents that can seriously impair a person's attention …

Utilizing functional magnetic resonance imaging (fMRI) to model functional brain networks
(FBNs) is increasingly prominent in attention-deficit/hyperactivity disorder (ADHD) research …

The inadequacy of labeled datasets for image quality assessment has led to the
development and popularity of self-supervised approaches. However, most existing self …

The actual collection of tabular data for sharing involves confidentiality and privacy
constraints, leaving the potential risks of machine learning for interventional data analysis …

The problems of incomplete data and insufficient feature utilization are commonly existing in
disease diagnosis with multi-modality ophthalmology images. In this paper, we firstly …

Attention deficit hyperactivity disorder (ADHD) is a common childhood developmental
disorder. In recent years, pattern recognition methods have been increasingly applied to …

Functional connectivity (FC) graph analysis is an important method for diagnosing brain
disorders using functional magnetic resonance imaging (fMRI). Existing FC graph diagnosis …

Autism Spectrum Disorder (ASD) is a heterogeneous disorder in children, and the current
clinical diagnosis is accomplished using behavioral, cognitive, developmental, and …