Time series are the primary data type used to record dynamic system measurements and
generated in great volume by both physical sensors and online processes (virtual sensors) …

The unprecedented growth of computational capabilities in recent years has allowed
Artificial Intelligence (AI) models to be developed for medical applications with remarkable …

Alzheimer's disease (AD) is a progressive neurodegenerative disorder that affects millions of
individuals worldwide, causing severe cognitive decline and memory impairment. The early …

Neurological disorders (NDs), such as Alzheimer's disease, have been a threat to human
health all over the world. It is of great importance to diagnose ND through combining artificial …

Leveraging recent advances in graph neural networks, our study introduces an application
of graph convolutional networks (GCNs) within a correlation-based population graph, aiming …

Abstract Context: Artificial Intelligence (AI) in the medical domain has achieved remarkable
results on various metrics primarily due to recent advancements in computational …

From the various perspectives of machine learning (ML) and the multiple models used in this
discipline, there is an approach aimed at training models for the early detection (ED) of …

Predicting the function magnetic resonance imaging (fMRI)-based brain network (fMRI-BN)
from structure MRI-based brain network is imperative in clinical practice because fMRIs are …

Background Cardiometabolic disorders pose significant health risks globally. Metabolic
syndrome, characterized by a cluster of potentially reversible metabolic abnormalities, is a …

Analyses of longitudinal brain networks, ie, graphs, are of significant interest to understand
the dynamics of brain changes with respect to aging and neurodegenerative diseases …