Unsupervised domain adaptation deals with a task from an unlabeled target domain by
leveraging the knowledge gained from labeled source domain (s). The fuzzy system is …

Fuzzy systems are the perfect systems for knowledge representation-the fuzzy rules on
which they are based can be interpreted and explained during the presentation of the …

Machine learning draws its power from various disciplines, including computer science,
cognitive science, and statistics. Although machine learning has achieved great …

Unsupervised domain adaptation leverages knowledge from source domain (s)/task (s) to
facilitate learning in target task, particularly in unsatisfied and complex scenarios with data …

Considering the failure of fully exploring the transferability of samples, domain adaptation
(DA) is still troubled by the problem of the disturbed distribution of the target domain. In this …

Test-time adaptation (TTA) leverages real-time updates with test samples to effectively
address distribution shifts. Previous TTA methods have achieved stable results in scenarios …

Universal source-free domain adaptation is an urgent, unsolved problem in transfer
learning. Existing source-free and universal domain adaptation methods ignore soft …

Current research on resting-state functional magnetic resonance imaging (rs-fMRI)-related
autism spectrum disorder (ASD) revealed two problems in the diagnosis of ASD. First, the …

The current diagnostic methods for Autism Spectrum Disorder (ASD) based on Resting-State
Functional Magnetic Resonance Imaging (rs-fMRI) face two significant challenges. Firstly …

In fluid mechanics research, understanding the motion behavior of particles in a flow field is
crucial for comprehending particle transport, mixing, and deposition processes. However …