IMPROVING FMRI-BASED AUTISM SEVERITY IDENTIFICATION VIA BRAIN NETWORK DISTANCE AND ADAPTIVE LABEL DISTRIBUTION LEARNING

Improving fMRI-Based Autism Severity Identification via Brain Network Distance and Adaptive Label Distribution Learning

Machine learning methodologies have been profoundly researched in the realm of autism spectrum disorder (ASD) diagnosis.Nonetheless, owing to the ambiguity of ASD severity labels and individual differences in ASD severity, Easter Ornament current fMRI-based methods for identifying ASD severity still do not achieve satisfactory performance.Besides,

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Effect of coupling agents on the dielectric properties and energy storage of Ba0.5Sr0.5TiO3/P(VDF-CTFE) nanocomposites

Dielectric materials with high electric energy density and low dielectric loss are critical for electric applications in ANTIOXIDANT FORMULA modern electronic and electrical power systems.To obtain desirable dielectric properties and energy storage, nanocomposites using Ba0.5Sr0.5TiO3 (BST) as the filler and poly(vinylidene fluoride-chlorotrifluoro

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