Results 211 to 220 of about 5,698,498 (295)

AI‐BioMech: Deep Learning Prediction of Mechanical Behavior in Aperiodic Biological Cellular Materials

Advanced Intelligent Discovery, EarlyView.
AI‐BioMech is a deep learning framework that predicts the mechanical behavior of biological cellular materials directly from 2D images. By replacing traditional finite element analysis with semantic segmentation, it identifies stress and strain distributions with 99% accuracy, offering a high‐speed, scalable alternative for analyzing complex, aperiodic
Haleema Sadia, Marcelo A. Dias, Parvez Alam   +2 more
wiley   +1 more source

A New Regression Model for Depression Severity Prediction Based on Correlation among Audio Features Using a Graph Convolutional Neural Network. [PDF]

Diagnostics (Basel), 2023
Ishimaru M, Okada Y, Uchiyama R, Horiguchi R, Toyoshima I.   +4 more
europepmc   +1 more source

Multimodal Learning with Rashomon Analysis for Battery Discharge Capacity Prediction

Advanced Intelligent Discovery, EarlyView.
Multimodal fusion integrates composition, crystal‐structure, and radial‐distribution descriptors to predict battery discharge capacity. Rashomon analysis across near‐optimal models reveals that explanatory variation is structured rather than arbitrary, separating stable mechanistic signals from model‐contingent attributions and providing a more ...
Jue Gong, Lejia Wang, Tianqing Zhu, Keith Butler, Sichao Li   +4 more
wiley   +1 more source

[Research on fatigue recognition based on graph convolutional neural network and electroencephalogram signals]. [PDF]

Sheng Wu Yi Xue Gong Cheng Xue Za Zhi
Li S, Fu Y, Zhang Y, Lu G.
europepmc   +1 more source

A fused biometrics information graph convolutional neural network for effective classification of patellofemoral pain syndrome. [PDF]

Front Neurosci, 2022
Xiong B, OuYang Y, Chang Y, Mao G, Du M, Liu B, Xu Y.   +6 more
europepmc   +1 more source

Materials Representation Learning Based on a Material–Motif Network and Heterogeneous Graphs

Advanced Intelligent Discovery, EarlyView.
Structure motifs in materials are used to construct a bipartite material–motif network that links each material to its constituent motifs and establishes connectivity among materials sharing common motifs. Network analysis reveals material clusters associated with different functional applications and supports motif‐guided screening of materials.
Anoj Aryal, Weiyi Gong, Huta Banjade, Qimin Yan   +3 more
wiley   +1 more source

A temporal-spectral graph convolutional neural network model for EEG emotion recognition within and across subjects. [PDF]

Brain Inform
Li R, Yang X, Lou J, Zhang J.
europepmc   +1 more source

Chemical toxicity prediction based on semi-supervised learning and graph convolutional neural network. [PDF]

J Cheminform, 2021
Chen J, Si YW, Un CW, Siu SWI.
europepmc   +1 more source

From Data to Discovery: Machine Learning–Enabled Intelligent Characterization of Two‐Dimensional Materials

Advanced Intelligent Discovery, EarlyView.
Machine learning serves as a central engine for the intelligent characterization of two‐dimensional materials by integrating multimodal techniques, including optical microscopy, spectroscopy, electron microscopy, and scanning probe microscopy (SPM). This unified framework enables automated, high‐throughput, and quantitative extraction of structural ...
Zhi‐Long Cao, Jia‐Xu Yan
wiley   +1 more source

Attention mechanism-enhanced graph convolutional neural network for unbalanced lithology identification. [PDF]

Sci Rep
Wang A, Zhao S, Xie K, Wen C, Tian HL, He JB, Zhang W.   +6 more
europepmc   +1 more source