Results 191 to 200 of about 36,148 (258)

EEG-based emotion recognition using graph convolutional neural network with dual attention mechanism. [PDF]

Front Comput Neurosci
Chen W, Liao Y, Dai R, Dong Y, Huang L.
europepmc   +1 more source

Heart disease detection based on internet of things data using linear quadratic discriminant analysis and a deep graph convolutional neural network. [PDF]

Front Comput Neurosci, 2022
Saikumar K, Rajesh V, Srivastava G, Lin JC.   +3 more
europepmc   +1 more source

Deep Learning–Based Extraction of Promising Material Groups and Common Features from High‐Dimensional Data: A Case of Optical Spectra of Inorganic Crystals

Advanced Intelligent Discovery, EarlyView.
We report a novel interpretation method for deep learning models based on feature extraction and clustering. Applying this method to an atomistic line graph neural network (ALIGNN) model trained on optical absorption spectra of 2,681 inorganic compounds obtained from first‐principles calculations, we successfully identify key factors underlying ...
Akira Takahashi, Yu Kumagai, Arata Takamatsu, Fumiyasu Oba   +3 more
wiley   +1 more source

Identifying ADHD-Related Abnormal Functional Connectivity with a Graph Convolutional Neural Network. [PDF]

Neural Plast
Hu Y, Ran J, Qiao R, Xu J, Xu J, Tan C, Hu L, Tian Y.   +7 more
europepmc   +1 more source

An Early Warning System for Earthquake Prediction from Seismic Data Using Batch Normalized Graph Convolutional Neural Network with Attention Mechanism (BNGCNNATT). [PDF]

Sensors (Basel), 2022
Bilal MA, Ji Y, Wang Y, Akhter MP, Yaqub M.   +4 more
europepmc   +1 more source

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

scRGCL: a cell type annotation method for single-cell RNA-seq data using residual graph convolutional neural network with contrastive learning. [PDF]

Brief Bioinform
Yuan L, Sun S, Jiang Y, Zhang Q, Ye L, Zheng CH, Huang DS.   +6 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

Spectral graph convolutional neural network for Alzheimer's disease diagnosis and multi-disease categorization from functional brain changes in magnetic resonance images. [PDF]

Front Neuroinform
Alharbi H, Juanatas RA, Al Hejaili A, Lim SJ.   +3 more
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