Results 211 to 220 of about 142,397 (310)

A survey of field programmable gate array (FPGA)-based graph convolutional neural network accelerators: challenges and opportunities. [PDF]

PeerJ Comput Sci, 2022
Li S, Tao Y, Tang E, Xie T, Chen R.
europepmc   +1 more source

Graph Convolutional Neural Networks for Attack Detection in Wireless Sensor Networks Security

S. Rakesh, RVS Praveen, M. Ramkumar Prabhu, Amit Chauhan, Subharun Pal, Gourav Kalra   +5 more
openalex   +1 more source

Explaining the Origin of Negative Poisson's Ratio in Amorphous Networks With Machine Learning

Advanced Intelligent Discovery, EarlyView.
This review summarizes how machine learning (ML) breaks the “vicious cycle” in designing auxetic amorphous networks. By transitioning from traditional “black‐box” optimization to an interpretable “AI‐Physics” closed‐loop paradigm, ML is shown to not only discover highly optimized structures—such as all‐convex polygon networks—but also unveil hidden ...
Shengyu Lu, Xiangying Shen
wiley   +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

Composition‐Aware Cross‐Sectional Integration for Spatial Transcriptomics

Advanced Intelligent Discovery, EarlyView.
Multi‐section spatial transcriptomics demands coherent cell‐type deconvolution, domain detection, and batch correction, yet existing pipelines treat these tasks separately. FUSION unifies them within a composition‐aware latent framework, modeling reads as cell‐type–specific topics and clustering in embedding space.
Qishi Dong, Zijian Huang, Xuanwu Wang, Zhenghui Feng, Wei Liu, Bingding Huang   +5 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

Harnessing Machine Learning to Understand and Design Disordered Solids

Advanced Intelligent Discovery, EarlyView.
This review maps the dynamic evolution of machine learning in disordered solids, from structural representations to generative modeling. It explores how deep learning and model explainability transform property prediction into profound physical insight.
Muchen Wang, Yue Fan
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

A novel graph convolutional neural network for predicting interaction sites on protein kinase inhibitors in phosphorylation. [PDF]

Sci Rep, 2022
Wang F, Chen YT, Yang JM, Akutsu T.
europepmc   +1 more source

Why Physics Still Matters: Improving Machine Learning Prediction of Material Properties With Phonon‐Informed Datasets

Advanced Intelligent Discovery, EarlyView.
Phonons‐informed machine‐learning predictive models are propitious for reproducing thermal effects in computational materials science studies. Machine learning (ML) methods have become powerful tools for predicting material properties with near first‐principles accuracy and vastly reduced computational cost.
Pol Benítez, Cibrán López, Edgardo Saucedo, Teruyasu Mizoguchi, Claudio Cazorla   +4 more
wiley   +1 more source