Results 281 to 290 of about 142,397 (310)

Dynamic geo‐hydrogeological monitoring‐driven situational awareness for real‐time floor water inrush risk prediction in deep mining

Deep Underground Science and Engineering, EarlyView.
The fused data extracted from the distributed monitoring system as the data basis, combined with dynamic geological data, are imported into a deep learning model. As the geological conditions of mining and excavation change, the risk of water inrush at the working face is retrieved in real time.
Yongjie Li, Huiyong Yin, Fangying Dong, Tao Wu, Chao Zhang   +4 more
wiley   +1 more source

A Novel Graph Convolutional Neural Network Method for Improving Phase Severance Formation Planning in Polymer Alloys

openalex   +1 more source

A Review of Grain Boundaries: Formation Mechanism, Synthesis Strategy, and Application in Electrocatalysis

EcoEnergy, EarlyView.
An overview of grain boundary engineering in the field of electrocatalysis. ABSTRACT Key electrocatalytic reactions such as HER, OER, ORR, CO2RR, and NRR offer promising routes for storing renewable energy as chemical fuels. However, their widespread application is constrained due to the lack of highly active and stable catalysts. Grain boundaries (GBs)
Jingyu Gao, Fengming Zhou, Xinying Li, Ying Deng, Zexing Wu, Siyuan Feng, Yujing Zhu, Dan Zhang, Zhenyu Xiao   +8 more
wiley   +1 more source

Malware Classification Using Ghost Strategy Northern Goshawk Optimization with a Spatial Temporal Graph Convolutional Neural Network Based on Image Patterns

openalex   +1 more source

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Convolutional Graph Neural Networks

2020
Applying deep learning to the pervasive graph data is significant because of the unique characteristics of graphs. Recently, substantial amounts of research efforts have been keen on this area, greatly advancing graph-analyzing techniques. In this study, the authors comprehensively review different kinds of deep learning methods applied to graphs. They
J. Joshua Thomas, Tran Huu Ngoc Tran, Gilberto Pérez Lechuga, Bahari Belaton   +3 more
openaire   +1 more source

Universal Readout for Graph Convolutional Neural Networks

2019 International Joint Conference on Neural Networks (IJCNN), 2019
Several machine learning problems can be naturally defined over graph data. Recently, many researchers have been focusing on the definition of neural networks for graphs. The core idea is to learn a hidden representation for the graph vertices, with a convolutive or recurrent mechanism.
Navarin N., Tran D. V., Sperduti A.
openaire   +1 more source

Explainability Methods for Graph Convolutional Neural Networks

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019
With the growing use of graph convolutional neural networks (GCNNs) comes the need for explainability. In this paper, we introduce explainability methods for GCNNs. We develop the graph analogues of three prominent explainability methods for convolutional neural networks: contrastive gradient-based (CG) saliency maps, Class Activation Mapping (CAM ...
Phillip E. Pope, Soheil Kolouri, Mohammad Rostami, Charles E. Martin, Heiko Hoffmann   +4 more
openaire   +1 more source

Weighted Feature Fusion of Convolutional Neural Network and Graph Attention Network for Hyperspectral Image Classification

IEEE Transactions on Image Processing, 2022
Yanni Dong, Quanwei Liu, BO DU
exaly  

Covid-19 classification by FGCNet with deep feature fusion from graph convolutional network and convolutional neural network

Information Fusion, 2021
shuihua Wang, Juan M Gorriz, Xin Zhang
exaly