Cross-device fault diagnosis method based on graph convolution and multi-sensor fusion by SUN Yuanshuai, KONG Fanqin, NIE Xiaoyin, XIE Gang

Subjects: “…Graph convolutional neural network…”
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Multi-Step Parking Demand Prediction Model Based on Multi-Graph Convolutional Transformer by Yixiong Zhou, Xiaofei Ye, Xingchen Yan, Tao Wang, Jun Chen

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Latent Graph Induction Networks and Dependency Graph Networks for Events Detection by Jing Yang, Hu Gao, Depeng Dang

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TAD-Net: An approach for real-time action detection based on temporal convolution network and graph convolution network in digital twin shop-floor [version 1; peer review: 2 approved] by Liang Fu, Yunfeng Xie, Tingyu Liu, Yifeng Sun, Qing Hong

“…We then reconstructed the Joint Classification-Regression Recurrent Neural Networks (JCR-RNN) based on Temporal Convolution Network (TCN) and Graph Convolution Network (GCN). …”
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A novel recommender system using light graph convolutional network and personalized knowledge-aware attention sub-network by Rasoul Hassanzadeh, Vahid Majidnezhad, Bahman Arasteh

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An Efficient Algorithm for Extracting Railway Tracks Based on Spatial-Channel Graph Convolutional Network and Deep Neural Residual Network by Yanbin Weng, Meng Xu, Xiahu Chen, Cheng Peng, Hui Xiang, Peixin Xie, Hua Yin

“…This study introduces an innovative algorithm that utilizes a graph convolutional network (GCN) and deep neural residual network to enhance feature extraction from high-resolution aerial imagery. …”
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A Joint Estimation Method of Distribution Network Topology and Line Parameters Based on Power Flow Graph Convolutional Networks by Yu Wang, Xiaodong Shen, Xisheng Tang, Junyong Liu

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Multimodal data fusion for Alzheimer's disease based on dynamic heterogeneous graph convolutional neural network and generative adversarial network by Xiaoyu Chen, Shuaiqun Wang, Wei Kong

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A Graph Convolutional Network-Based Fine-Grained Low-Latency Service Slicing Algorithm for 6G Networks by Yuan Ye, Caiming Zhang, Chenlan Wu, Xiaorong Zhu

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EEG Functional Connection Analysis Based on the Weight Distribution of Convolutional Neural Network by Jinglong Wu, Peiwen Huang, Tiantian Liu, Go Ritsu, Duanduan Chen, Tianyi Yan

“…In this paper, we innovatively combined the functional connection with the deep learning algorithms and analysed the matrices after the weight distribution of each layer of the convolutional neural network (CNN) to obtain the connections that play a vital role in the classification. …”
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Attention Adaptive Temporal Graph Convolutional Network for Long-Term Seasonal Sea Surface Temperature Forecasting by Ling Xiao, Peihao Yang, Yuxue Wang, Sheng Li, Baoqin Chen

“…To address this, we introduce a novel attention adaptive temporal graph convolutional network (AA-TGCN) specifically designed for long-term seasonal SST forecasting. …”
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An Elliptic Kernel Unsupervised Autoencoder—Graph Convolutional Network Ensemble Model for Hyperspectral Unmixing by Estefania Alfaro-Mejia, Carlos J. Delgado, Vidya Manian

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TSTA-GCN: trend spatio-temporal traffic flow prediction using adaptive graph convolution network by Xinlu Zong, Jiawei Guo, Fucai Liu, Fan Yu

“…A trend spatio-temporal adaptive graph convolution network (TSTA-GCN) model for metro passenger flow prediction is presented in this paper. …”
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Reservoir Fluid PVT High-Pressure Physical Property Analysis Based on Graph Convolutional Network Model by Binghuan Li, Shan Jiang

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Overseas short video recommendations: A multimodal graph convolutional network approach incorporating cultural preferences by Xishi Liu, Haolin Wang, Dan Li

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Research on Urban Road Traffic Flow Prediction Based on Sa-Dynamic Graph Convolutional Neural Network by Song Hu, Jian Gu, Shun Li

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MVHGCN: Predicting circRNA-disease associations with multi-view heterogeneous graph convolutional neural networks. by Yan Miao, Xuan Tang, Chunyu Wang, Zhenyuan Sun, Guohua Wang, Shan Huang

“…To address these challenges, a novel computational method, namely MVHGCN, is proposed based on multi-view and graph convolutional networks to predict potential associations between circRNA and diseases. …”
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Prediction of the water solubility by a graph convolutional-based neural network on a highly curated dataset by Nadin Ulrich, Karsten Voigt, Anton Kudria, Alexander Böhme, Ralf-Uwe Ebert

“…To this end, we developed a graph convolutional neural network model (GNN) to predict the water solubility in the form of log S w based on a highly curated dataset of 9800 chemicals. …”
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A Novel Model for Predicting PM2.5 Concentrations Utilizing Graph Convolutional Networks and Transformer by Yuan Huang, Feilong Han, Qimeng Feng

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Shape-based disease grading via functional maps and graph convolutional networks with application to Alzheimer’s disease by Julius Mayer, Daniel Baum, Felix Ambellan, Christoph von Tycowicz, for the Alzheimer’s Disease Neuroimaging Initiative

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