Lightweight graph convolutional network with multi-attention mechanisms for intelligent action recognition in online physical education by Yuhao You

“…To address this, we propose a lightweight graph convolutional network (GCN) that integrates an improved Ghost module with multi-attention mechanisms, including a global attention mechanism (GAM) and a channel attention mechanism (CAM), to enhance spatial and temporal feature extraction. …”
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LoadSeer: Exploiting Tensor Graph Convolutional Network for Power Load Forecasting With Spatio-Temporal Characteristics by Jiahao Zhang, Bin Yu, Hanbin Lai, Lin Liu, Jinghui Zhou, Fengliang Lou, Yili Ni, Yan Peng, Ziheng Yu

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Stability-Optimized Graph Convolutional Network: A Novel Propagation Rule with Constraints Derived from ODEs by Liping Chen, Hongji Zhu, Shuguang Han

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Multi-featured spatial-temporal and dynamic multi-graph convolutional network for metro passenger flow prediction by Chuan Zhao, Xin Li, Zezhi Shao, HongJi Yang, Fei Wang

“…To address these challenges, we developed a novel model called the multi-featured spatial-temporal (MFST) and dynamic multi-graph convolutional network (DMGCN) model. Temporal connections are learned from both the local and global information in a time-series sequence using the combination of a time-trend feature mapping block and a gated recurrent unit block. …”
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Cross-User Electromyography Pattern Recognition Based on a Novel Spatial-Temporal Graph Convolutional Network by Mengjuan Xu, Xiang Chen, Yuwen Ruan, Xu Zhang

“…Given that high-density surface EMG (HD-sEMG) signal contains rich temporal and spatial information, the multi-view spatial-temporal graph convolutional network (MSTGCN)is adopted as the basic classifier, and a feature extraction convolutional neural network (CNN) module is designed and integrated into MSTGCN to generate a new model called CNN-MSTGCN. …”
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A Spiking Neural Network With Adaptive Graph Convolution and LSTM for EEG-Based Brain-Computer Interfaces by Peiliang Gong, Pengpai Wang, Yueying Zhou, Daoqiang Zhang

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UPN-GCN: Update Positive-Negative Graph Convolution Neural Network in Non-Euclidean Structured Data by Lijun Fan, Shichao Yi, Wenrui Guan, Pingxin Wang

“…Graph Convolutional neural Networks (GCNs) demonstrate exceptional effectiveness when working with data that have non-Euclidean structures. …”
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Multi‐omics graph convolutional networks for digestive system tumour classification and early‐late stage diagnosis by Lin Zhou, Zhengzhi Zhu, Hongbo Gao, Chunyu Wang, Muhammad Attique Khan, Mati Ullah, Siffat Ullah Khan

“…Addressing this challenge, the authors introduce a novel methodology, denominated as the Multi‐omics Graph Transformer Convolutional Network (MGTCN). …”
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Bearing fault diagnosis based on a multiple-constraint modal-invariant graph convolutional fusion network by Zhongmei Wang, Pengxuan Nie, Jianhua Liu, Jing He, Haibo Wu, Pengfei Guo

“…Then, the spatial aggregation property of Graph Convolutional Neural Networks (GCN) is utilized to capture the dependency relationship between current and vibration modes with similar time step features for accurately fusing contextual semantic information. …”
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SignFormer-GCN: Continuous sign language translation using spatio-temporal graph convolutional networks. by Safaeid Hossain Arib, Rabeya Akter, Sejuti Rahman, Shafin Rahman

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ProG-SOL: Predicting Protein Solubility Using Protein Embeddings and Dual-Graph Convolutional Networks by Gen Li, Ning Zhang, Long Fan

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A road generalization method using graph convolutional network based on mesh-line structure unit by Tianyuan Xiao, Tinghua Ai, Dirk Burghardt, Pengcheng Liu, Min Yang, Aji Gao, Bo Kong, Huafei Yu

“…Aiming at the above problems, this study designs a simplification method using the Mesh-Line Structure Unit (MLSU) to consider polyline and polygon characteristics simultaneously with the support of graph-based deep learning networks. In order to make generalization decisions, a model based on graph convolutional network (GCN) is constructed and trained using real data, thus realizing the road network selective omission. …”
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Power Load Data Completion Method Based on Integrated Graph Convolutional Variational Transformer by YAN Li, HU Hailin, SHI Lei, WU Qinzheng, LÜ Tianguang, XU Yingdong, ZHANG Wenbin, WANG Gaozhou

Subjects: “…data imputation|graph convolutional networks|transformer model|power load data…”
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Graph Convolutional Recommendation System Based on Bilateral Attention Mechanism by Hui Yang, Changchun Yang

“…Therefore, numerous researchers have integrated knowledge graphs and graph convolutional networks into recommender systems to enhance their performance. …”
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Glaucoma detection from retinal fundus images using graph convolution based multi-task model by Satyabrata Lenka, Zefree Lazarus Mayaluri, Ganapati Panda

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Efficient Tactile Perception in Robotics: Reducing Data Redundancy through Compression and Normalization in Spiking Graph Convolutional Networks by Elahe Rezaee Ahvanooii, Sheis Abolmaali

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Combination of Graph and Convolutional Networks for Brain Tumor Segmentation from Multi-Modal MR Images In Clinical Applications by Marjan Vatanpour, Javad Haddadnia, Shahryar Salmani Bajestani

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Capturing Global Structural Features and Global Temporal Dependencies in Dynamic Social Networks Using Graph Convolutional Networks for Enhanced Analysis by Ling Wu, Boen Li, Kun Guo, Qishan Zhang

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Graph convolution-based adaptive feature fusion method for MRI brain tumor segmentation by Ye ZHANG, Muqing ZHANG, Xuegang YUAN, Datian NIU

Subjects: “…computer neural network; brain tumor segmentation; 3d u-net; graph convolution inference bottleneck layer; dynamic snake convolution; adaptive spatial feature fusion…”
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Towards lightweight model using non-local-based graph convolution neural network for SQL injection detection by Duc-Chinh Nguyen, Manh-Hung Ha, Manh-Tuan Do, Oscal Tzyh-Chiang Chen

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