CGV-Net: Tunnel Lining Crack Segmentation Method Based on Graph Convolution Guided Transformer by Kai Liu, Tao Ren, Zhangli Lan, Yang Yang, Rong Liu, Yuantong Xu

“…To address this issue, this study proposes CGV-Net (CNN, GNN, and ViT networks), a novel tunnel crack segmentation network model that integrates convolutional neural networks (CNNs), graph neural networks (GNNs), and Vision Transformers (ViTs). …”
Get full text

EEG-Based Multi-Level Mental State Classification Using Partial Directed Coherence and Graph Convolutional Networks: Impact of Binaural Beats on Stress Mitigation by Yara Badr, Fares Al-Shargie, M. N. Afzal Khan, Nour Faris Ali, Usman Tariq, Fadwa Almughairbi, Fabio Babiloni, Hasan Al-Nashash

Get full text

Hyperbolic multi-channel hypergraph convolutional neural network based on multilayer hypergraph by Libing Bai, Feng Hu, Chunyang Tang, Zhangyu Mei, Chuang Liu

“…Building on this foundation, we propose a novel Hyperbolic Multi-channel HyperGraph convolutional Neural Network (HMHGNN). …”
Get full text

Integrated perception-communication-logistics multi-objective oriented path planning for emergency UAVs by XU Yunpeng, XIE Yaqi, YU Ran, HOU Luyang, WANG Kailiang, XU Lianming

“…In the first stage, a temporal graph convolution networks-based model was introduced to predict the number of personnel at the relief sites to quantify its supply and communication needs. …”
Get full text

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

“…In our research, we propose a novel suggestion model of short video material for international video apps through user preference modelling via hybrid multi-modal GCN (graph convolutional network). Unlike traditional methods that rely on the overall metadata of the short movies only, our approach jointly considers visual, linguistic and audio features of short movies, as well as user interactions, to propose personalized recommendations. …”
Get full text

Efficient Real-Time Sports Action Pose Estimation via EfficientPose and Temporal Graph Convolution by Yuanzhe Ma, Hui Li, Hongqiao Yan

“…This paper presents a real-time pose estimation framework that integrates EfficientPose and T-GCN (Temporal Graph Convolutional Networks) to address the challenges of dynamic and complex sports scenarios. …”
Get full text

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. …”
Get full text

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. …”
Get full text

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

“…Then, we tailor the concepts of the multi-head adaptive graph convolution to SNN so that it can make good use of the intrinsic spatial topology information among distinct EEG channels. …”
Get full text

Establishing Two-Dimensional Dependencies for Multi-Label Image Classification by Jiuhang Wang, Yuewen Zhang, Tengjing Wang, Hongying Tang, Baoqing Li

Subjects: Get full text

Hyperledger Fabric Graph Isomorphism Network for Conflict Transactions Detection in Multi-Version Concurrency Control by Fawaz Alzahrani, Mohd Yazid Idris, Mohd Fo'Ad Rohani, Rahmat Budiarto

“…Evaluation results indicate that HFGIN substantially outperforms baseline models such as the Graph Convolutional Network (GCN) and Graph Isomorphism Network (GIN). …”
Get full text

Correction: Oluwasanmi et al. Multi-Head Spatiotemporal Attention Graph Convolutional Network for Traffic Prediction. <i>Sensors</i> 2023, <i>23</i>, 3836 by Ariyo Oluwasanmi, Muhammad Umar Aftab, Zhiguang Qin, Muhammad Shahzad Sarfraz, Yang Yu, Hafiz Tayyab Rauf

Get full text

YOLO-BCD: A Lightweight Multi-Module Fusion Network for Real-Time Sheep Pose Estimation by Chaojie Sun, Junguo Hu, Qingyue Wang, Chao Zhu, Lei Chen, Chunmei Shi

Get full text

A Method for Fault Localization in Distribution Networks with High Proportions of Distributed Generation Based on Graph Convolutional Networks by Xiping Ma, Wenxi Zhen, Haodong Ren, Guangru Zhang, Kai Zhang, Haiying Dong

“…To address the issues arising from the integration of a high proportion of distributed generation (DG) into the distribution network, which has led to the transition from traditional single-source to multi-source distribution systems, resulting in increased complexity of the distribution network topology and difficulties in fault localization, this paper proposes a fault localization method based on graph convolutional networks (GCNs) for distribution networks with a high proportion of distributed generation. …”
Get full text

MDGCN: Multiple Graph Convolutional Network Based on the Differential Calculation for Passenger Flow Forecasting in Urban Rail Transit by Chenxi Wang, Huizhen Zhang, Shuilin Yao, Wenlong Yu, Ming Ye

“…Firstly, we identify the heterogeneity of stations under two spaces by the Multi-graph convolutional layer. Secondly, we designed the Diff-graph convolutional layer to identify the changing trend of heterogeneous features and used the attention mechanism unit with the LSTM unit to achieve adaptive fusion of multiple features and modeling of temporal correlation. …”
Get full text

Robust Low-Snapshot DOA Estimation for Sparse Arrays via a Hybrid Convolutional Graph Neural Network by Hongliang Zhu, Hongxi Zhao, Chunshan Bao, Yiran Shi, Wenchao He

“…We propose a hybrid Convolutional Graph Neural Network (C-GNN) for direction-of-arrival (DOA) estimation in sparse sensor arrays under low-snapshot conditions. …”
Get full text

Real-time analysis of soccer ball–player interactions using graph convolutional networks for enhanced game insights by Fahad Majeed, Maria Nazir, Kamilla Swart, Marco Agus, Jens Schneider

“…Our approach leverages the power of deep computer vision models, employing a CSPDarknet53 backbone for detection and a Graph Convolutional Network (GCN) for predictive analytics. …”
Get full text

Measurement error evaluation method for voltage transformers in distribution networks based on self-attention and graph convolutional networks by Xiujuan Zeng, Tong Liu, Huiqin Xie, Dajiang Wang, Jihong Xiao

“…To address the challenge of extracting complex nonlinear features from multivariate electrical data, a combined model of a self-attention mechanism and a graph convolutional network (GCN) is proposed. The self-attention mechanism captures global dependencies among power parameters, while the GCN effectively constructs the multivariate data structures in distribution networks. …”
Get full text

Drug repositioning framework using embedding drug-protein-disease similarities with graph convolution network and ensemble learning by Hanaa Torkey, Heba El-Behery, Abdel-Fattah Atti, Nawal El-Fishawy

“…The effectiveness of utilizing Meta-Path instance, the number of attention heads, and Graph Convolutional Network (GCN) and ensemble learning algorithm is analyzed on gold-standard datasets to evaluate the accuracy of the model and validity of the discovered DTI. …”
Get full text

Grid Partition-Based Dynamic Spatial–Temporal Graph Convolutional Network for Large-Scale Traffic Flow Forecasting by Lifeng Gao, Liujia Chen, Agen Qiu, Qinglian Wang, Jianlong Wang, Cai Chen, Fuhao Zhang, Geli Ou’er

“…It includes the following: a dynamic graph convolution module to divide the traffic network into grid regions and thereby effectively capture the local spatial dependencies inherent in large-scale traffic topologies, an attention-based dynamic graph convolutional network to capture the local spatial correlations within each region, a global spatial dependency aggregation module to model inter-regional correlation weights using sequence similarity methods and comprehensively reflect the overall state of the traffic network, and multi-scale gated convolutions to capture both long- and short-term temporal correlations across varying time ranges. …”
Get full text