Deep Learning Scheduling on a Field-Programmable Gate Array Cluster Using Configurable Deep Learning Accelerators by Tianyang Fang, Alejandro Perez-Vicente, Hans Johnson, Jafar Saniie

“…The system demonstrated its capability to parallel-process diverse neural network (NN) models, manage compute graphs in a pipelined sequence, and allocate computational resources efficiently to intensive NN layers. …”
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Lightweight and efficient skeleton-based sports activity recognition with ASTM-Net. by Bin Wu, Mei Xue, Ying Jia, Ning Zhang, GuoJin Zhao, XiuPing Wang, Chunlei Zhang

“…To address these challenges, we propose ASTM‑Net, an Activity‑aware SpatioTemporal Multi‑branch graph convolutional network comprising two novel modules. …”
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Traffic flow prediction based on spatiotemporal encoder-decoder model. by Yuanming Ding, Wei Zhao, Lin Song, Chen Jiang, Yunrui Tao

“…To more effectively capture the periodic and dynamic changes in urban traffic flow and the spatiotemporal correlation of complex road networks, a new traffic flow prediction method, the Enhanced Spatiotemporal Graph Convolutional Network Encoder-Decoder Model (ESGCN-EDM), is proposed. …”
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GCN-based unsupervised community detection with refined structure centers and expanded pseudo-labeled set. by Bing Guo, Liping Deng, Tao Lian

“…Considering them as pseudo-labeled nodes, graph convolutional network (GCN) is recently exploited to realize unsupervised community detection. …”
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Rotten strawberry classification based on EfficientNet V2 algorithm fused with GCN and CA-Transformer by WANG Wei, YANG Shizhong, GONG Yucheng, GAO Sheng, DENG Zhaopeng

“…ObjectiveImproving the accuracy and efficiency of rotting strawberry classification using modern computer vision techniques and deep learning methods.MethodsA classification method for rotten strawberries based on EfficientNet V2 fusion with Graph Convolutional Network (GCN) and Channel-Attention Transformer (CA-Transformer) has been proposed. …”
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A malware classification method based on directed API call relationships. by Cuihua Ma, Zhenwan Li, Haixia Long, Anas Bilal, Xiaowen Liu

“…To effectively capture these features, we introduce First-order and Second-order Graph Convolutional Networks (FSGCN) to approximate the operations of a directed graph convolutional network (DGCN). …”
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Multilevel Feature Fusion-Based GCN for Rumor Detection with Topic Relevance Mining by Shenyu Chen, Meng Li, Weifeng Yang

“…In this paper, we propose a novel graph convolution network model, named multilevel feature fusion-based graph convolution network (MFF-GCN) which can employ multiple streams of GCNs to learn different level features of rumor data, respectively. …”
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The Prediction of Multistep Traffic Flow Based on AST-GCN-LSTM by Fan Hou, Yue Zhang, Xinli Fu, Lele Jiao, Wen Zheng

“…Aiming at the traffic flow prediction problem of the traffic network, this paper proposes a multistep traffic flow prediction model based on attention-based spatial-temporal-graph neural network-long short-term memory neural network (AST-GCN-LSTM). …”
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Federated Learning-Based Credit Card Fraud Detection: A Comparative Analysis of Advanced Machine Learning Models by Zheng Han

“…This paper introduced federated learning and discussed a few federated learning algorithms applied to the problem—these methods include Federated Graph Attention Network with Dilated Convolution Neural Network (FedGAT-DCNN), FedAvg with Convolutional Neural Network (CNN), and Federated Averaging with Distance-based Weighted Aggregation (FedAvg-DWA) with Random Forest (RF). …”
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A Deep Reinforcement Learning Approach for Portfolio Management in Non-Short-Selling Market by Ruidan Su, Chun Chi, Shikui Tu, Lei Xu

“…Moreover, stock spatial interrelation representing the correlation between two different stocks is captured by a graph convolution network based on fundamental data. …”
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Financial accounting management strategy based on business intelligence technology for sustainable development strategy by Jianben Feng

“…To solve this problem, the study proposes a corporate financial distress prediction model based on graph convolutional neural networks and dynamic time regularization. …”
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Data-driven personalized marketing strategy optimization based on user behavior modeling and predictive analytics: Sustainable market segmentation and targeting. by Bin Sun

“…In this study, we propose a novel framework called DP-GCN (Deterministic Policy Graph Convolutional Network), which integrates multi-level Graph Convolutional Networks (GCNs) with Deep Deterministic Policy Gradient (DDPG) reinforcement learning to model heterogeneous information networks composed of users, products, and search queries. …”
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A Research Approach to Port Information Security Link Prediction Based on HWA Algorithm by Zhixin Xia, Zhangqi Zheng, Lexin Bai, Xiaolei Yang, Yongshan Liu

“…The algorithm can obtain hypergraphs without knowing the attribute information of hypergraph nodes and combines the graph convolutional network (GCN) framework to capture node feature information for link prediction. …”
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Analysis of baseball behavior recognition model based on Dual-GCN improved by motion weights by Ji Li

“…A motion weight improvement model based on dual-graph convolutional network is proposed. The new model takes a dual-graph convolutional network for behavior recognition and key region segmentation of baseball video images, and enhances the correlation and contribution between characters through motion weights. …”
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Resource Optimization Method Based on Spatio-Temporal Modeling in a Complex Cluster Environment for Electric Vehicle Charging Scenarios by Hongwei Wang, Wei Liu, Chenghui Wang, Kao Guo, Zihao Wang

“…For spatial modeling, an innovative dual-view dynamic graph convolutional network architecture is utilized to accurately explore the static and dynamic correlation information of the spatial layout of charging piles. …”
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Action recognition using part and attention enhanced feature fusion by Danfeng Zhuang, Min Jiang, Lei Wang, Mohamed Sanim Akremi, Hedi Tabia

“…Abstract In various studies on skeleton-based action recognition, graph convolution has been widely used to extract crucial skeleton information. …”
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A Study on the Construction of Translation Curriculum System for English Majors from the Perspective of Human-Computer Interaction by Qi Li

“…First, a new data enhancement method is proposed for the bipartite graph structure. Then, the enhanced data is fed into a graph convolutional neural network for node feature extraction to obtain node representations of users and items. …”
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Multi-Step Peak Passenger Flow Prediction of Urban Rail Transit Based on Multi-Station Spatio-Temporal Feature Fusion Model by Jianan Sun, Xiaofei Ye, Xingchen Yan, Tao Wang, Jun Chen

“…A combination of a graph convolutional neural network and a Transformer is used. …”
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GNN for LoRa Device Fingerprint Identification by Bojun Zhang

“…The system employs GGC(Gated Graph Convolution) networks to extract feature representations from the graphs, which are then encoded by a time encoder, specifically an LSTM(Long Short-Term Memory) network, to obtain a coarse-grained temporal representation. …”
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A rolling bearing fault diagnosis method based on GADF-CWT-GCNN by ZHANG Xiaoli, LUO Xin, LI Min, LIANG Wang, WANG Fangzhen

“…The sample sub-graph is imported into the convolutional neural network with the group normalization algorithm for diagnostic detection. …”
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