Key Frame Detection in Badminton Swings and Its Application to Physical Education by Jen-Hao Hsu, Chi-Chuan Lee, Jing-Yuan Chang, Duan-Shin Lee

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Directed Knowledge Graph Embedding Using a Hybrid Architecture of Spatial and Spectral GNNs by Guoqiang Hou, Qiwen Yu, Fan Chen, Guang Chen

“…The graph transformer leverages multi-head attention mechanisms to capture the global connectivity of the feature graph from different perspectives in the spatial domain, which bridges the gap between frequency responses and, further, naturally couples the graph transformer and directed graph convolutional neural networks (GCNs). …”
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DGNMDA: Dual Heterogeneous Graph Neural Network Encoder for miRNA-Disease Association Prediction by Daying Lu, Qi Zhang, Chunhou Zheng, Jian Li, Zhe Yin

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Dual-stream dynamic graph structure network for document-level relation extraction by Yu Zhong, Bo Shen

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An OGFA+CNN Approach for Multi-Level Disease Identification in Fundus Images by Preethi Kulkarni, K. Srinivasa Reddy

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Cyber security entity recognition method based on residual dilation convolution neural network by Bo XIE, Guowei SHEN, Chun GUO, Yan ZHOU, Miao YU

“…In recent years,cybersecurity threats have increased,and data-driven security intelligence analysis has become a hot research topic in the field of cybersecurity.In particular,the artificial intelligence technology represented by the knowledge graph can provide support for complex cyberattack detection and unknown cyberattack detection in multi-source heterogeneous threat intelligence data.Cybersecurity entity recognition is the basis for the construction of threat intelligence knowledge graphs.The composition of security entities in open network text data is very complex,which makes traditional deep learning methods difficult to identify accurately.Based on the pre-training language model of BERT (pre-training of deep bidirectional transformers),a cybersecurity entity recognition model BERT-RDCNN-CRF based on residual dilation convolutional neural network and conditional random field was proposed.The BERT model was used to train the character-level feature vector representation.Combining the residual convolution and the dilation neural network model to effectively extract the important features of the security entity,and finally obtain the BIO annotation of each character through CRF.Experiments on the large-scale cybersecurity entity annotation dataset constructed show that the proposed method achieves better results than the LSTM-CRF model,the BiLSTM-CRF model and the traditional entity recognition model.…”
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Global information aware network with global interaction graph attention for infrared small target detection by Ruimin Yang, Yidan Zhang, Guangshuai Gao, Liang Liao, Chunlei Li

“…Additionally, the multi‐scale context fusion module utilises self‐attention and dilation convolution to complement richer feature details at different scales. …”
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Ultra-short-term Multi-region Power Load Forecasting Based on Spearman-GCN-GRU Model by Junying WU, Xin LU, Hong LIU, Bin ZHANG, Shouliang CHAI, Yunchun LIU, Jianan WANG

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Multi-Biometric Feature Extraction from Multiple Pose Estimation Algorithms for Cross-View Gait Recognition by Ausrukona Ray, Md. Zasim Uddin, Kamrul Hasan, Zinat Rahman Melody, Prodip Kumar Sarker, Md Atiqur Rahman Ahad

“…Subsequently, we employed a residual graph convolutional network (ResGCN) to extract features from the generated skeleton data. …”
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GKCAE: A graph-attention-based encoder for fine-grained semantic segmentation of high-voltage transmission corridors scenario LiDAR data by Su Zhang, Haibo Liu, Jingguo Rong, Yaping Zhang

“…To tackle this limitation, we propose a novel network architecture—Graph-Kernel Convolution Attention Encoder (GKCAE)—designed for multi-class, fine-grained semantic segmentation of transmission corridor point clouds. …”
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Graph Neural Network Classification in EEG-Based Biometric Identification: Evaluation of Functional Connectivity Methods Using Time-Frequency Metric by Roghaieh Ashenaei, Ali Asghar Beheshti Shirazi

“…Integrated with Graph Convolutional Neural Networks (GCNNs), our approach leverages graph-structured FC data for superior classification. …”
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DGL-STFA: Predicting lithium-ion battery health with dynamic graph learning and spatial–temporal fusion attention by Zheng Chen, Quan Qian

“…The framework employs multi-scale convolutional neural networks to capture diverse temporal patterns, a self-attention mechanism to construct dynamic adjacency matrices that adapt over time, and a temporal attention mechanism to identify and prioritize key moments that influence battery degradation. …”
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RMDNet: RNA-aware dung beetle optimization-based multi-branch integration network for RNA–protein binding sites prediction by Jiangbo Zhang, Yunhui Peng, Feifei Cui, Zilong Zhang, Shankai Yan, Qingchen Zhang

Subjects: “…Multi-branch deep learning network…”
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A Fusion Deep Learning Model for Predicting Adverse Drug Reactions Based on Multiple Drug Characteristics by Qing Ou, Xikun Jiang, Zhetong Guo, Jiayi Jiang, Zhanpeng Gan, Fangfang Han, Yongming Cai

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Visual language transformer framework for multimodal dance performance evaluation and progression monitoring by Lei Chen

“…To achieve this, we integrate contrastive self-supervised learning, spatiotemporal graph convolutional networks (STGCN), long short-term memory networks (LSTM), and transformer-based text prompting. …”
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Autonomous Driving Decision-Making Method Based on Spatial-Temporal Fusion Trajectory Prediction by Yutao Luo, Aining Sun, Jiawei Hong

“…Firstly, the spatial interaction between vehicles is implicitly modeled using a graph convolutional neural network and multi-head attention mechanism, and the gated loop unit is embedded to capture the sequential temporal relationship to establish a prediction model incorporating spatial-temporal features. …”
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Hybrid CNN-GCN Network for Hyperspectral Image Classification by Cuiping Shi, Diling Liao, Liguo Wang

“…Unlike CNN, graph convolutional networks (GCNs) can well handle the intrinsic manifold structures of hyperspectral images (HSIs). …”
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Rail Transit Prediction Based on Multi-View Graph Attention Networks by Li Wang, Xin Wang, Jiao Wang

“…Specifically, the proposed model maps multiple relationships into multiple views. A graph convolutional neural network of multiple views with multi-layer attention learns the optimal regression of nodes. …”
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MB-AGCL: multi-behavior adaptive graph contrast learning for recommendation by Xiaowen lv, Yiwei Zhao, Zhihu Zhou, Yifeng Zhang, Yourong Chen

“…Abstract Graph Convolutional Networks (GCNs) have achieved remarkable success in recommendation systems by leveraging higher-order neighborhoods. …”
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Improving healthy food recommender systems through heterogeneous hypergraph learning by Jing Wang, Jincheng Zhou, Muammer Aksoy, Nidhi Sharma, Md Arafatur Rahman, Jasni Mohamad Zain, Mohammed J.F. Alenazi, Aliyeh Aminzadeh

“…For example, IoT sensors tracking daily nutrient intake require complex, multi-faceted analysis that traditional methods struggle to handle. …”
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