PoseRL-Net: human pose analysis for motion training guided by robot vision by Bin Liu, Hui Wang

“…PoseRL-Net integrates multiple components, including a Spatial-Temporal Graph Convolutional Network (STGCN), attention mechanism, Gated Recurrent Unit (GRU) module, pose refinement, and symmetry constraints. …”
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Spatial-temporal deep learning model based on Similarity Principle for dock shared bicycles ridership prediction by Jiahui Zhao, Zhibin Li, Pan Liu, Mingye Zhang

Subjects: “…Keywords: Traffic demand prediction, Similarity-based Principle, Spatio-temporal Graph Convolutional Neural Network model; activity-based geographic information; prediction of bicycle sharing ridership.…”
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Rolling Bearing Fault Diagnosis Method Based on Fusion of CNN and CSSVM by LI Yunfeng, LAN Xiaosheng, SHEN Hongchang, XU Tongle

“…Secondly, the extended bearing vibration signal is converted into a two- dimensional wavelet time-frequency graph by continuous wavelet transform method. Then, the improved convolutional neural network model was used to train the divided two-dimensional image set to extract the deep features of time-frequency images. …”
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Smart City Traffic Flow and Signal Optimization Using STGCN-LSTM and PPO Algorithms by Tuxiang Lin, Rongliang Lin

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Seismic data denoising based on attention dual dilated CNN by Haixia Hu, Youhua Wei, Hui Chen, Xingan Fu, Ji Zhang, Quan Wang, Shiwei Cai

“…This study introduces an innovative Attention Dual-Dilated Convolutional Neural Network (ADDC-Net) to address random noise in seismic data. …”
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Graph Learning-Based Power System Health Assessment Model by Koji Yamashita, Nanpeng Yu, Evangelos Farantatos, Lin Zhu

“…The proposed framework leverages a physics-informed graph convolution network and graph attention network with ordinal encoders, which are benchmarked with multi-layer perceptron models. …”
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Enhancing Anti-Money Laundering Frameworks: An Application of Graph Neural Networks in Cryptocurrency Transaction Classification by Stefano Ferretti, Gabriele D'Angelo, Vittorio Ghini

“…The research specifically employs Graph Convolutional Networks (GCNs), Graph Attention Networks (GATs), the Chebyshev spatial convolutional neural networks, and GraphSAGE networks. …”
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Scalable recurrence graph network for stratifying RhoB texture dynamics in rectal cancer biopsies by Tuan D. Pham

“…SRGNet integrates spatial statistics, nonlinear dynamics, graph theory, and graph convolutional networks to address these challenges. …”
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STGATN: A novel spatiotemporal graph attention network for predicting pollutant concentrations at multiple stations. by Huazhen Xu, Wei Song, Lanmei Qian, Xiangxiang Mei, Guojian Zou

“…Both the encoder and decoder incorporate a spatiotemporal embedding mechanism, a spatiotemporal graph attention block, a gated temporal convolutional network, and a fusion gate. …”
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Spectro-Image Analysis with Vision Graph Neural Networks and Contrastive Learning for Parkinson’s Disease Detection by Nuwan Madusanka, Hadi Sedigh Malekroodi, H. M. K. K. M. B. Herath, Chaminda Hewage, Myunggi Yi, Byeong-Il Lee

“…Comprehensive experimental validation on multi-institutional datasets from Italy, Colombia, and Spain demonstrates that the proposed ViG-contrastive framework achieves superior classification performance, with the ViG-M-GELU architecture achieving 91.78% test accuracy. The integration of graph neural networks with contrastive learning enables effective learning from limited labeled data while capturing complex spectro-temporal relationships that traditional Convolution Neural Network (CNN) approaches miss, representing a promising direction for developing more accurate and clinically viable speech-based diagnostic tools for PD.…”
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T-RippleGNN: Predicting traffic flow through ripple propagation with attentive graph neural networks. by Anning Ji, Xintao Ma

“…Inspired by the propagation idea of graph convolutional networks, we propose ripple-propagation-based attentive graph neural networks for traffic flow prediction (T-RippleGNN). …”
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Securing Industrial IoT Environments: A Fuzzy Graph Attention Network for Robust Intrusion Detection by Safa Ben Atitallah, Maha Driss, Wadii Boulila, Anis Koubaa

“…Conventional machine learning methods and typical Graph Neural Networks (GNNs) often struggle to capture the complexity and uncertainty in IIoT network traffic, which hampers their effectiveness in detecting intrusions. …”
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Advancing Hate Speech Detection in Indonesian Language Using Graph Neural Networks and TF-IDF by Syaikha Amirah Zikrina, Fitriyani

“…Previous research has applied machine learning models such as Recurrent Neural Networks (RNN), Support Vector Machines (SVM), and Convolutional Neural Networks (CNN) to address this issue. …”
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An Efficient Model for Real-Time Traffic Density Analysis and Management Using Visual Graph Networks by Nikhil Nigam, Dhirendra Pratap Singh, Jaytrilok Choudhary, Surendra Solanki

“…The paper describes a method for improving urban traffic studies using Real-time Dense Analysis and Management using Visual Graph Networks (RDAMVGN) that utilizes deep learning techniques along with Visual Graph Networks based on visualizations. …”
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Image Reconstruction Through Multimode Polymer Optical Fiber for Potential Optical Recording of Neural Activity by Fengling Chen, Siyu Chen, Changjian Zhao, Yanan Zou, Kun Xiao, Zhuo Wang, Arnaldo Leal-Junior, Rui Min

“…Here, a conventional U-Net model within the framework of convolutional neural networks (CNNs) is applied to the reconstruction of speckle images obtained via POF. …”
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Deep graph representation learning: methods, applications, and challenges by ZHANG Xulong, QU Xiaoyang, XIAO Chunguang, WANG Jianzong

“…We discuss various techniques within these categories, including matrix factorization, random walks, graph convolutional networks, and graph Transformers. …”
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A novel anomaly detection method for multimodal WSN data flow via a dynamic graph neural network by Qinghao Zhang, Miao Ye, Xiaofang Deng

“…The simulation results obtained on a public dataset show that the proposed approach can significantly improve upon existing methods interms of robustness, and its F1 score reaches 0.90, which is 14.2% higher than that of the graph convolution network (GCN) with longshort-term memory (LSTM).…”
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ENDNet: Extra-Node Decision Network for Subgraph Matching by Masaki Shirotani, Motoki Amagasaki, Masato Kiyama

“…Although graph neural networks (GNNs) are commonly used in learning-based subgraph matching, they face challenges due to their convolution process. …”
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D3GNN: Double dual dynamic graph neural network for multisource remote sensing data classification by Teng Yang, Song Xiao, Jiahui Qu

“…Convolutional Neural Network (CNN) has garnered attention due to its outstanding performance in multisource remote sensing (RS) image classification. …”
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Histopathological Image Analysis Using Deep Learning Framework by Sudha Rani Vupulluri, Jogendra Kumar Munagala

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