A Sensor Data Prediction and Early-Warning Method for Coal Mining Faces Based on the MTGNN-Bayesian-IF-DBSCAN Algorithm by Mingyang Liu, Xiaodong Wang, Wei Qiao, Hongbo Shang, Zhenguo Yan, Zhixin Qin

“…The MTGNN (Multi-Task Graph Neural Network) is first employed to model the spatiotemporal coupling characteristics of gas concentration and wind speed data. …”
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Understanding and classification of innate immune response through weighted edge representation learning with dual hypergraph transformation by Mallikharjuna Rao Sakhamuri, Shagufta Henna, Leo Creedon, Kevin Meehan, Marion McAfee

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Anomaly traffic detection method based on data augmentation and feature mining by AN Yishuai, FU Yu, YU Yihan, LIU Taotao

“…Finally, a multi-layer graph convolutional network with a hierarchical attention mechanism was designed, in which local and global features were hierarchically extracted and fused through a multi-level neighborhood aggregation strategy, significantly enhancing the model’s capability to identify key features. …”
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Pose estimation for health data analysis: advancing AI in neuroscience and psychology by Juan Yu, Daoyu Zhu

“…The framework integrates multi-modal data sources and applies temporal graph convolutional networks, ensuring both scalability and adaptability to diverse tasks. …”
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Wheat Soil-Borne Mosaic Virus Disease Detection: A Perspective of Agricultural Decision-Making via Spectral Clustering and Multi-Indicator Feedback by Xue Hou, Chao Zhang, Yunsheng Song, Turki Alghamdi, Majed Aborokbah, Hui Zhang, Haoyue La, Yizhen Wang

“…First, for each site, field observation of infection symptoms are recorded and represented using intuitionistic fuzzy numbers (IFNs) to capture uncertainty in detection. Second, a Bayesian graph convolutional networks model (Bayesian-GCN) is used to construct a spatial trust propagation mechanism, inferring missing trust values and preserving regional dependencies. …”
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A densely connected framework for cancer subtype classification by Yu Li, Denggao Zheng, Kaijie Sun, Chi Qin, Yuchen Duan, Qingqing Zhou, Yunxia Yin, Hongxing Kan, Jili Hu

“…Results We propose DEGCN, a novel deep learning model that integrates a three-channel Variational Autoencoder (VAE) for multi-omics dimensionality reduction and a densely connected Graph Convolutional Network (GCN) for effective subtype classification. …”
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An Approach for Detecting Tomato Under a Complicated Environment by Chen-Feng Long, Yu-Juan Yang, Hong-Mei Liu, Feng Su, Yang-Jun Deng

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Diagnosis of depression based on facial multimodal data by Nani Jin, Renjia Ye, Peng Li

“…We use spatiotemporal attention module to enhance the extraction of visual features and combine the Graph Convolutional Network (GCN) and the Long and Short Term Memory (LSTM) to analyze the audio features. …”
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Development and Training Strategy of Badminton Action Recognition System Under the Background of Artificial Intelligence by Hongjun Ma, Fan Zhang, Ni Liang

“…In the experimental section, the performance of three mainstream models—Spatial-Temporal Graph Convolutional Network (ST-GCN), Vision-Attention Transformer for Real-time Motion Recognition (VATRM), and Multi-Modal Network for Sports Action Recognition (MM-Net)—is compared from two dimensions: recognition performance and computational efficiency. …”
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Lightweight hybrid transformers-based dyslexia detection using cross-modality data by Abdul Rahaman Wahab Sait, Yazeed Alkhurayyif

“…We introduce a model, leveraging hybrid transformer-based feature extraction, including SWIN-Linformer for MRI, LeViT-Performer for handwriting images, and graph transformer networks (GTNs) with multi-attention mechanisms for EEG data. …”
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GramSeq-DTA: A Grammar-Based Drug–Target Affinity Prediction Approach Fusing Gene Expression Information by Kusal Debnath, Pratip Rana, Preetam Ghosh

“…To address this limitation, graph-based representations have been used to some extent. …”
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Intelligent Interior Design Systems: Optimizing Layouts and Aesthetics Using AI and User Data by Zhe Ji, Yan Yu

“…Our computational framework leverages convolutional neural networks (CNNs) for layout parsing, graph neural networks (GNNs) for modeling spatial relationships, and Transformer-based architectures for context-aware reasoning. …”
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STARNet: A Deep‐Learning Algorithm for Surface Shortwave Radiation Retrieval From Fengyun‐4A by Mengmeng Song, Dazhi Yang, Hongrong Shi, Yun Chen, Bai Liu, Yanbo Shen, Zijing Ding, Xiang'ao Xia

“…The algorithm holds three technical innovations: (a) a data preprocessing method that highlights the correlation‐ and causality‐type climatology associations in the original reflectance and brightness temperature observations; (b) a graph network cascade that extracts topological spatio‐temporal features, and (c) a multi‐scale convolution network that extracts regular spatio‐temporal features. …”
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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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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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Joint QoS prediction for Web services based on deep fusion of features by Jianxun LIU, Linghang DING, Guosheng KANG, Buqing CAO, Yong XIAO

“…In order to solve the problem of insufficient accuracy of Web service QoS prediction, a joint QoS prediction method for Web services based on the deep fusion of features was proposed with considering of the hidden environmental preference information in QoS and the common features of multi-class QoS.First, QoS data was modeled as a user-service bipartite graph and multi-component graph convolution neural network was used for feature extraction and mapping, and the weighted fusion method was used for the same dimensional mapping of multi-class of QoS features.Subsequently, the attention factor decomposition machine was used to extract the first-order features, second-order interactive features, and high-order interactive features of the mapped feature vector.Finally, the results of each part were combined to achieve the joint QoS prediction.The experimental results show that the proposed method is superior to the existing QoS prediction methods in terms of root mean square error (RMSE) and average absolute error (MAE).…”
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Time–frequency ensemble network for wind turbine mechanical fault diagnosis by Haiyu Guo, Xingzheng Guo, Xiaoguang Zhang, Fanfan Lu, Chuang Liang

“…In the frequency domain module, a mixhop graph convolutional network is used to extract the multi-scale frequency domain features of different neighbours, and a Multi Head Attention (MHA) mechanism is introduced to capture the intra-feature dependencies. …”
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ToxACoL: an endpoint-aware and task-focused compound representation learning paradigm for acute toxicity assessment by Jiang Lu, Lianlian Wu, Ruijiang Li, Mengxuan Wan, Jun Yang, Peng Zan, Hui Bai, Song He, Xiaochen Bo

“…ToxACoL models endpoint associations via graph topology and achieves knowledge transfer via graph convolution. …”
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The 3D tooth model segmentation method based on GAC+PointMLP network by Jianjun Chen, Liyuan Zheng, Huilai Zou, Jiafa Mao, Weiguo Sheng

“…This study explores the application of Point Multi-Layer Perceptron (PointMLP) in processing 3D tooth models and introduces an innovative integration of the Graph Attentional Convolution (GAC) Layer with a graph attention mechanism. …”
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