PM2.5 prediction using population-based centrality weight by Hee Joon Choi, Won Kyung Lee, So Young Sohn

“…The proposed weight was applied to two types of deep learning models, the long-and-short term temporal neural network (LSTNet) and temporal-graph convolutional network (T-GCN) to forecast the PM2.5 in 25 districts of Seoul Metropolitan City in Korea for empirical experiments. …”
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Predicting trajectories of coastal area vessels with a lightweight Slice-Diff self attention by Jinxu Zhang, Jin Liu, Xiliang Zhang, Lai Wei, Zhongdai Wu, Junxiang Wang

“…Recently, graph-based methods have also been used to predict trajectories, however processing graph-structured data introduces significant increase in computation. …”
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Quality Judgment of 3D Face Point Cloud Based on Feature Fusion by Gong Gao, Hong Liu, Hongyu Yang

“…Firstly, the 3D point cloud was preprocessed to cut out the face area, and the image obtained from the point cloud and the corresponding 2D plane depth map projection was used as the input. Secondly, Dynamic Graph Convolutional Neural Network (DGCNN) was trained for point cloud learning and ShuffleNet was trained for image learning. …”
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Application of Deep Learning in Food Safety Detection and Risk Early Warning by DING Haohan, WANG Long, HOU Haoke, XIE Zhenqi, HAN Yu, CUI Xiaohui

“…This paper first introduces the basic concept of deep learning and its current development in the field of food safety, and discusses the application of convolutional neural network (CNN), recursive neural network (RNN), transformer architecture and graph neural network (GNN) in food safety detection and risk prediction. …”
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Multi-camera video collaborative analysis method based on edge computing by Zhibo QI, Lei DU, Ru HUO, Fan YANG, Tao HUANG

“…In order to reduce the processing volume of multi-camera real-time video data in smart city scenarios, a video collaborative analysis method based on machine learning algorithms at the edge was proposed.Firstly, for the important objects detected by each camera, different key windows were designed to filter the region of interest (RoI) in the video, reduce the video data volume and extract its features.Then, based on the extracted data features, the same objects in the videos from different cameras were annotated, and a strategy for calculating the association degree value between cameras was designed for further reducing the video data volume.Finally, the GC-ReID algorithm based on graph convolutional network (GCN) and re-identification (ReID) was proposed, aiming at achieving the collaborative analysis of multi-camera videos.The experimental results show that proposed method can effectively reduce the system latency and improve the video compression rate while ensuring the high accuracy, compared with the existing video analysis methods.…”
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Synergizing vision transformer with ensemble of deep learning model for accurate kidney stone detection using CT imaging by Arwa Alzughaibi, Adwan A. Alanazi, Mohammed Alshahrani, Ines Hilali Jaghdam, Abaker A. Hassaballa

“…Furthermore, the majority voting ensemble of three DL approaches, such as the graph convolutional network (GCN), temporal convolutional network (TCN), and three-dimensional convolutional autoencoder (3D-CAE) approaches, are employed to increase the precision and reliability of the kidney stone recognition. …”
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Demand prediction for shared bicycles around metro stations incorporating STAGCN. by Xue Xing, Le Wan, Fahui Luo

“…A predictive model integrating a Spatiotemporal Attention Graph Convolutional Network (STAGCN), Long Short-Term Memory (LSTM) network, and Informer is developed to forecast shared bike demand for metro connectivity. …”
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Research on prediction algorithm of college students' academic performance based on Bert-GCN multi-modal data fusion by Yan Wu

“…In this study, the BERT model was used to mine deep features from text data such as course notes and assignment feedback, and then the Graph Convolutional Network (GCN) was used to integrate students' social network information and learning behavior data to construct a multimodal fusion model. …”
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The Prediction of Cerebral Palsy in Infants Using Pose Estimation Techniques Within a Hybrid Deep Learning Framework by Lama A. Alghamdi, Maryam A. Aldahri, Saja A. Alzahrani, Rowla A. Alaofi, Rawaf F. Nafea, Noura M. Alotaibi

“…Three hybrid DL models were explored: a convolutional neural network (CNN) combined with long short-term memory (LSTM) networks (CNN-LSTM), a CNN with Gated Recurrent Units (CNN-GRU), and a Graph Convolutional Network (GCN) with GRU (GCN-GRU). …”
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The application of suitable sports games for junior high school students based on deep learning and artificial intelligence by Xueyan Ji, Shamsulariffin Bin Samsudin, Muhammad Zarif Bin Hassan, Noor Hamzani Farizan, Yubin Yuan, Wang Chen

“…This study intends to develop a Spatial Temporal-Graph Convolutional Network (ST-GCN) action detection algorithm based on the MediaPipe framework. …”
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Adaptive Pixel-Level and Superpixel-Level Feature Fusion Transformer for Hyperspectral Image Classification by Wei Huang, Dazhan Zhou, Le Sun, Qiqiang Chen, Junru Yin

“…Despite transformers possess strong long-range dependence modeling capabilities, they primarily extract nonlocal information from patches and often fail to fully capture global information, leading to incomplete spectral-spatial feature extraction. However, graph convolutional networks (GCNs) can effectively extract features from the global structure. …”
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Review of Research on Trajectory Prediction of Road Pedestrian Behavior by YANG Zhiyong, GUO Jieru, GUO Zihang, ZHANG Ruixiang, ZHOU Yu

“…Special emphasis is placed on deep learning methods, categorized by network architecture into sequential models, convolutional neural networks, graph convolutional networks,  generative adversarial networks, etc. …”
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A spatial scene reconstruction framework in emergency response scenario by Nan Zheng, Danhuai Guo

“…Additionally, a large number of manual experiments were conducted to explore quantitative biases in human spatial cognition, and based on the obtained biases, a greedy resolution method based on cost functions was used to fine tune the layout of conflicting spatial objects and solve the conflicting spatial information in the spatial information knowledge graph. Finally, use graph convolutional neural networks to obtain scene knowledge graph embeddings that consider spatial constraints. …”
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Edge-aware joint neural denoising and normal estimation for mobile and handheld laser point clouds by T. Zhang, S. Filin

“…To extract contextual information it introduces densely packed graph convolution layers and a global attention mechanism. …”
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A semantic‐based method for analysing unknown malicious behaviours via hyper‐spherical variational auto‐encoders by Yi‐feng Wang, Yuan‐bo Guo, Chen Fang

“…The authors further use a Graph Convolutional Network (GCN) to reduce the impact of different user behaviour patterns before projection. …”
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Leveraging spatial dependencies and multi-scale features for automated knee injury detection on MRI diagnosis by Jianhua Sun, Ye Cao, Ying Zhou, Baoqiao Qi

“…The research aims to provide an efficient and reliable tool for clinicians to aid in the diagnosis of knee joint disorders, particularly focusing on Anterior Cruciate Ligament (ACL) tears.MethodsKneeXNet leverages the power of graph convolutional networks (GCNs) to capture the intricate spatial dependencies and hierarchical features in knee MRI scans. …”
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Faithful novel machine learning for predicting quantum properties by Gavin Nop, Micah Mundy, Jonathan D. H. Smith, Durga Paudyal

“…Abstract Machine learning (ML) has accelerated the process of materials classification, particularly with crystal graph neural network (CGNN) architectures. However, advanced deep networks have hitherto proved challenging to build and train for quantum materials classification and property prediction. …”
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Time series prediction based on the variable weight combination of the T-GCN-Luong attention and GRU models by Yushu Guo, Jiacheng Huang, Xuchu Jiang

“…Considering the spatiotemporal features of temperature changes, this paper proposes a variable weight combination model based on a temporal graph convolutional network (T-GCN), Luong attention network (LUA) and gated recurrent unit (GRU) network, which fully utilizes spatiotemporal information to predict future temperature changes more accurately. …”
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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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A Hybrid Spatial–Temporal Deep Learning Method for Metro Tunnel Displacement Prediction Under “Dual Carbon” Background by Jianyong Chai, Limin Jia, Jianfeng Liu, Enguang Hou, Zhe Chen

“…This study introduces a hybrid spatial–temporal deep learning model, integrating graph convolutional network (GCN) and long short-term memory (LSTM) networks, to predict metro tunnel displacements under the imperatives of “dual carbon” goals. …”
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