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

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Harnessing hybrid perception on multi-scale features for hand-foot-mouth disease multi-region prediction based on Seq2Seq. by Bingbing Lei, Xuanjun Zhu, Tao Zhou, Yuxi Zhang

“…Secondly, a novel Spatio-Temporal Parallel Encoding(STPE) Cell is designed; multiple STPE Cells constitute an encoder capable of hybrid perception across multi-scale spatio-temporal features. Within this encoder, graph-based feature representation and iterative convolution operations enable the capture of cumulative influence of neighboring regions across temporal and spatial dimensions, facilitating efficient extraction of spatio-temporal dependencies between multiple regions. …”
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Spatio-Temporal Collaborative Perception-Enabled Fault Feature Graph Construction and Topology Mining for Variable Operating Conditions Diagnosis by Jiaxin Zhao, Xing Wu, Chang Liu, Feifei He

“…Finally, we develop a graph residual convolutional network to mine topological information from multi-source spatio-temporal features under complex operating conditions. …”
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Narrowband Radar Micromotion Targets Recognition Strategy Based on Graph Fusion Network Constructed by Cross-Modal Attention Mechanism by Yuanjie Zhang, Ting Gao, Hongtu Xie, Haozong Liu, Mengfan Ge, Bin Xu, Nannan Zhu, Zheng Lu

“…Subsequently, a cross-modal attention mechanism integrates these extracted features into a graph structure, achieving multi-level interactions among unimodal, bimodal, and trimodal features. …”
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TFF-Net: A Feature Fusion Graph Neural Network-Based Vehicle Type Recognition Approach for Low-Light Conditions by Huizhi Xu, Wenting Tan, Yamei Li, Yue Tian

“…To address the performance degradation caused by insufficient lighting, complex backgrounds, and light interference, this paper proposes a Twin-Stream Feature Fusion Graph Neural Network (TFF-Net) model. The model employs multi-scale convolutional operations combined with an Efficient Channel Attention (ECA) module to extract discriminative local features, while independent convolutional layers capture hierarchical global representations. …”
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Ligand-receptor dynamics in heterophily-aware graph neural networks for enhanced cell type prediction from single-cell RNA-seq data by Lian Duan, Mahshad Hashemi, Alioune Ngom, Luis Rueda

“…While standard GNN models like Graph Convolutional Networks (GCN), GraphSAGE, Graph Attention Networks (GAT), and MixHop often assume homophily (similar nodes are more likely to be connected), this assumption does not always hold in biological networks. …”
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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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EMFF-Net: Edge-Enhancement Multi-Scale Feature Fusion Network by Xuhui Guan, Jiwang Zhou, Jian Chen, Xiaodan Xu, Yizhang Jiang, Kaijian Xia

“…From these extracted features, we generate a global mapping graph as a bootstrap region. Additionally, we introduce Spatial Channel Convolution (SCEConv) and Reverse Gated Channel Transformer (RGCT) to incorporate boundary information into the segmentation network. …”
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Multi-View Collaborative Training and Self-Supervised Learning for Group Recommendation by Feng Wei, Shuyu Chen

“…By incorporating both group and individual recommendation tasks, MCSS leverages graph convolution and attention mechanisms to generate three sets of embeddings, enhancing the model’s representational power. …”
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APT Adversarial Defence Mechanism for Industrial IoT Enabled Cyber-Physical System by Safdar Hussain Javed, Maaz Bin Ahmad, Muhammad Asif, Waseem Akram, Khalid Mahmood, Ashok Kumar Das, Sachin Shetty

“…To overcome these issues, a new approach is suggested that is based on the Graph Attention Network (GAN), a multi-dimensional algorithm that captures behavioral features along with the relevant information that other methods do not deliver. …”
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An urban road traffic flow prediction method based on multi-information fusion by Xiao Wu, Hua Huang, Tong Zhou, Yudan Tian, Shisen Wang, Jingting Wang

“…Then, a superimposed one-dimensional inflated convolutional layer is used to extract long-term trends, a dynamic graph convolutional layer to extract periodic features, and a short-term trend extractor to learn short-term temporal features. …”
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Electricity theft detection in integrated energy systems considering multi-energy loads by Wenlong Liao, Dechang Yang, Leijiao Ge, Yixiong Jia, Zhe Yang

“…Furthermore, a Chebyshev graph convolutional network (ChebGCN) is proposed to detect malicious users by capturing latent features and correlations from the graphs. …”
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Multi-feature stock price prediction by LSTM networks based on VMD and TMFG by Zhixin Zhang, Qingyang Liu, Yanrong Hu, Hongjiu Liu

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A Non-Contact AI-Based Approach to Multi-Failure Detection in Avionic Systems by Chengxin Liu, Michele Ferlauto, Haiwen Yuan

“…To address these challenges, this paper aims to develop a fast, accurate, and non-destructive, multi-failure diagnosis algorithm for PCBs. The proposed method combines a self-attention mechanism with an adaptive graph convolutional neural network to enhance diagnostic precision. …”
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ADHD detection from EEG signals using GCN based on multi-domain features by Ling Li, Xueyang Guo, Zihan Yang, Yanping Zhao, Xu Liu, Junxian Yang, Yanyan Chen, Xinxian Peng, Lina Han

“…While many researchers have explored automated ADHD detection methods, developing accurate, rapid, and reliable approaches remains challenging.MethodsThis study proposes a graph convolutional neural network (GCN)-based ADHD detection framework utilizing multi-domain electroencephalogram (EEG) features. …”
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Urban Functional Zone Mapping by Integrating Multi-Source Data and Spatial Relationship Characteristics by Daoyou Zhu, Xu Dang, Wenjia Shi, Yixiang Chen, Wenmei Li

“…This framework leverages the OpenStreetMap (OSM) road network to partition the study area into functional units, employs a graph model to represent urban functional nodes and their intricate spatial topological relationships, and harnesses the capabilities of Graph Convolutional Network (GCN) to fuse these multi-dimensional features through end-to-end learning for accurate urban function discrimination. …”
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Multi-Neighborhood Sparse Feature Selection for Semantic Segmentation of LiDAR Point Clouds by Rui Zhang, Guanlong Huang, Fengpu Bao, Xin Guo

“…To address these problems, a sparse feature dynamic graph convolutional neural network, abbreviated as SFDGNet, is constructed in this paper for LiDAR point clouds of complex scenes. …”
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Deep learning for single-site solar irradiance forecasting using multi-station data by Daniel Guatibonza, Gabriel Narvaez, Luis Felipe Giraldo

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MPVF: Multi-Modal 3D Object Detection Algorithm with Pointwise and Voxelwise Fusion by Peicheng Shi, Wenchao Wu, Aixi Yang

“…This highlights the necessity of multi-modal fusion approaches to enhance detection performance. …”
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A Multi-Semantic Feature Fusion Method for Complex Address Matching of Chinese Addresses by Pengpeng Li, Qing Zhu, Jiping Liu, Tao Liu, Ping Du, Shuangtong Liu, Yuting Zhang

“…First, the address is resolved into address elements, and the Word2vec model is trained to generate word vector representations using these address elements. Then, multi-semantic features of the addresses are extracted using a Text Recurrent Convolutional Neural Network (Text-RCNN) and a Graph Attention Network (GAT). …”
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