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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SIAT: Pedestrian trajectory prediction via social interaction-aware transformer by Chengdong Wang, Jianming Wang, Wenbo Gao, Lei Guo

“…This paper introduces the Social Interaction-Aware Transformer (SIAT), a novel approach that leverages a Transformer encoder to process pedestrian embedding features and a Graph Convolutional Network (GCN) to construct a social graph for extracting spatial interaction features. …”
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Joint fusion of sequences and structures of drugs and targets for identifying targets based on intra and inter cross-attention mechanisms by Xin Zeng, Guang-Peng Su, Wen-Feng Du, Bei Jiang, Yi Li, Zi-Zhong Yang

“…MM-IDTarget integrates some cutting-edge deep learning techniques such as graph transformer, multi-scale convolutional neural networks (MCNN), and residual edge-weighted graph convolutional network (EW-GCN) to extract sequence and structure modal features of drugs and targets. …”
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Radio frequency fingerprint data augmentation for indoor localization based on diffusion model by Haojun AI, Weike ZENG, Jingjie TAO, Jinying XU, Hanxiao CHANG

“…The radio frequency fingerprint indoor localization method ensures the accuracy by collecting a sufficient amount of fingerprints in the offline state to build a dense fingerprint database.A data augmentation method called FPDiffusion was proposed based on diffusion model to reduce the cost of fingerprint acquisition.Firstly, a temporal graph representation of the fingerprint sequence was constructed, the forward process of the diffusion model was accomplished by adding Gaussian noise, and a U-Net was utilized for the reverse process.The loss function of the network was designed according to the characteristics of radio frequency fingerprints.Finally, the computational process for generating dense fingerprints based on sparse fingerprints was presented.Experimental results demonstrate that FPDiffusion achieves 76% and 28% localization error reduction on K-nearest neighbor (KNN) and convolutional neural network (CNN) respectively, and significantly improves localization accuracy on KNN compared to Gaussian process regression (GPR) and GPR-GAN when only a small amount of labeled fingerprints is available.…”
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MTAD-TF: Multivariate Time Series Anomaly Detection Using the Combination of Temporal Pattern and Feature Pattern by Q. He, Y. J. Zheng, C.L. Zhang, H. Y. Wang

“…In the prediction part, multiscale convolution and graph attention network are mainly used to capture information in temporal pattern with feature pattern. …”
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Explore human parsing modality for action recognition by Jinfu Liu, Runwei Ding, Yuhang Wen, Nan Dai, Fanyang Meng, Fang‐Lue Zhang, Shen Zhao, Mengyuan Liu

“…The first human pose branch feeds robust skeletons in the graph convolutional network to model pose features, while the second human parsing branch also leverages depictive parsing feature maps to model parsing features via convolutional backbones. …”
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Use of Artificial Intelligence in Imaging Dementia by Manal Aljuhani, Azhaar Ashraf, Paul Edison

“…Artificial intelligence algorithms (machine learning and deep learning) enable automation of neuroimaging interpretation and may reduce potential bias and ameliorate clinical decision-making. Graph convolutional network-based frameworks implicitly provide multimodal sparse interpretability to support the detection of Alzheimer’s disease and its prodromal stage, mild cognitive impairment. …”
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GCN-Based Issues Classification in Software Repository by Bader Alshemaimri, Nafla Alrumayyan, Reem Alqadi

“…Graph Convolutional Network (GCN) have demon- strated significant potential in various fields, particu- larly in classification tasks. …”
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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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Building Safer Social Spaces: Addressing Body Shaming with LLMs and Explainable AI by Sajedeh Talebi, Neda Abdolvand

“…A term co-occurrence network graph uncovers semantic links, such as “shame” and “depression,” revealing discourse patterns. …”
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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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A Dynamic Spatio-Temporal Deep Learning Model for Lane-Level Traffic Prediction by Bao Li, Quan Yang, Jianjiang Chen, Dongjin Yu, Dongjing Wang, Feng Wan

“…Specifically, we take advantage of the graph convolutional network (GCN) with a data-driven adjacent matrix for spatial feature modeling and treat different lanes of the same road segment as different nodes. …”
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Is Deep Learning for Image Recognition Applicable to Stock Market Prediction? by Hyun Sik Sim, Hae In Kim, Jae Joon Ahn

“…In this paper, we propose a stock price prediction model based on convolutional neural network (CNN) to validate the applicability of new learning methods in stock markets. …”
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Simultaneous Estimation of Wrist Joint Angle and Torque During Isokinetic Contraction Based on HD-sEMG by Mingjie Yan, Zhe Chen, Jianmin Li, Jinhua Li, Lizhi Pan

“…To decode these signals, a convolutional neural network (CNN) incorporating the global attention mechanism was established, named global attention convolutional neural network (GACNN). …”
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A cognition‐inspired trajectory prediction method for vehicles in interactive scenarios by Shanshan Xie, Jiachen Li, Jianqiang Wang

“…Specifically, in the perception stage, featured grids are used that are in the driver's view to encode perceptual information; in the decision stage, convolution and graph attention operations are combined to model the driver's interaction with the surrounding traffic elements; in the motion stage, the elements are constrained in one hidden layer by vehicles' actual control inputs and design the corresponding method to obtain probabilistic results. …”
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A method for feature division of Soccer Foul actions based on salience image semantics. by Jianming Wang, Lifeng Li

“…DLSPM combines the improved DeepPlaBV 3+architecture for salient region detection, Graph Convolutional Networks (GCN) for feature extraction and Deep Neural Network (DNN) for classification. …”
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Prescription Recommendation Algorithm Based on Herbal Property-Driven Compatibility Mechanism Semantic Modeling by Geng Xueru, Zhang Jiantong, Luo Tao, Hou Jianchen, Tao Xiaohua

“…This is followed by aggregating higher-order heterogeneous path information of nodes through a graph convolutional network model. Finally, an attention mechanism is employed to fuse information from symptom interaction graphs, symptom-herb interaction graphs, and herb interaction graphs, distinguishing the influence of different dimensions of TCM semantic information. …”
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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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A Day-Ahead PV Power Forecasting Method Based on Irradiance Correction and Weather Mode Reliability Decision by Haonan Dai, Yumo Zhang, Fei Wang

“…Firstly, based on the measured irradiance, K-means clustering method is used to obtain the daily actual weather mode labels; secondly, considering the coupling relationship of meteorological elements, the graph convolutional network (GCN) model is used to correct the predicted irradiance by using multiple meteorological elements of NWP data; thirdly, the weather mode label is converted into one-heat code, and a weather mode reliability prediction model based on a convolutional neural network (CNN) is constructed, and then the prediction strategy of the day to be forecasted is decided; finally, based on the weather mode reliability prediction results, transformer model are established for unreliable weather and credible weather respectively. …”
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Deep Forest Modeling: An Interpretable Deep Learning Method for Mineral Prospectivity Mapping by Yue‐Lin Dong, Zhen‐Jie Zhang

“…Predictions are made using Deep Forest with factors like Euclidean distance between faults and magmatic rock, fault line density, gravity anomalies, and stream‐sediment geochemical data. Deep neural networks, random forest, convolutional neural networks, transformer model and graph convolutional networks are also used for comparison. …”
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