Capturing Global Structural Features and Global Temporal Dependencies in Dynamic Social Networks Using Graph Convolutional Networks for Enhanced Analysis by Ling Wu, Boen Li, Kun Guo, Qishan Zhang

“…To address the above issues, this paper proposes a novel graph convolutional network considering global structural features and global temporal dependencies (GSTGCN). …”
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Yoga pose recognition using dual structure convolutional neural network by Xiang Meng, Zhaobing Liu

“…To recognize five different yoga postures, this article proposed a dual structure convolutional neural network with a feature fusion function, which consists of the convolutional neural network A (CNN A) and convolutional neural network B (CNN B). …”
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SICGNN: structurally informed convolutional graph neural networks for protein classification by YongHyun Lee, Eunchan Kim, Jiwoong Choi, Changhyun Lee

“…Therefore, we propose a structurally informed convolutional GNN (SICGNN) that utilizes information that can express the overall topological structure of a protein graph during GNN training and prediction. …”
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Unsupervised Structural Damage Detection Technique Based on a Deep Convolutional Autoencoder by Zahra Rastin, Gholamreza Ghodrati Amiri, Ehsan Darvishan

“…This paper proposes a new unsupervised deep learning-based method for structural damage detection based on convolutional autoencoders (CAEs). …”
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Dynamic Snake Convolution Neural Network for Enhanced Image Super-Resolution by Weiqiang Xin, Ziang Wu, Qi Zhu, Tingting Bi, Bing Li, Chunwei Tian

“…However, existing methods were often limited in their ability to effectively process and integrate multi-scales information from fine textures to global structures. To address these limitations, this paper proposes DSCNN, a dynamic snake convolution neural network for enhanced image super-resolution. …”
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CerviXpert: A multi-structural convolutional neural network for predicting cervix type and cervical cell abnormalities by Rashik Shahriar Akash, Radiful Islam, SM Saiful Islam Badhon, KSM Tozammel Hossain

“…This study aims to develop CerviXpert, a multi-structural convolutional neural network designed to classify cervix types and detect cervical cell abnormalities efficiently. …”
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Modeling Higher-Order Interactions in Graphs Through Combinatorial Arc-Transitive Structure Using Graph Convolutional Network by Qingwei Wen

“…This research addresses the fundamental challenge of learning robust representations in heterophilic graphs by introducing Higher-Order Graph Convolutional Network (HiGCN), a novel framework that effectively models both homophilic and heterophilic interactions through a structured Petal-Complex (PC) model. …”
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Binocular Video-Based Automatic Pixel-Level Crack Detection and Quantification Using Deep Convolutional Neural Networks for Concrete Structures by Liqu Liu, Bo Shen, Shuchen Huang, Runlin Liu, Weizhang Liao, Bin Wang, Shuo Diao

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GCN-based weakly-supervised community detection with updated structure centres selection by Liping Deng, Bing Guo, Wen Zheng

“…In view of this, a weakly-supervised community detection method based on graph convolutional neural network (WC-GCN). Firstly, it introduces a genetic evolution strategy to select and update the structure centres, which enables the updating structure centre process to not get stuck in the local optima, and get the structural centres that are closer to the global best, solving the problem of centre dependence. …”
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Graph Knowledge Structure for Attentional Knowledge Tracing With Self-Supervised Learning by Zhaohui Liu, Sainan Liu, Weifeng Gu

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Optimization of Table Tennis Swing Action Supported by the Temporal Convolutional Network Algorithm in Deep Learning by Shaoxuan Sun, Hongyu Zheng, Zhixin Lin

“…The research effectively addresses the vanishing gradient problem by replacing the traditional Rectified Linear Unit (ReLU) activation function with Leaky ReLU, while simplifying the network structure through the use of a Global Average Pooling layer to reduce model complexity. …”
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Cross-Filter Structured Pruning for Efficient Sparse CNN Acceleration by Ngoc-Son Pham, Sangwon Shin, Lei Xu, Weidong Shi, Taeweon Suh

“…Convolutional Neural Networks (CNNs) are widely used in vision tasks for resource-constrained environments due to their computational efficiency and strong generalization. …”
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VDMNet: A Deep Learning Framework with Vessel Dynamic Convolution and Multi-Scale Fusion for Retinal Vessel Segmentation by Guiwen Xu, Tao Hu, Qinghua Zhang

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Dual-stream dynamic graph structure network for document-level relation extraction by Yu Zhong, Bo Shen

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The optimization path of agricultural industry structure and intelligent transformation by deep learning by Xingchen Pan, Jinyu Chen

“…Abstract This study addresses key challenges in optimizing agricultural industry structures and facilitating intelligent transformation through the application of deep learning algorithms and advanced optimization techniques. …”
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A Neural Network with Multiscale Convolution and Feature Attention Based on an Electronic Nose for Rapid Detection of Common Bunt Disease in Wheat Plants by Zhizhou Ren, Kun Liang, Yihe Liu, Xiaoxiao Wu, Chi Zhang, Xiuming Mei, Yi Zhang

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A composite photovoltaic power prediction optimization model based on nonlinear meteorological factors analysis and hybrid deep learning framework by Mengji Yang, Haiqing Zhang, Xi Yu, Aicha Sekhari Seklouli, Abdelaziz Bouras, Yacine Ouzrout

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ARO-GNN: Adaptive relation-optimized graph neural networks by Yong Lu, Zhengguo Lin

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Removing Stripe Noise From Infrared Cloud Images via Deep Convolutional Networks by Pengfei Xiao, Yecai Guo, Peixian Zhuang

“…To further improve the performance, we propose a local-global combination structure model, which combines the representations of different layers for recovering the rich details of infrared cloud images. …”
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Multi-Source Data-Driven Local-Global Dynamic Multi-Graph Convolutional Network for Bike-Sharing Demands Prediction by Juan Chen, Rui Huang

“…The model utilizes a local-global structure to capture spatial dependencies among individual bike-sharing stations and all stations collectively. …”
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