GTN-GCN: Real-Time Traffic Forecasting Using Graph Convolutional Network and Transformer by Sadia Naj Jinia, Sumaiya Binte Azad, Rima Akter, Taivan Reza Dipto, Md. Khaliluzzaman

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Aspect-Based Sentiment Analysis Through Graph Convolutional Networks and Joint Task Learning by Hongyu Han, Shengjie Wang, Baojun Qiao, Lanxue Dang, Xiaomei Zou, Hui Xue, Yingqi Wang

“…The proposed unified framework achieves state-of-the-art performance, as evidenced by experimental results on four benchmark datasets. …”
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Multi-sensing node convolution fusion identity recognition algorithm for radio digital twin by Guofeng WEI, Guoru DING, Yutao JIAO, Yitao XU, Daoxing GUO, Peng TANG

“…Electromagnetic space is an important link to empower and coordinate sea, land, air, space and network.Electromagnetic target recognition provides important radio target identity information for the twin construction of electromagnetic space, so that it can describe and depict the identity situation of electromagnetic targets in digital space.However, a single sensing node is vulnerable to interference, and its recognition performance is limited.Wrong recognition results will provide radio digital twin with conflicting identity information.Therefore, based on the requirements of radio digital twin in electromagnetic space, a radio target recognition framework for radio digital twin was constructed and a multi-sensing node convolution neural network individual identity fusion recognition algorithm was proposed.Compared with the nearest single sensing node, the recognition performance is improved by 6.29% by deploying the multi-node recognition network in the actual scene, which provides more accurate individual identity information.…”
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Multi-sensing node convolution fusion identity recognition algorithm for radio digital twin by Guofeng WEI, Guoru DING, Yutao JIAO, Yitao XU, Daoxing GUO, Peng TANG

“…Electromagnetic space is an important link to empower and coordinate sea, land, air, space and network.Electromagnetic target recognition provides important radio target identity information for the twin construction of electromagnetic space, so that it can describe and depict the identity situation of electromagnetic targets in digital space.However, a single sensing node is vulnerable to interference, and its recognition performance is limited.Wrong recognition results will provide radio digital twin with conflicting identity information.Therefore, based on the requirements of radio digital twin in electromagnetic space, a radio target recognition framework for radio digital twin was constructed and a multi-sensing node convolution neural network individual identity fusion recognition algorithm was proposed.Compared with the nearest single sensing node, the recognition performance is improved by 6.29% by deploying the multi-node recognition network in the actual scene, which provides more accurate individual identity information.…”
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SL-GCNN: A Graph Convolutional Neural Network for Granular Human Motion Recognition by Yang Li, Jingyu Zhang

“…This paper presents SL-GCNN, a novel Graph Convolutional Neural Network framework specifically designed for granular skeletal motion recognition. …”
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Removing Stripe Noise From Infrared Cloud Images via Deep Convolutional Networks by Pengfei Xiao, Yecai Guo, Peixian Zhuang

“…We propose a new deep network architecture for removing a stripe noise from a single meteorological satellite infrared cloud image. In the proposed framework, a residual learning is utilized to directly reduce the mapping range from input to output, which speeds up the training process as well as boosts the destriping performance. …”
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A Novel Approach for Tomato Leaf Disease Classification with Deep Convolutional Neural Networks by Gizem Irmak, Ahmet Saygılı

“…In contrast, a novel convolutional neural network (CNN) framework, complete with unique parameters and layers, was utilized for deep learning. …”
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Emoji-Driven Sentiment Analysis for Social Bot Detection with Relational Graph Convolutional Networks by Kaqian Zeng, Zhao Li, Xiujuan Wang

“…To address this gap, we propose ESA-BotRGCN, an emoji-driven multi-modal detection framework that integrates semantic enhancement, sentiment analysis, and multi-dimensional feature modeling. …”
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Deep Convolutional Network Based Machine Intelligence Model for Satellite Cloud Image Classification by Kalyan Kumar Jena, Sourav Kumar Bhoi, Soumya Ranjan Nayak, Ranjit Panigrahi, Akash Kumar Bhoi

“…The proposed cloud image classification shows 94% prediction accuracy with the DCNN framework.…”
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Enhanced Attention-Driven Dynamic Graph Convolutional Network for Extracting Drug-Drug Interaction by Xiechao Guo, Dandan Song, Fang Yang

“…Our model combines the Attention-driven Dynamic Graph Convolutional Network (ADGCN) with a feature fusion method and multi-task learning framework. …”
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Enhancing biometric identification using 12-lead ECG signals and graph convolutional networks by Maram Al Alfi, Pedro Peris-Lopez, Carmen Camara

“…This study presents a novel real-time biometric authentication system integrating Graph Convolutional Networks (GCN) with Mutual Information (MI) indices extracted from 12-lead ECG signals.MethodsThe MI index quantifies the statistical dependencies among ECG leads and is computed using entropy-based estimations. …”
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Application of Dual-Stage Attention Temporal Convolutional Networks in Gas Well Production Prediction by Xianlin Ma, Long Zhang, Jie Zhan, Shilong Chang

“…The DA-TCN architecture integrates feature and temporal attention mechanisms within the Temporal Convolutional Network (TCN) framework, improving the model’s ability to capture complex temporal dependencies and emphasize significant features, resulting in robust forecasting performance across multiple time horizons. …”
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GMFLDA: Improved Prediction of lncRNA-Disease Association via Graph Convolutional Network by Kwangsu Kim, Jihwan Ha

“…In this study, we present GMFLDA, an advanced machine learning framework for inferring lncRNA-disease associations (LDA) by synergizing graph convolutional networks (GCNs) with deep matrix factorization. …”
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Hyperspectral Image Classification Method Based on Morphological Features and Hybrid Convolutional Neural Networks by Tonghuan Ran, Guangfeng Shi, Zhuo Zhang, Yuhao Pan, Haiyang Zhu

“…The CNN structure uses multiscale convolution, involving depthwise separable convolution, which can effectively reduce the amount of parameter calculation. …”
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Crack-ConvT Net: A Convolutional Transformer Network for Crack Segmentation in Underwater Dams by Pengfei Shi, Hongzhu Chen, Zaiming Geng, Xinnan Fan, Yuanxue Xin

“…Experimental results demonstrate that the proposed framework significantly outperforms existing methods in underwater dam crack segmentation, effectively improving segmentation accuracy under noise interference.…”
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GH-UNet: group-wise hybrid convolution-VIT for robust medical image segmentation by Shengxiang Wang, Ge Li, Min Gao, Linlin Zhuo, Mingzhe Liu, Zhizhong Ma, Wei Zhao, Xiangzheng Fu

“…We propose GH-UNet, a Group-wise Hybrid Convolution-ViT model within the U-Net framework, to address this limitation. …”
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Local kernel renormalization as a mechanism for feature learning in overparametrized convolutional neural networks by R. Aiudi, R. Pacelli, P. Baglioni, A. Vezzani, R. Burioni, P. Rotondo

“…In this work, we present a theoretical framework that provides a rationale for these differences in one-hidden-layer networks; we derive an effective action in the so-called proportional limit for an architecture with one convolutional hidden layer and compare it with the result available for fully-connected networks. …”
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KA-GCN: Kernel-Attentive Graph Convolutional Network for 3D face analysis by Francesco Agnelli, Giuseppe Facchi, Giuliano Grossi, Raffaella Lanzarotti

“…To address this limitation, we propose the Kernel-Attentive Graph Convolutional Network (KA-GCN). Our key finding is that integrating kernel-based and attention-based mechanisms to dynamically refine distances and learn the adjacency matrix within a Graph Structure Learning (GSL) framework enhances the model’s adaptability, making it particularly effective for 3D face analysis tasks and delivering strong performance in data-scarce scenarios. …”
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Sign Language Sentence Recognition Using Hybrid Graph Embedding and Adaptive Convolutional Networks by Pathomthat Chiradeja, Yijuan Liang, Chaiyan Jettanasen

“…The proposed HGE-ACN framework integrates graph-based embeddings to capture dynamic spatial–temporal relationships in motion and curvature data. …”
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BPDM-GCN: Backup Path Design Method Based on Graph Convolutional Neural Network by Wanwei Huang, Huicong Yu, Yingying Li, Xi He, Rui Chen

“…First, the BPDM-GCN backup path algorithm is constructed within a deep deterministic policy gradient training framework. It uses graph convolutional networks to detect changes in network topology, aiming to optimize data transmission delay and bandwidth occupancy within the network topology. …”
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