Dynamic convolutional model based on distribution-collaboration strategy for remote sensing scene classification by Chenjun Xu, Jingqian Shu, Zhenghan Wang, Jialin Wang

“…Finally, two types of dynamic convolution kernel (class consensus kernel and adaptive kernel) are proposed for scene classification. …”
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PhyICNet: Physics-informed interactive learning convolutional recurrent network for spatiotemporal dynamics by Ruohan Cao, Jin Su, Jinqian Feng, Qin Guo

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DPSTCN: Dynamic Pattern-Aware Spatio-Temporal Convolutional Networks for Traffic Flow Forecasting by Zeping Dou, Danhuai Guo

“…To address these complexities head-on, this paper introduces a novel solution in the form of Dynamic Pattern-aware Spatio-Temporal Convolutional Networks (DPSTCN). …”
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Multi-Scale Plastic Lunch Box Surface Defect Detection Based on Dynamic Convolution by Jing Yang, Gang Zhang, Yunwang Ge, Jingzhuo Shi, Yiming Wang, Jiahao Li

“…A multi-scale attention mechanism based on dynamic convolution is designed in this paper to solve the problems of large differences in surface defects of plastic lunch boxes and insensitive perception of multi-scale features. …”
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Convolution of the physical point cloud for predicting the self-assembly of colloidal particles by Seunghoon Kang, Young Jin Lee, Kyung Hyun Ahn

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Enhancing Neurodegenerative Disease Diagnosis Through Confidence-Driven Dynamic Spatio-Temporal Convolutional Network by Ning Yuan, Donghai Guan, Shengrong Li, Li Zhang, Qi Zhu

Subjects: “…Dynamic brain network…”
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A Dynamic Multi-Graph Convolutional Spatial-Temporal Network for Airport Arrival Flow Prediction by Yunyang Huang, Hongyu Yang, Zhen Yan

“…Specifically, in the spatial dimension, a novel dynamic multi-graph convolutional network is designed to adaptively model the heterogeneous and dynamic airport networks. …”
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DMoC-UNet: A Dynamic Mixture-of-Convolution Network for Enhanced Pathological Image Segmentation by Jingwei Zhu, Lining Qin, Zixin Teng, Xiaomin Li, Kuiwu Li, Haoran Chu

“…This paper proposes a novel pathological image segmentation method, DMoC-UNet, which integrates Dynamic Mixture-of-Convolution (DMoC) modules, Haar wavelet downsampling, and Dual Attention Fusion (DAF) modules to enhance multi-scale feature extraction and fine-grained boundary segmentation. …”
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Temporal representation learning enhanced dynamic adversarial graph convolutional network for traffic flow prediction by Linlong Chen, Linbiao Chen, Hongyan Wang, Jian Zhao

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GP-DGECN: Geometric Prior Dynamic Group Equivariant Convolutional Networks for Specific Emitter Identification by Yu Han, Xiang Chen, Manxi Wang, Long Shi, Zhongming Feng

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Drug-target interaction prediction based on graph convolutional autoencoder with dynamic weighting residual GCN by Ming Zeng, Min Wang, Fuqiang Xie, Zhiwei Ji

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Full-dimensional dynamic convolution and progressive learning strategy for strawberry recognition based on YOLOv8 by Liping Bai, Chenglei Xia, Fei Liu, Xing Yang, Tai Zhang

“…In this study, we enhanced the YOLOv8 architecture by replacing the traditional backbone with an EfficientNetV2 feature extraction network and using ODConv instead of the standard convolution. The loss function was modified with a dynamic nonmonotonic focusing mechanism, and WiseIoU was introduced to replace the traditional CIoU. …”
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Graph convolutional network as a fast statistical emulator for numerical ice sheet modeling by Younghyun Koo, Maryam Rahnemoonfar

“…However, considering ISSM is compatible only with central processing units (CPUs), it has limitations in economizing computational time to explore the linkage between climate forcings and ice dynamics. Although several deep learning emulators using graphic processing units (GPUs) have been proposed to accelerate ice sheet modeling, most of them rely on convolutional neural networks (CNNs) designed for regular grids. …”
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An Effective Temporal Convolutional Networks-Based Method for Detecting Android Malware Using Dynamic Extracted Features by Abdurraheem Joomye, Mee Hong Ling, Muhammed Basheer Jasser, Athirah Mohd Ramly, Kok-Lim Alvin Yau

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Translational approach for dementia subtype classification using convolutional neural network based on EEG connectome dynamics by Thawirasm Jungrungrueang, Sawrawit Chairat, Kasidach Rasitanon, Praopim Limsakul, Krit Charupanit

“…We also employed a convolutional neural network model, enhanced with these dynamic features, to differentiate between dementia subtypes. …”
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Dynamic Tracking and Real-Time Fall Detection Based on Intelligent Image Analysis with Convolutional Neural Network by Ching-Bang Yao, Cheng-Tai Lu

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Research on Urban Road Traffic Flow Prediction Based on Sa-Dynamic Graph Convolutional Neural Network by Song Hu, Jian Gu, Shun Li

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Multi-channel spatial-temporal traffic flow prediction based on hybrid static-dynamic graph convolution by Xiongtao ZHANG, Jingyu ZHENG, Qing SHEN, Danfeng SUN, Yunliang JIANG

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Multi-featured spatial-temporal and dynamic multi-graph convolutional network for metro passenger flow prediction by Chuan Zhao, Xin Li, Zezhi Shao, HongJi Yang, Fei Wang

“…To address these challenges, we developed a novel model called the multi-featured spatial-temporal (MFST) and dynamic multi-graph convolutional network (DMGCN) model. …”
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From data to dynamics: Reconstructing soliton collision phenomena in optical fibers using a convolutional autoencoder by Qibo Xu, Jifang Rong, Qilin Zeng, Xiaofang Yuan, Longnv Huang, Hua Yang

“…In this study, a convolutional autoencoder is constructed to extract and reconstruct the dynamical processes of soliton collisions in optical fibers. …”
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