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

“…To avoid this, GNNs typically use a limited number of layers, which leads to the problem of reflecting only the local structure and neighborhood information rather than the global structure of the graph. …”
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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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Cross-Filter Structured Pruning for Efficient Sparse CNN Acceleration by Ngoc-Son Pham, Sangwon Shin, Lei Xu, Weidong Shi, Taeweon Suh

“…To address this issue, this paper proposes a cross-filter structured pruning method, enforcing a uniform sparsity pattern across multiple filters to ensure balanced workload distribution among PEs. …”
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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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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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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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Modeling Higher-Order Interactions in Graphs Through Combinatorial Arc-Transitive Structure Using Graph Convolutional Network by Qingwei Wen

“…The proposed approach employs a two-step random walk mechanism between core and petal regions, facilitating bidirectional information transfer while preserving the inherent graph structure. Furthermore, adaptive spectral filters across distinct Petal-Complex Laplacian spectral domains enable the effective capture of both localized and global structural patterns in combinatorial arc-transitive complexes. …”
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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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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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Dual-stream dynamic graph structure network for document-level relation extraction by Yu Zhong, Bo Shen

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Transition state structure detection with machine learningś by Yitao Si, Yiding Ma, Tao Yu, Yifan Wu, Yingzhe Liu, Weipeng Lai, Zhixiang Zhang, Jinwen Shi, Liejin Guo, Oleg V. Prezhdo, Maochang Liu

“…Abstract Transition structure calculations via quantum chemistry methods have become a staple in modern chemical reaction research. …”
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A Computational Approach to Understanding Agglutinative Structures in Urdu by Muhammad Shoaib Tahir, Mahnoor Amjad

“…This study investigates the computational challenges and opportunities presented by the agglutinative structures in Urdu, a language characterized by its complex system of morpheme-based word formation. …”
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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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Underground low-light self-supervised image enhancement method based on structure and texture perception by Shan PAN, Ting YU, Wei CHEN, Zijian TIAN, Zhongwen YUE

“…To further exploit local texture features and global structural features in low-light images to improve the performance of the illumination estimation network, we introduce a local-global perception module into the illumination estimation network. …”
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Complex Network Analytics for Structural–Functional Decoding of Neural Networks by Jiarui Zhang, Dongxiao Zhang, Hu Lou, Yueer Li, Taijiao Du, Yinjun Gao

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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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Image Inpainting Algorithm Based on Structure-Guided Generative Adversarial Network by Li Zhao, Tongyang Zhu, Chuang Wang, Feng Tian, Hongge Yao

“…The proposed methodology advances a two-stage restoration paradigm: (1) Structural Prior Extraction, where adaptive edge detection algorithms identify residual contours in corrupted regions, and a transformer-enhanced network reconstructs globally consistent structural maps through contextual feature propagation; (2) Structure-Constrained Texture Synthesis, wherein a multi-scale generator with hybrid dilated convolutions and channel attention mechanisms iteratively refines high-fidelity textures under explicit structural guidance. …”
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Classification of Structural and Functional Development Stage of Cardiomyocytes Using Machine Learning Techniques by V. R. Bondarev, K. O. Ivanko, N. G. Ivanushkina

“…A pre-processed and augmented dataset is used for training of the convolutional neural network having an architecture with hierarchical structure and residual block usage. …”
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