CNN–Transformer gated fusion network for medical image super-resolution by Juanjuan Qin, Jian Xiong, Zhantu Liang

“…The network consists of two branches, one is the global branch based on residual Transformer network, and the other is the local branch based on dynamic convolutional neural network. …”
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Modeling Equatorial to Mid‐Latitudinal Global Night Time Ionospheric Plasma Irregularities Using Machine Learning by Ephrem Beshir Seba, Giovanni Lapenta

“…Abstract This study focuses on modeling the characteristics of nighttime topside Ionospheric Plasma Irregularities (PI) on a global scale. We utilize Random Forest (RF) and a one‐dimensional Convolutional Neural Network (1D‐CNN) model, incorporating data from the Swarm A, B, and C satellites, space weather data from the OMNIWeb data center, as well as zonal and meridional wind model data. …”
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Multi-Scale Geometric Feature Extraction and Global Transformer for Real-World Indoor Point Cloud Analysis by Yisheng Chen, Yu Xiao, Hui Wu, Chongcheng Chen, Ding Lin

“…Indoor point clouds often present significant challenges due to the complexity and variety of structures and high object similarity. The local geometric structure helps the model learn the shape features of objects at the detail level, while the global context provides overall scene semantics and spatial relationship information between objects. …”
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Utilizing GCN-Based Deep Learning for Road Extraction from Remote Sensing Images by Yu Jiang, Jiasen Zhao, Wei Luo, Bincheng Guo, Zhulin An, Yongjun Xu

“…This method integrates similarity and gradient information and employs graph convolution to capture the global contextual relationships among features. …”
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A Novel Dual-Branch Global and Local Feature Extraction Network for SAR and Optical Image Registration by Xuanran Zhao, Yan Wu, Xin Hu, Zhikang Li, Ming Li

“…Beyond merely extracting local features to generate feature descriptors, more importantly, the network also extracts the global feature to better mine the common structural features between SAR and optical images. …”
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GRU2-Net: Global response double U-shaped network for lesion segmentation in ultrasound images by Xiaokai Jiang, Xuewen Ding, Jinying Ma, Chunyu Liu, Xinyi Li

“…To improve global context modeling, this paper proposes the Global Response Transformer Block in the bottleneck, enabling the network to capture long-range dependencies and structural variability in lesion appearance. …”
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Innovative Framework for Historical Architectural Recognition in China: Integrating Swin Transformer and Global Channel–Spatial Attention Mechanism by Jiade Wu, Yang Ying, Yigao Tan, Zhuliang Liu

“…Focusing on the study of Chinese historical architecture, this research proposes an innovative architectural recognition framework that integrates the Swin Transformer backbone with a custom-designed Global Channel and Spatial Attention (GCSA) mechanism, thereby substantially enhancing the model’s capability to extract architectural details and comprehend global contextual information. …”
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Using a discrete global grid system for a scalable, interoperable, and reproducible system of land-use mapping by Richard M. Law, James Ardo

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LGC-YOLO: Local-Global Feature Extraction and Coordination Network With Contextual Interaction for Remote Sensing Object Detection by Qinggang Wu, Yang Li, Junru Yin, Xiaotian You

“…First, LGSFE captures local and global features of dense objects through receptive-field attention convolution and global pooling in a multibranch structure, which effectively alleviates the misalignment between the extracted features of objects and their intrinsic characteristics, thereby providing more accurate and abundant features for subsequent object detection. …”
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ED‐ConvLSTM: A Novel Global Ionospheric Total Electron Content Medium‐Term Forecast Model by Guozhen Xia, Fubin Zhang, Cheng Wang, Chen Zhou

“…Abstract In this paper, we proposed an innovative encoder‐decoder structure with a convolution long short‐term memory (ED‐ConvLSTM) network to forecast global total electron content (TEC) based on the International GNSS Service (IGS) TEC maps from 2005 to 2018 with 1‐hr time cadence. …”
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Deep Time Series Intelligent Framework for Power Data Asset Evaluation by Lihong Ge, Xin Li, Li Wang, Jian Wei, Bo Huang

“…In the evaluation of the complex and rich Solar-Power dataset and Electricity dataset, TSENet achieved significant performance improvements over other state-of-the-art baseline methods.Through the synergistic design of deep convolutional structures and an efficient memory mechanism, it effectively addresses issues such as inadequate modeling of long-term dependencies, insufficient extraction of short-term features, and high prediction volatility, thereby significantly enhancing both the accuracy and robustness of forecasting in power asset evaluation tasks.…”
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Diagnosis of Coronary Heart Disease Through Deep Learning-Based Segmentation and Localization in Computed Tomography Angiography by Bo Zhao, Jianjun Peng, Ce Chen, Yongyan Fan, Kai Zhang, Yang Zhang

“…Coronary computed tomography angiography (CCTA) has emerged as a non-invasive modality for detailed coronary artery visualization; however, automatic and accurate segmentation of coronary structures from CCTA images remains challenging. Conventional convolutional neural networks (CNNs), despite their success in medical imaging, face limitations in capturing the complex, long-range dependencies in coronary artery images due to their localized receptive fields. …”
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An Inverted Residual Cross Head Knowledge Distillation Network for Remote Sensing Scene Image Classification by Cuiping Shi, Mengxiang Ding, Liguo Wang

“…Then, a multiscale spatial attention module is constructed to further extract global and local features of the image through multiple dilated convolutions, using spatial attention to weight important features in each dilated convolution branch. …”
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GLAI-Net: Global–Local Awareness Integrated Network for Semantic Change Detection in Remote Sensing Images by Qing Ding, Fengyan Wang, Mingchang Wang, Ying Zhang, Gui Cheng

“…We design a parallel encoding structure and utilize convolutional neural networks and transformer to achieve multi-scale modeling of images and enhance feature expression ability. …”
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An OGFA+CNN Approach for Multi-Level Disease Identification in Fundus Images by Preethi Kulkarni, K. Srinivasa Reddy

“…Graph-based techniques are employed to capture the structural relationships between key elements such as blood vessels and the optic disc, providing valuable global context to the image. …”
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GHMSA-Net: Gated Hierarchical Multi-Scale Self-Attention for Perceptually-Guided AV1 Post-Processing by Bopu Zhao, Woowoen Gwun, Kiho Choi

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Multiscale Wavelet and Graph Network With Spectral Self-Attention for Hyperspectral Image Classification by Anyembe C. Shibwabo, Zou Bin, Tahir Arshad, Jorge Abraham Rios Suarez

“…Third, DH-GCN constructs a deep graph structure to model spatial topology and overcome oversmoothing. …”
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Multi-scale window transformer for cervical cytopathology image recognition by Jiaxiang Yi, Xiuli Liu, Shenghua Cheng, Li Chen, Shaoqun Zeng

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A novel deep neural network-based technique for network embedding by Sabrina Benbatata, Bilal Saoud, Ibraheem Shayea, Naif Alsharabi, Abdulraqeb Alhammadi, Ali Alferaidi, Amr Jadi, Yousef Ibrahim Daradkeh

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HETMCL: High-Frequency Enhancement Transformer and Multi-Layer Context Learning Network for Remote Sensing Scene Classification by Haiyan Xu, Yanni Song, Gang Xu, Ke Wu, Jianguang Wen

“…To solve this problem, we propose a novel method based on High-Frequency Enhanced Vision Transformer and Multi-Layer Context Learning (HETMCL), which can effectively learn the comprehensive features of high-frequency and low-frequency information in visual data. First, Convolutional Neural Networks (CNNs) extract low-level spatial structures, and the Adjacent Layer Feature Fusion Module (AFFM) reduces semantic gaps between layers to enhance spatial context. …”
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