GBNSS: A Method Based on Graph Neural Networks (GNNs) for Global Biological Network Similarity Search by Yi Wang, Feng Zhan, Cuiyu Huang, Yiran Huang

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Wi-Fi-Enabled Vision via Spatially-Variant Pose Estimation Based on Convolutional Transformer Network by Hyeon-Ju Lee, Seok-Jun Buu

“…To address these challenges, we propose a Convolutional Transformer Network. This architecture integrates convolutional layers for localized spatial feature extraction and transformer layers for global temporal dependency modeling. …”
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MSGEGA: Multiscale Gaussian Enhancement and Global-Aware Network for Infrared Small Target Detection by Yuyang Xi, Liuwei Zhang, Ying Jiang, Feng Qian, Fanjiao Tan, Qingyu Hou

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A Deep Learning Inversion Method for 3D Temperature Structures in the South China Sea with Physical Constraints by Dongcan Xu, Yahao Liu, Yuan Kong

“…This study develops a Convolutional Long Short-Term Memory (ConvLSTM) neural network, integrating multi-source satellite remote sensing data, to reconstruct the Ocean Subsurface Temperature Structure (OSTS). …”
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Measurement error evaluation method for voltage transformers in distribution networks based on self-attention and graph convolutional networks by Xiujuan Zeng, Tong Liu, Huiqin Xie, Dajiang Wang, Jihong Xiao

“…To address the challenge of extracting complex nonlinear features from multivariate electrical data, a combined model of a self-attention mechanism and a graph convolutional network (GCN) is proposed. The self-attention mechanism captures global dependencies among power parameters, while the GCN effectively constructs the multivariate data structures in distribution networks. …”
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A Lightweight Single-Image Super-Resolution Method Based on the Parallel Connection of Convolution and Swin Transformer Blocks by Tengyun Jing, Cuiyin Liu, Yuanshuai Chen

“…Specifically, through a parallel structure of channel feature-enhanced convolution and Swin Transformer, the network extracts, enhances, and fuses the local and global information. …”
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HMCFormer (hierarchical multi-scale convolutional transformer): a hybrid CNN+Transformer network for intelligent VIA screening by Bo Feng, Chao Xu, Zhengping Li, Chuanyi Zhang

“…The authors apply the structure of the Swin Transformer network with minor modifications in the global perception modeling process. …”
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ADMNet: adaptive deformable convolution large model combining multi-level progressive fusion for Building Change Detection by Liye Mei, Haonan Yu, Zhaoyi Ye, Chuan Xu, Cheng Lei, Wei Yang

“…Nevertheless, the mainstream detection methods utilizing traditional convolution and attention mechanisms are often prone to errors due to the loss of edge detail information and underutilization of global context information. …”
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Robust Low-Snapshot DOA Estimation for Sparse Arrays via a Hybrid Convolutional Graph Neural Network by Hongliang Zhu, Hongxi Zhao, Chunshan Bao, Yiran Shi, Wenchao He

“…The C-GNN architecture combines 1D convolutional layers for local spatial feature extraction with graph convolutional layers for global structural learning, effectively capturing both fine-grained and long-range array dependencies. …”
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Multi-convolutional neural network brain image denoising study based on feature distillation learning and dense residual attention by Huimin Qu, Haiyan Xie, Qianying Wang

“…The overall network structure contains four parts: a global sparse network (GSN), a dense residual attention network (DRAN), a feature distiller network (FDN), and a feature processing block (FPB). …”
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DualPlaqueNet with dual-branch structure and attention mechanism for carotid plaque semantic segmentation and size prediction by Lili Deng, Xingyu Duan, Yongxiang Sun, Yunling Wang, Dongmei Song, Xiaokai Duan

“…Notably, a multi-layer one-dimensional convolutional structure is introduced within the Efficient Channel Attention (ECA) module. …”
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An object detection model AAPW-YOLO for UAV remote sensing images based on adaptive convolution and reconstructed feature fusion by Yiming Wu, Xiaofang Mu, Hong Shi, Mingxing Hou

“…To overcome these challenges, this paper presents a model for detecting small objects, AAPW-YOLO, based on adaptive convolution and reconstructed feature fusion. In the AAPW-YOLO model, we improve the standard convolution and the CSP Bottleneck with 2 Convolutions (C2f) structure in the You Only Look Once v8 (YOLOv8) backbone network by using Alterable Kernel Convolution (AKConv), which improves the network’s proficiency in capturing features across various scales while considerably lowering the model’s parameter count. …”
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Multi-frequency EEG and multi-functional connectivity graph convolutional network based detection method of patients with Alzheimer’s disease by Yujian Liu, Libing An, Haiqiang Yang, Shuzhi Sam Ge

“…This network comprehensively captures abnormalities in brain network structures induced by AD, across different frequency bands and connectivity modes. …”
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Automatic melanoma and non-melanoma skin cancer diagnosis using advanced adaptive fine-tuned convolution neural networks by Muhammad Amir Khan, Tehseen Mazhar, Muhammad Danish Ali, Umar Farooq Khattak, Tariq Shahzad, Mamoon M. Saeed, Habib Hamam

“…Example: “Skin cancer accounts for 1 in 5 diagnosed cancers globally, with melanoma causing over 60,000 deaths annually. …”
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MolAttnNet: A Predictive Model for Organic Drug Solubility Based on Graph Convolutional Networks and Transformer-Attention by Chenxu Wang, Yijun Feng, Zhejie Xu, Xiaohui Xu, Bangguo Peng

“…The framework comprises three specialized modules: a Graph Convolutional Network for extracting local molecular structural features, a multi-granularity attention mechanism for capturing both local and global molecular dependencies, and an adaptive LSTM with chemically-informed forget gates for selective feature retention and noise attenuation. …”
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WT-HMFF: Wavelet Transform Convolution and Hierarchical Multi-Scale Feature Fusion Network for Detecting Infrared Small Targets by Siyu Li, Jingsi Huang, Qingwu Duan, Zheng Li

“…WTConv expands the receptive field through wavelet convolution, effectively capturing global contextual information while preserving target shape characteristics. …”
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Integrating Multiscale Spatial–Spectral Shuffling Convolution With 3-D Lightweight Transformer for Hyperspectral Image Classification by Qinggang Wu, Mengkun He, Qiqiang Chen, Le Sun, Chao Ma

“…The combination of convolutional neural networks and vision transformers has garnered considerable attention in hyperspectral image (HSI) classification due to their abilities to enhance the classification accuracy by concurrently extracting local and global features. …”
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Optimizing AlexNet for accurate tree species classification via multi-branch architecture and mixed-domain attention by Jianjianxian Liu, Tao Xing, Xiangyu Wang

“…The multi-branch convolutional module extracts diverse features by processing input with branches of different kernel sizes, capturing both fine and global details. …”
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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

“…First, high-dimensional features are extracted using ResNeXt, whose grouped convolution structure balances parameter efficiency and feature representation capability, significantly enhancing the expressiveness of the data. …”
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SVEA: an accurate model for structural variation detection using multi-channel image encoding and enhanced AlexNet architecture by Taixing Qiu, Jiawei Li, Yan Guo, Limin Jiang, Jijun Tang

“…Additionally, SVEA integrates multi-head self-attention mechanisms and multi-scale convolution modules, enhancing its ability to capture global context and multi-scale features. …”
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