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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Damage Identification of Conduit Rack in Offshore Platform Structures Based on a Novel Composite Neural Network by Jiaqiang Yan, Yuanchao Qiu, Renhe Shao, Ziqiao Ling, Ruixiang Zhang

“…First, the temporal convolutional network (TCN) breaks through the localisation of traditional convolutional neural networks in modelling the temporal dimension by efficiently extracting the long-term time since of the structural vibration response through an expansive causal convolution mechanism. …”
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SAR ship target detection method based on CNN structure with wavelet and attention mechanism. by Shiqi Huang, Xuewen Pu, Xinke Zhan, Yucheng Zhang, Ziqi Dong, Jianshe Huang

“…The new method uses the U-Net structure to construct the network, which not only effectively reduces the depth of the network structure, but also significantly improves the complexity of the network. …”
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A Segmentation Network with Two Distinct Attention Modules for the Segmentation of Multiple Renal Structures in Ultrasound Images by Youhe Zuo, Jing Li, Jing Tian

“…Furthermore, the triple-branch multi-head self-attention mechanism leverages the different convolution layers to obtain diverse receptive fields, capture global contextual information, compensate for the local receptive field limitations of convolution operations, and boost the segmentation performance. …”
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NAF-MEEF: A Nonlinear Activation-Free Network Based on Multi-Scale Edge Enhancement and Fusion for Railway Freight Car Image Denoising by Jiawei Chen, Jianhai Yue, Hang Zhou, Zhunqing Hu

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FD-YOLO11: A Feature-Enhanced Deep Learning Model for Steel Surface Defect Detection by Zichen Dang, Xingshuo Wang

“…To enhance the multiscale feature extraction process, self-calibrated convolution is integrated into the C3k2 module. Additionally, an FSPPF structure is designed to optimize the process of fusing local and global information, improving the defect recognition ability of the model in complex backgrounds. …”
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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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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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MDIGCNet: Multidirectional Information-Guided Contextual Network for Infrared Small Target Detection by Luping Zhang, Junhai Luo, Yian Huang, Fengyi Wu, Xingye Cui, Zhenming Peng

“…To address the issue of lacking texture and structural information in the target images, we employ an integrated differential convolution (IDConv) module to extract richer image features during both the encoding and decoding stages. …”
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Structural Similarity-Guided Siamese U-Net Model for Detecting Changes in Snow Water Equivalent by Karim Malik, Colin Robertson

“…We conclude with a discussion on the implications of the findings from our study of snow dynamics and climate variables using gridded SWE data, computer vision metrics, and fully convolutional deep neural networks.…”
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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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SAFH-Net: A Hybrid Network With Shuffle Attention and Adaptive Feature Fusion for Enhanced Retinal Vessel Segmentation by Yang Zhou Ling Ou, Joon Huang Chuah, Hua Nong Ting, Shier Nee Saw, Jun Zhao

“…Specifically, a parallel encoder architecture employs a Convolutional (Conv) block with a residual structure for local feature extraction alongside a hierarchical Swin Transformer with Shifted-Window Multi-head Self-Attention (SW-MSA) for global context modeling, thereby achieving comprehensive feature capture with minimal additional parameter overhead. …”
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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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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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Deep Learning Models for Multi-Part Morphological Segmentation and Evaluation of Live Unstained Human Sperm by Peiran Lei, Mozafar Saadat, Mahdieh Gol Hassani, Chang Shu

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Prediction of the Next Solar Rotation Synoptic Maps Using an Artificial Intelligence–based Surface Flux Transport Model by Hyun-Jin Jeong, Mingyu Jeon, Daeil Kim, Youngjae Kim, Ji-Hye Baek, Yong-Jae Moon, Seonghwan Choi

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Prompt-Gated Transformer with Spatial–Spectral Enhancement for Hyperspectral Image Classification by Ruimin Han, Shuli Cheng, Shuoshuo Li, Tingjie Liu

“…Hyperspectral image (HSI) classification is an important task in the field of remote sensing, with far-reaching practical significance. Most Convolutional Neural Networks (CNNs) only focus on local spatial features and ignore global spectral dependencies, making it difficult to completely extract spectral information in HSI. …”
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