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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A novel neuroimaging based early detection framework for alzheimer disease using deep learning by Areej Alasiry, Khlood Shinan, Abeer Abdullah Alsadhan, Hanan E. Alhazmi, Fatmah Alanazi, M. Usman Ashraf, Taseer Muhammad

“…Comparative analyses further validate the superiority of NEDA-DL over existing methods. By integrating structural and functional neuroimaging insights, this approach enhances diagnostic precision and supports clinical decision-making in Alzheimer’s disease detection.…”
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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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Multiscale Feature Fusion for Salient Object Detection of Strip Steel Surface Defects by Li Zhang, Xirui Li, Yange Sun, Yan Feng, Huaping Guo

“…For the first step, MFF uses ResNet (convolutions with downsampling operations) instead of sampling techniques to generate multiscale features because convolution excels at extracting local regional features (e.g., edge and contour information). …”
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Enhancement of Underwater Images through Parallel Fusion of Transformer and CNN by Xiangyong Liu, Zhixin Chen, Zhiqiang Xu, Ziwei Zheng, Fengshuang Ma, Yunjie Wang

“…Subsequently, to extract global features, both temporal and frequency domain features are incorporated to construct the convolutional neural network. …”
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Time-Series Forecasting Method Based on Hierarchical Spatio-Temporal Attention Mechanism by Zhiguo Xiao, Junli Liu, Xinyao Cao, Ke Wang, Dongni Li, Qian Liu

“…Breaking through traditional structural designs, the model employs a Squeeze-and-Excitation Network (SENet) to reconstruct the convolutional layers of the Temporal Convolutional Network (TCN), strengthening the feature expression of key time steps through dynamic channel weight allocation to address the redundancy issue of traditional causal convolutions in local pattern capture. …”
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Seismic data denoising based on attention dual dilated CNN by Haixia Hu, Youhua Wei, Hui Chen, Xingan Fu, Ji Zhang, Quan Wang, Shiwei Cai

“…Traditional noise suppression methods often result in the loss of critical signals, affecting subsurface structure characterization. This study introduces an innovative Attention Dual-Dilated Convolutional Neural Network (ADDC-Net) to address random noise in seismic data. …”
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SGRD: A Ship Group Relationship Description Method Based on Scene Graph Generation With a Global-Local Context Fusion Network by Qianwen Rui, Yanan You, Jingyi Cao, Kaiwen Zhu, Yuanyuan Qiao

“…The proposed network integrates global feature fusion through a transformer-based self-attention mechanism and enhances local feature fusion using a graph convolutional network focused on object-specific graph structures. …”
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Spatiotemporal Multivariate Weather Prediction Network Based on CNN-Transformer by Ruowu Wu, Yandan Liang, Lianlei Lin, Zongwei Zhang

“…After that, in order to ensure that the model has better prediction ability for global and local hotspot areas, we designed a composite loss function based on MSE and SSIM to focus on the global and structural distribution of weather to achieve more accurate multivariate weather prediction. …”
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Diagnosis of contamination discharge state of porcelain insulators based on GA-CNN by Kegeng Zhang, Jinyuan Liu, Junshuai Zhong, Yizhou Jing

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Fusion of syntactic enhancement and semantic enhancement for aspect-based sentiment analysis by LIU Yao, WU Yunfei, ZHOU Hongjing, HUANG Shaonian, ZHANG Zhen

“…The graph neural networks mainly focus on the syntactic structure when they are used to model the syntactic dependency tree of a sentence for aspect-based sentiment analysis (ABSA). …”
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Aircraft Multi-stage Altitude Prediction Under Satellite Signal Loss by Mengchan HUANG, Qiang MIAO

“…LTCA efficiently exploited attention mechanisms to extract key features from multi-dimensional flight parameter data samples through adaptive global average pooling (GAP) and one-dimensional convolution, considering global and local information. …”
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Fusion of syntactic enhancement and semantic enhancement for aspect-based sentiment analysis by LIU Yao, WU Yunfei, ZHOU Hongjing, HUANG Shaonian, ZHANG Zhen

“…The graph neural networks mainly focus on the syntactic structure when they are used to model the syntactic dependency tree of a sentence for aspect-based sentiment analysis (ABSA). …”
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A Synergistic CNN-DF Method for Landslide Susceptibility Assessment by Jiangang Lu, Yi He, Lifeng Zhang, Qing Zhang, Jiapeng Tang, Tianbao Huo, Yunhao Zhang

“…The complex structures and intricate hyperparameters of existing deep learning (DL) models make achieving higher accuracy in landslide susceptibility assessment (LSA) time-consuming and labor-intensive. …”
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Vision Transformers (ViTs) for Feature Extraction and Classification of AI-Generated Visual Designs by Qing Yun

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Coseismic Landslide Mapping Based on Trans-UNet and Transfer Learning by Tianhe Ren, Wenping Gong, Jun Chen, Liang Gao, Jiahao Wu, Xuyang Xiang

“…The Trans-UNet model integrates a UNet-like encoder–decoder structure with a Transformer module to enhance global context extraction, a U-shaped full-scale feature extraction module to preserve multiscale spatial details, and a convolutional decoder for effective feature fusion and resolution restoration. …”
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Bird Species Detection Net: Bird Species Detection Based on the Extraction of Local Details and Global Information Using a Dual-Feature Mixer by Chaoyang Li, Zhipeng He, Kai Lu, Chaoyang Fang

“…The dual-branch feature mixer extracts features from dichotomous feature segments using global attention and deep convolution, expanding the network’s receptive field and achieving a strong inductive bias, allowing the network to distinguish between similar local details. …”
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Single-Image Superresolution for RGB Remote Sensing Imagery via Multiscale CNN-Transformer Feature Fusion by Xudong Yao, Haopeng Zhang, Sizhe Wen, Zhenwei Shi, Zhiguo Jiang

“…MSTB generates multiscale tokens with multiple convolutional layers to obtain multiscale global information. …”
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An explainable hybrid feature aggregation network with residual inception positional encoding attention and EfficientNet for cassava leaf disease classification by M. Sundara Srivathsan, S. Alden Jenish, K. Arvindhan, R. Karthik

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