A Feature-Driven Inception Dilated Network for Infrared Image Super-Resolution Reconstruction by Jiaxin Huang, Huicong Wang, Yuhan Li, Shijian Liu

“…Therefore, an Inception Dilated Super-Resolution (IDSR) network with multiple branches is proposed. A dilated convolutional branch captures high-frequency information to reconstruct edge details, while a non-local operation branch captures long-range dependencies between any two positions to maintain the global structure. …”
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Hybrid Deep Learning Architecture with Adaptive Feature Fusion for Multi-Stage Alzheimer’s Disease Classification by Ahmad Muhammad, Qi Jin, Osman Elwasila, Yonis Gulzar

“…Unlike static fusion methods, our adaptive feature fusion layer employs an attention mechanism to dynamically integrate ResNet50’s localized structural features and vision transformer (ViT) global connectivity patterns, significantly enhancing stage-specific Alzheimer’s disease classification accuracy. …”
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Enhanced Conformer-Based Speech Recognition via Model Fusion and Adaptive Decoding with Dynamic Rescoring by Junhao Geng, Dongyao Jia, Zihao He, Nengkai Wu, Ziqi Li

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Seismic Waveform Feature Extraction and Reservoir Prediction Based on CNN and UMAP: A Case Study of the Ordos Basin by Lifu Zheng, Hao Yang, Guichun Luo

“…The UMAP-CNN framework leverages the strengths of manifold learning and deep learning, enabling multi-scale feature extraction and dimensionality reduction while preserving both local and global data structures. The evaluation experiments, which considered runtime, receiver operating characteristic (ROC) curves, embedding distribution maps, and other quantitative assessments, illustrated that the UMAP-CNN outperformed t-distributed stochastic neighbor embedding (t-SNE), locally linear embedding (LLE) and isometric feature mapping (Isomap). …”
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An Adaptive Methodology for Constructing Domain-Specific Sentiment Lexicons Based on Chinese Social Media Data by Xue Xu, Haidong Liu, Lei Liu

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Multi-Scale Venation Pattern Analysis for Medicinal Plant Species Recognition by Arnav Sanjay Karnik, Nikhil Nair, Yashas Sagili, P. B. Pb

“…Our method extracts and analyzes venation patterns at multiple spatial scales, capturing both global and fine-grained structural details to improve classification performance. …”
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DSMF-Net: Dual Semantic Metric Learning Fusion Network for Few-Shot Aerial Image Semantic Segmentation by Xiyu Qi, Yidan Zhang, Lei Wang, Yifan Wu, Yi Xin, Zhan Chen, Yunping Ge

“…To exploit multiscale global semantic context, we construct scale-aware graph prototypes from different stages of the feature layers based on graph convolutional networks (GCNs), while also incorporating prior-guided metric learning to further enhance context at the high-level convolution features. …”
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Frequency-Aware Learned Image Compression Using Channel-Wise Attention and Restormer by Hanwen Zhang, Cheolkon Jung, Xu Liu

“…However, learned image compression has a limitation of balancing global context and local texture because the global structure easily ignores local redundancy, especially for the non-repetitive textures, affecting the reconstruction performance. …”
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Joint Grid-Based Attention and Multilevel Feature Fusion for Landslide Recognition by Xinran Li, Tao Chen, Gang Liu, Jie Dou, Ruiqing Niu, Antonio Plaza

“…We complement CNNs by adding a transformer-based structure in a layer-by-layer fashion and improving methods for sequence generation and attention weight calculation. …”
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A seismic random noise suppression method based on CNN-Mamba by Xiujuan WEI, Xingye LIU, Huailai ZHOU

“…This limitation results in insufficient collaborative optimization between local details and macroscopic structures during denoising, further reducing the noise suppression accuracy. …”
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CSA-Net: Complex Scenarios Adaptive Network for Building Extraction for Remote Sensing Images by Dongjie Yang, Xianjun Gao, Yuanwei Yang, Minghan Jiang, Kangliang Guo, Bo Liu, Shaohua Li, Shengyan Yu

“…The HFE obtains high-level semantic information at different levels and fuses it with low-level detailed information by skipping connections to enhance the reasoning and perception ability of building structure in complex scenes. Then, the GFI acquires global-local features of buildings and their surrounding environment via dense multiscale dilated convolution. …”
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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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Causal inference-based graph neural network method for predicting asphalt pavement performance by CHEN Kai;WANG Xiaohe;SHI Xinli;CAO Jinde

“…The model comprises four modules: global feature extraction, local feature extraction,causal inference, and dual-channel graph convolution. …”
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Implicit Is Not Enough: Explicitly Enforcing Anatomical Priors inside Landmark Localization Models by Simon Johannes Joham, Arnela Hadzic, Martin Urschler

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MultiV_Nm: a prediction method for 2′-O-methylation sites based on multi-view features by Lei Bai, Fei Liu, Yile Wang, Junle Su, Lian Liu

“…MultiV_Nm extracts the features of Nm sites from multiple dimensions, including sequence features, chemical characteristics, and secondary structure features. By integrating the powerful local feature extraction ability of convolutional neural networks, the ability of graph attention networks to capture global structural information, and the efficient interaction advantage of cross-attention mechanisms for different features, it deeply explores and integrates multi-view features, and finally realizes the prediction of Nm modification sites. …”
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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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Multi-Scale Spatial Perception Attention Network for Few-Shot Hyperspectral Image Classification by Yang Li, Jian Luo, Haoyu Long, Qianqian Jin

“…In the encoder, the spatial contraction perception Transformer (SCPFormer) is first proposed to improve the model’s capacity for perceiving global-local joint features. Next, the multi-scale spatial attention (MSSA) module is proposed to capture spatial information at different convolution kernel scales and cascade them to form a more comprehensive representation structure. …”
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Dual-Branch Neural Network-Based In-Loop Filter for VVC Intra Coding Using Spatial-Frequency Feature Fusion by Zhen Feng, Xu Liu, Cheolkon Jung

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Online English teaching resource recommendation method design based on LightGCNCSCM by Jing Tang

“…The research proposes an online English teaching resource recommendation method. The local and global features of the user-resource interaction graph are captured through Lightweight graph convolutional networks, and the resource semantic vectors are extracted in combination with the content-based similarity calculation model. …”
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Vessel Traffic Flow Prediction in Port Waterways Based on POA-CNN-BiGRU Model by Yumiao Chang, Jianwen Ma, Long Sun, Zeqiu Ma, Yue Zhou

“…Aiming at the stage characteristics of vessel traffic in port waterways in time sequence, which leads to complexity of data in the prediction process and difficulty in adjusting the model parameters, a convolutional neural network (CNN) based on the optimization of the pelican algorithm (POA) and the combination of bi-directional gated recurrent units (BiGRUs) is proposed as a prediction model, and the POA algorithm is used to search for optimized hyper-parameters, and then the iterative optimization of the optimal parameter combinations is input into the best combination of iteratively found parameters, which is input into the CNN-BiGRU model structure for training and prediction. …”
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