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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Implicit Is Not Enough: Explicitly Enforcing Anatomical Priors inside Landmark Localization Models by Simon Johannes Joham, Arnela Hadzic, Martin Urschler

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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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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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Detecting SARS-CoV-2 in CT Scans Using Vision Transformer and Graph Neural Network by Kamorudeen Amuda, Almustapha Wakili, Tomilade Amoo, Lukman Agbetu, Qianlong Wang, Jinjuan Feng

“…Using the strength of CNN and GNN to capture complex relational structures and the ViT capacity to classify global contexts, ViTGNN achieves a comprehensive representation of CT scan data. …”
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From Image to Sequence: Exploring Vision Transformers for Optical Coherence Tomography Classification by Amirali Arbab, Aref Habibi, Hossein Rabbani, Mahnoosh Tajmirriahi

“…Current methods for OCT image classification encounter specific challenges, such as the inherent complexity of retinal structures and considerable variability across different OCT datasets. …”
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Fault diagnosis of marine electric thruster gearbox based on MPDCNN under strong noisy environments by Qianming SHANG, Wanying JIANG, Yi ZHOU, Zhengqiang WANG, Yubo SUN

“…Meanwhile, a novel parallel dual-channel convolutional neural network structure is designed to explore both global features and deeper, finer details of the data, thereby enhancing the diagnostic performance of the method in strong noise environments.ResultsExperimental evaluation results under different noise conditions show that the proposed method achieves a fault diagnosis accuracy of over 98% in environments with strong noise. …”
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Deep Learning Approach Predicts Longitudinal Retinal Nerve Fiber Layer Thickness Changes by Jalil Jalili, Evan Walker, Christopher Bowd, Akram Belghith, Michael H. Goldbaum, Massimo A. Fazio, Christopher A. Girkin, Carlos Gustavo De Moraes, Jeffrey M. Liebmann, Robert N. Weinreb, Linda M. Zangwill, Mark Christopher

“…We evaluated four models: linear regression (LR), support vector regression (SVR), gradient boosting regression (GBR), and a custom 1D convolutional neural network (CNN). The GBR model achieved the best performance in predicting pointwise RNFL thickness changes (MAE = 5.2 μm, R² = 0.91), while the custom 1D CNN excelled in predicting changes to average global and sectoral RNFL thickness, providing greater resolution and outperforming the traditional models (MAEs from 2.0–4.2 μm, R² from 0.94–0.98). …”
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Assessment of Scientific Creative-Potential by Near-Infrared Spectroscopy Using Brain-Network-Based Deep-Fuzzy Classifier by Sayantani Ghosh, Amit Konar, Atulya K. Nagar

“…The novelty of the classifier lies in: i) design of an enhanced graph convolution operation that encapsulates local and global structural information from the input graph, ii) use of the Smish activation function to improve performance, iii) inclusion of a one-dimensional spatial convolution layer for preserving relevant information within convolved embeddings, iv) design of a novel mapping function to mitigate uncertainty among the spatial convolved vectors in the type-2 fuzzy layer, and v) application of Takagi-Sugeno-Kang (TSK)-based fuzzy reasoning to reduce computational cost. …”
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DSGAU: Dual-Scale Graph Attention U-Nets for Hyperspectral Image Classification With Limited Samples by Hongzhuang Ji, Leying Song, Zhaohui Xue, Hongjun Su

“…This enables the simultaneous learning of local spectral features and global contextual patterns within HSI data. However, the convolutional operations in traditional GCNs require the inclusion of all data points during graph construction, leading to significant computational overhead, particularly for large-scale datasets. …”
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Fault diagnosis of ZDJ7 railway point machine based on improved DCNN and SVDD classification by Zengshu Shi, Yiman Du, Xinwen Yao

“…First, the depthwise separable convolution in the Xception structure is used to optimize the extraction of fault features. …”
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Enhancing Small Language Models for Graph Tasks Through Graph Encoder Integration by Dongryul Oh, Sujin Kang, Heejin Kim, Dongsuk Oh

“…Graphs inherently encode intricate structural dependencies, requiring models to effectively capture both local and global relationships. …”
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Category semantic and global relation distillation for object detection by Yanpeng LIANG, Zhonggui MA, Zongjie WANG, Zhuo LI

“…Knowledge distillation stands out as it transfers knowledge from large teacher models to compact student models without modifying the network structure, enabling the student models to perform nearly as well as their larger counterparts. …”
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Occlusion Removal in Light-Field Images Using CSPDarknet53 and Bidirectional Feature Pyramid Network: A Multi-Scale Fusion-Based Approach by Mostafa Farouk Senussi, Hyun-Soo Kang

“…To preserve efficiency without sacrificing the quality of the extracted feature, our model uses separable convolutional blocks. A simple refinement module based on half-instance initialization blocks is integrated to explore the local details and global structures. …”
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Background-Supported Global Feature Response Image Classification Network by JIANG Wentao, LI Weida, ZHANG Shengchong

“…Then, a full-domain feature response module BGR (background-supported global feature response) is proposed, and BGR is embedded into the residual branch to restore the image full domain features, which reduces the loss of feature information due to the convolution operation to a certain extent. …”
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Global Aerosol Climatology from ICESat-2 Lidar Observations by Shi Kuang, Matthew McGill, Joseph Gomes, Patrick Selmer, Grant Finneman, Jackson Begolka

“…This study presents a global aerosol climatology derived from six years (October 2018–October 2024) of the Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) observations, using a U-Net Convolutional Neural Network (CNN) machine learning algorithm for Cloud–Aerosol Discrimination (CAD). …”
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Global-Frequency-Domain Network: A Semantic Segmentation Method for High-Resolution Remote Sensing Images Based on Fine-Grained Feature Extraction and Global Context Integration by Ye Zhou, Mingyue Zhang, Yechenzi Wang

“…The inherent complex spatial structure and abundant contextual information in these images make segmentation challenges, such as feature recognition difficulties and segmentation discontinuities. …”
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Graph-Based Few-Shot Learning for Synthetic Aperture Radar Automatic Target Recognition with Alternating Direction Method of Multipliers by Jing Jin, Zitai Xu, Nairong Zheng, Feng Wang

“…To address this challenge, we propose a novel few-shot learning (FSL) framework: the alternating direction method of multipliers–graph convolutional network (ADMM-GCN) framework. ADMM-GCN integrates a GCN with ADMM to enhance SAR ATR under limited data conditions, effectively capturing both global and local structural information from SAR samples. …”
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GaitRGA: Gait Recognition Based on Relation-Aware Global Attention by Jinhang Liu, Yunfan Ke, Ting Zhou, Yan Qiu, Chunzhi Wang

“…To slove these issues, we propose a gait recognition method based on relational-aware global attention. Specifically, we introduce a Relational-aware Global Attention (RGA) module, which captures global structural information within gait sequences to enable more precise attention learning. …”
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ED‐Autoformer: A New Model for Precise Global TEC Forecast by Jiawei Zhou, Hongtao Cai, Xu Yan, Hong‐wen Xu, Kun Hu, Chao Xiong

“…Evaluated on global ionospheric maps TEC, our model achieves a 12.0% improvement (0.51 TECu) in the root mean squared error (RMSE) during solar maximum and an 8.9% improvement (0.14 TECu) in RMSE during solar minimum compared to the Convolutional Long‐Short‐Term Memory (ConvLSTM) method. …”
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