Œuvrer en neurodidactique de la production d’écrits by Jocelyn REULIER

“…Therefore, this article aims at presenting this study, examining the issues, the challenges, the precautions to take into account in such a research. …”
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AD-Diff: enhancing Alzheimer's disease prediction accuracy through multimodal fusion by Lei Han

“…Specifically, the AD-Diff model consists of two core components: the ADdiffusion module and the multimodal Mamba Classifier. The ADdiffusion module uses a 3D diffusion process to generate high-quality PET images, which are then fused with MRI images and tabular data to provide input for the Multimodal Mamba Classifier. …”
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Planning Design Competitions to Cultivate Innovative Design Capabilities Among Hong Kong Secondary School Students by Chun-Wang To, Yi-Teng Shih

“…Despite the recognized importance of creativity in secondary education, the role of design as a faculty for teaching abstract creative concepts effectively is seldom explored in depth, hindering students’ development. To address these issues, this study aimed to motivate secondary students to engage more deeply in innovative design by proposing a university-led workshop approach within a design competition module. …”
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Generative Adversarial Network-Based Distortion Reduction Adapted to Peak Signal-to-Noise Ratio Parameters in VVC by Weihao Deng, Zhenglong Yang

“…The lightweight attention module is integrated into the residual block of the generator module structure, thereby facilitating the extraction of image details and motion compensation. …”
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LMD-YOLO: A lightweight algorithm for multi-defect detection of power distribution network insulators based on an improved YOLOv8. by Weiyu Han, Zixuan Cai, Xin Li, Anan Ding, Yuelin Zou, Tianjun Wang

“…The network improves the backbone by introducing SCConv module to improve C2f module, which reduces spatial and channel redundancy, lowering both computational complexity and the number of parameters. …”
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Label-Guided Data Augmentation for Chinese Named Entity Recognition by Miao Jiang, Honghui Chen

“…The LGDA model consists of three key components: a data augmentation module, a label semantic fusion module, and an optimized loss function. …”
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Deep learning-based automatic detection and grading of disk herniation in lumbar magnetic resonance images by Yan Guo, Xiaoxiang Huang, Wei Chen, Ichiro Nakamoto, Weiqing Zhuang, Hua Chen, Jie Feng, Jianfeng Wu

“…The greatest challenges today are the repetitive and time-consuming process of interpreting these complex MR images and the problem of unequal diagnostic results from physicians with different levels of experience. To address these issues, in this study, an improved YOLOv8 model (GE-YOLOv8) that combines a gradient search module and efficient channel attention was developed. …”
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Research on Underwater Target Detection Technology Based on SMV-YOLOv11n by Dongcheng Liao, Yijun Shen, Jinyu Ou, Yanlian Du

“…Subsequently, the VoVGSCSP module is introduced to replace the C3K2 module, effectively leveraging contextual information to improve global detection performance. …”
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A Focal Attention-Based Large Convolutional Kernel Network for Anomaly Detection of Coated Fuel Particles by Zhaochuan Hu, Jiang Yu, Hang Zhang, Jian Liu, Ning Chen, Rong Li

“…Second, some anomalies exhibit weak morphological features, making them difficult to detect. To address these issues, this study proposes an innovative focal attention-based large convolutional kernel network detection framework comprising three core modules. …”
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CMSAF-Net: integrative network design with enhanced decoder for precision segmentation of pear leaf diseases by Jie Ding, Wenwen Xu, Xin Shu, Wenyu Wang, Shuxia Chen, Yunzhi Wu

“…CMSAF-Net integrates a Multi-scale Convolutional Attention Module (MBCA), a Self-adaptive Attention-augmented Upsampling Module (SAUP), and a Cross-layer Feature Alignment Module (CGAG) to enhance feature extraction, preserve edge information in complex disease regions, and optimize cross-layer information fusion. …”
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Multilevel Feature Interaction Network for Remote Sensing Images Semantic Segmentation by Hongkun Chen, Huilan Luo

“…Furthermore, to mitigate the semantic dilution issues caused by upsampling, a semantic-guided fusion module is introduced to enhance the propagation of rich semantic information among features. …”
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Optimization of TCN-BiLSTM for dissolved oxygen prediction based on improved sparrow search algorithm by Pei Shi, Mingjie Tang, Quan Wang, Xiaofei Ma

“…However, current DO prediction models often struggle with issues such as noise in the water quality data and insufficient feature extraction. …”
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HSF-YOLO: A Multi-Scale and Gradient-Aware Network for Small Object Detection in Remote Sensing Images by Fujun Wang, Xing Wang

“…Specifically, we introduce three tailored modules: Hybrid Atrous Enhanced Convolution (HAEC), a Spatial–Interactive–Shuffle attention module (C2f_SIS), and a Focal Gradient Refinement Loss (FGR-Loss). …”
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An insulator target detection algorithm based on improved YOLOv5 by Bing Zeng, Zhihao Zhou, Yu Zhou, Dilin He, Zhanpeng Liao, Zihan Jin, Yulu Zhou, Kexin Yi, Yunmin Xie, Wenhua Zhang

“…Firstly, in the backbone and neck networks, a lightweight CSP-SCConv module is employed to replace the original CSP-Darknet53 module, thereby reducing the parameter count and enhancing the feature extraction capabilities. …”
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Video Action Recognition Based on Two‑stream Feature Enhancement Network by ZHAO Chen, FENG Xiufang, DONG Yunyun, WEN Xin, CAO Ruochen

“…[Methods] In this paper, a two-stream network called the Two-stream Feature Enhancement Network (TFEN) was proposed to address these issues. To solve the problem of feature damage caused by temporal shift, a Spatial Enhancement-Temporal Shift Module (SE-TSM) and a Channel Enhancement-Temporal Shift Module (CE-TSM) were proposed to enhance features after each time shift, for improving damaged features. …”
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GESC-YOLO: Improved Lightweight Printed Circuit Board Defect Detection Based Algorithm by Xiangqiang Kong, Guangmin Liu, Yanchen Gao

“…First, a new lightweight module, C2f-GE, is designed to replace the C2f module of the backbone network, which effectively reduces the computational parameters, and at the same time increases the number of channels of the feature map to enhance the feature extraction capability of the model. …”
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DO-MDS&DSCA: A New Method for Seed Vigor Detection in Hyperspectral Images Targeting Significant Information Loss and High Feature Similarity by Liangquan Jia, Jianhao He, Jinsheng Wang, Miao Huan, Guangzeng Du, Lu Gao, Yang Wang

“…Secondly, a dual-stream spectral collaborative attention (DSCA) module is proposed. The DSCA module adopts a dual-modal fusion approach combining global feature capture and local feature enhancement, deepening the characterization capability of spectral features. …”
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IECI: A Pipeline Framework for Iterative Event Causal Identification with Dynamic Inference Chains by Hefei Chen, Yuanyuan Cai, Zexi Song, Yiyao Zhang, Hongbo Zhang

“…However, information about events described in documents is often distributed across sentences, which makes it difficult for existing studies to capture long-distance causal relations between events. To address these issues, this paper proposes Iterative Event Causal Identification (IECI), a pipelined framework for event causality identification that integrates two modules. …”
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Dual-Filter Cross Attention and Onion Pooling Network for Enhanced Few-Shot Medical Image Segmentation by Lina Ni, Yang Liu, Zekun Zhang, Yongtao Li, Jinquan Zhang

“…Additionally, we design an onion pooling (OP) module that combines eroding mask operations with masked average pooling to generate multiple prototypes, preserving contextual information and mitigating prototype bias. …”
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An improved DeepLabv3 + railway track extraction algorithm based on densely connected and attention mechanisms by Yanbin Weng, Jie Yang, Changfan Zhang, Jing He, Cheng Peng, Lin Jia, Hui Xiang

“…Abstract The railway track extraction using unmanned aerial vehicle (UAV) aerial images suffers from issues such as low extraction accuracy and high time consumption. …”
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