EMSPAN: Efficient Multi-Scale Pyramid Attention Network for Object Counting Under Size Heterogeneity and Dense Scenarios by Phu Nguyen Phan Hai, Bao Bui Quoc, Trang Hoang

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MSALNet: a multi-scale adaptive learning network for high-resolution remote sensing scene classification by Chao Yang, Chengbo Wei, Yiming Zhao, Liming Wang, Peigang Xu, Kunlun Qi, Yuanzheng Shao, Huayi Wu

“…The MSALNet begins by extracting original features using dilated convolution, effectively capturing information from objects of diverse sizes. …”
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Anomaly Detection of Acoustic Signals in Ultra-High Voltage Converter Valves Based on the FAVAE-AS by Shuyan Pan, Mingzhu Tang, Na Li, Jiawen Zuo, Xingpeng Zhou

“…A self-supervised learning module with six signal transformations improves generalization, while KL divergence-based correlation inconsistency quantization with dynamic thresholds enables accurate anomaly detection. …”
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Multi-Scale Differentiated Network with Spatial–Spectral Co-Operative Attention for Hyperspectral Image Denoising by Xueli Chang, Xiaodong Wang, Xiaoyu Huang, Meng Yan, Luxiao Cheng

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A seismic random noise suppression method based on CNN-Mamba by Xiujuan WEI, Xingye LIU, Huailai ZHOU

“…However, mainstream random noise intelligent methods based on convolutional neural networks (CNNs) are constrained by their local receptive fields. …”
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HFEF²-YOLO: Hierarchical Dynamic Attention for High-Precision Multi-Scale Small Target Detection in Complex Remote Sensing by Yao Lu, Biyun Zhang, Chunmin Zhang, Yifan He, Yanqiang Wang

“…Furthermore, a lightweight version (L-HFEF²-YOLO) is developed via dynamic convolution, reducing parameters by 42% while maintaining >95% accuracy, demonstrating real-time applicability on edge devices. …”
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A Comprehensive Drift-Adaptive Framework for Sustaining Model Performance in COVID-19 Detection From Dynamic Cough Audio Data: Model Development and Validation by Theofanis Ganitidis, Maria Athanasiou, Konstantinos Mitsis, Konstantia Zarkogianni, Konstantina S Nikita

“…Machine learning models, particularly convolutional neural networks, are promising in this regard. …”
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Towards precision diagnosis: a novel hybrid DC-CAD model for lung disease detection leveraging multi-scale capsule networks and temporal dynamics by Esther Stacy E. B. Aggrey, Qin Zhen, Seth Larweh Kodjiku, Linda Delali Fiasam, Collins Sey, Chiagoziem C. Ukwuoma, Evans Aidoo, Emmanuel Osei-Mensah

“…DC-CAD is innovative in its ability to combine multi-scale feature extraction and temporal dynamic analysis, enabling the model to capture intricate spatial relationships and sequential changes in lung tissue. …”
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Temporal and Modality Awareness-Based Lightweight Residual Network With Attention Mechanism for Human Activity Recognition Using a Lower-Limb Exoskeleton Robot by Chang-Sik Son, Won-Seok Kang

“…In addition, existing models frequently overlook the complementary nature of multimodal sensor signals, focusing primarily on temporal dynamics while underutilizing modality-specific information. …”
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CMHFE-DAN: A Transformer-Based Feature Extractor with Domain Adaptation for EEG-Based Emotion Recognition by Manal Hilali, Abdellah Ezzati, Said Ben Alla

“…The architecture tackles key challenges in EEG emotion recognition, including generalisability, inter-subject variability, and temporal dynamics modelling. The results highlight the effectiveness of combining convolutional feature learning with adversarial domain adaptation for robust EEG-ER.…”
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Whispered Speech Recognition Based on Audio Data Augmentation and Inverse Filtering by Jovan Galić, Branko Marković, Đorđe Grozdić, Branislav Popović, Slavko Šajić

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Analysis of space solar array arc images based on deep learning techniques by Afaf M. Abd El-Hameed, Ahmed S. Farahat, Khaled Y. Youssef, M. Elfarran, I. M. Selim

“…Leveraging advanced Deep Learning (DL) methodologies, including Convolutional Neural Networks (CNN) and Transfer Learning, a robust predictive model has been developed to analyze arc behavior and identify defective cells based on image data. …”
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Analyzing the extent and use of impervious land in rural landscapes by Andreas Moser, Jasper van Vliet, Ulrike Wissen Hayek, Adrienne Grêt-Regamey

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Optimizing the early diagnosis of neurological disorders through the application of machine learning for predictive analytics in medical imaging by Vijaya Bhaskar Sadu, Sathvik Bagam, Mohd Naved, Siva Krishna Reddy Andluru, Kamalakar Ramineni, Meshal Ghalib Alharbi, Sudhakar Sengan, Rahmaan Khadhar Moideen

“…However, these analysis methods frequently fail to accept temporal dynamics, which is significant for a complete test. …”
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LCC-Net: Swin transformer-CNN hybrid for enhanced land cover classification in natural disaster monitoring by P. Shailaja, Pala Mahesh Kumar, Nalla Nikhitha, Kunta Neeraj Kumar Reddy, Enthala Mukesh Reddy, Goli Ganesh Reddy, Vadde Indu

“…The core of LCC-Net employs the Swin Transformer Convolutional Neural Network (ST-CNN), which leverages self-attention mechanisms to capture intricate spatial features and temporal dynamics. …”
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An incremental learning framework for pipeline weld crack damage identification and leakage rate prediction by Jing Huang, Zhifen Zhang, Yanlong Yu, Yongjie Li, Shuai Zhang, Rui Qin, Ji Xing, Wei Cheng, Guangrui Wen, Xuefeng Chen

“…Microstructural variations and dynamic propagation lead to unpredictable changes in leakage rate over time and conditions. …”
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FAD-Net: Automated Framework for Steel Surface Defect Detection in Urban Infrastructure Health Monitoring by Nian Wang, Yue Chen, Weiang Li, Liyang Zhang, Jinghong Tian

“…The MSDFConv module, employing multi-scale dilated convolutions with optimized dilation rates to preserve fine details while expanding the receptive field. …”
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Swin-FSNet: A Frequency-Aware and Spatially Enhanced Network for Unpaved Road Extraction from UAV Remote Sensing Imagery by Jiwu Guan, Qingzhan Zhao, Wenzhong Tian, Xinxin Yao, Jingyang Li, Wei Li

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SFRADNet: Object Detection Network with Angle Fine-Tuning Under Feature Matching by Keliang Liu, Yantao Xi, Donglin Jing, Xue Zhang, Mingfei Xu

“…Existing detectors often utilize feature pyramid networks (FPN) and deformable (or rotated) convolutions to adapt to variations in object scale and orientation. …”
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Graph-Based Topological Embedding and Deep Reinforcement Learning for Autonomous Voltage Control in Power System by Hongtao Wei, Siyu Chang, Jiaming Zhang

“…To address these issues, this paper proposes a DRL method combining Graph Convolutional Networks (GCNs) and soft actor-critic (SAC) for voltage control through load shedding. …”
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