Currently, the traffic speed prediction model based on deep learning has become a research hotspot in the field of transportation. With the rapid development of deep learning and the improvement of ...

This study uses a hybrid deep learning technique to classify asphalt, pavement, and unpaved roads. In real-world circumstances, image data noise can damage image categorization algorithms. This issue ...

Bio-Digital Catalyst Design: Generative Deep Learning for Multi-Objective Optimization and Chemical Insights in CO 2 Methanation ...

CTSMamba is a multitask deep learning model trained on longitudinal CT images of neoadjuvant chemotherapy-treated locally advanced gastric cancer that accurately predicts lymph node metastasis and ...

Methods: This study proposes a novel approach for ASD detection utilizing deep learning and advanced feature selection techniques. A hybrid model combining Stacked Sparse Denoising Autoencoder (SSDAE) ...

Our presented plug-and-play denoising prior, DRBNet, is a learning deep model, which is different from existing plug-and-play prior. Our plug-and-play method with learning prior integrates flexibility ...

A self-supervised deep learning model has been developed to improve the quality of dynamic fluorescence images by leveraging temporal gradients. The method enables accurate denoising without ...

Abstract The rapid growth of unlabeled time-series data in domains such as wireless communications, radar, biomedical engineering, and the Internet of Things (IoT) has driven advancements in ...

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