The rapid evolution of cyber threats poses significant challenges to the adaptability and performance of anomaly detection systems. This study presents an innovative hybrid deep learning framework that integrates Convolutional Neural Networks (CNN), ...
Historical landscape restoration has become a crucial area of research in cultural heritage preservation, and with the advancement of digital technologies, effectively restoring damaged historical images has become a critical challenge. Traditional r...
OBJECTIVES: Retinal diseases, major causes of vision impairment and blindness, are assessed using optical coherence tomography (OCT) scans. Automated report generation for retinal OCT scans, powered by deep learning, can help standardize interpretati...
Using machine learning (ML) and deep learning (DL) algorithms for precise coagulant dosing in drinking water treatment plants (DWTPs) helps ensure drinking water safety and supports greenhouse gas (GHG) emission reduction. The effectiveness of these ...
Traditional manual inspection approaches face challenges due to the reliance on the experience and alertness of operators, which limits their ability to meet the growing demands for efficiency and precision in modern manufacturing processes. Deep lea...
Deep learning has emerged as the preeminent technique for semantic segmentation of brain MRI tumors. However, existing methods often rely on hierarchical downsampling to generate multi-scale feature maps, effectively capturing fine-grained global fea...
In the current environmental context, significant emissions generated by industrial and transportation activities, coupled with an unreasonable energy structure, have resulted in recurrent haze phenomena. This consequently leads to degraded image con...
Supervised deep learning methods have been widely employed to detect floating macroplastic litter (>5 mm) in (fresh)water bodies. However, few studies used them to quantify floating litter fluxes in rivers with wide cross-sections, that is important ...
OBJECTIVE: Identifying patients at high risk of mortality is crucial for emergency physicians to allocate hospital resources effectively, particularly in regions with limited medical services. This need becomes even more pressing during global health...
BACKGROUND: To investigate the feasibility and accuracy of PET radiomics features, along with their combination with CT radiomics, dosiomics, and deep learning (DL) features, in predicting radiation pneumonitis (RP) in lung cancer patients treated wi...
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