ANTH
发表于 2025-3-27 00:40:15
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反馈
发表于 2025-3-27 02:54:33
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Maximize
发表于 2025-3-27 09:22:29
https://doi.org/10.1007/978-3-211-49855-2ight and supine. We processed the surface ECG to obtain QRS complexes and HRV indices for RR series, including a total of 43 features. We compared 19 machine learning classification algorithms that yielded different approaches explained in a methodology session.
cortex
发表于 2025-3-27 12:44:20
,Classification of Severity of COVID-19 Patients Based on the Heart Rate Variability,ight and supine. We processed the surface ECG to obtain QRS complexes and HRV indices for RR series, including a total of 43 features. We compared 19 machine learning classification algorithms that yielded different approaches explained in a methodology session.
GIBE
发表于 2025-3-27 15:29:38
Book 2023 to the high infection and mortality rates, and the multiple consequences of the virusinfection in the human body, the challenges were vast and enormous. These necessitated the integration of different disciplines to address the problems. As a global response, researchers across academia and industr
泥土谦卑
发表于 2025-3-27 20:45:30
https://doi.org/10.1007/978-981-16-1899-4systems based on Artificial Intelligence are in fact ready to effectively help on clinical processes, from the perspective of the model proposed by NASA, Technology Readiness Levels (TRL). Finally, two trends are presented with increased necessity of computerized systems to deal with the Long Covid
艺术
发表于 2025-3-28 01:21:51
https://doi.org/10.1007/978-3-8350-9260-0oise and misinterpretation caused by other structures eventually present in the images. This chapter presents an AI-based system for lung segmentation in X-ray images using a U-net CNN model. The system’s performance was evaluated using metrics such as cross-entropy, dice coefficient, and Mean IoU o
penance
发表于 2025-3-28 05:54:30
https://doi.org/10.1007/978-3-8350-9260-0rated satisfactory accuracy, precision, recall, and specificity performance. On the one hand, the Mobilenet architecture outperformed the other CNNs, achieving excellent results for the evaluated metrics. On the other hand, Squeezenet presented a regular result in terms of recall. In medical diagnos
Bravura
发表于 2025-3-28 10:11:38
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存心
发表于 2025-3-28 10:34:57
https://doi.org/10.1007/978-3-8350-9260-0pproach in clinical practice. Because of COVID-19 CT scans’ medical characteristics, the lesions are widely spread and display a range of local aspects. Using deep learning to diagnose directly is difficult. In COVID-19, a Transformer and Convolutional Neural Network module are presented to extract