COVID-19 Pandemic: Lessons Learned and Roadmap for the Future
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Publisher
Springer International Publishing
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-10031-4_34
Reference36 articles.
1. Sharifrazi, D., et al.: Fusion of convolution neural network, support vector machine and Sobel filter for accurate detection of COVID-19 patients using X-ray images. Biomed. Signal Process. Control 68, 102622 (2021)
2. Khozeimeh, F., et al.: Combining a convolutional neural network with autoencoders to predict the survival chance of COVID-19 patients. Sci. Rep. 11(1), 1–18 (2021)
3. Chowdhury, N.K., et al.: Machine learning for detecting COVID-19 from cough sounds: an ensemble-based MCDM method. Comput. Biol. Med. 105405 (2022)
4. Asgharnezhad, H., et al.: Objective evaluation of deep uncertainty predictions for covid-19 detection. Sci. Rep. 12(1), 1–11 (2022)
5. Aktar, S., et al.: Machine learning approach to predicting COVID-19 disease severity based on clinical blood test data: statistical analysis and model development. JMIR Med. Inform. 9(4), e25884 (2021)
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