Machine Learning Models for Detection COVID-19
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Publisher
Springer International Publishing
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-47672-3_12
Reference16 articles.
1. Ahuja, S., Panigrahi, B.K., Dey, N., Rajinikanth, V., Gandhi, T.K.: Deep transfer learning-based automated detection of COVID-19 from lung CT scan slices. Appl. Intell. 51(1), 571–585 (2021). https://doi.org/10.1007/s10489-020-01826-w
2. Arpaci, I., Huang, S., Al-Emran, M., Al-Kabi, M.N., Peng, M.: Predicting the COVID-19 infection with fourteen clinical features using machine learning classification algorithms. Multimed. Tools Appl. 80(8), 11943–11957 (2021). https://doi.org/10.1007/s11042-020-10340-7
3. Chung, J.Y., Thone, M.N., Kwon, Y.J.: COVID-19 vaccines: the status and perspectives in delivery points of view. Adv. Drug Deliv. Rev. 170, 1–25 (2021). https://doi.org/10.1016/j.addr.2020.12.011
4. Das, A.K., Ghosh, S., Thunder, S., Dutta, R., Agarwal, S., Chakrabarti, A.: Automatic COVID-19 detection from X-ray images using ensemble learning with convolutional neural network. Pattern Anal. Appl. 24(3), 1111–1124 (2021). https://doi.org/10.1007/s10044-021-00970-4
5. El Massari, H., Mhammedi, S., Gherabi, N., Nasri, M.: Virtual OBDA mechanism Ontop for answering SPARQL queries over Couchbase. In: Saidi, R., El Bhiri, B., Maleh, Y., Mosallam, A., Essaaidi, M. (eds.) Advanced Technologies for Humanity. Lecture Notes on Data Engineering and Communications Technologies, pp. 193–205. Springer. https://doi.org/10.1007/978-3-030-94188-8_19
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