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Authorities all over the world are using COVID-19 episode expectation models to make informed decisions and maintain necessary control steps. AI (ML)-based deciding components have proven to be useful in predicting perioperative outcomes and improving the dynamic of possible operations. For a long time, machine learning models have been used in a variety of applications that required recognisable proof and the prioritisation of unfavourable factors for a risk. To cope with anticipating problems, a few expectation strategies are commonly used. Authorities all over the world are using COVID-19 episode expectation models to make informed decisions and maintain necessary controls. AI (ML)-based deciding components have shown their worth in predicting perioperative outcomes and improving the dynamic of future operations. For a long time, machine learning models have been used in a variety of applications that needed recognisable proof and prioritisation of unfavourable factors for a danger. To cope with expecting problems, a few expectation strategies are in use.
| Autorius: | Sheshang Degadwala, Brijesh Patel, Dhairya Vyas, |
| Leidėjas: | LAP LAMBERT Academic Publishing |
| Išleidimo metai: | 2021 |
| Knygos puslapių skaičius: | 68 |
| ISBN-10: | 6203863084 |
| ISBN-13: | 9786203863086 |
| Formatas: | 220 x 150 x 5 mm. Knyga minkštu viršeliu |
| Kalba: | Anglų |
Parašykite atsiliepimą apie „COVID-19 Cases Outbreak Prediction using Supervise Learning Models“
