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Different techniques, including LSTM, GAN, and ELM, have been created to incorporate diagnostic systems for COVID-19. Geographical concerns, high-risk groups, detection, and radiology are among the primary COVID-19 issues examined and discussed here. Using a variety of clinical and non-clinical datasets, we also showed how to choose appropriate models for parameter estimation and prediction. With the aid of these platforms, AI specialists may analyse sizable data sets, assist in the training of machines, create algorithms, and enhance the data they have already analysed to support quicker and more precise virus identification. I can manage it. For propagating neural networks with several hidden layers, gradient-based learning algorithms like backpropagation are helpful, but ELM approaches are very affordable and are thus advised for the prediction of appropriate medications.
| Autorius: | Geeta Tiwari |
| Leidėjas: | LAP LAMBERT Academic Publishing |
| Išleidimo metai: | 2024 |
| Knygos puslapių skaičius: | 60 |
| ISBN-10: | 6207647394 |
| ISBN-13: | 9786207647392 |
| Formatas: | 220 x 150 x 4 mm. Knyga minkštu viršeliu |
| Kalba: | Anglų |
Parašykite atsiliepimą apie „COVID 19: Deep Learning Approach for Diagnosis“
