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Mendeley readers
| Chapter title |
Artificial Intelligence Prediction for the COVID-19 Data Based on LSTM Neural Networks and H2O AutoML
|
|---|---|
| Chapter number | 5 |
| Book title |
Predictive Models for Decision Support in the COVID-19 Crisis
|
| Published by |
Springer, Cham, December 2020
|
| DOI | 10.1007/978-3-030-61913-8_5 |
| Book ISBNs |
978-3-03-061912-1, 978-3-03-061913-8
|
| Authors |
Joao Alexandre Lobo Marques, Francisco Nauber Bernardo Gois, José Xavier-Neto, Simon James Fong, Marques, Joao Alexandre Lobo, Gois, Francisco Nauber Bernardo, Xavier-Neto, José, Fong, Simon James |
Mendeley readers
The data shown below were compiled from readership statistics for 8 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 8 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 3 | 38% |
| Lecturer > Senior Lecturer | 2 | 25% |
| Student > Ph. D. Student | 1 | 13% |
| Professor > Associate Professor | 1 | 13% |
| Unknown | 1 | 13% |
| Readers by discipline | Count | As % |
|---|---|---|
| Engineering | 2 | 25% |
| Environmental Science | 1 | 13% |
| Mathematics | 1 | 13% |
| Pharmacology, Toxicology and Pharmaceutical Science | 1 | 13% |
| Social Sciences | 1 | 13% |
| Other | 1 | 13% |
| Unknown | 1 | 13% |