| Chapter title |
IoMT Potential Impact in COVID-19: Combating a Pandemic with Innovation
|
|---|---|
| Chapter number | 18 |
| Book title |
Computational Intelligence Methods in COVID-19: Surveillance, Prevention, Prediction and Diagnosis
|
| Published by |
Springer, Singapore, October 2020
|
| DOI | 10.1007/978-981-15-8534-0_18 |
| Book ISBNs |
978-9-81-158533-3, 978-9-81-158534-0
|
| Authors |
Mohd Faizan Siddiqui, Siddiqui, Mohd Faizan |
Twitter Demographics
The data shown below were collected from the profiles of 7 tweeters who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Arab Emirates | 2 | 29% |
| Kyrgyzstan | 1 | 14% |
| United States | 1 | 14% |
| India | 1 | 14% |
| Unknown | 2 | 29% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 5 | 71% |
| Practitioners (doctors, other healthcare professionals) | 1 | 14% |
| Scientists | 1 | 14% |
Mendeley readers
The data shown below were compiled from readership statistics for 23 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 23 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 2 | 9% |
| Lecturer | 2 | 9% |
| Student > Doctoral Student | 2 | 9% |
| Professor > Associate Professor | 2 | 9% |
| Student > Bachelor | 1 | 4% |
| Other | 4 | 17% |
| Unknown | 10 | 43% |
| Readers by discipline | Count | As % |
|---|---|---|
| Engineering | 3 | 13% |
| Medicine and Dentistry | 3 | 13% |
| Biochemistry, Genetics and Molecular Biology | 2 | 9% |
| Environmental Science | 1 | 4% |
| Mathematics | 1 | 4% |
| Other | 4 | 17% |
| Unknown | 9 | 39% |