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Mendeley readers
| Title |
Efficient-ECGNet framework for COVID-19 classification and correlation prediction with the cardio disease through electrocardiogram medical imaging
|
|---|---|
| Published in |
Frontiers in Medicine, November 2022
|
| DOI | 10.3389/fmed.2022.1005920 |
| Pubmed ID | |
| Authors |
Marriam Nawaz, Tahira Nazir, Ali Javed, Khalid Mahmood Malik, Abdul Khader Jilani Saudagar, Muhammad Badruddin Khan, Mozaherul Hoque Abul Hasanat, Abdullah AlTameem, Mohammed AlKhathami |
Mendeley readers
The data shown below were compiled from readership statistics for 14 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 14 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Unspecified | 1 | 7% |
| Lecturer | 1 | 7% |
| Student > Doctoral Student | 1 | 7% |
| Student > Bachelor | 1 | 7% |
| Professor | 1 | 7% |
| Other | 3 | 21% |
| Unknown | 6 | 43% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 3 | 21% |
| Engineering | 2 | 14% |
| Pharmacology, Toxicology and Pharmaceutical Science | 1 | 7% |
| Nursing and Health Professions | 1 | 7% |
| Unspecified | 1 | 7% |
| Other | 0 | 0% |
| Unknown | 6 | 43% |