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Mendeley readers
| Chapter title |
Non Linear Tensor Diffusion Based Unsharp Masking for Filtering of COVID-19 CT Images
|
|---|---|
| Chapter number | 22 |
| 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_22 |
| Book ISBNs |
978-9-81-158533-3, 978-9-81-158534-0
|
| Authors |
S. N. Kumar, A. Lenin Fred, L. R. Jonisha Miriam, Parasuraman Padmanabhan, Balazs Gulyas, H. Ajay Kumar, Kumar, S. N., Lenin Fred, A., Jonisha Miriam, L. R., Padmanabhan, Parasuraman, Gulyas, Balazs, Kumar, H. Ajay |
Mendeley readers
The data shown below were compiled from readership statistics for 10 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 10 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Other | 2 | 20% |
| Student > Ph. D. Student | 2 | 20% |
| Student > Bachelor | 2 | 20% |
| Researcher | 2 | 20% |
| Student > Doctoral Student | 1 | 10% |
| Other | 1 | 10% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 2 | 20% |
| Computer Science | 2 | 20% |
| Medicine and Dentistry | 2 | 20% |
| Mathematics | 1 | 10% |
| Energy | 1 | 10% |
| Other | 1 | 10% |
| Unknown | 1 | 10% |