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Timeline
X Demographics
Mendeley readers
| Title |
Are GRU Cells More Specific and LSTM Cells More Sensitive in Motive Classification of Text?
|
|---|---|
| Published in |
Frontiers in Artificial Intelligence, June 2020
|
| DOI | 10.3389/frai.2020.00040 |
| Pubmed ID | |
| Authors |
Nicole Gruber, Alfred Jockisch |
X Demographics
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Switzerland | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 136 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 136 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 19 | 14% |
| Student > Ph. D. Student | 11 | 8% |
| Student > Bachelor | 11 | 8% |
| Researcher | 8 | 6% |
| Professor > Associate Professor | 6 | 4% |
| Other | 16 | 12% |
| Unknown | 65 | 48% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 22 | 16% |
| Engineering | 17 | 13% |
| Psychology | 6 | 4% |
| Biochemistry, Genetics and Molecular Biology | 3 | 2% |
| Linguistics | 2 | 1% |
| Other | 12 | 9% |
| Unknown | 74 | 54% |