| Title |
An efficient hybrid system for anomaly detection in social networks
|
|---|---|
| Published in |
Cybersecurity, March 2021
|
| DOI | 10.1186/s42400-021-00074-w |
| Authors |
Md. Shafiur Rahman, Sajal Halder, Md. Ashraf Uddin, Uzzal Kumar Acharjee |
Mendeley readers
The data shown below were compiled from readership statistics for 79 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 79 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 10 | 13% |
| Student > Bachelor | 7 | 9% |
| Researcher | 4 | 5% |
| Lecturer | 3 | 4% |
| Other | 2 | 3% |
| Other | 8 | 10% |
| Unknown | 45 | 57% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 22 | 28% |
| Engineering | 5 | 6% |
| Arts and Humanities | 2 | 3% |
| Business, Management and Accounting | 1 | 1% |
| Unspecified | 1 | 1% |
| Other | 2 | 3% |
| Unknown | 46 | 58% |