| Title |
Malware detection based on deep learning algorithm
|
|---|---|
| Published in |
Neural Computing and Applications, July 2017
|
| DOI | 10.1007/s00521-017-3077-6 |
| Authors |
Ding Yuxin, Zhu Siyi |
X Demographics
The data shown below were collected from the profiles of 26 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 7 | 27% |
| India | 2 | 8% |
| Kuwait | 1 | 4% |
| Nepal | 1 | 4% |
| Netherlands | 1 | 4% |
| Venezuela, Bolivarian Republic of | 1 | 4% |
| Saudi Arabia | 1 | 4% |
| Belgium | 1 | 4% |
| Spain | 1 | 4% |
| Other | 2 | 8% |
| Unknown | 8 | 31% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 24 | 92% |
| Scientists | 2 | 8% |
Mendeley readers
The data shown below were compiled from readership statistics for 158 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 158 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 29 | 18% |
| Student > Master | 22 | 14% |
| Student > Bachelor | 15 | 9% |
| Student > Doctoral Student | 9 | 6% |
| Researcher | 5 | 3% |
| Other | 12 | 8% |
| Unknown | 66 | 42% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 74 | 47% |
| Engineering | 10 | 6% |
| Pharmacology, Toxicology and Pharmaceutical Science | 2 | 1% |
| Unspecified | 2 | 1% |
| Business, Management and Accounting | 1 | <1% |
| Other | 3 | 2% |
| Unknown | 66 | 42% |
Attention Score in Context
This research output has an Altmetric Attention Score of 16. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 27 July 2017.
All research outputs
#2,307,242
of 25,286,324 outputs
Outputs from Neural Computing and Applications
#21
of 2,531 outputs
Outputs of similar age
#42,457
of 322,924 outputs
Outputs of similar age from Neural Computing and Applications
#2
of 14 outputs
Altmetric has tracked 25,286,324 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 90th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 2,531 research outputs from this source. They receive a mean Attention Score of 1.4. This one has done particularly well, scoring higher than 99% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 322,924 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 86% of its contemporaries.
We're also able to compare this research output to 14 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 92% of its contemporaries.