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X Demographics
Mendeley readers
Attention Score in Context
| Chapter title |
Recurrent Auto-Encoder Model for Large-Scale Industrial Sensor Signal Analysis
|
|---|---|
| Chapter number | 17 |
| Book title |
Engineering Applications of Neural Networks
|
| Published in |
arXiv, September 2018
|
| DOI | 10.1007/978-3-319-98204-5_17 |
| Book ISBNs |
978-3-31-998203-8, 978-3-31-998204-5
|
| Authors |
Timothy Wong, Zhiyuan Luo |
X Demographics
The data shown below were collected from the profiles of 9 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 | 1 | 11% |
| Turkey | 1 | 11% |
| Germany | 1 | 11% |
| Unknown | 6 | 67% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 7 | 78% |
| Science communicators (journalists, bloggers, editors) | 1 | 11% |
| Scientists | 1 | 11% |
Mendeley readers
The data shown below were compiled from readership statistics for 33 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 33 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 8 | 24% |
| Researcher | 6 | 18% |
| Student > Ph. D. Student | 6 | 18% |
| Professor | 2 | 6% |
| Student > Doctoral Student | 2 | 6% |
| Other | 2 | 6% |
| Unknown | 7 | 21% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 12 | 36% |
| Engineering | 5 | 15% |
| Psychology | 1 | 3% |
| Physics and Astronomy | 1 | 3% |
| Decision Sciences | 1 | 3% |
| Other | 5 | 15% |
| Unknown | 8 | 24% |
Attention Score in Context
This research output has an Altmetric Attention Score of 12. 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 09 January 2024.
All research outputs
#2,845,042
of 25,331,507 outputs
Outputs from arXiv
#49,242
of 1,034,651 outputs
Outputs of similar age
#55,496
of 342,175 outputs
Outputs of similar age from arXiv
#1,103
of 21,749 outputs
Altmetric has tracked 25,331,507 research outputs across all sources so far. Compared to these this one has done well and is in the 88th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,034,651 research outputs from this source. They receive a mean Attention Score of 4.1. This one has done particularly well, scoring higher than 95% 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 342,175 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 83% of its contemporaries.
We're also able to compare this research output to 21,749 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 94% of its contemporaries.