| Title |
dLSTM: a new approach for anomaly detection using deep learning with delayed prediction
|
|---|---|
| Published in |
International Journal of Data Science and Analytics, May 2019
|
| DOI | 10.1007/s41060-019-00186-0 |
| Authors |
Shigeru Maya, Ken Ueno, Takeichiro Nishikawa |
X Demographics
The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Japan | 2 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 50% |
| Scientists | 1 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 102 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 102 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 21 | 21% |
| Student > Ph. D. Student | 18 | 18% |
| Researcher | 7 | 7% |
| Student > Bachelor | 6 | 6% |
| Lecturer | 2 | 2% |
| Other | 8 | 8% |
| Unknown | 40 | 39% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 35 | 34% |
| Engineering | 11 | 11% |
| Arts and Humanities | 3 | 3% |
| Physics and Astronomy | 2 | 2% |
| Unspecified | 2 | 2% |
| Other | 8 | 8% |
| Unknown | 41 | 40% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. 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 06 August 2019.
All research outputs
#15,855,488
of 23,560,187 outputs
Outputs from International Journal of Data Science and Analytics
#1
of 1 outputs
Outputs of similar age
#218,857
of 352,804 outputs
Outputs of similar age from International Journal of Data Science and Analytics
#1
of 1 outputs
Altmetric has tracked 23,560,187 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1 research outputs from this source. They receive a mean Attention Score of 0.0. This one scored the same or higher as 0 of them.
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 352,804 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 29th percentile – i.e., 29% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them