| Title |
A novel improved extreme learning machine algorithm in solving ordinary differential equations by Legendre neural network methods
|
|---|---|
| Published in |
Advances in Continuous and Discrete Models, December 2018
|
| DOI | 10.1186/s13662-018-1927-x |
| Authors |
Yunlei Yang, Muzhou Hou, Jianshu Luo |
X Demographics
The data shown below were collected from the profiles of 17 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 | 4 | 24% |
| France | 1 | 6% |
| Comoros | 1 | 6% |
| India | 1 | 6% |
| Singapore | 1 | 6% |
| Sri Lanka | 1 | 6% |
| Brazil | 1 | 6% |
| Unknown | 7 | 41% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 14 | 82% |
| Scientists | 3 | 18% |
Mendeley readers
The data shown below were compiled from readership statistics for 26 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 26 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 4 | 15% |
| Lecturer | 4 | 15% |
| Researcher | 2 | 8% |
| Lecturer > Senior Lecturer | 1 | 4% |
| Student > Doctoral Student | 1 | 4% |
| Other | 4 | 15% |
| Unknown | 10 | 38% |
| Readers by discipline | Count | As % |
|---|---|---|
| Mathematics | 4 | 15% |
| Engineering | 3 | 12% |
| Social Sciences | 2 | 8% |
| Computer Science | 2 | 8% |
| Environmental Science | 1 | 4% |
| Other | 3 | 12% |
| Unknown | 11 | 42% |
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 10 September 2020.
All research outputs
#2,998,165
of 25,385,509 outputs
Outputs from Advances in Continuous and Discrete Models
#5
of 189 outputs
Outputs of similar age
#64,798
of 443,605 outputs
Outputs of similar age from Advances in Continuous and Discrete Models
#1
of 2 outputs
Altmetric has tracked 25,385,509 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 189 research outputs from this source. They receive a mean Attention Score of 1.6. This one has done particularly well, scoring higher than 97% 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 443,605 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 85% of its contemporaries.
We're also able to compare this research output to 2 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