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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Classification of Structural MRI Images in Alzheimer's Disease from the Perspective of Ill-Posed Problems
|
|---|---|
| Published in |
PLOS ONE, October 2012
|
| DOI | 10.1371/journal.pone.0044877 |
| Pubmed ID | |
| Authors |
Ramon Casanova, Fang-Chi Hsu, for the Alzheimer's Disease Neuroimaging Initiative Mark A. Espeland |
| Abstract |
Machine learning neuroimaging researchers have often relied on regularization techniques when classifying MRI images. Although these were originally introduced to deal with "ill-posed" problems it is rare to find studies that evaluate the ill-posedness of MRI image classification problems. In addition, to avoid the effects of the "curse of dimensionality" very often dimension reduction is applied to the data. |
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 % |
|---|---|---|
| Egypt | 2 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Practitioners (doctors, other healthcare professionals) | 1 | 50% |
| Members of the public | 1 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 78 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 2 | 3% |
| Unknown | 76 | 97% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 14 | 18% |
| Student > Master | 14 | 18% |
| Student > Ph. D. Student | 12 | 15% |
| Student > Bachelor | 6 | 8% |
| Other | 5 | 6% |
| Other | 13 | 17% |
| Unknown | 14 | 18% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 12 | 15% |
| Psychology | 9 | 12% |
| Computer Science | 9 | 12% |
| Engineering | 8 | 10% |
| Agricultural and Biological Sciences | 6 | 8% |
| Other | 18 | 23% |
| Unknown | 16 | 21% |
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 10 October 2012.
All research outputs
#15,253,344
of 22,681,577 outputs
Outputs from PLOS ONE
#129,867
of 193,576 outputs
Outputs of similar age
#108,141
of 172,656 outputs
Outputs of similar age from PLOS ONE
#2,850
of 4,570 outputs
Altmetric has tracked 22,681,577 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 193,576 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 15.0. This one is in the 24th percentile – i.e., 24% of its peers scored the same or lower than it.
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 172,656 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 27th percentile – i.e., 27% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 4,570 others from the same source and published within six weeks on either side of this one. This one is in the 28th percentile – i.e., 28% of its contemporaries scored the same or lower than it.