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Human skin detection: An unsupervised machine learning way

Overview of attention for article published in Journal of Visual Communication & Image Representation, February 2024
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Title
Human skin detection: An unsupervised machine learning way
Published in
Journal of Visual Communication & Image Representation, February 2024
DOI 10.1016/j.jvcir.2024.104046
Authors

ABM Rezbaul Islam, Ali Alammari, Bill Buckles

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Attention Score in Context

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 11 January 2024.
All research outputs
#22,778,604
of 25,394,764 outputs
Outputs from Journal of Visual Communication & Image Representation
#515
of 668 outputs
Outputs of similar age
#203,674
of 251,537 outputs
Outputs of similar age from Journal of Visual Communication & Image Representation
#2
of 15 outputs
Altmetric has tracked 25,394,764 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 668 research outputs from this source. They receive a mean Attention Score of 3.0. This one is in the 1st percentile – i.e., 1% 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 251,537 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 15 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.