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Attention Score in Context
| Title |
VisionScaling: Dynamic Deep Learning Model and Resource Scaling in Mobile Vision Applications
|
|---|---|
| Published in |
IEEE Internet of Things Journal, January 2024
|
| DOI | 10.1109/jiot.2024.3349512 |
| Authors |
Pyeongjun Choi, Dongho Ham, Yeongjin Kim, Jeongho Kwak |
Attention Score in Context
This research output has an Altmetric Attention Score of 34. 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 31 March 2024.
All research outputs
#1,175,470
of 25,610,986 outputs
Outputs from IEEE Internet of Things Journal
#29
of 1,164 outputs
Outputs of similar age
#17,713
of 346,969 outputs
Outputs of similar age from IEEE Internet of Things Journal
#1
of 15 outputs
Altmetric has tracked 25,610,986 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 95th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,164 research outputs from this source. They receive a mean Attention Score of 4.8. 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 346,969 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 94% of its contemporaries.
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 has done particularly well, scoring higher than 99% of its contemporaries.