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Case report: Quantitative recognition of virtual human technology acceptance based on efficient deep neural network algorithm

Overview of attention for article published in Frontiers in Neurorobotics, October 2022
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1 X user

Readers on

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46 Mendeley
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Title
Case report: Quantitative recognition of virtual human technology acceptance based on efficient deep neural network algorithm
Published in
Frontiers in Neurorobotics, October 2022
DOI 10.3389/fnbot.2022.1009093
Pubmed ID
Authors

Xu Wang, Charles Chen

X Demographics

X Demographics

The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 46 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 46 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 3 7%
Researcher 3 7%
Student > Doctoral Student 2 4%
Student > Master 2 4%
Professor 1 2%
Other 2 4%
Unknown 33 72%
Readers by discipline Count As %
Computer Science 2 4%
Business, Management and Accounting 2 4%
Arts and Humanities 1 2%
Mathematics 1 2%
Nursing and Health Professions 1 2%
Other 5 11%
Unknown 34 74%
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 07 November 2022.
All research outputs
#21,148,008
of 23,801,276 outputs
Outputs from Frontiers in Neurorobotics
#738
of 929 outputs
Outputs of similar age
#358,188
of 447,520 outputs
Outputs of similar age from Frontiers in Neurorobotics
#39
of 61 outputs
Altmetric has tracked 23,801,276 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 929 research outputs from this source. They receive a mean Attention Score of 4.0. 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 447,520 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 61 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.