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An effective deep learning approach for the classification of Bacteriosis in peach leave

Overview of attention for article published in Frontiers in Plant Science, November 2022
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (54th percentile)
  • Good Attention Score compared to outputs of the same age and source (68th percentile)

Mentioned by

twitter
4 X users

Citations

dimensions_citation
11 Dimensions

Readers on

mendeley
13 Mendeley
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Title
An effective deep learning approach for the classification of Bacteriosis in peach leave
Published in
Frontiers in Plant Science, November 2022
DOI 10.3389/fpls.2022.1064854
Pubmed ID
Authors

Muneer Akbar, Mohib Ullah, Babar Shah, Rafi Ullah Khan, Tariq Hussain, Farman Ali, Fayadh Alenezi, Ikram Syed, Kyung Sup Kwak

X Demographics

X Demographics

The data shown below were collected from the profiles of 4 X users 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 13 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 13 100%

Demographic breakdown

Readers by professional status Count As %
Lecturer 2 15%
Professor > Associate Professor 1 8%
Researcher 1 8%
Student > Doctoral Student 1 8%
Unknown 8 62%
Readers by discipline Count As %
Computer Science 4 31%
Unknown 9 69%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 28 November 2022.
All research outputs
#14,418,035
of 23,206,358 outputs
Outputs from Frontiers in Plant Science
#8,105
of 20,945 outputs
Outputs of similar age
#197,711
of 440,226 outputs
Outputs of similar age from Frontiers in Plant Science
#410
of 1,359 outputs
Altmetric has tracked 23,206,358 research outputs across all sources so far. This one is in the 37th percentile – i.e., 37% of other outputs scored the same or lower than it.
So far Altmetric has tracked 20,945 research outputs from this source. They receive a mean Attention Score of 3.9. This one has gotten more attention than average, scoring higher than 60% 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 440,226 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 54% of its contemporaries.
We're also able to compare this research output to 1,359 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 68% of its contemporaries.