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Pulmonary disease detection and classification in patient respiratory audio files using long short-term memory neural networks

Overview of attention for article published in Frontiers in Medicine, November 2023
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1 X user

Citations

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Readers on

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10 Mendeley
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Title
Pulmonary disease detection and classification in patient respiratory audio files using long short-term memory neural networks
Published in
Frontiers in Medicine, November 2023
DOI 10.3389/fmed.2023.1269784
Pubmed ID
Authors

Pinzhi Zhang, Alagappan Swaminathan, Ahmed Abrar Uddin

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.
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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 10 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 10 100%

Demographic breakdown

Readers by professional status Count As %
Lecturer 2 20%
Researcher 2 20%
Other 1 10%
Unspecified 1 10%
Unknown 4 40%
Readers by discipline Count As %
Engineering 2 20%
Biochemistry, Genetics and Molecular Biology 1 10%
Unspecified 1 10%
Medicine and Dentistry 1 10%
Mathematics 1 10%
Other 0 0%
Unknown 4 40%
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 30 November 2023.
All research outputs
#22,314,112
of 24,904,819 outputs
Outputs from Frontiers in Medicine
#6,069
of 6,893 outputs
Outputs of similar age
#212,472
of 266,768 outputs
Outputs of similar age from Frontiers in Medicine
#140
of 171 outputs
Altmetric has tracked 24,904,819 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 6,893 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 13.8. 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 266,768 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 171 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.