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Biofouling detection and classification in Tidal Stream Turbines through soft voting ensemble transfer learning of video images

Overview of attention for article published in Engineering Applications of Artificial Intelligence, December 2024
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Title
Biofouling detection and classification in Tidal Stream Turbines through soft voting ensemble transfer learning of video images
Published in
Engineering Applications of Artificial Intelligence, December 2024
DOI 10.1016/j.engappai.2024.109316
Authors

Haroon Rashid, Mohamed Benbouzid, Yassine Amirat, Tarek Berghout, Hosna Titah-Benbouzid, Abdeslam Mamoune

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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 1 Mendeley reader of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 1 100%

Demographic breakdown

Readers by professional status Count As %
Student > Doctoral Student 1 100%
Readers by discipline Count As %
Unspecified 1 100%
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 20 September 2024.
All research outputs
#21,631,663
of 26,587,829 outputs
Outputs from Engineering Applications of Artificial Intelligence
#690
of 876 outputs
Outputs of similar age
#4,246
of 5,845 outputs
Outputs of similar age from Engineering Applications of Artificial Intelligence
#10
of 14 outputs
Altmetric has tracked 26,587,829 research outputs across all sources so far. This one is in the 10th percentile – i.e., 10% of other outputs scored the same or lower than it.
So far Altmetric has tracked 876 research outputs from this source. They receive a mean Attention Score of 3.4. This one is in the 9th percentile – i.e., 9% 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 5,845 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 15th percentile – i.e., 15% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 14 others from the same source and published within six weeks on either side of this one. This one is in the 14th percentile – i.e., 14% of its contemporaries scored the same or lower than it.