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Intrusion detection in smart meters data using machine learning algorithms: A research report

Overview of attention for article published in Frontiers in Energy Research, February 2023
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Mentioned by

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1 X user

Citations

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36 Dimensions

Readers on

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17 Mendeley
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Title
Intrusion detection in smart meters data using machine learning algorithms: A research report
Published in
Frontiers in Energy Research, February 2023
DOI 10.3389/fenrg.2023.1147431
Authors

M. Ravinder, Vikram Kulkarni

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 17 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 17 100%

Demographic breakdown

Readers by professional status Count As %
Professor 1 6%
Lecturer 1 6%
Student > Postgraduate 1 6%
Student > Master 1 6%
Unknown 13 76%
Readers by discipline Count As %
Engineering 2 12%
Social Sciences 1 6%
Computer Science 1 6%
Unknown 13 76%
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 23 March 2023.
All research outputs
#20,945,173
of 23,575,882 outputs
Outputs from Frontiers in Energy Research
#1,365
of 3,615 outputs
Outputs of similar age
#325,506
of 412,633 outputs
Outputs of similar age from Frontiers in Energy Research
#67
of 430 outputs
Altmetric has tracked 23,575,882 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 3,615 research outputs from this source. They receive a mean Attention Score of 1.7. 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 412,633 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 430 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.