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A Wide Learning Approach for Interpretable Feature Recommendation for 1-D Sensor Data in IoT Analytics

Overview of attention for article published in Machine Intelligence Research, July 2019
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About this Attention Score

  • Average Attention Score compared to outputs of the same age
  • Above-average Attention Score compared to outputs of the same age and source (55th percentile)

Mentioned by

twitter
2 X users

Citations

dimensions_citation
3 Dimensions

Readers on

mendeley
17 Mendeley
Title
A Wide Learning Approach for Interpretable Feature Recommendation for 1-D Sensor Data in IoT Analytics
Published in
Machine Intelligence Research, July 2019
DOI 10.1007/s11633-019-1185-8
Authors

Snehasis Banerjee, Tanushyam Chattopadhyay, Utpal Garain

X Demographics

X Demographics

The data shown below were collected from the profiles of 2 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 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 %
Unspecified 3 18%
Student > Bachelor 2 12%
Lecturer 1 6%
Student > Ph. D. Student 1 6%
Student > Master 1 6%
Other 1 6%
Unknown 8 47%
Readers by discipline Count As %
Unspecified 3 18%
Engineering 3 18%
Psychology 2 12%
Decision Sciences 1 6%
Unknown 8 47%
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 03 July 2019.
All research outputs
#16,053,755
of 25,385,509 outputs
Outputs from Machine Intelligence Research
#124
of 444 outputs
Outputs of similar age
#206,270
of 362,446 outputs
Outputs of similar age from Machine Intelligence Research
#4
of 9 outputs
Altmetric has tracked 25,385,509 research outputs across all sources so far. This one is in the 34th percentile – i.e., 34% of other outputs scored the same or lower than it.
So far Altmetric has tracked 444 research outputs from this source. They receive a mean Attention Score of 2.5. This one has gotten more attention than average, scoring higher than 70% 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 362,446 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 40th percentile – i.e., 40% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 9 others from the same source and published within six weeks on either side of this one. This one has scored higher than 5 of them.