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Effectiveness analysis of machine learning classification models for predicting personalized context-aware smartphone usage

Overview of attention for article published in Journal of Big Data, July 2019
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About this Attention Score

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

Mentioned by

patent
1 patent

Citations

dimensions_citation
166 Dimensions

Readers on

mendeley
350 Mendeley
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Title
Effectiveness analysis of machine learning classification models for predicting personalized context-aware smartphone usage
Published in
Journal of Big Data, July 2019
DOI 10.1186/s40537-019-0219-y
Authors

Iqbal H. Sarker, A. S. M. Kayes, Paul Watters

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 350 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 350 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 46 13%
Student > Master 38 11%
Student > Bachelor 25 7%
Lecturer 23 7%
Researcher 11 3%
Other 48 14%
Unknown 159 45%
Readers by discipline Count As %
Computer Science 88 25%
Engineering 28 8%
Business, Management and Accounting 10 3%
Social Sciences 8 2%
Biochemistry, Genetics and Molecular Biology 7 2%
Other 38 11%
Unknown 171 49%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 25 October 2022.
All research outputs
#7,755,938
of 23,575,882 outputs
Outputs from Journal of Big Data
#134
of 354 outputs
Outputs of similar age
#136,575
of 349,972 outputs
Outputs of similar age from Journal of Big Data
#10
of 23 outputs
Altmetric has tracked 23,575,882 research outputs across all sources so far. This one is in the 44th percentile – i.e., 44% of other outputs scored the same or lower than it.
So far Altmetric has tracked 354 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 11.6. This one has gotten more attention than average, scoring higher than 58% 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 349,972 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 55% of its contemporaries.
We're also able to compare this research output to 23 others from the same source and published within six weeks on either side of this one. This one is in the 47th percentile – i.e., 47% of its contemporaries scored the same or lower than it.