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Calibration Model of a Low-Cost Air Quality Sensor Using an Adaptive Neuro-Fuzzy Inference System

Overview of attention for article published in Sensors, December 2018
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (64th percentile)
  • High Attention Score compared to outputs of the same age and source (81st percentile)

Mentioned by

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6 X users
facebook
1 Facebook page

Readers on

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92 Mendeley
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Title
Calibration Model of a Low-Cost Air Quality Sensor Using an Adaptive Neuro-Fuzzy Inference System
Published in
Sensors, December 2018
DOI 10.3390/s18124380
Pubmed ID
Authors

Kemal Maulana Alhasa, Mohd Shahrul Mohd Nadzir, Popoola Olalekan, Mohd Talib Latif, Yusri Yusup, Mohammad Rashed Iqbal Faruque, Fatimah Ahamad, Haris Hafizal Abd. Hamid, Kadaruddin Aiyub, Sawal Hamid Md Ali, Firoz Khan, Azizan Abu Samah, Imran Yusuff, Murnira Othman, Tengku Mohd Farid Tengku Hassim, Nor Eliani Ezani

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X Demographics

X Demographics

The data shown below were collected from the profiles of 6 X users who shared this research output. Click here to find out more about how the information was compiled.
As of 1 July 2024, you may notice a temporary increase in the numbers of X profiles with Unknown location. Click here to learn more.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 92 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 92 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 17 18%
Researcher 9 10%
Student > Master 9 10%
Lecturer > Senior Lecturer 6 7%
Student > Bachelor 5 5%
Other 13 14%
Unknown 33 36%
Readers by discipline Count As %
Engineering 16 17%
Environmental Science 15 16%
Computer Science 7 8%
Business, Management and Accounting 3 3%
Chemistry 3 3%
Other 11 12%
Unknown 37 40%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 21 December 2018.
All research outputs
#8,190,103
of 25,385,509 outputs
Outputs from Sensors
#4,285
of 24,318 outputs
Outputs of similar age
#156,135
of 444,848 outputs
Outputs of similar age from Sensors
#96
of 529 outputs
Altmetric has tracked 25,385,509 research outputs across all sources so far. This one has received more attention than most of these and is in the 67th percentile.
So far Altmetric has tracked 24,318 research outputs from this source. They receive a mean Attention Score of 3.1. This one has done well, scoring higher than 82% 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 444,848 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 64% of its contemporaries.
We're also able to compare this research output to 529 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 81% of its contemporaries.