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An open-source tool for evaluating calibration techniques used in low-cost air pollutant monitors
  • +2
  • Daniel Tatsch,
  • Alejandro Ramirez,
  • Fernando Campo,
  • Leonardo Hoinaski,
  • Evelio González-Dalmau
Daniel Tatsch
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Alejandro Ramirez
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Fernando Campo
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Leonardo Hoinaski
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Evelio González-Dalmau
Cuban Neuroscience Center

Corresponding Author:[email protected]

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Low-cost air pollutant sensors suffer several interferences due to the variation of climatic elements. Recent studies look for calibration solutions based on different regression and classification machine learning algorithms. The present work brings together the implementation and extraction of performance metrics from these algorithms in a single open-source tool. Both the input data and parameters for each algorithm are automatically configured. This feature makes the tool compatible with any input dataset and removes the need to interact with complex codes.
05 Jan 2023Submitted to Electronics Letters
06 Jan 2023Submission Checks Completed
06 Jan 2023Assigned to Editor
24 Jan 2023Reviewer(s) Assigned
15 Mar 2023Review(s) Completed, Editorial Evaluation Pending
20 Mar 2023Editorial Decision: Revise Major
05 Apr 20231st Revision Received
06 Apr 2023Submission Checks Completed
06 Apr 2023Assigned to Editor
06 Apr 2023Review(s) Completed, Editorial Evaluation Pending
13 Apr 2023Reviewer(s) Assigned
26 Apr 2023Editorial Decision: Accept