2023
DOI: 10.1038/s41598-023-30064-6
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Explainable AI for unveiling deep learning pollen classification model based on fusion of scattered light patterns and fluorescence spectroscopy

Abstract: Pollen monitoring have become data-intensive in recent years as real-time detectors are deployed to classify airborne pollen grains. Machine learning models with a focus on deep learning, have an essential role in the pollen classification task. Within this study we developed an explainable framework to unveil a deep learning model for pollen classification. Model works on data coming from single particle detector (Rapid-E) that records for each particle optical fingerprint with scattered light and laser induc… Show more

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Cited by 11 publications
(1 citation statement)
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“…The data were also filtered to remove particles for which noise exceeded the signal. To do this, we focused on intensity of the scattering and fluorescence signals, as it was done in previous studies with Rapid-E (Tesendic et al, 2020;Matavulj et al, 2022;2023;Brdar et al, 2023). The particles, for which the maximum intensity of the spectrum did not exceed 4000 units or a sum of scattering measurements was below 50000 units after smoothing, were removed from the analysis (Table B2).…”
Section: Reference Data Collectionmentioning
confidence: 99%
“…The data were also filtered to remove particles for which noise exceeded the signal. To do this, we focused on intensity of the scattering and fluorescence signals, as it was done in previous studies with Rapid-E (Tesendic et al, 2020;Matavulj et al, 2022;2023;Brdar et al, 2023). The particles, for which the maximum intensity of the spectrum did not exceed 4000 units or a sum of scattering measurements was below 50000 units after smoothing, were removed from the analysis (Table B2).…”
Section: Reference Data Collectionmentioning
confidence: 99%