ArticleInternational journal of environmental research and public health2017
Quick Estimation Model for the Concentration of Indoor Airborne Culturable Bacteria: An Application of Machine Learning.
Article in International journal of environmental research and public health, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed.
- Bacterial Community Characteristics Shaped by Artificial Environmental PM2.5 Control in Intensive Broiler Houses.International journal of environmental research and public health · 2022Article
- Neural Classification of Compost Maturity by Means of theInternational journal of environmental research and public health · 2019Article
- Health damage assessment of particulate matter pollution in Jing-Jin-Ji region of China.Environmental science and pollution research international · 2019Article
- Microscale dispersion behaviors of dust particles during coal cutting at large-height mining face.Environmental science and pollution research international · 2018Article
- Estimation of PMInternational journal of environmental research and public health · 2018Article
- Characterization of Microbial Communities in Pilot-Scale Constructed Wetlands withArchaea (Vancouver, B.C.) · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Indoor airborne culturable bacteria are sometimes harmful to human health. Therefore, a quick estimation of their concentration is particularly necessary. However, measuring the indoor microorganism concentration (e.g., bacteria) usually requires a large amount of time, economic cost, and manpower. In this paper, we aim to provide a quick solution: using knowledge-based machine learning to provide quick estimation of the concentration of indoor airborne culturable bacteria only with the inputs of several measurable indoor environmental indicators, including: indoor particulate matter (PM
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Registered trials
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