A systematic approach to predict the behavior of cough droplets using feedforward neural networks method
Coronavirus disease 2019 (Covid-19) has been identified as being transmitted among humans with droplets from breath, cough, and sneezes. Understanding the droplets’ behavior can be critical information to avoid disease transmission, especially while designing a device deals with human air respirator...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021
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| Subjects: | |
| Online Access: | http://eprints.utm.my/id/eprint/95019/1/NurhazimahNazmi2021_ASystematicApproachtoPredict.pdf http://eprints.utm.my/id/eprint/95019/ http://dx.doi.org/10.3390/fluids6020076 |
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| Summary: | Coronavirus disease 2019 (Covid-19) has been identified as being transmitted among humans with droplets from breath, cough, and sneezes. Understanding the droplets’ behavior can be critical information to avoid disease transmission, especially while designing a device deals with human air respiratory. Although various studies have provided enormous computational fluid simulations, most cases are too specific and quite challenging to combine with other similar studies directly. Therefore, this paper proposes a systematic approach to predict the droplet behavior for coughing cases using machine learning. The approach consists of three models, which are droplet generator, mask model, and free droplet model modeled using feedforward neural network (FFNN). The evaluation has shown that the three FFNNs models’ accuracies are relatively high, with R-values of more than 0.990. The model has successfully predicted the evaporation effect on the diameter reduction and the completely evaporated state, which can be considered unlearned cases for machine learning models. The predicted horizontal distance pattern also agrees with the data in the literature. In summary, the proposed approach has demonstrated the capability to predict the diameter pattern according to the experimental or previous work data at various mask face types. |
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