A Systematic Approach to Predict the Behavior of Cough Droplets Using Feedforward Neural Networks Method
| Autor: | Ahdiar Fikri Maulana, Cahyo Adi Pandito, Setyawan Bekti Wibowo, M. Syairaji, Rian Mantasa Salve Prastica, Irfan Bahiuddin, Jimmy Trio Putra, Nurhazimah Nazmi |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
2019-20 coronavirus outbreak
010504 meteorology & atmospheric sciences Coronavirus disease 2019 (COVID-19) Computer science 02 engineering and technology lcsh:Thermodynamics 01 natural sciences % diameter reduction cough lcsh:QC310.15-319 empirical model lcsh:QC120-168.85 0105 earth and related environmental sciences Fluid Flow and Transfer Processes business.industry Mechanical Engineering Work (physics) Pattern recognition 021001 nanoscience & nanotechnology Condensed Matter Physics respiratory system machine learning Face (geometry) feedforward neural network Feedforward neural network lcsh:Descriptive and experimental mechanics droplet Artificial intelligence 0210 nano-technology business Disease transmission Generator (mathematics) |
| Zdroj: | Fluids Volume 6 Issue 2 Fluids, Vol 6, Iss 76, p 76 (2021) |
| ISSN: | 2311-5521 |
| DOI: | 10.3390/fluids6020076 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|