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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| Format: | Article |
| Language: | English |
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MDPI AG
2021-02-01
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| Series: | Fluids |
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| Online Access: | https://www.mdpi.com/2311-5521/6/2/76 |
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| author | Irfan Bahiuddin Setyawan Bekti Wibowo M. Syairaji Jimmy Trio Putra Cahyo Adi Pandito Ahdiar Fikri Maulana Rian Mantasa Salve Prastica Nurhazimah Nazmi |
| author_facet | Irfan Bahiuddin Setyawan Bekti Wibowo M. Syairaji Jimmy Trio Putra Cahyo Adi Pandito Ahdiar Fikri Maulana Rian Mantasa Salve Prastica Nurhazimah Nazmi |
| author_sort | Irfan Bahiuddin |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T05:00:22Z |
| format | Article |
| id | doaj.art-aad0fc89b2ac4dc7a32ace74aa77002d |
| institution | Directory Open Access Journal |
| issn | 2311-5521 |
| language | English |
| last_indexed | 2024-03-09T05:00:22Z |
| publishDate | 2021-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Fluids |
| spelling | doaj.art-aad0fc89b2ac4dc7a32ace74aa77002d2023-12-03T13:01:17ZengMDPI AGFluids2311-55212021-02-01627610.3390/fluids6020076A Systematic Approach to Predict the Behavior of Cough Droplets Using Feedforward Neural Networks MethodIrfan Bahiuddin0Setyawan Bekti Wibowo1M. Syairaji2Jimmy Trio Putra3Cahyo Adi Pandito4Ahdiar Fikri Maulana5Rian Mantasa Salve Prastica6Nurhazimah Nazmi7Department of Mechanical Engineering, Vocational College, Universitas Gadjah Mada, Yogyakarta 55281, IndonesiaDepartment of Mechanical Engineering, Vocational College, Universitas Gadjah Mada, Yogyakarta 55281, IndonesiaDepartment of Information and Medical Service, Vocational College, Universitas Gadjah Mada, Yogyakarta 55281, IndonesiaDepartment of Electrical Engineering and Informatics, Vocational College, Universitas Gadjah Mada, Yogyakarta 55281, IndonesiaDepartment of Mechanical Engineering, Vocational College, Universitas Gadjah Mada, Yogyakarta 55281, IndonesiaDepartment of Bioresources Technology and Veterinary, Vocational College, Universitas Gadjah Mada, Yogyakarta 55281, IndonesiaDepartment of Civil Engineering, Vocational College, Universitas Gadjah Mada, Yogyakarta 55281, IndonesiaMalaysia-Japan International Institute of Technology, Universiti Teknologi Malaysia, Jalan Sultan Yahya Petra, Kuala Lumpur 54100, MalaysiaCoronavirus 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.https://www.mdpi.com/2311-5521/6/2/76dropletcoughfeedforward neural networkmachine learningrespiratory systemempirical model |
| spellingShingle | Irfan Bahiuddin Setyawan Bekti Wibowo M. Syairaji Jimmy Trio Putra Cahyo Adi Pandito Ahdiar Fikri Maulana Rian Mantasa Salve Prastica Nurhazimah Nazmi A Systematic Approach to Predict the Behavior of Cough Droplets Using Feedforward Neural Networks Method Fluids droplet cough feedforward neural network machine learning respiratory system empirical model |
| title | A Systematic Approach to Predict the Behavior of Cough Droplets Using Feedforward Neural Networks Method |
| title_full | A Systematic Approach to Predict the Behavior of Cough Droplets Using Feedforward Neural Networks Method |
| title_fullStr | A Systematic Approach to Predict the Behavior of Cough Droplets Using Feedforward Neural Networks Method |
| title_full_unstemmed | A Systematic Approach to Predict the Behavior of Cough Droplets Using Feedforward Neural Networks Method |
| title_short | A Systematic Approach to Predict the Behavior of Cough Droplets Using Feedforward Neural Networks Method |
| title_sort | systematic approach to predict the behavior of cough droplets using feedforward neural networks method |
| topic | droplet cough feedforward neural network machine learning respiratory system empirical model |
| url | https://www.mdpi.com/2311-5521/6/2/76 |
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