https://www.socictopen.socict.org/files/original/f7569b0e18b9ab62ad57b5cba1e55e76.pdf 9018da602e08510f17a8042260e0bf26 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Coronavirus Description An account of the resource Dominio científico: Coronavirus Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource A Systematic Approach to Predict the Behavior of Cough Droplets Using Feedforward Neural Networks Method Creator An entity primarily responsible for making the resource Nurhazimah Nazmi, Irfan Bahiuddin, Setyawan Bekti Wibowo, M. Syairaji, Jimmy Trio Putra, Cahyo Adi Pandito, Ahdiar Fikri Maulana, Rian Mantasa Salve Prastica Description An account of the resource 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. Date A point or period of time associated with an event in the lifecycle of the resource 2021 Subject The topic of the resource feed-forward neural network, machine learning, respiratory system, droplet, cough, Empirical model Identifier An unambiguous reference to the resource within a given context 10.3390/fluids6020076 Source A related resource from which the described resource is derived Epidemiology and Health Publisher An entity responsible for making the resource available Korean Society of Epidemiology Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Descriptive and experimental mechanics, Thermodynamics