Cargando…
An ensemble learning approach to digital corona virus preliminary screening from cough sounds
This work develops a robust classifier for a COVID-19 pre-screening model from crowdsourced cough sound data. The crowdsourced cough recordings contain a variable number of coughs, with some input sound files more informative than the others. Accurate detection of COVID-19 from the sound datasets re...
Autores principales: | Mohammed, Emad A., Keyhani, Mohammad, Sanati-Nezhad, Amir, Hejazi, S. Hossein, Far, Behrouz H. |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8319422/ https://www.ncbi.nlm.nih.gov/pubmed/34321592 http://dx.doi.org/10.1038/s41598-021-95042-2 |
Ejemplares similares
-
Self-assembly of highly ordered micro- and nanoparticle deposits
por: Zargartalebi, Hossein, et al.
Publicado: (2022) -
Noncontact and Nonintrusive Microwave-Microfluidic Flow Sensor for Energy and Biomedical Engineering
por: Zarifi, Mohammad Hossein, et al.
Publicado: (2018) -
Machine learning for detecting COVID-19 from cough sounds: An ensemble-based MCDM method
por: Chowdhury, Nihad Karim, et al.
Publicado: (2022) -
A systematic review on cough sound analysis for Covid-19 diagnosis and screening: is my cough sound COVID-19?
por: Santosh, KC, et al.
Publicado: (2022) -
Sensitive, Real-time and Non-Intrusive Detection of Concentration and Growth of Pathogenic Bacteria using Microfluidic-Microwave Ring Resonator Biosensor
por: Narang, Rakesh, et al.
Publicado: (2018)