Cargando…
Supervised Machine Learning Algorithms for Bioelectromagnetics: Prediction Models and Feature Selection Techniques Using Data from Weak Radiofrequency Radiation Effect on Human and Animals Cells
The emergence of new technologies to incorporate and analyze data with high-performance computing has expanded our capability to accurately predict any incident. Supervised Machine learning (ML) can be utilized for a fast and consistent prediction, and to obtain the underlying pattern of the data be...
Autor principal: | Halgamuge, Malka N. |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7345599/ https://www.ncbi.nlm.nih.gov/pubmed/32604814 http://dx.doi.org/10.3390/ijerph17124595 |
Ejemplares similares
-
Bioelectromagnetic medicine /
Publicado: (2007) -
OpenMEEG: opensource software for quasistatic bioelectromagnetics
por: Gramfort, Alexandre, et al.
Publicado: (2010) -
Bioelectromagnetic Platform for Cell, Tissue, and In Vivo Stimulation
por: Ashbaugh, Ryan C., et al.
Publicado: (2021) -
Head phantoms for bioelectromagnetic applications: a material study
por: Hunold, Alexander, et al.
Publicado: (2020) -
DUNEuro—A software toolbox for forward modeling in bioelectromagnetism
por: Schrader, Sophie, et al.
Publicado: (2021)