Skip to content

In this work, we used five machine learning techniques (GWO_MLP, GWO_CMLP, MGWO_MLP, FDO_MLP, FDO_CMLP) all of which aim to classify Covid-19 patients into two categories (Positive and Negative). Experiments showed promising results for all used models. The applied methods showed very similar performance, typically in terms of accuracy.

Notifications You must be signed in to change notification settings

Tarik4Rashid4/covid19models

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

7 Commits
 
 
 
 

Repository files navigation

Covid19-models

Cite the following articles:

Maryam T. Abdulkhaleq, Tarik A. Rashid, Bryar A. Hassan, Abeer Alsadoon, Nebojsa Bacanin, Amit Chhabra, S. Vimal, 2023. Fitness dependent optimizer with neural networks for COVID-19 patients, Computer Methods and Programs in Biomedicine Update, Volume 3, 100090, DOI: https://doi.org/10.1016/j.cmpbup.2022.100090

Dosti Kh. Abbas, Tarik A. Rashid, Karmand H. Abdalla, Nebojsa Bacanin, Abeer Alsadoon. (2021) Using Fitness Dependent Optimizer for Training Multi-layer Perceptron. Journal of Internet Technology. Volume 22 (2021) No.7. DOI: https://doi.org/10.53106/160792642021122207011

J. M. Abdullah and T. A. Rashid (2019). Fitness Dependent Optimizer: Inspired by the Bee Swarming Reproductive Process," in IEEE Access, vol. 7, pp. 43473-43486. DOI:https://doi.org/10.1109/ACCESS.2019.2907012

Rashid TA, Abbas DK, Turel YK (2019) A multi hidden recurrent neural network with a modified grey wolf optimizer. PLoS ONE 14(3): e0213237. https://doi.org/10.1371/journal.pone.0213237

About

In this work, we used five machine learning techniques (GWO_MLP, GWO_CMLP, MGWO_MLP, FDO_MLP, FDO_CMLP) all of which aim to classify Covid-19 patients into two categories (Positive and Negative). Experiments showed promising results for all used models. The applied methods showed very similar performance, typically in terms of accuracy.

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published