Abstract:
COVID-19 is one of the most dangerous diseases that appeared during the
past 100 years, that caused millions of deaths worldwide. It caused hundreds of
billions of losses worldwide as a result of complete business paralysis. This reason
has attracted many researchers to attempt to find a suitable treatment for this dreaded
virus.
The search for a cure is still ongoing, but many researchers around the world have
begun to search for the safest ways to detect if a person carries the virus or not. Many
researchers resorted to artificial intelligence and machine learning techniques in
order to detect whether a person is carrying the virus or not.
However, many problems are arising when using these techniques, the most
important problem is the optimal selection of the parameter values for these
methods, as the choice of these values greatly affects the expected results.
In this chapter, Differential Evolution algorithm is used to determine the optimal
values for the hyperparameters of Convolutional Neural Networks, as Differential
Evolution is one of the most efficient optimization algorithms in the last two
decades. The results obtained showed that the use of Differential Evolution in
optimizing the hyperparameters of the Convolutional Neural Network was very
efficient.
