| dc.description.abstract | The flourishing of Artificial Intelligence (AI) in recent years, mainly due to the technological
advancements resulting in high-performing hardware that boosted the field’s
rise, has led to the research and development of many real-world applications. By
extension, since the field’s theoretical beginnings are now applied to solve real-world
problems, Machine Learning (ML), a subcategory of AI has been proven highly advantageous
for computer vision tasks including image classification. This led to the
development of various high-performing image classification Neural Networks (NN),
each one with a different architectural approach. Through Transfer Learning (TL)
these networks can be used for the development of real-world applications. However,
such applications come with challenges that require an NN performing a task to be
highly efficient, accurate, fast, stable generalized, and as less computational powerconsuming
as possible. There is constant research to improve models by designing
innovative architectures through various tools and techniques, including activation
functions. This work focuses on improving popular pre-trained image classification
NNs of high architecture and performance by altering the activation functions they
use in their core. The models are trained for five datasets, each time with a different
activation function in their entirety of architecture. Nine activation functions were
chosen for testing. The experiments show optimistic results as improvements in performance
in terms of accuracy or training time are possible and in many cases to a
high extent. | en_US |
