Deep neural networks have proven to be effective in various domains, especially in natural
language processing and image processing. However, one of the challenges associated with using
deep neural networks includes the long design time and expertise needed to apply these neural
networks to a particular domain. The research presented in this paper investigates the automation of
the design of the deep neural network pipeline to overcome this challenge. The deep learning pipeline
includes identifying the preprocessing needed, the feature engineering technique, the neural network
to use and the parameters for the neural network. A selection pertubative hyper-heuristic (SPHH)
is used to automate the design pipeline. The study also examines the reusability of the generated
pipeline. The effectiveness of transfer learning on the generated designs is also investigated. The
proposed approach is evaluated for text processing—namely, sentiment analysis and spam detection—
and image processing—namely, maize disease detection and oral lesion detection. The study revealed
that the automated design of the deep neural network pipeline produces just as good, and in some
cases better, performance compared to the manual design, with the automated design requiring
less design time than the manual design. In the majority of instances, the design was not reusable;
however, transfer learning achieved positive transfer of designs, with the performance being just as
good or better than when transfer learning was not used.The National Research Foundation of South Africa.https://www.mdpi.com/journal/applsciam2023Computer Scienc
