Alamri, Nawaf Mohammad H  ORCID: https://orcid.org/0000-0002-5641-0178, Packianather, Michael ORCID: https://orcid.org/0000-0002-9436-8206 and Bigot, Samuel ORCID: https://orcid.org/0000-0002-0789-4727
2022.
Optimization of convolutional neural network topology and training parameters using Bees Algorithm.
Presented at: IEEE 2nd International Symposium on Sustainable Energy, Signal Processing and Cyber Security (iSSSC),
Gunupur, Odisha, India,
15-17 December 2022.
Proceedings of 2nd International Symposium on Sustainable Energy, Signal Processing and Cyber Security.
IEEE,
pp. 1-6.
10.1109/iSSSC56467.2022.10051487
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Abstract
Designing a Convolutional Neural Network (CNN) topology with optimal performance is a challenge. This paper proposes a hybrid algorithm combining the nature-inspired Bees Algorithm (BA) with Bayesian Optimization (BO) technique to improve CNN performance (BA-BO-CNN). In addition, another hybrid algorithm is proposed which uses BA to optimize CNN hyperparameters (BA-CNN) to improve the network performance. Applying the hybrid BA-BO-CNN rather than BA-CNN on human electrocardiogram (ECG) signals the testing accuracy improved from 92.50% to 95%, on Cifar10DataDir benchmark data the accuracy on the validation set increased from 80.72% for BO-CNN to 82.22% for BA-BO-CNN, and finally, on benchmark digits images the training, validation and testing accuracies remained the same compared to the existing BO-CNN, but with more efficient computational time since it is reduced by 3 minutes and 12 seconds for BA-BO-CNN and 4 minutes and 14 seconds for BA-CNN.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Date Type: | Publication |
| Status: | Published |
| Schools: | Schools > Engineering |
| Publisher: | IEEE |
| ISBN: | 9781665490573 |
| Last Modified: | 05 Apr 2023 11:45 |
| URI: | https://orca.cardiff.ac.uk/id/eprint/158386 |
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