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Deep learning: parameter optimization using proposed novel hybrid bees Bayesian convolutional neural network

Alamri, Nawaf Mohammad, Packianather, Michael and Bigot, Samuel 2022. Deep learning: parameter optimization using proposed novel hybrid bees Bayesian convolutional neural network. Applied Artificial Intelligence 36 (1) , 2031815. 10.1080/08839514.2022.2031815

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Abstract

Deep Learning (DL) is a type of machine learning used to model big data to extract complex relationship as it has the advantage of automatic feature extraction. This paper presents a review on DL showing all its network topologies along with their advantages, limitations, and applications. The most popular Deep Neural Network (DNN) is called a Convolutional Neural Network (CNN), the review found that the most important issue is designing better CNN topology, which needs to be addressed to improve CNN performance further. This paper addresses this problem by proposing a novel nature inspired hybrid algorithm that combines the Bees Algorithm (BA), which is known to mimic the behavior of honey bees, with Bayesian Optimization (BO) in order to increase the overall performance of CNN, which is referred to as BA-BO-CNN. Applying the hybrid algorithm on Cifar10DataDir benchmark image data yielded an increase in the validation accuracy from 80.72% to 82.22%, while applying it on digits datasets showed the same accuracy as the existing original CNN and BO-CNN, but with an improvement in the computational time by 3 min and 12 s reduction, and finally applying it on concrete cracks images produced almost similar results to existing algorithms.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Engineering
Additional Information: This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/ licenses/by/4.0/)
Publisher: Taylor & Francis
ISSN: 0883-9514
Date of First Compliant Deposit: 17 March 2022
Date of Acceptance: 18 January 2022
Last Modified: 11 Jul 2022 12:21
URI: https://orca.cardiff.ac.uk/id/eprint/146825

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