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Applied Artificial Intelligence

An International Journal

Volume 36, 2022 - Issue 1

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Research Article

Deep Learning: Parameter Optimization Using Proposed Novel Hybrid Bees Bayesian Convolutional Neural Network

Nawaf Mohammad H Alamria King Abdulaziz University, Jeddah, Saudi Arabia;b School of Engineering, Cardiff University, Cardiff, UKCorrespondence[email protected]

https://orcid.org/0000-0002-5641-0178 View further author information

Michael Packianatherb School of Engineering, Cardiff University, Cardiff, UK

https://orcid.org/0000-0002-9436-8206 View further author information

Samuel Bigotb School of Engineering, Cardiff University, Cardiff, UK

https://orcid.org/0000-0002-0789-4727 View further author information

Article: 2031815 | Received 15 Oct 2020, Accepted 18 Jan 2022, Published online: 16 Feb 2022

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https://doi.org/10.1080/08839514.2022.2031815
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Citations (4)

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Articles from other publishers (4)

Masyitah Abu, Nik Adilah Hanin Zahri, Amiza Amir, Muhammad Izham Ismail, Azhany Yaakub, Fumiyo Fukumoto & Yoshimi Suzuki. (2023) Analysis of the Effectiveness of Metaheuristic Methods on Bayesian Optimization in the Classification of Visual Field Defects. Diagnostics 13:11, pages 1946.
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C. Erden. (2023) Genetic algorithm-based hyperparameter optimization of deep learning models for PM2.5 time-series prediction. International Journal of Environmental Science and Technology 20:3, pages 2959-2982.
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Nawaf Mohammad H. Alamri, Michael Packianather & Samuel Bigot. (2023) Optimizing the Parameters of Long Short-Term Memory Networks Using the Bees Algorithm. Applied Sciences 13:4, pages 2536.
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Nawaf Mohammad H Alamri, Michael Packianather & Samuel Bigot. (2022) Optimization of Convolutional Neural Network Topology and Training Parameters using Bees Algorithm. Optimization of Convolutional Neural Network Topology and Training Parameters using Bees Algorithm.

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