A review unveiling various machine learning algorithms adopted for biohydrogen productions from microalgae

Mohamad Zulfadhli Ahmad Sobri and Alya Redhwan and Fuad Ameen and Lim, Jun Wei and Liew, Chin Seng and Mong, Guo Ren and Hanita Daud and Rajalingam Sokkalingam and Ho, Chii-Dong and Anwar Usman and D. H. Nagaraju and Pasupuleti Visweswara Rao (2023) A review unveiling various machine learning algorithms adopted for biohydrogen productions from microalgae. Fermentation, 9. pp. 1-12. ISSN 2311-5637

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URL: https://www.mdpi.com/2311-5637/9/3/243

Abstract

Biohydrogen production from microalgae is a potential alternative energy source that is now intensively being researched. The complex natures of the biological processes involved have afflicted the accuracy of traditional modelling and optimization, besides being costly. Accordingly, machine learning algorithms have been employed to overcome setbacks, as these approaches have the capability to predict nonlinear interactions and handle multivariate data from microalgal biohydrogen studies. Thus, the review focuses on revealing the recent applications of machine learning techniques in microalgal biohydrogen production. The working principles of random forests, artificial neural networks, support vector machines, and regression algorithms are covered. The applications of these techniques are analyzed and compared for their effectiveness, advantages and disadvantages in the relationship studies, classification of results, and prediction of microalgal hydrogen production. These techniques have shown great performance despite limited data sets that are complex and nonlinear. However, the current techniques are still susceptible to overfitting, which could potentially reduce prediction performance. These could be potentially resolved or mitigated by comparing the methods, should the input data be limited.

Item Type:	Article
Keyword:	Machine learning, Biohydrogen, Microalgae, Nonlinear interaction, Prediction, Overfitting
Subjects:	Q Science > QH Natural history > QH1-278.5 Natural history (General) > QH1-(199.5) General Including nature conservation, geographical distribution R Medicine > RS Pharmacy and materia medica > RS153-441 Materia medica > RS160-167 Pharmacognosy. Pharmaceutical substances (Plant, animal, and inorganic)
Department:	FACULTY > Faculty of Medicine and Health Sciences
Depositing User:	SITI AZIZAH BINTI IDRIS -
Date Deposited:	16 Dec 2024 11:22
Last Modified:	16 Dec 2024 11:22
URI:	https://eprints.ums.edu.my/id/eprint/42230

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