Improve carbon dioxide emission prediction in the Asia and Oceania (OECD): nature-inspired optimisation algorithms versus conventional machine learning-Reference-Cited by-同舟云学术

Improve carbon dioxide emission prediction in the Asia and Oceania (OECD): nature-inspired optimisation algorithms versus conventional machine learning

Published:2024-08-23 Issue:1 Volume:18 Page:
ISSN:1994-2060
Container-title:Engineering Applications of Computational Fluid Mechanics
language:en
Short-container-title:Engineering Applications of Computational Fluid Mechanics

Author:

Foong Loke Kok¹²,Blazek Vojtech³,Prokop Lukas³,Misak Stanislav³,Atamurotov Farruh⁴⁵⁶,Khalilpoor Nima⁷

Affiliation:

1. Institute of Research and Development, Duy Tan University, Da Nang, Vietnam

2. School of Engineering & Technology, Duy Tan University, Da Nang, Vietnam

3. ENET Centre, VSB—Technical University of Ostrava, Ostrava, Czech Republic

4. Faculty of Pedagogy, Shahrisabz State Pedagogical Institute, Shahrisabz, Uzbekistan

5. Department of Nuclear Power Plants, Tashkent State Technical University, Tashkent, Uzbekistan

6. Faculty of Completer Engineering, University of Tashkent for Applied Sciences, Tashkent, Uzbekistan

7. Department of Energy Engineering, Graduate School of the Environment and Energy, Science and Research Branch, Islamic Azad University, Tehran, Iran

Funder

ExPEDite receives funding from the European Union’s Horizon Mission Programme

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/19942060.2024.2391988

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