Optimizing sustainable desalination plants with advanced ML-based uncertainty analysis DOI
Sani I. Abba, Jamilu Usman, Abdullah Bafaqeer

et al.

Applied Soft Computing, Journal Year: 2024, Volume and Issue: unknown, P. 112624 - 112624

Published: Dec. 1, 2024

Language: Английский

Digital Twin of Wind Turbine Surface Damage Detection Based on Deep Learning-Aided Drone Inspection DOI
Weifei Hu, Jianhao Fang,

Yaxuan Zhang

et al.

Renewable Energy, Journal Year: 2025, Volume and Issue: 241, P. 122332 - 122332

Published: Jan. 7, 2025

Language: Английский

Citations

1
A new adsorption desalination-cooling system with four beds and two evaporators to enhance freshwater production DOI
Ehab S. Ali, Ahmed S. Alsaman, Ridha Ben Mansour

et al.

Thermal Science and Engineering Progress, Journal Year: 2025, Volume and Issue: unknown, P. 103300 - 103300

Published: Jan. 1, 2025

Language: Английский

Citations

1
Strength and durability predictions of ternary blended nano-engineered high-performance concrete: Application of hybrid machine learning techniques with bio-inspired optimization DOI
Vikrant S. Vairagade, Boskey V. Bahoria, Haytham F. Isleem

et al.

Engineering Applications of Artificial Intelligence, Journal Year: 2025, Volume and Issue: 148, P. 110470 - 110470

Published: March 6, 2025

Language: Английский

Citations

1
Evaluation of emission of the hydrogen-enriched diesel engine through machine learning DOI

Tian Erlin,

Guoning Lv,

Zuhe Li

et al.

Energy, Journal Year: 2024, Volume and Issue: 307, P. 132303 - 132303

Published: July 3, 2024

Language: Английский

Citations

6
A systematic review of current AI techniques used in the context of the SDGs DOI Creative Commons
Lucas Greif,

Fabian Röckel,

Andreas Kimmig

et al.

International Journal of Environmental Research, Journal Year: 2024, Volume and Issue: 19(1)

Published: Oct. 24, 2024

Abstract This study aims to explore the application of artificial intelligence (AI) in resolution sustainability challenges, with a specific focus on environmental studies. Given rapidly evolving nature this field, there is an urgent need for more frequent and dynamic reviews keep pace innovative applications AI. Through systematic analysis 191 research articles, we classified AI techniques applied field sustainability. Our review found that 65% studies supervised learning methods, 18% employed unsupervised learning, 17% utilized reinforcement approaches. The highlights neural networks (ANN), are most commonly contexts, accounting 23% reviewed methods. comprehensive overview identifies key trends proposes new avenues address complex issue achieving Sustainable Development Goals (SDGs). Graphic abstract

Language: Английский

Citations

4
A machine learning based framework for the probability failure envelope analysis of foundations in spatially variable clay DOI

Shuntao Fan,

Yurong Zhang,

Sa Li

et al.

Engineering Applications of Artificial Intelligence, Journal Year: 2025, Volume and Issue: 143, P. 110005 - 110005

Published: Jan. 18, 2025

Language: Английский

Citations

0
Optimizing Arrhenius parameters for multi-step reactions via metaheuristic algorithms DOI

AliReza Eshaghi,

Zeinab Pouransari

Journal of Mathematical Chemistry, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 6, 2025

Language: Английский

Citations

0
A three layer stacked multimodel transfer learning approach for deep feature extraction from Chest Radiographic images for the classification of COVID-19 DOI
Baijnath Kaushik, Akshma Chadha, Abhigya Mahajan

et al.

Engineering Applications of Artificial Intelligence, Journal Year: 2025, Volume and Issue: 147, P. 110241 - 110241

Published: Feb. 25, 2025

Language: Английский

Citations

0
Modular surrogate modeling-based optimization framework for thermohydraulic systems assisted by machine learning DOI

Rong-Huan Fu,

Tian Zhao, Mei Yuan

et al.

Energy, Journal Year: 2025, Volume and Issue: unknown, P. 135738 - 135738

Published: March 1, 2025

Language: Английский

Citations

0
Pyrolysis and oxidation mechanisms of ethylene and ethanol blended fuel based on ReaxFF molecular dynamics simulation DOI
Liang Song,

Chun-Chen Xu,

Jing Ye

et al.

Fuel, Journal Year: 2024, Volume and Issue: 373, P. 132361 - 132361

Published: June 29, 2024

Language: Английский

Citations

2