Artificial intelligence-based solutions for climate change: a review
L Chen, Z Chen, Y Zhang, Y Liu, AI Osman… - Environmental …, 2023 - Springer
Climate change is a major threat already causing system damage to urban and natural
systems, and inducing global economic losses of over $500 billion. These issues may be …
systems, and inducing global economic losses of over $500 billion. These issues may be …
Machine learning and deep learning in energy systems: A review
With population increases and a vital need for energy, energy systems play an important
and decisive role in all of the sectors of society. To accelerate the process and improve the …
and decisive role in all of the sectors of society. To accelerate the process and improve the …
A machine learning based credit card fraud detection using the GA algorithm for feature selection
The recent advances of e-commerce and e-payment systems have sparked an increase in
financial fraud cases such as credit card fraud. It is therefore crucial to implement …
financial fraud cases such as credit card fraud. It is therefore crucial to implement …
A review on application of artificial neural network (ANN) for performance and emission characteristics of diesel engine fueled with biodiesel-based fuels
Biodiesel has been emerging as a potential and promising biofuel for the strategy of
reducing toxic emissions and improving engine performance. Computational methods …
reducing toxic emissions and improving engine performance. Computational methods …
Multi-objective optimization of a hydrogen-fueled Wankel rotary engine based on machine learning and genetic algorithm
H Wang, C Ji, C Shi, J Yang, S Wang, Y Ge, K Chang… - Energy, 2023 - Elsevier
Hydrogen is a promising way to achieve high efficiency and low emissions for Wankel rotary
engines. In this paper, the intake and exhaust phases and excess air ratios (λ) were …
engines. In this paper, the intake and exhaust phases and excess air ratios (λ) were …
A novel integrated approach of augmented grey wolf optimizer and ANN for estimating axial load carrying-capacity of concrete-filled steel tube columns
The purpose of this study is to offer a high-performance machine learning model for
determining the ultimate load-carrying capability of concrete-filled steel tube (CFST) …
determining the ultimate load-carrying capability of concrete-filled steel tube (CFST) …
Comparison and evaluation of advanced machine learning methods for performance and emissions prediction of a gasoline Wankel rotary engine
H Wang, C Ji, C Shi, Y Ge, H Meng, J Yang, K Chang… - Energy, 2022 - Elsevier
In order to improve the performance, reduce the emissions and enhance the calibration
efficiency of a gasoline Wankel rotary engine (WRE), three advanced machine learning (ML) …
efficiency of a gasoline Wankel rotary engine (WRE), three advanced machine learning (ML) …
Optimization of combustion, performance, and emission characteristics of a dual-fuel diesel engine powered with microalgae-based biodiesel/diesel blends and …
Z Said, DTN Le, P Sharma, VH Dang, HS Le… - Fuel, 2022 - Elsevier
The current study explores and improves the effects of engine load, injection time, and
oxyhydrogen fuel flow rate on the combustion and emissions characteristics of a diesel …
oxyhydrogen fuel flow rate on the combustion and emissions characteristics of a diesel …
[HTML][HTML] The application of machine learning to air pollution research: A bibliometric analysis
Y Li, Z Sha, A Tang, K Goulding, X Liu - Ecotoxicology and Environmental …, 2023 - Elsevier
Abstract Machine learning (ML) is an advanced computer algorithm that simulates the
human learning process to solve problems. With an explosion of monitoring data and the …
human learning process to solve problems. With an explosion of monitoring data and the …
Application of artificial neural network to forecast engine performance and emissions of a spark ignition engine
Increasing the application of machine learning algorithms in engine development has the
potential to reduce the number of experimental runs and the computation cost of …
potential to reduce the number of experimental runs and the computation cost of …