Progress in combustion science and engineering has led to the generation of large amounts
of data from large-scale simulations, high-resolution experiments, and sensors. This corpus …

A critical review of the existing Internal Combustion Engine (ICE) modeling, optimization,
diagnosis, and control challenges and the promising state-of-the-art Machine Learning (ML) …

Combustion science is an interdisciplinary study that involves nonlinear physical and
chemical phenomena in time and length scales, including complex chemical reactions and …

Legislations for greenhouse gas and pollutant emissions from light-duty vehicles are
pushing the spark ignition engine to be cleaner and more efficient. As one of the promising …

In response to the stringent emission regulations, artificial neural network (ANN) coupled
with genetic algorithm (GA) is employed to optimize a novel internal combustion engine …

Biodiesel has appeared as a renewable and clean energy resource and a means of
diminishing global warming. This study provides Bayesian optimization algorithm (BOA) …

Renewable methanol and hydrogen have emerged as great potential alternative fuels for
internal combustion engines in recent research. Hydrogen exhibits the ability to mitigate …

Traffic control optimization is a challenging task for various traffic centers around the world
and the majority of existing approaches focus only on develo** adaptive methods for …

Industrial revolution 4.0 has pushed industries worldwide to use machine learning (ML)
models to address real-world engineering problems. The industry generally faces two main …

Accurate prediction of domestic waste generation is a challenging task for municipalities to
implement sustainable waste management strategies. In the present study, domestic waste …