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Machine Learning Methods for the Prediction of Co2 Emissions in Spark-ignition Engines

https://doi.org/10.2139/ssrn.4663398
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Modeling the organic matter of water using the decision tree coupled with bootstrap aggregated and least-squares boosting
inferredcites →https://doi.org/10.1016/j.fuel.2019.116707openalex-extracted
Visualization of in-cylinder combustion using endoscope in spark ignition engine fueled with pine oil blended gasoline
inferredcites →https://doi.org/10.1016/j.fuel.2022.124131openalex-extracted
Precise prediction of performance and emission of a waste derived Biogas–Biodiesel powered Dual–Fuel engine using modern ensemble Boosted regression Tree: A critique to Artificial neural network
inferredcites →https://doi.org/10.1016/j.fuel.2023.127578openalex-extracted
A Machine Learning Approach for Carbon di oxide and Other Emissions Characteristics Prediction in a Low Carbon Biofuel-Hydrogen Dual Fuel Engine
inferredcites →https://doi.org/10.1016/j.ijhydene.2014.09.044openalex-extracted
Experimental investigation of the combustion characteristics, emissions and performance of hydrogen port fuel injection in a diesel engine
inferredcites →https://doi.org/10.1016/j.renene.2018.07.010openalex-extracted
Mitigation of carbon footprints through a blend of biofuels and oxygenates, combined with post-combustion capture system in a single cylinder CI engine
inferredcites →https://doi.org/10.1016/j.rser.2017.03.080openalex-extracted
Alcohol based automotive fuels from first four alcohol family in compression and spark ignition engine: A review on engine performance and exhaust emissions
inferredcites →https://doi.org/10.2139/ssrn.4231208openalex-extracted
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