首页|Nile University of Nigeria Researchers Publish New Data on Machine Learning (Com parative studies of machine learning models for predicting higher heating values of biomass)
Nile University of Nigeria Researchers Publish New Data on Machine Learning (Com parative studies of machine learning models for predicting higher heating values of biomass)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators discuss new findings in artificial intelligence. According to news reporting originating from Abuja, Nig eria, by NewsRx correspondents, research stated, “This study addresses the chall enge of efficiently determining the higher heating value (HHV) of biomass, a cru cial parameter in large-scale biomass-based energy systems.” Our news correspondents obtained a quote from the research from Nile University of Nigeria: “The conventional method of measuring HHV using an oxygen bomb calor imeter is time-consuming, expensive, and less accessible to researchers, particu larly in developing nations. To overcome these limitations, we employed four mac hine learning (ML) models, namely Random Forest (RF), Decision Tree (DT), Suppor t Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost). These models we re developed by using proximate and ultimate analysis parameters as input featur es. Up to 200 datasets were compiled from literature and used for the ML models. Our results demonstrate the effectiveness of all ML models in accurately predic ting the HHV of biomass materials. Notably, the XGBoost model exhibited superior performance with the highest R-squared (R2) values for both training (0.9683) a nd test datasets (0.7309), along with the lowest root mean squared error (RSME) of 0.3558.”
Nile University of NigeriaAbujaNiger iaAfricaCyborgsEmerging TechnologiesMachine Learning
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