首页|China University of Geosciences Reports Findings in Machine Learning (Thermograv imetric experiments based prediction of biomass pyrolysis behavior: A comparison of typical machine learning regression models in Scikit-learn)
China University of Geosciences Reports Findings in Machine Learning (Thermograv imetric experiments based prediction of biomass pyrolysis behavior: A comparison of typical machine learning regression models in Scikit-learn)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Machine Learning is th e subject of a report. According to news reporting out of Wuhan, People’s Republ ic of China, by NewsRx editors, research stated, “A variety of machine learning (ML) models have been extensively utilized in predicting biomass pyrolysis owing to their prowess in deciphering complex non-linear relationships between inputs and outputs, but there is still a lack of consensus on the optimal methods. Thi s study elaborates on the development, optimization, and evaluation of three ML methodologies, namely, artificial neural networks, random forest (RF), and suppo rt vector machines, aimed to determine the optimal model for accurate prediction of biomass pyrolysis behavior using thermogravimetric data.” Our news journalists obtained a quote from the research from the China Universit y of Geosciences, “This work assesses the utility of thermal data derived from t hese models in the computation of kinetic and thermodynamic parameters, alongsid e an analysis of their statistical performance. Eventually, the RF model exhibit s superior physical interpretability and the least discrepancy in predicting kin etic and thermodynamic parameters.”
WuhanPeople’s Republic of ChinaAsiaCyborgsEmerging TechnologiesMachine Learning
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