首页|New Findings in Machine Learning Described from Faculty of Engineering (Heteroge neous ensemble machine learning to predict the asiaticoside concentration in cen tella asiatica urban)
New Findings in Machine Learning Described from Faculty of Engineering (Heteroge neous ensemble machine learning to predict the asiaticoside concentration in cen tella asiatica urban)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on artificial intelligenc e is the subject of a new report.According to news reporting from Maha Sarakham,Thailand,by NewsRx journalists,research stated,"This study proposes a novel heterogeneous ensemble machine learning methodology to predict the concentratio n of asiaticoside in Centella asiatica (CA-CA) in the context of the lack of an effective prediction method capable of accurately estimating its quantity based on various growing environmental factors." The news correspondents obtained a quote from the research from Faculty of Engin eering:"The accurate prediction of the asi-aticoside concentration in CA-CA hol ds great significance in optimizing cultivation practices and improving the effi cacy of the derived medicinal products.The presented approach aims to address t his crucial need by employing a diverse ensemble of machine learning techniques.The proposed model integrates several machine learning tech-niques,including t he standard long short-term memory (LSTM),gated recurrent unit (GRU),convoluti onal long short-term memory (ConvLSTM),and attention-based LSTM,by utilizing a differential evolution algorithm to optimize the ensemble model's weights.The developed model is called the heterogeneous ensemble machine learning model (He- ML).Experimental results demonstrate that the He-ML achieves an im-pressive roo t-mean-square error (RMSE) value of 4.76,which is up to 12.48 % l ower than the RMSE.The findings highlight the advantages of employing an ensemb le model over a single model,as the ensemble model achieves an RMSE value that is 14.67 % lower than that of the individual machine learning mode l."
Faculty of EngineeringMaha SarakhamT hailandAsiaCyborgsDifferential EvolutionEmerging TechnologiesMachine L earning
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