首页|Findings from Chinese Academy of Agricultural Sciences Reveals New Findings on M achine Learning (Robust Prediction for Characteristics of Digestion Products In an Industrial-scale Biogas Project Via Typical Non-time Series and Time-series . ..)
Findings from Chinese Academy of Agricultural Sciences Reveals New Findings on M achine Learning (Robust Prediction for Characteristics of Digestion Products In an Industrial-scale Biogas Project Via Typical Non-time Series and Time-series . ..)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – Current study results on Machine Learning have be en published. According to news reporting from Beijing, People’s Republic of Chi na, by NewsRx journalists, research stated, “Anaerobic digestion (AD) is a well- established pathway for treating agricultural organic waste, and machine learnin g has emerged as a novel tool to predict its product performance. In prior resea rch, the majority of studies concentrated on non-time series models for laborato ry-scale fermentation data.” Financial supporters for this research include China Agriculture Research System of MOF and MARA, National Natural Science Foundation of China (NSFC), Agricultu ral Science and Technology Innovation Pro-gram (ASTIP) of China. The news correspondents obtained a quote from the research from the Chinese Acad emy of Agricultural Sciences, “Consequently, the generalization performance of t hese models was significantly constrained, particularly in the context of indust rial-scale biogas projects. Thus, in this study, typical non-time series models (GBR and RF) and time-series models (LSTM, CNN-LSTM, and DA-LSTM) after hyperpar ameter optimization were chosen to accurately predict the characteristics of dig estion products in a biogas project. The ideal GBR model for CH4 content was obt ained, and the R-2 values of the test set and training set were 0.93 (R-MSE=1.11 ) and 0.97 (R-MSE=0.69), respectively. Temperature was the most important parame ter for biogas production according to feature importance and SHAP analysis of t he RF model.”
BeijingPeople’s Republic of ChinaAsi aAlgorithmsCyborgsEmerging TechnologiesMachine LearningChinese Academy of Agricultural Sciences
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