首页|Chinese Academy of Sciences Reports Findings in Machine Learning (Prediction mod els for bioavailability of Cu and Zn during composting: Insights into machine le arning)
Chinese Academy of Sciences Reports Findings in Machine Learning (Prediction mod els for bioavailability of Cu and Zn during composting: Insights into machine le arning)
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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 originating in Changchun, Peo ple’s Republic of China, by NewsRx journalists, research stated, “Bioavailabilit y assessment of heavy metals in compost products is crucial for evaluating assoc iated environmental risks. However, existing experimental methods are time-consu ming and inefficient.” The news reporters obtained a quote from the research from the Chinese Academy o f Sciences, “The machine learning (ML) method has demonstrated excellent perform ance in predicting heavy metal fractions. In this study, based on the convention al physicochemical properties of 260 compost samples, including compost time, te mperature, electrical conductivity (EC), pH, organic matter (OM), total phosphor us (TP), total nitrogen, and total heavy metal contents, back propagation neural network, gradient boosting regression, and random forest (RF) models were used to predict the dynamic changes in bioavailable fractions of Cu and Zn during com posting. All three models could be used for effective prediction of the variatio n trend in bioavailable fractions of Cu and Zn; the RF model showed the best pre diction performance, with the prediction level higher than that reported in rela ted studies. Although the key factors affecting changes among fractions were dif ferent, OM, EC, and TP were important for the accurate prediction of bioavailabl e fractions of Cu and Zn.”
Changchun, People’s Republic of China, A sia, Cyborgs, Emerging Technologies, Machine Learning
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