首页|Nanjing University Reports Findings in Machine Learning [Expl oring the primary magnetic parameters affecting chemical fractions of heavy meta l(loid)s in lake sediment through an interpretable workflow]
Nanjing University Reports Findings in Machine Learning [Expl oring the primary magnetic parameters affecting chemical fractions of heavy meta l(loid)s in lake sediment through an interpretable workflow]
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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 Nanjing, People's Repu blic of China, by NewsRx editors, research stated, "The magnetic properties of l ake sediments account for close relationships with heavy metal(loid)s (HMs), but little is known about their relationships with chemical fractions (CFs) of HMs. Establishing an effective workflow to predict HMs risk among various machine le arning (ML) methods in conjunction with magnetic measurement remains challenging ." Our news journalists obtained a quote from the research from Nanjing University, "This study evaluated the simulation efficiency of nine ML methods in predictin g the risk assessment code (RAC) and ratio of the secondary and primary phases ( RSP) of HMs with magnetic parameters in sediment cores of a shallow lake. The se diment cores were collected and sliced, and the total amount and CFs of HMs, as well as magnetic parameters, were determined. Support vector machine (SVM) outpe rformed other models, as evidenced by coefficient of determination ® > 0.8. Interpretable machine learning (IML) methods were employed to identify key indicators of RAC and RSP among the magnetic parameters. Values of ch HIRM, ch, and ch/SIRM of sediments ranging in 220-500 x 10 m/kg, 30-40 x 10Am/kg, 15-25, and 0.5-1, respectively, indicated the potential ecological risks of Cd, Hg, and Sb."
NanjingPeople's Republic of ChinaAsi aCyborgsEmerging TechnologiesMachine Learning
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