首页|New Data from Shanghai University Illuminate Research in Machine Learning (Ensem ble learning for impurity prediction in high-purity indium purified via vertical zone refining)

New Data from Shanghai University Illuminate Research in Machine Learning (Ensem ble learning for impurity prediction in high-purity indium purified via vertical zone refining)

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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators publish new report on ar tificial intelligence. According to news reporting out of Shanghai, People’s Rep ublic of China, by NewsRx editors, research stated, “The complexity of raw mater ials and multi-step purification processes presents considerable technical chall enges in establishing universally applicable process parameters for the producti on of high-purity metals.” Our news journalists obtained a quote from the research from Shanghai University : “Machine learning has emerged as an indispensable tool in the field of materia ls science, facilitating the accurate prediction of target variables and acceler ating process optimization, thereby yielding substantial reductions in both expe rimental costs and time. This study explores the utilization of high-precision m achine learning models to predict the residual impurity content in high-purity i ndium after vertical zone refining. A dataset comprising 82 experimental dataset s was employed to determine the optimal hyperparameters for XGBoost and LightGBM models through Bayesian optimization. The XGBoost and LightGBM models demonstra ted mean absolute errors (MAEs) of 0.022 and 0.023, respectively, as determined via leave-oneout cross-validation (LOOCV). Their comparable predictive performa nce to the previously established Ridge regression model (MAE = 0.024) prompted the exploration of fusion techniques, including mean, weighted, and stacking fus ion, to further enhance accuracy. Remarkably, the weighted fusion model exhibite d the most optimal predictive capabilities, supported by comprehensive evaluatio n metrics, including an MAE of 0.020, root mean squared error (RMSE) of 0.026, a nd a coefficient of determination (R2 score) of 0.830.”

Shanghai UniversityShanghaiPeople’s Republic of ChinaAsiaCyborgsEmerging TechnologiesHeavy MetalsIndiumM achine Learning

2024

Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

ISSN:
年,卷(期):2024.(Jun.6)