首页|New Findings Reported from National Institute of Technology Jamshedpur Describe Advances in Machine Learning (Stokes Shift Prediction of Fluorescent Organic Dyes Using Machine Learning Based Hybrid Cascade Models)
New Findings Reported from National Institute of Technology Jamshedpur Describe Advances in Machine Learning (Stokes Shift Prediction of Fluorescent Organic Dyes Using Machine Learning Based Hybrid Cascade Models)
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NETL
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Elsevier
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators discuss new findings in Machine Learning. According to news reporting from Jharkhand, India, by NewsRx journalists, research stated, “Fluorescent organic dyes are widely used in various fields, including science and technology, research and development, medicine, and drug delivery. Multitudinous attempts have been made by experimentalists to develop such fluorescent organic dyes with the desired Stokes shift property at negligible cost and time.” Funders for this research include CSIR HRDG in India, CSIR NET-JRF fellowship. The news correspondents obtained a quote from the research from the National Institute of Technology Jamshedpur, “For quickly and accurately predicting the Stokes shift property of fluorescent organic dye, we proposed eight hybrid models based on the combination of nine single machine-learning models. To fulfill the objective, we considered a dataset of 3066 fluorescent organic materials and evaluated the performance of each model using three evaluation parameters: mean absolute error (MAE), root mean squared error (RMSE), and the coefficient of determination (R2). The hybrid cascade model of Extreme Gradient Boosting Regression and Light Gradient Boosting Machine Regression (XGBR + LGBMR) performed best for Stokes shift prediction, with MAE of 13.83 nm, RMSE of 19.95 nm, and R2 of 86.18 %. The prediction performance of all the undertaken models was validated by the experimental data of four xanthene dyes (Rh- 19, Rh-B, Rh-6G, and Rh-110). In this regard, the XGBR + DTR (Extreme Gradient Boosting Regression + Decision Tree Regression) model was the best performer, with errors ranging from 5 to 13 nm for four dyes. The resultant errors are much smaller than the recently re-ported synthesized material with an error of 30 nm.” According to the news reporters, the research concluded: “The proposed models allow for rapid and cost-effective screening of a wide range of fluorescent organic dyes, which assists the researchers in gaining prior knowl-edge of materials and accelerates the discovery of new materials.” This research has been peer-reviewed.
JharkhandIndiaAsiaCyborgsEmerging TechnologiesMa- chine LearningNational Institute of Technology Jamshedpur
NETL
NSTL
Elsevier
