首页|New Findings from Faculty of Computer Science in the Area of Machine Learning Re ported (Prediction of Anti-corrosion Performance of New Triazole Derivatives Via Machine Learning)
New Findings from Faculty of Computer Science in the Area of Machine Learning Re ported (Prediction of Anti-corrosion Performance of New Triazole Derivatives Via Machine Learning)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Research findings on Machine Learning are discussed in a new report. According to news reporting from Semarang, Indone sia, by NewsRx journalists, research stated, "This paper endeavors to present an in-depth investigation into the corrosion inhibition efficiency (CIE) of novel triazole derivatives serving as corrosion inhibitors. Among the array of models considered, the extreme gradient boosting (XGBoost) model emerged as the most ad ept predictor in forecasting the CIE of N-heterocyclic organic compounds." The news correspondents obtained a quote from the research from the Faculty of C omputer Science, "This resolute preference for the XGBoost model was consistentl y upheld when employed in the prediction of the CIE for three newly synthesized triazole derivatives, namely, 2-[5-Phenyl-1-(2 ‘-furanylmethy lene)imino-(1,3,4)homotriazole]thio-N-(2 ‘-furanyl)hypomethyl acetylhydrazine, 2-[5-(3 ‘-Methyl)Phenyl- 1(2 ‘-furanylidine)i mino-(1,3,4)homotriazole]thio-N-(2 ‘-furanylidine) acetylhydr azine, and 2-[5-(4 ‘- Methyl) Phenyl-1-(2 ‘-furanylidine)imino -(1,3,4)homotriazole]thio-N-(2 ‘-furanylidine) acetylhydrazin e. Remarkably, this application of the XGBoost model yielded notably elevated CI E values, spanning from 88.35 % to 93.41 %. Supplemen tary density functional theory (DFT) calculations for these derivative compounds further substantiated the predictive trends observed through machine learning a nd experimental predictions."
SemarangIndonesiaAsiaCyborgsEmer ging TechnologiesMachine LearningFaculty of Computer Science
NETL
NSTL
