首页|Reports Summarize Machine Learning Findings from Rajasthan Technical University (Assessment of Short and Long-term Pozzolanic Activity of Natural Pozzolans Usin g Machine Learning Approaches)
Reports Summarize Machine Learning Findings from Rajasthan Technical University (Assessment of Short and Long-term Pozzolanic Activity of Natural Pozzolans Usin g Machine Learning Approaches)
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Researchers detail new data in Machine Learning. According to news originating from Rajasthan, India, by NewsRx corres pondents, research stated, "This investigation introduces the optimal performanc e models for predicting the compressive strength (CS) and pozzolanic activity in dex (PAI) by comparing the machine learning models. The machine learning models, i.e., multilinear regression (MLR), support vector machine (SVM), gaussian proc ess regression (GPR), decision tree (DT), random forest (RF), and gene expressio n programming (GEP) have been trained (TRN) and tested (TST) by 28 and 7 data po ints." Our news journalists obtained a quote from the research from Rajasthan Technical University, "For the first time, the SiO2, Al2O3, Fe2O3, SiO2 +Al2O3 +Fe2O3, re active SiO2, Blaine specific surface area, and specific gravity have been used a s input variables to compute the CS, and 28 days PAI (28PAI), and 90 days PAI (9 0PAI) of the natural pozzolans. The multicollinearity analysis showed the SiO2, Al2O3, Fe2O3, SiO2 +Al2O3 +Fe2O3, reactive SiO2, and specific gravity have probl ematic multicollinearity (variance inflation factor - VIF > 10). Therefore, the root mean square error (RMSE), mean absolute error (MAE), c orrelation coefficient ®, performance index (PI), and variance accounted for (VA F) metrics have been implemented to evaluate the model's performance and multico llinearity impact. From the comparison of models, it has been recorded that mode l GPR outperformed the MLR, SVM, DT, RF, and GEP models in predicting CS (PI = 1 .29, VAF = 71.31, R = 0.8473, MAE = 0.9390 MPa), 28PAI (PI = 1.87, VAF = 94.88, R = 0.9744, MAE = 0.7295 %), and 90PAI (PI = 1.72, VAF = 88.11, R = 0.9393, MAE = 1.2444 %) in the TST phase, close to ideal values. T he score, generalizability."
RajasthanIndiaAsiaCyborgsEmergin g TechnologiesMachine LearningRajasthan Technical University
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