首页|Research from Xi’an Traffic Engineering Institute Reveals New Findings on Machin e Learning (A theoretical approach based on machine learning for estimation of p hysical properties of LLDPE in moulding process)
Research from Xi’an Traffic Engineering Institute Reveals New Findings on Machin e Learning (A theoretical approach based on machine learning for estimation of p hysical properties of LLDPE in moulding process)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – Investigators publish new report on artificial in telligence. According to news originating from the Xi’an Traffic Engineering Ins titute by NewsRx correspondents, research stated, “This study explores the predi ction of mechanical characteristics of linear polyethylene based on oven residen ce time, employing various regression models and hyper-parameter tuning through the Whale Optimization Algorithm.” The news journalists obtained a quote from the research from Xi’an Traffic Engin eering Institute: “The dataset comprises one input variable (oven residence time ) and three output parameters (Tensile Strength, Impact Strength, and Flexure St rength). The models investigated include Multilayer Perceptron, K-Nearest Neighb ors, Support Vector Regression, Polynomial Regression, and Theil-Sen Regression. The results showcased distinct performances across the models for each output p arameter. The Polynomial Regression (WOA-PR) method has been identified as the m ost suitable option for predicting Tensile Strength due to its ability to achiev e the lowest errors in terms of Mean Absolute Error, Root Mean Square Error, and Average Absolute Relative Deviation. K-Nearest Neighbors (WOA-KNN) outperforms other models in predicting Impact Strength due to its superior accuracy and reli ability. Additionally, Support Vector Regression (WOA-SVR) emerges as the best m odel for predicting Flexure Strength, showcasing notable performance in minimizi ng prediction errors.”
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