首页|Guangzhou University Researcher Adds New Study Findings to Research in Machine L earning (Towards a Reliable Design of Geopolymer Concrete for Green Landscapes:A Comparative Study of Tree-Based and Regression-Based Models)
Guangzhou University Researcher Adds New Study Findings to Research in Machine L earning (Towards a Reliable Design of Geopolymer Concrete for Green Landscapes:A Comparative Study of Tree-Based and Regression-Based Models)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators discuss new findings in artificial intelligence.According to news originating from Guangzhou,People's Republic of China,by NewsRx editors,the research stated,"The design of geopol ymer concrete must meet more stringent requirements for the landscape,so unders tanding and designing geopolymer concrete with a higher compressive strength cha llenging.In the performance prediction of geopolymer concrete compressive stren gth,machine learning models have the advantage of being more accurate and faste r." Financial supporters for this research include Guangdong Provincial Department o f Education Innovative Strong School Youth Innovative Talent Project; China Post doctoral Science Foundation.Our news reporters obtained a quote from the research from Guangzhou University:"However,only a single machine learning model is usually used at present,ther e are few applications of ensemble learning models,and model optimization proce sses is lacking.Therefore,this paper proposes to use the Firefly Algorithm (AF ) as an optimization tool to perform hyperparameter tuning on Logistic Regressio n (LR),Multiple Logistic Regression (MLR),decision tree (DT),and Random Fores t (RF) models.At the same time,the reliability and efficiency of four integrat ed learning models were analyzed.The model was used to analyze the influencing factors of geopolymer concrete and determine the strength of their influencing a bility.According to the experimental data,the RF-AF model had the lowest RMSE value.The RMSE value of the training set and test set were 4.0364 and 8.7202,r espectively.The R value of the training set and test set were 0.9774 and 0.8915,respectively."
Guangzhou UniversityGuangzhouPeople' s Republic of ChinaAsiaCyborgsEmerging TechnologiesMachine Learning
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