| 1 | Material selection of polymeric composite automotive bumper beam using analytical hierarchy process显示文摘Selection of materials,as an area of design research,has been under considerable interest over the years.Materials selection is one of the most important activities in the product development process.Inappropriate decision of materials can cause the product to be reproduced or remanufactured.To avoid this circumstance,one of the useful tools that can be employed in determining the most appropriate material is analytical hierarchy process(AHP).To illustrate the application of AHP,six different types of composite materials were considered.The most appropriate one for suitability of use in manufacturing automotive bumper beam was determined by considering eight main selection factors and 12 sub-factors.The AHP analysis reveals that the glass fibre epoxy is the most appropriate material because it has the highest value(25.7%,mass fraction) compared with other materials.The final material is obtained by performing six different scenarios of the sensitivity analysis.It is proved that glass fibre epoxy is the most optimum decision. | A.HAMBALI S.M.SAPUAN N.ISMAIL Y.NUKMAN | 2010 | Journal of Central South University2010，17，2： | 3 |
| 3 | GA-Stacking:A New Stacking-Based Ensemble Learning Method to Forecast the COVID-19 Outbreak显示文摘As a result of the increased number of COVID-19 cases,Ensemble Machine Learning(EML)would be an effective tool for combatting this pandemic outbreak.An ensemble of classifiers can improve the performance of single machine learning(ML)classifiers,especially stacking-based ensemble learning.Stacking utilizes heterogeneous-base learners trained in parallel and combines their predictions using a meta-model to determine the final prediction results.However,building an ensemble often causes the model performance to decrease due to the increasing number of learners that are not being properly selected.Therefore,the goal of this paper is to develop and evaluate a generic,data-independent predictive method using stacked-based ensemble learning(GA-Stacking)optimized by aGenetic Algorithm(GA)for outbreak prediction and health decision aided processes.GA-Stacking utilizes five well-known classifiers,including Decision Tree(DT),Random Forest(RF),RIGID regression,Least Absolute Shrinkage and Selection Operator(LASSO),and eXtreme Gradient Boosting(XGBoost),at its first level.It also introduces GA to identify comparisons to forecast the number,combination,and trust of these base classifiers based on theMean Squared Error(MSE)as a fitness function.At the second level of the stacked ensemblemodel,a Linear Regression(LR)classifier is used to produce the final prediction.The performance of the model was evaluated using a publicly available dataset from the Center for Systems Science and Engineering,Johns Hopkins University,which consisted of 10,722 data samples.The experimental results indicated that the GA-Stacking model achieved outstanding performance with an overall accuracy of 99.99%for the three selected countries.Furthermore,the proposed model achieved good performance when compared with existing baggingbased approaches.The proposed model can be used to predict the pandemic outbreak correctly and may be applied as a generic data-independent model 3946 CMC,2023,vol.74,no.2 to predict the epidemic trend for other countries when comparing preventive and control measures. | Walaa N.Ismail Hessah A.Alsalamah Ebtesam Mohamed | 2023 | Computers, Materials & Continua2023，，2： | 0 |
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