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Modern predictive modeling techniques are commonly used for modeling a target of interest based on a list of input variables. In general, these techniques are capable of identifying input variables associated with the target, but not for the purpose of identifying the causation relationship between target and inputs due to the fact that the data are observational data. Advanced technology has made...
Large data analysis problems often involve a large number of variables, and the corresponding analysis algorithms may examine all variable combinations to find the optimal solution. For example, to model the time required to complete a scientific workflow, we need to consider the impact of dozens of parameters. To reduce the model building time and reduce the likelihood of overfitting, we look to...
The infant mortality has been declining in the recent decades globally. However, it continues to be an international concern. The issues are not only on what types of diseases or incidents leading to the infant death for individual cases, but also on what factors; social, economic, environment, education and other; are associated with infant mortality in the society. In this study, we collect infant...
The aim of this paper is to investigate the potential of Visible and short-wave near-infrared spectroscopy (Vis/SWNIR) technique used for brand discrimination of milk powder. Fifty samples for each brand were studied. Based on the independent components (ICs) as the input variable obtained from fast fixed-point independent component analysis (FastICA), Least-squares Support Vector Machine (LS-SVM)...
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