A new study presents a machine learning model that accurately predicts the compressive strength of high-strength concrete, ...

Proper handling of continuous variables is crucial in healthcare research, for example, within regression modelling for descriptive, explanatory, or predictive purposes. However, inadequate methods ...

Nature is marked not only by struggles for survival, but also by remarkable feats of cooperation. From microbes to insects to humans, organisms work together in a variety of ways to gather and share ...

If the solution of the above equation for zm, zm = &eegr; (z1, ··· ; a1, ···) gives rise to a function &eegr; which is nonlinear in the ai, then one may rely on a version of Gaussian regression [1, 2] ...

The c-regression model is a method that simultaneously performs clustering and regression to obtain regression equations for each cluster and express the overall structure of the dataset. Gaussian ...

The performance of UHPC-CA was predicted in this paper based on five prediction models: multiple linear regression, multiple nonlinear regression, traditional neural network (T-BP), principal ...

Nonlinear regression analysis of several nitrobenzoic acid substances Compared with linear fitting methods, nonlinear regression analysis can predict the reaction process of thermal decomposition of ...

On the other hand, polynomial regression is a useful technique when the pattern of the data indicates a nonlinear relationship between the dependent and independent variables. Moreover, higher-degree ...