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It is clearly that the ANN models give better and most confident results than GLM models.
First, the proposed models give better forecasts than classical SVR when a suitable noise distribution is assumed.
It is clear that the ANN models give better and most confident results than the regression models.
Also using LRM, it was observed that ANN 4 and ANN 3 models give better fitness to predict data as compared to deterministic model.
Comparative assessment of rate-based and equilibrium-based models of the process in Lawal et al. (2009a, b) showed that rate-based models give better predictions.
The ANN models give better, more confident, and logic results than the regression models in terms of predicting V85 for both cars and trucks.
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A design demonstration of USBSD-HX reveals that the simultaneous use of multiple surrogate models gives better results.
Bur for the NaCl concentration of 3.997mol·L−1, using the ELECNRTL and ENRTL-HG models gives better results than using the PITZ-HG model.
Comparison of statistical parameters showed that partial least squares (PLS) models gave better fit than the multiple linear regression (MLR) models.
Simulations with these two models gave better results for the highest fluid height investigated, especially close to the interface between the fluid layer and the porous medium.
Page, user-defined polynomial, and fractional conversion models gave better fits for the experimental data with color parameters than the zeroth order, first order, exponential, and Peleg kinetic models.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com