The 17β-HSD3 enzyme plays a key role in treatment of prostate cancer and small inhibitorscan be used to efficiently target it. In the present study, the multiple linear regression (MLR),and support vector machine (SVM) methods were used to interpret the chemical structuralfunctionality against the inhibition activity of some 17β-HSD3inhibitors. Chemical structuralinformation were described thro...

1. Methods to be considered for multivariate calibration Many methods for multivariate calibration have been proposed. It turns out that many of the methods perform similarly. To avoid confusion due to use of many different methods, it is suggested that only the following should be considered: Multiple linear regression (MLR) Principal component regression (PCR) Partial least squares (PLS) Neur...

We propose a new set of meta-level features to be used for learning how to combine classifier predictions with stacking. This set includes the probability distributions predicted by the base-level classifiers and a combination of these with the certainty of the predictions. We use these features in conjunction with multi-response linear regression (MLR) at the meta-level. We empirically evaluat...

Six popular approaches of «NIR spectrum–property» calibration model building are compared in this work on the basis of a gasoline spectral data. These approaches are: multiple linear regression (MLR), principal component regression (PCR), linear partial least squares regression (PLS), polynomial partial least squares regression (Poly-PLS), spline partial least squares regression (Spline-PLS) an...

The significant rise in the frequency and sophistication of cyber-attacks their diversity necessitated various researchers to develop strong effective approaches address recurring cyber threat challenges. This study evaluated performance three selected meta-learning models for optimal multi-class detection using University New South Wales 2015 Network benchmark (UNSW-NB15) Intrusion Dataset. re...

In the present study, two different data-driven models, artificial neural network (ANN) and multiple linear regression (MLR) models, have been developed to predict the 28 days compressive strength of concrete. Seven different parameters namely 3/4 mm sand, 3/8 mm sand, cement content, gravel, maximums size of aggregate, fineness modulus, and water-cement ratio were considered as input variables...

The objectives of this trial were to develop multiple linear regression (MLR) models and three-layer Levenberg-Marquardt back propagation (BP3) neural network models using the Cornell Net Carbohydrate and Protein System (CNCPS) carbohydrate fractions as dietary variables for predicting in vitro rumen volatile fatty acid (VFA) production and further compare MLR and BP3 models. Two datasets were ...