in this work, quantitative structure-property relationship (qspr) approaches were used to predict the redox potential of 42 phenolic antioxidants. the structures of all compounds optimized by the am1 semi-empirical method and then a large number of molecular descriptors were calculated for each compound in the data set. subsequently, stepwise multilinear regression was applied to select the mos...

Predication is one of the fundamental tasks of data mining. In recent years, Artificial Intelligence techniques are widely being used in data mining applications where conventional statistical methods were used such as Regression and classification. The aim of this work is to show the applicability of Gene Expression Programming (GEP), a recently developed AI technique, for hydraulic data predi...

In this study, we present a new model that has been developed for the prediction of θ (lower critical solution temperature) using a database of 169 data points that include 12 polymers and 67 solvents. For the characterization of polymer and solvent molecules, a number of molecular descriptors (topological, physicochemical,steric and electronic) were examined. The best subset of descriptors was...

Multivariate statistical methods including pattern recognition (Principal Component Analysis--PCA) and modeling (Multiple Linear Regression--MLR, Partial Least Squares--PLS, as well as Principal Component Regression--PCR) methods were carried out to evaluate the state of ambient air in Miskolc (second largest city in Hungary). Samples were taken from near the ground at a place with an extremely...

Simple slopes, regions of significance, and confidence bands are commonly used to evaluate interactions in multiple linear regression (MLR) models, and the use of these techniques has recently been extended to multilevel or hierarchical linear modeling (HLM) and latent curve analysis (LCA). However, conducting these tests andplotting the conditional relations is often a tedious and error-prone ...

The aim of this study was to determine the probability of working days (PWD) for tillage operation using weather data with Multiple Linear Regression (MLR) and Radial Basis Function (RBF) artificial networks. In both models, seven variables were considered as input parameters, namely minimum, average and maximum temperature, relative humidity, rainfall, wind speed, and evaporation on a daily ba...

Predicting the mass of solid waste generation plays an important role in integrated solid waste management plans. In this study, the performance of two predictive models, Artificial Neural Network (ANN) and Multiple Linear Regression (MLR) was verified to predict mean Seasonal Municipal Solid Waste Generation (SMSWG) rate. The accuracy of the proposed models is illustrated through a case study ...

Over the years development of selective estrogen receptor (ER) ligands has been of great concern to researchers involved in the chemistry and pharmacology of anticancer drugs, resulting in numerous synthesized selective ER subtype inhibitors. In this work, a data set of 82 ER ligands with ERα and ERβ inhibitory activities was built, and quantitative structure-activity relationship (QSAR) method...

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...