Comparative Study of QSAR/QSPR Correlations Using Support Vector Machines, Radial Basis Function Neural Networks, and Multiple Linear Regression

An Accurate QSPR Study of O-H Bond Dissociation Energy in Substituted Phenols Based on Support Vector Machines

The support vector machine (SVM), as a novel type of learning machine, was used to develop a Quantitative Structure-Property Relationship (QSPR) model of the O-H bond dissociation energy (BDE) of 78 substituted phenols. The six descriptors calculated solely from the molecular structures of compounds selected by forward stepwise regression were used as inputs for the SVM model. The root-mean-squ...

Application of Artificial Neural Networks and Support Vector Machines for carbonate pores size estimation from 3D seismic data

This paper proposes a method for the prediction of pore size values in hydrocarbon reservoirs using 3D seismic data. To this end, an actual carbonate oil field in the south-western part ofIranwas selected. Taking real geological conditions into account, different models of reservoir were constructed for a range of viable pore size values.  Seismic surveying was performed next on these models. F...

Developing a Radial Basis Function Neural Networks to Predict the Working Days for Tillage Operation in Crop Production

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...

A Comparative Approximate Economic Behavior Analysis Of Support Vector Machines And Neural Networks Models

A preliminary experimental comparison of recursive neural networks and a tree kernel method for QSAR/QSPR regression tasks

We consider two different methods for QSAR/QSPR regression tasks: Recursive Neural Networks (RecNN) and a Support Vector Regression (SVR) machine using a Tree Kernel. Experimental results on two specific regression tasks involving alkanes and benzodiazepines are obtained for the two approaches.