introduction: with using multiple linear regression (mlr), can simultaneously analyses several different variables, but to get the desirable results from the mlr, the samples must be much and accurate. therefore, this method has high sensitivity and may cause errors in results. in addition, to use this method, the variable must have normal distribution and modification follow from a linear rela...

Chor:, Wooyoung. Ph.D., Purdue University, December 1999, Detection of Rare Events and Rule Extraction by Neural Networks and Decision Trees. Major Professor: Okan K. Ersoy . Sample stratification is a technique for making each class in a sample have equal influence on decision making. For classification with neural netwobrks, it is often preferable to use a stratified sample including an equal...

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...

Introduction: Artificial neural networks are a type of systems that use very complex technologies and non-algorithmic solutions for problem solving. These characteristics make them suitable for various medical applications. This study set out to investigate the application of artificial neural networks for differential diagnosis of thalassemia minor and iron-deficiency anemia. Methods: It is...

in addition to its primary role of providing financial protection for other industries the insurance industry also serves as a medium for fund mobilization. in spite of the harsh economic environment in nigeria, the insurance industry has been crucial to the consummation of business plans and wealth creation.  however, the continued downturn experienced by many countries, in the last decade, se...

Artificial neural networks are intelligent systems that have successfully been used for prediction in different medical fields. In this study, the efficiency of a neural network for predicting the survival of patients with acute pancreatitis is compared with days-of-survival obtained from patients. A three- layer back-propagation neural network was developed for this purpose. Clinical data (e.g...

The purpose of this article is to demonstrate the application of probabilistic neural networks (PNNs) as a classification tool in the slope stability estimation. PNNs are applied to estimate slope stability according to the slope geometric shapes and soil mechanical parameters. Unlike other neural network training paradigms, PNNs are characterized by high training speed and their ability to pro...

This paper presents a feed forward back-propagation neural network model to predict the retained tensile strength and design chart in order to estimation of the strength reduction factors of nonwoven geotextiles due to installation process. A database of 34 full-scale field tests were utilized to train, validate and test the developed neural network and regression model. The results show that t...