the aim of this paper is how to predict stock return by using accounting ratios and also by using the procedure of neural network. this paper has considered the prediction of stock return by using accounting ratios with two procedures, the artificial neural network and least square regression. the independent variables in this paper are accounting ratios and dependent variable of stock return, ...

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...

The application of artificial neural network to compressor performance map prediction is investigated. Different types of artificial neural networks such as general regression neural network, rotated general regression neural network proposed by the authors, radial basis function network, and multilayer perceptron network are considered. Two different models are utilized in simulating the perfo...

background: the purpose of this investigation was to compare empirically predictive ability of an artificial neu­ral network with a logistic regression in prediction of low back pain. methods: data from the second national health survey were considered in this investigation. this data in­cludes the information of low back pain and its associated risk factors among iranian people aged 15 years a...

Neural networks were used to estimate the cost of jet engine components, specifically shafts and cases. The neural network process was compared with results produced by the current conventional cost estimation software and linear regression methods. Due to the complex nature of the parts and the limited amount of information available, data expansion techniques such as doubling-data and data-cr...

abstract spatial prediction of soil organic carbon is a crucial proxy to manage and conserve natural resources, monitoring co2 and preventing soil erosion strategies within the landscape, regional, and global scale. the objectives of this study was to evaluate capability of artificial neural network and multivariate linear regression models in order to predict soil organic carbon using terrain ...

histamine h3 receptor subtype has been the target of several recent drug development programs. quantitative structure-activity relationship (qsar) methods are used to predict the pharmaceutically relevant properties of drug candidates whenever it is applicable. the aim of this study was to compare the predictive powers of three different qsar techniques, namely, multiple linear regression (mlr)...

investigation of soil properties like cation exchange capacity (cec) plays important roles in study of environmental reaserches as the spatial and temporal variability of this property have been led to development of indirect methods in estimation of this soil characteristic. pedotransfer functions (ptfs) provide an alternative by estimating soil parameters from more readily available soil data...

Cox regression model serves as a statistical method for analyzing the survival data, which requires some options such as hazard proportionality. In recent decades, artificial neural network model has been increasingly applied to predict survival data. This research was conducted to compare Cox regression and artificial neural network models in prediction of kidney transplant survival. The prese...