this study was conducted to investigate the prediction of growth performance using linear regression and artificial neural network (ann) in broiler chicken. artificial neural networks (anns) are powerful tools for modeling systems in a wide range of applications. the ann model with a back propagation algorithm successfully learned the relationship between the inputs of metabolizable energy (kca...

In this paper, application of artificial neural networks in typical disease diagnosis has been investigated. The real procedure of medical diagnosis which usually is employed by physicians was analyzed and converted to a machine implementable format. Then after selecting some symptoms of eight different diseases, a data set contains the information of a few hundreds cases was configured and app...

DoS attack tools have become increasingly sophisticated challenging the existing detection systems to continually improve their performances. In this paper we present a victimend DoS detection method based on Artificial Neural Networks (ANN). In the proposed method a Feed-forward Neural Network (FNN) is optimized to accurately detect DoS attack with minimum resources usage. The proposed method ...

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...

abstract tax is one of the main sources of financing government budget. therefore, having a clear picture about the attainable amount of taxes are not only necessary for optimal allocation of scarce resources for tax collection, but also helps the government to develop precise tax collection programs .in this article, the structural features of the tax revenues series have first been examined i...

this paper presents a prediction model based on a new neuro-fuzzy algorithm for estimating time in construction projects. the output of the proposed prediction model, which is employed based on a locally linear neuro-fuzzy (llnf) model, is useful for assessing a project status at different time horizons. being trained by a locally linear model tree (lolimot) learning algorithm, the model is int...

changes in the physicochemical conditions of process unit, even under control, may lead to what are generically referred to as faults. the cognition of causes is very important, because the system can be diagnosed and fault tolerated. in this article, we discuss and propose an artificial neural network that can detect the incipient and gradual faults either individually or mutually. the main fe...

ground vibrations caused by blasting are undesirable results in the mining industry and can cause serious damage to the nearby buildings and facilities; therefore, controlling such vibrations has an important role in reducing the environmental damaging effects. controlling vibration caused by blasting can be achieved once peak particle velocity (ppv) is predicted. in this paper, the values of p...

there is a complicated relation between cold flat rolling parameters such as effective input parameters of cold rolling, output cold rolling force and exit thickness of strips. in many mathematical models, the effect of some cold rolling parameters has been ignored and the outputs have not a desirable accuracy. in the other hand, there is a special relation among input thickness of strips, the ...