نتایج جستجو برای: layer perceptron artificial neural network ann
تعداد نتایج: 1275874 فیلتر نتایج به سال:
The water quality of the Karaj River was studied through collecting 2137 experimental data set gained by 20 sampling stations. The data included different parameters such as T (temperature), pH, NTU (turbidity), hardness, TDS (total dissolved solids), EC (electrical conductivity) and basic anion, cation concentrations. In this study a multi-layer perceptron artificial neural network model was d...
the purpose of this research is to detect manipulation of stock prices in tehran stock exchange that it has been done through hybrid genetic algorithm-artificial neural network (ann-ga) model and the simplified quadratic discriminant function (sqdf) model. in this study, the variables of price, trading volume and free float stock to match the results of the model and the actual data of price ma...
thermal conductivity is an important property of juices in the prediction of heat- and mass-transfer coefficients and in the design of heat- and mass-transfer equipment for the fruit juice industry. an artificial neural network (ann) was developed to predict thermal conductivity of pear juice. temperature and concentration were input variables. thermal conductivity of juices was outputs. the op...
In this work, a simplified Artificial Neural Network (ANN) based approach for recognition of various objects is explored using multiple features. The objective is to configure and train an ANN to be capable of recognizing an object using a feature set formed by Principal Component Analysis (PCA), Frequency Domain and Discrete Cosine Transform (DCT) components. The idea is to use these varied co...
rivers and runoff have always been of interest to human beings. in order to make use of the proper water resources, human societies, industrial and agricultural centers, etc. have usually been established near rivers. as the time goes on, these societies developed, and therefore water resources were extracted more and more. consequently, conditions of water quality of the rivers experienced rap...
objective(s): a fast and reliable evaluation of the binding energy from a single conformation of a molecular complex is an important practical task. artificial neural networks (anns) are strong tools for predicting nonlinear functions which are used in this paper to predict binding energy. we proposed a structure that obtains binding energy using physicochemical molecular descriptions of the se...
in this work, response surface methodology (rsm) and artificial neural network (ann) were used to predict the decolorization efficiency of reactive red 33 (rr 33) by o3/uv process in a bubble column reactor. the effects of four independent variables including time (20-60 min), superficial gas velocity (0.06-0.18 cm/s), initial concentration of dye (50-150 ppm) and ph (3-11) were investigated us...
discrimination between iron deficiency (ida) anemia and β-thalassemia trait (β-tt) is a time consuming and costly problem. because, they have approximate similar effects on routine blood test indices, in some cases, the complementary tests, which are expensive and time consuming, would be needed for differentiate the anemia. complete blood count (cbc) is a fast, inexpensive, and accessible medi...
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...
Background: Although experiments on water quality are time consuming and expensive, models are often employed as supplement to simulate water quality. Artificial neural network (ANN) is an efficient tool in hydrologic studies, yet it cannot predetermine its results in the forms of maps and geo-referenced data. Methods: In this study, ANN was applied to simulate groundwater quality ...
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