Evaluation of Artificial Immune System with Artificial Neural Network for Predicting Bombay Stock Exchange Trends

Considering these difficulties, this paper presents the evaluation of segmentation by artificial neural network (ANN) SOM to automatic detection of nodules contour in

Two methods of multiple linear regression and artificial neural network (ANN) models were developed to describe tannin content in sorghum grain from three input

In conclusion, an artificial neural network model with variables consisting of age, hematocrit, serum glucose, BUN and serum calcium may be useful for predicting the development

Abstract — This paper presents Artificial Neural Network (ANN) implementation for the Radio Frequency (RF) and Mechanical modeling of lateral RF Micro Electro

In line with this, artificial neural network technology of backpropagation neural network (BPNN), radial basis function neural network (RBFNN) and generalized regression

The results of the proposed GLSSVM approach are compared with the conventional artificial neural network (ANN) models, Autoregres- sive Integrated Moving Average (ARIMA) model, GMDH

The objective of this research is to optimize the EC value of nutrient solution on each generative stage using Artificial Neural Network (ANN) and Genetic Algorithms (GA).. ANN

Method: A neuro-fuzzy system was developed using the NEFCLASS (NEuro Fuzzy CLASSIfication) architecture and an artificial neural network with backpropagation learning