Optimized artificial neural network for classification of biological data

Prediction of Cardiovascular Diseases Using an Optimized Artificial Neural Network

Introduction:  It is of utmost importance to predict cardiovascular diseases correctly. Therefore, it is necessary to utilize those models with a minimum error rate and maximum reliability. This study aimed to combine an artificial neural network with the genetic algorithm to assess patients with myocardial infarction and congestive heart failure.   Materials & Methods: This study utilized a m...

scour modeling piles of kambuzia industrial city bridge using hec-ras and artificial neural network

today, scouring is one of the important topics in the river and coastal engineering so that the most destruction in the bridges is occurred due to this phenomenon. whereas the bridges are assumed as the most important connecting structures in the communications roads in the country and their importance is doubled while floodwater, thus exact design and maintenance thereof is very crucial. f...

Biological image classification using rough-fuzzy artificial neural network

This paper presents a methodology to biological image classification through a Rough-Fuzzy Artificial Neural Network (RFANN). This approach is used in order to improve the learning process by Rough Sets Theory (RS) focusing on the feature selection, considering that the RS feature selection allows the use of low dimension features from the image database. This result could be achieved, once the...

Evolvable Binary Artificial Neural Network for Data Classification

This paper describes a new evolvable hardware organization and its learning algorithm to generate binary logic artificial neural networks based on mutual information and statistical analysis. First, thresholds to convert analog signals of the training data to digital signals are established. In order to extract feature function for multidimensional data classification, conditional entropy is ca...

Representation of Adsorption Data for the Case of Energetically Heterogeneous Solid Surfaces Using Artificial Neural Network