In order to develop effective evolutionary artificial neural networks (EANNs) we have to address the questions on how to evolve EANNs more efficiently and how to achieve the best performance from the ANNs evolved. Most of the previous works, however, do not utilize all the information obtained with several ANNs but choose the one best network in the last generation. Some recent works indicate t...

The main objective of this paper is to introduce a new intelligent optimization technique that uses a predictioncorrection strategy supported by a recurrent neural network for finding a near optimal solution of a given objective function. Recently there have been attempts for using artificial neural networks (ANNs) in optimization problems and some types of ANNs such as Hopfield network and Bol...

This paper surveys recent literature in the domain of machine learning techniques and artificial intelligence used to predict stock market movements. Artificial Neural Networks (ANNs) are identified to be the dominant machine learning technique in stock market prediction area. Keywords— Artificial Neural Networks (ANNs); Stock Market; Prediction

In this paper, neural network based cryptology is performed. The system consists of two stages. In the first stage, neural network-based pseudo-random numbers (NPRNGs) are generated and the results are tested for randomness using National Institute of Standard Technology (NIST) randomness tests. In the second stage, a neural network-based cryptosystem is designed using NPRNGs. In this cryptosys...

In this study , after a new Artificial Intelligence technique inspired from the human immune system –the Artificial Immune System (AIS) is described, it is compared with the Artificial Neural Networks (ANNs) and some immune approaches in neural networks are emphasized as application. After the artificial intelligence (AI) has come into existence, especially artificial neural networks took a gre...

Introduction: Automatic mognition of handwritten characters and natural patterns has been one of the most difficult problems in artificial intelligence. Artificial neural networks (ANNs) constitute an important class of computational models for handling this class of problems. Many neural techniques have been proposed to solve this problem, such as those in [l 41. We propose an eficient way of ...

This paper proposes an intelligent first-warning system for virus code detection based on neural learning in an artificial neural network (ANN). The system operates in accordance with the basic principles of ANNs for pattern matching, in which the detectors detect a virus signature after training by means of analysis of the byte content of the executable code. ANNs provide the potential to iden...

An Artificial Neural Network (ANN) is an information processing paradigm that is inspired by the way biological nervous systems, such as the brain, process information. The key element of this paradigm is the novel structure of the information processing system. It is composed of a large number of highly interconnected processing elements (neurons) working in unison to solve specific problems. ...

Artificial Neural Network (ANN) has been used in nonlinear systems modeling and simulation. One of the most useful and interesting factors of ANNs is forecasting. This paper discusses the application of ANNs to predict the long range energy consumption for a country. In this study the long-term energy consumption for the years ahead is predicted, exploiting ANN computational speed, ability to h...