Face detection is an intelligent approach used in a variety of applications that identifies human faces digital images. This work presents new method which composes neural network and Techebycheve transforms for face detection. For feature extraction, Tchebychev transform was applied, discrete given different sampling patterns several samples here were performed on color A Levenberg-Marquardt b...

In today's digital landscape, Internet of Things (IoT) networking has grown dramatically broad. The major feature IoT network devices is their ability to connect the internet and interact with it through data collecting exchanging. Distributed Denial Service (DDoS) one form cyber-attacks in which hackers penetrate a single connection then multiple machines are operating together attack target. ...

Diabetes patients are increasing in number so it is necessary to predict , treat and diagnose the disease. Data Mining can help to provide knowledge about this disease. The knowledge extracted using Data Mining can help in treating and preventing the disease. Artificial Neural Network (ANN) can be used to create an classifier from the data. The neural network is trained using backpropagation al...

This paper explores the size and value effect in influencing performance of individual companies using backpropagation neural networks. According to existing theory, companies with small market capitalization and high book to market ratios have a tendency to perform better in the future. Data from over 300 Australian Stock Exchange listed companies between 2000–2004 is examined and a neural net...

Neural networks are a powerful computational architecture for modeling data, but optimizing the connection weights can be very difficult. Flexible neural trees (FNTs) are good at finding a globally near-optimal network to fit a dataset, using evolutionary algorithms and particle swarm optimization. We show that putting the two methods together can yield very good results. The FNT solution can b...

In this paper a new backpropagation learning method enhanced with type-2 fuzzy logic is presented. Simulation results and a comparative study among monolithic neural networks, neural network with type-1 fuzzy weights and neural network with type-2 fuzzy weights are presented to illustrate the advantages of the proposed method. In this work, type-2 fuzzy inference systems are used to obtain the ...

We propose a simple framework for critic-based training of recurrent neural networks and feedback controllers. We term the critics that are used primitive adaptive critics, since we represent them with the simplest possible architecture (bias weight only). We derive this framework from two main premises. The first of these is a natural similarity between a form of approximate dynamic programmin...

By studying adaptive critic designs (ACD) from the standpoint of practical use in training neural networks , we expect to establish the types of problems for which ACD might be preferable to more established methods. To restrict the scope, we have chosen to concentrate on applying ACD, speciically derivative critics, to the training of recurrent networks 1]. This is actually less restrictive th...