Scientific computations for diffusion equations and ANNs (Artificial Neural Networks) are data intensive tasks accompanied by heavy memory access; on the other hand, their computational complexities are relatively low. Thus, this type of tasks naturally maps onto SIMD (Single Instruction Multiple Data stream) parallel processing with distributed memory. This paper proposes a high performance ac...

introduction: with using multiple linear regression (mlr), can simultaneously analyses several different variables, but to get the desirable results from the mlr, the samples must be much and accurate. therefore, this method has high sensitivity and may cause errors in results. in addition, to use this method, the variable must have normal distribution and modification follow from a linear rela...

The primary aim of this chapter is to present an overview of the artificial neural network basics and operation, architectures, and the major algorithms used for training the neural network models. As can be seen in subsequent chapters, neural networks have made many useful contributions to solve theoretical and practical problems in finance and manufacturing areas. The secondary aim here is th...

Artificial Neural Networks (ANNs) are grouped within connectionist techniques of Artificial Intelligence. In particular, Recurrent Neural Networks are a type of ANN which is widely used in signal reproduction tasks and sequence analysis, where causal relationships in time and space take place. On the other hand, in many problems of science and engineering, signals or sequences under analysis do...

In recent years, artificial neural networks (ANNs) have been used for forecasting in time series in the literature. Although it is possible tomodel both linear and nonlinear structures in time series by using ANNs, they are not able to handle both structures equally well. Therefore, the hybrid methodology combining ARIMA and ANN models have been used in the literature. In this study, a new hybr...

Named-entity recognition (NER) aims at identifying entities of interest in a text. Artificial neural networks (ANNs) have recently been shown to outperform existing NER systems. However, ANNs remain challenging to use for non-expert users. In this paper, we present NeuroNER, an easyto-use named-entity recognition tool based on ANNs. Users can annotate entities using a graphical web-based user i...

This paper applies artificial neural networks (ANNs) to the survival analysis problem. Because ANNs can easily consider variable interactions and create a non-linear prediction model, they offer more flexible prediction of survival time than traditional methods. This study compares ANN results on two different breast cancer datasets, both of which use nuclear morphometric features. The results ...

Most Artificial Neural Networks (ANNs) have a fixed topology during learning, and typically suffer from a number of shortcomings as a result. Variations of ANNs that use dynamic topologies have shown ability to overcome many of these problems. This paper introduces Location-Independent Transformations (LITs) as a general strategy for implementing neural networks that use static and dynamic topo...

It seems obvious that the massively parallel computations inherent in artificial neural networks (ANNs) can only be realized by massively parallel hardware. However, the vast majority of the many ANN applications simulate their ANNs on sequential computers which, in turn, are not resource-efficient. The increasing availability of parallel standard hardware such as FPGAs, graphics processors, an...

A RECONFIGURABLE COMPUTING ARCHITECTURE FOR IMPLEMENTING ARTIFICIAL NEURAL NETWORKS ON FPGA Kristian Nichols University of Guelph, 2003 Advisor: Professor Medhat Moussa Professor Shawki Areibi Artificial Neural Networks (ANNs), and the backpropagation algorithm in particular, is a form of artificial intelligence that has traditionally suffered from slow training and lack of clear methodology to...