The goal of this paper is to evaluate artificial neural network in urinary diseases diagnosis. Artificial neural networks are widely used in medical problems. Artificial neural networks are used to disease diagnosis. Feed-forward back propagation neural network is used as a classifier to distinguish between infected or non-infected with two types of urinary disease. Inflammation of urinary blad...

In this research, it is concerned with constructing software effort estimation model based on artificial neural networks. The model is designed accordingly to improve the performance of the network that suits to the COCOMO model. In this paper, it is proposed to use multi layer feed forward neural network to accommodate the model and its parameters to estimate software development effort. The n...

This paper presents the use of various type of neural network architectures for the classification of medical data. Extensive research has indicated that neural networks generate significant improvements when used for the pre-processing of medical time-series data signals and have assisted in obtaining high accuracy in the classification of medical data. Up to date, most of hospitals and health...

A new class of Neural Networks (NN), designated the Multiple Feed-Forward (MFF) networks, and a new gradient-based learning algorithm, Multiple Back-Propagation (MBP), are proposed and analyzed. MFF are obtained by integrating two feed-forward networks (a main network and a space network) in a novel manner. A major characteristic is their ability to partition the input space by using selective ...

This paper presents one of the soft computing methods, specifically the artificial neural network technique, that has been used to model the temperature dependence of dynamic mechanical properties and visco-elastic behavior of widely exploited thermoplastic polyurethane over the wide range of temperatures. It is very complex and commonly a highly non-linear problem with no easy analytical metho...

In this paper, Multilayer Feed Forward Artificial Neural Network with weight initialization method is Proposed for Image Compression. Image compression helps to reduce the storage space and transmission cost. Artificial Neural network (ANNs) is a training algorithm has used to compress the image. Artificial neural network is exceptionally Feed Forward Back propagation neural network (FFBPNN) in...

Our study is about feed-forward neural network’s learning method. Generally, the method of improving its learning is focused on learning rate and moment term. We focus on sigmoid functions. Sigmoid functions are used for converting input signal into output signal and adjusting connection weight of learning in feed-forward neural network. We change gradient of sigmoid functions and investigate o...

A neural network classification based noise identification method is presented by isolating some representative noise samples, and extracting their statistical features for noise type identification. The isolation of representative noise samples is achieved using prevalent used image filters whereas noise identification is performed using statistical moments features based classification system...

This paper deals with a neural network approach for Short term wind speed forecasting. Now a day, short-term wind speed forecasts have become gradually more important for the power system management or energy trading due to the large penetration of wind power technology and development of wind energy markets. In this new era, short-term wind speed forecasting is necessary for producers and cons...

Since past few years, researchers have been concentrating on the classification of Electromyography Signal. This method is very useful in diagnosing the neuro-muscular disorders, which consists of wide spread diseases affecting peripheral nervous system. Progressive muscle weakness is the major form of these disorders. Out of various proposed methods, scholars are commonly focusing on Neural Ne...