This paper explains works in speech recognition using neural network. The main objective of the experiment is to choose suitable number of nodes in hidden layer and learning parameters for malay iIsolated digit speech problem through trial and error method. The network used in the experiment is feed forward multilayer perceptron trained with back propagation scheme. Speech data for the study ar...

Predicting corporate failure or bankruptcy is one of the most important problems facing business and government. The recent Savings and Loan crisis is one example, where bankruptcies cost the United States billions of dollars and became a national political issue. This paper provides a ‘meta analysis’ of the use of neural networks to predict corporate failure. Fifteen papers are reviewed and co...

This study shows the usefulness of Artificial Neural Network (ANN) in maintenance planning and man-agement. An ANN model based on the multi-layer perceptron having three hidden layers and four processing elements per layer was built to predict the expected downtime resulting from a breakdown or a maintenance activity. The model achieved an accuracy of over 70% in predicting the expected downtime.

The extreme learning machine (ELM) that is proposed by Huang is designed based on single-hidden layer feedforward neural networks (SLFNs), which can randomly choose the parameters of hidden nodes and the output weights gotten analytically. So it can get the solution fastly. However, the learning time of ELM is mainly spent on calculating the Moore-Penrose generalized inverse matrices of the hid...

This paper addresses the synchronization problem in outer topology networks using chaotic nodes with hidden attractors. Specifically, we analyze bidirectionally coupled various inner–outer coupling topologies to identify optimal configuration that encourages synchronization. The incorporate a system capable of generating To assess stability state, conduct numerical simulations and examine maxim...

in this study, response surface methodology (rsm) was used to optimize osmo-dehydration of orange slice. effect of osmotic solution temperature in the range of 30 to 60 °c, immersion time from 0 to 300 min and sucrose concentration from 35 to 65 brix degree on water loss, solid gain, moisture content, water loss to solid gain ratio and brix change were investigated by central composite design (...

Introduction: The possibility of depression is common in the elderly. Novel technologies allow us to monitor people related to depression. Hence, a model was provided to detect depression in elderly based on artificial neural network (ANN). Methods: The present study is an applied descriptive-survey research. Forty elderly people were randomly selected from the Elderly Care Center in Gonbad Ka...

An artificial neural network was used to optimize the release of salbutamol sulfate from hydrophilic matrix formulations. Model formulations to be used for training, testing and validating the neural network were manufactured with the aid of a central composite design with varying the levels of Methocel® K100M, xanthan gum, Carbopol® 974P and Surelease® as the input factors. In vitro dissolutio...

Objective(s): This paper investigates the validity of Artificial Neural Networks (ANN) model in the prediction of electrospun kefiran nanofibers diameter using 4 effective parameters involved in electrospinning process. Polymer concentration, applied voltage, flow rate and nozzle to collector distance were used as variable parameters to design various sets of electrospinning ex...

Artificial neural networks (ANNs) have been successfully applied to solve a variety of classification and function approximation problems. Although ANNs can generally predict better than decision trees for pattern classification problems, ANNs are often regarded as black boxes since their predictions cannot be explained clearly like those of decision trees. This paper presents a new algorithm, ...