Automatic monitoring, data collection, analysis and prediction of environmental changes is essential for all living things. Understanding future climate changes does not only helps in measuring the influence on people life, habits, agricultural and health but also helps in avoiding disasters. Giving the high emission of chemicals on air, scientist discovered the growing depletion in ozone layer...

The purpose of this study is to model the nonparametric realized volatility of the futures contract as traded in domestic U.S. markets for exchange involving the South African rand and the U.S. dollar (ZAR). The study embraces a Bayesian regularization radial basis function (RBF) artificial neural network (ANN) to model the complex volatility patterns. The modeling characteristics revealed by t...

An existing low complexity, black box artificial neural network model (ANN model) is investigated towards its more general applicability in the field of high isobaric±isothermal inactivation of enzymes. The use of this non-linear modeling technique makes it possible to describe accurately synergistic effects of pressure and temperature in contrast with more classical models used in this novel a...

Among modeling languages Unified Modeling Language (UML) has become most popular. UML is commonly used in the design and implementation of any system and software architectures. To achieve functional and non functional requirements of the system UML model helps. In order to initiate the programming phase of building software UML tools enabled the creation of source code from UML diagram. The ma...

These are the guidelines for the program Generalised Regression Neural Networks (GRNNs). Engineering has been researching the use of artificial neural networks (ANNs) for water resources modeling applications, such as flow forecasting, water quality forecasting and water treatment process modeling since the early 1990s. While Multi‐Layer Perceptrons (MLPs) are the most widely used ANN architect...

Stylized facts are uncovered for a domestic (U.S.) examination of the South African Rand futures contract (ZAR). In this preliminary study, we model complex volatility patterns by a nonparametric artificial neural network (ANN) that incorporates a performance enhancing closed-form regularization technique. The modeling characteristics revealed by the Kajiji-4 radial basis function (RBF) ANN pro...

Pronunciation variation in conversational speech has caused significant amount of word errors in large vocabulary automatic speech recognition. Rule-based approaches and decision-tree based approaches have been previously proposed to model pronunciation variation. In this paper, we report our work on modeling pronunciation variation using artificial neural networks (ANN). The results we achieve...

This paper discusses the performance comparison of an artificial neural network (ANN) model and a memory polynomial (MP) model for modeling the dynamic nonlinear input-output characteristics of power amplifier (PA) with memory. The ANN model was based on time delay neural network (TDNN) and the memory polynomial model was developed using analytical polynomial function. Both models were develope...

abstract autoregressive integrated moving average (arima) has been one of the widely used linear models in time series forecasting during the past three decades. recent studies revealed the superiority of artificial neural network (ann) over traditional linear models in forecasting. but neither arima nor anns can be adequate in modeling and forecasting time series since the first model cannot d...

in this work quantitative structure activity relationship (qsar) methodology was applied for modeling and prediction of cellular response to polymers that have been designed for tissue engineering. after calculation and screening of molecular descriptors, linear and nonlinear models were developed by using multiple linear regressions (mlr) and artificial neural network (ann) methods. the root m...