In this work, a simplified Artificial Neural Network (ANN) based approach for recognition of various objects is explored using multiple features. The objective is to configure and train an ANN to be capable of recognizing an object using a feature set formed by Principal Component Analysis (PCA), Frequency Domain and Discrete Cosine Transform (DCT) components. The idea is to use these varied co...

Background and aims: Since accurate forecasts help inform decisions for preventive health-careintervention and epidemic control, this goal can only be achieved by making use of appropriatetechniques and methodologies. As much as forecast precision is important, methods and modelselection procedures are critical to forecast precision. This study aimed at providing an overview o...

if both reference station (rs) and navigational device in differential global positioning system (dgps) receive signals from the same satellite, rs position components error (rpce) can be used to compensate for navigational device error. this research used hybrid method for rpce prediction which was collected by a low-cost gps receiver. it is a combination of genetic algorithm (ga) computing an...

The paper investigates the powerful of hybridizing two computational intelligence methods viz., Gray Wolf Optimization (GWO) and Artificial Neural Networks (ANN) for prediction of heart disease. Gray wolf optimization is a global search method while gradient-based back propagation method is a local search one. The proposed algorithm implies the ability of ANN to find a relationship between the ...

Artificial neural network (ANN) is a major tool in artificial intelligence computing and it is also extensively used across all disciplines of coastal and ocean engineering including offshore, deep-ocean and marine engineering. A review of important research studies reported so far in these areas is presented. ANN in general has provided either substitutive or complementary option to traditiona...

This study introduces the classiication of musical instrument sounds by artiicial neural networks (ANN). The time varying spectral contents of sounds are estimated based on Short-time Fourier Transform (STFT) and are applied to ANN structures for classiication. Recognition results obtained from a multilayer perceptron (MLP), time delay neural network (TDNN) and a hybrid self organizing map radi...

A hybrid algorithm of the BP-ANN/GA (Back Propagation Artificial Neural Network and Genetic Algorithm) is developed to optimize the initial weights and make fast convergence of the BP-ANN. This algorithm is applied to data fusion of Landsat ETM+ and ERS-2 SAR for classification of the urban terrain surfaces. An example of the Shanghai Pudong area using both the Landsat ETM+ and ERS-2 SAR image ...

the objective of this study was to explore the application of hybrid artificial neural network methods to predict heavy metals in rainwater based on major elements. measurements of the heavy metals pb, cu, zn, as, ni, hg, and fe in soluble rain fractions were performed in rainwater collected at the arak plain during the rainy seasons of 2012. in the soluble fractions, the concentrations of the ...

This paper illustrates a hybrid prediction system consists of Rough Set Theory (RST) and Artificial Neural Network (ANN) for processing medical data. In the process of developing a new data mining technique and software to aid efficient solutions for medical data analysis, we propose a hybrid tool that incorporates RST and ANN to make efficient data analysis and suggestive predictions. In the e...