In this paper, we present an intelligent method of human action recognition based on hybrid features. These features are calculated from the space-time cubes (interest points). The Blocks or cubes are derived from the difference of consecutive frames. These features include average number of blocks per frame, velocity of the blocks i.e. average angle and displacement, some of the kinematic feat...

This work illustrates the use and some results of Artificial Neural Networks (ANNs) for data quality control of air pollutants. ANNs are applied to the short-term predicting of air pollutant concentrations in urban areas. Observed versus predicted data are compared to test the efficacy of ANNs in simulating environmental processes. Statistical analysis is used for choice of neural structure. Th...

Artificial Neural Networks (ANNs) have been used to perform classification for Automatic Speech Recognition (ASR). In this paper, we propose a new neural network, the Contenders' Network (CN) which requires little initial knowledge of the classification problem and lesser neurons than other ANNS

A feature transformation method based on domain knowledge for artificial neural networks (ANNs) is proposed. The method of feature transformation based on domain knowledge converts continuous values into discrete values in accordance with the knowledge of experts in specific application domains. This approach effectively filters data, trains the classifier, and extracts the rules from the class...

Here, we examine the capability of artificial neural networks (ANNs) in sensitivity analysis of the parameters of efficiency analysis model, namely data envelopment analysis (DEA). We are mainly interested to observe the required change of a group of parameters when another group goes under a managerial change, maintaining the score of the efficiency. In other words, this methodology provides a...

In this paper, a new approach of modeling for Artificial Neural Networks (ANNs) models based on the concepts of fuzzy regression is proposed. For this purpose, we reformulated ANN model as a fuzzy nonlinear regression model while it has advantages of both fuzzy regression and ANN models. Hence, it can be applied to uncertain, ambiguous, or complex environments due to its flexibility for forecas...

Artificial Neural Networks (ANNs) are non-linear computational tools suitable to a great host of practical application due to their flexibility and adaptability. However, their application to the resolution of chemometric problems is relatively recent (early ‘90s). In this communication, different artificial neural networks architectures are presented and their application to different kinds of...

The purpose of the work described in this paper is to investigate the use of autoregressive (AR) model by using maximum likelihood estimation (MLE) also interpretation and performance of this method to extract classifiable features from human electroencephalogram (EEG) by using Artificial Neural Networks (ANNs). ANNs are evaluated for accuracy, specificity, and sensitivity on classification of ...

Pyrolysis-mass spectrometry and artificial neural networks (ANNs) were used in combination to provide quantitative analyses of mixtures of casamino acids in glycogen, as representatives of complex proteins and carbohydrates. We studied fully interconnected feedforward networks, whose weights were modified using various types of back-propagation algorithms, and which exploited a sigmoidal activa...

This study presents a new procedure based on Artificial Neural Network (ANN) for identification of double cracks in Functionally Graded Beams (FGBs). A cantilever beam is modeled using Finite Element Method (FEM) for analyzing a double-cracked FGB and evaluation of its first four natural frequencies for different cracks depths and locations. The obtained FEM results are verified against availab...