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The classification of the sound heart into different valve-physiological heart disease categories is a complex pattern recognition task. This paper will purpose sound heart recognition for diagnosing heart disease with 4 type of Artificial Neural Network (ANN). We develop a simple model for the recognition of heart sounds, and demonstrate its utility in identifying features useful in diagnosis. We...
In this paper, BP neural network is applied to fault pattern recognition of pipeline leakage. When the pipeline pressure falls suddenly, the pressure sensors on both sides of the pipeline get pressure signals. The fundamental principal of using wavelet transform to decompose the pressure signal is introduced, using wavelet transform in pressure de-noising and pipeline feature vector extraction, and...
Fault feature extraction and application is the key technology of gearbox fault diagnosis. In this paper, a fault diagnosis method using bispectrum entropy as the fault feature parameters is put forward. Bispectrum entropy as the information entropy in bispectrum domain can reflect the complexity of information energy. When the structure is failed, the distribution of bispectrum will be changed. bispectrum...
A novel method of crowd estimation is proposed in this paper: Firstly, surveillance image is divided into bit planes by OSTU algorithm, the pixel ratio of foreground to background and complexity of bit planes are taken as feature vectors of crowd estimation. The degree of crowd density of the scene is classified into several grades, BP neural network is used for training and then the classification...
Gabor texture descriptor have gained much attention for different aspects of computer vision and pattern recognition. Recently, on the Rayleigh nature of Gabor filter out-puts Rayleigh model Gabor texture descriptor is proposed.In this paper, we investigate the performance of these two Gabor texture descriptor in texture classification. We built a texture classification system based on BPNN, and use...
This paper introduces a novel method for human face recognition that employs a new back propagation neural network (BPN) training algorithm performed with an ant colony optimization (ACO) to get the optimal connection weights of the BPN of the classification phase. The aim is to automate the face recognition system using computational intelligence. The input image undergoes histogram equalization...
Karyotyping is a common method in cytogenetics. Automatic classification of the chromosomes within the microscopic images is the first step in designing an automatic karyotyping system. This is a difficult task especially if the chromosome is highly curved within the image. This paper introduces a new wavelet transform based linear discriminant analysis based feature vector for discriminating both...
The identification of the state of human skin tissues is discussed here. The bio-optical signals recorded in vitro have been analyzed by extracting various statistical features. Using LAB VIEW 7.1 programs/tools, different statistical features are extracted from both normal and pathology spectra. Each spectrum is filttered and normalized. Then different features like skewness, summation, median residuals,...
With the emerging of the new applications like virtual reality in image processing and machine vision, it is necessary to have more perfect interfaces than mouse and keyboard for human computer interaction. To cope with this problem, variety of tools has been presented to interact with computers. Hand gesture recognition is one of the proper methods for this purpose. This paper presents a new algorithm...
In the framework of multiple classifier systems, we suggest to reformulate the classifier combination problem as a pattern recognition one. Following this approach, each input pattern is associated to a feature vector composed by the output of the classifiers to be combined. A Bayesian Network is used to automatically infer the probability distribution for each class and eventually to perform the...
With the growing population of Web services, the discovery of services is a key to the development of Web services. While extensive researches focus mainly on service matchmaking algorithms, service classification that is also a meaningful approach to accelerating service discovery only receives little attention. In this paper, we propose to use adaptive back-propagation neural network model (BPM)...
The subject of word recognition has been receiving considerable attention in recent years due to the increasing dependence on computer data processing. Several methods for recognizing Latin, Chinese words have been proposed. However, works on recognition of Farsi words has been relatively sparse. Techniques developed for recognizing other language can not been used for recognizing Farsi words. In...
We propose a method that uses independent component analysis (ICA) and backpropagation neural network to classify electrocardiogram (ECG) signals. In this study, ICA is used to extract important features from ECG signals. A backpropagation neural network follows to classify the input ECG beats into one of eight beat types. The independent components are calculated from the training ECG beats and serve...
We propose a method that uses independent component analysis (ICA) and backpropagation neural network to classify electrocardiogram (ECG) signals. In this study, ICA is used to extract important features from ECG signals. A backpropagation neural network follows to classify the input ECG beats into one of eight beat types. The independent components are calculated from the training ECG beats and serve...
In this work, the spikes in the electroencephalogram (EEG) signals are analyzed by using artificial neural networks (ANN). Multiple layer perceptron (MLP) networks utilizing between 3 and 15 hidden neurons are used in the network architecture. For training the MLP network backpropagation algorithm, backpropagation with adaptive learning rate, Levenberg-Marquardt (LM) algorithm, early stopping and...
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