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Processing and classifying galaxy information is one of the most important challenges and intensive research area for astronomers. In this paper; analyzing and classifying photographic images of galaxies are presented, with interesting scientific findings gleaned from the processed photographic data. In addition, the performance of ten artificial neural networks (ANNs) based classifiers was evaluated,...
The performance and regression precision of weak learners (accuracies should be greater than 0.5) for pattern recognition and forecasting can be upgraded using AdaBoost algorithm. Support vector machine (SVM) is a state-of-the-art learning machines and have been widely used in pattern recognition area since 90's of 20th contrary, however the performance of SVM is not stable and easily influenced due...
In this paper, classification of pavement surface distress and the statistics of the distress data are discussed. In order to improve the accuracy and efficiency to identify the pavement surface distress by the image information, a new algorithm based on SVM is discussed. In this study, support vector classification (SVC), which is a novel and effective classification algorithm, is applied to crack...
Syndrome is a unique TCM concept, which is an abstractive collection of symptoms and signs. Several modern algorithms have been applied to classify syndromes, but no satisfied results have been obtained because of the complexity of diagnosis procedure. Support vector machine (SVM) has been found to be very efficient to solve the classification problems, especially for binary classification with good...
The multi-DOF prosthetic hand's myocontrol needs to recognize more hand gestures (or motions) based on myoelectric signals. This paper presents a classification method, which is based on the support vector machine (SVM), to classify 19 different hand gesture modes through electromyographic (EMG) signals acquired from six surface myoelectric electrodes. All hand gestures are based on a 3-DOF configuration,...
Scope: Commercial banks, as the key of the nation's economy and the center of financial credit, play a multiple irreplaceable role in the financial system. Credit risks threaten the economic system as a whole. Therefore, predicting bank financial credit risks is crucial to prevent and lessen the incoming negative effects on the economic system. Objective: This study aims to apply a credit risk assessment...
To solve the problem that the performance of speech recognition systems declines in the noisy environment, this paper used the linear predictive Mel frequency cepstrum coefficients according with human hearings characteristic as speech feature parameters, adopted two recognition machines, the support vector machine and the wavelet neural network, realized respectively a speech recognition system of...
Due to the fact that the detection of intrusion is inefficient and lacks intelligence in current intrusion detection system, this paper integrates BP neural network and support vector machine (SVM) based on the theory of neural network integration, applying fuzzy clustering technology to cluster data, choosing data from the cluster centre to train ensemble individuals, then selecting and integrating...
Classification of underwater objects remains challenging and significant problem because of the complexity of underwater environments. In this paper,a probabilistic neural network (PNN) is used as a classifier to the automatic classification of underwater objects. Firstly, a process of multi-field feature extraction is employed to construct a feature vector.The multi-field feature extraction involves...
In the force control of multi-functional prosthetic hands, it is important to extract grasp force information besides mode specifications directly from the myoelectric signals. In this paper, a force sensor is adopted to record the hand's enveloping force when the hand is performing several grasp modes, synchronously with 6 channels surface electromyography (EMG) which are extracting from the subject's...
In this study, we assessed the large sample population of patients with chronic gastritis based on three methods with supervised learning function, i.e., the regression analysis, BP neural network and support vector machine. On basis of the results, we constructed the diagnostic models to predict the types of traditional Chinese medicine (TCM) syndromes of chronic gastritis, and compared the correct...
Classification of EEG signals is core issues on EEG-based brain computer interface (BCI). Typically, such classification has been performed using signals from a set of selected EEG sensors. Because EEG sensor signals are mixtures of effective signals and noise, which has low signal-to-noise ratio, motor imagery EEG signals can be difficult to classification. In this paper, the energy entropy was used...
The paper will present the original NBV (Nearest Boundary Vector) classifier whose structure has been inspired by the structure of CP (Counter Propagation) neural network, which uses the methods applied in the minimum-distance classification while in its operation drawn on the idea of functioning of SVM (Support Vector Machines) classifiers. The classification algorithm which is used by it relies...
Brain Computer Interface (BCI) is a technology that developed over the last three decades has provided a novel and promising alternative method for interacting with the environment. BCI is a system which translates a subject's intentions into a control signal for a device, e.g., a computer application, a wheelchair or a neuroprosthesis. Electroencephalogram-based BCI has become a hot spot in the research...
This paper discusses a feature extraction technique with genetic programming (GP) and bootstrap to improve interpretation accuracy of dissolved gas analysis (DGA) fault classification in power transformers, dealing with highly versatile or noise corrupted data. Initial DGA data are preprocessed with bootstrap to equalize the sample numbers for different fault classes, thus improving subsequent extraction...
The alcoholism is one of psychiatric phenotype, which results from interplay between genetic and environmental factors. Not only it leads to brain defects but also associated cognitive, emotional, and behavioral impairments. It can be detected by analyzing EEG signals. In this research, the power spectrum of the Haar mother wavelet is extracted as features. Then the principle component analysis is...
Gas sensor array is an important part of electronic nose. The gas analysis performance of electronic nose is affected badly by the cross sensitivity of gas sensor array. In order to solve the problem of the cross sensitivity, in this work a new method based on support vector machine (SVM) is used for pattern analysis of gas mixture quantitative analysis. The proposed method has been used for processing...
A classification model is obtained after a classifier is trained on training data. Decision region is the region in which data are predicted the same class label by a classifier. Decision boundary is the boundary between regions of different classes. We view classification as dividing the data space into decision regions. The formal definitions of decision region and decision boundary are presented...
A new approach to the measurement of wet gas flows is introduced in this paper. Support Vector Machine (SVM) was employed in wet gas metering. Typical features were extracted from the signals obtained by a throat-extended Venturi meter. The features and the corresponding flow rates (targets) were used to train the SVM model. The trained model was then used to predict the flow rates of wet gas. Experimental...
Industrial equipments that employ element identification tend to be expensive as they utilize built-in spectroscopes and computers for post processing. In this paper we present an in situ fully automatic method for detecting constituent elements in a sample specimen using computer vision and machine learning techniques on Laser Induced Breakdown Spectroscopy (LIBS) spectra. This enables the development...
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