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Feasibility of Random Forest and Support Vector Machine classifiers is tested for the discrimination of 7 types of vegetation near lake Poosjärvi in Western Finland. Four sets of features grouped as basic, textural, ICA or PCA based, and rotational features are applied. The results indicate that the Random Forest classification scheme outperforms the Support Vector Machine classifier. For both classifiers...
This paper presents a novel feature selection and fuzzy-neural classification scheme to decode motor imagery signals during driving. To perform this, we would consider the fuzziness involved in sudden left bent, where the driver is supposed to take sudden 90º left turn during acceleration. This requires classification of motor imagery signals during acceleration and steering left control. The fuzzy-recurrent...
We propose a novel noise-robust classification scheme to discriminate one walking person and two walking persons. Non-parametric Bayesian techniques with the Beta process prior are applied to the Principal Component Analysis (PCA) model for the automatic choice of the number of principal components. Noise reduction is accomplished via reconstructing the echo within the subspace composed of the selected...
A number of papers has presented a pattern recognition method for Parkinson's Disease (PD) detection. However, the literatures only able to classify subjects as either healthy of suffering from PD. This paper presents a pattern recognition method for multi stage classification of PD utilizing voice features. 22 features are obtained from University of California-Irvine (UCI) data repository. These...
The bank direct marketing campaign for offering products that meet the customers' needs is the challenge problems. The bank direct marketing data analysis is important work that helps the banks predict whether customers will sign a long term deposits with the banks. The method that can predict such customers' needs can be profitable to the banks for improving their marketing campaign strategies. Unfortunately,...
This paper presents a quadratic neural unit with error backpropagation learning algorithm to classify electrocardiogram arrhythmia disease. The electrocardiogram arrhythmia classification scheme consists of data acquisition, feature extraction, feature reduction, and a quadratic neural unit classifier to discriminate three different types of arrhythmia. A total of 44 records were obtained from MIT-BIH...
This paper presents an improved stochastic simulation method for calculating current dependent energy losses in distribution networks. The method is based on power load curves and integrates the stochastic nature of the load curves with power and voltage covariance matrices. The method reduces calculation effort using the factor analysis of covariance matrices and provides a few quantities needed...
This study considers the problem of in-depth document analysis. We propose a new document analysis method, named Multi-Dimensional Linear Discriminant Analysis (MDL-DA), which enables us to formulate an efficient class specific semantic representation of local information from a document with respect to term associations and spatial distributions. MDL-DA works by firstly partitioning each document...
This paper introduces a novel approach to examine the scope of touch perception as a possible modality of treatment of patients suffering from certain mental disorder using a Radial Basis function induced Back Propagation Neural Network. Experiments are designed to understand the perceptual difference of schizophrenic patients from normal and healthy subjects with respect to four different touch classes,...
This paper proposes a novel method for discriminating the supraventricular tachycardias and the ventricular tachycardias via a high dimensional linear discriminant function and a perceptron with a multi-piece domain activation function having multi-level functional values. The algorithm is implemented via the mobile application. First, the discrete cosine transform is applied to each training electrocardiogram...
Conventional dimensionality reduction methods are not applicable for hybrid data as they require the data set to be pure numerical. In this study, the mutual information (MI)-based unsupervised feature transformation (UFT) method which can transform symbolic features into numerical features without information loss was integrated with principle component analysis (PCA) for dimensionality reduction...
In this paper, we propose a new temporal coherent face descriptor for video gender recognition. The proposed face descriptor is constructed from detected faces of continuous video frames. Because it describes detected faces under variant changes in continuous video frames and provides a unified feature description, face normalization and alignment processes can be avoided during gender recognition...
As the number of cooking recipes posted on the Web increases, it becomes difficult to find a cooking recipe that a user needs. Moreover, even if it can be done, it is still difficult for users to arrange the cooking recipe, for example, by replacing ingredients with different ones. To deal with such problems, we propose a framework for typicality analysis of the combination of ingredients. The framework...
Peer-to-peer system is a promising solution to manage a large amount of data, but similarity search on peer-to-peer network with a restricted small number of messages is a challenging problem. Existing methods that can perform similarity search work only with low-dimensional data. We propose a method to transform the very high-dimensional data into low-dimensional vectors in order to perform similarity...
The different ways of describing the characteristics of the image. The application of autoencoder for image classification. The results of experiments showing the effectiveness of autoencoder for solving pattern classification.
Face recognition is the process of identification of a person by their facial images. This technique makes it possible to use the facial image of a person to authenticate him into a secure system. Face is the main part of human being to be distinguished from one another. Face recognition system mainly takes an image as an input and compares this image with a number of images stored in database to...
Dimensionality reduction techniques are convenient for data aggregation to reduce battery energy consumption in sensor nodes. Normally, principal component analysis (PCA), a dimensionality reduction technique, has been used for data aggregation in WSNs. However, PCA yields to data errors when the sensing data are not related. The PCA processing time is also an issue in an urgent situation that the...
As modern technology evolves, the use of face recognition system is scattering in different sectors of commercial markets rather than in security purposes only. Various approaches are introduced for face recognition system, among them principal component analysis is one of the simplest and efficient method. To improve the performance of face recognition, choosing a threshold value and minimum number...
The tracking of moving points in image sequences requires unique features that can be easily distinguished. However, traditional feature descriptors are of high dimension, leading to larger storage requirement and slower computation. In this paper, Principal Component Analysis (PCA) is applied to the 64-Dimension (D) Speeded Up Robust Features (SURF) descriptor to reduce the descriptor dimensionality...
To perform many common industrial robotic tasks, e.g. deburring a work-piece, in small and medium size companies where a model of the work-piece may not be available, building a geometrical model of how to perform the task from a data set of human demonstrations is highly demanded. In many cases, however, the human demonstrations may be sub-optimal and noisy solutions to the problem of performing...
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