ÐThe primary goal of pattern recognition is supervised or unsupervised classification. Among the various frameworks in which pattern recognition has been traditionally formulated, the statistical approach has been most intensively studied and used in practice. More recently, neural network techniques and methods imported from statistical learning theory have been receiving increasing attention....

purpose: to assess three main emotions (happy, sad and calm) by various classifiers, using appropriate feature extraction and feature selection. materials and methods: in this study a combination of power spectral density and a series of statistical features are proposed as statistical-frequency features. next, a feature selection method from pattern recognition (pr) tools is presented to extra...

The concept, structures, and algorithms of principal feature classification (PFC) are presented in this paper. PFC is intended to solve complex classification problems with large data sets. A PFC network is designed by sequentially finding principal features and removing training data which has already been correctly classified. PFC combines advantages of statistical pattern recognition, decisi...

Purpose: To assess three main emotions (happy, sad and calm) by various classifiers, using appropriate feature extraction and feature selection. Materials and Methods: In this study a combination of Power Spectral Density and a series of statistical features are proposed as statistical-frequency features. Next, a feature selection method from pattern recognition (PR) Tools is presented to e...

This paper presents a pattern recognition system for the identification of the vegetable crops diseases from images. The proposed system is based on three main phases: Segmentation, feature extraction and classification. The segmentation of the images is carried out using k-means clustering method. Then, three type of features are extracted from the segmented images including color, texture and...

Precise and fast control chart pattern (CCP) recognition is important for monitoring process environments to achieve appropriate control and to produce high quality products. CCPs can exhibit six types of pattern: normal, cyclic, increasing trend, decreasing trend, upward shift and downward shift. Except for normal patterns, all other patterns indicate that the process being monitored is not fu...

Optical Characters Recognition (OCR) is one of the active subjects of research in the field of pattern recognition. The two main stages in the OCR system are feature extraction and classification. In this paper, a new hybrid feature extraction technique and a neural network classifier are proposed for off-line handwritten English character recognition system. The hybrid features are obtained by...

In the present paper a set of artificial intelligence methods are presented, with special focus on the pattern recognition algorithms and neural networks techniques, applied to the evaluation of deformed speech signal. The principal idea of the paper is the statement that in the presented problems the standard methods of signal processing and classification, widely applied for analysis and reco...

in this study, a low-cost, rapid and qualitative evaluation procedure is presented using dynamic pattern recognition analysis to assess liquefaction potential which is useful in the planning, zoning, general hazard assessment, and delineation of areas, dynamic pattern recognition using neural networks is generally considered to be an effective tool for assessing of hazard potential on the basis...

In order to realize machines that work in the complex environments of the “real world,” the most fundamental problem, I believe, is to devise a probabilistic model which can capture the nature of the world. Without such models, machines cannot learn, because there are too much to learn about the environment. Stuart Geman and his colleagues have developed a theory of “composition systems,” which...