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Soft Computing for Recognition based on Biometrics (Studies in Computational Intelligence)

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We describe in this book, bio-inspired models and applications of hybrid intelligent systems using soft computing techniques for image analysis and pattern recognition based on biometrics and other information sources. Soft Computing (SC) consists of several intelligent computing paradigms, including fuzzy logic, neural networks, and bio-inspired optimization algorithms, which can be used to produce powerful hybrid intelligent systems. The book is organized in five main parts, which contain a group of papers around a similar subject. The first part consists of papers with the main theme of classification methods and applications, which are basically papers that propose new models for classification to solve general problems and applications. The second part contains papers with the main theme of modular neural networks in pattern recognition, which are basically papers using bio-inspired techniques, like modular neural networks, for achieving pattern recognition based on biometric measures. The third part contains papers with the theme of bio-inspired optimization methods and applications to diverse problems. The fourth part contains papers that deal with general theory and algorithms of bio-inspired methods, like neural networks and evolutionary algorithms. The fifth part contains papers on computer vision applications of soft computing methods.

In the part of classification methods and applications there are 5 papers that describe different contributions on fuzzy logic and bio-inspired models with application in classification for medical images and other data. The first paper, by Carlos Alberto Reyes et al., deals with soft computing approaches to the problem of infant cry classification with diagnostic purposes. The second paper, by Pilar Gomez et al., deals with neural networks and SVM-based classification of leukocytes using the morphological pattern spectrum. The third paper, by Eduardo Ramirez et al., describes a hybrid system for cardiac arrhythmia classification with fuzzy KNearest Neighbors and neural networks combined by a fuzzy inference system. The fourth paper, by Christian Romero et al., offers a comparative study of blog comments spam filtering with machine learning techniques. The fifth paper, by Victor Sosa et al., describes a distributed implementation of an intelligent data classifier.
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