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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. |
