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We describe in this book, bio-inspired models and applications of hybrid intel- gent systems using soft computing techniques for image analysis and pattern r- ognition 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 pr- lems 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 r- ognition 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 - scribe different contributions on fuzzy logic and bio-inspired models with appli- tion in classification for medical images and other data.
List of contents
Classification Algorithms and Applications.- Soft Computing Approaches to the Problem of Infant Cry Classification with Diagnostic Purposes.- Neural Networks and SVM-Based Classification of Leukocytes Using the Morphological Pattern Spectrum.- Hybrid System for Cardiac Arrhythmia Classification with Fuzzy K-Nearest Neighbors and Neural Networks Combined by a Fuzzy Inference System.- A Comparative Study of Blog Comments Spam Filtering with Machine Learning Techniques.- Distributed Implementation of an Intelligent Data Classifier.- Pattern Recognition.- Modular Neural Network with Fuzzy Integration and Its Optimization Using Genetic Algorithms for Human Recognition Based on Iris, Ear and Voice Biometrics.- Comparative Study of Type-2 Fuzzy Inference System Optimization Based on the Uncertainty of Membership Functions.- Modular Neural Network for Human Recognition from Ear Images Using Wavelets.- Modular Neural Networks for Person Recognition Using the Contour Segmentation of the Human Iris Biometric Measurement.- Real Time Face Identification Using a Neural Network Approach.- Comparative Study of Feature Extraction Methods of Fuzzy Logic Type 1 and Type-2 for Pattern Recognition System Based on the Mean Pixels.- Optimization Methods.- Application of the Bee Swarm Optimization BSO to the Knapsack Problem.- An Approach Based on Neural Networks for Gas Lift Optimization.- A New Evolutionary Method with Particle Swarm Optimization and Genetic Algorithms Using Fuzzy Systems to Dynamically Parameter Adaptation.- Local Survival Rule for Steer an Adaptive Ant-Colony Algorithm in Complex Systems.- Using Consecutive Swaps to Explore the Insertion Neighborhood in Tabu Search Solution of the Linear Ordering Problem.- A New Optimization Method Based on a Paradigm Inspired by Nature.-Theory and Algorithms.- Improvement of the Backpropagation Algorithm Using (1+1) Evolutionary Strategies.- Parallel Genetic Algorithms for Architecture Optimization of Neural Networks for Pattern Recognition.- Scene Recognition Based on Fusion of Color and Corner Features.- Improved Tabu Solution for the Robust Capacitated International Sourcing Problem (RoCIS).- Variable Length Number Chains Generation without Repetitions.- Comparative Analysis of Hybrid Techniques for an Ant Colony System Algorithm Applied to Solve a Real-World Transportation Problem.- Computer Vision Applications.- Comparison of Fuzzy Edge Detectors Based on the Image Recognition Rate as Performance Index Calculated with Neural Networks.- Intelligent Method for Contrast Enhancement in Digital Video.- Method for Obstacle Detection and Map Reconfiguration in Wheeled Mobile Robotics.- Automatic Dust Storm Detection Based on Supervised Classification of Multispectral Data.
Summary
We describe in this book, bio-inspired models and applications of hybrid intel- gent systems using soft computing techniques for image analysis and pattern r- ognition 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 pr- lems 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 r- ognition 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 - scribe different contributions on fuzzy logic and bio-inspired models with appli- tion in classification for medical images and other data.
