Artificial Intelligence and Soft Computing: 13th International Conference, ICAISC 2014, Zakopane, Poland, June 1-5, 2014, Proceedings, Part I [Paperback]

The two-volume set LNAI 8467 and LNAI 8468 constitutes the refereed proceedings of the 13th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2014, held in Zakopane, Poland in June 2014. The 139 revised full papers presented in the volumes, were carefully reviewed and selected from 331 submissions. The 69 papers included in the first volume are focused on the following topical sections: Neural Networks and Their Applications, Fuzzy Systems and Their Applications, Evolutionary Algorithms and Their Applications, Classification and Estimation, Computer Vision, Image and Speech Analysis and Special Session 3: Intelligent Methods in Databases. The 71 papers in the second volume are organized in the following subjects: Data Mining, Bioinformatics, Biometrics and Medical Applications, Agent Systems, Robotics and Control, Artificial Intelligence in Modeling and Simulation, Various Problems of Artificial Intelligence, Special Session 2: Machine Learning for Visual Information Analysis and Security, Special Session 1: Applications and Properties of Fuzzy Reasoning and Calculus and Clustering.Hybrid System of ART and RBF Neural Networks for Classification of Vibration Signals and Operational States of Wind Turbines.- The Parallel Approach to the Conjugate Gradient Learning Algorithm for the Feedforward Neural Networks.- A Cascade Neural Network Architecture Investigating Surface Plasmon Polaritons Propagation for Thin Metals in OpenMP.- Application of Support Vector Machines, Convolutional Neural Networks and Deep Belief Networks to Recognition of Partially Occluded Objects.- A New Multilayer Perceptron Initialisation Method with Selection of Weights on the Basis of the Function Variability.- Soft Committee Machine Using Simple Derivative Term.- Representations of Highly-Varying Functions by One-Hidden-Layer Networks.- Non-Euclidean Principal Component Analysis for Matrices by Hebbian Learning.- Spin-Glass Implementation of a Hopfield Neural StructlÃa