The main objective of the annual International Conference on Intelligent Technologies (InTech) is to bring together researchers and practitioners who implement intelligent and fuzzy technologies in real-world environment. The Fifth International Conference on Intelligent Technologies InTech'04 was held in Houston, Texas, on December 2-4, 2004. Topics of InTech'04 included mathematical foundations of intelligent technologies, traditional Artificial Intelligent techniques, uncertainty processing and methods of soft computing, learning/adaptive systems/data mining, and applications of intelligent technologies. This special issue contains versions of 15 selected papers originally presented at InTech'04. These papers cover most of the topics of the conference. Several papers describe new applications of the existing intelligent techniques. R. Aló{o} et al. show how traditional <I>statistical</I> hypotheses testing techniques – originally designed for processing measurement results – need to be modified when applied to simulated data – e.g., when we compare the quality of two algorithms. Y. Frayman et al. use <I>mathematical morphology</I> and <I>genetic algorithms</I> in the design of a machine vision system for detecting surface defects in aluminum die casting. Y. Murai et al. propose a new faster <I>entropy</I>-based placement algorithm for VLSI circuit design and similar applications. A. P. Salvatore et al. show how <I>expert system</I>-type techniques can help in scheduling botox treatment for voice disorders. H. Tsuji et al. propose a new method, based on <I>partial differential equations</I>, for automatically identifying and extracting objects from a video. N. Ward uses <I>Ordered Weighted Average</I> (OWA) techniques to design a model that predicts admission of computer science students into different graduate schools. An important aspect of intelligence is ability to <I>learn</I>. In A. Mahaweerawat et al., neural-based machine learning is <I>used</I> to identify and predict software faults. J. Han et al. show that we can drastically <I>improve</I> the quality of machine learning if, in addition to discovering traditional (positive) rules, we also search for negative rules. A serious problem with many neural-based machine learning algorithms is that often, the results of their learning are un-intelligible rules and numbers. M. I. Khan et al. show, on the example of robotic arm applications, that if we allow neurons with different input-output dependencies – including linear neurons – then we can <I>extract</I> meaningful <I>knowledge</I> from the resulting network. Several papers analyze the Equivalent Transformation (ET) model, that allows the user to <I>automatically generate code from specifications</I>. A general description of this model is given by K. Akama et al. P. Chippimolchai et al. describe how, within this model, we can transform a user's query into an equivalent more efficient one. H. Koike et al. apply this approach to <I>natural language processing</I>. Y. Shigeta et al. show how the existing <I>constraint</I> techniques can be translated into equivalent transformation rules and thus, combined with other specifications. I. Takarajima et al. extend the ET approach to situations like <I>parallel computations</I>, where the order in which different computations are performed on different processors depends on other processes and is, thus, non-deterministic. Finally, a paper by J. Chandra – based on his invited talk at InTech'04 – describes a <I>general framework</I> for robust and resilient critical infrastructure systems, with potential applications to transportation systems, power grids, communication networks, water resources, health delivery systems, and financial networks. We want to thank all the authors for their outstanding work, the participants of InTech'04 for their helpful suggestions, the anonymous reviewers for their thorough analysis and constructive help, and – last but not the least – to Professor Kaoru Hirota for his kind suggestion to host this issue and to the entire staff of the journal for their tireless work.
A Review on Machine Learning for Audio Applications