Special issue on intelligent computing theory and methodology

We are very pleased to offer this special issue named as Intelligent Computing Theory and Methodology to the readers of Applied Mathematics and Computation by selecting the candidate papers from The 2005 International Conference on Intelligent Computing, held in Hefei, Anhui Province, China, on 23–26 August 2005. Thirty-six papers were selected for inclusion in this special issue, which represent less than 6% of all eligible papers accepted at the ICIC 2005. In recent years, we have witnessed intelligent computing techniques, such as artificial intelligence, machine learning, colony intelligence, etc., are being dedicated to the various aspects of applications in bioinformatics, neuroinformatics, chemoinformatics, computational biology, and computational intelligence, etc. Intelligent computing knowledge has been enriched by developing more solid mathematical frameworks, by elaborating more efficient and powerful algorithms as well as structures. The goal of intelligent computing technique is to form the theory from the data automatically, and its main objective is to find the rule from limited observation examples, which cannot be obtained using the classical theory. It further extends the rule to all the objects of interest, and predicts and infers the development of the things. Hence, intelligent computing technique is supplementary to the conventional methods. Intelligent computing technique makes it possible to use computer to extract and discover knowledge from large amount of biological, commercial, etc., information. This special issue is divided into five parts, which can be respectively introduced as follows. A subsection named as Feature Extraction and Classifications in the issue includes nine papers. Li et al. discussed English sentence stress detection system based on HMM framework for computer assisted language learning. Jia et al. addressed a regression-based algorithm for recently frequent patterns in multiple time granularity data streams. Wu et al. presented multiple features data fusion method in color texture analysis. Guo et al. discussed extraction of higher order coupling feature using three and one half dimension spectrum. Li presented an analytic solution for estimating two-dimensional hidden Markov models. Wang et al. addressed enhanced gradient-based algorithm for the estimation of fingerprint orientation fields. Yuan et al. discussed a sequential subspace learning method and its application to dynamic texture analysis. Pan et al. presented an approach for incorporating domain knowledge into medical image clustering. Jiang et al. addressed the spherical harmonic based linear face de-lighting and compensation method. A subsection named as Image Processing Technology and Applications in the issue includes nine papers. Liu et al. addressed an image fragile watermark scheme based on chaotic image pattern and pixel-pairs. Du et al. investigated and discussed an approach for plant leaf shape based plant species recognition. Wang et al. presented how to perform the recognition and location of the internal corners of planar checkerboard calibration pattern image. Ye et al. discussed a new adaptive watermarking for real-time MPEG videos. Zhu et al. investigated a GA-based query optimization method for web information retrieval. Han et al. discussed the method and technique for fingerprint images encryption via multi-scroll chaotic attractors. Hong et al. proposed an improved mean shift segmentation approach for natural images. Li et al. gave an approach