JACIII Vol.11 No.3 pp. 259-260
doi: 10.20965/jaciii.2007.p0259


Special Issue on Advances in Intelligent Data Processing

Kok Wai Wong*, Tamás Gedeon**, and Chun Che Fung*

*School of Information Technology, Murdoch University, Western Australia 6150

**Department of Computer Science, The Australian National University, Acton ACT 0200, Australia

March 20, 2007
Technological advancement using intelligent techniques has provided solutions to many applications in diverse engineering disciplines. In application areas such as web mining, image processing, medical, and robotics, just one intelligent data processing technique may be inadequate for handling a task, and a combination or hybrid of intelligent data processing techniques becomes necessary. The sharp increase in activities in the development of innovative intelligent data processing technologies also attracted the interest of many researchers in applying intelligent data processing techniques in other application domains. In this special issue, we presented 12 research papers focusing on different aspects of intelligent data processing and its applications. We start with a paper entitled "An Activity Monitor Design Based on Wavelet Analysis and Wireless Sensor Networks," which focuses on using wavelet analysis and wireless sensor networks for monitoring the human physical condition. The second paper, "An Approach in Designing Hierarchy of Fuzzy Behaviors for Mobile Robot Navigation," presents a hierarchical approach using fuzzy theory to assist in the task of mobile robot navigation. It also discusses the design of hierarchical behavior of mobile robots using sensors. The third paper, "Toward Natural Communication: Human-Robot Gestural Interaction Using Pointing," also works with robots focusing more on the interaction between users and robots in which the robot recognizes pointing by a human user through intelligent data processing. The fourth paper, "Embodied Conversational Agents for H5N1 Pandemic Crisis," examines the use of intelligent software bots as an interaction tool for crisis communication. linebreaknewpage The work is based on a novel Automated Knowledge Extraction Agent (AKEA). There are many interests of using intelligent data processing techniques for image processing and analysis, as shown in the next few papers. The fifth paper, "A Feature Vector Approach for Inter-Query Learning for Content-Based Image Retrieval," presents relevance feedback based technique for content based image retrieval. It extends the relevance feedback approach to capture the inter-query relationship between current and previous queries. The sixth paper, "Abstract Image Generation Based on Local Similarity Pattern," also falls in the area of image retrieval using local similarity patterns to generate abstract images from a given set of images. Along the same line of similarity measure for image retrieval, the seventh paper, "Cross-Resolution Image Similarity Modeling," works on cross resolution using probabilistic and fuzzy theory to formulate cross resolution image similarity modeling. The eighth paper, "Bayesian Spatial Autoregressive for Reducing Blurring Effect in Image," presents a Bayesian Spatial Autoregressive technique developed by Geweke and LeSage. The ninth paper, "Logistic GMDH-Type Neural Network and its Application to Identification of X-Ray Film Characteristic Curve," presents a class of neural networks for X-Ray Film processing and compares results with some conventional techniques. As digital entertainment and games grow increasingly popular, the tenth paper, "Classification of Online Game Players Using Action Transition Probability and Kullback Leibler Entropy," looks into the use of intelligent data processing for classifying of online game players. The eleventh paper, "Parallel Learning Model and Topological Measurement for Self-Organizing Maps," presents the concept of a SOM parallel learning model that appears both robust and efficient. The twelfth paper, "Optimal Size Fuzzy Models," delineates concepts on how to make fuzzy systems more efficient. As guest editors for this issue, we thank the authors for their hard work. We also thank the reviewers for their assistance in the review process. All full papers submitted to this special issue have been peer-reviewed by at least two international reviewers in the area.
Cite this article as:
K. Wong, T. Gedeon, and C. Fung, “Special Issue on Advances in Intelligent Data Processing,” J. Adv. Comput. Intell. Intell. Inform., Vol.11 No.3, pp. 259-260, 2007.
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