International Journal of Pattern Recognition and Artificial Intelligence

In this paper, we propose a formal approach of Artificial Intelligence (AI) in securing object oriented database systems. We combine the specification of object oriented database with security policies and provide its formal syntax and semantics. The properties in the inheritance of authorizations in object oriented database system and reasoning about authorizations on data objects are also investigated in detail.

Image retrieval plays an important role in a broad spectrum of applications. Contentbased retrieval (CBR) is one of the popular choices in many biomedical and industrial applications. Discrete image transforms have been widely studied and suggested for many image retrieval applications. The Discrete Wavelet Transform (DWT) is one of the most popular transforms recently applied to many image processing applications. The Daubechies wavelet can be used to form the basis for extracting features in retrieving images based on the description of a particular object within the scene. This wavelet is widely used for image compression. In this paper we highlight the common features between compression and retrieval. Several examples are used to test the DWT retrieval system. A comparison between DWT and Discrete Cosine Transform (DCT) is also made. The retrieval system using DWT requires preprocessing and normalization of images, which might slow down the retrieval process. The accuracy of the retrieval using DWT has been significantly improved by incorporating efficient K-Neighbor Nearest Distance (KNND) measure in our system.

In this paper we outline a human-centered multilayered multiagent distributed soft computing architecture for providing support to practitioners and problem solvers at four levels, namely, clerical, tool, optimization and task level, respectively. We describe the emergent characteristics of the architecture from a architectural, management and domain perspective. We illustrate the application of these four levels with the help of two image processing applications namely, face detection and annotation, and unstained mammalian cell classification.

Determining causality has been a tantalizing goal throughout human history. Proper sacrifices to the gods were thought to bring rewards; failure to make suitable observations were thought to lead to disaster. Today, data mining holds the promise of extracting unsuspected information from very large databases. Methods have been developed to build association rules from large data sets. Association rules indicate the strength of association of two or more data attributes. In many ways, the interest in association rules is that they offer the promise (or illusion) of causal, or at least, predictive relationships. However, association rules only calculate a joint probability; they do not express a causal relationship. If causal relationships could be discovered, it would be very useful. Our goal is to explore causality in the data mining context.