Aboul Ella Hassanien (Abo)

In this paper we are looking for a relationship between the intent of Web pages, their architecture and the communities who take part in their usage and creation. For us, the Web page is entity carrying information about these communities. Our paper describes techniques, which can be used to extract mentioned information as well as tools usable in analysis of these information. Information about communities could be used in several ways thanks to our approach. Finally we present an experiment which proves the feasibility of our approach.

Rough set theory offers a novel approach to manage uncertainty that has been used for the discovery of data dependencies, importance of features, patterns in sample data, feature space dimensionality reduction, and the classification of objects. Consequently, rough sets have been successfully employed for various image processing tasks including image segmentation, enhancement and classification. Nevertheless, while rough sets on their own provide a powerful technique, it is often the combination with other computational intelligence techniques that results in a truly effective approach. In this paper we show how rough sets have been combined with various other methodologies such as neural networks, wavelets, mathematical morphology, fuzzy sets, genetic algorithms, Bayesian approaches, swarm optimization, and support vector machines in the image processing domain.

The objective of this paper is to introduce a rough neural intelligent approach for rule generation and image classification. Hybridization of intelligent computing techniques has been applied to see their ability and accuracy to classify breast cancer images into two outcomes: malignant cancer or benign cancer. Algorithms based on fuzzy image processing are first applied to enhance the contrast of the whole original image; to extract the region of interest and to enhance the edges surrounding that region. Then, we extract features characterizing the underlying texture of the regions of interest by using the gray-level co-occurrence matrix. Then, the rough set approach to attribute reduction and rule generation is presented. Finally, rough neural network is designed for discrimination of different regions of interest to test whether they represent malignant cancer or benign cancer. Rough neural network is built from rough neurons, each of which can be viewed as a pair of sub-neurons, corresponding to the lower and upper bounds. To evaluate performance of the presented rough neural approach, we run tests over different mammogram images. The experimental results show that the overall classification accuracy offered by rough neural approach is high compared with other intelligent techniques