Aboul Ella Hassanien (Abo)

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The use of computational intelligence systems such as rough sets, neural networks, fuzzy set, genetic algorithms, etc., for predictions and classification has been widely established. This paper presents a generic classification model based on a rough set approach and decision rules. To increase the efficiency of the classification process, boolean reasoning discretization algorithm is used to discretize the data sets. The approach is tested by a comparatif study of three different classifiers (decision rules, naive bayes and k-nearest neighbor) over three distinct discretization techniques (equal bigning, entropy and boolean reasoning). The rough set reduction technique is applied to find all the reducts of the data which contains the minimal subset of attributes that are associated with a class label for prediction. In this paper we adopt the genetic algorithms approach to reach reducts. Finally, decision rules were used as a classifier to evaluate the performance of the predicted reducts and classes. To evaluate the performance of our approach, we present tests on breast cancer data set. The experimental results obtained, show that the overall classification accuracy offered by the employed rough set approach and decision rules is high compared with other classification techniques including Bayes and k-nearest neighbor.

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In this paper, a novel approach is introduced to compare web sites by analysing their web page content. Each web page can be expressed as a set of entities called MicroGenres, which in turn are abstractions about design patterns and genres for representing the page content. This description is useful for web page and web site classification and for a deeper insight into the web site׳s social context.

The web site comparison is useful for extracting patterns which can be used for improving Web search engine effectiveness, the identification of best practices in web site design and of course in the organization of web page content to personalize the web user experience on a web site.

The effectiveness of the proposed approach was tested in a real world case, with e-shop web sites showing that a web site can be represented in a high level of abstraction by using MicroGenres, the contents of which can then be compared and given a measure corresponding to web site similarity. This measure is very useful for detecting web communities on the Web, i.e., a group of web sites sharing similar contents, and the result is essential in performing a focused and effective information search as well as minimizing web page retrieval.

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This article introduces a hybrid approach that combines the advantages of fuzzy sets, pulse coupled neural networks (PCNNs), and support vector machine, in conjunction with wavelet-based feature extraction. An application of MRI breast cancer imaging has been chosen and hybridization approach have been applied to see their ability and accuracy to classify the breast cancer images into two outcomes: normal or non-normal.
The introduced approach starts with an algorithm based on type-II fuzzy sets to enhance the contrast of the input images. This is followed by performing PCNN-based segmentation algorithm in order to identify the region of interest and to detect the boundary of the breast pattern. Then, wavelet-based features are extracted and normalized. Finally, a support vector machine classifier were employed to evaluate the ability of the lesion descriptors for discrimination of different regions of interest to determine whether they represent cancer or not. To evaluate the performance of presented approach, we present tests on different breast MRI images. The experimental results obtained, show that the overall accuracy offered by the employed machine learning techniques is high compared with other machine learning techniques including decision trees, rough sets, neural networks, and fuzzy ARTMAP.

This paper proposes an elementary pattern detection approach for viruses propagated through e-mail and address books using the non-uniform pheromone deposition mechanism of ant colony. The local temporary tabu memory has been used to learn the pattern and it can combine known information from past viruses with a type of prediction for future viruses. This is achieved through certain generated test signature of viruses associated with e-mail over landscape. A non-uniform and non-decreasing time function for pheromone deposition and evaporation ensures that subsequent ants who are close enough to a previously selected trial solution will follow the trajectory or test landscape. They are capable to examine gradually thicker deposition of pheromone over the trajectory. It is empirically shown that the proposed modified pheromone learning mechanism can be an alternative approach to detect virus pattern for e-mail messages.