Shawki A. Al-Dubaee

Computational Intelligence Methods for Data Analysis and Mining of eLearning Activities

Enhancing the the effectiveness of web-based eduction has become one of the most important concerns within both educational engineering and information system fields. The development of information technologies has contributed to the growth in elearning as an important education method. This learning environment enables learners to participate in ’any time, any place’ personalized training. It has been known that the application of data mining and computational intelligent approaches can provide better learning environments, and in their effort to participate in this field, the authors introduced this study which consists in its first part of a survey of the applications of data mining and computational intelligence in web based education during (2004-2009), and the second part is a case study that aims to analyze students’ activities performed in a Learning Management System.

New Direction of Applied Wavelet Transform in Multilingual Web Information Retrieval

This paper presents a novel approach based on one of the signal processing tools in soft computing applied to Web information retrieval, namely wavelet transform. The influence of two parameters, wavelet functions (Mother wavelets) and decomposition level, on feature extraction and information retrieval ability of calibration model was investigated. The experimental results show that the proposed method performs accurate retrieval. This work is a step towards multilingual (English, Spanish, Arabic, Chinese (Simplified and Traditional), Korean, and Japanese) search engine.

New Strategy of Lossy Text Compression

This paper proposes a new strategy that is based on the signal processing tools applied to text compression of files namely, the wavelet transform and the fourier transform. The influence of compression size and threshold of wavelet filters and the fourier transform as well as two parameters: families of wavelet filters and decomposition levels, on compression factor of text files are investigated. The experimental results are shown that the wavelet and the fourier transforms are suitable for lossy text compression with non-stationary text signal files. In addition, the fourier transform is the most suitable with files which have same characters such as aaa.txt and aaaa.txt files. However, the results of wavelet and fourier transforms are loss less text compression with stationary text signal files (aaa.txt and aaaa.txt files). This research also represents a step forwards dealing with both images and text compression i.e. multimedia compression.

Multilingual Lossy Text Compression Using Wavelet Transform

Most of the algorithms of data compression were developed for English language. However, the aptitude of wavelet transform to be multilingual lossy text compression is promised. This paper proposes a new strategy that is based on the wavelet transform applied to text compression of files. The influences of two parameters, namely families of wavelet filters and decomposition levels, on compression factor of seven languages of text files are investigated. The experimental results are shown that the proposed method gives the satisfactory performance of wavelet transform. This work is a step forwards dealing with both images and text compression i.e. multimedia compression.

Language Identification Using Wavelet Transform and Artificial Neural Network

In traditional language identification methods, it is not so easy for search engines to find relevant language database of a given query. Therefore, there is a need to identify the relevant user’s natural language query of unknown document database in a better way by automatic language identification. This novel approach presents an automatic method for classification of English and Arabic language identification. The classifier used is a three-layered feed-forward artificial neural network and the feature vector is formed by calculating the wavelet coefficients. Three wavelet decomposition functions (filters), namely Haar, Bior 2.2 and Bior 3.1 have been used to extract the feature vector set and their performance has been compared.

A New Search Result Clustering Using Haar Wavelet Transform

In traditional search engines, it is not so easy for users to find relevant Web pages list (snippet) of a given query. Therefore, there is a need to retrieve and find the relevant information in a better way by clustering the search results. This paper presents a new approach for clustering search results using Haar wavelet transform. We explain how uses of Haar wavelet transform offer a reasonably good solution for the above.

Using Wavelet and Multi-wavelet Transforms for Web Information Retrieval of Czech Language

In this paper, we apply our novel approaches based on wavelet and multiwavelet transforms in Web information retrieval of Czech language [1, 2, 3]. The influence of wavelet and multiwavelet transforms on feature extraction and information retrieval ability of calibration model and solve problem of selecting optimum wavelet transform for sentence query entered of any language by Internet user was investigated. The empirical results show that performs accurate retrieval of the multiwavelet transform than scalar wavelet transform. The aptitude of multiwavelet transform to represent one language or domain of the multilingual world regardless of type, script, word order, direction of writing and difficult font problems of the language. This work is a step towards multilingual search engine.

Search Result Clustering Using Fuzzy C-Mean and Gustafon Kessel Algorithms: A Comparative Study

During the last few years, the search result clustering has attracted a substantial amount of research. In this paper, we present a comparative study of the performance of fuzzy clustering algorithms, namely Fuzzy C-Means (FCM), and Gustafson-Kessel (GK) algorithms with clustering search results. Therefore, there is a need to reduce the information, help filtering out irrelevant items, and favors exploration of unknown or dynamic domains in a better way by clustering the search results.