Wesam M. Ashour

Work place: Islamic University of Gaza, Gaza, Palestine

E-mail: washour@iugaza.edu.ps


Research Interests:


Wesam Ashour has graduated in 2000 with B.Sc. in Electrical and Computer Engineering from Islamic University of Gaza. He has worked at IUG for 3 years as a teaching assistant before getting a studentship and traveling to UK for M.Sc. Dr. Ashour has finished his M.Sc. in Multimedia with Distinction in 2004 from the University of Birmingham, UK. During his M.Sc. study, he was one of the top two students in the class and he was awarded a prize for the best project 2003/2004. The project title is: Speech Recognition based on Lip Information. After that, he has returned back to Gaza and he has joined the staff of Electrical and Computer Engineering for one year. In 2005, he has got a scholarship from the University of the West of Scotland (UWS), UK, for his PhD. During his PhD study, he has worked in UWS as a teaching assistant and lab demonstrator for some modules. After he has graduated and got his PhD degree, he returned back again to the Islamic University of Gaza and he has joined the staff of Computer Engineering. Dr. Ashour is a researcher in Applied Computational Intelligence Research Unit in the University of the West of Scotland, UK since October, 2005. Dr. Ashour has been the head of the Computer Engineeing Department 2009-2010.

Author Articles
A Hybrid Unsupervised Density-based Approach with Mutual Information for Text Outlier Detection

By Ayman H. Tanira Wesam M. Ashour

DOI: https://doi.org/10.5815/ijisa.2023.05.04, Pub. Date: 8 Oct. 2023

The detection of outliers in text documents is a highly challenging task, primarily due to the unstructured nature of documents and the curse of dimensionality. Text document outliers refer to text data that deviates from the text found in other documents belonging to the same category. Mining text document outliers has wide applications in various domains, including spam email identification, digital libraries, medical archives, enhancing the performance of web search engines, and cleaning corpora used in document classification. To address the issue of dimensionality, it is crucial to employ feature selection techniques that reduce the large number of features without compromising their representativeness of the domain. In this paper, we propose a hybrid density-based approach that incorporates mutual information for text document outlier detection. The proposed approach utilizes normalized mutual information to identify the most distinct features that characterize the target domain. Subsequently, we customize the well-known density-based local outlier factor algorithm to suit text document datasets. To evaluate the effectiveness of the proposed approach, we conduct experiments on synthetic and real datasets comprising twelve high-dimensional datasets. The results demonstrate that the proposed approach consistently outperforms conventional methods, achieving an average improvement of 5.73% in terms of the AUC metric. These findings highlight the remarkable enhancements achieved by leveraging normalized mutual information in conjunction with a density-based algorithm, particularly in high-dimensional datasets.

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Data Clustering Using Wave Atom

By Bilal A. Shehada Mahmoud Z.Alkurdi Wesam M. Ashour

DOI: https://doi.org/10.5815/ijisa.2012.09.05, Pub. Date: 8 Aug. 2012

Clustering of huge spatial databases is an important issue which tries to track the densely regions in the feature space to be used in data mining, knowledge discovery, or efficient information retrieval. Clustering approach should be efficient and can detect clusters of arbitrary shapes because spatial objects cannot be simply abstracted as isolated points they have different boundary, size, volume, and location. In this paper we use discrete wave atom transformation technique in clustering to achieve more accurate result .By using multi-resolution transformation like wavelet and wave atom we can effectively identify arbitrary shape clusters at different degrees of accuracy. Experimental results on very large data sets show the efficiency and effectiveness of the proposed wave atom bases clustering approach compared to other recent clustering methods. Experimental result shows that we get more accurate result and denoised output than others.

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Efficient and Fast Initialization Algorithm for Kmeans Clustering

By Mohammed El Agha Wesam M. Ashour

DOI: https://doi.org/10.5815/ijisa.2012.01.03, Pub. Date: 8 Feb. 2012

The famous K-means clustering algorithm is sensitive to the selection of the initial centroids and may converge to a local minimum of the criterion function value. A new algorithm for initialization of the K-means clustering algorithm is presented. The proposed initial starting centroids procedure allows the K-means algorithm to converge to a “better” local minimum. Our algorithm shows that refined initial starting centroids indeed lead to improved solutions. A framework for implementing and testing various clustering algorithms is presented and used for developing and evaluating the algorithm.

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Cosine-Based Clustering Algorithm Approach

By Mohammed A. H. Lubbad Wesam M. Ashour

DOI: https://doi.org/10.5815/ijisa.2012.01.07, Pub. Date: 8 Feb. 2012

Due to many applications need the management of spatial data; clustering large spatial databases is an important problem which tries to find the densely populated regions in the feature space to be used in data mining, knowledge discovery, or efficient information retrieval. A good clustering approach should be efficient and detect clusters of arbitrary shapes. It must be insensitive to the outliers (noise) and the order of input data. In this paper Cosine Cluster is proposed based on cosine transformation, which satisfies all the above requirements. Using multi-resolution property of cosine transforms, arbitrary shape clusters can be effectively identified at different degrees of accuracy. Cosine Cluster is also approved to be highly efficient in terms of time complexity. Experimental results on very large data sets are presented, which show the efficiency and effectiveness of the proposed approach compared to other recent clustering methods.

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Other Articles