Natheer Khasawneh

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In this work, an efficient automated new approach for sleep stage identification based on the new standard of the American academy of sleep medicine (AASM) is presented. The propose approach employs time-frequency analysis and entropy measures for feature extraction from a single electroencephalograph (EEG) channel. Three time-frequency techniques were(More)
This work presents a new methodology for automated sleep stage identification in neonates based on the time frequency distribution of single electroencephalogram (EEG) recording and artificial neural networks (ANN). Wigner-Ville distribution (WVD), Hilbert-Hough spectrum (HHS) and continuous wavelet transform (CWT) time frequency distributions were used to(More)
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Clustering Web Usage data is one of the important tasks of Web Usage Mining, which helps to find Web user clusters and Web page clusters. Web user clusters establish groups of users exhibiting similar browsing patterns and Web page clusters provide useful knowledge to personalized Web services. Different types of clustering algorithms such as partition(More)
Web usage mining is the area of web mining which deals with extraction of useful knowledge from web log information produced by web servers. One of the most important tasks of Web Usage Mining (WUM) is web user clustering which forms groups of users exhibiting similar interests or similar browsing patterns. This paper presents results of clustering(More)