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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)
User identification and session identification are two major steps in preprocessing Web log data for Web usage mining. This paper introduces a fast active user-based user identification algorithm with time complexity O(n). The algorithm uses both an IP address and a finite users' inactive time to identify different users in the Web log. Web site ontology is(More)
This paper compares and contrasts two feature selection techniques when applied to Arabic corpus; in particular; stemming, and light stemming were employed. With stemming, words are reduced to their stems. With light stemming, words are reduced to their light stems. Stemming is aggressive in the sense that it reduces words to their 3-letters roots. This(More)
ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM FOR AUTOMATIC SLEEP MULTISTAGE LEVEL SCORING EMPLOYING EEG, EOG, AND EMG EXTRACTED FEATURES Natheer Khasawneh a , Mohammad Abdel Kareem Jaradat b , Luay Fraiwan c & Mohamed Al-Fandi b a Department of Software Engineering, Faculty of Computer & Information Technology , Jordan University of Science and Technology , Irbid,(More)
PURPOSE This study describes stressors of Jordanian nurses and the social support they received to decrease the influence of these stressors. The relationships between the two concepts, and each with the sample's demographics were assessed. Predictors of nurses' stressors as well as social supportive behaviours were also studied. METHODS A descriptive(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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This paper studies the use of a rough set based learning program for predicting Web usage. In our approach, Web usage patterns are represented as rules generated by the inductive learning program, BLEM2. Inputs to BLEM2 are clusters generated by a hierarchical clustering algorithm applied to preprocessed Web log records. Empirical results show that the(More)
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)