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Osamah Ali Abdullah
Researcher at Western Michigan University
Publications - 16
Citations - 55
Osamah Ali Abdullah is an academic researcher from Western Michigan University. The author has contributed to research in topics: Indoor positioning system & Divergence (statistics). The author has an hindex of 4, co-authored 12 publications receiving 38 citations.
Papers
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Book ChapterDOI
Machine Learning Algorithm for Wireless Indoor Localization
TL;DR: A new framework based on symmetric Bregman divergence, which incorporates k-nearest neighbor (kNN) classification in signal space, was proposed, which was compared with the probabilistic neural network and multivariate Kullback-Leibler divergence.
Proceedings ArticleDOI
K-means-Jensen-Shannon divergence for a WLAN indoor positioning system
TL;DR: The K-Means-Jensen-Shannon divergence is proposed, which is the original k-means algorithm extended into a meta-algorithm, and the results indicate that the integrated system outperforms other results with around 1m accuracy in an academic building.
Proceedings ArticleDOI
A probability neural network-Jensen-Shannon divergence for a fingerprint based localization
TL;DR: The proposed technique is based on a probabilistic neural network (PNN) scheme in which the Jensen-Shannon divergence method is incorporated and results indicate that the integrated system outperforms this method in terms of nearest neighbor estimation.
Journal ArticleDOI
Convex Optimization via Symmetrical Hölder Divergence for a WLAN Indoor Positioning System
TL;DR: This paper proposes that symmetrical Hölder divergence, which is a statistical model of entropy that encapsulates both the skew Bhattacharyya divergence and Cauchy–Schwarz divergence that can be used to measure the statistical dissimilarities between the same exponential family for the signals that have multivariate distributions.
Proceedings ArticleDOI
A PNN- Jensen-Bregman Divergence symmetrization for a WLAN Indoor Positioning System
TL;DR: A framework that incorporates the probabilistic neural network (PNN) and Jensen-Bregman Divergence (JBD) is proposed and the algorithm results have high accuracy with an error of less than 1m distance.