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Adel Al-Jumaily
Researcher at University of Technology, Sydney
Publications - 204
Citations - 3535
Adel Al-Jumaily is an academic researcher from University of Technology, Sydney. The author has contributed to research in topics: Feature extraction & Support vector machine. The author has an hindex of 25, co-authored 197 publications receiving 2694 citations. Previous affiliations of Adel Al-Jumaily include Auckland University of Technology & Information Technology University.
Papers
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Journal ArticleDOI
Computer Aided Diagnostic Support System for Skin Cancer: A Review of Techniques and Algorithms
Ammara Masood,Adel Al-Jumaily +1 more
TL;DR: A framework for comparative assessment of skin cancer diagnostic models is suggested and results based on these models are reviewed, including those specifically developed for skin lesion diagnosis.
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Active Exoskeleton Control Systems: State of the Art
TL;DR: In this paper, a review of the control systems in the existing active exoskeleton in the last decade is presented, which can be categorized according to the model system, the physical parameters, the hierarchy and the usage.
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Feature subset selection using differential evolution and a statistical repair mechanism
TL;DR: The proposed DEFS is used to search for optimal subsets of features in datasets with varying dimensionality and is then utilized to aid in the selection of Wavelet Packet Transform best basis for classification problems, thus acting as a part of a feature extraction process.
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Orthogonal Fuzzy Neighborhood Discriminant Analysis for Multifunction Myoelectric Hand Control
TL;DR: A new dimensionality reduction method, referred to as orthogonal fuzzy neighborhood discriminant analysis (OFNDA), is proposed as a response to such a challenge, and practical results indicate the significance of OFNDA in comparison to many other feature projection methods.
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A Framework of Temporal-Spatial Descriptors-Based Feature Extraction for Improved Myoelectric Pattern Recognition
TL;DR: A new feature extraction framework that aims to give an enhanced representation of muscular activities through increasing the amount of information that can be extracted from individual and combined electromyogram (EMG) channels by using time-domain descriptors (TDDs).