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Othman Ibrahim

Researcher at Universiti Teknologi Malaysia

Publications -  178
Citations -  6487

Othman Ibrahim is an academic researcher from Universiti Teknologi Malaysia. The author has contributed to research in topics: Government & Collaborative filtering. The author has an hindex of 36, co-authored 173 publications receiving 4682 citations. Previous affiliations of Othman Ibrahim include Islamic Azad University & University of Manchester.

Papers
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Collaborative filtering recommender systems

TL;DR: This study presents an overview of the field of recommender systems with current generation of recommendation methods and examines comprehensively CF systems with its algorithms.
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A recommender system based on collaborative filtering using ontology and dimensionality reduction techniques

TL;DR: This research solves two main drawbacks of recommender systems, sparsity and scalability, using dimensionality reduction and ontology techniques, and uses ontology to improve the accuracy of recommendations in CF part.
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A systematic review and meta-Analysis of SWARA and WASPAS methods: Theory and applications with recent fuzzy developments

TL;DR: In this article, a systematic review of methodologies and applications with recent fuzzy developments of two new MCDM utility determining approaches including step-wise weight assessment ratio analysis (SWARA) and the Weighted Aggregated Sum Product Assessment (WASPAS) is presented.
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A Systematic Review and Meta-Analysis of Swara and Waspas Methods: Theory and Applications with Recent Fuzzy Developments

TL;DR: A systematic review of methodologies and applications with recent fuzzy developments of two new MCDM utility determining approaches including Step-wise Weight Assessment Ratio Analysis (SWARA) and the Weighted Aggregated Sum Product Assessment (WASPAS) and fuzzy extensions which discussed in recent years are presented.
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A knowledge-based system for breast cancer classification using fuzzy logic method

TL;DR: Experimental results on Wisconsin Diagnostic Breast Cancer and Mammographic mass datasets show that proposed methods remarkably improves the prediction accuracy of breast cancer.