M
Mohd Hanafi Ahmad Hijazi
Researcher at Universiti Malaysia Sabah
Publications - 63
Citations - 843
Mohd Hanafi Ahmad Hijazi is an academic researcher from Universiti Malaysia Sabah. The author has contributed to research in topics: Computer science & Sentiment analysis. The author has an hindex of 11, co-authored 57 publications receiving 550 citations. Previous affiliations of Mohd Hanafi Ahmad Hijazi include Universiti Teknologi MARA & University of Liverpool.
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Automated "disease/no disease" grading of age-related macular degeneration by an image mining approach.
TL;DR: This study has demonstrated a proof-of-concept, image-mining technique for automated AMD grading that has the potential to be further developed as an automated grading tool for future whole-scale AMD screening programs.
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Self-adaptive population sizing for a tune-free differential evolution
TL;DR: Empirical testing results showed that DE with self-adaptive population size using relative encoding performed well in terms of the average performance as well as stability compared to absolute encoding version aswell as the original DE.
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A survey on the architecture, application, and security of software defined networking: challenges and open issues
Kashif Nisar,Kashif Nisar,Kashif Nisar,Emilia Rosa Jimson,Mohd Hanafi Ahmad Hijazi,Ian Welch,Rosilah Hassan,Azana Hafizah Mohd Aman,Ali Hassan Sodhro,Sandeep Pirbhulal,Sohrab Khan +10 more
TL;DR: The state-of-the-art contribution to Software Defined Networking is surveyed such as a comparison between SDN and traditional networking, and future direction of SDN security solutions is discussed in detail.
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A survey on Proof of Retrievability for cloud data integrity and availability: Cloud storage state-of-the-art, issues, solutions and future trends
TL;DR: The state-of-the-art of PoR is examined and the issues and challenges as a result of employing PoR specifically and cloud storage generally are described and some possible countermeasures to address the identified issues are suggested.
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Data mining techniques for the screening of age-related macular degeneration
TL;DR: Two data mining techniques are proposed and compared to support the automated screening for AMD that is founded on a hierarchical decomposition of the image set so that a tree representation is generated.