Mohammad Majid al-Rifaie

Dr. Mohammad Majid al-Rifaie

School of Computing & Mathematical Sciences, Faculty of Engineering and Science, University of Greenwich, London SE10 9LS, UK
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Research Focus & Metrics

Academic Career Length

16Years (2011-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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6 March 2012 in Cognitive Computation
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Mohammad Majid al-RifaieMohammad Majid al-Rifaie
Mohammad Majid al-Rifaie
John BishopJohn Bishop
John Bishop
SCSuzanne Caines
Suzanne Caines

29 September 2014
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Mohammad Majid al-RifaieMohammad Majid al-Rifaie
Mohammad Majid al-Rifaie

1 December 2015 in IET Systems Biology
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Mohammad Majid al-RifaieMohammad Majid al-Rifaie
Mohammad Majid al-Rifaie
AAAhmed Aber
Ahmed Aber
Jude HemanthJude Hemanth
Jude Hemanth
This study proposes an umbrella deployment of swarm intelligence algorithm, such as stochastic diffusion search for medical imaging applications. After summarising the results of some previous works which shows how the algorithm assists in the identification of metastasis in bone scans and microcalcifications on mammographs, for the first time, the use of the algorithm in assessing the CT images of the aorta is demonstrated along with its performance in detecting the nasogastric tube in chest X‐ray. The swarm intelligence algorithm presented in this study is adapted to address these particular tasks and its functionality is investigated by running the swarms on sample CT images and X‐rays whose status have been determined by senior radiologists. In addition, a hybrid swarm intelligence‐learning vector quantisation (LVQ) approach is proposed in the context of magnetic resonance (MR) brain image segmentation. The particle swarm optimisation is used to train the LVQ which eliminates the iteration‐dependent nature of LVQ. The proposed methodology is used to detect the tumour regions in the abnormal MR brain images.

22 March 2017
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Mohammad Majid al-RifaieMohammad Majid al-Rifaie
Mohammad Majid al-Rifaie
AUAnna Ursyn
Anna Ursyn
RZRobert Zimmer
Robert Zimmer
MJMohammad Ali Javaheri Javid
Mohammad Ali Javaheri Javid

12 July 2011
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Mohammad Majid al-RifaieMohammad Majid al-Rifaie
Mohammad Majid al-Rifaie
MBMark John Bishop
Mark John Bishop
TBTim Blackwell
Tim Blackwell

27 January 2013 in Paladyn
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Mohammad Majid al-RifaieMohammad Majid al-Rifaie
Mohammad Majid al-Rifaie
John BishopJohn Bishop
John Bishop
Stochastic Diffusion Search, first incepted in 1989, belongs to the extended family of swarm intelligence algorithms. In contrast to many nature-inspired algorithms, stochastic diffusion search has a strong mathematical framework describing its behaviour and convergence. In addition to concisely exploring the algorithm in the context of natural swarm intelligence systems, this paper reviews various developments of the algorithm, which have been shown to perform well in a variety of application domains including continuous optimisation, implementation on hardware and medical imaging. This algorithm has also being utilised to argue the potential computational creativity of swarm intelligence systems through the two phases of exploration and exploitation.