Dr. Syed Islam
Edith Cowan University, Perth, Western Australia, Australia
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Academic Career Length
22Years (2005-Present)
(Calculated once Scilit publication data is synced: current year − year of first publication + 1)
Representative Publications
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Journal Article
The k-means clustering algorithm is considered one of the most powerful and popular data mining algorithms in the research community. However, despite its popularity, the algorithm has certain limitations, including problems associated with random initialization of the centroids which leads to unexpected convergence. Additionally, such a clustering algorithm requires the number of clusters to be defined beforehand, which is responsible for different cluster shapes and outlier effects. A fundamental problem of the k-means algorithm is its inability to handle various data types. This paper provides a structured and synoptic overview of research conducted on the k-means algorithm to overcome such shortcomings. Variants of the k-means algorithms including their recent developments are discussed, where their effectiveness is investigated based on the experimental analysis of a variety of datasets. The detailed experimental analysis along with a thorough comparison among different k-means clustering algorithms differentiates our work compared to other existing survey papers. Furthermore, it outlines a clear and thorough understanding of the k-means algorithm along with its different research directions.
16 July 2019 in IEEE Access
Journal Article
FAFouzia Altaf
Fouzia AltafSyed IslamNANAVEED AKHTAR
NAVEED AKHTARNJNaeem Janjua
Naeem JanjuaMedical image analysis is currently experiencing a paradigm shift due to deep learning. This technology has recently attracted so much interest of the Medical Imaging Community that it led to a specialized conference in “Medical Imaging with Deep Learning” in the year 2018. This paper surveys the recent developments in this direction and provides a critical review of the related major aspects. We organize the reviewed literature according to the underlying pattern recognition tasks and further sub-categorize it following a taxonomy based on human anatomy. This paper does not assume prior knowledge of deep learning and makes a significant contribution in explaining the core deep learning concepts to the non-experts in the Medical Community. This paper provides a unique computer vision/machine learning perspective taken on the advances of deep learning in medical imaging. This enables us to single out “lack of appropriately annotated large-scale data sets” as the core challenge (among other challenges) in this research direction. We draw on the insights from the sister research fields of computer vision, pattern recognition, and machine learning, where the techniques of dealing with such challenges have already matured, to provide promising directions for the Medical Imaging Community to fully harness deep learning in the future.
1 April 2022 in Robotics and Autonomous Systems
Journal Article
MMMD Moniruzzaman
MD MoniruzzamanARAlexander Rassau
Alexander RassauDCDouglas Chai
Douglas ChaiSyed Islam23 November 2017
Book Chapter
MMMD Moniruzzaman
MD MoniruzzamanSyed Islam
Mohammed BennamounPLPaul Lavery
Paul LaveryJournal Article
Today’s rapid growth of elderly populations and aging problems coupled with the prevalence of obstructive sleep apnea (OSA) and other health related issues have affected many aspects of society. This has led to high demands for a more robust healthcare monitoring, diagnosing and treatments facilities. In particular to Sleep Medicine, sleep has a key role to play in both physical and mental health. The quality and duration of sleep have a direct and significant impact on people’s learning, memory, metabolism, weight, safety, mood, cardio-vascular health, diseases, and immune system function. The gold-standard for OSA diagnosis is the overnight sleep monitoring system using polysomnography (PSG). However, despite the quality and reliability of the PSG system, it is not well suited for long-term continuous usage due to limited mobility as well as causing possible irritation, distress, and discomfort to patients during the monitoring process. These limitations have led to stronger demands for non-contact sleep monitoring systems. The aim of this paper is to provide a comprehensive review of the current state of non-contact Doppler radar sleep monitoring technology and provide an outline of current challenges and make recommendations on future research directions to practically realize and commercialize the technology for everyday usage.
1 April 2011 in International Journal of Computer Vision
Journal Article
Syed IslamRDRowan Davies
Rowan DaviesMohammed BennamounAMAjmal Mian
Ajmal MianAbout Me
Dr Syed completed his PhD with Distinction in Computer Engineering (CE) from the University of Western Australia (UWA) in 2011. He is a founding member of the School of Science Centre for AI and ML (CAIML).
Upon joining ECU, he established the 3D sensing, visualization and analytics lab with the state-of-the-art computing and imaging devices in 2017, securing an internal funding of USD 115,368. He has also secured the NHMRC Ideas Grant 2019 (AUD 467,980) with Dr Joshua Lewis and 12 other external research grants, totaling over a million Australian dollars.
He has published over 65 fully refereed scholarly articles including 20 journals (13 in WoS Quartile-1 including one in top 1%), and three conference papers with the best paper awards. He also attracted tens of public media releases including TV news (that reached an audience of 7.75 million people), and awards including the High Achieving Researcher 2021 award by ECU. He is active within national and international professional organizations, e.g., Coordinator of Educational activities and Chair of the Signal Processing Chapter of IEEE WA Section, Associate Editor of IEEE Access, a regular reviewer of over 10 Q1 journals, Organizing/Technical Committee member of over 35 conferences and a Senior member of both IEEE and Australian Computer Society. His research expertise in the areas of Artificial Intelligence, Machine Learning, Medical Imaging, and Computer Vision is invaluable for the success of the project.