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Biography and Research Information
OverviewAI-generated summary
Syed M Rahman is a professor at Southern Arkansas University. His research interests include computer science and engineering, with a focus on cybersecurity and information security. He has published on topics such as phishing techniques and mitigation strategies, e-commerce security, and the application of multimedia technology in manufacturing. Rahman has also investigated factors influencing cloud computing adoption in developing countries and the evaluation of key success factors for Enterprise Resource Planning (ERP) implementation.
With over 18 years of teaching experience, Rahman has served as a dissertation chair and supervisor for numerous Ph.D. graduates. He holds editorial roles, including Editor-in-Chief for the International Journal on Cryptography and Information Security (IJCIS), and regularly serves as a reviewer for journals and conferences. His work extends to educational assessment and policy, including contributions to the Hawaii Statewide Assessment Program for Science. Rahman also engages in outreach to foster cybersecurity career pathways, collaborating with local educational institutions.
Rahman has managed grants from agencies such as the National Science Foundation (NSF) and the United States Department of Agriculture (USDA). His scholarly output includes 114 publications, with an h-index of 13 and 533 citations. He actively maintains a lab website to showcase his research activities and collaborates with other researchers, including Hong Cheng at Southern Arkansas University.
Metrics
- h-index: 13
- Publications: 114
- Citations: 533
Positions
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Professor 2009–presentUniversity of Hawaii at Hilo Computer Science ORCID
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ProfessorSouthern Arkansas University ORCID
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Part-time Faculty 2007–2021Capella University Information Security and Information Assurance ORCID
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Assistant Professor 2006–2009University of Wisconsin Platteville Computer Science and Software Engineering ORCID
Selected Publications
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CFCT: A Framework Using ConvNeXt With FPN, CBAM and A Topology Enhanced Loss Function for Skin Lesion Segmentation (2026)
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Skin Lesion Segmentation Using Unet With A Topology Term in Loss Function (2025)
Collaboration Network
Top Collaborators
- Skin Lesion Segmentation Using Unet With A Topology Term in Loss Function
- CFCT: A Framework Using ConvNeXt With FPN, CBAM and A Topology Enhanced Loss Function for Skin Lesion Segmentation
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