S
S. M. Riazul Islam
Researcher at Sejong University
Publications - 133
Citations - 8093
S. M. Riazul Islam is an academic researcher from Sejong University. The author has contributed to research in topics: Computer science & Communication channel. The author has an hindex of 24, co-authored 125 publications receiving 5501 citations. Previous affiliations of S. M. Riazul Islam include University of Dhaka & Inha University.
Papers
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Nonorthogonal Multiple Access (NOMA): How It Meets 5G and Beyond
TL;DR: An overview of non-orthogonal multiple access principles and applications is provided and several opportunities and challenges associated with the compatibility of NOMA with other advanced communication paradigms such as heterogeneous networks and millimeter wave communications are addressed.
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Medical Diagnostic Systems Using Artificial Intelligence (AI) Algorithms: Principles and Perspectives
Simarjeet Kaur,Jimmy Singla,Lewis Nkenyereye,Sudan Jha,Deepak Prashar,Gyanendra Prasad Joshi,Shaker El-Sappagh,Md. Saiful Islam,S. M. Riazul Islam +8 more
TL;DR: Some important insights are revealed into current and previous different AI techniques in the medical field used in today’s medical research, particularly in heart disease prediction, brain disease, prostate, liver disease, and kidney disease.
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Multiomics Analysis Reveals that GLS and GLS2 Differentially Modulate the Clinical Outcomes of Cancer.
Subbroto Kumar Saha,S. M. Riazul Islam,Mohammad Abdullah-Al-Wadud,Saiful Islam,Farman Ali,Kyoung Sik Park +5 more
TL;DR: A systematic multiomic analysis to determine whether glutaminases function as prognostic biomarkers in human cancers suggested that GLS and GLS2 expression differentially modulate the clinical outcomes of cancers.
Posted Content
Resource Allocation for Downlink NOMA Systems: Key Techniques and Open Issues
TL;DR: This article presents advances in resource allocation for downlink non-orthogonal multiple access (NOMA) systems, focusing on user pairing and power allocation algorithms, and introduces the concept of cluster fairness.
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Machine Learning and Deep Learning Approaches for Brain Disease Diagnosis: Principles and Recent Advances
Protima Khan,Md. Fazlul Kader,S. M. Riazul Islam,Aisha B. Rahman,Md. Shahriar Kamal,Masbah Uddin Toha,Kyung Sup Kwak +6 more
TL;DR: This study aims at finding the most accurate technique for detecting different brain diseases which can be employed for future betterment through a review on recent machine learning and deep learning approaches in detecting four brain diseases such as Alzheimer's disease, brain tumor, epilepsy, and Parkinson's disease.