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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The Internet of Things for Health Care: A Comprehensive Survey
TL;DR: An intelligent collaborative security model to minimize security risk is proposed; how different innovations such as big data, ambient intelligence, and wearables can be leveraged in a health care context is discussed; and various IoT and eHealth policies and regulations are addressed to determine how they can facilitate economies and societies in terms of sustainable development.
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Power-Domain Non-Orthogonal Multiple Access (NOMA) in 5G Systems: Potentials and Challenges
TL;DR: This paper comprehensively surveys the recent progress of NOMA in 5G systems, reviewing the state-of-the-art capacity analysis, power allocation strategies, user fairness, and user-pairing schemes in NomA.
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Power-Domain Non-Orthogonal Multiple Access (NOMA) in 5G Systems: Potentials and Challenges
TL;DR: In this paper, the authors comprehensively survey the recent progress of NOMA in 5G systems, reviewing the state-of-the-art capacity analysis, power allocation strategies, user fairness, and user-pairing schemes in NOMAs.
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A Smart Healthcare Monitoring System for Heart Disease Prediction Based On Ensemble Deep Learning and Feature Fusion
Farman Ali,Shaker El-Sappagh,Shaker El-Sappagh,S. M. Riazul Islam,Daehan Kwak,Amjad Ali,Muhammad Imran,Kyung Sup Kwak +7 more
TL;DR: A smart healthcare system is proposed for heart disease prediction using ensemble deep learning and feature fusion approaches and obtains accuracy of 98.5%, which is higher than existing systems.
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Resource Allocation for Downlink NOMA Systems: Key Techniques and Open Issues
TL;DR: In this paper, the authors proposed a divide-and-next-largest-difference-based user pairing algorithm to distribute the capacity gain among the NOMA clusters in a controlled manner.