Jamal Bzai
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Jamal Bzai has a total of 6 co-authors. Here are the top co-authors by number of shared publications:
- Arwa Dhafer
College of Computing and Informatics, Saudi Electronic University, Riyadh 13316, Saudi Arabia
1 shared publication - Furqan Alam
Faculty of Computing and Information Technology (FoCIT), Sohar University, Sohar 311, Oman
1 shared publication - Imran Khan Niazi
New Zealand College of Chiropractic
1 shared publication - Miroslav Bojović
School of Electrical Engineering, University of Belgrade, 11000 Belgrade, Serbia
1 shared publication - Rashid Mehmood
Islamic University of Madinah
1 shared publication - Saleh M. Altowaijri
Department of Information Systems, Faculty of Computing and Information Technology, Northern Border University, Rafha 91911, Saudi Arabia
1 shared publication
Latest Publications
- Open Access
- 26 August 2022
- Published by MDPI AG in Electronics
- Vol. 11 (17) , 2676
Machine Learning-Enabled Internet of Things (IoT): Data, Applications, and Industry Perspective
Machine learning (ML) allows the Internet of Things (IoT) to gain hidden insights from the treasure trove of sensed data and be truly ubiquitous without explicitly looking for knowledge and data patterns. Without ML, IoT cannot withstand the future requirements of businesses, governments, and individual users. The primary goal of IoT is to perceive what is happening in our surroundings and allow automation of decision-making through intelligent methods, which will mimic the decisions made by humans. In this paper, we classify and discuss the literature on ML-enabled IoT from three perspectives: data, application, and industry. We elaborate with dozens of cutting-edge methods and applications through a review of around 300 published sources on how ML and IoT work together to play a crucial role in making our environments smarter. We also discuss emerging IoT trends, including the Internet of Behavior (IoB), pandemic management, connected autonomous vehicles, edge and fog computing, and lightweight deep learning. Further, we classify challenges to IoT in four classes: technological, individual, business, and society. This paper will help exploit IoT opportunities and challenges to make our societies more prosperous and sustainable.
- 26 August 2022
- Published by MDPI AG in Electronics
- Vol. 11 (17) , 2676
Open AccessMachine Learning-Enabled Internet of Things (IoT): Data, Applications, and Industry Perspective
Machine learning (ML) allows the Internet of Things (IoT) to gain hidden insights from the treasure trove of sensed data and be truly ubiquitous without explicitly looking for knowledge and data patterns. Without ML, IoT cannot withstand the future requirements of businesses, governments, and individual users. The primary goal of IoT is to perceive what is happening in our surroundings and allow automation of decision-making through intelligent methods, which will mimic the decisions made by humans. In this paper, we classify and discuss the literature on ML-enabled IoT from three perspectives: data, application, and industry. We elaborate with dozens of cutting-edge methods and applications through a review of around 300 published sources on how ML and IoT work together to play a crucial role in making our environments smarter. We also discuss emerging IoT trends, including the Internet of Behavior (IoB), pandemic management, connected autonomous vehicles, edge and fog computing, and lightweight deep learning. Further, we classify challenges to IoT in four classes: technological, individual, business, and society. This paper will help exploit IoT opportunities and challenges to make our societies more prosperous and sustainable.