Internet of Things with Deep Learning Techniques for Pandemic Detection: A Comprehensive Review of Current Trends and Open Issues

被引:7
作者
Ajagbe, Sunday Adeola [1 ,2 ]
Mudali, Pragasen [1 ]
Adigun, Matthew Olusegun [1 ]
机构
[1] Univ Zululand, Dept Comp Sci, ZA-3886 Kwa Dlangezwa, South Africa
[2] First Tech Univ, Dept Comp Engn, Ibadan 200255, Nigeria
关键词
artificial intelligence (AI); Internet of Things (IoT); sensor; deep learning techniques; Machine Learning Techniques; pandemic detection; optimization algorithms; COVID-19; TECHNOLOGIES; ALGORITHM;
D O I
10.3390/electronics13132630
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Technological advancements for diverse aspects of life have been made possible by the swift development and application of Internet of Things (IoT) based technologies. IoT technologies are primarily intended to streamline various processes, guarantee system (technology or process) efficiency, and ultimately enhance the quality of life. An effective method for pandemic detection is the combination of deep learning (DL) techniques with the IoT. IoT proved beneficial in many healthcare domains, especially during the last worldwide health crisis: the COVID-19 pandemic. Using studies published between 2019 and 2024, this review seeks to examine the various ways that IoT-DL models contribute to pandemic detection. We obtained the titles, keywords, and abstracts of the chosen papers by using the Scopus and Web of Science (WoS) databases. This study offers a comprehensive review of the literature and unresolved problems in applying IoT and DL to pandemic detection in 19 papers that were eligible to be read from start to finish out of 2878 papers that were initially accessed. To provide practitioners, policymakers, and researchers with useful information, we examine a range of previous study goals, approaches used, and the contributions made in those studies. Furthermore, by considering the numerous contributions of IoT technologies and DL as they help in pandemic preparedness and control, we provide a structured overview of the current scientific trends and open issues in this field. This review provides a thorough overview of the state-of-the-art routing approaches currently in use, as well as their limits and potential future developments, making it an invaluable resource for DL researchers and practitioners and it is a useful tool for multidisciplinary research.
引用
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页数:27
相关论文
共 85 条
[1]   Optimization Algorithms for Wireless Sensor Networks Node Localization: An Overview [J].
Ahmad, Rami ;
Alhasan, Waseem ;
Wazirali, Raniyah ;
Aleisa, Noura .
IEEE ACCESS, 2024, 12 :50459-50488
[2]   An IoT-Based Deep Learning Framework for Early Assessment of Covid-19 [J].
Ahmed, Imran ;
Ahmad, Awais ;
Jeon, Gwanggil .
IEEE INTERNET OF THINGS JOURNAL, 2021, 8 (21) :15855-15862
[3]   Deep learning modelling techniques: current progress, applications, advantages, and challenges [J].
Ahmed, Shams Forruque ;
Alam, Md. Sakib Bin ;
Hassan, Maruf ;
Rozbu, Mahtabin Rodela ;
Ishtiak, Taoseef ;
Rafa, Nazifa ;
Mofijur, M. ;
Ali, A. B. M. Shawkat ;
Gandomi, Amir H. .
ARTIFICIAL INTELLIGENCE REVIEW, 2023, 56 (11) :13521-13617
[4]  
Ajagbe SA, 2023, IoT-enabled convolutional neural networks: techniques and applications, P27, DOI [10.1201/9781003393030, DOI 10.1201/9781003393030]
[5]  
Ajagbe SA, 2023, 2023 IEEE AFRICON, P1, DOI [10.1109/AFRICON55910.2023.10293525, DOI 10.1109/AFRICON55910.2023.10293525]
[6]   Deep learning techniques for detection and prediction of pandemic diseases: a systematic literature review [J].
Ajagbe, Sunday Adeola ;
Adigun, Matthew O. .
MULTIMEDIA TOOLS AND APPLICATIONS, 2023, 83 (2) :5893-5927
[7]  
Ajwad A., 2023, J. Wirel. Mob. Netw. Ubiquitous Comput. Dependable Appl, V14, P1, DOI [10.58346/JOWUA.2023.I1.001, DOI 10.58346/JOWUA.2023.I1.001]
[8]  
Al-Khaleefa AS, 2023, SCI-BASEL, V6, DOI 10.3390/sci6010002
[9]   Enhancing patient information performance in internet of things-based smart healthcare system: Hybrid artificial intelligence and optimization approaches [J].
Ala, Ali ;
Simic, Vladimir ;
Pamucar, Dragan ;
Bacanin, Nebojsa .
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 2024, 131
[10]   Deep Learning Approaches for Detecting COVID-19 From Chest X-Ray Images: A Survey [J].
Alghamdi, Hanan S. ;
Amoudi, Ghada ;
Elhag, Salma ;
Saeedi, Kawther ;
Nasser, Jomanah .
IEEE ACCESS, 2021, 9 (09) :20235-20254