The DOI matches its Crossref record; 15 of the 16 references it cites were found in a registry or an index.
Explainable AI and Mass Surveillance System-Based Healthcare Framework to Combat COVID-I9 Like Pandemics
Abstract
Tactile edge technology that focuses on 5G or beyond 5G reveals an exciting approach to control infectious diseases such as COVID-19 internationally. The control of epidemics such as COVID-19 can be managed effectively by exploiting edge computation through the 5G wireless connectivity network. The implementation of a hierarchical edge computing system provides many advantages, such as low latency, scalability, and the protection of application and training model data, enabling COVID-19 to be evaluated by a dependable local edge server. In addition, many deep learning (DL) algorithms suffer from two crucial disadvantages: first, training requires a large COVID-19 dataset consisting of various aspects, which will pose challenges for local councils; second, to acknowledge the outcome, the findings of deep learning require ethical acceptance and clarification by the health care sector, as well as other contributors. In this article, we propose a B5G framework that utilizes the 5G network's low-latency, high-bandwidth functionality to detect COVID-19 using chest X-ray or CT scan images, and to develop a mass surveillance system to monitor social distancing, mask wearing, and body temperature. Three DL models, ResNet50, Deep tree, and Inception v3, are investigated in the proposed framework. Furthermore, blockchain technology is also used to ensure the security of healthcare data.
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“With sophisticated XAI, the reason for the diagnosis of a certain cancer is apparent and visible to the system’s user [169].”
From Federated learning-based AI approaches in smart healthcare: Concepts, taxonomies, challenges and open issues (2023) · cites this paper as [169]
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Cites
From local explanations to global understanding with explainable AI for trees
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5G-based smart healthcare network: architecture, taxonomy, challenges and future research directions
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Review of blockchain-based distributed energy: Implications for institutional development
Ahl, A., Yarime, M., Tanaka, K., & Sagawa, D. (2019) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2019.03.002 · Cited by 304
Secure UAV Communication Networks over 5G
Li, B., Fei, Z., Zhang, Y., & Guizani, M. (2019) · IEEE Wireless Communications · doi:10.1109/mwc.2019.1800458 · Cited by 202
Edge Intelligence in the Cognitive Internet of Things: Improving Sensitivity and Interactivity
Zhang⋆, Y., Ma, X., Zhang, J., Hossain, M. S., Muhammad, G., & Amin, S. U. (2019) · IEEE Network · doi:10.1109/mnet.2019.1800344 · Cited by 149
Self-citationFuture of Ultra-Dense Networks Beyond 5G: Harnessing Heterogeneous Moving Cells
Andreev, S. D., Петров, В. И., Döhler, M., & Yanıkömeroğlu, H. (2019) · IEEE Communications Magazine · doi:10.1109/mcom.2019.1800056 · Cited by 126
B5G and Explainable Deep Learning Assisted Healthcare Vertical at the Edge: COVID-I9 Perspective
Rahman, M. A., Hossain, M. S., Alrajeh, N. A., & Guizani, N. (2020) · IEEE Network · doi:10.1109/mnet.011.2000353 · Cited by 115
Self-citationEmotion recognition using secure edge and cloud computing
Hossain, M. S., & Muhammad, G. (2019) · Information Sciences · doi:10.1016/j.ins.2019.07.040 · Cited by 104
Self-citationAli, Z., Muhammad, G., & Alhamid, M. F. (2017) · IEEE Access · doi:10.1109/access.2017.2680467 · Cited by 90
Self-citationComputing and Processing on the Edge: Smart Pathology Detection for Connected Healthcare
Muhammad, G., Alhamid, M. F., & Long, X. (2019) · IEEE Network · doi:10.1109/mnet.001.1900045 · Cited by 76
Self-citationReinforcement Learning-Based Radio Resource Control in 5G Vehicular Network
Zhou, Y., Tang, F., Kawamoto, Y., & Kato, N. (2019) · IEEE Wireless Communications Letters · doi:10.1109/lwc.2019.2962409 · Cited by 61
A Deep-Tree-Model-Based Radio Resource Distribution for 5G Networks
Hossain, M. S., & Muhammad, G. (2020) · IEEE Wireless Communications · doi:10.1109/mwc.001.1900286 · Cited by 37
Self-citationTree-Based Deep Networks for Edge Devices
Muhammad, G., Hossain, M. S., & Yassine, A. (2019) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2019.2950326 · Cited by 32
Self-citationLogical Explanations for Deep Relational Machines Using Relevance InformationPreprint
Srinivasan, A., Vig, L., & Bain, M. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1807.00595 · Cited by 17
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Yang, G., Ye, Q., & Xia, J. (2022) · Information Fusion · doi:10.1016/j.inffus.2021.07.016 · Cited by 717
A Review of Explainable Artificial Intelligence in Healthcare
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A Survey of Recent Advances in Edge-Computing-Powered Artificial Intelligence of Things
Chang, Z., Liu, S., Xiong, X., Cai, Z., & Tu, G. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3088875 · Cited by 431
Rahman, A., Hossain, M. S., Muhammad, G., Kundu, D., Debnath, T., Rahman, M., Khan, M. S. I., Tiwari, P., & Band, S. S. (2023) · Cluster Computing · doi:10.1007/s10586-022-03658-4 · Cited by 399
Self-citationFan, Z., Yan, Z., & Wen, S. (2023) · Sustainability · doi:10.3390/su151813493 · Cited by 417
Metadata
- Authors
- Hossain, M. S., Muhammad, G., & Guizani, N.
- Affiliations
- King Saud University (SA)
- Washington State University (US)
- Venue
- IEEE Network
- Published
- 2020-07
- Volume
- 34
- Issue
- 4
- Pages
- 126-132
- ISSN
- 0890-8044 (ISSN-L)
- 1558-156X
- OpenAlex
- W3041006163
- Cited by
- 347
- References
- 16
- Citations by year
- 2026: 14 · 2025: 35 · 2024: 36 · 2023: 58 · 2022: 69 · 2021: 112 · 2020: 23
- FWCI
- 30.78
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Network (publishedVersion)
- Funders
- King Saud University (RGP 228)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › COVID-19 diagnosis using AI
- Physical Sciences › Computer Science › Information Systems › Internet of Things and AI
- Keywords
- explainable AI, COVID-19 detection, mass surveillance, edge computing, 5G, beyond 5G, chest radiography, computed tomography, social distancing monitoring, face mask detection, body temperature monitoring, blockchain, healthcare data security, deep learning models, ResNet50, InceptionV3
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