Hassan, Md Rafiul and Hassan, Mohammad Mehedi and Altaf, Meteb and Yeasar, Mostafa Shamin and Hossain, M. Imtiaz and Fatema, Kanis and Shaharin, Rubya and Ahmed, Adel F. (2021) B5g-enabled distributed artificial intelligence on edges for COVID-19 pandemic outbreak prediction. IEEE Nework, 35 (3). pp. 48-55. ISSN 0890-8044, DOI https://doi.org/10.1109/MNET.011.2000718.
Full text not available from this repository.Abstract
In this study, we leverage the fusion of edge computing, artificial intelligence (AI) methods, and facilities provided by B5G to build a heterogeneous set of AI techniques for COVID-19 outbreak prediction. Advancement in the areas of AI, edge computing, the Internet of Things (IoT), and fast communication networks provided by beyond 5G (B5G) networks has opened doors for new possibilities by fusing these technologies and techniques. In a pandemic outbreak, such as COVID-19, the need for rapid analysis, decision making, and prediction of future trends becomes paramount. On a global map, the distributed processing and analysis of data at the source is now possible and much more efficient. With the features provided by B5G, such as low latency, larger area coverage, higher data rate, and realtime communication, building new intelligent and efficient frameworks is becoming easier. In this study, our aim is to achieve higher accuracy in prediction by fusing multiple AI methods and leveraging the B5G communication architecture. We propose a distributed architecture for training AI methods on edge devices, with the results of edge-trained models then propagated to a central cloud AI method, which then combines all the received edge-trained models into a global and final prediction model. The experimental results of five countries (United States, India, Italy, Bangladesh, and Saudi Arabia) show that the proposed distributed AI on edges can predict COVID-19 outbreak better than that of each individual AI method in terms of correlation coefficient scores.
| Item Type: | Article |
|---|---|
| Funders: | King Saud University |
| Uncontrolled Keywords: | COVID-19; Training; Pandemics; Architecture; Distributed databases; Computer architecture; Predictive models |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science T Technology > TA Engineering (General). Civil engineering (General) |
| Divisions: | Faculty of Computer Science & Information Technology > Department of Artificial Intelligence |
| Depositing User: | Ms Zaharah Ramly |
| Date Deposited: | 12 Jul 2022 04:15 |
| Last Modified: | 12 Jul 2022 04:15 |
| URI: | http://eprints.um.edu.my/id/eprint/33927 |
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