| Autor: |
Saeedvand, Saeed, Jafari, Masoumeh, Aghdasi, Hadi S., Baltes, Jacky, Rahmani, Amir Masoud |
| Předmět: |
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| Zdroj: |
Concurrency & Computation: Practice & Experience; 12/10/2022, Vol. 34 Issue 27, p1-18, 18p |
| Abstrakt: |
Summary: The Covid‐19 pandemic has affected many lives over the past year. In addition to the enormous health cost, the necessary lockdowns and government‐mandated suspension to prevent the spread of the virus had a huge economic impact. The new challenges in 2021 were combating new virus mutations and providing effective vaccines globally. Artificial intelligent (AI) and machine learning have made significant improvements in many different applications during the last decades. One of the advanced and robust technologies in machine learning is deep learning (DL), which can be employed to help prevent initial infections and detect and monitor their progress and side effects. Fast and accurate Covid‐19 infection detection and treatment of suspected patients is essential to make better decisions, ensure treatment, and even save patients' lives. Modern technologies are required to achieve these objectives and create a sustainable society. This article presents a taxonomy in DL algorithms to cover both the technical novelties and empirical results techniques for Covid‐19 in smart cities. In this regard, (i) we demonstrate possible DL algorithms capable of combating Covid‐19; (ii) we propose an up‐to‐date perspective of DL algorithms in social prevention and medical treatment; and (iii) we identify the challenges in combating Covid‐19 outbreaks. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
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