Deep learning: A taxonomy of modern weapons to combat Covid-19 similar pandemics in smart cities

Saeed Saeedvand, Masoumeh Jafari, Hadi S. Aghdasi, Jacky Baltes, Amir Masoud Rahmani*


研究成果: 雜誌貢獻期刊論文同行評審


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.

期刊Concurrency Computation Practice and Experience
出版狀態已發佈 - 2022 12月 10

ASJC Scopus subject areas

  • 軟體
  • 理論電腦科學
  • 電腦網路與通信
  • 電腦科學應用
  • 計算機理論與數學


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