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*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Article numbere7314
JournalConcurrency Computation Practice and Experience
Issue number27
Publication statusPublished - 2022 Dec 10


  • Covid-19
  • artificial intelligent
  • coronavirus
  • deep learning
  • machine learning
  • smart cities

ASJC Scopus subject areas

  • Software
  • Theoretical Computer Science
  • Computer Networks and Communications
  • Computer Science Applications
  • Computational Theory and Mathematics


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