@article{2b6ca86de15249deaf543aedee3d6c60,
title = "Deep learning: A taxonomy of modern weapons to combat Covid-19 similar pandemics in smart cities",
abstract = "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.",
keywords = "Covid-19, artificial intelligent, coronavirus, deep learning, machine learning, smart cities",
author = "Saeed Saeedvand and Masoumeh Jafari and Aghdasi, {Hadi S.} and Jacky Baltes and Rahmani, {Amir Masoud}",
note = "Funding Information: information Ministry of Science and Technology, Taiwan, Grant/Award Numbers: MOST 107-2811-E-003-503; MOST 108-2634-F-003-004; MOST 108-2634-F-003-003; MOST 108-2634-F-003-002; Ministry of Education (MOE) in Taiwan, National Taiwan Normal UniversityThis work was financially supported by the “Chinese Language and Technology Center” of National Taiwan Normal University (NTNU) from the Featured Areas Research Center Program within the framework of the Higher Education Sprout Project by the Ministry of Education (MOE) in Taiwan, and the Ministry of Science and Technology, Taiwan, under Grant Nos. MOST 108-2634-F-003-002, MOST 108-2634-F-003-003, and MOST 108-2634-F-003-004 (administered through Pervasive Artificial Intelligence Research [PAIR] Labs) as well as MOST 107-2811-E-003-503. We are grateful to the National Center for High-performance Computing for computer time and facilities to conduct this research. Funding Information: Ministry of Science and Technology, Taiwan, Grant/Award Numbers: MOST 107‐2811‐E‐003‐503; MOST 108‐2634‐F‐003‐004; MOST 108‐2634‐F‐003‐003; MOST 108‐2634‐F‐003‐002; Ministry of Education (MOE) in Taiwan, National Taiwan Normal University Funding information Funding Information: This work was financially supported by the “Chinese Language and Technology Center” of National Taiwan Normal University (NTNU) from the Featured Areas Research Center Program within the framework of the Higher Education Sprout Project by the Ministry of Education (MOE) in Taiwan, and the Ministry of Science and Technology, Taiwan, under Grant Nos. MOST 108‐2634‐F‐003‐002, MOST 108‐2634‐F‐003‐003, and MOST 108‐2634‐F‐003‐004 (administered through Pervasive Artificial Intelligence Research [PAIR] Labs) as well as MOST 107‐2811‐E‐003‐503. We are grateful to the National Center for High‐performance Computing for computer time and facilities to conduct this research. Publisher Copyright: {\textcopyright} 2022 John Wiley & Sons, Ltd.",
year = "2022",
month = dec,
day = "10",
doi = "10.1002/cpe.7314",
language = "English",
volume = "34",
journal = "Concurrency Computation Practice and Experience",
issn = "1532-0626",
publisher = "John Wiley and Sons Ltd",
number = "27",
}