Literature DB >> 34131453

AI in Fighting Covid-19: Pandemic Management.

Abhishek Tripathi1, Parmeet Kaur1, Shwetha Suresh1.   

Abstract

Coronaviruses are a family of viruses found in several animal species, such as bats, cattle, cats, camels, and humans. With more than 1.6 million people dead worldwide, as of December 2020, the Covid-19 pandemic has brought about a unified need to address global health crises more aggressively. There is great urgency in decreasing the impact of a potential future outbreak, which can be done by gathering information about the disease and its effects on humans. Various artificial intelligence (AI) techniques can be utilized for the pandemic, such as COVID (CoV) management, a vast scientific field involving computers performing tasks capable of only human brains. Among the subsets of AI, there are Machine Learning (ML) techniques, which can learn from historical data examples without programming. While no prior data regarding the virus exists, the growing cases make for more data. In this research, we employ a literature review method to understand pandemic management's current state and how it can benefit by utilizing AI capabilities. Published by Elsevier B.V.

Entities:  

Keywords:  Artificial Intelligence; Coronavirus; Covid-19; Intelligent Cities; Machine Learning; Pandemic Management; Smart Cities

Year:  2021        PMID: 34131453      PMCID: PMC8191524          DOI: 10.1016/j.procs.2021.05.039

Source DB:  PubMed          Journal:  Procedia Comput Sci


  10 in total

1.  Artificial intelligence in health care.

Authors:  David Isaacs
Journal:  J Paediatr Child Health       Date:  2020-10       Impact factor: 1.954

Review 2.  Artificial Intelligence in Healthcare: Past, Present and Future.

Authors:  Ahmet İlker Tekkeşin
Journal:  Anatol J Cardiol       Date:  2019-10       Impact factor: 1.596

3.  Stopping Covid-19: A pandemic-management service value chain approach.

Authors:  Alok Baveja; Ajai Kapoor; Benjamin Melamed
Journal:  Ann Oper Res       Date:  2020-05-14       Impact factor: 4.854

4.  Role of biological Data Mining and Machine Learning Techniques in Detecting and Diagnosing the Novel Coronavirus (COVID-19): A Systematic Review.

Authors:  A S Albahri; Rula A Hamid; Jwan K Alwan; Z T Al-Qays; A A Zaidan; B B Zaidan; A O S Albahri; A H AlAmoodi; Jamal Mawlood Khlaf; E M Almahdi; Eman Thabet; Suha M Hadi; K I Mohammed; M A Alsalem; Jameel R Al-Obaidi; H T Madhloom
Journal:  J Med Syst       Date:  2020-05-25       Impact factor: 4.460

5.  Artificial intelligence vs COVID-19: limitations, constraints and pitfalls.

Authors:  Wim Naudé
Journal:  AI Soc       Date:  2020-04-28

Review 6.  Utility of Artificial Intelligence Amidst the COVID 19 Pandemic: A Review.

Authors:  Agam Bansal; Rana Prathap Padappayil; Chandan Garg; Anjali Singal; Mohak Gupta; Allan Klein
Journal:  J Med Syst       Date:  2020-08-01       Impact factor: 4.460

7.  Deep learning COVID-19 detection bias: accuracy through artificial intelligence.

Authors:  Shashank Vaid; Reza Kalantar; Mohit Bhandari
Journal:  Int Orthop       Date:  2020-05-27       Impact factor: 3.075

Review 8.  Emergence of New Disease: How Can Artificial Intelligence Help?

Authors:  Yurim Park; Daniel Casey; Indra Joshi; Jiming Zhu; Feng Cheng
Journal:  Trends Mol Med       Date:  2020-05-03       Impact factor: 11.951

9.  Can Building "Artificially Intelligent Cities" Safeguard Humanity from Natural Disasters, Pandemics, and Other Catastrophes? An Urban Scholar's Perspective.

Authors:  Tan Yigitcanlar; Luke Butler; Emily Windle; Kevin C Desouza; Rashid Mehmood; Juan M Corchado
Journal:  Sensors (Basel)       Date:  2020-05-25       Impact factor: 3.576

10.  Country-level pandemic risk and preparedness classification based on COVID-19 data: A machine learning approach.

Authors:  Jordan J Bird; Chloe M Barnes; Cristiano Premebida; Anikó Ekárt; Diego R Faria
Journal:  PLoS One       Date:  2020-10-28       Impact factor: 3.240

  10 in total

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