Selected article for: "contact tracing and new virus"
Author: Kamalov, Firuz; Cherukuri, Aswani; Sulieman, Hana; Thabtah, Fadi; Hossain, Akbar
Title: Machine learning applications for COVID-19: A state-of-the-art review
  • Cord-id: 7ibvlt6v
  • Document date: 2021_1_19
    • ID: 7ibvlt6v
    Snippet: The COVID-19 pandemic has galvanized the machine learning community to create new solutions that can help in the fight against the virus. The body of literature related to applications of machine learning and artificial intelligence to COVID-19 is constantly growing. The goal of this article is to present the latest advances in machine learning research applied to COVID-19. We cover four major areas of research: forecasting, medical diagnostics, drug development, and contact tracing. We review a
    Document: The COVID-19 pandemic has galvanized the machine learning community to create new solutions that can help in the fight against the virus. The body of literature related to applications of machine learning and artificial intelligence to COVID-19 is constantly growing. The goal of this article is to present the latest advances in machine learning research applied to COVID-19. We cover four major areas of research: forecasting, medical diagnostics, drug development, and contact tracing. We review and analyze the most successful state of the art studies. In contrast to other existing surveys on the subject, our article presents a high level overview of the current research that is sufficiently detailed to provide an informed insight.

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