Artificial Intelligence Technologies Combating against COVID-19

Kumar, Anikesh; Sharma, Gopal Krishna

doi:10.36018/dsiij.v16i.164

Cited by 7 publications

(1 citation statement)

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“…The molecule can block the virus’s ability to replicate. SRI International and AI company Iktos announced that they are collaborating together to develop new antiviral therapies to treat COVID-19 (Kumar and Sharma 2020 ). The Researchers of Huazhong University of Science and Technology and Tongji Hospital in Wuhan developed XGBoost machine learning-based prognostic model which helps in diagnosing COVID-19 using blood samples with 90% accuracy (Dananjayan and Raj 2020 ).…”

Section: Disease Spread Prevention In Public Spherementioning

confidence: 99%

The role of data-driven artificial intelligence on COVID-19 disease management in public sphere: a review

Pillai¹,

Kumar²

2021

Decision

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Coronavirus disease 2019 (COVID-19) is an infectious disease with acute intense respiratory syndrome which spread around the world for the very first time impacting the way of life with drastic uncertainty. It rapidly reached almost every nook and corner of the world and the World Health Organization (WHO) has announced COVID-19 as a pandemic. The health care institutions around the globe are looking for viable and real-time technological solutions to handle the virus for evading its spread and circumvent probable demises. Importantly, the artificial intelligence tools and techniques are playing a major role in fighting the effect of virus on the economic jolt by mimicking human intelligence by screening, analyzing, predicting and tracking the existing and likely future patients. Since the first reported case, all the government organizations in the world jumped into action to prevent it and many studies reported the role of AI in taking decisions analyzing big data available in public sphere. Thereby, this review focuses on identifying the significant implication of AI techniques used for the COVID-19 disease management in the public sphere by agglomerating the latest available information. It also discusses the pitfalls and future directions in handling sensitive big data required for advanced neural networks.

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Section: Disease Spread Prevention In Public Spherementioning

confidence: 99%