Applications of machine learning and artificial intelligence for Covid-19 (SARS-CoV-2) pandemic: A review

Lalmuanawma, Samuel; Hussain, Jamal; Chhakchhuak, Lalrinfela

doi:10.1016/j.chaos.2020.110059

Cited by 714 publications

(523 citation statements)

References 40 publications

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“…Rafiq et al [13] used a single input single output (SISO) based machine learning model for COVID-19 estimation. One can find a few relevant pieces of literature in Fong et al [14] , Mahalle et al [15] and Lalmuanawma et al [16] . Table 1 shows selected works on the prediction of COVID-19 using machine learning/ artificial intelligence (AI) models.…”

Section: Introductionmentioning

confidence: 99%

Modelling and forecasting of COVID-19 spread using wavelet-coupled random vector functional link networks

Hazarika

Gupta

2020

Applied Soft Computing

105

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Researchers around the world are applying various prediction models for COVID-19 to make informed decisions and impose appropriate control measures. Because of a high degree of uncertainty and lack of necessary data, the traditional models showed low accuracy over the long term forecast. Although the literature contains several attempts to address this issue, there is a need to improve the essential prediction capability of existing models. Therefore, this study focuses on modelling and forecasting of COVID-19 spread in the top 5 worst-hit countries as per the reports on 10th July 2020. They are Brazil, India, Peru, Russia and the USA. For this purpose, the popular and powerful random vector functional link (RVFL) network is hybridized with 1-D discrete wavelet transform and a wavelet-coupled RVFL (WCRVFL) network is proposed. The prediction performance of the proposed model is compared with the state-of-the-art support vector regression (SVR) model and the conventional RVFL model. A 60 day ahead daily forecasting is also shown for the proposed model. Experimental results indicate the potential of the WCRVFL model for COVID-19 spread forecasting.

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Section: Introductionmentioning

confidence: 99%

Modelling and forecasting of COVID-19 spread using wavelet-coupled random vector functional link networks

Hazarika

Gupta

2020

Applied Soft Computing

105

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“…CNN is one of deep learning algorithms and can automatically learn the most predictive representations in a manner of layer-by-layer feature combinations while it is also notorious for consuming large amounts of data [11] . The current structure of CNN has achieved much success in the detection of COVID-19 from X-Rays [12] , treatment, medication, screening and prediction for the COVID -19 [ 13 , 14 ], etc. To data, several work groups have implemented the use of CNN for automatic detection of COVID-19 on chest CT [15] , [16] , [17] , [18] .…”

Section: Introductionmentioning

confidence: 99%

Automatic distinction between COVID-19 and common pneumonia using multi-scale convolutional neural network on chest CT scans

Yan

Wong²,

Ren

et al. 2020

Chaos, Solitons & Fractals

107

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Highlights A CT dataset contains 416 COVID-19 positive CT scans and 412 common pneumonia CT scans is publicly available. A multi-scale convolutional neural network can accurately differentiate COVID-19 and other common pneumonia on chest CT scans with limited number of training data. An AI system has comparable diagnostic sensitivity (89.1% vs 84.8%, p -value = 0.724), specificity (85.7% vs 83.3%, p -value = 1.000), and accuracy (87.5% vs 84.1%, p -value = 0.804) than experienced radiologists.

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“…Artificial intelligence and its panoply of methodologies were rendered only partly effective during the pandemic ( 2 ). The following not an exhaustive review but rather a summary of some of the activities in using artificial intelligence in the 1) epidemiology, 2) diagnosis, and 3) therapy during the pandemic, with each section followed by a short personal observation: Epidemiology of COVID-19 …”

Section: Global Health Primer With Relevance To Artificial Intelligenmentioning

confidence: 99%

Artificial intelligence and COVID-19: Present state and future vision

Chang¹

2020

Intelligence-Based Medicine

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The COVID-19 pandemic has lead to catastrophic number of deaths and revealed that much work still remains with data and artificial intelligence. To fully comprehend the dynamics of a pandemic with relevance to artificial intelligence, a primer on global health concepts is first presented. Following this, various aspects of diagnosis and therapy and the relationship to artificial intelligence are presented along with a future projection of an ideal deployment of artificial intelligence in a pandemic. Final thoughts are made about lessons learned and what lies ahead.

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Applications of machine learning and artificial intelligence for Covid-19 (SARS-CoV-2) pandemic: A review

Cited by 714 publications

References 40 publications

Modelling and forecasting of COVID-19 spread using wavelet-coupled random vector functional link networks

Modelling and forecasting of COVID-19 spread using wavelet-coupled random vector functional link networks

Automatic distinction between COVID-19 and common pneumonia using multi-scale convolutional neural network on chest CT scans

Artificial intelligence and COVID-19: Present state and future vision

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