Artificial Intelligence for Forecasting the Prevalence of COVID-19 Pandemic: An Overview

Coronavirus disease 2019 (COVID-19) is a novel disease that affects healthcare on a global scale and cannot be ignored because of its high fatality rate. Computed tomography (CT) images are presently being employed to assist doctors in detecting COVID-19 in its early stages. In several scenarios, a combination of epidemiological criteria (contact during the incubation period), the existence of clinical symptoms, laboratory tests (nucleic acid amplification tests), and clinical imaging-based tests are used to diagnose COVID-19. This method can miss patients and cause more complications. Deep learning is one of the techniques that has been proven to be prominent and reliable in several diagnostic domains involving medical imaging. This study utilizes a convolutional neural network (CNN), stacked autoencoder, and deep neural network to develop a COVID-19 diagnostic system. In this system, classification undergoes some modification before applying the three CT image techniques to determine normal and COVID-19 cases. A large-scale and challenging CT image dataset was used in the training process of the employed deep learning model and reporting their final performance. Experimental outcomes show that the highest accuracy rate was achieved using the CNN model with an accuracy of 88.30%, a sensitivity of 87.65%, and a specificity of 87.97%. Furthermore, the proposed system has outperformed the current existing state-of-the-art models in detecting the COVID-19 virus using CT images.

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“…An SAE is an unsupervised learning algorithm having a multilayer neural network with SAE [ 37 ]. It consists of input, hidden, and output layers.…”

Section: Methodsmentioning

confidence: 99%

Automated System for Identifying COVID-19 Infections in Computed Tomography Images Using Deep Learning Models

Abdulkareem

Mostafa

Al-Qudsy

et al. 2022

Journal of Healthcare Engineering

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“…The precision level of the selected algorithms can also be enhanced by increasing the dataset and input parameters. The other statistical checks, such as singular spectrum analysis (SPA), with the inclusion of other statistical metrics, such as normalized root-mean-square error (NRMSE), coefficient of variation (COV), overall index (OI), efficiency coefficient (EC), mean relative error (MRE), and residual mass coefficient (RMC), can also be applied to cross-verify the obtained results from the selected models [ 91 ]. The other ML approaches, such as ANN, Adaboost, and bagging regressor, can also be investigated to check the accuracy level for the prediction of required outcomes.…”

Section: Discussionmentioning

confidence: 99%

Application of Machine Learning Approaches to Predict the Strength Property of Geopolymer Concrete

Cao

Fang

et al. 2022

Materials

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Geopolymer concrete (GPC) based on fly ash (FA) is being studied as a possible alternative solution with a lower environmental impact than Portland cement mixtures. However, the accuracy of the strength prediction still needs to be improved. This study was based on the investigation of various types of machine learning (ML) approaches to predict the compressive strength (C-S) of GPC. The support vector machine (SVM), multilayer perceptron (MLP), and XGBoost (XGB) techniques have been employed to check the difference between the experimental and predicted results of the C-S for the GPC. The coefficient of determination (R2) was used to measure how accurate the results were, which usually ranged from 0 to 1. The results show that the XGB was a more accurate model, indicating an R2 value of 0.98, as opposed to SVM (0.91) and MLP (0.88). The statistical checks and k-fold cross-validation (CV) also confirm the high precision level of the XGB model. The lesser values of the errors for the XGB approach, such as mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE), were noted as 1.49 MPa, 3.16 MPa, and 1.78 MPa, respectively. These lesser values of the errors also indicate the high precision of the XGB model. Moreover, the sensitivity analysis was also conducted to evaluate the parameter’s contribution towards the anticipation of C-S of GPC. The use of ML techniques for the prediction of material properties will not only reduce the effort of experimental work in the laboratory but also minimize the cast and time for the researchers.

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“…It proves itself to be robust to noise, immune to multicollinearity, and sufficiently accurate for engineering applications [28]. The selected models are currently the most popular for similar regression problems [11,17,19,[29][30][31].…”

Section: Machine Learning Modelsmentioning

confidence: 99%

Machine Learning to Rate and Predict the Efficiency of Waterflooding for Oil Production

2022

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Waterflooding is a widely used secondary oil recovery technique. The oil and gas industry uses a complex reservoir numerical simulation and reservoir engineering analysis to forecast production curves from waterflooding projects. The application of such standard methods at the stage of assessing the potential of a huge number of projects could be computationally inefficient and requires a lot of effort. This paper demonstrates the applicability of machine learning to rate the outcome of waterflooding applied to an oil reservoir. We also explore the relationship of project evaluations by operators at the final stages with several performance metrics for forecasting. Real data about several thousand waterflooding projects in Texas are used in the current study. We compare the ML models rankings of the waterflooding efficiency and the expert rankings. Linear regression models along with neural networks and gradient boosting on decision threes are considered. We show that machine learning models allow reducing computational complexity and can be useful for rating the reservoirs, with respect to the effectiveness of waterflooding.

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Artificial Intelligence for Forecasting the Prevalence of COVID-19 Pandemic: An Overview

Cited by 38 publications

References 102 publications

Automated System for Identifying COVID-19 Infections in Computed Tomography Images Using Deep Learning Models

Automated System for Identifying COVID-19 Infections in Computed Tomography Images Using Deep Learning Models

Application of Machine Learning Approaches to Predict the Strength Property of Geopolymer Concrete

Machine Learning to Rate and Predict the Efficiency of Waterflooding for Oil Production

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