Comparative study of artificial neural network versus parametric method in COVID-19 data analysis

Shafiq, Anum; Çolak, Andaç Batur; Sindhu, Tabassum Naz; Lone, Showkat Ahmad; Alsubie, Abdelaziz; Jarad, Fahd

doi:10.1016/j.rinp.2022.105613

Cited by 50 publications

(28 citation statements)

References 60 publications

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“…The algorithms of Artificial Intelligence (AI) have been used frequently to model complex functions for which the traditional mathematical methods no longer suffice. The artificial neural networks are one of the artificial intelligence methods that have the capacity to perform highly accurate simulations with potent learning algorithms and training capacities [21]. For [22], the neural network is an automatic learning algorithm.…”

Section: Related Workmentioning

confidence: 99%

The Use of ICTs in the Digital Culture for Virtual Learning of University Students Applying an Artificial Neural Network Model

Rocha¹,

Zela²,

Torres³

et al. 2022

IJACSA

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Artificial neural networks are mathematical models of artificial intelligence that intend to reproduce the behavior of the human brain and whose main objective is the construction of systems that are capable of demonstrating certain intelligent behavior. The purpose of the investigation is to determine the influence of the use of Information and Communication Technologies (ICTs) in the digital culture in the learning process of university students in Peru and Bolivia in the context of the Coronavirus -COVID 19 sanitary emergency, through the application of artificial neural network models. The investigation has a quantitative focus, the applied type, with a correlational level and a non-experimental design. Data was recollected by means of a digital questionnaire, applied to students of two universities. The population is composed of 3980 students of the Universidad Privada Domingo Savio (UPDS, Tarija, Bolivia) and 1506 of the Universidad Nacional de Moquegua (UNAM, Moquegua, Peru). The sample consists of 496 students. The hypothetical-deductive and the artificial intelligence methods were used. It was determined that the ability to install software and data protection programs, the use of mobile devices for academic purposes and the command of specialized software are the most influential factors in the digital culture of the students at UNAM and UPDS.

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Section: Related Workmentioning

confidence: 99%

The Use of ICTs in the Digital Culture for Virtual Learning of University Students Applying an Artificial Neural Network Model

Rocha¹,

Zela²,

Torres³

et al. 2022

IJACSA

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“…Shafiq et al [19] developed this kind of model to predict the boundary layer flow of a single-walled carbon nanotube nanofluid. A comparative study of artificial neural network versus parametric method in COVID-19 data analysis was carried out in [20].…”

Section: Introductionmentioning

confidence: 99%

“…Note that the bound ‖y (n) i − y(t ni − τ)‖ in last term stands for the Lagrange interpolation error of the delay term (see (20)). It follows that…”

mentioning

confidence: 99%

Analysis of Exponential Runge–Kutta Methods for Differential Equations with Time Delay

Zhan

2022

Mathematical Problems in Engineering

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Numerous mathematical models simulating the phenomenon in science and engineering use delay differential equations. In this paper, we focus on the semilinear delay differential equations, which include a wide range of mathematical models with time lags, such as reaction-diffusion equation with delay, model of bacteriophage predation on bacteria in a chemostat, and so on. This paper is concerned with the stability and convergence properties of exponential Runge–Kutta methods for semilinear delay differential equations. GDN-stability and D-convergence of exponential Runge–Kutta methods are investigated. These two concepts are generalizations of the classical AN-stability and B-convergence for ordinary differential equations to delay differential equations. Sufficient conditions for GDN-stability are given by a newly introduced concept of strong exponential algebraic stability. Further, with the aid of diagonal stability, we show that exponential Runge–Kutta methods are D-convergent. The D-convergent orders are also examined. Numerical experiments are presented to illustrate the theoretical results.

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“…Recent studies have shown the versatility and usefulness of ANN models, for example, Shaquid et al [19] employed an ANN model to investigate a reliability model centered on the exponentiated Weibull distribution and the inverse power-law model. Moreover, Shaquid et al [20] demonstrated that ANNs are an excellent engineering tool for predicting survival and mortality rates.…”

Section: Introductionmentioning

confidence: 99%

Use of Artificial Neural Networks to Predict Wind‐Induced External Pressure Coefficients on a Low‐Rise Building: A Comparative Study

Rodríguez-Alcántara¹,

Pozos‐Estrada²

2022

Advances in Civil Engineering

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Wind flow on a bluff body is a complex and nonlinear phenomenon that has been mainly studied experimentally or analytically. Several mathematical methods have been developed to predict the wind-induced pressure distribution on bluff bodies; however, most of them result unpractical due to the mathematical complexity required. Long-short term memory artificial neural networks with deep learning have proven to be efficient tools in the solution of nonlinear phenomena, although the choice of a more efficient network model remains a topic of open discussion for researchers. The main objective of this study is to develop long-short term memory artificial neural network models to predict the external pressure distribution of a low-rise building. For the development of the artificial neural network models, the multilayer perceptron and the recurrent neural network were also employed for comparison purposes. To train the artificial neural networks, a database with the external pressure coefficients from boundary layer wind tunnel tests of a low-rise building is employed. The analysis results indicate that the long-short term memory artificial neural network model and the multilayer perceptron neural network outperform the recurrent neural network.

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Comparative study of artificial neural network versus parametric method in COVID-19 data analysis

Cited by 50 publications

References 60 publications

The Use of ICTs in the Digital Culture for Virtual Learning of University Students Applying an Artificial Neural Network Model

The Use of ICTs in the Digital Culture for Virtual Learning of University Students Applying an Artificial Neural Network Model

Analysis of Exponential Runge–Kutta Methods for Differential Equations with Time Delay

Use of Artificial Neural Networks to Predict Wind‐Induced External Pressure Coefficients on a Low‐Rise Building: A Comparative Study

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