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  • Open Access

    ARTICLE

    Optimization of Coronavirus Pandemic Model Through Artificial Intelligence

    Manal. M. Alqarni1, Arooj Nasir2,3, Dumitru Baleanu4,5,6, Ali Raza7, Tahir Nawaz Cheema8,*, Nauman Ahmed9, Muhammad Rafiq10, Umbreen Fatima11, Emad E. Mahmoud12

    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 6807-6822, 2023, DOI:10.32604/cmc.2023.033283 - 28 December 2022

    Abstract Artificial intelligence is demonstrated by machines, unlike the natural intelligence displayed by animals, including humans. Artificial intelligence research has been defined as the field of study of intelligent agents, which refers to any system that perceives its environment and takes actions that maximize its chance of achieving its goals. The techniques of intelligent computing solve many applications of mathematical modeling. The research work was designed via a particular method of artificial neural networks to solve the mathematical model of coronavirus. The representation of the mathematical model is made via systems of nonlinear ordinary differential equations.… More >

  • Open Access

    ARTICLE

    Machine learning techniques applied to the coronavirus pandemic: a systematic and bibliometric analysis from January 2020 to June 2021

    Maria Teresinha Arns Steiner1, David Gabriel de Barros Franco2, Pedro José Steiner Neto3

    Revista Internacional de Métodos Numéricos para Cálculo y Diseño en Ingeniería, Vol.38, No.3, pp. 1-15, 2022, DOI:10.23967/j.rimni.2022.09.001 - 09 September 2022

    Abstract During the pandemic caused by the Coronavirus (Covid-19), Machine Learning (ML) techniques can be used, among other alternatives, to detect the virus in its early stages, which would aid a fast recovery and help to ease the pressure on healthcare systems. In this study, we present a Systematic Literature Review (SLR) and a Bibliometric Analysis of ML technique applications in the Covid-19 pandemic, from January 2020 to June 2021, identifying possible unexplored gaps. In the SLR, the 117 most cited papers published during the period were analyzed and divided into four categories: 22 articles that… More >

  • Open Access

    ARTICLE

    Structure Preserving Algorithm for Fractional Order Mathematical Model of COVID-19

    Zafar Iqbal1,2, Muhammad Aziz-ur Rehman1, Nauman Ahmed1,2, Ali Raza3,4, Muhammad Rafiq5, Ilyas Khan6,*, Kottakkaran Sooppy Nisar7

    CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 2141-2157, 2022, DOI:10.32604/cmc.2022.013906 - 07 December 2021

    Abstract In this article, a brief biological structure and some basic properties of COVID-19 are described. A classical integer order model is modified and converted into a fractional order model with as order of the fractional derivative. Moreover, a valued structure preserving the numerical design, coined as Grunwald–Letnikov non-standard finite difference scheme, is developed for the fractional COVID-19 model. Taking into account the importance of the positivity and boundedness of the state variables, some productive results have been proved to ensure these essential features. Stability of the model at a corona free and a corona existing More >

  • Open Access

    ARTICLE

    Prediction Model for Coronavirus Pandemic Using Deep Learning

    Mamoona Humayun1,*, Ahmed Alsayat2

    Computer Systems Science and Engineering, Vol.40, No.3, pp. 947-961, 2022, DOI:10.32604/csse.2022.019288 - 24 September 2021

    Abstract The recent global outbreak of COVID-19 damaged the world health systems, human health, economy, and daily life badly. None of the countries was ready to face this emerging health challenge. Health professionals were not able to predict its rise and next move, as well as the future curve and impact on lives in case of a similar pandemic situation happened. This created huge chaos globally, for longer and the world is still struggling to come up with any suitable solution. Here the better use of advanced technologies, such as artificial intelligence and deep learning, may… More >

  • Open Access

    ARTICLE

    Implementing Effective Learning with Ubiquitous Learning Technology During Coronavirus Pandemic

    Hosam F. El-Sofany1,2,*, Samir A. El-Seoud3

    Computer Systems Science and Engineering, Vol.40, No.1, pp. 389-404, 2022, DOI:10.32604/csse.2022.018619 - 26 August 2021

    Abstract Ubiquitous computing supports U-learning to develop and implement a new educational environment that provides effective and interactive learning to students wherever they are. This study aims to present a qualitative evaluation for using U-learning instead of traditional education to avoid the spread of the Coronavirus pandemic. The authors introduce a UTAUT (Unified Theory of Acceptance and Use of Technology) model to assess the capability of the given factors that are expected to affect the learners’ intention and behavior for accepting the U-learning technology for full E-learning. The research study shows a promising impact on the… More >

  • Open Access

    ARTICLE

    An Effective Numerical Method for the Solution of a Stochastic Coronavirus (2019-nCovid) Pandemic Model

    Wasfi Shatanawi1,2,3, Ali Raza4,5,*, Muhammad Shoaib Arif4, Kamaledin Abodayeh1, Muhammad Rafiq6, Mairaj Bibi7

    CMC-Computers, Materials & Continua, Vol.66, No.2, pp. 1121-1137, 2021, DOI:10.32604/cmc.2020.012070 - 26 November 2020

    Abstract Nonlinear stochastic modeling plays a significant role in disciplines such as psychology, finance, physical sciences, engineering, econometrics, and biological sciences. Dynamical consistency, positivity, and boundedness are fundamental properties of stochastic modeling. A stochastic coronavirus model is studied with techniques of transition probabilities and parametric perturbation. Well-known explicit methods such as Euler Maruyama, stochastic Euler, and stochastic Runge–Kutta are investigated for the stochastic model. Regrettably, the above essential properties are not restored by existing methods. Hence, there is a need to construct essential properties preserving the computational method. The non-standard approach of finite difference is examined More >

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