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  • Open Access

    ARTICLE

    Automatic Detection of COVID-19 Using Chest X-Ray Images and Modified ResNet18-Based Convolution Neural Networks

    Ruaa A. Al-Falluji1,*, Zainab Dalaf Katheeth2, Bashar Alathari2

    CMC-Computers, Materials & Continua, Vol.66, No.2, pp. 1301-1313, 2021, DOI:10.32604/cmc.2020.013232 - 26 November 2020

    Abstract The latest studies with radiological imaging techniques indicate that X-ray images provide valuable details on the Coronavirus disease 2019 (COVID-19). The usage of sophisticated artificial intelligence technology (AI) and the radiological images can help in diagnosing the disease reliably and addressing the problem of the shortage of trained doctors in remote villages. In this research, the automated diagnosis of Coronavirus disease was performed using a dataset of X-ray images of patients with severe bacterial pneumonia, reported COVID-19 disease, and normal cases. The goal of the study is to analyze the achievements for medical image recognition… More >

  • Open Access

    ARTICLE

    Fuzzy Based Decision Making Approach for Evaluating the Severity of COVID-19 Pandemic in Cities of Kingdom of Saudi Arabia

    Abdullah Baz1,*, Hosam Alhakami2

    CMC-Computers, Materials & Continua, Vol.66, No.2, pp. 1155-1174, 2021, DOI:10.32604/cmc.2020.013215 - 26 November 2020

    Abstract The World Health Organization declared COVID-19 a pandemic on March 11, 2020 stating that it is a worldwide danger and requires imminent preventive strategies to minimise the loss of lives. COVID-19 has now affected millions across 211 countries in the world and the numbers continue to rise. The information discharged by the WHO till June 15, 2020 reports 8,063,990 cases of COVID-19. As the world thinks about the lethal malady for which there is yet no immunization or a predefined course of drug, the nations are relentlessly working at the most ideal preventive systems to… More >

  • Open Access

    ARTICLE

    Approach for Training Quantum Neural Network to Predict Severity of COVID-19 in Patients

    Engy El-shafeiy1, Aboul Ella Hassanien2, Karam M. Sallam3,*, A. A. Abohany4

    CMC-Computers, Materials & Continua, Vol.66, No.2, pp. 1745-1755, 2021, DOI:10.32604/cmc.2020.013066 - 26 November 2020

    Abstract Currently, COVID-19 is spreading all over the world and profoundly impacting people’s lives and economic activities. In this paper, a novel approach called the COVID-19 Quantum Neural Network (CQNN) for predicting the severity of COVID-19 in patients is proposed. It consists of two phases: In the first, the most distinct subset of features in a dataset is identified using a Quick Reduct Feature Selection (QRFS) method to improve its classification performance; and, in the second, machine learning is used to train the quantum neural network to classify the risk. It is found that patients’ serial More >

  • Open Access

    ARTICLE

    Intelligent Decision Support System for COVID-19 Empowered with Deep Learning

    Shahan Yamin Siddiqui1,2, Sagheer Abbas1, Muhammad Adnan Khan3,*, Iftikhar Naseer4, Tehreem Masood4, Khalid Masood Khan3, Mohammed A. Al Ghamdi5, Sultan H. Almotiri5

    CMC-Computers, Materials & Continua, Vol.66, No.2, pp. 1719-1732, 2021, DOI:10.32604/cmc.2020.012585 - 26 November 2020

    Abstract The prompt spread of Coronavirus (COVID-19) subsequently adorns a big threat to the people around the globe. The evolving and the perpetually diagnosis of coronavirus has become a critical challenge for the healthcare sector. Drastically increase of COVID-19 has rendered the necessity to detect the people who are more likely to get infected. Lately, the testing kits for COVID-19 are not available to deal it with required proficiency, along with-it countries have been widely hit by the COVID-19 disruption. To keep in view the need of hour asks for an automatic diagnosis system for early… More >

  • Open Access

    ARTICLE

    A Hybrid Deep Learning Model for COVID-19 Prediction and Current Status of Clinical Trials Worldwide

    Shwet Ketu*, Pramod Kumar Mishra

    CMC-Computers, Materials & Continua, Vol.66, No.2, pp. 1896-1919, 2021, DOI:10.32604/cmc.2020.012423 - 26 November 2020

    Abstract Infections or virus-based diseases are a significant threat to human societies and could affect the whole world within a very short time-span. Corona Virus Disease-2019 (COVID-19), also known as novel coronavirus or SARS-CoV-2 (Severe Acute Respiratory Syndrome-Coronavirus-2), is a respiratory based touch contiguous disease. The catastrophic situation resulting from the COVID-19 pandemic posed a serious threat to societies globally. The whole world is making tremendous efforts to combat this life-threatening disease. For taking remedial action and planning preventive measures on time, there is an urgent need for efficient prediction models to confront the COVID-19 outbreak.… More >

  • Open Access

    ARTICLE

    Analysis and Dynamics of Fractional Order Mathematical Model of COVID-19 in Nigeria Using Atangana-Baleanu Operator

    Olumuyiwa J. Peter1, Amjad S. Shaikh2,*, Mohammed O. Ibrahim1, Kottakkaran Sooppy Nisar3, Dumitru Baleanu4,5,6, Ilyas Khan7, Adesoye I. Abioye1

    CMC-Computers, Materials & Continua, Vol.66, No.2, pp. 1823-1848, 2021, DOI:10.32604/cmc.2020.012314 - 26 November 2020

    Abstract We propose a mathematical model of the coronavirus disease 2019 (COVID-19) to investigate the transmission and control mechanism of the disease in the community of Nigeria. Using stability theory of differential equations, the qualitative behavior of model is studied. The pandemic indicator represented by basic reproductive number R0 is obtained from the largest eigenvalue of the next-generation matrix. Local as well as global asymptotic stability conditions for the disease-free and pandemic equilibrium are obtained which determines the conditions to stabilize the exponential spread of the disease. Further, we examined this model by using Atangana–Baleanu fractional derivative… More >

  • Open Access

    ARTICLE

    Performance Estimation of Machine Learning Algorithms in the Factor Analysis of COVID-19 Dataset

    Ashutosh Kumar Dubey1,*, Sushil Narang1, Abhishek Kumar1, Satya Murthy Sasubilli2, Vicente García-Díaz3

    CMC-Computers, Materials & Continua, Vol.66, No.2, pp. 1921-1936, 2021, DOI:10.32604/cmc.2020.012151 - 26 November 2020

    Abstract Novel Coronavirus Disease (COVID-19) is a communicable disease that originated during December 2019, when China officially informed the World Health Organization (WHO) regarding the constellation of cases of the disease in the city of Wuhan. Subsequently, the disease started spreading to the rest of the world. Until this point in time, no specific vaccine or medicine is available for the prevention and cure of the disease. Several research works are being carried out in the fields of medicinal and pharmaceutical sciences aided by data analytics and machine learning in the direction of treatment and early… More >

  • Open Access

    ARTICLE

    Is Social Distancing, and Quarantine Effective in Restricting COVID-19 Outbreak? Statistical Evidences from Wuhan, China

    Salman A. Cheema1, Tanveer Kifayat2, Abdu R. Rahman2, Umair Khan3, A. Zaib4, Ilyas Khan5,*, Kottakkaran Sooppy Nisar6

    CMC-Computers, Materials & Continua, Vol.66, No.2, pp. 1977-1985, 2021, DOI:10.32604/cmc.2020.012096 - 26 November 2020

    Abstract The flow of novel coronavirus (COVID-19) has affected almost every aspect of human life around the globe. Being the emerging ground and early sufferer of the virus, Wuhan city-data remains a case of multifold significance. Further, it is of notable importance to explore the impact of unique and unprecedented public health response of Chinese authorities—the extreme lockdown of the city. In this research, we investigate the statistical nature of the viral transmission concerning social distancing, extreme quarantine, and robust lockdown interventions. We observed highly convincing and statistically significant evidences in favor of quarantine and social… 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 >

  • Open Access

    ARTICLE

    Qualitative Analysis of a Fractional Pandemic Spread Model of the Novel Coronavirus (COVID-19)

    Ali Yousef1,*, Fatma Bozkurt1,2, Thabet Abdeljawad3,4,5

    CMC-Computers, Materials & Continua, Vol.66, No.1, pp. 843-869, 2021, DOI:10.32604/cmc.2020.012060 - 30 October 2020

    Abstract In this study, we classify the genera of COVID-19 and provide brief information about the root of the spread and the transmission from animal (natural host) to humans. We establish a model of fractional-order differential equations to discuss the spread of the infection from the natural host to the intermediate one, and from the intermediate one to the human host. At the same time, we focus on the potential spillover of bat-borne coronaviruses. We consider the local stability of the co-existing critical point of the model by using the Routh–Hurwitz Criteria. Moreover, we analyze the More >

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