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Artificial Intelligence and Healthcare Analytics for COVID-19

Submission Deadline: 25 January 2021 (closed)

Guest Editors

Dr. Senthilkumar Mohan, Vellore Institute of Technology, India.
Dr. Mohammad Tabrez Quasim, University of Bisha, Saudi Arabia.
Dr. Ayoub khan, University of Bisha, Saudi Arabia.

Summary

In the era of Machine learning, deep learning has extreme growth in the healthcare and medical field. Using AI / ML techniques in hospitals, providing optimal solution becomes important. This aims to find new challenges and overcome the difficulties of novel models/techniques by using AI. However, around the globe corona disease increases every day, prediction and prevention become essential and based on AI techniques like image classification/ segmentation, object detection, healthcare analytics techniques become unavoidable. This calls for bringing novel ideas in artificial intelligence, deep learning, neural networks, machine learning, and healthcare analytics based on COVID-19 which will provide values for researchers and scientists with advanced AI techniques. The proposal aims to collect innovative and unpublished work that focused on Machine learning, Deep learning, Healthcare analytics techniques, and models for COVID-19. This special issue provides an opportunity for researchers around the globe to share their novel ideas in an exciting area.

Keywords

• Machine learning and deep learning-based techniques with medical image analyses.
• Machine learning and deep learning-based heart and lung infection.
• Deep learning and Neural networks for lung infections.
• Machine learning and deep learning techniques for prediction/ forecasting Artificial Intelligence methods for COVID -19 diagnostic models.
• Detection of COVID-19 disease based on Healthcare analytics and Machine learning features.
• Healthcare analytics and deep learning-based on CT images and other image processing models.
• Prediction / preventions models for hospitals.
• Early prevention/prediction of COVID-19 based on advanced machine learning and deep learning techniques.
• Novel methods with AI for COVID -19.

Published Papers

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  • Open Access

    ARTICLE

    Bayesian Rule Modeling for Interpretable Mortality Classification of COVID-19 Patients

    Jiyoung Yun, Mainak Basak, Myung-Mook Han
    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 2827-2843, 2021, DOI:10.32604/cmc.2021.017266
    （This article belongs to this Special Issue: Artificial Intelligence and Healthcare Analytics for COVID-19)
    Abstract Coronavirus disease 2019 (COVID-19) has been termed a “Pandemic Disease” that has infected many people and caused many deaths on a nearly unprecedented level. As more people are infected each day, it continues to pose a serious threat to humanity worldwide. As a result, healthcare systems around the world are facing a shortage of medical space such as wards and sickbeds. In most cases, healthy people experience tolerable symptoms if they are infected. However, in other cases, patients may suffer severe symptoms and require treatment in an intensive care unit. Thus, hospitals should select patients who have a high risk… More >

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    2409

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    1338

  • Open Access

    ARTICLE

    Emergency Decision-Making Based on q-Rung Orthopair Fuzzy Rough Aggregation Information

    Ahmed B. Khoshaim, Saleem Abdullah, Shahzaib Ashraf, Muhammad Naeem
    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 4077-4094, 2021, DOI:10.32604/cmc.2021.016973
    （This article belongs to this Special Issue: Artificial Intelligence and Healthcare Analytics for COVID-19)
    Abstract With the frequent occurrences of emergency events, emergency decision making (EDM) plays an increasingly significant role in coping with such situations and has become an important and challenging research area in recent times. It is essential for decision makers to make reliable and reasonable emergency decisions within a short span of time, since inappropriate decisions may result in enormous economic losses and social disorder. To handle emergency effectively and quickly, this paper proposes a new EDM method based on the novel concept of q-rung orthopair fuzzy rough (q-ROPR) set. A novel list of q-ROFR aggregation information, detailed description of the… More >

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    2023

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    1114

    Cited by

    4

  • Open Access

    ARTICLE

    Classification and Categorization of COVID-19 Outbreak in Pakistan

    Amber Ayoub, Kainaat Mahboob, Abdul Rehman Javed, Muhammad Rizwan, Thippa Reddy Gadekallu, Mustufa Haider Abidi, Mohammed Alkahtani
    CMC-Computers, Materials & Continua, Vol.69, No.1, pp. 1253-1269, 2021, DOI:10.32604/cmc.2021.015655
    （This article belongs to this Special Issue: Artificial Intelligence and Healthcare Analytics for COVID-19)
    Abstract Coronavirus is a potentially fatal disease that normally occurs in mammals and birds. Generally, in humans, the virus spreads through aerial droplets of any type of fluid secreted from the body of an infected person. Coronavirus is a family of viruses that is more lethal than other unpremeditated viruses. In December 2019, a new variant, i.e., a novel coronavirus (COVID-19) developed in Wuhan province, China. Since January 23, 2020, the number of infected individuals has increased rapidly, affecting the health and economies of many countries, including Pakistan. The objective of this research is to provide a system to classify and… More >

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    2612

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    1439

    Cited by

    11

  • Open Access

    ARTICLE

    Impact Assessment of COVID-19 Pandemic Through Machine Learning Models

    Fawaz Jaber Alsolami, Abdullah Saad Al-Malaise ALGhamdi, Asif Irshad Khan, Yoosef B. Abushark, Abdulmohsen Almalawi, Farrukh Saleem, Alka Agrawal, Rajeev Kumar, Raees Ahmad Khan
    CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 2895-2912, 2021, DOI:10.32604/cmc.2021.017469
    （This article belongs to this Special Issue: Artificial Intelligence and Healthcare Analytics for COVID-19)
    Abstract Ever since its outbreak in the Wuhan city of China, COVID-19 pandemic has engulfed more than 211 countries in the world, leaving a trail of unprecedented fatalities. Even more debilitating than the infection itself, were the restrictions like lockdowns and quarantine measures taken to contain the spread of Coronavirus. Such enforced alienation affected both the mental and social condition of people significantly. Social interactions and congregations are not only integral part of work life but also form the basis of human evolvement. However, COVID-19 brought all such communication to a grinding halt. Digital interactions have failed to enthuse the fervor… More >

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    2853

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    1596

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    1

    Cited by

    1

  • Open Access

    ARTICLE

    Describe the Mathematical Model for Exchanging Waves Between Bacterial and Cellular DNA

    Mohamed S. Mohamed, Sayed K. Elagan, Saad J. Almalki, Muteb R. Alharthi, Mohamed F. El-Badawy, Amr M. S. Mahdy
    CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3615-3628, 2021, DOI:10.32604/cmc.2021.017208
    （This article belongs to this Special Issue: Artificial Intelligence and Healthcare Analytics for COVID-19)
    Abstract In this article, we have shown that bacterial DNA could act like some coils which interact with coil-like DNA of host cells. By decreasing the separating distance between two bacterial cellular DNA, the interaction potential, entropy, and the number of microstates of the system grow. Moreover, the system gives its energy to the medium and the temperature of the host body grows. This could be seen as fever in diseases. By emitting some special waves and changing the temperature of the medium, the effects of bacterial waves could be reduced and bacterial diseases could be controlled. Many investigators have shown… More >

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    1989

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    1439

  • Open Access

    ARTICLE

    Intelligent Autonomous-Robot Control for Medical Applications

    Rihem Farkh, Haykel Marouani, Khaled Al Jaloud, Saad Alhuwaimel, Mohammad Tabrez Quasim, Yasser Fouad
    CMC-Computers, Materials & Continua, Vol.68, No.2, pp. 2189-2203, 2021, DOI:10.32604/cmc.2021.015906
    （This article belongs to this Special Issue: Artificial Intelligence and Healthcare Analytics for COVID-19)
    Abstract The COVID-19 pandemic has shown that there is a lack of healthcare facilities to cope with a pandemic. This has also underscored the immediate need to rapidly develop hospitals capable of dealing with infectious patients and to rapidly change in supply lines to manufacture the prescription goods (including medicines) that is needed to prevent infection and treatment for infected patients. The COVID-19 has shown the utility of intelligent autonomous robots that assist human efforts to combat a pandemic. The artificial intelligence based on neural networks and deep learning can help to fight COVID-19 in many ways, particularly in the control… More >

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    3087

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    1980

    Cited by

    2

  • Open Access

    ARTICLE

    Prediction Models for COVID-19 Integrating Age Groups, Gender, and Underlying Conditions

    Imran Ashraf, Waleed S. Alnumay, Rashid Ali, Soojung Hur, Ali Kashif Bashir, Yousaf Bin Zikria
    CMC-Computers, Materials & Continua, Vol.67, No.3, pp. 3009-3044, 2021, DOI:10.32604/cmc.2021.015140
    （This article belongs to this Special Issue: Artificial Intelligence and Healthcare Analytics for COVID-19)
    Abstract The COVID-19 pandemic has caused hundreds of thousands of deaths, millions of infections worldwide, and the loss of trillions of dollars for many large economies. It poses a grave threat to the human population with an excessive number of patients constituting an unprecedented challenge with which health systems have to cope. Researchers from many domains have devised diverse approaches for the timely diagnosis of COVID-19 to facilitate medical responses. In the same vein, a wide variety of research studies have investigated underlying medical conditions for indicators suggesting the severity and mortality of, and role of age groups and gender on,… More >

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    2761

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    1664

    Cited by

    4

  • Open Access

    ARTICLE

    CNN Ensemble Approach to Detect COVID-19 from Computed Tomography Chest Images

    Haikel Alhichri
    CMC-Computers, Materials & Continua, Vol.67, No.3, pp. 3581-3599, 2021, DOI:10.32604/cmc.2021.015399
    （This article belongs to this Special Issue: Artificial Intelligence and Healthcare Analytics for COVID-19)
    Abstract In January 2020, the World Health Organization declared a global health emergency concerning the spread of a new coronavirus disease, which was later named COVID-19. Early and fast diagnosis and isolation of COVID-19 patients have proven to be instrumental in limiting the spread of the disease. Computed tomography (CT) is a promising imaging method for fast diagnosis of COVID-19. In this study, we develop a unique preprocessing step to resize CT chest images to a fixed size (256 × 256 pixels) that preserves the aspect ratio and reduces image loss. Then, we present a deep learning (DL) method to classify… More >

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    2283

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    1576

    Cited by

    1

  • Open Access

    ARTICLE

    Deep Learning Approach for COVID-19 Detection in Computed Tomography Images

    Mohamad Mahmoud Al Rahhal, Yakoub Bazi, Rami M. Jomaa, Mansour Zuair, Naif Al Ajlan
    CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 2093-2110, 2021, DOI:10.32604/cmc.2021.014956
    （This article belongs to this Special Issue: Artificial Intelligence and Healthcare Analytics for COVID-19)
    Abstract With the rapid spread of the coronavirus disease 2019 (COVID-19) worldwide, the establishment of an accurate and fast process to diagnose the disease is important. The routine real-time reverse transcription-polymerase chain reaction (rRT-PCR) test that is currently used does not provide such high accuracy or speed in the screening process. Among the good choices for an accurate and fast test to screen COVID-19 are deep learning techniques. In this study, a new convolutional neural network (CNN) framework for COVID-19 detection using computed tomography (CT) images is proposed. The EfficientNet architecture is applied as the backbone structure of the proposed network,… More >

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    2915

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    1650

    Cited by

    3

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