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Computational Intelligence-Based Diagnosis of COVID-19

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Computational Intelligence for COVID-19 and Future Pandemics

Abstract

As the newly emerged novel coronavirus (COVID-19) spread to 210 countries worldwide, it was declared pandemic. It influences society as well as each country with a remarkable impact on the health of the people, economics, social life, and education setups at the society and country levels. Due to the increase in the cases of COVID-19 at the national and international levels, there is a need for innovative ways in initial screen out and final diagnostic evidence of COVID-19 patients in a short time. To achieve that goal, various computer-associated models belonging to the fourth industrial revolution technology have been under research and are known as computational intelligence (CI) systems. CI works as a collaborative domain where the science disciplines together with computer science work for designing systems using computer-assisted tools such as algorithms and hardware. The CI technology is an optimized tool that works on the basis of the behavior of living organisms. The CI models search to get the required objective with a set of limitations to be fulfilled, and the performance index indicates an optimal solution known as the objective function. The CI saved inside the computer can exploit the required search objective using nature-inspired algorithms. Neural networks, fuzzy logic, probabilistic theorems, and artificial intelligence tools can exploit and explore the search space using the naturally supplied irregular trapping competency of animals to search out a solution for this worldwide problem. This chapter will present a review of computational models (such as neural networks, fuzzy logic, probabilistic models, evolutionary computation, computation learning theory, real-world systems and tools, big data analysis, artificial intelligence, and nature-inspired computation) for rapid diagnosis of COVID-19 cases.

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Authors and Affiliations

  1. Institute of Microbiology, University of Agriculture, Faisalabad, 38000, Pakistan

    Muhammad Shoaib

  2. Department of Medicine, Cholistan University of Veterinary and Animal Sciences, Bahawalpur, 63100, Pakistan

    Amjad Islam Aqib

  3. The Royal (Dick) School of Veterinary Studies, University of Edinburgh, Easter Bush Campus, Midlothian, E25 9RG, Scotland, UK

    Zeeshan Ahmad Bhutta

  4. Key Laboratory of New Animal Drug Project, Gansu Province/Key Laboratory of Veterinary Pharmaceutical Development, Ministry of Agriculture and Rural Affairs, Lanzhou Institute of Husbandry and Pharmaceutical Sciences of Chinese Academy of Agriculture Sciences, Lanzhou, 730050, PR China

    Muhammad Shoaib & Wanxia Pu

  5. Department of Veterinary Medicine, University of Veterinary and Animal Sciences, Lahore, 54000, Pakistan

    Iqra Muzammil

  6. Department of Clinical Medicine and Surgery, University of Agriculture, Faisalabad-38000, Pakistan

    Muhammad Aamir Naseer

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  1. Muhammad Shoaib
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  3. Zeeshan Ahmad Bhutta
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Correspondence to Amjad Islam Aqib .

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Editors and Affiliations

  1. Department of Computer Engineering, Suleyman Demirel University, Isparta, Turkey

    Utku Kose

  2. Graduate School of Information, Waseda Univeristy, Shinjuku, Japan

    Junzo Watada

  3. Dept. of Computer Technology, Afyon Kocatepe University, Afyonkarahisar, Turkey

    Omer Deperlioglu

  4. Facultad de Ingeniería, Universidad Panamericana, Mexico City, Mexico

    Jose Antonio Marmolejo Saucedo

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Shoaib, M., Aqib, A.I., Bhutta, Z.A., Pu, W., Muzammil, I., Naseer, M.A. (2022). Computational Intelligence-Based Diagnosis of COVID-19. In: Kose, U., Watada, J., Deperlioglu, O., Marmolejo Saucedo, J.A. (eds) Computational Intelligence for COVID-19 and Future Pandemics. Disruptive Technologies and Digital Transformations for Society 5.0. Springer, Singapore. https://doi.org/10.1007/978-981-16-3783-4_11

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