Artificial Intelligence in Covid-19 Diagnosis
Keywords:
COVID-19, Artificial Intelligence, WHO, Algorithms, Testing, Evaluation
Abstract
The Coronavirus has quickly spread to more than 200 nations, infecting several million people globally in a matter of weeks. Artificial intelligence (AI) tools have been shown to be beneficial in testing, diagnosing, and efficiently limiting viral propagation. However, several flaws or limitations have been discovered in existing AI approaches. Patients are diagnosed after the onset of symptoms, making it difficult to determine the appropriate patient management. Doctors' involvement is required, which may result in viral infection via direct contact with patients. The present project attempts to create AI approaches to address such issues. This work suggests AI capable of estimating the timing of viral infection in patients and the degree of medical treatment required prior to the development of COVID-19 symptoms. Many vital biological and human functions are measured by the proposed device, including the state of the brain and neurotransmitters, mental/mood conditions, tension level of face muscles, hands, and body temperature, rate of pulse and pressure, oxygen level in the blood, rate of breathing and difficulty, degree of redness of eye(s), and general body imbalances. This work makes use of both hardware and software designs. To explore, test, and evaluate the integration/work of each subsystem component and the overall system architecture, an experimental research is used. The findings demonstrated that the suggested approach might be utilised to differentiate between healthy and infected persons prior to the manifestation of COVID-19 symptoms.
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3. M. Akagi, Y. Nakamura, T. Higaki, Correction to: Deep learning reconstruction improves image quality of abdominal ultra-highresolution CT, J. European Radiology, 2019, 29(8).
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8. S. Wang, B. Kang, J. Ma, X. Zeng, M. Xiao, J. Guo, M. Cai, J. Yang, Y. Li, X. Meng, B. Xu, A deep learning algorithm using CT images to screen for Corona Virus Disease (COVID-19). medRxiv preprint doi: https://doi.org/10.1101/2020.02.14.20023028, 1-26, 2020
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13. A. w. Linda Wang, ʺCOVID‐Net: A Tailored Deep Convolutional Neural Network Design for Detection of COVID‐19 Cases from Chest Radiography Images,ʺ 2020.
14. S. Wang, B. Kang, J. Ma, X. Zeng, M. Xiao, J. Guo, M. Cai, J. Yang, Y. Li, X. Meng, B. Xu, A deep learning algorithm using CT images to screen for Corona Virus Disease (COVID-19), doi: https://doi.org/10.1101/2020.02.14.20023028; February 17, 2020, p.
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17. M. A. Al-Qaness, A. A. Ewees, H. Fan, and M. Abd El Aziz, “Optimization method for forecasting confirmed cases of covid-19 in china,” Journal of Clinical Medicine, vol. 9, no. 3, p. 674, 2020.
18. B. Schuller and A. Batliner, Computational Paralinguistics: Emotion, Affect and Personality in Speech and Language Processing. Wiley, 2013.
19. A. S. S. Rao and J. A. Vazquez, “Identification of covid-19 can be quicker through artificial intelligence framework using a mobile phonebased survey in the populations when cities/towns are under quarantine,”Infection Control & Hospital Epidemiology, pp. 1–18, 2020.
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22. A. Elfasakhany "Modeling of Pulverised Wood Flames", PhD Thesis, fluid mechanics dept., Lund, Sweden, ISBN-13/EAN: 9789162864255, 2005.
23. A. Elfasakhany, X.S. Bai, “Modeling of Pulverised Wood Combustion: A Comparison of Different Models”, Prog. Comp. Fluid Dynamics (PCFD), Scopus, ISI , Vol. 6, No. 4/5, p. 188–199, 2006.
24. A. Elfasakhany, T. Klason ,X.S. Bai, “Modeling of Pulverised Wood Combustion Using a Functional Group Model”, Combustion Theory and Modeling, Scopus, ISI , Vol. 12, 5, 883– 904, 2008.
25. A. Elfasakhany, E.Y. Rezola, K.B. Quiñones, R.S. Sánc, "Design and Development of a Competitive Low-cost Robot Arm with Four Degrees of Freedom", Modern Mechanical Eng, vol. 1, 47–55, 2011.
26. A. Elfasakhany, J. Hernández, J. C. García, M. Reyes, F. Martell, "Design and Development of House-Mobile Security System", Engineering, vol. 3, 1213–1224, 2011.
27. A. Elfasakhany, A. Arrieta, D. M. Ramírez, F. Rodríguez, "Design and Development of an Autonomous Trash Sorting System". Global J. of Pure and Applied Sciences and Tech., vol. 01i3, 56–64, 2011.
28. A. Elfasakhany, L. Tao, B. Espenas, J. Larfeldt, X.S. Bai "Pulverised Wood Combustion in a Vertical Furnace: Experimental and Computational Analyses" International Conference of Applied Energy, 2012.
29. A. Elfasakhany, J. Marquez, E.Y. Rezola, J. Benitez "Design and Development of an Economic Autonomous Beverage Cans Crusher" Int. J. of Mech. Eng. Tech. Vol. 3, Issue 3, 107–122, 2012.
30. A. Elfasakhany "Improving Performance and Development of Two-Stage Reciprocating Compressors" Int. J. of Advanced Research in Eng. Tech. Vol. 3, Issue 2, 119–136, 2012.
31. A. Elfasakhany "Modeling of Secondary Reactions of Tar (SRT) Using a Functional Group Model" Int. J. of Mech. Eng. Tech. Vol. 3, Issue 3, 123–136, 2012.
32. A. Elfasakhany, J. A. Alarcón, D. O. S. Montes " Design and Development of an Automotive Vertical Doors Opening System (AVDOS)" Int. J. of Advanced Research.
Published
2021-12-15
How to Cite
Chakraborty, A. (2021). Artificial Intelligence in Covid-19 Diagnosis. CENTRAL ASIAN JOURNAL OF MATHEMATICAL THEORY AND COMPUTER SCIENCES, 2(12), 42-55. Retrieved from https://cajmtcs.centralasianstudies.org/index.php/CAJMTCS/article/view/141
Section
Articles
