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COVID-19 Detection Mechanism in Vehicles Using a Deep Extreme Machine Learning Approach

COVID-19 is a rapidly spreading pandemic, and early detection is important to halting the spread of infection. Recently, the outbreak of this virus has severely affected people around the world with increasing death rates. The increased death rates are because of its spreading nature among people, m...

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Autores principales: Fatima, Areej, Shahzad, Tariq, Abbas, Sagheer, Rehman, Abdur, Saeed, Yousaf, Alharbi, Meshal, Khan, Muhammad Adnan, Ouahada, Khmaies
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9858069/
https://www.ncbi.nlm.nih.gov/pubmed/36673080
http://dx.doi.org/10.3390/diagnostics13020270
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author Fatima, Areej
Shahzad, Tariq
Abbas, Sagheer
Rehman, Abdur
Saeed, Yousaf
Alharbi, Meshal
Khan, Muhammad Adnan
Ouahada, Khmaies
author_facet Fatima, Areej
Shahzad, Tariq
Abbas, Sagheer
Rehman, Abdur
Saeed, Yousaf
Alharbi, Meshal
Khan, Muhammad Adnan
Ouahada, Khmaies
author_sort Fatima, Areej
collection PubMed
description COVID-19 is a rapidly spreading pandemic, and early detection is important to halting the spread of infection. Recently, the outbreak of this virus has severely affected people around the world with increasing death rates. The increased death rates are because of its spreading nature among people, mainly through physical interactions. Therefore, it is very important to control the spreading of the virus and detect people’s symptoms during the initial stages so proper preventive measures can be taken in good time. In response to COVID-19, revolutionary automation such as deep learning, machine learning, image processing, and medical images such as chest radiography (CXR) and computed tomography (CT) have been developed in this environment. Currently, the coronavirus is identified via an RT-PCR test. Alternative solutions are required due to the lengthy moratorium period and the large number of false-negative estimations. To prevent the spreading of the virus, we propose the Vehicle-based COVID-19 Detection System to reveal the related symptoms of a person in the vehicles. Moreover, deep extreme machine learning is applied. The proposed system uses headaches, flu, fever, cough, chest pain, shortness of breath, tiredness, nasal congestion, diarrhea, breathing difficulty, and pneumonia. The symptoms are considered parameters to reveal the presence of COVID-19 in a person. Our proposed approach in Vehicles will make it easier for governments to perform COVID-19 tests timely in cities. Due to the ambiguous nature of symptoms in humans, we utilize fuzzy modeling for simulation. The suggested COVID-19 detection model achieved an accuracy of more than 90%.
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spelling pubmed-98580692023-01-21 COVID-19 Detection Mechanism in Vehicles Using a Deep Extreme Machine Learning Approach Fatima, Areej Shahzad, Tariq Abbas, Sagheer Rehman, Abdur Saeed, Yousaf Alharbi, Meshal Khan, Muhammad Adnan Ouahada, Khmaies Diagnostics (Basel) Article COVID-19 is a rapidly spreading pandemic, and early detection is important to halting the spread of infection. Recently, the outbreak of this virus has severely affected people around the world with increasing death rates. The increased death rates are because of its spreading nature among people, mainly through physical interactions. Therefore, it is very important to control the spreading of the virus and detect people’s symptoms during the initial stages so proper preventive measures can be taken in good time. In response to COVID-19, revolutionary automation such as deep learning, machine learning, image processing, and medical images such as chest radiography (CXR) and computed tomography (CT) have been developed in this environment. Currently, the coronavirus is identified via an RT-PCR test. Alternative solutions are required due to the lengthy moratorium period and the large number of false-negative estimations. To prevent the spreading of the virus, we propose the Vehicle-based COVID-19 Detection System to reveal the related symptoms of a person in the vehicles. Moreover, deep extreme machine learning is applied. The proposed system uses headaches, flu, fever, cough, chest pain, shortness of breath, tiredness, nasal congestion, diarrhea, breathing difficulty, and pneumonia. The symptoms are considered parameters to reveal the presence of COVID-19 in a person. Our proposed approach in Vehicles will make it easier for governments to perform COVID-19 tests timely in cities. Due to the ambiguous nature of symptoms in humans, we utilize fuzzy modeling for simulation. The suggested COVID-19 detection model achieved an accuracy of more than 90%. MDPI 2023-01-11 /pmc/articles/PMC9858069/ /pubmed/36673080 http://dx.doi.org/10.3390/diagnostics13020270 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Fatima, Areej
Shahzad, Tariq
Abbas, Sagheer
Rehman, Abdur
Saeed, Yousaf
Alharbi, Meshal
Khan, Muhammad Adnan
Ouahada, Khmaies
COVID-19 Detection Mechanism in Vehicles Using a Deep Extreme Machine Learning Approach
title COVID-19 Detection Mechanism in Vehicles Using a Deep Extreme Machine Learning Approach
title_full COVID-19 Detection Mechanism in Vehicles Using a Deep Extreme Machine Learning Approach
title_fullStr COVID-19 Detection Mechanism in Vehicles Using a Deep Extreme Machine Learning Approach
title_full_unstemmed COVID-19 Detection Mechanism in Vehicles Using a Deep Extreme Machine Learning Approach
title_short COVID-19 Detection Mechanism in Vehicles Using a Deep Extreme Machine Learning Approach
title_sort covid-19 detection mechanism in vehicles using a deep extreme machine learning approach
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9858069/
https://www.ncbi.nlm.nih.gov/pubmed/36673080
http://dx.doi.org/10.3390/diagnostics13020270
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