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...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-01-01
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| Series: | Diagnostics |
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| Online Access: | https://www.mdpi.com/2075-4418/13/2/270 |
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| author | Areej Fatima Tariq Shahzad Sagheer Abbas Abdur Rehman Yousaf Saeed Meshal Alharbi Muhammad Adnan Khan Khmaies Ouahada |
| author_facet | Areej Fatima Tariq Shahzad Sagheer Abbas Abdur Rehman Yousaf Saeed Meshal Alharbi Muhammad Adnan Khan Khmaies Ouahada |
| author_sort | Areej Fatima |
| collection | DOAJ |
| 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%. |
| first_indexed | 2024-03-09T13:02:50Z |
| format | Article |
| id | doaj.art-74c14f41faa049f59b11b38ce9e872fe |
| institution | Directory Open Access Journal |
| issn | 2075-4418 |
| language | English |
| last_indexed | 2024-03-09T13:02:50Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj.art-74c14f41faa049f59b11b38ce9e872fe2023-11-30T21:52:23ZengMDPI AGDiagnostics2075-44182023-01-0113227010.3390/diagnostics13020270COVID-19 Detection Mechanism in Vehicles Using a Deep Extreme Machine Learning ApproachAreej Fatima0Tariq Shahzad1Sagheer Abbas2Abdur Rehman3Yousaf Saeed4Meshal Alharbi5Muhammad Adnan Khan6Khmaies Ouahada7Department of Computer Science, Lahore Garrison University, Lahore 54000, PakistanDepartment of Electrical and Computer Engineering, COMSATS University Islamabad, Sahiwal Campus, Sahiwal 57000, PakistanSchool of Computer Science, National College of Business Administration and Economics, Lahore 54000, PakistanSchool of Computer Science, National College of Business Administration and Economics, Lahore 54000, PakistanDepartment of Information Technology, University of Haripur, Haripur 22620, PakistanDepartment of Computer Science, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Alkharj 11942, Saudi ArabiaDepartment of Software, Faculty of Artificial intelligence and Software, Gachon University, Seongnam 13120, Republic of KoreaDepartment of Electrical and Electronic Engineering Science, University of Johannesburg, Auckland Park, P.O. Box 524, Johannesburg 2006, South AfricaCOVID-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%.https://www.mdpi.com/2075-4418/13/2/270coronavirusDELMWHOCOVID-19diagnosishealthcare |
| spellingShingle | Areej Fatima Tariq Shahzad Sagheer Abbas Abdur Rehman Yousaf Saeed Meshal Alharbi Muhammad Adnan Khan Khmaies Ouahada COVID-19 Detection Mechanism in Vehicles Using a Deep Extreme Machine Learning Approach Diagnostics coronavirus DELM WHO COVID-19 diagnosis healthcare |
| 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 | coronavirus DELM WHO COVID-19 diagnosis healthcare |
| url | https://www.mdpi.com/2075-4418/13/2/270 |
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