Version 1
: Received: 23 September 2022 / Approved: 26 September 2022 / Online: 26 September 2022 (05:46:03 CEST)
How to cite:
Mehaboob, A.K.; Natarajan, P.D.; Vayalil, M.P.; Marakkar, S.; Koyilot, M.C.; Shameer, K.; Yadav, K.K. A Novel Design For A COVID-19 Diagnosis System Using Machine Learning And Drone Technology (COVIDRONE-20). Preprints2022, 2022090380. https://doi.org/10.20944/preprints202209.0380.v1
Mehaboob, A.K.; Natarajan, P.D.; Vayalil, M.P.; Marakkar, S.; Koyilot, M.C.; Shameer, K.; Yadav, K.K. A Novel Design For A COVID-19 Diagnosis System Using Machine Learning And Drone Technology (COVIDRONE-20). Preprints 2022, 2022090380. https://doi.org/10.20944/preprints202209.0380.v1
Mehaboob, A.K.; Natarajan, P.D.; Vayalil, M.P.; Marakkar, S.; Koyilot, M.C.; Shameer, K.; Yadav, K.K. A Novel Design For A COVID-19 Diagnosis System Using Machine Learning And Drone Technology (COVIDRONE-20). Preprints2022, 2022090380. https://doi.org/10.20944/preprints202209.0380.v1
APA Style
Mehaboob, A.K., Natarajan, P.D., Vayalil, M.P., Marakkar, S., Koyilot, M.C., Shameer, K., & Yadav, K.K. (2022). A Novel Design For A COVID-19 Diagnosis System Using Machine Learning And Drone Technology (COVIDRONE-20). Preprints. https://doi.org/10.20944/preprints202209.0380.v1
Chicago/Turabian Style
Mehaboob, A.K., Khader Shameer and Kamlesh K. Yadav. 2022 "A Novel Design For A COVID-19 Diagnosis System Using Machine Learning And Drone Technology (COVIDRONE-20)" Preprints. https://doi.org/10.20944/preprints202209.0380.v1
Abstract
Tracking and early identification of suspected cases are essential to control and prevent potential COVID-19 outbreaks. One of the most popular techniques used to track this disease is the use of Infrared cameras to identify individuals with elevated body temperatures. However, they are limited by their inability to be implemented in open public settings such as public parks or even outdoor recreational centers. This limits their ability to effectively track possible COVID-19 patients as open public recreational places such as parks, concert venues and other public venues are hotspots for the spreading of the virus. Other technological solutions such as thermal scanners require an individual to perform the actual testing as they are not individual standalone technologies. This method of testing can potentially cause the transmission of the virus between the tester and the individual getting tested. As can be seen, an alternative solution is essential to solving this issue. In this study, we aim to present the system, design and potential scope of a non-invasive system that can diagnose and identify potential COVID-19 patients using thermal and optical images of the individual using drone technology. The proposed system (COVIDRONE) combines multi-modal machine intelligence, computer vision and real-time monitoring to enable scalable monitoring. The system will also involve the use of machine learning algorithms for better and more accurate diagnosis. We envisage that development of such technologies may help in developing technological solutions to combat infectious disease threats in the future pandemics.
Keywords
UAV Technology; Information Processing; Machine Learning; IR Technology; Covid-19; Multi-modal machine learning; Machine vision; Computer vision
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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