Browsing by Author "Liyanarachchi, R. K"

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    InCOV Chamber: An IoT based Intelligent Chamber to monitor and identify potential COVID-19 positive patients
    (IEEE, 2021-12-09) Liyanarachchi, R. K; Premathilaka, M; Samarawickrama, H; Thilakasiri, N; Wellalage, S; Wijekoon, J
    COVID-19, the infectious disease with common symptoms such as tiredness, fever, cough, and severe symptoms such as shortness of breath has become a global pandemic that has an enormous negative impact on society. Because of the disease’s negative influence o n o rganizational operations, the entire world is concerned about its spread within their organization. Despite the fact that fever is currently the only symptom used to identify COVID-19 suspects, there may be COVID-19 patients who may not show any signs of fever. The goal of this study is to use an IoT-based chamber to detect potential COVID-19 suspects by taking into account the aforementioned symptoms. When a person enters the chamber, our system employs Neural Networks and Artificial Intelligence (AI) to detect COVID-19 symptoms like Fever, Anosmia, Cough, and Shortness of Breath. The proposed system yields accuracies of 95% for fever detection, 96% for Anosmia detection, and 94% for cough analysis.
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    InCOV Chamber: Intelligent Chamber to Detect Potential COVID-19 Positive Patients
    (IEEE, 2022-01-12) Liyanarachchi, R. K; Premathilaka, M; Samarawickrama, H; Thilakasiri, N; Hettiarachchi, N. U; Wellalage, S; Wijekoon, J
    COVID-19, the infectious disease with common symptoms such as tiredness, fever, cough, and severe symptoms such as shortness of breath has become a global pandemic that has an enormous negative impact on society.Due to its adverse impact on the operations of organizations, the entire world is highly concerned about the spread of the disease within their organization. Even though fever is the only symptom considered currently to detect suspects, there may be COVID19 patients without any indications of fever. The purpose of this study is to identify potential COVID-19 suspects by taking the aforementioned symptoms into account with the help of an IoT-based chamber. Once a person enters the chamber, our solution uses Neural Networks and Artificial Intelligence(AI) to identify COVID-19 symptoms such as Fever, Anosmia, Cough, and Shortness of Breath. The proposed system yields accuracies of 95% for fever detection, 96% for anosmia detection and 94% for cough analysis.