2023

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Author: search.filters.author.Wijekoon, J

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    PublicationOpen Access
    COVID-19 symptom identification using Deep Learning and hardware emulated systems
    (Elsevier, 2023-06-28) Liyanarachchi, R; Wijekoon, J; Premathilaka, M; Vidhanaarachchi, S
    The COVID-19 pandemic disrupted regular global activities in every possible way. This pandemic, caused by the transmission of the infectious Coronavirus, is characterized by main symptoms such as fever, fatigue, cough, and loss of smell. A current key focus of the scientific community is to develop automated methods that can effectively identify COVID-19 patients and are also adaptable for foreseen future virus outbreaks. To classify COVID-19 suspects, it is required to use contactless automatic measurements of more than one symptom. This study explores the effectiveness of using Deep Learning combined with a hardware-emulated system to identify COVID-19 patients in Sri Lanka based on two main symptoms: cough and shortness of breath. To achieve this, a Convolutional Neural Network (CNN) based on Transfer Learning was employed to analyze and compare the features of a COVID-19 cough with other types of coughs. Real-time video footage was captured using a FLIR C2 thermal camera and a web camera and subsequently processed using OpenCV image processing algorithms. The objective was to detect the nasal cavities in the video frames and measure the breath cycles per minute, thereby identifying instances of shortness of breath. The proposed method was first tested on crowd-sourced datasets (Coswara, Coughvid, ESC-50, and a dataset from Kaggle) obtained online. It was then applied and verified using a dataset obtained from local hospitals in Sri Lanka. The accuracy of the developed methodologies in diagnosing cough resemblance and recognizing shortness of breath was found to be 94% and 95%, respectively.
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    PublicationEmbargo
    Effects of the Organizational Knowledge Management Systems on Psychological Well-Being Among Employees in Private Large-Scale IT Organizations in Sri Lanka
    (IEEE, 2023-06-12) Aratthanage, K; Wijekoon, J
    The goal of this study basically focuses on evaluating the Organizational Knowledge Management Systems (KMS) and their impact on psychological well-being among employees in selected large-scale private IT organizations in Sri Lanka. It evaluates Knowledge Management Systems quality dimensions, KMS adoption of users, and psychological well-being. Knowledge Management is an essential part in IT industry. Gaining domain knowledge from one starting point to sharing knowledge among organizations is a very complex process, therefore, different types of Knowledge Management systems are implemented within IT organizations. There are several quality dimension factors introduced to determine better knowledge management systems. This research evaluates eight quality dimensions against four psychological aspects. In this study, statistical analysis was used to determine the statistically significant levels, correlations and relationships between the independent and dependent variables. Ultimately the results can be used to improve the Human-centered Knowledge Management System's design approach with balanced employee psychological well-being. For further improvements, the results can be used to build interactive knowledge management software-based solutions.