Browsing by Author "Senevirathne, C"

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    Digital Pathways in Smart Tourism: A Systematic Literature Review on Key Drivers of Travelers' Decision-Making
    (Institute of Electrical and Electronics Engineers Inc., 2025) Karunaratna, D; Senevirathne, C; Fernando, K; Peksha, P; Jayasinghe, P; Samarakkody, T
    This study systematically investigates how digital components including information reliability, accessibility, cost efficiency, virtual reality and mobile adoption influence travelers' preferences in smart tourism. To do this, the study utilizes the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) framework to aggregate evidence from across the globe - showing both regional technology disparities, and a variety of different methodological approaches. In developed countries, technology integration is quite strong while developing regions face constraints of finance and infrastructure. These tech-centric factors are important to traveler decision-making, as indicated by the review; however, greater coverage of understudied regions is needed. The resulting insights provide the basis for targeted and holistic solutions to improve tourism services for the benefit of policymakers, industry practitioners and academics.
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    SmartCare: Detecting Heart Failure and Diabetes Using Smartwatch
    (IEEE, 2022-09-08) Colombage, L; Amarasiri, T; Sanjeewani, T; Senevirathne, C
    Busy lifestyles of people which resulted in an increase in non-communicable diseases have demanded a revolution in the healthcare system. This has prompted active research in developing smart sensing devices to automatically monitor the health status of a user with less human intervention. This could be more challenging when the disease is asymptomatic, hence smart solutions for early detection of such diseases are vital to help people to maintain a healthy and long life. In this study, we focus on the most common non-communicable diseases, Heart Failure, and Diabetes which are asymptomatic in their early stages. We propose a SmartCare solution for the real-time detection of heart failure and diabetes disease using a smartwatch. Data collected through a smartwatch along with health data provided by the user are used to detect heart failure, severity levels of the heart failure, diabetes disease, and types of diabetes. Random Forest and Logistic Regression algorithms are used to develop the four prediction models. Extensive evaluations performed on patients' data collected from local hospitals show our SmartCare system can detect the heart failure, severity levels of the heart failure, diabetes disease, and types of diabetes with an F1 score of 0.72, 0.7, 0.72, and 0.86 respectively.