Browsing by Author "Subasinghe, C"

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    PublicationEmbargo
    Computer Vision Enabled Drowning Detection System
    (IEEE, 2021-12-09) Handalage, U; Nikapotha, N; Subasinghe, C; Prasanga, T; Thilakarthna, T; Kasthurirathna, D
    Safety is paramount in all swimming pools. The current systems expected to address the problem of ensuring safety at swimming pools have significant problems due to their technical aspects, such as underwater cameras and methodological aspects such as the need for human intervention in the rescue mission. The use of an automated visual-based monitoring system can help to reduce drownings and assure pool safety effectively. This study introduces a revolutionary technology that identifies drowning victims in a minimum amount of time and dispatches an automated drone to save them. Using convolutional neural network (CNN) models, it can detect a drowning person in three stages. Whenever such a situation like this is detected, the inflatable tube-mounted self-driven drone will go on a rescue mission, sounding an alarm to inform the nearby lifeguards. The system also keeps an eye out for potentially dangerous actions that could result in drowning. This system’s ability to save a drowning victim in under a minute has been demonstrated in prototype experiments' performance evaluations.
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    PublicationOpen Access
    Predictive Model for Monthly Made Tea Production in Sri Lanka
    (Department of Mathematics and Statistics, Faculty of Humanities and Sciences, SLIIT, 2025-10-10) Subasinghe, C; Wattegedara, N; Silva, T; Balasooriya, S; Dassanayake, K; Guruge, M.L
    This study forecasts monthly tea production in Sri Lanka by developing a suitable time series model to identify future trends in the national tea industry. The analysis is based on monthly made tea production data from January 2000 to June 2025, obtained from the Central Bank of Sri Lanka and the Sri Lanka Tea Board. After confirming the non-stationarity of the original series through the Augmented Dickey-Fuller test, both first-order and seasonal differencing were applied to achieve stationarity. The Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF) plotswere used to identify potential model structures.