Research Publications
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Item Embargo Developing Predictive Models for Future Stress Likelihood and Recovery Time Using Behavioral and Emotional Data(Institute of Electrical and Electronics Engineers Inc., 2025) Weerasinghe W.P.D.J.N; Gunasekera H.D.P.M; Wickramasinghe B.G.W.M.C.R; Jayathunge K.A.D.T.R; Wijesiri, P; Dassanayake, TStress has a serious impact on mental and physical well-being, but treatments as usual are often unavailable and not effective over the long term. The AyurAura application combines imaginative Ayurvedic therapies with modern AI techniques to deliver customized stress reduction by way of Mandala art and music. This research develops two predictive models for the application. In its first model, the stress prediction probability is estimated from users' behavior in a questionnaire and the result can be used to proactively intervene. The second model forecasts time needed for recovery into a stress-free state by using the changes in daily emotional state and participation in app activities. Machine learning algorithms are used to prepare behavioral and emotional data for improved prediction performance. Trained on multi-institution datasets, both models delivered 90-95% accuracy, enabling the user to detect behavior eliciting stress and the degree needed for recovery. These results highlight the possibility of combining conventional therapeutics with contemporary tech for ongoing, affordable stress relief interventions with personalized needs in mind.Publication Embargo Child Head Gesture Classification through Transformers(Institute of Electrical and Electronics Engineers Inc., 2022-11-04) Wedasingha, N; Samarasinghe, P; Singarathnam, D; Papandrea, M; Puiatti, A; Seneviratne, LThis paper proposes a transformer network for head pose classification (HPC) which outperforms the existing SoA for HPC. This robust model is then extended to overcome the limited child data challenge by applying transfer learning resulting in an accuracy of 95.34% for child HPC in the wild.Publication Open Access A System to Notify Real-Time Radio Signal Failures and Predict the Possibility of Failures - LOST TRANSMISSION(University Of Bahrain, 2022-03-31) Sumithraarachchi, G; Ahamed, R; Vithana, NThe focal point of this work was to build a troubleshooting mobile application, which provides an alert notification when RT (Radio Transmission) failures happen at radio outstations and enables predicting the possibilities of radio signal failures based on weather components. The current radio signal failure notifying process is being done half-manual at most of the radio stations while not providing immediate notifications to the radio station staff. A cloud platform, IoT (Internet of Things) technology, and machine learning technique are combined with the aforementioned system to provide fast service to the radio station end-users. The IoT-based Wi-Fi module distinguishes RT failures of each outstation. When weather data is detected, the predictive model displays the possibilities of radio signal failures. The cloud-based functionalities push instant notifications which make the system highly reliable. A key benefit of this system is that even though the users are out of the radio station, the system will be one notification away from the users to notify sudden RT failures.Publication Open Access A System to Notify Real-Time Radio Signal Failures and Predict the Possibility of Failures - LOST TRANSMISSION(University Of Bahrain, 2022-02-15) Sumithraarachchi, G; Ahamed, R; Vithana, NThe focal point of this work was to build a troubleshooting mobile application, which provides an alert notification when RT (Radio Transmission) failures happen at radio outstations and enables predicting the possibilities of radio signal failures based on weather components. The current radio signal failure notifying process is being done half-manual at most of the radio stations while not providing immediate notifications to the radio station staff. A cloud platform, IoT (Internet of Things) technology, and machine learning technique are combined with the aforementioned system to provide fast service to the radio station end-users. The IoT-based Wi-Fi module distinguishes RT failures of each outstation. When weather data is detected, the predictive model displays the possibilities of radio signal failures. The cloud-based functionalities push instant notifications which make the system highly reliable. A key benefit of this system is that even though the users are out of the radio station, the system will be one notification away from the users to notify sudden RT failures.