Browsing by Author "Dassanayake, T"

Filter results by typing the first few letters
Now showing 1 - 2 of 2
  • Thumbnail Image
    ItemEmbargo
    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, T
    Stress 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.
  • Thumbnail Image
    PublicationEmbargo
    SMARKET-Shopping in Supercenters (Hypermarkets) with Augmented Reality
    (IEEE, 2021-12-17) Jayagoda, N. M; Jayawardana, O. R; Welivita, W. W. T. P; Weerasinghe, L; Dassanayake, T
    Not so long ago, online shopping for groceries, electronics, and furniture items seemed futuristic. But today, it has become a norm to order requisites through online platforms using smart devices and deliver them to customers' doorstep. With the emerge of technologies such as artificial intelligence, machine learning, deep learning, augmented reality, retail becomes progressively effortless. One such emerging futuristic technology involved recently in online shopping is Augmented Reality (AR) which is rapidly adopted by many industries. In multi-story supercenters, also known as “Hypermarkets”, the customer often feels lost due to difficulty in finding exactly what they looking for, and also in conventional online shopping, often customers are in two minds whether to purchase an item or not since it lacks the proper visualization, touch, and feel of the product. In this research study, we propose a mobile-based solution with augmented reality, which assists the customer when shopping in-store as well as when shopping online to mitigate the difficulties and hesitancies faced while shopping. The results are commendable with 96.21 % accuracy in suggesting visually similar items and 89.59% accuracy in detecting emotional implications of product reviews.