Browsing by Author "Senanayake, S"

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    Diagnostic Intervention for Mental Disorder
    (IEEE, 2021-12-01) Senanayake, S; Karunanayaka, C; Dananjaya, L; Chamodya, L; Kumari, S; Chandrasiri, S
    Mental health is one of the essential factors in the topic of healthcare and wellbeing. However, mental health disorders could cause severe damage, even loss of life to the person or the surroundings, if mental health disorders were not identified and appropriately cured. Unfortunately, though there is good help there, some people have a hard time detecting whether they are suffering from mental health disorders or not. In this study, the team proposes a system to detect mental health issues using facial emotion recognition (FER), sleeping patterns, social media web scraping, and heart rate. The intention is to give an accurate prediction of the mental health status of a person using these three nodes.
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    Google map and camera based fuzzified adaptive networked traffic light handling model
    (IEEE, 2018-12-05) Nirmani, A; Thilakarathne, L; Wickramasinghe, A; Senanayake, S; Haddela, P. S
    Rising traffic congestion has turned into a certain issue as the number of vehicles on roads are increasing. This research study was conducted to develop `Google Map and Camera Based Fuzzified Adaptive Networked Traffic Light Handling Model'. The main road with six major junctions was selected as the target route for the project. During this study, we were able to plan a limit and control traffic congestion utilizing two neural networks which process together to provide an efficient, productive and optimized solution based on real-time situations. Real-time video streams and Google Map traffic layer were used as primary input sources to the system. The Main algorithm was used to reduce traffic at a specific point whereas secondary algorithm was used to produce optimum decisions for the overall network. As a further advancement, REST endpoint was implemented to get the best route considering all the accessible data. With the aid of the previously mentioned techniques, an optimal traffic management model was developed.