Browsing by Author "Siriwardana, S."

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Academic Depression Detection Using Behavioral Aspects for Sri Lankan University Students
(2021 3rd International Conference on Advancements in Computing (ICAC) -SLIIT, 2021-12-09) Gamage, M.A.; Matara Arachchi, R.; Naotunna, S.; Rubasinghe, T.; Silva, C.; Siriwardana, S.
Academic Depression is a widespread problem among undergraduate students in Sri Lanka. It is exhausting and has a detrimental impact on students' academic life. Therefore, the development of a technique to estimate the probability of depression among undergraduates is a blessed respite. Depression is mostly caused by a failure to check students' psychological well-being on a regular basis. Identifying depression at the college level, leading the students to get proper therapy treatments. If a counselor detects depression in a student early enough, he/she can successfully assist the student in overcoming depression. However, keeping track of the substantial changes that occur in students because of depression becomes challenging for the counselor with a considerable number of undergraduates. The advancement of image processing and machine learning fields has contributed to the creation of effective algorithms capable of identifying depression probability. Depression Possibility Detection Tool (DPDT) is considered an effective automated tool that brings the depression probability of a certain student. In DPDT, the result is generated by concerning four main strategies. They are facial expressions, eye movements, behavior changes (step count and phone usage), and physical conditions (heart rate and sleep rate). Convolutional Neural Network (CNN) with Visual Geometry Group 16 (VGG16) model, Residual Neural Network (ResNet-50), Random Forest (RF) classifier is the main models and techniques used in the system. More than 93% of accuracy was generated in every trained model. The paper concludes the system overview along with four strategies, literature review, methodologies, conclusion, and future works.
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E-Learning Platform for Hearing Impaired Students
(2021 3rd International Conference on Advancements in Computing (ICAC), SLIIT, 2021-12-09) Krishnamoorthy, N.; Raveendran, A.; Vadiveswaran, P.; Arulraj, S.R.; Manathunga, K.; Siriwardana, S.
With the Spread of global pandemic Covid-19, the traditional education was transformed to online from traditional learning drastically. Hence the use of e-Learning platforms was increased. But this idea has issues with certain communities of people around the world. The hearing-impaired people have many issues with eLearning platforms because of their deficiency in hearing sound. Therefore, through this paper authors are introducing a learning platform for hearing impaired communities to aid in learning effectively. The proposed platform uses sign language to facilitate communication among students and tutors while providing sign language learning materials, practicing opportunities and Q&A sessions. The system has a low light enhancement module to enhance the videos uploaded by the tutor, module to convert the uploaded videos to American Sign Language and it also converts the questions asked via sign language to text.
Embargo
Mobile Based Solution to Weight Loss Planning for Children (with Obesity) in Sri Lanka
(2021 3rd International Conference on Advancements in Computing (ICAC), SLIIT, 2021-12-09) Rajapakse, R.M.M.P.K.; Mudalige, J.M.A.I.; Perera, L.A.D.Y.S.; Warakagoda, R.N.A.M.S.C.B.; Siriwardana, S.
Obesity is a condition where there is excess fat in the body, and it is one of the world's most extreme and dangerous dietary diseases. Genetic factors, lack of physical activity, unhealthy eating patterns, or a combination of these factors are the most common causes of obesity. This is important because it influences every part of a child's life. More, in particular, this disorder leads to poor health and negative social standing with perceptions. Nowadays, children are paying keen interest in technology and related devices. Therefore, in this research, we are planning to give a mobile-based solution with a smart band that is used to monitor the child. In this solution, we are mainly focusing on Sri Lankan children with obesity who are aged between 5-10. In our solution, there are four main sections which are, monitoring child activities, recognizing the activities, and getting relevant data, then based on those data and previous activity completion levels, this solution will suggest activities for losing weight, provide specific diet plans for each child considering the health conditions and predict the probability of having main obesity-