Scopus Index Publications
Permanent URI for this communityhttps://rda.sliit.lk/handle/123456789/2162
This collection consists of all Scopus-indexed publications produced by SLIIT researchers. Scopus is recognized worldwide as a leading and reputable academic indexing database.
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Item Embargo An Adaptive E-Learning Platform for Individuals with Down Syndrome(Institute of Electrical and Electronics Engineers Inc., 2025) Sandaruwan U.V.S.; Dias A.H.J.S.S; Shamindi H.M.H; Priyawansha N.G.D.; Chandrasiri L.H.S.S; Attanayaka B.Children with Down Syndrome (DS) encounter varying degrees of learning disabilities within the traditional education framework, requiring personalized interventions. This paper presents Blooming Minds, an adaptive, Machine Learning (ML) driven e-learning platform designed to support the development of cognitive, linguistic, and motor skills in children with DS. Built on the VARK (Visual, Auditory, Reading/Writing, Kinematic) theory, the platform provides personalized activities using real-time feedback mechanisms. The system includes nine interactive modules that cover the above VARK theory. It uses ML algorithms, including Support Vector Machine (SVM) and Random Forest (RF) for screening, Convolutional Neural Networks (CNN) for handwriting and speech analysis, Long Short-Term Memory (LSTM) for sequence prediction, and Reinforcement Learning (RL) for adaptive difficulties. Handwritten letters and voice samples from children with DS, both domestic and international, were specifically considered as inputs for this research. Progress tracking dashboards provide visual insights for educators, parents, and caregivers, improving support and adaptability. The system achieved 91.26% accuracy in letter recognition and 88% in speech classification. This e-learning platform has been recognized as an effective solution in Sri Lanka, allowing for further correlations and investigations to assess the knowledge capacity and ability to express that knowledge in children with DSPublication Embargo An Enhanced Virtual Fitting Room using Deep Neural Networks(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Ileperuma, I.C.S.; Gunathilake, H.M.Y.V.; Dilshan, K.P.A.P.; Nishali, S.A.D.S.; Gamage, A.I.; Priyadarshana, Y.H.P.P.As the customer's experience in present fit-on rooms is considered as an essential part of the textile industry, these fit-on rooms play a huge role in the textile shops. It is quite an arduous method and generates problems like long queues, having to change clothes individually, privacy problems and wasting time. The proposed convolutional neural network-based Virtual Fit-on Room helps to prevent the above mentioned problems. This product contains a TV screen, two web cameras, and a PC. It captures the customer's body by using two web cameras and displays the customer's dressed body. The combination of CNN in Deep learning and AR processes the body detection and generates the customer's dressed object. The application uses the stereo vision concept to get body measurements. The system detects customer age, gender, face type, and skin tones which are used to recommend cloth styles to customers. Another requirement of this system is customizing styles according to the customer requirements and suggests different styles of clothes. The system achieved 99% accuracy when suggesting different styles using FFNN. Customers can choose clothes for another person who does not physically appear with the customer in the textile shop. The expected output delivers the most realistic dressed object to the customer which allows the efficient customizations for the textile products according to customer requirements. This product can highly influence the textile and fashion industry. Therefore, this product is suitable to compete with other applications in the industry.