Research Papers - Dept of Software Engineering

Permanent URI for this collectionhttps://rda.sliit.lk/handle/123456789/1022

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Author: search.filters.author.Chandrasiri, SHas files: Yes

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    Revolutionalize Your Learning Experience with EQU ACCESS
    (IEEE, 2024-07-25) Raveenthiran, G; Sivarajah, K; Kugathasan, V; Chandrasiri, S; Mohamed Riyal, A. A; Rajendran, K
    This paper introduces a novel approach aimed at enhancing online education by placing a central focus on students' emotional well-being and improving their learning experiences. The approach integrates four key machine learning technologies: behavioral expression analysis, a personalized chatbot for emotional support, voice stress detection, and visual content description. Through empirical findings, the study illustrates the effectiveness of these methods in bolstering students' emotional well-being and academic performance. By providing a roadmap for the advancement of online education and emotional support, this research holds promise for delivering substantial benefits to learners worldwide. The study showcases notable advancements in online education, reporting a 30% rise in perceived emotional support and a 25% increase in overall satisfaction. The personalized emotional support chatbot achieved an 85% accuracy in addressing students' emotional needs, while voice stress detection boasted a 90% accuracy in identifying anxiety. Additionally, visual content description led to a 20% improvement in comprehension. These findings highlight the approach's potential to elevate both emotional well-being and academic performance in online learners.
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    E-Learning Assistive System for Deaf and Mute Students
    (IEEE, 2022-12-09) Ranasinghe, P; Akash, K; Nanayakkara, L; Perera, H; Chandrasiri, S; Kumari, S
    E-learning has become a popular digital platform among both students and teachers. When using an e-learning system, deaf-mute students can get significant benefits. It allows students to better grasp their studies by providing additional details. The major problem that the deaf and mute community encounters in the e-learning environment is that they are no longer attempting to enroll in normal institutions, which do not provide many facilities for them due to a lack of resources, a lack of learning facilities, and some social disturbances. To achieve that problem this system will translate the lecturer’s voice into text, map words with pre-created sign language animations, generate subtitles for lecture videos, clearly identify the face position of the lecturer, detect difficult words, track the hand gestures, and practice sign language so that it will increase learning resources, facilities, usability and help teachers to execute their teaching process through this platform. Therefore, normal institutions can use this system as their learning management system. It will approach deaf and mute students to enroll in normal institutions and do their studies as typical students.
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    Real-time Smart Navigation System for Visually Impaired People
    (IEEE, 2022-12-09) Sudaraka Keshara, S.R.D.; Weragoda, W.R.J.M.; Chandrasiri, S; Ellankovan, J; Madushan, W.A
    Visual sense plays a primary role in guiding sighted people through an unfamiliar environment and assisting them to reach their destination safely. Visual impairment describes the actual damage that makes it difficult to accomplish visual tasks because it makes it difficult to see clearly. This paper proposes an approach to overcome the challenges faced by visually impaired people with the help of machine learning. This proposed system combines a smart cane and a wearable smart glass. The detection of obstacles and potholes helps to increase the safety and comfort of visually impaired users by detecting and displaying obstacles, and the Smart Walk-lane Navigation assists in navigating through the walk-lanes without letting them enter the main roads and helps to prevent accidents. Road sign detection allows users to follow road signs and cross the roads safely, while face and emotion detection allows users to recognize well-known people and their emotions.