Faculty of Computing

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Subject: search.filters.subject.emotion detection

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    Gamified Smart Mirror 􀁗o Leverage Autistic Education – Aliza
    (2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Najeeb, R.S.; Uthayan, J.; Lojini, R.P.; Vishaliney, G.; Alosius, J.; Gamage, A.
    Autism is a neurodevelopmental disorder that causes difficulties in communication, emotional responsiveness and social skills. There has been a global increase rate in autism and lack of resources locally to educate ASD children. As this condition affects children at an early stage, it remains a challenge in learning. Even though today's world there are ample of teaching methods and technologies, people are unaware of the use and impact of them. This paper presents “Aliza” Gamified smart mirror to teach basic education for autism children. “Aliza” consists of four core components such as writing mentor for pre-writing, math tutor for mathematics, verbal trainer for speech and attentiveness tracker for emotion detection. These components assist and enhance their competency in education. The users of the “Aliza" will be constantly monitored and evaluated during their training using Convolutional Neural Network (CNN). The interactive games are given to impact their learning process while the generated report from the Deep Learning evaluation system can acquaint parents and the tutors with the progress of the children. Through this research, it is expected to improve autistic children's basic education with assistance of “Aliza".
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    Stress Analysis and Care Prediction System for Online Workers
    (2021 3rd International Conference on Advancements in Computing (ICAC), SLIIT, 2021-12-09) Amarasinghe, A.A.S.M.; Malassri, I.M.S.; Weerasinghe, K.C.N.; Jayasingha, I.B.; Abeygunawardhana, P.K.W.; Silva, S.
    Working from home (WFH) online during the covid-19 pandemic has caused increased stress level. Online workers/students have been affecting by the crisis according to new researches. Natural response of body, to external and internal stimuli is stress. Even though stress is a natural occurrence, prolonged exposure while working Online to stressors can lead to serious health problems if any action will not be applied to control it. Our research has been conducted deeply to identify the best parameters, which have connection with stress level of online workers. As a result of our research, a desktop application has been created to identify the users stress level in real time. According to the results, our overall system was able to provide outputs with more than 70% accuracy. It will give best predictions to avoid the health problems. Our main goal is to provide best solution for the online workers to have healthy lifestyles. Updates for the users will be provided according to the feedback we will have in the future from the users. Our System will be a most valuable application in the future among online workers.
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    AI Base E-Learning Solution to Motivate and Assist Primary School Students
    (2021 3rd International Conference on Advancements in Computing (ICAC), SLIIT, 2021-12-09) Silva, P.H.D.D.; Sudasinghe, S.A.V.D.; Hansika, P.D.U.; Gamage, M.P.; Gamage, M.P.A.W.
    E-learning is a form of providing education by using electronic devices. Lack of proper mechanisms for encouraging and assisting students are key issues faced by many students in an e-learning environment. The ‘Vidu Mithuru’ is a question-based e-learning application which has been developed as a solution to overcome these problems. This mobile application will auto generate and categorize the questions, evaluate the answers and track the performance while providing motivational quotes by detecting the emotions of the student. This mobile application is based on Neural Networks, Natural Language Processing and Machine Learning concepts. In order to developing this application, the information provided by the primary education professionals was used to comply with the standards. The core objective of the proposed solution is to track the performance level and assist the students to improve in their studies while keeping them motivated. The trained Machine Learning models have achieved the accuracy of 75%, 78%, 99% and 86% for question categorization model, speech emotion detection model, facial emotion detection model and model to evaluate answers as respectively. We have received favorable responses as the results after testing the developed ‘Vidu Mithuru’ mobile application