Research Publications Authored by SLIIT Staff
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This collection includes all SLIIT staff publications presented at external conferences and published in external journals. The materials are organized by faculty to facilitate easy retrieval.
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Publication Embargo 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".Publication Embargo Computer Vision Enabled Drowning Detection System(2021 3rd International Conference on Advancements in Computing (ICAC), SLIIT, 2021-12-09) Handalage, U.; Nikapotha, N.; Subasinghe, C.; Prasanga, T.; Thilakarthna, T.; Kasthurirathna, D.Safety is paramount in all swimming pools. The current systems expected to address the problem of ensuring safety at swimming pools have significant problems due to their technical aspects, such as underwater cameras and methodological aspects such as the need for human intervention in the rescue mission. The use of an automated visual-based monitoring system can help to reduce drownings and assure pool safety effectively. This study introduces a revolutionary technology that identifies drowning victims in a minimum amount of time and dispatches an automated drone to save them. Using convolutional neural network (CNN) models, it can detect a drowning person in three stages. Whenever such a situation like this is detected, the inflatable tube-mounted selfdriven drone will go on a rescue mission, sounding an alarm to inform the nearby lifeguards. The system also keeps an eye out for potentially dangerous actions that could result in drowning. This system's ability to save a drowning victim in under a minute has been demonstrated in prototype experiments' performance evaluations.