Research Papers - Dept of Computer Systems Engineering

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Author: search.filters.author.Wijesundara, M.

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    A Geophone Based Surveillance System Using Neural Networks and IoT
    (2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Supun Hettigoda, Chamath Jayaminda; Amarathunga, U.; Wijesundara, M.; Wijekoon, J.; Thaha, S.
    Securing our assets and properties from intruders and thieves has become increasingly challenging as intruders become technology aware. The most common approach to monitor physical assets is CCTV. However, this approach has a number of technical limitations in addition to the cost. The CCTV camera location is visible to the intruder and intruder can also identify possible blind spots in the CCTV coverage area. In this paper, we introduce a novel method to secure physical assets using Geophones, Neural Networks, and IoT Platforms. This can either be used stand alone or to complement existing CCTV systems. In this approach, the system monitors vibrations on ground to detect intruders. We have achieved up to 93.90% overall accuracy for person identification. The system is invisible to intruders and covers a large area with a smaller number of nodes, thereby reducing the cost of ownership.
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    APlus: An Intelligent Lecture Delivery System using Artificial Intelligence Learning Analytics
    (2021 3rd International Conference on Advancements in Computing (ICAC), SLIIT, 2021-12-09) Kodagoda, C.; Kodagoda, V.; Ekanayake, R.; De Silva, N.; Wijesundara, M.; Atapattu, P.
    As technology advances, new ways of teaching and learning can be discovered. Online learning platforms are able to provide easy, informative, and effective methods of delivering information since people are getting more accustomed to the use of e-learning. With the help of Artificial Intelligence, these methods can be refined to understand the users on a deeper level and provide services that are helpful and are more tailored to each user. The purpose of this project is to design an e-learning platform equipped for both educators and students. The system will be able to allow users to record/upload lecture sessions and other related study materials to the service which in return, with the use of Artificial Intelligence, try to understand the context of each lesson, segment videos, generate questions and deliver the materials to the Learner. The system will use various inputs from the Learner including video observation time, watched duration, types of videos watched, answers for questions, etc. to build a comprehensive profile of Learners. The system will use the knowledge gathered to provide services such as recommendations to the Learner, track the progress of the Learner as well as provide useful statistics of lecture materials to the Content Providers.