Browsing by Author "Pathirana, M"

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Aqua Safe: Blockchain Based Maritime Communication System using Ad hoc Network
(IEEE, 2022-12-26) Lokuliyana, S; Wellalage, S; Warusavithana, L; Pathirana, M; Kodithuwakku, S
The lack of a pre-existing infrastructure for facilitating long-range connectivity with the land makes maritime communications extremely difficult. Satellite connections, which are expensive, and use much power, are generally used to communicate on the high seas. For better connectivity between fisheries and land stations, different functional methods such as whether detection, boundary detection, data security, barrier detection, and data communication without interruption have been implemented in the system. Implementing a LoRa WAN system is to inform about emergencies in the deep sea and send the information indicated above. IoT node-based Ad hoc network is used to communicate with the land station without an internet connection. In case of emergency or when the fisheries need to send data and information to the land station in real-time, the fishing boat can interact with the land station through this proposed system. Blockchain technology is used in the system to ensure secure communication of information. In this scenario, the blockchain technology has enabled it to deploy distributed networks and perform secure peer-to-peer transmission and data integrity without a third party accessing the network.
Embargo
Personalized Adaptive System for Enhancing University Student Performance in Sri Lanka
(Institute of Electrical and Electronics Engineers Inc., 2025) Dissanayake, N; Samarakoon, C; Wickramasinghe, D; Pathirana, M; Gamage N.D.U; Attanayaka, B
The growing need for personalized learning strategies has driven the development of data-driven solutions to meet the diverse needs of Sri Lankan university students. A key challenge lies in identifying optimal learning paths that align with individual capabilities, learning styles, and engagement behaviors to improve academic performance. While previous research has explored generalized learning models, these often fail to adapt to the specific demands of individual learners. Traditional strategies lack personalization, resulting in inconsistent learning progress. To address this gap, the research introduces an assistive, data-driven approach that leverages Self-Organizing Maps (SOMs), Adaptive Learning (AL), Content-Based Filtering, Graph Neural Networks (GNNs), and Social Network Analysis (SNA) to create optimized, personalized learning strategies. Clustering algorithms and predictive analysis were used to segment learners and deliver tailored interventions based on their behavior. The proposed system integrates advanced machine learning techniques to enhance student engagement and improve overall academic outcomes through personalized pathways.