Browsing by Author "Mapa, N"

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    PublicationOpen Access
    A light weight provenance aware trust negotiation algorithm for smart objects in IoT
    (Annual Technical Conference 2016 - IET- Sri Lanka, 2016) Jayakody, A; Rupasinghe, L; Mapa, N; Disanayaka, T; Kandawala, D; Dinusha, K
    Internet of Things can be considered as the next big tide which advances towards the ICT realm. Many research communities have shown enthusiastic interest towards the variety of research topics which has been emerged into a discussion related to this novel concept. The research taxonomy of IoT is built upon several key pillars by considering its Complexity, Heterogeneity, and Versatility nature. Among these, security related research challenges can be considered as a key impacting domain. This particular research has been conducted with the special consideration towards Trust Negotiation among smart objects in order to satisfy provenance related criteria. Therefore this paper has suggested a light –weight, lesscomplex, comprehensive encryption algorithm by applying shuffling techniques in order to satisfy the origin identification.
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    MindBridge: Early Identification of Learning Difficulties in Children as a Supporting Tool for Teachers
    (Institute of Electrical and Electronics Engineers Inc., 2025) Mapa, N; Deshapriya, M; Premathilake, M; Samarakoon, S; Thelijjagoda, S; Vidanaralage, A.J
    Learning difficulties in children significantly impede academic success by affecting information processing, mathematical performance, and the learning of proper reading and writing. This paper proposes a Progressive Web Application (PWA) based on artificial intelligence (AI) and machine learning (ML) for identifying potential learning barriers. In contrast with standard diagnostic instruments, the proposed system is designed as a prediction tool with the potential for teachers to conduct timely and focused interventions. By automating feature extraction and reducing manual processing, the system overcomes the limitations of existing learning systems and improves early detection accuracy. Preliminary evaluations indicate that the PWA can effectively identify at-risk students and improve intervention methods and overall academic performance. This research contributes to the integration of computational methods and pedagogy, offering a scalable and low-cost solution for helping slow learners overcome their learning challenges.