Browsing by Author "Kumara, S"

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    PublicationEmbargo
    Exploring Public Perceptions of COVID-19 Vaccine Adverse Effects Through Social Media Analysis
    (IGI Global, 2023) Nimanthika, S; Kuhaneswaran, B; Wijeratne, A.K; Kumara, S
    This study examines social media content to identify adverse effects of COVID-19 vaccination as perceived by the public. Existing studies did not categorize tweets on vaccine adverse effects as personal experience, informative, or advice-seeking. Authors manually classified tweets into categories and used the data to train four machine learning models. LSTM algorithm yielded the highest accuracy of 90.13%. The LSTM model with GloVe embedding was determined to be most suitable. This research aims to fill a research gap and increase public awareness of COVID-19 vaccine side effects. The study highlights the importance of analyzing social media content to better understand public perception of vaccines.
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    PublicationOpen Access
    Leveraging LLMs for Dynamic Content Generation and Creating Contextual Quizzes to Enhance Learning Outcomes in Personalized Education
    (School of Education, Faculty of Humanities and Sciences, SLIIT, 2025-10-10) Wanigasekara, S; Gunawardhane, K; Kumara, S
    The research study developed an adaptive learning system based on LLMs and RAG technology to deliver customized educational content. The system differentiates from traditional LLM educational software by accepting complete lecture materials, which ensure quiz responses and feedback match the specific content of the current course. The application retrieves dynamic, relevant content from lecture slides to provide focused, structured learning that goes beyond standardized, pre-trained responses. Pinecone serves as a vector database for semantic content retrieval, and OpenAI provides GPT for natural language generation from the system architecture. The educational materials undergo Sentence Transformers processing to create semantic embeddings that enable both precise content retrieval as well as contextual adjustments.