Conference papers

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    EDU-SENSE: AI-Driven Adaptive E-Learning System for Primary Education
    (Institute of Electrical and Electronics Engineers Inc., 2025-12-09) Shanmugalingam, N; Maheshwaran, L; Rizvee, I; Roshni R.J; Samarakoon, U; Rajendran, K
    This research addresses critical gaps in primary education by integrating AI-driven e-learning tools with traditional classroom teaching, ensuring technology supports rather than replaces teachers while improving student learning outcomes. In getting ready for high-pressure exams like Sri Lanka's Grade 5 Scholarship Examination, maintaining student engagement and emotional well-being remains a significant challenge. EDU-SENSE is an AI-driven web platform designed for primary education that integrates personalized content delivery, stress detection, and an emotion-aware chatbot. The system begins with a diagnostic pre-test to assess individual skill levels,and provides syllabus-aligned questions, and delivers targeted revision based on identified knowledge gaps. A s tress-aware module and motivational chatbot provide emotional support, while peer collaboration features promote teamwork and knowledge sharing. Using Machine Learning, Natural Language Processing, and fine-tuned Large Language Models, EDU-SENSE offers a holistic solution that enhances both academic performance and emotional well-being in young learners.
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    Agile User Story Builder: Transform Real-Time User Requirement Conversations into Well-Structured Agile User Stories
    (Institute of Electrical and Electronics Engineers Inc., 2025-12-09) Sathsarani, S; Sandun, Y; Rusith, R; Himaranga, S; Kumari, S; Supunya Swarnakantha, N.H.P. R
    Agile software development methodologies rely heavily on well-structured user stories to bridge technical and business requirements. However, the traditional manual process of converting stakeholder conversations into structured user stories presents significant challenges including transcription errors, communication ambiguity, and inefficient processing. This research presents an automated Agile User Story Builder system that transforms requirement conversations into well-structured user stories. The system integrates cloud speech recognition, Natural Language Processing (NLP), machine learning, and Retrieval-Augmented Generation (RAG) for automated requirement processing within a microservices architecture. The system was developed using synthesized audio from 1,247 SRS documents (achieving 88% transcription accuracy) and validated on 75 real stakeholder conversations. Real-world validation demonstrates 78% speech-to-text accuracy (95% CI: 74-82%), 82% task classification precision, 73% reduction in manual documentation effort, and 86% format compliance in generated stories. The system achieves 20% timeline and 62% budget prediction improvement while providing measurable benefits including enhanced collaboration and streamlined Agile workflow efficiency.
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    NoFake Trustworthy Reviewer Scoring System for Detecting Fake Reviews
    (Institute of Electrical and Electronics Engineers Inc., 2025-12-09) Dissanayaka S.D; Jayawardhana R.A.D.G.S; Jayalath, T; De Silva, H
    The proliferation of fake online reviews has severely undermined consumer trust and market fairness in digital platforms. Traditional detection systems relying on rule-based filters or shallow machine learning models often fail to identify sophisticated, artificially intelligent-generated, or behaviorally deceptive content. This research proposes an intelligent system for fake review detection that integrates advanced Natural Language Processing, behavioral anomaly analysis, and explainable artificial intelligence to enhance accuracy, transparency, and usability. The system employs a hybrid feature set of about 212 dimensions, including Term Frequency-Inverse Document Frequency scores, sentiment-rating inconsistency, readability metrics, and reviewer behavioral indicators such as review frequency and account metadata. A neural network trained on the Deceptive Opinion Spam dataset (40,000 reviews) classifies reviews as genuine or fake, achieving 81.55% accuracy and an Area Under the Receiver Operating Characteristic Curve of 0.9019. Anomaly detection models Isolation Forest generate a dynamic trust score for reviewers, improving detection of coordinated spam campaigns. To ensure transparency, SHapley Additive exPlanations are integrated into a full-stack web application, providing human-readable insights such as Sentiment-Rating Mismatch or Robotic Writing Detected. Results show the proposed system outperforms traditional methods in accuracy and interpretability, offering a scalable and trustworthy solution for digital trust ecosystems.
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    AI-Driven Integrated Caregiving and Health Monitoring Framework for Elderly Well-Being
    (Institute of Electrical and Electronics Engineers, 2026-07-22) Nugaliyadde, S; Nikeshi, N; Marasinghe, M; Rajapaksha, C; Rajapaksha, S; Thelijjagoda, S
    The rapid growth of the aging population has brought about some serious challenges, particularly in managing illnesses, feelings of loneliness, cognitive decline, and mental health issues. Traditional caregiving methods often depend on occasional assessments and hands-on supervision, which can fall short in providing the ongoing and adaptable support that’s really needed. This paper introduces an innovative caregiving and monitoring framework powered by AI, aimed at offering integrated, real-time, and comprehensive assistance for older adults. The system harnesses the power of Artificial Intelligence (AI), Machine Learning (ML), Natural Language Processing (NLP), and health data analytics to combine physical health monitoring, nutrition planning, smart routine coaching, and therapist-led mental health support all in one platform. With features like voice-based conversations and journaling, it makes emotional expression and behavioral analysis more accessible, helping to gain a deeper insight into users’ mental well-being. Predictive analytics and anomaly detection are used to spot early signs of health risks and shifts in behavior, allowing for timely interventions. Plus, remote access means caregivers and healthcare professionals can keep an eye on users and offer informed advice. By shifting caregiving from a reactive approach to a proactive and preventive one, this system not only improves quality of life but also encourages independent living and eases the burden on caregivers.