Research Publications Authored by SLIIT Staff
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This collection includes all SLIIT staff publications presented at external conferences and published in external journals. The materials are organized by faculty to facilitate easy retrieval.
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Publication Embargo OcupHI: knowledge-driven colorimetric interpretation framework for high-precision real-time ocular pH diagnostics(Springer Nature, 2026-08-12) Kahandawala, B, S; Sandaruwan, H. H. P. B; Liyanage, P; Dassanayake, R.S; Costha, N.P; Liyanage, R.N; Wijenayake, U; Wijesinghe, R.E; Silva, B.N; Manatunga, D.COcular injuries due to chemical spills pose a substantial concern, representing 10–22% of all ocular trauma. Although precise detection of ocular pH is crucial for determining the optimal medical treatment, many existing methods remain invasive, biased, or insufficiently precise. Reliance on subjective visual assessment of subtle color differences limits the objectivity and hinders high-throughput analysis. Therefore, an advanced colorimetric knowledge-driven ocular pH detection method was developed using a biosensor (OcupHI) based on a Clitoria ternatea (Butterfly Pea) anthocyanin sensing agent. The proposed work delivers fast, high-precision, and easily measurable pH prediction across clinically relevant ranges, while supporting real-time decision support for eye physicians. The pH range from 1 to 12 was tested and compared with six different anthocyanin concentrations: 5, 10, 20, 30, 40, and 50 ppm, and five different machine learning models, namely, Decision Tree (DT), K-Nearest Neighbors (KNN), Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Support Vector Machines (SVM). The results revealed that the 40 ppm anthocyanin concentration trained with the XGBoost model produced the most accurate ocular pH values, achieving superior performance with an overall accuracy of 96%, a significantly higher F1-score for early detection. Experimental validation clearly demonstrates strong predictive accuracy, robustness, and interpretability, highlighting the potential for next-generation ocular diagnostics. Further research findings support Sustainable Development Goal (SDG) 3 – good health and well-being through a real-time ocular pH monitoring kit, and SDG 12 – responsible consumption and production by optimizing the use of the natural colorant anthocyanin for sensor development.Publication Embargo Exploring the Usage of AI Tools in Education: Insights from Gen Z Undergraduates in Sri Lanka(University of Nigeria Department of Mass Communication, 2025-06-02) Nishshanka, N; Karunarathna, N; Dayapathirana, N; Karunarathna, R. V; Hewage, H. K; Anthony, PBackground: This study investigates the patterns of use and adoption of AI tools in Sri Lanka, with a special emphasis on Generation Z undergraduates who will enter the industry next. As AI is an emerging technology, how this generation interacts with and enriches knowledge through AI tools becomes a vital area of concern. Objective: To identify key subjective factors influencing the adoption and usage of AI tools in education among Gen Z undergraduates in Sri Lanka. Methodology: This study employs qualitative research methods, specifically semi-structured interviews, to gather insights from 18 university students across various disciplines. Thematic analysis was used to identify recurring themes related to undergraduates' subjective experiences, benefits received, and attitudes, for which MAXQDA is used as the analytical software. Results: The findings demonstrate four key subjective factors that influence adoption and usage, such as academic work, awareness and adoption, challenges and risk, and helpful and supportive factors. The frequently used AI tool in Sri Lanka was noted as ChatGPT, which showed a high usage pattern in the analysis. Conclusion: Understanding the usage patterns and adoption factors helps the community use AI tools effectively, as it makes them aware of the risks and helpful factors. Also, the facilities that aid in adopting these AI tools could elevate the efficiency of their usage by making many students, future undergraduates, AI developers, and educational institutions aware of its benefits. Unique Contribution: This research provides insights for future research by helping to understand the usage of emerging AI tools among Gen Z undergraduates in a developing country like Sri Lanka. The findings can be applied to understanding different generations and emerging generations, such as Generation Alpha.Publication Embargo ASD Screening for Toddlers via Physical Interpretation through Advanced AI(2021 3rd International Conference on Advancements in Computing (ICAC), SLIIT, 2021-12-09) Jayasekera, D.; Alwis, H.; Dissanayaka, H.; Mudalinayake, R.; Piyawardana, V.; Pulasinghe, K.Autism Spectrum Disorders (ASD) are generally causing challenges for significant communication, social interaction, and behavioral patterns to elderly people and children. Providing early treatments can make a huge advancement in the lives of children. Meanwhile, there is a limited number of systems to screen and identify ASD children. This research project is about developing a set of tools bonding together to one system called “AI - Bot Simon” to screen kids with ASD by filling the gap. In the system development process mainly, Audio, Facial expressions, Gestures, and the Gates of a targeted group of children are considered for screening. Since the target group is 6 months to 4 years, they are in early language development age. On the technical side of view Machine Learning (ML) and Deep Learning (DL) with Neural Networks (NN) are used for advanced screening and monitoring for automation of the process. In the last step of the development, all the outputs or information gathered from each tool or model, processed, analyzed, and provided to the users of the system by an Artificial Intelligence (AI) bot implemented with a web application and a mobile application whether children are suffering from ASD or not.