Ms. Dulanjali Wijesekara, Lecturer attached to the Faculty of Computing at NSBM Green University, represented the University at the 11th International Conference on Information Technology Trends (ITT 2026), held on 28–29 October 2026 at the Higher Colleges of Technology, Dubai Academic City Campus, United Arab Emirates.

Held under the theme “Intelligence in Action: Autonomous Agents, Embodied AI, and the Cognitive Economy,” the conference provided an international platform for researchers and professionals to present innovative research and explore technology-driven solutions to real-world challenges.

At the conference, Ms. Wijesekara presented two research papers and also contributed to the academic programme as an evaluator for the Poster Presentation Competition, reflecting her active engagement in international research and scholarly activities.

The first paper, titled “An Intelligent Decision Support System for Tea Leaf Disease Identification and Severity Analysis Using Hybrid Image Segmentation Techniques,” was presented under the Smart Cities & Autonomous Systems track. The research originated as an undergraduate project undertaken by Mr. Akila Premachandra under the supervision of Ms. Wijesekara. Integrating Artificial Intelligence and image segmentation techniques, the system identifies tea leaf diseases, visualises affected areas, and estimates disease severity, while providing guidance in Sinhala, Tamil, and English. Building on its initial research outcomes, the project has progressed into a pilot initiative in collaboration with commercial tea planters in Sri Lanka.

The second paper, titled “Explainable and Actionable Machine Learning for Reduced Kidney Function Screening Using Multi-Regional Sri Lankan Data,” was presented under the AI for Government, Public Services & National Resilience track. This ongoing research initiative brings together Ms. Dulanjali Wijesekara of NSBM Green University; Dr. Sagara Sumathipala of Commonwealth Bank of Australia; and Mr. M. N. M. Afsaan and Dr. B. H. Sudantha of the University of Moratuwa, with additional collaboration from the University of Kelaniya. The research explores the application of explainable and actionable machine learning approaches to kidney function screening using data from multiple regions of Sri Lanka.

Both papers are intended for publication in IEEE Xplore and Scopus indexing, subject to the conference’s publication and indexing processes.