Recent Submissions
Balancing User Engagement and Sustainable Web Design: A Comparative Study of 2D and 3D Hero Sections Using Kansei Engineering
(Sri Lanka Institute of Information Technology, 2026-05-21) Sandaruwan, R. M .T.; Somaweera, W. T. S.; Vasanthapriyan, S.; Sathyanjana, W.W.N.C.
With the increasing usage of digital platforms across the globe, the decisions made by designers in regards to web interfaces affect not only the interaction but also the sustainability of digital systems in the long-term. The primary visual entry point is the hero section which is usually the initial section of the page that the visitor looks at. However, the cost that the environment of the world would bear in case of using heavy 3-dimensional (3D) graphics in place of lighter 2-dimensional (2D) elements is rather unknown. This paper explores trade-off, applied to assess the 2D and 3D hero designs on user engagement and sustainability with the help of Kansei Engineering. A total of one hundred respondents (20-30 years old) rated 20 hero designs in ten themes. They rated clarity, immersion, excitement and rofessionalism. The PCA was used to identify the important perceptual attributes used to make decisions. Findings indicate that 3D graphics enhanced immersion and excitement with average scores of more than 4.5
out of 5 and 65 percent of the participants preferring 3D graphics. On the contrary, 2D graphics obtained a higher clarity and professionalism score, averages were 3.2 to 3.9 and were more relied upon in a situation where reliability was important.
Although 3D components enhance emotional appeal, they significantly increase the computing capabilities, which is why the consideration of experience and smart design are crucial. These results provide guidelines to designers of how they can maximize user interaction and how they can facilitate sustainable web interfaces.
EcoSort: An Edge-Deployable Hybrid AI-IoT Framework with Decision Fusion for Automated Waste Sorting and Real-Time Bin Monitoring
(Sri Lanka Institute of Information Technology, 2026-05-21) Liyanage, V; Seneviratne, O
Abstract—Effective waste segregation remains a major challenge in urban environments because manual sorting, isolated sensor systems, and stand-alone vision models often fail to deliver the accuracy, integration, and operational visibility required for reliable deployment. This paper presents EcoSort, an edgedeployable hybrid AI-IoT framework that combines imagebased waste classification, sensor-assisted validation, decision fusion, automated sorting, and real-time bin monitoring within a single architecture. A MobileNetV3-based classifier performs lightweight visual recognition, while complementary sensor readings provide physical cues for validating ambiguous cases. The independent outputs are merged using a priority-based decision fusion layer that produces the final class label used to trigger
the sorting actuator. The system is implemented as a lowcost prototype using embedded controllers, ultrasonic sensing, servo-based actuation, and a web dashboard for live fill-level visualization. In addition to end-to-end sorting, the monitoring layer generates threshold-based collection alerts when bin capacity approaches critical levels, improving operational responsiveness. The study contributes a unified design that addresses the fragmentation seen in prior waste management solutions, where classification, segregation, and monitoring are typically treated as separate subsystems. Prototype-level evaluation and implementation observations indicate that the hybrid pipeline improves classification dependability and sorting robustness compared with single-modality operation while remaining feasible for resource-constrained edge deployment. The proposed framework therefore offers a practical foundation for scalable, data-driven, and sustainable waste management in institutions and smart-city settings.
A Data-Driven Framework for Prioritizing Post-Disaster Non-Food Relief Needs: Evidence from District-Level Analysis following the ”Dithwa” disaster in Sri Lanka
(Sri Lanka Institute of Information Technology, 2026-05-21) Lokuliyana, S; Wijesiri, P; Jayakody, A; Peiris, H
Effective disaster response requires timely and data-driven allocation of relief resources. This study presents a statistical analysis of district-level and camp-level non-food relief requirements following the Dithwa disaster in Sri Lanka. A consolidated dataset comprising multiple districts and relief camps was analyzed using descriptive statistics, frequency analysis, cross-tabulation, and inferential statistical tests, including the Chi-square test and Kruskal–Wallis test.
The results reveal that relief demand is highly heterogeneous across districts, with a small number of regions accounting for the majority of total requirements. Shelter and bedding items dominate the demand profile, followed by water, sanitation, and hygiene (WASH) supplies, indicating significant needs related to temporary living conditions and public health. Frequently requested items such as bed sheets, blankets, and sanitary packs suggest the feasibility of developing standardized core relief packages. However, statistically significant differences across districts highlight the necessity for adaptive, location-specific allocation strategies.
The findings demonstrate the value of transforming operational disaster data into structured statistical insights to support evidence-based decision-making. The proposed approach contributes to improving resource prioritization and enhancing the efficiency of humanitarian response planning in disaster-prone regions.
Evaluating the Effectiveness of Virtual Reality-Based Fire Safety Training Using Performance Metrics and User Behavior Analysis
(Sri Lanka Institute of Information Technology, 2026-05-21) Nisme, N; Wijayasiri, U; Abeygunawardhana, P
Fire safety training is essential for improving emergency response; however, conventional methods often lack interactivity and fail to provide measurable performance data. Existing Virtual Reality (VR)-based training systems enhance immersion but primarily rely on subjective evaluation, limiting quantitative assessment of effectiveness. To address this limitation, a VR-based fire safety training system integrated with performance metrics and user behavior analysis was developed and evaluated. An experimental study involving 47 participants was conducted using a pre-test and post-test design. Learning outcomes, behavioral data, and user feedback were analyzed. Results indicate a significant improvement, with median scores increasing from 4 to 8 and 70.21% of participants showing improvement. The Wilcoxon signed-rank test confirmed statistical significance (p = 0.00016) with a moderate to large effect size (r = 0.525). Behavioral metrics further demonstrated measurable user performance, supporting the effectiveness of VR-based fire safety training.
Review-Based Conceptual Framework for Generative AI in Urban Disaster Management
(Sri Lanka Institute of Information Technology, 2026-05-21) Samadhi, L. A. S. S. S; Dilshan, O.A.P; Galappaththi, K
Natural disasters, including floods and earthquakes and cyclones and wildfires, create ongoing dangers to human life and critical infrastructure and worldwide socioeconomic stability. The rising occurrence and intensity of these events require disaster management systems that can think and respond and adapt to changing needs. Generative Artificial Intelligence (GenAI) has become the dominant framework for
disaster response activities. The system can create realistic simulations of disaster situations by processing extensive multimodal data. The research investigates how GenAI functions in disaster management while focusing on disaster prediction and damage assessment and response planning and resilience building. The system enables crisis management through its advanced satellite image analysis and social media text processing and sensor data evaluation and visual record assessment. The collaborative process of creating models enables urban resilience planning through scenario simulation because it shows disaster effects under different conditions. Data privacy issues together with ethical challenges and model reliability problems and misinformation risks remain as major obstacles. The study combines existing research to describe what GenAI currently achieves and what it cannot do in disaster management while presenting future research routes that will help build AI systems which are safe and reliable and
beneficial for human users and will boost worldwide disaster resilience.
The SLIIT Research Document Archive (RDA) is the institutional repository of SLIIT, managed by the SLIIT Library. The primary purpose of SLIIT RDA is to manage, store, and disseminate SLIIT research output with its community and beyond, reaching the wider public. This plays a pivotal role in preserving the academic legacy of the institute.
The collection comprises the research output of SLIIT staff and postgraduate research students, including research publications, conference and symposium papers, books, book chapters, theses, and other scholarly materials. Access to full texts may be restricted depending on the access and licensing terms.