Research Publications

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Start date: 2025

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Now showing 1 - 10 of 628
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    ItemOpen Access
    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.
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    ItemOpen Access
    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.
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    ItemOpen Access
    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.
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    ItemOpen Access
    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.
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    ItemOpen Access
    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.
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    ItemOpen Access
    Work in Progress: Applying Model-Based Systems Engineering to Decentralised Earthquake Early Warning System Design
    (Sri Lanka Institute of Information Technology, 2026-05-21) Mirzaei, S; Prasanna, R; Tan, M.L; Herath, P
    Earthquake Early Warning (EEW) systems built on low-cost MEMS sensors and decentralised processing architectures represent a promising path toward affordable, largescale seismic warning coverage. Yet despite considerable progress at the component level, the field lacks a formal engineering infrastructure capable of binding these components into a coherent, verifiable, and maintainable system. This paper presents work in progress toward filling that gap through the systematic application of Model-Based Systems Engineering (MBSE) to the design of a decentralised EEW framework. Grounded in Design Science Research Methodology (DSRM), the approach uses Systems Modelling Language (SysML) to produce a formal, traceable representation of system architecture and behaviour. We report on three completed stages: a systematic literature review that identified the core research gaps, a component analysis that produced a conceptual framework for the decentralised architecture, and the formalisation of node-level detection and alert logic. The ongoing SysML modelling activity and the path toward simulation-based validation are also described. The central argument is that MBSE is not merely a documentation exercise for EEW research; it is the missing engineering foundation that makes these systems verifiable, scalable, and transferable.
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    ItemOpen Access
    How We Apply Agent-Based Modeling of Climate-Induced Internal Migration in Sri Lanka
    (Sri Lanka Institute of Information Technology, 2026-05-21) Wickramanayaka, D.M.R.
    Climate change has become a major driver of internal migration, especially in developing countries where agriculture-dependent populations are highly vulnerable to environmental shocks. This study presents an agent-based model (ABM) for simulating climate-induced internal migration in Sri Lanka. The model integrates seasonal climate variability, household economic conditions, crop failures, food insecurity, aid interventions, and return migration. Implemented in NetLogo, the simulation represents 300 heterogeneous household agents distributed across coastal, dry, and wet climatic zones. Results show that migration emerges through the interaction of climate stress, repeated crop failure, and economic hardship. Climate stress contributes 40% of migration pressure, crop failure 32%, and economic factors 28%. Aid interventions reduce displacement by 35% and increase return migration by 47%. The model offers a practical framework for understanding migration dynamics and evaluating climate adaptation policies in vulnerable regions.
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    ItemOpen Access
    Integrated Flood, Transport, and Economic Exposure Assessment Using Sentinel-1 SAR: A Case Study of Cyclone Ditwah
    (Sri Lanka Institute of Information Technology, 2026-05-21) Hettikankanama, H.S.R.; Dassanayake, S.M.; De Silva,T.S.; Mahakalanda, I.; Kaluarachchi, N.C.N.; Abeysiriwardhana,N.S.
    Flooding remains one of the most destructive natural hazards worldwide, causing substantial infrastructure damage, economic disruption, and social vulnerability, particularly in rapidly urbanising tropical regions. In November 2025, Cyclone Ditwah brought intense rainfall to western Sri Lanka, leading to widespread inundation in the Gampaha Divisional Secretariat Division. This study develops and evaluates an integrated geospatial framework combining flood detection, transport network disruption analysis, and economic exposure assessment using Sentinel-1 Synthetic Aperture Radar (SAR) data. Pre-flood (1–15 November 2025) and post-flood (28 November–4 December 2025) C-band SAR imagery were processed through radiometric calibration, speckle filtering, terrain correction, and logarithmic backscatter differencing to derive a binary flood mask. The inundation layer was subsequently integrated with a connectivity-classified road network and a geocoded economic establishment dataset to evaluate transport disruption and spatial-economic exposure using an Economic Activity Exposure Index (EAEI) and Local Moran’s I clustering. The proposed workflow was evaluated using 540 stratified validation points derived from independent high-resolution reference data. Validation results yielded an overall accuracy of 81.5%, precision of 79.3%, recall of 85.2%, F1-score of 82.1%, and a Cohen’s Kappa coefficient of 0.63, indicating substantial agreement and suitability for rapid post-flood assessment. Results reveal that flooding disproportionately affected highly connected transport corridors and economically clustered zones, suggesting localised but structurally significant disruption. The study demonstrates the operational usefulness of Sentinel-1 SAR for rapid flood assessment and highlights the importance of integrating hazard mapping with infrastructure topology and economic clustering to support geospatial decision-making for disaster risk management.
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    ItemOpen Access
    ResQNet: A Multi-Layer Hybrid AI Framework for Priority-Aware Disaster Communication over Decentralized Mesh Networks
    (Sri Lanka Institute of Information Technology, 2026-05-21) Perera,W.M.R
    Effective emergency communication is critical during disaster events, yet conventional infrastructure frequently fails through physical damage, power outages, and network congestion precisely when reliable coordination is most needed. This paper presents ResQNet, a decentralized hybrid intelligent framework designed to prioritize and route distress messages under post-disaster network conditions. The system employs a multi-layer processing pipeline integrating rule-based keyword scoring, TF-IDF with Linear Support Vector Classification (LinearSVC), a novel Distress-Aware Priority Evaluation (DAPE) heuristic, fuzzy logic-based trust evaluation, and mesh network routing using Breadth-First Search (BFS). An iterative development process upgraded the ML classifier from Multinomial Naive Bayes to LinearSVC with an expanded training corpus of 100 labeled bilingual messages, improving standalone ML accuracy from 32.5% to 70.0%. End-to-end evaluation on 80 live dashboard-tested messages demonstrates that the full hybrid pipeline achieves 71.2% classification accuracy — a 38.7 percentage-point improvement over the original ML baseline — with HIGH-priority message recall of 0.87, confirming reliable detection of safety-critical content. The system maintains stable message delivery across Dense, Sparse, and Damaged network topologies with a mean hop count of 3.4, and priority ordering effectiveness of 79.9%. Trust evaluation identified 21.2% of messages as low-credibility inputs, demonstrating resilience against noisy and misleading content. These results confirm that combining complementary AI techniques produces a significantly more robust disaster communication system than single-method approaches, with meaningful potential for real-world deployment.
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    ItemOpen Access
    Automated Flood Impact Detection from Remote Sensing Data using Transfer Learning: A New Zealand Case Study
    (Sri Lanka Institute of Information Technology, 2026-05-21) Pathirana, N; Ranasinghe, M; Prasanna, R
    Proper flood damage assessment is required for rapid emergency response and recovery planning. Highresolution drone imagery provides greater visual detail compared to satellite imagery and can support more accurate damage assessment after flood events. This work-in-progress study investigates the use of transfer learning with a pre-trained ResNet50 convolutional neural network to classify post-flood drone images. A manually annotated dataset of drone images representing damaged and non-damaged areas was used to finetune the pre-trained model and evaluate its performance using several metrics. The training process employed hyperparameter tuning to select the best model which achieved an accuracy of approximately 87%. These preliminary results demonstrate the potential of transfer learning for flood damage classification using a limited drone image dataset. Future work will focus on comparing the performance of additional deep learning models, incorporating satellite imagery and extending the approach from image-level classification to object-level damage detection.