Recent Submissions
AI-Driven Decision Support System for Sustainable Agarwood Cultivation and Export Readiness
(Institute of Electrical and Electronics Engineers Inc., 2026-05-22) Malmali W.S.V.M.O; Jayawardena L.P.G.K.; Rathnamalala R.M.B.I.T.; Kandage T.P.; Weerasinghe, Lokesha
Agarwood cultivation and export involve multiple critical decision points that directly affect resin quality, economic value, and market acceptance. In current practice, decisions related to resin induction timing, disease identification, and export readiness are largely based on manual inspection and subjective judgment, leading to inconsistent assessments and avoidable losses. This paper presents an AI-based decision support system to support sustainable agarwood cultivation and export by integrating three analytical components: resin induction stage classification, export readiness and quality assessment, and leaf disease detection with remedy recommendation. A multimodal deep learning approach combining bark images and numerical tree parameters is used for resin induction stage classification, achieving a test accuracy of 93% using an EfficientNetB0 with a Multi-Layer Perceptron (MLP). Agarwood resin and chip quality grading is performed using an EfficientNetB0-based Convolutional Neural Network (CNN), while export readiness is evaluated using a Random Forest-based numerical model. Leaf disease detection is implemented using a CNN-based classifier, achieving an overall accuracy of 80% across four common agarwood leaf disease classes. Explainability mechanisms, including Gradient-weighted Class Activation Mapping (Grad-CAM) and reason based readiness analysis, are incorporated to enhance transparency and user trust. Experimental results indicate that the proposed system reduces subjectivity and supports data-driven decision-making across key stages of the agarwood value chain.
The Multi-Tenant Customization Paradox: Formalising the Intrinsic Conflict Between Scalable Shared Codebases and Tenant-Specific Operational Customization in SaaS Architecture
(Institute of Electrical and Electronics Engineers Inc., 2026-07-07) Jayasuriya, R; Piyarisi, T; Awandya, S; Wickramasooriya, S; Thelijjagoda, S; Kasthurirathna, D
Multi-tenant Software-as-a-Service (SaaS) architectures promise economies of scale through a single shared codebase serving multiple tenants. However, enterprise tenants increasingly demand deep operational customization (bespoke workflows, domain-specific business rules, and industry-specific computational logic) that fundamentally conflicts with the sharedcodebase constraint. Despite the centrality of this tension to SaaS architecture, no formal definition exists in the literature. This paper formally defines the Multi-Tenant Customization Paradox (MTCP): the structural impossibility of simultaneously maximizing codebase unity and tenant customization depth without incurring costs that grow super-linearly with the number of tenants. We introduce a formal model quantifying this tension through the Paradox Coefficient $P(S)$, establish a five-dimensional customization taxonomy with per-dimension formal measures, derive the Feasibility Region governed by an architectural sophistication parameter $α(B)$, propose an operational rubric for estimating $α(B)$ in practice, and derive an upper bound on the achievable unity-depth trade-off for four canonical resolution strategies. Our formalization demonstrates that the paradox is inherent to the mathematical structure of multi-tenancy rather than incidental to implementation choices, providing architects and researchers with a theoretical foundation for reasoning about customization trade-offs.
Integrating industry 4.0 and 5.0 technologies in luxury fashion retail interiors: A systematic review of digital transformation, sensory design, and brand storytelling
(Elsevier Ltd, 2026-05-12) Ratnayake, Janitha C; Jayasuriya, N; Suraweera, T; De Silva, L
Emerging developments in Industry 4.0 and Industry 5.0 are transforming the cultural, sensory, and experiential character of luxury fashion retail interiors. These changes, driven by artificial intelligence, the Internet of Things, augmented and virtual reality, influence how individuals perceive space, form emotional connections, and engage with brand narratives across both physical and digitally enriched environments. Against this backdrop, this review aims to systematically synthesise how Industry 4.0 and Industry 5.0 technologies intersect with sensory experience, spatial design, and narrative expression in luxury fashion retail interiors, addressing the tendency of prior reviews to examine digital technologies or experiential aspects of retail in isolation. The study adopts a systematic literature review approach guided by the SPIDER framework and the PRISMA protocol. Fifty peer reviewed publications published between 2014 and 2025 were analysed to examine how digital transformation, sensory experience, and narrative expression intersect within luxury retail culture. The thematic analysis identified three closely connected areas: branding and customer experience, the interior environment of luxury stores, and the integration of advanced technologies within retail spaces. Together, these themes illustrate how contemporary luxury interiors operate as culturally expressive and emotionally charged settings shaped by new forms of technological mediation. Building on these insights, the study introduces the Multi-Layered Integration Framework, which explains the interaction between digital systems, spatial atmospheres, and brand storytelling in the creation of culturally responsive and human-centred retail interiors. The review contributes to the social sciences and humanities by demonstrating how emerging technologies reshape sensory engagement, symbolic identity, and cultural expression in luxury retail settings, while offering an expanded understanding of human experience within digitally influenced interior environments.
Multimodal AI Framework for Personalized and Context-Aware Skin Disease Diagnosis, Monitoring, and Treatment Support
(Institute of Electrical and Electronics Engineers, 2026-05-22) Wijesinghe H.W.M.O.P.; Laksopan R; Mihisandali W.K.M.; Devindi K.P.T.; Weerasinghe, L; De Silva, A
Dermatoscopic assessment of skin diseases based on visual morphology may not provide sufficient discrimination due to differences in cutaneous appearance, the severity of disease symptoms and individual biological or environmental factors. Current artificial intelligence (AI)-based dermatological systems mainly integrate unimodal image-based data which is constrained by comparative diagnostic performance in visually ambiguous conditions and across different skin complexions. Image-only approaches also do not utilize patient-reported symptoms needed to tailor treatment plans. In this study, we explore the potential of a unified multimodal AI framework towards robust, context-aware and patient- centric skin disease diagnosis, monitoring and treatment support. The framework combines deep learning-based image analysis with symptom-aware inputs extracted from voice recordings and structured text, which allows for improved diagnostic reliability. The proposed framework also introduces an explainable severity assessment module which evaluates disease progression via interpretable features and rule-based score. Domain adaptation methods further employed lead to better generalization for out-of-distribution data originating from different populations and reduce model bias. A knowledge-driven recommendation module generates context-aware personalized treatment recommendations based on predicted disease categories and patient-related information. Experimental results demonstrate that the proposed multimodal framework improves contextual understanding and robustness in visually ambiguous cases while enhancing interpretability, improved generalization, and practical applicability in teledermatology environments, while adding interpretability, fairness and real-world applicability of teledermatology systems.
Determinants of under-five mortality in Africa: evidence from a two-decade panel analysis for public health policy
(BioMed Central Ltd, 2026-06-17) Rathnasekara, H; Jayathilaka, R
Background: Child survival is a critical indicator for a nation’s health and its progress is important in attaining the Sustainable Development Goals. Understanding the regional and country-specific dynamic and interplay of various determinants of under-five child mortality is vital for the African continent, which remains one of the most vulnerable regions for child mortality globally. Methods: This study investigates the association of economic, health-related, social and demographic, environmental, and infrastructure-related factors with under-five child mortality. It integrates generalisable regional associations using a standard fixed-effects panel model and examines illustrative country-specific associations of the selected determinants through multiple linear regression, based on a balanced panel dataset of 45 countries over a 22-year period. Results: Fixed-effect analysis reveals that the diphtheria-tetanus-pertussis (DTP) immunisation and total fertility rate (TFR) are robust regional determinants of under-five mortality across specifications. While health expenditure, sanitation services, and malaria incidence show significant associations in the baseline model, these findings are sensitive to the inclusion of year fixed effects, suggesting they are influenced by broader temporal trends or common regional shocks rather than serving as stable independent factors within the study period. Conclusion: Regional analysis, which controls for unobserved country-specific heterogeneity over an extended period and is complemented by country-specific analysis, facilitates the formulation of policy implications at both national and international levels. Recommended policy measures include increasing immunisation coverage, implementing malaria control programmes, strengthening community health infrastructure, enhancing girls’ education, promoting widespread access to modern family planning, and improving sanitation services. These strategies are expected to contribute to the progress toward Sustainable Development Goal 3.2 in the African region.
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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.