Institutional-Repository, University of Moratuwa
Welcome to the University of Moratuwa Digital Repository, which houses postgraduate theses and dissertations, research articles presented at conferences by faculties and departments, university-published journal articles and research publications authored by academic staff. This online repository stores, preserves and distributes the University's scholarly work. This service allows University members to share their research with a larger audience.
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Recent Submissions
item: Conference-Full-text
Graphene oxide incorporated silica nanocomposite: facile low-cost synthesis for application in concrete and cement mortar mixtures
(IEEE, 2025) Rathnayake, ARMAM; Samanmali, VGC; Dharmaweera, N; Bandara, T; De Silva, R; Perera, HCS; Mantilaka, MMMGPG
Graphene oxide incorporated silica nanocomposite (GO-NS) was successfully synthesized using a simple and cost-effective sol-gel approach using sodium silicate (Na2SiO3) as silicate precursor, while graphene oxide (GO) was synthesized via a modified Hummer’s method. The resulting nanocomposite was analyzed to assess its morphological, elemental, and chemical features. SEM-EDS (Scanning Electron Microscopy with Energy Dispersive X-ray Spectroscopy) confirmed the successful deposition of silica nanoparticles (NS) on the GO surface. Complementary analyses using X-ray photoelectron spectroscopy (XPS), Fourier transform infrared spectroscopy (FTIR), and Raman spectroscopy further validated the presence of strong interfacial covalent bonding between GO and NS. The effective integration of these components is anticipated to yield synergistic effects, enhancing the overall functionality of the nanocomposite. Owing to these promising attributes, the GO-NS nanocomposite demonstrates considerable potential as an additive in concrete formulations, with the ability to improve mechanical strength, durability, and the overall performance of cementitious materials.
item: Conference-Full-text
A Comparative analysis of openstack autoscaling engines: evaluating performance, scalability, and usability of heat and senlin under real-world workload patterns
(IEEE, 2025) Wijayasiri, A; Liyanage, D
Cloud computing enables flexible and on-demand resource provisioning over the internet. Among its key features is auto-scaling, which is the automatic allocation and deallocation of resources based on the needs of the application without intervention by the user. Although auto-scaling is fairly mature in public cloud environments, its application in private clouds, particularly OpenStack meant for private cloud computing, is still limited. This gap in literature has been caused largely by a lack of performance data and comparative studies on OpenStack-based auto-scaling solutions. Manual scaling remains prevalent in most organizations but is often considered inefficient and difficult to manage. Public clouds pose challenges like higher costs, limited transparency, lower administrative control, and vendor lock-in. Hence, this paper presents a comparative analysis for performance evaluation of two native OpenStack auto-scaling engines Heat and Senlin, using ten realistic workloads generated using Apache JMeter. The ten workloads are evaluated in a fully-fledged OpenStack Zed environment, and results are analyzed using a Multi-Criteria Decision Analysis (MCDA) method. The conclusions provide practical guidance on selecting the best OpenStack auto-scaling engine based on workload patterns, thus filling a gap in the literature.
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AI chatbots for humanized service touch and customer satisfaction towards promoting AI-enabled banking interactions
(IEEE, 2025) Wikeshani, AHTJS; Jayamanna, RPAI; Wanasinghe, WDHBU; Arachchi, HADM; Samarasinghe, GD; Sisara, HT
This study examines the effects of AI chatbot communication techniques on customer satisfaction and intention to use AI enabled banking in the banking sector. Drawing on Expectancy Disconfirmation Theory (EDT) and Privacy Calculus Theory (PCT), the study investigates the effects of socially focused conversational styles and text-based communication mode on bank customers’ perceptions of humanness, happiness, and privacy concerns. A structured survey was completed by 135 Gen Z students, and Smart PLS software were used for analysis. The results demonstrate that both text-based and social-oriented communication styles significantly boost perceived humanness, which positively affects user satisfaction and intention to use. Additionally, it was demonstrated that privacy concerns moderated the relationship between pleasure and intention. The study fills in theoretical gaps and provides helpful information for chatbot designers and banking managers by applying the dual frameworks (EDT and PCT) in the context of AI-driven banking interactions. Enhancing user-centric chatbot capabilities while addressing privacy concerns may help to improve the customer experience and encourage broader AI use in digital banking.
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Predictive analytics for tea leaf aging and quality degradation
(IEEE, 2025) Bandara, RJ; Kuruppu, A
Tea quality directly influences flavor, marketability, and economic value. Traditional approaches to assessing tea leaf quality rely on manual inspection and cannot anticipate future degradation, leading to post-harvest losses. This paper introduces a two-stage, microservices-based predictive analytics system that empowers tea producers with forward-looking insights. In the first stage, a state-of-the-art object detection model processes harvested leaf images to classify quality into four tiers. In the second stage, a Random Forest classifier forecasts daily quality degradation over a fifteen-day horizon by combining leaf characteristics with environmental data-temperature, humidity, and rainfallfetched from public APIs and efficiently cached to minimize redundant calls. Explainable AI techniques distill each day’s prediction into the top three driving factors, presented in farmer-friendly language alongside actionable harvest recommendations. Deployed as serverless services and accessed through a mobile interface, the framework delivers scalable, low-latency predictions. This research addresses the critical need for proactive quality management in tea production by uniting image-based classification, environmental data integration, and explainability into a novel, end-to-end solution.
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ARGO-SLSA: software supply chain security in argo workflows
(IEEE, 2025) Mohomed, T; Ekanayake, I
Kubernetes has become the de facto standard when it comes to managing microservices. Automating complex, multi-step workflows is a common requirement in Kubernetes. Argo Workflows is a Kubernetes-native engine for managing these workflows in an automated fashion. These workflows generate artifacts such as executables, logs, container images, and packages. These artifacts require proper governance. Open-Source Security Foundation (OpenSSF), in collaboration with Google, introduced Supply-chain Levels for Software Artifacts (SLSA), a security governance framework that includes detailed technical requirements for producing artifacts. However, Argo Workflows doesn't have any built-in ways to provide the ability to incorporate the SLSA framework. This vacuum creates silos because practitioners need to rely on third-party tools to meet software supply chain security standards. This paper proposes a Kubernetes-native controller written to run in parallel to the existing open-source Argo Workflows to enhance the security of artifacts. Cryptographic signing and provenance attestations for the artifacts produced by the controller, which allows Argo Workflows to comply with SLSA standards. Evaluations were conducted in a real-world, self-hosted environment to demonstrate ARGO-SLSA’s ability to elevate artifacts to Level 2 of the SLSA compliance build track. Experimental results indicate that the ARGO-SLSA controller surpasses existing software supply chain security solutions.