Research Publications Authored by SLIIT Staff
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This collection includes all SLIIT staff publications presented at external conferences and published in external journals. The materials are organized by faculty to facilitate easy retrieval.
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Publication Embargo Unveiling the Current Extent of the Gig Economy Engagement in Developing Asian Countries(University of Nigeria Department of Mass Communication, 2025-05-21) Dilmith, C; Jayathilaka, R; Jayalal, S; Devhara, T; Rathnayake, N; Jayasuriya, NBackground: The gig economy, driven by technological advancements, has shifted the labour market from traditional jobs to mainstream freelance and contract work via online platforms. Statistical evidence highlights the importance of examining gig economy engagement in developing Asian countries, which are key contributors to global platforms. Objective: This study sought to systematically analyse the rise of gig economy engagement in developing Asian countries and its implications for the future of work while providing insights for platform users. Methodology: This study was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, drawing on past research and numerous reliable resources from 1999 to 2024. Results: Findings reveal a growing research focus on the gig economy, particularly since 2016, with a significant increase in publications from 2019 to 2024. This highlights gaps in understanding gig workers' well-being, including stress, quality of life, and gender-specific barriers. Conclusion: Scholars must pay adequate attention to the expanding contributions of the gig economy, considering its potential to reshape workforce dynamics and drive economic innovation. Unique contribution: This study presents a graphical representation that illustrates the evolution of existing scholarly contributions, highlighting key gaps that require further exploration, and emphasises the vital importance of investigating this area. Key Recommendation: Policymakers need to focus on adopting a fair work framework while addressing the underexplored areas of gig workers' experiences and challenges to foster equitable and sustainable growth of the gig economy in developing Asian countries. © 2025, University of Nigeria Department of Mass Communication.Publication Open Access Efficient Hotspot Detection in Solar Panels via Computer Vision and Machine Learning(Multidisciplinary Digital Publishing Institute (MDPI), 2025-07-15) Fernando, N; Seneviratne, L; Weerasinghe, N; Rathnayake, N; Hoshino, YSolar power generation is rapidly emerging within renewable energy due to its cost-effectiveness and ease of deployment. However, improper inspection and maintenance lead to significant damage from unnoticed solar hotspots. Even with inspections, factors like shadows, dust, and shading cause localized heat, mimicking hotspot behavior. This study emphasizes interpretability and efficiency, identifying key predictive features through feature-level and What-if Analysis. It evaluates model training and inference times to assess effectiveness in resource-limited environments, aiming to balance accuracy, generalization, and efficiency. Using Unmanned Aerial Vehicle (UAV)-acquired thermal images from five datasets, the study compares five Machine Learning (ML) models and five Deep Learning (DL) models. Explainable AI (XAI) techniques guide the analysis, with a particular focus on MPEG (Moving Picture Experts Group)-7 features for hotspot discrimination, supported by statistical validation. Medium Gaussian SVM achieved the best trade-off, with 99.3% accuracy and 18 s inference time. Feature analysis revealed blue chrominance as a strong early indicator of hotspot detection. Statistical validation across datasets confirmed the discriminative strength of MPEG-7 features. This study revisits the assumption that DL models are inherently superior, presenting an interpretable alternative for hotspot detection; highlighting the potential impact of domain mismatch. Model-level insight shows that both absolute and relative temperature variations are important in solar panel inspections. The relative decrease in “blueness” provides a crucial early indication of faults, especially in low-contrast thermal images where distinguishing normal warm areas from actual hotspot is difficult. Feature-level insight highlights how subtle changes in color composition, particularly reductions in blue components, serve as early indicators of developing anomalies.Publication Open Access A novel application with explainable machine learning (SHAP and LIME) to predict soil N, P, and K nutrient content in cabbage cultivation(Elsevier B.V., 2025-03-06) Abekoon, T; Sajindra, H; Rathnayake, N; Ekanayake, I, U; Jayakody, A; Rathnayake, UCabbage (Brassica oleracea var. capitata) is commonly cultivated in high altitudes and features dense, tightly packed leaves. The Green Coronet variety is well-known for its robust growth and culinary versatility. Maximizing yield is crucial for food sustainability. It is essential to predict the soil’s major nutrients (nitrogen, phosphorus, and potassium) to maximize the yield. Artificial intelligence is widely used for non-linear predictions with explainability. This research assessed the predictive capabilities of soil nitrogen, phosphorus, and potassium levels with explainable machine learning methods over an 85-day cabbage growth period. Experiments were conducted on cabbage plants grown in central hills of Sri Lanka. SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME) were used to clarify the model’s predictions. SHAP analysis showed that high feature values of the number of days and plant average leaf area negatively impacted for nutrient predictions, while high feature values of leaf count and plant height had a positive effect on the nutrient predictions. To validate the results, 15 greenhouse-grown cabbage plants at various growth stages were selected. The nitrogen, phosphorus, and potassium levels were measured and compared with the predicted values. These insights help refine predictive models and optimize agricultural practices. A user-friendly application was developed to improve the accessibility and interpretation of predictions. This tool is a user-friendly platform for end-users, enabling effective use of the model’s predictive capabilities.Publication Open Access Identifying the causes of adolescent malnutrition in Nuwara-Eliya District, Sri Lanka(Nature Research, 2025-05-06) Nandajeewa, S; Aluthwatta, S; Weerarathna, R; Rathnayake, N; Rajapakse, V; Wijesinghe, N; Liyanaarachchi, TMalnutrition, a persistent illness, significantly reduces fat, muscle and bone levels, harming internal organs. The economic crisis in Sri Lanka has led to widespread malnutrition among children, including adolescents experiencing growth spurts. This study identifies factors influencing malnutrition in grade 10 pupils in the Nuwara-Eliya District, with the highest rates of malnutrition and also a multicultural area with many estate sector residents. Using a cross-sectional, quantitative approach, the data was collected from 379 respondents via a Likert scale questionnaire. Structural Equation Model (SEM) analysis was conducted using Smart PLS 4.0. Key findings indicate that environmental factors, such as access to clean water and sanitation, significantly influence adolescent malnutrition. A comprehensive strategy incorporating education, healthcare, and environmental improvements is essential for this. Ongoing observation, community engagement, and cooperative tactics are crucial for sustainable solutions. Addressing environmental issues and promoting a holistic approach to health education and infrastructure improvements are vital to combat adolescent malnutrition in vulnerable populationsPublication Open Access Beyond compensation: effect of employee benefits on job motivation, performance, and turnover intention(Cogent OA, 2025-12-31) Peemanee, J; Weerarathna, R; Issarapaibool, A; Boonlua, S; Rathnayake, NThis study investigates the influence of employee benefits on motivation, performance, and turnover intention within contemporary workplaces that increasingly emphasize employee well-being. Addressing a key gap in the literature, it employs Structural Equation Modeling (SEM) and analyzes data from 387 Generation Y and Generation Z employees in Small and Medium Enterprises (SMEs) in Thailand. The analysis examines how diverse benefit types influence employees’ motivation, performance, and decisions to remain with their organizations. The findings reveal a direct and positive link between employee benefits, enhanced motivation, and improved performance, which together significantly reduce turnover intention. Specifically, attraction and retention strategies, organizational support mechanisms, and a growth-oriented, well-being-focused environment emerged as critical factors in motivating employees and elevating their performance. Overall, the study demonstrates that strategically designed employee benefit packages—aligned with employee needs and workplace realities—foster engagement, productivity, and loyalty. This study contributes valuable insights for organizational leaders seeking to refine benefit systems and extends the academic understanding of the strategic importance of non-monetary benefits in promoting employee satisfaction and retention.Publication Open Access Factors influencing migration intention of undergraduates in Sri Lanka: ‘About more than employment(Elsevier Ltd, 2026-01-26) Marawila, R; Weerarathna, R; Rathnayake, N; Guruge, R; Wehella, B; Udugahapattuwa, T; Weligodapola, MThe objective of this study is to examine the factors influencing Sri Lankan undergraduates' intention to migrate. Persistent economic, social, and political challenges have driven many youngsters and professionals to leave their Country of Origin (COO). The economic collapse triggered by COVID-19 further intensified this trend, leading to a sharp increase in outward migration. Recently, a growing number of Sri Lankan undergraduates and skilled professionals have expressed a strong desire to relocate abroad, often immediately after completing secondary education. For this study, a sample of 385 undergraduates from state and non-state universities across Sri Lanka was analysed. Given the national concerns of brain drain and shortages of trained and skilled workers, the study specifically focused on understanding undergraduates' aspirations to migrate. Structural Equation Modeling (SEM) was applied to identify and test the variables influencing migration intentions within the Sri Lankan context. The findings provide a holistic picture of the drivers of undergraduate migration. These carry important implications not only for students but also for policymakers and Higher Education Institutions (HEIs), by informing policies and strategies that could encourage young people to realise their potential within Sri Lanka rather than abroad.Publication Open Access Carbon emissions across income groups: exploring the role of trade, energy use, and economic growth(Springer Nature, 2025-07-10) Dharmapriya, N; Gunawardena, V; Methmini, D; Jayathilaka, R; Rathnayake, NThis study investigates the interplay of trade openness, energy consumption, and gross domestic product (GDP) on carbon emissions across different income groups, analysing data from 163 countries from 2000 to 2019. Using panel regression and multiple linear regression techniques, the findings highlight energy consumption as the principal driver of carbon emissions across all income categories, underscoring its central role in environmental sustainability challenges. High-income countries, despite technological advancements, continue to exhibit substantial emissions due to their reliance on fossil fuels. In contrast low-income nations face difficulties in balancing economic growth with environmental sustainability, often lacking the resources to adopt cleaner energy alternatives. The study emphasises the urgent need for income-specific strategies to reduce carbon emissions, advocating for the widespread adoption of renewable energy sources and tailored policy interventions. These insights align with the United Nations Sustainable Development Goals, particularly SDG 13 (Climate Action), by promoting the integration of economic development with environmental stewardship. By addressing disparities across income levels, this research offers actionable recommendations for policymakers to support equitable and sustainable practices globally.Publication Open Access Enhancing the Understanding of climate dynamics: analysis of global warming’s influence on Climatic changes across continents(Springer, 2025-07-14) Dharmapriya, N; Edirisinghe, S; Gunawardena, V; Methmini, D; Rathnayake, N; Jayathilaka, RGlobal warming, primarily due to increased atmospheric carbon dioxide, poses a significant threat to climate stability, yet research on its combined effects across different geographical areas is limited. In order to fill that gap, this study examines how carbon emissions (CE) are impacted by greenhouse gas emissions (GHG), agricultural nitrogen oxide (ANO), urban population (UP), and fossil fuel consumption (FFC) in 185 different nations between 2000 and 2019. With the exception of urban population, which was expressed as a percentage, all variables were standardised to metric tonnes per capita using panel regression analysis. The results draw attention to geographic disparities. Africa has the lowest carbon and greenhouse gas emissions due to its extensive forest cover and minimal industrial production. Although Oceania’s greenhouse gas emissions have decreased, the region continues to emit high amounts of agricultural nitrous oxide. Rapid industrialisation is the primary cause of Asia’s growing consumption of fossil fuels. Agricultural nitrous oxide and carbon emissions have a negative correlation in Asia, Oceania, and the globe, but a positive correlation in Africa, America, and Europe. Carbon emissions and the use of fossil fuels are strongly positively correlated in every region but Asia. These results highlight the complex, location-specific factors affecting carbon emissions. For policymakers to effectively cut emissions, they must develop customised, geographically specific initiatives. In order to accomplish Sustainable Development Goal 13: Climate Action by 2030, emission controls should be strengthened, and sustainable practices should be encouraged, particularly in the use of fossil fuels and farming.