Scopus Index Publications
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This collection consists of all Scopus-indexed publications produced by SLIIT researchers. Scopus is recognized worldwide as a leading and reputable academic indexing database.
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Publication Open Access CALCOM: An integrated techno-economic and life-cycle environmental framework for electric vehicle assessment in Sri Lanka(Elsevier B.V., 2026-08-01) Abeygunawardena, N; Wijayapala, A; Jathunga, TElectric vehicle (EV) adoption is accelerating worldwide, creating a need for comprehensive frameworks that assess economic feasibility and environmental performance. This study presents a context-adaptive levelized cost of mileage (CALCOM) framework to evaluate the economic and environmental performance of battery electric vehicles (BEVs), hybrid electric vehicles (HEVs), and internal combustion engine vehicles (ICEVs) under Sri Lankan conditions. The framework integrates discounted life-cycle cost (LCC), net present value (NPV), and greenhouse gas (GHG) emissions into a unified assessment model. Real-world operational ad cost data were collected from owners representing Nissan Leaf (BEV), Toyota Aqua (HEV), and Toyota Vitz (ICEV). The analysis included purchase cost, energy consumption, maintenance, battery replacement, salvage value, and environmental costs over a 10-year ownership period. The BEV achieved the lowest levelized cost of mileage of 0.080 USD/km which further decreased to 0.050 USD/km under renewable charging. Life-cycle GHG emissions were 59% lower than those of ICEV. Sensitivity analysis identified electricity price, annual distance travelled, and charging efficiency as the primary determinants of BEV competitiveness. Threshold analysis indicated that BEVs remain economically attractive when domestic electricity tariffs are maintained below 0.32 USD/kWh. The findings demonstrate that BEVs offer the greatest economic and environmental benefits and offer evidence-based guidance for policies supporting electric mobility in developing countries.Item Embargo Enhancing Chronic Kidney Disease Prediction : A Hybrid Approach Combining Logistic Regression and Random Forest Models(Institute of Electrical and Electronics Engineers Inc., 2025) Jathunga, T; Abeygunawardena, NThis study investigates the use of Machine Learning (ML) models for Chronic Kidney Disease (CKD) prediction, comparing Logistic Regression with L1 and L2 regularization, Random Forest , and a Hybrid Voting Classifier. The models were evaluated using performance metrics including accuracy, precision, recall, and F1-score, with the hybrid model demonstrating the highest accuracy of 99 percent, followed by Random Forest at 98 percent. Logistic Regression models achieved accuracies of 97 percent and 98 percent , with slight variations in recall for different classes. Cross-validation and learning curve analyses indicated minimal overfitting in ensemble models. These results emphasize the potential of ML models for accurate CKD prediction, suggesting further research into model optimization and data preprocessing techniques.