SLIIT Conference and Symposium Proceedings
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All SLIIT faculties annually conduct international conferences and symposiums. Publications from these events are included in this collection.
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Publication Open Access A Data-Driven Approach to Predicting Ischemic Heart Disease Risk in Monaragala: Integrating Lifestyle and Symptom Factors with Machine Learning(Faculty of Engineering, 2025-09-09) Meddepola, M.A.R.L.; Wickramasinghe, B.M.G.S.T.S.K.Ischemic Heart Disease (IHD) remains a leading cause of mortality worldwide and presents a critical challenge in underserved rural areas such as Monaragala, Sri Lanka. Traditional IHD prediction methods predominantly depend on clinical diagnostics like ECGs and blood tests, which are often unavailable or inaccessible in such regions. This study aims to bridge this gap by developing a machine learning-based prediction model that utilizes only lifestyle and symptom-related data, eliminating the need for invasive clinical procedures. A dataset comprising lifestyle habits (e.g., diet, smoking, alcohol use, exercise) and symptom indicators (e.g., chest pain, fatigue, dizziness) was collected via surveys. Feature selection using Logistic Regression identified the top eight most relevant predictors. Five machine learning algorithms, Logistic Regression, K-Nearest Neighbors, Support Vector Machine, Decision Tree, and Random Forest, were trained and evaluated. Among them, the Random Forest model achieved the highest performance with an accuracy of 83.5%, precision of 0.86, recall of 0.78, and F1- score of 0.81, demonstrating strong predictive capability based solely on non-clinical features. In addition, a web-based self-assessment tool was developed to make the model accessible to the public, particularly targeting individuals in rural areas with limited healthcare access. The tool enables users to input basic lifestyle and symptom information and receive a real-time risk assessment. The findings confirm that the model leveraging lifestyle and symptom data can effectively identify individuals at risk of IHD. This approach supports the development of scalable, low-cost, and user-friendly screening tools that can enhance early detection and preventive care, especially in rural and resource-constrained settings.Publication Open Access Identification of Antibiotic-Resistant Bacteria in Processed Meat Products Available in Local Markets from Five Selected Localities in Sri Lanka(Department of Applied Sciences. Faculty of Humanities and Sciences,SLIIT, 2025-10-10) Jayakody, U; Vithanage, DThis study focused on identifying antibiotic-resistant bacteria in processed meat products available in Sri Lankan local markets, considering the potential risks caused by inappropriate packaging and storage conditions. Five processed meat samples were purchased from five localities in Sri Lanka and examined on Luria-Bertani (LB) agar medium using both homogenised and direct culture techniques. The Kirby-Bauerdisc diffusion method was used in the Antibiotic Sensitivity Test (ABST) to determine how bacteria responded to various antibiotics. Samples that were improperly packaged revealed the presence of antibiotic-resistant bacterial strains, exhibiting resistance to both ampicillin and amoxicillin, while ciprofloxacin sensitivity was observed in every tested bacterium. DNA was extracted from the antibioticresistant bacteria. Escherichia coli and Staphylococcus sp. were confirmed using Polymerase Chain Reaction(PCR) and agarose gel electrophoresis. Although PCR identified many isolates, it was unable to confirm two bacterial species; after additional DNA sequencing analysis, these two unidentified organisms were determined as Enterobacter sp. and Psychrobacter piechaudii. These results demonstrate the significance of appropriate packaging in avoiding the foodborne transmission of bacteria that are resistant to antibiotics.The study additionally indicates that to improve food safety and decrease antibiotic overuse, public awareness and stronger regulations are required. This study improves the understanding of how antibiotic resistance can spread through regularly consumed food products, which helps protect public health.Publication Open Access Modelling the Indicative Rate of the USD/LKR SPOT Exchange Rate in Sri Lanka(Department of Mathematics and Statistics, Faculty of Humanities and Sciences, SLIIT, 2025-10-10) Rajapaksha, R. G. S. N.; Kumarasiri, P. V. A. L.; Sathsarani, T. V. I. A.; Rambukkana, P. P.; Botheju, W. S. R.; Guruge, M. L.; Peiris, T. S. G.This study develops and validates a time series model to forecast Sri Lanka’s daily indicative USD/LKR spot exchange rate using ARIMA and ARCH methods using data from 1st of January 2021 to 4th of June 2025, sourced from Central Bank of Sri Lanka. The original series was first differenced to achieve stationarity since it is not stationary. According to the sample ACF and PACF of stationary series, three candidate models were augmented with an ARCH(2) variance specification based on residual diagnostics. After comparing AIC, SIC, Hannan Quinn metrics and log likelihood, the ARIMA(1,1,1)+ARCH(2) was identified as the best possible model. The diagnostic tests confirmed that residuals are identically and independently distributed without remaining heteroskedasticity. Insample forecasting yielded a MAPE of 0.32% and a Theil U statistic of 0.0036, while out-of-sample validation (June 5 to July 4, 2025) produced a MAPE of 0.087% and a bias proportion near zero, highlighting the model the model’s predictive accuracy. By focusing only on the internal pattern of the exchange rate, this research creates a strong short term forecasting tool for Sri Lanka's volatile currencyenvironment laying ground work for adding outside factors in future improvements.Publication Open Access Quantifying Future Flood Risk in Sri Lanka: A Smart Data Approach for Insurance Pricing and Strategy(Department of Mathematics and Statistics, Faculty of Humanities and Sciences, SLIIT, 2025-10-10) Premaratne, CSri Lanka is increasingly vulnerable to flooding due to climate change, unplanned urban expansion, and insufficient infrastructure resilience. Despite this, the current insurance regulatory framework under the Risk-Based Capital (RBC) regime does not explicitly incorporate a catastrophic (CAT) risk charge for natural disasters such as floods. This paper proposes a novel framework for quantifying future flood risk in Sri Lanka using a smart data approach that integrates hydraulic modeling (HEC-RAS), geographic information systems (GIS), and machine learning (ML). The framework enables the generation of flood hazard maps, estimation of event probabilities, and calculation of expected losses at property level. A simulation-based approach is then used to determine the capital required to cover extreme loss events, which can serve as the basis for a CAT risk charge. Although full implementation is pending, this paperpresents an illustrative model using synthetic data to demonstrate the methodology and its potential implications. By embedding flood risk into pricing and strategic decisions, this approach aims to improve insurance sector resilience and inform regulatory advancement. The results highlight the feasibility and urgency of adopting data-driven tools to better manage climate-induced risks in Sri Lanka.Publication Open Access Climate-Based Agri-Insurance Method for Paddy Production in Sri Lanka(Mathematics and Statistics, Faculty of Humanities and Sciences, SLIIT, 2025-10-10) Vijayakumar, JClimate change has emerged as a major threat to agriculture globally, and Sri Lanka is no exception. The districts of Ampara, Anuradhapura, and Polonnaruwa are the main producers of paddy. In recent decades, these regions have experienced greater climate variability, leading to unstable harvests and posing financial risks for paddy farmers. This study examines the potential of Weather Index Insurance (WII) as an effective tool to mitigate income losses caused by extreme weather events. Historical data on paddy yields were combined with daily weather records. The analysis focused on relationships between paddy yields and weather variables: total rainfall, average temperature, maximum and minimum temperatures, and extreme monthly temperatures in the regions for both the Maha and Yala cultivation seasons. Regression models identified significant correlations, and insurance indices were designed for each district and season, with pure premiums calculated based on these relationships. The results indicate that total rainfall is the most significant factor influencing yield variability across all three districts in different seasons. The proposed insurance models were able to reduce income variability by 15–19%. These findings indicate that rainfall is the most reliable basis for climate-resilient paddy insurance in these regions. This data-driven framework for index-based agricultural insurance provides insights to enhance farmer resilience, reduce economic vulnerability for farmers, and support the long-term sustainability of production in those regions.Publication Open Access Event Detection and Latency Analysis in High Frequency Trading Dashboards(Department of Mathematics and Statistics, Faculty of Humanities and Sciences, SLIIT, 2025-10-10) de Silva, U; Perera, S; Liyanage, U.P; Erandi, HHigh frequency trading relies on millisecond-level decisions, where profitability is strongly influenced by both market responsiveness and system latency. Traditional dashboards offer real-time visualizations but fall short in detecting abrupt regime shifts or quantifying latency. This study presents an AI-aided Market Pulse and Latency Panel that integrates candlestick pattern recognition, change point detection and latency measurement into a unified dashboard. The system detects technical patterns, identifies structural market shifts, and quantifies infrastructural bottlenecks. Experimental results demonstrate that the panel enhances situational awareness by combining event detection with latency analytics, providing traders with actionable insights for strategy adjustment and infrastructural optimization.Publication Open Access Designing an Economic Scenario Generator for Financial Risk Management of Low-Income Households in Sri Lanka: A Review(Department of Mathematics and Statistics, Faculty of Humanities and Sciences, SLIIT, 2025-10-10) Peiris, K. G. H. S.; Premarathna, L. P. N. DLow-income households in Sri Lanka face increasing financial vulnerabilities driven by unstable income, high dependence on essential goods, and exposure to inflation and external shocks. Economic Scenario Generators (ESGs), widely used in institutional risk management, offer a structured way to model uncertainty but have rarely been adapted for household-level applications. This review synthesizes literature on ESG methodologies, household financial risk in developing economies, and Sri Lanka’s socio-economic realities. It highlights the need for a household-oriented ESG framework that integrates macroeconomic shocks with micro-level financial behavior to support budgeting, debt avoidance, and policy interventions.Publication Open Access The Impact of Digital Learning Readiness on Academic Performance and Student Engagement in Sri Lanka(Department of Mathematics and Statistics, Faculty of Humanities and Sciences, SLIIT, 2025-10-10) Nuwanthika, W. A. N.; Thathsarani, U.S.The rapid shift to virtual learning settings has unveiled disparities in preparedness, involvement, and scholarly performance among Sri Lankan government university undergraduate students. This study investigates the impact of Digital Learning Readiness (DLR), Teacher Support (TS), Perceived Usefulness (PU), and Motivation (MDL) on Student Engagement (ENG) and Academic Performance (AP). The general aim is to develop and validate a structural model that explains the mechanisms by which psychological and environmental factors lead to academic performance in online learning contexts. Quantitative research design was employed. A standardized questionnaire was completed by 301 undergraduate students sampled through simple random sampling across ten government universities. Data were analyzed using Structural Equation Modeling (SEM) supplemented by Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA). Reliability and validity were tested using Cronbach's alpha, Average Variance Extracted (AVE), Composite Reliability (CR),and fit indices for the model through SPSS and SmartPLS. Greater digital learning readiness strongly facilitates student motivation, engagement, and academic achievement. Perceived digital tool usefulness mediates the influence of readiness on academic performance to some extent. Motivation and engagement also have central mediating roles. Support from teachers has a positive impact on motivation, which reinforces student engagement. The study confirms that digital readiness, motivational factors, perceived technology usefulness, and supportive pedagogy are integrated to influence academic performance in digital learning settings. The results have theoretical and practical suggestions for increasing the efficiency of digital learning in the higher education system of Sri Lanka.Publication Open Access On the Fundamentals of Angle Trisectors of a Triangle(Department of Mathematics and Statistics, Faculty of Humanities and Sciences, SLIIT, 2025-10-10) Amarasinghe, IOver the years of the history of elementary and advanced geometry, trisecting a given angle into three equal parts, was prominent and given more attention. Nevertheless, it is evident that there is a significant research gap of the standard angle trisectors, the lengths of the angle trisectors and the relationships amongst other standard line segments in a triangle. In this paper, we address this gap by developing a purely geometric framework, supplemented with advanced algebraic methods, to obtainclosed-form expressions for internal angle trisectors in a Euclidean triangle. Using the circumcircle, similarity arguments, and Ptolemy’s Theorem, we derive polynomial relations and solve the associated cubic equations explicitly through Cardano’s method. The explicit determination of angle trisector lengths has not been previously available in closed form. Most approaches are trigonometric, but the trisector and related lengths were implicit or incomplete. Moreover, we present few very useful, novel, interesting lemmas, fundamental theorems, and corollaries related to two-dimensional angle trisectors in Euclidean triangles without using any trigonometric, vector algebra or complex number methods.Publication Open Access A Poisson Mixture Model of Claim Counts to Improve Insurance Claim Predictions Using Incomplete Data/ Asymmetric Data: A Case Study with Telematics Insurance(2025-10-10) Peiris, K. G. H. S.; Sampath, J. K. H.; Premarathna, L. P. N. DIn the evolving landscape of insurance analytics, integrating traditional and telematics data is pivotal for enhancing the accuracy of claim predictions. This study introduces a two-fold approach utilizing a Poisson mixture model to merge these distinct data streams effectively. Initially, we apply the Poisson mixture model to traditional insurance features common to both datasets, employing Hamiltonian Monte Carlo (HMC) and Metropolis-Hastings algorithms separately for model fitting. Subsequently,the predicted claim counts derived from the Poisson mixture model are used as an offset to fit a Poisson generalized linear model (GLM) exclusively with telematics-based features. Our focus is on assessing the suitability of HMC and Metropolis-Hastings for addressing data integration challenges within Poisson mixture frameworks. Comparative analysis reveals that while HMC demands more computational time to achieve convergence, it exhibits superior performance in parameter estimation in scenarios with increased model complexity. This study underscores the potential of advanced Monte Carlo methods in refining predictive models by leveraging the synergy between traditional and telematics data sources.