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.Item Open Access Predictive Modeling for Personalized Cancer Therapy Using Reinforcement Learning(Faculty of Engineering, 2025-09-09) Edirisinghe M.M; Gunarathne,J H M S MAdaptive therapy is transforming cancer treatment by enabling dynamic, patient-specific interventions that adapt to tumor progression and individual variability. Unlike traditional fixed-dose regimens, adaptive therapy leverages the evolutionary dynamics of tumors to extend treatment effectiveness and delay resistance. Reinforcement Learning (RL), an area of artificial intelligence focused on sequential decision-making, offers a robust framework for optimizing these adaptive strategies. RL can learn optimal treatment policies by interacting with computational models of tumor growth and drug response, continuously adjusting regimens based on observed tumor states, resistant cell populations, and biomarkers. This approach allows for the creation of personalized therapies that maintain long-term tumor control while minimizing toxicity and the emergence of resistance. The integration of RL into predictive modeling for cancer therapy represents a paradigm shift, enabling smarter, safer, and more effective treatments that are dynamically tailored to each patient’s evolving disease. This paper reviews the foundational concepts of adaptive therapy and RL discusses tumor modeling approaches, examines RL algorithms, and addresses current challenges and future directions in the field.Item Open Access Development of Low-cost Slipper by using NR/EVA Blend with Recycling Materials for reducing Environment Pollution in Footwear Industry(Faculty of Engineering, 2025-09-09) Randika K.G.; Perera K.P.M; Gunaratne R.D.This study aims to develop a low-cost slipper compound by blending low grade Natural Rubber (NR), Ethylene Vinyl Acetate (EVA), and recycled LDPE plastics granules with crumb rubber in different phr (parts per hundred rubber) amounts. Low grade natural rubber (off grade brown scrape) and ethylene vinyl acetate (19 wt.% of vinyl acetate) were used during formulation in order to reduce cost. During this compounding process, polymeric material and other chemical ingredients were masticated by using a kneader and two roll mills then sheet was prepared by using calendaring techniques, eventually curing was performed by using a compressing molding method. Blowing agents were used to obtain the Slipper sheets’ inter cellular structure. Peroxide curing system has used due to natural rubber blend with ethylene vinyl acetate. When preparing compound batch, different phr amount of crumb rubber and recycled LDPE plastics granules blended. Firstly, crumb rubber sheets which were punctured and waste scrap sheets were obtained, then converted into 30 mesh size small particles by using grinding and crush method. Hardness and Abrasion tested of prepared slipper. After curing process higher hardness value observed when increasing crumb rubber phr. As particle size increases, there was a tendency of asymmetrical spread of compounding ingredients through the mixture and this was mitigated by additions of processing oils which increased the dispersion of the particles. In summary, through this work, ideal compounding formulation with phr values was able to determine that can be used to manufacture to low-cost slipper sheet at industrial scale.Item Open Access Enhancing Modern Education Through an AI-Integrated Learning Management and Support System (LMSS)(Faculty of Engineering, 2025-09-09) Rashminda, JThe rapid advancement of educational technologies has underscored the limitations of conventional Learning Management Systems (LMS) in effectively supporting the evolving demands of learners and educators. While traditional LMS platforms primarily focus on content delivery and administrative tasks, they often lack the capacity to foster active engagement, facilitate meaningful collaboration, and promote participation in broader learning experiences. This paper presents the design and functional implementation of a prototype for a Learning Management and Support System (LMSS), an AI-enhanced platform built to address these limitations by offering a more holistic and studentcentered approach to digital education. LMSS integrates course management with interactive features that encourage student collaboration, peer-to-peer communication, and involvement in academic and extracurricular events. These capabilities are designed to support a more engaging and socially connected learning experience while also simplifying instructional workflows for educators. The system incorporates adaptive learning tools and real-time insights to better align learning processes with individual needs and institutional goals. This paper reviews the existing literature, highlights gaps in current LMS implementations, and details the development methodology, architecture, and feature set of LMSS. The system’s anticipated impact is grounded in established research findings demonstrating that adaptive learning approaches can significantly enhance student engagement, AI-driven early intervention can improve retention rates among at-risk learners, and realtime analytics can reduce instructor workload related to feedback provision. By integrating these evidence-based practices into a unified platform, LMSS is designed to foster learner motivation, deepen engagement, and support teaching effectiveness. Ethical considerations such as user privacy and data governance are also addressed to ensure responsible and transparent use.Item Open Access Synergistic Charge Dynamics and Light Harvesting in TiO₂/MgO Composites for Efficiency Enhancement in CdS Quantum Dot-Sensitized Solar CellsSynergistic Charge Dynamics and Light Harvesting in TiO₂/MgO Composites for Efficiency Enhancement in CdS Quantum Dot-Sensitized Solar Cells(Faculty of Engineering, 2025-09-09) Ajward, N.F.; Fernando, J.V.P.; Perera, V.P.S.Quantum dot-sensitized solar cells (QDSSCs) represent a promising advancement in renewable energy technologies, with recent improvements achieving power conversion efficiencies close to 6%. Structurally similar to dye-sensitized solar cells (DSSCs), QDSSCs employ quantum dots (QDs) as sensitizers that absorb photons and inject excited electrons into the conduction band of a wide-bandgap semiconductor electrode. while a redox electrolyte removes the generated holes, completing the circuit by regenerating them at the counter electrode. Quantum dots composed of materials such as CdS, CdSe, PbS, and InP are increasingly studied for use in QDSSCs, offering the advantage of tunable optical band gaps through particle size manipulation. This adaptability enhances QDSSCs’ design potential, enabling the integration of third-generation solar cell configurations, including multiple exciton generation (MEG), to further enhance energy conversion efficiency. Despite these advancements, QDSSC performance is currently limited by issues such as reduced photovoltage and recombination losses at the TiO₂-QD-electrolyte interface. This study explores the use of magnesium oxide (MgO) coatings on TiO₂ nanoparticles to address these limitations, focusing on improving the fill factor (FF) and overall cell efficiency. MgO serves as an electron-blocking layer, effectively reducing recombination and associated energy losses. Furthermore, MgO facilitates electron transport from QDs to the TiO₂ electrode, improving charge collection. The light-scattering properties of MgO also increase the photon's path length within the cell, enhancing light absorption and consequently boosting the short-circuit current. In this study, MgO powder was incorporated in specific mass ratios with TiO₂, followed by the application of CdS quantum dots (QDs) on the TiO₂/MgO composite layer using the SILAR method. Results indicated a significant improvement in the fill factor (FF) at an optimal MgO-to-TiO₂ ratio, attributed to synergistic effects of MgO on interface stabilization, reduced recombination, and enhanced charge transport. The optimized MgO-modified TiO₂ films achieved a current density of 1.946 mA, voltage of 437 V, and power of 0.121 W, reaching an efficiency of 0.311 (18.7% higher than TiO₂/CdS QDSCs), with improved interfacial impedance, Incident Photon to Current Efficiency (IPCE), and FF of 37.4%.Item Open Access Solar Hotspot Detection Using VHDL-Simulated Fixed-Point SVM: A Methodology Toward FPGA Realization Solar Hotspot Detection via FPGASVM(Faculty of Engineering, 2025-09-09) Fernando, N; Seneviratne, L; Weerasinghe, N; Rathnayake, N; Hoshino, YThe early and accurate detection of thermal hotspots in photovoltaic modules is critical to ensure the efficiency, safety, and longevity of solar power systems. This study presents a complete end-to-end methodology for implementing a fixed-point Medium Gaussian Support Vector Machine classifier using Very High-Speed Integrated Circuit - Hardware Description Language, optimized for Field Programmable Logic Array. The approach begins with feature extraction from thermal images, focusing on MPEG-7 descriptors and blue chrominance. The SVM model is trained in MATLAB and converted into a fixed-point Q1.15 format for hardware compatibility. Key parameters, including support vectors, Lagrange multipliers, bias, and kernel scale, are extracted and verified in a custom Python environment to ensure numerical alignment with MATLAB results. The validated model is then implemented in synthesizable VHDL and verified using GHDL and GNU Tool Kit waveform viewer, confirming bit-accurate hardware behavior. Results show classification accuracy exceeding 99.3% with negligible performance loss due to quantization. The design achieves deterministic latency based on FSM structure and parallel feature processing, completing classification within 2702 clock cycles for a 300-support-vector, 222-feature system. Unlike floating-point models, this approach enables low-power, real-time inference on edge platforms such as drones.Item Open Access Predicting Cognitive Test Performance from New Onset Behavioral and Personality Changes in Adults over 50 using Post-Selection Boosted Random Forest Classifier(Faculty of Engineering, 2025-09-09) Mervyn M.; Welhenge A.; Creese B.Mild Behavioral Impairment (MBI) refers to neuropsychiatric symptoms of various severity levels that might not be discovered by conventional psychiatric nosology. These symptoms should persist for more than six (06) months. MBI is typically observed in adults of age 50 and above. This study investigates the prediction of cognitive test performance of cognitive and behavioral changes in adults over 50 years of age using a post-selection boosted Random Forest (RF) Classifier. The baseline cognitive aging data of the Simple Reaction Time (SRT) metric and Mild Behavioral Impairment Checklist (MBI-C) from the ongoing PROTECT study in the United Kingdom was used to classify the participants’ cognitive ability into five classes. Using the post-selected boosted RF classifier, the study obtained an accuracy of 96.26% which was an improvement compared to the 95.52% accuracy obtained by the RF classifier. These findings suggest that machine learning-based prediction models can provide valuable insights into analyzing the cognitive decline of adults of a late age.Item Open Access Macaranga peltata Leaf Extract Mediated Green Synthesis of Iron Nanoparticles and Their Application in Organic Dye Removal(Faculty of Engineering, 2025-09-09) Dissanayake D.M.K.N.; Perera M.A.D.; Karunaratne M.S.A.; Pahalagedara M.N.This study presents an eco-friendly method for synthesizing iron nanoparticles (FeNPs) using Macaranga peltata leaf extract, evaluating their potential in degrading the organic dye methyl orange (MO). The synthesis exploits phytochemicals in the leaf extract as natural reducing and stabilizing agents. The synthesized FeNPs were characterized using UV-Vis spectroscopy, FTIR, XRD, and SEM, confirming amorphous structure and particle sizes ranging from 34–94 nm. Catalytic activity was evaluated via MO degradation experiments, achieving 85.16% efficiency within 200 minutes. The study demonstrates a sustainable solution for wastewater treatment through green nanotechnology.Item Open Access Improving Post-Harvest Rice Drying Efficiency through a Low-Cost Halogen Dryer Design for Rural Communities(Faculty of Engineering, 2025-09-09) Sadeepa, S; Thilakarathna, RSmall-scale rice farmers in Sri Lanka often depend on traditional sun-drying methods, which are inefficient, weather-dependent, and contribute to significant post-harvest losses. This research focuses on the conceptual design and evaluation of a low-cost wet rice dryer using halogen lamps as the heat source, aimed at improving drying efficiency prior to milling. Field surveys were conducted to identify the common challenges faced by rural farmers, such as uneven drying, weather interruptions, and grain rejection by millers due to high moisture content. Based on the survey results, key user requirements were identified, including low operating cost, simple structure, and potential for multi-crop drying. A conceptual design was developed accordingly, with a drying chamber and tray system optimized for 1 cm thick rice layers. The full assembly was modeled in 3D using CAD software, allowing for virtual evaluation of airflow, heat source positioning, and accessibility. Finite Element Analysis (FEA) was applied to simulate the mechanical response of the tray under typical loads, confirming its structural soundness. Preliminary thermal experiments were conducted using a controlled test box setup to evaluate the heating performance of a 1000W halogen lamp. The system successfully achieved drying temperatures up to 82°C, suitable for surface moisture reduction. Temperature trends were recorded over time, and manual quality checks showed promising results for further development. These findings indicate the technical feasibility of the design and its potential to improve post-harvest efficiency in rural settings. The study provides a foundation for future stages of prototype fabrication, sensor integration, and field validation.Item Open Access Highly Efficient 3D Object Transmission System for HTC Services in 6G Networks(Faculty of Engineering, 2025-09-09) Svechnikov, D; Volkov, A; Marochkina, A; Muthanna, A; Kouhceryavy, AIn recent years, advancements in technology have brought forth a new frontier in visual communication. Holography is a technique that captures and reproduces threedimensional (3D) images with an unprecedented level of realism and depth, has emerged as a groundbreaking method for conveying visual information. Unlike traditional images and videos, holography recreates scenes with full parallax, enabling viewers to perceive objects from various angles. The transmission of holographic images presents both exciting possibilities and unique challenges. To this end, this article conducts a comparative analysis of a previously developed application system for transmitting dynamic 3D human movements with a ready-made solution for transmitting 2D video streams in order to provide conference calling services. The network characteristics of the systems were collected and compared. The opportunities that programs currently provide and will provide in the future are examined.