Research Publications

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This main community comprises five sub-communities, each representing the academic contribution made by SLIIT-affiliated personnel.

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2D Pose Estimation based Child Action Recognition
(Institute of Electrical and Electronics Engineers Inc., 2022-11) Mohottala, S; Abeygunawardana, S; Samarasinghe, P; Kasthurirathna, D; Abhayaratne, C
We present a graph convolutional network with 2D pose estimation for the first time on child action recognition task achieving on par results with LRCN on a benchmark dataset containing unconstrained environment based videos.
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6-REXOS: Upper limb exoskeleton robot with improved pHRI
(SAGE Publications, 2015-04-29) Gunasekara, M; Gopura, R; Jayawardena, T. S. S
Close interaction can be observed between an exoskeleton robot and its wearer. Therefore, appropriate physical human-robot interaction (pHRI) should be considered when designing an exoskeleton robot to provide safe and comfortable motion assistance. Different features have been used in recent studies to enhance the pHRI in upperlimb exoskeleton robots. However, less attention has been given to integrating kinematic redundancy into upper-limb exoskeleton robots to improve the pHRI. In this context, this paper proposes a six-degrees-of-freedom (DoF) upperlimb exoskeleton robot (6-REXOS) for the motion assistance of physically weak individuals. The 6-REXOS uses a kinematically different structure to that of the human lower arm, where the exoskeleton robot is worn. The 6-REXOS has four active DoFs to generate the motion of the human lower arm. Furthermore, two flexible bellow couplings are attached to the wrist and elbow joints to generate two passive DoFs. These couplings not only allow translational motion in wrist and elbow joints but also a redundancy in the robot. Furthermore, the compliance of the flexible coupling contributes to avoiding misalignments between human and robot joint axes. The redundancy in the 6- REXOS is verified based on manipulability index, mini‐ mum singular value, condition number and manipulability ellipsoids. The 6-REXOS and a four-DoF exoskeleton robot are compared to verify the manipulation advantage due to the redundancy. The four-DoF exoskeleton robot is designed by excluding the two passive DoFs of the 6- REXOS. In addition, a kinematic model is proposed for the human lower arm to validate the performance of the 6- REXOS. Kinematic analysis and simulations are carried out to validate the 6-REXOS and human-lower-arm model.
Embargo
A BI Approach for Student Engagement and Retention along with Cognitive Load Analysis for Educator
(979-833153098-3, 2025) Algewatta, M. N; Manathunga, K
This research presents a systematic approach to monitoring student engagement, retention, and cognitive load within higher education by integrating Business Intelligence (BI) tools with cognitive load analysis. The proposed framework utilizes a diverse range of data sources -including attendance, academic performance, mental health indicators, demographic variables, and student feedback to generate real-time insights into student behavior patterns. The BI system identified critical trends, such as irregular attendance, declining academic performance, and the influence of demographic factors, enabling educators to identify at-risk students and intervene proactively. Additionally, cognitive load analysis was employed to evaluate the mental demands of course content, categorizing learning objectives in alignment with Bloom's Taxonomy. This allowed for the identification of content that could potentially overwhelm students, facilitating adjustments in instructional complexity. The integration of BI insights with cognitive load data provided a holistic approach that not only enhanced the monitoring of student engagement but also supported the tailoring of instructional content to optimize learning without inducing cognitive overload. The findings suggest that combining BI tools with cognitive load metrics offers a robust framework for both improving student retention and assisting educators in creating a balanced, engaging, and supportive learning environment. This study contributes a practical model for institutions seeking to leverage data-driven insights to promote student success and address the dynamic challenges of modern higher education.
Open Access
A Blend of Arbitration and Mediation: Analysis of the Possibilities and Challenges in Utilising MedArb Practice in Sri Lanka
(Sri Lanka Institute of Information Technology, 2024-12-31) Vithanage, P
The ADR landscape is evolving at a rapid level across the world and one of the latest trends in such is transferring dispute resolution into Arb-Med-Arb and Med-Arb. Arbitration and Mediation are recognised as two favourable dispute resolution methods, especially in commercial dispute resolution. When considered in isolation, both mediation and arbitration have unique features. Despite a few substantive and procedural drawbacks in both methods, mediation, and arbitration have gained popularity recently. While transferring into a mixed approach of MedArb and Arb-Med-Arb is still in its infancy in the context of Sri Lanka, this paper examines the possibility of utilising MedArb practice within the existing landscape in the country. Arbitration practice in Sri Lanka has a long history and the Arbitration Act No. 11 of 1995 and its amendments suggested which are to be in force in the future lays the statutory framework for arbitration. Notably, the background for Mediation in Sri Lanka started with community mediation and now it has reached a significant milestone in commercial mediation as the enabling legislation for the Singapore Convention was enacted recently. This paper uses a doctrinal approach in dealing with primary resources as well as secondary resources when conducting the research. This paper uses an exploratory analytical method. It also includes a comparative study that examines the MedArb practice in Hong Kong as a progressive Common law jurisdiction. In its findings, the paper highlights that the MedArb practice is adaptable in Sri Lanka within the existing statutory framework and the institutional setup. However, it urges that the professionals and the commercial community pay attention to the points discussed in the recommendations for the successful functioning of the MedArb practice.
Open Access
A Comparative Investigation of Infiltration and Channel Roughness of Ephemeral and Perennial Streams in a Mountainous Catchment
(John Wiley, 2025-06) Khaniya, B; Gomes, P.I.A; Perera,M. D.D; Wai, O, W.H
Infiltration and channel roughness, two major factors that govern stream discharge, were studied in similar-sized ephemeral and perennial streams in a mountainous tropical catchment. Seasons were defined based on two ephemeral flow conditions, i.e., with (wet season) and without (dry season) surface flow. A stream was divided transversely into low-flow areas (close to the thalweg) and high-flow areas (close to the channel margin). The highest average infiltration (~50 mm/h) was observed in the low flow areas around the thalweg of ephemeral streams in the dry season and was significantly higher than for any other spatial scale or temporal period. The infiltration in high-flow areas did not show a statistically significant difference between the two stream types, and surprisingly, perennial streams in the dry season showed higher infiltration than ephemeral streams. Since sediment moisture and organic content showed negative and positive correlations with infiltration, respectively, for both stream types and ephemeral streams showed statistically significant negative correlations between litter and infiltration during the dry season, the low infiltration in ephemeral high flow areas was attributed to stream-type dependent litter processing. The litter of ephemeral stream high-flow areas was subject to partial decomposition due to rapid drying and had residue of previously buried litter. Ephemeral channels were two to three times rougher than perennial channels. Standing crop biomass and mean particle size increased stream roughness in both stream types but were less prominent in ephemeral streams due to the presence of litter. The study demonstrated that litter has a special role in defining the infiltration pattern, channel roughness, and flood control potential of ephemeral streams.
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A Comparative Study on Narrative Techniques in the Novel Deutschstunde and its 2019 Film Adaptation
(Department of Linguistics, Faculty of Humanities and Sciences, SLIIT, 2025-10-10) Bandara, S.I; Wijewardhana, S; Sandaruwan, L.G.S.U.
The adaptation of literary works into films presents unique challenges and opportunities in translating narrative techniques across media. This study provides a comparative analysis of narrative techniques employed in Siegfried Lenz’s novel Deutschstunde and its 2019 film adaptation directed by Christian Schwochow. It explores the complexities of transforming a thematically dense literary narrative into the visual and auditory medium of film. The primary research problem addressed is the extent to whichnarrative strategies in Lenz’s novel Deutschstunde differ from its 2019 adaptation. The methodology consists of a qualitative comparative approach incorporating close reading of the novel and detailed frmal analysis of the film. Central narrative aspects focused on are focalisation, temporal structure, character representation, and the presentation of symbolic motifs. Results indicate that while the film effectively utilises cinematic techniques to depict the oppressive setting and Siggi Jepsen’s inner turmoil, it alters the pervasive frame narrative and extensive interior monologue utilised in the novel, subtly shifting the portrayal of the protagonist’s inner journey and the engagement of the audience with memory. This comparative analysis elucidates the adaptive processes, the impact of different media on narrative delivery, and the continuing relevance of Deutschstunde to adaptation studies.
Open Access
A Comparative Study on TiO₂/Graphite–PEG and Graphite/Carbon Fibre- Paraffin Shape Stabilized Phase Change Materials for Thermal Energy Storage Applications
(Faculty of Engineering, 2025-09-09) Dananjaya, V; Wen,Q; Abeykoon, C
Shape-stabilized phase change materials (SSPCMs) are promising candidates for latent heat thermal energy storage systems due to their high energy density and ability to prevent leakage during phase transitions. This study presents a comparative analysis of two SSPCM systems: TiO₂/graphite–polyethylene glycol (PEG) and graphite/carbon fibre/graphene–paraffin composites. Both composites were prepared by vacuum-assisted infiltration of molten PCMs into porous expanded graphite networks, with the addition of functional fillers to enhance structural integrity and thermal stability. Scanning electron microscopy (SEM) revealed distinct microstructural features for each system; TiO₂ nanoparticles were uniformly dispersed within the PEG matrix and anchored onto graphite surfaces, while carbon fibres and graphene nanoplatelets formed a hierarchical interconnected network within the paraffin-based composites. Differential scanning calorimetry (DSC) demonstrated that both systems preserved high latent heat storage capacities with slight shifts in phase transition temperatures compared to pure PCMs. Thermogravimetric analysis (TGA) showed improved thermal stability of the SSPCMs relative to neat PCMs, with filler composition significantly affecting degradation onset temperatures. In TiO₂/graphite–PEG composites, DSC analysis showed melting temperatures of 61.4-62.7 °C and solidification temperatures of 53.1-54.0 °C, with latent heats of 185-210 J g⁻¹ depending on TiO₂ content. Graphite/carbon fibre/graphene–paraffin composites exhibited melting temperatures of 54.8-55.6 °C and solidification temperatures of 48.9-49.7 °C, with latent heats of 140-160 J g⁻¹. Thermogravimetric analysis revealed improved degradation onset temperatures: TiO₂/graphite-PEG composites showed higher thermal stability compared to pure PEG, while carbon fibre/graphene–paraffin composites exhibited enhanced thermal resistance relative to pure paraffin. The TiO₂/graphite-PEG composites exhibited higher latent heat capacities and enhanced thermal resistance, whereas the graphite/carbon fibre/graphene–paraffin composites provided superior mechanical reinforcement and phase change reliability. These findings offer insight into the design optimization of SSPCMs tailored for specific thermal management applications.
Embargo
A Comprehensive Approach to Secure, Accessible, and Engaging Voting Systems
(Springer Science and Business Media Deutschland GmbH, 2026) Jayasinghe J.A.M.P; Bandara S.Y.T.D; Shabry S.M; Wickramasinghe W.A.R.M.; Rajapakse, K; Silva, N
This research presents a secure and accessible e-voting system for polling booths in Sri Lankan context, to overcome issues with the traditional voting system. It incorporates block-chain for fair vote storage, and homomorphic encryption for privacy preserving computation of results. The identity of voters is confirmed by face recognition, which includes measures to deterring any voting by impostors. Special identification model with multiple digits is beneficial for disabled voters. Public opinion is effectively gauged through sentiment analysis from social media and it puts concerns to rest, thus a whole lot of enhancement in the whole of customer engagement. Ease of use is also assured thanks to a very user-friendly interface which eliminates mistakes a lot with only a little effort generally. Experimental results demonstrate that security is greatly strengthened, transparency and usability are significantly increased traditional procedural integrity is still maintained efficiently.
Embargo
A Comprehensive Investigation of Microplastic Contamination and Polymer Toxicity in Farmed Shrimps; L. vannamei and P. monodon
(Springer Nature, 2025-02-20) Jayaweera, Y. U; Hennayaka, H. M.A.I; Herath, H.M.L.P.B; Kumara, G. M.P; Mahagamage, M.G.Y.L; Rodrigo, U.D; Manatunga, D. C
Microplastic (MP) pollution poses a significant threat to marine ecosystems, seafood safety, and human health. This study investigates the accumulation of microplastics in two commercially important shrimp species, Litopenaeus vannamei (L. vannamei) and Penaeus monodon (P. monodon), sourced from cluster farming sites in Puttalam, Sri Lanka. Shrimp exoskeletons and edible soft tissues underwent rigorous microplastic analysis, including density separation, alkali digestion, stereo microscopy, and Raman spectroscopy. The results revealed high microplastic contamination, with L. vannamei containing an average of 4.99 ± 1.81 MP particles/g and P. monodon containing 1.87 ± 0.55 MP particles/g. Microplastic sizes varied, with L. vannamei predominantly contaminated with 100–250 µm particles and P. monodon with 500 µm—1000 µm particles. Fiber morphotypes were prevalent in L. vannamei, while blue-colored microplastics were dominant in P. monodon. These comprised polystyrene (PS), nylon 6,6, and polyethylene (PE) which were identified by Raman spectroscopy. Additionally, the study investigated the acute toxicity effects of microplastic polymer combinations using a zebrafish embryo model (FET236 assay). Zebrafish embryos exposed to polyethylene-nylon 6,6 combinations exhibited significant adverse effects on hatching, survival, and heart function at lower concentrations, while polyethylene terephthalate-polystyrene combinations showed no considerable effects. These findings underscore the urgent need for monitoring and managing microplastic contamination in shrimp farming areas. Future research should focus on elucidating the ecological impacts and human health risks associated with microplastic exposure.
Open Access
A Comprehensive Review of Most Influential Risk Factors for Dementia among Elderly People in Asian Countries
(School of Nursing, Faculty of Humanities and Sciences, SLIIT, 2025-10-10) Ranaweera, G; Dasanayake, C; Kanchana, T
Dementia is characterised by a group of symptoms that are typically defined by memory loss, behavioural changes, and the subsequent loss of cognitive and social functioning caused by progressive neurological disorders. It represents one of the greatest global challenges for health and social care in the 21st century. This review aimed to identify the most influential risk factors for dementia among elderly populations in Asian countries.
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A Context-Aware Doorway Alignment and Depth Estimation Algorithm for Assistive Wheelchairs
(Multidisciplinary Digital Publishing Institute (MDPI), 2025-07-17) Tennekoon, S; Wedasingha, N; Welhenge, A; Abhayasinghe, N; Murray, I
Navigating through doorways remains a daily challenge for wheelchair users, often leading to frustration, collisions, or dependence on assistance. These challenges highlight a pressing need for intelligent doorway detection algorithm for assistive wheelchairs that go beyond traditional object detection. This study presents the algorithmic development of a lightweight, vision-based doorway detection and alignment module with contextual awareness. It integrates channel and spatial attention, semantic feature fusion, unsupervised depth estimation, and doorway alignment that offers real-time navigational guidance to the wheelchairs control system. The model achieved a mean average precision of 95.8% and a F1 score of 93%, while maintaining low computational demands suitable for future deployment on embedded systems. By eliminating the need for depth sensors and enabling contextual awareness, this study offers a robust solution to improve indoor mobility and deliver actionable feedback to support safe and independent doorway traversal for wheelchair users.
Open Access
A cost effective machine learning based network intrusion detection system using Raspberry Pi for real time analysis
(PLOS ONE, 2025-12-29) Wijethilaka R.W.K.S; Yapa, K; Siriwardena, D
In an increasingly interconnected world, the security of sensitive data and critical operations is paramount. This study presents the development of a Network Intrusion Detection System (NIDS) that analyzes both inbound and outbound network traffic to detect and classify various cyber attacks. The research begins with an extensive review of existing intrusion detection techniques, highlighting the limitations of traditional methods when addressing the unique security challenges posed by distributed networks. To overcome these limitations, advanced machine learning algorithms, including Random Forest, Long Short Term Memory (LSTM) networks, Artificial Neural Networks (ANN), XGBoost, and Naive Bayes, are employed to create a robust and adaptive intrusion detection system. The practical implementation utilizes a Raspberry Pi as the central processing unit for real time traffic analysis, supported by hardware components such as Ethernet cables, LEDs, and buzzers for continuous monitoring and immediate threat response. A comprehensive alert system is developed, sending email notifications to administrators and activating physical indicators to signify detected threats. Our proposed NIDS achieves 96.5 detection accuracy on the NF-UQ-NIDS dataset, with a significantly reduced false positive rate after applying SMOTE. The system processes real time network traffic with an average response time of 50 milliseconds, outperforming traditional IDS solutions in accuracy and efficiency. Evaluation using the NF-UQ-NIDS dataset demonstrates a significant improvement in detection accuracy and response time, establishing the system as an effective tool for safeguarding networks against emerging cyber threats.
Open Access
A Cost-Effective Battery Retrofit for Non-Hybrid Grid-Tied PV Systems to Reduce Solar Energy Loss During Grid Outages
(Faculty of Engineering, 2025-09-09) Jayamanne, N
Conventional grid-tied, non-hybrid photovoltaic systems disconnect from the utility grid during outages, resulting in complete solar energy loss despite continued solar generation. This paper proposes a cost-effective retrofit solution that enables energy harvesting and storage during such outages without requiring inverter replacement or voiding manufacturer warranties. The proposed retrofit integrates three blocking diodes, a two-pole DC magnetic contactor, a DC relay, a bidirectional maximum power point tracking (MPPT) DC–DC battery charger, and a microcontroller-based supervisory controller. Under normal grid-connected conditions, the retrofit remains electrically isolated to preserve the inverter’s original operation. During grid outages, available solar energy is redirected to charge a battery through the bidirectional charger. At night, the stored energy is discharged via controlled current injection into one of the inverter’s MPPT inputs through the diode-protected pathway, enabling up to 12 hours of energy utilization depending on local night duration. This approach mitigates solar energy loss during grid failures, eliminates the need for costly hybrid inverter upgrades, and offers a scalable retrofit pathway for residential and small-commercial PV systems.
Open Access
A cross-category analysis of high impact low occurrence (HILO) disasters
(Elsevier Ltd, 2026-03-19) Samaraweera, U; Kulatunga, U; Dias, P
This paper explores six High Impact Low Occurrence (HILO) disasters, generating insights from five different categories associated with them, namely causes (geophysical, technological, biological, sociological), phases (preparedness, response, recovery), dimensions (socio-economics, governance, equity), sectors (health, education, infrastructure, economy) and national contexts with differing levels of economic development. The process involved the generation of a questionnaire, based on a literature review; and the subsequent analysis and discussion of the questionnaire responses made by six experts nominated by six academies of science in Asia. The findings highlight the limitations of probabilistic, frequency-based risk models for HILO disasters and instead emphasise the importance of scenario-based (worst-case) analyses; mechanisms that preserve inter‐generational knowledge, institutional continuity and community‐based early‐response networks; strengthening community resilience while ensuring equity; and making appropriate investments for increasing preparedness, if not through structural interventions, at least through sustained awareness programs and periodic drills. Theoretical contributions include arguments that institutional capacity, governance quality, and social resilience are more decisive determinants of HILO event outcomes than probabilistic risk analyses; and that effective preparedness depends more on anticipatory planning, scenario-based training and institutionalised memory rather than experiential learning; thus advancing HILO theory beyond event-centred and frequency-driven interpretations towards a more governance- and resilience-oriented understanding.
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.
Open Access
A Deep Learning-Based Dual-Model Framework for Real-Time Malware and Network Anomaly Detection with MITRE ATT&CK Integration
(Science and Information Organization, 2025) Migara H.M.S; Sandakelum M.D.B; Maduranga D.B.W.N; Kumara D.D.K.C; Fernando, H; Abeywardena, K
The contemporary world of high connectivity in the digital realm has presented cybersecurity with more advanced threats, such as advanced malware and network attacks, which in most cases will not be detected using traditional detection tools. Static cybersecurity tools, which are traditional, often fail to deal with dynamic and hitherto unseen attacks, including signature-based antivirus systems and rule-based intrusion detection. To ad-dress this issue, we would suggest a two-part, AI-powered solution to cybersecurity which would allow real-time threat detection on an endpoint and a network level. The first element uses a Feedfor-ward Neural Network (FNN) to categorize Windows Portable Ex-ecutable (PE) files, whether they are benign or malicious, by using structured static features. The second component improves net-work anomaly detection with a deep learning model that is aug-mented by Generative Adversarial Networks (GAN) and effec-tively addresses the data imbalance issue and sensitivity to rare cyber-attacks. To enhance its performance further, the system is integrated with the MITRE ATT&CK adversarial tactics and techniques, which correlate real-time detection results with adver-sarial tactics and techniques, thus offering actionable context to incident response teams. Tests based on open-source datasets pro-vided accuracies of 98.0 per cent of malware detection and 96.2 per cent of network anomaly detection. Data augmentation using GAN was very effective in improving the detection of less popular attacks, including SQL injections and internal reconnaissance. Moreover, the system is horizontally scalable and responsive in real-time due to Docker-based deployment. The suggested frame-work is an effective, explainable and scalable cybersecurity de-fense system, which is perfectly applicable to Managed Security Service Providers (MSSPs) and Security Operations Centers (SOCs), greatly increasing the precision rate and contextual in-sight of threat detection. © (2025), (Science and Information Organization)
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A Dual-Branch CNN and Metadata Analysis Approach for Robust Image Tampering Detection
(Institute of Electrical and Electronics Engineers Inc., 2025) Zakey, A; Bawantha, D; Shehara, D; Hasara, N; Abeywardena, K.Y; Fernando, H
Image tampering has become a widespread issue due to the availability of advanced tools such as Photoshop, GIMP, and AI-powered technologies like Generative Adversarial Networks (GANs). These advancements have made it easier to create deceptive images, undermining their reliability and fueling misinformation. To address this growing problem, we propose a hybrid approach for image forgery detection, combining deep learning with traditional forensic techniques. Our study integrates a dual-branch Convolutional Neural Network (CNN) with handcrafted features derived from Error Level Analysis (ELA), noise residuals from the Spatial Rich Model, and metadata analysis to enhance detection capabilities. Metadata analysis plays a crucial role in identifying inconsistencies in image properties such as timestamps, geotags, and camera details, which often accompany tampered images. The CASIA dataset, a publicly available benchmark for tampered images, was used to train and evaluate the proposed model. After 30 epochs of training, the hybrid method achieved an accuracy of 95%, demonstrating its effectiveness in distinguishing between authentic and tampered images. This research highlights the advantages of combining deep learning models with traditional feature extraction methods and metadata analysis, offering a robust solution for detecting manipulated images. Our findings contribute to advancing image forensics by improving detection accuracy, even in cases involving sophisticated tampering methods driven by AI.
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A Fly in the Ointment; Undue Liability on E-commerce Platforms
(Faculty of Humanities and Sciences, SLIIT, 2022) Rajaguru, K.
Trade is now largely internet-centric, meaning the internet is the medium through which most commercial transactions occur in today’s (information) economy. As e-commerce uptake has accelerated globally, it has opened new possibilities for buyers and sellers alike, helping them integrate into a global marketplace and promoting innovations across different business lines. Ecommerce is considered one of the main drivers of recent economic and social developments. In Sri Lanka, e-commerce is emerging and in its infancy. The industry is expected to operate within the margin of the law and be self-regulated. In the absence of a separate law for e-commerce, ecommerce platforms (e-commerce marketplaces) meaning, digital storefronts that connect sellers and customers to transact online, are exposed to a higher risk of being unreasonably penalized by applying the existing laws without mitigation. On the other hand, the platform users are left in a desperate situation with no remedy for harm caused. However, there are many developments globally around e-commerce and platform liability. Therefore, this article explores the responses of advanced jurisdictions such as China, the EU, and the USA regarding platform liability. This concludes that facilitating a business-enabled environment with holistic and innovative strategies that are aligned with the social and economic status of the country with a business-friendly legal landscape that matches the reality of the industry is imperative.
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A Game Centric E-Learning Application For Preschoolers
(Institute of Electrical and Electronics Engineers Inc., 2025) Kulasekara D.A.M.N.; Nipun P.G.I.; Dombawela H.M.D.L.B.A; Manilka G.S; Manilka G.S; De Silva D.I.
This research explores the potential of advanced technologies such as pose detection (PD), augmented reality (AR), object detection (OD), and voice recognition (VR) in creating a game-centric e-learning application for preschoolers. The proposed application, Kidstac, integrates cognitive and physical development through interactive activities with real world interaction, addressing gaps in traditional e-learning methods that often neglect physical engagement. The app features real-time feedback mechanisms and structured modules like virtual zoo explorations, exercise games, treasure hunts, and pronunciation activities. Testing results indicate significant improvements in motor skills, knowledge retention, problem-solving abilities, and language proficiency. These findings demonstrate the effectiveness of blending physical and digital learning experiences to enhance early childhood education. The study establishes a foundation for scalable, activity-based learning tools, emphasizing the holistic development of young learners.
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A Machine Learning Approach to Actuarial Life Table Estimation in Lung Cancer Patients
(Department of Mathematics and Statistics, Faculty of Humanities and Sciences, SLIIT, 2025-10-10) Tharushika, D. D. H.; Napagoda, N. A. D. N.
Cancer-related mortalities worldwide are most caused by lung cancer, and one of the major causes of passing worldwide is still cancer. A dangerous disease is lung cancer, which requires accurate survival modelling to assist in actuarial evaluations, public health planning, and clinical decisions. Life expectancy and mortality risk across age groups are calculated using essential tools such as actuarial life tables, but complex real-world data is frequently struggled with by traditional methods. Actuarial life tables for patients with lung cancer are created using a data set of more than 500,000 patient records with 15 key variables from 2014 to 2024 across European countries, employing Extreme Gradient Boost Accelerated Failure Time (XGBoost AFT) based survival analysis. The main objective is to develop agespecific mortality rates and life expectancy for patients with lung cancer. In contrast to earlier research that was reliant on traditional models, the nonlinear learning capabilities of XGBoost AFT models areutilized in this study to allow for more accurate estimation of mortality trends. A data-driven, machine learning approach to actuarial life table development is contributed by this study, with information about lung cancer survival patterns being provided. The understanding of survival trends, treatment planning, efficient use of healthcare resources, and assessment of the results of initiatives is aided by physicians, researchers, and policymakers. Public health initiatives focused on early identification and prevention are also guided, as well as future healthcare requirements being forecast.