Browsing by Author "Fernando, N"

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Embargo
Automated vehicle insurance claims processing using computer vision, natural language processing
(IEEE, 2022-11-30) Fernando, N; Kumarage, A; Thiyaganathan, V; Hillary, R; Abeywardhana, L
Traditional insurance claims processing systems are no match for the modern world due to the increasing population of vehicles and the resulting number of accidents. In this paper, the authors present a novel idea to automate the tedious processes in the insurance industry. The presented system consists of three main components namely, re-identify the make and model of the vehicle, identify the damaged automobile component, type, and severity, and compute an accurate repair estimate using damage component identification. Also, automate the documentation process by identifying the relevant fields in the voice input provided by the user. This ensures both the parties involved in this process will be benefited from the proposed system. Presented solutions Were designed using the aid of Artificial Intelligence techniques, mainly CNN models and Natural language processing techniques.
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, Y
Solar 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.
Embargo
Emergency Patient Identification System
(IEEE, 2019-12-05) Sandamal, T; Fernando, N; Jayasinghe, I; Xavier, J; Kuruwitaarachchi, N; Rupasinghe, L
Emergency patient identification system (EPIS) will enable the more powerful quality system in the health industry. This research study was conducted to develop an EPI system, which is a complete patient-based medical information recording system. This system will help to identify the patients uniquely. Doctors can get patients' latest situation and can make quick decisions to do the treatments in emergencies. To achieve this approach, authors use the patient's fingerprint, face recognition technology, and eye recognition to identify the person. The patient can view his medical records; system reminds the notifications. The system helps the patients in storing and tracking the diet weight, medications, allergies, health history, fitness, lab results, x-rays, blood pressure, ongoing surgeries, drug reminders, doctor visits, doctor's appointments, images and more. The system helps the user in receiving and sending necessary health-related information to the doctors and hospitals. This system has many unique features; an emergency will help to find nearest hospitals. This can add patient's family member's history so it can help to remind his present medical situation. The main system is working on web interface; another part is offered as a mobile application.
Open Access
Exploring the Determinants of Medical Insurance Expenses: A Quantile Regression Approach
(Department of Mathematics and Statistics, Faculty of Humanities and Sciences, SLIIT, 2025-10-10) Rathnayake, K; Somasiri, D; Abeygunawardana, T; Nugegoda, K; Fernando, N; Guruge, M. L.; Peiris, T. S. G.
Healthcare insurance costs are influenced by a combination of biological and socioeconomic factors. This study investigates how age, body mass index (BMI), gender, and discount eligibility affect medical insurance expenses in the United States, using data from 1,338 individuals. Due to the right-skewed distribution of expenses, quantile regression was applied at the 25th, 50th, and 75th percentiles, providing insights across low-, medium-, and high-cost groups. Results show that age and BMI consistently increase insurance expenses, with stronger effects among high-cost patients. Genderdifferences also emerged, with females incurring higher costs than males at certain expenditure levels. Discount eligibility significantly reduced expenses across all quantiles. In contrast, the number of children was not a significant predictor and was excluded from the final model. Compared to ordinary least squares regression, quantile regression provided a more accurate assessment of cost determinants in skewed data. These findings highlight the importance of adopting advanced modeling approachesin insurance pricing and suggest that targeted policies addressing individuals having high BMI and equitable discount programs could improve healthcare affordability and risk management.
Embargo
Geo-enabled FOSS tool supports for immediate flood disaster response planning
(IEEE, 2014-12-22) Ramanayake, K; Vithanage, D; Hettiarachchi, N; Rathnayake, G; Rajapaksha, S, K; Fernando, N
—Flood is a major natural hazard occur recurrently in Sri Lanka. Allocating victims to camps and provide medical facilities are two main activities at the immediate response phase of a flood and use of manual methods delayed this process. This project developed a geoenabled application to support immediate response planning, mainly focusing on allocation victims to IDP camps, provide medical facilities, and supporting access avoiding already blocked roads based on administrative divisions of the affected area. Capacities and facilities in camps and hospitals are matched against the needs of the victims. It identifies the blocked roads, alternative routes to reach resource centers, camps and hospitals and provide navigation guidance. The tool can be used after a flood disaster, assuming basic demographic data and the current flood affected area data are available. The tool is developed as a plug-in for QGIS, a free and open source desktop Geographic Information System software. The tool is verified with sample data related to “Kaluthara” area. It is intended to integrate with InaSAFE disaster response support tool at a later stage.
Open Access
Optimum Synchronization of Grid-connected Renewable Energy Source
(Sri Lanka Institute of Information Technology, 2023-03-25) Fernando, N; Ganepola, D; Hettiwatte, S
In the last decades, wind power production has become one of the major concerns to investigate in enhancing the utilization of renewable energy resources in microgrids. Wind power can regulate environmental-friendly power generation which helps to satisfy the power demand in the grid whenever it is essential. This research has been carried out for analyzing behavior of Wind Energy Conversion System (WECS) and appropriate technique for grid synchronization in optimum way. Therefore, this includes the analysis of synchronization procedures and design an optimization technique for synchronization of WECS which is connected to the grid via an inverter. Also, it comprises existing renewable energy systems and applications on synchronization techniques. Mainly, this paper proposes an optimal synchronizing control scheme which verifies deterministic and reliable reconnection to the grid. The control scheme was designed using MATLAB Simulink software and the results were interpreted that the concept is efficient and reliable to optimize the microgrid operations.
Open Access
The Professional Life of Counsellors During the Economic Crisis of Sri Lanka
(School of Psychology. Faculty of Humanities and Sciences, SLIIT, 2025-10-10) Ekanayake, T; Fernando, N
Economic recession periods can significantly heighten risks to the population's mental health and wellbeing while posing additional challenges to health systems. Despite being central to mental health care delivery, the experiences of professionals working through such crises remain underexplored. This qualitative study seeks to illuminate those experiences by addressing two core research questions: (1) What challenges have mental health counsellors in Sri Lanka faced during the economic crisis, and (2) What motivational factors have sustained their commitment under such adverse conditions? Semistructured interviews were conducted with six counsellors from Colombo, who participated voluntarily. Using Interpretative Phenomenological Analysis (IPA), the study uncovered four superordinate themes: ‘economic adversity and emotional dynamics’, ‘coping resources and protective factors’, ‘sense of fulfilment and personal growth’, and ‘professional support and availability of services’. The findings reveal that counsellors were deeply committed to providing psychological care despite economic uncertainty, social stigma, and limitations in service infrastructure. Participants emphasized the importance of both internal and external coping mechanisms, including personal resilience, peer support, and ongoing motivation rooted in a strong sense of purpose. Notably, many counsellors reflected on their growth and sense of fulfilment derived from working with vulnerable populations, highlighting the transformative nature of their roles during crises. While the study is limited by a small sample and the interpretative nature of qualitative research, it offers valuable insights for stakeholders in the mental health sector. Recommendations include strengthening practitioner support systems, enhancing professional infrastructure, and ensuring counsellors’ voices guide future policy and planning.
Embargo
A trilateral influence model for online shopping
(IEEE, 2017-01-27) Samaraweera, S. A. K. G; Gamage, N. G. H. P; Gallage, I. G; Gunathilaka, D. D. T. M; Fernando, N; Kasthurirathna, D
Application of social influence toward E-commerce has brought a significant benefit for the stakeholders. Consequently, it has enhanced the consumer satisfaction as well as spread of experiences. However, even with the collaboration of social influence there are some visible short comings potentially appearing in such systems. In fact, the contribution of social influence is still in an evolving state. The reliability of products is such recognized key issue that still appears in exiting social E-commerce systems. In this context we introduce a social influence model combined with a built in social network which further improves the customer reliability and satisfaction on available products. Thus, it can propagate reliable knowledge among community and optimize product recommendation process. The implemented model considers the personal preferences of respective consumers, their social influences in social network and external social influences to the system for the execution. Furthermore, it operates as a multi-agent system. The model has been validated by two sample data sets of consumers and products. As the results, majority have picked products suggested by combining external influences, internal social influences, and personal preferences. Therefore it has concluded that recommendation of products considering above three combinations is more effective.