Research Publications
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Item Embargo Throat AI - An Intelligent System For Detecting Foreign Objects In Lateral Neck X-Ray Images(Institute of Electrical and Electronics Engineers Inc., 2025) Baddewithana, P; Krishara, J; Yapa, KForeign Object ingestion is a commonly encountered medical condition within the Ear, Nose, and Throat clinical domain. Timely and accurate detection of such objects is vital, as it often guides the need for surgical intervention. Among the available imaging techniques, lateral neck X-rays are the most widely used radiographs to visualize and assess the presence of FOs in the throat. However, manual interpretation of these images can be time-consuming and subject to human error, potentially leading to misdiagnosis or delayed treatment. This research presents a deep learning-based software solution, deployable via web and mobile platforms, aimed at assisting medical professionals with the automated detection of FOs in lateral neck X-rays. The system leverages state-of-the-art YOLO object detection models, specifically evaluating novel versions such as YOLO-NAS-s, YOLOv11s, and YOLOv8s-OBB to ensure high detection accuracy and deployment efficiency. The best-performing model, YOLO-NAS-s, achieved a validation accuracy of 96.3%. For deployment, the model was hosted on the Roboflow platform and accessed via a FastAPI-based middleware server. Performance evaluation showed an average inference time of approximately 2 seconds and a memory footprint of around 100 MB on standard computing hardware, demonstrating its suitability for integration into resource-constrained clinical environments. This setup highlights the system's lightweight design and real-world applicability. Training, evaluation, and testing of the deep learning models were conducted using a dataset curated from public local healthcare institutions and online medical imaging repositories.Item Embargo MindBridge: Early Identification of Learning Difficulties in Children as a Supporting Tool for Teachers(Institute of Electrical and Electronics Engineers Inc., 2025) Mapa, N; Deshapriya, M; Premathilake, M; Samarakoon, S; Thelijjagoda, S; Vidanaralage, A.JLearning difficulties in children significantly impede academic success by affecting information processing, mathematical performance, and the learning of proper reading and writing. This paper proposes a Progressive Web Application (PWA) based on artificial intelligence (AI) and machine learning (ML) for identifying potential learning barriers. In contrast with standard diagnostic instruments, the proposed system is designed as a prediction tool with the potential for teachers to conduct timely and focused interventions. By automating feature extraction and reducing manual processing, the system overcomes the limitations of existing learning systems and improves early detection accuracy. Preliminary evaluations indicate that the PWA can effectively identify at-risk students and improve intervention methods and overall academic performance. This research contributes to the integration of computational methods and pedagogy, offering a scalable and low-cost solution for helping slow learners overcome their learning challenges.Publication Open Access Blockchain–AI–Geolocation Integrated Architecture for Mobile Identity and OTP Verification(Multidisciplinary Digital Publishing Institute (MDPI), 2025-11-23) SULOCHANA, G. G. D.; De Silva, D.IOne-Time Passwords (OTPs) are a core component of multi-factor authentication in banking, e-commerce, and digital platforms. However, conventional delivery channels such as SMS and email are increasingly vulnerable to SIM-swap fraud, phishing, spoofing, and session hijacking. This study proposes an end-to-end mobile authentication architecture that integrates a permissioned Hyperledger Fabric blockchain for tamper-evident identity management, an AI-driven risk engine for behavioral and SIM-swap anomaly detection, Zero-Knowledge Proofs (ZKPs) for privacy-preserving verification, and geolocation-bound OTP validation for contextual assurance. Hyperledger Fabric is selected for its permissioned governance, configurable endorsement policies, and deterministic chaincode execution, which together support regulatory compliance and high throughput without the overhead of cryptocurrency. The system is implemented as a set of modular microservices that combine encrypted off-chain storage with on-chain hash references and smart-contract–enforced policies for geofencing and privacy protection. Experimental results show sub-0.5 s total verification latency (including ZKP overhead), approximately 850 transactions per second throughput under an OR-endorsement policy, and an F1-score of 0.88 for SIM-swap detection. Collectively, these findings demonstrate a scalable, privacy-centric, and interoperable solution that strengthens OTP-based authentication while preserving user confidentiality, operational transparency, and regulatory compliance across mobile network operators.Publication Embargo Machine learning study of shoreline change in Western and Southwestern coastlines of Sri Lanka(Emerald Publishing, 2025-12-05) Dananjaya, H.G. D.V; Gomes,P.I.AShoreline change per year, also known as end point rate (EPR), showed a skewed normal distribution but without a clear spatial trend for the period 2013–2023 in the western and southern coastal belts. The performance of four machine learning (ML) algorithms was evaluated by dividing the EPR into three or five classes. The three-class EPR approach gave more predictive power. With hyperparameter tuning, the random forest (RF) algorithm demonstrated 0.69 accuracy in EPR prediction, whereas the artificial neural network, support vector machine, and k-nearest neighbour showed accuracies at 0.63, 0.58, and 0.52, respectively. The RF model in any EPR class showed more than 50% accuracy and was thus used as the ML prediction tool. Global Shapely additive explanations illustrated that the presence of port structures, distance to the river mouth, and geomorphology contributed significantly to the overall predictions. Model validation using a separate coastal stretch resulted in a 0.66 accuracy, demonstrating the model’s generalisation ability.Publication Open Access Nutria: An AI-Driven Personalized Meal and Exercise Recommender System for Diabetes Management(SLIIT City UNI, 2025-07-08) Kumari, V.W.I.D; Seneviratne, OThe prevalence of diabetes has led to a growing demand for personalized dietary management tools, leading to the development of Nutria, a web-based food recommendation system tailored for individuals with diabetes. Nutria application is leveraging artificial intelligence, machine learning, and image processing. Nutria analyzes individual health data to provide realtime meal suggestions. The system also features predicting blood glucose level, feature of a chatbot that supports user engagement by offering dietary advice, tracking user progress and exercise recommendation for control their disease condition. The inclusion of a chatbot serves as a vital component of Nutria, facilitating ongoing user engagement and support. Users can interact with the chatbot to receive personalized dietary advice, track their progress over time. This interactive feature not only helps users stay motivated but also fosters a sense of accountability in their dietary choices. Findings from the system evaluation revealed a high level of user satisfaction, with over 85% of participants reporting improved dietary awareness and adherence.Publication Open Access Developing AI-Powered Android Application about Self-financial Management for Individuals “FinGuard”(SLIIT City UNI, 2025-07-08) Samarakoon, S.M.A; Nallaperuma, N.A.PIn this paper the author presented the development of “FinGuard” an artificial intelligence powered android application intended to aid individuals in effectively managing their finances. The application addresses common money issues like executive daily expenditures, lack of income, and financial ignorance. They lead to individuals taking loans, pawning items or borrowing money actions that compromise long-term financial stability. FinGuard offers income and expense tracking services, automated report generation, monthly predictive analysis, customizable reminder feature, advice for financial management feature and a chatbot for get answers for user problems inside the application. The user credentials are shielded from unauthorized use through secure login functionality. FinGuard applies a full and smart process to improve personal financial wellness and promote good financial management habits.Publication Embargo Converting high resolution multi-lingual printed document images in to editable text using image processing and artificial intelligence(IEEE, 2022-06-21) Jayakody, A; Premachandra, H. W. H; Kawanaka, HThe optical character recognition technique is used to convert information, mainly printed or handwritten text in paper materials, into an electronic format that the computers can edit. According to the literature, there are few competent OCR systems for recognizing multilingual characters in the form of Sinhala and English characters together. The lack of an appropriate technology to recognize multilingual text still remains as a problem that the current research community must address, and it has been designated as the key problem for this study. The main goal of this research is to develop a multilingual character recognition system that uses character image geometry features and Artificial Neural Networks to recognize printed Sinhala and English scripts together. It is intended that the solution would be improved to cover three Sri Lanka’s most commonly spoken languages, with the addition of Tamil as a later upgrade. The primary technologies for this study were character geometry features and Artificial Neural Networks. At the moment almost an 85% of success rate has been achieved with a database containing around 800 images, which are divided into 46 characters (20 Sinhala and 26 English), and each character is represented in 20 different forms of character images. Recognition of text from printed bi-lingual documents is experimented by extracting individual character data from such printed text documents and feeding them to the system.Publication Embargo Learning platform for visually impaired children through artificial intelligence and computer vision(IEEE, 2018-02-19) Balasuriya, B. K; Lokuhettiarachchi, N. P; Ranasinghe, A. R. M. D. N; Shiwantha, K. D. C; Jayawardena, CThe topic Visual Disabilities and Computer Vision are the most researched topics of recent years. Researchers have been trying to combine two topics to create most usable systems to the visually disabled to aid them in their day to day tasks. In this research, we are trying to create an application which is targeting children between the age of 6-14 who suffers from visual disabilities to aid them in their primary learning task of learning to identify objects without a supervision of a third-party. We are trying to achieve this task by combining latest advancements of Computer Vision and Artificial Intelligence technologies by using Deep Region Based Convolutional Networks (R-CNN), Recurrent Neural Networks (RNN) and Speech models to provide an interactive learning experience to such individuals. The paper discusses.Publication Embargo Effectiveness of artificial intelligence, decentralized and distributed systems for prediction and secure channelling for Medical Tourism(IEEE, 2020-11-04) Subasinghe, M; Magalage, D; Amadoru, N; Amarathunga, L; Bhanupriya, N; Wijekoon, JGood health and wellbeing, a sustainable development goal introduced by the United Nations to be achieved by 2030. Sri Lanka is a country that highly depends on tourism. A healthcare system which consists of high quality and low-cost services and an abundance of tourist attractions makes Sri Lanka to be one of the best medical tourism destinations. Tourism and travel have contributed to the GDP of Sri Lanka by 11.1 billion USD by 2018. Lack of technological advancements within the medical sector has drawn back the ability to smoothly cater medical tourism. The proposed system aims for an advanced technological improvement that would help in further developing and contributing to medical tourism. To this end, this paper introduces an Intelligent System for Secure Channeling platform that aids medical tourism with the help of artificial intelligence and blockchain technologies. System proposes a treatment prediction and suggesting the best doctor for it and a secured network to store and access electronic health records (EHR). The yielded results show that the proposed method successfully performed treatment prediction with 79-88% accuracy.