Browsing by Author "De Silva, D.I"

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    PublicationOpen 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.I
    One-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.
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    PublicationOpen Access
    Bridging Language Barriers in Programming Education: Java Programming Assistance Tool for Sinhala Native Speakers
    (International Association of Computer Science and Information Technology, 2025-09-12) Athukorala, K. S.N; De Silva, D.I
    This study presents an innovative programming assistance tool designed to address language barriers faced by Sinhala-speaking novice Java programmers. The tool provides real-time Java code generation and diagram creation based on Sinhala programming queries, enhancing conceptual understanding. Developed using a Design-Based Research methodology, the tool underwent iterative testing with 122 Sinhala-speaking learners, incorporating user feedback to refine usability and performance. Central to the system is Generative Pre-trained Transformer, version 3.5 Turbo, ensuring accurate translations and programming assistance, alongside a transformer-based model that translates Sinhala queries into English for processing. The translation model achieved 91.37% accuracy, with strong Bilingual Evaluation Understudy scores validating its contextual relevance. The tool’s practical applications extend beyond academia, supporting educational institutions, self-learners, and industry professionals in learning and skill development. Statistical evaluation of user performance demonstrated significant improvements in programming comprehension, reinforcing its effectiveness. By promoting inclusivity and expanding access to programming knowledge, this research contributes to the advancement of Sri Lanka’s technology sector and establishes a scalable framework for broader implementation in multilingual programming education. Copyright
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    Enhancing Environmental Awareness for Hard of Hearing Individuals: A Mobile Application Approach
    (Springer Science and Business Media Deutschland GmbH, 2025) Dharmasiri, K.G; Rathnasooriya, C.V; Balasuriya, M.K; Yapa, L.N; De Silva, D.I; Thilakarathne, T
    This research focuses on developing a mobile application to enhance environmental awareness for deaf and hard of hearing individuals. At its core is an advanced audio classification system using a convolutional neural network model optimized for recognizing environmental sounds. Extensive experimentation identified the best performing convolutional neural network architecture, trained on spectrograms to classify diverse environmental sounds accurately. The model balances accuracy and computational efficiency, making it ideal for real-time mobile deployment. The application includes a user-friendly admin interface, enabling individuals without machine learning expertise to manage and train models, ensuring adaptability to various auditory environments. Leveraging cloud technologies like Amazon Web Services for data storage, processing, and model deployment, the platform provides a scalable solution for safe interaction with surroundings. This empowers users to navigate their environments confidently, enhancing awareness of crucial auditory cues. The study demonstrates the potential of mobile technology to improve inclusivity and environmental consciousness for underserved populations through real-time, tailored sound recognition.
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    IoT-Enabled Smart Solution for Rice Disease Detection, Yield Prediction, and Remediation
    (IEEE, 2023-06-26) Wanninayake, K.M.I.S; Bambaranda, L.G.S. W; Wickramaarachchi, T.I; Pathirana, U.C.S.L; Vidhanaarachchi, S; Nanayakkara, A.A.E.; Gunapala, K.R.D.; Sarathchandra, S.R.; Gamage, A.I; De Silva, D.I
    Sri Lanka's rice cultivation is a vital industry supporting over 1.8 million cultivators and providing staple sustenance for 21.8 million people. According to Sri Lanka's Central Bank, rice cultivation contributed 2.7% to the country's GDP in 2020 [3]. Pests and diseases, particularly rice thrips damage and rice blast disease, are a challenge for the industry, as they cause yield loss. This paper describes an intelligent solution that aids stakeholders by detecting and classifying the disease, forecasting its dispersion, and providing remedies. The proposed solution is approached with deep learning techniques for real-time detection and classification of the disease, location tracking of infected areas, and pesticide application on the target. In addition, it predicts the spread of disease based on the locations of infected individuals. In addition, the solution enables Machine-learning algorithms to recommend appropriate rice varieties and predict yields. In controlled experiments utilizing data from Sri Lankan paddy fields, the proposed method obtained high accuracy rates of 89%-98% in identifying disease and rice varieties and yield prediction. This system has the potential to increase rice production and productivity, decrease yield loss, and benefit the Sri Lankan rice industry and producers.
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    PublicationOpen Access
    Optical Insight: Enhancing Ophthalmic Diagnostics with Automated Detection of Retinal Abnormalities
    (International Association of Computer Science and Information Technology, 2025-06-11) De Silva, D.I; Wijendra, D. R; Siriwardana, K.S; Gunasekara, S.N.W; Piyumantha, U; Thilakaratne, S.P
    Early and accurate detection of retinal diseases is crucial for preventing vision loss, yet traditional diagnostic methods remain limited by subjectivity and inefficiencies. This study introduces an Artificial Intelligence (AI)-driven diagnostic system leveraging hybrid deep learning models to detect Glaucoma, Macular Hole, Central Serous Retinopathy, and Drusen using fundus images. By integrating multiple architectures, including Residual Network (ResNet), Visual Geometry Group 16-layer network (VGG16), Densely Connected Convolutional Network (DenseNet), U-shaped Network (U-Net), and You Only Look Once version 8 (extra-large variant) (YOLOv8x), the system enhances diagnostic precision and generalization across diverse imaging conditions. Key innovations include the hybrid ResNet-VGG16 and DenseNet-VGG16 models, which significantly improve detection accuracy for Drusen and Central Serous Retinopathy, respectively. Additionally, the U-Net-ResNet hybrid architecture mitigates overfitting, ensuring more reliable Macular Hole detection, while the YOLOv8x object detection model outperforms traditional approaches in Glaucoma localization by accurately identifying the optic disc. These models, integrated into a web-based diagnostic platform, achieved sensitivities and specificities exceeding 95%, establishing a new performance benchmark for automated ophthalmic diagnostics. This research advances medical image analysis by demonstrating the efficacy of hybrid deep learning models, offering a scalable AI solution for early retinal disease detection. Its integration into clinical workflows highlights its potential to transform ophthalmic care, enhancing accessibility and improving patient outcomes.