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Author: search.filters.author.De Silva, HHas files: Yes

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    An Integrated Smart Framework for Post-Harvest Optimization and Market Intelligence
    (Institute of Electrical and Electronics Engineers Inc., 2026-08-04) Gamage U.V.A; Wimalarathna B.P.K; Dharmappriya W.A.I.U.; Rathnayaka S.J.; Tissera, W; Rupasinghe, S; De Silva, H; Priyadarshana W.H.D.
    Post-harvest losses in Sri Lanka's fruit and vegetable supply chain remain a critical challenge, attributed to the absence of integrated quality assessment tools, real-time market intelligence, and accessible decision support systems tailored to local conditions. Existing approaches address these problems in isolation, leaving smallholder farmers without a unified platform for quality grading, price forecasting, post-harvest advisory, and cultivation planning. This paper presents CropShield, a novel four-component AI framework designed to address these gaps for Sri Lankan agricultural stakeholders. The first component employs YOLOv8 for fruit detection followed by MobileNetV2 fine-tuned on a papaya dataset for defect classification across six categories and maturity classification across three stages, with Grad-CAM explainability and a Random Forest recommendation engine integrated with Department of Agriculture knowledge. The second component delivers price forecasting across eleven crop varieties using a hybrid ensemble of ARIMAX, XGBoost, and LightGBM trained on HARTI market data from 2008 to 2025. The third component provides a bilingual post-harvest risk advisory assistant supporting Sinhala and English, integrating real-time weather data with an NLP-driven prediction engine, with SHAP-based explainability for transparent advisory outputs. The fourth component implements a Random Forest-based crop suitability and yield estimation model using district-level agronomic data with SHAP explainability for interpretable crop recommendations. The defect detection model achieved 95.65% accuracy and F1-score under clean conditions and 93.48% under robust augmentation, while the price forecasting model achieved R2=0.986 and MAPE=3.16%. CropShield delivers a scalable, explainable, and farmer-accessible platform for evidence-based agricultural decision-making across Sri Lanka
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    Ai-Based Urine Microscopy Image Analysis for Predicting Urinary Tract and Renal Diseases
    (Institute of Electrical and Electronics Engineers, 2026-05-29) Senanayake, K; Panagoda, P; Dharmapriya, S; Chathurya, R; Wijendra, D; De Silva, H; Jayawardana, D
    The manual microscopic examination of urine is a crucial step in diagnosis of urinary tract and renal diseases. However, the process is time-consuming and operator dependent. Most of the existing automated urinalysis techniques only consider the microscopic examination of individual components or the black-box-based prediction models. There is a lack of a comprehensive framework that incorporates the microscopic examination of the microscopic components with the clinical diagnostic logic. In this regard, the present work proposes an artificial intelligence-based urinalysis system for the microscopic examination of the components in the urine sample to generate diagnostic outcomes. In the proposed system, the microscopic components like white blood cells, red blood cells, bacteria, yeast, crystals, and casts are detected and analyzed to generate the diagnostic outcomes for the diagnosis of urinary tract infection, kidney stone risk, hematuria causes, and casts-related renal diseases. In the proposed system, efficient lightweight models ensure precision and effectiveness in identifying various biological entities. White blood cells are detected with a mAP@0.5 score of 0.96, yeast with over 0.94, and crystals with more than 0.91, yielding a classification accuracy of 99.41% for crystals. The system detects microscopic elements like casts with a mAP@0.5 score of 0.80. The system also incorporates auxiliary clinical data to enhance diagnostic results for various diseases.
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    Estimation of Switching Overvoltages during Energization of Transmission Lines using Recurrent Neural Networks
    (979-833153728-9, 2025) Pelendagamage, S; De Silva, H
    Overvoltages frequently pose significant challenges during the energization of transmission lines. During restoration, transmission lines have to be energized from zero voltage to the nominal voltage and the switching of transmission lines is a primary source of these overvoltages. The magnitude and waveform of switching overvoltages are influenced by system parameters, network configuration, and the specific point in the wave cycle at which switching occurs. The ability to estimate peak overvoltages in real-time is crucial for operators, during power system restoration. Traditional methods rely on extensive simulations or empirical formulas, which may not provide the necessary speed or accuracy for operational decisions. It is crucial for operators to ensure that peak overvoltages from switching actions remain within safe limits. This paper introduces a compact long short term memory recurrent neural network (LSTM RNN) based methodology to estimate the peak overvoltages induced during line energization. The developed RNN is trained and tested using extensive simulated data in PSCAD. The results demonstrate that the proposed RNN technique accurately estimates the peak values of switching overvoltages, offering a reliable tool for operators during power system restoration.