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Author: search.filters.author.Krishara, JSubject: search.filters.subject.machine learning

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    DiverseMind: An Integrated Framework for Children with Multi-Dimensional Challenges as Slow Learners
    (Institute of Electrical and Electronics Engineers Inc., 2025-07-03) Jayasundara, H; Neewin, S; Kiriwaththuduwa, C; Herath, R; Krishara, J; Thelijjagoda, S
    Education systems worldwide struggle to support slow learners, who face difficulties in traditional classrooms due to learning challenges in writing, mathematics, attention, and memory. Slow learners, characterized by an Intelligence Quotient (IQ) between 70 and 85, require additional time and adaptive learning methods to grasp concepts effectively. However, existing educational frameworks lack comprehensive screening and targeted interventions. This research introduces "DiverseMind", an integrated framework designed to identify and assist slow learners among Grade 4 primary school children in Sri Lanka using advanced Machine Learning (ML) algorithms, image processing, and multi-model architecture. The system evaluates academic abilities through four key assessments of writing skills, mathematical proficiency, attention span, and short-term memory. A Convolutional Neural Network (CNN) based model, achieving a training accuracy of 98% combined with a Python-based weighted condition function, classifies writing accuracy, while Decision Tree (DT) classifiers analyze mathematical capabilities with 98% accuracy. Attention span is assessed using facial landmark detection, gaze tracking, and emotion recognition, where the CNN model trained on 28,709 images achieved a training accuracy of 80%. Short-term memory is evaluated through ML driven cognitive tasks, with the DT model achieving 99% accuracy. In addition to comprehensive assessments and interventions, the system provides a dedicated dashboard for the teachers to monitor the student progress. By integrating gamification and AI-driven learning analytics, "DiverseMind"promotes inclusive education and bridges the gap in support for slow learners, ensuring they receive the necessary resources to reach their full potential.
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    AloeGreen: Smart IOT-Based System for Aloe Vera
    (Institute of Electrical and Electronics Engineers Inc., 2026-05-21) Megasooriya, E; Rajapaksha, H; Rajapaksha, H; Bandara, A; Krishara, J; Wijendra, D
    Agriculture plays a vital role in food security, yet Aloe vera cultivation remains vulnerable to environmental variability, nutrient imbalance, disease occurrence, and unstable market conditions. This study presented AloeGreen, a crop-specific AI-IoT smart agriculture framework designed to support Aloe vera cultivation through integrated sensing, forecasting, and decision-support modules. The system combined real-time IoT-based field monitoring with machine learning models for yield prediction, environmental forecasting, disease detection, fertilizer recommendation, and price forecasting. A key contribution of the study was a forecast-informed yield prediction strategy in which short-term environmental forecasts were incorporated into the yield estimation pipeline to support future-aware decision-making. In addition, domain-specific agronomic features, including water stress and heat stress indices, were introduced to better represent Aloe vera growth conditions. For the yield prediction module, the cleaned hourly cultivation dataset contained 1,048,330 observations after removing missing critical fields and duplicates. Experimental results showed that XGBoost achieved the best yield prediction performance with an RMSE of $\mathbf{1 0. 0 2}$ and an $\mathbf{R}^{\mathbf{2}}$ of $\mathbf{0. 8 9 2}$, while the environmental forecasting module achieved strong performance for temperature and humidity prediction, although rainfall prediction remained comparatively weaker. The disease detection module achieved balanced classification performance of approximately 77% accuracy, and Random Forest performed best in both price forecasting and fertilizer recommendation tasks. Overall, the findings showed that integrating IoT sensing with intelligent analytics in a unified Aloe vera cultivation platform can improve decision support, reduce uncertainty, and contribute to more sustainable smart agriculture practices.