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Item Embargo Mobile Application for Enhance Sustainable Tea Farming in Sri Lanka(Institute of Electrical and Electronics Engineers Inc., 2025-10-27) Lokuliyana, S; Wijesiri, P; Kulathunga H.A.S.C; Perera K.N.T.; Koongahage M.GEnsuring sustainable tea farming requires intensive monitoring of plant conditions, nutritional status, and disease infections. To that end, this research presents a smartphone application that is powered by machine learning to assist Sri Lankan tea farmers in identifying fertilizer and chemical deficiencies, predicting tea yield quality, and detecting diseases at early stages. The system makes use of a trained machine-learning model to scan images of leaves for relevant characteristics to provide instant feedback through a user-friendly smartphone interface. The app offers advice to farmers to improve yield and reduce crop loss. This approach enhances accuracy in farming, minimizes reliance on over-fertilization, and assists in efficient farming methods. The given system is designed to target small scale and far-away farmers to make it more popular in diversified agricultural lands. The research involves mass-scale agricultural image dataset collection and processing, deep learning model training, and deployment of a robust mobile application for field implementation. Outputs strive to contribute to Sri Lankan smart agriculture by allowing farmers to make data-driven decisions to ultimately improve productivity and sustainability.Item Embargo LexAyudha : Personalized AI-Driven Rehabilitation for Adolescents with Dyslexia and Dyscalculia(Institute of Electrical and Electronics Engineers Inc., 2025-07-03) Silva, U; Madusanka, I; Thalangama, T; Dissanayake, T; Thelijjagoda, S; Vidanaralage, A. JDyslexia and dyscalculia, the most common learning disabilities, produce a considerably challenging environment for adolescents and lead to frustration, disengagement, and reduced self-esteem. While assistive technologies with influential functionalities exist, they lack personalization for effective and supportive learning. LexAyudha is an AI-powered platform addressing these gaps by integrating proven medical methodologies such as chromatic variation, Touch Math, and multisensory teaching strategies. Advanced AI technologies like Convolutional and Recurrent Neural Networks have been used in LexAyudha to dynamically adjust reading content, visual layouts, and lesson plans in the gamified app based on students' performances to cater for their requirements. Moreover, a novel emotion recognition algorithm even adjusts difficulty levels of activities and voice output with altered audio features to ensure a stress-free learning process and a stimulating environment. Initial findings based on the user performances tests conducted with the dyslexic and dyscalculia adolescents in Sri Lanka, represents significant improvements in reading fluency, comprehension, and motivation, showing that adaptive learning with AI has the potential to revolutionize learning for dyslexic and dyscalculia students. The research identifies the potential of rehabilitation with AI-driven technology as a flexible and scalable solution for personalized education in dyslexia and dyscalculia.Item Embargo 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, SEducation 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.Item Open Access Enhancing IoT Resilience: Machine Learning Techniques for Autonomous Anomaly Detection and Threat Mitigation(Elsevier B.V., 2025-03-05) Lokuliyana, S; Kalupahanage A.G.A; Herath H.M.S.D; Siriwardana, D; Bulathsinhala D.N.; Herath H.M.T.MThe explosive growth of the Internet of Things (IoT) has had a substantial impact on daily life and businesses, allowing for real-time monitoring and decision-making. However, increased connectivity also brings higher security risks, such as botnet attacks and the need for stronger user authentication. This research explores how machine learning can enhance Internet of Things security by identifying abnormal activity, utilizing behavioral biometrics to secure cloud-based dashboards, and detecting botnet threats early. Researchers tested numerous machine learning methods, including K-Nearest Neighbors (KNN), Decision Trees, Logistic Regression, and XGBoost on publicly available datasets. The Decision Tree model earned an impressive accuracy rate of 0.73 for anomaly identification, proving its supremacy in dealing with complex security risks, while the XGBoost model demonstrated strong performance with a 92% accuracy rate for detecting TCP SYN flood attacks. Research findings show the effectiveness of these strategies in enhancing the security and reliability of IoT devices. This study provides significant insights into the use of machine learning to protect IoT devices while also addressing crucial concerns such as power consumption and privacy.Item Embargo AI-Driven Prediction of Optimal Player Positions Based on Fundamental Skills in Netball(Institute of Electrical and Electronics Engineers Inc., 2025-07-03) Karunanayaka S.K.N.C; Wijayasiri B.M.G; Weerathunga W.A.R.N; Hemashi T.G.B; Wimalaratne, G; Rajapaksha, SThis research presents an innovative, video-based performance monitoring system designed to assess and enhance the fundamental skills of netball players while predicting optimal playing positions. Using advanced computer vision and machine learning techniques, the system evaluates fundamental skills through automated video analysis. The framework integrates Convolutional Neural Networks (CNNs), with feature extraction powered by pre-trained ResNet50 models, and temporal sequence analysis using Long-Short-Term Memory (LSTM) networks. In addition, random forest classifiers, supported by SMOTE-balanced data sets, are used for accurate position prediction. The system also incorporates centroid-based motion tracking through OpenCV, enabling precise monitoring of player movements. Unlike traditional subjective coaching methods, data-driven insights offer tailored training recommendations. The predictive analytics component also anticipates player development trajectories and role assignment. By transforming conventional netball training into an AI-driven predictive process, this research provides a powerful tool for coaches and players, improving skill evaluation accuracy, optimizing player positioning, and fostering informed evidence-based decision-making. Ultimately, this innovation contributes to improved individual performance, more cohesive team dynamics, and increased competitive success.Item Embargo AgriSense LK: Weekly Automated Machine Learning for Sri Lankan Produce Prices with Business Continuity Plan, Market Opportunity Ranking, Cultivation Targeting, and Yield Quality Valuation(Institute of Electrical and Electronics Engineers Inc., 2026-05-22) Matharaarachchi, Charaka J.; Samarasinghe, Ravindu T; Vidyasarani G.G.T.; Fasnas, M; Siriwardana, D; Wijesooriya, AIn Sri Lanka, agricultural decision-making remains largely traditional: decisions are often based on historical practices, informal consultation, and heuristic judgment. The primary barrier is that market price data is difficult to interpret without analytical expertise, resulting in unpredictable price volatility and suboptimal farmer income. AgriSense LK is a machine learning platform that converts historical price records into actionable recommendations for farmers and traders. The system comprises four components: business strategy classification, market opportunity ranking, cultivation targeting, and smartphone-based produce quality grading. The platform was trained on 123,985 real price records sourced from the Central Bank of Sri Lanka (CBSL), spanning 2017 to 2025. Key results include a MAPE of 0.7% and MAE of Rs. 1.86 on weekly price forecasting (a 98.1% improvement over the naive baseline), a ROC-AUC of 0.9056 on cultivation targeting, and 91.49% crop classification accuracy with 89.84% quality grade accuracy in the computer vision component. Direct price regression over a seven-day horizon proved unreliable; a binary profitability classifier was adopted instead and substantially outperformed the regression approach. While results are promising, further validation under real-world deployment conditions is required.Item Embargo HireGenius: Automated Interviewing System for Software Engineers(Springer Science and Business Media Deutschland GmbH, 2026-08-01) Hewamadduma N.A.; Nalinka G.K; Mahawaththa N.T.M.A.S.M; Rosa S.R.T.L; De Silva D.I.; Gunathilake P.Recruiting the right software engineers is a critical challenge, with traditional manual screening being time-consuming, subjective, and often inconsistent. Recruiters typically rely on Curriculum vitae reviews and interviews, which lack the depth needed for evaluating technical roles. For software engineers, it is essential to assess programming skills, academic performance, and personality traits. To overcome these limitations, this study developed an automated candidate selection and interview system using artificial intelligence, natural language processing, and deep learning. Ensemble learning and artificial intelligence models incorporating natural language processing were used to rank candidates and predict job match percentages. Top-ranked individuals were further evaluated through analysis of GitHub profiles, LinkedIn activity, and academic transcripts using machine learning and natural language processing techniques. Each candidate’s technical skills, experience, and education were assessed to generate accurate shortlists for technical interviews. These shortlisted candidates then participated in an automated interview process powered by advanced natural language processing and deep learning. A gamified human resource interview system was introduced, leveraging a machine learning model and structured scoring criteria to identify the best-fit candidates while streamlining and enhancing the hiring process.Item Embargo 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, DAgriculture 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.Item Embargo Bovitrack:Animal behavior monitoring using Machine learning and IoT(Institute of Electrical and Electronics Engineers Inc., 2025) Viraj, H; Wijesekara, S; Tharuka, K; Fernando, S; Jayakody, A; Wijesiri, PAnalyzing dairy cattle behavior and anomalies is a critical component of precision livestock farming, allowing farmers to remotely monitor animals for health and behavior. In order to accomplish this task better, the use of IoT technology and machine learning algorithms is more appropriate as per the time. The YOLO (you only look once) object recognition algorithm is more suitable for that, and the use of this algorithm allows these processes to be performed automatically and in real time with high accuracy. YOLO's ability to recognize multiple objects in images or videos makes Yolo ideal for cattle detection and tracking.Item Embargo Hybrid Model-Based Automated Exterior Vehicle Damage Assessment and Severity Estimation for Insurance Operations(Institute of Electrical and Electronics Engineers Inc., 2025) Jayagoda, N.M; Kasthurirathna, DAfter a vehicle accident, insurance companies face the critical task of assessing the damage sustained by the involved vehicles, a process essential for maintaining the insurer's credibility, building consumer trust, and meeting legal and ethical obligations. This assessment is crucial for ensuring clients' financial protection and proper compensation, upholding the integrity of the insurance process. Traditionally, evaluations have been conducted through manual inspections by experienced professionals who meticulously document vehicle damage. Despite its thoroughness, this approach suffers from significant inefficiencies, high costs, and extended time requirements. Moreover, the method is vulnerable to human errors and subjective biases, which can result in inflated valuations. To overcome these challenges, this research introduces an innovative system designed to leverage technology for analyzing images of damaged vehicles uploaded by the user. This system aims to accurately identify the damaged external components, assess the severity of the damage, and determine the repair needs based on the compromised sections of the vehicle. The findings reveal that the hybrid model used in this research is capable of determining vehicle damage severity with an overall accuracy of 73.3%. This level of accuracy demonstrates the model's robust capability to effectively navigate and analyze complex damage patterns, underscoring its practical applications. By accurately determining damage levels on the first assessment, the model reduces the need for further assessments and disagreements, which frequently cause claim delays. This enhancement increases productivity, reduces administrative costs, and improves the customer experience, resulting in a more efficient, transparent, and satisfactory resolution of vehicle insurance claims.