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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 Identify Dyscalculia, Dysgraphia Learning Disabilities in Deaf and Mute Primary Students and Help to Improve Learning Abilities(Institute of Electrical and Electronics Engineers, 2026-01-22) Perera, G; Neththasinghe, H; Rasanjana, D; Thalakotunna, Y; Krishara, J; Rajendran, KLearning disabilities, particularly Dyscalculia and Dysgraphia, significantly hinder students' academic, social, and future occupational outcomes. Deaf and mute primary students face amplified challenges due to limited availability of specialized educational resources and tools that cater to their unique communication needs. This research presents an innovative, intelligent learning environment designed specifically to identify and mitigate Dyscalculia and Dysgraphia among deaf and mute primary students. Leveraging advanced artificial intelligence, computer vision, and machine learning (ML) technologies, the developed system incorporates Sinhala Sign Language (SLSL) and interactive, adaptive learning methodologies. The identification process employs Convolutional Neural Networks (CNNs) to analyze handwritten numerical inputs and sign language gestures, categorizing students based on the severity of their conditions. Subsequently, personalized, engaging instructional activities facilitate gradual skill development and continuous improvement. Robust data privacy measures ensure ethical standards, while automated tracking provides actionable insights for educators and parents. Initial findings indicate significant improvements in student performance and engagement, demonstrating the system's efficacy in delivering inclusive, culturally relevant, and accessible educational interventions tailored for deaf and mute students.Item Embargo LegalVision: A Knowledge-Driven AI Framework for Legal Understanding and Trust Assessment(Institute of Electrical and Electronics Engineers, 2026-06-12) Sharan K; Wicramasinghe D.A.T.N.; Maxwell L.Y; Sivanuja S; Kuruppu, D.S; Dissanayake, ALegal documents are often lengthy, complex, and written in highly technical language, making them difficult for citizens and even legal professionals to interpret efficiently. This creates a need for an intelligent legal support system that can improve accessibility, transparency, and trust in document understanding. This study proposes LegalVision, a knowledgedriven AI framework that integrates multi-perspective legal summarization and visualization, explainable legal reasoning, clause-level bias and risk evaluation, and a dynamic legal knowledge graph for property law documents. This research is conducted within the Sri Lankan legal context using a dataset collected from real Sri Lankan legal documents, including property-related deeds and agreements. The framework processes legal texts through clause segmentation, entity and relation extraction, perspective-based summary generation, infographic visualization, risk classification, and graphsupported reasoning, while preserving links to the original clauses for traceability within a single platform. Therefore, this research contributes a unified and explainable legal AI framework that supports both legal professionals and nonexpert users in understanding property law documents more accurately and efficiently.Item Embargo AI-Powered Mobile Application for Supporting Mental Wellness in Children(Institute of Electrical and Electronics Engineers, 2026-06-26) Pathiraja G.P.K; Navarathne N.M.D.C; Navodya D.P.D.D.; Dharmapriya R.A.P.; Vidanaralage, A.J; Vidanaralage, A.JChildren often struggle to express their emotions verbally, making early detection of mental health issues challenging and limiting timely intervention. This research proposes an AI-powered mobile application designed to support children's mental well-being through multimodal emotional analysis and personalized interventions. The significance of this study lies in addressing the lack of accessible, child-friendly, and culturally relevant digital mental health solutions, particularly for Sinhala-speaking users, while reducing dependence on subjective expert interpretation. The proposed system integrates four main components, including a culturally grounded therapeutic story generator using a GRU-based deep learning model, an emotion analysis module for children's drawings utilizing DenseNet121 and YOLOv8 with colour and spatial feature extraction, an emotion-aware music recommendation and adaptive puzzle game powered by EfficientNet-based facial emotion recognition, and a Sinhala voice-first mood prediction system using fine-tuned XLM-RoBERTa.Experimental results demonstrate strong performance, including improved contextual coherence and adaptive therapeutic storytelling capabilities, 79% accuracy in drawing-based emotion classification, 87% accuracy in emotion-aware music recommendation, and 96% accuracy in Sinhala text-based mood prediction, along with improved user engagement through personalized storytelling and adaptive feedback mechanisms. The system further enhances interpretability by generating parent-friendly emotional reports using large language models. In conclusion, this research contributes a comprehensive, multimodal, and culturally adaptive AI solution for supporting children's mental health, enabling early emotional detection, improved communication, and scalable intervention through an engaging mobile platform.Item Embargo Intelligent Traffic Management Using Fuzzy Logic and Machine Learning(Institute of Electrical and Electronics Engineers Inc., 2026-06-12) Gunarathna R.P; Randima K.M.G.D; Tennakoon I.M.S.R.; Palihakkara P.I; Rajapaksha, S; Kahatapitiya, Kfic violations, and inefficient signal con-trol. Conventional traffic management systems rely on manual monitoring and fixed signal timings, making them ineffective in handling dynamic real-time traffic conditions. This research proposes an Intelligent Traffic Management System (ITMS) that integrates real-time traffic monitoring, adaptive signal control, traffic violation detection, and accident risk prediction through a unified analytical dashboard. The system is designed for an IoT-based four-way junction where sensors and cameras detect vehicle density and dynamically prioritize lanes with higher traffic volume. Using video-based vehicle detection, the system measures vehicle speed in real time and identi-fies violations such as over-speeding, red-light violations, and illegal parking. Drivers receive notifications through a mobile application where they can check violation details and pay fines calculated based on predefined traffic rules. Additionally, a dynamic accident risk scoring mechanism combines real-time vehicle speed data with historical violation records to identify high-risk driving behavior. Analytical dashboards visualize traffic density, violations, and risk levels to support data-driven decision making. The proposed system demonstrates how real-time traffic monitoring, violation detection, and dynamic signal control can improve road safety and traffic efficiency, contributing to the development of advanced smart city traffic management solutions.Item Embargo Agri-Sense: Autonomous Tomato Farming Robotic Vehicle(Institute of Electrical and Electronics Engineers Inc., 2026-05-21) Bandara K.M.D.I.; Yomal M.S.; Weerasekara W.M.G.V; Nilessh P; Jayakody J.A.B.C.; Gamage N.D.U.The precision greenhouse cultivation of tomatoes is often hindered by the expensive labor costs, the easy spread of diseases, and the unavailability of robust digital technologies. This paper proposes Agri-Sense, a comprehensive autonomous robotic system that covers the entire scope of greenhouse management. The system includes a robotic platform that allows the crops to be traversed autonomously without the need for human input. The ripeness and leaf disease detection are done using advanced YOLO-based computer vision techniques. The robotic arm has 4 degrees of freedom, which includes a modular gripper and a precise sprayer system for disease treatment. This reduces the usage of chemicals and protects the healthy crops from disease. To solve the problem of connectivity in rural greenhouse environments, the system includes a sensor mesh using LoRa sensors and a Software-Defined Networking controller. This controller uses Quality of Service and VPN tunnels to provide robust communication services even in the presence of unreliable connectivity. Experimental results showed that the proposed system bridges the gap between passive monitoring and autonomous greenhouse management. The system is robust and efficient in greenhouse management.Item Embargo Huruwa: An AI-IoT Robotic System for Adaptive Speech Therapy and Parental Support for Sinhala-Speaking Children with Speech Sound Disorders(Institute of Electrical and Electronics Engineers Inc., 2026-05-21) Iddamalgoda, R; Piyathilaka, D; Pallebathgala, D; Abeyrathne, H; Thelijjagoda, S; Vidanaralage, A. JEarly phonological intervention for Sinhalaspeaking children with speech sound disorders requires scalable, engaging tools that bridge clinical expertise and home practice; however, low-resource language constraints limit available solutions. This paper introduces Huruwa, an AI-IoT robotic platform designed to support speech therapy for Sinhala-speaking children. The system integrates LLM-guided conversational interaction, phoneme-adaptive exercises (80-90% task suitability), and SVM-RBF-based phoneme error detection achieving 78% accuracy. It further employs knowledge-graph-driven therapy generation and a RAG-based parent guidance system to deliver grounded, hallucination-controlled support. Evaluations confirm real-time feasibility across components, offering a deployable model for Sinhala child speech therapy in resource-limited settings like Sri Lanka.Item Embargo MaizeGenie - Mobile Platform for Sustainable and Profitable Corn Cultivation(Institute of Electrical and Electronics Engineers Inc., 2026-03-26) Suraweera A.G.S.S.; Pasindu J.D; Rathnayaka R.M.T.P.B; Perera P.D.P.N; Samarakoon, U; Hathurusinghe, SMaize is an important crop in Sri Lanka, but many farmers still face low yields and income losses due to pest and leaf disease attacks, uncertain weather, and market price changes. Farmers also have limited access to real-time, easy-to-understand decision support in Sinhala, especially in areas with weak internet coverage. This research proposes MaizeGenie, an AI-powered mobile advisory platform that supports sustainable and profitable corn cultivation. The system combines computer vision and machine learning to deliver four main services: (1) pest identification and control guidance using object detection and image classification, (2) leaf disease identification with severity-based advice, (3) yield prediction with prediction-based site-specific real time fertilizer advisory system, and (4) price forecasting and cultivation timing decision support using time-series forecasting. The application is designed for farmerfriendly use, provides Sinhala/English guidance with voice/text output, and supports operation where possible. Overall, the proposed solution aims to reduce trial-and-error farming, improve timely actions against pests and diseases, and help farmers plan inputs and selling decisions with more confidence.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 From AI Assistance to Critical Thinking: Exploring Cognitive Offloading and Metacognition as Mechanisms within Personalized Learning Environments(Institute of Electrical and Electronics Engineers Inc., 2026-02-18) Gunathilake, N; Gamage, A; Rajapakshe, D; Jayasooriya, M; Wisenthige, K; Yapa, C.GThe rapid adoption of AI-assisted learning tools in higher education has completely transformed the undergraduate study system, but empirical evidence on their impact on deep cognition and learning processes is limited. This study investigates the effects of AI-assisted learning tool usage on metacognition, cognitive offloading, personalized learning, and critical thinking among Sri Lankan undergraduates. Using positivism philosophy and a deductive quantitative approach, data were collected from 379 students in computing, management, and engineering subjects through a 35-items, five-point Likert scale questionnaire, and analyzed using PLS-SEM. The findings reveal significant direct, indirect, and moderating relationships among key variables, suggesting that the use of AI-assisted learning tools influences students’ learning regulation, reliance on external support, and development of higher-order thinking. This study provides empirical evidence for the cognitive and psychological effects of AI-assisted learning tools, and helps address an important research gap, and offers practical insights for educators, curriculum developers, and policymakers to use balanced and effective AI integration in higher education.