Browsing by Author "Rajendran, K"

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An AI based Chatbot to Self-Learn and Self-Assess Performance in Ordinary Level Chemistry
(IEEE, 2020-12-10) Mahroof, A; Gamage, V; Rajendran, K; Rajkumar, S; Rajapaksha, S, K; Wijendra, D
Education is one of the fast-growing fields in the global perspective. Advancement of technology can be used in this sector to provide an effective and a valuable education system. In general, the students are more attracted to displays rather than the textbooks. In Sri Lanka, there is an inadequacy of resources and teachers cannot provide one on one attention to the students. Sri Lanka is not equipped with any platform to self-learn or self-evaluate their performance using an application either. Fortunately, “Edubot” acts as a solution for the stated research gap by providing a self-learning and self-evaluating AI based chatbot platform for Ordinary Level students in Chemistry domain. The self-learning component will provide the students a classroom environment by providing interactive tutorials. Explanatory responses would be given by Edubot by capturing doubts raised by the students and the self-evaluating component will provide an exam-based environment in which the Edubot auto generates the question and answers. The research finding shows that each component has an accuracy of more than 70 percent and helps to achieve the main goal of increasing the resources available to the ordinary level students in the Chemistry domain. This would then lead to an increase in the pass rate of the chemistry subject in the G.C.E Ordinary Level exam.
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AI Driven Smart Tourism Platform for Personalized Safe and Sustainable Travel Planning
(Institute of Electrical and Electronics Engineers Inc., 2025-12-08) Srikanthan, S; Senevirathne, C; Rasarathnam, T; Jayalath, T; Rajendran, K
Tourism planning remains challenging due to the need for group preference alignment, personalized itinerary generation, and real-time budget control, challenges that are not adequately supported by existing platforms. This paper presents an AI-driven modular framework that integrates three components: a semantic-aware group recommender that uses Sentence-BERT embeddings with a learning-to-rank model to match travelers; a hybrid itinerary planner that fuses content-based filtering, collaborative filtering, and machine-learning-based rating prediction to generate preference-aligned and geographically coherent travel plans and a predictive budgeting system that applies regression-based forecasting with live API data to provide dynamic cost estimation. The platform is developed specifically for the Sri Lankan tourism context, incorporating regional travel behavior patterns and destination characteristics into its models. Experiments indicate strong performance across all modules, including high-quality group matching, accurate itinerary prediction, and a substantial improvement in budget estimation accuracy compared with static baselines. Early user testing further highlights increased satisfaction with itinerary relevance and budget transparency. Overall, the framework demonstrates a scalable and adaptive approach to smart tourism planning, advancing personalization, collaboration, and sustainable travel support.
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Artificial Intelligence-Based Shoreline Monitoring and Hatchling Behavioral Analysis for Sea Turtle Conservation
(Institute of Electrical and Electronics Engineers Inc., 2026-05-21) Palamure, S; Mirahawaththa, G; Krishara, J; Rajendran, K; Vidhanaarachchi, S
Coastal erosion, climate change, and changes in shorelines are serious threats to sea turtle populations. Abnormal behavior in hatchlings raised in hatcheries also affects their survival. Traditional monitoring methods are mainly based on manual observation, which limits continuous and scalable assessment in various conservation areas. Although recent advances in artificial intelligence (AI) show potential in wildlife monitoring and environmental analysis, these issues are often tackled separately. This study introduces an AI-based monitoring framework that combines two related conservation efforts: assessing environmental risks at natural nesting sites and monitoring the behavior of hatchlings in hatcheries. The proposed system merges deep learning for shoreline segmentation with YOLOv8 for hatchling detection. It also includes trajectory based behavioral analysis and spatial risk modeling. This integrated approach allows simultaneous evaluation of environmental changes and hatchling behaviors within a unified analytical framework. Experimental results from video data gathered in coastal nesting areas and hatchery environments show reliable performance. The shoreline segmentation model achieved an Intersection over Union (IoU) of 0.71, with a segmentation mAP@0.5 of 0.71. The hatchling detection model reached a mAP@0.5 of 0.977 and can provide real-time results. By combining environmental and behavioral information, the system supports better risk assessments, including detecting shoreline encroachment and unusual hatchling behavior. In general, this framework offers an efficient and practical solution to automatically monitor sea turtle conservation.
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EDU-SENSE: AI-Driven Adaptive E-Learning System for Primary Education
(Institute of Electrical and Electronics Engineers Inc., 2025-12-09) Shanmugalingam, N; Maheshwaran, L; Rizvee, I; Roshni R.J; Samarakoon, U; Rajendran, K
This research addresses critical gaps in primary education by integrating AI-driven e-learning tools with traditional classroom teaching, ensuring technology supports rather than replaces teachers while improving student learning outcomes. In getting ready for high-pressure exams like Sri Lanka's Grade 5 Scholarship Examination, maintaining student engagement and emotional well-being remains a significant challenge. EDU-SENSE is an AI-driven web platform designed for primary education that integrates personalized content delivery, stress detection, and an emotion-aware chatbot. The system begins with a diagnostic pre-test to assess individual skill levels,and provides syllabus-aligned questions, and delivers targeted revision based on identified knowledge gaps. A s tress-aware module and motivational chatbot provide emotional support, while peer collaboration features promote teamwork and knowledge sharing. Using Machine Learning, Natural Language Processing, and fine-tuned Large Language Models, EDU-SENSE offers a holistic solution that enhances both academic performance and emotional well-being in young learners.
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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, K
Learning 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.
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Intelligent Adaptive Lighting Control: Reinforcement Learning-Based Optimization for Smart Home Energy Efficiency
(Institute of Electrical and Electronics Engineers Inc., 2025) Hewakapuge M.M; Gamage W.G.T; Surendra D.M.B.G.D; Thejan K.G.T; Rajapaksha, S; Rajendran, K
This study introduces a novel research paper outlining a behavioral-based adaptive lighting system that aims to revolutionise smart home lighting by integrating user behavior tracking to enhance energy efficiency and user comfort. Unlike traditional motion-sensor-based lighting, the novelty of this approach is the ability to adapt dynamically to evolving user behaviors through reinforcement learning. The system utilises Wi-Fi-based positioning, GPS and accelerometer data to monitor user movements and classify different areas of the house. Users initially calibrate the home layout through a mobile application, marking room locations and lighting configurations. The system then collects movement data over time to predict optimal lighting schedules based on user routines and refines the predictions and updates lighting adjustments accordingly, minimising energy wastage while maximising user convenience. A serverless backend architecture ensures scalability, cost-effectiveness, and seamless data processing. The adaptive framework continuously refines lighting automation, responding to evolving behavioral patterns.
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Revolutionalize Your Learning Experience with EQU ACCESS
(IEEE, 2024-07-25) Raveenthiran, G; Sivarajah, K; Kugathasan, V; Chandrasiri, S; Mohamed Riyal, A. A; Rajendran, K
This paper introduces a novel approach aimed at enhancing online education by placing a central focus on students' emotional well-being and improving their learning experiences. The approach integrates four key machine learning technologies: behavioral expression analysis, a personalized chatbot for emotional support, voice stress detection, and visual content description. Through empirical findings, the study illustrates the effectiveness of these methods in bolstering students' emotional well-being and academic performance. By providing a roadmap for the advancement of online education and emotional support, this research holds promise for delivering substantial benefits to learners worldwide. The study showcases notable advancements in online education, reporting a 30% rise in perceived emotional support and a 25% increase in overall satisfaction. The personalized emotional support chatbot achieved an 85% accuracy in addressing students' emotional needs, while voice stress detection boasted a 90% accuracy in identifying anxiety. Additionally, visual content description led to a 20% improvement in comprehension. These findings highlight the approach's potential to elevate both emotional well-being and academic performance in online learners.
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Revolutionizing Tamil Language Analysis: A Natural Language Processing Model Development Approach
(IEEE, 2024-07-25) Ravichandira, G; Sivabaskaran, V; Uthayakumar, T; Vyravanathan, S; Krishara, J; Rajendran, K
This study proposes a web-based platform utilizing Natural Language Processing (NLP) techniques to identify and rectify spelling and grammar errors in Tamil, a language with intricate nuances. Users can input Tamil text, which undergoes automated scrutiny for linguistic inaccuracies. Additionally, the research delves into contextual text summarization and real-time transcription of spoken Tamil. The overarching aim is to devise a holistic solution amalgamating various components to facilitate the detection and rectification of Tamil spelling and grammatical errors. The envisioned subgoals encompass a spell-checking tool capable of identifying misspelled words and suggesting appropriate replacements based on context, a grammar correction feature adept at identifying and rectifying grammatical inaccuracies while accommodating the unique grammatical structures of Tamil, a summarization component adept at condensing paragraphs while retaining core concepts, and a transcription feature enabling the real-time conversion of spoken Tamil into accurate text. By addressing the complexities of the Tamil language, this research endeavor seeks to contribute to the expansion of language processing tools. The ultimate objective is to empower users with the means to detect and rectify errors while enhancing their proficiency in spoken Tamil. This synthesis of components represents a significant stride towards the development of a comprehensive web-based platform for identifying and rectifying Tamil spelling and grammar errors.