Other Conference and Symposium Proceedings

Permanent URI for this communityhttps://rda.sliit.lk/handle/123456789/4774

Browse

Filters

202552

Advanced Search

Filter by

Settings

Start date: 2025End date: 2025

Search Results

Now showing 1 - 10 of 52
  • No Thumbnail Available
    ItemEmbargo
    Autonomous Water Quality Monitoring: Integrating UWB Ad-Hoc Networks, Sensor Calibration, and Kubernetes Cloud Architecture
    (IEEE Computer Society, 2025) Tharindu, K; Abeysinghe, M; Karunarathne, S; Dilshan, K; Primal, D; Jayakody, A
    Water quality monitoring plays a critical role in ensuring environmental sustainability and public health. Traditional methods, while accurate, are time-consuming and lack the ability to provide real-time insights. This study proposes a secure, scalable IoT-based solution utilizing autonomous sensor-equipped boats designed to measure pH, turbidity, and temperature in aquatic environments. The boats navigate predefined grid coordinates generated through a Python-based script and communicate data using UWB in a decentralized ad hoc network operating under the AODV routing protocol. Preprocessed sensor data is transmitted to a base station and securely forwarded to a Kubernetes-based cloud infrastructure for real-time processing and visualization. Communication between the base station and cloud services is secured using HTTPS/TLS encryption. Experimental trials confirm reliable navigation, high sensor accuracy, low latency, and robust security. The system remains cloud-agnostic and is compatible with a range of open-source Kubernetes distributions, enabling deployment flexibility across various environments. This research demonstrates an effective, autonomous approach to real-time water quality monitoring, advancing scalable and sustainable environmental management
  • No Thumbnail Available
    ItemOpen Access
    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.
  • No Thumbnail Available
    ItemOpen Access
    Predictive Modeling for Identifying Early Warning Signs of Underperformance in Vocational Education
    (Institute of Electrical and Electronics Engineers Inc., 2025) Hettiarachchi D.S.S; Harshanath S.M.B
    This study focuses on developing a predictive modeling system to identify early signs of underperformance in vocational education, critical for building a skilled workforce. Addressing challenges like high dropout rates and inadequate graduate preparedness, the system utilizes machine learning techniques such as Neural Networks, Decision Trees, and Logistic Regression. Implemented in Python, it analyzes key features like academic records, attendance, engagement, and socioeconomic factors. Preprocessing steps, such as data cleaning and feature engineering, were implemented, and transfer learning was employed to adapt the model. This combination of feature engineering and transfer learning enables the transfer of knowledge from academic settings to vocational education by identifying and leveraging shared characteristics between the two domains. The system provides real time insights through automated reports and notifications, enabling targeted interventions to improve retention and graduation rates. This scalable approach advances educational technology and informs policies to enhance vocational education outcomes.
  • No Thumbnail Available
    ItemEmbargo
    Voice from the Control Room : Government Officials' Perspectives on How Politics, Funding and Technology Shape Sri Lanka's Transport Future
    (Institute of Electrical and Electronics Engineers Inc., 2025) Bandara, S; Perera, Y; Premathilaka, H; Wijethunga, J; Karunarathna, N; Dayapathirana, N
    The daily struggles of Sri Lanka's public transpo rtation system affect millions of lives, yet the voices of those who run it often go unheard. This study spoke with eight senior government officials from the National Transport Commission and Ceylon Government Railways to uncover what happens behind the scenes. Through detailed interviews, three main problems weighing on their minds were identified: political interference disrupts their work, money shortages block necessary improvements, and finally, worker satisfaction has hit rock bottom. Many transport workers feel stuck with low wages and unclear career paths, which makes it hard to keep services running smoothly. However, it is not all about bad news. These officials shared smart ideas about fixing issues, from better resource management to innovative technology implementation that could help riders track their buses and trains. They believe Sri Lanka's public transport can improve with the right changes. This research study goes beyond just listing problems - we talked to people who live these challenges every day and know what needs to change. Their stories show that improving public transport is not just about new buses or trains; it is about supporting the people who keep everything moving, listening to what riders need, and Equipping transport workers with the required equipment to perform their jobs well.
  • No Thumbnail Available
    ItemEmbargo
    Enhancing Chronic Kidney Disease Prediction : A Hybrid Approach Combining Logistic Regression and Random Forest Models
    (Institute of Electrical and Electronics Engineers Inc., 2025) Jathunga, T; Abeygunawardena, N
    This study investigates the use of Machine Learning (ML) models for Chronic Kidney Disease (CKD) prediction, comparing Logistic Regression with L1 and L2 regularization, Random Forest , and a Hybrid Voting Classifier. The models were evaluated using performance metrics including accuracy, precision, recall, and F1-score, with the hybrid model demonstrating the highest accuracy of 99 percent, followed by Random Forest at 98 percent. Logistic Regression models achieved accuracies of 97 percent and 98 percent , with slight variations in recall for different classes. Cross-validation and learning curve analyses indicated minimal overfitting in ensemble models. These results emphasize the potential of ML models for accurate CKD prediction, suggesting further research into model optimization and data preprocessing techniques.
  • No Thumbnail Available
    ItemOpen Access
    Designing Culturally Adaptive Emotional Gestures to Enhance Child-Robot Interaction with NAO Robots in ASD Therapy
    (Institute of Electrical and Electronics Engineers Inc., 2025) Manukalpa, C.S; Pulasinghe, K; Rajapakshe, S
    Integrating social robots into human-robot interactions demands advancements in natural language processing, navigation, computer vision, and expressive gestures to foster meaningful interactions. However, a gap remains in designing culturally relevant and developmentally appropriate gestures, particularly in the Sri Lankan context. Autism Spectrum Disorder (ASD), a neurodevelopmental condition impacting early education, often remains underdiagnosed, exacerbating learning challenges. This study introduces a novel approach utilizing robot-child interactions for ASD screening to minimize such delays. Expressive gestures were developed for the NAO6 humanoid robot to engage Sinhala-speaking children aged 2 to 6 years, including those with ASD, in Sri Lanka. Using the NAOqi Python API and Choregraphe simulator, culturally aligned gestures expressing emotions like happiness, sadness, fear, anger, and more were designed and synchronized with voice and LED effects. Pilot studies with typical children demonstrated the significance of linguistic and cultural alignment in enhancing engagement, emotional response, and trust. By addressing cultural nuances and advancing early ASD screening, this framework holds potential for broader applications in education, therapy, and diagnosis, improving human-robot interactions globally.
  • No Thumbnail Available
    ItemEmbargo
    Enhancing Environmental Awareness for Hard of Hearing Individuals: A Mobile Application Approach
    (Springer Science and Business Media Deutschland GmbH, 2025) Dharmasiri, K.G; Rathnasooriya, C.V; Balasuriya, M.K; Yapa, L.N; De Silva, D.I; Thilakarathne, T
    This research focuses on developing a mobile application to enhance environmental awareness for deaf and hard of hearing individuals. At its core is an advanced audio classification system using a convolutional neural network model optimized for recognizing environmental sounds. Extensive experimentation identified the best performing convolutional neural network architecture, trained on spectrograms to classify diverse environmental sounds accurately. The model balances accuracy and computational efficiency, making it ideal for real-time mobile deployment. The application includes a user-friendly admin interface, enabling individuals without machine learning expertise to manage and train models, ensuring adaptability to various auditory environments. Leveraging cloud technologies like Amazon Web Services for data storage, processing, and model deployment, the platform provides a scalable solution for safe interaction with surroundings. This empowers users to navigate their environments confidently, enhancing awareness of crucial auditory cues. The study demonstrates the potential of mobile technology to improve inclusivity and environmental consciousness for underserved populations through real-time, tailored sound recognition.
  • No Thumbnail Available
    ItemEmbargo
    Blockchain-Based Custody Evidence Management System for Healthcare Forensics
    (Institute of Electrical and Electronics Engineers Inc., 2025) Jayasinghe R.D.D.L.K; Sasanka M.W.K.L; Athukorala D.A.S.M; Sandeepani M.A.D; Jayakody, A; Senarathna, A
    As digital evidence increasingly growing in significance in healthcare forensics, safeguarding sensitive medical data's confidentiality, integrity, and limited access remains to be an important issue. Existing forensic evidence management systems are subject to data breaches and illegal access since they frequently lack significant privacy-preserving measures. In order to overcome such challenges, this research suggests a Blockchain-Based Custody Evidence Management System for Healthcare Forensics, which combines blockchain technology, machine learning, and encryption methods to improve security, privacy, and accessibility. To ensure accurate and efficient gathering of information, machine learning algorithms are used to extract handwritten and printed text from medical photographs. AES encryption ensures safe storage, while Fully Homomorphic Encryption (FHE) is used for dynamic access level control to protect gathered evidence. Identity verification is made possible via a web-based authentication system that uses Zero-Knowledge Proofs (ZKP) to protect privacy by preventing the disclosure of personal data. By preventing unintended modifications, blockchain technology is used to preserve the custody chain's integrity. Furthermore, machine learning-driven PII detection and masking methods balance the requirement for forensic investigation with privacy compliance by controlling data accessibility according to access entitlements. Based on permitted access levels, the system makes it possible to share safe evidence with law enforcement agencies, such as courts, the police, and other forensic groups. Using blockchain to guarantee data immutability, cryptographic security to restrict access, and artificial intelligence (AI) to safeguard data, this approach enhances the privacy, security, and dependability of handling forensic evidence in medical investigations
  • No Thumbnail Available
    ItemEmbargo
    A BI Approach for Student Engagement and Retention along with Cognitive Load Analysis for Educator
    (979-833153098-3, 2025) Algewatta, M. N; Manathunga, K
    This research presents a systematic approach to monitoring student engagement, retention, and cognitive load within higher education by integrating Business Intelligence (BI) tools with cognitive load analysis. The proposed framework utilizes a diverse range of data sources -including attendance, academic performance, mental health indicators, demographic variables, and student feedback to generate real-time insights into student behavior patterns. The BI system identified critical trends, such as irregular attendance, declining academic performance, and the influence of demographic factors, enabling educators to identify at-risk students and intervene proactively. Additionally, cognitive load analysis was employed to evaluate the mental demands of course content, categorizing learning objectives in alignment with Bloom's Taxonomy. This allowed for the identification of content that could potentially overwhelm students, facilitating adjustments in instructional complexity. The integration of BI insights with cognitive load data provided a holistic approach that not only enhanced the monitoring of student engagement but also supported the tailoring of instructional content to optimize learning without inducing cognitive overload. The findings suggest that combining BI tools with cognitive load metrics offers a robust framework for both improving student retention and assisting educators in creating a balanced, engaging, and supportive learning environment. This study contributes a practical model for institutions seeking to leverage data-driven insights to promote student success and address the dynamic challenges of modern higher education.
  • No Thumbnail Available
    ItemEmbargo
    WORDEX: Early Dyslexia Detection and Support
    (Institute of Electrical and Electronics Engineers Inc., 2025) Ganegoda, S.H; Dissanayake, O; Samarakoon, S; Jayawardana, N; Thelijjagoda, S; Gunathilake, P
    Dyslexia is a prevalent and complex learning disability that affects approximately 5% of primary school students worldwide. It often manifests as persistent difficulties in reading, writing, spelling, and overall academic performance, which can lead to long-term educational and psychological impacts if not addressed early. To facilitate the early identification and support of dyslexic learners aged 7 to 10, this paper introduces Wordex, an innovative and adaptive educational platform. Wordex is designed to screen for multiple dyslexia subtypes and provide targeted interventions through engaging, interactive, and personalized learning activities. The platform features an integrated machine learning-based screening system that analyzes user interactions and performance metrics to assess the risk of dyslexia. Upon identification, the platform delivers tailored remedial exercises that align with national school curricula, aiming to strengthen specific cognitive and linguistic skills. Wordex is developed using a modern technology stack including Spring Boot, Flutter, Python libraries, Firebase, and MongoDB, and incorporates capabilities such as image processing, supervised learning algorithms, real-time progress tracking, and cloud-based data management. A user-centered design approach and iterative testing cycles were employed to ensure the platform is accessible, intuitive, and pedagogically effective. Wordex contributes significantly to the field of educational technology by offering a scalable, research-informed intervention tool. Future enhancements include multilingual support, broader age group coverage, and integration with classroom learning environments.