Faculty of Computing
Permanent URI for this collectionhttps://rda.sliit.lk/handle/123456789/4776
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Item Embargo A BI Approach for Student Engagement and Retention along with Cognitive Load Analysis for Educator(979-833153098-3, 2025) Algewatta, M. N; Manathunga, KThis 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.Item Embargo A Comprehensive Approach to Secure, Accessible, and Engaging Voting Systems(Springer Science and Business Media Deutschland GmbH, 2026) Jayasinghe J.A.M.P; Bandara S.Y.T.D; Shabry S.M; Wickramasinghe W.A.R.M.; Rajapakse, K; Silva, NThis research presents a secure and accessible e-voting system for polling booths in Sri Lankan context, to overcome issues with the traditional voting system. It incorporates block-chain for fair vote storage, and homomorphic encryption for privacy preserving computation of results. The identity of voters is confirmed by face recognition, which includes measures to deterring any voting by impostors. Special identification model with multiple digits is beneficial for disabled voters. Public opinion is effectively gauged through sentiment analysis from social media and it puts concerns to rest, thus a whole lot of enhancement in the whole of customer engagement. Ease of use is also assured thanks to a very user-friendly interface which eliminates mistakes a lot with only a little effort generally. Experimental results demonstrate that security is greatly strengthened, transparency and usability are significantly increased traditional procedural integrity is still maintained efficiently.Item Embargo A Dual-Branch CNN and Metadata Analysis Approach for Robust Image Tampering Detection(Institute of Electrical and Electronics Engineers Inc., 2025) Zakey, A; Bawantha, D; Shehara, D; Hasara, N; Abeywardena, K.Y; Fernando, HImage tampering has become a widespread issue due to the availability of advanced tools such as Photoshop, GIMP, and AI-powered technologies like Generative Adversarial Networks (GANs). These advancements have made it easier to create deceptive images, undermining their reliability and fueling misinformation. To address this growing problem, we propose a hybrid approach for image forgery detection, combining deep learning with traditional forensic techniques. Our study integrates a dual-branch Convolutional Neural Network (CNN) with handcrafted features derived from Error Level Analysis (ELA), noise residuals from the Spatial Rich Model, and metadata analysis to enhance detection capabilities. Metadata analysis plays a crucial role in identifying inconsistencies in image properties such as timestamps, geotags, and camera details, which often accompany tampered images. The CASIA dataset, a publicly available benchmark for tampered images, was used to train and evaluate the proposed model. After 30 epochs of training, the hybrid method achieved an accuracy of 95%, demonstrating its effectiveness in distinguishing between authentic and tampered images. This research highlights the advantages of combining deep learning models with traditional feature extraction methods and metadata analysis, offering a robust solution for detecting manipulated images. Our findings contribute to advancing image forensics by improving detection accuracy, even in cases involving sophisticated tampering methods driven by AI.Item Embargo A Game Centric E-Learning Application For Preschoolers(Institute of Electrical and Electronics Engineers Inc., 2025) Kulasekara D.A.M.N.; Nipun P.G.I.; Dombawela H.M.D.L.B.A; Manilka G.S; Manilka G.S; De Silva D.I.This research explores the potential of advanced technologies such as pose detection (PD), augmented reality (AR), object detection (OD), and voice recognition (VR) in creating a game-centric e-learning application for preschoolers. The proposed application, Kidstac, integrates cognitive and physical development through interactive activities with real world interaction, addressing gaps in traditional e-learning methods that often neglect physical engagement. The app features real-time feedback mechanisms and structured modules like virtual zoo explorations, exercise games, treasure hunts, and pronunciation activities. Testing results indicate significant improvements in motor skills, knowledge retention, problem-solving abilities, and language proficiency. These findings demonstrate the effectiveness of blending physical and digital learning experiences to enhance early childhood education. The study establishes a foundation for scalable, activity-based learning tools, emphasizing the holistic development of young learners.Item Embargo A Lightweight YOLOv8n-Based Binary Waste Classification Model for Autonomous Garbage-Collecting Robots(Institute of Electrical and Electronics Engineers Inc., 2026-07-02) Perera W.B.N.; Sendanayaka H.K; Nuwanthi B.D.T.; Jayasekara R.G.S; Lokuliyana, S; Silva, SEfficient waste classification is critical for automation in modern waste management systems, especially in places where it is hard to use manual classification. In this work, a model for waste and non-waste classification based on the YOLOv8n framework, known for high efficiency and applicability for embedded systems, is designed. For this purpose, a customized dataset was collected using real photos under different lighting conditions, different backgrounds, and with varying objects to reflect a more realistic environment. The training process included augmentation, normalization, and hyperparameters tuning to improve the efficiency of the model. Evaluation metrics such as accuracy, precision, recall, F1 score, and confusion matrix were used. According to the experimental results, the developed model provides an accuracy of 92.5% and F1 score of 0.94 with maintaining fast inference performance for real-time applications. The designed framework aims to become a module in an autonomous waste collecting robot to validate objects before manipulating them by robots, ensuring reliable and efficient real-world system performance.Item Embargo A Secure Protocol for Computer-Based Assessments in Disrupted Environments(Institute of Electrical and Electronics Engineers Inc., 2025) Navin, D; De Zoysa, K; Karunaratna D.D; Harshanath, BExaminations are fundamental to education, yet conducting secure computer-based exams in disrupted environments presents significant challenges. This research introduces a Secure by Design protocol leveraging Delay Tolerant Networks (DTN) to overcome connectivity gaps in remote and resource-constrained areas. The proposed solution integrates physical, administrative, and technical controls to ensure the confidentiality, integrity, and availability of examination data. Through an iterative action research approach, the system evolved from a centralized Moodle server to standalone local servers, enabling offline functionality and enhanced resilience. Tested across over 180,000 candidates in Sri Lanka's largest computer-based examination, the framework effectively addressed power outages, internet disruptions, and logistical constraints. The findings demonstrate the protocol's effectiveness in promoting equitable and reliable access to education, ensuring examination continuity despite adverse conditions.Item Embargo 'AAYU', Paralyze Ease Home Suite and Mobility Companion(IEEE Computer Society, 2025) Tharushi N.K.; Ranaweera D.G.K.T.T.; Munasinghe A.S.; Wijesekara P.N; Gamage N.D.U; Pandithage DEnsuring the safety and well-being of paralyzed individuals remains a critical challenge, particularly in resource-limited settings. Limited access to assistive technology and real-time monitoring increases health risks and dependency. This paper presents AAYU (Assistive Automation for Your Upliftment) Paralyze Ease Home Suite and Mobility Companion, an intelligent system integrating home automation and wearable technology to enhance patient safety, communication, and autonomy. AAYU addresses four key challenges: (1) optimizing home environments through automated adjustments based on vital signs, (2) enabling nonverbal communication via a voice-to-text smart device, (3) detecting falls with a real-time positioning belt, and (4) preventing deep vein thrombosis (DVT) using a sensor-equipped monitoring belt. An initial evaluation demonstrates AAYU's potential to improve the quality of life for paralyzed individuals through proactive and adaptive support.Item Embargo Adaptive Robotic Voice Modulation for ASD Kids: Tailored Voice Pitch, Tone, and Speed(Institute of Electrical and Electronics Engineers Inc., 2025) Panduwawala, P; Pulasinghe, K; Rajapaksha, SChildren with Autism Spectrum Disorder (ASD) often experience sensory sensitivities, particularly auditory hypersensitivity, which can make interactions and communication challenging. This study explores the customization of the NAO robot's voice pitch, tone, and speech speed using the Kaldi Speech Recognition Toolkit to align with the preferences of children with ASD. Eight distinct voice profiles were created, offering a range of variations in pitch, tone, and speech speed. Parents or caretakers were asked to select the voice profile they felt would be most suitable for their child. Based on this feedback, we created a spectrum of voices tailored to each child's needs. Results indicate that medium-pitch and moderate-speed combinations are most effective in enhancing engagement, with Voice 2 emerging as the preferred profile. The findings underscore the potential of adaptive voice modulation in improving robotic interactions for ASD therapy and highlight opportunities for further research in real-time adaptability and long-term impact assessment.Item Embargo Adaptive Voice Communication in Emotion-Aware Digital Companions(Institute of Electrical and Electronics Engineers Inc., 2025) Rathnayake, P; Rathnaweera, C; Jithma, U; Aththanayake, I; Rathnayake, S; Gunaratne, MThis paper presents an adaptive voice communication system for emotion-aware digital companions that dynamically responds to users' affective states through expressive speech and synchronized 3D avatar animation. The system integrates real-time voice input, emotion recognition, and context-aware dialogue generation using GPT-3.5, followed by emotional text-to-speech synthesis via neural TTS. Lip-sync data is generated using phoneme alignment and rendered in sync with the avatar's facial expressions and gestures. To enhance user trust and engagement, the avatar visually mirrors the emotional tone of the speech. A cultural adaptation layer is introduced to align voice output and speech style with Sri Lankan communication norms, including tone, pacing, and formality. Implemented using a Node.js backend and React + Three.js frontend, the system demonstrates strong potential for emotionally intelligent, culturally adaptive AI interactions. This work contributes a modular pipeline for building empathetic voice agents capable of enhancing realism and trust in human-AI communication.Item Embargo Advancing Speech Therapy for Sinhala-Speaking Children with Autism Spectrum Disorder Through an Intelligent Dialog System(Institute of Electrical and Electronics Engineers Inc., 2025) Jayawardena, A; Pulasinghe, K; Rajapakshe, SThis paper presents a dialog system integrated with a NAO socially assistive robot, designed to support Sinhala-speaking children with Autism Spectrum Disorder (ASD). The system leverages a pipeline-based architecture implemented using the RASA framework, consisting of Natural Language Understanding (NLU), Dialog Management (DMU), and Natural Language Generation (NLG) units. The NLU unit processes user input by identifying intents, entities, and dialogue acts, incorporating custom tools like the SpokenSinhalaVerbTokenizer for handling spoken Sinhala. The DMU includes a Dialog State Tracker (DST) to maintain conversation context and a Dialog Policy Generator, which employs rule-based, TED, and UnexpecTED policies to adapt conversation flows dynamically. The NLG unit generates natural responses to foster interactive and goal-oriented conversations. Integrated with the NAO robot, the system engages children through meaningful dialogues, such as discussing toy preferences, aiming to enhance social interaction and communication skills. This work highlights the potential of conversational AI and robotics in therapeutic interventions for ASD in low-resource languages.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 AI Interviews with Facial Emotion Recognition for Real-Time Feedback and Career Recommendations(Institute of Electrical and Electronics Engineers Inc., 2025) Herath R.P.N.M; Arachchi D.S.U.; Gunaratne M.H.B.P.T.; Hansana K.T.; Wijayasekara, S.K; Jayasinghe, DThe hiring process is complex, requiring evaluation of candidates across multiple dimensions, including technical proficiency, behavioral traits, and credibility. Traditional interviews often suffer from biases and inefficiencies. This research presents an AI-driven Interview System integrating Machine Learning (ML), Natural Language Processing (NLP), and Computer Vision to automate and enhance recruitment. The system generates contextual interview questions, evaluates candidate responses using LLM-based scoring models, and provides real-time feedback for engagement. It includes speech-to-text transcription and offensive word detection to ensure professionalism. The behavioral analysis module leverages facial emotion recognition and computer vision to assess non-verbal cues such as confidence and attentiveness. Additionally, Curriculum Vitae (CV) parsing and LinkedIn data extraction use NLP-based entity recognition to extract educational background, work experience, and key skills, enabling personalized interviews. The technical assessment module administers real-time coding challenges, evaluating solutions for correctness, efficiency, and best practices while providing AI-generated feedback. By automating these key hiring aspects, this system enhances objectivity, efficiency, and decision-making, ensuring a data-driven, unbiased, and scalable selection process while improving the candidate's experience and employer insightsItem Embargo AI Powered Integrated Code Repository Analyzer for Efficient Developer Workflow(Institute of Electrical and Electronics Engineers Inc., 2025) Akalanka, I; Silva, S.D; Ganeshalingam, M; Abeykoon, A; Wijendra, D; Krishara, JTransitioning between new and legacy codebases in diverse project environments poses significant challenges for developers, especially with traditional Knowledge Transfer (KT) methods, which are often resource intensive and prone to obsolescence. These limitations hinder the Software Development Life Cycle (SDLC), particularly in fast-paced industrial settings. This research introduces an AI-driven automation solution that leverages large language models (LLMs) and advanced artificial intelligence technologies to address critical gaps in technical knowledge transfer, with a focus on modern software frameworks. The proposed system reduces development costs, improves team performance, and accelerates adaptation to complex codebases. Key features include a documentation generation tool that cuts manual effort by up to 90%, with an average generation time of 6.8 minutes. Additionally, a virtual knowledge transfer assistant enhances onboarding efficiency, potentially reducing senior developer involvement by 50-60%. The system also includes an automated diagram generator that achieves 97% validation accuracy and a code smell detection tool with 71% accuracy, resulting in better code quality assessments. These findings demonstrate the effectiveness of AI-driven automation in improving developer productivity, streamlining onboarding processes, and optimizing software development workflowItem Embargo AI-Driven Fault-Tolerant ETL Pipelines for Enhanced Data Integration and Quality(Institute of Electrical and Electronics Engineers Inc., 2025) Wickramaarachchi, C.K; Perera, S.K; Thelijjagoda, SThe reliability and fault tolerance of ETL (Extract, Transform, Load) pipelines are essential for maintaining data integrity in corporate environments. Traditional ETL systems often depend on manual interventions to resolve data inconsistencies, leading to errors, inefficiencies, and increased operational costs. This study introduces an AI-driven framework designed to improve the fault tolerance of ETL processes by automating data cleaning, standardization, and integration tasks. Using machine learning models, the framework reduces the need for human intervention, enhances data quality, and supports scalability across various data formats. Using real-world data sets, the proposed solution demonstrates its ability to improve operational efficiency and reduce errors within corporate data pipelines. This research addresses a crucial gap in ETL automation, offering a scalable and proactive approach to robust data integration in large-scale corporate settings. The findings highlight the ability of the framework to improve fault tolerance, improve data quality, and offer organizations a competitive advantage in managing complex data ecosystems.Item Embargo AI-Driven Vehicle Valuation and Market Trend Analysis for Sri Lanka's Automotive Sector(Institute of Electrical and Electronics Engineers Inc., 2025) De Silva K.P.N.T.; Shehan H.A.; Jayawardhane A.S; Premarathne A.P.S.; Krishara, J; Wijendra, D.RThe automotive sector in Sri Lanka faces challenges in vehicle valuation accuracy and market trend analysis due to fluctuating prices, varying vehicle conditions, and environmental concerns. This paper presents an AI-driven vehicle valuation system integrating machine learning models for automated vehicle identification, damage detection, market trend analysis, and environmental sustainability assessments. Using deep learning techniques such as Convolutional Neural Networks (CNNs) and time-series models like Long Short-Term Memory (LSTM), the system delivers accurate valuation and market trend insights. Experimental results demonstrate 9 2% accuracy in damage classification and a mean absolute error (MAE) of 5.3% in repair cost estimation, supporting informed decision-making. This research bridges gaps in valuation transparency and sustainability in emerging automotive markets.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 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 An Adaptive E-Learning Platform for Individuals with Down Syndrome(Institute of Electrical and Electronics Engineers Inc., 2025) Sandaruwan U.V.S.; Dias A.H.J.S.S; Shamindi H.M.H; Priyawansha N.G.D.; Chandrasiri L.H.S.S; Attanayaka B.Children with Down Syndrome (DS) encounter varying degrees of learning disabilities within the traditional education framework, requiring personalized interventions. This paper presents Blooming Minds, an adaptive, Machine Learning (ML) driven e-learning platform designed to support the development of cognitive, linguistic, and motor skills in children with DS. Built on the VARK (Visual, Auditory, Reading/Writing, Kinematic) theory, the platform provides personalized activities using real-time feedback mechanisms. The system includes nine interactive modules that cover the above VARK theory. It uses ML algorithms, including Support Vector Machine (SVM) and Random Forest (RF) for screening, Convolutional Neural Networks (CNN) for handwriting and speech analysis, Long Short-Term Memory (LSTM) for sequence prediction, and Reinforcement Learning (RL) for adaptive difficulties. Handwritten letters and voice samples from children with DS, both domestic and international, were specifically considered as inputs for this research. Progress tracking dashboards provide visual insights for educators, parents, and caregivers, improving support and adaptability. The system achieved 91.26% accuracy in letter recognition and 88% in speech classification. This e-learning platform has been recognized as an effective solution in Sri Lanka, allowing for further correlations and investigations to assess the knowledge capacity and ability to express that knowledge in children with DSItem Embargo An Explainable Deep Learning Framework for Coconut Disease Detection Using MobileNetV2, Super-Resolution, and Grad-CAM++(Institute of Electrical and Electronics Engineers Inc., 2025) Balasooriya R.C.; Adithya E.L.A.Y; Gunarathne M.M.S.U; Silva T.C.D; Lokuliyana, S; Wijesiri, PCoconut production is a significant industry in Sri Lanka's economy and food security. However, it is constantly under threat from diseases such as Grey Leaf Spot and pests such as Coconut Mites (Aceria guerreronis). Detection must be early, but it is difficult, especially in field conditions where image quality is low and symptoms are not visually distinguishable. This paper proposes a two-stage deep learning solution to enhance and automate disease and pest recognition with a lightweight and mobile system. The system combines Real-ESRGAN based image super-resolution to restore visual detail in poor-quality mobile images and MobileNetV2-based classification, a lightweight convolutional neural network. The model recognizes grey leaf spot with over 97% accuracy and greatly enhanced mite recognition performance when combined with super-resolution preprocessing. In the interest of transparency and trust for users, the Grad-CAM++ and LIME interpretation techniques are utilized, and visual explanations of the predictions are presented. A mobile application was created with React Native and integrated with a Flask-based backend to enable real-time image enhancement and classification to facilitate practical deployment. Smartphone-captured field-level photos were preprocessed and categorized into healthy, diseased, and non-coconut samples. Farmers can use the proposed system in real time because it maintains good accuracy while being computationally efficient. This framework provides a scalable method for intelligent and sustainable agriculture.Item Open Access An integrated data-driven approach for Chronic Kidney Disease of Unknown Etiology (CKDu) risk profiling and prediction in Sri Lanka(SPIE, 2025) Rajapaksha, N; Rajawasan, H; Ubeysinghe, R; Perera,S; Swarnakantha, N.H.P.R.S; Gamage, M; Nanayakkara, N; Wijayakulasooriya, J; Herath, D; Lakmali, MChronic kidney disease of unknown etiology is a significant public health issue in Sri Lanka, especially in rural farming communities. The exact causes remain unclear, with potential links to environmental and socio-economic factors. This research employs Biological Data and Geographic Information Systems to analyze risk factors such as water quality, agricultural practices, climatic conditions, Demographic Factors, Socio-economic Factors. This study uses data from government health records, the Centre for Research-National Hospital Kandy, and field surveys. By identifying patterns and correlations, the study aims to inform public health interventions and reduce the impact of CKDu, ultimately improving health outcomes for affected populations. This will greatly contribute to preventing the disease, reducing the risk, and identifying patients at an early stage.