Faculty of Computing

Permanent URI for this collectionhttps://rda.sliit.lk/handle/123456789/4776

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"Cropmaster" - Real-Time Coordination of Multirobot Systems for Autonomous Crop Harvesting: Design and Implementation
(Institute of Electrical and Electronics Engineers Inc., 2025) Pramod, I; Arachchi, A.M; Rashen, C; Chinthaka, G; Pandithage, D; Gamage, N
The CropMaster is an autonomous rover system designed to enhance Scotch Bonnet production by improving disease management, crop sorting, autonomous navigation, and real-time environmental monitoring. Equipped with sensors to measure sunlight, humidity, pH, NPK content, and soil moisture, the rover securely transmits analyzed data to a web-based dashboard. LIDAR technology enables efficient autonomous navigation, allowing the rover to move around fields and avoid obstacles. The MQTT protocol facilitates communication between multiple rovers, preventing duplicate measurements and ensuring data is sent to the dashboard for comprehensive data collection across large areas. TensorFlow's machine learning models allow the rover to accurately assess crop health and detect early-stage diseases, followed by automated pesticide and fertilizer application through a spraying system. To maintain reliability, the rover's operations, including data transfer and task execution, are continuously monitored for Quality of Service (QoS). All collected data is stored in the cloud for long-term access. Built with a lightweight aluminum and plastic chassis and robotic arms, the rover is designed for adaptability and operational efficiency, aiming to improve crop management and increase yields across extensive agricultural fields.
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Context-Aware Behavior-Driven Pipeline Generation
(Institute of Electrical and Electronics Engineers Inc., 2025) Gunathilaka, P; Senadheera, D; Perara, S; Gunawardana, C; Thelijjagoda, S; Krishara, J
Enterprise networks increasingly rely on cloud platforms, remote collaboration tools, and real-time communication, placing high demands on bandwidth availability and responsiveness. Static bandwidth allocation approaches often fail to adapt to dynamic traffic conditions, leading to congestion, inefficiency, and degraded Quality of Service (QoS) for critical services such as VoIP and video conferencing. This research introduces a novel real-time bandwidth allocation system that integrates Deep Packet Inspection (DPI), supervised machine learning, and Linux traffic control (tc). Unlike prior solutions that focus only on classification or simulation, our system actively enforces bandwidth policies based on live predictions. Traffic is captured and analyzed in the WAN, while adaptive policies are deployed in the LAN. A web dashboard offers real-time traffic and bandwidth visibility. The proposed system addresses realworld enterprise challenges by enabling intelligent, responsive bandwidth management without requiring costly infrastructure changes, achieving measurable improvements in latency, throughput, and application-level prioritization
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Dynamic Bandwidth Allocation in Enterprise Network Architecture: A Real-Time Optimization Approach
(Institute of Electrical and Electronics Engineers Inc., 2025) Wickramasinghe T.M.L.D; Costa M.M.R.S; Dissanayake S.C.W.; Abayakoon A.M.W.Y.; Lokuliyana, S; Gamage, N
Enterprise networks increasingly rely on cloud platforms, remote collaboration tools, and real-time communication, placing high demands on bandwidth availability and responsiveness. Static bandwidth allocation approaches often fail to adapt to dynamic traffic conditions, leading to congestion, inefficiency, and degraded Quality of Service (QoS) for critical services such as VoIP and video conferencing. This research introduces a novel real-time bandwidth allocation system that integrates Deep Packet Inspection (DPI), supervised machine learning, and Linux traffic control (tc). Unlike prior solutions that focus only on classification or simulation, our system actively enforces bandwidth policies based on live predictions. Traffic is captured and analyzed in the WAN, while adaptive policies are deployed in the LAN. A web dashboard offers real-time traffic and bandwidth visibility. The proposed system addresses realworld enterprise challenges by enabling intelligent, responsive bandwidth management without requiring costly infrastructure changes, achieving measurable improvements in latency, throughput, and application-level prioritization.
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UrbanGreen - E-Waste Detection and Analysis using YOLOv5
(Institute of Electrical and Electronics Engineers Inc., 2025) Madusanka A.R.M.S; Nawaratne D.M.R.S.; Gamage, N; Attanayaka, B
E-waste has become a global concern that challenges environmental sustain ability. The disposal of electronic devices is often poorly managed, especially in urban areas. This research aims to develop an innovative e-waste management system suitable for urban areas, focusing on accurately identifying electronic devices and their harmful components through advanced image processing techniques. (Y olov5) The system identifies various electronic devices, harmful components and materials and assesses their recyclability, improper disposal's environmental and health impacts, empowering users to make informed decisions about disposal and recycling. The system will integrate tools to identify E-waste, promote the reuse of electronic devices, educate the public through interactive educational platforms, and locate nearby e-waste collection centers. By addressing these critical aspects of e-waste management, the project aims to provide a useful platform to manage e-waste effectively in urban areas. This paper was developed to discuss E-waste detection and analysis using YOLOv5 object detection model.
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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 DS
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Personalized Adaptive System for Enhancing University Student Performance in Sri Lanka
(Institute of Electrical and Electronics Engineers Inc., 2025) Dissanayake, N; Samarakoon, C; Wickramasinghe, D; Pathirana, M; Gamage N.D.U; Attanayaka, B
The growing need for personalized learning strategies has driven the development of data-driven solutions to meet the diverse needs of Sri Lankan university students. A key challenge lies in identifying optimal learning paths that align with individual capabilities, learning styles, and engagement behaviors to improve academic performance. While previous research has explored generalized learning models, these often fail to adapt to the specific demands of individual learners. Traditional strategies lack personalization, resulting in inconsistent learning progress. To address this gap, the research introduces an assistive, data-driven approach that leverages Self-Organizing Maps (SOMs), Adaptive Learning (AL), Content-Based Filtering, Graph Neural Networks (GNNs), and Social Network Analysis (SNA) to create optimized, personalized learning strategies. Clustering algorithms and predictive analysis were used to segment learners and deliver tailored interventions based on their behavior. The proposed system integrates advanced machine learning techniques to enhance student engagement and improve overall academic outcomes through personalized pathways.
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IoT-Based Smart Hydroponics for Automated Nutrient, Climate, Irrigation, and Health Monitoring
(Institute of Electrical and Electronics Engineers Inc., 2025) Ashik M.A.M; Bogahawatta C.A; Perera M.R.D; Dassanayake D.R.I.P; Jayakody, A; Gamage, N
This study presents HydroNutraLeaf as a selfgoverning hydroponic tower system built with Internet of Things technology to automate the critical aspects of hydroculture farming by uniting water supply management with environmental control and watering systems and plant health monitoring capabilities. The system unites multiple essential components to operate as one unit. The system incorporates an automatic plant disease detection system through real-time image acquisition which uses Convolutional Neural Network (CNN) algorithms and cloud-based warning protocols for classification purposes. An automated system comprising Raspberry Pi actuators, NPK sensors, and machine learning functions delivers nutrients at proper stages during plant growth. A reinforcement learning system directs the management of climate factors including temperature and humidity together with Light Emitting Diode (LED) spectrums to achieve superior yield production and product quality. The system includes a self-operated irrigation system with Electrical Conductivity (EC), potential of hydrogen (pH) regulation features which utilizes SVM-based prediction methods in combination with real-time monitoring to achieve optimum root environment conditions. Users can access a dashboard in Grafana to monitor and control the system by using cloud platforms which include Firebase and AWS. The experimental findings reveal water consumption decreased by 30% along with improved nutritional efficiency reaching 25% and enhanced crop yield reaching 15% with better health performance. The sustainable farming operations and commercial greenhouse implementation benefit from HydroNutraLeaf solution which operates through a scalable model based on data analysis and requires minimal human intervention.
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Dynamic Resource Allocation and Management in SDN for Multi-Service Network
(Institute of Electrical and Electronics Engineers Inc., 2025) Fernando U.S.K; Pieris M.H.N; Abhayathunge H.I; Athapattu A.R.B.L; Dharmakeerthi, U; Senarathne, A
The increase in network traffic due to technological advancements has led to considerable network congestion, complicating manual network management. Software Defined Networking (SDN) addresses these limitations by decoupling the control plane from the data plane, thereby facilitating centralized and programmable network management. This study introduces a novel dynamic resource allocation method utilizing the Ryu controller and Mininet to optimize traffic flow by identifying congestion-free channels based on critical network attributes such as link bandwidth utilization, latency, and packet loss. The proposed method comprises four principal components: traffic categorization, a dynamic queuing system, an advanced status collection module, and a path selection module. The Ryu controller's dynamic packet direction based on priority levels ensures efficient resource utilization and improved Quality of Service (QoS). Simulated traffic scenarios demonstrate the efficacy of the proposed algorithm, hence highlighting its ability to enhance network performance beyond traditional static routing methods.
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Predictive Policing with Neural Networks: A Big Data Approach to Crime Forecasting in Sri Lanka
(Institute of Electrical and Electronics Engineers Inc., 2025) Nauzad, H; Dayawansa, D; Dias, N.Y; Haddela, P.S; Ratnayake, S
The surge in crime rates, particularly in urban regions, has underscored the importance of predictive policing within law enforcement strategies. This research introduces a neural network-based crime prediction model, specifically tailored to address the complexities of Sri Lanka's crime landscape. By combining big data analytics with advanced machine learning methods - including ensemble models such as Random Forest and Gradient Boosting, alongside Artificial Neural Networks (ANNs) - our study presents a robust framework to forecast crime incidents, locations, and time spans. While neural networks excel in predictive accuracy, their "black-box"nature can hinder practical applications in critical fields like law enforcement. To address this, our model integrates Explainable AI (XAI), making the decision-making process of the system transparent and interpretable for end-users. XAI helps break down complex neural network predictions, ensuring trust and clarity in the model's insights. With a prediction accuracy rate of 85%, this approach demonstrates substantial potential to improve crime prevention efforts and optimize resource allocation. Our research not only highlights the predictive strengths of neural networks but also showcases the essential role of interpretability for deploying these models effectively in real-world policing.
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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, J
Transitioning 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 workflow
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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, S
This 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.
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Intelligent Water Quality Monitoring and Prediction System
(Institute of Electrical and Electronics Engineers Inc., 2025) Shiraz, S; Karunasena, K; Mudelige, H; Kumarasinghe, O; Nawinna, D; Perera, J
The paper aims to develop an integrated approach to improve water treatment processes using predictive modeling and SCADA integration in order to meet the specific needs of water purification systems in Sri Lanka. The current systems utilized for this need are outdated since these systems are based on traditional technologies and do not have the means for predictions or real-time data accessibility outside the system. The proposed solution will focus on raw water quality prediction, optimization of chemical usage to bring in efficiency, sustainability, and resource management, ensuring seamless access to all the relevant data required to manage and monitor. In order to achieve this, past data provided by the Meewatura water plant in Sri Lanka, sourced from the Mahawali river, is utilized for the relevant predictions alongside of the data gathered through the SCADA system. The data is not directly accessible since the SCADA system is mainly built for monitoring, and in order to get the data, a MODBUS connection through the PLC is utilized alongside of an IOT device. In addition to the extracted data, past data that was provided by the water plant is also incorporated. The combined data set is utilized for the predictions while continuously improving itself with new data. The present study contributes to the establishment of sustainable and adaptable water treatment frameworks for a wide range of operational needs within the water plants by addressing the gaps in the existing water quality management systems and improving upon them.
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Smart Agricultural Platform for Sri Lankan Farmers with Price Prediction, Blockchain Security, and Adaptive Interfaces
(Institute of Electrical and Electronics Engineers Inc., 2025) Kuruppu K.A.G.S.R.; Kandambige S.T; Perera W.H.T.H; Cooray N.T.L; Nawinna, D; Perera, J
Improper management of seed demand in Sri Lanka's agricultural sector can result in market imbalances, affecting farmers' decision-making and supply chain efficiency. This research introduces an integrated system for monitoring vegetable seed demand using digital technologies. The proposed system utilizes machine learning techniques to predict vegetable prices, a blockchain network for secure transactions, and a reward-based system to encourage user engagement. It also incorporates an adaptive user interface to accommodate different levels of digital literacy, ensuring accessibility for all farmers, especially senior citizens. Furthermore, the system features an AI Chatbot powered by Langchain and Pinecone, offering domain-specific responses and real-time support for farmers. The solution aims to combine advanced technology with agricultural practices to improve seed demand forecasting, promote transparency in transactions, and ensure a more efficient supply chain. This paper presents a multi-component agricultural platform that integrates predictive analytics, blockchain-secured transactions, gamified incentives, and adaptive user interfaces to support farming decision-making. The system combines machine learning for price forecasting, dynamic reward mechanisms to drive user engagement, and personalized UI/UX optimizations tailored for diverse user groups, including senior farmers. A multilingual AI-powered chatbot enhances accessibility and real-time support, enabling a robust, transparent, and inclusive digital solution for agricultural supply chain management.
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Multimodal Knowledge Graph for Domain-Specific Intelligence
(Institute of Electrical and Electronics Engineers Inc., 2025) Mohan, K; Munasinghe, M; Bandara, L; Wijesinghe, H; Rathnayake, S; Abeywardhana, L
In the era of information abundance, transforming vast amounts of data into meaningful knowledge remains a critical challenge, especially in domains like medicine, engineering, and education, where visual and multimodal elements play a vital role. Traditional Knowledge Graphs (KGs) excel in organizing structured and textual data but struggle to incorporate multimodal information and implicit relationships, limiting their effectiveness. This paper explores the potential of Multimodal Knowledge Graphs (MMKGs) to address these limitations by integrating text, images, videos, and audio into a unified framework. We investigate how MMKGs enhance knowledge retrieval, comprehension, and interactive learning through advanced techniques, including Natural Language Processing and deep learning. Our findings demonstrate that MMKGs significantly improve knowledge retention and application in specialized fields, offering a foundation for more intuitive and effective domain-specific knowledge ecosystems.
Open Access
Model Optimization for Personalized Health Metrics Analysis
(Institute of Electrical and Electronics Engineers Inc., 2025) Perera, M; Wijesiriwardena, A; Pathirana, A; Gamaathige, L; Wijesiri, P; Jayakody, A
This paper investigates the development and application of four machine learning models designed to enhance personalized health management, specifically targeting young adults aged 15-30. The research addresses common health challenges, such as obesity and lifestyle-induced diseases, through data-driven methodologies that provide personalized meal plans, workout recommendations, and progress monitoring. The first model generates optimized personalized recommendations according to the user's health condition using Random Forest and Decision Tree algorithms. The second model utilizes an ensemble of Random Forest, LightGBM, and XGBoost, combined through a stacking technique with Linear Regression as the meta-model, to generate optimized personalized meal plans according to health condition. The third model generates optimized workout plans using Gradient Boosting and XGBoost classifiers, accounting for individual fitness objectives, body compositions, and medical conditions. A fourth model predicts goal achievement timelines by analyzing features such as caloric balance and hydration efficiency, providing users with actionable feedback using XGBoost. The integration of these AI-driven components into a scalable digital platform demonstrates the potential of machine learning in transforming health management. Future enhancements include improving model accuracy, enabling real-time feedback, and deploying the system as an accessible mobile application. ensemble
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Precision Agriculture with Centralized IoT-Enabled Greenhouse Management for Sustainable Vanilla Production
(Institute of Electrical and Electronics Engineers Inc., 2025) Karunathilaka M.M.D.N; Samaraweera H.M.C.D; Balachandra B.A.D.K.M; Thenabandu W.S.D; Silva, S; Fernando, H
After saffron, vanilla is the second most significant spice in terms of economic impact worldwide. The vanilla business faces challenges from pests, illnesses, and environmental variables, especially fungal diseases like fusarium wilt and unfavorable climatic circumstances that can significantly reduce productivity and lower bean quality. This study offers a clever remedy that helps all parties involved by identifying and categorizing plant illnesses, predicting vanilla bean growth and quality, vanilla bean market value analysis and future prediction and build cost prediction and improve operational efficiency. Stakeholders can also obtain forecasts for the quality and growth of vanilla beans in the future. Deep learning algorithms are used in the suggested solution to track the location of diseased areas, diagnose and classify plant diseases in real-time, and apply pesticides or growth-regulating chemicals selectively. For sustainable vanilla production, machine learning algorithms are used to forecast yields, advise ideal greenhouse conditions, and recommend the best vanilla beans. In precision agriculture, the types, applications, and monitoring of IoT devices and sensors are also discussed. Data analysis and management, disease and pest control, fertilization and irrigation management, and environmental monitoring are a few examples. The suggested method produced high accuracy rates in identifying illnesses, evaluating bean quality, estimating yields, and optimizing greenhouse conditions in controlled studies using data from vanilla farms and greenhouses. This technology could assist the vanilla business, producers, and sustainable agricultural practices. It could also boost productivity and production of vanilla, decrease yield loss, and maintain constant bean quality with the help of our suggesting vanilla greenhouse application.
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Enhancing the Performance of Supply Chain using Artificial Intelligence
(Institute of Electrical and Electronics Engineers Inc., 2025) Wijedasa, S; Gnanathilake, K; Alahakoon, T; Warunika, R; Krishara, J; Tissera, W
Optimizing warehouse operations is essential to meet dynamic customer demands while maintaining efficiency in the rapidly changing supply chain landscape. Using four key components, this research presents a comprehensive AI-based approach to improve supply chain management performance. The first component uses Long Short-Term Memory (LSTM) networks to predict demand and returns, allowing for accurate forecasting of product demand and returns based on historical sales data. The second component uses Q-learning, a Reinforcement Learning (RL) technique that optimizes the scheduling of product replenishments by prioritizing critical stock shortages based on inventory levels and predicted demand. The third component analyzes customer purchasing patterns using FP Growth and clustering algorithms to analyze customer buying patterns, strategically placing items in aisles to reduce selection time and improve picking efficiency. The final component involves customer churn prediction using machine learning techniques to identify at-risk customers and facilitate proactive retention strategies. To bridge the gap between complex AI models and practical warehouse operations, a web-based application named 'OptiFlow AI' has been developed. This platform provides warehouse workers with user-friendly interfaces to access demand forecasts, replenishment priorities, optimized product placements, and customer retention insights. The proposed system significantly enhances operational efficiency, reduces time delays, and improves customer satisfaction, contributing to a more resilient and intelligent supply chain ecosystem.
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Child's Age Range Prediction Using Sinhala Speech Recognition System
(Institute of Electrical and Electronics Engineers Inc., 2025) Kathriarachchi, A; Pulasinghe, K
This study predicts the age range of a child speaking Sinhala by analyzing voice characteristics and acoustic features. Identifying speech impairments in children aged 6 to 72 months is critical for early intervention, mainly when using a system that recognizes their native language. The developed system generates accurate insights to assist speech pathologists in diagnosing speech disorders. A Multilayer Perceptron neural network is proposed for age group prediction, leveraging Mel Frequency Cepstral Coefficients (MFCC) and pitch features to enhance recognition accuracy. The system demonstrated an overall accuracy rate of 77% in age range identification, providing a valuable tool for healthcare professionals to evaluate and monitor speech development in Sinhala-speaking children
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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, H
Image 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.
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MindBridge: Early Identification of Learning Difficulties in Children as a Supporting Tool for Teachers
(Institute of Electrical and Electronics Engineers Inc., 2025) Mapa, N; Deshapriya, M; Premathilake, M; Samarakoon, S; Thelijjagoda, S; Vidanaralage, A.J
Learning difficulties in children significantly impede academic success by affecting information processing, mathematical performance, and the learning of proper reading and writing. This paper proposes a Progressive Web Application (PWA) based on artificial intelligence (AI) and machine learning (ML) for identifying potential learning barriers. In contrast with standard diagnostic instruments, the proposed system is designed as a prediction tool with the potential for teachers to conduct timely and focused interventions. By automating feature extraction and reducing manual processing, the system overcomes the limitations of existing learning systems and improves early detection accuracy. Preliminary evaluations indicate that the PWA can effectively identify at-risk students and improve intervention methods and overall academic performance. This research contributes to the integration of computational methods and pedagogy, offering a scalable and low-cost solution for helping slow learners overcome their learning challenges.