Research Publications
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This main community comprises five sub-communities, each representing the academic contribution made by SLIIT-affiliated personnel.
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Item Embargo Throat AI - An Intelligent System For Detecting Foreign Objects In Lateral Neck X-Ray Images(Institute of Electrical and Electronics Engineers Inc., 2025) Baddewithana, P; Krishara, J; Yapa, KForeign Object ingestion is a commonly encountered medical condition within the Ear, Nose, and Throat clinical domain. Timely and accurate detection of such objects is vital, as it often guides the need for surgical intervention. Among the available imaging techniques, lateral neck X-rays are the most widely used radiographs to visualize and assess the presence of FOs in the throat. However, manual interpretation of these images can be time-consuming and subject to human error, potentially leading to misdiagnosis or delayed treatment. This research presents a deep learning-based software solution, deployable via web and mobile platforms, aimed at assisting medical professionals with the automated detection of FOs in lateral neck X-rays. The system leverages state-of-the-art YOLO object detection models, specifically evaluating novel versions such as YOLO-NAS-s, YOLOv11s, and YOLOv8s-OBB to ensure high detection accuracy and deployment efficiency. The best-performing model, YOLO-NAS-s, achieved a validation accuracy of 96.3%. For deployment, the model was hosted on the Roboflow platform and accessed via a FastAPI-based middleware server. Performance evaluation showed an average inference time of approximately 2 seconds and a memory footprint of around 100 MB on standard computing hardware, demonstrating its suitability for integration into resource-constrained clinical environments. This setup highlights the system's lightweight design and real-world applicability. Training, evaluation, and testing of the deep learning models were conducted using a dataset curated from public local healthcare institutions and online medical imaging repositories.Item Embargo An Integrated Deep Learning Framework for Early Detection of Vision Disorders(Institute of Electrical and Electronics Engineers Inc., 2025) Jayathilaka, S; Balaruban, D; Kumanayake, I; Elladeniya, A; Wijendra, D; Krishara, J; De Silva, MVision impairment due to retinal diseases like Diabetic Retinopathy (DR), Age-Related Macular Degeneration (AMD), Glaucoma, and Retinal Vein Occlusion (RVO) poses a significant health challenge in Sri Lanka, where these conditions are leading causes of blindness. This research presents a novel multi-disease prediction system leveraging advanced deep learning techniques for early detection of DR, AMD, Glaucoma, and RVO. The study utilized publicly available datasets, including retinal fundus images from repositories such as RFMiD, IDRiD, APTOS validated by medical professionals to ensure diagnostic reliability. These images were preprocessed and augmented to train robust convolutional neural network (CNN) models tailored to each disease. The predictive models were developed and optimized using hybrid architectures, integrating attention mechanisms and feature fusion for enhanced performance. This approach achieved high accuracies 93% for DR, 92% for AMD, 94% for Glaucoma, and 94% for RVO demonstrating robustness and consistency across diverse retinal conditions. To validate real-world applicability, the models underwent further testing in clinical settings using a Sri Lankan dataset, reflecting local disease prevalence and imaging conditions. By combining validated public data with clinical testing, this scalable system supports ophthalmologists in early diagnosis, reducing diagnostic delays and improving patient outcomes. This work offers a reliable, innovative solution to mitigate the burden of blindness in Sri Lanka and beyond.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 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, WOptimizing 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.Item 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 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, JEnterprise 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 prioritizationItem Open Access Intelligent Systems for Comprehensive Dog Management(Association for Computing Machinery, 2025-06-28) Katipearachchi, M.E; Sachethana, O; Gunawardena, G. N.A; Ruwanara, D.C; Krishara, J; Kasthurirathna, DIn recent years, the integration of advanced technologies with canine welfare has gained significant attention, leading to the development of comprehensive platforms for dog management. The "Research Pooch-Paw"initiative addresses the multifaceted needs of dog owners and stray dog populations through an innovative platform that incorporates machine learning, wearable sensors, and real-time data processing. The platform facilitates early disease detection, behaviour analysis, and health monitoring using IoT-enabled devices, and provides personalized care guidance. Additionally, it includes features for stray dog identification and emergency response using deep learning algorithms and image processing techniques. The research underscores the potential of leveraging modern technology to enhance the quality of life for dogs and improve the effectiveness of canine welfare strategies.Publication Embargo A Comprehensive Mobile Platform for Fostering Communication, Literacy, Numeracy, and Emotion Understanding in Children with ASD(IEEE, 2024-07-25) Bandara, T.W.M.I.P.S; Deshan, M.A.D.; Prasanth, P.; Nadeera, M.S.; Krishara, JThis study presents SIPNENA, a novel mobile application designed to aid the learning and communication development of Sinhala-speaking autistic children aged six, particularly in rural areas of Sri Lanka. It offers a unique approach to teaching challenging subjects like English and Mathematics, tailored to the specific needs of children with Autism Spectrum Disorder (ASD). The application integrates interactive methodologies and gamification elements to facilitate better communication, understanding, and engagement. Additionally, it incorporates real-time emotion recognition features to monitor and respond to children's emotional states during learning activities. This research evaluates SIPNENA's effectiveness in improving communication abilities, academic skills, and emotion understanding among autistic children. The findings indicate promising results in catering to the unique educational needs of this target population, particularly in under-resourced rural regions, where specialized interventions are often scarce.Publication Embargo 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, KThis 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.Publication Embargo IoT-Based Solution for Fish Disease Detection and Controlling a Fish Tank Through a Mobile Application(IEEE, 2024-04-05) Bodaragama, B.D.T; Miyurangana, E.H.A.D.M; Jayakod, Y.T.W.S.L; Vipulasiri, D.M.H.D; Rajapaksha, U. U. S; Krishara, JThis research project seeks to enhance fish tank management and improve the well-being of aquatic life by leveraging modern technological solutions. It focuses on four key areas: monitoring water quality, detecting fish diseases, preventing algae growth, and developing an automatic fish feeder with remote control capabilities. The project’s first goal is to establish a comprehensive water quality monitoring and control system that predicts future water conditions, continuously assesses key parameters, and provides real-time data to users for proactive interventions. Additionally, the research project aims to develop an image-processing-based mobile application for early detection of fish diseases, eliminating the need for manual inspection and improving overall fish health management. The project also involves the creation of a mobile app to predict and prevent algae growth by analyzing factors like lighting, nutrient levels, and water flow, providing personalized recommendations for algae control. Lastly, an automatic fish feeder with remote control capabilities will be designed, allowing fish owners to schedule and adjust feeding times and portion sizes through a mobile app. This innovative approach ensures fish receive consistent and appropriate nutrition even when owners are away from home.