Theses

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Postgraduate students are required to submit a thesis as part of fulfilling the requirements of their respective postgraduate degree programmes. This community features merit-based graduate theses submitted by SLIIT postgraduate students. Abstracts are available for public viewing, while the full texts can be accessed on-site within the library.
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Theses and Dissertations of the Sri Lanka Institute of Information Technology (SLIIT) are licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License .

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Subject: search.filters.subject.AI-Driven

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Now showing 1 - 4 of 4
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    PublicationOpen Access
    Edge-Aware Optimization for AI-Driven Anomaly Detection in Smart City Wireless Networks
    (Sri Lanka Institute of Information Technology, 2025-12) Kumarasiri, D.G.S.M.
    The rapid growth of smart cities has led to increased reliance on wireless networks and IoT devices, raising critical security concerns, particularly regarding anomaly detection. Traditional methods fall short in dynamic and resource constrained environments due to their limited adaptability and high computational demands. This paper presents a comprehensive review of AI-driven approaches to anomaly detection in smart city wireless networks, focusing on real-time adaptability and edge-aware optimization. It evaluates state of the art techniques such as federated learning, lightweight deep learning models, and hybrid AI frameworks that balance detection accuracy with efficiency. Public datasets like CICIDS 2017 and IoT-23 are discussed in detail for their role in training and evaluating these systems. Despite significant progress, existing models struggle with scalability, high false positive rates, and limited deployment readiness for edge devices. The paper proposes a conceptual framework to address these gaps and lays a foundation for future research in secure, scalable smart city networks. Additionally, this study incorporates architectural visualization and proposes feasible real-time deployment through edge-based simulators, addressing gaps in prior implementations and privacy safeguards.
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    PublicationOpen Access
    Smart-Split: Ai-Driven Context-Aware System Decomposition For Small And Medium-Sized Businesses
    (Sri Lanka Institute of Information Technology, 2025-11) Subasinghe, L. R. S.
    The transition from monolithic to microservices architecture has become essential for software modernization, yet small and medium-sized enterprises (SMEs) face significant barriers, including prohibitively expensive commercial tools, resource-intensive processes, and context-unaware decomposition approaches. Existing solutions like IBM Mono2Micro and AWS Microservice Extractor rely primarily on static analysis, overlooking critical runtime behavior patterns and domain knowledge, resulting in suboptimal service boundaries misaligned with business capabilities. This research proposes SMART-Split, a resource-efficient multi-agent Retrieval-Augmented Generation (RAG) framework for automated monolith decomposition, specifically designed for Go applications under 50,000 lines of code. The framework employs specialized agents—Static Analyzer, Runtime Profiler, Domain Knowledge Agent, and Decomposer Agent coordinated through a supervisor pattern to integrate multiple analysis perspectives. By combining Abstract Syntax Tree analysis, runtime execution traces, and domain knowledge extraction through RAG, SMART-Split addresses critical gaps in existing decomposition tools. The framework introduces three key innovations: (1) a multi-agent collaborative architecture that synthesizes static, dynamic, and domain context; (2) a lightweight RAG implementation optimized for resourceconstrained environments; and (3) a hybrid decomposition algorithm that produces business-aligned service boundaries. Validation across three open-source Go monoliths demonstrates improved decomposition quality through metrics including Modularity Quality (MQ > 0.7), Service Independence Score (SIS > 0.8), and Business Alignment Index (BAI > 0.9). Results indicate SMART-Split achieves comparable decomposition quality to commercial tools while requiring significantly fewer computational resources, making microservices modernization accessible and affordable for SMEs.
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    PublicationOpen Access
    Localization of AI-Driven Sign Language Recognition
    (SLIIT, 2024-12) Samaranayake, H.T.M.D
    Sign language recognition (SLR) is a vital research area promoting inclusivity and interaction for the deaf and hard-of-hearing communities. This thesis focuses on automatic SLR using the American Sign Language (ASL) dataset, emphasizing preprocessing, feature extraction, and Long Short-Term Memory (LSTM) networks to enhance accuracy. The process begins with data collection, where video frames from the ASL dataset are resized, normalized, and converted into grayscale to reduce computational load while retaining key features. Data augmentation techniques like rotation, flipping, and scaling are applied to improve the model’s generalization. Feature extraction captures spatial and temporal information critical for SLR. Optical flow is employed to detect hand motion and facial expressions, while Convolutional Neural Networks (CNNs) extract spatial patterns from the video frames. These features are fed into an LSTM network, designed to learn sequential dependencies in the data. LSTMs are effective for understanding dynamic gestures, as they capture both short- and long-term dependencies between frames. The model predicts sign language symbols or words, facilitating real-time recognition. The thesis further integrates semantic sentence prediction, enabling the system to recognize isolated signs and predict entire sentences. Using Natural Language Processing (NLP), input sentences are mapped to sign language sequences, which are visualized through synthesis models that generate animations. This approach captures handshapes, movements, and expressions essential in ASL. By combining preprocessing, feature extraction, and deep learning, this research improves SLR accuracy and contributes to communication accessibility. It lays a foundation for advancements in SLR systems for applications in education, healthcare, and human-computer interaction.
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    PublicationOpen Access
    AI-Driven Nutrient Management in Hydroponics for Urban Agriculture Enhancing Food Security through Technology
    (SLIIT, 2024-12) De Silva, G.P.S.N
    This research investigates the integration of artificial intelligence (AI) into hydroponic farming systems to tackle challenges in urban agriculture, particularly food security and resource optimization. Urban expansion and shrinking arable land necessitate innovative agricultural solutions, and hydroponics—a soilless cultivation method—is increasingly recognized for its efficiency and scalability in urban environments. By leveraging AI and Internet of Things (IoT) technologies, this study develops an automated nutrient management system that optimizes critical parameters such as pH, electrical conductivity (EC), and nutrient concentrations (NPK: Nitrogen, Phosphorus, Potassium) to enhance plant growth and resource efficiency. The experimental design includes two hydroponic systems: an AI-driven system and a manual control setup, both operating under identical conditions. The AI-driven system utilizes real-time sensor data, processed by machine learning models, to automate nutrient adjustments. Data collected from sensors, including pH, EC, and temperature, is transmitted via AWS IoT Core and stored in DynamoDB for real-time monitoring and historical analysis. The system's performance is visualized through an Angular-based dashboard, enabling continuous monitoring and decision-making. Results demonstrate that the AI-driven system significantly outperforms manual nutrient management in terms of plant growth, resource efficiency, and environmental stability. Plants grown in the automated system exhibited a 48% increase in weight and improved root development compared to those grown in the manual system. The automated system 4 also maintained optimal pH (6.3–6.7) and EC (1.8–2.4) levels with minimal deviations, reducing nutrient waste and ensuring precise dosing. This research contributes to the field of smart agriculture by showcasing the transformative potential of AI and IoT technologies in hydroponic farming. The findings emphasize the viability of AI-driven systems to enhance the sustainability, scalability, and efficiency of hydroponics for urban agriculture. The integration of advanced