MSc in Information Technology

Permanent URI for this collectionhttps://rda.sliit.lk/handle/123456789/2484

Students enrolled in the MSc in Information Technology programme are required to submit a thesis as a compulsory component of their degree requirements. This collection features merit-based theses submitted by postgraduate students specialising in Information Technology. Abstracts are available for public viewing, while the full texts can be accessed on-site within the library.

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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Open 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
Open 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.
Open 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.