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.
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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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    PublicationOpen Access
    Behaviour-based AI Algorithm for Crime Against a Person and Inchoate Crime in Sri Lanka
    (Sri Lanka Institute of Information Technology, 2025-12) Peiris,H.S.H
    The growing integration of digital technologies into daily life has led to a steady rise in crimes that leave behind digital footsteps in the form of text logs, voice recordings, and observation footage. In Sri Lanka, crime investigation systems such as the On-Scene Crime Reporting System (OSCRS- LK) have built up evidence collection practices and improved documentation at crime scenes. although, these systems remain limited in their ability to performance automated behaviour analysis and often rely heavily on manual explanation by investigators, which can be time- consuming and prone to human error. This research addresses this gap by proposing a behaviour- based artificial intelligence (AI) algorithm capable of analyzing multimodal evidence including text, audio, and video to detect unsure behaviour associated with crimes against a person and inchoate crimes, such as threats, harassment, and attempted violence. The proposed system employs machine learning models tailored to each data type; LLM for text processing, CNN for image processing, and YOLOv8 for video-based behavioural recognition. Extracted features from these models are combine into a unified representation, which is then used to calculate a risk score for each incident. To ensure transparency, fairness, and accountability in decision-making, SHAP explainability techniques are integrated, enabling law enforcement officers to explain the model’s predictions and understand the underlying reasoning. By combining multimodal evidence with understandable AI, this research aims to improve early threat detection, increase digital forensic capabilities, and accelerate criminal investigations in the Sri Lankan context. The proposed framework not only strengthens OSCRS-LK but also provides a scalable foundation for future AI-driven crime solutions.
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    PublicationOpen Access
    IoT and AI Application for Implementation of Smart Gardening and Irrigation System
    (2024-12) Sanjeetha, M.B.F.
    This thesis presents a smart gardening and irrigation system that utilizes the Internet of Things and artificial intelligence. This system uses innovative technologies to enhance plant maintenance's efficiency and long-term viability. The system integrated a network of sensors and analytics driven by artificial intelligence to monitor environmental conditions and plant health. This technique facilitated precise gardening and disease management. A smartphone application was created for that, which aims to provide gardeners with real-time data and control over their gardening systems. The mobile application was enhanced with user-friendly and intuitive features, making it even easier to use. This innovative technology represents a significant advancement in smart home systems and sustainable living practices. It aims to save water, optimize plant growth, and provide a personalized and intelligent gardening experience. The research provides a concise overview of the design, implementation, and potential impact of this technology, emphasizing its significance in promoting gardening solutions that are visually appealing and ecologically sustainable.