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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Now showing 1 - 4 of 4
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    PublicationOpen Access
    Development of an AI-Integrated Online Counseling and Self-Improvement Platform for Mental Health Support
    (Sri Lanka Institute of Information Technology, 2026-01) Wijewardena T P
    Mental health challenges such as stress and anxiety remain a growing global concern, particularly in low-resource settings like Sri Lanka, where access to professional counseling is limited and stigma discourages many individuals from seeking support. This thesis presents the development of an offline AI-based counseling chatbot designed to provide accessible, empathetic, and private mental health support without relying on high-bandwidth internet connections. The system was implemented using a TF-IDF-based natural language processing pipeline to classify user inputs into predefined intent categories and deliver evidence-based therapeutic responses. Training data were compiled from clinical counseling transcripts, standardized affective word databases, anonymized peer support forums, and publicly available datasets, ensuring both linguistic diversity and clinical relevance. Evaluation of the system demonstrated an intent detection accuracy of 91.2% across 387 test queries. A preliminary user study involving 10 participants revealed that 80% reported noticeable stress reduction after interaction, while responses were rated at an average of 4.3/5 for relevance. The chatbot maintained a lightweight design with an average response time of 0.19 seconds and a memory footprint of just 2MB, enabling reliable operation on low-end devices in offline settings. The findings confirm that simple, transparent AI techniques can effectively bridge treatment gaps in underserved regions. While the current system is English-only, future enhancements will focus on incorporating multilingual support, contextual emotion analysis, and improved personalization, providing a scalable and culturally adaptive solution for equitable mental health care.
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    PublicationOpen Access
    Development of a Non-Invasive Algorithm for Anemia Detection in Women in Sri Lanka
    (SLIIT, 2024-12) Senanayake, W.I. Umaya
    Anemia continues to be a considerable health issue for women in Sri Lanka, impacting physical and cognitive growth, general health, and economic efficiency. Diagnostic methods, like blood tests, are invasive, time-consuming, and could be out of reach for populations with limited resources. A non-invasive algorithm is created to detect anemia in Sri Lankan women in this thesis. The algorithm utilizes readily available clinical and demographic information to decrease reliance on conventional blood tests. According to that ―Development of a Non-Invasive Algorithm for Anemia Detection in Women in Sri Lanka‖ entitled as the research title of this thesis. The research involves data collection from women across varied demographics and regions, combined with vital health parameters and physical indicators relevant to anemia detection. Advanced machine learning models are trained on this data to identify patterns associated with anemia, offering accurate predictions without the need for invasive procedures. A core aim of the study is to enhance early detection, enabling timely intervention and reducing the overall prevalence of anemia among women. The high sensitivity rate of the algorithm allows for effective anemia detection with minimal input data, according to key findings. Furthermore, its non-invasive characteristics make it appropriate for application in rural regions where healthcare resources are scarce. The system successfully provides a non-invasive, accurate, and accessible method for anemia detection, using fingertip imaging and machine learning to predict anemia in real-time. With a compact device integrated into a web app, users can monitor their health easily, while healthcare providers can remotely access patient data for timely interventions. The system’s cost-effectiveness and ease of use make it particularly valuable for resource-limited settings, offering a scalable solution for anemia management and broader public health impact.
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    PublicationOpen Access
    Development and Integration of an AI-Driven PHP Adapter for Automated Mathematical Question Classification and Assessment: Enhancing Student Profiling and Feedback Mechanisms
    (SLIIT, 2024-12) Nishamali, M.K.C.P.
    The transformative growth of AI can be seen in almost every sector. AI can be a useful application for the educational domain as well. This research aims to combine IT to develop mathematics subjects by leveraging AI in practice mainly introducing Capabilities of Open AI. The primary objective is to create OpenAI API through a specially created PHP adapter to classify mathematical questions into six main themes Sets and Probability, Algebra, Numbers, Geometry, Measurements, and Statistics. This automated AI-driven classification system helps to create online assessments within the blink of an eye. The Integration of Open AI API with a PHP-based framework makes a bridge between AI capabilities and education needs. This framework is the ideal solution for manual and traditional school assessments. This plugin can be implemented in other university-level courses as well. The sample of the adapter plugin is only created and tested for secondary school mathematics classes for grade 10. This AI-driven mathematics classification system is designed to optimize the assessment process by providing additional objectives such as leveraging automated student grading feedback so teachers and students can see the result instantly. Additionally, answers are automatically generated after the assessment, displaying the solving steps that help students identify their mistakes. Meanwhile, this system also predicts the student’s mathematics pass mark based on the results of the tests taken from this system.
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    PublicationOpen Access
    Development Of An Elephant Detection And Repellent System Based On EfficientDet-Lite models
    (2023-02) Pemasinghe, W.D.S.S
    Human-elephant conflict (HEC) has become a major concern in Sri Lanka that results in many unfortunate human and elephant deaths. Methods that are currently in place to mitigate HEC, such as electrical fences have undesirable consequences resulting in both human and elephant casualties. In this paper, we have proposed a method based on computer vision and deep learning that has promising potential for detecting and repelling elephants without endangering the lives of elephants or humans. We have used EfficientDet-Lite models that provide a good compromise between accuracy and performance to be usable with a resource-constrained device like a Raspberry Pi.