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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    Decision Support System for Overcoming the Challenges in Vocational Education in Sri Lanka
    (2021) Lakshani, J. K. A. M.
    The vocational education is undergoing continuous changes. In the past, high youth unemployment has taken place due to unfamiliarity with vocational education. Researchers and policy makers are paying attention to the vocational education because of the hidden importance of the vocational education. In Sri Lanka, there is a vocational education system as the 13 years mandatory education system. The project is going to discover the challenges of the vocational education and give some solution to enhance the effectiveness of vocational education using the sample scenario of the professional entry. There are several issues in vocational education system. Among them, the major challenge is the lower rate of successfully completed students than commencing students. The main objective of this research is to develop a Data-driven decision support system to mitigate the students’ dropouts from vocational education using deep learning model with higher level of accuracy rate than previous systems. Accurate data collection helps to maintain the integrity of the research in any field. The project has collected real data set from the students and teachers in selected government schools in Sri Lanka. Data has collected mainly in three categories as demographic factors, academic performance and candidate interest. Collected data has analyzed according to the data analysis techniques. Decision support system has used machine learning model to predict the suitable vocational education pathways to the students. The model has used deep neural network (DNN) with PyTorch library. After training the model, the model has predicted the accuracy level as 96.06%.
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    Unsupervised Sinhala Cyberbullying Categorization
    (2021) Chandrasena, B.G.M
    The objective of unsupervised machine learning is to categorize the social media comments into a given number of pre-learned categories. The earlier studies of this domain have used many the dataset for supervised learning & introduced a large number of techniques, methodologies. A major challenge there was training labels. Although words with training comments are easy to find, separating them manually is not an easy task. Through this research, we hope to find a solution to this using unsupervised machine learning techniques. the proposed technique divides the comments into words and removed special characters, emojis, and links from the comments & categorized each comment using a keyword list of each category and similarity findings. And then this was used to categorize comments for training. The implemented method shows the same performance, by Comparison with other supervised machine learning techniques for cyberbullying. Therefore, this mechanism can be used in any other places where low-cost cyberbullying identification is needed. This also can be used to create train comments.