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.Deep Learning

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    PublicationOpen Access
    A Multi-Modal Deep Learning and Explainable AI Framework for Transparent Job Matching and Career Development
    (Sri Lanka Institute of Information Technology, 2025-12) Warnasooriya,D. M. D. W. R
    In the digital age, career and professional growth are being influenced with advanced systems which enable finding employment, reviewing applicants and skill advancement. The changing aspect of Explainable Artificial Intelligence (XAI) is essential to allow contextual job matching and reduce discrimination in AI-based job processes. However, most of the existing systems are still non-transparent and restrictive, perpetuating prejudice and weakening credibility. The study presents a new career development and recruitment platform using XAI and surpasses the traditional methods. The proposed system uses a hybrid two-stage system to combine deep learning with the Graph Neural Networks to encode candidate job relevance as well as structural dynamics of career progression, skills dependencies, and mentorship networks. New feature engineering algorithms simulate the dynamics of temporal profiles development and skill acquisition, which allow dynamic and context-sensitive candidate representations. In order to guarantee interpretability, a recruitment-specific explainability engine offers stakeholder-specific explanations such as comparative explanations between a candidate and a job, trajectory correspondence insights, and visualizations of fair trade-offs. The system is tested to execute its functions: a real-world evaluation, which is a combination of fairness statistical measures and accuracy with user-centric interpretability measures, proves the effectiveness of the system. The results highlight the potential of radically changing the current state of hybrid AI architectures and domain-specific explainability to create ethical, equitable, and adaptive solutions in the future of work.
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    PublicationEmbargo
    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%.