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
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Publication Open Access Enhancing Student Performance Prediction Using Real-Time Data and Explainable Artificial Intelligence in Higher Education(Sri Lanka Institute of Information Technology, 2025-12) Dewduni, P.H.H.C.Accurate prediction of student performance has become a strategic priority in higher education, driven by increasing learner diversity, rising dropout rates, and limited institutional resources. Traditional statistical models based on historical records are limited in capturing the dynamic and multidimensional nature of student learning. This study proposes a real-time predictive framework that integrates machine learning with explainable artificial intelligence to achieve both predictive accuracy and interpretability. Data were collected from 2175 undergraduate students from Faculty of Humanities & Social Sciences, University of Ruhuna across five course modules, combining institutional academic and learning management system records with a structured survey capturing financial and psychosocial indicators. After a systematic preprocessing pipeline, five machine learning classifiers were developed and evaluated, including Logistic Regression, Decision Tree, K Nearest Neighbours, Support Vector Machine, and Random Forest. Model performance was assessed using accuracy, precision, recall, F1 score, and area under the curve. Random Forest achieved the highest accuracy of 96.92%, while Support Vector Machine achieved the strongest discriminative capability with the highest area under the curve. To address interpretability, SHapley Additive exPlanations provided global feature attributions, and Local Interpretable Model Agnostic Explanations generated case-specific insights. Results consistently identified attendance and assignment performance as dominant predictors, while behavioural engagement and financial and psychological dimensions offered complementary contributions. A decision support dashboard was designed to operationalize these insights by enabling early identification of at-risk learners, explaining contributing factors, and generating individualized reports for timely intervention. The proposed framework demonstrates that robust predictive accuracy and interpretability can be achieved simultaneously, providing a practical tool for enhancing student retention, equitable support, and efficient resource allocation in higher education.Publication Open Access A Machine Learning Approach for Context-Aware Input/Output Validation in Mobile Applications(SLIIT, 2024-12) De Dilva, H.K.BThere is still insecurity in mobile application since, input/output validation is not well implemented since the rule-based methods cannot adapt the new attacking forms and the new environments. Thus, this work puts forward a novel approach for context-aware input/output validation in mobile applications to overcome these challenges with the use of machine learning. The work is targeted towards investigating a sequence of previous data, application context, and user input for identifying abnormal patterns in real-time using a machine learning model. In line with the formulated model, an adaptive validation system will be employed so that the validation criteria are fluid with the detected context and possible threats. To measure the impact and satisfaction level of the proposed system, this study will use both penetration test and users. Penetration testing will establish the effectiveness of the model in discovering and even preventing security threats while user research will determine the ease of use of the application with the implemented security method. The hope is that this research will be of significant value in formulating mobile applications that are more secure while at the same time providing users with a positive experience. In conclusion, this machine learning integrated method of validation seeks to enhance application security as well as satisfaction levels of the users.Publication Embargo 10-Year Cardiovascular Disease (CVD) Risk Prediction of Sri Lankans: A Longitudinal Cohort Study(2021) Solangaarachchige, M.BCardiovascular diseases are one of the leading causes of mortality in the world. A cornerstone of preventive cardiology is identifying individuals at risk of cardiovascular diseases (CVD) at the earliest. Clinical guideline primarily recommends risk prediction models that are based on a limited number of predictors that perform poorly across all patient groups. Predicting cardiovascular risk is crucial for making treatment decisions, especially in the primary prevention of CVDs using a total risk approach. Despite the fact that several cardiovascular risk prediction models exist, only a handful are specifically designed for Asians, and none are generated from South Asians, including Sri Lankans. Machine learning (ML) and neural networks appear to be increasingly promising in supporting decision-making and forecasting from the huge amounts of data generated by the healthcare industry. This led us to develop a CVD model using Machine Learning to predict 10-year risk of developing a CVD in Sri Lankans. We investigated whether we could adopt ML to develop a model and whether there is an improvement in including nontraditional variables for the accuracy of CVD risk estimates and how to validate the ML model with existing WHO risk charts. Using data on 2596 participants without CVD at baseline data collection of Ragama Medical Officer of Health (MOH) area in Sri Lanka, we developed a ML-based model for predicting CVD risk based on 75 available variables. However, the ratio of developing a CVD vs no CVD in 10 years was 7:93, which is extremely unbalanced. Therefore, at first, we derived a balanced dataset from the main dataset and build a ML model and it recorded an 80.56% accuracy. Secondly, to alleviate the dataset's imbalance, we adopted two techniques, which are 10-fold cross validation and stratified 10-fold cross validation (SKF) and trained six ML classification algorithms. They are Random Forest (RF), Decision Tree, AdaBoost, Gradient Boosting, K-Nearest Neighbor and 2D Neural Network. Out of these six algorithms RF model with SKF showed the highest accuracy in predicting a CVD event with an accuracy of 93.11%. Our ML model included predictors that are not usually considered in existing risk prediction models. Systolic blood pressure was the most important variable in this model. There were six non-traditional variables in the most ten important variable list and three of them were non-laboratory variables. To validate the model with existing WHO risk charts, we explored an experimental approach by developing a simple logistic regression function using the same techniques as the best selected model, with the seven traditional risk factors used in WHO risk charts and our Random Forest model indicated the highest accuracy compared to the WHO model, with a difference of 26.20 %. Our ML model improves the accuracy of CVD risk prediction in the Sri Lankan population. This approach justifies that the CVD prediction models also can be derived using ML for each subregion individually. Additionally, our research discovered novel CVD disease factors that may now be investigated in prospective studies.