MSc in Information Systems

Permanent URI for this collectionhttps://rda.sliit.lk/handle/123456789/2481

Students enrolled in the MSc in Information Systems programme are required to submit a thesis as a compulsory component of their degree requirements. This collection contains merit-based theses submitted by postgraduate students specialising in Information Systems. 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 Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License .

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Open Access
A Flask-Based System for Measuring and Analyzing Confidence in Interviewee Speech Using Speech Recognition Technology
(Sri Lanka Institute of Information Technology, 2025-12) Dangalla, H.P.
Confidence is vital in the interview process, as it is a key determinant of credibility and competence. Nevertheless, conventional approaches to evaluating confidence are highly dependent on human judgment, which brings in bias and variability. This article suggests an effective machine learning platform which uses convolutional neural networks (CNNs) and long short-term memory networks (LSTMs) to overcome these shortcomings, offering an objective and scalable method to determine confidence levels in speech during interviews. In this architecture, CNNs extract the spatial characteristics of audio spectrograms, paying attention to the key prosodic variations in pitch and tone that act as confidence indicators. Meanwhile, LSTMs learn the time-varying behavior of these features, enabling the system to identify change in speech rate and time-varying pauses. These models can jointly identify speech as confident or non-confident with 92.5 percent accuracy on labeled data. This system is more precise, recalls higher, and has a better F1 score than current methods. Although the model demonstrates potential in confidence detection, it struggles with extrapolating across accents and languages due to overfitting. But it has a lot of potential in the future as a tool. To overcome future challenges, more diverse datasets and sophisticated methods such as data augmentation and transfer learning can be implemented to enhance the adaptability of the system. Such a framework might be of immense use in practical situations when conducting job interviews, educational evaluations, and coaching in speech delivery, giving consistent, objective measures of confidence. The resultant system might help enhance fairer judgments, offer constructive criticism to applicants, and contribute to making informed choices, benefiting the science of affective computing. It also paves the way to scalable, real-time solutions that could improve human-AI interaction and enhance communication dynamics in various areas.
Open Access
Analyzing the Performance of Different Text Classification Algorithms for “Dhivehi” Documents
(SLIIT, 2024-12) Mohamed, F.R
This research investigates the effectiveness of various machine learning classification algorithms applied to Dhivehi text-based documents. Dhivehi, the official language of the Maldives, presents unique linguistic challenges for text classification due to its limited digital resources and distinct grammatical structure. The study aims to identify the most suitable algorithm for classifying Dhivehi documents and to provide insights into optimizing text classification approaches for less- resourced languages. The research systematically evaluates the performance of several machine learning algorithms, including Support Vector Machines (SVM), Naive Bayes, Decision Trees, XGboost , Random Forest and Neural Networks. These algorithms are applied to a diverse dataset of Dhivehi text, encompassing various genres and topics. The study employs a rigorous methodology involving data preprocessing, feature extraction, and model training and testing. Performance metrics such as accuracy, precision, recall, and F1-score are used to compare the efficacy of each algorithm. Additionally, the research explores the impact of different text representation techniques, including bag-of-words, TF-IDF, and word embeddings, on classification performance. The findings offer valuable insights into optimizing text classification methods for low-resource languages and aim to advance natural language processing tools specifically tailored for “Dhivehi.” The evaluation highlights that K-Neighbors achieved the highest performance, with an accuracy of 64.7% and F1 scores (macro: 0.640, weighted: 0.642), demonstrating a strong balance between precision and recall. Support Vector Machines (accuracy: 63.9%) and XGBoost (accuracy: 62.8%) also showed competitive results, with SVM slightly outperforming XGBoost in F1 metrics. Decision Tree exhibited the lowest performance across all metrics. By identifying the most effective classification algorithms and representation techniques, this research aims to enhance the accuracy and efficiency of Dhivehi text classification tasks. The results will have practical applications in areas such as sentiment analysis, document categorization, and information retrieval systems tailored for the Dhivehi language. Furthermore, the dataset is publicly available on Mendeley data under the name “Dhivehi Categories data set” to foster future research and innovation in this domain.