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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Publication 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.Publication Open Access Analyzing the Impact of Key Factors on the Likelihood of Cash-on-Delivery Parcel Returns: A Seller’s Perspective in Sri Lankan E-Commerce(Sri Lanka Institute of Information Technology, 2025-09) Panditharathna, S.M. VCash-on-Delivery (COD) remains the dominant payment method in Sri Lankan e-commerce, but it generates disproportionately high return rates compared to prepaid orders. Returned COD parcels impose dual costs on sellers, who must absorb forward delivery fees even when the order is refused, while also incurring inventory, administrative, and reconciliation burdens. Despite the prevalence of COD, there is little structured research that distinguishes between seller-, courier-, and buyer-related drivers of returns in this context. The purpose of this study is to develop and validate a predictive framework that estimates the likelihood of COD parcel returns from the seller’s perspective, and to identify operational levers that can reduce such returns. Three independent constructs are examined: Seller’s Operational Quality, Courier Service Delivery Performance, and Buyer Acceptance Behavior at Delivery. Each construct is operationalized using seller-perspective indicators over a fixed 90-day recall window and measured on a five-point Likert scale. Data were collected through a structured survey of active Sri Lankan e-commerce sellers using purposive sampling, with analysis conducted via Partial Least Squares Structural Equation Modelling (PLS-SEM). The results demonstrate that all three constructs significantly influence the likelihood of COD returns, with courier performance and buyer acceptance behaviors exerting the strongest predictive effects. Findings highlight the critical importance of first-attempt delivery success, pre-shipment accuracy, and effective communication in minimizing return rates. This research contributes a context-specific framework for understanding COD returns in Sri Lanka, offering actionable insights for sellers, courier firms, and policymakers seeking to enhance e-commerce sustainability.Publication Open Access Analyzing the Influence of Automated Water Distribution Systems on Precision irrigation for Orchids A Case Study Using Dendrobium Phalaenopsis Orchid Group(SLIIT, 2024-12) Maleesha, R. P. G. S.This research seeks to establish the efficiency of an automated water treatment of the Dendrobium Phalaenopsis orchids using remote monitoring and controlling through a dash- board in Audino Cloud. Soil moisture, temperature and humidity levels in the terrain are Other environment factors monitored and the application controls water discharge in response to the results. Water is only added once the soil moisture level gets to a low level of 30 percent as to avoid unnecessarily using water. The system Water Use Efficiency was 60 to 95 percent, thus the system was good at maintaining the moisture level without wasting much water. Temperature ranged from 22-28 and humidity ranged from 40-95 percent affected water demand but the system took into consideration the soil moisture values. It operated correspondingly under principles of precision irrigation that is they provided water where it was needed and when it was needed. , which might be added in the future to the algorithm parameters, include temperature and humidity, as well as predictions of possible changes to environmental climates for even greater water savings. Through the results, it is noticed the prospect for automation supply systems to reestablish the cultivation practices of orchids, having special concern with the rational use of resources and sustainability in the agricultural activityPublication Open Access Analyzing the Performance of Different Text Classification Algorithms for “Dhivehi” Documents(SLIIT, 2024-12) Mohamed, F.RThis 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.