Research Publications Authored by SLIIT Staff
Permanent URI for this communityhttps://rda.sliit.lk/handle/123456789/4195
This collection includes all SLIIT staff publications presented at external conferences and published in external journals. The materials are organized by faculty to facilitate easy retrieval.
Browse
2 results
Search Results
Publication Open Access Digitalisation dynamics: Developing a global index for digital pioneers, adapters, and followers(Science Direct, 2025-04-25) Kumara, U; Wijerathna, D; Jayathilaka, RDigitalisation has become a transformative force revamping economies, societies, and governance systems. It has fostered innovation and enhanced global competitiveness in an interconnected world. This study aims to construct a composite index for digitalisation to evaluate global digitalisation levels and categorise nations as digital pioneers, adapters, and followers. The index is developed using a Principal Component based on Factor Analysis, utilising secondary data gathered from World Development Indicators from 2010 to 2022. The study highlights that the United States, Hong Kong, Singapore, China, and Korea dominate the top tier as digital pioneers through adopting emerging fourth-industrial revolution technologies such as artificial intelligence, blockchain, etc. Moreover, nations like Japan, Switzerland, Estonia, Czechia, and Iceland are categorised as digital adapters due to less digital investments in digital technologies and building digital ecosystems. At the same time, Madagascar, Paraguay, Ecuador, Guatemala, and Egypt remain at the bottom of the index as digital followers due to existing digital gap and digital literacy and skills among the population. This evidence provides digitalisation index an effective tool for policymakers and researchers to assess each nation's digitalisation levels and technological readiness, to formulate strategies and policies to enhance digital interaction, foster innovation, and promote economic growth.Publication Open Access Support Vector Machine Based an Efficient and Accurate Seasonal Weather Forecasting Approach with Minimal Data Quantities(SLIIT, 2022-02-11) Chandrasekara, S; Tennekoon, S; Abhayasinghe, N; Seneviratne, LClimate change makes a big impact in our daily activities. Therefore, forecasting climate changes prior to its actual occurrences is important. Even though highly accurate weather prediction systems throughout the world are available, they require mass amounts of data exceeding thousands of data points to obtain a significant accuracy. This study was aimed at proposing a Support Vector Machine based approach to carryout seasonal weather predictions up to thirty-minute intervals, the results of which would be considerably effective with respect to predictions carried out with models trained with annual datasets. The model was trained utilizing a dataset corresponding to the district of Kandy which consisted of 136 samples, 20 features, and 5 labels. By means of carrying out numerous data preprocessing steps, the model was trained, and the relevant hyperparameters were optimized considering the grid search algorithm to yield a maximum accuracy of 86%, once tested via the k-fold cross validation. The performance of the Support Vector Machine was also then compared for the same dataset with that of the K-Nearest Neighbor algorithm which consumed relatively fewer computing resources. An optimal accuracy of 61% was observed for this model for a K-value of 27. This approach supported the concept of a Support Vector Machine’s ability to perceive time series forecasts to a relatively higher degree and its ability to perform effectively in higher dimensional datasets with smaller number of samples. As per the future work, the Receiver Operating Characteristic analysis is proposed to be carried out to evaluate the performance of the model and the dataset size is proposed to be further enhanced to a maximum of a thousand samples to yield the best performance results.