SLIIT Journal Publications

Permanent URI for this communityhttps://rda.sliit.lk/handle/123456789/294

All SLIIT indexed journals are included in this collection, with access to full texts subject to restrictions based on the access and licensing terms.

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Open Access
Assessing Statistical Methods for Generating Forecasts for COVID-19
(Faculty of Humanities and Sciences, SLIIT, 2024-06-09) Siriwardena, S. M. D. G. A; Dharmaratne, G.; Amaratunga, D.
The COVID-19 pandemic, a persistent global health emergency that has affected almost all facets of daily life, was initially discovered in Wuhan, China, in December 2019. Since that time, the virus has rapidly spread over the globe, causing serious social and economic upheavals necessitating the need for reliable forecasting methods. This study compares ten distinct models to predict the number of confirmed COVID-19 cases in Sri Lanka, aiming to assess the performance of statistical models using limited and volatile real-world data characterized by trends, random peaks, and autocorrelations. In addition to the classical ARIMA model, various smoothing and filtering techniques were explored to capture the unique characteristics of the data. The model consistencies in multiple-day predictions were demonstrated, and robust evaluation criteria, along with non-robust measures, were utilized to enhance the effectiveness of the evaluation process. The results highlight the effectiveness of traditional smoothing and filtering strategies such as Simple Exponential Smoothing, Holt’s Exponential Smoothing, and the Smoothing Splines technique coupled with the ARIMA model. This study also discovered that the ARIMA model, when applied directly to the original data without using any smoothing or filtering approaches, failed to forecast adequately, thereby demonstrating the insufficiency of the ARIMA model on its own to provide credible forecasts when given a volatile set of data.
Open Access
Identifying Ordinal Nature Inherited Proteins Associated with a Certain Disease
(Faculty of Humanities and Sciences, SLIIT, 2022-10-07) Samarawickrama, O.; Jayatillake, R.; Amaratunga, D.
Proteomic studies are studies of protein expression levels. They are growing swiftly with the steady improvement in technology and knowledge of cell biology. Since differentially expressed proteins have an influence on overall cell functionality, this improves discrimination between healthy and diseased states. Identifying prime proteins offers prospective insights for developing optimized and targeted treatments. This research involves analyzing data from an early-stage study of which the main purpose was to identify differentially expressed proteins. There are three progressively serious disease states (healthy to mild to severe) in this study. The analysis can be categorized into 2 stages as univariate and multi-protein analysis. The approach of the univariate analysis was to implement continuation ratio modeling considering one protein at a time to pick those that exhibit potential ordinality. Penalized continuation ratio modeling using lasso regularization incorporated with bootstrapping proteins was performed as the next stage to identify protein combinations that perform well together. Combining results of the univariate and multi-protein analyses identified 20 proteins that join forces to discriminate disease severity with an ordinal setting and 21 proteins that are effective each on its own.