Statistics & Computer Science
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Item Forecasting COVID-19 Cases Using Alpha-Sutte Indicator: A Comparison with Autoregressive Integrated Moving Average (ARIMA) Method(Department of Statistics & Computer Science, Faculty of Science, University of Kelaniya, Sri Lanka, 2020) Attanayake, A. M. C. H.; Perera, S. S. N.COVID-19 is a pandemic which has spread to more than 200 countries. Its high transmission rate makes it difficult to control. To date, no specific treatment has been found as a cure for the disease. Therefore, prediction of COVID-19 cases provides a useful insight to mitigate the disease. This study aims to model and predict COVID-19 cases. Eight countries: Italy, New Zealand, the USA, Brazil, India, Pakistan, Spain, and South Africa which are in different phases of COVID-19 distribution as well as in different socioeconomic and geographical characteristics were selected as test cases. The Alpha-Sutte Indicator approach was utilized as the modelling strategy. The capability of the approach in modelling COVID-19 cases over the ARIMA method was tested in the study. Data consist of accumulated COVID-19 cases present in the selected countries from the first day of the presence of cases to September 26, 2020. Ten percent of the data were used to validate the modelling approach. The analysis disclosed that the Alpha-Sutte modelling approach is appropriate in modelling cumulative COVID-19 cases over ARIMA by reporting 0.11%, 0.33%, 0.08%, 0.72%, 0.12%, 0.03%, 1.28%, and 0.08% of the mean absolute percentage error (MAPE) for the USA, Brazil, Italy, India, New Zealand, Pakistan, Spain, and South Africa, respectively. Differences between forecasted and real cases of COVID-19 in the validation set were tested using the paired t-test. The differences were not statistically significant, revealing the effectiveness of the modelling approach. Thus, predictions were generated using the Alpha-Sutte approach for each country. Therefore, the Alpha-Sutte method can be recommended for short-term forecasting of cumulative COVID-19 incidences. The authorities in the health care sector and other administrators may use the predictions to control and manage the COVID-19 casesItem FUZZY LINEAR REGRESSION: AN APPLICATION TO HEART DISEASE(Department of Statistics and Computer Science, University of Kelaniya Sri Lanka, 2021) Attanayake, A. M. C. H.Disorders in heart condition refer to heart disease. Several risk factors are associated with causing the heart disease. Physical inactivity and smoking are leading risk factors among other risk factors. The aim of this study is to investigate the relationship of heart disease with physical activity and smoking. Regression analysis is one of the key areas that can be utilized in finding the relationship of variables. By considering heart disease as the output variable (dependent variable) and correlated other factors as input variables, one can model the relationship through multiple linear regression. Fuzzy regression is an application of fuzzy platform for conventional regression analysis. Fuzzy regression analysis gives a fuzzy relationship between dependent and independent variables which represents vagueness in the data. The input data may be crisp values or fuzzy numbers whereas the conventional ordinary least squares regression can handle only crisp measures. The model output is in the form of fuzzy representative which has lower and upper approximation models to represent the fuzziness of the output. Fuzzy models are especially suitable in modelling and predicting heart disease as the diseaseItem Instrument to Measure Safety Climate: An Application to a Tyre Manufacturing Plant(Department of Statistics & Computer Science, Faculty of Science, University of Kelaniya, Dalugama, Kelaniya (SRI LANKA);, 2021) Madurangee, L. H. L. S.; Attanayake, A. M. C. H.; Jayasundara, D. D. M.ABSTRACT Occupational health and safety is a key feature in good governance. It depends on the safety culture of each and every person relevant to a work place. Culture means people think or act according to their opinions and beliefs by themselves without any external force. Positive safety culture gives benefits to both employee and employer. Therefore, measure the current status of safety culture is important to identify the areas which already improved and areas need to be improved. Safety climate is a descriptive measure that implies the status of the safety culture. Safety climate in a work place can be measured through the employees’ attitudes regarding the work place. A selfadministrative questionnaire can be used to collect the data as a productive method. The objective of the study was to develop a questionnaire as an instrument to measure safety climate in a work place through employees’ attitudes and validate the theoretical structure of safety climate with five dimensions. The questionnaire was designed based on literature survey under five dimensions. 30 Likert item questions were used to measure the 5 dimensions and Likert scaling technique was used to measure those five dimensions. Data were collected based on a tire manufacturing plant. Since these dimensions are highly correlated a pilot survey was conducted to identify ambiguities and difficult questions. A representative sample was selected using stratified sampling technique. The reliability of the questionnaire was measured using Chonbach’s Alpha statistic and Split –half Test. Confirmatory factor analysis was used to validate the theoretical structure. According to its results common factor was explained more than 80% of variance in each variables and model diagnostic tests showed that errors were satisfied the assumptions. The goodness of fit statistics showed that fitted model was acceptable. It can be concluded that the theoretically assumed structure to measure the safety climate with five dimensions is acceptable. This study provides a complete guidance on how to measure safety climate through a questionnaire and any interested parties may able to make their own measuring system based on the study.Item Proactive Dengue Management System Synergize by an Exponential Smoothing Model(Research & Development Centre for Mathematical Modelling,Department of Mathematics, University of Colombo,Sri Lanka, Department of Statistics & Computer Science,Faculty of Science, University of Kelaniya, Kelaniya, Sri Lanka., 2021) Wetthasinghe, W. A. U. K.; Attanayake, A. M. C. H.; Liyanage, U. P.; Perera, S. S. N.In a critical area like health sector centralized computer system helps to improve the efficiency of the health system. In particular, controlling an epidemic is usually difficult in developing countries. In this study we introduce a multi-platform, centralized proactive management system to manage dengue controlling activities in Sri Lanka. The system make common platform (ProDMS) for all sectors who contribute their services for mitigating dengue [1]. We mainly focused to the special feature of the system which enhance the centralized property. Cross platform environment was developed under this feature as a bridge to connect researches and general public. ProDMS is a internet base web application and researches can plug their dengue forecasting models to the system and publish their outputs as graphs through the web system. The ProDMS web application, which consisting of plug and play system architecture concepts, fully support for any statistical or mathematical model to publish its results online. In this work we use one of the univariate time series modelling approaches; namely exponential smoothing to plug with the system. This research helps to enhance efficiency of Dengue controlling process and support to generalize centralization.Item TREND ANALYSIS OF FINANCIAL PERFORMANCE OF THE BANK OF CEYLON, SRI LANKA(Department of Statistics and Computer Science University of Kelaniya Sri Lanka, 2021) Attanayake, A. M. C. H.; Liyanage, N.Trend analysis is important in banking to analyze and predict their financial statements. The aim of this study is to analyze the trend of key financial performance indicators; profit, advances and deposits of the Bank of Ceylon, Sri Lanka. Non-parametric loess analysis, Sen’s slope, linear/quadratic trend modeling, growth curve fitting and change point analysis were implemented to understand the trend pattern of the data. Results revealed that quadratic trend model was suited for advances and growth curves for both deposits and profits. The loess analysis detected upward trends for both advances and deposits but it was nearly horizontal for profit. The estimated slopes of the trends were significant in the Sen’s slope of three financial indicators. Potential significant changing points were detected in all