ICAPS 2022
Permanent URI for this collectionhttp://repository.kln.ac.lk/handle/123456789/25482
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Item Predicting a top rank batsman in an ODI match, using the first few balls faced: A case study(Faculty of Science, University of Kelaniya Sri Lanka, 2022) Madhuranga, W. P. K.; Kavinga, H. W. B.; Chandrasekara, N. V.Predicting the success of a top-rank batsman will play a crucial role in the decision-making process in the game of cricket, on the field as well as off the field. This research is carried out with the purpose of achieving the aforementioned task. The proposed procedure explicitly followed to rank one, two and three players in the world by August 2021. Therefore, the results cannot be generalized to a wider set of players. Among several models tried out, Decision Tree (DT) model with a training ratio of 0.9 showed the highest accuracy of 72% in predicting whether the batsman will be successful, i.e., scoring fifty or more runs on a given day. Probabilistic Neural Network (PNN) and Support Vector Machine (SVM) models with a similar test ratio resulted in an accuracy of around 65% for the three players, Rohit Sharma, Babar Azam and Virat Kholi. PNN recorded a maximum accuracy of 64.2% when predicting the performance of Rohit Sharma and the SVM model recorded a maximum accuracy of 59% when predicting the success of Babar Azam. The aforementioned accuracy of the DT model was achieved using the first five balls for Virat Kholi and Rohit Sharma and the first seven balls for Babar Azam. The findings of the study can be used to make accurate decisions in the game of cricket.Item Forecasting foreign exchange reserves in Sri Lanka(Faculty of Science, University of Kelaniya Sri Lanka, 2022) Jayawardhana, K. J. U. M.; Wijesuriya, H. P. A. D.; Kaushalya, R. A. D.; Chandrasekara, N. V.Foreign exchange reserves are mainly used by governments to stabilize the exchange rate and balance international payments. They play a major role in the current financial crisis in Sri Lanka too. The purpose of this study was to build a suitable forecasting model and to detect factors affecting foreign exchange reserves in the context of Sri Lanka. The findings of this study can be used to provide suggestions for some policy measures taken by the government for the overall improvement of foreign exchange reserves. Monthly data on the foreign exchange reserves, United States Dollar (USD) exchange rate, foreign direct investments (FDI), gold reserves, imports, inflation rate, remittance, and total exports from January 2010 to September 2021 were used for the model fitting procedure. To transform quarterly data on gold reserves into monthly data, the cubic spline interpolation approach was utilized. The preliminary analysis identified a significant association between the foreign reserves and predictor variables: exchange rate, FDI, gold reserves, imports, and remittance. Augmented Dicky Fuller (ADF), Kwiatkowski Phillips Schmidt Shin (KPSS), and Phillips-Perron (PP) unit root tests were used to examine the stationarity. A time series regression model was fitted, adhering to the assumptions of residual diagnostics: multicollinearity, homoscedasticity, serial correlation, and autocorrelation, except for the normality. Further, the presence of co-integration was tested with the Johansen cointegration test revealed long-run equilibrium. Hence a vector error correction (VEC) model was fitted which adhered to assumptions of model residuals, including serial correlation, heteroscedasticity, and except for normality. The forecasted VEC model has a Mean Absolute Percentage Error (MAPE) of 5.30%, indicating that the VEC model is better for forecasting compared to the fitted time series regression model with a MAPE of 9.52%. The results of the analysis further revealed that foreign exchange reserves have a positive significant impact on the remittance to Sri Lanka and foreign reserves of seven months ago.Item A study on factors associated with child sexual abuse and recognizing the severity: Special reference to Galle district(Faculty of Science, University of Kelaniya Sri Lanka, 2022) Dilshan, L. H. K.; Withanage, N.; Chandrasekara, N. V.Child Sexual Abuse (CSA) has been a universal and social crisis with serious life-long consequences. One in four girls and one in six boys worldwide have experienced some form of sexual abuse in their childhood. According to Police statistics, CSA cases have been increasing rapidly in recent years in Sri Lanka. Galle is among the four districts where the reported child abuse cases are high, and the reported CSA complaints are rising drastically. Further, no previous study has been carried out in the Southern part of the island regarding the crisis of CSA. Therefore, the main objective of this study is to determine the key risk factors affecting the CSA cases in Galle Police Division and to develop suitable statistical and machine learning models to recognize the severity of CSA. All the 225 CSA cases reported to the Police Child and Women Bureau of Galle Police Division during the 2017 – 2020 period were considered for this study. The severity of CSA can be categorized into not fatal, child sexual exploitation, and fatal categories. Out of the twenty-one risk factors, which were found from the literature and knowledge of domain experts, sixteen factors showed a significant relationship with the severity of CSA at 10% significance level according to the chi-square test of association. These significant risk factors were area, child’s age, gender, whether mother lives with child, reason, the willingness of child, frequency of abuses, place of incident, relationship to the perpetrator, perpetrator’s age, education level of the perpetrator, perpetrator’s job, marriage status, whether the perpetrator has children, the number of children he has, and drug addiction of perpetrator. The Ordinal Logistic Regression (OLR) model was trained using a backward selection method with different data selection criteria. Next, the machine learning techniques: Decision Tree (DT), Support Vector Machine (SVM), and Probabilistic Neural Network (PNN) were employed to predict the severity of CSA. The random over-sampling technique was used to overcome the class imbalance problem that persists in the dataset. The bagging technique was implemented to preserve the robustness of the models and to improve their performance. The adequacy of the OLR model with the oversampling technique was examined and it was selected as the best model after considering the proportional odds assumption and analysis of deviance. The model classified the severity of CSA with 68.85% accuracy and area, gender, reason, frequency of abuses, place, perpetrator’s job, and whether the perpetrator has children can be identified as the significant predictors for CSA. The DT, SVM and PNN models classified the severity of CSA with an accuracy of 82.15%, 77.68% and 81.25%, respectively for the bagging technique. The PNN model performed better than the other fitted models with higher accuracy. The results obtained from this study can be used to get precautions and to arrange awareness sessions for parents and adults to reduce CSA in Galle Police Division. Similarly, the scope of the study can be extended to the whole island to reduce CSA and to make a better place for children.Item Performance of seasonal and double seasonal autoregressive integrated moving average models with ARCH/GARCH in forecasting exchange rates in Sri Lanka(Faculty of Science, University of Kelaniya Sri Lanka, 2022) Basnayake, B. R. P. M.; Chandrasekara, N. V.The exchange rate is one of the most essential economic indices and forecasting its chaotic and uncertain behaviour is challenging for business practitioners and academic researchers. This study mainly evaluated the performance of Seasonal Autoregressive Integrated Moving Average (SARIMA) and Double SARIMA (DSARIMA) with Autoregressive Conditional Heteroskedasticity (ARCH)/ Generalized ARCH (GARCH) models in forecasting daily exchange rates in Sri Lanka. This is the first study that used DSARIMA models with ARCH/GARCH of different specifications of error distributions, as previous studies focused on either on annual or weekly seasonality separately in forecasting exchange rates. The study considered USD, EURO, JPY, GBP, AUD, CAD, SGD and CHF against LKR, daily exchange rates from 1st January 2008 to 28th February 2022. Data were split non-randomly for training from 1st January 2008 to 07th January 2022 and the remainder for testing. The stationary of the exchange rates was checked, and the weekly and annual seasonality patterns were examined from the tests of Webel-Ollech (WO), Friedman rank (FR), and Kruskal-Wallis (KW). Model diagnostics checking was carried out with the tests of Ljung-Box, Jarque–Bera, and ARCH to check the presence of autocorrelation, normality, and heteroskedasticity in the residuals, respectively. The ARCH/GARCH specifications of normal, skew-normal, student-t, and skew-t were applied, as the correct innovation of the appropriate error distribution increases the accuracy of the fitted volatile model. Moreover, DSARIMA models were compared with the Seasonal Autoregressive Integrated Moving Average (SARIMA) models considering several performance criteria which were calculated from the original test values and forecasted values. Transformations of log and differencing were applied respectively to convert all the non-stationary exchange rates to stationary. Overall, weekly and annual seasonality patterns were observed for all the exchange rates from the results of WO, FR, and KW tests, except for FR test results, indicated that there is no annual seasonality in every exchange rate. Hence, SARIMA and DSARIMA models were fitted incorporating weekly and annual seasonality separately and together, respectively. Here, the seasonality feature was included using Fourier terms as external regressors to the ARIMA process. In conclusion, the compared results between fitted models favoured SARIMA for CHF against LKR, SARIMA with ARCH/GARCH for USD, EURO, JPY, GBP, and AUD against LKR, and DSARIMA with ARCH/GARCH models for CAD and SGD against LKR with the lower values. Overall, predicted values captured the behaviour of the exchange rates. However, a considerable number of volatile movements of the currency exchange rates were not very well captured, and they were observed by the graphs of actual vs fitted. Hence, as future work, this study proposes to build a time-series extension model incorporating the real distribution of the exchange rates. Nevertheless, the knowledge from the results of this study is important in managerial and financial decision makings and many others. Further, this study will add more value to the existing literature.