Browsing by Author "Chandrasekara, N. V."

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An application of time series techniques to forecast the Open market weekly average retail price of lime in Sri Lanka
(Faculty of Science, University of Kelaniya Sri Lanka, 2023) Wickramarathne, R. A. S.; Wickramanayaka, M. P. A. T.; Mahanama, K. R. T. S.; Chandrasekara, N. V.
Limes are known for their acidic and tangy flavour and are commonly used in cooking, as a garnish, or to add flavour to drinks. The lime market in Sri Lanka is highly volatile, with prices fluctuating significantly on a weekly basis. In this research study, the main objective is to forecast the weekly lime price in Sri Lanka. Even though some research has been conducted on forecasting fruit prices in Sri Lanka, there is currently a lack of research on forecasting lime prices. The weekly price of lime from 1st week of January 2010 to 3rd week of February 2023 was considered for this study (632 observations). The first 600 observations were used as the training set and reserved data were used as the testing set. The time series plot of the weekly lime price of Sri Lanka indicates a slight upward trend and a non-constant variance with a seasonal pattern. The presence of a seasonal pattern motivated the development of a Seasonal Autoregressive Integrated Moving Average (SARIMA) model. When comparing Akaike’s Information Criterion (AIC), ARIMA(1,1,2)(0,1,1)[24] generated the minimum AIC value (-1.125469). Assumptions of autocorrelation and heteroscedasticity were not violated and the normality was violated. Although, the performance measures of ARIMA(1,1,2)(0,1,1)[24] were very low, ARIMA(1,1,2)(0,1,1)[24] was identified as the better model with mean absolute error of 40.799, mean absolute percentage error of 7.543, and root mean squared error of 49.793. The results obtained from this analysis would be helpful to mitigate price risks and uncertainties in the lime industry.
Changes in the Antioxidant Micronutrients and Volatile Metabolomics Profile of Selected Edible Vegetables Cooked with Coconut Milk and Heat Extracted Coconut Oil
(Journal of Culinary Science & Technology, 2022) Dewangania, H. G. Nadini; Jayawardena, Bimali; Chandrasekara, N. V.; Chandrasiri Waliwita, W. A. L.
Coconut milk, coconut oil, spices, condiments, and herbs are added to enhance flavor and aroma. The objective of the present study was to evaluate the effect of cooking methods on the total polyphenol content (TPC), antioxidant capacity and the metabolomics profile of selected vegetables. Incorporation of coconut oil and coconut milk with the spices and condiments had minimal effect on the evaluated parameters highlighting the positive role of traditional culinary methods in retaining nutritional qualities of vegetables (p < .05). The increase in TPC, 2,2-diphenyl-1-picryl-hydrazyl-hydrate (DPP scavenging activity, and Ferric Reducing Antioxidant Power assay (FRAP) values were probably due to the synergistic effect of added ingredients which may have increased the bio accessibility of bioactives. Fifty eight metabolites were identified using a gas chromatograph-coupled to mass spectrometer. Vanillic acid, 3,5-di-tert-butyl-4-hydroxyphenylpropionic acid, Phytol, Phenol, 2,2’-methylenebis[6-(1,1-dimethylethyl)-4-methyl-, and phenol, 2,2’-[(1-methyl-1,2-ethanediyl)bis(nitrilomethylidyne)] bis- were identified in analyzed samples. In conclusion, by adopting optimal cooking method the health promoting bioactives can be preserved.
Detection of β - Thalassemia carriers using data mining techniques
(The Institute of Applied Statistics, Sri Lanka, 2024) Subasinghe, G. K.; Chandrasekara, N. V.; Premawardhena, A. P.
Thalassemia, a genetic blood disorder, presents a significant challenge in Sri Lanka due to its high prevalence. Traditional methods of identifying tha-lassemia carriers, such as genetic and blood testing, are both costly and time-consuming, and potentially not available for certain demographic groups. However, there haven’t been many studies done on the efficacy of data mining models for thalassemia carrier detection, therefore the field is still in its in fancy. As such, evaluating their accuracy and utility in clinical practice is crucial. This study aims to develop a time-efficient model to detect the β-thalassemia carriers, which can reduce the time to take a decision and develop the built model as a decision support tool. Eight blood parameters - includ-ing RBC, HGB, HCT, MCV, MCH, MCHC, RDW, and HbA2 were selected based on literature. Two model-fitting approaches were introduced, each un-der different data selection methods: Method 1: Model fitting before handling the class imbalance problem and Method 02: Model fitting with random over-sampling technique. Support Vector Machine (SVM) and Probabilistic Neural Network (PNN) models were utilized for β-thalassemia carrier detection. Method 2 exhibited superior performance, especially with the PNN Model 2, achieving an impressive 98.75% overall classification accuracy. Moreover, the implemented PNN Model 2 could be utilized as an efficient decision-support tool, offering both time and cost savings in identifying β-thalassemia carriers. Nonetheless, for further investigation, consulting a medical expert is recommended.
The effect of food commodity price fluctuation on inflation in Sri Lanka
(Faculty of Science, University of Kelaniya Sri Lanka, 2023) Nadeekantha, H. A. D. D.; Lakshitha, W. A. D. M.; Lakshitha, W. A. D. M.; Chandrasekara, N. V.
In Sri Lanka, the intersection of inflation and food price fluctuations holds profound significance, affecting not only the nation's economic stability but also the daily lives of its citizens. While existing research has extensively focused on the impact of rice prices on inflation, no published studies have been found that specifically investigate the influence of fluctuations in vegetable and fish commodity prices on inflation. Hence, there is a research gap to have a comprehensive understanding about price fluctuation on inflation. Thus, the objectives of this research are to primarily consider the effect of price fluctuations in mostly consuming vegetable and fish commodities on inflation using suitable techniques. The study focuses on key commodities, including beetroot, cabbage, potato, and various fish types (Seer, Mullet, Kelawalla, and Hurulla). Monthly data from January 2014 to June 2022, sourced from the Central Bank of Sri Lanka and the Department of Census and Statistics, were utilized for the analysis, with no missing values. To measure inflation, the National Consumer Price Index (NCPI) was used. Since all the time series of monthly observations of fish and vegetable prices and NCPI were non-stationary, the first differencing of logarithm for all the series was used where it proved the stationary by both graphical and theoretical techniques. After investigating the lag structures for fish and vegetable models, the optimum and the better lags were found. The cointegration test for both models proved that there were correlations between several time series in the long run based on the optimal lag length. Hence, two Vector Error Correction (VEC) models were fitted for two groups of food commodity prices namely, Fish and Vegetables where VEC models are well-suited for examining the relationships between food commodity prices and inflation over time. Strong cointegration relationships were identified inside these two groups. According to the VEC Granger causality test, it was found that beetroot, cabbages and potatoes do Granger-cause in NCPI but cabbages and other selected fishes do not Granger-cause in NCPI. To study the impact on inflation, the impulse response function was used. It was found that price shocks of the Hurulla fish type have a significant positive impact on inflation than other fish types of Seer, Mullet, and Kelawalla. Beetroot price shocks have a significantly more positive impact on inflation than other vegetable types of potatoes, tomatoes, and cabbage. The model, which was fitted for fish prices, the percentage of forecasting errors for NCPI increases over time for each type of fish, according to the forecast error variance decompositions. In the model, which was fitted for vegetable prices, the percentage also increases with time, but it remains smaller compared to the fish. Sri Lanka needs effective strategies and policies to mitigate the challenges of unstable inflation, hence the understanding of price fluctuation on inflation empowers policymakers to craft targeted strategies to mitigate the impact of inflation on daily life.
Exploring data mining avenues in β-Thalassemia carrier identification
(Faculty of Science, University of Kelaniya Sri Lanka, 2023) Subasinghe, G. K.; Chandrasekara, N. V.; Premawardhena, A. P.
Thalassemia is a genetic blood disorder that affects the production of haemoglobin and is a global health problem. In comparison to many other nations in the region, Sri Lanka also has a high prevalence of thalassemia. The traditional methods for identifying thalassemia carriers, such as genetics and blood tests, are expensive and time-consuming and may not be available to all demographic groups. Nevertheless, the use of data mining models for thalassemia carrier detection is still in its infancy, and there are few studies on its efficacy. Therefore, it is vital to investigate the efficacy and accuracy of data mining approaches for detecting thalassemia carriers, as well as the viability of employing these methods in clinical practice. Thus, the objective of this study is to develop a time-efficient model to detect the β-thalassemia carriers, which can reduce the time to take a decision and develop the built model as a decision support tool. Also, the earlier detection will help individuals to refer to necessary treatments further. This study is carried out with the data obtained from Hemal's Adolescent and Adult Thalassemia Care Centre, Mahara, one of the treatments centres for thalassemia. As the study population, 343 individuals’ data values were considered from August 2019 to December 2019. When processing the dataset, 112 (36%) individuals were declared as β-thalassemia carriers, whereas 200 (64%) were identified as β- thalassemia non-carriers. Eight blood parameters, such as RBC, HGB, HCT, MCV, MCH, MCHC, RDW and HbA2 were identified by revealing the literature and the Chi-square and Mann- Whitney U tests were used to identify the association between the variables at 5% level of significance. A random over-sampling technique was used to overcome the class-imbalanced problem in the dataset, and based on that, model fitting was performed under the two data selection methods, i.e., Method 1: Model fitting before handling the class imbalance problem and Method 02: Model fitting with random over-sampling technique. Then 80% of the data was used for training the models, and 20% of the data was used for the evaluation. Support Vector Machine (SVM) and Probabilistic Neural Network (PNN) models were used to detect the β-thalassemia carriers. In comparison among methods, the better-performing models were given under Method 2, and the PNN model fitted under Method 2 (PNN Model 2) exhibits 98.75% overall classification accuracy. Here, the PNN model’s network architecture consisted of eight nodes in the input layer, 320 nodes in the pattern layer, two nodes in the summation layer, and two nodes in the output layer. Further, the fitted PNN Model 2 can be utilised as a cost-effective and timesaving option to detect β-thalassemia carriers in a few seconds with acceptable accuracy and can be implemented as a decision support tool. However, it is recommended to get advice from a medical doctor for further investigation.
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.
Identification of factors leading to elephant deaths in human-elephant conflicts
(Faculty of Science, University of Kelaniya Sri Lanka, 2023) Lakshitha, W. A. D. M.; Chandrasekara, N. V.; Kavinga, H. W. B.; Withanage, N.
Human-elephant conflicts (HEC) have emerged as one of the main challenges that Sri Lanka faces throughout several decades. According to the official data of the Department of Wildlife Conservation (DWC), the number of elephant deaths is higher than the number of human deaths due to HEC per year. This research focused on the North Central Province, where the highest number of elephant deaths have been recorded. Hence, the objectives of this research are to identify the main factors that have affected the deaths of elephants and to identify suitable models to predict the causes of elephant deaths due to human-elephant conflict. Although there has been much research related to HEC worldwide, no published research studies were found in the literature that utilized advanced statistical techniques such as Multinomial Logistic Regression (MLR), LASSO regression, Decision Tree (DT), Support Vector Machine (SVM), and Probabilistic Neural Network (PNN) for their studies. However, this research will address that research gap by constructing models for classifying the causes of elephant deaths resulting from HEC. Data was collected from various departments, including DWC, the Department of Meteorology, and the crop calendar of the Department of Agriculture. Furthermore, Pearson's Chi-square and Fisher's exact tests were used to identify the association between the cause of death and influencing factors. Five variables, including the elephant age group, grass levels, gender, rainfall season, and place of death, were found to significantly influence the causes of death of an elephant. MLR and Data Mining (DM) techniques were initially utilized, but due to multicollinearity arising in MLR, the LASSO technique was employed as a remedial method. To overcome the class imbalanced problem, 90% of the data were randomly selected for model building while maintaining the class ratio of the response variable, and the remaining 10% of the data were used for testing. Performance measures, overall classification accuracy (OCA), and Misclassification Percentage of Critical Cases (MPCC) were used to evaluate and compare the classification potential of models. Models such as final MLR, LASSO, DT, SVM with Polynomial and Gaussian Kernels, and PNN with spread 0.801 illustrated 42.30%, 50%, 53.84%, 69.23%, 73.07%, and 73.07% of OCA. In addition, the above models showed 34.61%, 30.76%, 7.69%, 11.53%, 19.23%, and 26.92% MPCC respectively. Finally, the SVM model with Gaussian Kernel exhibited high OCA (73.07%) with 19.23% of MPCC as the better model since the PNN showed a high MPCC of about 26.92%. These findings will be helpful for authorities in their future and existing projects.
Introducing a novel hybrid algorithm to resolve class imbalance problem for binary classification in two-dimensional space
(Faculty of Science, University of Kelaniya Sri Lanka, 2024) Madhuwanthi, U. S. P.; Chandrasekara, N. V.
Classification is a task that involves categorizing data into predefined classes or categories based on their features. The class imbalance problem (CIP) in which the number of instances within the classes of the response variable is unevenly distributed, is crucial in many real-world datasets when classifying the instances into class labels or categories. Typically, the number of minority class instances (positive class) which is often, the class of interest is significantly less than the number of majority class instances (negative class). The presence of the imbalance within the classes leads to biased predictions towards the majority class. Different techniques such as oversampling, under-sampling, and hybrid techniques can be used to handle CIP. Oversampling increases the number of instances in the minority class by either duplicating existing instances or generating synthetic examples while under-sampling lowers the number of instances in the majority class. However, applying oversampling alone causes data replication while under-sampling causes loss of valuable information. The objective of the study is to propose a novel hybrid resampling technique to handle CIP, overcoming those disadvantages caused by oversampling and under-sampling alone. Binary classification problems are related to cases where the target variable has only two classes. This study has mainly focused on such datasets where only two classes are present in the target variable. The proposed algorithm aims to an application of a hybrid resampling technique, that is oversampling and under-sampling the imbalanced data together and leveling the number of instances of both majority and minority classes to half the size of the original dataset using a quartile-based approach. The proposed hybrid resampling technique is evaluated using the Pima Indian Diabetes medical dataset with imbalanced class distributions. Logistic regression was employed to identify the two most influential variables for testing in two-dimensional space. Performance metrics including accuracy, recall, precision, and F-measure are employed to assess the effectiveness of the approach. To carry out the classification process, Support Vector Machine (SVM) with one of the simplest kernel functions, the polynomial kernel function has been applied as the classifier. A training-testing split of 85% to 15% was employed for the evaluation. To compare the performance with existing oversampling techniques; ROS, SMOTE, and ADASYN and undersampling techniques; RUS, NCL, and Tomek Links, and a hybrid technique; SMOTETomek were used. In the performance evaluation process, an average recall of 100 iterations was considered. The highest average recall, 86.96%, has been obtained by the proposed algorithm while that for ROS is 42%, SMOTE is 42.57%, ADASYN is 47.1%, RUS is 40.7%, NCL is 73.7%, TomekLinks is 27.46% and SMOTETomek is 49.95%. Experimental results demonstrate significant improvements in classification performance using this proposed algorithm compared to existing oversampling, under-sampling, and hybrid techniques for handling class imbalance. Future studies will extend this work to multi-class classification problems and increase the number of explanatory variables.
Modeling and forecasting global oil price on Sri Lankan inflation rate
(Faculty of Science, University of Kelaniya Sri Lanka, 2024) Priyadarshana, D. A. D. S.; Wijesekara, J. M. C. D.; Chandrasekara, N. V.
Inflation serves as a key indicator of overall economic well-being. Indeed, inflation is the continuing increase in the general level of prices for goods and services over time. Moderate inflation may connote high economic growth, while high inflation is usually damaging to both long-term economic growth and financial stability. Since 1977, Sri Lanka has been undergoing continuous inflationary pressure due to power outages, energy shortages, reduced production in the agricultural sector, and others. Furthermore, the prices of the World oil market have been fluctuating, owing to changes in the taxes of crude oil, costs of refining and transport, and other related factors. All these dynamics bear directly on Sri Lanka's inflation. Therefore, policymakers, corporate leaders, and the general public needed to understand the dynamics of inflation. Therefore, this study brings out a research gap in the study of the impact of Gasoline Unl 92 (PATROL) and Gasoil 500ppm (DIESEL) in the Singapore World Oil Market on the inflation rate in Sri Lanka (NCPI). This is the novelty of this research since previous studies have not covered it. The main objective of this study is to develop a predictive model illustrating the influence of global oil prices on the inflation rate in Sri Lanka. Data for this research was gathered monthly from the Central Bank of Sri Lanka and the Singapore Platts, covering the period from January 2015 to December 2021. Moderate relationships were observed among the inflation rate and prices of Gasoline Unl 92 and Gasoil 500ppm from the Pearson correlation matrix. All the time series variables have been made stationary through log transformation and first differencing, which were checked through the ADF, PP, and KPSS tests. Assumptions in the residual diagnostics procedure of the time series regression model have not violated the characteristics of the absence of multicollinearity, autocorrelation, serial Correlation, and heteroscedasticity among the residuals. In addition, the residuals are normally distributed. The final predictive model ∆[𝑙𝑜𝑔(𝑁𝐶𝑃𝐼)]𝑡 = 0.0040 + 0.3453 ∆[𝑙𝑜𝑔(𝑁𝐶𝑃𝐼)]𝑡-1 + 0.4247 ∆[𝑙𝑜𝑔(𝑁𝐶𝑃𝐼)]𝑡-3 + 0.0511 ∆[𝑙𝑜𝑔(𝐷𝐼𝐸𝑆𝐸𝐿)]𝑡 + 0.0355 ∆[𝑙𝑜𝑔(𝑃𝐴𝑇𝑅𝑂𝐿)]𝑡 + 0.0283 ∆[𝑙𝑜𝑔(𝑃𝐴𝑇𝑅𝑂𝐿)]𝑡-1 included lagged terms of past inflation and gasoline and gas oil prices, which ended up quite accurate; given the RMSE value was 1.729, the MAE value came to 1.289, and the MAPE value was 0.901. Further validation of the strength of the model was in the 𝑅2 value of 53%. This model can underline the strong influences of world oil prices in determining Sri Lanka's inflation dynamics at the same time. Also, this only considered the global oil price of petrol and diesel because of the inflation rate of Sri Lanka, and all the other factors were limited. It can give important facts for policymakers to devise appropriate strategies for the management of inflation in the economy. Future researchers can improve this model using different methodological approaches and consider more designs for the global oil market decision.
Modeling open market monthly average retail price of potatoes in Colombo
(Faculty of Science, University of Kelaniya, Sri Lanka, 2021) Weerakoon, H. L. A.; Mendis, G. L. L. S.; Umagiliya, , K. S.; Imbulana, I. D. D. C.; Chandrasekara, N. V.
Potato is a vastly consumed commodity, which is both exported and imported in Sri Lanka in the form of a fresh vegetable or as an ingredient in several processed food products. The availability of potatoes in the market is mainly affected by the quantity supplied to the local market, trade agreements and government policies. A considerable impact is also made by uncontrollable factors such as weather conditions, diseases, and catastrophes. The resulting demand and supply imbalances cause fluctuations in the price levels of potatoes in the market. Hence, capturing such fluctuations onto a suitable time series model for forecasting the potato price levels was considered as the main objective of the study where the findings of the study will benefit to the farmers, retail & wholesale markets, the government and many other stakeholders. The scope of the study was built upon the open market monthly average retail price (per kilogram) of potatoes, centring Colombo. The Central Bank archives, published under the Economic & Social Statistics discipline from January 2006 to December 2018 were used to obtain data. The trend component observed in the original data set was removed using the differencing technique. The cutoff lags at intervals of 12 in the Autocorrelation Function (ACF) plot of the stationary series, limited the candidate models to Seasonal Autoregressive Integrated Moving Average (SARIMA) models as the series exhibits seasonality. Since the SARIMA model with the minimum Akaike’s Information Criterion (AIC), hinted the presence of autocorrelation in the residuals, it was replaced by (ARIMA) (4, 1, 3) (0, 1, 3) [12] model which was recognized as a better model of fit. The model validation was done using Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE) and Mean Absolute Error (MAE) and the corresponding values were 14.74, 8.13% and 12.24 respectively. Since forecasting the retail price of potatoes concerns various interested parties, this analysis would be helpful in decision making and in determining optimal settings for the demand and supply criteria.
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.
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.
A statistical approach to assess faceted blue sapphire gemstones
(Faculty of Science, University of Kelaniya Sri Lanka, 2023) Mahanama, K. R. T. S.; Chandrasekara, N. V.; Ranatunga, G. D.
The gem industry is a promising contributor to Sri Lankan economic development. The gemstone market prices are set by professional gem evaluators based on their tacit knowledge. Although the valuation of gemstones is complex due to the high variability in their characteristics, establishing a standard model that minimizes overpricing or under-pricing of gemstones helps stakeholders and preserves the reputation of the gem industry. This research aims to develop a statistical model to assess faceted blue sapphires based on affecting factors of gemstones such as colour, inclusions, cracks, cut, weight, state of treatment, and calibration. All exported gemstone records from February to September 2022 were collected from the National Gem and Jewellery Authority. A total of 881 records composed of single (409) and batch assessments (472) of faceted blue sapphire were utilized for modelling. Multiple linear regression (MLR), quantile regression (QR), support vector regression (SVR), feedforward neural network (FFNN), and generalized regression neural network (GRNN) were employed in developing pricing models. However, MLR and QR models showed a reduction of some important variables from the model. Further, the MLR model was not adequate due to the violation of the assumptions for both heteroscedasticity and autocorrelation. The performances of SVR, FFNN, and GRNN models were compared using mean squared error (MSE), root mean squared error and mean absolute percentage error. MSE for SVR, FFNN, and GRNN were 0.0697, 0.0733, and 0.0730 respectively. Even though all three models exhibit similar performances, GRNN provided a closer approximation for most of the cases. Further SVR (MSE=0.0419) and GRNN (MSE=0.0700) models were separately developed to address the most common single-piece assessment. Results revealed that the SVR model with Gaussian kernel outperforms in single assessments while GRNN provides closer predictions to all assessments. Future studies can be conducted to develop a model using the generalized method of moments which is widely used in violation of both heteroscedasticity and autocorrelation. Moreover, this study can be extended to developing statistical models to assess other varieties of gemstones. Finally, developing and implementing an application decision support tool to assess gemstones would be highly beneficial.
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.
Study on tension detection and acceptance of glove liners
(Faculty of Science, University of Kelaniya Sri Lanka, 2023) Pathirana, G. P. N. M.; Jayasundara, D. D. M.; Chandrasekara, N. V.
The glove industry plays a leading role in the Sri Lankan economy. The quality of the final product is crucial when it comes to mass production. A significant shrink or extension of a glove can cause great losses to the company by increasing the number of defective products. The dimensions of knitted liners vary due to various factors in the knitting process. In finding a solution to this problem, the Six Sigma “DMAIC” approach is being used. This research investigated how the tension of the main yarn and yarn conditioning time affect liner dimension changes in a controlled temperature and humidity level. As for finding the dimension changes, the total length, cuff length, and the cuff width of the liners were considered. Relevant data was gathered from a leading glove manufacturing company in Sri Lanka. The Randomized Complete Block Design with 9-12 replicates, considering yarn conditioning time as blocks and tension ranges as treatments, was set up. Analysis of Variance suggested that there is a significant difference among the population means in all three dimensions. Hence, a multiple comparison test (Tucky’s test) is used to compare means. The results confirmed that the changes in yarn conditioning time had a significant impact on total length and cuff width. Nonetheless, factorial designs suggested that the interactions of tension and yarn conditioning time had a significant impact on the dimensions of knitted glove liners. As the tension increased, the length of the liners decreased. As tension levels increased, cuff lengths began to shorten. In contrast, the increase in tension of the main yarn caused the cuff widths to lengthen. Low-conditioned yarns contained significantly different dimensions than the rest of the liners knitted with yarns that had been conditioned for at least 24 hours. Generally, industries determine the optimal tension values of the main yarn manually using test gloves, which is time-consuming and costly. As a solution, this research used statistical modelling concepts, which aided in the development of a model to predict the level of tension required when the relevant liner length parameters and conditioning times were provided. Multiple linear regression and data mining techniques were used, and the models were compared. By having the lowest Root Mean Square Error, the Generalized Regression Neural Network (GRNN) outperformed the regression model and decision tree model. The error of the implemented GRNN model is 0.1521, and the independent variables explained more than 90% of the mean tension.
Time series modeling and forecasting of total primary energy consumption in Sri Lanka
(Faculty of Science, University of Kelaniya, Sri Lanka., 2021) Caldera, P. A. D. S. P.; Malshika, N. N. D.; Nikapitiya, S. H. A. S.; Udugedara, U. S. C. B.; Chandrasekara, N. V.
Primary energy is the energy that is harvested directly from natural resources. Forecasting total primary energy consumption in Sri Lanka is significant as primary energy consumption worldwide is expected to continue increasing. This study aimed to model and forecast total primary energy consumption in Sri Lanka, which has not yet been analysed using Time Series Analysis. For this purpose, the annual data of total primary energy consumption in Sri Lanka from 1960 to 2019 in terawatt-hours was extracted from the world wide web and analysed with Auto- Regressive Integrated Moving-Average (ARIMA) model. The stationary of the series was tested using the Kwiatkowski–Phillips–Schmidt–Shin (KPSS) test, Phillips-Perron (PP) test, and Augmented Dickey-Fuller (ADF) test. The study revealed the ARIMA(4,2,1) model as a best- fitting model, which gave the minimum value of Akaike Information Criterion (AIC). Total primary energy consumption from 2008 to 2019 was forecasted using ARIMA(4,2,1) model as it satisfied the model diagnostics, which are ARCH test, autocorrelation function, and normality of residuals. With Mean Absolute Error (MAE) of 5.0283 and Root Mean Squared Error (RMSE) of 5.9216, the results illustrate that ARIMA(4,2,1) model captures the trend in total primary energy consumption accurately. Based on the results, the study suggests ARIMA(4,2,1) is more convenient in determining the trends and the patterns of the future in total primary energy consumption in Sri Lanka.