Browsing by Author "Mahanama, K. R. T. S."

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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.
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.
Survey on the acceptance of online education in state universities of Sri Lanka during the COVID-19 pandemic situation
(Faculty of Science, University of Kelaniya, Sri Lanka., 2021) Mahanama, K. R. T. S.; Mohamed, A. R. W.; Wickramarathne, R. A. S.; Pathirana, G. P. N. M.; Kumara, H. H. D.; Pathirana, M. P. R. L.; Wickramanayaka, M. P. A. T.; Gunawardena, S. L. H.; Dias, M. J. R.; Ihsan, M. I.; Kaushalya, K. D.; Kumara, M. S. M. S.
Online education is a mode of electronically facilitated distance education method. Due to the COVID-19 pandemic situation, global educational institutions transformed into online platforms. As a developing nation, Sri Lanka had to make a rapid transition from face-to-face to the online teaching-learning process. According to the Department of Census and Statistics, Sri Lanka, only 22.2% of households owned desktop or laptop computers (2020). Consequently, the availability and accessibility of infrastructure to transform into an online education platform are at a question. Hence, to appraise this current situation based on students’ points of view, a sample survey was conducted to explore the acceptance of online education mechanisms in state universities of Sri Lanka during the COVID-19 pandemic situation. As a first step, a pilot study was conducted on 44 undergraduates, who were selected by convenience sampling. With the experience of the pilot survey, the final questionnaire was fine-tuned with 27 questions, and it was delivered to the undergraduates in 14 state universities employing the snowball sampling technique. Based on observation of the pilot study, the required minimum sample size was found to be 570 with a margin of error of 0.04. Finally, a descriptive analysis was performed using 574 responses using Minitab software. Most of the students are more inclined to use online lectures (33%) and videos (55.3%). From 64.2% who had online sessions for practical courses, 38.9% are dissatisfied. Even though 36.3% had faced network problems, regular and usual participation figures were approximately 70%. 57.6% of the respondents in the sample are females, and among them, a higher percentage (44.8%) were participating in online lectures regularly compared to that of males (25.4%). The majority of the student has complained of difficulties in health problems (81%), inability in raising questions (64.9%), understanding course contents (86.9%), and heavy workload (89.4%). Overall, comparisons of face-to-face and online lectures revealed that the majority preferred face-to-face lectures (43.8%), and a significant proportion accepted both study modes (39.3%). On average, the acceptance of online education is ranked 2.86 on a scale of 1 (highly reject) to 5 (highly accept). Based on the findings, it is recommended to strengthen the interactions between students and lecturers, conduct break-through room assignments during the lectures, and use multiple communication platforms. In addition, student grievances can be accommodated by relaxing deadlines on assessment, aiding of educational, technical, and financial needs.