http://ir.lib.ruh.ac.lk/handle/iruor/8 Scholarly publications produced by the members of University of Ruhuna 2026-09-17T17:25:10Z http://ir.lib.ruh.ac.lk/handle/iruor/21786 Forecasting Green Tea Production in Sri Lanka: A Time Series Analysis. De Silva, S.H.C.B.; Dilshani, S.D.M. Tea is the main agricultural value-added export product in Sri Lanka, providing a significant contribution to the national economy. Specifically, green tea has drawn worldwide attention in this sector due to its growing consumer demand. Therefore, the main objective of this study was to obtain a reliable forecasting model for green tea production using time series analysis, which is essential for strategic planning to maintain competitiveness in the international market. This study develops time series forecasting models for monthly green tea production using both traditional Seasonal Auto Regressive Integrated Moving Average (SARIMA) models and modern approaches such as Recurrent Neural Network (RNN) models, specifically Long Short-Term Memory (LSTM) models. Monthly production data from 2013 to 2024 were obtained from the Sri Lanka Tea Board for analysis. Model forecasts were evaluated against observed values using standard forecasting accuracy measures such as MAE, MAPE, and RMSE to identify the best-performing model. SARIMA (2,1,2) (1,1,1)12 and LSTM models were developed to forecast monthly green tea production for the test data months. The dataset was split into training and testing data, focusing on capturing shortterm production patterns. The results indicate that the LSTM model achieves the lowest prediction error, demonstrating superior forecasting performance.The results demonstrate that modern forecasting approaches, such as LSTM based on Recurrent Neural Networks (RNNs), can effectively capture the nonlinear dynamics of tea production and outperform traditional SARIMA models. These findings suggest that industry stakeholders and policymakers can use RNN-based forecasts to improve production planning, resource allocation, and market supply management, thereby supporting more informed decision-making in the green tea sector. 2026-03-04T00:00:00Z http://ir.lib.ruh.ac.lk/handle/iruor/21785 Modeling Conditional Value-at-Risk Using Tukey’s g-and-h Family of Distributions with Maximum Approximated Likelihood Estimation. Abeygunasekara, S.M.; De Mel, W.A.R. The main purpose of this study was to model Conditional Value-at-Risk using Tukey’s g-and-h distribution and employ the maximum approximated likelihood method for parameter estimation, subsequently contrasting Tukey’s distribution with Cornish-Fisher and classical approaches. While the Cornish-Fisher method modifies the normal quantile to account for skewness and kurtosis, the classical methodology assumes that asset returns follow a normal distribution. Historical daily return data from January 1, 2003, to January 17, 2011, from Spain were used in the study. Conditional Value-at-Risk (CVaR), also known as Expected Shortfall (ES), is a risk indicator that shows the expected loss at a given confidence level when a loss exceeds the Value-at-Risk. The maximum approximated likelihood estimator is one approach used to estimate the parameters of Tukey’s distribution. Because of its light-tailed assumption, the classical technique results in the least extreme losses. Due to its sensitivity to skewness and kurtosis modifications, the Cornish-Fisher approach produces noticeably larger tail losses; however, at very high confidence levels, this may introduce instability. Without the dramatic divergence observed in the Cornish-Fisher method, the MALE-based Tukey’s model captures heavy-tailed traits with smoother and more stable tail behavior. These findings imply that MALE provides a more reliable paradigm for measuring high risk. These findings have applications in stress testing, regulatory calculation, and risk management, where precise extreme tail risk estimation is essential. Model errors, parameter uncertainty, data selection, and inadequate data may still impact the proposed strategy. Consequently, even while MALE enhances tail risk prediction, its efficacy depends on adequate data and trustworthy estimation techniques. As a recommendation, to further confirm the MALE model’s resilience in measuring severe risk, additional research should examine its performance in various market scenarios and on larger datasets. 2026-03-04T00:00:00Z http://ir.lib.ruh.ac.lk/handle/iruor/21784 Digital Image Security on Elliptic Curve using Visual Cryptography. Bandara, D. M. S.; Kumara, W.A.D.L. In the modern digital landscape, images are integral to daily communication and operations, making their security a critical concern. While no encryption method can guarantee absolute security, some approaches offer significantly stronger protection than others. This paper presents a novel hybrid image encryption scheme that combines the strengths of Visual Cryptography (VC) and Elliptic Curve Cryptography (ECC). The proposed method begins by using Visual Cryptography to split a secret image into two distinct shares. One share functions as an encryption key, while the other is encrypted using ECC 128 bits curve parameters to produce a cipher image. Subsequently, both shares are merged into a single secured image file. The effectiveness of the encryption and decryption processes is rigorously evaluated through standard security analyses. Performance metrics such as the Number of Pixel Change Rate (NPCR), Unified Average Changing Intensity (UACI), histogram analysis, and processing speed are used to assess the scheme’s robustness and efficiency. 2026-03-04T00:00:00Z http://ir.lib.ruh.ac.lk/handle/iruor/21783 Optimization and Validation of a UV Spectrophotometric Assay for Empagliflozin: A Cost-Effective Approach for Pharmaceutical Quality Control. Malwatta, M.A.L.D.; Wasana, P.W.D.; Gunawardena, S. Empagliflozin, a selective sodium-glucose co-transporter 2 (SGLT2) inhibitor, is widely used for the management of type 2 diabetes mellitus (T2DM). Ensuring the quality and consistency of empagliflozin-containing pharmaceutical products requires validated analytical methods that are simple, accurate, and cost-effective. This study aimed to develop and validate a UV spectrophotometric method for the quantitative estimation of empagliflozin in bulk drug and tablet formulations, following ICH Q2(R1) guidelines. A UV spectrophotometric method was developed using a 50:50 methanol-deionised water solvent system. The λmax (223 nm) was identified from the absorption spectrum, and calibration curves were prepared within the 2–20 μg/mL range. Validation parameters—including linearity, specificity, accuracy, precision, limits of detection and quantification (LOD/LOQ), and solution stability—were evaluated per ICH Q2(R1). Commercial empagliflozin 10 mg tablets were assayed using both the developed UV method (200–400 nm range) and a validated HPLC method to confirm method accuracy. Empagliflozin exhibited optimal solubility and spectral stability in the selected solvent system, with a prominent λmax at 223 nm. Calibration data demonstrated excellent linearity (R = 0.9994). Method validation confirmed excellent specificity, with no interference from excipients. Accuracy studies demonstrated recovery values ranging from 97.1% to 99.8%, while intra-day and inter-day precision yielded %RSD values of 0.57% and 0.53%, respectively. The LOD and LOQ were 0.34 μg/mL and 1.03 μg/mL, respectively. Analytical solutions remained stable for up to 24 hours at room temperature with less than 1% change in absorbance. API content obtained using UV spectrophotometry (97.8%) closely matched that obtained by HPLC (98.2%), supporting the reliability of the developed method. Overall, the developed UV spectrophotometric method is simple, rapid, environmentally friendly, and cost-effective, meeting all regulatory validation requirements. It is suitable for routine quality control of empagliflozin formulations and provides an accessible alternative to chromatographic techniques, particularly in laboratories with limited resources. 2026-03-04T00:00:00Z