Research Publications Authored by SLIIT Staff
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This collection includes all SLIIT staff publications presented at external conferences and published in external journals. The materials are organized by faculty to facilitate easy retrieval.
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Publication Embargo Comparison of ARIMA and LSTM in Forecasting the Retail Prices of Vegetables in Colombo, Sri Lanka(IEEE, 2022-12-09) Fonseka, D.D; Karunasena, AIdentification of vegetable price trends is important to make better decisions in the production and market. Due to several factors, including seasonality, perishability, an imbalanced supply-demand market, customer choice, and the availability of raw materials, vegetable prices fluctuate quickly and are highly unstable. In this study price prediction was concluded using two models ARIMA and LSTM with retail price data for Cabbage, Carrot, and Green beans in Colombo from 2009 to 2018. According to the decision criteria of RMSE and MAPE, the LSTM model is superior to the ARIMA model in predicting the retail prices of vegetables. There were no studies have focused on predicting prices with novel technology in the Sri Lankan vegetable market. Hence the results of this study can be used to build an advanced forecasting model by the government and decision-makers in agriculture in Sri Lanka.Publication Embargo Machine Learning Based Solution for Improving the Efficiency of Sugar Production in Sri Lanka(IEEE, 2022-12-26) Kulasekara, S; Kumarasiri, K; Sirimanna, T; Dissanayake, D; Karunasena, A; Pemadasa, NAlthough sugar is a popularly used commodity in Sri Lanka, sugar manufactured within the country fulfill only a very small portion of the demanded amount. Sugar production is an intricate process which requires a considerable amount of expertise especially in the areas of cultivation, production and revenue prediction which may not exist in novice farmers. This research proposes a methodology which provides novice sugarcane farmers with expert knowledge on four main areas related to farming including weather forecast, sugarcane maturity estimation, production forecast and prediction of return sugarcane amounts from lands. ARIMA model is used for weather forecast whereas machine learning methods and multiple regression models were used for sugarcane maturity estimation and production of forecasts and returns respectively. The final ARIMA time series model was validated with p-value greater than 0.05 for Ljung-Box test with three different lag values. The Support Vector Machines model was identified as the best model with an accuracy of 81.19% for the sugarcane maturity estimation. The SVM model was trained using the HSV and texture features extracted from sugarcane stalk images using image processing techniques. The prediction of sugar production received a testing R-squared score of 87.75% and mean squared error of 0. Prediction of yield received a mean squared error of approximately 0 and R squared score of 98% on test data. The methodology used in this research could be used by novice farmers to increase their cultivation as well as sugar production.Publication Embargo Influencial Factors of Adopting Digital Banking by Users in Western Province of Sri Lanka(IEEE, 2020-12-10) Rathnaweera, L; Karunasena, AInformation Technology has transformed entire society while changing our day to day lives immensely in the past few decades. The emergence of digital technologies enables new business models and inline with that establishes much potential in transforming banking industry. In the process of innovation, banks have adopted numerous cutting-edge technologies to develop computer frameworks, computer system networks, and digital communication infrastructures. Distinct to many developed countries, digital banking is a new phenomenon for Sri Lankan context as majority of banking service providers and clients appears to be fairly unacquainted with different aspects of this service. The purpose of this study is to identify influential factors for adopting digital banking of customers residing in Western Province of Sri Lanka. For the above purpose, technology acceptance model (TAM) has been used to define the research framework and the study employs the quantitative research methods for data collection and analysis. The results of the research show perceived usefulness, perceived ease of use, security and trust, attitude towards digital banking and subjective norm has a great influence towards to adopting digital banking.