Research Papers - Dept of Information Technology

Permanent URI for this collectionhttps://rda.sliit.lk/handle/123456789/593

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    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, N
    Although 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.
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    E-Pod: E-learning System for Improving Student Engagement in Asynchronous Mode
    (IEEE, 2021-10-27) Tennakoon, S; Wickramaarachchi, T; Weerakotuwa, R; Sulochana, P; Karunasena, A; Piyawardana, V
    Over the last decade, e-learning has grown significantly as the internet and education have merged to give individuals the possibility to learn new skills. With the COVID-19 pandemic, the use of e-learning has increased in an exponential manner. The asynchronous e-learning mode is found to be appealing to students due to its adoption at any time and in any location. Yet, this mode of learning suffers from lack of interactivity. Under such circumstances, this research proposes E-Pod, an asynchronous e-learning system, which promotes student engagement. Through attention monitoring, when the students are found to be inattentive they are provided with opportunities to engage in a wide range of activities such as summarization activities, puzzles and answering questions to improve the interactivity. The accuracy achieved for the gaze estimation model is 89.5 % and the accuracy achieved for the facial emotion recognition model is 83%. In order to generate FBQ and MCQ questions for students, a SVM model was trained to an accuracy of 95.56%. E-Pod includes a MaLSTM model with 83.98% accuracy for short answer evaluation and a DistilBERT model with 86.8% accuracy for essay answer evaluation. The system is developed using a blend of cutting-edge technologies including image processing, Natural Language Processing, machine learning algorithms and language models. With these features, E-Pod is proposed as an all-inclusive system which stands out from existing e-learning systems and will be helpful for educational institutions to deliver flexible and self-paced learning to their students in asynchronous mode.