Faculty of Computing

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Author: search.filters.author.Karunasena, ASubject: search.filters.subject.Machine Learning

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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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    Know More: Social Media based Student Centric E-learning platform with Machine Learning Approaches
    (IEEE, 2022-07-18) Malavige, O; Nasome, V; Costa, M; Jayasinghe, B; Karunasena, A; Samarakoon, U
    Social media has become increasingly popular among the younger generation in the last decade. Students engage with social media on daily basis, and it affects their interests, lifestyle, and attitude. There are many existing e-learning applications used by higher educational institutes, but such applications are mainly focused on delivering teaching content rather than facilitating active and interactive learning. This paper proposes a novel e-learning platform to create an active and interactive learning environment for students leveraging social media strategies, especially those of “Facebook.” The objective of this platform is to promote self-motivation, self-learning, and interaction. The platform features were built on considering three aspects important for learning, which are personal knowledge management, learning management, and collaborative learning. Features of the proposed platform that it comprises are Newsfeed, Classmates, Profile, Cluster, Repository, Knowledgebase, Bookmark, Topic Map, Search Engine, Test Mark Prediction, and Slide Show Summary generator. Machine Learning techniques and Natural Language Processing were used to build some of the platform features. The feedback collected on the proposed system, “KnowMore,” shows that the satisfaction of the students has increased with the system.
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    Machine Learning Based Emotion Level Assessment
    (IEEE, 2021-12-09) Wickremesinghe, L; Madanayake, D; Karunasena, A; Samarasinghe, P
    With recent advancements of technology, identification of emotions of humans via facial recognition is done with the application of numerous methods including machine learning and deep learning. In this paper, machine learning techniques are applied for identifying different levels of emotions of individuals for unannotated video clips using Facial Action Coding System. In order to archive the above, first, two methods were experimented to obtain a labeled image data set to train classification models where in the first method, clustering of images was done using Action Units(AU) identified from literature and the emotion levels of the images were determined through the resulted clusters and images are labeled according to the cluster they belonged to. In the second method, the image set is analyzed explicitly to identify AUs contributing to emotions rather than relying on those identified in literature and then the clustering of image set was done using those identified AUs to label the images similar to the first method. The two labeled data sets were used to train classification models with Random Forest, Support Vector Machine and K-Nearest Neighbour algorithms separately.Classification models showed better accuracy with data set produced using the second method. An overall F1 score, accuracy, precision and recall of 87% was obtained for the best classification model which is developed using the Random Forest algorithm to identify levels of emotions. Identifying the AU combinations related to emotions and developing a classification model for identifying levels of emotions are the major contributions of this paper. The results of this research would be especially useful to identify levels of emotions of individuals who are having issues in verbal communication.