Browsing by Author "Kumara, B. T. G. S."

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Data Mining Approach for Identifying Suitable Sport for Beginners
(IEEE International Research Conference on Smart computing & Systems Engineering (SCSE) 2019, Department of Industrial Management, Faculty of Science, University of Kelaniya, Sri Lanka, 2019) Amarasena, P.T.; Kumara, B. T. G. S.; Jointion, S.
Anthropometric measurements are generally used to determine and predict achievement in different sports. An athlete’s anthropometric and physical characteristics may perform important preconditions for successful participation in any given sport. Further, anthropometric profiles indicate whether the player would be suitable for the competition at the highest level in a specific sport. Recently, more researches have been carried out on Sport Data mining. In this study, we propose an approach to identify the most suitable sport for beginners using data mining and anthropometric profiles. We propose clustering base approach. We apply a spatial clustering technique called the Spherical Associated Keyword Space which is projected clustering result from a three-dimensional sphere to a two dimensional (2D) spherical surface for 2D visualization. Empirical study of our approach has proved the effectiveness of clustering results
Docker incorporation is different from other computer system infrastructures: A review
(Department of Industrial Management, Faculty of Science, University of Kelaniya Sri Lanka, 2021) Kithulwatta, W. M. C. J. T.; Jayasena, K. P. N.; Kumara, B. T. G. S.; Rathnayaka, R. M. K. T.
Currently the computing world is getting complex, innovating and maturing with modern technologies. Virtualization is one of the old concepts and currently containerization has arrived as an alternative and innovative technology. Docker is the most famous and trending container management technology. Different other container management technologies and virtualization technologies are respective other corresponding technologies and mechanisms for Docker containerization. This research study aims to identify how Docker incorporation is different from other computer system infrastructure technologies in the perspective of architecture, features and qualities. By considering forty-five existing literatures, this research study was conducted. To deliver a structured review process, a thorough review protocol was conducted. By considering four main research questions, the research study was lined up. Ultimately, Docker architecture and Docker components, Docker features, Docker integration with other computing domains and Docker & other computing infrastructures were studied. After synthesizing all the selected research studies, the cream was obtained with plenty of knowledge contribution to the field of computer application deployment and infrastructure.
Novel machine learning ensemble approach for landslide prediction
(IEEE International Research Conference on Smart computing & Systems Engineering (SCSE) 2019, Department of Industrial Management, Faculty of Science, University of Kelaniya, Sri Lanka, 2019) Madawala, C. N.; Kumara, B. T. G. S.; Indrathilaka, L.
Haphazard development activities on mountain slopes and inadequate attention to construction aspects have led to the increase of landslides and consequently sustaining damage to lives and infrastructure. Nearly 3275 sq.km of area spread over the Ratnapura District, seems to be highly prone to landslides and mass wastage of 2178 sq.km. Landslides occur in many regions of Ratnapura district and nearly 90 deaths have been reported according to National Research Building Organization (NBRO) in 2017. Most landslides or potential failures could be predicted fairly accurately if proper investigations were performed in time. The primary objective of this study is landslide-hazard mapping and risk evaluation to determine the real extent, timing, and severity of landslide processes in Ratnapura district. Such knowledge will provide the most significant benefit to government officials, consulting engineering firms, and the general public in avoiding the landslide hazard or in mitigating the losses. Hybrid Machine Learning techniques can be used to develop prediction models using existing data. Ensemble approach based on Support Vector Machine (SVM), Naïve Bayes model were combined and implemented for the final prediction. This study possesses a strong capability to predict landslides by causative factors, slope, land use, elevation, geology, soil materials and triggering factor; rainfall was extracted and applied to the machine learning algorithms. This research introduces a novel architecture to produce a more relevant and accurate prediction of the landslide vulnerability within the study area. Moreover, it was revealed that all of the factors had relatively positive relationship with occurrence of landslides. An improvement in hazard monitoring, accuracy of early warning and disaster mitigation is documented.
Sentiment Analysis on Twitter Data Related to Online Learning During the Covid-19 Pandemic
(Department of Industrial Management, Faculty of Science, University of Kelaniya Sri Lanka, 2022) Senadhira, K. I.; Rupasingha, R. A. H. M.; Kumara, B. T. G. S.
With the outbreak of the Corona Virus Disease (COVID-19), nearly all educational associations throughout the world have been working tirelessly to supply online education. Students with opportunities for ongoing learning ensure their well-being. This study is being conducted to learn more about real community experiences with online learning facilities during the pandemic situation and the adaptation of online learning around the world following the pandemic circumstances. The Twitter API has been used to collect tweets for this study and a suitable result was produced after pooling the tweets. Out of the 8976 tweets, 4486 were positive, whereas 4490 were negative. After completing the pre-processing process of tweets, extract the feature vectors using the Term Frequency-Inverse Document Frequency (TF-IDF) vectorizer. Then, the dataset was loaded into supervised machine learning techniques such as Support Vector Machine (SVM) and Artificial Neural Network (ANN) to construct a forecast paradigm for predicting the probability of the society using the online learning procedure. According to the results, ANN beat SVM and achieved an accuracy of 81.97% with higher precision, recall, f-measure values, and lowest error values. The unexpected outbreak of the pandemic caused significant disruptions to students' educational practice. They have a lack of access to technology gadgets, bad internet connectivity, and improper learning conditions. This effort also identifies the peculiarities of current technical techniques knowledge? in the development of distance learning theory. Additional financing and feasible strategies were determined to be required for the development of an efficient teaching-learning procedure for the aforementioned technique in the context of education across the globe.