Browsing by Author "Rupasingha, R. A. H. M."

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A Comparative Study of Clustering English News Articles Using Clustering Algorithms
(Department of Industrial Management, Faculty of Science, University of Kelaniya Sri Lanka, 2022) Disayiram, N.; Rupasingha, R. A. H. M.
The news informs us of what is going on in the world. People nowadays read their interesting news on news websites. There are numerous categories of news. Each newsreader has a different preference for news categories. Sportspeople prioritize sports news, whereas technology fans pay attention to the technology segment of the news. At the end of the day, each news category is important. Every day, a large amount of information is released on news websites. News sites usually categorize the news however, not all of the categories are published on those sites. Some categories are given higher attention by news outlets, while others receive less coverage. As a result, finding an appropriate category of news is tough. These issues make it difficult for newsreaders and content seekers to find relevant sections on news websites. The clustering of English news articles by relative category provides solutions to these issues. This research aims to use clustering algorithms to cluster news articles depending on the relevant domain/cluster. We consider five news categories: politics, sports, health, technology, and business. The data collected online was converted into a vector format using the term frequency-inverse document frequency (TF-IDF) vectorization. Then, on the body of the news and the news heading, the three clustering algorithms: Expectation-Maximization (EM), Simple K-means, and Hierarchical Clustering based on an agglomerative approach were applied individually. The Waikato Environment for Knowledge Analysis (WEKA) tool's classes to clusters evaluation model are used to calculate the accuracy. The EM method had the maximum accuracy of 88.5% with the best results in terms of correctly clustered instances. The comparison between the heading of news and the body of news demonstrates that the body of news clustered the news items better than the heading of news.
Predicting Employee Preference of Teleworking Using Machine Learning Techniques in the Post COVID-19 Period in Sri Lanka
(Department of Industrial Management, Faculty of Science, University of Kelaniya Sri Lanka, 2022) Abesiri, S. A. D. D.; Rupasingha, R. A. H. M.
Coronavirus Disease (COVID-19) is a new disease that has begun since December 2019. As COVID-19 continues to spread around the world, some governments around the world have locked up. Many countries strictly enforce laws to force their citizens to stay at home and away from society. Since the start of COVID-19, many organizations in Sri Lanka have also been looking for the opportunity to work remotely under the teleworking concept instead of traditional working. It has an enormous impact on work and family culture. The study’s main purpose is to predict the user preference for continuing the teleworking concept after the COVID-19 pandemic. A sample of 325 employees who worked online during the COVID-19 pandemic served as the study's data. Online employees in Sri Lanka were selected using convenience sampling and surveyed about their preferences for working online. A questionnaire was designed to cover all the objectives of the study. The Waikato Environment for Knowledge Analysis (WEKA) tool was used for data pre-processing and implementation. Furthermore, Nave Bayes, Decision Tree (J48), Random Forest, Multi-Layer Perceptron (MLP), Support Vector Machine (SVM), and Logistic Regression algorithms were used to generate the prediction models. Based on the accuracy, precision, recall, and f-measure evaluations, The Random Forest algorithm outperforms the other six algorithms with a score of 87.84%.
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.