http://dlib.iit.ac.lk/xmlui/handle/123456789/2310 2026-04-06T23:51:42Z 2026-04-06T23:51:42Z Perera, Andrea Fernando, Pumudu http://dlib.iit.ac.lk/xmlui/handle/123456789/2265 2025-05-03T02:23:29Z 2024-01-01T00:00:00Z

Cyberbullying Detection System on Social Media Using Supervised Machine Learning Perera, Andrea; Fernando, Pumudu The use of digital and social media is growing every day as technology advances. People in the twenty-first century are growing up in a social media and internet-enabled society. Digital media offers a lot of opportunities, but people frequently tend to misuse them. On social networking sites, people spread anger toward a person. People are affected by cyberbullying in various ways. It has an impact on more than just health; numerous other factors put life in danger. Cyberbullying is a widespread modern phenomenon that people cannot completely avoid but can prevent. The author proposes a system for automatic cyberbullying detection and prevention using supervised machine learning. The system considers key characteristics of cyberbullying, such as the intention to harm, repeated behavior, and the use of abusive language. Support vector machines and logistic regression are employed to identify cyberbullying and related themes/categories such as race, physical, sexuality, and politics. This proposed method offers a novel theory for the detection of cyberbullying: texting has evolved over time due to changes in context usage, and language. In the dataset that includes tweets, Support Vector Machine (SVM), Naïve Bayes, and Logistic Regression (LR) models were tested along with different Natural Language Processing methods. The accuracy of the system is improved by sentiment analysis, N-gram analysis, and other non-traditional feature extraction methods like Term Frequency-Inverse Document Frequency (TF-IDF) and profanity detection.

2024-01-01T00:00:00Z Fernando, Pumudu A. Premadasa, H.K. Salinda http://dlib.iit.ac.lk/xmlui/handle/123456789/2263 2025-05-03T02:22:43Z 2024-01-01T00:00:00Z

Game-based Activity Design in Primary School Students’ Learning Style Detection Fernando, Pumudu A.; Premadasa, H.K. Salinda Generation Alpha, the present primary school cohort born after 2010, has significant exposure to mobile devices and gaming. Adopting a "One Size Fits All" approach in modern teaching methods may not be effective, as it overlooks individual learning preferences. Personalized learning can be facilitated by identifying a student’s learning style (LS). Adaptive learning based on LS has been found to have positive effects in several studies. However, traditional learning style detection techniques such as questionnaires and self-assessments can be time-consuming and demotivating for primary school students. This study aims to propose a game-based activity framework as an alternative to the Index of Learning Style (ILS) questionnaire linked with Felder Silverman Learning Style Model for LS detection. The proposed game was evaluated with a sample of sixty students, and preliminary results indicate that the game outperforms the original ILS questionnaire in terms of student engagement and motivation to complete LS activities, achieving an overall satisfaction rate of 87.5%. The second phase of the research will focus on evaluating the accuracy of LS prediction using the designed game, which is currently ongoing.

2024-01-01T00:00:00Z