Computing and Technology
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Item Detection of Cyberbullying to Reduce Mental Health Problems using Machine Learning Algorithms(2025 International Research Conference on Smart Computing and Systems Engineering (SCSE), 2025) Perera, M.V.V.; Piyumal, K. M.Social networks and other online platform services are where people are more likely to experience issues with cyberbullying, including kids, young and older adults who are addicted to them. Cyberbullying is an activity that takes place on digital platforms. Victims are threatened or bullied individually or in groups by messages or comments online. Various cyberbullying detection techniques are continuously used on social media platforms. However, not all online platform services follow those cyberbullying mechanisms, which may lead to psychological problems that can cause depression and lead to suicide because people are unaware of taking action to prevent it. Many past cyberbullying detection studies used a small data set and omitted to disclose the total number of features used to train the model. To fill this gap, this study explores how the model performance changes with the feature count and what happens to the model performance when the data set size increases. Therefore, two cyberbullying datasets with a combined total of 47,183 and 120,556 were used, which contained suspicious activities on Twitter and Facebook that most commonly belong to the cyberbullying category. To compare the performance metrics of each model, three methods for feature extraction and three classifiers were used, namely, Logistic Regression (LR), Random Forest (RF), and Support Vector Machine (SVM). The highest accuracy for the models created utilizing 47,183 data under the three feature extraction approaches was 94.43%, and the highest accuracy for the 120,556 data was 89.96%.Item Forecasting Monthly Ad Revenue from Blogs using Machine Learning(3rd International Conference on Advances in Computing and Technology (ICACT ‒ 2018), Faculty of Computing and Technology, University of Kelaniya, Sri Lanka., 2018) Dias, D.S.; Dias, N.G.J.Blogs emerged in the late 1990s as a technology that allows Internet users to share information. Since then, blogging has evolved to become a source of living to some and a hobby to others. A blog with rich content and regular traffic could easily be monetized through a number of methods. Affiliate marketing, Google AdSense, offering courses or services, selling eBooks and paid banner advertisements are some of the methods in which a blog could be monetized. There exists, a direct relationship on the revenue that can be generated through any of the above methods and the traffic that the blog gets. Google AdSense is the leader in providing ads from publishers to website owners. All bloggers or blogging website owners who have monetized their blogs, attempt to maximize their revenue by publishing articles in hope that it will generate the targeted revenue. On the other hand, bloggers or blogging website owners that hope to monetize their blog will be greatly benefitted if there was a way to forecast the monthly ad revenue that could be generated through the blog. But there exists no tool in the market that can help the bloggers forecast their ad revenue from the blog. In this research, we are looking at the possibility of finding an appropriate machine learning technique by comparing a linear regression, neural network regression and decision forest regression approaches in order to forecast the monthly ad revenue that a blog can generate to a greater accuracy, using statistics from Google Analytics and Google AdSense. As conclusion, the Decision Forest Regression model came out as the best fit with an accuracy of over 70%Item Mobile Telecommunication Customers Churn Prediction Model(3rd International Conference on Advances in Computing and Technology (ICACT ‒ 2018), Faculty of Computing and Technology, University of Kelaniya, Sri Lanka., 2018) Chathuranga, L. L. G.; Rathnayaka, R.M.T.B.; Arumawadu, H.I.The present Sri Lankan mobile industry is extremely dynamic, with new services, technologies, and carriers constantly altering the landscape. Then customers have more choices. So, Predict customer churn is one of the most challengeable target in the telecommunication industry today. The major aim of the study is develop a customer churn prediction model by considering some soft factors like monthly bill, billing complaints, promotions, hotline call time, arcade visit time, negative ratings sent, positive ratings sent, complaint resolve duration, total complaints, and coverage related complaints. This study introduces a Mobile Telecommunication customer churn prediction model using data mining techniques. In this study, three machine learning algorithms namely logistic regression, naive bayes and decision tree are used. Indeed, twenty attributes are mainly carried out to train these three algorithms. Furthermore, the back propagation neural network was trained to predict customer churn. Data set used in this study contains 3,334 subscribers, including 1,289 churners and 2,045 non-churners. According to the results, the trained neural network has two hidden layers with 25 total neurons. The proposed Artificial Neural Network result gives 96% accuracy for mobile telecommunication customer churn prediction. The estimated results suggested that the proposed algorithm gives high performances than traditional machine learning algorithm.