The modern online society requires everyone, especially children and young people, to learn how to use the Internet. Cyberbullying is one misuse that can be detrimental to the cyberbullied individuals’ mental health and lifestyle, and it often ends up with the victim becoming depressed, fearful of society, and in the worst cases, suicidal ideation. The aim of this study is to investigate the awareness, perception, and perpetration of cyberbullying by high school students and undergraduates to find ways to prevent cyberbullying in the future. For this cross-sectional study, data were collected in 2020 from 14 schools throughout Thailand and 4 universities in Chiang Mai, Thailand, using two-stage sampling. Chi-squared tests were used to compare differences between the groups. Of the 2,683 high school students, girls perceived cyberbullying more than boys (81.6% vs. 75.4%; p <0.001), with those from the later academic years being more aware of cyberbullying (p = 0.033) and more likely to conduct cyberbullying behavior (p = 0.027). Of the 721 undergraduates, women were more aware of cyberbullying than men (92.1% vs. 82.7%; p <0.001). The most common cause of cyberbullying was aiming to tease the target (67.6% of high school students vs. 82.5% of undergraduates). The most commonly cyberbullying victimization was sending mocking or rebuking messages (29.6% of high school students and 39.6% of undergraduates). The most popular solutions for cyberbullying were to avoid leaving a trace on social media and be with friends who accept who you are. Our findings show that most of the cyberbullying perpetrators did not consider that their actions would have serious consequences and only carried out cyberbullying because of wanting to tease their victims. This is useful information for the cyberbullying solution center, teachers, and parents to recognize how to make the students realize the effects of cyberbullying on the victims.
A stock price index measures the change in several share prices, which can describe the market and assist investors in deciding on a specific investment. Thus, foreseeing the stock price index benefits investors in creating a better investment strategy. However, forecasting the stock price index can be challenging due to its non-linearity, non-stationary and high uncertainty. Gaussian process regression (GPR) is an attractive and powerful approach for prediction, especially when the data fluctuates over time with fewer restrictions. Besides, the GPR gains advantages over other forecasting techniques as it can offer predictions with uncertainty to provide margin errors. In this study, we evaluate the use of GPR to predict the stock price of Thailand (SET). The SET data are divided into 2 datasets; the data in the year 2015 - 2020 and the data in the year 2020 due to the massive change during the COVID-19 pandemic. The prediction results from the GPR are then compared to the machine learning approaches, artificial neural network (ANN) and recurrent neural network (RNN) using evaluation scores; the root mean square error (RMSE), the mean absolute error (MAE), the mean absolute percentage error (MAPE) and the Nash-Sutcliffe efficiency (NSE). The results indicate that the GPR is superior to the ANN and RNN for both datasets as it provides a high prediction accuracy. Moreover, the results suggest that the GPR is less sensitive to the number of input lags in the model. Therefore, the GPR is more favorable for the prediction of SET than the ANN and RNN. HIGHLIGHTS The Gaussian process regression (GPR) was applied to predict the stock price index of Thailand (SET) The predictive performance of the GPR was compared to artificial neural networks (ANNs) and recurrent neural networks (RNNs) The results indicate that GPR outperformed the other methods as it provided a high prediction accuracy along with prediction intervals GRAPHICAL ABSTRACT
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