Cheam Yu Chein, a third-year student from USM, is set to embark on a research internship in Madrid, Spain, facilitated by the Erasmus+ Program. With the program’s support, Cheam aims to develop an automated classification model during the internship, focusing on categorizing memes as either hateful or not hateful. The research will explore the model’s performance with different inputs, such as text, image, or a combination of both.

The overarching goal of Cheam’s research is to determine the effectiveness of the classification model in various input scenarios. The anticipated outcomes of this investigation hold potential significance in advancing the development of a predictive model for evaluating trustworthiness. The research aligns with broader efforts to contribute valuable insights to the ongoing progress in trustworthiness prediction models.

Ammar has developed MENDA (Mental Health Dashboard), a tool tailored for educational institutions with the primary goal of offering a comprehensive overview of students’ mental health. In response to the escalating mental health concerns among students at SMK Datuk Hj. Mohamed Nor Ahmad, Ammar’s MENDA seamlessly integrates yearly student survey data, including GAD-7 and PHQ-9 surveys. This information is presented in a user-friendly dashboard powered by PowerBI.

MENDA’s dashboard provides visualizations, group comparisons, and the capability to identify students exhibiting depressive and anxiety symptoms. Additionally, Ammar has incorporated a machine learning model for early case identification. By utilizing various features from student surveys, this proactive approach aims to predict potential mental health issues, not only supporting students with existing symptoms but also emphasizing prevention.

Teachers can leverage MENDA to gain profound insights into their students’ mental health, enabling them to effectively provide the necessary support and intervention.