Arsenyan Ani’s research while affiliated with Xi’an University and other places

In recent times, the rapid growth of e-learning has brought about increased concerns regarding cybersecurity risks within digital learning environments. Despite the growing importance of cybersecurity awareness among e-learning students, there is limited research on the factors that influence students' understanding and adherence to cyber safety practices. This study investigates the relationship between e-learning engagement, cybersecurity awareness, and cyber safety practices, with a focus on the moderating roles of gender and e-learning experience. The research emphasizes the need to consider demographic factors such as gender and experience level when designing cybersecurity interventions for online learners. The investigation uses structural equation modeling to analyze how engagement in e-learning platforms influences students' cybersecurity awareness and their adherence to security protocols. Among the key findings from SEM, the study reveals that e-learning engagement positively impacts cybersecurity awareness, with gender and experience level acting as significant moderators. However, the study also found that experienced learners may initially face challenges in applying security protocols effectively, though their understanding improves over time with continued engagement. In addition, the research found that students with higher e-learning engagement are more likely to adopt cyber safety practices and comply with information security policies. Interestingly, the results suggest that educational institutions should tailor cybersecurity programs to address the unique needs of students based on their gender and experience levels. Furthermore, our research can inform future policies and interventions aimed at promoting cybersecurity awareness among e-learning students, ultimately contributing to safer and more secure digital learning environments.

Understanding and predicting drivers' gaze patterns is essential for improving road safety and optimizing in-vehicle displays. This study delves into the nuanced dynamics of drivers’ visual attention across varied road segments, employing both statistical analyses and machine learning models. Ten participants, spanning diverse demographics, participated in a real driving experiment, navigating curves and straight stretches while their eye movements were tracked using Tobii Pro Glasses 2. Statistical analysis unveiled significant variations in gaze behavior, emphasizing specific Areas of Interest (AOIs) like the instrumental panel, left view, main view, and right view during curves. Machine learning models, including XGBoost (XGB), Adaboost, Support Vector Machine (SVM), and an Ensemble Model, were deployed to predict gaze patterns. Adaboost emerged as the top-performing model, showcasing robust accuracy (82.50%). The Ensemble Model, capitalizing on the strengths of individual models, demonstrated a well-balanced performance with a remarkable training accuracy of 99.36% and testing accuracy of 82.50%, coupled with an F1-Score of 83.72%. Despite participant-related limitations, this study provides indispensable insights into the intricate dynamics of driver gaze behavior. It underscores the effectiveness of machine learning in understanding and predicting drivers' gaze patterns, offering valuable implications for applications aimed at fortifying road safety measures and optimizing in-vehicle displays. The balanced performance of the Ensemble Model affirms the potential of amalgamating diverse models, presenting a promising avenue for future research and practical applications in the realm of driver behavior analysis.