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One of the most important aspects of learning is attention. This is even more pronounced in online education due to the instructor’s need for sufficient control over the learner’s environment. This study aims to identify the pattern of changes in the cognitive state of learners, from unconsciousness to consciousness. By observing the brain’s response while learners watch micro videos, we sought to understand the impact of these ego states on learners’ performance in E-Learning environments. Our findings suggest that learners' ego states significantly impact their learning performance. The first phase aimed to precisely detect the transition point from the unconscious to the conscious state. In the second phase, we tried to differentiate between these two states by comparing their learning performance. Finally, the obtained results led us to believe that learning outcomes are subject to a significant increase when the brain state changes. These findings emphasize the importance of early engagement strategies in online learning, as improving the initial phase of content delivery significantly increases overall learning outcomes. By understanding the transition between different ego states, educational content authors would create more effective learning materials that maintain continuous learner engagement.
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Social media platforms are vital repositories of user-generated content, reflecting a range of emotions, interests, and discussions. Among these interactions, hate speech has emerged as a significant issue, influencing user behavior. While prior studies have attempted to analyze user characteristics to understand hate attitudes, they often rely on simple statistical comparisons and lack robust methods for causal effect estimation. This study investigates the causal effects of hate speech on user behavior on Twitter (now known as X) during the COVID-19 pandemic, characterized by heightened online discourse and harmful rhetoric. We focus on users who broadcast hate speech to determine how such expressions affect emotional responses. Using a Bayesian structural time-series modeling approach, we isolate the effects of hate speech from confounding factors, providing a solid framework for causal inference. Our findings indicate a significant shift in user emotions following instances of hate speech, demonstrating a measurable impact on user dynamics. We also analyze hashtag usage during this period, emphasizing their role in shaping online discourse. This study enhances understanding of the relationship between hate speech and user behavior, offering insights crucial for researchers, policymakers, and social media platforms in developing strategies to mitigate the adverse effects of online hate speech.
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The digital era has introduced mental health challenges, especially for youth. Despite increasing awareness, comprehensive analyses of these challenges remain limited. This study collects and examines the prevalence of 15 key mental health challenges related to digital engagement, based on a sample of 555 participants. The prevalence of these challenges varied, with pressures related to parenting, hoarding, and inappropriate content being the most common, affecting 60.13%, 52.76%, and 45.39% of the participants, respectively. The research also highlights gender and age differences, noting that males report higher levels of issues like FOMO and Nomophobia compared to females. Adults (18+) face more severe challenges, such as memory decline, while younger individuals report fewer problems. Correlation analysis revealed significant relationships between several mental health challenges, such as Nomophobia and TAD (r = 0.68) and FOMO and TAD (r = 0.50), indicating that individuals experiencing one challenge are likely to face others. A decision tree analysis was used to predict mental health challenges by examining the relationships between different mental health conditions, uncovering specific patterns and rules associated with the occurrence of these challenges. Additionally, cluster analysis in this study identified distinct population segments, with 21% of individuals falling into a cluster that experiences severe mental health challenges. The findings suggest that a significant portion of the population is at risk for severe mental health issues, highlighting the need for targeted interventions.
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The Internet of Things (IoT) has become increasingly prevalent, and recent advances in machine learning, particularly in healthcare, have gained significant attention from researchers. One prominent interdisciplinary topic in these fields is human activity recognition (HAR). Despite extensive research, several challenges remain in this area, especially concerning the application of modern machine learning techniques for HAR. This study proposes a novel method for human activity recognition by combining radial basis function neural networks (RBFNN) and support vector machines (SVM). The approach enhances recognition accuracy and algorithm efficiency by extracting relevant features using RBFNN and convolutional neural networks (CNN). Classification is then performed using SVM. The proposed method was evaluated using the UCI HAR dataset, which includes six distinct human activities. Results demonstrate that the proposed approach achieves an accuracy of 99%, surpassing existing methods.
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The Internet of Multimedia Things (IoMT) represents a significant advancement in the evolution of IoT technologies, focusing on the transmission and management of multimedia streams. As the volume of data continues to surge and the number of connected devices grows exponentially, internet traffic has reached unprecedented levels, resulting in challenges such as server overloads and deteriorating service quality. Traditional computer network architectures were not designed to accommodate this rapid increase in demand, leading to the necessity for innovative solutions.
In response, Software-Defined Networks (SDNs) have emerged as a promising framework, offering enhanced management capabilities by decoupling the control layer from the data layer. This study explores the load balancing of servers within software-defined multimedia IoT networks. The Long Short-Term Memory (LSTM) prediction algorithm is employed to accurately estimate server loads and fuzzy systems are integrated to optimize load distribution across servers. The findings from the simulations indicate that the proposed approach enhances the optimization and management of IoT networks, resulting in improved service quality, reduced operational costs, and increased productivity.
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This study explores the potential of metaverse technology in advancing sustainable tourism development in alignment with the United Nations' Sustainable Development Goals. Through a meta-synthesis analysis following a seven-step approach, 17 articles were selected from an initial pool of 149 and analyzed, identifying six key categories that illustrate how the metaverse contributes to tourism sustainability. The findings emphasize the metaverse's significant role in sustainable tourism by offering economic, educational, technological, and environmental benefits. The metaverse supports long-term sustainability in the sector by enhancing tourism experiences while reducing environmental impact, promoting responsible travel behaviors, and leveraging digital innovations. This study provides valuable insights for policymakers and stakeholders seeking to harness metaverse technology to drive innovation, foster sustainability, and develop models that balance economic growth with environmental stewardship.
