Hybrid machine learning approach for Arabic medical web page credibility assessment.

For many people, the Internet is their primary source of knowledge in today's modern world. Internet users frequently seek health-related information in order to better understand a health problem, seek guidance, or diagnose symptoms. Unfortunately, most of this information is inaccurate or unreliable, making it difficult for regular users to discern reliable sources of information. To determine online source reliability, specific knowledge and domain expertise are necessary. Researchers in health informatics studied a number of linguistic and non-linguistic indicators to assist ordinary individuals in judging medical web page credibility. This study proposes a method that automates the process of assessing the reliability of online medical sites based on textual and non-textual characteristics. To evaluate the proposed approach, we developed a real-world dataset of Arabic web pages that provide medical information. This dataset is the first Arabic medical web page dataset for content credibility evaluation. The hybrid approach was assessed using multiple machine learning and deep learning algorithms on the dataset, providing an accuracy and F1-score of 79% and 77%, respectively. We also identify the most observable patterns that help evaluate or detect unreliable web pages written in Arabic.

Student behavior analysis to measure engagement levels in online learning environments.

After the COVID-19 pandemic, no one refutes the importance of smart online learning systems in the educational process. Measuring student engagement is a crucial step towards smart online learning systems. A smart online learning system can automatically adapt to learners' emotions and provide feedback about their motivations. In the last few decades, online learning environments have generated tremendous interest among researchers in computer-based education. The challenge that researchers face is how to measure student engagement based on their emotions. There has been an increasing interest towards computer vision and camera-based solutions as technology that overcomes the limits of both human observations and expensive equipment used to measure student engagement. Several solutions have been proposed to measure student engagement, but few are behavior-based approaches. In response to these issues, in this paper, we propose a new automatic multimodal approach to measure student engagement levels in real time. Thus, to offer robust and accurate student engagement measures, we combine and analyze three modalities representing students' behaviors: emotions from facial expressions, keyboard keystrokes, and mouse movements. Such a solution operates in real time while providing the exact level of engagement and using the least expensive equipment possible. We validate the proposed multimodal approach through three main experiments, namely single, dual, and multimodal research modalities in novel engagement datasets. In fact, we build new and realistic student engagement datasets to validate our contributions. We record the highest accuracy value (95.23%) for the multimodal approach and the lowest value of "0.04" for mean square error (MSE).