Study Purpose: This study aims to critically evaluate ChatGPT's impact on cybersecurity in healthcare and education sectors.
Methods: This study employed a cross-sectional survey design, collecting data from healthcare and educational professionals in Saudi Arabia through a structured questionnaire, with 205 healthcare workers' and 214 educators. The survey assessed perceptions of ChatGPT's impact on cybersecurity opportunities and challenges, with data analyzed using descriptive statistics and ANOVA to explore differences across professional roles.
This study aims to assess the effectiveness of ChatGPT in remote learning among medical students. This cross-sectional survey study recruited 386 medical students from three public universities in Saudi Arabia. Participants completed an online questionnaire designed to assess perceptions of ChatGPT's effectiveness in remote learning.
View Article and Find Full Text PDFStudy Purpose: This study aims to assess the effectiveness of ChatGPT in critical thinking skills among medical students.
Methods: This cross-sectional survey study recruited 392 medical students from three public universities in Saudi Arabia. Participants completed an online questionnaire assessing perceptions of ChatGPT's impact on critical thinking skills.
In the rapidly evolving landscape of modern technology, the convergence of blockchain innovation and machine learning advancements presents unparalleled opportunities to enhance computer forensics. This study introduces SentinelFusion, an ensemble-based machine learning framework designed to bolster secrecy, privacy, and data integrity within blockchain systems. By integrating cutting-edge blockchain security properties with the predictive capabilities of machine learning, SentinelFusion aims to improve the detection and prevention of security breaches and data tampering.
View Article and Find Full Text PDFCOVID-19, a novel pathogen that emerged in late 2019, has the potential to cause pneumonia with unique variants upon infection. Hence, the development of efficient diagnostic systems is crucial in accurately identifying infected patients and effectively mitigating the spread of the disease. However, the system poses several challenges because of the limited availability of labeled data, distortion, and complexity in image representation, as well as variations in contrast and texture.
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