Analysis of web design visual element attention based on user educational background.

The evolution of Internet technology has led to an increase in online users. This study focuses on the pivotal role of visual elements in web content conveyance and their impact on user browsing behavior. Therefore, the use of visual elements in web design based on big data has aroused widespread concern among web designers, they apply visual elements to their web design works to make the web more attractive. This study examines the composition and distribution characteristics of key visual elements identified through user behavior data in a big data environment and discusses the use of visual elements in web design in the era of network economy. In addition, this paper issued 200 questionnaires to investigate the degree of attention to visual elements in web pages for users of different occupations and different educational backgrounds. Our survey indicated that visual elements captured the attention of 41% of corporate employees, whereas a mere 1% of social welfare workers focused on web content; 36% of undergraduates pay attention to visual elements of web pages, but only 5% and 4% of postgraduates and doctoral degrees and above. Therefore, the visual elements of the designed web page need to conform to the user's cultural background and professional background.

Automated Brain Tumor Detection Using Machine Learning: A Bibliometric Review.

To develop a research overview of brain tumor classification using machine learning, we conducted a systematic review with a bibliometric analysis. Our systematic review and bibliometric analysis included 1747 studies of automated brain tumor detection using machine learning reported in the previous 5 years (2019-2023) from 679 different sources and authored by 6632 investigators. Bibliographic data were collected from the Scopus database, and a comprehensive bibliometric analysis was conducted using Biblioshiny and the R platform. The most productive and collaborative institutes, reports, journals, and countries were determined using citation analysis. In addition, various collaboration metrics were determined at the institute, country, and author level. Lotka's law was tested using the authors' performance. Analysis showed that the authors' publication trends followed Lotka's inverse square law. An annual publication analysis showed that 36.46% of the studies had been reported in 2022, with steady growth from previous years. Most of the cited authors had focused on multiclass classification and novel convolutional neural network models that are efficient for small training sets. A keyword analysis showed that "deep learning," "magnetic resonance imaging," "nuclear magnetic resonance imaging," and "glioma" appeared most often, proving that of the several brain tumor types, most studies had focused on glioma. India, China, and the United States were among the highest collaborative countries in terms of both authors and institutes. The University of Toronto and Harvard Medical School had the highest number of affiliations with 132 and 87 publications, respectively.

Enhanced Approach Using Reduced SBTFD Features and Modified Individual Behavior Estimation for Crowd Condition Prediction.

Sensor technology provides the real-time monitoring of data in several scenarios that contribute to the improved security of life and property. Crowd condition monitoring is an area that has benefited from this. The basic context-aware framework (BCF) uses activity recognition based on emerging intelligent technology and is among the best that has been proposed for this purpose. However, accuracy is low, and the false negative rate (FNR) remains high. Thus, the need for an enhanced framework that offers reduced FNR and higher accuracy becomes necessary. This article reports our work on the development of an enhanced context-aware framework (EHCAF) using smartphone participatory sensing for crowd monitoring, dimensionality reduction of statistical-based time-frequency domain (SBTFD) features, and enhanced individual behavior estimation (IBEenhcaf). The experimental results achieved 99.1% accuracy and an FNR of 2.8%, showing a clear improvement over the 92.0% accuracy, and an FNR of 31.3% of the BCF.