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Artificial intelligence (AI) has emerged as an innovative technology with the potential to revolutionize various industries including libraries and information science. Academic libraries are increasingly adopting artificial intelligence (AI) to enhance services, improve efficiency, and enhance user experience. This study utilizes a bibliometric approach to comprehensively analyze current research on AI in academic libraries (AI in ALs). This study employed bibliometric indicators to identify key trends, patterns, and research gaps in the existing literature. A comprehensive dataset of 373 research papers on AI in ALs published between 2002 and 2022 was collected and analyzed using the Scopus database. Various bibliometric tools, such as Biblioshiny, VOSviewer, and BibExcel, have enhanced this analysis. The findings of this study provide important insights. By 2022, there were 64 publications, constituting 17.16% of the total corpus, accompanied by 65 citations. In contrast, 2019 witnessed only 33 publications yet accumulated a substantial number of citations, amounting to 294, representing 8.85% of the overall citations. Conference papers exhibited the highest frequency among different publication types, with 165 publications, whereas journal articles had the highest citation count, accumulating 217 citations. Geographically, China emerged as the leading contributor with 119 publications, and Wuhan University stood out as the most prominent affiliation. Notably, the "Lecture Notes in Computer Science" series emerged as the most prolific source title, publishing 15 articles, of which eight were cited. The authors Wang J., Wang C., and Wang X. from China demonstrated significant contributions, consistently publishing four papers annually from 2010 to 2022. [ABSTRACT FROM AUTHOR]

In this study, a bibliometric analysis technique is used for performance analysis and science mapping of artificial intelligence (AI) applications in streamflow research. This paper examines the current trends in the literature using the Scopus database over the last 37 years. RStudio Bibliometrix software was used to analyse the titles, keywords, abstracts, and full texts of 3000 publications to identify trends in AI models, publication types, journals, citations, authors, countries, and regions. The highest frequency AI-related keyword is "artificial neural networks," which was used in a total of 25587 times. The most common publication type, at 82.1%, is journal articles, and the highest rate of country production is 25% for China. In recent years, streamflow research studies have significantly increased their use of AI applications. [ABSTRACT FROM AUTHOR]

AbstractThe user experience and usability of Wearable Health Devices (WHD-UXU) play a pivotal role in determining user acceptance. However, there is a scarcity of comprehensive research systematically addressing the current focus and future research directions of WHD-UXU using bibliometric analysis. Therefore, to unveil the latest research progress and the evolutionary trajectory of WHD-UXU research hotspots over the past decade, this study leverages 365 papers related to WHD-UXU gathered from the Web of Science (WOS) database. Employing bibliometric methods and integrating tools like VOSviewer and CiteSpace, a knowledge map is formulated, encompassing aspects such as keyword co-occurrence, references co-citation, and research collaboration networks. The research findings highlight that current WHD-UXU research hotspots predominantly revolve around (1) the study of usability in wearable health devices, (2) research on mobile health of wearable health devices, (3) the study of health monitoring in wearable health devices, (4) research on rehabilitation treatment of wearable health devices, and (5) the study of user acceptance in wearable health devices. Future research is anticipated to focus on areas such as human-computer interaction, affective computing, artificial intelligence, human-machine trust, user self-disclosure, privacy algorithms, and system design evaluation. Finally, the study addresses future research directions and challenges in this field. [ABSTRACT FROM AUTHOR]