Over the past decade, decentralized digital currencies have gained prominence in finance and technology, but their growth has also drawn adversaries exploiting security vulnerabilities. This paper reviews the literature on cryptocurrency and security using bibliometric analysis of Web of Science and Scopus articles published between 2013 and May 2025. Tools such as Biblioshiny and VOSviewer were employed to explore key trends, influential contributors, collaborative networks, and emerging themes. A novel contribution of this study is the use of Large Language Models (LLMs) to address inconsistent affiliation formats, enabling accurate identification of leading academic organizations. The results demonstrate that LLM-based harmonization effectively prevents misrepresentation in bibliometric datasets. Overall, this study not only summarizes evolving research trends in cryptocurrency and security but also highlights the potential of LLMs to enhance bibliometric methods, suggesting broader applications for improving the accuracy and reliability of future scholarly analyses.

This scientometric analysis of 87 publications provides a detailed and structured overview of the research trends and improvements in the study of automatic brake pedal systems, after a thorough evaluation of these publications, which were selected from the Scopus database using special databases like brake pedal, pedal ratio, and pedal mechanism. It tracks the evolution of research advancements over time. Three primary, interconnected research streams have been identified by the analysis that shapes the modern studies in this domain. The first stream focuses on the mechanical improvement of the pedal assemblies. The second and rapidly growing research stream survey focuses on the integration of brake pedal assemblies into vehicles' structure, especially for electric and automated vehicles. Human vehicle interaction (HVI) is highlighted in the third research stream. This area of study focuses on the ergonomics, biometrics of the driver, role of pedal feedback in situational awareness, and advanced driver-assistance systems (ADAS). The analysis shows an evident progress from the studies focusing on only significant mechanical parameters of the system to a system-scale approach that integrates mechanical engineering, electrical engineering, and control systems. This paper focuses on summarizing these major findings to obtain a basic reference for the field. It highlights the current state of research, emphasizes major studies and future scope of research, including adaptive pedal systems and evaluation of pedal feel in software-defined vehicles.

Consumer decision-making has been transformed by digital platforms and social media. User-generated content (UGC) and electronic word of mouth (eWOM) have a leading role in influencing purchase intention. Despite the increased academic interest. Nevertheless, studies are still disjointed in terms of industries, regions and platforms. This paper is a systematic bibliometric review of eWOM and UGC, and purchase intention. Studies that are published within the last four years, 2020-2024. Based on the Dimensions AI database. 249 open-access articles were analysed using VOSviewer software in terms of performance, co-citation, and co-word analysis. The results indicate that publications have been on a consistent increase, which indicates more academic attention to the subject of digital consumer behaviour. The volume of publications is mostly at the forefront of Southeast Asian universities, in Indonesia. Whereas Western establishments like Swansea University generate fewer but well-cited works. Three themes were identified: eWOM as a marketing strategy, credibility and brand equity in purchase decisions, and platform-specific influences in social commerce. The research has identified credibility, content quality, and platform adaptation as some of the most important elements of consumer trust, which can be used by marketers, researchers, and policymakers.

This paper presents a scientometric and analytical examination of machine learning applications in human factors research using a Scopus indexed dataset of 1,708 publications from 2019 to 2025. Keyword cooccurrence analysis was conducted using VOSviewer with a minimum occurrence threshold of 10, resulting in a network of 75 keywords grouped into eight thematic clusters. The thematic structure indicates a dominant empirical stream focused on cognitive state assessment, especially mental workload and fatigue detection using physiological signals such as electroencephalography and deep learning methods. Complementary clusters emphasize human AI interaction, explainable AI, decision support, automation and human reliability, affective computing, ergonomics in Industry 4.0/5.0 contexts, and healthcare oriented clinical decision support and patient safety. Overlay analysis shows recent growth in generative AI, large language models, fairness, and human centered AI concepts in 2024-2025. An analytical synthesis identifies major gaps, including limited human-in-the-loop validation, weak cross domain generalizability, insufficient longitudinal evaluation of human AI collaboration, and emerging explainability work that remains under validated empirically. The results provide a structured map of the field and a focused research agenda for responsible, transparent, and effective human centered intelligent systems.

Lung cancer remains a major global health burden, and deep learning, especially convolutional neural networks, has become a widely used approach for CT-based detection and diagnostic support. This scientometric study maps how this research area has developed and how its main themes and collaborations are organized, using a workflow that combines bibliometrix (R) with VOSviewer. From a broader Scopus query result set, an exported core subset of 200 journal articles published between 2017 and 2025 was selected for in-depth bibliometric analysis, spanning 139 sources and including 1,659 cited references. The citation overview reports 7,167 total citations across 175 cited documents, corresponding to a corpus h-index of 42. Thematic mapping and keyword co-occurrence analyses show a stable CT-centric backbone that connects most studies, alongside faster-growing streams focused on model generalization and optimization, and smaller niche lines such as 3D CNN–driven lung nodule detection. Collaboration indicators point to moderate internationalization, with 43 multi-country publications (21.5%). Overall, the field is consolidating around CT-based deep learning pipelines, while a key gap remains in translation-ready evidence, multi-center external validation, robustness to protocol heterogeneity, and more consistent reporting practices to support reproducibility and clinical adoption.