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.

Heart disease is one of the most common causes for death among human nations for many years. There have been substantial efforts to reduce heart diseases in the world. It is essential to implement the recent advances of data science to discover any symptoms of cardiovascular disease (CVD). Machine learning (ML) has given scientists a tool to detect early causes of such disease and this survey uses the combination of ML and CVD as a search keyword to determine 200 highly cited articles from the Scopus database. The study performs a survey on the data which were published from 2018 to 2025 and present possible road-map for future studies. The results indicate that a significant number of highly cited articles are published in Open Access journals such as PlosOne, IEEE Access and Scientific Report. In addition, the study presents seven different areas of research which have been under significant progress.

Acute Myeloid Leukemia (AML) remains a significant hematological malignancy where early and accurate diagnosis is critical for improving patient outcomes and therapeutic stratification. With the emerging understanding of liquid biopsies, research on extracellular vesicles (EVs), such as exosomes, and their microRNA (miRNA) cargo has evolved as a promising domain for AML diagnostics. However, a systematic review is imperative not only to consolidate existing knowledge but also to chart relevant and timely pathways for future research in the AML diagnostics domain. Against this backdrop, in this study, we conduct a bibliometric analysis of research at the intersection of Acute Myeloid Leukemia and exosome-based biomarkers. Our aim is to map the intellectual structure of the field, identify thematic clusters and influential works, and surface emerging and underexplored directions. The Web of Science database was queried on October 01, 2025, using a comprehensive Boolean search string against three thematic pillars: (1) Disease Focus, with the terms “Acute Myeloid Leukemia*”, “AML”, “APL”, "AML-M1", "AML-M6", and related synonyms; (2) Vesicle/RNA Technology, including “Exosome*”, “extracellular vesicle*”, "microvesicle*", and “miRNA” ; and (3) Biomarker Application, using keywords such as “Biomarker*”, “Early Diagnosis”, and “Liquid Biopsies”. We retrieved an initial corpus of 714 documents. Upon applying a two-stage curation protocol based on predefined inclusion criteria (such as language restrictions), a final analytical sample of 710 documents was established for the robust bibliometric analysis. From our analysis, we confirm that the burgeoning domains of AML diagnostics and exosome-based biomarkers are rapidly expanding, with specific molecules like miRNAs and exosomal proteins emerging as central enablers, research focusing on prognostic stratification and residual disease monitoring (MRD) integration, and clear gaps remaining in methodological standardization (e.g., EV isolation) and clinical validation, highlighting promising directions for future investigation.

In contemporary medicine, two trends are of particular significance: the establishment of Telehealth as a fundamental component of healthcare delivery, and the emergence of Large Language Models (LLMs) as a transformative category of artificial intelligence. The convergence of these domains has created a new, dynamic, and rapidly expanding research frontier. Despite this escalating interest, a comprehensive map of the field's intellectual foundations, key contributors, and thematic progression has yet to be established. This study addresses this critical knowledge gap by employing a quantitative scientometric methodology to systematically map the intellectual structure and evolutionary trajectory of research at the intersection of Telehealth and LLMs. We conducted a comprehensive bibliometric analysis of 670 scientific documents extracted from the Web of Science (WoS) Core Collection database, spanning the period from 1997 to Oct 2025. The analysis utilized performance metrics and network mapping tools to identify publication dynamics, influential actors, and core conceptual themes. The findings reveal a field in a state of “Hypergrowth“, characterized by an exponential increase in scientific production, particularly after 2021. The United States and China are identified as the dominant leaders in research output. Thematic analysis demonstrates a clear paradigm shift within the literature: an evolution from a broad focus on general artificial intelligence and machine learning applications toward a more specialized and intense concentration on the capabilities and implications of LLMs and Generative AI. This research provides the first large-scale quantitative map of the Telehealth and LLM landscape. It documents a field that is maturing at an accelerated rate, creating an urgent need for scholarly and practical frameworks that bridge the gap between rapid technological innovation and the slower-moving, yet critical, domains of clinical validation, regulatory oversight, and ethical considerations. The insights provided herein offer a data-driven foundation for researchers, policymakers, and practitioners to navigate and contribute to this critical and rapidly evolving field.

The COVID-19 pandemic has created an exponential trend in global research mobilization, with vaccines as a primary objective. This present survey gives a scientometric study on the landscape of research concerning the COVID-19 pandemic and vaccines, using a dataset of 19,749 records from the Scopus database where only 200 records are chosen for the review. The analysis maps the conceptual structure and dynamics of this area by looking at the publication trends, key contributors, core research themes, and impact of the published papers. The results have disclosed an exponential trend in publications, peaking in 2021-2022, driven by the quick requirement for development, evaluating, and deploying vaccines. The study was investigated by large-scale international collaborations, with prolific contributions from universities as well as vaccine makers in the United States, China, and Europe. High-impact journals like The Lancet and The New England Journal of Medicine considered critical dissemination channels. Thematic clusters are dominated by vaccine development and immunology, real-world effectiveness, SARS-CoV-2 variants and immune evasion, vaccine safety, and public acceptance. The evolution of study is concentrated on from initial clinical trials to real-world evidence, variant-specific challenges, and eventually, long-term impact and systemic lessons. The survey gives a comprehensive review of a defining scientific effort, showing the collaborative and rapid-response nature of research during a global health crisis.

Bidirectional Encoder Representations from Transformers (BERT) has become a paradigm shift in natural language processing (NLP). This scientometric study analyzes a curated dataset of 200 highly-cited Scopus publications to map the intellectual landscape and research trajectories catalyzed by BERT and other transformer models. The study discloses a quick evolution from foundational architectural innovations and pre-training paradigms to widespread domain adaptation, rigorous model optimization for efficiency, and critical examination of model capabilities and societal effects. The literature shows BERT's role as a foundational model, successfully implemented in diverse fields such as biomedicine, education, and software engineering, while simultaneously spurring substantial research into compression, quantization, and efficient inference. A parallel and influential strand of studies emerged concentrated on “BERTology”, giving the linguistic knowledge and biases encoded within these models, and addressing ethical concerns regarding their deployment. The study synthesizes these developments, presenting how BERT not only set new performance benchmarks but also built a new paradigm for transfer learning and spurred a self-critical research community, ultimately paving the way for the present era of large language models.

At the edge of the network, the exponential increase of data produced along with the growing concerns over data privacy coming from regulations and society have all together triggered the rise of Federated Learning (FL) as the main approach in distributed machine learning (DML). Fed learning allows the model training to be performed on decentralized devices or data silos even without the raw data being transferred. Hence, FL is completely in line with the objectives of the privacy-preserving techniques. In this paper, we carry out a scientometric analysis on the 200 most cited papers, which are the first 200 papers at the intersection of "Distributed Machine Learning," "Federated Learning," and "Privacy-Preserving" published between 2020 and 2024, and the Scopus database is where they are indexed. The literature of publication trends, prominent authors and works, the thematic clusters, and research fronts that are changing are all systematically examined in this study; hence, the intellectual landscape of this fast developing field is mapped out. Our findings point to the existence of certain streams of research such as the algorithms with differential privacy being the mainstay, secure aggregation methods through the use of homomorphic encryption and multi-party computation, blockchain-based FL systems which ensure security and trust, and resource-efficient FL that supports IoT and edge computing. The results also show an area that is nearly enjoying a complete transformation as a result of the overpowering need to address the triad of model quality, data protection, and system efficiency. The review not only encourages researchers, and practitioners but also helps the policymakers by providing the current trend to which the key challenges can be identified and the future directions in privacy-preserving distributed intelligence anticipated.