Cybersecurity and Artificial Intelligence Applications: A Bibliometric Analysis Based on Scopus Database

This study investigates the scientific development of Artificial Intelligence (AI) and Machine Learning (ML) applications in Human Resource Management (HRM) through bibliometric analysis. To this end, 522 academic publications indexed in the Scopus database between 2020 and 2024 were analyzed using the R-based bibliometrix package and VOSviewer software. Descriptive analysis, scientific productivity metrics, and content analysis techniques were employed. The findings revealed three main patterns. First, research on AI and ML applications in HRM has grown significantly—particularly between 2022 and 2024—driven by post-pandemic digital transformation. Second, India, China, and the USA lead in research output, while the UK and France demonstrate strong citation impact, indicating a globally expanding research ecosystem. Third, the thematic focus of research is shifting from technical infrastructure toward more human-centered and ethical dimensions. Additionally, keyword co-occurrence network analysis identified three major thematic clusters: HRM functions, AI applications, and machine learning analytics, highlighting the field’s interdisciplinary nature. Compared to the previous studies, this research provides a more comprehensive bibliometric analysis of AI and ML applications in HRM. It is the first extensive study to map the intellectual evolution of the field from a multidisciplinary perspective. Furthermore, it charts research trends and collaboration networks, revealing a shift from technical implementations to strategic integration. In conclusion, this analysis offers new insights to the literature by illustrating the technological evolution in HRM and highlighting the growing significance of cutting-edge approaches such as AI and ML, reaffirming the field as a timely and impactful area of research.

This research aims to describe and map information and research ideas that can be developed in the application of artificial intelligence in higher education. The method applied in this research is Preferred Reporting Items For Systematic Reviews And Meta-Analyses (PRISMA) which uses scientific articles or proceedings sourced from the Scopus database which are analyzed using Vos Viewer and Publish Or Perish as well as Google Trends and Dimensions. The research results reveal that artificial intelligence research covers three aspects that can be applied in higher education: development of AI tools, application of AI in learning, and digitalization of higher education.

Purpose Significant interest in Integrated Management Systems (IMS), as a key area within ISO-related Management System Standards (MSS) literature, has been evident from both academia and industry over the past three decades. This study aims to map the evolution and current state of IMS research and propose possible directions for future studies. Design/methodology/approach A comprehensive content and bibliometric analysis of 846 documents from the Scopus database across the period 1995 to 2023 was conducted. This included performance analysis to track publication trends and identify key contributors, and content analysis to specify dominant research methodologies and the MSS most commonly integrated. Furthermore, science mapping techniques—such as co-authorship networks, keyword co-occurrence analysis, and bibliographic coupling—were utilized to outline the collaborative networks and the conceptual and intellectual structure of the field. Findings The study identifies three principal IMS research themes: the practical implementation of IMS, their role in promoting sustainability and social responsibility, and their impact on continuous performance improvement. It also highlights the field’s evolution and key research constituents—including influential works, prolific authors, leading academic institutions and countries, and top publishing journals. It further reveals that IMS research exhibits strong collaboration across authors and countries, and a rich methodological plurality, notably with a marked increase in empirical surveys in recent years. Additionally, it identifies the most frequently referenced MSS for integration, prominently featuring ISO 9001, ISO 14001, and ISO 45001/OHSAS 18001. Originality/value This study is original in its application of a dual analytical approach—bibliometric and content analysis—to provide a holistic overview of IMS research. It offers new insights into the integration of diverse MSS and proposes several promising paths for future research. Among the most prominent are standardizing IMS fundamental specifications, conducting more empirical research with advanced methods to evaluate the effects of MSS integration, providing practical support for organizations in IMS implementation through tailored methodologies and tools, and exploring the potential of Industry 4.0 and 5.0 technologies to advance IMS practices.

Drug discovery has traditionally been a time consuming and expensive endeavor. Additionally, drugs weren’t as effectively designed as those that are being predicted and developed through AI and ML today. Machine learning is a form of artificial intelligence that develops and evolves based on experience (similarly to the human mind), and is more recently being utilized in drug discovery and design. The integration of AI and ML into the drug discovery and development process has allowed for higher target precision, lower toxicity, and better dosage formulations. AI more generally has been introduced to and has been leveraged at, each step of drug development, including target identification and validation, hit identification, as well as hit to lead optimization, and has been key in shortening the previously lengthy drug screening process. AI and ML has also been applied downstream in drug formulation where it has maximized resource utilization and is allowing for web-based 3D printing of drugs. Application of AI in the drug development process has also been extended to the modeling of novel drug-like compounds to predict their ADMET properties. This review will address the stages of drug discovery and development in which the application of AI and ML modeling has altered the traditional development of drugs. Keywords: Drug discovery, machine learning, artificial intelligence, computational drug development.

Artificial Intelligent (AI) applications in e-health have evolved considerably inthe last 25 years. To track the current research progress in this field, thereis a need to analyze the most recent trend of adopting AI applications ine-health. This bibliometric analysis study covers AI applications in e-health.It differs from the existing literature review as the journal articles areobtained from the Scopus database from its beginning to late 2021 (25 years),which depicts the most recent trend of AI in e-health. The bibliometric analysisis employed to find the statistical and quantitative analysis of availableliterature of a specific field of study for a particular period. An extensiveglobal literature review is performed to identify the significant research area,authors, or their relationship through published articles. It also provides theresearchers with an overview of the work evolution of specific research fields.The study's main contribution highlights the essential authors, journals,institutes, keywords, and states in developing the AI field in e-health.

This study examines publication trends and research developments in the field of digital literacy in education using bibliometric analysis. A total of 389 documents were retrieved from the Scopus database (2005 to mid-2025) using the keyword Digital Literacy and related educational terms. The PRISMA method was applied to ensure data validity and relevance. The analysis shows a significant increase in publications starting in 2013, peaking in 2023. Universitas Negeri Yogyakarta was identified as the most productive institution, while Indonesia led in publication volume (n=54), although its international collaboration rate remained low (12.85%). Authors from the United States and Indonesia contributed most frequently. The journal “Education and Information Technologies” was found to be the most productive. The most cited articles focused on informal learning, digital generations, and non-formal educational settings. Thematic evolution revealed conceptual deepening and the emergence of multidimensional approaches to digital literacy. Eight keyword clusters were identified, with new topics such as “Artificial Intelligence”, “Self-efficacy”, “Augmented Reality”, and “Inclusive Education” highlighting future research directions. These findings indicate that digital literacy in education now spans technical, pedagogical, affective, and social dimensions. However, this study is limited to the Scopus database and only includes data up to mid-2025. Future research is encouraged to explore digital literacy in specific educational settings such as vocational, out-of-school, and hybrid learning. Additional gaps include teacher competence in AI integration, challenges in inclusive digital learning, and the need for locally relevant digital literacy indicators.

Adverse Drug Reactions (ADRs) pose significant health and economic burdens, yet underreporting and inconsistent standards persist. Digital health innovations, particularly mobile and web-based ADR reporting applications, offer potential to enhance pharmacovigilance by improving data accuracy and patient-reported outcomes. The study provides a comprehensive mapping of ADR mobile and web application literature, analysing publication trends, key contributors, and core themes through bibliometric and NLP-based content analysis. A systematic two-stage approach was applied to 289 Web of Science articles on ADR reporting applications. Bibliometric analysis explored publication trends, co-authorship networks, and keyword occurrences, while NLP-based topic modelling identified prevalent themes, ensuring thematic coherence and interpretability. Bibliometric analysis showed a rise in ADR application-related publications, primarily from the United Kingdom, United States of America, and Switzerland. Content analysis identified ten key themes, including pharmacovigilance, chemotherapy adherence, and psychiatry research. A distinct focus on digital tools in ADR reporting and management was evident, with keywords such as "mobile," "application," and "patient" becoming increasingly prominent in recent years. Co-authorship and collaboration networks, however, showed limited cross-national research partnerships. The study highlights the transformative role of digital solutions in pharmacovigilance, demonstrating the potential of ADR applications to enhance reporting accuracy and improve patient safety. However, adoption remains early-stage and fragmented by regional affiliations. Future research should focus on patient-centric app development, effectiveness assessment, and fostering global collaboration. Strengthening digital literacy and robust investment in ADR reporting applications is crucial for optimizing their impact in healthcare.

In recent years, the integration of smartphones into language learning environments has gained significant attention due to its potential to enhance accessibility and effectiveness. This paper presents a bibliometric analysis focusing on the topic of "Arabic Vocabulary Learning Through Smartphone Application". The proliferation of smartphones and mobile applications has revolutionized various aspects of education, including language learning. Arabic language learning, in particular, has witnessed increased interest in leveraging smartphone applications to facilitate vocabulary acquisition. The lack of a systematic analysis of research on Arabic vocabulary learning through smartphone applications hampers the ability to identify emerging trends, key contributors, and high-impact studies in the field. This study employs bibliometric analysis techniques to examine the scholarly output related to Arabic vocabulary learning through smartphone applications. Data were retrieved from the Scopus database, focusing on publications from 2001 to 2023. Various bibliometric indicators, VosViewer version 1.16.20 tools to analyze publication trends, citation counts, co-authorship networks, and thematic analysis, were utilized to analyze the research landscape comprehensively. The analysis reveals a steady increase in publications over time, reflecting the growing interest and research activity in Arabic vocabulary learning through smartphone applications. This bibliometric analysis provides valuable insights into the trends, patterns, and impact of research on Arabic vocabulary learning through smartphone applications. The identified themes and high-impact articles contribute to our understanding of the field's development and offer directions for future research endeavors, educational practices, and policy decisions.

Artificial intelligence applications in supply chain management

Artificial Intelligence is changing the future of museums, opening the door to unprecedented innovations that are revolutionising the way collections are managed and experiences are designed. This bibliometric analysis, covering four decades of research (1983-2024) from the Scopus database, explores how Artificial Intelligence is transforming museum activities — from creating personalised visitor experiences to preserving cultural treasures through advanced digitisation processes. The study shows that the number of publications on Artificial Intelligence has increased dramatically in recent years, reflecting the growing momentum in the field. The integration of AI in museums demands careful reflection on its implications for human rights, cultural values, and social responsibility. As AI adoption accelerates, addressing ethical concerns—such as potential biases, transparency, and accountability—has become increasingly vital. Institutions are encouraged to develop robust ethical frameworks that mitigate these risks, ensuring AI does not reinforce inequalities or distort cultural representation. By embracing AI responsibly, museums can not only enhance visitor engagement and streamline operations but also uphold their commitment to inclusivity, safeguarding their role as stewards of knowledge, culture, and education.

Current insights and future outlooks of energy-efficient desalination plants: A comprehensive bibliometric analysis

Comprehensive guide offering actionable strategies for enhancing human-centered AI, efficiency, and productivity in industrial and systems engineering through the power of AI. Advances in Artificial Intelligence Applications in Industrial and Systems Engineering is the first book in the Advances in Industrial and Systems Engineering series, offering insights into AI techniques, challenges, and applications across various industrial and systems engineering (ISE) domains. Not only does the book chart current AI trends and tools for effective integration, but it also raises pivotal ethical concerns and explores the latest methodologies, tools, and real-world examples relevant to today’s dynamic ISE landscape. Readers will gain a practical toolkit for effective integration and utilization of AI in system design and operation. The book also presents the current state of AI across big data analytics, machine learning, artificial intelligence tools, cloud-based AI applications, neural-based technologies, modeling and simulation in the metaverse, intelligent systems engineering, and more, and discusses future trends. Written by renowned international contributors for an international audience, Advances in Artificial Intelligence Applications in Industrial and Systems Engineering includes information on: Reinforcement learning, computer vision and perception, and safety considerations for autonomous systems (AS)(NLP) topics including language understanding and generation, sentiment analysis and text classification, and machine translationAI in healthcare, covering medical imaging and diagnostics, drug discovery and personalized medicine, and patient monitoring and predictive analysisCybersecurity, covering threat detection and intrusion prevention, fraud detection and risk management, and network securitySocial good applications including poverty alleviation and education, environmental sustainability, and disaster response and humanitarian aid. Advances in Artificial Intelligence Applications in Industrial and Systems Engineering is a timely, essential reference for engineering, computer science, and business professionals worldwide.

Recent advancements in artificial intelligence (AI) applications in medicine have been significant over the past 30 years. To monitor current research developments, it is crucial to examine the latest trends in AI adoption across various medical fields. This bibliometric analysis focuses on AI applications in cardiology. Unlike existing literature reviews, this study specifically examines journal articles published in the last decade, sourced from both Scopus and Web of Science databases, to illustrate the recent trends in AI within cardiology. The bibliometric analysis involves a statistical and quantitative evaluation of the literature on AI application in cardiovascular medicine over a defined period. A comprehensive global literature review is conducted to identify key research areas, authors, and their interrelationships through published works. The leading institutions and most influential authors in research on the role of AI in cardiology were located in the United States, the United Kingdom, and China. This study also provides researchers with an overview of the evolution of research in AI and cardiology. The main contribution of this study is to highlight the prominent authors, countries, journals, institutions, keywords, and trends in the development of AI in cardiology.

This research delves into the application of artificial intelligence in precision farming and crop monitoring, focusing on four AI algorithms: “Support Vector Machine (SVM), Random Forest (RF), Convolutional Neural Networks (CNN), and K-Nearest Neighbors (KNN)”. The objective of improving crop disease detection accuracy and prediction efficiency, along with yield prediction and resource optimization, is targeted. The models were trained to predict crop health and optimize agricultural practices, using a dataset of crop images and environmental data. For the test result, CNN took the leads with an accuracy of 92.5% in disease detection, followed by RF with an accuracy of 89.3%, SVM with an accuracy of 86.7%, and KNN with an accuracy of 81.5%. Additionally, crop yield prediction using a hybrid AI model incorporating meteorological and soil data showed an R-squared value of 0.88, demonstrating strong prediction capabilities. The integration of AI with UAVs and remote sensing technologies allowed for real-time monitoring of crops, providing farmers with actionable insights to optimize resource use. These results demonstrate the possible significant impact of AI on facilitating sustainable farming practices through cost savings, reduced environmental impact, and improved productivity. In general, AI applications in agriculture will revolutionize precision farming by coming up with intelligent data-driven solutions for crop management.

BackgroundArtificial intelligence (AI) has played an important role in the field of medical risk prediction with its strong learning ability and data processing capabilities. With the rapid development of research in this field, it is necessary to conduct quantitative literature analysis to understand the development trends and research hotspots of AI in the field of medical risk prediction.ObjectiveThrough a comprehensive bibliometric analysis, this paper summarizes the development stage and key research hotspots of the application of AI in the field of medical risk prediction in the past 20 years. Additionally, we provide a thorough analysis of emerging trends and future directions, offering insights into how advancements in AI are likely to shape risk prediction methodologies and their clinical applications in the years to come.MethodsRelevant articles from the establishment of the database to 2024 were retrieved through Science Citation Index and Social Sciences Citation Index of the Web of Science Core Collection. Citespace, VOSviewer, Scimago Graphica, Pajek, and other software were used for bibliometric and visual analysis.ResultA total of 2080 articles were included. From 1986 to 2004, this field experienced a slow development period, with the number of papers published per year less than 10. From 2005 to 2020, the number of papers published increased with a linear trend, and entered an exponential rapid growth stage after 2020, with the development entering a mature stage. The United States was the country with the most extensive cooperation and the largest number of publications (652 articles, 31.35%). The diseases, AI technologies, and functions that have received the most attention in this field are cancer, machine learning, and prediction, respectively.ConclusionsArtificial intelligence in medical risk prediction has transitioned from technical exploration to a critical component of clinical practice, expanding from single-disease forecasts to complex, multimodal assessments. Advances in machine learning and personalized medicine have integrated AI into medical decision-making and management, yet widespread adoption requires addressing challenges related to interpretability, privacy, ethics, reliability, and standardization. In the future, AI is expected to significantly enhance prediction accuracy, optimize health management, and advance personalized medicine.