Search

Highly cited articles capture the attention of significant contributors in the research community as an opportunity to improve knowledge, source of ideas or solutions, and advance their research in general. Typically, these articles are authored by a large number of scientists with international collaboration. However, this could not be the only reason for an article to be highly cited, there might be several other characteristics for an article to be more attractive to researchers and readers. In other words, there are a few other characteristics that help articles/papers to be more than others to appear in search engines or to grab readers’ attention. In this study, we modeled several machine-learning methods with a set of articles, and journal characteristics including authors-count, title characteristics, abstract length, international collaboration, number of keywords, funding information, journal characteristics, etc. We extracted 20 characteristics and developed multiple machine-learning models to automate highly-cited papers recognition from regular papers. In experiments conducted with an ensemble machine learning algorithm, 97% recognition accuracy was achieved. Other algorithms including a deep learning method using LSTMs also achieved high recognition accuracy. Such high performances can be utilized for a promising HCP auto-detection system in the future. [ABSTRACT FROM AUTHOR]

Background: Natural language processing, such as ChatGPT, demonstrates growing potential across numerous research scenarios, also raising interest in its applications in public health and epidemiology. Here, we applied a bibliometric analysis for a systematic assessment of the current literature related to the applications of ChatGPT in epidemiology and public health. Methods: A bibliometric analysis was conducted on the Biblioshiny web-app, by collecting original articles indexed in the Scopus database between 2010 and 2023. Results: On a total of 3431 original medical articles, "Article" and "Conference paper", mostly constituting the total of retrieved documents, highlighting that the term "ChatGPT" becomes an interesting topic from 2023. The annual publications escalated from 39 in 2010 to 719 in 2023, with an average annual growth rate of 25.1%. In terms of country production over time, the USA led with the highest overall production from 2010 to 2023. Concerning citations, the most frequently cited countries were the USA, UK, and China. Interestingly, Harvard Medical School emerges as the leading contributor, accounting for 18% of all articles among the top ten affiliations. Conclusions: Our study provides an overall examination of the existing research interest in ChatGPT's applications for public health by outlining pivotal themes and uncovering emerging trends. [ABSTRACT FROM AUTHOR]

The advancement of Artificial Intelligence (AI) is progressing rapidly, compelling companies to integrate it within their operational frameworks to sustain competitiveness, primarily driven by its impact on organizational agility (OA). Nevertheless, the absence of a robust theoretical framework underscores the limited understanding of the relationship between AI and OA. Within this context, the research aims to establish foundational knowledge, delineate the evolutionary trajectory of the topic, and identify prospective avenues for inquiry. To achieve this objective, bibliometric analysis is employed to gain comprehensive insights into the interplay between these variables and discern trends within this research domain. The utilization of the Web of Science (WoS) and Scopus databases up to January 2024 facilitates data collection, while Bibliometrix and Visme are instrumental in crafting a scientific production map. The analysis corroborates the novelty and growth potential of the subject matter, underscoring heightened author interest, particularly evident in 2023, against a backdrop of sparse and temporally dispersed publications until 2017. Notably, the prevalence of conference papers on this topic stands significantly high at 26.98 % in comparison to the total contributions, indicative of the research community's engagement. Furthermore, the findings underscore a robust association between the keywords AI and OA, delineating a burgeoning research domain that converges with the digital transformation of enterprises and the Theory of Standardization Process. The effective integration of AI into corporate operational frameworks marks the zenith of this transformative process, ushering in the genesis and overhaul of organizational routines. This study represents a pioneering endeavour within the literature, as it constitutes the inaugural bibliometric exploration of this subject matter. Moreover, it serves to underpin the establishment of theoretical underpinnings for future research endeavours as it outlines current trends and emerging future research trajectories, concerning the role of AI in OA. [ABSTRACT FROM AUTHOR]

Keeping up with the hastily growing economy implies undergoing unremitting transformation permanently. In the field of supply chain, such progress can only be guaranteed via the exploration of new horizons and innovative solutions in response to the constraints of the global market. Emerging technologies, particularly artificial intelligence, offer promising avenues for enhancing supply chain processes, with sustainability ascending as a critical consideration. Despite the recent surfacing of AI-driven applications, scant attention has been devoted to exploring their full potential within supply chain operations, particularly in conjunction with SDGs. Recognizing the untapped opportunities presented by the implementation of AI for a sustainable supply chain this study undertakes a bibliometric analysis of 236 research papers sourced from the Web of science database. The analysis utilizes R language BiblioShiny to examine the extracted papers, and dissect patterns, trends, and relationships among key concepts and themes as well as prominent topics, impactful authors, and leading journals and countries in this domain. The findings reveal substantial growth in research related to SCM, AI, and sustainability as the UK leads this field of study with 132 articles followed by India, China and the USA. Eventually, the National University of Singapore came first in terms of paper affiliations, followed by De La Salle University, and London Metropolitan University. These results only prove that sustainability is becoming more critical in the equation of AI-driven supply chains especially with the current socio-political and economic circumstances, constituting a solid base for further academic research and more innovations in the managerial and business-related policies in this field. [ABSTRACT FROM AUTHOR]

This paper presents a bibliometric systematic review on model-based learning analytics (MbLA), which enable coupling between teachers and intelligent systems to support the learning process. This is achieved through systems that make their models of student learning and instruction transparent to teachers. We use bibliometric network analysis and topic modelling to explore the synergies between the related research groups and the main research topics considered in the 42 reviewed papers. Network analysis depicts an early stage community, made up of several research groups, mainly from the fields of learning analytics and intelligent tutoring systems, which have had little explicit and implicit collaboration but do share a common core literature. Th resulting topics from the topic modelling can be grouped into the ones related to teacher practices, such as awareness and reflection, learning orchestration, or assessment frameworks, and the ones related to the technology used to open up the models to teachers, such as dashboards or adaptive learning architectures. Moreover, results show that research in MbLA has taken an individualistic approach to student learning and instruction, neglecting social aspects and elements of collaborative learning. To advance research in MbLA, future research should focus on hybrid teacher–AI approaches that foster the partnership between teachers and technology to support the learning process, involve teachers in the development cycle from an early stage, and follow an interdisciplinary approach. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to explore the trend and status of international collaboration in the field of artificial intelligence (AI) and to understand the hot topics, core groups, and major collaboration patterns in global AI research. Design/methodology/approach: We selected 38,224 papers in the field of AI from 1985 to 2019 in the core collection database of Web of Science (WoS) and studied international collaboration from the perspectives of authors, institutions, and countries through bibliometric analysis and social network analysis. Findings: The bibliometric results show that in the field of AI, the number of published papers is increasing every year, and 84.8% of them are cooperative papers. Collaboration with more than three authors, collaboration between two countries and collaboration within institutions are the three main levels of collaboration patterns. Through social network analysis, this study found that the US, the UK, France, and Spain led global collaboration research in the field of AI at the country level, while Vietnam, Saudi Arabia, and United Arab Emirates had a high degree of international participation. Collaboration at the institution level reflects obvious regional and economic characteristics. There are the Developing Countries Institution Collaboration Group led by Iran, China, and Vietnam, as well as the Developed Countries Institution Collaboration Group led by the US, Canada, the UK. Also, the Chinese Academy of Sciences (China) plays an important, pivotal role in connecting the these institutional collaboration groups. Research limitations: First, participant contributions in international collaboration may have varied, but in our research they are viewed equally when building collaboration networks. Second, although the edge weight in the collaboration network is considered, it is only used to help reduce the network and does not reflect the strength of collaboration. Practical implications: The findings fill the current shortage of research on international collaboration in AI. They will help inform scientists and policy makers about the future of AI research. Originality/value: This work is the longest to date regarding international collaboration in the field of AI. This research explores the evolution, future trends, and major collaboration patterns of international collaboration in the field of AI over the past 35 years. It also reveals the leading countries, core groups, and characteristics of collaboration in the field of AI. [ABSTRACT FROM AUTHOR]

Fake news is an explosive subject, being undoubtedly among the most controversial and difficult challenges facing society in the present-day environment of technology and information, which greatly affects the individuals who are vulnerable and easily influenced, shaping their decisions, actions, and even beliefs. In the course of discussing the gravity and dissemination of the fake news phenomenon, this article aims to clarify the distinctions between fake news, misinformation, and disinformation, along with conducting a thorough analysis of the most widely read academic papers that have tackled the topic of fake news research using various machine learning techniques. Utilizing specific keywords for dataset extraction from Clarivate Analytics' Web of Science Core Collection, the bibliometric analysis spans six years, offering valuable insights aimed at identifying key trends, methodologies, and notable strategies within this multidisciplinary field. The analysis encompasses the examination of prolific authors, prominent journals, collaborative efforts, prior publications, covered subjects, keywords, bigrams, trigrams, theme maps, co-occurrence networks, and various other relevant topics. One noteworthy aspect related to the extracted dataset is the remarkable growth rate observed in association with the analyzed subject, indicating an impressive increase of 179.31%. The growth rate value, coupled with the relatively short timeframe, further emphasizes the research community's keen interest in this subject. In light of these findings, the paper draws attention to key contributions and gaps in the existing literature, providing researchers and decision-makers innovative viewpoints and perspectives on the ongoing battle against the spread of fake news in the age of information. [ABSTRACT FROM AUTHOR]

The importance of artificial intelligence in the banking industry is reflected in the speed at which financial institutions are adopting and implementing AI solutions to improve their services and adapt to new market demands. The aim of this research is to conduct a bibliometric analysis of the involvement of artificial intelligence in the banking sector to provide a comprehensive overview of the current state of research to guide future directions and support the sustainable development of this rapidly expanding field. Another important objective is to identify research gaps and underexplored areas in the field of artificial intelligence in banking. The methodology used is a bibliometric analysis using VOSviewer, analysing 1089 papers from the Web of Science database. The results of the study provide relevant information for banking professionals but also for policy makers. Thus, the study highlights key areas where banks are using artificial intelligence to gain competitive advantage, thereby guiding practitioners in strategic decision making. Moreover, by identifying emerging trends and patterns in AI adoption, the study helps banking practitioners with foresight, enabling them to anticipate and prepare for future developments in the field. In terms of governmental implications, the study can contribute to the development of more nuanced regulatory frameworks that effectively balance the promotion of AI innovation with the protection of ethical standards and consumer protection. [ABSTRACT FROM AUTHOR]

The intersection of Cybersecurity and AI has garnered increasing attention in recent years due to the growing importance of securing digital assets in an interconnected world. This bibliometric analysis aims to provide valuable insights into the research trends and developments within this interdisciplinary domain. Using data extracted from the Scopus database, a total of 501 papers were selected and analyzed to uncover key patterns and themes. The methodology involved conducting a comprehensive literature search using specific keywords related to Cybersecurity and AI applications. The initial search yielded 736 papers, which were subsequently filtered to include research articles, conference papers, editorial papers, and review papers, resulting in the final dataset of 501 papers. The analysis of publication trends revealed a remarkable surge in research output since 2015, indicating the escalating interest in this field. Collaboration patterns among researchers and institutions were analyzed through co-authorship networks, highlighting a well-connected research community that fosters knowledge exchange. Keyword analysis exposed common areas of application, such as network security, deep learning, and the Internet of Things, underscoring the importance of AI technologies in enhancing Cybersecurity measures. Furthermore, examination of the most cited documents showcased influential contributions that have shaped the trajectory of Cybersecurity and AI research. The study emphasizes the significance of Cybersecurity and AI applications research, considering the ever-increasing reliance on technology in various aspects of modern life. By integrating AI technologies, Cybersecurity measures can be fortified with automated threat detection, adaptive defense mechanisms, and proactive risk mitigation, thereby bolstering overall cybersecurity resilience. The findings of this bibliometric analysis have several implications for researchers and policymakers. Researchers can leverage the identified trends and gaps to explore new research directions and potential collaborations. Policymakers can utilize these insights to make informed decisions regarding resource allocation for research initiatives aimed at addressing emerging Cybersecurity challenges. This bibliometric analysis provides a comprehensive overview of the evolving landscape of Cybersecurity and AI applications research. It underscores the growing importance of this interdisciplinary field and its potential to reshape the future of cybersecurity. As technology continues to advance, the integration of AI in Cybersecurity will play a pivotal role in safeguarding digital assets and ensuring the secure functioning of critical systems in an increasingly interconnected world. [ABSTRACT FROM AUTHOR]

Objectives: Early gastric cancer (EGC) is defined as aggressive gastric cancer involving the gastric mucosa and submucosa. Early detection and treatment of gastric cancer are beneficial to patients. In recent years, many studies have focused on endoscopic diagnosis and therapy for EGC. Exploring new methods to analyze data to enhance knowledge is a worthwhile endeavor, especially when numerous studies exist. This study aims to investigate research trends in endoscopy for EGC over the past 20 years using bibliometric analysis. Methods: Original articles and reviews examining the use of endoscopy for EGC published from 2000 to 2022 were retrieved from the Web of Science Core Collection, and bibliometric data were extracted. Microsoft Office Excel 2016 was used to show the annual number of published papers for the top 10 countries and specific topics. VOSviewer software was used to generate network maps of the cooccurrences of keywords, authors, and topics to perform visualization network analysis. Results: In total, 1,009 published papers met the inclusion criteria. Japan was the most productive country and had the highest number of publications (452, 44.8%), followed by South Korea (183, 18.1%), and China (150, 14.9%). The National Cancer Center of Japan was the institution with the highest number of publications (48, 4.8%). Ono was the most active author and had the highest number of cited publications. Through the network maps, exploring endoscopic diagnosis and therapy were major topics. Artificial intelligence (AI), convolutional neural networks (CNNs), and deep learning are hotspots in endoscopic diagnosis. Helicobacter pylori eradication, second-look endoscopy, and follow-up management were examined. Conclusions: This bibliometric analysis investigated research trends regarding the use of endoscopy for treating EGC over the past 20 years. AI and deep learning, second-look endoscopy, and management are hotspots in endoscopic diagnosis and endoscopic therapy in the future. [ABSTRACT FROM AUTHOR]

The field of artificial intelligence is starting to permeate all aspects of society and management is no exception. From digital transformation, marketing and industry 4.0 to privacy and ethics, the significant growth of the number of papers being published each year makes it difficult to assess the state of research, the current topics that academia is focusing on and how these topics are evolving over the years. This paper aims to highlight the main topics of this complex and central theme and showcase the evolution of the field through a structured bibliometric analysis of all business-relevant articles and conference paper published in this interval. Leveraging the SCOPUS database, a number of 4763 papers have been identified and analysed, revealing a number of new insights into how the study of artificial intelligence is evolving. Although we are looking at a global perspective, in subsidiary we also observe and compare how Romania is faring against the other global players. [ABSTRACT FROM AUTHOR]

Delayed graft function (DGF) is an early complication after kidney transplantation. The literature on DGF has experienced substantial growth. However, there is a lack of bibliometric analysis of DGF. This study aimed to analyze the scientific outputs of DGF and explore its hotspots from 2013 to 2023 by using CiteSpace and VOSviewer. The 2058 pieces of literature collected in the Web of Science Core Collection (WOSCC) from 1 January 2013 to 31 December 2023 were visually analyzed in terms of the annual number of publications, authors, countries, journals, literature co-citations, and keyword clustering by using CiteSpace and VOSviewer. We found that the number of papers published in the past ten years showed a trend of first increasing and then decreasing; 2021 was the year with the most posts. The largest number of papers was published by the University of California System, and the largest number of papers was published by the United States. The top five keyword frequency rankings are: 'delayed graft function', 'kidney transplantation', 'renal transplantation', 'survival', and 'recipients'. These emerging trends include 'brain death donors', 'blood absence re-injection injuries', 'tacrolimus', 'older donors and recipients', and 'artificial intelligence and DGF'. In summary, this study reveals the authors and institutions that could be cooperated with and discusses the research hotspots in the past ten years. It provides a reference and direction for future research and application of DGF. [ABSTRACT FROM AUTHOR]

In an era where all information can be reached with one click and by using the internet, the risk has increased in a significant manner. Deepfakes are one of the main threats on the internet, and affect society by influencing and altering information, decisions, and actions. The rise of artificial intelligence (AI) has simplified the creation of deepfakes, allowing even novice users to generate false information in order to create propaganda. One of the most prevalent methods of falsification involves images, as they constitute the most impactful element with which a reader engages. The second most common method pertains to videos, which viewers often interact with. Two major events led to an increase in the number of deepfake images on the internet, namely the COVID-19 pandemic and the Russia–Ukraine conflict. Together with the ongoing "revolution" in AI, deepfake information has expanded at the fastest rate, impacting each of us. In order to reduce the risk of misinformation, users must be aware of the deepfake phenomenon they are exposed to. This also means encouraging users to more thoroughly consider the sources from which they obtain information, leading to a culture of caution regarding any new information they receive. The purpose of the analysis is to extract the most relevant articles related to the deepfake domain. Using specific keywords, a database was extracted from Clarivate Analytics' Web of Science Core Collection. Given the significant annual growth rate of 161.38% and the relatively brief period between 2018 and 2023, the research community demonstrated keen interest in the issue of deepfakes, positioning it as one of the most forward-looking subjects in technology. This analysis aims to identify key authors, examine collaborative efforts among them, explore the primary topics under scrutiny, and highlight major keywords, bigrams, or trigrams utilized. Additionally, this document outlines potential strategies to combat the proliferation of deepfakes in order to preserve information trust. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) technologies are increasingly being applied to the shipping industry to advance its development. In this study, 476 articles published in the Science Citation Index Expanded (SCI-EXPANDED) and the Social Sciences Citation Index (SSCI) of the Web of Science Core Collection from 2001 to 2022 were collected, and bibliometric methods were applied to conduct a systematic literature of the field of AI technology applications in the shipping industry. The review commences with an annual publication trend analysis, which shows that research in the field has been growing rapidly in recent years. This is followed by a statistical analysis of journals and a collaborative network analysis to identify the most productive journals, countries, institutions, and authors. The keyword "co-occurrence analysis" is then utilized to identify major research clusters, as well as hot research directions in the field, providing directions for future research in the field. Finally, based on the results of the keyword co-occurrence analysis and the content analysis of the papers published in recent years, the research gaps in AIS data applications, ship trajectory, and anomaly detection, as well as the possible future research directions, are discussed. The findings indicate that AIS data in the future research direction are mainly reflected in the analysis of ship behavior and AIS data repair. Ship trajectory in the future research direction is mainly reflected in the deep learning-based method research and the discussion of ship trajectory classification. Anomaly detection in the future research direction is mainly reflected in the application of deep learning technology in ship anomaly detection and improving the efficiency of ship anomaly detection. These insights offer guidance for researchers' future investigations in this area. In addition, we discuss the implications of research in the field of shipping AI from both theoretical and practical perspectives. Overall, this review can help researchers understand the status and development trend of the application field of AI technology in shipping, correctly grasp the research direction and methodology, and promote the further development of the field. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to analyze and explore the research studies on Big Data in relation with Artificial Intelligence domain, which published in Peer Review Journals and indexed in Web of Science Core Collection for the period of 2008-2019 years. The publication data for our research analysis "Big Data in relation with Artificial Intelligence" has been derived from the Web of Science (WoS) Core Collection database (Indexes included SCI Expanded and SSCI). The Bibliometric Analysis Methods is applied for the study in order to find out the relations between two domains and to investigate the status of scientific development level in the research era. Therefore, our research concentrates and highlights the current issues discussed and studied by the scholars around the globe. This paper would useful for researchers to show the publication trends on big data in relation with artificial intelligence research outcomes in highly reputable SCI-Exp and SSCI journal (ranked by WoS). [ABSTRACT FROM AUTHOR]

An in-depth analysis of Internet of Things (IoT) applications expertise for agriculture. This article's primary purpose is to provide a comprehensive and organized review of IoT research in agriculture themes. Although recent research has offered some pertinent information regarding the analysis of IoT applications in agriculture, further information is needed. Bibliometric analysis is utilized to objectively investigate, and develop information knowledge of IoT applications in agriculture. The papers investigated and examined the themes of IoT-agriculture by analyzing the co-occurrence keywords. The analysis began by picking 550 papers from the Scopus database that were published from 2003 to May 2023. The results show that IoT agriculture papers have grown rapidly since 2015 until now. The three journals that published the most IoT agricultural publications are Sensors (Switzerland), Computers and Electronics in Agriculture, and IEEE Access. Based on the co-occurrence keywords, the focus of IoT paper in agriculture is wireless sensors network (WSN) and radio frequency identification (RFID) for agricultural monitoring, smart agriculture with IoT blockchain and machine learning, IoT greenhouses with cloud computing and artificial intelligence (AI), components of IoT agriculture, precision agriculture with low power low range (LoRa) communication network and IoT cloud platform, smart farming with sensor networks and automation. The study provided an understanding of themes of the IoT agriculture that has been carried out and its future growth. The future of IoT application will elaborate a system efficient, consumes less energy, and emits less carbon dioxide. It has begun by combining IoT-agriculture with the technology edge-fog-cloud layer. [ABSTRACT FROM AUTHOR]

Delayed graft function (DGF) is an early complication after kidney transplantation. The literature on DGF has experienced substantial growth. However, there is a lack of bibliometric analysis of DGF. This study aimed to analyze the scientific outputs of DGF and explore its hotspots from 2013 to 2023 by using CiteSpace and VOSviewer. The 2058 pieces of literature collected in the Web of Science Core Collection (WOSCC) from 1 January 2013 to 31 December 2023 were visually analyzed in terms of the annual number of publications, authors, countries, journals, literature co-citations, and keyword clustering by using CiteSpace and VOSviewer. We found that the number of papers published in the past ten years showed a trend of first increasing and then decreasing; 2021 was the year with the most posts. The largest number of papers was published by the University of California System, and the largest number of papers was published by the United States. The top five keyword frequency rankings are: ‘delayed graft function’, ‘kidney transplantation’, ‘renal transplantation’, ‘survival’, and ‘recipients’. These emerging trends include ‘brain death donors’, ‘blood absence re-injection injuries’, ‘tacrolimus’, ‘older donors and recipients’, and ‘artificial intelligence and DGF’. In summary, this study reveals the authors and institutions that could be cooperated with and discusses the research hotspots in the past ten years. It provides a reference and direction for future research and application of DGF. [ABSTRACT FROM AUTHOR]

Digital twin is the digital representation of an entity, and it drives Industry 4.0. This paper presents a bibliometric analysis of digital twin in the supply chain to help researchers, industry practitioners, and academics to understand the trend, development, and focus of the areas of digital twin in the supply chain. This paper found several key clusters of research, including the designing of a digital twin model, integration of a digital twin model, application of digital twin in quality control, and digital twin in digitalization. In the embryonic stage of research, digital twin was tested in the production line with limited optimization. In the development stage, the importance of digital twin in Industry 4.0 was observed, as big data, machine learning, Industrial Internet of Things, blockchain, edge computing, and cloud-based systems complemented digital twin models. Digital twin was applied to improve sustainability in manufacturing and production logistics. In the current prosperity stage with high annual publications, the recent trends of this topic focus on the integration of deep learning, data models, and artificial intelligence for digitalization. This bibliometric analysis also found that the COVID-19 pandemic drove the start of the prosperity stage of digital twin research in the supply chain. Researchers in this field are slowly moving towards applying digital twin for human-centric systems and mass personalization to prepare to transit to Industry 5.0. [ABSTRACT FROM AUTHOR]

Financial technology, or Fintech, has captured the attention of scholars, students, and institutions across worldwide for over a decade. With a plethora of new financial services, products, and innovative methods to engage with clients, the impact of technology on the financial sector has been extensively studied. This research paper provides a summary of scientific research on FinTech by using bibliometric analysis. Using the Scopus database, the paper analyzed 665 publications and identified research gaps and new study topics through "VOS-Viewer" software and "Biblioshiny" using RStudio. The study focused on FinTech's functions and research constraints in digital finance by assessing citation links between the most significant articles. The findings provide a starting point for further investigation and offer opportunities for researchers to expand their expertise in exciting and innovative studies. Overall, this study seeks to help researchers discover new avenues for exploration in Fintech while advancing their present understanding. There exists much scope in the area of Digital Lending, Supply Chain Finance, the Internet of Things, and RoboAdvisers. [ABSTRACT FROM AUTHOR]

This study aims to understand the development trends and research structure of articles on artificial intelligence (AI) and information processing in the past 10 years. In particular, this study analyzed 13,294 papers published from 2012 to 2021 in the Web of Science, used the bibliometric analysis method to visualize the data of the papers, and drew a scientific knowledge map. By exploring the development of mainstream journals, author and country rankings, keyword evolution, and research field rankings in the past 10 years, this study uncovered key trends affecting AI progress and information processing that provide insights and serve as an important reference for future AI research and information processing. The results revealed a gradual increase in publications over the past decade, with explosive growth after 2020. The most prolific researchers in this field were Xu, Z.S.; Pedrycz, W.; Herrera-Viedma, E.; the major contributing countries were China, the USA, and Spain. In the AI and information processing research, keywords including "Deep learning", "Machine learning", and "Feature extraction" are components that play a crucial role. Additionally, the most representative research areas were "Engineering", "Operations Research and Management Science", and "Automation Control Systems". Overall, this study used bibliometric analysis to provide an overview of the latest trends in artificial intelligence and information processing. Although AI and information processing have been applied to various research areas, many other sub-topics can be further applied. Based on the findings, this study presented research insights and proposed suggestions for future research directions on AI and information processing. [ABSTRACT FROM AUTHOR]

In the rapidly evolving landscape of education, the pivotal axis around which transformation revolves is human-AI interaction. In this sense, this paper adopts a data mining and analytic approach to understand what the related literature tells us regarding the trends and patterns of generative AI research in educational praxis. Accordingly, this systematic exploration spotlights the following research themes: Interaction and communication with generative AI-powered chatbots; impact of the LLMs and generative AI on teaching and learning, conversational educational agents and their opportunities, challenges, and implications; leveraging Generative AI for enhancing social and cognitive learning processes; promoting AI literacy for unleashing future opportunities; harnessing Generative AI to expand academic capabilities, and lastly, augmenting educational experiences through human-AI interaction. Beyond the identified research themes and patterns, this paper argues that emotional intelligence, AI literacy, and prompt engineering are the trending research topics that require further exploration. Accordingly, it's in this praxis that emotional intelligence emerges as a pivotal attribute, as AI technologies often struggle to comprehend and respond to the nuanced emotional cues. Generative AI literacy then takes center stage, becoming an indispensable asset in an era permeated with AI technologies, equipping students with the tools to critically engage with AI systems, thereby ensuring they become active, discerning users of these powerful tools. Concurrently, prompt engineering, the art of crafting queries that yield precise and valuable responses from AI systems, empowers both educators and students to maximize the utility of AI-driven educational resources. [ABSTRACT FROM AUTHOR]

This research aims to conduct a bibliometric analysis of published research on the use of artificial intelligence in accessible museums in the Web of Science Core Collection database. Through bibliometric research, the general framework of research on specific subject areas can be determined. A total of 30 articles were reached in the research. The research findings were analyzed and visualized through the "Analyze Results" option provided in the WOS viewer and Bibliometric tool and ten articles were selected from a systematic review. The findings of the research revealed that research on the use of "artificial intelligence in accessible museums" started in 1997, and then continued to be published with an increase in 2017 after a twenty-year break. The year with the most publications was 2022. The vast majority of research has been published in English. Research on this topic has also been published across Europe. Research has been widely disseminated in the USA, Italy, Spain, France, Romania, India, Bulgaria, Greece, and the Netherlands. However, the majority of the research is indexed in Springer and some in IEEE and MDPI. Most of the studies were prepared in the form of papers and some of them were prepared in the form of articles. Finally, the findings reveal that the research prepared on this subject has been addressed every year since 1997 with the keyword "artificial intelligence" until 2022, while the keyword "cultural heritage" has been mostly used since 2008. As a result, the results obtained from the bibliometric research show that the use of artificial intelligence in museums has become increasingly widespread year by year. [ABSTRACT FROM AUTHOR]

Background: Stroke stands as a prominent cause of mortality and disability worldwide, posing a major public health concern. Recent years have witnessed rapid advancements in artificial intelligence (AI). Studies have explored the utilization of AI in imaging analysis, assistive rehabilitation, treatment, clinical decision-making, and outcome and risk prediction concerning stroke. However, there is still a lack of systematic bibliometric analysis to discern the current research status, hotspots, and possible future development trends of AI applications in stroke. Methods: The publications on the application of AI in stroke were retrieved from the Web of Science Core Collection, spanning 2004-2024. Only articles or reviews published in English were included in this study. Subsequently, a manual screening process was employed to eliminate literature not pertinent to the topic. Visualization diagrams for comprehensive and in-depth analysis of the included literature were generated using CiteSpace, VOSviewer, and Charticulator. Results: This bibliometric analysis included a total of 2,447 papers, and the annual publication volume shows a notable upward trajectory. The most prolific authors, countries, and institutions are Dukelow, Sean P., China, and the University of Calgary, respectively, making significant contributions to the advancement of this field. Notably, stable collaborative networks among authors and institutions have formed. Through clustering and citation burst analysis of keywords and references, the current research hotspots have been identified, including machine learning, deep learning, and AI applications in stroke rehabilitation and imaging for early diagnosis. Moreover, emerging research trends focus on machine learning as well as stroke outcomes and risk prediction. Conclusion: This study provides a comprehensive and in-depth analysis of the literature regarding AI in stroke, facilitating a rapid comprehension of the development status, cooperative networks, and research priorities within the field. Furthermore, our analysis may provide a certain reference and guidance for future research endeavors. [ABSTRACT FROM AUTHOR]

This study aims to provide a comprehensive bibliometric analysis covering the common areas between artificial intelligence (AI) applications and research focused on local or regional contexts. The analysis covers the period between the year 2002 and the year 2023, utilizing data sourced from the Web of Science database. Employing the Bibliometrix package within RStudio and VOSviewer software, the study identifies a significant increase in AI-related publications, with an annual growth rate of 22.67%. Notably, key journals such as Remote Sensing, PLOS ONE, and Sustainability rank among the top contributing sources. From the perspective of prominent contributing affiliations, institutions like Duy Tan University, Ton Duc Thang University, and the Chinese Academy of Sciences emerge as leading contributors, with Vietnam, Portugal, and China being the countries with the highest citation counts. Furthermore, a word cloud analysis is able to highlight the recurring keywords, including "model", "classification", "prediction", "logistic regression", "innovation", "performance", "random forest", "impact", "machine learning", "artificial intelligence", and "deep learning". The co-occurrence network analysis reveals five clusters, amongst them being "artificial neural network", "regional development", "climate change", "regional economy", "management", "technology", "risk", and "fuzzy inference system". Our findings support the fact that AI is increasingly employed to address complex regional challenges, such as resource management and urban planning. AI applications, including machine learning algorithms and neural networks, have become essential for optimizing processes and decision-making at the local level. The study concludes with the fact that while AI holds vast potential for transforming local and regional research, ongoing international collaboration and the development of adaptable AI models are essential for maximizing the benefits of these technologies. Such efforts will ensure the effective implementation of AI in diverse contexts, thereby supporting sustainable regional development. [ABSTRACT FROM AUTHOR]

This article seeks to conduct a bibliometric analysis focusing on exponential technologies such as big data, internet of thing (IoT), artificial intelligence (AI), blockchain and cloud computing. It aims to outline research trends in this domain and explore their correlation with resilience. The study aims to track the evolution of research trends in this field over time and identify less explored dimensions of exponential technologies. Leveraging performance analysis and science mapping techniques, the paper highlights the significant growth and potential in these areas, considering them as pivotal agendas of the twenty-first century. By examining scientific productivity metrics such as publications, authors, institutions, countries and keywords, the article offers insights into emerging areas within exponential technologies. As the first comprehensive study of its kind, it provides a broad overview of the main trends and patterns in resilience research encompassing big data, IoT, AI, blockchain and cloud computing, consolidating them into a single cohesive narrative. [ABSTRACT FROM AUTHOR]

Background: With the development of digital pathology and the renewal of deep learning algorithm, artificial intelligence (AI) is widely applied in tumor pathology. Previous researches have demonstrated that AI-based tumor pathology may help to solve the challenges faced by traditional pathology. This technology has attracted the attention of scholars in many fields and a large amount of articles have been published. This study mainly summarizes the knowledge structure of AI-based tumor pathology through bibliometric analysis, and discusses the potential research trends and foci.Methods: Publications related to AI-based tumor pathology from 1999 to 2021 were selected from Web of Science Core Collection. VOSviewer and Citespace were mainly used to perform and visualize co-authorship, co-citation, and co-occurrence analysis of countries, institutions, authors, references and keywords in this field.Results: A total of 2753 papers were included. The papers on AI-based tumor pathology research had been continuously increased since 1999. The United States made the largest contribution in this field, in terms of publications (1138, 41.34%), H-index (85) and total citations (35,539 times). We identified the most productive institution and author were Harvard Medical School and Madabhushi Anant, while Jemal Ahmedin was the most co-cited author. Scientific Reports was the most prominent journal and after analysis, Lecture Notes in Computer Science was the journal with highest total link strength. According to the result of references and keywords analysis, "breast cancer histopathology" "convolutional neural network" and "histopathological image" were identified as the major future research foci.Conclusions: AI-based tumor pathology is in the stage of vigorous development and has a bright prospect. International transboundary cooperation among countries and institutions should be strengthened in the future. It is foreseeable that more research foci will be lied in the interpretability of deep learning-based model and the development of multi-modal fusion model. [ABSTRACT FROM AUTHOR]

Background: Artificial intelligence (AI) is widely applied in cancer field nowadays. The aim of this study is to explore the hotspots and trends of AI in cancer research. Methods: The retrieval term includes four topic words ("tumor," "cancer," "carcinoma," and "artificial intelligence"), which were searched in the database of Web of Science from January 1983 to December 2022. Then, we documented and processed all data, including the country, continent, Journal Impact Factor, and so on using the bibliometric software. Results: A total of 6,920 papers were collected and analyzed. We presented the annual publications and citations, most productive countries/regions, most influential scholars, the collaborations of journals and institutions, and research focus and hotspots in AI-based cancer research. Conclusion: This study systematically summarizes the current research overview of AI in cancer research so as to lay the foundation for future research. [ABSTRACT FROM AUTHOR]

Land use change detection (LUCD) is a critical technology with applications in various fields, including forest disturbance, cropland changes, and urban expansion. However, the current review articles on LUCD tend to be limited in scope, rendering a comprehensive review challenging due to the vast number of publications. This paper systematically reviewed 3512 articles retrieved from the Web of Science Core database between 1985 and 2022, utilizing a combination of bibliometric analysis and machine learning methods with LUCD as the main focus. The results indicated an exponential increase in the number of LUCD studies, indicating continued growth in this research field. Commonly used methods include classification-based, threshold-based, model-based, and deep learning-based change detection, with research themes encompassing forest logging and vegetation succession, urban landscape dynamics, and biodiversity conservation and management. To build an intelligent change detection system, researchers need to develop a flexible framework that integrates data preprocessing, feature extraction, land use type interpretation, and accuracy evaluation, given the continuous evolution and application of remote sensing data, deep learning, big data, and artificial intelligence. [ABSTRACT FROM AUTHOR]

AI-enabled blockchain refers to the use of AI to enable the analysis and decision-making processes based on data collected, shared, and stored by blockchain. This helps overcome some of the existing challenges in blockchain applications. Despite the growing number of review papers on blockchain and AI, there is a dearth of literature on AI-enabled blockchain in business scenarios. This study uses bibliometric-content analysis to (1) identify three stages of development of AI-enabled blockchain literature and point out the increasing diversity of technological applications; (2) identify the strongest foci of extant literature; (3) unveil the roles of AI-enabled blockchain in 10 application sectors, and identify the key roles of AI in enabling blockchain applications; (4) conclude the referred ethical issues from three levels and make further discussion. The findings present the trends of AI-enabled blockchain and could help developers and service providers better manage the use and ethical issues of AI in blockchain applications. [ABSTRACT FROM AUTHOR]

This study employs bibliometric analysis to investigate the evolving research landscape of Artificial intelligence (AI) within Africa, focusing on the years 2013 to 2022. The central objective is to discern and analyze AI studies conducted in Africa, using a dataset compiled from research papers within the Scopus database. By conducting a comprehensive analysis, this research uncovers crucial insights, including primary authors, influential journals and publishers, nations with the highest research productivity, noteworthy funding sources, influential organizations, and prevalent research domains. Additionally, the study examines year-by-year growth trends and authorship patterns. Employing the VOSviewer software, it creates visual representations that illustrate the dynamic evolution of AI research within the African context. Notably, the analysis of 1646 publications reveals a significant increase in publications over the last decade, with South Africa emerging as a global leader in AI development, and the IEEE, Elsevier, and Springer as prominent publishers. The study also highlights the leading institutions, with the University of the Witwatersrand, University of Johannesburg, University of KwaZulu-Natal, University of Cape Town, and University of Pretoria at the forefront of AI research in Africa. The National Research Foundation is identified as the primary funder supporting AI research across the continent. In conclusion, this research aims to provide a comprehensive understanding of AI's role in addressing African challenges, fostering innovation, and contributing to the continent's technological advancement, shedding light on prevalent research areas and significant funding sources in the process. [ABSTRACT FROM AUTHOR]

Featured Application: Application of main conclusions when developing a complexity indicator for giving information about air traffic. Air traffic complexity is an indicator that allows air traffic controllers to understand the airspace situation. Controllers need support tools to reduce their workload. For this reason, complexity is a parameter that is being studied more and more, as it makes it possible to know a large amount of information about air traffic. In this article, we perform a bibliometric analysis in the field of air traffic complexity. Through Web of Science (WoS), a collection of complexity-related articles from 2001 to 2022 is compiled. Subsequently, the bibliometric analysis itself is performed. Then, a summary of five main contributions is presented, identifying the strengths and weaknesses of the contributions, and thus the topic. The results of the bibliometric analysis show that future air traffic complexity indicators should consider aircraft trajectories but also take into account other aspects, such as regulations. In addition, future complexity indicators should introduce artificial intelligence predictions to foresee areas of conflict in airspace but taking into account the main limitations, such as uncertainty of the air traffic trajectories. This study helps in the study of complexity due to being able to know previous studies in a summarised form and being able to draw conclusions on future lines. [ABSTRACT FROM AUTHOR]

The discourse on urban flood resilience and the notions associated with the word has grown enormously and has been discovered to be in staggering form. This paper intends to provide a bibliometric analysis of nearly 30 years of urban flood resilience by mapping the knowledge domain, identifying evolving themes & trends over time, and extracting publications addressing urban flood resilience from the Scopus database. Trends, evolution, and mapping purpose of subject and field information analysis are undertaken using VOSviewer and SciMAT tools. The VOSviewer programme highlights the emphasis areas, whereas SciMAT traces the field's intellectual evolution across time and the essential themes that drove the transition. The study period is divided into four segments: 1996–2005, 2006–2010, 2011–2015, and 2016–2022 based on the significant international policy milestones. The findings of the analysis highlight that the urban flood resilience field initially concentrated on a few themes and subsequently expanded to encompass the multi-dimensional characteristics of urban area resilience. In recent years, spatial planning approaches, nature-based and sustainable solutions, community-based and citizen science approaches, application of artificial intelligence, urban–rural relationship in resilience aspect, health services, and critical infrastructure networks have been the field's primary themes and concerns for achieving and assessing urban flood resilience. Despite a lack of emphasis on resilience assessment methods and approaches favouring conceptual framework and factors, it is observed that these themes are emerging in the developing scenario to solve the resilience concepts. The research may serve as a springboard for people's and policymakers' concerns about climate change and its effects, infrastructure development to improve the health and social well-being of societies, and a desire to learn more about resolving urban flooding problems in order to enhance flood management concepts pertaining to urban flood resilience and its history. Hence, a comprehensive list of themes and thematic areas concerning urban flood resilience with varying weights according to contexts is recommended, leading to the development of the urban flood resilient assessment tool and modifying the existing tools to reflect better the holistic understanding of sustainable and resilient cities/areas. [ABSTRACT FROM AUTHOR]

From 1990 to 2024, this study presents a groundbreaking bibliometric and sentiment analysis of nanocomposite literature, distinguishing itself from existing reviews through its unique computational methodology. Developed by our research group, this novel approach systematically investigates the evolution of nanocomposites, focusing on microstructural characterization, electrical properties, and mechanical behaviors. By deploying advanced Boolean search strategies within the Scopus database, we achieve a meticulous extraction and in-depth exploration of thematic content, a methodological advancement in the field. Our analysis uniquely identifies critical trends and insights concerning nanocomposite microstructure, electrical attributes, and mechanical performance. The paper goes beyond traditional textual analytics and bibliometric evaluation, offering new interpretations of data and highlighting significant collaborative efforts and influential studies within the nanocomposite domain. Our findings uncover the evolution of research language, thematic shifts, and global contributions, providing a distinct and comprehensive view of the dynamic evolution of nanocomposite research. A critical component of this study is the "State-of-the-Art and Gaps Extracted from Results and Discussions" section, which delves into the latest advancements in nanocomposite research. This section details various nanocomposite types and their properties and introduces novel interpretations of their applications, especially in nanocomposite films. By tracing historical progress and identifying emerging trends, this analysis emphasizes the significance of collaboration and influential studies in molding the field. Moreover, the "Literature Review Guided by Artificial Intelligence" section showcases an innovative AI-guided approach to nanocomposite research, a first in this domain. Focusing on articles from 2023, selected based on citation frequency, this method offers a new perspective on the interplay between nanocomposites and their electrical properties. It highlights the composition, structure, and functionality of various systems, integrating recent findings for a comprehensive overview of current knowledge. The sentiment analysis, with an average score of 0.638771, reflects a positive trend in academic discourse and an increasing recognition of the potential of nanocomposites. Our bibliometric analysis, another methodological novelty, maps the intellectual domain, emphasizing pivotal research themes and the influence of crosslinking time on nanocomposite attributes. While acknowledging its limitations, this study exemplifies the indispensable role of our innovative computational tools in synthesizing and understanding the extensive body of nanocomposite literature. This work not only elucidates prevailing trends but also contributes a unique perspective and novel insights, enhancing our understanding of the nanocomposite research field. [ABSTRACT FROM AUTHOR]

Background: The past decade has seen a substantial rise in the employment of modern data‐driven methods to study atopic dermatitis (AD)/eczema. The objective of this study is to summarise the past and future of data‐driven AD research, and identify areas in the field that would benefit from the application of these methods. Methods: We retrieved the publications that applied multivariate statistics (MS), artificial intelligence (AI, including machine learning‐ML), and Bayesian statistics (BS) to AD and eczema research from the SCOPUS database over the last 50 years. We conducted a bibliometric analysis to highlight the publication trends and conceptual knowledge structure of the field, and applied topic modelling to retrieve the key topics in the literature. Results: Five key themes of data‐driven research on AD and eczema were identified: (1) allergic co‐morbidities, (2) image analysis and classification, (3) disaggregation, (4) quality of life and treatment response, and (5) risk factors and prevalence. ML&AI methods mapped to studies investigating quality of life, prevalence, risk factors, allergic co‐morbidities and disaggregation of AD/eczema, but seldom in studies of therapies. MS was employed evenly between the topics, particularly in studies on risk factors and prevalence. BS was focused on three key topics: treatment, risk factors and allergy. The use of AD or eczema terms was not uniform, with studies applying ML&AI methods using the term eczema more often. Within MS, papers using cluster and factor analysis were often only identified with the term AD. In contrast, those using logistic regression and latent class/transition models were "eczema" papers. Conclusions: Research areas that could benefit from the application of data‐driven methods include the study of the pathogenesis of the condition and related risk factors, its disaggregation into validated subtypes, and personalised severity management and prognosis. We highlight BS as a new and promising approach in AD and eczema research. [ABSTRACT FROM AUTHOR]

In order to understand the current research status and development direction of driving risk identification at home and abroad, relevant literatures in the field of driving risk identification from the China National Knowledge Infra-structure (CNKI) and Web of Science (WOS) in recent 12 years (2011e2022) were selected as research samples, and literature metrology tools VOSviewer and Citespace were used for visual analysis. The situation was analyzed from the aspects of chronological distribution, national cooperation network, distribution of domestic institutions, journal performance and keywords overview, literature coupling clustering and research hotspots. The results show that the number of published papers fluctuates year by year, and China, the United States and Germany have the largest number of published papers. The United States is at the center of international cooperation. The CNKI shows that universities in China such as Chang'an University and Chongqing Jiaotong University have published a large number of documents. According to the statistics of WOS, Accident Analysis & Prevention is the most widely published journal in the world. The average level of the journal is high and the quality of articles is better. Combining the research contents of CNKI and WOS, the main research directions can be clustered into five cluster themes by using the coupling function in VOSviewer, including driving risk assessment considering driver factors, the influence of driving environment on driving risk, driving risk assessment considering multi-source characteristic data, multiaspect research on driving risk and risk identification of non-traditional vehicles in specific scenarios. Human-machine co-driving, artificial intelligence, intelligent driving, risk identification and natural driving are the current research hotspots and the future research trends. [ABSTRACT FROM AUTHOR]

Background: In recent years, the integration of Artificial Intelligence (AI) into various fields has led to transformative changes in research and practice. One such domain that has seen substantial impact is the medical field, where AI-powered tools like ChatGPT have emerged as potential alternatives to traditional methods for generating medical literature. This study presents a comprehensive comparative bibliometric analysis of medical literature generated by ChatGPT and traditional methods, aiming to uncover the emerging trends and potential shifts in the landscape. Materials and Methods: This study collects and analyzes a substantial corpus of articles, reviews, and papers generated by both ChatGPT and traditional methodologies. Bibliometric indicators such as publication frequency, citation counts, collaboration patterns, and keyword usage are examined to discern differences in output and impact. We extract data from Web of Science citation database and selected 18087 publications from the year 2019 to 2023 for our study. The data and descriptive analysis were categorised, collected one at a time, and imported into the Bibliometric R-package programme to produce science maps and statistical graphs. They were exported to MS-Excel for bibliometric analysis and VOSviewer software was used to analyse Co-Occurrence networks. Results and Discussion: A total of 18087 publications on ChatGPT and traditional methodologies from the year 2019 to 2023, namely 12519 (69.29%) original articles, 2836 (15%) reviews, 233 (01.2%) letters, and others. The most productive institution was found to be the Indian Institute of Technology System IIT System (n=1718, 0.09%), followed by National Institute of Technology NIT System (n=1275, 0.07%). the most productive author was found to be the Kumar, Atul, All India Institute of Medical Sciences (AIIMS) New Delhi (n=421, 2.39%), followed by Kumar, Satish, Indian Institute of Management Nagpur (n=405, 2.24%). The most productive journal was found that the IEEE Acess (n=373, 2.063%, TC=6740, ACP=18.06) followed by Multimedia Tools and Applications (n=279, 1.543%, TC=1552, ACP=5.56). The most frequent of authors keywords and occurrences was found that the 'artificial intelligence' 1517 occurrences and 1898 total link strength followed by 'deep learning' 1156 occurrences and 1764 total link strength. Conclusions: This bibliometric analysis sheds light on the evolving landscape of medical literature production, comparing the outputs of ChatGPT and traditional methods. While ChatGPT shows promise in its ability to quickly generate content on cutting-edge topics, traditional methods maintain their dominance in terms of research depth and impact. The findings have implications for researchers, clinicians, and policymakers, suggesting potential ways to leverage both approaches for a more comprehensive and impactful medical research ecosystem. Further research is warranted to monitor the trajectory of this evolving paradigm shift in medical literature and its long-term implications. [ABSTRACT FROM AUTHOR]

Different standard methods for the assessment of the thermal performance of the building envelope are used: analogy with coeval building, theoretical method, heat flow meter measurement, simple hot box, infrared thermography, and thermometric method. Review papers on these methods, applied in situ and in laboratory, have been published, focusing on theory, equipment, metrological performance, test conditions and data acquisition, data analysis, benefits, and limitations. However, steps forward have been done and not been deepened in previous works: in fact, the representative points method and the weighted area method have been proposed, too, whilst artificial intelligence and data-driven methods have begun to prove the reliability also in the U-value prevision using available datasets. Considering this context, this work aims at updating the literature background considering exclusively in situ methods. The work starts from bibliometric and scientometric analysis not previously conducted: this helped to group the methods and to sketch the innovations and the future perspectives. Indeed, from the bibliometric and scientometric literature analysis what emerged was (i) the richness of the background on this topic, especially in the recent years, (ii) two macro-groups (methods with and without measurements), and (iii) the importance of paper keywords (otherwise, interesting papers are eluded by the output of simple database queries). The method study that followed aims at providing (i) a broader view of the thermal transmittance (U-value) assessment procedures, including the utmost recent applications, proposal, and outlooks in this field, (ii) the understanding on the fundamental theories of the techniques, (iii) practical advice for building-envelope assessment, focusing on the advantages and limitations useful for professionals and researchers involved in the energy audit, conservation, or refurbishment of building stock, (iv) the identification of the interconnection between the techniques that often rely on one another, and (v) final remarks and future perspective of the procedures, which embrace the use of artificial intelligence (AI). From the topic analysis, as a result, it emerged that this is an open field for future research, especially with the implementation of AI, which requires good datasets and trials on the models' architectures, in terms of input layer, number of hidden layer and neurons, and percentage of data to be employed for model training and testing. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) has recently become the focus of academia and practitioners, reflecting the substantial evolution of scientific production in this area, particularly during the COVID-19 era. However, there is no known academic work exploring the major trends and the extant and emerging themes of scientific research production of AI leading journals. To this end, this study is to specify the research progress on AI among the top-tier journals by highlighting the development of its trends, topics, and key themes. This article employs an integrated bibliometric analysis using evaluative and relational metrics to analyze, map, and outline the key trends and themes of articles published in the leading AI academic journals, based on the latest CiteScore of Scopus-indexed journals between 2020 and 2021. The findings depict the major trends, conceptual and social structures, and key themes of AI leading journals' publications during the given period. This paper represents valuable implications for concerned scholars, research centers, higher education institutions, and various organizations within different domains. Limitations and directions for further research are outlined. [ABSTRACT FROM AUTHOR]

Academicians and practitioners have recently begun to accord Artificial Intelligence (AI) and Big Data Analytics (BDA) significant consideration when exploring emerging research trends in different fields. The technique of bibliometric review has been extensively applied to the AI and BDA literature to map out existing scholarships. We summarise 711 bibliometric articles on AI & its sub-sets and BDA published in multiple fields to identify academic disciplines with significant research contributions. We pulled bibliometric review papers from the Scopus Q1 and Q2 journal database published between 2012 and 2022. The Scopus database returned 711 documents published in journals of different disciplines from 59 countries, averaging 17.9 citations per year. Multiple software and Database Analysers were used to investigate the data and illustrate the most active scientific bibliometric indicators such as authors and co-authors, citations, co-citations, countries, institutions, journal sources, and subject areas. The USA was the most influential nation (101 documents; 5405 citations), while China was the most productive nation (204 documents; 2371 citations). The most productive institution was Symbiosis International University, India (32 documents; 4.5%). The results reveal a substantial increase in bibliometric reviews in five clusters of disciplines: (a) Business & Management, (b) Engineering and Construction, (c) Healthcare, (d) Sustainable Operations & I4.0, and (e) Tourism and Hospitality Studies, the majority of which investigate the applications and use cases of AI and BDA to address real-world problems in the field. The keyword co-occurrence in the past bibliometric analyses indicates that BDA, AI, Machine Learning, Deep Learning, NLP, Fuzzy Logic, and Expert Systems will remain conspicuous research areas in these five diverse clusters of domain areas. Therefore, this paper summarises the bibliometric reviews on AI and BDA in the fields of Business, Engineering, Healthcare, Sustainable Operations, and Hospitality Tourism and serves as a starting point for novice and experienced researchers interested in these topics. [ABSTRACT FROM AUTHOR]

This research provides a comprehensive analysis of the dynamic interplay between agent-based modeling (ABM) and artificial intelligence (AI) through a meticulous bibliometric study. This study reveals a substantial increase in scholarly interest, particularly post-2006, peaking in 2021 and 2022, indicating a contemporary surge in research on the synergy between AI and ABM. Temporal trends and fluctuations prompt questions about influencing factors, potentially linked to technological advancements or shifts in research focus. The sustained increase in citations per document per year underscores the field's impact, with the 2021 peak suggesting cumulative influence. Reference Publication Year Spectroscopy (RPYS) reveals historical patterns, and the recent decline prompts exploration into shifts in research focus. Lotka's law is reflected in the author's contributions, supported by Pareto analysis. Journal diversity signals extensive exploration of AI applications in ABM. Identifying impactful journals and clustering them per Bradford's Law provides insights for researchers. Global scientific production dominance and regional collaboration maps emphasize the worldwide landscape. Despite acknowledging limitations, such as citation lag and interdisciplinary challenges, our study offers a global perspective with implications for future research and as a resource in the evolving AI and ABM landscape. [ABSTRACT FROM AUTHOR]

The emerging generative artificial intelligence (AI) chatbots, such as Chat Generative Pre-Trained Transformer (ChatGPT), have recently taken different disciplines by surprise. Very few scholarly papers show the collaborative effort by researchers on the impact of generative AI in higher education (HE) and its implication on HE disciplines and activities. This paper uses citation network analysis by deep searching the different research articles using Scopus databases. A bibliographic analysis tool is used to reveal patterns of application of generative AI chatbots in HE, collaboration of generative AI research, and generative AI in HE among different countries of the world. Furthermore, it investigates the thematic topics that generative AI in HE researchers are studying. Research papers from Scopus databases were sampled. The sampling timeframe is from January 2022 to September 2023. This paper presents the preliminary findings of the work that was conducted for a doctoral thesis in computer science and information technology. The preliminary findings show that Spain, the United States of America (USA), and Australia are countries that collaborate more often on the development of generative AI chatbots. In the study, generative AI chatbots were most likely to cover plagiarism and essay inclusion, but very few covered research for postgraduate enterprises. The study concludes that Chatbots are an important part of teaching technology that should be embraced by higher education. [ABSTRACT FROM AUTHOR]

With the unparallel advance of leading-edge technologies like artificial intelligence (AI), the healthcare systems are transforming and shifting for more digital health. In recent years, scientific productions have reached unprecedented levels. However, a holistic view of how AI is being used for digital health remains scarce. Besides, there is a considerable lack of studies on responsible AI and ethical issues that identify and suggest practitioners' essential insights towards the digital health domain. Therefore, we aim to rely on a bibliometric approach to explore the dynamics of the interplay between AI and digital health approaches, considering the responsible AI and ethical aspects of scientific production over the years. We found four distinct periods in the publication dynamics and the most popular approaches of AI in the healthcare field. Also, we highlighted the main trends and insightful directions for scholars and practitioners. In terms of contributions, this work provides a framework integrating AI technologies approaches and applications while discussing several barriers and benefits of AI-based health. In addition, five insightful propositions emerged as a result of the main findings. Thus, this study's originality is regarding the new framework and the propositions considering responsible AI and ethical issues on digital health. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI) plays an increasingly important role in language education; however, the trends, research issues, and applications of AI in language learning remain largely under-investigated. Accordingly, the present paper, using bibliometric analysis, investigates these issues via a review of 516 papers published between 2000 and 2019, focusing on how AI was integrated into language education. Findings revealed that the frequency of studies on AI-enhanced language education increased over the period. The USA and Arizona State University were the most active country and institution, respectively. The 10 most popular topics were: (1) automated writing evaluation; (2) intelligent tutoring systems (ITS) for reading and writing; (3) automated error detection; (4) computer-mediated communication; (5) personalized systems for language learning; (6) natural language and vocabulary learning; (7) web resources and web-based systems for language learning; (8) ITS for writing in English for specific purposes; (9) intelligent tutoring and assessment systems for pronunciation and speech training; and (10) affective states and emotions. The results also indicated that AI was frequently used to assist students in learning writing, reading, vocabulary, grammar, speaking, and listening. Natural language processing, automated speech recognition, and learner profiling were commonly applied to develop automated writing evaluation, personalized learning, and intelligent tutoring systems. [ABSTRACT FROM AUTHOR]

Purpose: Due to the recent development of Big Data and artificial intelligence (AI) technology solutions in customer relationship management (CRM), this paper provides a systematic overview of the field, thus unveiling gaps and providing promising paths for future research. Design/methodology/approach: A total of 212 peer-reviewed articles published between 1989 and 2020 were extracted from the Scopus database, and 2 bibliometric techniques were used: bibliographic coupling and keywords' co-occurrence. Findings: Outcomes of the bibliometric analysis enabled the authors to identify three main subfields of the AI literature within the CRM domain (Big Data and CRM as a database, AI and machine learning techniques applied to CRM activities and strategic management of AI–CRM integrations) and capture promising paths for future development for each of these subfields. This study also develops a three-step conceptual model for AI implementation in CRM, which can support, on one hand, scholars in further deepening the knowledge in this field and, on the other hand, managers in planning an appropriate and coherent strategy. Originality/value: To the best of the authors' knowledge, this study is the first to systematise and discuss the literature regarding the relationship between AI and CRM based on bibliometric analysis. Thus, both academics and practitioners can benefit from the study, as it unveils recent important directions in CRM management research and practices. [ABSTRACT FROM AUTHOR]

Deep learning is a significant research field in today's society, especially in the field of artificial intelligence. To gain a more comprehensive understanding of the current state of research in the field of deep learning and to identify areas of intense interest, this paper uses Citespace for research and learning, samples 1863 articles from the Web of Science (WoS) core dataset, which spans the years 2017 to 2023, and adopts the methods of co-occurrence analysis, keyword burst analysis, and co-citation analysis. Information visualization is used to conduct bibliometric analysis, examining keywords, research institutions, authors, and countries. The analysis demonstrates a significant increase in the literature record from 2017 to 2022, followed by a decline from 2022 to 2023. The research on deep learning spans various fields, including computer science, engineering, and telecommunications, which are considered crucial areas of study. China, the USA, and England are leading the way in the field of deep learning research. These countries have established extensive cooperative relationships with many countries, with research institutions and authors playing a vital role in the cooperative network. The current theoretical research hotspots of deep learning mainly include "convolutional neural networks", "neural networks", "models", "classification", etc. The focus of research in deep learning has shifted from theoretical research to practical applications. It is now being applied in various areas, such as deep reinforcement learning, bearing fault, and semi-supervised learning. In the future, deep learning technology will be applied to more cuttingedge technologies. [ABSTRACT FROM AUTHOR]

Background: Artificial intelligence (AI) is more and more widely used in cancer, which is of great help to doctors in diagnosis and treatment. This study aims to summarize the current research hotspots in the Application of Artificial Intelligence in Cancer (AAIC) and to assess the research trends in AAIC. Methods: Scientific publications for AAIC-related research from 1 January 1998 to 1 July 2022 were obtained from the Web of Science database. The metrics analyses using bibliometrics software included publication, keyword, author, journal, institution, and country. In addition, the blustering analysis on the binary matrix was performed on hot keywords. Results: The total number of papers in this study is 1592. The last decade of AAIC research has been divided into a slow development phase (2013-2018) and a rapid development phase (2019-2022). An international collaboration centered in the USA is dedicated to the development and application of AAIC. Li J is the most prolific writer in AAIC. Through clustering analysis and highfrequency keyword research, it has been shown that AI plays a significantly important role in the prediction, diagnosis, treatment and prognosis of cancer. Classification, diagnosis, carcinogenesis, risk, and validation are developing topics. Eight hotspot fields of AAIC were also identified. Conclusion: AAIC can benefit cancer patients in diagnosing cancer, assessing the effectiveness of treatment, making a decision, predicting prognosis and saving costs. Future AAIC research may be dedicated to optimizing AI calculation tools, improving accuracy, and promoting AI. [ABSTRACT FROM AUTHOR]