Exploring the Application Sphere of the Internet of Things in Industry 4.0: A Review, Bibliometric and Content Analysis

Kumar, Raman; Rani, Sita; Awadh, Mohammed Al

doi:10.3390/s22114276

Cited by 55 publications

(30 citation statements)

References 80 publications

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“…The study design was based on compliance with the fundamental principles of bibliometrics ( 14 , 15 ). The study employed a two-stage structured approach to bibliometric analysis and visual assessment of published scientific literature.…”

Section: Methodsmentioning

confidence: 99%

Exploring changes in depression and radiology-related publications research focus: A bibliometrics and content analysis based on natural language processing

et al. 2022

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ObjectiveThis study aims to construct and use natural language processing and other methods to analyze major depressive disorder (MDD) and radiology studies’ publications in the PubMed database to understand the historical growth, current state, and potential expansion trend.MethodsAll MDD radiology studies publications from January 2002 to January 2022 were downloaded from PubMed using R, a statistical computing language. R and the interpretive general-purpose programming language Python were used to extract publication dates, geographic information, and abstracts from each publication’s metadata for bibliometric analysis. The generative statistical algorithm “Latent Dirichlet allocation” (LDA) was applied to identify specific research focus and trends. The unsupervised Leuven algorithm was used to build a network to identify relationships between research focus.ResultsA total of 5,566 publications on MDD and radiology research were identified, and there is a rapid upward trend. The top-cited publications were 11,042, and the highly-cited publications focused on improving diagnostic performance and establishing imaging standards. Publications came from 76 countries, with the most from research institutions in the United States and China. Hospitals and radiology departments take the lead in research and have an advantage. The extensive field of study contains 12,058 Medical Subject Heading (MeSH) terms. Based on the LDA algorithm, three areas were identified that have become the focus of research in recent years, “Symptoms and treatment,” “Brain structure and imaging,” and “Comorbidities research.”ConclusionLatent Dirichlet allocation analysis methods can be well used to analyze many texts and discover recent research trends and focus. In the past 20 years, the research on MDD and radiology has focused on exploring MDD mechanisms, establishing standards, and constructing imaging methods. Recent research focuses are “Symptoms and sleep,” “Brain structure study,” and “functional connectivity.” New progress may be made in studies on MDD complications and the combination of brain structure and metabolism.

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Section: Methodsmentioning

confidence: 99%

Exploring changes in depression and radiology-related publications research focus: A bibliometrics and content analysis based on natural language processing

et al. 2022

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“…Bibliometric analysis is a well-established form of meta-analytical study [ 23 ] and a statistical technique that finds qualitative and quantitative changes in a particular research topic [ 4 ]. This research strategy has been utilised extensively to assess enormous quantities of publications in disciplines and domains as diverse as sustainability [ 24 , 25 ], healthcare [ 26 , 27 , 28 ] and green supply chain management [ 22 , 29 ], blockchain [ 30 ] and IoT [ 31 , 32 ]. Bibliometric analysis is a common and thorough technique for examining and interpreting vast quantities of scientific data [ 33 ].…”

Section: Introductionmentioning

confidence: 99%

An Integrated Methodology for Bibliometric Analysis: A Case Study of Internet of Things in Healthcare Applications

Ullah

Asghar

Griffiths

2022

Sensors

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This paper presents an integrated and easy methodology for bibliometric analysis. The proposed methodology is evaluated on recent research activities to highlight the role of the Internet of Things in healthcare applications. Different tools are used for bibliometric studies to explore the breadth and depth of different research areas. However, these Methods consider only the Web of Science or Scopus data for bibliometric analysis. Furthermore, bibliometric analysis has not been fully utilised to examine the capabilities of the Internet of Things for medical devices and their applications. There is a need for an easy methodology to use for a single integrated analysis of data from many sources rather than just the Web of Science or Scopus. A few bibliometric studies merge the Web of Science and Scopus to conduct a single integrated piece of research. This paper presents a methodology that could be used for a single bibliometric analysis across multiple databases. Three freely available tools, Excel, Perish or Publish and the R package Bibliometrix, are used for the purpose. The proposed bibliometric methodology is evaluated for studies related to the Internet of Medical Things (IoMT) and its applications in healthcare settings. An inclusion/exclusion criterion is developed to explore relevant studies from the seven largest databases, including Scopus, Web of Science, IEEE, ACM digital library, PubMed, Science Direct and Google Scholar. The study focuses on factors such as the number of publications, citations per paper, collaborative research output, h-Index, primary research and healthcare application areas. Data for this study are collected from the seven largest academic databases for 2012 to 2022 related to IoMT and their applications in healthcare. The bibliometric data analysis generated different research themes within IoMT technologies and their applications in healthcare research. The study has also identified significant research areas in this field. The leading research countries and their contributions are another output from the data analysis. Finally, future research directions are proposed for researchers to explore this area in further detail.

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“…This bibliometric approach has been used by researchers for various reasons, such as to assess the intellectual structure of a specific domain in the literature, to look for the emerging research trends ( 23 ) and hotspots in certain topics ( 24 ), and to uncover journal performance and research constituents ( 25 ). Bibliometric analysis has gained popularity in a variety of hot fields, such as interpretive structural modeling ( 26 ), Internet of Things ( 27 ), fuzzy theory ( 28 ), applied intelligence ( 29 ), sustainable tourism ( 30 ), and rough sets research ( 31 ). It has also been used for many physical and chronic medical conditions, such as obesity ( 32 ), cancer ( 24 ), and diabetes ( 33 ).…”

Section: Introductionmentioning

confidence: 99%

Intellectual disability and COVID-19: A bibliometric review

2022

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BackgroundDuring the COVID-19 pandemic, the vulnerabilities of certain groups of people have been highlighted, such as people with intellectual disability (ID). Although related research on ID has developed rapidly during the COVID-19 pandemic, the quantitative analysis of those research results has not been systematically performed through bibliometric analysis. Bibliometric analysis is a useful and rigorous method to explore large volumes of research data, and it allows researchers to extract quantitative information on distribution by author, time, country, and journal.AimThe aim of the present study is to comprehensively analyze the current status and developing trends in publications on ID research related to and conducted during the COVID-19 pandemic.MethodsA bibliometric analysis was performed using the Web of Science database. Biblioshiny software was used to analyze and visualize the following information: main information of dataset, annual scientific production, journals which published the most relevant sources, most-cited authors, most-cited countries, most-cited global documents, word-cloud of keywords authors have used, and both the co-occurrence and co-citation networks.ResultsA total of 450 publications were included. The average number of citations per document was 5.104. Among the top three journals, Journal of Applied Research in Intellectual Disabilities published 32 articles, Journal of Intellectual Disability Research published 29 articles, and British Journal of Learning Disabilities published 17 articles. The article with the title COVID-19 and People with Intellectual Disability: Impact of a Pandemic was the most cited with total 144 citations The United Kingdom had the most publications and had strong cooperative relationships with the United States, Canada, and Australia. The most popular keywords included mental health, autism, developmental disability, and lockdown. Thematic map analysis identified several possible clusters, including telemedicine, physical activities, and mental health.ConclusionThe present study provides a better understanding in this research field and may help clinicians, researchers and stakeholders to obtain more comprehensive view of ID and COVID-19. The insights gained from this analysis could inform future research.

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Exploring the Application Sphere of the Internet of Things in Industry 4.0: A Review, Bibliometric and Content Analysis

Cited by 55 publications

References 80 publications

Exploring changes in depression and radiology-related publications research focus: A bibliometrics and content analysis based on natural language processing

Exploring changes in depression and radiology-related publications research focus: A bibliometrics and content analysis based on natural language processing

An Integrated Methodology for Bibliometric Analysis: A Case Study of Internet of Things in Healthcare Applications

Intellectual disability and COVID-19: A bibliometric review

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