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Image Processing is an emerging field that is used to extract information from images. In recent years, this field has received immense attention from researchers, especially in the research domains of object detection, Biomedical Imaging and Semantic segmentation. In this study, a bibliometric analysis of publications related to image processing in the Science Expanded Index Extended (SCI-Expanded) has been performed. Several parameters have been analyzed such as annual scientific production, citations per article, most cited documents, top 20 articles, most relevant authors, authors evaluation using y-index, top and most relevant sources (journals) and hot topics.

The Bibliographic data has been extracted from the Web of Science which is well known and the world's top database of bibliographic citations of multidisciplinary areas that covers the various journals of computer science, engineering, medical and social sciences.

The research work in image processing is meager in the past decade, however, from 2014 to 2019, it increases dramatically. Recently, the IEEE Access journal is the most relevant source with an average of 115 publications per year. The USA is most productive and its publications are highly cited while China comes in second place. Image Segmentation, Feature Extraction and Medical Image Processing are hot topics in recent years. The National Natural Science Foundation of China provides 8% of all funds for Image Processing. As Image Processing is now becoming one of the most critical fields, the research productivity has enhanced during the past five years and more work is done while the era of 2005–2013 was the area with the least amount of work in this area.

This research is novel in this regard that no previous research focuses on Bibliometric Analysis in the Image Processing domain, which is one of the hot research areas in computer science and engineering.

Equity, diversity, inclusion and accessibility (EDIA) are the core values of librarianship. This study aims to examine the EDIA literature in the library and information science (LIS) profession, focusing on publishing trends, authorship patterns, thematic evolution, co-occurrence network, international collaboration network and three-factor analysis.

A scientometrics analysis was performed, and data were retrieved from the Web of Science database following a four-phase data extraction and filtration process. Data analysis was performed using specialized scientometrics tools.

Most of the published work comes from the developed world. The thematic evolution identified that the initial period (1971–2010) was dedicated to the diversity of library resources, services and users. The second phase (2011–2015) concentrated on accessibility and information literacy. The recent period (2016–2021) is linked with social inclusion, diversity and accessibility. Furthermore, the co-cluster citation and content analysis identified five major interconnected streams in the EDIA literature in LIS: disability and accessibility; diversity, inclusion and recruitment; social justice and libraries; libraries and immigrants; and libraries and the lesbian, gay, bisexual, transgendered, queer community.

Theoretical, practical and implications for policy makers and curriculum developers were added to create awareness and better observe EDIA in LIS field.

The identification of research streams is one of the most important findings of this study, which shows that some areas of EDIA are maturing and others are emerging in the profession.

The study aims to identify and discuss influential aspects of corporate environmental disclosure (CED) literature, including key streams, themes, authors, keywords, journals, affiliations and countries. This review also constructs agendas for future CED research.

Using a bibliometric review approach, the authors reviewed 560 articles on CED from 215 journals published between 1982 and 2020.

The authors' insights are three-fold. First, the authors identified three core streams of CED research: “legitimization of environmental hazards via environmental disclosures,” “the role of environmental accounting in achieving corporate environmental sustainability” and “integrating environmental social and governance (ESG) reporting into the global reporting initiatives (GRI) guidelines”. Second, the authors also deployed a thematic map that classifies CED research into four themes: niche themes (e.g. institutional theory and environmental management system), motor themes (e.g. stakeholder engagement), emerging/declining themes (e.g. legitimacy theory) and basic/transversal themes (e.g. voluntary CED, environmental reporting and corporate social responsibility). Third, the authors highlighted important CED authors, keywords, journals, articles, affiliations and countries.

This study assists researchers, journal editors and consultants in the corporate sector to comprehensively understand various dimensions of CED research and practices and suggests potential emerging research areas. Although this paper appears to have been thoroughly conducted, using authors' keywords to identify themes was a key limitation. Thus, the authors call upon using a more comprehensive data mining technique that uses keywords in abstracts, titles and the whole body of papers and then identifies inclusive trends in CED literature.

The authors contribute to the extant accounting literature by investigating the organizational-level CED, both mandatory and voluntary, using a systematic and bibliometric literature review model to summarize the key research streams, themes, authors, journals, affiliations and countries. By doing so, the authors construct a future research agenda for CED literature.

The Grey System Theory (GST) is an emerging area of research within artificial intelligence. Since its founding in 1982, it has seen a lot of multidisciplinary applications. In just a short period, it has garnered some considerable strengths. Based on the 1987–2021 data collected from the Web of Science (WoS), the current study reports the advancement of the GST.

Research papers utilizing the GST in the fields of economics and education were retrieved from the Web of Science (WoS) platform using a set of predetermined keywords. In the final stage of the process, the papers that underwent analysis were manually chosen, with selection criteria based on the information presented in the titles and abstracts.

The study identifies prominent authors, institutions, publications and journals closely associated with the subject. In terms of authors, two major clusters are identified around Liu SF and Wang ZX, while the institution with the highest number of publications is Nanjing University of Aeronautics and Astronautics. Moreover, significant keywords, trends and research directions have been extracted and analyzed. Additionally, the study highlights the regions where the theory holds substantial influence.

The study is subject to certain limitations stemming from factors such as the language employed in the chosen literature, the papers included within the Web of Science (WoS) database, the designation of works categorized as “articles” in the database, the specific selection of keywords and keyword combinations, and the meticulous manual process employed for paper selection. While the manual selection process itself is not inherently limiting, it demands a greater investment of time and meticulous attention, contributing to the overall limitations of the study.

The significance of the study extends not only to scholars and practitioners but also to readers who observe the development of emerging scientific disciplines.

The analysis of trends revealed a growing emphasis on the application of GST in diverse domains, including supply chain management, manufacturing and economic development. Notably, the emergence of COVID-19 as a new research focal point among GST scholars is evident. The heightened interest in COVID-19 can be attributed to its global impact across various academic disciplines. However, it is improbable that this interest will persist in the long term, as the pandemic is gradually brought under control.

This study aims to investigate the research productivity in terms of publications count of the top four premiers Indian Institute of Management (IIM) institutions and to explore the current research trends.

Bibliometric techniques were employed to assess the performance in terms of research productivity of authors affiliated with IIMs. The Elsevier Scopus database was selected as a tool to extract the prospective publications data limiting the time frame for 2010–2021. The IIM-Ahmedabad, IIM-Bangalore, IIM-Calcutta and IIM-Lucknow have been selected for the study. The harvested data were analyzed by using the standard bibliometric indicators and scientometric parameters to measure the research landscape such as average growth rate, compound average growth rate, relative growth rate, doubling time, degree of collaboration, collaborative index, collaborative coefficient and modified collaborative coefficient. VOSviewer 1.6.17, BibExcel and Microsoft Excel were used for data analysis and visualization.

The research productivity of selected four IIMs has shown an upward trend during the study period from 2010–2021 and accrued 4,397 publications with an average of 366 publications per year. The authorship patterns demonstrate the collaborative trends as most of the publications were produced by the multiple-authors (81.03%). IIM-Ahmedabad has produced the maximum number of publications (32.20%). The research productivity of IIMs has come out in collaboration with the 125 nations across the world and the USA, the UK, Canada, Germany and China are the front runners with IIMs in the collaborative network. The high magnitude and density of collaboration are evident from the calculated mean values of the degree of collaboration (0.82). The mean values of the collaborative index (2.64), collaborative coefficient (0.51) and modified collaborative coefficient (0.51) demonstrated a positive trend, but indicate the fluctuation in the collaborative pattern as time proceeds.

The study is limited to the publications data indexed in the Scopus database, therefore the outcome may not be generalized across other databases available in the public domain like Web of Science (WoS), PubMed, Dimensions and Google Scholars.

The findings of the study may aid academics and library professionals in identifying research trends, collaboration networks and evaluating other academic and research institutions by using the current advancement in data analysis.

The present study is the first effort to evaluate the research productivity of IIMs. The expanding literature will make an important contribution to identifying patterns and evaluating current research trends on a worldwide scale.

VINE Journal of Information and Knowledge Management Systems (VJIKMS) is one of the oldest and leading journals in the knowledge management (KM) field. The purpose of this study is to conduct bibliometrics analysis of publications published in VJIKMS for the past two decades. For doing this, this paper covered the past two decades of publications and carried out a science mapping analysis of publications.

The methodology included bibliometrics and the science mapping analysis process. This paper imported the bibliographic information of VJIKMS from the abstract and citation database Scopus. Through bibliometrics method, this paper examined the citation results, author productivity, authorship pattern, research collaboration and other parameters of the selected publications. Afterward, this paper used VOSviewer software to carry out the science mapping of bibliometric networks.

The findings showed that VJIKMS published 718 publications during 2000–2020, which got cited 4,311 times (6 times per article) till date. Joint authorship and international collaboration have been increasing where 1,417 authors from 66 countries have published. The USA, the UK and Iran lead the KM publications in this journal. Nonaka’s publications and Journal of Knowledge Management (JKM) are highly cited references and journals in the VJIKMS.

The findings of this study would help the KM students, researchers and practitioners to see the diffusion of KM globally, what are the promising areas to work and helps to know the various patterns of publications if they aim to publish in VJIKMS.

This is the first time a bibliometric analysis has been conducted to analysis of research publications published in VJIKMS. This presents a comprehensive analysis of publications between 2000 and 2020.

This research conducts bibliometric analyses and network mapping on smart libraries worldwide. It examines publication profiles, identifies the most cited publications and preferred sources and considers the cooperation of the authors, organizations and countries worldwide. The research also highlights keyword trends and clusters and finds new developments and emerging trends from the co-cited references network.

A total of 264 records with 1,200 citations were extracted from the Web of Science database from 2003 to 2021. The trends in the smart library were analyzed and visualized using BibExcel, VOSviewer, Biblioshiny and CiteSpace.

The People’s Republic of China had the most publications (119), the most citations (374), the highest H-index (12) and the highest total link strength (TLS = 25). Wuhan University had the highest H-index (6). Chiu, Dickson K. W. (H-index = 4, TLS = 22) and Lo, Patrick (H-index = 4, TLS = 21) from the University of Hong Kong had the highest H-indices and were the most cooperative authors. Library Hi Tech was the most preferred journal. “Mobile library” was the most frequently used keyword. “Mobile context” was the largest cluster on the research front.

This study helps librarians, scientists and funders understand smart library trends.

There are several studies and solid background research on smart libraries. However, to the best of the author’s knowledge, this study is the first to conduct bibliometric analyses and network mapping on smart libraries around the globe.

Assessing the research performance of researchers offers inducement toward excellence in research. This study aims to analyze the research productivity of the most prolific authors of Pakistan considering their trends toward publications, citations and collaboration.

Top 100 authors from the top 10 Pakistani universities from Web of Science over the 10 years with the rigorous data cleaning process were selected. Scientometric analysis techniques were carried out to evaluate the research profile of these authors.

The findings revealed that majority of the productive authors were male working in the position of Professor in the physical sciences area. The publications and citations gradually increase with time. They preferred to collaborate for their publications, while first authorship publications were found less in number. Moreover, the propensity to collaborate at the international level increases double-fold from the first five years to the next five years period. In addition, the position of the authors was explored among different performance metrics. The finding exhibits variation in the ranking of authors among them. The impact of numbers of authors, funding status, publication of articles, presence of collaboration and international collaboration on the dependent variable and citation count was insignificant. However, the publication of review papers has a significant impact on the citation counts.

Findings have significant implications for policymakers to make maximum opportunities for researchers to strengthen linkages for collaboration and increase the funding prospects.

Studies on this topic are scarce, and therefore, this study provides useful recommendations to researchers and institutes to improve research productivity.

This study aims to provide a more complete picture of blockchain development by combining numerous methodologies with diverse data sources, such as academic papers and news articles. This study displays the developmental status of each subject based on the interrelationships of each topic cluster by analyzing high-frequency keywords extracted from the collected data. Moreover, applying above methodologies will help understanding top research topics, authors, venues, institutes and countries. The differences of blockchain research and new are identified.

To identify and find blockchain development linkages, researchers have used search terms such as co-occurrence, bibliographic coupling, co-citation and co-authorship to help us understand the top research topics, authors, venues, institutes and countries. This study also used text mining analysis to identify blockchain articles' primary concepts and semantic structures.

The findings show the fundamental topics based on each topic cluster's links. While “technology”, “transaction”, “privacy and security”, “environment” and “consensus” were most strongly associated with blockchain in research, “platform”, “big data and cloud”, “network”, “healthcare and business” and “authentication” were closely tied to blockchain news. This article classifies blockchain principles into five patterns: hardware and infrastructure, data, networking, applications and consensus. These statistics helped the authors comprehend the top research topics, authors, venues, publication institutes and countries.

Since Web of Science (WoS) and LexisNexis Academic data are used, the study has few sources. Others advise merging foreign datasets. WoS is one of the world's largest and most-used databases for assessing scientific papers.

This study has several uses and benefits. First, key concept discoveries can help academics understand blockchain research trends so they can prioritize research initiatives. Second, bibliographic coupling links academic papers on blockchain. It helps information seekers search and classify the material. Co-citation analysis results can help researchers identify potential partners and leaders in their field. The network's key organizations or countries should be proactive in discovering, proposing and creating new relationships with other organizations or countries, especially those from the journal network's border, to make the overall network more integrated and linked. Prominent members help recruit new authors to organizations or countries and link them to the co-authorship network. This study also used concept-linking analysis to identify blockchain articles' primary concepts and semantic structures. This may lead to new authors developing research ideas or subjects in primary disciplines of inquiry.

While technical vocational education training (TVET) has been studied in-depth, the evolution and performance patterns of the subject remain unknown and limited. A bibliometric analysis was performed to examine the global scientific literature to assess the state of the art in TVET research over the past 23 years.

The Web of Science (WoS) database was searched to explore TVET-related research from 1999 to 2021, resulting in the identification of 7,512 articles. The VOSviewer software was used to investigate the network of collaboration between authors, institutions, countries and author keywords.

The results reveal that the subject categories of “education” and “educational research” are the most prolific contributors to TVET-related research, with 3,314 articles. Most of the previous studies in Phase I (1999–2006) focussed on human capital resources development in the TVET sector. Phase II (2007–2014) follows with the centralisation of TVET, focussing on technology transition in education. However, in Phase III (2015–2021), researchers appear to focus on vocational studies in higher education towards increasing the productivity of human resources via the implementation of technology transition.

The valuable findings of this study can facilitate better understanding among scholars on the trends of TVET research developments and on the direction of future research.