Bibliometric Analysis of Fuzzy Logic Research in International Scientific Databases

The purpose of this study is to explore the Web of Science Database (WOS) and review the significant contributions to the research of Fuzzy Logic or Fuzzy Sets theory from the beginning to the present. This study analyzes the most eminent authors, institutions, countries, and journals in Fuzzy Logic research by applying science mapping methods and bibliometric measures. Also, we paid attention to link strength and h-index to represent the visibility, influence, and link between the representative authors. Moreover, we added descriptive statistics to highlight strong linearity and a connection between fuzzy publications and Fuzzy Logic research. Also, we applied regression analyses and prevision functions to predict the evolution of the Fuzzy Logic topic. The results showed a significant increase in the number of papers published annually in a portfolio of internationally representative journals. This leads us to the idea that Fuzzy Logic research is now a transdisciplinary topic that continually develops. Therefore, it can be found in more and more related areas such as artificial intelligence, IoT, medicine, economics, or the environment. Most of the results are consistent with other bibliometric studies. Still, some results are different, results related to the current cited works that show a polarization in the Asia area and the top journals that is continuously changing depending on the number of papers and the quotations of scientific personalities that publish. We used the VOS Viewer software to map the main trends in the field. The results indicate that the use of concepts has long exceeded traditional boundaries

A Bibliometric Analysis of International Journal of Nursing Studies (1963 – 2018)

Purpose- This study set to present a general overview of the International Journal of Nursing Studies (IJNS) from 1963 to 2018 using bibliometric indicators to assess its performance. Design/methodology/approach- This study is a bibliometric analysis. The articles of the IJNS were analyzed. Scopus database was used for collecting articles. Excel software was used to analyze the process of publishing journal articles, identify top countries, institutes, and authors and extract highly cited articles in the journal. The visualization of the keywords in the articles published in the journal was done using the VOSviewer software. Findings- The results of the bibliometric analysis showed that the United Kingdom with 966 papers, King\u27s College London with 130 papers and Bergman R with 25 papers were the most productive and influential countries, universities, and authors contributing to the IJNS. The paper of Keeney S. (2001), entitled A critical review of the Delphi technique as a research methodology for nursing , was the most highly cited article in the IJNS from the beginning of 1963 to the end of 2018. The clustering of published keywords suggests that psychometric and nursing care issues are emerging journal clusters added to the journal in the last two decades. Conclusion- The evolution process of the IJNS is positive and growing. This Journal has attracted much attention from researchers and authors around the world as a pioneer and leading journalist in the field of nursing. Originality/value- This is the first comprehensive study offering a bibliometric overview of the leading trends of the IJNS over its history

Pre-research Study based on Bibliometrics, Deep Learning Research for Aspect-Based Sentiment Analysis

 Background: Massive publications on deep learning research for aspect-based sentiment analysis are challenging for interested researchers who want to research this area. Purpose: to provide an overview and comprehensive analysis on the research trend, which include the growth of publications, the most used keywords, the most popular publication sources to publish and find literature, the most cited publication, the most productive researcher, the most productive institution and country affiliation. Method: This study used a bibliometric method to analyze Scopus's indexed publications from 2014 (the year when the first publication was first indexed) to 2020. A total of 222 publications were analyzed and visualized using the VosViewer software. Result: In general, there is an increase in the number of publications from year to year. Keyword visualization shows keywords related to text-based processing, deep learning architectures, the research object and media, and the application of the method. The most popular sources to publish and to find literature are the “Lecture Notes in Computer Science” and the “Expert Systems with Applications''. The most cited publication is “Deep Learning for Aspect-Based Sentiment Analysis: A Comparative Review”, written by Do, Prasad (cited 81 times). The most productive researcher is Zhang Y from China. The most productive institution is Nanyang Technological University (6 publications), and China has the highest number of publications (76 documents). Conclusion: The bibliometric method can provide a conclusive and comprehensive preliminary overview of research trends for interested researchers who want to start research about deep learning for aspect-based sentiment analysis.   Keywords: Bibliometrics; Deep learning; Aspect-based sentiment analysis; VosViewer    Abstrak  Latar Balakang: Banyaknya publikasi mengenai penelitian deep learning untuk aspect-based sentiment analysis menjadi tantangan tersendiri bagi peneliti yang tertarik dan ingin memulai penelitian terkait topik ini. Tujuan: memberikan gambaran umum serta analisis komprehensif tren penelitian meliputi pertumbuhan jumlah publikasi, kata kunci yang banyak digunakan, sumber publikasi populer yang dapat dimanfaatkan untuk tujuan publikasi maupun menemukan literatur, publikasi utama yang paling banyak disitir, peneliti paling produktif dan pola kolaborasi peneliti, serta afiliasi institusi dan negara paling produktif. Metode: Kajian ini menggunakan metode bibliometrik untuk menganalisis publikasi terindeks Scopus dari tahun 2014 (tahun pertama kali publikasi terindeks) hingga tahun 2020. Sebanyak 222 judul publikasi dianalisis, kemudian divisualisasikan dengan software VosViewer. Hasil: Secara umum jumlah publikasi mengalami peningkatan dari tahun ke tahun. Visualisasi kata kunci menggambarkan kata kunci yang berkaitan dengan pemrosesan berbasis teks, arsitektur deep learning, obyek dan media penelitian, serta aplikasi aspect-based sentiment analysis dengan metode deep learning. Sumber publikasi terpopuler untuk tujuan publikasi dan sumber literatur utama berturut-turut adalah Lecture notes in Computer Science dan Expert Systems with Applications. Publikasi yang paling banyak disitir adalah Deep Learning for Aspect-Based Sentiment Analysis: A Comparative Review oleh Do, Prasad (disitir 81 kali). Peneliti paling produktif adalah Zhang Y dari Cina. Institusi yang paling produktif adalah Nanyang Technological University (6 publikasi), dan Cina menjadi negara paling produktif dengan jumlah publikasi sebanyak 76 dokumen. Kesimpulan: Kajian menggunakan metode bibliometrik dapat memberikan gambaran awal tren penelitian yang konklusif dan komprehensif bagi peneliti yang tertarik dan ingin memulai penelitian tentang topik deep learning untuk aspect-based sentiment analysis.   Kata kunci: Bibliometrika; Deep learning; Aspect-based sentiment analysis; VosViewer&nbsp

Scientometric Analysis of Optimisation and Machine Learning Publications

Introduction: Optimisation is an important aspect of machine learning because it helps improve accuracy and reduce errors in the model's predictions. Purpose: The purpose of this research is to identify the global structure of optimization and machine learning. The work specifically looks at the collaborative network of countries in these fields, the top 20 authors in terms of production from 2015–2021, and the co-citation network of articles. Methodology: In this study, co-word analysis and social network analysis were used to conduct a descriptive study based on the scientometric approach and the content analysis method. In this research, around 17,500 articles on optimization and machine learning published between 2015 and 2021 were extracted. An ANOVA was performed to evaluate whether there was a significant difference between betweenness, closeness, and pagerank. The Dimensions database was utilised for the investigation without language constraints. Moreover, Bibliometrix was used for calculation and visualization. Findings: The results revealed a substantial difference between betweenness, proximity, and pagerank, indicating that this research has the potential to bring vital insights into future optimization and machine learning research

A bibliometric overview of the International Journal of Strategic Property Management between 2008 and 2019

The International Journal of Strategic Property Management (IJSPM) is an interdisciplinary journal which provides a forum for a broad range of strategic property management research. The topics such as asset and facilities management, property policy, risk management, residential property value enhancement, and housing finance are included in the scope of the IJSPM’s investigation. The aim of this study is to provide a bibliometric analysis of the papers published by the IJSPM which is collected in the well-known Social Science Citation Index database and analyse the current status and the emerging trends of the research outputs in the IJSPM with some broadly utilized as well as diversely designed indicators. By analysing the annual publication distribution, the citation structure, the co-citation and cooperation networks, and the influential contributors on the aspects of specific countries/regions, institutions, cited journals, and authors, the status quo of the IJSPM publications is presented. Also, the emerging trends are explored through the analyses of timeline view and burst detection. We make the contributions in terms of visualizing the complex and significant results based on the objective and quantitative data. This paper assists researchers with an understanding of the development of the IJSPM, which gives useful information for further researches and submitting works

BIBLIOMETRIC STUDY ON THE DEVELOPMENT AND IMPLEMENTATION OF CYBERSECURITY IN AUTONOMOUS VEHICLES

The main objective was to examine the trajectory of scientific research in this domain, identify the most influential publications related to cybersecurity in autonomous vehicles and pinpoint research opportunities, supported by the PRISMA method. Additionally, the study explores cybersecurity themes in autonomous vehicles, emphasizing the significance of concepts like blockchain, machine learning, and deep learning essential in formulating business strategies. Furthermore, the research identifies influential scientific publications, predominant journals, the most productive countries, and authors with the most publications on cybersecurity in autonomous vehicles. It identifies research opportunities organized into two distinct clusters to provide a comprehensive understanding of the current state of research in this field and offer insights for companies and academics interested in contributing to future advancements in the cybersecurity of autonomous vehicles. The article demonstrates that cybersecurity is a fundamental area for the development and implementation of secure and reliable autonomous vehicles.info:eu-repo/semantics/publishedVersio

Data Science, Machine learning and big data in Digital Journalism: A survey of state-of-the-art, challenges and opportunities

Digital journalism has faced a dramatic change and media companies are challenged to use data science algo-rithms to be more competitive in a Big Data era. While this is a relatively new area of study in the media landscape, the use of machine learning and artificial intelligence has increased substantially over the last few years. In particular, the adoption of data science models for personalization and recommendation has attracted the attention of several media publishers. Following this trend, this paper presents a research literature analysis on the role of Data Science (DS) in Digital Journalism (DJ). Specifically, the aim is to present a critical literature review, synthetizing the main application areas of DS in DJ, highlighting research gaps, challenges, and op-portunities for future studies. Through a systematic literature review integrating bibliometric search, text min-ing, and qualitative discussion, the relevant literature was identified and extensively analyzed. The review reveals an increasing use of DS methods in DJ, with almost 47% of the research being published in the last three years. An hierarchical clustering highlighted six main research domains focused on text mining, event extraction, online comment analysis, recommendation systems, automated journalism, and exploratory data analysis along with some machine learning approaches. Future research directions comprise developing models to improve personalization and engagement features, exploring recommendation algorithms, testing new automated jour-nalism solutions, and improving paywall mechanisms.Acknowledgements This work was supported by the FCT-Funda?a ? o para a Ciência e Tecnologia, under the Projects: UIDB/04466/2020, UIDP/04466/2020, and UIDB/00319/2020

BIBLIOMETRIC STUDY ON THE DEVELOPMENT AND IMPLEMENTATION OF CYBERSECURITY IN AUTONOMOUS VEHICLES

The main objective was to examine the trajectory of scientific research in this domain, identify the most influential publications related to cybersecurity in autonomous vehicles and pinpoint research opportunities, supported by the PRISMA method. Additionally, the study explores cybersecurity themes in autonomous vehicles, emphasizing the significance of concepts like blockchain, machine learning, and deep learning essential in formulating business strategies. Furthermore, the research identifies influential scientific publications, predominant journals, the most productive countries, and authors with the most publications on cybersecurity in autonomous vehicles. It identifies research opportunities organized into two distinct clusters to provide a comprehensive understanding of the current state of research in this field and offer insights for companies and academics interested in contributing to future advancements in the cybersecurity of autonomous vehicles. The article demonstrates that cybersecurity is a fundamental area for the development and implementation of secure and reliable autonomous vehicles.info:eu-repo/semantics/publishedVersio