From Data to Insights: A Bibliometric Assessment of Agent-Based Modeling Applications in Transportation

Domenteanu, Adrian; Delcea, Camelia; Chiriță, Nora; Ioanăș, Corina

doi:10.3390/app132312693

Cited by 11 publications

(10 citation statements)

References 83 publications

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“…In order to conduct the bibliometric analysis, the chosen data set was thoroughly examined using Biblioshiny [40], a well-known research tool included in R studio. With its assistance, a sizable number of visually appealing and functional graphs, tables, and visualizations were retrieved, emphasizing crucial information about the investigation of machine learning approaches in fake news research as well as hidden features, unknown details, current trends, and tactics [41,42].…”

Section: Bibliometric Analysismentioning

confidence: 99%

Numbers Do Not Lie: A Bibliometric Examination of Machine Learning Techniques in Fake News Research

Sandu,

Ioanăș,

Delcea

et al. 2024

Algorithms

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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.

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Section: Bibliometric Analysismentioning

confidence: 99%

Numbers Do Not Lie: A Bibliometric Examination of Machine Learning Techniques in Fake News Research

Sandu,

Ioanăș,

Delcea

et al. 2024

Algorithms

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“…The research took into account all ten indexes provided by WoS [33,34]: Several filters have been applied while selecting the database, as presented in Table 2. The first filter was referring to the titles, which should contain the word "Industry 5.0", resulting in 276 papers.…”

Section: Exploratorymentioning

confidence: 99%

Mapping the Research Landscape of Industry 5.0 from a Machine Learning and Big Data Analytics Perspective: A Bibliometric Approach

Domenteanu,

Cibu,

Delcea

2024

Sustainability

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Over the past years, machine learning and big data analysis have emerged, starting as a scientific and fictional domain, very interesting but difficult to test, and becoming one of the most powerful tools that is part of Industry 5.0 and has a significant impact on sustainable, resilient manufacturing. This has garnered increasing attention within scholarly circles due to its applicability in various domains. The scope of the article is to perform an exhaustive bibliometric analysis of existing papers that belong to machine learning and big data, pointing out the capability from a scientific point of view, explaining the usability of applications, and identifying which is the actual in a continually changing domain. In this context, the present paper aims to discuss the research landscape associated with the use of machine learning and big data analysis in Industry 5.0 in terms of themes, authors, citations, preferred journals, research networks, and collaborations. The initial part of the analysis focuses on the latest trends and how researchers lend a helping hand to change preconceptions about machine learning. The annual growth rate is 123.69%, which is considerable for such a short period, and it requires a comprehensive analysis to check the boom of articles in this domain. Further, the exploration investigates affiliated academic institutions, influential publications, journals, key contributors, and most delineative authors. To accomplish this, a dataset has been created containing researchers’ papers extracted from the ISI Web of Science database using keywords associated with machine learning and big data, starting in 2016 and ending in 2023. The paper incorporates graphs, which describe the most relevant authors, academic institutions, annual publications, country collaborations, and the most used words. The paper ends with a review of the globally most cited documents, describing the importance of machine learning and big data in Industry 5.0.

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“…Bibliometric analysis has gained momentum and is increasingly used in all fields because its main benefit is the complex scientific mapping [21], over a specific time period, highlighting new trends that practitioners or decision-makers in various domains can apply [22,23]. Lately, there has been a growing number of bibliometric studies where authors emphasize the importance of utilizing fuzzy logic in various domains such as marketing [24], big data [25], decision management [26], financial modeling [27], and more [28].…”

Section: Literature Reviewmentioning

confidence: 99%

Mathematical Patterns in Fuzzy Logic and Artificial Intelligence for Financial Analysis: A Bibliometric Study

Nica,

Delcea,

Chiriță

2024

Mathematics

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In this study, we explored the dynamic field of fuzzy logic and artificial intelligence (AI) in financial analysis from 1990 to 2023. Utilizing the bibliometrix package in RStudio and data from the Web of Science, we focused on identifying mathematical models and the evolving role of fuzzy information granulation in this domain. The research addresses the urgent need to understand the development and impact of fuzzy logic and AI within the broader scope of evolving technological and analytical methodologies, particularly concentrating on their application in financial and banking contexts. The bibliometric analysis involved an extensive review of the literature published during this period. We examined key metrics such as the annual growth rate, international collaboration, and average citations per document, which highlighted the field’s expansion and collaborative nature. The results revealed a significant annual growth rate of 19.54%, international collaboration of 21.16%, and an average citation per document of 25.52. Major journals such as IEEE Transactions on Fuzzy Systems, Fuzzy Sets and Systems, the Journal of Intelligent & Fuzzy Systems, and Information Sciences emerged as significant contributors, aligning with Bradford’s Law’s Zone 1. Notably, post-2020, IEEE Transactions on Fuzzy Systems showed a substantial increase in publications. A significant finding was the high citation rate of seminal research on fuzzy information granulation, emphasizing its mathematical importance and practical relevance in financial analysis. Keywords like “design”, “model”, “algorithm”, “optimization”, “stabilization”, and terms such as “fuzzy logic controller”, “adaptive fuzzy controller”, and “fuzzy logic approach” were prevalent. The Countries’ Collaboration World Map indicated a strong pattern of global interconnections, suggesting a robust framework of international collaboration. Our study highlights the escalating influence of fuzzy logic and AI in financial analysis, marked by a growth in research outputs and global collaborations. It underscores the crucial role of fuzzy information granulation as a mathematical model and sets the stage for further investigation into how fuzzy logic and AI-driven models are transforming financial and banking analysis practices worldwide.

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From Data to Insights: A Bibliometric Assessment of Agent-Based Modeling Applications in Transportation

Cited by 11 publications

References 83 publications

Numbers Do Not Lie: A Bibliometric Examination of Machine Learning Techniques in Fake News Research

Numbers Do Not Lie: A Bibliometric Examination of Machine Learning Techniques in Fake News Research

Mapping the Research Landscape of Industry 5.0 from a Machine Learning and Big Data Analytics Perspective: A Bibliometric Approach

Mathematical Patterns in Fuzzy Logic and Artificial Intelligence for Financial Analysis: A Bibliometric Study

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