Keyword Trends Research Articles

Dual Monetary System

Islamic economy grows in Muslim-majority countries within the monetary system, although the monetary system dominates with central banks and paper money. As a result, the Islamic monetary system developed its regulations and transmission mechanisms. The central bank must implement policies of both conventional and Islamic banking systems. From an Islamic point of view, the monetary policy effectively extends overall macroeconomic conditions in countries with multiple financial systems. Bibliometric studies in research related to the development of Dual Monetary System trends published by leading journals from 1974 to 2021, indexed by Scopus. Analysis of keyword trends, authors, and institutions. The data analyzed were 77 research publications using descriptive statistical methods and bibliometric analysis. The results show that the number of articles discussing the theme of the Dual Monetary System has an increasing trend from year to year, in line with the development of the Islamic economy. The most popular keywords used are monetary policy, dual banking system, and credit channels. The most prolific writer is Majid M.S.A. Meanwhile, the country with the most famous authors in the United States. This research provides information for researchers who focus on research in the field of the Monetary System. In the end, this theme is likely to continue to be developed.

Applying text mining to identify relevant literature in food science: Cold denaturation as a case study.

In a research environment characterized by the five V's of big data, volume, velocity, variety, value, and veracity, the need to develop tools that quickly screen a large number of publications into relevant work is an increasing area of concern, and the data-rich food industry is no exception. Here, a combination of latent Dirichlet allocation and food keyword searches were employed to analyze and filter a dataset of 6102 publications about cold denaturation. After using the Python toolkit generated in this work, the approach yielded 22 topics that provide background and insight on the direction of research in this field, as well as identified the publications in this dataset which are most pertinent to the food industry with precision and recall of 0.419 and 0.949, respectively. Precision is related to the relevance of a paper in the filtered dataset and the recall represents papers which were not identified in the screening method. Lastly, gaps in the literature based on keyword trends are identified to improve the knowledge base of cold denaturation as it relates to the food industry. This approach is generalizable to any similarly organized dataset, and the code is available upon request. Practical Application: A common problem in research is that when you are an expert in one field, learning about another field is difficult, because you may lack the vocabulary and background needed to read cutting edge literature from a new discipline. The Python toolkit developed in this research can be applied by any researcher that is new to a field to identify what the key literature is, what topics they should familiarize themselves with, and what the current trends are in the field. Using this structure, researchers can greatly speed up how they identify new areas to research and find new projects.

A bibliometric analysis of Iranian Journal of Fuzzy Systems (2007-2020)

Iranian Journal of Fuzzy Systems (IJFS) is an international journal, based on the WoS core collection, this paper uses bibliometric methods and tools (VOSviewer and Bibliometrix) to analyze the publications of IJFS from 2007 to 2020. A total of 716 publications are analyzed. Firstly, according to the basic information of the publications, including the type of publications, the number of publications, the number of citations, the average number of citations and the h-index, we conduct a general statistical analysis to explore the basic characteristics and internal structure of the publications. Afterwards, by studying the citation structure of publications, this paper analyzes which groups and fields the journal is more concerned about. For the journal itself, we combine the number of citations and the number of publications to identify the most popular publications, the most productive and influential authors/institutes/countries/regions. In the collaboration network analysis, we map the collaboration network of countries/regions, institutes and authors to explore the different levels of collaboration. The co-citation network of the journal is studied to explore the relationship between IJFS and other international academic journals. In the analysis of keywords, we discuss the development trend and hot topics of keywords over time through co-occurrence analysis, word growth graph and thematic evolution analysis. Finally, based on the above analysis, we discuss the problems and challenges that the journal may face, and some main conclusions are drawn. This study has certain reference value for the further development of the related researchers�� scientific research and the journal.

Need of care in interpreting Google Trends-based COVID-19 infodemiological study results: potential risk of false-positivity

BackgroundGoogle Trends (GT) is being used as an epidemiological tool to study coronavirus disease (COVID-19) by identifying keywords in search trends that are predictive for the COVID-19 epidemiological burden. However, many of the earlier GT-based studies include potential statistical fallacies by measuring the correlation between non-stationary time sequences without adjusting for multiple comparisons or the confounding of media coverage, leading to concerns about the increased risk of obtaining false-positive results. In this study, we aimed to apply statistically more favorable methods to validate the earlier GT-based COVID-19 study results.MethodsWe extracted the relative GT search volume for keywords associated with COVID-19 symptoms, and evaluated their Granger-causality to weekly COVID-19 positivity in eight English-speaking countries and Japan. In addition, the impact of media coverage on keywords with significant Granger-causality was further evaluated using Japanese regional data.ResultsOur Granger causality-based approach largely decreased (by up to approximately one-third) the number of keywords identified as having a significant temporal relationship with the COVID-19 trend when compared to those identified by Pearson or Spearman’s rank correlation-based approach. “Sense of smell” and “loss of smell” were the most reliable GT keywords across all the evaluated countries; however, when adjusted with their media coverage, these keyword trends did not Granger-cause the COVID-19 positivity trends (in Japan).ConclusionsOur results suggest that some of the search keywords reported as candidate predictive measures in earlier GT-based COVID-19 studies may potentially be unreliable; therefore, caution is necessary when interpreting published GT-based study results.

A Scientometric Analysis of Zakat Literature Published in times of COVID-19 Pandemic

This bibliometric study investigates the development of trends from various studies on the role of zakat in the COVID-19 pandemic published by journals equipped with a digital-object-identifier. The analysis is focused on descriptions of the characteristics and trends of keywords, authors, journals and citations. The data analyzed were 32 DOI-equipped publications indexed by Google Scholar. The search used to determine the research dataset was last updated on March 16, 2021. The data was then processed and analyzed using the VOSviewer application program to determine the bibliometric mapping of research developments on the role of zakat in the COVID-19 pandemic. The results showed that the visualization of the mapping of research developments in this theme was divided into 4 clusters. Cluster 1 consists of 17 topics, cluster 2 consists of 17 topics, cluster 3 consists of 16 topics and cluster 4 consists of 8 topics. The most popular authors in the results of this bibliometric study are Dzarfan Abdul Kadir, then Margaret Adamek. Then the most popular journals are the Journal of Islamic Accounting and Business Research and the International Conference of Zakat (ICONZ) Proceeding. Most citations are occupied by papers written by Bin-Nashwan (2020)

Scientific Production on the Social Economy: A Review of Worldwide Research.

The aim of this article is to assess the use of the term Social Economy, while being aware of its lack of concreteness, and to analyze the level of scientific production by means of a bibliometric analysis using WoS (JCR) and Scopus (SJR) as sources. Starting in 2004 and related to the Charter of Principles of the Social Economy, the material development of articles began. The most receptive countries are Spain, the USA, China, the UK and Canada. In terms of the most productive journals, Voluntas in JCR and CIRIEC-Spain and REVESCO in SJR stand out. Scientific production on this issue is linked to university institutions, namely the Chinese Academy of Sciences, the University of Valencia and the University of Quebec. The most prevalent subject are Economics and Business in the case of JCR and Social Sciences in SJR. The most recognized term is that of cooperatives and the most prevalent keyword trends being related to sustainable development, climate change, urbanization, management and China.

Sustainability and Tourism Marketing: A Bibliometric Analysis of Publications between 1997 and 2020 Using VOSviewer Software

Several studies have empirically explored the association between practices in sustainable tourism and their impact on tourism marketing. However, bibliometric studies that organize the production in this field are still scarce. The objective of this study is thus to provide a bibliometric analysis of research on sustainable practices in tourism related to marketing, identifying the state of the art, trends and other indicators, by monitoring the articles published on the Web of Science (WoS) platform. A sample of 694 materials was obtained. The data were processed and the results graphically illustrated using the VOSviewer software. The study analyzed the simultaneous occurrence of publications by year, keyword trends, cocitations, bibliographic coupling and analysis of coauthorship, countries and institutions, and indicates that the literature on tourism sustainability issues in the field of tourism marketing is growing at a quick pace; merely five papers accounted for more than 2193 citations, but there are several prolific authors. Of the 694 sources included in the review, the most important ones published 40.34% of the papers; Spain is the leading country in this topic. This research provides insight about the state of the art and identifies gaps and research opportunities in sustainability and tourism marketing.

Current Research Trends in IoT Security: A Systematic Mapping Study

The smart mobile Internet-of-things (IoT) network lays the foundation of the fourth industrial revolution, the era of hyperconnectivity, hyperintelligence, and hyperconvergence. As this revolution gains momentum, the security of smart mobile IoT networks becomes an essential research topic. This study aimed to provide comprehensive insights on IoT security. To this end, we conducted a systematic mapping study of the literature to identify evolving trends in IoT security and determine research subjects. We reviewed the literature from January 2009 to August 2020 to identify influential researchers and trends of keywords. We additionally performed structural topic modeling to identify current research topics and the most promising ones via topic trend estimation. We synthesized and interpreted the results of the systematic mapping study to devise future research directions. The results obtained from this study are useful to understand current trends in IoT security and provide insights into research and development of IoT security.

A bibliometric analysis and visualization of photothermal therapy on cancer.

BackgroundCancer is one of the most lethal diseases in the world, and photothermal therapy was reported recently as a new and effective therapy for cancer. This study offers the bibliometric and visualization analysis of photothermal therapy on cancer.MethodsA record of 6,233 papers in this field from 1995 to 2019 was obtained based on the Web of Science Core Collection (WoSCC). And CiteSpace was used to analyze the annual trends of publications, countries, institutions, journals, co-cited references, and keywords in the field of photothermal therapy on cancer.ResultsWe identified that the number of publications continually increased over the time. The most productive country and institution in this field was China and Chinese Academy of Sciences, respectively. The ACS Appl Mater Interfaces was the most active journal. Co-cited references analysis revealed the top landmark articles in the field. Co-occurrence keywords and their clustered network were analyzed, revealing that materials, especially nanomaterials, used in photothermal therapy, remained the hotspots in this research field. Timezone view and burst detection of keywords showed that nanomaterials were always the hotspots and the frontier topics in this field.ConclusionsThe current study revealed that photothermal therapy has become a subject of growing study and a very important research area. In addition, the research of materials in photothermal therapy, especially nanomaterials, which were applied in photothermal therapy to treat cancer effectively, is the foci and the frontier topic in this field.

Bibliometric analysis of productivity and keyword trends of articles on physical therapy and aging (1990-2014)

IntroductionThe increase in population longevity has led the subject of ageing to take an important role in research and innovation policies. Considerable efforts have been made worldwide to promote research on physiotherapy and ageing. Therefore, applying bibliometric measures can be useful to identify emerging and innovative scientific indicators. This study identifies productivity and topic trends in physiotherapy in ageing and the main publishing authors, institutions, and countries.MethodsA bibliometric analysis was applied in original articles published on the Science Citation Index Expanded of Web of Science Core Collection (1990–2014). Bibliometric indicators were obtained by using specific bibliometric software. Pearsons’ correlation coefficient (r) was applied for correlations; statistical significance was considered for <i>p</i> < 0.01.ResultsOverall, 2237 original articles were included. Topic trends show an initial focus on illness and evolve towards the study of prevention. Authors and institutions increased productivity and citation. The most productive country was the United States, and there was a significant correlation between productivity and gross domestic product (<i>p</i> < 0.01). No statistically significant relationship was observed between productivity and the countries’ aged population.ConclusionsProductivity increased, with a greater scientific interest in prevention. The published research was mainly related to wealthy and developed countries.

Predicting Emerging Trends of Keywords based on Graph Neural Network

Predicting Emerging Trends of Keywords based on Graph Neural Network

Urban Ecosystem Services: A Review of the Knowledge Components and Evolution in the 2010s

In an effort to reconnect urban populations to the biosphere, which is an urgent task to ensure human sustainability, the concept of urban ecosystem services (UES) has recently garnered scholarly and political attention. With an aim to examine the emerging research trends and gaps in UES, we present an up-to-date, computer-based meta-analysis of UES from 2010 to 2019 by implementing a keyword co-occurrence network (KCN) approach. A total of 10,247 author keywords were selected and used to analyze undirected and weighted networks of these keywords. Specifically, power-law distribution fitting was performed to identify overall UES keyword trends, and clusters of keywords were examined to understand micro-level knowledge trends. The knowledge components and structures of UES literature exhibited scale-free network characteristics, which implies that the KCN of the UES throughout the 2010s was dominated by a small number of keywords such as “urbanization”, “land use and land cover”, “urban green space” and “green infrastructure”. Finally, our findings indicate that knowledge of stakeholder involvement and qualitative aspects of UES are not as refined as spatial UES approaches. The implications of these knowledge components and trends are discussed in the context of urban sustainability and policy planning.

Scientific Trends, Characterization, and Future Directions of Regional Development Research Based on a Bibliometric Analysis

To understand the research trends and areas of focus of regional development, this study conducted bibliometric research based on the Science Citation Index Expanded and Social Science Citation Index databases. The study presents a comprehensive overview of the field of regional development research by exploring its annual output, journals, countries, institutions, temporal keyword trends, citations, and high-impact authors. The countries that had the greatest academic impact were the United Kingdom and United States, and the University of California, Los Angeles, was the institution that had the highest relative citation impact. The popular key words changed from those related to industry to those related to mechanisms and regulation. Later, China emerged as a popular topic, after which integrated research gained prominence. Three main research directions emerged: enterprise and theoretical studies on regional development, China’s regional development, and the dynamic mechanism of regional development.

Keyword Trends for Mother-Child Oral Health in Korea Based on Social Media Big Data from Naver.

ObjectivesThe present study examined trends in search keywords related to the oral health of infants and pregnant women using “social media cafés” on the Korean portal site, Naver.MethodsWe obtained data from January 2015 to December 2017, collected by searching for common terms related to oral health, such as “dental caries”, “oral health”, “scaling”, “tooth brushing”, and “oral examination”. Search results for these terms were organized by frequency and visualized by increase in the font size with increasing frequency.ResultsThe ranking of keywords on Naver cafés for pregnant women and women with infants was as follows (in descending order): “oral examination”, “tooth filling”, and “tooth brushing”. The “oral health” network was linked to “dental caries”, “oral health education”, and “tooth brushing”. In addition, the analysis of trends of keyword frequencies according to time periods showed that “dental caries” and “oral examination” were of highest interest to the café users.ConclusionsWe found a high interest in keywords related to preventive measures for the oral health of infants and children, but there was a lack of awareness regarding the oral health of pregnant women. These findings suggest that prevention in infants and pregnant women is necessary, and that public awareness regarding education about oral healthcare needs to be raised.

Analysis of key words published with the Korea Society of Emergency Medical Services journal using text mining

Purpose: The purpose of this study was to analyze the English abstract key words found within the Korea Society of Emergency Medical Services journal using text mining techniques to determine the adherence of these terms with Medical Subject Headings (MeSH) and identify key word trends. Methods: We analyzed 212 papers that were published from 2012 to 2019. R software, web scraping, and frequency analysis of key words were conducted using R's basic and text mining packages. Additionally, the Word Clouds package was used for visualization. Results: The average number of key words used per study was 3.9. Word cloud visualization revealed that CPR was most prominent in the first half and emergency medical technician was most frequently used during the second half. There were a total of 542 (64.9%) words that exactly matched the MeSH listed words. A total of 293 (35%) key words did not match MeSH listed words. Conclusion: Researchers should obey submission rules. Further, journals should update their respective submission rules. MeSH key words that are frequently cited should be suggested for use.

Systematic Review of Smart Tourism Research

In recent decades, smart tourism has been attracting attention from practitioners and scholars. The current study used multiple analysis methods to conduct a systematic review of 124 related articles on smart tourism. Qualitative analysis was conducted to identify 10 categories of smart tourism articles. Results showed that the largest proportion focus on the influence of technology on tourists’ perceptions, behaviors, and experiences. Co-occurrence analysis was performed to investigate the development trend of keywords used by academics in the last five years, while co-authorship (country) analysis was conducted to examine the collaborative relationship between different countries. The research regions, industries, methods, and theories applied in these articles were also analyzed. Theoretical and practical/managerial implications, as well as future research directions, were provided.

A calculation method for the development trend of subject keywords

SIGNIFICANCE: The complex network structure information such as citation network and co-authored network provides rich structural information for subject trend research, thus research on deep mining of existing static structure information to obtain

Patent Data Analysis of Artificial Intelligence Using Bayesian Interval Estimation

Technology analysis is one of the important tasks in technology and industrial management. Much information about technology is contained in the patent documents. So, patent data analysis is required for technology analysis. The existing patent analyses relied on the quantitative analysis of the collected patent documents. However, in the technology analysis, expert prior knowledge should also be considered. In this paper, we study the patent analysis method using Bayesian inference which considers prior experience of experts and likelihood function of patent data at the same time. For keyword data analysis, we use Bayesian predictive interval estimation with count data distributions such as Poisson. Using the proposed models, we forecast the future trends of technological keywords of artificial intelligence (AI) in order to know the future technology of AI. We perform a case study to provide how the proposed method can be applied to real areas. In this paper, we retrieve the patent documents related to AI technology, and analyze them to find the technological trend of AI. From the results of AI technology case study, we can find which technological keywords are more important or critical in the entire structure of AI industry. The existing methods for patent keyword analysis were depended on the collected patent documents at present. But, in technology analysis, the prior knowledge by domain experts is as important as the collected patent documents. So, we propose a method based on Bayesian inference for technology analysis using the patent documents. Our method considers the patent data analysis with the prior knowledge from domain experts.

Can Users Search Trends Predict People Scares or Disease Breakout? An Examination of Infectious Skin Diseases in the United States.

Background:In health and medicine, people heavily use the Internet to search for information about symptoms, diseases, and treatments. As such, the Internet information can simulate expert medical doctors, pharmacists, and other health care providers.Aim:This article aims to evaluate a dataset of search terms to determine whether search queries and terms can be used to reliably predict skin disease breakouts. Furthermore, the authors propose and evaluate a model to decide when to declare a particular month as Epidemic at the US national level.Methods:A Model was designed to distinguish a breakout in skin diseases based on the number of monthly discovered cases. To apply this model, the authors correlated Google Trends of popular search terms with monthly reported Rubella and Measles cases from Centers for Disease Control and Prevention (CDC). Regressions and decision trees were used to determine the impact of different terms to trigger the occurrence of epidemic classes.Results:Results showed that the volume of search keywords for Rubella and Measles rises when the volume of those reported diseases rises. Results also implied that the overall process was successful and should be repeated with other diseases. Such process can trigger different actions or activities to be taken when a certain month is declared as “Epidemic.” Furthermore, this research has shown great interest for vaccination against Measles and Rubella.Conclusions:The findings suggest that the search queries and keyword trends can be truly reliable to be used for the prediction of disease outbreaks and some other related knowledge extraction applications. Also search-term surveillance can provide an additional tool for infectious disease surveillance. Future research needs to re-apply the model used in this article, and researchers need to question whether characterizing the epidemiology of Coronavirus Disease 2019 (COVID-19) pandemic waves in United States can be done through search queries and keyword trends.

Engineering applications of artificial intelligence: A bibliometric analysis of 30 years (1988–2018)

The Engineering Applications of Artificial Intelligence (EAAI) is a journal of very high repute in the domain of Engineering and Computer Science. This paper gives a broad view of the publications in EAAI from 1988–2018, which are indexed in Web of Science (WoS) and Scopus. The main purpose of this research is to bring forward the prime impelling factors that bring about the EAAI publications and its citation structure. The publication and citation structure of EAAI is analyzed, which includes the distribution of publication over the years, citations per year and a bird’s eye view of the citation structure. Then the co-citation analysis and over the year’s trend of top keywords is given. The co-authorship networks and a geographic analysis of the sources is also provided. Further, a country-wise temporal and quantitative analysis of the publications is given along with the highly cited documents among the EAAI publications.