Informatics in Medicine Unlocked (23529148)38
With the increasing development of information and scientific databases, scientific collaboration has expanded in health sciences. This study aims to prioritize the criteria that affect finding potential author matches in bioinformatics using fuzzy Multiple Criteria Decision Making (MCDM) methods such as Analytical Hierarchy Process (AHP), Fuzzy Delphi Method (FDM), and Triangular Fuzzy Numbers (TFN). To answer the research questions, a mix of documentary analysis and fuzzy methods is utilized. The documentary analysis stage involves collecting relevant documents and resources using the purposive sampling approach and ranking the effective criteria. The subsequent step involves experts determining the priorities of the effective criteria using pairwise comparisons and the Delphi questionnaire. The final weights are obtained based on the research purpose. The study shows that 79 criteria related to the research purpose can be grouped into three general categories: behavioral, topological, and content-based criteria. The most effective criteria in finding and recommending a potential author match are “journal titles”, “citations”, “paper titles”, “affiliations”, “keywords”, and “abstracts”. Among these criteria, citation and paper titles have a higher priority compared to others. The results indicate that content-based criteria have the most significant impact on finding potential author matches in static scholar networks and networks with text information. Furthermore, among the content-based criteria, the number of publications in common specialized journals and the number of common citations are the most sought-after criteria for finding a potential author match with the highest similarity. © 2023 The Authors
Informatics in Medicine Unlocked (23529148)36
Purpose: The COVID-19 pandemic has indisputably impacted every aspect of human life, and a host of studies have investigated its different aspects. This paper models the contents of Persian literature on COVID-19. Method: This is a descriptive-exploratory study in which 815 articles were collected from the Magiran database. The articles were published before March 2022. The abstracts and titles were used in the modeling. The modeling was performed by combining the latent Dirichlet allocation (LDA) algorithm with ParsBERT. Findings: Topic modeling indicated ten major topics, including medicine, psychology, humanities, politics, management, biology, economics, culture, engineering, and religion. The articles under the category of medicine had the largest cluster (42.3%), while engineering and religion had the smallest clusters (1.1% each). Conclusion: The found topics in the created clusters have structural relationships. The COVID-19 effect on physical and mental health (medical and psychological topics) is the most crucial factor. These clusters provide evidence that COVID-19 affects all facets of human society at three levels: the individual, family, and society. Aside from the ten critical clusters in the humanities field, the utmost disorder is related to teaching and learning. For the first time, this research has presented a model of scientific communication in the field of COVID-19 based on the data collected from a Persian database – Magiran. © 2022 The Authors
Informatics in Medicine Unlocked (23529148)32
Health communication is a new field focusing on the “powerful role” of human and media communication in health care services and health promotion. This study intends to explore the intellectual structure of knowledge in health communication literature using the co-word analysis technique. The applied descriptive-analytical method was used in this study to analyze the literature content with a hierarchical clustering approach. For data collection, the descriptors of the keyword “Health Communication” were searched in the medical subject heading (MeSH) in the PubMed database on November 18, 2021, for the period of 1959–2021. Data analysis and clustering were performed using SPSS software (version 20), RavarPremap software, Excell, Ucinet and VosViewer software. Data analysis indicates that scientific articles on communication health have experienced ascending growth pattern. Moreover, the findings on hierarchical clustering led to the formation of six subject clusters with the predominant subjects of “ COVID-19 Pandemic, Health Education & Vaccine Hesitancy.” The present study revealed a structural relationship among subject concepts in the clusters created with common features within each group. This study provided valuable insights into scientific communication patterns in health communication research produced in the PubMed database. © 2022 The Authors
Journal of Big Data (21961115)8(1)
Finding the most suitable co-author is one of the most important ways to conduct effective research in scientific fields. Data science has contributed to achieving this possibility significantly. The present study aims at designing a mathematical model of co-author recommender system in bioinformatics using graph mining techniques and big data applications. The present study employed an applied-developmental research method and a mixed-methods research design. The research population consisted of all scientific products in bioinformatics in the PubMed database. To achieve the research objectives, the most appropriate effective features in choosing a co-author were selected, prioritized, and weighted by experts. Then, they were weighted using graph mining techniques and big data applications. Finally, the mathematical co-author recommender system model in bioinformatics was presented. Data analysis instruments included Expert Choice, Excel, Spark, Scala and Python programming languages in a big data server. The research was conducted in four steps: (1) identifying and prioritizing the criteria effective in choosing a co-author using AHP; (2) determining the correlation degree of articles based on the criteria obtained from step 1 using algorithms and big data applications; (3) developing a mathematical co-author recommender system model; and (4) evaluating the developed mathematical model. Findings showed that the journal titles and citations criteria have the highest weight while the abstract has the lowest weight in the mathematical co-author recommender system model. The accuracy of the proposed model was 72.26. It was concluded that using content-based features and expert opinions have high potentials in recommending the most appropriate co-authors. It is expected that the proposed co-author recommender system model can provide appropriate recommendations for choosing co-authors on various fields in different contexts of scientific information. The most important innovation of this model is the use of a combination of expert opinions and systemic weights, which can accelerate the finding of co-authors and consequently saving time and achieving a greater quality of scientific products. © 2021, The Author(s).
International Journal Of Information Science And Management (20088310)19(2)pp. 1-18
Nowadays, scientific collaboration has dramatically increased due to web-based technologies, advanced communication systems, and information and scientific databases. The present study aims to provide a predictive model for author collaborations in bioinformatics research output using graph mining techniques and big data applications. The study is applied-developmental research adopting a mixed-method approach, i.e., a mix of quantitative and qualitative measures. The research population consisted of all bioinformatics research documents indexed in PubMed (n=699160). The correlations of bioinformatics articles were examined in terms of weight and strength based on article sections including title, abstract, keywords, journal title, and author affiliation using graph mining techniques and big data applications. Eventually, the prediction model of author collaboration in bioinformatics research was developed using the abovementioned tools and expert-assigned weights. The calculations and data analysis were carried out using Expert Choice, Excel, Spark, and Scala, and Python programming languages in a big data server. Accordingly, the research was conducted in three phases: 1) identifying and weighting the factors contributing to authors' similarity measurement; 2) implementing co-authorship prediction model; and 3) integrating the first and second phases (i.e., integrating the weights obtained in the previous phases). The results showed that journal title, citation, article title, author affiliation, keywords, and abstract scored 0.374, 0.374, 0.091, 0.075, 0.055, and 0.031. Moreover, the journal title achieved the highest score in the model for the co-author recommender system. As the data in bibliometric information networks is static, it was proved remarkably effective to use content-based features for similarity measures. So that the recommender system can offer the most suitable collaboration suggestions. It is expected that the model works efficiently in other databases and provides suitable recommendations for author collaborations in other subject areas. By integrating expert opinion and systemic weights, the model can help alleviate the current information overload and facilitate collaborator lookup by authors. © 2021, International Journal of Information Science and Management, All Rights Reserved.
International Journal of Distributed Systems and Technologies (19473540)11(2)pp. 1-17
This research was carried out using the bibliometric method to thematically analyze the articles on IoT in the Web of Science with Hierarchical Agglomerative Clustering approach. First, the descriptors of the related articles published from 2002 to 2016 were extracted from WoS, by conducting a keyword search using the “Internet of Things” keyword. Data analysis and clustering were carried out in SPSS, UCINET, and PreMap. The analysis results revealed that the scientific literature published on IoT during the period had grown exponentially, with an approximately 48% growth rate in the last two years of the study period (i.e. 2015 and 2016). After analyzing the themes of the documents, the resulting concepts were classified into twelve clusters. The twelve main clusters included: Privacy and Security, Authentication and Identification, Computing, Standards and Protocols, IoT as a component, Big Data, Architecture, Applied New Techniques in IoT, Application, Connection and Communication Tools, Wireless Network Protocols, and Wireless Sensor Networks. Copyright © 2020, IGI Global.
