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In this research, the conceptual dimensions of research in the field of scientific authority were analyzed with the aim of analysis of the conceptual structure of knowledge in this field in Iran. In order to achieve this goal, the main questions is: What is the conceptual structure of “scientific authority” knowledge in Iran based on existing scientific documents? In this research, the technique of co-word analysis, information clustering and social network analysis were used. The study population of this research includes all scientific documents including Persian articles, books, theses and research projects from the beginning to January 2023. Research tools include software in the field of scientometrics such as Ravar_matrix, Ucinet, NetDraw, and VOSviewer. To collect data from databases such as Noormags Database, Magiran Database, Magiran, ISC Database and SID Database, List of National and University Libraries, GANJ Database, other specific indexes, search engines such as Google, etc. were used. To retrieve documents related to scientific authority, from all search strategies including advanced, simple and review, and sometimes semi-automatic text mining with keywords “scientific authority”, “scientific originality”, “scientific leadership”, “academic leadership”, “scientific reviewer”, and “university professors” were used to extract related scientific documents. The results related to co-lexical analysis showed that more than 241 concepts or keywords were mentioned in this field and the concepts of “scientific authority” in particular with the highest frequency, Ahl al-Bayt, Holy Qur’an, gaining or achieving authority, University of Medical Sciences, University “Leadership perspective and higher education” have had the most frequency among all concepts. Also, 30 concepts such as “indigenization of science”, “scientific dynamism”, “scientific vitality”, “scientific capital”, “scientific authority”, “civilization-building research”, “scientific authority”, “scientism”, “scientific flow”, “promotion chair”, “frontiers of knowledge”, “social innovation”, “scientific heritage”, “leaders of science” were identified as emerging concepts in the researches. The results related to clustering showed that the knowledge of this field in Iran consists of 13 main clusters. Drawing conceptual maps of the field of knowledge of scientific authority showed that the formed network of concepts has discontinuity and its concepts are less connected with each other. Considering that its density is around 0.0268, it cannot be said that its conceptual network is dense. Network density is a number that is in the range between one and zero. The closer the network density score is to one, it indicates more density and the closer to zero, it indicates the laxity and disconnection of the network. According to the density of the network, it can be concluded that the number of 20 to 30 concepts has caused cohesion in a part of the conceptual network of this field. In this regard, the density map visualized this situation well and showed where the hot spots in the field of scientific authority are concentrated in the network and in the vicinity of what concepts. The term scientific authority was first proposed by the Supreme Leader and after that it was explained many times and various researchers have defined, interpreted and explained the concept of scientific authority. This research was done on the basis of what has been written so far about the scientific authority of research and writing, not what it should be. In this study, an attempt was made to express the concepts from the point of view of information visualization technology, and by drawing conceptual maps, their relationships were depicted. Surely, readers and those interested in this field can find more conceptual relationships governing the field. This research is done for the first time with this method. Also, no research has been done with this method in the field of scientific authority so far. Therefore, matching the findings with some of the previous results of this research are not equivalent. Therefore, the findings were also different from them. Perhaps it can be claimed that these findings were the result of the activities of Iranian researchers in the field of scientific authority from the time it was raised by the Supreme Leader in 2014 until today. This research provided a conceptual macro picture of the field of scientific authority. It is hoped that officials, curators and researchers who work for the development of this field will use the results of this research. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this research is to analyze the knowledge structure of Hawraman research in Iran.Methodology: This study employed content analysis techniques, specifically co-word analysis and information clustering. The research community encompassed all scientific outputs from the beginning until 2022.Findings: The findings indicate a significant growth in research on Hawraman over the past two decades, with over 15 geographical areas being studied. Co-word analysis revealed more than 1500 concepts or keywords mentioned, with "Hawraman," "Tourism," "local architecture," and "Hawrami dialect" being the most frequently used concepts. Notably, the conceptual pairs "Hawraman-tourist attractions," "Hawraman-history," and "Hawraman-Palangan" exhibited the highest co-occurrences. Additionally, clustering analysis uncovered 15 main clusters within the field's knowledge. Furthermore, the analysis of conceptual maps showed extensive relationships between concepts, indicating a dense network of interconnected ideas.Conclusion: The Hawraman region has gained substantial attention from researchers due to its unique environmental conditions and UNESCO registration. Consequently, there has been a surge in research, leading to the exploration of numerous concepts. This research provides a comprehensive overview of the current state of Hawraman research and sheds light on the key issues being investigated.Value: This study represents the first attempt at analyzing research data for an area in western Iran.

Nowadays, with the expansion of semantic web services, the need of search engines for conceptual networks and domain ontology to infer semantically from user queries and to retrieve optimal (accurate and relevant) information has also increasedThe present study is an applied research aiming to analyze the conceptual network of “Information security”, which its domain structure was discovered using a mixed method of co-word and social networks analysis. The statistical society included 10227 scientific documents (books and international journal and conference papers) which was searched on Scopus and Web of Science citation databases from 2013 to 2017. After preprocessing keywords and tags by Zotero, an extension of the Firefox web-browser, Excel was used to match 8 prominent information security glossaries. Gephi and VOSviewer were used to visualize and analyze the conceptual network. By analyzing 19648 keywords and tags, a total of 207 keywords were extracted from the last edition of the information security glossary. The results showed that this network includes 14 clusters, of which 5 are mature, 7 are half-mature, and 2 are immature, and it was sufficiently coherent and dense. The most important concepts of this network with the highest betweenness centrality are “Security”, “Information Security”, “Information Systems”, “Privacy”, “Information” “Telecommunication, “Encryption” and “Cryptography”, “Authentication”, “Cyber security”, “Network”, “Cloud Computing”, “Security Attacks”, “Access Control”, “Intrusion Detection Systems (IDS)”, “Security Protocols”, “Risk”, “Risk Management and its Frameworks”, and “Service Level Agreement (SLA)”. These concepts are directly inter-related and connected.

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In this paper, a manifold-based dictionary learning method for the bag-of-features (BoF) representation optimized toward information clustering is proposed. First, the spectral representation, which unwraps the manifolds of the data and provides better clustering solutions, is formed. Then, a new dictionary is learned in order to make the histogram space, i.e., the space where the BoF historgrams exist, as similar as possible to the spectral space. The ability of the proposed method to improve the clustering solutions is demonstrated using a wide range of datasets: two image datasets, the 15-scene dataset and the Corel image dataset, one video dataset, the KTH dataset, and one text dataset, the RT-2k dataset. The proposed method improves both the internal and the external clustering criteria for two different clustering algorithms: 1) the k -means and 2) the spectral clustering. Also, the optimized histogram space can be used to directly assign a new object to its cluster, instead of using the spectral space (which requires reapplying the spectral clustering algorithm or using incremental spectral clustering techniques). Finally, the learned representation is also evaluated using an information retrieval setup and it is demonstrated that improves the retrieval precision over the baseline BoF representation. [ABSTRACT FROM PUBLISHER]

The present paper offers a basic introduction to data clustering and demonstrates the application of clustering methods in drawing maps of science. All approaches towards classification and clustering of information are briefly discussed. Their application to the process of visualization of conceptual information and drawing of science maps are illustrated by reviewing similar researches in this field. By implementing aggregated hierarchical clustering algorithm, which is an algorithm based on complete-link method, the map for urban management science as an emerging, interdisciplinary scientific field is analyzed and reviewed.

Linked data describes a method of publishing structured data which it can be interlinked and become more effective through semantic queries. This enables data from different sources to be connected and queried. It builds upon standard web technologies such as HTTP, RDF and URIs. This method helps human readers to share information in a way that can be read automatically by computers. Regarding the importance of Linked data, the main aim of this article is visualizing scientific mapping of linked data to show its progress through one decade. The scientometric study employs hierarchical cluster analysis, strategic diagrams and network analysis to map and visualize the linked data landscape of the "Scopus" publications through the use of co-word analysis. The study quantifies and describes the thematic evolution of the field based on a total of 717 Scopus articles and their associated 19977 keywords published between 1970s and 2014. According to the results the thematic visualization and the clusters show most concepts concentrated around computer related terms, such as big data; cloud computing semantic data; semantic technologies; semantic web; artificial intelligence; computer programming; semantic search, etc. In addition, we found that in recent years after librarians and information scientists doing researches in linked data on the behalf of computer scientist the "user" studies became important. [ABSTRACT FROM AUTHOR]

Highlights: [•] We incorporate the spatial context to the conventional fuzzy c-means (FCM) algorithm for image segmentation. [•] We incorporate the information-theoretic framework into the FCM algorithm to improve its robustness. [•] The new clustering algorithm can effectively improve the image segmentation results and is useful for brain image analysis. [ABSTRACT FROM AUTHOR]

Search engines usually publish search results on web pages that are filled with a certain number of matched records that can be further navigated through successive back and forth operations. While this approach proves to be useful, it fails to show the context and relationship among displayed results. We argue that new and intuitive user interfaces, such as 3-D virtual environment interfaces, may provide an alternative to the traditional presentation techniques, particularly in a learning environment where learners are looking for multimedia learning resources that match their interests. We propose a 3-D car gaming environment such as a search metaphor and analyze the suitability of such an approach in terms of user perception, interaction, visual quality, graphics-rendering performance, and other parameters. Our experiment suggests that the proposed approach not only shows an intuitive way of information visualization but also integrates education and entertainment to serve the vision of Edutainment. [ABSTRACT FROM AUTHOR]

This study examines the individuation versus categorization of men and women. Several researchers have argued for structural status differences between men and women — men occupy societal positions of high status, and women positions of low status. This line of research predicts that male participants will individuate other men, bur categorize women. Conversely, female participants will individuate men as well as women. In the present study, male and female participants were presented with eight stimulus persons, four men and four women, each described by four attributes. In addition, stimulus-category fit was manipulated such that the attributes were either stereotypical of the gender group they described or gender neutral. Information clustering in free recall and name-matching were main dependent measures. The results support the hypotheses and are discussed in terms of status differentials and contemporary theories of person perception. [ABSTRACT FROM AUTHOR]