Academic Journal
Recent Synergies of Machine Learning and Neurorobotics: A Bibliometric and Visualized Analysis.
| Τίτλος: | Recent Synergies of Machine Learning and Neurorobotics: A Bibliometric and Visualized Analysis. |
|---|---|
| Συγγραφείς: | Lin, Chien-Liang, Zhu, Yu-Hui, Cai, Wang-Hui, Su, Yu-Sheng |
| Πηγή: | Symmetry (20738994); Nov2022, Vol. 14 Issue 11, p2264, 15p |
| Θεματικοί όροι: | Bibliometrics, Machine learning, Compilers (Computer programs), Cluster analysis (Statistics), Problem solving |
| Περίληψη: | Over the past decade, neurorobotics-integrated machine learning has emerged as a new methodology to investigate and address related problems. The combined use of machine learning and neurorobotics allows us to solve problems and find explanatory models that would not be possible with traditional techniques, which are basic within the principles of symmetry. Hence, neuro-robotics has become a new research field. Accordingly, this study aimed to classify existing publications on neurorobotics via content analysis and knowledge mapping. The study also aimed to effectively understand the development trend of neurorobotics-integrated machine learning. Based on data collected from the Web of Science, 46 references were obtained, and bibliometric data from 2013 to 2021 were analyzed to identify the most productive countries, universities, authors, journals, and prolific publications in neurorobotics. CiteSpace was used to visualize the analysis based on co-citations, bibliographic coupling, and co-occurrence. The study also used keyword network analysis to discuss the current status of research in this field and determine the primary core topic network based on cluster analysis. Through the compilation and content analysis of specific bibliometric analyses, this study provides a specific explanation for the knowledge structure of the relevant subject area. Finally, the implications and future research context are discussed as references for future research. [ABSTRACT FROM AUTHOR] |
| Copyright of Symmetry (20738994) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Βάση Δεδομένων: | Complementary Index |
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| Items | – Name: Title Label: Title Group: Ti Data: Recent Synergies of Machine Learning and Neurorobotics: A Bibliometric and Visualized Analysis. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Lin%2C+Chien-Liang%22">Lin, Chien-Liang</searchLink><br /><searchLink fieldCode="AR" term="%22Zhu%2C+Yu-Hui%22">Zhu, Yu-Hui</searchLink><br /><searchLink fieldCode="AR" term="%22Cai%2C+Wang-Hui%22">Cai, Wang-Hui</searchLink><br /><searchLink fieldCode="AR" term="%22Su%2C+Yu-Sheng%22">Su, Yu-Sheng</searchLink> – Name: TitleSource Label: Source Group: Src Data: Symmetry (20738994); Nov2022, Vol. 14 Issue 11, p2264, 15p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Bibliometrics%22">Bibliometrics</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Compilers+%28Computer+programs%29%22">Compilers (Computer programs)</searchLink><br /><searchLink fieldCode="DE" term="%22Cluster+analysis+%28Statistics%29%22">Cluster analysis (Statistics)</searchLink><br /><searchLink fieldCode="DE" term="%22Problem+solving%22">Problem solving</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Over the past decade, neurorobotics-integrated machine learning has emerged as a new methodology to investigate and address related problems. The combined use of machine learning and neurorobotics allows us to solve problems and find explanatory models that would not be possible with traditional techniques, which are basic within the principles of symmetry. Hence, neuro-robotics has become a new research field. Accordingly, this study aimed to classify existing publications on neurorobotics via content analysis and knowledge mapping. The study also aimed to effectively understand the development trend of neurorobotics-integrated machine learning. Based on data collected from the Web of Science, 46 references were obtained, and bibliometric data from 2013 to 2021 were analyzed to identify the most productive countries, universities, authors, journals, and prolific publications in neurorobotics. CiteSpace was used to visualize the analysis based on co-citations, bibliographic coupling, and co-occurrence. The study also used keyword network analysis to discuss the current status of research in this field and determine the primary core topic network based on cluster analysis. Through the compilation and content analysis of specific bibliometric analyses, this study provides a specific explanation for the knowledge structure of the relevant subject area. Finally, the implications and future research context are discussed as references for future research. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Symmetry (20738994) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/sym14112264 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 15 StartPage: 2264 Subjects: – SubjectFull: Bibliometrics Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Compilers (Computer programs) Type: general – SubjectFull: Cluster analysis (Statistics) Type: general – SubjectFull: Problem solving Type: general Titles: – TitleFull: Recent Synergies of Machine Learning and Neurorobotics: A Bibliometric and Visualized Analysis. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Lin, Chien-Liang – PersonEntity: Name: NameFull: Zhu, Yu-Hui – PersonEntity: Name: NameFull: Cai, Wang-Hui – PersonEntity: Name: NameFull: Su, Yu-Sheng IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 11 Text: Nov2022 Type: published Y: 2022 Identifiers: – Type: issn-print Value: 20738994 Numbering: – Type: volume Value: 14 – Type: issue Value: 11 Titles: – TitleFull: Symmetry (20738994) Type: main |
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