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Uncertainty analysis in document publications using single-valued neutrosophic set and collaborative entropy

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Abstract

Recent time characterization of domain based expert of given field is considered as one of the crucial tasks due to uncertainty and randomness in document publication. It becomes more crucial when interdisciplinary, collaborative and other uncertain papers are published by an author or institute only to receive the ranking. In this case precise characterization of founding author or institute of given domain generate uncertainty. This problem starts because document publications, collaboration or expert analysis of given field is totally dark data set which contains lots of unstructured, incomplete or uncertain data. Due to which, less attention has been paid towards this direction. However, it impacts more to recruitment process analysis, brain drain analysis, reviewer comment analysis, collaborative publication analysis, loyal or honest author analysis, or even conflict of interest analysis. To control this issue, a method is proposed in this paper to characterize the the document published by any institute or author in true, false or indeterminant zone of given domain using the properties of single–valued neutrosophic set. The expert of the given domain is classified based on defined (\(\alpha, \beta, \gamma \))–cut. Same time another method is proposed to deal the case of higher randomness in publication due to multiple author or collaboration using Shannon entropy. The obtained results from both of the methods are compared with each other as well as recently available approaches for validation.

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Data availability

The data can be taken from Google Scholar/SCOPUS/Web of Science for the analysis of the proposed method.

Notes

  1. https://en.wikipedia.org/wiki/Dark_data.

  2. https://www.bmc.com/blogs/dark_data.

  3. https://www.investopedia.com/terms/b/brain_drain.asp.

  4. https://retractionwatch.com/2014/12/19/elsevier-retracting-16-papers-faked-peer-review/.

  5. https://retractionwatch.com/.

  6. https://en.wikipedia.org/wiki/Elsevier.

  7. https://en.wikipedia.org/wiki/Retractions_in_academic_publishing.

  8. https://www.ugc.ac.in/page/fake-universities.aspx.

  9. https://www.livemint.com/Opinion/wXuMY2QSBNnCsRVzUUzJgN/In-Indian-science-and-technology-research-quantity-trumps-q.html.

  10. https://en.wikipedia.org/wiki/Citation_analysis.

  11. https://en.wikipedia.org/wiki/Co-citation.

  12. https://www.frontiersin.org/articles/10.3389/fnhum.2016.00556/full.

  13. https://timesofindia.indiatimes.com/city/guwahati/fake-phd-degrees-under-government-scanner/articleshow/63726060.cms,

  14. https://www.news18.com/news/india/engineering-for-rs-75000-law-certificate-for-rs-2-lakh-how-the-fake-degree-market-flourishes-2289559.html.

  15. https://indianexpress.com/article/education/two-vcs-alleged-with-fake-phd-thesis-hrd-5289123/.

  16. https://journosdiary.com/2018/05/30/iit-dhanbad-retraction-madhuri-sharma/.

  17. https://journosdiary.com/2018/08/09/image-duplication-bose-institute-retracted-corrected-pubpeer/.

  18. https://retractionwatch.com/

  19. https://www.nature.com/articles/d41586-018-06185-8.

  20. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5089989/.

  21. https://www.researchtrends.com/issue20-november-2010/does-a-nobel-prize-lead-to-more-citations/.

  22. https://www.idashboards.com/blog/2019/01/30/dark-data-the-blind-spots-in-your-analytics/.

References

  • Alonso S, Cabrerizo FC, Herrera-Viedma E, Herrera F (2009) h-index: a review focused in its variants, computation and standardization for different scientific fields. J Informetr 3(4):273–289

    Article  Google Scholar 

  • Bevern R, Komusiewicz C, Niedermeierd R, Sorge M, Walsh T (2016) H-index manipulation by merging articles: models, theory, and experiments. Artif Intell 240:19–35

    Article  MathSciNet  MATH  Google Scholar 

  • Aksnes DW, Langfeldt L, Wouters P (2019) Citations, citation indicators, and research quality: an overview of basic concepts and theories. SAGE Open 9:1. https://doi.org/10.1177/2158244019829575

    Article  Google Scholar 

  • Bornmann L, Daniel HD (2008) What do citation counts measure? A review of studies on citing behavior. J Document 64(1):45–80

    Article  Google Scholar 

  • Broumi S, Bakali A, Talea M, Singh PK, Smarandache F (2019) Energy and spectrum analysis of interval-valued neutrosophic graph using MATLAB. Neutrosoph Set Syst 24:46–60

    Google Scholar 

  • Breuer T, Schaer P, Tunger D (2022) Relevance assessments, bibliometrics, and altmetrics: a quantitative study on PubMed and arXiv. Scientometrics. https://doi.org/10.1007/s11192-022-04319-4

  • Chandresh K, Singh PK (2019) Scopus-based Comparative Analysis of Computer Science Research in India and USA. In: Proceedings of 10th international conference on computing, communication and networking technologies (ICCCNT), Kanpur, India, 2019, pp. 1–7, https://doi.org/10.1109/ICCCNT45670.2019.8944444.

  • Dutta R, Chakrabarti D, Gadgil A, Roy N (2021) Do authorship disputes deter Indian medical students from pursuing research? Indian J Med Ethics. https://doi.org/10.20529/IJME.2021.053

  • Ganter B, Wille R (1999) Formal concept analysis: mathematical foundation. Springer, Berlin

    Book  MATH  Google Scholar 

  • Gartner (2015) Magic quadrant for business intelligence and analytic platforms

  • Gao C, Wang Z, Li X, Zhang Z, Zeng W (2016) PR-index: using the h-index and PageRank for determining true impact. PLoS ONE 11(9):e0161755. https://doi.org/10.1371/journal.pone.0161755

    Article  Google Scholar 

  • Garfeld E (1955) Citation indexes to science: a new dimension in documentation through association of ideas. Science 122:108–111

    Article  Google Scholar 

  • Gimpel G (2020) Bringing dark data into the light: illuminating existing IoT data lost within your organization. Business Horizons 63(4):519–530

    Article  Google Scholar 

  • Heidorn PB (2008) Shedding light on the dark data in the long tail of science. Library Trends 57(2):280–299

    Article  Google Scholar 

  • Hirsch JE (2019) \(h_\alpha \): an index to quantify an individuals scientific leadership. Scientometrics 118:673–686

    Article  Google Scholar 

  • Hu BQ (2016) Three-way decision spaces based on partially ordered sets and three-way decisions based on hesitant fuzzy sets. Knowl-Based Syst 91:16–31

    Article  Google Scholar 

  • Jin T, Wu Q, Ou X, Yu J (2021) Community detection and co-author recommendation in co-author networks. Int J Mach Learn Cybern 12:597–609

    Article  Google Scholar 

  • Ioannidis John PA, Boyack Kevin W, Baas Jeroen (2020) Updated science-wide author databases of standardized citation indicators. PLoS Biol. https://doi.org/10.1371/journal.pbio.3000918

    Article  Google Scholar 

  • Kevin NM, Wanyaga FM, Kibaara D, Dinda WA, Ngatia JK (2016) Dark data: business analytical tools and facilities for illuminating dark data. Sci Res J 4(4):1–10

    Google Scholar 

  • Kim H, Jeong YK, Song M (2018) Content and proximity-based author co-citation analysis using citation sentences. J Informetr 10(4):954–966

    Article  Google Scholar 

  • Kosmulski M (2021) Posthumous co-authorship revisited. Scientometrics 126:8227–8231

    Article  Google Scholar 

  • Kreiner G (2016) The slavery of the h-index measuring the unmeasurable. Front Hum Neurosci. https://doi.org/10.3389/fnhum.2016.00556

    Article  Google Scholar 

  • Liu S, Wang B, Liu J (2020) Group decision making under social influences based on information entropy. Granul Comput 5:303–308

    Article  Google Scholar 

  • Li JH, Huang C, Qi J, Qian Y, Liu W (2017) Three-way cognitive concept learning via multi-granularity. Inf Sci 378:244–263

    Article  MATH  Google Scholar 

  • Majumdar P (2015) Neutrosophic sets and its applications to decision making. In: Acharjya D, Dehuri S, Sanyal S (eds) Computational intelligence for big data analysis. Adaptation, learning, and optimization, vol 19. Springer, Cham

    Google Scholar 

  • Mussard M, Pappachen James A (2018) Engineering the global university rankins: gold Standard, limitations and implications. IEEE Access 6:6765–6776

    Article  Google Scholar 

  • Noorden RV, Chawla DS (2019) Hundreds of extreme self-citing scientists revealed in new database. Nature 572:578–579

    Article  Google Scholar 

  • Singh PK (2017) Three-way fuzzy concept lattice representation using neutrosophic set. Int J Mach Learn Cybern 8(1):69–79

    Article  Google Scholar 

  • Singh PK (2019) Single-valued neutrosophic context analysis at distinct multi-granulation. Comput Appl Math 38(80):1–18. https://doi.org/10.1007/s40314-019-0842-4

    Article  MathSciNet  Google Scholar 

  • Singh PK (2019) Vague concept lattice reduction using granular computing and vague entropy. Math Comput Simul 165:56–73. https://doi.org/10.1016/j.matcom.2019.02.007

    Article  MathSciNet  MATH  Google Scholar 

  • Singh PK (2020) Multi-granular based n-valued neutrosophic contexts analysis. Granul Comput 5:287–301

    Article  Google Scholar 

  • Singh PK (2021) Turiyam set a fourth dimension data representation. J Appl Math Phys 9(7):1821–1828

    Article  MathSciNet  Google Scholar 

  • Singh PK (2021) Data with Turiyam set for fourth dimension quantum information processing. J Neutrosoph Fuzzy Syst 1(1):9–23. https://doi.org/10.54216/JNFS.010101

    Article  Google Scholar 

  • Singh P (2021) A type-2 neutrosophic-entropy-fusion based multiple thresholding method for the brain tumor tissue structures segmentation. Appl Soft Comput 103:107–119

    Article  Google Scholar 

  • Singh PK (2022a) t-index: Entropy based random document and citation analysis using average h-index. Scientrometrics 127:637–660. https://doi.org/10.1007/s11192-021-04222-4

    Article  Google Scholar 

  • Singh PK (2022b) The consistency measurement in document publications and citation using t-index. J Appl Math Phys 10:1000–1011. https://doi.org/10.4236/jamp.2022.103067

    Article  Google Scholar 

  • Singh PK (2022c) Complex plithogenic set. Int J Neutrosoph Sci 18(1):57–72. https://doi.org/10.54216/IJNS.180106

    Article  MathSciNet  Google Scholar 

  • Singh PK (2022d) NeutroAlgebra and NeutroGeometry for Dealing Heteroclinic Patterns. In: Theory and Applications of NeutroAlgebras as Generalizations of Classical Algebras, IGI Global Publishers, Chapter 6. https://doi.org/10.4018/978-1-6684-3495-6, pp 90-102, ISBN13: 9781668434956

  • Singh PK, Gani Abdullah (2015) Fuzzy concept lattice reduction using Shannon entropy and Huffman coding. J Appl Non-Classic logic 25(2):101–119

    Article  MathSciNet  MATH  Google Scholar 

  • Singh PK, Singh CK (2019) Bibliometric Study of Indian Institutes of Technology in Computer Science. In: Proceedings of Amity International Conference on Artificial Intelligence held at Dubai: pp 384–393. https://doi.org/10.1109/AICAI.2019.8701422

  • Singh P, Singla A (2020) A neutrosophic-entropy based adaptive thresholding segmentation algorithm (NEATSA): a special application in MR images of Parkinson’s disease (PD). Artif Intell Med 104:101838. https://doi.org/10.1016/j.artmed.2020.101838

    Article  Google Scholar 

  • Singh PK, Aswani Kumar Ch, Li JH (2017) Concepts reduction in formal concept analysis with fuzzy setting using Shannon entropy. Int J Mach Learn Cybern 8(1):179–189. https://doi.org/10.1007/s13042-014-0313-6

    Article  Google Scholar 

  • Singh P, Torek MW, Ceglarek A, Fafrowicz M, Lewandowska K, Marek T, Sikora-Wachowicz B, Oswiecimka P (2022) Analysis of fMRI signals from working memory tasks and resting-state of brain: neutrosophic-entropy-based clustering algorithm. Int J Neural Syst 32(4):2250012. https://doi.org/10.1142/S0129065722500125

    Article  Google Scholar 

  • Qi J, Wei L, Yao Y (2014) Three-way formal concept analysis. Lect Notes Comput Sci 8818:732–741

    Article  MATH  Google Scholar 

  • Rivieccio U (2007) Neutrosophic logics: prospects and problems. Fuzzy Sets Syst 159:1860–1868

    Article  MathSciNet  MATH  Google Scholar 

  • Saarikko T, Westergren UH, Blomquist T (2017) The internet of things: are you ready for what is coming? Bussiness Horizons 60(5):667–67

    Article  Google Scholar 

  • Sandelowski M (2002) Real qualitative researchers do not count: the use of numbers in qualitative research. Res Nursing Health 24:230–240

    Article  Google Scholar 

  • Senanayake U, Piraveenan M, Zomaya A (2015) The Pagerank-index: going beyond citation counts in quantifying scientific impact of researchers. PLoS ONE 10(8):e0134794. https://doi.org/10.1371/journal.pone.0134794

    Article  Google Scholar 

  • Shannon CE (1948) A mathematical theory of communication. Bell Syst Tech J 27(3):342–379

    Article  MathSciNet  Google Scholar 

  • Smarandache F (1999) A unifying field in logics neutrosophy: neutrosophic probability, set and logic. American Research Press, Rehoboth

    MATH  Google Scholar 

  • Smarandache F, Broumi S, Talea M, Bakali A (2016) Single-valued neutrosophic graphs: degree, order and size. In: Proceedings of international on fuzzy systems (FUZZ), pp 2444–2551

  • Smith R (1998) Beyond conflict of interest: transparency is the key. Br Med J 317(7154):291–292

    Article  Google Scholar 

  • Subbotin A, Aref S (2021) Brain drain and brain gain in Russia: analyzing international migration of researchers by discipline using Scopus bibliometric data 1996–2020. Scientometrics 126:7875–7900

    Article  Google Scholar 

  • Teixeira da Silva JA, Vuong QH (2021) The right to refuse unwanted citations: rethinking the culture of science around the citation. Scientometrics 126:5355–5360

    Article  Google Scholar 

  • Thelwall M (2019) Should citations be counted separately from each originating section? J Informetr 13(2):658–678

    Article  Google Scholar 

  • Wille R (1982) Restructuring lattice theory: an approach based on hierarchies of concepts. In: Rival I (ed) Ordered Sets. Reidel Dordrect–Boston, pp. 445-470

  • Yang B, Li JH (2021) Complex network analysis of three-way decision researches. Int J Mach Learn Cybern 11:973–987

    Article  Google Scholar 

  • Yao Y (2010) Three-way decisions with probabilistic rough sets. Inf Sci 180(3):341–353

    Article  MathSciNet  Google Scholar 

  • Yao Y (2017) Interval sets and three-way concept analysis in incomplete contexts. Int J Mach Learn Cybern 8(1):3–20

    Article  MathSciNet  Google Scholar 

  • Yao Y (2018) Three-way decision and granular computing. Int J Approx Reason 103:107–123

    Article  MATH  Google Scholar 

  • Yao Y (2021) The geometry of three-way decision. Appl Intell. https://doi.org/10.1007/s10489-020-02142-z

    Article  Google Scholar 

  • Zerem E (2017) The ranking of scientists based on scientific publications assessment. J Biomed Inf 75:107–109

    Article  Google Scholar 

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Acknowledgements

Author sincerely thanks the anonymous reviewer’s and editor’s for their valuable time and suggestions to improve the quality of this paper.

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Author sincerely acknowledges the funding from Gandhi Institute of Technology and Management under Ref. No.: 2021/0050 for this research.

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  1. Department of Computer Science and Engineering, Gandhi Institute of Technology and Management, Visakhapatnam, Andhra Pradesh, 530045, India

    Prem Kumar Singh

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Singh, P.K. Uncertainty analysis in document publications using single-valued neutrosophic set and collaborative entropy. Artif Intell Rev 56, 2785–2809 (2023). https://doi.org/10.1007/s10462-022-10249-7

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