Published 2026-07-22
Keywords
- Literature Review,
- Bibliometrics,
- Neutrosophic Sets,
- Scopus Database,
- VOSviewer
Copyright (c) 2026 Muhammad Saqlain, José M. Merigó, Vladimir Simic, Broumi Said (Author)

This work is licensed under a Creative Commons Attribution 4.0 International License.
How to Cite
Abstract
In this study, a bibliometric analysis of the neutrosophic set research is conducted based on the Scopus database. The chronological time distribution of documents and citations is analyzed. Influential documents, authors, institutions, journals, countries, as well as thematic trends are identified. The findings showed that the neutrosophic set research has been growing rapidly, especially since 2011. Annual publication output, citation accumulation, and high–impact methodological contributions have been substantially developed. Key authors and institutions in fostering the field's intellectual development are identified, as well as major contributing nations to its global growth, such as the United States, India, China, Turkey, and Egypt. A co–citation, bibliographic coupling, and keyword co–occurrence analyses show that the neutrosophic set research is built on strong methodological bases connected to uncertainty modeling, decision–making frameworks, aggregation operators, and similarity measures. Thematic clusters indicated that multi–criteria decision–making, computational intelligence, medical diagnosis, and engineering optimization have remained the core areas of research interest. The analysis proves that the neutrosophic set research has reached some important conceptual maturity, and its interdisciplinary relevance continues to expand.
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References
- Smarandache, F. (2002). Neutrosophy: A new branch of philosophy. Multiple–Valued Logic: An International Journal, 8(3), 297–384.
- Smarandache, F. (1998). A unifying field in logics: Neutrosophy–Neutrosophic probability, set and logic. American Research Press.
- Fujita, T., & Mehmood, A. (2026). Fuzzy MultiLinear Sets, Neutrosophic MultiLinear Sets, abd Plithogenic MultiLinear Sets. International Scientific Spectrum, 2(1), 99-113. https://doi.org/10.66972/iscis2120265.
- Smarandache, F. (2005). Neutrosophic set: A generalization of the intuitionistic fuzzy sets. International Journal of Pure and Applied Mathematics, 24, 287–297.
- Smarandache, F. (2006). Neutrosophic set: A generalization of the intuitionistic fuzzy set. In Y.–Q. Zhang & T. Y. Lin (Eds.), Proceedings of the 2006 IEEE International Conference on Granular Computing, Georgia State University. https://doi.org/10.1109/GRC.2006.1635754.
- Fujita, T., Anitha, K., Mehmood, A., Ghaib, A. A., & Ur Rahman, A. (2025). Modeling Directional Uncertainty for Sustainability: IndetermSoft and IndetermHyperSoft Multi-Directed Sets. International Scientific Spectrum, 1(1), 43-79. https://doi.org/10.66972/iscis1120256.
- Smarandache, F. (2003). Definition of neutrosophic logic–A generalization of the intuitionistic fuzzy logic. In Proceedings of the Third Conference of the European Society for Fuzzy Logic and Technology (EUSFLAT 2003). University of Applied Sciences at Zittau/Goerlitz.
- Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338–353. https://doi.org/10.1016/S0019-9958(65)90241-X.
- Atanassov, K. T. (1986). Intuitionistic fuzzy sets. Fuzzy Sets and Systems, 20(1), 87–96. https://doi.org/10.1016/S0165-0114(86)80034-3.
- Abdel–Basset, M., Ding, W., Mohamed, R., Metawaet, N. (2020). An integrated plithogenic MCDM approach for financial performance evaluation of manufacturing industries. Risk Management, 22, 192–218. https://doi.org/10.1057/s41283-020-00061-4.
- Hashmi, M.R., Riaz, M. & Smarandache, F. (2020). m–Polar Neutrosophic Topology with Applications to Multi–criteria Decision–Making in Medical Diagnosis and Clustering Analysis. International Journal of Fuzzy Systems, 22, 273–292. https://doi.org/10.1007/s40815-019-00774-8.
- Abdel–Basset, M., Atef, A., & Smarandache, F. (2019). A hybrid neutrosophic multiple criteria group decision making approach for project selection. Cognitive Systems Research, 57, 216–227. https://doi.org/10.1016/j.cogsys.2018.09.019.
- Peng, J. J., Wang, J. Q., Zhang, H. Y., & Chen, X. H. (2014) An Outranking Approach for Multi–Criteria Decision–Making Problems with Simplified Neutrosophic Sets. Applied Soft Computing, 25, 336–346. https://doi.org/10.1016/j.asoc.2014.08.059.
- Wang, Jq., Yang, Y. & Li, L. (2018). Multi–criteria decision–making method based on single–valued neutrosophic linguistic Maclaurin symmetric mean operators. Neural Computing & Applications, 30, 1529–1547. https://doi.org/10.1007/s00521-016-2785-5.
- Zhang, H., Wang, J. & Chen, X. (2016). An outranking approach for multi–criteria decision–making problems with interval–valued neutrosophic sets. Neural Computing & Applications, 27, 615–627. https://doi.org/10.1007/s00521-015-1892-z.
- Saqlain M., S. Moin, M. N. Jafar, M. Saeed, & F. Smarandache. (2020). Aggregate Operators of Neutrosophic Hypersoft Set. Neutrosophic Sets and Systems, 32, 294–306. https://doi.org/10.5281/zenodo.3723167.
- Amalini, L. N., Kamal, M., Abdullah, L., & Saqlain. M. (2020). Multi–Valued Interval Neutrosophic Linguistic Soft Set Theory and Its Application in Knowledge Management. CAAI Transactions on Intelligence Technology, 5, 200–208. https://doi.org/10.1049/trit.2020.0012.
- Saqlain, M., Sana, M., Jafar, N., Saeed, M., & Said, B. (2020). Single and Multi–valued Neutrosophic Hypersoft set and Tangent Similarity Measure of Single valued Neutrosophic Hypersoft Sets. Neutrosophic Sets and Systems, 32, 317–329. https://doi.org/10.5281/zenodo.3723169.
- Ye, J., & Fu, J. (2015). Multi–period medical diagnosis method using a single valued neutrosophic similarity measure based on tangent function. Computer Methods and Programs in Biomedicine, 123, 142–149. https://doi.org/10.1016/j.cmpb.2015.10.002.
- Ye, J. (2015). Improved cosine similarity measures of simplified neutrosophic sets for medical diagnoses. Artificial Intelligence in Medicine, 63(3), 171–179. https://doi.org/10.1016/j.artmed.2014.12.007.
- Chai, J. S., Selvachandran, G., Smarandache, F., Gerogiannis, V. C., Son, L. H., Bui, Q.–T., & Vo, B. (2021). New similarity measures for single–valued neutrosophic sets with applications in pattern recognition and medical diagnosis problems. Complex & Intelligent Systems, 7, 703–723. https://doi.org/10.1007/s40747-020-00255-9.
- Nguyen, G. N., Son, L. H., Ashour, A. S., & Dey, N. (2019). A survey of the state–of–the–art on neutrosophic sets in biomedical diagnoses. International Journal of Machine Learning and Cybernetics, 10, 1–13. https://doi.org/10.1007/s13042-017-0741-2.
- Abdel–Basset, M., Gamal, A., Manogaran, G., Son, L. H., & Long, H. V. (2020). A novel group decision–making model based on neutrosophic sets for heart disease diagnosis. Multimedia Tools and Applications, 79, 9977–10002. https://doi.org/10.1007/s11042-019-07801-2.
- Guo, Y., Şengür, A., & Ye, J. (2014). A novel image thresholding algorithm based on neutrosophic similarity score. Measurement, 58, 175–186. https://doi.org/10.1016/j.measurement.2014.08.007.
- Zhang, M., Zhang, L., & Cheng, H. (2010). A neutrosophic approach to image segmentation based on watershed method. Signal Processing, 90(5), 1510–1517. https://doi.org/10.1016/j.sigpro.2009.10.021.
- Guo, Y., & Cheng, H. (2009). New neutrosophic approach to image segmentation. Pattern Recognition, 42(5), 587–595. https://doi.org/10.1016/j.patcog.2008.10.002.
- Abdel–Basset, M., Manogaran, G., Gamal, A., & Smarandache, F. (2018). A hybrid approach of neutrosophic sets and DEMATEL method for developing supplier selection criteria. Design Automation for Embedded Systems, 22(3), 257–278. https://doi.org/10.1007/s10617-018-9203-6.
- Mohamed, Z., Ismail, M. M., & Abd El–Gawad, A. F. (2023). Sustainable supplier selection using neutrosophic multi–criteria decision–making methodology. Sustainable Machine Intelligence Journal, 2(2), 9. https://doi.org/10.61185/SMIJ.2023.229.
- Kara, K., Yalçın, G. C., Simic, V., Önden, I., Edinsel, S., & Bacanin, N. (2024). A single–valued neutrosophic–based methodology for selecting warehouse management software in sustainable logistics systems. Engineering Applications of Artificial Intelligence, 129, 107626. https://doi.org/10.1016/j.engappai.2023.107626.
- Jiang, P. (2024). LogTODIM framework for MAGDM with neutrosophic sets: energy conservation and emission reduction case. International Journal of Knowledge–Based and Intelligent Engineering Systems, 28(1), 149–161. https://doi.org/10.3233/KES-230238.
- Fetanat, A., & Tayebi, M. (2024). Sustainability and reliability–based hydrogen technologies prioritization for decarbonization in the oil refining industry: A decision support system under single–valued neutrosophic set. International Journal of Hydrogen Energy, 52, 765–786. https://doi.org/10.1016/j.ijhydene.2023.10.159.
- Abdel–Basset, M., Gamal, A., Chakrabortty, R. K., Ryan, M., & El–Saber, N. (2021). A comprehensive framework for evaluating sustainable green building indicators under an uncertain environment. Sustainability, 13(11), 6243. https://doi.org/10.3390/su13116243.
- Broadus, R.N. (1987). Toward a definition of „Bibliometrics”. Scientometrics, 12(5–6), 373–379. https://doi.org/10.1007/BF02016680.
- Pritchard, A. (1969). Statistical bibliography or bibliometrics?. Journal of Documentation, 25(4), 348–349.
- Rousseau, R. (2014). Forgotten founder of bibliometrics. Nature, 510, 218. https://doi.org/10.1038/510218e.
- Scopus. (2024). Scopus Database. Retrieved from https://www.scopus.com/.
- Ding, Y., Rousseau, R., & Wolfram, D. (2014). Measuring scholarly impact: Methods and practice, Springer, Switzerland. https://doi.org/10.1007/978-3-319-10377-8.
- Garfield, E. (1972). Citation analysis as a tool in journal evaluation. Science, 178, 471–479. https://doi.org/10.1126/science.178.4060.471.
- Waltman, L. (2016). A review of the literature on citation impact indicators. Journal of Informetrics, 10(2), 365–391. https://doi.org/10.1016/j.joi.2016.02.007.
- Small, H. (1973). Co–citation in the scientific literature: A new measure of the relationship between two documents. Journal of the American Society for Information Science, 32, 265–269. https://doi.org/10.1002/asi.4630240406
- Kessler, M. (1963). Bibliographic coupling between scientific papers. American Documentation, 14(1), 10–25. https://doi.org/10.1002/asi.5090140103
- Callon, M., Courtial, J.–P., Turner, W. A., & Bauin, S. (1983). From translations to problematic networks: An introduction to co–word analysis. Social Science Information, 22(2), 191–235. https://doi.org/10.1177/053901883022002003.
- Van Eck, N., & Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84, 523–538. https://doi.org/10.1007/s11192-009-0146-3.
- Van Eck NJ, Waltman L (2023). VOSviewer Manual: Manual for VOSviewer version 1.6.20. Leiden University.
- Donthu, N., Kumar, S., Mukherjee, D., Pandey, N., & Lim, W. M. (2021). How to conduct a bibliometric analysis: An overview and guidelines. Journal of Business Research, 133, 285–296. https://doi.org/10.1016/j.jbusres.2021.04.070.
- Bar–Ilan, J. (2007). Informetrics at the beginning of the 21st century–A review. Journal of Informetrics, 2(1), 1–52. https://doi.org/10.1016/j.joi.2007.11.001.
- Blanco–Mesa, F., Merigó, J. M., & Gil–Lafuente, A. M. (2017). Fuzzy decision making: A bibliometric–based review. Journal of Intelligent & Fuzzy Systems, 32(3), 2033–2050. https://doi.org/10.3233/JIFS-161640.
- Aria, M., & Cuccurullo, C. (2017). Bibliometrix: An R–tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959–975. https://doi.org/10.1016/j.joi.2017.08.007.
- Merigó, J. M., Mas–Tur, A., Roig–Tierno, N., & Ribeiro–Soriano, D. (2015). A bibliometric overview of the Journal of Business Research between 1973 and 2014. Journal of Business Research, 68(12), 2645–2653. https://doi.org/10.1016/j.jbusres.2015.04.006.
- Glänzel, W., Moed, H. F., Schmoch, U., & Thelwall, M. (Eds.). (2019). Springer handbook of science and technology indicators (p. 850). Dordrecht: Springer. https://doi.org/10.1007/978-3-030-02511-3.
- Moral–Muñoz, J. A., Herrera–Viedma, E., Santisteban–Espejo, A., & Cobo, M. J. (2020). Software tools for conducting bibliometric analysis in science: An up–to–date review. Profesional de la Información, 29(1). https://doi.org/10.3145/epi.2020.ene.03.
- Podsakoff, P. M., MacKenzie, S. B., Podsakoff, N. P., & Bachrach, D. G. (2008). Scholarly influence in the field of management: A bibliometric analysis of the determinants of university and author impact in the management literature in the past quarter century. Journal of Management, 34(4), 641–720. https://doi.org/10.1177/0149206308319533.
- Zupic, I., & Cater, T. (2015). Bibliometric methods in management and organization. Organizational Research Methods, 18(3), 429–472. https://doi.org/10.1177/1094428114562629.
- Aksnes, D. W., Langfeldt, L., & Wouters, P. (2019). Citations, citation indicators, and research quality: An overview of basic concepts and theories. Sage Open, 9(1), 2158244019829575. https://doi.org/10.1177/2158244019829575.
- Saqlain, M., Merigó, J. M., Kumam, P. (2025). Neutrosophic Sets and Systems: A Decade of Scientific Contribution and Growth. Neutrosophic Sets and Systems, 81, 438–465. https://doi.org/10.5281/zenodo.15340442.
- Laengle, S., Merigó, J. M., Miranda, J., Słowiński, R., Bomze, I., Borgonovo, E., Dyson, R. G., Oliveira, J. F., & Teunter, R. (2017). Forty years of the European Journal of Operational Research: A bibliometric overview. European Journal of Operational Research, 262(3), 803–816. https://doi.org/10.1016/j.ejor.2017.04.027.
- Akpan, I. J. (2023). Thirty years of International Transactions in Operational Research: Past, present, and future direction. International Transactions in Operational Research, 30(6), 2709–2728. https://doi.org/10.1111/itor.13208.
- Calma, A., Ho, W., Shao, L., & Li, H. (2021). Operations research: Topics, impact, and trends from 1952–2019. Operations Research, 69(5), 1487–1508. https://doi.org/10.1287/opre.2021.2142.
- Yu, D., Xu, Z., Kao, Y., & Lin, C.–T. (2017). The structure and citation landscape of IEEE Transactions on Fuzzy Systems (1994–2015). IEEE Transactions on Fuzzy Systems, 26(2), 430–442. https://doi.org/10.1109/TFUZZ.2017.2690212.
- Saqlain, M., Merigó, J. M., Amirbagheri, K., & Maurer, H. (2025). 30 years of the Journal of Universal Computer Science: A bibliometric retrospective. JUCS – Journal of Universal Computer Science, 31(13), 1416–1462. https://doi.org/10.3897/jucs.127229.
- Saqlain, M., Merigó, J. M., Kumam, P., Alam, M. T., & Elali, B. (2025). A Golden Anniversary of the Arabian Journal for Science and Engineering: A Bibliometric Retrospective. Arabian Journal for Science and Engineering. https://doi.org/10.1007/s13369-025-10194-0.
- Liao, H., Jin, X., Shi, Y., & Kou, G. (2024). A bibliometric overview and visualization of the International Journal of Information Technology and Decision Making between 2012 and 2022. International Journal of Information Technology & Decision Making, 23(1), 171–195. https://doi.org/10.1142/S0219622024500067.
- Blanco–Mesa, F., Merigó, J. M., & Gil–Lafuente, A. M. (2017). Fuzzy decision making: A bibliometric–based review. Journal of Intelligent & Fuzzy Systems, 32(3), 2033–2050. https://doi.org/10.3233/JIFS-161640.
- Liu, W., & Liao, H. (2017). A bibliometric analysis of fuzzy decision research during 1970–2015. International Journal of Fuzzy Systems, 19(1), 1–14. https://doi.org/10.1007/s40815-016-0272-z.
- Alfaro–García, V. G., Merigó, J. M., Pedrycz, W., & Gómez Monge, R. (2020). Citation analysis of fuzzy set theory journals: Bibliometric insights about authors and research areas. International Journal of Fuzzy Systems, 22(5), 2414–2448. https://doi.org/10.1007/s40815-020-00900-0.
- Cobo, M. J., López–Herrera, A. G., Herrera–Viedma, E., & Herrera, F. (2011). An approach for detecting, quantifying, and visualizing the evolution of a research field: A practical application to the Fuzzy Sets Theory field. Journal of Informetrics, 5(1), 146–166. https://doi.org/10.1016/j.joi.2010.10.002.
- Peng, X., & Dai, J. (2020). A bibliometric analysis of neutrosophic set: Two decades review from 1998 to 2017. Artificial Intelligence Review, 53, 199–255. https://doi.org/10.1007/s10462-019-09698-3.
- Yu, D., Xu, Z., & Wang, W. (2018). Bibliometric analysis of fuzzy theory research in China: A 30–year perspective. Knowledge–Based Systems, 141, 188–199. https://doi.org/10.1016/j.knosys.2017.11.018.
- Merino–Arteaga, I., Alfaro–García, V. G., & Merigó, J. M. (2022). Fuzzy systems research in the United States of America and Canada: A bibliometric overview. Information Sciences, 617, 277–292. https://doi.org/10.1016/j.ins.2022.10.023.
- Saqlain, M. (2025). Half a century of fuzzy decision making in Italy: A bibliometric analysis. Management Science Advances, 2(1), 133–143. https://doi.org/10.31181/msa2120257.
- Kumam, P., Saqlain, M., Merigo, J. M., Thounthong, P., & Edalatpanah, S. A. (2025). From Foundations to Frontiers: Half a Century of Fuzzy Logic Research in Iran. (e228889). Journal of Fuzzy Extension and Applications, e228889. https://doi.org/10.22105/jfea.2025.228889.1178.
- Saqlain, M., Merigó, J. M., Kumam, P. (2025). Turkey Contributions to Fuzzy Research: A Bibliometric Review. In: Kahraman, C., et al. Intelligent and Fuzzy Systems. INFUS 2025. Lecture Notes in Networks and Systems, vol 1528. Springer, Cham. https://doi.org/10.1007/978-3-031-98545-4_89.
- Saqlain, M., Merigó, J. M., Gulistan, M., Saeed, M., & Razaq, F. (2025). Scientometric Exploration of Fuzzy Research in Saudi Arabia. Multicriteria Algorithms with Applications, 9, 18–50. https://doi.org/10.31181/mcaa9520252.
- Saqlain, M., Merigó, J. M., Kumam, P., & Riaz, M. (2025). Advancements in fuzzy research in Pakistan: A bibliometric perspective between 1989 and 2023. Punjab University Journal of Mathematics, 57(3), 219–264.
- Paul, J., Lim, W. M., O’Cass, A., Hao, A. W., & Bresciani, S. (2021). Scientific procedures and rationales for systematic literature reviews (SPAR–4–SLR). International Journal of Consumer Studies, 45(4), O1–O16. https://doi.org/10.1111/ijcs.12695.
- Alaminos, D., Guillén–Pujadas, M., Vizuete–Luciano, E., & Merigó, J. M. (2024). What is going on with studies on financial speculation? Evidence from a bibliometric analysis. International Review of Economics & Finance, 89, 429–445. https://doi.org/10.1016/j.iref.2023.08.037.
- Ye, J. (2014). A multicriteria decision-making method using aggregation operators for simplified neutrosophic sets. Journal of Intelligent & Fuzzy Systems, 26(5), 2459-2466. https://doi.org/10.3233/IFS-130916.
- Ye, J. (2013). Multicriteria decision-making method using the correlation coefficient under single-valued neutrosophic environment. International Journal of General Systems, 42(4), 386-394. https://doi.org/10.1080/03081079.2012.761609.
- Biswas, P., Pramanik, S., & Giri, B. C. (2016). TOPSIS method for multi-attribute group decision-making under single-valued neutrosophic environment. Neural Computing and Applications, 27(3), 727-737. https://doi.org/10.1007/s00521-015-1892-2.
- Ye, J. (2014). Similarity measures between interval neutrosophic sets and their applications in multicriteria decision-making. Journal of Intelligent & Fuzzy Systems, 26(1), 165-172. https://doi.org/10.3233/IFS-120724.
- Peng, J. J., Wang, J. Q., Wang, J., Zhang, H. Y., & Chen, X. H. (2016). Simplified neutrosophic sets and their applications in multi-criteria group decision-making problems. International Journal of Systems Science, 47(10), 2342-2358. https://doi.org/10.1080/00207721.2014.994050.
- Majumdar, P., & Samanta, S. K. (2014). On similarity and entropy of neutrosophic sets. Journal of Intelligent & Fuzzy Systems, 26(3), 1245-1252. https://doi.org/10.3233/IFS-130810.
- Abdel-Basset, M., Saleh, M., Gamal, A., & Smarandache, F. (2019). An approach of TOPSIS technique for developing supplier selection with group decision making under type-2 neutrosophic number. Applied Soft Computing, 77, 438-452. https://doi.org/10.1016/j.asoc.2019.01.035.
- Zhang, H. Y., Wang, J. Q., & Chen, X. H. (2014). Interval neutrosophic sets and their application in multicriteria decision making problems. The Scientific World Journal, 2014(1), 645953. https://doi.org/10.1155/2014/645953.
- Ye, J. (2014). Single valued neutrosophic cross-entropy for multicriteria decision making problems. Applied Mathematical Modelling, 38(3), 1170-1175. https://doi.org/10.1016/j.apm.2013.07.020.
- Liu, P., & Wang, Y. (2014). Multiple attribute decision-making method based on single-valued neutrosophic normalized weighted Bonferroni mean. Neural Computing and Applications, 25(7), 2001-2010. https://doi.org/10.1007/s00521-014-1688-8.
- Peng, X., & Dai, J. (2018). Approaches to single-valued neutrosophic MADM based on MABAC, TOPSIS and new similarity measure with score function. Neural Computing and Applications, 29(10), 939-954. https://doi.org/10.1007/s00521-016-2604-z.
- Deli, I., & Şubaş, Y. (2017). A ranking method of single valued neutrosophic numbers and its applications to multi-attribute decision making problems. International Journal of Machine Learning and Cybernetics, 8(4), 1309-1322. https://doi.org/10.1007/s13042-016-0500-5.
- Özyurt, F., Sert, E., Avci, E., & Dogantekin, E. (2019). Brain tumor detection based on Convolutional Neural Network with neutrosophic expert maximum fuzzy sure entropy. Measurement, 147, 106830. https://doi.org/10.1016/j.measurement.2019.106830.
- Abdel-Basset, M., Mohamed, M., & Smarandache, F. (2018). An extension of neutrosophic AHP–SWOT analysis for strategic planning and decision-making. Symmetry, 10(4), 116. https://doi.org/10.3390/sym10040116.
- Abdel-Basset, M., Manogaran, G., Gamal, A., & Smarandache, F. (2019). A group decision making framework based on neutrosophic TOPSIS approach for smart medical device selection. Journal of Medical Systems, 43(2), 38. https://doi.org/10.1007/s10916-019-1156-1.
- Ye, J. (2015). Trapezoidal neutrosophic set and its application to multiple attribute decision-making. Neural Computing and Applications, 26(5), 1157-1166. https://doi.org/10.1007/s00521-014-1751-5.
