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A novel distance measure for simplified neutrosophic sets with its applications in pattern recognition

Year 2022, , 1207 - 1215, 15.10.2022
https://doi.org/10.17714/gumusfenbil.1179414

Abstract

The simplified neutrosophic set (SNS) is an essential modelling technique for effectively modelling and expressing inconsistent and ambiguous information. A crucial tool often utilized in a number of contexts, from clustering approach to medical diagnostics, is the distance measure. Although there are several distance measures in neutrosophic literature, some of them have the drawback of not providing the general requirements of the distance measure for certain particular values. We introduce a brand-new distance measure in this paper to deal with the relationship between two SNSs. When compared to alternative distance measures that are already in use, it appears that the new distance measure produces better results. The suggested distance measure has been put to use in a number of numerical instance concerning pattern recognition that is medical diagnosis.

References

  • Atanassov, K. T. (1986). Intuitionistic fuzzy sets. Fuzzy Sets and Systems, 20(1), 87–96.
  • Altun, F., Şahin, R., and Güler, C. (2020). Multi-criteria decision making approach based on PROMETHEE with probabilistic simplified neutrosophic sets. Soft Computing, 24(7), 4899-4915.
  • Balopoulos, V., Hatzimichailidis, A.G., Papadopoulos, B.K. (2007). Distance and similarity measures for fuzzy operators. Inf Sci , 177(11):2336–2348.
  • Broumi S, Smarandache F (2015) Extended Hausdorff distance and similarity measures for neutrosophic refined sets and their applications in medical diagnosis. J New Theory 7:64–78.
  • Hatzimichailidis, A.G., Papakostas, G.A. and Kaburlasos, V.G. (2012). A novel distance measure of intuitionistic fuzzy sets and its application to pattern recognition problems, International Journal of Intelligent Systems 27(4) 396-409.
  • Khatibi, V. and Montazer, G.A. (2009). Intuitionistic fuzzy set vs. fuzzy set application in medical pattern recognition Artificial Intelligence in Medicine. 47, 43-52.
  • Köseoğlu, A. and Şahin, R. (2021). Correlation coefficients of simplified neutrosophic multiplicative sets and their applications in clustering analysis. Journal of Ambient Intelligence and Humanized Computing, 1-22.
  • Köseoğlu, A. (2022a). Subsethood measure for picture fuzzy sets and its applications on multicriteria decision making. Gümüşhane Üniversitesi Fen Bilimleri Dergisi, 12(2), 385-394.
  • Köseoğlu, A. (2022b). Intuitionistic multiplicative set approach for green supplier selection problem using TODIM method. Journal of Universal Mathematics, 5(2), 149-158.
  • Luo, M. and Zhao, R.A. (2018). Distance measure between intuitionistic fuzzy sets and its application in medical diagnosis, Artificial Intelligence in Medicine 89, (2018), 34-39.
  • Majumdar, P., and Samanta, S.K. (2014). On Similarity and Entropy of Neutrosophic Sets, Journal of Intelligent and Fuzzy Systems, 26(3): 1245–1252.
  • Mondal K, Pramanik S, Giri BC. (2018) Hybrid Binary Logarithm Similarity Measure for MAGDM Problems under SVNS Assessments, Neutrosophic Sets and Systems, 20:12-25.
  • Pramanik S., Biswas, P. and Giri, B.C. (2017). Hybrid vector similarity measures and their applications to multi-attribute decision making under neutrosophic environment. Neural Computing & Applications, 28(5): 1163-1176
  • Ren HP, Xiao SX, Zhou H (2019) A Chi-square distance-based similarity measure of single valued neutrosophic set and applications. Int J Comput Commun control 14:78–89.
  • Shahzadi, G., Akram, M., Borumand A. and Saeid, A.B. (2017). An Application of Single-Valued Neutrosophic Sets in Medical Diagnosis, Neutrosophic Sets and Systems, 18.
  • Şahin, R. and Liu, P.D. (2015). Maximizing deviation method for neutrosophic multiple attribute decision making with incomplete weight information, Neural Computing and Applications, https://doi.org/10.1007/s00521-015-1995-8.
  • Şahin, R. and Küçük, A. (2015). Subsethood measure for single valued neutrosophic sets. J. Intell. Fuzzy Syst., 29(2): 525–530.
  • Şahin, R. (2019). An approach to neutrosophic graph theory with applications. Soft Comput. 23(2), 569-581 Smarandache, F. (1999). A unifying field in logics. neutrosophy: Neutrosophic probability, set and logic, American Research Press, Rehoboth.
  • Tian, M.Y. (2013). A new fuzzy similarity based on cotangent function for medical diagnosis, Adv. Model. Optim. 15 (2) 151–156.
  • Wang, Z., Xu, Z., Liu, S. and Tang, J. (2011). A Netting Clustering Analysis Method under Intuitionistic Fuzzy Environment, Applied Soft Computing, 11, 5558–5564.
  • Wang, H., Smarandache, F., Zhang, Y.Q. and Sunderraman, R. (2010). Single valued neutrosophic sets, Multispace and Multistructure 4: 410–413.
  • Ye, J. (2014a) Single Valued Neutrosophic Minimum Spanning Tree and Its Clustering Method, Journal of Intelligent Systems, 23(3) 311–324.
  • Ye, J. (2014b). Vector similarity measures of simplified neutrosophic sets and their application in multicriteria decision making. International Journal of Fuzzy Systems, 16, 204–211.
  • Ye, J. (2014c). A multicriteria decision-making method using aggregation operators for simplified neutrosophic sets, J. Intell. Fuzzy Syst. 26 (5) 2459–2466.
  • Ye, J. (2015). Improved cosine similarity measures of simplified neutrosophic sets for medical diagnoses. Artif Intell Med. 63(3):171–179.
  • Ye, J. and Fu, J. (2016). 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.
  • Ye, J. (2016). Fault Diagnoses of Hydraulic Turbine Using the Dimension Root Similarity Measure of Single-valued Neutrosophic Sets. Intelligent Automation & Soft Computing. https://doi.org/10.1080/10798587.2016.1261955.
  • Ye, J. (2017a). Single-valued neutrosophic similarity measures based on cotangent function and their application in the fault diagnosis of steam turbine. Soft Computing 21(3): 817-825.
  • Ye, J. (2017b). Single-Valued Neutrosophic Clustering Algorithms Based on Similarity Measures, Journal of Classification 34(1) 148-162.
  • Zadeh, L.A. (1965). Fuzzy sets, Inf. Control 8: 338–353.
  • Zhang, H., Xu, Z. and Chen, Q. (2007). Clustering Method of Intuitionistic Fuzzy Sets, Control and Decision, 22(8), 882–888.

Örüntü tanımadaki uygulamalarıyla basitleştirilmiş nötrosofik kümeler için yeni bir mesafe ölçüsü

Year 2022, , 1207 - 1215, 15.10.2022
https://doi.org/10.17714/gumusfenbil.1179414

Abstract

Basitleştirilmiş nötrosofik küme (BNK), tutarsız ve belirsiz bilgileri etkili bir şekilde ifade etmek ve işlemek için temel bir modelleme tekniğidir. Kümeleme analizinden tıbbi teşhise kadar birçok bağlamda sıklıkla kullanılan önemli bir araç, mesafe ölçümüdür. Nötrosofik literatüründe birkaç uzaklık ölçütü olmasına rağmen, bunlardan bazıları belirli değerler için mesafe ölçüsünün genel gereksinimlerini sağlamama dezavantajına sahiptir. Bu yazıda iki basitleştirilmiş nötrosofik küme arasındaki ilişkiyi ele almak için yepyeni bir mesafe ölçüsü sunuyoruz. Hâlihazırda kullanımda olan alternatif mesafe ölçüleri ile karşılaştırıldığında, yeni mesafe ölçüsünün daha iyi sonuçlar verdiği görülmektedir. Önerilen mesafe ölçüsü, tıbbi teşhis de olmak üzere örüntü tanıma ile ilgili sayısal bir örnekte kullanıma sunulmuştur.

References

  • Atanassov, K. T. (1986). Intuitionistic fuzzy sets. Fuzzy Sets and Systems, 20(1), 87–96.
  • Altun, F., Şahin, R., and Güler, C. (2020). Multi-criteria decision making approach based on PROMETHEE with probabilistic simplified neutrosophic sets. Soft Computing, 24(7), 4899-4915.
  • Balopoulos, V., Hatzimichailidis, A.G., Papadopoulos, B.K. (2007). Distance and similarity measures for fuzzy operators. Inf Sci , 177(11):2336–2348.
  • Broumi S, Smarandache F (2015) Extended Hausdorff distance and similarity measures for neutrosophic refined sets and their applications in medical diagnosis. J New Theory 7:64–78.
  • Hatzimichailidis, A.G., Papakostas, G.A. and Kaburlasos, V.G. (2012). A novel distance measure of intuitionistic fuzzy sets and its application to pattern recognition problems, International Journal of Intelligent Systems 27(4) 396-409.
  • Khatibi, V. and Montazer, G.A. (2009). Intuitionistic fuzzy set vs. fuzzy set application in medical pattern recognition Artificial Intelligence in Medicine. 47, 43-52.
  • Köseoğlu, A. and Şahin, R. (2021). Correlation coefficients of simplified neutrosophic multiplicative sets and their applications in clustering analysis. Journal of Ambient Intelligence and Humanized Computing, 1-22.
  • Köseoğlu, A. (2022a). Subsethood measure for picture fuzzy sets and its applications on multicriteria decision making. Gümüşhane Üniversitesi Fen Bilimleri Dergisi, 12(2), 385-394.
  • Köseoğlu, A. (2022b). Intuitionistic multiplicative set approach for green supplier selection problem using TODIM method. Journal of Universal Mathematics, 5(2), 149-158.
  • Luo, M. and Zhao, R.A. (2018). Distance measure between intuitionistic fuzzy sets and its application in medical diagnosis, Artificial Intelligence in Medicine 89, (2018), 34-39.
  • Majumdar, P., and Samanta, S.K. (2014). On Similarity and Entropy of Neutrosophic Sets, Journal of Intelligent and Fuzzy Systems, 26(3): 1245–1252.
  • Mondal K, Pramanik S, Giri BC. (2018) Hybrid Binary Logarithm Similarity Measure for MAGDM Problems under SVNS Assessments, Neutrosophic Sets and Systems, 20:12-25.
  • Pramanik S., Biswas, P. and Giri, B.C. (2017). Hybrid vector similarity measures and their applications to multi-attribute decision making under neutrosophic environment. Neural Computing & Applications, 28(5): 1163-1176
  • Ren HP, Xiao SX, Zhou H (2019) A Chi-square distance-based similarity measure of single valued neutrosophic set and applications. Int J Comput Commun control 14:78–89.
  • Shahzadi, G., Akram, M., Borumand A. and Saeid, A.B. (2017). An Application of Single-Valued Neutrosophic Sets in Medical Diagnosis, Neutrosophic Sets and Systems, 18.
  • Şahin, R. and Liu, P.D. (2015). Maximizing deviation method for neutrosophic multiple attribute decision making with incomplete weight information, Neural Computing and Applications, https://doi.org/10.1007/s00521-015-1995-8.
  • Şahin, R. and Küçük, A. (2015). Subsethood measure for single valued neutrosophic sets. J. Intell. Fuzzy Syst., 29(2): 525–530.
  • Şahin, R. (2019). An approach to neutrosophic graph theory with applications. Soft Comput. 23(2), 569-581 Smarandache, F. (1999). A unifying field in logics. neutrosophy: Neutrosophic probability, set and logic, American Research Press, Rehoboth.
  • Tian, M.Y. (2013). A new fuzzy similarity based on cotangent function for medical diagnosis, Adv. Model. Optim. 15 (2) 151–156.
  • Wang, Z., Xu, Z., Liu, S. and Tang, J. (2011). A Netting Clustering Analysis Method under Intuitionistic Fuzzy Environment, Applied Soft Computing, 11, 5558–5564.
  • Wang, H., Smarandache, F., Zhang, Y.Q. and Sunderraman, R. (2010). Single valued neutrosophic sets, Multispace and Multistructure 4: 410–413.
  • Ye, J. (2014a) Single Valued Neutrosophic Minimum Spanning Tree and Its Clustering Method, Journal of Intelligent Systems, 23(3) 311–324.
  • Ye, J. (2014b). Vector similarity measures of simplified neutrosophic sets and their application in multicriteria decision making. International Journal of Fuzzy Systems, 16, 204–211.
  • Ye, J. (2014c). A multicriteria decision-making method using aggregation operators for simplified neutrosophic sets, J. Intell. Fuzzy Syst. 26 (5) 2459–2466.
  • Ye, J. (2015). Improved cosine similarity measures of simplified neutrosophic sets for medical diagnoses. Artif Intell Med. 63(3):171–179.
  • Ye, J. and Fu, J. (2016). 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.
  • Ye, J. (2016). Fault Diagnoses of Hydraulic Turbine Using the Dimension Root Similarity Measure of Single-valued Neutrosophic Sets. Intelligent Automation & Soft Computing. https://doi.org/10.1080/10798587.2016.1261955.
  • Ye, J. (2017a). Single-valued neutrosophic similarity measures based on cotangent function and their application in the fault diagnosis of steam turbine. Soft Computing 21(3): 817-825.
  • Ye, J. (2017b). Single-Valued Neutrosophic Clustering Algorithms Based on Similarity Measures, Journal of Classification 34(1) 148-162.
  • Zadeh, L.A. (1965). Fuzzy sets, Inf. Control 8: 338–353.
  • Zhang, H., Xu, Z. and Chen, Q. (2007). Clustering Method of Intuitionistic Fuzzy Sets, Control and Decision, 22(8), 882–888.
There are 31 citations in total.

Details

Primary Language English
Journal Section Articles
Authors

Mesut Karabacak 0000-0002-0057-8115

Publication Date October 15, 2022
Submission Date September 23, 2022
Acceptance Date October 13, 2022
Published in Issue Year 2022

Cite

APA Karabacak, M. (2022). A novel distance measure for simplified neutrosophic sets with its applications in pattern recognition. Gümüşhane Üniversitesi Fen Bilimleri Dergisi, 12(4), 1207-1215. https://doi.org/10.17714/gumusfenbil.1179414