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An approach toward decision-making and medical diagnosis problems using the concept of spherical fuzzy sets

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Abstract

Human opinion cannot be restricted to yes or no as depicted by conventional fuzzy set (FS) and intuitionistic fuzzy set (IFS) but it can be yes, abstain, no and refusal as explained by picture fuzzy set (PFS). In this article, the concept of spherical fuzzy set (SFS) and T-spherical fuzzy set (T-SFS) is introduced as a generalization of FS, IFS and PFS. The novelty of SFS and T-SFS is shown by examples and graphical comparison with early established concepts. Some operations of SFSs and T-SFSs along with spherical fuzzy relations are defined, and related results are conferred. Medical diagnostics and decision-making problem are discussed in the environment of SFSs and T-SFSs as practical applications.

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References

  1. Suapang, P et al (2010) Medical image processing and analysis for nuclear medicine diagnosis. In: 2010 International conference on control automation and systems (ICCAS)., IEEE

  2. Akbarizadeh G, Moghaddam AE (2016) Detection of lung nodules in CT scans based on unsupervised feature learning and fuzzy inference. J Med Imaging Health Inform 6(2):477–483

    Article  Google Scholar 

  3. Shanmugan KS et al (1981) Textural features for radar image analysis. IEEE Trans Geosci Remote Sens 3:153–156

    Article  Google Scholar 

  4. Akbarizadeh G (2012) A new statistical-based kurtosis wavelet energy feature for texture recognition of SAR images. IEEE Trans Geosci Remote Sens 50(11):4358–4368

    Article  Google Scholar 

  5. Akbarizadeh G (2013) Segmentation of SAR satellite images using cellular learning automata and adaptive chains. J Remote Sens Technol 1(2):44

    Article  Google Scholar 

  6. Akbarizadeh G, Rahmani M (2015) A new ensemble clustering method for PolSAR image segmentation. In: 2015 7th conference on information and knowledge technology (IKT). IEEE. A new computer vision algorithm for classification of POLSAR images

  7. Akbarizadeh G et al (2014) A new curvelet-based texture classification approach for land cover recognition of SAR satellite images. Malays J Comput Sci 27(3):218–239

    Google Scholar 

  8. Modava M, Akbarizadeh G (2017) Coastline extraction from SAR images using spatial fuzzy clustering and the active contour method. Int J Remote Sens 38(2):355–370

    Article  Google Scholar 

  9. Akbarizadeh G, Tirandaz Z (2015) Segmentation parameter estimation algorithm based on curvelet transform coefficients energy for feature extraction and texture description of SAR images. In: 2015 7th conference on information and knowledge technology (IKT). IEEE

  10. Akbarizadeh G, Rahmani M (2017) Efficient combination of texture and color features in a new spectral clustering method for PolSAR image segmentation. Natl Acad Sci Lett 40(2):117–120

    Article  MathSciNet  Google Scholar 

  11. Faraji Z, Akbarizadeh G (2015) A new computer vision algorithm for classification of POLSAR images. In: 2015 7th conference on information and knowledge technology (IKT). IEEE

  12. Modava M, Akbarizadeh G (2017) A level set based method for coastline detection of SAR images. In: 2017 3rd international conference on pattern recognition and image analysis (IPRIA). IEEE

  13. Rahmani M, Akbarizadeh G (2015) Unsupervised feature learning based on sparse coding and spectral clustering for segmentation of synthetic aperture radar images. IET Comput Vis 9(5):629–638

    Article  Google Scholar 

  14. Tirandaz Z, Akbarizadeh G (2016) A two-phase algorithm based on kurtosis curvelet energy and unsupervised spectral regression for segmentation of SAR images. IEEE J Sel Top Appl Earth Observ Remote Sens 9(3):1244–1264

    Article  Google Scholar 

  15. Gong M et al (2012) Change detection in synthetic aperture radar images based on image fusion and fuzzy clustering. IEEE Trans Image Process 21(4):2141–2151

    Article  MathSciNet  MATH  Google Scholar 

  16. Benz UC et al (2004) Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information. ISPRS J Photogramm Remote Sens 58(3–4):239–258

    Article  Google Scholar 

  17. Chanussot J et al (1999) Fuzzy fusion techniques for linear features detection in multitemporal SAR images. IEEE Trans Geosci Remote Sens 37(3):1292–1305

    Article  Google Scholar 

  18. Daugman J (2009) How iris recognition works. In: The essential guide to image processing, pp 715–739. https://doi.org/10.1016/B978-0-12-374457-9.00025-1

  19. Ahmadi N, Akbarizadeh G (2017) Hybrid robust iris recognition approach using iris image pre-processing, two-dimensional gabor features and multi-layer perceptron neural network/PSO. IET Biom. https://doi.org/10.1049/iet-bmt.2017.0041

    Article  Google Scholar 

  20. Andekah ZA et al (2017) Semi-supervised Hyperspectral image classification using spatial-spectral features and superpixel-based sparse codes. In: 2017 Iranian conference on electrical engineering (ICEE). IEEE

  21. Perić N (2015) Fuzzy logic and fuzzy set theory based edge detection algorithm. Serb J Electr Eng 12(1):109–116

    Article  Google Scholar 

  22. Myers DG (2009) Image processing. Electr Eng 1:396

    Google Scholar 

  23. Zadeh LA (1965) Fuzzy sets. Inf Control 8(3):338–353

    Article  MATH  Google Scholar 

  24. Atanassov KT (1986) Intuitionistic fuzzy sets. Fuzzy Sets Syst 20(1):87–96

    Article  MATH  Google Scholar 

  25. Atanassov KT (1999) Intuitionistic fuzzy sets. Springer, New York, pp 1–137

    Book  MATH  Google Scholar 

  26. Yager RR (2013) Pythagorean fuzzy subsets. 2013 Joint IFSA world congress and NAFIPS annual meeting (IFSA/NAFIPS). IEEE

  27. Yager RR, Abbasov AM (2013) Pythagorean membership grades, complex numbers, and decision making. Int J Intell Syst 28(5):436–452

    Article  Google Scholar 

  28. Yager RR (2014) Pythagorean membership grades in multicriteria decision making. IEEE Trans Fuzzy Syst 22(4):958–965

    Article  Google Scholar 

  29. Peng X, Yang Y (2015) Some results for pythagorean fuzzy sets. Int J Intell Syst 30(11):1133–1160

    Article  MathSciNet  Google Scholar 

  30. Cuong BC (2013) Picture fuzzy sets—first results. Part 1, in preprint of seminar on neuro-fuzzy systems with applications, Institute of Mathematics, Hanoi, May

  31. Wang C et al (2017) Some geometric aggregation operators based on picture fuzzy sets and their application in multiple attribute decision making. Ital J Pure Appl Math 37:477–492

    MathSciNet  MATH  Google Scholar 

  32. Sanchez E (1976) Resolution of composite fuzzy relation equations. Inf Control 30(1):38–48

    Article  MathSciNet  MATH  Google Scholar 

  33. Xu Z (2007) Intuitionistic fuzzy aggregation operators. IEEE Trans Fuzzy Syst 15(6):1179–1187

    Article  Google Scholar 

  34. Xu Z, Yager RR (2006) Some geometric aggregation operators based on intuitionistic fuzzy sets. Int J Gen Syst 35(4):417–433

    Article  MathSciNet  MATH  Google Scholar 

  35. Sanchez E (1993) Solutions in composite fuzzy relation equations: application to medical diagnosis in Brouwerian logic. Readings in fuzzy sets for intelligent systems, Elsevier, pp 159–165

  36. Burillo PJ, Bustince H (1995) Intuitionistic fuzzy relations (part I). Mathw Soft Comput 2(1):5–38

    MATH  Google Scholar 

  37. De SK et al (2001) An application of intuitionistic fuzzy sets in medical diagnosis. Fuzzy Sets Syst 117(2):209–213

    Article  MathSciNet  MATH  Google Scholar 

  38. Phong PH et al (2014). Some compositions of picture fuzzy relations. In: Proceedings of the 7th national conference on fundamental and applied information technology research (FAIR’7), Thai Nguyen

  39. Cuong BC (2013) Picture fuzzy sets—first results. Part 2, in preprint of seminar on neuro-fuzzy systems with applications, Institute of Mathematics, Hanoi, June

  40. Cuong BC, Kreinovich V (2013) Picture fuzzy sets—a new concept for computational intelligence problems. In: 2013 Third world congress on information and communication technologies (WICT). IEEE

  41. Cuong BC, Van Hai P (2015) Some fuzzy logic operators for picture fuzzy sets. In: 2015 Seventh international conference on knowledge and systems engineering (KSE). IEEE

  42. Cuong BC et al (2016) A classification of representable t-norm operators for picture fuzzy sets. In: 2016 Eighth international conference on knowledge and systems engineering (KSE). IEEE

  43. Toh CK (2001) Ad hoc mobile wireless networks: protocols and systems. Pearson Education, London

    Google Scholar 

  44. Eze EC, Zhang S, Liu E (2014) Vehicular ad hoc networks (VANETs): current state, challenges, potentials and way forward. In: 2014 20th international conference on automation and computing (ICAC). IEEE, pp 176–181

  45. Alam T, Aljohani M (2015) Design and implementation of an ad hoc network among android smart devices. In: 2015 International conference on green computing and internet of things (ICGCIoT). IEEE, pp 1322–1327

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Acknowledgements

The authors highly appreciate the efforts of the reviewers and editors and thankful for the useful suggestions and comments of reviewers.

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Authors and Affiliations

  1. Department of Mathematics and Statistics, International Islamic University Islamabad, Islamabad, Pakistan

    Tahir Mahmood, Kifayat Ullah, Qaisar Khan & Naeem Jan

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  1. Tahir Mahmood
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  2. Kifayat Ullah
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  3. Qaisar Khan
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  4. Naeem Jan
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Correspondence to Kifayat Ullah.

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Mahmood, T., Ullah, K., Khan, Q. et al. An approach toward decision-making and medical diagnosis problems using the concept of spherical fuzzy sets. Neural Comput & Applic 31, 7041–7053 (2019). https://doi.org/10.1007/s00521-018-3521-2

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