T-Bipolar Soft Semigroups and Related Results
DOI:
https://doi.org/10.31181/smeor11202421Keywords:
T-bipolar soft set, Semigroup, AND product, OR product, Extended union, Extended intersectionAbstract
The role of semigroups (SGs) is to operate as the tool for the study and modeling of systems and phenomena in which inverse operation is not relevant or necessary. SGs are encountered in the subject of automata theory, coding theory, language theory, and the study of discrete dynamical systems. Moreover, T-bipolar soft set is closer to bipolarity as compared to other theories on bipolar soft sets. Based on the idea of T-bipolar soft sets, here we explore the notion and properties of the T-bipolar soft semigroup (T-BSSG). These properties include the AND and OR product, the Restricted Union (Res-Union) and Restricted Intersection (Res-Intersection), and the Extended Union (Ext-Union) and Extended Intersection (Ext-Intersection) on T-BSSG. Further, we also devise the related algebraic properties of T-BSSG.
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References
Howie, J. M. (1995). Fundamentals of semigroup theory. oxford university Press.
Shain, B. M. (1963). Representation of semigroups by means of binary relations. Matematicheskii Sbornik, 102(3), 293-303.
McKenzie, R., & Schein, B. (1997). Every semigroup is isomorphic to a transitive semigroup of binary relations. Transactions of the American Mathematical Society, 349(1), 271-285.
Molodtsov, D. (1999). Soft set theory—first results. Computers & mathematics with applications, 37(4-5), 19-31. https://doi.org/10.1016/S0898-1221(99)00056-5
Ali, M. I., Feng, F., Liu, X., Min, W. K., & Shabir, M. (2009). On some new operations in soft set theory. Computers & Mathematics with Applications, 57(9), 1547-1553. https://doi.org/10.1016/j.camwa.2008.11.009
Zahedi Khameneh, A., & Kılıçman, A. (2019). Multi-attribute decision-making based on soft set theory: A systematic review. Soft Computing, 23, 6899-6920. https://doi.org/10.1007/s00500-018-3330-7
Xiao, Z., Chen, L., Zhong, B., & Ye, S. (2005). Recognition for soft information based on the theory of soft sets. In Proceedings of ICSSSM'05. 2005 International Conference on Services Systems and Services Management, 2005. (Vol. 2, pp. 1104-1106). IEEE. https://doi.org/10.1109/ICSSSM.2005.1500166
Tripathy, B. K., Sooraj, T. R., & Mohanty, R. K. (2016). A new approach to fuzzy soft set theory and its application in decision making. In Computational Intelligence in Data Mining—Volume 2: Proceedings of the International Conference on CIDM, 5-6 December 2015 (pp. 305-313). Springer India. https://doi.org/10.1007/978-81-322-2731-1_28
Cagman, N., Enginoglu, S., & Citak, F. (2011). Fuzzy soft set theory and its applications. Iranian journal of fuzzy systems, 8(3), 137-147.
Mushrif, M. M., Sengupta, S., & Ray, A. K. (2006). Texture classification using a novel, soft-set theory based classification algorithm. In Computer Vision–ACCV 2006: 7th Asian Conference on Computer Vision, Hyderabad, India, January 13-16, 2006. Proceedings, Part I 7 (pp. 246-254). Springer Berlin Heidelberg. https://doi.org/10.1007/11612032_26
Min, W. K. (2012). Similarity in soft set theory. Applied Mathematics Letters, 25(3), 310-314. https://doi.org/10.1016/j.aml.2011.09.006
Xu, W., Xiao, Z., Dang, X., Yang, D., & Yang, X. (2014). Financial ratio selection for business failure prediction using soft set theory. Knowledge-Based Systems, 63, 59-67. https://doi.org/10.1016/j.knosys.2014.03.007
Danjuma, S., Ismail, M. A., & Herawan, T. (2017). An alternative approach to normal parameter reduction algorithm for soft set theory. IEEE Access, 5, 4732-4746. https://doi.org/10.1109/ACCESS.2016.2645179
Maji, P. K., Biswas, R., & Roy, A. R. (2003). Soft set theory. Computers & mathematics with applications, 45(4-5), 555-562. https://doi.org/10.1016/S0898-1221(03)00016-6
Jun, Y. B., Lee, K. J., & Khan, A. (2010). Soft ordered semigroups. Mathematical Logic Quarterly, 56(1), 42-50. https://doi.org/10.1002/malq.200810030
Feng, F., Ali, M. I., & Shabir, M. (2013). Soft relations applied to semigroups. Filomat, 27(7), 1183-1196.
Khan, A., Khan, R., & Jun, Y. B. (2017). Uni-soft structure applied to ordered semigroups. Soft Computing, 21, 1021-1030. https://doi.org/10.1007/s00500-015-1837-8
Hamouda, E. H. (2017). Soft ideals in ordered semigroups. Rev. Union Matematica Argentina, 58(1), 85-94.
Shabir, M., & Ali, M. I. (2009). Soft ideals and generalized fuzzy ideals in semigroups. New Mathematics and Natural Computation, 5(3), 599-615. https://doi.org/10.1142/S1793005709001544
Muhiuddin, G., & Mahboob, A. (2020). Int-soft ideals over the soft sets in ordered semigroups. AIMS Mathematics, 5(3), 2412-2423. https://doi.org/10.3934/math.2020159
Khan, A., Jun, Y. B., Ali Shah, S. I., & Khan, R. (2016). Applications of soft union sets in ordered semigroups via uni-soft quasi-ideals. Journal of Intelligent & Fuzzy Systems, 30(1), 97-107. https://doi.org/10.3233/IFS-151734
Yousafzai, F., Khalaf, M. M., Ali, A., & Saeid, A. B. (2019). Non-associative ordered semigroups based on soft sets. Communications in Algebra, 47(1), 312-327. https://doi.org/10.1080/00927872.2018.1476524
Shabir, M., & Naz, M. (2013). On bipolar soft sets. arXiv preprint arXiv:1303.1344.
Karaaslan, F., & Karataş, S. (2015). A new approach to bipolar soft sets and its applications. Discrete Mathematics, Algorithms and Applications, 7(4), 1550054. https://doi.org/10.1142/S1793830915500548
Mahmood, T. (2020). A novel approach towards bipolar soft sets and their applications. Journal of Mathematics, 2020, 4690808. https://doi.org/10.1155/2020/4690808
Al-Shami, T. M. (2021). Bipolar soft sets: relations between them and ordinary points and their applications. Complexity, 2021, 6621854. https://doi.org/10.1155/2021/6621854
Kamacı, H., & Petchimuthu, S. (2020). Bipolar N-soft set theory with applications. Soft Computing, 24, 16727-16743. https://doi.org/10.1007/s00500-020-04968-8
Ali, M., Son, L. H., Deli, I., & Tien, N. D. (2017). Bipolar neutrosophic soft sets and applications in decision making. Journal of Intelligent & Fuzzy Systems, 33(6), 4077-4087. https://doi.org/10.3233/JIFS-17999
Shabir, M., Mubarak, A., & Naz, M. (2021). Rough approximations of bipolar soft sets by soft relations and their application in decision making. Journal of Intelligent & Fuzzy Systems, 40(6), 11845-11860. https://doi.org/10.3233/JIFS-202958
Saleh, H. Y., Salih, A. A., Asaad, B. A., & Mohammed, R. A. (2023). Binary Bipolar Soft Points and Topology on Binary Bipolar Soft Sets with Their Symmetric Properties. Symmetry, 16(1), 23. https://doi.org/10.3390/sym16010023
Ali, G., & Ansari, M. N. (2022). Multiattribute decision-making under Fermatean fuzzy bipolar soft framework. Granular Computing, 7(2), 337-352. https://doi.org/10.1007/s41066-021-00270-6
Yi, J., Ahmmad, J., Mahmood, T., Ur Rehman, U., & Zeng, S. (2024). Complex Fuzzy Rough Set: An Application in Digital Marketing for Business Growth. IEEE Access, 12, 66453-66465, https://doi.org/10.1109/ACCESS.2024.3397699
Mahmood, T., & Ur Rehman, U. (2022). A novel approach towards bipolar complex fuzzy sets and their applications in generalized similarity measures. International Journal of Intelligent Systems, 37(1), 535-567. https://doi.org/10.1002/int.22639
Mahmood, T., Waqas, H. M., Rehman, U. U., & Aslam, M. (2024). Selection of cloud services provider by utilizing multi-attribute decision-making based on hesitant bipolar complex fuzzy Dombi aggregation operators. IEEE Access, 12, 35417-35447. https://doi.org/10.1109/ACCESS.2024.3369893
Javed, M., Javeed, S., Ullah, K., & Haleemzai, I. (2023). An Approach to Multi-Attribute Decision-Making for Olive Trees Plantation Site Selection using Spherical Fuzzy Neutrality Aggregation Operators. IEEE Access, 11, 117403-117422. https://doi.org/10.1109/ACCESS.2023.3325359
Ullah, K., Naeem, M., Hussain, A., Waqas, M., & Haleemzai, I. (2023). Evaluation of electric motor cars based frank power aggregation operators under picture fuzzy information and a multi-attribute group decision-making process. IEEE Access, 11, 67201-67219. https://doi.org/10.1109/ACCESS.2023.3285307
Khan, M. R., Ullah, K., Khan, Q., & Haleemzai, I. (2024). Confidence Levels Measurement of Mobile Phone Selection Using a Multiattribute Decision‐Making Approach with Unknown Attribute Weight Information Based on T‐Spherical Fuzzy Aggregation Operators. Discrete Dynamics in Nature and Society, 2024, 6572374. https://doi.org/10.1155/2024/6572374
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