Double framed T-bipolar soft sets and their applications in decision making
DOI:
https://doi.org/10.31181/jdaic10010112025aKeywords:
Double framed T-bipolar soft sets, AND product, OR product, Digital signal processingAbstract
Handling symmetric information that involves both positive and negative aspects, while avoiding data loss and a two-dimensional nature, has always been a problem for researchers. So far, no structure is available to address this issue. In addition to bipolarity, symmetry, and avoiding data loss, the notion of a T-bipolar soft set is more advanced. However, to address the issue of two dimensions simultaneously, we have developed the concept of double-framed T-bipolar soft sets in this manuscript. Moreover, we have defined the ideas of “AND” product, “OR” product, extended union, extended intersection, restricted union, and restricted intersection for double-framed T-bipolar soft sets. To utilize the introduced approach, we have developed an algorithm and provided an example to demonstrate its benefits and usefulness. We have utilized our work in the prioritization of advanced techniques in Digital Signal Processing (DSP). The comparative analysis of the delivered work shows the superiority of the introduced work.
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References
Ahmmad, J., Labassi, F., Alsuraiheed, T., Mahmood, T., & Khan, M. A. (2024). Classification of Feature Engineering Techniques for Machine Learning under the Environment of Lattice Ordered T-bipolar Fuzzy Soft Rings. IEEE Access, 12, 77514 – 77522.
Akram, M., Ali, G., Alcantud, J. C., & Fatimah, F. (2021). Parameter reductions in N‐soft sets and their applications in decision‐making. Expert Systems, 38(1), e12601.
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.
Biglieri, E., & Yao, K. (1989). Some properties of singular value decomposition and their applications to digital signal processing. Signal Processing, 18(3), 277-289.
Çağman, N., & Enginoğlu, S. (2010). Soft set theory and uni–int decision making. European journal of operational research, 207(2), 848-855.
Cappello, P., & Steiglitz, K. (1984). Some complexity issues in digital signal processing. IEEE Transactions on Acoustics, Speech, and Signal Processing, 32(5), 1037-1041.
Cartledge, J. C., Guiomar, F. P., Kschischang, F. R., Liga, G., & Yankov, M. P. (2017). Digital signal processing for fiber nonlinearities. Optics express, 25(3), 1916-1936.
Constantinides, G. A., Cheung, P. Y., & Luk, W. (2003). Wordlength optimization for linear digital signal processing. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 22(10), 1432-1442.
Durand, L. G., & Pibarot, P. (1995). Digital signal processing of the phonocardiogram: review of the most recent advancements. Critical Reviews™ in biomedical engineering, 23(3-4), 163-219.
Hegde, R., & Shanbhag, N. R. (2001). Soft digital signal processing. IEEE Transactions on Very Large Scale Integration (VLSI) Systems, 9(6), 813-823.
Herawan, T., Ghazali, R., & Deris, M. M. (2010). Soft set theoretic approach for dimensionality reduction. International Journal of Database Theory and Application, 3(2), 47-60.
Jun, Y. B., & Ahn, S. S. (2012). Double framed soft sets with applications in BCK/BCI-algebras. Journal of Applied Mathematics, 2012, 178159.
Jun, Y. B., Lee, K. J., & Park, C. H. (2008). Soft set theory applied to commutative ideals in BCK-Algebras. Journal of applied mathematics and informatics, 26(3-4), 707-720.
Jun, Y. B., Lee, K. J., & Park, C. H. (2009). Soft set theory applied to ideals in d-algebras. Computers & Mathematics with Applications, 57(3), 367-378.
Karaaslan, F., & Karataş, S. (2015). A new approach to bipolar soft sets and its applications. Discrete Mathematics, Algorithms and Applications, 7(4), 1550054.
Mahmood, T. (2020). A novel approach towards bipolar soft sets and their applications. Journal of Mathematics, 2020, 4690808.
Mahmood, T., Hussain, K., Ahmmad, J., Shahab, S., & Anjum, M. (2024). T-Bipolar soft groups and their fundamental laws. Journal of Intelligent & Fuzzy Systems, 46(4), 9479-9490.
Molodtsov, D. (1999). Soft set theory—first results. Computers & Mathematics with Applications, 37(4-5), 19-31.
Sahambi, J. S., Tandon, S. N., & Bhatt, R. K. P. (1997). Using wavelet transforms for ECG characterization. An on-line digital signal processing system. IEEE Engineering in Medicine and Biology Magazine, 16(1), 77-83.
Santos-Buitrago, B., Riesco, A., Knapp, M., Alcantud, J. C. R., Santos-García, G., & Talcott, C. (2019). Soft set theory for decision making in computational biology under incomplete information. IEEE Access, 7, 18183-18193.
Shabir, M., & Naz, M. (2013). On bipolar soft sets. arXiv preprint arXiv:1303.1344.
Taş, N., Özgür, N. Y., & Demir, P. (2017). An application of soft set and fuzzy soft set theories to stock management. Journal of Natural and Applied Sciences, 21(3), 791-796.
Tessier, R., & Burleson, W. (2001). Reconfigurable computing for digital signal processing: A survey. Journal of VLSI signal processing systems for signal, image and video technology, 28, 7-27.
Vaidyanathan, P. P., & Doganata, Z. (1989). The role of lossless systems in modern digital signal processing: A tutorial. IEEE Transactions on Education, 32(3), 181-197.
Van Straten, W., & Bailes, M. (2011). DSPSR: digital signal processing software for pulsar astronomy. Publications of the Astronomical Society of Australia, 28(1), 1-14.
Zahedi Khameneh, A., & Kılıçman, A. (2019). Multi-attribute decision-making based on soft set theory: A systematic review. Soft Computing, 23, 6899-6920.
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