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Relevant bibliographies by topics / Fuzzy Soft Sets

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Books
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Academic literature on the topic 'Fuzzy Soft Sets'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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Journal articles on the topic "Fuzzy Soft Sets"

1

Liu, Xiaoyan, Feng Feng, and Hui Zhang. "On Some Nonclassical Algebraic Properties of Interval-Valued Fuzzy Soft Sets." Scientific World Journal 2014 (2014): 1–11. http://dx.doi.org/10.1155/2014/192957.

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Interval-valued fuzzy soft sets realize a hybrid soft computing model in a general framework. Both Molodtsov’s soft sets and interval-valued fuzzy sets can be seen as special cases of interval-valued fuzzy soft sets. In this study, we first compare four different types of interval-valued fuzzy soft subsets and reveal the relations among them. Then we concentrate on investigating some nonclassical algebraic properties of interval-valued fuzzy soft sets under the soft product operations. We show that some fundamental algebraic properties including the commutative and associative laws do not hold

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Meng, Dan, Xiaohong Zhang, and Keyun Qin. "Soft rough fuzzy sets and soft fuzzy rough sets." Computers & Mathematics with Applications 62, no. 12 (2011): 4635–45. http://dx.doi.org/10.1016/j.camwa.2011.10.049.

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Sivasankari, H., and J. Subhashini. "Intuitionistic Fuzzy Binary Soft Sets and Its Properties." Indian Journal Of Science And Technology 17, no. 35 (2024): 3636–42. http://dx.doi.org/10.17485/ijst/v17i35.1161.

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Objective : The objective of Intuitionistic fuzzy binary soft sets is to extend the existing frameworks of binary soft sets and Intuitionistic fuzzy sets to accommodate both uncertainty and ambiguity in decision-making processes. This extension aims to provide a more flexible, representation of real-world data and phenomena, allowing for analysis and reasoning. Specifically, the objective involves defining the fundamental structures of Intuitionistic fuzzy binary soft sets over two initial universal sets, U1 and U2. Method: Reviewing the existing literature on binary soft sets, Intuitionistic

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H, Sivasankari, and Subhashini J. "Intuitionistic Fuzzy Binary Soft Sets and Its Properties." Indian Journal of Science and Technology 17, no. 35 (2024): 3636–42. https://doi.org/10.17485/IJST/v17i35.1161.

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Abstract <strong>Objective :</strong> The objective of Intuitionistic fuzzy binary soft sets is to extend the existing frameworks of binary soft sets and Intuitionistic fuzzy sets to accommodate both uncertainty and ambiguity in decision-making processes. This extension aims to provide a more flexible, representation of real-world data and phenomena, allowing for analysis and reasoning. Specifically, the objective involves defining the fundamental structures of Intuitionistic fuzzy binary soft sets over two initial universal sets, U1 and U2. <strong>Method:</strong> Reviewing th

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5

Shi, Fu-Gui, and Chao-Zan Fan. "Fuzzy soft sets as L-fuzzy sets." Journal of Intelligent & Fuzzy Systems 37, no. 4 (2019): 5061–66. http://dx.doi.org/10.3233/jifs-182828.

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Shao, Yingchao, and Keyun Qin. "Fuzzy Soft Sets and Fuzzy Soft Lattices." International Journal of Computational Intelligence Systems 5, no. 6 (2012): 1135–47. http://dx.doi.org/10.1080/18756891.2012.747667.

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Min, Won-Keun. "Interval-Valued Intuitionistic Fuzzy Soft Sets." Journal of Korean Institute of Intelligent Systems 18, no. 3 (2008): 316–22. http://dx.doi.org/10.5391/jkiis.2008.18.3.316.

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Al-shami, Tareq M., José Carlos R. Alcantud, and Abdelwaheb Mhemdi. "New generalization of fuzzy soft sets: $ (a, b) $-Fuzzy soft sets." AIMS Mathematics 8, no. 2 (2023): 2995–3025. http://dx.doi.org/10.3934/math.2023155.

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<abstract><p>Many models of uncertain knowledge have been designed that combine expanded views of fuzziness (expressions of partial memberships) with parameterization (multiple subsethood indexed by a parameter set). The standard orthopair fuzzy soft set is a very general example of this successful blend initiated by fuzzy soft sets. It is a mapping from a set of parameters to the family of all orthopair fuzzy sets (which allow for a very general view of acceptable membership and non-membership evaluations). To expand the scope of application of fuzzy soft set theory, the restricti

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9

Zhang, Zhiming. "The Parameter Reduction of Fuzzy Soft Sets Based on Soft Fuzzy Rough Sets." Advances in Fuzzy Systems 2013 (2013): 1–12. http://dx.doi.org/10.1155/2013/197435.

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Fuzzy set theory, rough set theory, and soft set theory are three effective mathematical tools for dealing with uncertainties and have many wide applications both in theory and practise. Meng et al. (2011) introduced the notion of soft fuzzy rough sets by combining fuzzy sets, rough sets, and soft sets all together. The aim of this paper is to study the parameter reduction of fuzzy soft sets based on soft fuzzy rough approximation operators. We propose some concepts and conditions for two fuzzy soft sets to generate the same lower soft fuzzy rough approximation operators and the same upper sof

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10

Fujita, Takaaki. "Fuzzy MultiDirected Sets, Neutrosophic MultiDirected Sets, Plithogenic MultiDirected Sets, Soft MultiDirected Sets, and Hypersoft MultiDirected Sets." HyperSoft Set Methods in Engineering 3 (April 23, 2025): 70–88. https://doi.org/10.61356/j.hsse.2025.3470.

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A fuzzy set generalizes classical sets by assigning each element a membership value in the interval [0, 1], thereby capturing partial or uncertain membership. A Neutrosophic Set extends fuzzy sets by introducing three independent membership degrees: truth, indeterminacy, and falsity, each ranging within [0, 1]. A Plithogenic Set further generalizes classical and fuzzy sets by incorporating attributes, their possible values, and a measure of contradiction. Other notable frameworks for handling uncertainty include the Soft Set and Hypersoft Set, which provide flexible parameterized approaches. A

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Dissertations / Theses on the topic "Fuzzy Soft Sets"

1

Keukelaar, J. H. D. "Topics in Soft Computing." Doctoral thesis, KTH, Numerical Analysis and Computer Science, NADA, 2002. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-3294.

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Saltos, Atiencia Ramiro Javier. "Soft-clustering in static and dynamic environments." Tesis, Universidad de Chile, 2016. http://repositorio.uchile.cl/handle/2250/142058.

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Doctor en Sistemas de Ingeniería<br>En la actualidad, el entorno macro y micro económico en el cual op eran las empresas está cambiando constantemente y a gran velo cidad. Esto se deb e princip almente al auge de las nuevas tecnologías, la revolución de la información y la gran facilidad con la que ahora p o demos comu nicarnos con cualquier parte d el mundo. Debido a la gran canti dad de información que está siendo generada segundo tras segundo, junto con la facilidad con la que se puede acceder a ella, la minería de datos y la investigación de op eracion es se han convertido en una de l

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Lucic, Panta. "Modeling Transportation Problems Using Concepts of Swarm Intelligence and Soft Computing." Diss., Virginia Tech, 2002. http://hdl.handle.net/10919/26396.

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Many real-world problems could be formulated in a way to fit the necessary form for discrete optimization. Discrete optimization problems can be solved by numerous different techniques that have developed over time. Some of the techniques provide optimal solution(s) to the problem and some of them give â good enoughâ solution(s). The fundamental reason for developing techniques capable of producing solutions that are not necessarily optimal is the fact that many discrete optimization problems are NP-complete. Metaheuristic algorithms are a common name for a set of general-purpose techniques

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4

Ferone, A. "EXPLOITING HIGHER ORDER UNCERTAINTY IN IMAGE ANALYSIS." Doctoral thesis, Università degli Studi di Milano, 2011. http://hdl.handle.net/2434/155479.

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Soft computing is a group of methodologies that works synergistically to provide flexible information processing capability for handling real-life ambiguous situations. Its aim is to exploit the tolerance for imprecision, uncertainty, approximate reasoning, and partial truth in order to achieve tractability, robustness, and low-cost solutions. Soft computing methodologies (involving fuzzy sets, neural networks, genetic algorithms, and rough sets) have been successfully employed in various image processing tasks including image segmentation, enhancement and classification, both individually or

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Bennert, Reinhard. "Soft Computing-Methoden in Sanierungsprüfung und -controlling : Entscheidungsunterstützung durch Computional Intelligence /." Wiesbaden : Dt. Univ.-Verl, 2004. http://www.gbv.de/dms/zbw/386511489.pdf.

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Vural, Hulya. "Comparison Of Rough Multi Layer Perceptron And Rough Radial Basis Function Networks Using Fuzzy Attributes." Master's thesis, METU, 2004. http://etd.lib.metu.edu.tr/upload/12605293/index.pdf.

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The hybridization of soft computing methods of Radial Basis Function (RBF) neural networks, Multi Layer Perceptron (MLP) neural networks with back-propagation learning, fuzzy sets and rough sets are studied in the scope of this thesis. Conventional MLP, conventional RBF, fuzzy MLP, fuzzy RBF, rough fuzzy MLP, and rough fuzzy RBF networks are compared. In the fuzzy neural networks implemented in this thesis, the input data and the desired outputs are given fuzzy membership values as the fuzzy properties &ldquo<br>low&rdquo<br>, &ldquo<br>medium&rdquo<br>and &ldquo<br>high&rdquo<br>. In the roug

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Books on the topic "Fuzzy Soft Sets"

1

G, Chen. Introduction to fuzzy sets, fuzzy logic, and fuzzy control systems. CRC Press, 2000.

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Tat, Pham Trung, ed. Introduction to fuzzy sets, fuzzy logic, and fuzzy control systems. CRC Press, 2001.

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Yager, R. R. Fuzzy sets, neural networks and soft computing. Van Nostrand Reinhold, 1994.

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1941-, Yager Ronald R., and Zadeh Lotfi Asker, eds. Fuzzy sets, neural networks, and soft computing. Van Nostrand Reinhold, 1994.

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International Conference on Rough Sets, Fuzzy Sets and Soft Computing (2009 Dept. of Mathematics, Tripura University). Proceedings, International Conference on Rough Sets, Fuzzy Sets, and Soft Computing, November 5-7, 2009. Serials Publications, 2011.

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1948-, Bouchon-Meunier B., Yager Ronald R. 1941-, and Zadeh Lotfi Asker, eds. Fuzzy logic and soft computing. World Scientific, 1995.

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Y, Baaklini George, Vary Alex, and United States. National Aeronautics and Space Administration., eds. Soft computing in design and manufacturing of advanced materials. National Aeronautics and Space Administration, 1993.

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8

NATO Advanced Research Workshop on Systematic Organisation of Information in Fuzzy Systems (2001 Vila Real, Portugal). Systematic organisation of information in fuzzy systems. IOS Press, 2003.

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1959-, Nikravesh Masoud, Kacprzyk Janusz, and Zadeh Lotfi Asker, eds. Forging the new frontiers: Fuzzy pioneers. Springer, 2007.

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10

Cao, Bing-Yuan. Fuzzy information and engineering. Springer, 2009.

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Book chapters on the topic "Fuzzy Soft Sets"

1

Chakraverty, Snehashish, Deepti Moyi Sahoo, and Nisha Rani Mahato. "Fuzzy Sets." In Concepts of Soft Computing. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-7430-2_2.

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Fathima Perveen, P. A., Sunil Jacob John, and K. V. Babitha. "Spherical Fuzzy Soft Sets." In Decision Making with Spherical Fuzzy Sets. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-45461-6_6.

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3

Naeem, Khalid, and Muhammad Riaz. "Pythagorean Fuzzy Soft Sets-Based MADM." In Pythagorean Fuzzy Sets. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-1989-2_16.

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4

Mukherjee, Anjan. "Soft Rough Intuitionistic Fuzzy Sets." In Generalized Rough Sets. Springer India, 2015. http://dx.doi.org/10.1007/978-81-322-2458-7_3.

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Pawlak, Zdzislaw. "Hard and Soft Sets." In Rough Sets, Fuzzy Sets and Knowledge Discovery. Springer London, 1994. http://dx.doi.org/10.1007/978-1-4471-3238-7_15.

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6

Miyamoto, Sadaaki. "Bags, Toll Sets, and Fuzzy Sets." In Advances in Intelligent and Soft Computing. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-11960-6_28.

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Mukherjee, Anjan, and Ajoy Kanti Das. "Introduction to Fuzzy Sets, Soft Sets, Multisets and Their Generalizations." In Essentials of Fuzzy Soft Multisets. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-2760-7_1.

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Murai, Tetsuya, Michinori Nakata, and Masaru Shimbo. "Modal Logic, Rough Sets, and Fuzzy Sets." In Soft Computing and Human-Centered Machines. Springer Japan, 2000. http://dx.doi.org/10.1007/978-4-431-67907-3_3.

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Mukherjee, Anjan. "Introduction to Fuzzy Sets, Rough Sets, and Soft Sets." In Generalized Rough Sets. Springer India, 2015. http://dx.doi.org/10.1007/978-81-322-2458-7_1.

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Park, Jin Han, Ki Moon Lim, Jong Seo Park, and Young Chel Kwun. "Correlation Coefficient between Intuitionistic Fuzzy Sets." In Advances in Soft Computing. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03664-4_66.

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Conference papers on the topic "Fuzzy Soft Sets"

1

Juang, Chia-Feng, and Wen-Chieh Chiang. "An Interpretable Fuzzy Classifier Learned Through Soft-Margin Minimization with Transparent Fuzzy Sets." In 2024 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2024. http://dx.doi.org/10.1109/fuzz-ieee60900.2024.10611900.

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2

Singh, Rashmi, Dechen Wangmo, Keshav Kaushik, and Aryan Chaudhary. "An Algorithmic Approach Using Fuzzy Soft Sets in Decision- Making for Patient-Specific Diagnosis." In 2024 International Conference on Emerging Innovations and Advanced Computing (INNOCOMP). IEEE, 2024. http://dx.doi.org/10.1109/innocomp63224.2024.00040.

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Arunadevi.S, D.Jayanthi, M.Thirumalaimuthukumaran, and M. Mohan Kumar. "An approach for solving transportation problem using possibility interval valued intuitionistic fuzzy soft sets." In 2025 3rd International Conference on Advancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA). IEEE, 2025. https://doi.org/10.1109/icaeca63854.2025.11012625.

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Kaur, Kamalpreet, and Asha Gupta. "q-rung orthopair neutrosophic fuzzy rough sets with an application based on multi-criteria decision-making." In 2024 11th International Conference on Soft Computing & Machine Intelligence (ISCMI). IEEE, 2024. https://doi.org/10.1109/iscmi63661.2024.10851579.

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Liu, Jin-hang, Rui-xia Yan, and Bing-xue Yao. "Fuzzy soft sets and fuzzy soft groups." In 2008 Chinese Control and Decision Conference (CCDC). IEEE, 2008. http://dx.doi.org/10.1109/ccdc.2008.4597801.

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Shokeen, Jyoti, and Chhavi Rana. "Fuzzy sets, advanced fuzzy sets and hybrids." In 2017 International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS). IEEE, 2017. http://dx.doi.org/10.1109/icecds.2017.8389911.

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FENG, FENG, YONGMING LI, and HONGXUAN LEI. "LEVEL SOFT SETS BASED ON FUZZY SETS." In Proceedings of the QL&SC 2012. WORLD SCIENTIFIC, 2012. http://dx.doi.org/10.1142/9789814401531_0065.

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Selvachandran, Ganeshsree, Nisren A. Hafeed, and Abdul Razak Salleh. "Complex fuzzy soft expert sets." In THE 4TH INTERNATIONAL CONFERENCE ON MATHEMATICAL SCIENCES: Mathematical Sciences: Championing the Way in a Problem Based and Data Driven Society. Author(s), 2017. http://dx.doi.org/10.1063/1.4980969.

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Athira, T. M., Sunil Jacob John, and P. Rajish Kumar. "Incomplete pythagorean fuzzy soft sets." In PROCEEDINGS OF INTERNATIONAL CONFERENCE ON ADVANCES IN MATERIALS RESEARCH (ICAMR - 2019). AIP Publishing, 2020. http://dx.doi.org/10.1063/5.0017218.

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Feng, Feng. "Generalized Rough Fuzzy Sets Based on Soft Sets." In 2009 International Workshop on Intelligent Systems and Applications. IEEE, 2009. http://dx.doi.org/10.1109/iwisa.2009.5072885.

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