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Готові списки джерел за темами / Soft Fuzzy Coset

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Добірка наукової літератури з теми "Soft Fuzzy Coset"

Автор: Grafiati

Опубліковано: 3 червня 2025

Оновлено: 31 липня 2025

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Статті в журналах з теми "Soft Fuzzy Coset"

1

Dehghan, O. R. "Quotient bipolar fuzzy soft sets of hypervector spaces and bipolar fuzzy soft sets of quotient hypervector spaces." Journal of Algebraic Hyperstructures and Logical Algebras 4, no. 2 (2023): 67–90. http://dx.doi.org/10.61838/kman.jahla.4.2.5.

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Анотація:

In this paper, two related quotient structures are investigated utilizing the concept of coset. At first, a new hypervector space F/V = (F/V,\circ,\circledcirc,K) is created, which is composed of all cosets of a bipolar fuzzy soft set (F;A) over a hypervector space V . Then it will be shown that dim F/V = dim V/W, where the quotient hypervector space V/W includes all cosets of an especial subhyperspace W of V. Also, three bipolar fuzzy soft sets over the quotient hypervector space V/W are presented and in this way some new bipolar fuzzy soft hypervector spaces are defined.

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2

Geetha, K., N. Anitha, S. Noeiaghdam, U. Fernandez-Gamiz, S. S. Santra, and K. M. Khedher. "Generalization of (Q,L)-Fuzzy Soft Subhemirings of a Hemiring." Advances in Fuzzy Systems 2022 (September 30, 2022): 1–12. http://dx.doi.org/10.1155/2022/6102211.

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This paper investigates the properties and results of (Q,L)-fuzzy soft subhemirings ((Q,L)-FSSHR) of a hemiring R. The motivation behind this study is to utilize the concept of L-fuzzy soft set of a hemiring and to derive a few specific outcomes on (Q, L)-FSSHR. The concepts of strongest Q-fuzzy soft set relation, Q-isomorphism, pseudo-Q-fuzzy soft coset, and some of their related properties are implemented while analyzing the results. Finally, the properties are verified with a numerical example from the 2000 AMS subject classification: 05C38, 05A15, and 15A18.

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3

S., Subramanian, and Seethalakshmi E. "CERTAIN APPLICATIONS OF P- FUZZY SOFT STRUCTURES." International Journal of Applied and Advanced Scientific Research 2, no. 2 (2017): 294–98. https://doi.org/10.5281/zenodo.1115610.

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In this paper, we investigate the notion of P-fuzzy soft intersection groups which is a generalization of that fuzzy soft groups is provided. By introducing the notion soft fuzzy cosets, soft fuzzy quotient groups based on P-fuzzy soft intersection ideals are established. Finally, isomorphism theorems of   P-fuzzy soft intersection groups related to invariant fuzzy soft sets are discussed.

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4

Dr., S. V. Manemaran *1 &. Dr. R. Nagarajan2. "APPLICATIONS OF STEP N-FUZZY FACTOR GROUP UNDER FUZZY VERSION." GLOBAL JOURNAL OF ENGINEERING SCIENCE AND RESEARCHES 6, no. 7 (2019): 105–17. https://doi.org/10.5281/zenodo.3354318.

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In this paper, we define the notion of Step N-Fuzzy Soft subgroup and investigate the condition under which a Fuzzy Soft subgroup is Step N-Fuzzy Soft subgroup. We introduce the notion of Step N-Fuzzy Soft cosets and establish their algebraic properties. We also initiate the study of Step N-Fuzzy Soft normal subgroups and quotient group with respect to Step N-Fuzzy Soft normal subgroup and prove some of their various group theoretic properties.

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5

Hameed, M. Shazib, Salman Mukhtar, Haq Nawaz Khan, Shahbaz Ali, M. Haris Mateen, and Muhammad Gulzar. "Pythagorean Fuzzy N-Soft Groups." Indonesian Journal of Electrical Engineering and Computer Science 21, no. 2 (2021): 1030–38. https://doi.org/10.11591/ijeecs.v21i2.pp1030-1038.

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We elaborate in this paper a new structure Pythagorean fuzzy N-soft groups which is the generalization of intuitionistic fuzzy soft group initiated by Karaaslan in 2013. In Pythagorean fuzzy N-soft sets concepts of fuzzy sets, soft sets, N-soft sets, fuzzy soft sets, intuitionistic fuzzy sets, intuitionistic fuzzy soft sets, Pythagorean fuzzy sets, Pythagorean fuzzy soft sets are generalized. We also talk about some elementary basic concepts and operations on Pythagorean fuzzy N-soft sets with the assistance of illusions. We additionally define three different sorts of complements for Pythagor

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6

Mateen, MUHAMMAD Haris. "Pythagorean Fuzzy N-Soft Groups." Indonesian Journal of Electrical Engineering and Computer Science 21, no. 2 (2021): 1030. http://dx.doi.org/10.11591/ijeecs.v21.i2.pp1030-1038.

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<p>We elaborate in this paper a new structure Pythagorean fuzzy<br />$N$-soft groups which is the generalization of intuitionistic fuzzy<br />soft group initiated by Karaaslan in 2013. In Pythagorean fuzzy<br />N-soft sets concepts of fuzzy sets, soft sets, N-soft sets, fuzzy<br />soft sets, intuitionistic fuzzy sets, intuitionistic fuzzy soft<br />sets, Pythagorean fuzzy sets, Pythagorean fuzzy soft sets are<br />generalized. We also talk about some elementary basic concepts and<br />operations on Pythagorean fuzzy N-soft sets with the assistanc

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7

V., Ramadas, and Anitha B. "ON PSEUDO COMPATIBLE P-FUZZY SOFT RELATIONS." International Journal of Applied and Advanced Scientific Research 3, no. 1 (2017): 7–11. https://doi.org/10.5281/zenodo.1133940.

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Дисертації з теми "Soft Fuzzy Coset"

1

Chen, Chen. "Soft Computing-based Life-Cycle Cost Analysis Tools for Transportation Infrastructure Management." Diss., Virginia Tech, 2007. http://hdl.handle.net/10919/28214.

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Анотація:

Increasing demands, shrinking financial and human resources, and increased infrastructure deterioration have made the task of maintaining the infrastructure systems more challenging than ever before. Life-cycle cost analysis (LCCA) is an important tool for transportation infrastructure management, which is used extensively to support project level decisions, and is increasingly being applied to enhance network level analysis. However, traditional LCCA tools cannot practically and effectively utilize expert knowledge and handle ambiguous uncertainties. The main objective of this dissertation w

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Книги з теми "Soft Fuzzy Coset"

1

Dompere, Kofi Kissi. Cost-Benefit Analysis and the Theory of Fuzzy Decisions: Fuzzy Value Theory. Springer, 2014.

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2

Dompere, Kofi Kissi. Cost-Benefit Analysis and the Theory of Fuzzy Decisions: Fuzzy Value Theory. Springer London, Limited, 2013.

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3

Dompere, Kofi Kissi. Cost-Benefit Analysis and the Theory of Fuzzy Decisions: Fuzzy Value Theory. Springer Berlin Heidelberg, 2010.

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4

Dompere, Kofi Kissi. Cost-Benefit Analysis and the Theory of Fuzzy Decisions: Identification and Measurement Theory. Springer London, Limited, 2013.

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5

Dompere, Kofi Kissi. Cost-Benefit Analysis and the Theory of Fuzzy Decisions: Identification and Measurement Theory. Springer, 2014.

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6

Dompere, Kofi Kissi. Cost-Benefit Analysis and the Theory of Fuzzy Decisions: Identification and Measurement Theory. Springer Berlin Heidelberg, 2010.

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7

Dostál, Petr, and Chia-Yang Lin. Business Applications of Fuzzy Logic. Edited by Shu-Heng Chen, Mak Kaboudan, and Ye-Rong Du. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780199844371.013.14.

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Анотація:

The chapter focuses on the use of fuzzy logic, or soft computing, among the different methods used as supports for decision making in business applications. The processes are focused on private corporate attempts at making money or decreasing expenses; therefore, the details of applications, successful or not, are not published very often. Fuzzy logic helps in decentralization of decisionmaking processes that are to be standardized, reproduced, and documented. Fuzzy logic plays very important roles, especially in business, because it helps reduce costs. It differs from conventional (hard) comp

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8

Cost-Benefit Analysis and the Theory of Fuzzy Decisions: Fuzzy Value Theory (Studies in Fuzziness and Soft Computing). Springer, 2004.

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9

Dompere, Kofi K. Cost-Benefit Analysis and the Theory of Fuzzy Decisions: Identification and Measurement Theory (Studies in Fuzziness and Soft Computing). Springer, 2004.

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Частини книг з теми "Soft Fuzzy Coset"

1

Xu, Ruo-ning, and Xiao-yan Zhai. "Fuzzy Model for Portfolio Selection with Transaction Cost." In Advances in Soft Computing. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03664-4_145.

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2

Idri, Ali, Alain Abran, and T. M. Khoshgoftaar. "Fuzzy Case-Based Reasoning Models for Software Cost Estimation." In Soft Computing in Software Engineering. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-44405-3_3.

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3

Xu, Ruo-ning, and Xiao-yan Zhai. "Fuzzy Portfolio Model with Transaction Cost Based on Downside Risk Measure." In Advances in Intelligent and Soft Computing. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-14880-4_40.

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4

Miao, Zhi-hong, Zhi-hui Li, Yong-li Zhang, and Hong-xing Li. "Design of Optimal Cost Fuzzy Controller for Spatial Double Inverted Pendulum System." In Advances in Intelligent and Soft Computing. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-28592-9_13.

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5

Zhihong, Miao, Wang Jiayin, and Li Zhihui. "Design of Fuzzy Control System with Optimal Guaranteed Cost for Planar Inverted Pendulum." In Advances in Intelligent and Soft Computing. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-14880-4_19.

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6

Bera, Sukhendu, Dipak Kumar Jana, Kajla Basu, and Manoranjan Maiti. "Novel Multi-objective Green Supply Chain Model with $$CO_2$$ Emission Cost in Fuzzy Environment via Soft Computing Technique." In Recent Advances in Intelligent Information Systems and Applied Mathematics. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-34152-7_36.

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7

Sasirekha, S., and S. Swamynathan. "Fuzzy Rule Based Environment Monitoring System for Weather Controlled Laboratories Using Arduino." In Fuzzy Systems. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-1908-9.ch038.

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Weather controlled laboratories such as blood banks, plasma centers, biomedical, research, pharmacy and healthcare always require a portable, low cost and web-based centralized wireless monitoring system. However, it has become more stringent to monitor various weather controlling devices of these laboratories in order to reduce the risk of non-compliance with accreditation requirements. In literature, it is inferred that the majority of existing event detection approaches relies only on precise value to specify event thresholds, but those values cannot adequately handle the imprecise sensor r

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8

Ghosal, Kajal, Partha Haldar, and Goutam Sutradhar. "Application of Fuzzy Expert System in Medical Treatment." In Fuzzy Systems. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-1908-9.ch045.

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The incidence of breast cancer is increasing day by day. Emotional significance of females for the fear of removal of breast demands attention and carries a particular terror. Fuzzy logic-based expert system is a powerful tool that is used in this chapter to get the benefits of soft computing in modern medical science. This chapter deals with reasoning for medical implementation in breast cancer diagnosis. The motto of this expert system using MATLAB software is to make the people of the world healthier, free from breast cancer and its metastasis through the power of information. Special revol

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9

Pillai, Jyothi, and O. P. Vyas. "Exploration of Soft Computing Approaches in Itemset Mining." In Advances in Data Mining and Database Management. IGI Global, 2014. http://dx.doi.org/10.4018/978-1-4666-5063-3.ch012.

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Data Mining is largely known to extract knowledge from large databases in an attempt to discover existing trends and newer patterns. While data mining refers to information extraction, soft computing is more inclined to information processing. Using Soft Computing, the tolerance for imprecision, uncertainty, approximate reasoning, and partial truth for achieving tractability, robustness, and low-cost solutions can be revealed. For effective knowledge discovery from large databases, both Soft Computing and Data Mining can be merged. Soft computing techniques are Fuzzy Logic (FL), Neural Network

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10

Pillai, Jyothi, and O. P. Vyas. "Exploration of Soft Computing Approaches in Itemset Mining." In Business Intelligence. IGI Global, 2016. http://dx.doi.org/10.4018/978-1-4666-9562-7.ch091.

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Анотація:

Data Mining is largely known to extract knowledge from large databases in an attempt to discover existing trends and newer patterns. While data mining refers to information extraction, soft computing is more inclined to information processing. Using Soft Computing, the tolerance for imprecision, uncertainty, approximate reasoning, and partial truth for achieving tractability, robustness, and low-cost solutions can be revealed. For effective knowledge discovery from large databases, both Soft Computing and Data Mining can be merged. Soft computing techniques are Fuzzy Logic (FL), Neural Network

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Тези доповідей конференцій з теми "Soft Fuzzy Coset"

1

Yoneyama, Jun, and Kenta Hoshino. "Output feedback control design with guaranteed cost of Takagi-Sugeno fuzzy systems." In 2014 Joint 7th International Conference on Soft Computing and Intelligent Systems (SCIS) and 15th International Symposium on Advanced Intelligent Systems (ISIS). IEEE, 2014. http://dx.doi.org/10.1109/scis-isis.2014.7044721.

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2

Ghassemi, Payam, and Souma Chowdhury. "Decentralized Task Allocation in Multi-Robot Systems via Bipartite Graph Matching Augmented With Fuzzy Clustering." In ASME 2018 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/detc2018-86161.

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Robotic systems, working together as a team, are becoming valuable players in different real-world applications, from disaster response to warehouse fulfillment services. Centralized solutions to coordinating multi-robot teams often suffer from poor scalability and vulnerability to communication disruptions. This paper develops a decentralized multi-agent task allocation (Dec-MATA) algorithm for multi-robot applications. The task planning problem is posed as a maximum-weighted matching of a bipartite graph, the solution of which using the blossom algorithm allows each robot to autonomously ide

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