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Journal articles on the topic 'Fuzzy-set approach'

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Published: 14 March 2023

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1

Sivakumar, P. S., and R. Kavitha. "Fuzzy Measure: A Fuzzy Set Theoretic Approach." Annals of Pure and Applied Mathematics 14, no. 1 (June 16, 2017): 21–31. http://dx.doi.org/10.22457/apam.v14n1a3.

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2

Szmidt, Eulalia, and Janusz Kacprzyk. "A Fuzzy Set Corresponding to an Intuitionistic Fuzzy Set." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 06, no. 05 (October 1998): 427–35. http://dx.doi.org/10.1142/s0218488598000343.

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We propose a new approach to find a fuzzy set which corresponds to a given intuitionistic fuzzy set (Atanassov, 1986) in a certain sense. This will help extend a huge array of fuzzy properties, models, etc. available to the case of intuitionistic fuzzy sets.
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3

Ahmed, Waseem, M. M. Sufyan Beg, and Tanvir Ahmad. "A fuzzy rough set based fitting approach for fuzzy set-valued information system." International Journal of Information Technology 12, no. 4 (April 11, 2019): 1355–64. http://dx.doi.org/10.1007/s41870-019-00304-y.

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4

Bisht, Raj Kishor, and H. S. Dhami. "Fuzzy Set Theoretic Approach To Collocation Extraction." International Journal of Computer Applications 5, no. 3 (August 10, 2010): 43–49. http://dx.doi.org/10.5120/895-1269.

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5

Dutta, Ambar. "Fuzzy Set Theoretic Approach to Image Thresholding." International Journal of Computer Science, Engineering and Applications 1, no. 6 (December 31, 2011): 63–72. http://dx.doi.org/10.5121/ijcsea.2011.1605.

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6

Sachs, Tillmn, Ribert Tiong, and Daniel Wagner. "Political risk quantification using fuzzy set approach." Journal of Financial Management of Property and Construction 12, no. 2 (August 2007): 107–26. http://dx.doi.org/10.1108/13664380780001098.

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7

Tamir, Dan E., Cao Zhi-Qiang, Abraham Kandel, and Joe L. Mott. "An axiomatic approach to fuzzy set theory." Information Sciences 52, no. 1 (October 1990): 75–83. http://dx.doi.org/10.1016/0020-0255(90)90036-a.

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8

Botteldooren, Dick. "Noise limits: A fuzzy set theoretical approach." Noise Control Engineering Journal 51, no. 5 (2003): 306. http://dx.doi.org/10.3397/1.2839727.

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9

Lee, Yong W., Mohamed F. Dahab, and Istvan Bogardi. "Nitrate-Risk Assessment Using Fuzzy-Set Approach." Journal of Environmental Engineering 121, no. 3 (March 1995): 245–56. http://dx.doi.org/10.1061/(asce)0733-9372(1995)121:3(245).

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10

Zeng, L., and H. P. Wang. "Machine-fault classification: A fuzzy-set approach." International Journal of Advanced Manufacturing Technology 6, no. 1 (January 1991): 83–93. http://dx.doi.org/10.1007/bf02601548.

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11

Rheem, Abdul, and Musheer Ahmad. "APPLICATION OF INTERVAL VALUED FUZZY SET AND SOFT SET." jnanabha 50, no. 02 (2020): 114–21. http://dx.doi.org/10.58250/jnanabha.2020.50213.

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Molodtsov was a father of soft set approach. We can’t easily settle the membership degree in some practical application. So it must be much better to describe interval-valued data instead of explaining membership degree. In this paper, we introduce the latest approach of the interval-valued fuzzy soft set by combining the interval-valued fuzzy set and soft set models. This approach successfully follows distributive, associative and DeMorgan’s laws as well. In the end, a decision problem is solved by this approach.
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12

KWIOTKOWSKA, Anna. "The success of science-industry R&D cooperation. A fuzzy-set approach." Scientific Papers of Silesian University of Technology. Organization and Management Series 2020, no. 146 (2020): 219–32. http://dx.doi.org/10.29119/1641-3466.2020.146.17.

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Purpose: This study focuses on the causal mechanisms by which a series of organizational factors like commitment, communication, experience, dependence and trust collectively affect on the success of science-industry R&D cooperation. The purpose of this paper is to identify multiple paths of complex causal recipes that can lead to success of science-industry R&D cooperation. Design/methodology/approach: The study uses fuzzy-set qualitative comparative analysis (fsQCA), a technique that provides a holistic view of the examined interrelationships, compared to traditional net effect approaches that assume symmetric and linear relationships among variables. Findings: Results indicate that different causal paths, exactly five configurations, explain success R&D contracts. Particularly, the findings reveal that the availability of commitment and communication are important, sufficient conditions because they appear in at least three of the five configurations that result from the analysis. In this way, a series of conclusions and implications have been obtained that can be very useful, both in the academic world and when trying to lead and manage cooperation agreements. Research implications: A comprehensive theoretical model was developed and tested that identifies the organizational factors of the success of science-industry R&D cooperation. The presented model and comprehensive research using fs/QCA allows to overcome the fragmentation of this specialized literature. Practical implications: The results contain a number of practical recommendations that can be useful in the conduct and management of cooperation agreements. During the establishing and developing contract stages, it is recommended to design managerial and organizational mechanisms that ensure a high degree of commitment and communication in combination with experience (configuration number 1) and/or with dependences (configuration number 4). Originality/value: Vital value of this paper is the use of fs/QCA, a technique that is an important novelty, at least in the field of R&D cooperation relationships between companies and research organizations. This method allows testing the configuration of conditions in relation to a specific outcome (e.g. success of science-industry R&D cooperation) in a way that is not possible using a linear additive approach.
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13

Singh, Shiva Raj, Surendra Singh Gautam, and Abhishekh . "An Intuitionistic Fuzzy Soft Set Theoretic Approach to Decision Making Problems." MATEMATIKA 34, no. 1 (May 28, 2018): 49–58. http://dx.doi.org/10.11113/matematika.v34.n1.890.

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In general most of real life problem of decision making involve imprecise parameters. In recent past the major emphasis of research workers in this area have been to develop the reliable models to deal with such imprecision and vagueness effectively. Several theories have been developed such as fuzzy set theory, interval valued fuzzy set, intuitionistic fuzzy set, and interval valued intuitionistic fuzzy set, rough set and soft set. The primary objectives of all the above developed theories are to deal with various kinds of uncertainty, imprecision and vagueness but unfortunately every theory has certain limitations. In the present paper we briefly introduced the notion of soft set, fuzzy soft set and intuitionistic fuzzy soft set. We extend the Jurio et al construction method of converting fuzzy set into intuitionistic fuzzy set to fuzzy soft set into intuitionistic fuzzy soft set. Here we consider a problem of decision making in fuzzy soft set and presented a method to generalize it into intuitionistic fuzzy soft set based decision making problem for modelling the problem in a better way. In the process we used the construction method and score function of intuitionistic fuzzy number.
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14

Arfi, Badredine. "Fuzzy Decision Making in Politics: A Linguistic Fuzzy-Set Approach (LFSA)." Political Analysis 13, no. 1 (2005): 23–56. http://dx.doi.org/10.1093/pan/mpi002.

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In this article I use linguistic fuzzy-set theory to analyze the process of decision making in politics. I first introduce a number of relevant elements of (numerical and linguistic) fuzzy-set theory that are needed to understand the terminology as well as to grasp the scope and depth of the approach. I then explicate a linguistic fuzzy-set approach (LFSA) to the process of decision making under conditions in which the decision makers are required to simultaneously satisfy multiple criteria. The LFSA approach is illustrated through a running (hypothetical) example of a situation in which state leaders need to decide how to combine trust and power to make a choice on security alignment.
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15

Mandal, Debabrata. "A Hesitant Intuitionistic Fuzzy Set Approach to Study Ideals of Semirings." International Journal of Fuzzy System Applications 10, no. 3 (July 2021): 1–17. http://dx.doi.org/10.4018/ijfsa.2021070101.

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The classical set theory was extended by the theory of fuzzy set and its several generalizations, for example, intuitionistic fuzzy set, interval valued fuzzy set, cubic set, hesitant fuzzy set, soft set, neutrosophic set, etc. In this paper, the author has combined the concepts of intuitionistic fuzzy set and hesitant fuzzy set to study the ideal theory of semirings. After the introduction and the priliminary of the paper, in Section 3, the author has defined hesitant intuitionistic fuzzy ideals and studied several properities of it using the basic operations intersection, homomorphism and cartesian product. In Section 4, the author has also defined hesitant intuitionistic fuzzy bi-ideals and hesitant intuitionistic fuzzy quasi-ideals of a semiring and used these to find some characterizations of regular semiring. In that section, the author also has discussed some inter-relations between hesitant intuitionistic fuzzy ideals, hesitant intuitionistic fuzzy bi-ideals and hesitant intuitionistic fuzzy quasi-ideals, and obtained some of their related properties.
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16

Shahzadi, Sundas, Musavarah Sarwar, and Muhammad Akram. "Decision-Making Approach with Fuzzy Type-2 Soft Graphs." Journal of Mathematics 2020 (November 17, 2020): 1–25. http://dx.doi.org/10.1155/2020/8872446.

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Molodtsov’s theory of soft sets is free from the parameterizations insufficiency of fuzzy set theory. Type-2 soft set as an extension of a soft set has an essential mathematical structure to deal with parametrizations and their primary relationship. Fuzzy type-2 soft models play a key role to study the partial membership and uncertainty of objects along with underlying and primary set of parameters. In this research article, we introduce the concept of fuzzy type-2 soft set by integrating fuzzy set theory and type-2 soft set theory. We also introduce the notions of fuzzy type-2 soft graphs, regular fuzzy type-2 soft graphs, irregular fuzzy type-2 soft graphs, fuzzy type-2 soft trees, and fuzzy type-2 soft cycles. We construct some operations such as union, intersection, AND, and OR on fuzzy type-2 soft graphs and discuss these concepts with numerical examples. The fuzzy type-2 soft graph is an efficient model for dealing with uncertainty occurring in vertex-neighbors structure and is applicable in computational analysis, applied intelligence, and decision-making problems. We study the importance of fuzzy type-2 soft graphs in chemical digestion and national engineering services.
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17

Deutsch, Stuart Jay, and Charles J. Malmborg. "A fuzzy set approach to data-set evaluation for decision support." IEEE Transactions on Systems, Man, and Cybernetics SMC-15, no. 6 (November 1985): 777–83. http://dx.doi.org/10.1109/tsmc.1985.6313462.

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18

González, José, Luis Castro, Víctor Lachos, and Alexandre Patriota. "A Confidence Set Analysis for Observed Samples: A Fuzzy Set Approach." Entropy 18, no. 6 (May 30, 2016): 211. http://dx.doi.org/10.3390/e18060211.

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19

Wu, Rui-Rong, and He-Ming Zhang. "Object-oriented and fuzzy-set-based approach for set-up planning." International Journal of Advanced Manufacturing Technology 14, no. 6 (June 1998): 406–11. http://dx.doi.org/10.1007/bf01304619.

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20

Li, Mingming, Jiao Dai, Fuqing Zhu, Liangjun Zang, Songlin Hu, and Jizhong Han. "A Fuzzy Set Based Approach for Rating Bias." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 9969–70. http://dx.doi.org/10.1609/aaai.v33i01.33019969.

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In recommender systems, the user uncertain preference results in unexpected ratings. This paper makes an initial attempt in integrating the influence of user uncertain degree into the matrix factorization framework. Specifically, a fuzzy set of like for each user is defined, and the membership function is utilized to measure the degree of an item belonging to the fuzzy set. Furthermore, to enhance the computational effect on sparse matrix, the uncertain preference is formulated as a side-information for fusion. Experimental results on three real-world datasets show that the proposed approach produces stable improvements compared with others.
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21

KIKUCHI, Shinya. "The fuzzy set theory approach to transportation problems." Doboku Gakkai Ronbunshu, no. 458 (1993): 1–6. http://dx.doi.org/10.2208/jscej.1993.1.

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22

Grivas, Dimitri A., and Yung-Ching Shen. "A fuzzy set approach for pavement damage assessments." Civil Engineering Systems 8, no. 1 (March 1991): 37–47. http://dx.doi.org/10.1080/02630259108970604.

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23

Kavitha, R., and E. Kannan. "Efficient Rule Reduction Approach Using Fuzzy-Rough Set." Journal of Computational and Theoretical Nanoscience 15, no. 6 (June 1, 2018): 2238–41. http://dx.doi.org/10.1166/jctn.2018.7443.

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24

Huang, David, Toly Chen, and Mao-Jiun J. Wang. "A fuzzy set approach for event tree analysis." Fuzzy Sets and Systems 118, no. 1 (February 2001): 153–65. http://dx.doi.org/10.1016/s0165-0114(98)00288-7.

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25

Singer, Dennis. "A fuzzy set approach to non-equilibrium thermodynamics." Fuzzy Sets and Systems 47, no. 1 (April 1992): 39–48. http://dx.doi.org/10.1016/0165-0114(92)90058-c.

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26

Chen, Toly, and Mao-Jiun J. Wang. "Applying fuzzy set approach to signal detection theory." Fuzzy Sets and Systems 72, no. 1 (May 1995): 39–49. http://dx.doi.org/10.1016/0165-0114(94)00272-9.

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27

Tso, S. K. "Fuzzy-set approach to dynamic voltage security assessment." IEE Proceedings - Generation, Transmission and Distribution 142, no. 2 (1995): 190. http://dx.doi.org/10.1049/ip-gtd:19951714.

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28

Gianini, Gabriele, and Alessandro Rizzi. "A fuzzy set approach to Retinex spray sampling." Multimedia Tools and Applications 76, no. 23 (June 5, 2017): 24723–48. http://dx.doi.org/10.1007/s11042-017-4877-5.

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29

Ammar, Salwa. "A fuzzy set-theoretic approach to decision making." International Journal of Approximate Reasoning 2, no. 2 (April 1988): 95. http://dx.doi.org/10.1016/0888-613x(88)90067-9.

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30

Gutierrez, Isabel, and Salvador Carmona. "A fuzzy set approach to financial ratio analysis." European Journal of Operational Research 36, no. 1 (July 1988): 78–84. http://dx.doi.org/10.1016/0377-2217(88)90008-2.

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31

Diker, Murat. "Textures and fuzzy unit operations in rough set theory: An approach to fuzzy rough set models." Fuzzy Sets and Systems 336 (April 2018): 27–53. http://dx.doi.org/10.1016/j.fss.2017.06.009.

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32

Biswas, Biswajit, and Biplab Kanti Sen. "Medical image fusion using type-2 fuzzy and near-fuzzy set approach." International Journal of Computers and Applications 42, no. 4 (December 1, 2017): 399–414. http://dx.doi.org/10.1080/1206212x.2017.1402588.

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33

Pham, Van Nha, Long Thanh Ngo, and Witold Pedrycz. "Interval-valued fuzzy set approach to fuzzy co-clustering for data classification." Knowledge-Based Systems 107 (September 2016): 1–13. http://dx.doi.org/10.1016/j.knosys.2016.05.049.

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34

Chu, Ta‐Chung. "Ranking alternatives via maximizing set and minimizing set based fuzzy MCDM approach." Journal of the Chinese Institute of Engineers 27, no. 1 (January 2004): 153–59. http://dx.doi.org/10.1080/02533839.2004.9670858.

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35

Mordeson, John N., Terry D. Clark, Nicholas R. Miller, Peter C. Casey, and Michael B. Gibilisco. "The uncovered set and indifference in spatial models: A fuzzy set approach." Fuzzy Sets and Systems 168, no. 1 (April 2011): 89–101. http://dx.doi.org/10.1016/j.fss.2010.10.016.

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36

Zhang, Zhiming. "A rough set approach to intuitionistic fuzzy soft set based decision making." Applied Mathematical Modelling 36, no. 10 (October 2012): 4605–33. http://dx.doi.org/10.1016/j.apm.2011.11.071.

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37

Sugano, Naotoshi. "Fuzzy Set Theoretical Approach to the Tone Triangle System." International Journal of Software Science and Computational Intelligence 5, no. 3 (July 2013): 33–54. http://dx.doi.org/10.4018/ijssci.2013070103.

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The present study considers a fuzzy color system in which three fuzzy sets are constructed on the tone triangle. This system processes a fuzzy input and outputs a color on the color triangle system. Two fuzzy sets (not black and white) are applied to the tone triangle relationship. By evaluating the attributes of chromaticness, whiteness, and blackness on the tone triangle, a target color can be easily obtained as the center of gravity of the resulting fuzzy set. The output of the system is a tone triangle, which includes a compound vector with three weights (scalars) in color space. The differences between a fuzzy input and the resulting inference output is shown by the input-output characteristic (linear shape and right triangle shape) between the chromaticness, the whiteness, and the blackness of the input and the chromaticness of the output.
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38

Ahmed, Dliouah, and Binxiang Dai. "Picture Fuzzy Rough Set and Rough Picture Fuzzy Set on Two Different Universes and Their Applications." Journal of Mathematics 2020 (November 29, 2020): 1–17. http://dx.doi.org/10.1155/2020/8823580.

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The major concern of this article is to propose the notion of picture fuzzy rough sets (PFRSs) over two different universes which depend on δ , ζ , ϑ -cut of picture fuzzy relation ℛ on two different universes (i.e., by combining picture fuzzy sets (PFSs) with rough sets (RSs)). Then, we discuss several interesting properties and related results on the PFRSs. Furthermore, we define some notions related to PFRSs such as (Type-I/Type-II) graded PFRSs, the degree α and β with respect to ℛ δ , ζ , ϑ on PFRSs, and (Type-I/Type-II) generalized PFRSs based on the degree α and β with respect to ℛ δ , ζ , ϑ and investigate the basic properties of above notions. Finally, an approach based on the rough picture fuzzy approximation operators on two different universes in decision-making problem is introduced, and we give an example to show the validity of this approach.
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39

SUGANO, Naotoshi. "Fuzzy Set Theoretical Approach to the RGB Triangular System." Journal of Japan Society for Fuzzy Theory and Intelligent Informatics 19, no. 1 (2007): 31–40. http://dx.doi.org/10.3156/jsoft.19.31.

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40

Kwiotkowska, Anna. "Organizational Capabilities and Social Entrepreneurship: A Fuzzy-set Approach." EUROPEAN RESEARCH STUDIES JOURNAL XXV, Issue 1 (January 1, 2022): 272–85. http://dx.doi.org/10.35808/ersj/2842.

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41

Harode, Shraddha, Manoj Jha, Namita Srivastava, and Sujoy Das. "Fuzzy soft set based decision approach for financial trading." Advances in Modelling and Analysis C 73, no. 3 (September 30, 2018): 102–11. http://dx.doi.org/10.18280/ama_c.730305.

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42

Bezrukova, Tatyana, Svetlana Kirillova, F. Sattorov, and Aliya Bazieva. "Fuzzy-set approach for the simulation of efficient cooperatives." Актуальные направления научных исследований XXI века: теория и практика 3, no. 5 (December 2, 2015): 30–33. http://dx.doi.org/10.12737/16200.

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43

Crump, Eric L., Timothy L. Jacobs, and P. Aarne Vesilind. "Fuzzy‐Set Approach for Optimizing Sludge Application Land Selection." Journal of Urban Planning and Development 119, no. 2 (June 1993): 53–71. http://dx.doi.org/10.1061/(asce)0733-9488(1993)119:2(53).

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44

Murthy, Sudhir, and Kumares C. Sinha. "A fuzzy set approach for bridge traffic safety evaluation1." Civil Engineering Systems 7, no. 1 (March 1990): 36–43. http://dx.doi.org/10.1080/02630259008970568.

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45

Perdikaris, Steve. "Mathematizing the van Hiele levels: a fuzzy set approach." International Journal of Mathematical Education in Science and Technology 27, no. 1 (January 1996): 41–47. http://dx.doi.org/10.1080/0020739960270106.

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46

Dragicevic, Suzana, and Danielle J. Marceau. "A fuzzy set approach for modelling time in GIS." International Journal of Geographical Information Science 14, no. 3 (April 2000): 225–45. http://dx.doi.org/10.1080/136588100240822.

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47

Hagen, Alex. "Fuzzy set approach to assessing similarity of categorical maps." International Journal of Geographical Information Science 17, no. 3 (April 2003): 235–49. http://dx.doi.org/10.1080/13658810210157822.

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48

HAGEN-ZANKER, ALEX, BAS STRAATMAN, and INGE ULJEE. "Further developments of a fuzzy set map comparison approach." International Journal of Geographical Information Science 19, no. 7 (August 2005): 769–85. http://dx.doi.org/10.1080/13658810500072137.

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49

Ducey, Mark J., and Bruce C. Larson. "A fuzzy set approach to the problem of sustainability." Forest Ecology and Management 115, no. 1 (March 1999): 29–40. http://dx.doi.org/10.1016/s0378-1127(98)00433-2.

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50

Raschia, G., and N. Mouaddib. "SAINTETIQ: a fuzzy set-based approach to database summarization." Fuzzy Sets and Systems 129, no. 2 (July 2002): 137–62. http://dx.doi.org/10.1016/s0165-0114(01)00197-x.

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