Relevant bibliographies by topics / INTUITIONISTIC FUZZY SET

Academic literature on the topic 'INTUITIONISTIC FUZZY SET'

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Published: 16 January 2024

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Journal articles on the topic "INTUITIONISTIC FUZZY SET"

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Jeon, Joung Kon, Young Bae Jun, and Jin Han Park. "Intuitionistic fuzzy alpha-continuity and intuitionistic fuzzy precontinuity." International Journal of Mathematics and Mathematical Sciences 2005, no. 19 (2005): 3091–101. http://dx.doi.org/10.1155/ijmms.2005.3091.

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A characterization of intuitionistic fuzzyα-open set is given, and conditions for an IFS to be an intuitionistic fuzzyα-open set are provided. Characterizations of intuitionistic fuzzy precontinuous (resp.,α-continuous) mappings are given.
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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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Pratama, Dian. "STRUKTUR IMAGE DAN PRE-IMAGE HOMOMORFISMA PADA TRANSLASI RING FUZZY INTUITIONISTIK." Jurnal Ilmiah Matematika dan Pendidikan Matematika 11, no. 1 (May 18, 2020): 59. http://dx.doi.org/10.20884/1.jmp.2020.12.1.1937.

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A set that is characterized by membership function and a non-membership function with the sum of both at intervals of 0 to 1 is called intuitionistik fuzzy set.. When it’s applied in ring’s theory, it will called intuitionistic fuzzy ring. In this research,if the topics is membership fuction and non-membership function then there are translates operator. This operator only changes the values of the membership and non-membership function while the properties are fixed. This journal discussed the structure of image ad pre-image homomorphism of translates on intuitionistic fuzzy rings. The result obtained that the structure of image and pre-image is also intuitionistic fuzzy rings. Full Article
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Bashir, Maruah, Abdul Razak Salleh, and Shawkat Alkhazaleh. "Possibility Intuitionistic Fuzzy Soft Set." Advances in Decision Sciences 2012 (March 13, 2012): 1–24. http://dx.doi.org/10.1155/2012/404325.

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Possibility intuitionistic fuzzy soft set and its operations are introduced, and a few of their properties are studied. An application of possibility intuitionistic fuzzy soft sets in decision making is investigated. A similarity measure of two possibility intuitionistic fuzzy soft sets has been discussed. An application of this similarity measure in medical diagnosis has been shown.
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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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Roh, Eun Hwan, Eunsuk Yang, and Young Bae Jun. "Intuitionistic Fuzzy Ordered Subalgebras in Ordered BCI-algebras." European Journal of Pure and Applied Mathematics 16, no. 3 (July 30, 2023): 1342–58. http://dx.doi.org/10.29020/nybg.ejpam.v16i3.4832.

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In this paper, we apply the concept of an intuitionistic fuzzy set to ordered subalgebras in ordered BCI-algebras in the sense of intuitionistic fuzzy point. We introduce the notion of an intuitionistic fuzzy (ordered) subalgebra in ordered BCI-algebras, and investigate some related properties. We provide relations between an intuitionistic fuzzy ordered subalgebra and an intuitionistic fuzzy subalgebra. We give characterizations of an intuitionistic fuzzy (ordered) subalgebra. Finally, we provide relations between a $q_{(t,s)}$-level set of intuitionistic fuzzy set and an intuitionistic fuzzy ordered subalgebra.
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Balamurugan, Manivannan, Nazek Alessa, Karuppusamy Loganathan, and M. Sudheer Kumar. "Bipolar Intuitionistic Fuzzy Soft Ideals of BCK/BCI-Algebras and Its Applications in Decision-Making." Mathematics 11, no. 21 (October 28, 2023): 4471. http://dx.doi.org/10.3390/math11214471.

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In this paper, we merge the concepts of soft set theory and a bipolar intuitionistic fuzzy set. A bipolar intuitionistic fuzzy soft ideal in a BCK-algebra is defined as a soft set over the set of elements in the BCK-algebra, with each element associated with an intuitionistic fuzzy set. This relationship captures degrees of uncertainty, hesitancy, and non-membership degrees within the context of BCK-algebras. We investigate basic operations on bipolar intuitionistic fuzzy soft ideals such as union, intersection, AND, and OR. The intersection, union, AND, and OR of two bipolar intuitionistic fuzzy soft ideals is a bipolar intuitionistic fuzzy soft ideal. We also demonstrate how to use a bipolar intuitionistic fuzzy soft set to solve a problem involving decision making. Finally, we provide a general approach for handling decision-making problems using a bipolar intuitionistic fuzzy soft set.
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Jana, Chiranjibe, and Madhumangal Pal. "Application of Bipolar Intuitionistic Fuzzy Soft Sets in Decision Making Problem." International Journal of Fuzzy System Applications 7, no. 3 (July 2018): 32–55. http://dx.doi.org/10.4018/ijfsa.2018070103.

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This article describes how recently, a paper by D. Ezhilmaran and K. Sankar called Morphism of bipolar intuitionistic fuzzy graphs, has introduced bipolar intuitionistic fuzzy sets and morphism of bipolar intuitionistic fuzzy graphs. By using this concept, the authors of this article have combined a bipolar intuitionistic fuzzy set and a soft set. They introduce the notion of bipolar intuitionistic fuzzy soft set and study their basic properties. Also, presented in this article are the basic operations on bipolar intuitionistic fuzzy soft sets, extended unions, and the intersection of two bipolar intuitionistic fuzzy soft sets. An application of bipolar intuitionistic fuzzy soft set provides into a decision-making problem and a general algorithm to solve this decision making problem.
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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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Wijaya, Ongky Denny, Abdul Rouf Alghofari, Noor Hidayat, and Mohamad Muslikh. "The Properties of Intuitionistic Anti Fuzzy Module t-norm and t-conorm." CAUCHY 7, no. 2 (March 11, 2022): 207–19. http://dx.doi.org/10.18860/ca.v7i2.13351.

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Zadeh have introduced fuzzy set in 1965 and Atanassov have introduced intuitionistic fuzzy set in 1986 in theirs paper. Now, many of researcher connecting intuitionistic fuzzy set with algebraic structure. We interested to combine some concepts over intuitionistic fuzzy set, module of a ring, t-norm, t-conorm, and intuitionistic anti fuzzy. In this paper, we discusses about intuitionistic anti fuzzy module t-norm and t-conorm (IAFMTC) and their properties with respect to module homomorphism, maps, pre-image, and anti-image from intuitionistic fuzzy sets. We have investigate all properties of IAFMTC.
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Dissertations / Theses on the topic "INTUITIONISTIC FUZZY SET"

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KUMARI, RANJEETA, and SHIVAM SHARMA. "HIERARCHICAL CLUSTERING OF PICTURE FUZZY RELATION." Thesis, 2023. http://dspace.dtu.ac.in:8080/jspui/handle/repository/20420.

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The paper aims to find the (𝛼̃,𝛽̃𝛾̃) - cuts of picture fuzzy relation and apply it to find its hierarchical clustering. we studied the previous results of Intuitionistic fuzzy relation and its (𝛼̃,𝛽̃,𝛾̃) - cuts. We propose a technique of finding 𝛼̃ - cuts of picture fuzzy sets using results of intuitionistic fuzzy set. Membership of PFS mainly deals with positive, negative and neutral membership while IFS only deals with positive and negative membership. In this paper, we attempt to study picture fuzzy set more deeply and providing the more results in picture fuzzy relation that contains 𝛼̃ - cuts of picture fuzzy set. Further we studied about the various applications of Intuitionistic fuzzy relation and picture fuzzy relation. Intuitionistic fuzzy relation deals with the application on the comparision of distant measures using normalised hamming distance measure. The two applications of picture fuzzy relation is discussed. First application deals with the determination of higher study area selection on the basis of composition of picture fuzzy relations. We found picture fuzzy relation on topic knowledge, study area and the skills. Then, we applied picture fuzzy transformation to obtain new picture fuzzy relation. After defuzzifying the PFR matrix we found the suitable study areas for students to continue higher studies. Second application picture-fuzzy medical diagnosis deals with the medical diagnosis of an illness and patients suffering from a particular disease is identified with the help of composition of picture fuzzy relation.
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Peng, Jen-pin, and 彭仁賓. "The Study of Optimizing Multi-response Problems with Intuitionistic Fuzzy Set." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/79486084912217317899.

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博士
國立中央大學
機械工程學系
103
The Taguchi method provides an effective framework for improving quality in industry. However, it determines the optimal setting of process parameters according to only single response. For the sake of optimizing multi-response problems, multiple criteria decision making (MCDM) methods have been extensively utilized in recent years. In considering an engineer's opinion in optimizing a multi-response problem, it must be paid to vagueness and hesitancy in revealing his or her perceptions of a fuzzy concept such as 'importance' or 'excellence'. Recently, the notion of intuitionistic fuzzy sets (IFSs) has been found to be more effective than that of fuzzy sets for dealing with vagueness and hesitancy. This thesis focuses on state systems and explores optimization of multi-response problems with IFSs, in which the importance of each response is given by an engineer as IFS. In the proposed methods, the TOPSIS method, VIKOR method and the similarity measure method are proposed for optimizing multi-response problems, where the weight of various responses are assessed in terms of IFSs. This scheme can eliminate the need for complicated intuitionistic fuzzy arithmetic operations and increase the efficiency of solving multi- response optimization problems in intuitionistic fuzzy environments. Two case studies of plasma-enhanced chemical vapor deposition (PECVD) and double-sided surface mount technology electronic assembly operation are used to demonstrate the effectiveness of the proposed methods. These case studies show that the proposed methods are useful schemes to efficiently determine the optimal factor-level combination. The proposed methods differ from previous approaches for optimizing multi-response problems, not only in that the proposed methods use IFSs rather than fuzzy sets, but also in that the calculations are more efficient.
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Kabir, Sohag, T. K. Goek, M. Kumar, M. Yazdi, and F. Hossain. "A method for temporal fault tree analysis using intuitionistic fuzzy set and expert elicitation." 2019. http://hdl.handle.net/10454/17992.

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Yes
Temporal fault trees (TFTs), an extension of classical Boolean fault trees, can model time-dependent failure behaviour of dynamic systems. The methodologies used for quantitative analysis of TFTs include algebraic solutions, Petri nets (PN), and Bayesian networks (BN). In these approaches, precise failure data of components are usually used to calculate the probability of the top event of a TFT. However, it can be problematic to obtain these precise data due to the imprecise and incomplete information about the components of a system. In this paper, we propose a framework that combines intuitionistic fuzzy set theory and expert elicitation to enable quantitative analysis of TFTs of dynamic systems with uncertain data. Experts’ opinions are taken into account to compute the failure probability of the basic events of the TFT as intuitionistic fuzzy numbers. Subsequently, for the algebraic approach, the intuitionistic fuzzy operators for the logic gates of TFT are defined to quantify the TFT. On the other hand, for the quantification of TFTs via PN and BN-based approaches, the intuitionistic fuzzy numbers are defuzzified to be used in these approaches. As a result, the framework can be used with all the currently available TFT analysis approaches. The effectiveness of the proposed framework is illustrated via application to a practical system and through a comparison of the results of each approach.
This work was supported in part by the Mobile IOT: Location Aware project (grant no. MMUE/180025) and Indoor Internet of Things (IOT) Tracking Algorithm Development based on Radio Signal Characterisation project (grant no. FRGS/1/2018/TK08/MMU/02/1). This research also received partial support from DEIS H2020 project (grant no. 732242).
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Chia-Yi, Chien, and 簡嘉毅. "An Improved Cross-Entropy Approach for Pattern Recognition Based on Intuitionistic Fuzzy Set." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/98868305678012283391.

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碩士
國防大學管理學院
運籌管理學系
97
The thesis addresses the issue of information-theoretic discrimination measures for intuitionistic fuzzy sets (IFSs). Although many measures of distance, similarity, dissimilarity, and correlation between IFSs have been proposed, there is no reference regarding information-driven measures used for comparison between sets. In this work, we introduce the concepts of discrimination information and cross-entropy in the intuitionistic fuzzy sets and improve non-probabilistic entropy proposed by Vlachos & Sergiadis (2007) for IFSs. Based on this entropy measure, we add information of hesitation and reveal an intuitive and mathematical connection between the notions of entropy for IFSs in terms of fuzziness and intuitionism. Finally, we demonstrate the applications of the proposed discrimination information measure for pattern recognition, medical diagnosis, and bacteria detection.
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Ching-Lin, Lin, and 林敬霖. "Kernel Intuitionistic Fuzzy C-Means Clustering Algorithms with Rough Set for Customer Analysis." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/69926836270687990934.

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碩士
龍華科技大學
資訊管理系碩士班
101
Fuzzy C-mean (FCM) algorithms have been widely used in variety of different places. This paper proposes a kernel intuitionistic fuzzy c-means clustering algorithms with rough set (KIFCMRS), and this method is applied to the E-learning data analysis. The rule generation can be divided into two stages for effective rule generation. In the first stage, KIFCM takes advantages of kernel function and intuitionistic fuzzy sets to cluster raw data into similarity groups. In the second stage, the rough set theory is employed to generate rules with different groups. Finally, this paper compared different methods, the first stages comparative KIFCM and the other two methods (KM, FCM), the second stages compare the KIFCMRS and the other two methods (ID3, RS). Comparison with other approaches demonstrate the superior performance of the proposed KIFCMRS.
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Books on the topic "INTUITIONISTIC FUZZY SET"

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Som, Tanmoy, Oscar Castillo, Anoop Kumar Tiwari, and Shivam Shreevastava, eds. Fuzzy, Rough and Intuitionistic Fuzzy Set Approaches for Data Handling. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-8566-9.

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Intuitionistic fuzzy measures: Theory and applications. New York: Nova Science Publishers, 2006.

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Atanassov, Krassimir T. On Intuitionistic Fuzzy Sets Theory. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.

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Xu, Zeshui. Intuitionistic Fuzzy Information Aggregation: Theory and Applications. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.

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Zhi jue mo hu cu cao ji li lun ji ying yong. Beijing: Ke xue chu ban she, 2013.

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Chaira, Tamalika. Fuzzy Set and Its Extension: The Intuitionistic Fuzzy Set. Wiley & Sons, Incorporated, John, 2019.

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Chaira, Tamalika. Fuzzy Set and Its Extension: The Intuitionistic Fuzzy Set. Wiley, 2019.

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Chaira, Tamalika. Fuzzy Set and Its Extension: The Intuitionistic Fuzzy Set. Wiley & Sons, Incorporated, John, 2019.

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Chaira, Tamalika. Fuzzy Set and Its Extension: The Intuitionistic Fuzzy Set. Wiley & Sons, Limited, John, 2019.

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Tiwari, Anoop Kumar, Shivam Shreevastava, Oscar Castillo, and Tanmoy Som. Fuzzy, Rough and Intuitionistic Fuzzy Set Approaches for Data Handling: Theory and Applications. Springer, 2023.

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Book chapters on the topic "INTUITIONISTIC FUZZY SET"

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Li, Deng-Feng. "Intuitionistic Fuzzy Set Theories." In Decision and Game Theory in Management With Intuitionistic Fuzzy Sets, 1–46. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40712-3_1.

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Wu, Le-tao, and Xue-hai Yuan. "Intuitionistic Fuzzy Rough Set Based on the Cut Sets of Intuitionistic Fuzzy Set." In Advances in Intelligent Systems and Computing, 37–45. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-66514-6_4.

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Gupta, Krishna Kumar, and Sanjay Kumar. "Probabilistic Intuitionistic Fuzzy Set Based Intuitionistic Fuzzy Time Series Forecasting Method." In Mathematical Modelling and Scientific Computing with Applications, 315–24. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-1338-1_23.

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Zhang, Yanqin, and Xibei Yang. "An Intuitionistic Fuzzy Dominance–Based Rough Set." In Bio-Inspired Computing and Applications, 665–72. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-24553-4_88.

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Cornelis, Chris, and Etienne Kerre. "Inclusion Measures in Intuitionistic Fuzzy Set Theory." In Lecture Notes in Computer Science, 345–56. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-45062-7_28.

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Kahraman, Cengiz, Alexander Bozhenyuk, and Margarita Knyazeva. "Internally Stable Set in Intuitionistic Fuzzy Graph." In Lecture Notes in Networks and Systems, 566–72. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-09173-5_65.

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Atanassov, Krassimir T. "On the Intuitionistic Fuzzy Implications and Negations. Part 1." In 35 Years of Fuzzy Set Theory, 19–38. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-16629-7_2.

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Gupta, Ananya, and Shahin Ara Begum. "Fuzzy Rough Set-Based Feature Selection for Text Categorization." In Fuzzy, Rough and Intuitionistic Fuzzy Set Approaches for Data Handling, 65–85. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-8566-9_4.

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Bisht, Kamlesh, Dheeraj Kumar Joshi, and Sanjay Kumar. "Dual Hesitant Fuzzy Set-Based Intuitionistic Fuzzy Time Series Forecasting." In Advances in Intelligent Systems and Computing, 317–29. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-7386-1_28.

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Chaira, Tamalika, and Tridib Chaira. "Intuitionistic Fuzzy Set: Application to Medical Image Segmentation." In Computational Intelligence in Medical Informatics, 51–68. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-75767-2_3.

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Conference papers on the topic "INTUITIONISTIC FUZZY SET"

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Xu, Yong-jie, Yong-kan Sun, and Deng-feng Li. "Intuitionistic Fuzzy Soft Set." In 2010 2nd International Workshop on Intelligent Systems and Applications (ISA). IEEE, 2010. http://dx.doi.org/10.1109/iwisa.2010.5473444.

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Lu, Yanli, Yingjie Lei, and Yang Lei. "Intuitionistic fuzzy rough set based on intuitionistic similarity relation." In 2008 Chinese Control and Decision Conference (CCDC). IEEE, 2008. http://dx.doi.org/10.1109/ccdc.2008.4597422.

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Xu, Yon-Hong, and Wei-Zhi Wu. "On intuitionistic fuzzy rough set algebras." In 2010 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 2010. http://dx.doi.org/10.1109/icmlc.2010.5580541.

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Nair, Premchand S. "Consolidation operator for Intuitionistic Fuzzy Set." In NAFIPS 2009 - 2009 Annual Meeting of the North American Fuzzy Information Processing Society. IEEE, 2009. http://dx.doi.org/10.1109/nafips.2009.5156483.

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Zenian, Suzelawati, Tahir Ahmad, and Amidora Idris. "Intuitionistic fuzzy set: FEEG image representation." In PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON MATHEMATICAL SCIENCES AND TECHNOLOGY 2018 (MATHTECH2018): Innovative Technologies for Mathematics & Mathematics for Technological Innovation. AIP Publishing, 2019. http://dx.doi.org/10.1063/1.5136486.

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Kumar, Deepak, and S. B. Singh. "Evaluating fuzzy reliability using rough intuitionistic fuzzy set." In 2014 Innovative Applications of Computational Intelligence on Power, Energy and Controls with their Impact on Humanity (CIPECH). IEEE, 2014. http://dx.doi.org/10.1109/cipech.2014.7019037.

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Khatibi, Vahid, and Gholam Ali Montazer. "Intuitionistic fuzzy set application in bacteria recognition." In 2009 14th International CSI Computer Conference (CSICC 2009) (Postponed from July 2009). IEEE, 2009. http://dx.doi.org/10.1109/csicc.2009.5349609.

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Chaira, Tamalika. "Medical image enhancement using intuitionistic fuzzy set." In 2012 1st International Conference on Recent Advances in Information Technology (RAIT). IEEE, 2012. http://dx.doi.org/10.1109/rait.2012.6194479.

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Kaushal, Meenakshi, Rinki Solanki, Q. M. Danish Lohani, and Pranab K. Muhuri. "A Novel Intuitionistic Fuzzy Set Generator with Application to Clustering." In 2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2018. http://dx.doi.org/10.1109/fuzz-ieee.2018.8491602.

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XU, Yong-jie. "Some New Operations on Triangular Fuzzy Number Intuitionistic Fuzzy Set." In 2019 Chinese Control And Decision Conference (CCDC). IEEE, 2019. http://dx.doi.org/10.1109/ccdc.2019.8833267.

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Reports on the topic "INTUITIONISTIC FUZZY SET"

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Atanasso, Krassimir. Elliptic Intuitionistic Fuzzy Sets. "Prof. Marin Drinov" Publishing House of Bulgarian Academy of Sciences, June 2021. http://dx.doi.org/10.7546/crabs.2021.06.02.

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Ismaili, Shpend, and Stefka Fidanova. Application of Intuitionistic Fuzzy Sets on Agent Based Modelling. "Prof. Marin Drinov" Publishing House of Bulgarian Academy of Sciences, June 2018. http://dx.doi.org/10.7546/crabs.2018.06.12.

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Atanassov, Krassimir. A New Modal Type Operator over Intuitionistic Fuzzy Sets. "Prof. Marin Drinov" Publishing House of Bulgarian Academy of Sciences, August 2020. http://dx.doi.org/10.7546/crabs.2020.08.01.

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