Relevant bibliographies by topics / Fuzziness measure

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Fuzziness measure'

Author: Grafiati
Published: 4 June 2025
Last updated: 15 July 2025

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Journal articles on the topic "Fuzziness measure"

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Lee, Sang-Hyuk, Keum-Boo Kang, and Sung shin Kim. "Measure of Fuzziness with fuzzy entropy function." Journal of Korean Institute of Intelligent Systems 14, no. 5 (2004): 642–47. http://dx.doi.org/10.5391/jkiis.2004.14.5.642.

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Delgado, M., M. A. Vila, and W. Voxman. "A fuzziness measure for fuzzy numbers: Applications." Fuzzy Sets and Systems 94, no. 2 (1998): 205–16. http://dx.doi.org/10.1016/s0165-0114(96)00247-3.

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Verma, Rohit Kumar, and M. Bhagya Laxmi. "Fuzzy Events of Theoretic Characteristics by Employing Pseudo Probabilistic Information Measure of Type Alpha." International Journal of Mathematics and Statistics Invention 13, no. 3 (2025): 24–35. https://doi.org/10.35629/4767-13032435.

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This study first examines a number of widely used fuzziness metrics for discrete fuzzy collections. We present a novel informative measure for discriminating between two fuzzy sets after examining a few of the current fuzzy set measures. We demonstrate that every class fulfills five well-known fuzziness measure axioms and show how a number of current measurements are related to these classes. The non-negative, monotony rising concave functions form the foundation of the multiplicative class. The only functions needed for the additive class are non-negative concave functions. There are also som
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Cardone, Barbara, and Ferdinando Di Martino. "A Novel Method Based on the Fuzzy Entropy Measure to Optimize the Fuzziness in Trapezoidal Strong Fuzzy Partitions." Information 15, no. 10 (2024): 615. http://dx.doi.org/10.3390/info15100615.

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Analyzing the uncertainty of outcomes based on estimates of the data’s membership degrees to fuzzy sets is essential for making decisions. These fuzzy sets are often designated by experts as strong fuzzy partitions of the data domain with trapezoidal fuzzy numbers. Some indices of the fuzzy set’s fuzziness provide an assessment of the degree of uncertainty of the results. It is feasible to bring the fuzzy sets’ fuzziness below a tolerable level by suitably redefining the strong fuzzy partition. Significant differences in the original fuzzy partition, however, result in disparities concerning t
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Mao, Junjun, Yuan Zhao, and Chuang Ma. "A New Type of Compositive Information Entropy for IvIFS and Its Applications." Mathematical Problems in Engineering 2016 (2016): 1–13. http://dx.doi.org/10.1155/2016/7652540.

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We first show the interval-valued intuitionistic fuzzy entropy which reflects intuitionism and fuzziness of interval-valued intuitionistic fuzzy set (IvIFS) based on interval-valued intuitionistic fuzzy cross-entropy. As for intuitionism and fuzziness of IvIFS, we propose interval-valued intuitionistic entropy and interval-valued fuzzy entropy, respectively. Furthermore, we establish the interval-valued span entropy describing the uncertainty of membership degree and nonmembership degree and show some concrete measure formulas. Combining intuitionistic factor, fuzzy factor, and span factor, we
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Li, Xiang, and Baoding Liu. "Chance measure for hybrid events with fuzziness and randomness." Soft Computing 13, no. 2 (2008): 105–15. http://dx.doi.org/10.1007/s00500-008-0308-x.

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Weiss, Brandi A., and William Dardick. "An Entropy-Based Measure for Assessing Fuzziness in Logistic Regression." Educational and Psychological Measurement 76, no. 6 (2016): 986–1004. http://dx.doi.org/10.1177/0013164415623820.

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This article introduces an entropy-based measure of data–model fit that can be used to assess the quality of logistic regression models. Entropy has previously been used in mixture-modeling to quantify how well individuals are classified into latent classes. The current study proposes the use of entropy for logistic regression models to quantify the quality of classification and separation of group membership. Entropy complements preexisting measures of data–model fit and provides unique information not contained in other measures. Hypothetical data scenarios, an applied example, and Monte Car
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DAS, SHUBHABRATA. "ON MEASURING IMPRECISION IN HUMAN RESPONSE DUE TO RESPONDENT AND ATTRIBUTE AND ITS UTILITY IN QUESTIONNAIRE DESIGN." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 14, no. 02 (2006): 155–73. http://dx.doi.org/10.1142/s0218488506003923.

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Human Response to a question with multiple alternatives can be imprecise or fuzzy for various reasons. Part of this fuzziness can be attributed to the lack of sincerity and clarity in the thought process of the respondent. A formal measure of this aspect of fuzziness is formulated in this work on the basis of consistency of the respondent's response to the same question repeated in multiple scales. Discarding haphazard and insincere respondents can improve the quality of data resulting in more efficient survey analysis. This may be achieved in the framework of statistical testing of hypothesis
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Han, Joon H., and Tae Y. Kim. "Ambiguity distance : an edge evaluation measure using fuzziness of edges." Fuzzy Sets and Systems 126, no. 3 (2002): 311–24. http://dx.doi.org/10.1016/s0165-0114(01)00037-9.

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Amato, P., and G. F. Cerofolini. "Measure, error and intrinsic fuzziness: a mathematical study of vagueness." Soft Computing 5, no. 3 (2001): 194–200. http://dx.doi.org/10.1007/s005000100081.

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Dissertations / Theses on the topic "Fuzziness measure"

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Fischer, Manfred M., and Josef Benedikt. "The Use of Fuzzy Set Theory in Remote Sensing Pattern Recognition." WU Vienna University of Economics and Business, 1996. http://epub.wu.ac.at/4174/1/WSG_DP_5096.pdf.

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Satellite images increasingly become a major data source for monitoring changes in the natural environment. A main task in the analysis of satellite images is concerned with the modelling of land use classes by reducing uncertainty during a classification process. In the approach presented in this paper uncertainty is perceived to be due to the vagueness of geographical categories caused by either the complexity of the category (like 'urban area') or by the use of the category in several application contexts. Two circumstances of use of an extended set theoretical concept (fuzzy sets) ar
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Books on the topic "Fuzziness measure"

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Fuzzy Measures and Integrals: Theory and Applications (Studies in Fuzziness and Soft Computing). Physica-Verlag Heidelberg, 2000.

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Intuitionistic Fuzzy Sets Studies in Fuzziness and Soft Computing. Physica-Verlag, 2010.

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Karmeshu. Entropy Measures, Maximum Entropy Principle and Emerging Applications (Studies in Fuzziness and Soft Computing). Springer, 2003.

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Book chapters on the topic "Fuzziness measure"

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Virant, Jernej. "Fuzziness and Its Measure." In Applied Optimization. Springer US, 2000. http://dx.doi.org/10.1007/978-1-4615-4673-3_1.

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Lee, Hsuan-Shih. "A Fuzziness Measure of Rough Sets." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-44967-1_6.

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Mahapatra, Rupkumar, Prasenjit Mandal, Sovan Samanta, Vivek Kumar Dubey, Madhumangal Pal, and Tofigh Allahviranloo. "Centrality Measure Using Linguistic Z-graph and Its Application." In Studies in Fuzziness and Soft Computing. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-65854-9_13.

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Ali, Abder-Rahman, Micael Couceiro, Ahmed M. Anter, Aboul Ella Hassanien, Mohamed F. Tolba, and Václav Snášel. "Liver CT Image Segmentation with an Optimum Threshold Using Measure of Fuzziness." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08156-4_9.

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Zimmermann, H. J. "Fuzzy Measures and Measures of Fuzziness." In Fuzzy Set Theory—and Its Applications. Springer Netherlands, 1996. http://dx.doi.org/10.1007/978-94-015-8702-0_4.

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Zimmermann, H. J. "Fuzzy Measures and Measures of Fuzziness." In Fuzzy Set Theory—and Its Applications. Springer Netherlands, 2001. http://dx.doi.org/10.1007/978-94-010-0646-0_4.

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Zimmermann, H. J. "Fuzzy Measures and Measures of Fuzziness." In Fuzzy Set Theory — and Its Applications. Springer Netherlands, 1991. http://dx.doi.org/10.1007/978-94-015-7949-0_4.

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Zimmermann, H. J. "Fuzzy Measures and Measures of Fuzziness." In Fuzzy Set Theory — and Its Applications. Springer Netherlands, 1985. http://dx.doi.org/10.1007/978-94-015-7153-1_4.

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Seising, Rudolf. "Measures of Observables and Measures of Fuzziness." In Communications in Computer and Information Science. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-31715-6_7.

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Balagopalan, Sarada. "Children’s Participation in Their Right to Education: Learning from the Delhi High Court Cases, 1998–2001." In The Politics of Children’s Rights and Representation. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-04480-9_4.

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AbstractWith several states in the majority world having passed legislation around free and compulsory education and millions of marginal children now enrolled in schools, the question of how we frame children’s participation in their right to education assumes considerable significance. By drawing together discussions around children’s representations, participation and educational equity, this chapter critically opens up the particular dynamic that has helped produce educational equity as a continually deferrable goal. It argues that the dominant representation of first-generation learners as economically marginal children is variously, and continually, leveraged to justify their presence within unequal and deeply segregated school spaces. To help problematize this narrative of assumed victimhood, the chapter discusses a set of court cases adjudicated in the Delhi High Court between 1997 and 2001. These cases not only highlight the state’s role in perpetuating existing inequalities but also draw attention to how these dominant representations had a deleterious effect on marginal children’s school experiences. By countering a simplistic narrative around school attendance as an adequate measure of children’s learning and participation in education, these Delhi High Court cases foreground marginal children’s primary identity as learners. They thus help expose how the current fuzziness around children’s participation in schooling has helped produce schooling as a critical compensatory technology that is no longer about guaranteeing educational equity.
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Conference papers on the topic "Fuzziness measure"

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Li, Fa-chao, Li-min Liu, and Chen-xia Jin. "Structural Description for Fuzziness Measure." In 2006 International Conference on Machine Learning and Cybernetics. IEEE, 2006. http://dx.doi.org/10.1109/icmlc.2006.258900.

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Lv, Yue-jin, Hong-yun Zhang, and Zhi-cheng Chen. "The Fuzziness Measure in Fuzzy Rough Sets." In 2008 Fifth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD). IEEE, 2008. http://dx.doi.org/10.1109/fskd.2008.377.

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Bukhensky, K. V., А. N. Konyukhov, А. B. Dubois, and A. S. Safoshkin. "OPERATIONS ON FUZZY NUMBERS AND MEASURE OF FUZZINESS." In Modern Technologies in Science and Education MTSE-2020. Ryazan State Radio Engineering University, 2020. http://dx.doi.org/10.21667/978-5-6044782-3-3-12-19.

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Zhi-Chen Tong, Zhi-Jun Wang, and Chen-Xia Jin. "Uncertain information measure system based on F⊕S fuzziness degree." In 2009 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 2009. http://dx.doi.org/10.1109/icmlc.2009.5212658.

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Pratamasunu, Gulpi Qorik Oktagalu, Zhencheng Hu, Agus Zainal Arifin, et al. "Image thresholding based on index of fuzziness and fuzzy similarity measure." In 2015 IEEE 8th International Workshop on Computational Intelligence and Applications (IWCIA). IEEE, 2015. http://dx.doi.org/10.1109/iwcia.2015.7449483.

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Cat Ho, Nguyen. "Complete and Linear Hedge Algebras, Fuzziness Measure of Vague Concepts and Linguistic Hedges and Application." In COMPUTING ANTICIPATORY SYSTEMS: CASYS'05 - Seventh International Conference. AIP, 2006. http://dx.doi.org/10.1063/1.2216642.

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Tan, Ruiguang. "Research methods of product perceptual image recognition in Kansei Engineering." In 13th International Conference on Applied Human Factors and Ergonomics (AHFE 2022). AHFE International, 2022. http://dx.doi.org/10.54941/ahfe1001764.

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The paper studies the image recognition in Kansei engineering. Firstly, the development history of Kansei Engineering is reviewed. The early research object of ergonomics is the explicit physiological and psychological reaction generated after the interaction between human and artificial objects. This type of research object is characterized by objectivity, universality, stability and easiness to measure. Kansei Engineering extends the research object of ergonomics to the implicit psychological reaction generated by the interaction between human beings and artificial objects, which is characte
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Yoshida, Yuji. "Mean Values of Fuzzy Numbers with Evaluation Measures and the Measurement of Fuzziness." In 9th Joint Conference on Information Sciences. Atlantis Press, 2006. http://dx.doi.org/10.2991/jcis.2006.298.

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Bajaj, R. K., and Nitin Gupta. "Utility and Fuzziness Aspects of the Information Measures in Experimental Observations and Computations." In 2012 International Conference on Advances in Computing and Communications (ICACC). IEEE, 2012. http://dx.doi.org/10.1109/icacc.2012.3.

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Yoshida, Yuji. "Mean Values of Fuzzy Numbers and the Measurement of Fuzziness by Evaluation Measures." In 2006 IEEE Conference on Cybernetics and Intelligent Systems. IEEE, 2006. http://dx.doi.org/10.1109/iccis.2006.252245.

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