Gotowe bibliografie tematyczne / Anti fuzzy soft set

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Gotowa bibliografia na temat „Anti fuzzy soft set”

Autor: Grafiati
Data publikacji: 3 czerwca 2025
Data aktualizacji: 31 lipca 2025

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Artykuły w czasopismach na temat "Anti fuzzy soft set"

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V., Chinnadurai, and Arulmozhi K. "ANTI FUZZY SOFT GAMMA REGULAR SEMIGROUPS." International Journal of Scientific Research and Modern Education 2, no. 1 (2017): 159–70. https://doi.org/10.5281/zenodo.810156.

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In this paper, we have introduce the notion of anti fuzzy soft -semigroups, anti fuzzy soft -ideal, bi-ideal, interior ideal-regular, soft-regular anti fuzzy soft-regular and obtain some interesting properties results and discusse in this paper
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Alsager,, K. M. "n-polar Z-hesitant Complementary Fuzzy Soft Set in BCK/BCI-Algebras." Malaysian Journal of Mathematical Sciences 17, no. 4 (2023): 629–44. http://dx.doi.org/10.47836/mjms.17.4.07.

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This paper introduces an innovative concept known as n-polar Z-hesitant Anti-Fuzzy Soft Sets (MZHAFSs) within the framework of BCK/BCI-algebras. Soft set theory originates in the captivating field of fuzzy set theory. Our approach is a harmonious synthesis of n-polar anti-fuzzy set theory, soft set models, and Z-hesitant anti-fuzzy sets, skillfully applied within the framework of BCK/BCI-algebras. This effort leads to the introduction of a new variant of fuzzy sets termed MZHAFSs (n-polar Z-hesitant anti-fuzzy soft sets) in the context of BCK/BCI-algebras. Additionally, we elucidate the concep
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Alshehri, Halimah, and Noura Alshehri. "Hesitant Anti-Fuzzy Soft Set in BCK-Algebras." Mathematical Problems in Engineering 2017 (2017): 1–13. http://dx.doi.org/10.1155/2017/3634258.

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We introduce the notions of hesitant anti-fuzzy soft set (subalgebras and ideals) and provide relation between them. However, we study new types of hesitant anti-fuzzy soft ideals (implicative, positive implicative, and commutative). Also, we stated and proved some theorems which determine the relationship between these notions.
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Alshehri, Noura, and Halimah Alshehri. "Corrigendum to “Hesitant Anti-Fuzzy Soft Set in BCK-Algebras”." Mathematical Problems in Engineering 2017 (2017): 1. http://dx.doi.org/10.1155/2017/6523784.

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HUANG, CHONGFU. "INFORMATION DIFFUSION TECHNIQUES AND SMALL-SAMPLE PROBLEM." International Journal of Information Technology & Decision Making 01, no. 02 (2002): 229–49. http://dx.doi.org/10.1142/s0219622002000142.

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Strong interests in the small-sample problem have been given towards for establishing several information diffusion techniques for pattern recognition. In this paper, we review and formalize three techniques: the soft histogram, the self-study discrete regression, and the interior-outer-set model. To promote the development of this area, in this paper we suggest two open topics: the anti-accuracy principle and the digital image compression technique based on the fuzzy if-then rules extracted by using information matrix technique.
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Kharola, Ashwani, and Pravin P. Patil. "A Comparative Study for Position Regulation and Anti-Swing Control of Highly Non-Linear Double Inverted Pendulum (DIP) System Using Different Soft Com." International Journal of Fuzzy System Applications 6, no. 2 (2017): 59–81. http://dx.doi.org/10.4018/ijfsa.2017040104.

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This paper presents a comparative analysis for stabilization and control of highly non-linear, complex and multi-variable Double Inverted Pendulum on cart. A Matlab-Simulink model of DIP has been built using governing mathematical equations. The objective is to control both the pendulums at vertical position while cart is free to move in horizontal direction. The control of DIP was achieved using three different soft-computing techniques namely Fuzzy logic reasoning, Neural networks (NN's) and Adaptive neuro fuzzy inference system (ANFIS). The results show that the ANFIS controller is more eff
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Adam, Fatma, and Nasruddin Hassan. "Q-fuzzy soft set." Applied Mathematical Sciences 8 (2014): 8689–95. http://dx.doi.org/10.12988/ams.2014.410865.

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Alkhazaleh, Shawkat, Abdul Razak Salleh, and Nasruddin Hassan. "Possibility Fuzzy Soft Set." Advances in Decision Sciences 2011 (September 8, 2011): 1–18. http://dx.doi.org/10.1155/2011/479756.

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We introduce the concept of possibility fuzzy soft set and its operation and study some of its properties. We give applications of this theory in solving a decision-making problem. We also introduce a similarity measure of two possibility fuzzy soft sets and discuss their application in a medical diagnosis problem.
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G., Muhiuddin A. Mahboob and M. Balamurugan. "Hesitant anti-intuitionistic fuzzy soft commutative ideals of BCK-algebras." Annals of Communications in Mathematics 3, no. 2 (2020): 158–70. https://doi.org/10.5281/zenodo.10044331.

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In this paper, the notions of hesitant anti-intuitionistic fuzzy soft BCI-commutative ideals of BCI-algebras and hesitant anti-intuitionistic fuzzy soft subcommutative ideals of BCK-algebras are introduced and their related properties are investigate. Relations between a hesitant anti-intuitionistic fuzzy soft ideals and hesitant anti-intuitionistic fuzzy soft BCI-commutative ideals are discussed. Conditions for a hesitant anti-intuitionistic fuzzy soft ideal to be a hesitant anti-intuitionistic fuzzy soft BCIcommutative ideal are provided. Finally, it is proved that a hesitant anti-intuitioni
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Borah, Manash, and Bipan Hazarika. "Soft ideal topological space and mixed fuzzy soft ideal topological space." Boletim da Sociedade Paranaense de Matemática 37, no. 1 (2017): 141–51. http://dx.doi.org/10.5269/bspm.v37i1.29647.

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In this paper we introduce fuzzy soft ideal and mixed fuzzy soft ideal topological spaces and some properties of this space. Also we introduce fuzzy soft $I$-open set, fuzzy soft $\alpha$-$I$-open set, fuzzy soft pre-$I$-open set, fuzzy soft semi-$I$-open set and fuzzy soft $\beta$-$I$-open set and discuss some of their properties.
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Rozprawy doktorskie na temat "Anti fuzzy soft set"

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Abbas, Mujahid. "Soft Set Theory: Generalizations, Fixed Point Theorems, and Applications." Doctoral thesis, Universitat Politècnica de València, 2015. http://hdl.handle.net/10251/48470.

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Mathematical models have extensively been used in problems related to engineering, computer sciences, economics, social, natural and medical sciences etc. It has become very common to use mathematical tools to solve, study the behavior and different aspects of a system and its different subsystems. Because of various uncertainties arising in real world situations, methods of classical mathematics may not be successfully applied to solve them. Thus, new mathematical theories such as probability theory and fuzzy set theory have been introduced by mathematicians and computer scientists to
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Saeed, Mehreen. "Soft AI methods and visual speech recognition." Thesis, University of Bristol, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.299270.

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Hill, Carla. "Mass assignments for inductive logic programming." Thesis, University of Bristol, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.325748.

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Chung, Hsiang-Yu, and 鍾翔宇. "Integrating soft set and fuzzy linguistic model to evaluate maintenance and operating decisions of training simulation systems." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/rgeaqa.

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博士<br>國立交通大學<br>工業工程與管理系所<br>106<br>The advancement of high technologies and the arrival of the information age have caused changes to the modern warfare; more technologically exquisite weapons are designed, increasing in cost and complexity. Training maneuvers with live ammunition are expensive and are prone to accidental casualties. Thus, the military forces of many countries have replaced partially real training drills with training simulation systems to achieve combat readiness. Although considerable types of training simulation systems are used in military settings, the training effective
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Hsu, Hua-Chi, and 徐華騏. "Stream flood forecasting using a soft-computing approach (neural networks and fuzzy set theory)--A case study of Wu-Tu basin." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/93037991270334908944.

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碩士<br>清雲科技大學<br>土木與防災研究所<br>96<br>The weather in Taiwan is high precipitation, and the heavy rainfall is generally in the typhoon season. Therefore, how to forecast stream flow is a worthy research subject for hydraulic engineers to avoid damages by typhoon. In this study, a flow-trend index based on the fuzzy set theory is developed for neural network models to systematically establish the relationship between the rainfall history and stream flow. The precipitation records of Wu-Tu including 18 typhoon events from 1973 to 2001 are used to train and test the stream flow forecasting model. The
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Shu, Hua-Chi, and 徐華騏. "Stream flood forecasting using a soft-computing approach (neural networks and fuzzy set theory)--A case study of Wu-Tu basin." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/08429472489760610812.

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碩士<br>清雲科技大學<br>空間資訊與防災科技研究所<br>96<br>The weather in Taiwan is high precipitation, and the heavy rainfall is generally in the typhoon season. Therefore, how to forecast stream flow is a worthy research subject for hydraulic engineers to avoid damages by typhoon. In this study, a flow-trend index based on the fuzzy set theory is developed for neural network models to systematically establish the relationship between the rainfall history and stream flow. The precipitation records of Wu-Tu including 18 typhoon events from 1973 to 2001 are used to train and test the stream flow forecasting model.
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Rautenbach, Helperus Ritzema. "An empirically derived system for high-speed rendering." Thesis, 2012. http://hdl.handle.net/2263/28196.

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This thesis focuses on 3D computer graphics and the continuous maximisation of rendering quality and performance. Its main focus is the critical analysis of numerous real-time rendering algorithms and the construction of an empirically derived system for the high-speed rendering of shader-based special effects, lighting effects, shadows, reflection and refraction, post-processing effects and the processing of physics. This critical analysis allows us to assess the relationship between rendering quality and performance. It also allows for the isolation of key algorithmic weaknesses and possible
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Książki na temat "Anti fuzzy soft set"

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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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1964-, Feuring Thomas, ed. Fuzzy and neural: Interactions anmd applications. Physica-Verlag, 1999.

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North American Fuzzy Information Processing Society. Conference. 18th International Conference of the North American Fuzzy Information Processing Society--NAFIPS: June 10-12, 199 [sic], New York, New York, U.S.A. Institute of Electrical and Electronics Engineers, 1999.

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1944-, Nguyen Hung T., ed. A first course in fuzzy and neural control. Chapman & Hall/CRC Press, 2003.

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1944-, Nguyen Hung T., ed. A first course in fuzzy and neural control. Chapman & Hall/CRC Press, 2003.

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Huang, Chongfu. Towards efficient fuzzy information processing: Using the principle of information diffusion. Physica-Verlag, 2002.

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Halder, S. Bhattacharya. Rough Sets, Fuzzy Sets and Soft Computing. Narosa Publishing House, 2015.

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An Introduction to Fuzzy Logic and Fuzzy Sets (Advances in Soft Computing). Physica-Verlag Heidelberg, 2002.

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Orlowska, Ewa. Incomplete Information: Rough Set Analysis. Physica-Verlag, 2013.

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Similarity and Compatibility in Fuzzy Set Theory Studies in Fuzziness and Soft Computing. Physica-Verlag HD, 2010.

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Części książek na temat "Anti fuzzy soft set"

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Shanahan, James G. "Fuzzy Set Theory." In Soft Computing for Knowledge Discovery. Springer US, 2000. http://dx.doi.org/10.1007/978-1-4615-4335-0_3.

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Chakrabarty, Kankana. "Roughness Indicator Fuzzy Set." In Developments in Soft Computing. Physica-Verlag HD, 2001. http://dx.doi.org/10.1007/978-3-7908-1829-1_7.

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Al-Hijjawi, Sumyyah, and Shawkat Alkhazaleh. "Effective Time Fuzzy Soft Set." In Springer Proceedings in Mathematics & Statistics. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-4876-1_9.

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Jayanthiladevi, A., L. Devi, R. Kannadasan, Ved P. Mishra, Piyush Mishra, and A. Mohamed Uvaze Ahamed. "Implying Fuzzy Set for Computing Agricultural Vulnerability." In Applied Soft Computing. Apple Academic Press, 2021. http://dx.doi.org/10.1201/9781003186885-14.

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Raha, Swapan, and Sanaul Hossain. "Fuzzy Set in Default Reasoning." In Advances in Soft Computing — AFSS 2002. Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-45631-7_4.

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Yao, Dengbao, Cuicui Wang, Junjun Mao, and Yanping Zhang. "Granularity Analysis of Fuzzy Soft Set." In Rough Sets and Knowledge Technology. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-31900-6_50.

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Zhao, Xiao-yan, Jian-guo Xu, and Jing-gui Gao. "An Extension of Rough Fuzzy Set on the Fuzzy Approximation Space." In Advances in Soft Computing. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03664-4_68.

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Niskanen, Vesa A. "Brief Introduction to Fuzzy Set Theory." In Soft Computing Methods in Human Sciences. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-36421-4_2.

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Muralikrishna, P., P. Hemavathi, and K. Palanivel. "Stress Level Analysis Using Bipolar Picture Fuzzy Set." In Soft Computing and Optimization. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-6406-0_26.

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Tas, Nihal, and Elif Kaplan. "Unmanned Aerial Vehicle Application to Fuzzy Soft Set Based Decision-Making." In Soft Computing. CRC Press, 2024. http://dx.doi.org/10.1201/9781003466260-4.

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Streszczenia konferencji na temat "Anti fuzzy soft set"

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Yao, Bing-xue, Jin-liang Liu, and Rui-xia Yan. "Fuzzy Soft Set and Soft Fuzzy Set." In 2008 Fourth International Conference on Natural Computation. IEEE, 2008. http://dx.doi.org/10.1109/icnc.2008.137.

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Fu Li and Wang Pingshu. "Comparison of the concepts of soft set, fuzzy soft set and L-fuzzy set." In 2011 Eighth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2011). IEEE, 2011. http://dx.doi.org/10.1109/fskd.2011.6019607.

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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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YANG, HAI-LONG. "ON FUZZY SOFT SET RELATIONS." In Proceedings of the QL&SC 2012. WORLD SCIENTIFIC, 2012. http://dx.doi.org/10.1142/9789814401531_0083.

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Jiang, Yi, Hailiang Zhao, and Junxuan He. "The relation between fuzzy soft set and integrative fuzzy set." In 2017 12th International Conference on Intelligent Systems and Knowledge Engineering (ISKE). IEEE, 2017. http://dx.doi.org/10.1109/iske.2017.8258761.

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Khan, Muhammad Sadiq, and Tutut Herawan. "Substantial Differences between Fuzzy Set and Soft Set Theories." In 3rd International Conference of Computer, Environment, Agriculture, Social Science, Health Science, Engineering and Technology. SCITEPRESS - Science and Technology Publications, 2018. http://dx.doi.org/10.5220/0010043403840387.

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Yanto, Iwan Tri Riyadi, Edi Sutoyo, Ani Apriani, and Okki Verdiansyah. "Fuzzy Soft Set for Rock Igneous Clasification." In 2018 International Symposium on Advanced Intelligent Informatics (SAIN). IEEE, 2018. http://dx.doi.org/10.1109/sain.2018.8673383.

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Ma, Xir;ao, and Guoyin Wang. "An extended soft set model: Type-2 fuzzy soft sets." In 2011 IEEE International Conference on Cloud Computing and Intelligence Systems (CCIS). IEEE, 2011. http://dx.doi.org/10.1109/ccis.2011.6045046.

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Medynskaya, M. K. "Fuzzy set theory. The concept of fuzzy sets." In 2015 XVIII International Conference on Soft Computing and Measurements (SCM). IEEE, 2015. http://dx.doi.org/10.1109/scm.2015.7190402.

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Thota, Nageswara Rao, T. Rishika Devi, Muneera Abdul, E. S. Rama Ravi Kumar, and Jonnalagadda Venkateswara Rao. "Algorithms on dominating set of fuzzy and anti-fuzzy graphs." In 2ND INTERNATIONAL CONFERENCE ON ADVANCED INFORMATION SCIENTIFIC DEVELOPMENT (ICAISD) 2021: Innovating Scientific Learning for Deep Communication. AIP Publishing, 2023. http://dx.doi.org/10.1063/5.0143096.

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