Relevant bibliographies by topics / Fuzzy selection

Contents

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

Academic literature on the topic 'Fuzzy selection'

Author: Grafiati
Published: 3 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "Fuzzy selection"

1

Ramze Rezaee, M., B. Goedhart, B. P. F. Lelieveldt, and J. H. C. Reiber. "Fuzzy feature selection." Pattern Recognition 32, no. 12 (1999): 2011–19. http://dx.doi.org/10.1016/s0031-3203(99)00005-9.

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K, Afsath, and Linjesh Sebastian. "Fuzzy MATLAB Approach to Construction Contractor Selection." International Journal of Scientific Engineering and Research 4, no. 8 (2016): 45–49. https://doi.org/10.70729/ijser15919.

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Roh, Seok-Beom, Yong Soo Kim, and Tae-Chon Ahn. "Feature Selection of Fuzzy Pattern Classifier by using Fuzzy Mapping." Journal of Korean Institute of Intelligent Systems 24, no. 6 (2014): 646–50. http://dx.doi.org/10.5391/jkiis.2014.24.6.646.

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Tafazzoli, Mohammadsoroush, Ayoub Hazrati, Kishor Shrestha, and Krishna Kisi. "Enhancing Contractor Selection through Fuzzy TOPSIS and Fuzzy SAW Techniques." Buildings 14, no. 6 (2024): 1861. http://dx.doi.org/10.3390/buildings14061861.

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Contractors play an integral role in construction projects, and their qualifications directly impact various aspects of a project’s success. The unbiased selection of contractors is a challenge in the construction industry worldwide, particularly in public projects where impartiality in the final selection is essential. Numerous factors must be considered when evaluating contractors, making the selection process challenging for the human brain. This paper introduces and compares two methods for assessing contractor prequalification by applying the fuzzy theory. The idea is to facilitate using
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Beseiso, Majdi, and Gulshan Kumar. "A fuzzy computational approach for selecting interdependent projects using prioritized criteria." Journal of Intelligent & Fuzzy Systems 40, no. 6 (2021): 11341–54. http://dx.doi.org/10.3233/jifs-202506.

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This paper presents a fuzzy computational approach for selecting project portfolio by combining fuzzy logic, Quality Function Deployment (QFD) and Genetic algorithm (GA) approaches with the consideration of prioritized selection criteria as per objectives of the organization to make decisions effectively with incomplete and ambiguous information to help in portfolio selection. This approach addresses the issues of the uncertainty of experts in selecting projects, prioritizing criteria before initiating project selection process and evaluating the number of interdependent projects for their max
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Ghaffari, S., and MR Homaeinezhad. "Autonomous path following by fuzzy adaptive curvature-based point selection algorithm for four-wheel-steering car-like mobile robot." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 232, no. 15 (2017): 2655–65. http://dx.doi.org/10.1177/0954406217723363.

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Autonomous path following in mobile robots with nonholonomic constraints can be divided into two problems: first, selecting the tracking point, and then designing an appropriate controller to follow the selected point. When selecting tracking point, considering the kinematics of the robot as well as characteristics of the desired path is of considerable importance. For these purposes, a curvature-based point selection algorithm is first proposed for the car-like mobile robot with independent steering mechanisms. Each instant, the proposed algorithm finds a point which enables the robot to be t
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BASHIRI, MAHDI, HOSSEIN BADRI, and JAFAR TALEBI. "A NEW FUZZY APPROACH FOR PROJECT SELECTION WITH OUTSOURCING VIEWPOINT." International Journal of Innovation and Technology Management 08, no. 02 (2011): 227–51. http://dx.doi.org/10.1142/s0219877011002349.

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Nowadays most of the organizations tend to outsource their projects. In these cases, two major problems arise: project selection and contractor selection. There are many papers which have argued separately about project and contractor selections. In this paper, we propose a decision support system for selecting projects and related contractors. The proposed approach contains three phases including contractors' prequalification (Phase 1), computing success coefficient of each contractor in projects using fuzzy TOPSIS method (Phase 2), and selecting projects and assigning each project to the mos
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Vasanti, G., and B. Venkata Rao. "Fuzzy Modelling for Selection of Overall Best Performer." DJ Journal of Engineering and Applied Mathematics 2, no. 1 (2016): 1–6. http://dx.doi.org/10.18831/djmaths.org/2016011001.

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Tinda Mangongo, Yves, Christian Tassak Deffo, Louis Aime Fono, Joseph Desire Kyemba Bukweli, and Justin Dupar Busili Kampempe. "Portfolio Selection with fuzzy return without target values." International Journal of Scientific Engineering and Research 6, no. 1 (2018): 18–29. https://doi.org/10.70729/ijser172072.

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Petrovic‐Lazarevic, Sonja. "Personnel Selection Fuzzy Model." International Transactions in Operational Research 8, no. 1 (2001): 89–105. http://dx.doi.org/10.1111/1475-3995.00008.

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Dissertations / Theses on the topic "Fuzzy selection"

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Talwanga, Matiki. "Counting of finite fuzzy subsets with applications to fuzzy recognition and selection strategies." Thesis, Rhodes University, 2015. http://hdl.handle.net/10962/d1018186.

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The counting of fuzzy subsets of a finite set is of great interest in both practical and theoretical contexts in Mathematics. We have used some counting techniques such as the principle of Inclusion-Exclusion and the Mõbius Inversion to enumerate the fuzzy subsets of a finite set satisfying different conditions. These two techniques are interdependent with the M¨obius inversion generalizing the principle of Inclusion-Exclusion. The enumeration is carried out each time we redefine new conditions on the set. In this study one of our aims is the recognition and identification of fuzzy subsets wit
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Jensen, Richard. "Combining rough and fuzzy sets for feature selection." Thesis, University of Edinburgh, 2004. http://hdl.handle.net/1842/24740.

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Feature selection (FS) refers to the problem of selecting those input attributes that are most predictive of a given outcome; a problem encountered in many areas such as machine learning, pattern recognition and signal processing. Unlike other dimensionality reduction methods, feature selectors preserve the original meaning of the features after reduction. This has found application in tasks that involve datasets containing huge numbers of features (in the order of tens of thousands), which would be impossible to process further. Recent examples include text processing and web content classifi
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Chiang, Kenneth H. "Fuzzy logic mode selection for a recuperative turboshaft engine." Thesis, Massachusetts Institute of Technology, 1992. http://hdl.handle.net/1721.1/74846.

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Patterson, Frank H. "Fuzzy framework for robust architecture identification in concept selection." Diss., Georgia Institute of Technology, 2015. http://hdl.handle.net/1853/54413.

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An evolving set of modern physics-based, multi-disciplinary conceptual design methods seek to explore the feasibility of a new generation of systems, with new capabilities, capable of missions that conventional vehicles cannot be empirically redesigned to perform. These methods provide a more complete understanding of a concept's design space, forecasting the feasibility of uncertain systems, but are often computationally expensive and time consuming to prepare. This trend creates a unique and critical need to identify a manageable number of capable concept alternatives early in the design pro
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Canbolat, Mustafa Serdal. "Supplier selection in e-procurement using fuzzy analytic hierarchy process." Thesis, University of Ottawa (Canada), 2005. http://hdl.handle.net/10393/26862.

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In today's competitive business world, it is important for decision makers to have access to decision support tools. One of these decision making areas is supplier selection in e-procurement. Supplier selection is a multi-criteria decision making process that deals with the optimization of conflicting objectives. If it is not supported by a system, this would be a complex and time consuming process. In spite of the fact that the term "supplier selection" is commonly used in the literature, few efforts have been dedicated to develop a system based on any of these methods. In this thesis, we pro
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Wong, Yin-king. "Integrating environmental criteria into the supplier selection process." Hong Kong : University of Hong Kong, 2000. http://sunzi.lib.hku.hk/hkuto/record.jsp?B22505659.

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Zábranská, Barbora. "Aplikace fuzzy logiky pro vyhodnocení dodavatelů firmy." Master's thesis, Vysoké učení technické v Brně. Fakulta podnikatelská, 2021. http://www.nusl.cz/ntk/nusl-444551.

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This Master’s thesis presents a design of models which are intended for evaluation of Braintrast’s suppliers. Based on the evaluation, there is selected the most optimal supplier according to parameters. The model uses fuzzy logic theory to support decision making. The solution is designed in programs MATLAB and MS Excel.
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Adewunmi, Adrian. "Selection of simulation variance reduction techniques through a fuzzy expert system." Thesis, University of Nottingham, 2010. http://eprints.nottingham.ac.uk/11260/.

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In this thesis, the design and development of a decision support system for the selection of a variance reduction technique for discrete event simulation studies is presented. In addition, the performance of variance reduction techniques as stand alone and combined application has been investigated. The aim of this research is to mimic the process of human decision making through an expert system and also handle the ambiguity associated with representing human expert knowledge through fuzzy logic. The result is a fuzzy expert system which was subjected to three different validation tests, the
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Rodrigues, Letícia Reis. "Seleção de fornecedores sustentáveis utilizando Fuzzy DEMATEL-ANP." Universidade Federal de São Carlos, 2017. https://repositorio.ufscar.br/handle/ufscar/9381.

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Submitted by Bruna Rodrigues (bruna92rodrigues@yahoo.com.br) on 2017-10-02T12:13:16Z No. of bitstreams: 1 DissLRR.pdf: 4462451 bytes, checksum: cd5651c604894410c0918ddc5d0b7551 (MD5)<br>Approved for entry into archive by Ronildo Prado (bco.producao.intelectual@gmail.com) on 2018-02-05T17:09:32Z (GMT) No. of bitstreams: 1 DissLRR.pdf: 4462451 bytes, checksum: cd5651c604894410c0918ddc5d0b7551 (MD5)<br>Approved for entry into archive by Ronildo Prado (bco.producao.intelectual@gmail.com) on 2018-02-05T17:09:45Z (GMT) No. of bitstreams: 1 DissLRR.pdf: 4462451 bytes, checksum: cd5651c604894410c0
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Mohamad, Khalid Y. "Restimulation candidate selection using virtual intelligence." Morgantown, W. Va. : [West Virginia University Libraries], 2000. http://etd.wvu.edu/templates/showETD.cfm?recnum=1722.

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Thesis (M.S.)--West Virginia University, 2000.<br>Title from document title page. Document formatted into pages; contains ix, 176 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 64-65).
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Books on the topic "Fuzzy selection"

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Ramaswamy, Srichander. Portfolio selection using fuzzy decision theory. Bank for International Settlements, Monetary and Economic Dept., 1998.

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Jensen, Richard. Computational intelligence and feature selection: Rough and fuzzy approaches. Wiley, 2008.

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Kitainik, Leonid. Fuzzy decision procedures with binary relations: Towards a unified theory. Kluwer Academic Publishers, 1993.

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United States. National Aeronautics and Space Administration., ed. Fuzzy sets, rough sets, and modeling evidence: Theory and application : a Dempster-Shafer based approach to compromise decision making with multiattributes applied to product selection. Research Institute for Computing and Information Systems, University of Houston-Clear Lake, 1992.

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Korvin, Andre De. Fuzzy sets, rough sets, and modeling evidence: Theory and application : a Dempster-Shafer based approach to compromise decision making with multiattributes applied to product selection. Research Institute for Computing and Information Systems, University of Houston-Clear Lake, 1992.

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Cao, Bing-Yuan. Optimal Models and Methods with Fuzzy Quantities. Springer-Verlag Berlin Heidelberg, 2010.

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Babanli, Mustafa B. Fuzzy Logic-Based Material Selection and Synthesis. World Scientific Publishing Co Pte Ltd, 2019.

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Jensen, Richard, and Qiang Shen. Computational Intelligence and Feature Selection: Rough and Fuzzy Approaches. Wiley & Sons, Incorporated, John, 2008.

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Jensen, Richard, and Qiang Shen. Computational Intelligence and Feature Selection: Rough and Fuzzy Approaches. Wiley & Sons, Incorporated, John, 2008.

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Kitainik, Leonid. Fuzzy Decision Procedures with Binary Relations: Towards a Unified Theory. Springer London, Limited, 2012.

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Book chapters on the topic "Fuzzy selection"

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Vieira, Susana M., João M. C. Sousa, and Uzay Kaymak. "Ant Feature Selection Using Fuzzy Decision Functions." In Fuzzy Optimization. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13935-2_16.

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Kahraman, Cengiz, Selcuk Cebi, and Fatih Tuysuz. "Fuzzy Location Selection Techniques." In Production Engineering and Management under Fuzziness. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-12052-7_14.

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Hasuike, Takashi, and Hiroaki Ishii. "Mathematical Approaches for Fuzzy Portfolio Selection Problems with Normal Mixture Distributions." In Fuzzy Optimization. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13935-2_19.

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Milioli, Maria Adele. "Variable Selection In Fuzzy Clustering." In Studies in Classification, Data Analysis, and Knowledge Organization. Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/978-3-642-60126-2_9.

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Gil-Aluja, Jaime. "Selection Based on Fuzzy Logic." In Applied Optimization. Springer US, 1998. http://dx.doi.org/10.1007/978-1-4613-3329-6_11.

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Wang, Shuming, and Junzo Watada. "Case Study II: Location Selection for Frozen Food Plants." In Fuzzy Stochastic Optimization. Springer US, 2012. http://dx.doi.org/10.1007/978-1-4419-9560-5_9.

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Antonelli, Michela, Pietro Ducange, and Francesco Marcelloni. "Feature Selection Based on Fuzzy Mutual Information." In Fuzzy Logic and Applications. Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-03200-9_4.

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Baláte, Jaroslav, and Bronislav Chramcov. "Selection of Mathematics Model for Daily Diagram Prediction According to Box-Jenkins Method." In Fuzzy Control. Physica-Verlag HD, 2000. http://dx.doi.org/10.1007/978-3-7908-1841-3_21.

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Peray, Kurt. "Selection of Mutual Funds." In Investing in Mutual Funds Using Fuzzy Logic. CRC Press, 2024. http://dx.doi.org/10.4324/9781003579229-5.

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Lee, Sang-Hyuk, Youn-Tae Kim, Seong-Pyo Cheon, and Sungshin Kim. "Reliable Data Selection with Fuzzy Entropy." In Fuzzy Systems and Knowledge Discovery. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11539506_27.

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Conference papers on the topic "Fuzzy selection"

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Loor, Marcelo, and Guy De Tré. "Automatic Context Selection in Explainable Support Vector Machine Classification." In 2024 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2024. http://dx.doi.org/10.1109/fuzz-ieee60900.2024.10611823.

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Mandal, Pawan Kumar, Sourabh Garg, and Xiao-Zhi Gao. "Modeling Mean-Semivariance Portfolio Selection Problems Using Coherent Trapezoidal Fuzzy Numbers." In 2024 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2024. http://dx.doi.org/10.1109/fuzz-ieee60900.2024.10612081.

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Jensen, Richard, and Chris Cornelis. "Fuzzy-rough instance selection." In 2010 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2010. http://dx.doi.org/10.1109/fuzzy.2010.5584791.

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Diao, Ren, Shangzhu Jin, and Qiang Shen. "Antecedent selection in fuzzy rule interpolation using feature selection techniques." In 2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2014. http://dx.doi.org/10.1109/fuzz-ieee.2014.6891818.

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Diao, Ren, and Qiang Shen. "Fuzzy-rough classifier ensemble selection." In 2011 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2011. http://dx.doi.org/10.1109/fuzzy.2011.6007400.

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Vieira, S. M., J. M. C. Sousa, and T. A. Runkler. "Fuzzy classification in ant feature selection." In 2008 IEEE 16th International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2008. http://dx.doi.org/10.1109/fuzzy.2008.4630609.

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Lin, Pei-Chun, Junzo Watada, and Berlin Wu. "Statistic test on fuzzy portfolio selection model." In 2011 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2011. http://dx.doi.org/10.1109/fuzzy.2011.6007343.

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Diao, Ren, Neil Mac Parthalain, and Qiang Shen. "Dynamic feature selection with fuzzy-rough sets." In 2013 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2013. http://dx.doi.org/10.1109/fuzz-ieee.2013.6622410.

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Jensen, Richard, Neil Mac Parthalain, and Chris Cornells. "Feature grouping-based fuzzy-rough feature selection." In 2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2014. http://dx.doi.org/10.1109/fuzz-ieee.2014.6891692.

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Vluymans, Sarah, Yvan Saeys, Chris Cornelis, Ankur Teredesai, and Martine De Cock. "Fuzzy Rough Set Prototype Selection for Regression." In 2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2015. http://dx.doi.org/10.1109/fuzz-ieee.2015.7337926.

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Reports on the topic "Fuzzy selection"

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Osipov, G. S. Fuzzy elective selection model. Редакция журнала «ОПиПМ», 2019. http://dx.doi.org/10.18411/oppm-2019-26-1.

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Arai, Yoichi, and Hidehiko Ichimura. Optimal bandwidth selection for the fuzzy regression discontinuity estimator. Institute for Fiscal Studies, 2015. http://dx.doi.org/10.1920/wp.cem.2015.4915.

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Buyak, Bogdan B., Ivan M. Tsidylo, Victor I. Repskyi, and Vitaliy P. Lyalyuk. Stages of Conceptualization and Formalization in the Design of the Model of the Neuro-Fuzzy Expert System of Professional Selection of Pupils. [б. в.], 2018. http://dx.doi.org/10.31812/123456789/2669.

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The article describes the problem of designing a neuro-fuzzy expert system of professional selection at the stages of conceptualization and formalization, which involves the definition of concepts, relationships and management mechanisms necessary to describe the solution of problems in the chosen subject field. The structural model of the decision making system for determining the professional selection of students for training in IT specialties is substantiated. Three subsystems are proposed as structural components for studying: psychological peculiarities, personal qualities, factual knowl
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Holman, Allyson, William Gilbraith, and Holly Flynn. Applications of fuzzy logic and best-worst method for tritium sensor selection. Office of Scientific and Technical Information (OSTI), 2024. http://dx.doi.org/10.2172/2406473.

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Chang, Shing I., E. S. Lee, and Steven R. Hanna. A Comparative Study of Multivariate Analysis for Selection and Classification Using Fuzzy Measures and Reasoning. Defense Technical Information Center, 2001. http://dx.doi.org/10.21236/ada397639.

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Tsidylo, Ivan M., Serhiy O. Semerikov, Tetiana I. Gargula, Hanna V. Solonetska, Yaroslav P. Zamora, and Andrey V. Pikilnyak. Simulation of intellectual system for evaluation of multilevel test tasks on the basis of fuzzy logic. CEUR Workshop Proceedings, 2021. http://dx.doi.org/10.31812/123456789/4370.

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The article describes the stages of modeling an intelligent system for evaluating multilevel test tasks based on fuzzy logic in the MATLAB application package, namely the Fuzzy Logic Toolbox. The analysis of existing approaches to fuzzy assessment of test methods, their advantages and disadvantages is given. The considered methods for assessing students are presented in the general case by two methods: using fuzzy sets and corresponding membership functions; fuzzy estimation method and generalized fuzzy estimation method. In the present work, the Sugeno production model is used as the closest
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Yusrina, Asri, Ulfah Alifia, Shintia Revina, Rezanti Putri Pramana, and Luhur Bima. Is the Game Worth the Candle? Examining the Effectiveness of Initial Teacher Education in Indonesia. Research on Improving Systems of Education (RISE), 2022. http://dx.doi.org/10.35489/bsg-rise-wp_2022/106.

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An impactful teacher education programme equips teachers with knowledge and skills to improve their effectiveness. Empirical findings on the effectiveness of teacher preparation programmes show that the accountability of institutions and teachers should not only be based on the knowledge or skills produced but also on student learning. Our study aims to evaluate the effectiveness of a pre-service teacher education programme in Indonesia, known as Pendidikan Profesi Guru Prajabatan or PPG. PPG is a one-year full-time programme in addition to four years of undergraduate teacher education (Bachel
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Kryzhanivs'kyi, Evstakhii, Liliana Horal, Iryna Perevozova, Vira Shyiko, Nataliia Mykytiuk, and Maria Berlous. Fuzzy cluster analysis of indicators for assessing the potential of recreational forest use. [б. в.], 2020. http://dx.doi.org/10.31812/123456789/4470.

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Cluster analysis of the efficiency of the recreational forest use of the region by separate components of the recreational forest use potential is provided in the article. The main stages of the cluster analysis of the recreational forest use level based on the predetermined components were determined. Among the agglomerative methods of cluster analysis, intended for grouping and combining the objects of study, it is common to distinguish the three most common types: the hierarchical method or the method of tree clustering; the K-means Clustering Method and the two-step aggregation method. For
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Karam, Sofia, Morteza Nagahi, Vidanelage Dayarathna, Junfeng Ma, Raed Jaradat, and Michael Hamilton. Integrating systems thinking skills with multi-criteria decision-making technology to recruit employee candidates. Engineer Research and Development Center (U.S.), 2021. http://dx.doi.org/10.21079/11681/41026.

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The emergence of modern complex systems is often exacerbated by a proliferation of information and complication of technologies. Because current complex systems challenges can limit an organization's ability to efficiently handle socio-technical systems, it is essential to provide methods and techniques that count on individuals' systems skills. When selecting future employees, companies must constantly refresh their recruitment methods in order to find capable candidates with the required level of systemic skills who are better fit for their organization's requirements and objectives. The pur
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Householder, Allen D., and Jonathan M. Foote. Probability-Based Parameter Selection for Black-Box Fuzz Testing. Defense Technical Information Center, 2012. http://dx.doi.org/10.21236/ada610472.

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