Relevant bibliographies by topics / Fuzzy logic regression analysis

Contents

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

Academic literature on the topic 'Fuzzy logic regression analysis'

Author: Grafiati
Published: 2 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "Fuzzy logic regression analysis"

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Xizhao, Wang, and Ha Minghu. "Fuzzy linear regression analysis." Fuzzy Sets and Systems 51, no. 2 (1992): 179–88. http://dx.doi.org/10.1016/0165-0114(92)90190-f.

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Pedrycz, Witold. "From fuzzy data analysis and fuzzy regression to granular fuzzy data analysis." Fuzzy Sets and Systems 274 (September 2015): 12–17. http://dx.doi.org/10.1016/j.fss.2014.04.017.

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Shi-quan, Chen. "Analysis for multiple fuzzy regression." Fuzzy Sets and Systems 25, no. 1 (1988): 59–65. http://dx.doi.org/10.1016/0165-0114(88)90099-1.

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Zhen-Yuan, Wang, and Li Shou-Mei. "Fuzzy linear regression analysis of fuzzy valued variables." Fuzzy Sets and Systems 36, no. 1 (1990): 125–36. http://dx.doi.org/10.1016/0165-0114(90)90086-l.

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Ishibuchi, Hisao, and Hideo Tanaka. "Fuzzy regression analysis using neural networks." Fuzzy Sets and Systems 50, no. 3 (1992): 257–65. http://dx.doi.org/10.1016/0165-0114(92)90224-r.

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Nassif, Ali Bou, Mohammad Azzeh, Ali Idri, and Alain Abran. "Software Development Effort Estimation Using Regression Fuzzy Models." Computational Intelligence and Neuroscience 2019 (February 20, 2019): 1–17. http://dx.doi.org/10.1155/2019/8367214.

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Software effort estimation plays a critical role in project management. Erroneous results may lead to overestimating or underestimating effort, which can have catastrophic consequences on project resources. Machine-learning techniques are increasingly popular in the field. Fuzzy logic models, in particular, are widely used to deal with imprecise and inaccurate data. The main goal of this research was to design and compare three different fuzzy logic models for predicting software estimation effort: Mamdani, Sugeno with constant output, and Sugeno with linear output. To assist in the design of
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Evangelides, Chris, George Arampatzis, and Christos Tzimopoulos. "Fuzzy logic regression analysis for groundwater quality characteristics." DESALINATION AND WATER TREATMENT 95 (2017): 45–50. http://dx.doi.org/10.5004/dwt.2017.21525.

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Ahmed, Reham A., Muhammad Ammar Shafi, Nor Faezan Abdul Rashid, Suraya Othman, Rozin Badeel, and Banan Badeel Abdal. "Fuzzy logic and linear regression modelling in breast cancer detection: A review." Edelweiss Applied Science and Technology 9, no. 4 (2025): 1101–9. https://doi.org/10.55214/25768484.v9i4.6182.

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The research investigates the effectiveness of breast cancer detection using linear regression models and fuzzy logic approaches, together with an analysis of their medical diagnostic applications and their associated limitations. The research evaluates performance results by analyzing both methods through a review of current studies, where linear regression demonstrates ease of interpretation alongside simplicity, but fuzzy logic shows strength in dealing with uncertainty along with nonlinear relationships. The research shows that while linear regression works simply, it fails to handle the c
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Doz, Daniel, Darjo Felda, and Mara Cotič. "Demographic Factors Affecting Fuzzy Grading: A Hierarchical Linear Regression Analysis." Mathematics 11, no. 6 (2023): 1488. http://dx.doi.org/10.3390/math11061488.

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Several factors affect students’ mathematics grades and standardized test results. These include the gender of the students, their socio-economic status, the type of school they attend, and their geographic region. In this work, we analyze which of these factors affect assessments of students based on fuzzy logic, using a sample of 29,371 Italian high school students from the 2018/19 academic year. To combine grades assigned by teachers and the students’ results in the INVALSI standardized tests, a hybrid grade was created using fuzzy logic, since it is the most suitable method for analyzing q
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Doz, Daniel, Mara Cotič, and Darjo Felda. "Random Forest Regression in Predicting Students’ Achievements and Fuzzy Grades." Mathematics 11, no. 19 (2023): 4129. http://dx.doi.org/10.3390/math11194129.

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The use of fuzzy logic to assess students’ knowledge is not a completely new concept. However, despite dealing with a large quantity of data, traditional statistical methods have typically been the preferred approach. Many studies have argued that machine learning methods could offer a viable alternative for analyzing big data. Therefore, this study presents findings from a Random Forest (RF) regression analysis to understand the influence of demographic factors on students’ achievements, i.e., teacher-given grades, students’ outcomes on the national assessment, and fuzzy grades, which were ob
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Dissertations / Theses on the topic "Fuzzy logic regression analysis"

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Beranová, Michaela. "Aspekty zásob v maloobchodě: modely přirozených úbytků zásob a ztratného." Doctoral thesis, Vysoké učení technické v Brně. Fakulta podnikatelská, 2009. http://www.nusl.cz/ntk/nusl-233730.

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The dissertation thesis deals with the problem of relevant volume of natural shrinkage and accidental losses of retail stock quota calculation. In frame of the dissertation thesis, factors affecting an extent of accidental shortage of inventories in retail business are investigated here. Then, possible approaches to a calculation of relevant volume of such a quota are recognized as well. By its scope, the dissertation thesis reacts on a problem that exists within the income taxes law since 1995, but any conceptual solution of this problem is still missing. This current problem that is felt esp
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Reynolds, Robert. "Gene Expression Data Analysis Using Fuzzy Logic." Fogler Library, University of Maine, 2001. http://www.library.umaine.edu/theses/pdf/REynoldsR2001.pdf.

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Chen, Carla Chia-Ming. "Bayesian methodology for genetics of complex diseases." Thesis, Queensland University of Technology, 2010. https://eprints.qut.edu.au/43357/1/Carla_Chen_Thesis.pdf.

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Genetic research of complex diseases is a challenging, but exciting, area of research. The early development of the research was limited, however, until the completion of the Human Genome and HapMap projects, along with the reduction in the cost of genotyping, which paves the way for understanding the genetic composition of complex diseases. In this thesis, we focus on the statistical methods for two aspects of genetic research: phenotype definition for diseases with complex etiology and methods for identifying potentially associated Single Nucleotide Polymorphisms (SNPs) and SNP-SNP interacti
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Touzé, Patrick A. "Applications of fuzzy logic to mechanical reliability analysis." Thesis, Virginia Tech, 1993. http://hdl.handle.net/10919/41583.

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Touz'e, Patrick A. "Applications of fuzzy logic to mechanical reliability analysis /." This resource online, 1993. http://scholar.lib.vt.edu/theses/available/etd-03142009-040345/.

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Dingerson, Lynne M. "Predicting Future Shoreline Condition Based on Land Use Trends, Logistic Regression, and Fuzzy Logic." W&M ScholarWorks, 2005. http://www.vims.edu/library/Theses/Dingerson05.pdf.

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Campbell, Jonathan G. "Fuzzy logic and neural network techniques in data analysis." Thesis, University of Ulster, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.342530.

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Atves, Colleen E. "A Fuzzy Logic Analysis of Sustainable Concrete Pile Foundations." The Ohio State University, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=osu1282670915.

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Nderu, Lawrence. "Fuzzy logic pattern in text and image data analysis." Electronic Thesis or Diss., Paris 8, 2015. http://www.theses.fr/2015PA080084.

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La logique floue est aujourd'hui universellement admise comme discipline ayant fait ses preuves à l'intersection des mathématiques, de l'informatique, des sciences cognitives et de l'Intelligence Artificielle. En termes formels, la logique floue est une extension de la logique classique ayant pour but de mesurer la flexibilité du raisonnement humain, et permettant la modélisation des imperfections des données, en particulier, les deux imperfections les plus fréquentes : l'imprécision et l'incertitude. En outre, la logique floue ignore le principe du tiers exclu et celui de non-contradiction.No
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Nderu, Lawrence. "Fuzzy logic pattern in text and image data analysis." Thesis, Paris 8, 2015. http://www.theses.fr/2015PA080084.

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La logique floue est aujourd'hui universellement admise comme discipline ayant fait ses preuves à l'intersection des mathématiques, de l'informatique, des sciences cognitives et de l'Intelligence Artificielle. En termes formels, la logique floue est une extension de la logique classique ayant pour but de mesurer la flexibilité du raisonnement humain, et permettant la modélisation des imperfections des données, en particulier, les deux imperfections les plus fréquentes : l'imprécision et l'incertitude. En outre, la logique floue ignore le principe du tiers exclu et celui de non-contradiction.No
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Books on the topic "Fuzzy logic regression analysis"

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1949-, Fedrizzi Mario, and Kacprzyk Janusz, eds. Fuzzy regression analysis. Omnitech Press, 1992.

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Bandemer, Hans. Fuzzy data analysis. Kluwer Academic Publishers, 1992.

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1939-, Bezdek James C., International Fuzzy Systems Association, and FIP-84 (1984 :. Kauai), eds. Analysis of fuzzy information. CRC Press, 1987.

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Ishikawa, Akira. Analysis and Evaluation of Fuzzy Systems. Springer Netherlands, 1995.

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Tucha, Thomas. Fuzzy transfer pricing world: On the analysis of transfer pricing with fuzzy logic techniques. Indian Institute of Management, 2005.

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Campbell, Jonathan George. Fuzzy logic and neural network network techniques in data analysis. The Author], 2000.

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Chernozhukov, Victor. Inference on counterfactual distributions. Massachusetts Institute of Technology, Dept. of Economics, 2008.

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Chernozhukov, Victor. Inference on counterfactual distributions. Massachusetts Institute of Technology, Dept. of Economics, 2008.

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Maria, Bojadziev, ed. Fuzzy logic for business, finance, and management. 2nd ed. World Scientific, 2007.

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Maria, Bojadziev, ed. Fuzzy logic for business, finance, and management. World Scientific, 1997.

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Book chapters on the topic "Fuzzy logic regression analysis"

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Gu, Cui-Ling, Wei Wang, and Han-Yu Wei. "Regression Analysis Model Based on Normal Fuzzy Numbers." In Quantitative Logic and Soft Computing 2016. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46206-6_46.

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Das, Monalisa, Sasmita Sahu, and Dayal R. Parhi. "Crack Detection in a Cantilever Composite Beam Using Fuzzy Logic System with Regression Analysis." In Lecture Notes in Mechanical Engineering. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-4606-6_79.

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Berthold, Michael. "Fuzzy Logic." In Intelligent Data Analysis. Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/978-3-662-03969-4_8.

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Diamond, Phil, and Hideo Tanaka. "Fuzzy Regression Analysis." In The Handbooks of Fuzzy Sets Series. Springer US, 1998. http://dx.doi.org/10.1007/978-1-4615-5645-9_11.

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Näther, Wolfgang. "Fuzzy Regression." In New Approaches in Classification and Data Analysis. Springer Berlin Heidelberg, 1994. http://dx.doi.org/10.1007/978-3-642-51175-2_54.

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Bandemer, Hans. "Fuzzy Analysis of Fuzzy Data." In Fuzzy Logic. Springer Netherlands, 1993. http://dx.doi.org/10.1007/978-94-011-2014-2_36.

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Appel, Orestes, Francisco Chiclana, Jennifer Carter, and Hamido Fujita. "Consensus in Sentiment Analysis." In Fuzzy Logic. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-66474-9_3.

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Staiano, Antonino, Roberto Tagliaferri, and Witold Pedrycz. "Linear Regression Model-Guided Clustering for Training RBF Networks for Regression Problems." In Fuzzy Logic and Applications. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/10983652_17.

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Cimino, Mario G. C. A., Beatrice Lazzerini, Francesco Marcelloni, and Witold Pedrycz. "Granular Data Regression with Neural Networks." In Fuzzy Logic and Applications. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23713-3_22.

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Diamond, Phil. "Least Squares Methods in Fuzzy Data Analysis." In Fuzzy Logic. Springer Netherlands, 1993. http://dx.doi.org/10.1007/978-94-011-2014-2_38.

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Conference papers on the topic "Fuzzy logic regression analysis"

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Murro, Rocco. "CLIMATE CHANGE AND INVESTMENTS FOR URBAN RENOVATION: ASSESSING THE FINANCIAL SUSTAINABILITY WITH THE APPLICATION OF FUZZY LOGIC PRINCIPLES TO REAL ESTATE APPRAISAL." In 24th SGEM International Multidisciplinary Scientific GeoConference 2024. STEF92 Technology, 2024. https://doi.org/10.5593/sgem2024/5.1/s21.54.

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Climate change requires significant measures to adapt existing cities to new requirements; extensive urban renewal actions are therefore necessary. In order to be financially sustainable, such investments are mainly based on real estate and market operations, also at long term. The current valuation practice would require to resort to appraisal methods based on actual, historical market data, also known as Revealed Preference Methods (RPMs), which allow to derive the preferences expressed by the actions of market. Because of the instability, complexity and uncertainty of real estate markets as
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Rahmadani, Rukmana, Andani Achmad, and Sri Mawar Said. "Quality Performance of X-Ray Aircraft Using Fuzzy Logic Based on Linear Regression." In 2024 8th International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE). IEEE, 2024. http://dx.doi.org/10.1109/icitisee63424.2024.10730570.

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Kumar, Ramesh, Mrinal Dutta, and Sachit Rathee. "Solar photovoltaic MPPT analysis using PO-fuzzy logic controller and I-C fuzzy logic controller." In 2024 International Conference on Control, Computing, Communication and Materials (ICCCCM). IEEE, 2024. https://doi.org/10.1109/iccccm61016.2024.11039985.

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Ding, Xiaoyin, Jie Shen, Yingxuan Zheng, and Lili Huang. "Research on Risk Prediction Model of Power Grid Marketing Project Based on Fuzzy Logic Regression." In 2025 IEEE International Conference on Electronics, Energy Systems and Power Engineering (EESPE). IEEE, 2025. https://doi.org/10.1109/eespe63401.2025.10987084.

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Cardoso, Samuel, Juliano Buss, Javier Gomez, et al. "Fuzzy Logic for Neonatal EEG Analysis: A Systematic Review." In 27th International Conference on Enterprise Information Systems. SCITEPRESS - Science and Technology Publications, 2025. https://doi.org/10.5220/0013362800003929.

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Prabhu, K., S. Vijayachitra, Sachin A, Subasri J, and Kavin E. "Fuzzy Logic based Temperature Control System for Boilers." In 2025 4th International Conference on Sentiment Analysis and Deep Learning (ICSADL). IEEE, 2025. https://doi.org/10.1109/icsadl65848.2025.10933188.

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Kugu, Emin, and Ozgur Koray Sahingoz. "Simulation based multiple regression analysis of fuzzy logic crowd injury model." In 2013 7th International Conference on Application of Information and Communication Technologies (AICT). IEEE, 2013. http://dx.doi.org/10.1109/icaict.2013.6722697.

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Abdullayev, P. S. "Fuzzy Statistics Approach for Aviation GTE Condition Estimation Technique." In ASME 8th Biennial Conference on Engineering Systems Design and Analysis. ASMEDC, 2006. http://dx.doi.org/10.1115/esda2006-95791.

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In this paper is shown that the probability-statistic methods application, especially at the early stage of the aviation gas turbine engine (GTE) technical condition diagnosing, when the flight information has property of the fuzzy, limitation and uncertainty is unfounded. Hence is considered the efficiency of application of new technology Soft Computing at these diagnosing stages with the using of the Fuzzy Logic and Neural Networks methods. Training with high accuracy of fuzzy multiple linear and non-linear models (fuzzy regression equations) which received on the statistical fuzzy data basi
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Pashayev, A. M., D. D. Askerov, R. A. Sadiqov, and P. S. Abdullayev. "Identification Technique of Aircraft Gas Turbine Engine’s Health." In ASME 7th Biennial Conference on Engineering Systems Design and Analysis. ASMEDC, 2004. http://dx.doi.org/10.1115/esda2004-58168.

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Groundlessness of probability-statistic methods application is shown, especially at an early stage of the aviation gas turbine engine (GTE) technical condition diagnosing, when the volume of the information has property of the fuzzy, limitation and uncertainty. Hence efficiency of application of new technology Soft Computing at these diagnosing stages with the using of the fuzzy logic and neural networks methods is considered. Training with high accuracy of multiple linear and nonlinear models (the regression equations) received on the statistical fuzzy data basis is made. For models choice is
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Abdullayev, P. S., A. M. Pashayev, R. A. Sadiqov, and A. J. Mirzoyev. "Fuzzy Condition Monitoring System for Aviation Gas Turbine Engines." In ASME 2007 International Mechanical Engineering Congress and Exposition. ASMEDC, 2007. http://dx.doi.org/10.1115/imece2007-43902.

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In this paper is shown the efficiency of the new Soft Computing technology application at different diagnosing stages of aviation gas turbine engine (GTE) technical condition with using Fuzzy Logic and Neural Networks methods, when the flight information has property of a fuzzy, limitation and uncertainty. On the fuzzy statistical data basis and with high accuracy is made the training of Fuzzy Multiple Linear and Non-Linear models (Fuzzy Regression Equations). Thus for GTE technical condition more adequate model making are analysed dynamics of skewness and kurtosis coefficients’ changes. Resea
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Reports on the topic "Fuzzy logic regression analysis"

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Liang, Qilian, and Haining Shu. Wireless Sensor Network Lifetime Analysis Using Interval Type-2 Fuzzy Logic Systems. Defense Technical Information Center, 2004. http://dx.doi.org/10.21236/ada428428.

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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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Pinzón-Puerto, Freddy, and Mauricio Villamizar-Villegas. Do Actions Speak Louder than Words? A Foreign Exchange Intervention Analysis. Banco de la República Colombia, 2023. http://dx.doi.org/10.32468/be.1223.

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We revisit an old question but with a new identification strategy, namely the difference in exchange rate effects between announced (“vocal”) and secret (“dirty”) foreign exchange intervention. Using a Regression Discontinuity Design, we exploit a rule-based intervention mechanism enacted by the Central Bank of Colombia that, under observable and deterministic conditions, triggered either the issuance of FX options or the ability to exercise them. We take the former (issuance) as central bank announcements under a sharp setting, since the rule and information that triggered the issuance of opt
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Matthews, Lisa, Guanming Wu, Robin Haw, et al. Illuminating Dark Proteins using Reactome Pathways. Reactome, 2022. http://dx.doi.org/10.3180/poster/20221027matthews.

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Diseases are often the consequence of proteins or protein complexes that are non-functional or that function improperly. An active area of research has focused on the identification of molecules that can interact with defective proteins and restore their function. While 22% percent of human proteins are estimated to be druggable, less than fifteen percent are targeted by FDA-approved drugs, and the vast majority of untargeted proteins are understudied or so-called "dark" proteins. Elucidation of the function of these dark proteins, particularly those in commonly drug-targeted protein families,
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Willson. L51756 State of the Art Intelligent Control for Large Engines. Pipeline Research Council International, Inc. (PRCI), 1996. http://dx.doi.org/10.55274/r0010423.

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Computers have become a vital part of the control of pipeline compressors and compressor stations. For many tasks, computers have helped to improve accuracy, reliability, and safety, and have reduced operating costs. Computers excel at repetitive, precise tasks that humans perform poorly - calculation, measurement, statistical analysis, control, etc. Computers are used to perform these type of precise tasks at compressor stations: engine / turbine speed control, ignition control, horsepower estimation, or control of complicated sequences of events during startup and/or shutdown. For other task
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Paule, Bernard, Flourentzos Flourentzou, Tristan de KERCHOVE d’EXAERDE, Julien BOUTILLIER, and Nicolo Ferrari. PRELUDE Roadmap for Building Renovation: set of rules for renovation actions to optimize building energy performance. Department of the Built Environment, 2023. http://dx.doi.org/10.54337/aau541614638.

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In the context of climate change and the environmental and energy constraints we face, it is essential to develop methods to encourage the implementation of efficient solutions for building renovation. One of the objectives of the European PRELUDE project [1] is to develop a "Building Renovation Roadmap"(BRR) aimed at facilitating decision-making to foster the most efficient refurbishment actions, the implementation of innovative solutions and the promotion of renewable energy sources in the renovation process of existing buildings. In this context, Estia is working on the development of infer
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