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Journal articles on the topic 'Fuzzy logic regression analysis'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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1

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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3

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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4

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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Szulczyński, Bartosz, Jacek Gębicki, and Jacek Namieśnik. "Application of fuzzy logic to determine the odour intensity of model gas mixtures using electronic nose." E3S Web of Conferences 28 (2018): 01036. http://dx.doi.org/10.1051/e3sconf/20182801036.

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The paper presents the possibility of application of fuzzy logic to determine the odour intensity of model, ternary gas mixtures (α-pinene, toluene and triethylamine) using electronic nose prototype. The results obtained using fuzzy logic algorithms were compared with the values obtained using multiple linear regression (MLR) model and sensory analysis. As the results of the studies, it was found the electronic nose prototype along with the fuzzy logic pattern recognition system can be successfully used to estimate the odour intensity of tested gas mixtures. The correctness of the results obta

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Jantunen, Erkki. "Diagnosis of tool wear based on regression analysis and fuzzy logic." IMA Journal of Management Mathematics 17, no. 1 (2006): 47–60. http://dx.doi.org/10.1093/imaman/dpi027.

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Dalkiliç, Türkan Erbay, and Seda Sağirkaya. "Parameter Prediction Based on Type-2 Fuzzy Clustering." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 26, no. 06 (2018): 877–92. http://dx.doi.org/10.1142/s0218488518500393.

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In regression analysis, the data have different distributions which requires to go beyond the classical analysis during the prediction process. In such cases, the analysis method based on fuzzy logic is preferred as alternative methods. There are couple important steps in the regression analysis based on fuzzy logic. One of them is identification of the clusters that generate the data set, the other is the degree of memberships that are determined the grades of the contributions of the data contained in these clusters. In this study, parameter prediction based on type-2 fuzzy clustering is dis

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Vicky, Ariandi, Yanto Musli, Izzaty Jamhur Annisak, Firdaus, and Afira Riandana. "Optimization artificial neural network classification analysis model diagnosis Gingivitis disease." Optimization artificial neural network classification analysis model diagnosis Gingivitis disease 29, no. 3 (2023): 1648–56. https://doi.org/10.11591/ijeecs.v29.i3.pp1648-1656.

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Gingivitis is a disease that can be caused by the buildup of bacteria and plaque caused by leftover food. This disease can attack anyone, especially children who are not aware of maintaining dental and oral health. This study aims to build and optimize the classification analysis model for the diagnosis of Gingivitis. The classification analysis model was built using the artificial neural network (ANN) method which was optimized using fuzzy logic and the multiple linear regression (MRL) method. Optimization with fuzzy aims to develop a pattern of rules in the detection. The MRL method is also

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Abhang, L. B., and M. Hameedullah. "Modeling and Analysis of Surface Roughness with Statistical and Soft Computing Approach." Advances in Science and Technology 106 (May 2021): 109–15. http://dx.doi.org/10.4028/www.scientific.net/ast.106.109.

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The objective of this study focuses on developing empirical prediction models using response regression analysis and fuzzy-logic. These models latter can be used to predict surface roughness according to technological variables. The values of surface roughness produced by these models are compared with experimental results. Experimental investigation has been carried out by using scientific composite factorial design on precision lathe machine with tungsten carbide inserts. Surface roughness measured at end of each experimental trial (three times), to get the effect of machining conditions and

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Jain, Mohit, and Satish Chand. "Modeling Connectivity of Ad Hoc Network Using Fuzzy Logic & Regression Analysis." International Journal of Computer Trends and Technology 42, no. 1 (2016): 17–25. http://dx.doi.org/10.14445/22312803/ijctt-v42p104.

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Minh, Vu Trieu, Dmitri Katushin, Maksim Antonov, and Renno Veinthal. "Regression Models and Fuzzy Logic Prediction of TBM Penetration Rate." Open Engineering 7, no. 1 (2017): 60–68. http://dx.doi.org/10.1515/eng-2017-0012.

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AbstractThis paper presents statistical analyses of rock engineering properties and the measured penetration rate of tunnel boring machine (TBM) based on the data of an actual project. The aim of this study is to analyze the influence of rock engineering properties including uniaxial compressive strength (UCS), Brazilian tensile strength (BTS), rock brittleness index (BI), the distance between planes of weakness (DPW), and the alpha angle (Alpha) between the tunnel axis and the planes of weakness on the TBM rate of penetration (ROP). Four (4) statistical regression models (two linear and two n

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Ellina, G., G. Papaschinopoulos, and B. K. Papadopoulos. "Research of fuzzy implications via fuzzy linear regression in data analysis for a fuzzy model." Journal of Computational Methods in Sciences and Engineering 20, no. 3 (2020): 879–88. http://dx.doi.org/10.3233/jcm-194015.

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The effect of eutrophication is characterized by dense algal and plant growth due to the enrichment of nutrients for photosynthesis. As a result, it often plays an important role to the formation of plants that float in the surface of a water body. When nutrients are increasing in aquatic ecosystems, the photosynthetic plants grow rapidly. As a result, the algae limit the amount of dissolved oxygen required for respiration by other species in the water. Multi-criteria analysis has helped us towards the understanding and estimation of all physical, chemical and biological functions. In this pap

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Jiang, Lisheng, and Huchang Liao. "Mixed fuzzy least absolute regression analysis with quantitative and probabilistic linguistic information." Fuzzy Sets and Systems 387 (May 2020): 35–48. http://dx.doi.org/10.1016/j.fss.2019.03.004.

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20

Ergun, Murat. "Estimation of friction coefficient of asphalt concrete road surfaces using the fuzzy logic approach." Canadian Journal of Civil Engineering 34, no. 9 (2007): 1110–18. http://dx.doi.org/10.1139/l07-045.

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Vehicle speeds have increased very dramatically with new developments in todays automotive industry, and thus the friction behavior of roads has become very important from the safety point of view. The main aim of this paper is to estimate the friction behavior of asphalt concrete road surfaces at any speed using the fuzzy logic approach. Friction is defined in the paper, and the effects of road surface characteristics, mainly macrotexture and microtexture properties, on the friction behavior of asphalt concrete road surfaces are explained. The data measured from the different asphalt concret

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Mebarkia, Mohamed, Asma Abdelmalek, Zoubir Aoulmi, Messaoud Louafi, Abdelhak Tabet, and Aissa Benselhoub. "Synergistic prediction of penetration rate in Boukhadhra mining using regression, design of experiments, fuzzy logic, and artificial neural networks." Technology audit and production reserves 4, no. 1(78) (2024): 32–42. http://dx.doi.org/10.15587/2706-5448.2024.309965.

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The comparative analysis of predictive methodologies highlights the original contribution of this study in optimizing the prediction of Rate of Penetration (ROP) in mining drilling operations. The emphasis on employing advanced Artificial Neural Networks (ANN), fuzzy logic, and linear regression models provides new insights into enhancing predictive accuracy and operational efficiency in mining practices. This study aims to quantify the effects of three pivotal drilling parameters: compressive strength, rotational pressure, and thrust pressure on the rate of penetration, a critical performance

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Alimudin, Arasy, Agus Sukoco, and Achmad Zakki Falani. "Strategy Development of Revolving Fund for Small Business Grocery Store Using Information System." IJEBD (International Journal of Entrepreneurship and Business Development) 4, no. 6 (2021): 965–76. http://dx.doi.org/10.29138/ijebd.v4i6.1574.

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Purpose: This study aims at analyzing the factors influencing the ability to return capital of MSMEs in Indonesia (case study of grocery store in Surabaya). Design/methodology/approach: The sample in this study is 171 pioneering grocery stores of cooperative Crosstabulation analysis was implemented to seek the relationship between factors of store internal resources and indicators of quality cooperatives on the ability to pay MSMEs. After that, a system architechture was designed which is then formulated in fuzzy logic to create decision-making system using MYSQL database. To compare the

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23

Mohammed, Mohammed Jasim. "Literature Review of Fuzzy Set Theory: Applications and Methodologies." Journal of Economics and Administrative Sciences 31, no. 146 (2025): 197–216. https://doi.org/10.33095/9p0kjy98.

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This paper represents a comprehensive literature survey of the research published in the Journal of Economics and Administrative Sciences (JEAS) on applications and methodologies of fuzzy set theory. The review traced how fuzzy logic has been evolving in decision-making, optimization, and modeling uncertainties in published articles such as economics, management, and engineering. The categorization of fuzzy methodologies into various domains such as fuzzy linear programming, fuzzy regression, fuzzy control systems, and fuzzy multi-criteria decision-making relies heavily on the study of existin

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Kobayashi, Takahiro, and Tetsuji Tani. "Application of Cooperative Control to Petroleum Plants Using Fuzzy Supervisory Control and Model Predictive Multi-variable Control." Journal of Advanced Computational Intelligence and Intelligent Informatics 5, no. 6 (2001): 333–37. http://dx.doi.org/10.20965/jaciii.2001.p0333.

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This paper describes hierarchical control with fuzzy supervisory control and model predictive multivariable control (MPC) in a petroleum plant. MPC is effective in time delay, interference, and handling constraints. Fuzzy logic controllers are effective for plants with large time delay and non-linearity. Our proposed hierarchical control combines their advantages. Fuzzy supervisory control, which determines set points for MPC, consists of an estimation block and a compensation block. We use a statistical model with multi-regression analysis for the estimation block to estimate parameters of pl

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Hrytsiuk, Petro, and Tetyana Babych. "MODELING OF GRAIN PRODUCTION PROFITABILITY BY FUZZY LOGIC." International Journal of New Economics and Social Sciences 4, no. 2 (2016): 42–52. http://dx.doi.org/10.5604/01.3001.0010.4538.

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Ukraine is an agrarian state. One of the most important brunches of agriculture sector is grain production. High yield of grain is a basis of Ukrainian food security. Therefore the task of developing a reliable mathematical model forecasting the grain production profitability is actually. Regression analysis and fuzzy simulation principles have been used for building of the grain production profitability depending model. The values profitability forecasting for 2015 obtained by three different methods are convergent to each other.

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Ariandi, Vicky, Musli Yanto, Annisak Izzaty Jamhur, Firdaus Firdaus, and Riandana Afira. "Optimization artificial neural network classification analysis model diagnosis Gingivitis disease." Indonesian Journal of Electrical Engineering and Computer Science 29, no. 3 (2023): 1648. http://dx.doi.org/10.11591/ijeecs.v29.i3.pp1648-1656.

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<span lang="EN-US">Gingivitis is a disease that can be caused by the buildup of bacteria and plaque caused by leftover food. This disease can attack anyone, especially children who are not aware of maintaining dental and oral health. This study aims to build and optimize the classification analysis model for the diagnosis of Gingivitis. The classification analysis model was built using the artificial neural network (ANN) method which was optimized using fuzzy logic and the multiple linear regression (MRL) method. Optimization with fuzzy aims to develop a pattern of rules in the detection

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27

Wang, Hongyi. "Research on influencing factors of financial performance of listed companies based on multiple linear regression and fuzzy logic system." Journal of Intelligent & Fuzzy Systems 40, no. 4 (2021): 8549–61. http://dx.doi.org/10.3233/jifs-189675.

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The ultimate goal of listed companies is to maximize shareholders’ wealth. With the increasingly fierce market competition, enterprise managers are constantly exploring the key indicators that have an important impact on the financial performance (FP) of enterprises, and achieve the expected FP of shareholders by improving these key indicators. On the basis of the existing enterprise performance measurement system and index research, through expert scoring to determine the secondary indicators, this paper selects 87 small and medium-sized board listed companies which officially announced the i

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ÖZDEM, Beyza, Muharrem DÜĞENCİ, and Mümtaz İPEK. "Determination of Electricity Production by Fuzzy Logic Method." Academic Platform Journal of Engineering and Smart Systems 12, no. 1 (2024): 14–20. http://dx.doi.org/10.21541/apjess.1326975.

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With the increase in the need for electrical energy, production amount planning is of great importance in order not to experience restrictions in terms of use, to meet the required electricity production, and to evaluate the excess production efficiently. In this study, a generation forecasting model was created with the fuzzy logic method to determine the electricity generation strategy. The created model is aimed to determine the electrical energy that needs to be produced daily by using the previous day's production amount, temperature, and season data. Three separate sets of data were used

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Alamaniotis, Miltiadis, Sangkyu Lee, and Tatjana Jevremovic. "Intelligent Analysis of Low-Count Scintillation Spectra Using Support Vector Regression and Fuzzy Logic." Nuclear Technology 191, no. 1 (2015): 41–57. http://dx.doi.org/10.13182/nt14-75.

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Kovac, P., D. Rodic, V. Pucovsky, B. Savkovic, and M. Gostimirovic. "Application of fuzzy logic and regression analysis for modeling surface roughness in face milliing." Journal of Intelligent Manufacturing 24, no. 4 (2012): 755–62. http://dx.doi.org/10.1007/s10845-012-0623-z.

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Chandrashekar P. "Comparative Analysis of Classification Models for Steel Rods Using Convolution Neural Networks and Fuzzy Logic Systems." Journal of Electrical Systems 20, no. 11s (2024): 3454–64. https://doi.org/10.52783/jes.8123.

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Maintaining high-quality steel rods is crucial in the competitive steel-strip production industry. Human visual inspection has traditionally been the primary method for detecting flaws, but it has limitations in accuracy, processing time, cost, and reliability. Automated visual inspection technologies have been developed to address these issues. However, in-depth research on vision-based approaches for identifying and categorizing surface flaws in steel products has also proven ineffective. This paper presents a comparative analysis of various classification models for different types of steel

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Kol'tsov, Nikolay I. "SOLUTION OF THE INVERSE PROBLEM OF CHEMICAL KINETICS BASED ON FUZZY LOGIC MODELS." ChemChemTech 67, no. 12 (2024): 54–63. https://doi.org/10.6060/ivkkt.20246712.7067.

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The possibility of using “soft” calculation methods to solve the inverse problem of chemical kinetics to identify fuzzy kinetic laws based on the data of stationary experiments without a priori specifying the type of the kinetic law has been investigated. “Soft” computing combines artificial intelligence methods such as fuzzy logic, neural networks, genetic algorithms, etc. (F. Rosenblatt, L. Zadeh, J. Holland, etc.). Combinations of these methods will allow the creation of various hybrid systems. Dynamic kinetic models of chemical reactions are described by systems of ordinary differential eq

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Poornima, K. A., and Dr G. Dheepa. "ANALYSIS OF CROP YIELD PREDICTION USING FUZZY CLUSTERING TECHNIQUES." International Journal of Advanced Research in Computer Science 11, no. 6 (2020): 33–35. http://dx.doi.org/10.26483/ijarcs.v11i6.6671.

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Nowadays the most important field in the real world is agriculture and it is the main occupation and backbone of our Indian economy. Agriculture data analysis is one of the latest drift research fields in data mining. Crop yield prediction is vital as it can support decision makers in agriculture zone. Data mining have modern techniques and algorithms for finding best yield prediction. This paper presents a brief comparative study on different views that deals with various performances used to figure out the different crop yield with less error rate. Fuzzy C-Means(FCM), Fuzzy logic (FL), Adapt

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De Andrés-Sánchez, Jorge. "Fitting Insurance Claim Reserves with Two-Way ANOVA and Intuitionistic Fuzzy Regression." Axioms 13, no. 3 (2024): 184. http://dx.doi.org/10.3390/axioms13030184.

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A highly relevant topic in the actuarial literature is so-called “claim reserving” or “loss reserving”, which involves estimating reserves to be provisioned for pending claims, as they can be deferred over various periods. This explains the proliferation of methods that aim to estimate these reserves and their variability. Regression methods are widely used in this setting. If we model error terms as random variables, the variability of provisions can consequently be modelled stochastically. The use of fuzzy regression methods also allows modelling uncertainty for reserve values using tools fr

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Singh, Arunesh Kumar, Abhinav Saxena, Nathuni Roy, and Umakanta Choudhury. "Inter-turn fault stability enrichment and diagnostic analysis of power system network using wavelet transformation-based sample data control and fuzzy logic controller." Transactions of the Institute of Measurement and Control 43, no. 12 (2021): 2788–98. http://dx.doi.org/10.1177/01423312211007006.

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In this paper, performance analysis of power system network is carried out by injecting the inter-turn fault at the power transformer. The injection of inter-turn fault generates the inrush current in the network. The power system network consists of transformer, current transformer, potential transformer, circuit breaker, isolator, resistance, inductance, loads, and generating source. The fault detection and termination related to inrush current has some drawbacks and limitations such as slow convergence rate, less stability and more distortion with the existing methods. These drawbacks motiv

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36

Devaraj, Rajamani, Emad Abouel Nasr, Balasubramanian Esakki, Ananthakumar Kasi, and Hussein Mohamed. "Prediction and Analysis of Multi-Response Characteristics on Plasma Arc Cutting of Monel 400™ Alloy Using Mamdani-Fuzzy Logic System and Sensitivity Analysis." Materials 13, no. 16 (2020): 3558. http://dx.doi.org/10.3390/ma13163558.

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Nickel-based alloys, especially Monel 400™, is gaining its significance in diverse applications owing to its superior mechanical properties and high corrosion resistance. Machining of these materials is extremely difficult through the traditional manufacturing process because of their affinity to rapid work hardening and deprived thermal conductivity. Owing to these difficulties a well-established disruptive metal cutting process namely plasma arc cutting (PAC) can be widely used to cut the sheet metals with intricate profiles. The present work focuses on an intelligent modeling of the PAC pro

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Gokulachandran, Jaganathan, and K. Mohandas. "Prediction of cutting tool life based on Taguchi approach with fuzzy logic and support vector regression techniques." International Journal of Quality & Reliability Management 32, no. 3 (2015): 270–90. http://dx.doi.org/10.1108/ijqrm-06-2012-0084.

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Purpose – The accurate assessment of tool life of any given tool is a great significance in any manufacturing industry. The purpose of this paper is to predict the life of a cutting tool, in order to help decision making of the next scheduled replacement of tool and improve productivity. Design/methodology/approach – This paper reports the use of two soft computing techniques, namely, neuro-fuzzy logic and support vector regression (SVR) techniques for the assessment of cutting tools. In this work, experiments are conducted based on Taguchi approach and tool life values are obtained. Findings

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Bitar, S. D., C. P. Campos, and C. E. C. Freitas. "Applying fuzzy logic to estimate the parameters of the length-weight relationship." Brazilian Journal of Biology 76, no. 3 (2016): 611–18. http://dx.doi.org/10.1590/1519-6984.20014.

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Abstract We evaluated three mathematical procedures to estimate the parameters of the relationship between weight and length for Cichla monoculus: least squares ordinary regression on log-transformed data, non-linear estimation using raw data and a mix of multivariate analysis and fuzzy logic. Our goal was to find an alternative approach that considers the uncertainties inherent to this biological model. We found that non-linear estimation generated more consistent estimates than least squares regression. Our results also indicate that it is possible to find consistent estimates of the paramet

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Zermane, Hanane, Ahcene Ziar, Hassina Madjour, and Djamel Touahar. "Transforming Industrial Supervision Systems: A Comprehensive Approach Integrating Machine Learning Techniques and Fuzzy Logic." Scientific Bulletin of Electrical Engineering Faculty 24, no. 2 (2024): 52–66. https://doi.org/10.2478/sbeef-2024-0021.

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Abstract In addressing the mounting challenges of industrial supervision systems grappling with intricate processes, this study pioneers a transformative paradigm centered on the SCIMAT cement factory. By seamlessly integrating Machine Learning and Fuzzy Logic, the primary aim is to revolutionize real-time control systems, with a keen focus on cement production. SVM integration into the supervision system, coupled with connectivity to a Programmable Logic Controller (PLC), is complemented by fuzzy real-time controllers’ regression analysis. Rigorous testing and evaluation validate the proposed

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Sahil, Mehta, and Basak Prasenjit. "Solar irradiance forecasting using fuzzy logic and multilinear regression approach: A case study of Punjab, India." International Journal of Advances in Applied Sciences (IJAAS) 8, no. 2 (2019): 125–35. https://doi.org/10.11591/ijaas.v8.i2.pp125-135.

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The accurate forecasting of solar irradiance depends on various uncertain parameters like time of day, temperature, wind speed, humidity, and atmospheric pressure. All these play an important role in calculating PV power output. In this paper, a novel approach for forecasting of solar irradiance using flexible and accurate fuzzy logic and robust multi-linear regression approach has been proposed considering the above mentioned five variables. Based on the simultaneous consideration of those five variables, the solar irradiance is forecasted using the proposed methodology at a particular locati

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Erçen, Hüseyin İlker, Hüseyin Özdeşer, and Turgut Türsoy. "The Impact of Macroeconomic Sustainability on Exchange Rate: Hybrid Machine-Learning Approach." Sustainability 14, no. 9 (2022): 5357. http://dx.doi.org/10.3390/su14095357.

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This paper constructed a robust methodology to investigate the impact of news regarding macroeconomic policies on exchange rate fluctuations, and to examined the applicability of qualitative information alongside historical data to predict exchange rates. To do so, hybrid machine learning algorithms comprised of natural language processing, fuzzy logic, and support vector regression have been constructed. This study emphasizes the significance of qualitative information on investors’ subjective consideration, the decision-making process, and causality on exchange rate volatility. To perceive t

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Agayan, Sergey, Shamil Bogoutdinov, Dmitriy Kamaev, Boris Dzeboev, and Michael Dobrovolsky. "Trends and Extremes in Time Series Based on Fuzzy Logic." Mathematics 12, no. 2 (2024): 284. http://dx.doi.org/10.3390/math12020284.

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The authors develop the theory of discrete differentiation and, on its basis, solve the problem of detecting trends in records, using the idea of the connection between trends and derivatives in classical analysis but implementing it using fuzzy logic methods. The solution to this problem is carried out by constructing fuzzy measures of the trend and extremum for a recording. The theoretical justification of the regression approach to classical differentiation in the continuous case given in this work provides an answer to the question of what discrete differentiation is, which is used in cons

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Gkountakou, Fani I., Kosmas E. Bantilas, Ioannis E. Kavvadias, Anaxagoras Elenas, and Basil K. Papadopoulos. "Fuzzy Multivariate Regression Models for Seismic Assessment of Rocking Structures." Applied Sciences 13, no. 17 (2023): 9602. http://dx.doi.org/10.3390/app13179602.

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The assessment of rocking response is a challenging task due to its high nonlinearity. The present study investigates two methodologies to evaluate finite rocking rotations and overturn of three typical rocking systems. In particular, fuzzy linear regression (FLR) with triangular fuzzy numbers and a hybrid model combining logistic regression and fuzzy logic were adopted. To this end, three typical rocking structures were considered, and nonlinear time history analyses were performed to obtain their maximum response. Eighteen seismic intensity measures (IMs) extracted from recorded seismic acce

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M. Jamal Rashid, Kawa, and Suzan S. Haydar. "FEWMA and Fuzzy Regression Model Control Chart one a-cut With Application." Academic Journal of Nawroz University 11, no. 4 (2022): 82–89. http://dx.doi.org/10.25007/ajnu.v11n4a881.

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Abstract: In many real-world applications, the data to be used in a control charting method are not crisp since they are approximated due to environmental uncertainties, In these situations, fuzzy numbers and linguistic variables are used to grab such uncertainties. That is why the use of a fuzzy control chart, in which fuzzy data are used, is justiﬁed. As an exponentially weighted moving average (EWMA) scheme is usually used to detect small shifts, in this paper a fuzzy EWMA (F-EWMA) control chart is proposed to detect small shifts in the process mean when fuzzy data are available. As w

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Koretskiy, Vladimir, Marina Degtiareva-Galiakhmetova, and Evgeniy Kostitsyn. "Banking front-line personnel assessment by fuzzy logic approaches." SHS Web of Conferences 116 (2021): 00057. http://dx.doi.org/10.1051/shsconf/202111600057.

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The article is dedicated to the qualitative assessment of banking personnel and the interpretation of the results with a developed fuzzy logic expert system. The authors proposed to evaluate human resources based on the Company Loyalty, Customer Service Quality and Intra-Corporate Communication which are linguistic terms for personnel to be assessed. To interpret the results of the received staff assessment, a fuzzy expert system was developed which enables the Business Efficiency of Personnel to be estimated. The expert system was tested at the front-line office of the regional bank. Regressi

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Goutam, Siddharth, and Srija Unnikrishnan. "Design, Implementation, and Analysis of Vertical Handoff Decision Algorithm." International Journal of Interdisciplinary Telecommunications and Networking 13, no. 3 (2021): 33–53. http://dx.doi.org/10.4018/ijitn.2021070103.

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The increasing use of mobile communication and computing has made accurate and efficient methods of executing vertical handoff a necessity. This research paper captures the design and implementation of an effectual algorithm for making decision for vertical handoff based on fuzzy logic. The input parameters considered are bandwidth, battery status, and cost. The authors present analysis of the handover value, which gives the handoff decision, in relation to the input attributes, using regression analysis, correlation analysis, and ANOVA.

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Jenarthanan, M. P., A. Ram Prakash, and R. Jeyapaul. "Experimental investigation of machinability characteristics in Al-TiB2 metal matrix composite (MMC) based on the Taguchi method with fuzzy logics." Multidiscipline Modeling in Materials and Structures 12, no. 1 (2016): 177–93. http://dx.doi.org/10.1108/mmms-04-2015-0018.

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Purpose – The purpose of this paper is to develop a mathematical model for metal removal rate and surface roughness through Taguchi method and analyse the influence of the individual input machining parameters (cutting speed, feed rate, helix angle, depth of cut and wt% on the responses in milling of aluminium-titanium diboride metal matrix composite (MMC) with solid carbide end mill cutter coated with nano-crystals. Design/methodology/approach – Taguchi OA is used to optimise the material removal rate (MRR) and Surface Roughness by developing a mathematical model. End Milling is used to creat

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Prokudin, Aleksandr A., and Maria P. Silich. "Analysis of crime factors with hybrid cognitive maps." Proceedings of Tomsk State University of Control Systems and Radioelectronics 26, no. 1 (2023): 107–15. http://dx.doi.org/10.21293/1818-0442-2023-26-1-107-115.

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A hybrid cognitive map has been created to assess the socioeconomic factors of crime and their relationships based on statistical data. The current state and change dynamics of every selected factor for each region of Russia were assessed with linguistic terms using fuzzy logic. Regression analysis was used to assess the impact that the factors have on each other. The resulting model can be used for diagnostics or comparative analysis, an example is given that compares crime rates in different federal districts.

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Tung Khuat, Thanh, and My Hanh Le. "An Application of Artificial Neural Networks and Fuzzy Logic on the Stock Price Prediction Problem." JOIV : International Journal on Informatics Visualization 1, no. 2 (2017): 40. http://dx.doi.org/10.30630/joiv.1.2.20.

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The financial industry has been becoming more and more dependent on advanced computing technologies in order to maintain competitiveness in a global economy. Hence, the stock price prediction problem using data mining techniques is one of the most important issues in finance. This field has attracted great scientific interest and has become a crucial research area to provide a more precise prediction process. Fuzzy logic (FL) and Artificial Neural Network (ANN) present an exciting and promising technique with a wide scope for the applications of prediction. There is a growing interest in both

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Habib, A., and U. Yildirim. "Simplified modeling of rubberized concrete properties using multivariable regression analysis." Materiales de Construcción 72, no. 347 (2022): e289. http://dx.doi.org/10.3989/mc.2022.13621.

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The studies on rubberized concrete have increased dramatically over the last few years due to being an environmentally friendly material with enhanced vibration behavior and energy dissipation capabilities. Nevertheless, multiple resources in the literature have reported reductions in its mechanical properties directly proportional to the rubber content. Over the last few years, various mathematical models have been proposed to estimate rubberized concrete properties using artificial intelligence, machine learning, and fuzzy logic-based methods. However, these models are relatively complicated

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