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Author: Grafiati
Published: 11 September 2023

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1

JENA, KALYAN KUMAR, SASMITA MISHRA, SAROJANANDA MISHRA, SOURAV KUMAR BHOI, and SOUMYA RANJAN NAYAK. "MRI Brain Tumor Image Analysis Using Fuzzy Rule based Approach." Journal of Research on the Lepidoptera 50, no. 2 (June 25, 2019): 98–112. http://dx.doi.org/10.36872/lepi/v50i2/201012.

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Mama, Rachid, and Mustapha Machkour. "Fuzzy querying with SQL: Fuzzy view-based approach." Journal of Intelligent & Fuzzy Systems 40, no. 5 (April 22, 2021): 9937–48. http://dx.doi.org/10.3233/jifs-202551.

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Nowadays several works have been proposed that allow users to perform fuzzy queries on relational databases. But most of these systems based on an additional software layer to translate a fuzzy query and a supplementary layer of a classic database management system (DBMS) to evaluate fuzzy predicates, which induces an important overhead. They are not also easy to implement by a non-expert user. Here we have proposed a simple and intelligent approach to extend the SQL language to allow us to write flexible conditions in our queries without the need for translation. The main idea is to use a view to manipulate the satisfaction degrees related to user-defined fuzzy predicates, instead of calculating them at runtime employing user functions embedded in the query. Consequently, the response time of executing a fuzzy query statement will be reduced. This approach allows us to easily integrate most fuzzy request characters such as fuzzy modifiers, fuzzy quantifiers, fuzzy joins, etc. Moreover, we present a user-friendly interface to make it easy to use fuzzy linguistic values in all clauses of a select statement. The main contribution of this paper is to accelerate the execution of fuzzy query statements.
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Chandra, Vishal, and Savita Shiwani. "Fuzzy based Effort Estimation Approach." International Journal of Computer Applications 103, no. 17 (October 18, 2014): 39–42. http://dx.doi.org/10.5120/18305-9438.

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4

Abuhijleh, Eman A., Mourad Massa’deh, Amani Sheimat, and Abdulazeez Alkouri. "Complex Fuzzy Groups Based on Rosenfeld’s Approach." WSEAS TRANSACTIONS ON MATHEMATICS 20 (August 4, 2021): 368–77. http://dx.doi.org/10.37394/23206.2021.20.38.

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Complex fuzzy sets (CFS) generalize traditional fuzzy sets (FS) since the membership functions of CFS reduces to the membership functions of FS. FS values are always at [0, 1], unlike CFS which has values in the unit disk of C. This paper merges notion and concept in group theory and presents the notion of a complex fuzzy subgroup of a group. This proposed idea represents a more general and better optional mathematical tool as one of the approaches in the fuzzy group. However, this research defines the notion of complex fuzzy subgroupiod, complex fuzzy normal subgroup, and complex fuzzy left(right) ideal. Therefore, the lattice, homomorphic preimage, and image of complex fuzzy subgroupiod and ideal are introduced and studied its properties. Finally, complex fuzzy subgroups and their properties are presented and investigated
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., Malkhan. "Fuzzy Logic Based Approach for Analysis and Synthesis of Failure Modes." Journal of Advanced Research in Production and Industrial Engineering 8, no. 1&2 (April 19, 2021): 18–22. http://dx.doi.org/10.24321/2456.429x.202102.

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Failure analysis and management has been gaining a lot of importance for the last few years. Failure mode and effect analysis is the process used for failure analysis and risk management in judging various businesses and managements. The FMEA is the methodology extensively used in different sectors to detect, prioritize and investigate the potential failures which occur during the production or design of the product. This whole process is computed with the Risk Priority Number (RPN). The blurriness and uncertainty in the FMEA associated with the human expertise and experience is responded using Fuzzy Logic based approach. In today’s time, organizations mainly focus on improving the quality of their product and building the reputation of their organizations. So, they consider every aspect of failure which can occur during the production. So, they use the FMEA team to overcome these issues before their existence. This paper is based on the approach of FMEA methodology and Fuzzy logic which describes multiple risks, insecurities associated with the different production processes and gives the solution to overcome the possible failures. FMEA is a methodology which suggests that such type of failures could occur during the design of the product or the production of the product.
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Zarinbal, M., and M. H. Fazel Zarandi. "Type-2 fuzzy image enhancement: Fuzzy rule based approach." Journal of Intelligent & Fuzzy Systems 26, no. 5 (2014): 2291–301. http://dx.doi.org/10.3233/ifs-130902.

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7

Watanabe, Toshihiko, and Hirosato Seki. "Modeling Approach Based on Modular Fuzzy Model." Journal of Advanced Computational Intelligence and Intelligent Informatics 16, no. 5 (July 20, 2012): 653–61. http://dx.doi.org/10.20965/jaciii.2012.p0653.

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Fuzzy modeling is one of the most important techniques for nonlinear modeling. SIRMs (Single Input Rule Modules) has been studied as a useful modeling method for real-life applications such as control and pattern recognition. Although the SIRMs is a practical modeling approach based on fuzzy reasoning, its performance is adversely affected by high-dimensional or complicated characteristics of the problems. The modular fuzzy model is an extension of the SIRMs for overcoming such a performance problem. In this paper, we study a modeling approach based on the modular fuzzy model by extending the SIRMs architecture. We show that the construction of error objective functions for modeling the modular fuzzy model and the SIRMs affects the prediction performance of the model. Through numerical experiments on modeling problems and reinforcement learning problems, we study the model construction based on the error objective functions. We find that the error objective function should be selected according to the number of dimensions of projection in the modular fuzzy model.
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EFE, Burak. "An integrated fuzzy approach based failure mode and effects analysis for a risk assessment." Bitlis Eren Üniversitesi Fen Bilimleri Dergisi 11, no. 2 (June 30, 2022): 681–96. http://dx.doi.org/10.17798/bitlisfen.1088988.

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This paper provides to cope with the limitations of traditional FMEA by using an integrated fuzzy multi-criteria decision making method, which considers fuzzy extension of AHP (Analytic Hierarchy Process) and fuzzy TOPSIS (Technique for Order Preference by Similarity to Ideal Solution), and a linear programming. The proposed method is shown for an application to failure mode and effects analysis (FMEA) based risk assessment of a construction firm. Firstly, fuzzy extension of AHP approach is utilized to define the weights of criteria in risk evaluation. Secondly, fuzzy TOPSIS approach is used to determine the most important failure mode in the construction firm. This work handles a sensitivity analysis and a comparison with the other methods. FMEA related papers in the literature presents only ranking of failure modes by using various methods. This study aims to handle the limited resources such as budget and time in a linear programming to establish a suitable occupational health and safety policy.
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9

Shyi-Ming Chen. "A fuzzy reasoning approach for rule-based systems based on fuzzy logics." IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics) 26, no. 5 (1996): 769–78. http://dx.doi.org/10.1109/3477.537318.

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10

ÖZTÜRK, Müslüm, Belkız TORĞUL, and Turan PAKSOY. "Sürdürülebilir Tedarikçi Seçimi için Aralık Tip-2 Bulanık Kural Tabanlı BWM Yaklaşımı." Konya Journal of Engineering Sciences 10, no. 2 (June 1, 2022): 312–36. http://dx.doi.org/10.36306/konjes.991295.

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Fuzzy logic is a theory based on human-specific approximate reasoning. Therefore, fuzzy logic applications can bring simple and more effective solutions to situations that classical methods cannot overcome. The type-1 fuzzy set is a set, which has a continuous (crisp) membership degree to which a membership degree between 0 and 1 is assigned, and is characterised by membership functions. Type-2 fuzzy sets, which have the power to express uncertainty better, are expressed by membership functions, where the membership degrees of each element belonging to that set also specify a fuzzy set.Therefore, type-2 fuzzy sets allow us to include the membership functions uncertainty in fuzzy set theory. Using expert knowledge and using sensitivity of human to reflect the level of the decision maker influence is expressed as a fuzzy rule based system. Recently, it has been seen that fuzzy rules are frequently used together with multi-criteria decision making (MCDM) methods. Again, combining fuzzy rules with type-2 fuzzy numbers is also found. In this study, the Best Worst Method (BWM), one of the MCDM methods, has been integrated with fuzzy rules based interval type-2. The developed hybrid method was defined as Interval Type-2 Fuzzy Rule-Based BWM (IT2 FRB BWM). The proposed hybrid method has an important place when there are alternatives with similar ranking positions. Thus, even if there is a small difference in each alternative, it will show the difference better (more sensitively). This makes the proposed hybrid method forceful and unique.The proposed approach has been applied to a sustainable supplier selection problem comparatively with the BWM. The results show that the IT2 FRB BWM approach is more successful in ordering alternatives than the classical BWM method.
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Margaliot, Michael, and Gideon Langholz. "Fuzzy Lyapunov-based approach to the design of fuzzy controllers." Fuzzy Sets and Systems 106, no. 1 (August 1999): 49–59. http://dx.doi.org/10.1016/s0165-0114(98)00356-x.

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Tam, P. K. S., and A. Wu. "A fuzzy neural network based on fuzzy hierarchy error approach." IEEE Transactions on Fuzzy Systems 8, no. 6 (2000): 808–16. http://dx.doi.org/10.1109/91.890349.

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13

Luo, Yan Fen. "Research of Fuzzy RBF Approach Method Based on IMGA." Applied Mechanics and Materials 719-720 (January 2015): 311–15. http://dx.doi.org/10.4028/www.scientific.net/amm.719-720.311.

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A research of fuzzy RBF approach method based on IMGA is proposed, depending on the equivalency between RBF network and fuzzy inference, a fuzzy RBF network is designed. At the same time, the parameters and weights of the fuzzy RBF are optimized based on the immune memory genetic algorithm (IMGA), and the speed of convergence is accelerated. The optimized system is simulated by MATLAB, and compared with the original system, the approach effect of the fuzzy RBF is improved by IMGA.
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14

Lam, H. K., and L. D. Seneviratne. "Chaotic Synchronization Using Sampled-Data Fuzzy Controller Based on Fuzzy-Model-Based Approach." IEEE Transactions on Circuits and Systems I: Regular Papers 55, no. 3 (April 2008): 883–92. http://dx.doi.org/10.1109/tcsi.2008.916413.

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15

Huang, Y. L., and L. T. Fan. "A fuzzy-logic-based approach to building efficient fuzzy rule-based expert systems." Computers & Chemical Engineering 17, no. 2 (February 1993): 181–92. http://dx.doi.org/10.1016/0098-1354(93)80013-d.

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KUMAR, S. SRINIVAS, and B. N. CHATTERJI. "STEREO MATCHING ALGORITHMS BASED ON FUZZY APPROACH." International Journal of Pattern Recognition and Artificial Intelligence 16, no. 07 (November 2002): 883–99. http://dx.doi.org/10.1142/s0218001402002040.

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Stereo matching is the central problem of stereovision paradigm. Area-based techniques provide the dense disparity maps and hence they are preferred for stereo correspondence. Normalized cross correlation (NCC), sum of squared differences (SSD) and sum of absolute differences (SAD) are the linear correlation measures generally used in the area-based techniques for stereo matching. In this paper, similarity measure for stereo matching based on fuzzy relations is used to establish the correspondence in the presence of intensity variations in stereo images. The strength of relationship of fuzzified data of two windows in the left image and the right image of stereo image pair is determined by considering the appropriate fuzzy aggregation operators. However, these measures fail to establish correspondence of the pixels in the stereo images in the presence of occluded pixels in the corresponding windows. Another stereo matching algorithm based on fuzzy relations of fuzzy data is used for stereo matching in such regions of images. This algorithm is based on weighted normalized cross correlation (WNCC) of the intensity data in the left and the right windows of stereo image pair. The properties of the similarity measures used in these algorithms are also discussed. Experiments with various real stereo images prove the superiority of these algorithms over normalized cross correlation (NCC) under nonideal conditions.
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Ates, Çagatay, Süleyman Özdel, and Emin Anarim. "Graph-based fuzzy approach against DDoS attacks1." Journal of Intelligent & Fuzzy Systems 39, no. 5 (November 19, 2020): 6315–24. http://dx.doi.org/10.3233/jifs-189099.

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While internet technologies have been evolving day by day, threats against them have been increasing with the same pace. One of the most serious and commonly executed attack type is Distributed Denial of Service (DDoS) attacks. Despite there are many security mechanisms against this type of attack, there is still need for new solutions due to the occurred DDoS attacks worldwide. In this work, a DDoS attack detection approach based on fuzzy logic and entropy is proposed. Network is modelled as a graph and graph-based features are used for discriminating attack traffic from attack-free traffic. Fuzzy-c-means clustering is applied based on these features in order to show the tendencies of IP addresses or port numbers to be in a same cluster or not. Based on this uncertainty, attack and attack-free traffic are modelled. In detection phase, fuzzy membership function is used. This algorithm is tested on the real data collected from Bogaziçi University network.
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Muqeem, Mohd, and Dr Md Rizwan Beg. "A FUZZY BASED APPROACH FOR EARLY REQUIREMENT PRIORITIZATION." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 15, no. 2 (November 10, 2015): 6480–90. http://dx.doi.org/10.24297/ijct.v15i2.566.

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The importance of the prioritization in commercial software development has been analyzed by many researchers. The gathered requirements are required to be put into an order of some priority. In other words we can say that there is a need to prioritize the requirements. It is evident that most of the approaches and techniques proposed in recent research to prioritize the requirements have not been widely adopted. These approaches are too complex, time consuming, or inconsistent and difficult to implement In this paper we propose a fuzzy based approach for requirement prioritization in which requirement are prioritized in early phase of requirement engineering as post elicitation step. This category of prioritization is known as early requirement prioritization. The proposed fuzzy based approach considers the nature of requirements by modeling their attributes as fuzzy variables. As such, these variables are integrated into a fuzzy based inference system in which the requirements represented as input attributes and ranked via the expected value operator of a fuzzy variable.
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Yung-Cheng Yao, Yung-Cheng Yao, Hsin-Te Wu Yung-Cheng Yao, Lai-Fu Shu Hsin-Te Wu, and Chun-Yi Lu Lai-Fu Shu. "Developing a Multifunctional Heating Pad Based on Fuzzy-Edge Computations and IoMT Approach." 網際網路技術學刊 23, no. 7 (December 2022): 1519–25. http://dx.doi.org/10.53106/160792642022122307007.

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<p>Many medical assistive devices, such as rehabilitation and heating pads, have developed toward AI IoT functions during the past decade. Heating pads can be used for rehabilitation or to relieve pain while patients use them to relax muscles and enhance blood circulation; however, this assistive device is single-functional. Additionally, older adults usually lack information adaptability in using multifunctional heating pads, and the heating sensors of the pads need to have high stability and low value of temperature error to avoid unstable measurement and unexpected results. This article proposes a multifunctional heating pad system by fuzzy-edge computations, primarily setting the functions by IoT development board and Bluetooth and the temperature ranges by fuzzy edge technique; users can adjust heating modes based on their needs. Our research team designed the heating sensor in the system to achieve high stability and durability; the IoT development board can calibrate the temperature by digital voltages, which enables the sensor to achieve expected temperatures. The IoT development board and fuzzy-edge technology employed in the system can self-calibrate and maintain the temperature ranges without needing a server to calculate, reaching the goal of edge computations. The experimental result has proved the feasibility of the system.</p> <p>&nbsp;</p>
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Wang, Gui Xiang, and Guang Tao Zhou. "Ranking Fuzzy Numbers Based on the Mean and Fuzzy Degree of Fuzzy Number." Applied Mechanics and Materials 220-223 (November 2012): 2102–8. http://dx.doi.org/10.4028/www.scientific.net/amm.220-223.2102.

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In this paper, a novel approach to ranking fuzzy numbers based on the mean and the fuzzy degree of fuzzy number is proposed. In the approach, a new ranking index that is comprehensive consideration of the mean and the fuzzy degree of fuzzy number is constructed, and then the properties of the ranking index are given. Moreover, to compare the proposed approach with the existing approaches, numerical examples are given. The comparative results illustrate that the approach proposed in this paper is better and has simpler calculations.
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Rana, Dinesh, and Sudha Rani. "Fuzzy logic based control system for fresh water aquaculture: A MATLAB based simulation approach." Serbian Journal of Electrical Engineering 12, no. 2 (2015): 171–82. http://dx.doi.org/10.2298/sjee1502171r.

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Fuzzy control is regarded as the most widely used application of fuzzy logic. Fuzzy logic is an innovative technology to design solutions for multiparameter and non-linear control problems. One of the greatest advantages of fuzzy control is that it uses human experience and process information obtained from operator rather than a mathematical model for the definition of a control strategy. As a result, it often delivers solutions faster than conventional control design techniques. The proposed system is an attempt to apply fuzzy logic techniques to predict the stress factor on the fish, based on line data and rule base generated using domain expert. The proposed work includes a use of Data acquisition system, an interfacing device for on line parameter acquisition and analysis, fuzzy logic controller (FLC) for inferring the stress factor. The system takes stress parameters on the fish as inputs, fuzzified by using FLC with knowledge base rules and finally provides single output. All the parameters are controlled and calibrated by the fuzzy logic toolbox and MATLAB programming.
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SUGENO, MICHIO, and GYEI-KARK PARK. "AN APPROACH TO LINGUISTIC INSTRUCTION BASED LEARNING." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 01, no. 01 (September 1993): 19–56. http://dx.doi.org/10.1142/s0218488593000036.

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In this paper, we notice the fact that a human learning process is characterized by a process under a natural language environment, and discuss an approach of learning based on indirect linguistic instructions. An instruction is interpreted through some meaning elements and each trend. Fuzzy evaluation rules are constructed for the searched meaning elements of the given instruction, and the performance of a system to be learned is improved by the evaluation rules. In this paper, we propose a framework of learning based on indirect linguistic instruction based learning using fuzzy theory: FULLINS(FUzzy-Learning based on Linguistic INStruction). The validity of FULLINS is shown by applying it to two control examples: truck backer-upper control and helicopter flight control problem.
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23

Makki, Anas A., and Reda M. S. Abdulaal. "A Hybrid MCDM Approach Based on Fuzzy MEREC-G and Fuzzy RATMI." Mathematics 11, no. 17 (September 2, 2023): 3773. http://dx.doi.org/10.3390/math11173773.

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Multi-criteria decision-making (MCDM) assists in making judgments on complex problems by evaluating several alternatives based on conflicting criteria. Several MCDM methods have been introduced. However, real-world problems often involve uncertain and ambiguous decision-maker inputs. Therefore, fuzzy MCDM methods have emerged to handle this problem using fuzzy logic. Most recently, the method based on the removal effects of criteria using the geometric mean (MEREC-G) and ranking the alternatives based on the trace to median index (RATMI) were introduced. However, to date, there is no fuzzy extension of the two novel methods. This study introduces a new hybrid fuzzy MCDM approach combining fuzzy MEREC-G and fuzzy RATMI. The fuzzy MEREC-G can accept linguistic input terms from multiple decision-makers and generates consistent fuzzy weights. The fuzzy RATMI can rank alternatives according to their fuzzy performance scores on each criterion. The study provides the algorithms of both fuzzy MEREC-G and fuzzy RATMI and demonstrates their application in adopted real-world problems. Correlation and scenario analyses were performed to check the new approach’s validity and sensitivity. The new approach demonstrates high accuracy and consistency and is sufficiently sensitive to changes in the criteria weights, yet not too sensitive to produce inconsistent rankings.
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Bouacha, Ismail, and Safia Bekhouche. "A Generic Fuzzy-Based Recommendation Approach (GFBRA)." International Journal of Fuzzy System Applications 11, no. 1 (January 2022): 1–29. http://dx.doi.org/10.4018/ijfsa.292461.

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Recommender Systems aim to automatically provide users with personalized information in an overloaded search space. To dual with vagueness and imprecision problems in RS, several researches have been proposed fuzzy based approaches. Even though, these works have incorporated experimental evaluation; they were used in different recommendation scenarios which makes it difficult to have a fair comparison between them. Also, some of them performed an items and/or users clustering before generating recommendations. For this reason they need additional information such as item attributes or trust between users which are not always available. In this paper, we propose to use fuzzy set techniques to predict the rating of a target user for each unrated item. It uses the target user's history in addition with rating of similar users which allows to the target user to contribute in the recommendation process. Experimental results on several datasets seem to be promising in term of MAE (Mean Average Error), RMSE (Root Mean Square Error), accuracy, precision, recall and F-measure.
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Subash, Jenita, and Dr Kalaivani .S. "IMAGE DENOISING USING IMPROVED FUZZY BASED APPROACH." Indian Journal of Computer Science and Engineering 12, no. 5 (October 20, 2021): 1325–33. http://dx.doi.org/10.21817/indjcse/2021/v12i5/211205068.

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Mascioli, F. M. Frattale, A. Rizzi, M. Panella, and G. Martinelli. "Scale-based approach to hierarchical fuzzy clustering." Signal Processing 80, no. 6 (June 2000): 1001–16. http://dx.doi.org/10.1016/s0165-1684(00)00016-5.

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Choudhary, Vishal, and Dr P. K. Bharti. "Fuzzy Based Approach for Predicting Software Maintainability." International Journal of Engineering Trends and Technology 10, no. 1 (April 25, 2014): 8–12. http://dx.doi.org/10.14445/22315381/ijett-v10p203.

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Shehata, Tamer, Mohamed S. Abdul moteleb, and Essam Mohamed Abou El-Zahab. "STATCOM controllers based on fuzzy PI approach." Indonesian Journal of Electrical Engineering and Computer Science 18, no. 3 (June 1, 2020): 1138. http://dx.doi.org/10.11591/ijeecs.v18.i3.pp1138-1147.

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<span>The electricity companies enforced some legislation on the electricity consumption contracts because the electricity network cannot bear the increasing demands of consumers. Therefore, a new controlling system is required to improve the frequent variations of the power system operating point (OP). Consequently, the flexible AC transmission systems (FACTS) controllers should be able to integrate with recent OP. Coordination of FACTS controller is more sophisticated due to various OP and uncertainties parametric in cooperation with the non linearity of power system (PS). Static Synchronous Compensator (STATCOM) plays very important role like the stability support of large and small transient-disturbance in PS. Therefore, the aim of this research is presents fuzzy logic (FL) with the PI controller (a novel controller) its ability to improve the performance of the power system along with the capability of switch irregular and rough actual world data. This new controlling system may be suitable for a wide range of applications especially the models which deal with huge and complicated data analysis. This new controller system carries out the adjustments of the voltage on DC capacitor under transient and steady-state conditions.</span>
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Wang, Dabuxilatu, and Olgierd Hryniewicz. "A fuzzy nonparametric Shewhart chart based on the bootstrap approach." International Journal of Applied Mathematics and Computer Science 25, no. 2 (June 1, 2015): 389–401. http://dx.doi.org/10.1515/amcs-2015-0030.

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Abstract In this paper, we consider a nonparametric Shewhart chart for fuzzy data. We utilize the fuzzy data without transforming them into a real-valued scalar (a representative value). Usually fuzzy data (described by fuzzy random variables) do not have a distributional model available, and also the size of the fuzzy sample data is small. Based on the bootstrap methodology, we design a nonparametric Shewhart control chart in the space of fuzzy random variables equipped with some L2 metric, in which a novel approach for generating the control limits is proposed. The control limits are determined by the necessity index of strict dominance combined with the bootstrap quantile of the test statistic. An in-control bootstrap ARL of the proposed chart is also considered.
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Makariadis, Stefanos, and Basil Papadopoulos. "A Fuzzy Implication-Based Approach for Validating Climatic Teleconnections." Mathematics 10, no. 15 (July 29, 2022): 2692. http://dx.doi.org/10.3390/math10152692.

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Fuzzy logic, during recent decades, has evolved into one of the most influential scientific fields. To an extent, this is due to its applications which have a profound impact on the daily life of people worldwide. The goal of this paper is to focus on the applications of fuzzy logic to the study of our climate, and especially if fuzzy implications can validate the climatic teleconnections observed on climatic data. To achieve this goal, a real world case study is provided which focuses on the relationship between temperature anomalies observed at different European cities. The results of this case study are that, indeed, fuzzy implications can validate the climatic teleconnections observed on climatic data. The conclusions drawn are that fuzzy logic can assist to the definitive proof of phenomena which, till now, could only be researched experimentally.
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Krishnapuram, R., S. Medasani, Sung-Hwan Jung, Young-Sik Choi, and R. Balasubramaniam. "Content-based image retrieval based on a fuzzy approach." IEEE Transactions on Knowledge and Data Engineering 16, no. 10 (October 2004): 1185–99. http://dx.doi.org/10.1109/tkde.2004.53.

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Saleh, Emran, Aida Valls, Antonio Moreno, Pedro Romero-Aroca, Humberto Bustince, and Vicenç Torra. "A Hierarchically ⊥-Decomposable Fuzzy Measure-Based Approach for Fuzzy Rules Aggregation." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 27, Supp01 (November 5, 2019): 59–76. http://dx.doi.org/10.1142/s0218488519400038.

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A Fuzzy Decision Tree is a classification method consisting of a set of rules defined on fuzzy variables. The final class assignment is done according to the output of all the rules of the tree. Generally, the maximum operator is used to aggregate the results of the rules. However, some approaches based on more complex aggregation operators have appeared recently. In this work we propose to use Sugeno and Choquet integrals together with a Hierarchically ⊥-Decomposable Fuzzy Measure (HDFM) to aggregate the rules' values. The HDFM exploits the hierarchical structure of the fuzzy decision tree and takes into account the confidence value of the output together with the classification ambiguity of the rules. The HDFM is built using Sugeno-Weber t-conorms.We validate this approach on several classification problems and make a comparison of the performance with the state of art aggregation operators. Finally, a case study with a real dataset of diabetic patients is analyzed to predict the risk of suffering from diabetic retinopathy.
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Hadi-Vencheh, A., and M. N. Mokhtarian. "A new fuzzy MCDM approach based on centroid of fuzzy numbers." Expert Systems with Applications 38, no. 5 (May 2011): 5226–30. http://dx.doi.org/10.1016/j.eswa.2010.10.036.

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Arefi, Mohsen, and S. Mahmoud Taheri. "A new approach for testing fuzzy hypotheses based on fuzzy data." International Journal of Computational Intelligence Systems 6, no. 2 (April 2013): 318–27. http://dx.doi.org/10.1080/18756891.2013.769768.

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Su, Te-Jen, Sheng-Yi Lin, and Gwo-Jia Jong. "FIR fuzzy equalizer design for nonlinear channels LMI-based fuzzy approach." Electrical Engineering 88, no. 6 (December 16, 2005): 527–34. http://dx.doi.org/10.1007/s00202-005-0309-z.

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Jaya, Eppili, and B. T. Krishna. "Fuzzy-based MTD." Data Technologies and Applications 54, no. 1 (January 7, 2020): 66–84. http://dx.doi.org/10.1108/dta-03-2019-0049.

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Purpose Synthetic aperture radar exploits the receiving signals in the antenna for detecting the moving targets and estimates the motion parameters of the moving objects. The limitation of the existing methods is regarding the poor power density such that those received signals are essentially to be transformed to the background ratio. To overcome this issue, fractional Fourier transform (FrFT) is employed in the moving target detection (MTD) process. The paper aims to discuss this issue. Design/methodology/approach The proposed MTD method uses the fuzzy decisive approach for detecting the moving target in the search space. The received signal and the FrFT of the received signal are subjected to the calculation of correlation using the ambiguity function. Based on the correlation, the location of the target is identified in the search space and is fed to the fuzzy decisive module, which detects the target location using the fuzzy linguistic rules. Findings The simulation is performed, and the analysis is carried out based on the metrics, like detection time, missed target rate, and MSE. From the analysis, it can be shown that the proposed Fuzzy-based MTD process detected the object in 5.0237 secs with a minimum missed target rate of 0.1210 and MSE of 23377.48. Originality/value The proposed Fuzzy-MTD is the application of the fuzzy rules for locating the moving target in search space based on the peak energy of the original received signal and FrFT of the original received signal.
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Ngo, Long Thanh, Long The Pham, and Phuong Hoang Nguyen. "AN APPROACH DESIGNING AUTONOMOUS ROBOT NAVIGATION SYSTEM BASED ON BEHAVIOR COORDINATION." ASEAN Journal on Science and Technology for Development 24, no. 4 (November 16, 2017): 353–67. http://dx.doi.org/10.29037/ajstd.212.

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Robot navigation using fuzzy behavior is suited in unknown and unstructured environment in which each behavior have an individual task. This paper deals with an approach designing autonomous robot navigation system based on fuzzy behaviors including collision avoidance, wall-following, go-to-target. The proposed hierarchy of fuzzy behaviors is used to fuse the command in which each behavior is a fuzzy inference system and its outputs are fuzzy sets. Its inputs are information fused from sensors using fuzzy directional relationship. The simulation results with some statistics show that the system works correctly.
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Benzian, Yaghmorasan, and Nacéra Benamrane. "New FCM Segmentation Approach Based on Multi-Resolution Analysis." International Journal of Fuzzy System Applications 7, no. 4 (October 2018): 100–114. http://dx.doi.org/10.4018/ijfsa.2018100105.

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This article presents a modified Fuzzy C Means segmentation approach based on multi-resolution image analysis. Fuzzy C-Means standard methods are improved through fuzzy clustering at different image resolution levels by propagating fuzzy membership values pyramidally from a lower to a higher level. Processing at a lower resolution image level provides a rough pixel classification result, thus, a pixel is assigned to a cluster to which the majority of its neighborhood pixels belongs. The aim of fuzzy clustering with multi-resolution images is to avoid pixel misclassification according to the spatial cluster of the neighbourhood of each pixel in order to have more homogeneous regions and eliminate noisy regions present in the image. This method is tested particularly on samples and medical images with gaussian noise by varying multiresolution parameter values for better analysis. The results obtained after multi-resolution clustering are giving satisfactory results by comparing this approach with standard FCM and spatial FCM ones.
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Sharma, Parikshit. "A Fuzzy Approach to Educational Grading Systems “Fuzzy Logic Based Grade Card”." International Journal of Advanced Engineering and Nano Technology 10, no. 6 (June 30, 2023): 1–8. http://dx.doi.org/10.35940/ijaent.g9582.0610623.

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This study aims at developing a fuzzy approach for the educational grading systems. Through this study a fuzzy logic-based grading card is suggested. The grading system based on crisp approach just deals with numbers. Fuzziness, being an important property of language, motivates us to work and study in this fuzzy environment. Before discussing the actual grade card some introduction about the key concepts is given for the readers. We have also analyzed the difference in grading and evaluating systems followed from the decades and the new fuzzy logic-based evaluation system. It also includes connectivity levels, advantages, and disadvantages between both evolution methods. The future scope of the fuzzy grading system is also discussed. The report will conclude with the answer to the question, “Is a fuzzy logic- based grade card worth for the educational grading systems?”. Moreover, towards the end, suggestions will be provided on how to bring more of these fuzzy approaches into education systems.
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Postorino, Maria Nadia, and Mario Versaci. "A Geometric Fuzzy-Based Approach for Airport Clustering." Advances in Fuzzy Systems 2014 (2014): 1–12. http://dx.doi.org/10.1155/2014/201243.

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Airport classification is a common need in the air transport field due to several purposes—such as resource allocation, identification of crucial nodes, and real-time identification of substitute nodes—which also depend on the involved actors’ expectations. In this paper a fuzzy-based procedure has been proposed to cluster airports by using a fuzzy geometric point of view according to the concept of unit-hypercube. By representing each airport as a point in the given reference metric space, the geometric distance among airports—which corresponds to a measure of similarity—has in fact an intrinsic fuzzy nature due to the airport specific characteristics. The proposed procedure has been applied to a test case concerning the Italian airport network and the obtained results are in line with expectations.
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Can, Gülin Feryal, and Pelin Toktas. "A novel fuzzy risk matrix based risk assessment approach." Kybernetes 47, no. 9 (October 1, 2018): 1721–51. http://dx.doi.org/10.1108/k-12-2017-0497.

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Purpose Traditional risk assessment (RA) methodologies cannot model vagueness in risk and cannot prioritize corrective-preventive measures (CPMs) by considering effectiveness of those on risk types (RTs). These cannot combine and reflect accurately different subjective opinions and cannot be used in a linguistic manner. Risk factors (RFs) are assumed to have the same importance and interrelations between RFs are not considered. This study aims to overcome these disadvantages by combining fuzzy logic with multi-criteria decision-making in a dynamic manner. Design/methodology/approach This study proposes a novel three-stage fuzzy risk matrix-based RA integrating fuzzy decision-making trial and evaluation laboratory (F-DEMATEL) and fuzzy multi-attributive border approximation area comparison (F-MABAC). At the first stage, importance weights of RFs are computed by F-DEMATEL. At the second stage, risk degrees of RTs are computed via using fuzzy risk matrix. At the third stage, CPMs are ranked by F-MABAC. Finally, a numerical example for RA in a warehouse is given. Findings Results show that developing instructions for material loading or unloading is the most important CPM and severity is the most important RF for the warehouse. Originality/value This study has originality in terms of having fuzzy dynamic structure. At first, RFs are assumed to be criteria sets then, RTs are assumed to be criteria set considering their risk degrees to rank CPMs in a fuzzy manner. Risk degrees of RTs are used for weights of RTs and effectiveness of CPMs are used for performance values of CPMs.
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Riali, Ishak, Messaouda Fareh, and Hafida Bouarfa. "Fuzzy Probabilistic Ontology Approach." International Journal on Semantic Web and Information Systems 15, no. 4 (October 2019): 1–20. http://dx.doi.org/10.4018/ijswis.2019100101.

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In spite of the undeniable success of the ontologies, where they have been widely applied successfully to represent the knowledge in lots of real-world problems, they cannot represent and reason with uncertain knowledge which inherently appears in most domains. To cope with this issue, this article presents a new approach for dealing with rich-uncertainty domains. In fact, it is mainly based on integrating hybrid models which combine both fuzzy logic and Bayesian networks. On the other hand, the Fuzzy multi-entity Bayesian network (FzMEBN) proposed as a hybrid model which enhances the classical multi-entity Bayesian network using fuzzy logic, it can be used to represent and reason with probabilistic and vague knowledge simultaneously. Thus, as a language belongs to the proposed approach, this study proposes a promising solution to overcome the weakness of the Probabilistic Ontology Web Language (PR-OWL) based on FzMEBN to allow dealing with vague and probabilistic knowledge in ontologies. The proposed extension is evaluated with a case study in the medical field (diabetes diseases).
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Honda, K., A. Notsu, T. Matsui, and H. Ichihashi. "Fuzzy Cluster Validation Based on Fuzzy PCA-Guided Procedure." International Journal of Fuzzy System Applications 1, no. 1 (January 2011): 49–60. http://dx.doi.org/10.4018/ijfsa.2011010104.

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Cluster validation is an important issue in fuzzy clustering research and many validity measures, most of which are motivated by intuitive justification considering geometrical features, have been developed. This paper proposes a new validation approach, which evaluates the validity degree of cluster partitions from the view point of the optimality of objective functions in FCM-type clustering. This approach makes it possible to evaluate the validity degree of robust cluster partitions, in which geometrical features are not available because of their possibilistic natures.
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ZHANG, ZILI, and YANHUI ZHOU. "A FUZZY LOGIC BASED APPROACH FOR SOFTWARE TESTING." International Journal of Pattern Recognition and Artificial Intelligence 21, no. 04 (June 2007): 709–22. http://dx.doi.org/10.1142/s0218001407005636.

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How to provide cost-effective strategies for Software Testing has been one of the research focuses in Software Engineering for a long time. Many researchers in Software Engineering have addressed the effectiveness and quality metric of Software Testing, and many interesting results have been obtained. However, one issue of paramount importance in software testing — the intrinsic imprecise and uncertain relationships within testing metrics — is left unaddressed. To this end, a new quality and effectiveness measurement based on fuzzy logic is proposed. Related issues like the software quality features and fuzzy reasoning for test project similarity measurement are discussed, which can deal with quality and effectiveness consistency between different test projects. Experiments were conducted to verify the proposed measurement using real data from actual software testing projects. Experimental results show that the proposed fuzzy logic based metrics is effective and efficient to measure and evaluate the quality and effectiveness of test projects.
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Martin, A. John, K. P. Padmanaban, and K. Thiagarajan. "MANUFACTURING TOLERANCE DESIGN BASED ON FUZZY BINARY APPROACH." Transactions of the Canadian Society for Mechanical Engineering 39, no. 1 (March 2015): 85–95. http://dx.doi.org/10.1139/tcsme-2015-0007.

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In this paper, the concept of binary, fuzzy systems has been applied. These concepts optimize tolerance as a percentage through the fuzzy approach (based on binary values 0 and 1). Finally, the tolerance fit into six different levels along with optimized cost has been characterized, namely, best fit with minimum cost, better fit with minimum cost, good fit with minimum cost, acceptable range for minimum required fit with minimum cost, non acceptable range, and worst fit range.
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Mattar, Mohamed Abdel-Aziz, Mohamed S. El-Marazky, and Khaled A. Ahmed. "Modeling sprinkler irrigation infiltration based on a fuzzy-logic approach." Spanish Journal of Agricultural Research 15, no. 1 (April 20, 2017): e1201. http://dx.doi.org/10.5424/sjar/2017151-9179.

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In this study, the irrigation water infiltration rate (IR) is defined by input variables in linguistic terms using a fuzzy-logic approach. A fuzzy-logic model was developed using data collected from published data. The model was trained with three fuzzy membership functions: triangular (‘trimf’), trapezoid (trapmf), and pi (‘pimf’). The fuzzy system considered the number of irrigation events, applied water depth, polyacrylamide application rate, water application time, water electrical conductivity, soil surface slope, and soil texture components as input variables. The inputs were classified in terms of low, medium, and high levels. The output variable (i.e., IR) was rated in terms of five levels: very low, low, medium, high, and very high. Using statistical analysis, the values of IR resulting from the developed fuzzy-logic model were compared with the observations from the experiments. The results confirm that the agreement between the observations and predictive results was acceptable, except for fuzzy 'trimf'. The coefficient of determination provided the greatest value when using the 'trapmf' and 'pimf', with the value estimated for the 'pimf' slightly higher than that of 'trapmf'. Based on the results that were obtained, irrigation managers can use the fuzzy-logic approach to modify their field practices during the growing season to improve on-farm water management.
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Atalik, Gultekin, and Sevil Senturk. "A noval ranking approach based on incircle of triangular intuitionistic fuzzy numbers." Journal of Intelligent & Fuzzy Systems 39, no. 5 (November 19, 2020): 6271–78. http://dx.doi.org/10.3233/jifs-189095.

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Since proposed by Zadeh in 1965, ordinary fuzzy sets help us to model uncertainty and developed many types such as type 2 fuzzy, intuitionistic fuzzy, hesitant fuzzy etc. Intuitionistic fuzzy sets include both membership and non-membership functions for their each element. Ranking of a number is to identify a relationship of scalar quantity between these numbers. Ranking of fuzzy numbers play an important role in modeling problems such as fuzzy decision making, fuzzy linear programming problems. In this study, a new ranking method for triangular intuitionistic fuzzy numbers is proposed. The method based on the incircle of the membership function and non-membership function of TIFN uses lexicographical order to rank intuitionistic fuzzy numbers. Two examples are provided to illustrate the applicability of the method. Also, a comparative study is performed to demonstrate the validity of the proposed method. The results indicate that proposed method is consistent with other methods in the literature. Also, the method overcomes the problems such as numbers being very small or close to each other.
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Liu, Fung-Bao, and Cheng-Feng Hu. "A TOPSIS-BASED ENTROPIC REGULARIZATION APPROACH FOR SOLVING FUZZY MULTI-OBJECTIVE NONLINEAR PROGRAMMING PROBLEMS." Journal of the Operations Research Society of Japan 55, no. 4 (2012): 235–44. http://dx.doi.org/10.15807/jorsj.55.235.

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Li, Mingming, Jiao Dai, Fuqing Zhu, Liangjun Zang, Songlin Hu, and Jizhong Han. "A Fuzzy Set Based Approach for Rating Bias." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 9969–70. http://dx.doi.org/10.1609/aaai.v33i01.33019969.

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In recommender systems, the user uncertain preference results in unexpected ratings. This paper makes an initial attempt in integrating the influence of user uncertain degree into the matrix factorization framework. Specifically, a fuzzy set of like for each user is defined, and the membership function is utilized to measure the degree of an item belonging to the fuzzy set. Furthermore, to enhance the computational effect on sparse matrix, the uncertain preference is formulated as a side-information for fusion. Experimental results on three real-world datasets show that the proposed approach produces stable improvements compared with others.
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Qin, Hongwu, Chengjun Gu, Xiuqin Ma, Weiyi Wei, and Yibo Wang. "S-Score Table-Based Parameter-Reduction Approach for Fuzzy Soft Sets." Symmetry 14, no. 8 (August 17, 2022): 1719. http://dx.doi.org/10.3390/sym14081719.

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A fuzzy soft set is a mathematical tool used to deal with vagueness and uncertainty. Parameter reduction is an important issue when applying a fuzzy soft set to handle decision making. However, existing methods neglect newly added parameters and have higher computational complexities. In this paper, we propose a new S-Score table-based parameter-reduction approach for fuzzy soft sets. Compared with two existing methods of parameter reduction for a fuzzy soft set, our method takes newly added parameters into account, which brings about greater flexibility and is beneficial to the extension of fuzzy soft sets and a combination of multiple fuzzy soft sets. Additionally, our method accesses fewer elements from the dataset, which results in lower computation compared with the two existing approaches. The experimental results from two applications show the availability and feasibility of our approach.
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