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Peide Liu - One of the best experts on this subject based on the ideXlab platform.
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Multiple-Attribute Group Decision-Making Based on q-Rung Orthopair Fuzzy Power Maclaurin Symmetric Mean Operators
IEEE Transactions on Systems Man and Cybernetics, 2020Co-Authors: Peide Liu, Shyiming Chen, Peng WangAbstract:To be able to describe more complex fuzzy uncertainty information effectively, the concept of ${q}$ -rung orthopair fuzzy sets ( ${q}$ -ROFSs) was first proposed by Yager. The ${q}$ -ROFSs can dynamically adjust the range of indication of decision information by changing a parameter ${q}$ based on the different hesitation degree from the decision-makers, where ${q} {\ge } {1}$ , so they outperform the traditional intuitionistic fuzzy sets and Pythagorean fuzzy sets. In real decision-making problems, there is often an interaction phenomenon between attributes. For aggregating these complex fuzzy information, the Maclaurin Symmetric Mean (MSM) operator is more superior by considering interrelationships among attributes. In addition, the power average (PA) operator can reduce the effects of extreme evaluating data from some experts with prejudice. In this paper, we introduce the PA operator and the MSM operator based on ${q}$ -rung orthopair fuzzy numbers ( ${q}$ -ROFNs). Then, we put forward the ${q}$ -rung orthopair fuzzy power MSM ( ${q}$ -ROFPMSM) operator and the ${q}$ -rung orthopair fuzzy power weighed MSM ( ${q}$ -ROFPWMSM) operator of ${q}$ -ROFNs and present some of their properties. Finally, we present a novel multiple-attribute group decision-making (MAGDM) method based on the ${q}$ -ROFPWA and the ${q}$ -ROFPWMSM operators. The experimental results show that the novel MAGDM method outperforms the existing MAGDM methods for dealing with MAGDM problems.
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Multiattribute decision method for comprehensive logistics distribution center location selection based on 2-dimensional linguistic information
Information Sciences, 2020Co-Authors: Peide LiuAbstract:Abstract The comprehensive logistics distribution center location selection (CLDCLS) problem is a multiattribute group decision-making (MAGDM) problem in which multiple commodity preference weights are considered. To better describe the preference information and expert evaluation information, this paper utilizes 2-dimensional linguistic (2DL) information to express the preference information of various commodities and the expert evaluation, which can represent not only the evaluation information of experts but also the reliability of the evaluation information. Additionally, for solving the CLDCLS problem, this paper puts forward improved operational rules a score function, a distance formula and a correlation coefficient measure. Based on the 2DL information and the improved operational rules, we propose a 2-dimensional linguistic similarity-degree-based clustering analysis method, the 2-dimensional linguistic partitioned Maclaurin Symmetric Mean (2DLPMSM) operator, and the 2-dimensional linguistic weighted partitioned Maclaurin Symmetric Mean (2DLWPMSM) operator. The corresponding properties and special cases are demonstrated. By using these proposed methods, this paper constructs a MAGDM solution framework for the CLDCLS problem. A practical case of the CLDCLS problem is presented to demonstrate the effectiveness, rationality, robustness and superior performance of the proposed method.
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Multiple attribute decision making based on q-rung orthopair fuzzy generalized Maclaurin symmetic Mean operators
Information Sciences, 2020Co-Authors: Peide Liu, Yumei WangAbstract:Abstract In the article, we establish two multiple attribute decision making (MADM) approaches using the developed weighted generalized Maclaurin Symmetric Mean (q-ROFWGMSM) and weighted generalized geometric Maclaurin Symmetric Mean (q-ROFWGGMSM) operator concerning q-rung orthopair fuzzy numbers (q-ROFNs). Firstly, inspired by the generalized Maclaurin Symmetric Mean (G-MSM) and geometric Maclaurin Symmetric Mean (Geo-MSM) operators, we establish the q-rung orthopair fuzzy G-MSM (q-ROFGMSM) and q-rung orthopair fuzzy Geo-MSM (q-ROFGGMSM) operators, which assumes the grades of membership and non-membership to evaluate information can take any values in interval [0,1] respectively and the attributes are relevant to other multiple attributes. Then, we present its characteristics and some special cases. Moreover, we propose the weighted forms of the q-ROFGMSM and q-ROFGGMSM operator, which is called the q-ROFWGMSM and q-ROFWGGMSM operators, respectively. Then, we present their some characteristics and special examples. Finally, we put forward two new MADM approaches founded on the developed q-ROFWGMSM and q-ROFWGGMSM operators. The developed approaches are more general and more practicable than Liu and Wang's MADM approach (2018), Wei and Lu's MADM method (2017), Qin and Liu's MADM method (2014) and Shen et al.’s MADM approach (2018).
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Multiattribute group decision making based on intuitionistic fuzzy partitioned Maclaurin Symmetric Mean operators
Information Sciences, 2020Co-Authors: Peide Liu, Shyiming Chen, Yumei WangAbstract:Abstract In this paper, we propose a new multiattribute group decision making (MAGDM) method based on the proposed weighted partitioned Maclaurin Symmetric Mean (IFWPMSM) operators for intuitionistic fuzzy numbers (IFNs). Firstly, motivated by the partitioned Bonferroni Mean (PBM) and the Maclaurin Symmetric Mean (MSM), we present the partitioned MSM (PMSM) operator considering the hypothesis that all attributes can be cut into some groups, where the attributes in the same group are relevant to other multiple attributes of the same group, but the attributes in different groups are irrelevant. Then, we present its characteristics and some special cases. Then, we generalize the PMSM operator to propose the intuitionistic fuzzy PMSM (IFPMSM) operator for IFNs and its weighted form (IFWPMSM) for IFNs. Then, we present several characteristics and special examples of the presented IFPMSM operator and the proposed IFWPMSM operator. Finally, we propose a new MAGDM method based on the proposed IFWPMSM operator and make a comparison with the existing approaches to interpret the usability and the validity of the proposed method.
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Three-Way Decisions with Intuitionistic Uncertain Linguistic Decision-Theoretic Rough Sets Based on Generalized Maclaurin Symmetric Mean Operators
International Journal of Fuzzy Systems, 2019Co-Authors: Peide Liu, Hongyu YangAbstract:As a typical model of three-way decisions (3WDs), decision-theoretic rough set (DTRS) has received extensive attention from researchers in the decision-making fields. Intuitionistic uncertain linguistic variables (IULVs) combine the advantages of intuitionistic fuzzy sets (IFSs) and uncertain linguistic variables (ULVs), IULV is more flexible in dealing with uncertain information in decision-making process, and provides a novel Means for obtaining loss function (LF) of DTRSs. To get more comprehensive results, a new 3WD model is proposed to solve the multi-attribute group decision-making (MAGDM) problem. First, we gave the LF of DTRSs with IULVs, combined the IULVs and the generalized Maclaurin Symmetric Mean (GMSM), and proposed the IULGMSM and WIULGMSM operators to aggregate decision information; further, we proposed an intuitionistic uncertain linguistic DTRS model. Then, a method for deducing a new DTRS model is constructed, which can give the corresponding semantic interpretation of the decision results of each alternative. Finally, an example is applied to elaborate the proposed method in detail, and the effects of different conditional probabilities on decision results are discussed.
Levon Nurbekyan - One of the best experts on this subject based on the ideXlab platform.
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Radially Symmetric Mean-field games with congestion
2017 IEEE 56th Annual Conference on Decision and Control (CDC), 2017Co-Authors: David Evangelista, Diogo A. Gomes, Levon NurbekyanAbstract:Here, we study radial solutions for first- and second-order stationary Mean-Field Games (MFG) with congestion on Rd. MFGs with congestion model problems where the agents' motion is hampered in high-density regions. The radial case, which is one of the simplest non one-dimensional MFG, is relatively tractable. As we observe in this paper, the Fokker-Planck equation is integrable with respect to one of the unknowns. Consequently, we obtain a single equation substituting this solution into the Hamilton-Jacobi equation. For the first-order case, we derive explicit formulas; for the elliptic case, we study a variational formulation of the resulting equation. In both cases, we use our approach to compute numerical approximations to the solutions of the corresponding MFG systems.
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CDC - Radially Symmetric Mean-field games with congestion
2017 IEEE 56th Annual Conference on Decision and Control (CDC), 2017Co-Authors: David Evangelista, Diogo A. Gomes, Levon NurbekyanAbstract:Here, we study radial solutions for first- and second-order stationary Mean-Field Games (MFG) with congestion on Rd. MFGs with congestion model problems where the agents' motion is hampered in high-density regions. The radial case, which is one of the simplest non one-dimensional MFG, is relatively tractable. As we observe in this paper, the Fokker-Planck equation is integrable with respect to one of the unknowns. Consequently, we obtain a single equation substituting this solution into the Hamilton-Jacobi equation. For the first-order case, we derive explicit formulas; for the elliptic case, we study a variational formulation of the resulting equation. In both cases, we use our approach to compute numerical approximations to the solutions of the corresponding MFG systems.
Yumei Wang - One of the best experts on this subject based on the ideXlab platform.
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Multiattribute group decision making based on intuitionistic fuzzy partitioned Maclaurin Symmetric Mean operators
Information Sciences, 2020Co-Authors: Peide Liu, Shyiming Chen, Yumei WangAbstract:Abstract In this paper, we propose a new multiattribute group decision making (MAGDM) method based on the proposed weighted partitioned Maclaurin Symmetric Mean (IFWPMSM) operators for intuitionistic fuzzy numbers (IFNs). Firstly, motivated by the partitioned Bonferroni Mean (PBM) and the Maclaurin Symmetric Mean (MSM), we present the partitioned MSM (PMSM) operator considering the hypothesis that all attributes can be cut into some groups, where the attributes in the same group are relevant to other multiple attributes of the same group, but the attributes in different groups are irrelevant. Then, we present its characteristics and some special cases. Then, we generalize the PMSM operator to propose the intuitionistic fuzzy PMSM (IFPMSM) operator for IFNs and its weighted form (IFWPMSM) for IFNs. Then, we present several characteristics and special examples of the presented IFPMSM operator and the proposed IFWPMSM operator. Finally, we propose a new MAGDM method based on the proposed IFWPMSM operator and make a comparison with the existing approaches to interpret the usability and the validity of the proposed method.
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Multiple attribute decision making based on q-rung orthopair fuzzy generalized Maclaurin symmetic Mean operators
Information Sciences, 2020Co-Authors: Peide Liu, Yumei WangAbstract:Abstract In the article, we establish two multiple attribute decision making (MADM) approaches using the developed weighted generalized Maclaurin Symmetric Mean (q-ROFWGMSM) and weighted generalized geometric Maclaurin Symmetric Mean (q-ROFWGGMSM) operator concerning q-rung orthopair fuzzy numbers (q-ROFNs). Firstly, inspired by the generalized Maclaurin Symmetric Mean (G-MSM) and geometric Maclaurin Symmetric Mean (Geo-MSM) operators, we establish the q-rung orthopair fuzzy G-MSM (q-ROFGMSM) and q-rung orthopair fuzzy Geo-MSM (q-ROFGGMSM) operators, which assumes the grades of membership and non-membership to evaluate information can take any values in interval [0,1] respectively and the attributes are relevant to other multiple attributes. Then, we present its characteristics and some special cases. Moreover, we propose the weighted forms of the q-ROFGMSM and q-ROFGGMSM operator, which is called the q-ROFWGMSM and q-ROFWGGMSM operators, respectively. Then, we present their some characteristics and special examples. Finally, we put forward two new MADM approaches founded on the developed q-ROFWGMSM and q-ROFWGGMSM operators. The developed approaches are more general and more practicable than Liu and Wang's MADM approach (2018), Wei and Lu's MADM method (2017), Qin and Liu's MADM method (2014) and Shen et al.’s MADM approach (2018).
Harish Garg - One of the best experts on this subject based on the ideXlab platform.
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Generalized Maclaurin Symmetric Mean aggregation operators based on Archimedean t-norm of the intuitionistic fuzzy soft set information
Artificial Intelligence Review, 2020Co-Authors: Harish Garg, Rishu AroraAbstract:Intuitionistic fuzzy soft set (IFSS) accommodates more uncertainties within the information by considering the parameterization feature than the intuitionistic fuzzy sets and hence its applications are more extensive. Archimedean T-conorm and T-norm (ATT), consists of T-norm and T-conorm classes, is as an essential source to make the comprehensive operational laws. Meanwhile, the Maclaurin Symmetric Mean (MSM) has a prominent characteristic and the advantage that it can take into account the interrelation between multi-input arguments, including different attributes or different experts. Motivated by these chief characteristics, in this article, we extend the MSM operators to the IFSS based on ATT. In this paper, a method is exploited to solve the multi-criteria decision-making (MCDM) problems under the IFSS environment. To it, firstly, some generalized intuitionistic fuzzy soft operational laws are introduced based on ATT. Secondly, we reveal some averaging and geometric aggregation operators based on MSM operator. Further, some desirable features and particular cases of it are tested and build up with a new technique for illustrating MCDM problems. Finally, an illustration is given to exhibit the methodology and approach’s supremacy is shown through a comparative study with prevailing techniques.
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Maclaurin Symmetric Mean aggregation operators based on t-norm operations for the dual hesitant fuzzy soft set
Journal of Ambient Intelligence and Humanized Computing, 2019Co-Authors: Harish Garg, Rishu AroraAbstract:The objective of this paper is to present a Maclaurin Symmetric Mean (MSM) operator to aggregate dual hesitant fuzzy (DHF) soft numbers. The salient feature of MSM operators is that it can reflect the interrelationship between the multi-input arguments. Under DHF soft set environment, we develop some aggregation operators named as DHF soft MSM averaging (DHFSMSMA) operator, the weighted DHF soft MSM averaging (WDHFSMSMA) operator, DHF soft MSM geometric (DHFSMSMG) operator, and the weighted DHF soft MSM geometric (WDHFSMSMG) operator. Further, some properties and the special cases of these operators are discussed. Then, by utilizing these operators, we develop an approach for solving the multicriteria decision-making problem and illustrate it with a numerical example. Finally, a comparison analysis has been done to analyze the advantages of the proposed operators.
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Interval-Valued Pythagorean Fuzzy Maclaurin Symmetric Mean Operators in Multiple Attribute Decision Making
IEEE Access, 2018Co-Authors: Harish GargAbstract:Interval-valued Pythagorean fuzzy (IVPF) set is one the successful extension of the existing theories for handling the uncertainties during the decision-making process. Under that environment, various aggregation operators have been developed by the authors to aggregate the different preferences of the decision makers under the different attributes. But these studies have conducted under the assumption that their corresponding pairs are independent and don’t consider the interaction between the pairs of the membership degrees. In this paper, these conditions have been relaxed by considering the interrelationship between the different inputs by using Maclaurin Symmetric Mean (MSM) operator. Further, based on the input and MSM operator, we proposed two aggregation operators namely, IVPF Maclaurin Symmetric Mean and IVPF weighted Maclaurin Symmetric Mean operators and studied their desirable properties. A decision-making method based on these operators has been discussed for solving the decision-making problems under IVPF set environment. Finally, an illustrative example and a comparative analysis have been presented to demonstrate the proposed approach.
Marleen De Bruijne - One of the best experts on this subject based on the ideXlab platform.
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CSI@MICCAI - Automated Estimation of the Spinal Curvature via Spine Centerline Extraction with Ensembles of Cascaded Neural Networks
Lecture Notes in Computer Science, 2020Co-Authors: Florian Dubost, Benjamin Collery, Antonin Renaudier, Axel Roc, Nicolas Posocco, Wiro J. Niessen, Marleen De BruijneAbstract:Scoliosis is a condition defined by an abnormal spinal curvature. For diagnosis and treatment planning of scoliosis, spinal curvature can be estimated using Cobb angles. We propose an automated method for the estimation of Cobb angles from X-ray scans. First, the centerline of the spine was segmented using a cascade of two convolutional neural networks. After smoothing the centerline, Cobb angles were automatically estimated using the derivative of the centerline. We evaluated the results using the Mean absolute error and the average Symmetric Mean absolute percentage error between the manual assessment by experts and the automated predictions. For optimization, we used 609 X-ray scans from the London Health Sciences Center, and for evaluation, we participated in the international challenge “Accurate Automated Spinal Curvature Estimation, MICCAI 2019” (100 scans). On the challenge’s test set, we obtained an average Symmetric Mean absolute percentage error of 22.96.
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Automated Estimation of the Spinal Curvature via Spine Centerline Extraction with Ensembles of Cascaded Neural Networks
arXiv: Image and Video Processing, 2019Co-Authors: Florian Dubost, Benjamin Collery, Antonin Renaudier, Axel Roc, Nicolas Posocco, Wiro J. Niessen, Gerda Bortsova, Marleen De BruijneAbstract:Scoliosis is a condition defined by an abnormal spinal curvature. For diagnosis and treatment planning of scoliosis, spinal curvature can be estimated using Cobb angles. We propose an automated method for the estimation of Cobb angles from X-ray scans. First, the centerline of the spine was segmented using a cascade of two convolutional neural networks. After smoothing the centerline, Cobb angles were automatically estimated using the derivative of the centerline. We evaluated the results using the Mean absolute error and the average Symmetric Mean absolute percentage error between the manual assessment by experts and the automated predictions. For optimization, we used 609 X-ray scans from the London Health Sciences Center, and for evaluation, we participated in the international challenge "Accurate Automated Spinal Curvature Estimation, MICCAI 2019" (100 scans). On the challenge's test set, we obtained an average Symmetric Mean absolute percentage error of 22.96.
