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Hu Y, Pang Z. A novel MCGDM technique based on correlation coefficients under probabilistic hesitant fuzzy environment and its application in clinical comprehensive evaluation of orphan drugs. PLoS One 2024; 19:e0303042. [PMID: 38709744 PMCID: PMC11073718 DOI: 10.1371/journal.pone.0303042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Accepted: 04/07/2024] [Indexed: 05/08/2024] Open
Abstract
Probabilistic hesitant fuzzy sets (PHFSs) are superior to hesitant fuzzy sets (HFSs) in avoiding the problem of preference information loss among decision makers (DMs). Owing to this benefit, PHFSs have been extensively investigated. In probabilistic hesitant fuzzy environments, the correlation coefficients have become a focal point of research. As research progresses, we discovered that there are still a few unresolved issues concerning the correlation coefficients of PHFSs. To overcome the limitations of existing correlation coefficients for PHFSs, we propose new correlation coefficients in this study. In addition, we present a multi-criteria group decision-making (MCGDM) method under unknown weights based on the newly proposed correlation coefficients. In addition, considering the limitations of DMs' propensity to use language variables for expression in the evaluation process, we propose a method for transforming the evaluation information of the DMs' linguistic variables into probabilistic hesitant fuzzy information in the newly proposed MCGDM method. To demonstrate the applicability of the proposed correlation coefficients and MCGDM method, we applied them to a comprehensive clinical evaluation of orphan drugs. Finally, the reliability, feasibility and efficacy of the newly proposed correlation coefficients and MCGDM method were validated.
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Affiliation(s)
- Yubo Hu
- School of Statistics, Lanzhou University of Finance and Economics, Lanzhou, Gansu, China
| | - Zhiqiang Pang
- School of Statistics, Lanzhou University of Finance and Economics, Lanzhou, Gansu, China
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2
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Wang L, Liu M, Lai S. Wealth exchange and decision-making psychology in epidemic dynamics. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:9839-9860. [PMID: 37322913 DOI: 10.3934/mbe.2023431] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
A binary wealth exchange mechanism, which involves the influence of the epidemic environment and agents' psychology on trading decisions, is introduced to discuss the wealth distribution of agents under the background of an epidemic. We find that the trading psychology of agents may affect wealth distribution and make the tail of the steady-state wealth distribution slimmer. The steady-state wealth distribution displays a bimodal shape under appropriate parameters. Our results suggest that government control measures are essential to curb the spread of epidemics, and vaccination may help to improve the economy, while contact control measures may aggravate wealth inequality.
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Affiliation(s)
- Lingling Wang
- School of Mathematics and Statistics, Yili Normal University, Yining 835000, China
- School of Mathematics, Southwestern University of Finance and Economics, Chengdu 611130, China
| | - Miao Liu
- School of Mathematics and Statistics, Yili Normal University, Yining 835000, China
| | - Shaoyong Lai
- School of Mathematics and Statistics, Yili Normal University, Yining 835000, China
- School of Mathematics, Southwestern University of Finance and Economics, Chengdu 611130, China
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3
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Wan S, Cheng X, Dong J. Group decision-making with interval multiplicative preference relations. Knowl Inf Syst 2023. [DOI: 10.1007/s10115-022-01816-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
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4
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Fang B. Some uncertainty measures for probabilistic hesitant fuzzy information. Inf Sci (N Y) 2023. [DOI: 10.1016/j.ins.2022.12.101] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
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5
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Lei F, Cai Q, Wei G, Mo Z, Guo Y. Probabilistic double hierarchy linguistic MADM for location selection of new energy electric vehicle charging stations based on the MSM operators. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022. [DOI: 10.3233/jifs-221979] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
The emergence of new energy electric vehicles (NEEV) can effectively reduce vehicle fuel consumption and alleviate the contradiction between fuel supply and demand. It has made great contributions to improving the atmospheric environment and promoting the development of environmental protection. However, the insufficient number of new energy electric vehicle charging stations (NEEVCSs) and unreasonable coverage areas have become obstacles to the large-scale promotion of new energy electric vehicles. Therefore, we build a multi-attribute decision making (MADM) model based on probabilistic double hierarchy linguistic weight Maclaurin symmetric mean (PDHLWMSM) operator and a MADM model based on probabilistic double hierarchy linguistic weight power Maclaurin symmetric mean (PDHLWPMSM) operator to select the best charging station construction point from multiple alternative sites. In addition, the model constructed in this paper is compared with the existing MADM models to verify the scientificity of the model proposed in this paper.
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Affiliation(s)
- Fan Lei
- School of Mathematical Sciences, Sichuan Normal University, Chengdu, P.R. China
| | - Qiang Cai
- School of Business, Sichuan Normal University, Chengdu, P.R. China
| | - Guiwu Wei
- School of Mathematical Sciences, Sichuan Normal University, Chengdu, P.R. China
- School of Business, Sichuan Normal University, Chengdu, P.R. China
| | - Zhiwen Mo
- School of Mathematical Sciences, Sichuan Normal University, Chengdu, P.R. China
| | - Yanfeng Guo
- School of Finance, Southwestern University ofFinance and Economics, Chengdu, P.R. China
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6
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Zhang S, Liu X, Garg H, Zhang S. Investment decision making in the fuzzy context: An integrated model approach. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022. [DOI: 10.3233/jifs-223059] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Abstract
With the implementation and steady progress of the Belt and Road (B&R) initiative, China’s investment in countries along the B&R has maintained a high growth trend. Generally speaking, investment problems are often accompanied by high risk and uncertainty, and how to make the suitable investment decision is a difficult issue. This paper investigates an investment decision approach under the probabilistic hesitant fuzzy environment. Firstly, a new probabilistic hesitant fuzzy distance and correlation coefficient are defined to overcome the defects of the existing probabilistic hesitant fuzzy information measures. Secondly, an attribute weight integrated model is constructed by combining the maximum deviation method, the CRITIC method and the maximum entropy principle, which is able to take into account the correlation between attributes and make full use of the decision information. In addition, a disappointment theory-based probabilistic hesitant fuzzy multi-attribute decision making (PHFMADM) method is proposed to solve the investment decision problem, which can integrate the psychological behavior of decision makers into the decision making process and make the decision results more authentic and reliable. Finally, the rationality and validity of the method are verified by comparing with the existing methods.
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Affiliation(s)
- Shasha Zhang
- School of Mathematics and Physics, Anhui University of Technology, Ma’anshan, Anhui, China
| | - Xiaodi Liu
- School of Mathematics and Physics, Anhui University of Technology, Ma’anshan, Anhui, China
| | - Harish Garg
- School of Mathematics, Thapar Institute of Engineering and Technology, Deemed University, Patiala
| | - Shitao Zhang
- School of Mathematics and Physics, Anhui University of Technology, Ma’anshan, Anhui, China
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Xu G, Zhang J, Cliff UGO, Ma C. An efficient blockchain‐based privacy‐preserving scheme with attribute and homomorphic encryption. INT J INTELL SYST 2022. [DOI: 10.1002/int.22946] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Guangxia Xu
- Cyberspace Institute of Advanced Technology Guangzhou University Guangzhou China
- School of Software Engineering Chongqing University of Posts and Telecommunications Chongqing China
| | - Jiajun Zhang
- School of Software Engineering Chongqing University of Posts and Telecommunications Chongqing China
| | - Uchani Gutierrez Omar Cliff
- School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing China
| | - Chuang Ma
- School of Software Engineering Chongqing University of Posts and Telecommunications Chongqing China
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A Novel Approach to Skin Lesion Segmentation: Multipath Fusion Model with Fusion Loss. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:2157322. [PMID: 35936380 PMCID: PMC9355768 DOI: 10.1155/2022/2157322] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/17/2022] [Revised: 06/17/2022] [Accepted: 06/24/2022] [Indexed: 11/30/2022]
Abstract
Segmentation of skin lesions plays a very important role in the early detection of skin cancer. However, indistinguishability due to various artifacts such as hair and contrast between normal skin and lesioned skin is an important challenge for specialist dermatologists. Computer-aided diagnostic systems using deep convolutional neural networks are gaining importance in order to cope with difficulties. This study focuses on deep learning-based fusion networks and fusion loss functions. For the automatic segmentation of skin lesions, U-Net (U-Net + ResNet 2D) with 2D residual blocks and 2D volumetric convolutional neural networks were fused for the first time in this study. Also, a new fusion loss function is proposed by combining Dice Loss (DL) and Focal Tversky Loss (FTL) to make the proposed fused model more robust. Of the 2594 image dataset, 20% is reserved for test data and 80% for training data. In test data training, a Jaccard score of 0.837 and a dice score of 0.918 were obtained. The proposed model was also scored on the ISIC 2018 Task 1 test images, whose ground truths were not shared. The proposed model performed well and achieved a Jaccard index of 0.800 and a dice score of 0.880 in the ISIC 2018 Task 1 test set. In addition, it has been observed that the new fused loss function obtained by fusing Focal Tversky Loss and Dice Loss functions in the proposed model increases the robustness of the model in the tests. The proposed new loss function fusion model has outstripped the cutting-edge approaches in the literature.
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Feature Extraction Approach for Speaker Verification to Support Healthcare System Using Blockchain Security for Data Privacy. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:8717263. [PMID: 35924113 PMCID: PMC9343193 DOI: 10.1155/2022/8717263] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Revised: 06/19/2022] [Accepted: 07/12/2022] [Indexed: 11/17/2022]
Abstract
Speech is one form of biometric that combines both physiological and behavioral features. It is beneficial for remote-access transactions over telecommunication networks. Presently, this task is the most challenging one for researchers. People’s mental status in the form of emotions is quite complex, and its complexity depends upon internal behavior. Emotion and facial behavior are essential characteristics through which human internal thought can be predicted. Speech is one of the mechanisms through which human’s various internal reflections can be expected and extracted by focusing on the vocal track, the flow of voice, voice frequency, etc. Human voice specimens of different ages can be emotions that can be predicted through a deep learning approach using feature removal behavior prediction that will help build a step intelligent healthcare system strong and provide data to various doctors of medical institutes and hospitals to understand the physiological behavior of humans. Healthcare is a clinical area with data concentrated where many details are accessed, generated, and circulated periodically. Healthcare systems with many existing approaches like tracing and tracking continuously disclose the system’s constraints in controlling patient data privacy and security. In the healthcare system, majority of the work involves swapping or using decisively confidential and personal data. A key issue is the modeling of approaches that guarantee the value of health-related data while protecting privacy and observing high behavioral standards. This will encourage large-scale perception, especially as healthcare information collection is expected to continue far off this current ongoing pandemic. So, the research section is looking for a privacy-preserving, secure, and sustainable system by using a technology called Blockchain. Data related to healthcare and distribution among institutions is a very challenging task. Storage of facts in the centralized form is a targeted choice for cyber hackers and initiates an accordant sight of patients’ facts which will cause a problem in sharing information over a network. So, this research paper’s approach based on Blockchain for sharing sufferer data in a secured manner is presented. Finally, the proposed model for extracting optimum value in error rate and accuracy was analyzed using different feature removal approaches to determine which feature removal performs better with different voice specimen variations. The proposed method increases the rate of correct evidence collection and minimizes the loss and authentication issues and using feature extraction based on text validation increases the sustainability of the healthcare system.
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Nour M, Kandaz D, Ucar MK, Polat K, Alhudhaif A. Machine Learning and Electrocardiography Signal-Based Minimum Calculation Time Detection for Blood Pressure Detection. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:5714454. [PMID: 35903432 PMCID: PMC9325348 DOI: 10.1155/2022/5714454] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Revised: 06/21/2022] [Accepted: 06/28/2022] [Indexed: 11/18/2022]
Abstract
Objective Measurement and monitoring of blood pressure are of great importance for preventing diseases such as cardiovascular and stroke caused by hypertension. Therefore, there is a need for advanced artificial intelligence-based systolic and diastolic blood pressure systems with a new technological infrastructure with a noninvasive process. The study is aimed at determining the minimum ECG time required for calculating systolic and diastolic blood pressure based on the Electrocardiography (ECG) signal. Methodology. The study includes ECG recordings of five individuals taken from the IEEE database, measured during daily activity. For the study, each signal was divided into epochs of 2-4-6-8-10-12-14-16-18-20 seconds. Twenty-five features were extracted from each epoched signal. The dimension of the dataset was reduced by using Spearman's feature selection algorithm. Analysis based on metrics was carried out by applying machine learning algorithms to the obtained dataset. Gaussian process regression exponential (GPR) machine learning algorithm was preferred because it is easy to integrate into embedded systems. Results The MAPE estimation performance values for diastolic and systolic blood pressure values for 16-second epochs were 2.44 mmHg and 1.92 mmHg, respectively. Conclusion According to the study results, it is evaluated that systolic and diastolic blood pressure values can be calculated with a high-performance ratio with 16-second ECG signals.
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Affiliation(s)
- Majid Nour
- Department of Electrical and Computer Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia
| | - Derya Kandaz
- Electrical-Electronics Engineering, Faculty of Engineering, Sakarya University, 54187 Sakarya, Turkey
| | - Muhammed Kursad Ucar
- Electrical-Electronics Engineering, Faculty of Engineering, Sakarya University, 54187 Sakarya, Turkey
| | - Kemal Polat
- Department of Electrical and Electronics Engineering, Faculty of Engineering, Bolu Abant Izzet Baysal University, Bolu 14280, Turkey
| | - Adi Alhudhaif
- Department of Computer Science, College of Computer Engineering and Sciences in Al-Kharj, Prince Sattam Bin Abdulaziz University, P.O. Box 151, Al-Kharj 11942, Saudi Arabia
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11
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Effectiveness evaluation method of constellation satellite communication system with acceptable consistency and consensus under probability hesitant intuitionistic fuzzy preference relationship. Soft comput 2022. [DOI: 10.1007/s00500-022-07220-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
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12
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Shi X, Lin Z, Zhou L, Bao H. Linguistic q-rung orthopair fuzzy multiple-attribute group decision making based on the grey similarity degree and PROMETHEE II method. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022. [DOI: 10.3233/jifs-220579] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Linguistic q-rung orthopair fuzzy numbers (Lq-ROFNs) are an effective tool for representing fuzzy linguistic information, and they can obtain a wider expression scope than linguistic intuitionistic fuzzy numbers and linguistic Pythagorean fuzzy numbers by increasing the value of parameter q. In this paper, we propose a new similarity measure called the grey similarity degree between any two Lq-ROFNs based on the concept of the grey correlation degree. Considering the significance of determining unknown weights, we also propose a grey correlation method to determine each expert’s weight under different alternatives and attributes, and we construct an optimization model to determine incompletely known attribute weights. Furthermore, an approach to linguistic q-rung orthopair fuzzy multiple-attribute group decision making is proposed that combines the grey similarity degree with the PROMETHEE II method. Finally, a numerical example is given to illustrate the effectiveness of the proposed method, and a sensitivity analysis and comparison analysis are also performed.
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Affiliation(s)
- Xuecheng Shi
- School of Mathematical Sciences, Anhui University, Hefei, Anhui, China
| | - Zhichao Lin
- School of Mathematical Sciences, Anhui University, Hefei, Anhui, China
- Anhui University Center for Applied Mathematics, Anhui University, Hefei, Anhui, China
| | - Ligang Zhou
- School of Mathematical Sciences, Anhui University, Hefei, Anhui, China
- Anhui University Center for Applied Mathematics, Anhui University, Hefei, Anhui, China
| | - Hengjia Bao
- School of Mathematical Sciences, Anhui University, Hefei, Anhui, China
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Jin C, Mi J, Li F, Liang M. A novel probabilistic hesitant fuzzy rough set based multi-criteria decision-making method. Inf Sci (N Y) 2022. [DOI: 10.1016/j.ins.2022.06.085] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
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14
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Krishankumar R, Garg H, Arun K, Saha A, Ravichandran KS, Kar S. An integrated decision-making COPRAS approach to probabilistic hesitant fuzzy set information. COMPLEX INTELL SYST 2021. [DOI: 10.1007/s40747-021-00387-w] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Abstract
AbstractThe paper aims to present an integrated approach to solve the decision-making problem under the probabilistic hesitant fuzzy information (PHFI) features, which is an extension of the hesitant fuzzy set. The considered PHFI not only allows multiple opinions, but also associates occurrence probability to each opinion, which increases the reliability of the information. Motivated by these features of PHFI, an approach is presented to solve the decision problem with partial known information about the attribute and expert weights. In addition, an algorithm for finding some missing values in the preference information is presented and stated their properties. Afterward, the Hamy mean operator has been used to aggregate the different collective information into a single one. Also, we presented a COPRAS method to the PHFI for ranking the given alternatives. The presented algorithm has been demonstrated through a case study of cloud vendor selection and its validity has been revealed by comparing the approach results with the several existing algorithm results.
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Score function based on concentration degree for probabilistic linguistic term sets: An application to TOPSIS and VIKOR. Inf Sci (N Y) 2021. [DOI: 10.1016/j.ins.2020.10.061] [Citation(s) in RCA: 72] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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16
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Krishankumar R, Ravichandran KS, Liu P, Kar S, Gandomi AH. A decision framework under probabilistic hesitant fuzzy environment with probability estimation for multi-criteria decision making. Neural Comput Appl 2021. [DOI: 10.1007/s00521-020-05595-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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17
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Guo J, Xu J, Du Q, He Z. Risk assessment on multimodal transport network based on quality function deployment. INT J INTELL SYST 2020. [DOI: 10.1002/int.22348] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Affiliation(s)
- Jingni Guo
- School of Transportation and Logistics Southwest Jiaotong University Chengdu Sichuan China
- National United Engineering Laboratory of Integrated and Intelligent Transportation Southwest Jiaotong University Chengdu Sichuan China
| | - Junxiang Xu
- School of Transportation and Logistics Southwest Jiaotong University Chengdu Sichuan China
- National United Engineering Laboratory of Integrated and Intelligent Transportation Southwest Jiaotong University Chengdu Sichuan China
| | - Qian Du
- School of Transportation and Logistics Southwest Jiaotong University Chengdu Sichuan China
| | - Zhenggang He
- School of Transportation and Logistics Southwest Jiaotong University Chengdu Sichuan China
- National United Engineering Laboratory of Integrated and Intelligent Transportation Southwest Jiaotong University Chengdu Sichuan China
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Yang J, Xu Z. A measure of probabilistic hesitant I‐fuzzy sets and decision makings for strategy choice. INT J INTELL SYST 2020. [DOI: 10.1002/int.22340] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- Jie Yang
- Business School Sichuan University Chengdu China
- College of Management Fujian Agriculture and Forestry University Fujian China
| | - Zeshui Xu
- Business School Sichuan University Chengdu China
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