Uncertain database retrieval with measure – Based belief function attribute values under intuitionistic fuzzy set

Xue, Yige; Deng, Yong; Garg, Harish

doi:10.1016/j.ins.2020.08.096

Cited by 59 publications

(26 citation statements)

References 40 publications

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“…Many mathematical tools such as fuzzy sets [11][12][13][14], neutrosophic sets [15], and Z-numbers [16] are used. For example, Xue et al [17] addressed the uncertain database retrieval problem based on intuitionistic fuzzy set. Li et al [18] proposed a new uncertainty measure of discrete Z-numbers and applied it to solve MCDM problems.…”

Section: Introductionmentioning

confidence: 99%

Fire Safety Evaluation for Scenic Spots: An Evidential Best-Worst Method

Chen

Xie

2021

Journal of Mathematics

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Fire safety plays a vital role in tourism management, which can cause significant loss of life and property. It is necessary to present an efficient fire safety evaluation for scenic spots. However, some key issues are not well addressed in existing methods. For example, how to model experts’ opinions and how to combine them are still open problems. To address these issues, a new evaluation method based on the Dempster–Shafer evidence theory and best-worst method is presented. First, a fire safety evaluation index system is constructed using the domino model. Domain experts can evaluate different factors with linguistic assessments. The best-worst method is used to determine the weights of different factors. Then, these weighted linguistic assessments are efficiently fused by Dempster’s combination rule to obtain the evaluation result. Finally, a case study is illustrated to demonstrate the efficiency of the proposed method in fire safety evaluation for scenic spots. The main contribution of the proposed method is to represent and handle the uncertainty in experts’ linguistic assessments, so as to decrease the uncertainty and improve decision making. In addition, the weight determination method BWM is easier and more reliable than the existing method AHP.

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Section: Introductionmentioning

confidence: 99%

Fire Safety Evaluation for Scenic Spots: An Evidential Best-Worst Method

Chen

Xie

2021

Journal of Mathematics

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“…Efficiency of the algorithm is tested over a numerical example as well as a case study and results were compared to those in the literature. As future study the proposed problem can be considered in other type uncertain environments like Pythagorean fuzzy uncertainty (see [7,39,40], or belief degree based uncertainty [42]).…”

Section: Resultsmentioning

confidence: 99%

Fractional two-stage transshipment problem under uncertainty: application of the extension principle approach

Garg

Mahmoodirad

Niroomand

2021

Complex Intell. Syst.

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In this paper, a fuzzy fractional two-stage transshipment problem where all the parameters are represented by fuzzy numbers is studied. The problem uses the ratio of costs divided by benefits as the objective function. A solution method which employs the extension principle is used to find the fuzzy objective value of the problem. For this purpose, the fuzzy fractional two-stage transshipment problem is decomposed into two sub-problems where each of them is tackled individually using various $$\alpha$$ α levels to obtain the fuzzy objective function value and its associated membership function. To deal with the nonlinearity of the objective function the Charnes–Cooper transformation method is embedded to the proposed approach. The superior efficiency of the presented formulation and the proposed solution method is examined over a numerical example as well as a case study comparing to the literature.

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“…Multi-attribute decision-making has received much attention [ 81 , 82 , 83 ]. Because of the complexity of various applications, it is still an open issue to handle uncertainty in this field.…”

Section: Algorithm and Applicationmentioning

confidence: 99%

Complex Pignistic Transformation-Based Evidential Distance for Multisource Information Fusion of Medical Diagnosis in the IoT

Xiao

2021

Sensors

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Multisource information fusion has received much attention in the past few decades, especially for the smart Internet of Things (IoT). Because of the impacts of devices, the external environment, and communication problems, the collected information may be uncertain, imprecise, or even conflicting. How to handle such kinds of uncertainty is still an open issue. Complex evidence theory (CET) is effective at disposing of uncertainty problems in the multisource information fusion of the IoT. In CET, however, how to measure the distance among complex basis belief assignments (CBBAs) to manage conflict is still an open issue, which is a benefit for improving the performance in the fusion process of the IoT. In this paper, therefore, a complex Pignistic transformation function is first proposed to transform the complex mass function; then, a generalized betting commitment-based distance (BCD) is proposed to measure the difference among CBBAs in CET. The proposed BCD is a generalized model to offer more capacity for measuring the difference among CBBAs. Additionally, other properties of the BCD are analyzed, including the non-negativeness, nondegeneracy, symmetry, and triangle inequality. Besides, a basis algorithm and its weighted extension for multi-attribute decision-making are designed based on the newly defined BCD. Finally, these decision-making algorithms are applied to cope with the medical diagnosis problem under the smart IoT environment to reveal their effectiveness.

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Uncertain database retrieval with measure – Based belief function attribute values under intuitionistic fuzzy set

Cited by 59 publications

References 40 publications

Fire Safety Evaluation for Scenic Spots: An Evidential Best-Worst Method

Fire Safety Evaluation for Scenic Spots: An Evidential Best-Worst Method

Fractional two-stage transshipment problem under uncertainty: application of the extension principle approach

Complex Pignistic Transformation-Based Evidential Distance for Multisource Information Fusion of Medical Diagnosis in the IoT

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