scholarly journals Complex Entropy and Its Application in Decision-Making for Medical Diagnosis

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Fuyuan Xiao ◽  
Xiao-Guang Yue
Keyword(s):  
Decision Making ◽  
Information Fusion ◽  
Medical Diagnosis ◽  
Uncertain Information ◽  
Fusion Algorithm ◽  
Numerical Examples ◽  
Open Issue ◽  
Complex Planes ◽  
Complex Valued ◽  
Complex Entropy

In decision-making systems, how to measure uncertain information remains an open issue, especially for information processing modeled on complex planes. In this paper, a new complex entropy is proposed to measure the uncertainty of a complex-valued distribution (CvD). The proposed complex entropy is a generalization of Gini entropy that has a powerful capability to measure uncertainty. In particular, when a CvD reduces to a probability distribution, the complex entropy will degrade into Gini entropy. In addition, the properties of complex entropy, including the nonnegativity, maximum and minimum entropies, and boundedness, are analyzed and discussed. Several numerical examples illuminate the superiority of the newly defined complex entropy. Based on the newly defined complex entropy, a multisource information fusion algorithm for decision-making is developed. Finally, we apply the decision-making algorithm in a medical diagnosis problem to validate its practicability.

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

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 840
Author(s):  
Fuyuan Xiao
Keyword(s):  
Decision Making ◽  
Information Fusion ◽  
Medical Diagnosis ◽  
Evidence Theory ◽  
Mass Function ◽  
Communication Problems ◽  
Multi Attribute Decision Making ◽  
Open Issue ◽  
The Difference ◽  
Complex Basis

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.

scholarly journals Some Similarity and Distance Measures between Complex Interval-Valued q-Rung Orthopair Fuzzy Sets Based on Cosine Function and their Applications

2021 ◽  
Vol 2021 ◽  
pp. 1-25
Author(s):  
Harish Garg ◽  
Zeeshan Ali ◽  
Tahir Mahmood ◽  
Sultan Aljahdali
Keyword(s):  
Decision Making ◽  
Solution Method ◽  
Similarity Measures ◽  
Cosine Similarity ◽  
Distance Measures ◽  
Uncertain Information ◽  
Cosine Similarity Measures ◽  
Complex Valued ◽  
Interval Valued ◽  
The Ideal

The purpose of this paper is to present a new method to solve the decision-making algorithm based on the cosine similarity and distance measures by utilizing the uncertain and vague information. A complex interval-valued q-rung orthopair fuzzy set (CIVQROFS) is a reliable and competent technique for handling the uncertain information with the help of the complex-valued membership grades. To address the degree of discrimination between the pairs of the sets, cosine similarity measures (CSMs) and distance measures (DMs) are an accomplished technique. Driven by these, in this manuscript, we defined some CSMs and DMs for the pairs of CIVQROFSs and investigated their several properties. Choosing that the CSMs do not justify the axiom of the similarity measure (SM), then we investigate a technique to developing other CIVQROFSs-based SMs using the explored CSMs and Euclidean DMs, and it fulfills the axiom of the SMs. In addition, we find the cosine DMs (CDMs) by considering the inter-relationship between the SM and DMs; then, we have modified the procedure for the rank of partiality by similarity to the ideal solution method for the CDMs under investigation, which can deal with the associated decision-making problems not only individually from the argument of the opinion of geometry but also the fact of the opinion of algebra. Finally, we provide a numerical example to demonstrate the practicality and effectiveness of the proposed procedure, which is also in line with existing procedures. Graphical representations of the measures developed are also used in this manuscript.

scholarly journals Complex q-Rung Orthopair Fuzzy Aggregation Operators and Their Applications in Multi-Attribute Group Decision Making

Information ◽  
2019 ◽  
Vol 11 (1) ◽  
pp. 5 ◽  
Author(s):  
Liu ◽  
Mahmood ◽  
Ali
Keyword(s):  
Decision Making ◽  
Fuzzy Sets ◽  
Imaginary Part ◽  
Group Decision Making ◽  
Group Decision ◽  
Aggregation Operators ◽  
Uncertain Information ◽  
Membership Degree ◽  
The Real ◽  
Complex Valued

In this manuscript, the notions of q-rung orthopair fuzzy sets (q-ROFSs) and complex fuzzy sets (CFSs) are combined is to propose the complex q-rung orthopair fuzzy sets (Cq-ROFSs) and their fundamental laws. The Cq-ROFSs are an important way to express uncertain information, and they are superior to the complex intuitionistic fuzzy sets and the complex Pythagorean fuzzy sets. Their eminent characteristic is that the sum of the qth power of the real part (similarly for imaginary part) of complex-valued membership degree and the qth power of the real part (similarly for imaginary part) of complex-valued non‐membership degree is equal to or less than 1, so the space of uncertain information they can describe is broader. Under these environments, we develop the score function, accuracy function and comparison method for two Cq-ROFNs. Based on Cq-ROFSs, some new aggregation operators are called complex q-rung orthopair fuzzy weighted averaging (Cq-ROFWA) and complex q-rung orthopair fuzzy weighted geometric (Cq-ROFWG) operators are investigated, and their properties are described. Further, based on proposed operators, we present a new method to deal with the multi‐attribute group decision making (MAGDM) problems under the environment of fuzzy set theory. Finally, we use some practical examples to illustrate the validity and superiority of the proposed method by comparing with other existing methods.

Research on Vague Set of Target Threat Information Fusion Algorithm

2014 ◽  
Vol 599-601 ◽  
pp. 1671-1678
Author(s):  
Ke Hu Xu ◽  
Jin Yu Chen ◽  
De Peng Kong ◽  
Pu Fan
Keyword(s):  
Information Fusion ◽  
Scoring Function ◽  
Score Function ◽  
Quantitative Information ◽  
Uncertain Information ◽  
Fusion Technology ◽  
Fusion Algorithm ◽  
Information Process ◽  
Vague Set ◽  
Research Areas

Information fusion technology is one of the most active research areas currently, which is affected by the fact that the computer can only handle quantitative information and the result cannot reflect the actual feature of the target sometimes. This paper makes use of advantages of the vague set in dealing with uncertain information process to establish the target threat sequencing model based on vague set and take both quantitative and qualitative information of target into account. Using the improved scoring function, this paper comes up with the target threat sequence steps based on extreme score function method to provide a better data supporting for the decision-making function of information fusion technology.

scholarly journals A Chi-square Distance-based Similarity Measure of Single-valued Neutrosophic Set and Applications

2019 ◽  
Vol 14 (1) ◽  
pp. 78-89 ◽  
Author(s):  
Haiping Ren ◽  
Shixiao Xiao ◽  
Hui Zhou
Keyword(s):  
Decision Making ◽  
Similarity Measure ◽  
Medical Diagnosis ◽  
Distance Measure ◽  
Similarity Measures ◽  
Neutrosophic Set ◽  
Chi Square ◽  
Neutrosophic Sets ◽  
Numerical Examples ◽  
Multi Attribute Decision Making

The aim of this paper is to propose a new similarity measure of singlevalued neutrosophic sets (SVNSs). The idea of the construction of the new similarity measure comes from Chi-square distance measure, which is an important measure in the applications of image analysis and statistical inference. Numerical examples are provided to show the superiority of the proposed similarity measure comparing with the existing similarity measures of SVNSs. A weighted similarity is also put forward based on the proposed similarity. Some examples are given to show the effectiveness and practicality of the proposed similarity in pattern recognition, medical diagnosis and multi-attribute decision making problems under single-valued neutrosophic environment.

scholarly journals A New Total Uncertainty Measure from A Perspective of Maximum Entropy Requirement

Entropy ◽  
2021 ◽  
Vol 23 (8) ◽  
pp. 1061
Author(s):  
Yu Zhang ◽  
Fanghui Huang ◽  
Xinyang Deng ◽  
Wen Jiang
Keyword(s):  
Maximum Entropy ◽  
Information Fusion ◽  
Uncertainty Measure ◽  
Total Uncertainty ◽  
Numerical Examples ◽  
Dempster Shafer Theory ◽  
Basic Probability ◽  
Open Issue ◽  
Shafer Theory ◽  
Fusion Framework

The Dempster-Shafer theory (DST) is an information fusion framework and widely used in many fields. However, the uncertainty measure of a basic probability assignment (BPA) is still an open issue in DST. There are many methods to quantify the uncertainty of BPAs. However, the existing methods have some limitations. In this paper, a new total uncertainty measure from a perspective of maximum entropy requirement is proposed. The proposed method can measure both dissonance and non-specificity in BPA, which includes two components. The first component is consistent with Yager’s dissonance measure. The second component is the non-specificity measurement with different functions. We also prove the desirable properties of the proposed method. Besides, numerical examples and applications are provided to illustrate the effectiveness of the proposed total uncertainty measure.

scholarly journals An Uncertainty Measure for Interval-valued Evidences

2017 ◽  
Vol 12 (5) ◽  
pp. 631 ◽  
Author(s):  
Wen Jiang ◽  
Shiyu Wang
Keyword(s):  
Evidence Theory ◽  
Uncertain Information ◽  
Uncertainty Measure ◽  
Interval Numbers ◽  
Numerical Examples ◽  
Belief Structure ◽  
Belief Structures ◽  
Open Issue ◽  
Deng Entropy ◽  
Interval Valued

Interval-valued belief structure (IBS), as an extension of single-valued belief structures in Dempster-Shafer evidence theory, is gradually applied in many fields. An IBS assigns belief degrees to interval numbers rather than precise numbers, thereby it can handle more complex uncertain information. However, how to measure the uncertainty of an IBS is still an open issue. In this paper, a new method based on Deng entropy denoted as UIV is proposed to measure the uncertainty of the IBS. Moreover, it is proved that UIV meets some desirable axiomatic requirements. Numerical examples are shown in the paper to demonstrate the efficiency of UIV by comparing the proposed UIV with existing approaches. 

scholarly journals An Improved Belief Entropy and Its Application in Decision-Making

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-15 ◽  
Author(s):  
Deyun Zhou ◽  
Yongchuan Tang ◽  
Wen Jiang
Keyword(s):  
Decision Making ◽  
Mass Function ◽  
The Body ◽  
Uncertain Information ◽  
Uncertainty Measure ◽  
Numerical Examples ◽  
Fusion Applications ◽  
Belief Entropy ◽  
The Difference ◽  
Available Information

Uncertainty measure in data fusion applications is a hot topic; quite a few methods have been proposed to measure the degree of uncertainty in Dempster-Shafer framework. However, the existing methods pay little attention to the scale of the frame of discernment (FOD), which means a loss of information. Due to this reason, the existing methods cannot measure the difference of uncertain degree among different FODs. In this paper, an improved belief entropy is proposed in Dempster-Shafer framework. The proposed belief entropy takes into consideration more available information in the body of evidence (BOE), including the uncertain information modeled by the mass function, the cardinality of the proposition, and the scale of the FOD. The improved belief entropy is a new method for uncertainty measure in Dempster-Shafer framework. Based on the new belief entropy, a decision-making approach is designed. The validity of the new belief entropy is verified according to some numerical examples and the proposed decision-making approach.

A Research on the Improved Information Fusion Algorithm

2014 ◽  
Vol 543-547 ◽  
pp. 1909-1912
Author(s):  
Guo Dong Zhang ◽  
Rui Min Qi
Keyword(s):  
Decision Making ◽  
Information Fusion ◽  
Evidence Theory ◽  
Source Information ◽  
Fusion Algorithm ◽  
Mutually Independent

In the field of information confusion, evidence theory takes advantage of its uncertainties. But in practical evidences are always mutually independent, However, multi-source information fusion is a comprehensive integration and then obtaining decision-making, sometimes the result of information fusion give us wrong conclusion. So an improved information fusion algorithm is proposed in this paper. It can heighten the information confusion reliability and accuracy in a practical example.

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