Uncertain Information Processing Research Articles

Evaluating edge artificial intelligence-driven supply chain management platforms using collaborative large-scale fuzzy information fusion

The rapid development of edge artificial intelligence (AI) has brought about revolutionary changes in supply chain management (SCM). It not only provides real-time data processing capabilities but also accelerates the decision-making process, injecting more innovative elements into SCM. In this context, SCM platforms require new technological developments and effective evaluations, as they collectively drive the efficient coordination of complex business processes. SCM platforms enable the seamless coordination of various supply chain elements, facilitating streamlined operations and enhanced decision-making processes. The evaluation of these platforms not only serves to validate their performance and effectiveness, but also contributes to continual improvements in design and application, and addressing the evolving demands within the realms of global commerce and dynamic market conditions. The ongoing assessment and enhancement of SCM platforms play a crucial role in optimizing resource allocation, improving production efficiency, and fostering adaptability to changing market dynamics. However, the acquisition of assessment data often introduces imprecise data. Additionally, the large-scale nature and bounded rationality of decision-makers (DMs) significantly impact the evaluation of SCM platforms. This article aims to explore a collaborative large-scale information fusion approach and provide a robust fuzzy framework for evaluating SCM platforms. This approach employs a combination of spherical fuzzy sets (SFSs), large-scale group decision-making (LSGDM), behavioral theories and three-way decisions (TWD) to thoroughly explore collaborative large-scale information fusion and its practical application in assessing SCM platforms. It introduces an innovative spherical fuzzy (SF) LSGDM technique and integrates TWD with the inclusion of prospect theory (PT) and regret theory (RT) to mitigate potential decision risks. The developed collaborative large-scale information fusion approach is assessed for validity, effectiveness and practicality in the context of evaluating SCM platforms using online data. Experimental results demonstrate that this approach provides reasonable evaluation outcomes, considering uncertain information processing capabilities, large-scale characteristics, bounded rationality and decision risks.

A local rough set method for feature selection by variable precision composite measure

Feature selection using variable precision neighborhood rough sets (VPNRS) has garnered considerable attention in data mining and knowledge discovery. Nevertheless, the positive region of VPNRS may not be strictly divided due to the introduction of variable parameters, which could reduce the credibility of feature significance. Meanwhile, the calculation of approximate space is also complex and expensive. Hence, how to improve the computation efficiency is also an investigated issue. As for these issues, we propose a variable precision composite measure and design a novel local method for the feature selection of decision data. Firstly, we introduce the variable precision neighborhood rough set model to process uncertain information from global and local viewpoints. Furthermore, the variable precision composite measure is defined to evaluate the model’s accuracy and further used to select the essential features. Finally, a local forward algorithm is provided for feature selection to improve computing efficiency. All experiments on twelve datasets show that the local method is efficient, and the feature selection algorithm based on variable precision composite measure performs well in classification performance. Our work will provide a convenient tool for feature selection methods with uncertainty measures.

Advantage prioritization of digital carbon footprint awareness in optimized urban mobility using fuzzy Aczel Alsina based decision making

City governments prioritize mobility in urban planning and policy. Greater mobility in a city leads to happier citizens. Although enhanced urban mobility is helpful, it comes with costs, notably in terms of climate change. Transportation systems that enable urban mobility often emit greenhouse gases. Cities must prioritize digital carbon footprint awareness. Cities may reduce the environmental impact of urban mobility while keeping its benefits by close monitoring and reducing the carbon footprint of digital technologies like transportation applications, ride-sharing platforms, and smart traffic control systems. The aim is to advantage prioritize three alternatives, namely doing nothing, upgrading and optimizing data centers and networks, and using renewable energy sources for data centers and networks to minimize the digital carbon footprint using the proposed decision making tool. This study consists of two stages. In the first stage, fuzzy Aczel-Alsina functions (fuzzy Aczel-Alsina weighted assessment — ALWAS method) based Ordinal Priority Approach (OPA) is proposed to find the weights of criteria. Secondly, fuzzy ALWAS Combined Compromise Solution (CoCoSo) model is improved to evaluate and choose the best alternative among the three alternatives. The improved ALWAS-CoCoSo model enables flexible nonlinear processing of uncertain information and simulation of different risk levels. Besides, we proposed the improved fuzzy OPA algorithm for processing uncertain and incomplete information. The case study is provided to the decision-makers to advantage prioritize the alternatives based on twelve criteria organized into four aspects, including digital carbon footprint, externalities, technical capability, and economics. The ranking results reveal that A3=2.445 is the best among the three alternative, while A1=1.705 is the worst alternative. The results show that the best way to reduce the digital carbon footprint is to use renewable energy sources to power data centers and networks (A3).

Higher Order Fractal Belief Rényi Divergence With Its Applications in Pattern Classification.

Information can be quantified and expressed by uncertainty, and improving the decision level of uncertain information is vital in modeling and processing uncertain information. Dempster-Shafer evidence theory can model and process uncertain information effectively. However, the Dempster combination rule may provide counter-intuitive results when dealing with highly conflicting information, leading to a decline in decision level. Thus, measuring conflict is significant in the improvement of decision level. Motivated by this issue, this paper proposes a novel method to measure the discrepancy between bodies of evidence. First, the model of dynamic fractal probability transformation is proposed to effectively obtain more information about the non-specificity of basic belief assignments (BBAs). Then, we propose the higher-order fractal belief Rényi divergence (HOFBReD). HOFBReD can effectively measure the discrepancy between BBAs. Moreover, it is the first belief Rényi divergence that can measure the discrepancy between BBAs with dynamic fractal probability transformation. HoFBReD has several properties in terms of probability transformation as well as measurement. When the dynamic fractal probability transformation ends, HoFBReD is equivalent to measuring the Rényi divergence between the pignistic probability transformations of BBAs. When the BBAs degenerate to the probability distributions, HoFBReD will also degenerate to or be related to several well-known divergences. In addition, based on HoFBReD, a novel multisource information fusion algorithm is proposed. A pattern classification experiment with real-world datasets is presented to compare the proposed algorithm with other methods. The experiment results indicate that the proposed algorithm has a higher average pattern recognition accuracy with all datasets than other methods. The proposed discrepancy measurement method and multisource information algorithm contribute to the improvement of decision level.

Research on the evaluation of green suppliers of high energy-consuming enterprises--based on rough number-grey correlation TOPSIS method

With the Goal-Establishment of “carbon compliance” and “carbon neutrality”, enterprises should upgrade “green development” to a strategic position and implement it through all aspects of business operations. Green supplier selection is the initial phase of supply chain management, therefore a green supplier evaluation system is needed to achieve green development. Based on a literature analysis, We selected 45 metrics as candidates for evaluating suppliers. Then through expert interviews, some indicators were revised and supplemented, and finally a green supplier evaluation index system for high energy-consuming enterprises was constructed. A unique aspect of this paper is the introduction of rough number theory into the supplier evaluation process to improve the indicator assignment and the grey correlation TOPSIS method, which optimizes the processing of uncertain semantic information in the evaluation process. The rough number-grey correlation TOPSIS supplier evaluation model developed in this paper has been verified to be applicable and stable in case studies and successfully implemented.

Accelerating the integration of the metaverse into urban transportation using fuzzy trigonometric based decision making

Metaverse is defined as a fictional universe that could serve as a simulation environment of reality. Beginning in the past with games, it becomes increasingly integrated into human life as time passes. Metaverse usage is inevitable in every aspect of life. One of its potential application areas could be urban transportation. A novel fuzzy trigonometric based on the combination of the Full Consistency Method (FUCOM) and Combined Compromise Solution (CoCoSo) is proposed to rank three alternatives with twelve criteria under four major aspects: managerial, safety, user, and urban mobility. In the first stage, fuzzy FUCOM methods are used to calculate the weights of the criteria. In the second stage, the fuzzy trigonometric based CoCoSo method is applied to evaluate and rank the alternatives. The proposed model enables the nonlinear processing of complex and uncertain information using fuzzy trigonometric functions. The findings demonstrate focusing on a particular age group can make it easier to integrate the metaverse with urban transportation. The findings of this study have the potential to serve as a guide for decision-makers. The metaverse-based applications could be started by policymakers, which is a promising opportunity with potential boundaries beyond human comprehension making this statement weaker.

Attribute subset selection via neighborhood composite entropy-based fuzzy β-covering

Attention has been widely paid to fuzzy β-covering, a powerful tool for processing uncertain information, and has recently turned into a hot topic. However, the ubiquity of unbalanced data makes fuzzy β-covering models are unavailable since they ignore the decision distribution normally. To address this drawback, a novel fuzzy β-covering attribute subset selection based on neighborhood composite entropy is proposed in this paper. First, the similarity measure of objects is discussed based on fuzzy β-neighborhoods for unbalanced fuzzy data. Subsequently, a neighborhood composite entropy is developed, which fully considers the decision distribution. Furthermore, an algorithm for attribute selection is presented according to the framework of the above measure mechanism. Furthermore, experimental results on 12 reality data from UCI and KRBM show that this novel method performs better classification on the proposed model to prove the performance and effectiveness by comparing several existing models.

Fuzzy Intelligence Learning Based on Bounded Rationality in IoMT Systems: A Case Study in Parkinson’s Disease

As a cause of interfering with routine activities, freezing of gait (FOG) is a severe syndrome of Parkinson’s disease (PD) and usually performs as an abrupt and momentary inability to effective stepping forward. Advanced wearable acceleration sensors based on socially implemented Internet of medical things (IoMT) devices can remotely provide a platform for recognizing FOG. However, due to the diverse data acquisition modes that appear in classic IoMT devices, the obtained data may contain imprecise, hesitant, and incomplete ones. Meanwhile, the bounded rationality owned by neurologists usually has a big impact on using wearable acceleration sensors to predict illnesses. Therefore, the objective of this article lies in exploring a fuzzy intelligence learning approach based on bounded rationality in IoMT systems and providing a valid scheme for biomedical data analysis. Specifically, a brand-new three-way group decision-making approach by means of TODIM (an acronym in Portuguese for interactive multicriteria decision-making) with incomplete dual hesitant fuzzy (DHF) information and its applications in detecting FOG in PD using IoMT devices are systematically explored. First, taking advantage of DHF sets (DHFSs) when depicting realistic group decision information, the concept of multigranulation (MG) incomplete DHF information systems is built. Second, adjustable MG DHF probabilistic rough sets (PRSs) are further put forward via DHF similarity relations. Third, a three-way group decision-making approach is constructed by virtue of adjustable MG DHF PRSs and TODIM. Finally, the validity, effectiveness, and practicality of the constructed three-way group decision-making approach are investigated by a University of California, Irvine (UCI) dataset with several experimental analyses in the background of FOG detection in PD using IoMT devices. The experimental result indicates that the developed fuzzy intelligence learning approach achieves reasonable diagnostic conclusions for FOG detection in PD from the perspective of uncertain information processing abilities, decision risks, and bounded rationality.

Assessing alternatives of including social robots in urban transport using fuzzy trigonometric operators based decision-making model

Current trends point to a not-too-distant future with qualitatively advanced interactions between humans and social robots. It is critical to consider the possibility of forming meaningful social relationships with robots when defining the future of human-robot interactions, as well as studying how these interactions will evolve to the point where humans are unable to distinguish between humans and robots in urban transportation. In this study, the advantages of using social robots in urban transportation are prioritized by using a multi-criteria decision-making tool, which consists of two consecutive stages, namely: i) a novel fuzzy sine trigonometry based on the logarithmic method of additive weights (fuzzy ST-LMAW) that is proposed to calculate the criteria weights; ii) a nonlinear fuzzy Aczel-Alsina function based the weighted aggregate sum product assessment (fuzzy ALWAS-WASPAS) that is developed to select and rank the alternatives. The proposed model enables flexible nonlinear processing of complex and uncertain information encountered in real applications. A case study is developed to rank three alternatives with twelve sub-criteria grouped into four aspects using the proposed method. The results show that the most advantageous alternative is to replace people with social robots as safety drivers in level four autonomous vehicles due to their possible impact on transportation.

A New Correlation Measure for Belief Functions and Their Application in Data Fusion.

Measuring the correlation between belief functions is an important issue in Dempster-Shafer theory. From the perspective of uncertainty, analyzing the correlation may provide a more comprehensive reference for uncertain information processing. However, existing studies about correlation have not combined it with uncertainty. In order to address the problem, this paper proposes a new correlation measure based on belief entropy and relative entropy, named a belief correlation measure. This measure takes into account the influence of information uncertainty on their relevance, which can provide a more comprehensive measure for quantifying the correlation between belief functions. Meanwhile, the belief correlation measure has the mathematical properties of probabilistic consistency, non-negativity, non-degeneracy, boundedness, orthogonality, and symmetry. Furthermore, based on the belief correlation measure, an information fusion method is proposed. It introduces the objective weight and subjective weight to assess the credibility and usability of belief functions, thus providing a more comprehensive measurement for each piece of evidence. Numerical examples and application cases in multi-source data fusion demonstrate that the proposed method is effective.

Failure Mode and Effects Analysis on the Air System of an Aero Turbofan Engine Using the Gaussian Model and Evidence Theory.

Failure mode and effects analysis (FMEA) is a proactive risk management approach. Risk management under uncertainty with the FMEA method has attracted a lot of attention. The Dempster-Shafer (D-S) evidence theory is a popular approximate reasoning theory for addressing uncertain information and it can be adopted in FMEA for uncertain information processing because of its flexibility and superiority in coping with uncertain and subjective assessments. The assessments coming from FMEA experts may include highly conflicting evidence for information fusion in the framework of D-S evidence theory. Therefore, in this paper, we propose an improved FMEA method based on the Gaussian model and D-S evidence theory to handle the subjective assessments of FMEA experts and apply it to deal with FMEA in the air system of an aero turbofan engine. First, we define three kinds of generalized scaling by Gaussian distribution characteristics to deal with potential highly conflicting evidence in the assessments. Then, we fuse expert assessments with the Dempster combination rule. Finally, we obtain the risk priority number to rank the risk level of the FMEA items. The experimental results show that the method is effective and reasonable in dealing with risk analysis in the air system of an aero turbofan engine.

Learning versus researching in a desktop virtual reality: How reception goals influence the processing of uncertain information marked by verbal and visual cues

Research on the impact of virtual reality (VR) on learning and the integrated model of processing evidence and its uncertainty values (Glaser, Lengyel, Toulouse, & Schwan, 2022) indicates that learners are in danger of mistakenly confusing the representational fidelity of information with scientific certainty and neglect uncertain information during the learning process. In order to find a solution to this problem, we have examined how high-fidelity VR presentations may be supplemented by markers of scientific uncertainty in a way that provides learners with an understanding of the evidential nature of the VR's content and, at the same time, have them elaborate on particular elements of the VR, irrespective of their status of scientific uncertainty. We assumed that certain information is better retained than uncertain information, and additionally inferred that reception goals might influence the retention of uncertain information. Participants (n = 75) were assigned to one of two experimental conditions: learning goal and research goal (between-subjects factor). In both conditions, participants walked through the virtual reconstruction of a historic building with architectural elements including uncertain, medium, and certain architectural elements (within-subject factor). Uncertainty was expressed visually by stop light colors and verbally in accompanying audio explanations. Retention of the appearance of architectural elements, uncertainty values, their verbal scientific justifications, and also exploratorily historical experience were measured in both reception goal conditions. Results showed that with a learning goal but not a research goal the appearance of certain architectural elements was better remembered than that of uncertain architectural elements. The memory of uncertainty values and their verbal scientific justifications was equal for uncertain, medium, or certain architectural elements in both reception goal conditions. Affective engagement during the historical experience was higher with a research than with a learning goal. The study extends existing theories on processing uncertain information and shows that orchestrating basic characteristics of VR with carefully chosen instructional strategies for successful knowledge acquisition is especially important for VRs containing uncertain information.

Dealing with Belief Uncertainty in Domain Models

There are numerous domains in which information systems need to deal with uncertain information. These uncertainties may originate from different reasons such as vagueness, imprecision, incompleteness, or inconsistencies, and in many cases, they cannot be neglected. In this article, we are interested in representing and processing uncertain information in domain models, considering the stakeholders’ beliefs (opinions). We show how to associate beliefs to model elements and how to propagate and operate with their associated uncertainty so that domain experts can individually reason about their models enriched with their personal opinions. In addition, we address the challenge of combining the opinions of different domain experts on the same model elements, with the goal to come up with informed collective decisions. We provide different strategies and a methodology to optimally merge individual opinions.

A New Reliability Coefficient Using Betting Commitment Evidence Distance in Dempster-Shafer Evidence Theory for Uncertain Information Fusion.

Dempster-Shafer evidence theory is widely used to deal with uncertain information by evidence modeling and evidence reasoning. However, if there is a high contradiction between different pieces of evidence, the Dempster combination rule may give a fusion result that violates the intuitive result. Many methods have been proposed to solve conflict evidence fusion, and it is still an open issue. This paper proposes a new reliability coefficient using betting commitment evidence distance in Dempster-Shafer evidence theory for conflict and uncertain information fusion. The single belief function for belief assignment in the initial frame of discernment is defined. After evidence preprocessing with the proposed reliability coefficient and single belief function, the evidence fusion result can be calculated with the Dempster combination rule. To evaluate the effectiveness of the proposed uncertainty measure, a new method of uncertain information fusion based on the new evidence reliability coefficient is proposed. The experimental results on UCI machine learning data sets show the availability and effectiveness of the new reliability coefficient for uncertain information processing.

The determination and elimination of hidden inherent preference using q-ROFNs multicriteria group decision making problem

The group decision-making problem usually involves decision makers (DMs) from different professional backgrounds, which leads to a considerable point, that it is the fact that there will be a certain difference in the professional cognition, risk preference and other hidden inherent factors of these DMs to the objective things that need to be evaluated. To improve the reasonability of decision-making, these hidden inherent preference (HIP) of DMs should be determined and eliminated prior to decision making. As a special form of fuzzy set, q-rung orthopair fuzzy numbers (q-ROFNs) is a useful tool to process uncertain information in decision making problems. Hence, under the environment of q-ROFNs, the determination of HIP based on distance from average score is proposed and a risk model is established to eliminate the HIP by analyzing the possible impact. Meanwhile, a dominant function is proposed, which extends the comparison method between q-ROFNs and an integrated decision-making method is provided. Finally, considering the application background of double carbon economy, an example by selecting the best design of electric vehicles charging station (EVCS) is conducted to illustrate the proposed method, and the feasibility and efficiency are verified.

A fuzzy Einstein-based decision support system for public transportation management at times of pandemic

Optimal decision-making has become increasingly more difficult due to their inherent complexity exacerbated by uncertain and rapidly changing environmental conditions in which they are defined. Hence, with the aim of improving the uncertainty management and facilitating the weighting criteria, this paper introduces an improved fuzzy Einstein Combined Compromise Solution (CoCoSo) methodology. Such a CoCoSo model improves previous CoCoSo proposals by using nonlinear fuzzy weighted Einstein functions for defining weighted sequences. In addition, it proposes a novel algorithm for determining the criteria weights based on the fuzzy logarithmic function, therefore it allows decision-makers a better perception of the relationship between the criteria, as it considers the relationships between adjacent criteria; high consistency of expert comparisons; and enables the definition of weighting coefficients of a larger set of criteria, without the need to cluster (group) the criteria. Nonlinear fuzzy Einstein functions implemented in the fuzzy Einstein CoCoSo methodology enable the processing of complex and uncertain information. Such characteristics contribute to the rational definition of compromise strategies and enable objective reasoning when solving real-world decision problems. The efficiency, effectiveness, and robustness of the proposed fuzzy Einstein CoCoSo model are illustrated by a case study to create a conceptual framework to evaluate and rank the prioritization of public transportation management at the time of the COVID-19 pandemic. The results reveal its good performance in determining the transportation management systems strategy.

An optimization viewpoint on evaluation-based interval-valued multi-attribute three-way decision model

Multi-attribute decision-making (MADM) aims to rank alternatives based on their attributes and evaluation information, which provides decision support for decision-makers. Most existing MADM methods can only obtain ranking results, decision-makers usually need to subjectively choose priority alternatives to make a decision based on the preset evaluation level and ranking results, which does not meet the requirements of complex decision situations and uncertain information processing. A method is urgently needed, which can objectively produce classification results and automatically provide priority objects for decisions. Three-way decisions (3WDs) offer an effective research technique to solve decision-making problems under uncertainty and risk by objectively dividing a universal set into three pairwise disjoint parts: acceptance, non-commitment and rejection, and implementing the corresponding strategy for each part. Considering that it is difficult to accurately determine the exact value of attributes in some cases, interval numbers can be a useful concept to flexibly describe uncertain information and satisfy the decision-maker’s cognition. Therefore, under an interval-valued MADM environment, this paper proposes an evaluation-based interval-valued multi-attribute three-way decision (IVMA3WD) model from an optimization viewpoint. The model achieves an effective fusion of 3WDs and MADM problems, and further expands evaluation-based 3WD models on the totally ordered set. Specifically speaking, in order to depict uncertain information in decision-making, interval numbers are used to describe the evaluation information included in the multi-attribute evaluation matrix. Then, considering a general case that there are no decision attributes in the multi-attribute evaluation matrix, we determine the evaluation function by use of the idea of the interval-valued TOPSIS method. In addition, unlike the fixed-valued loss functions in the majority of 3WD models, the relative loss function in this article is not provided straightly but computed by attribute evaluation values expressed by interval numbers. Furthermore, based on an optimization viewpoint, we construct two optimization models and attempt to find the optimal solution to obtain the threshold pair. A housing market investment decision problem is utilized as an illustrative example to find out the applicability of our built IVMA3WD model. In the end, we adopt a sensitivity analysis and a comparative analysis to demonstrate our proposed model’s characteristics and advantages.

A new method of conflicting evidence management based on non-extensive entropy and Lance distance in uncertain scenarios

Handing uncertain information is one of the research focuses currently. For the sake of great ability of handing uncertain information, Dempster-Shafer evidence theory (D-S theory) has been widely used in various fields of uncertain information processing. However, when highly contradictory evidence appears, the results of the classical Dempster combination rules (DCR) can be counterintuitive. Aiming at this defect, by considering the relationship between the evidence and its own characteristics, the proposed method is a new method of conflicting evidence management based on non-extensive entropy and Lance distance in uncertain scenarios. Firstly, the Lance distance function is used to measure the degree of discrepancy and conflict between evidences, and the credibility of evidence is expressed by matrix. Introducing non-extensive entropy to measure the amount of information about evidence and express the uncertainty of evidence. Secondly, the discount coefficient of the final fusion evidence is measured by considering the credibility and uncertainty of the evidence, and the original evidence is modified by the discount coefficient. Then, the final result is obtained by evidence fusion with DCR. Finally, two numerical examples are provided to illustrate the efficiency of the proposed method, and the utility of our work is demonstrated through an application of the active lane change to avoid obstacles to the autonomous driving of new energy vehicles. The proposed method has a better identification accuracy, reaching 0.9811.

Research on reliability analysis strategy of high-speed train ATP on-board equipment under uncertain information

In view of the incomplete knowledge and lack of data, the system has epistemic uncertainties. This paper uses Bayesian network fusion evidence theory to analyze the reliability of high-speed railway ATP on-board equipment. According to the requirements of safety-critical system reliability analysis, this paper starts with the analysis of system structure and unit module status, comprehensively considering the influence of uncertain information, common cause failure, recovery mechanism, and degraded operation on system reliability. In this paper, with the advantage of the Bayesian network in the description of events in multiple states, evidence theory is used to reason about the system under incomplete information conditions, obtain the availability interval of on-board subsystems and discuss the impact of the degraded operation on system availability. The [Formula: see text] factor model is used to analyze common cause failures, and then the Bayesian network modeling of common cause failures is realized by adding common cause failure nodes. The results show that the method enhances the Bayesian network’s ability to process uncertain information, and the common cause failure data of the train control on-board subsystem is continuously accumulated. The factor model can be used to obtain a more practical common cause failure rate.

A new distance measure for pythagorean fuzzy sets based on earth mover’s distance and its applications

In uncertain information processing, new knowledge can be discovered by measuring the proximity between discovered and undiscovered knowledge. Pythagorean Fuzzy Sets (PFSs) is one of the important tools to describe the natural attributes of uncertain information. Therefore, how to appropriately measure the distance between PFSs is an important topic. The earth mover’s distance (EMD) is a real distance metric that can be used to describe the difference between two distribution laws. In this paper, a new distance measure for PFSs based on EMD is proposed. It is a new perspective to measure the distance between PFSs from the perspective of distribution law. First, a new distance measure namely DEMD is presented and proven to satisfy the distance measurement axiom. Second, an example is given to illustrate the advantages of DEMD compared with other distance measures. Third, the problem statements and solving algorithms of pattern recognition, medical diagnosis and multi-criteria decision making (MCDM) problems are given. Finally, by comparing the application of different methods in pattern recognition, medical diagnosis and MCDM, the effectiveness and practicability of DEMD and algorithms presented in this paper are demonstrated.