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The purpose of this paper is to define an evaluation model for cloud services to deal with the fuzzy information and propose a novel fuzzy evaluation method based on consistency intensity to analyze the quantitative value from the fuzzy information.

The cloud service evaluation framework is constructed, and different trusted indicators for the infrastructure services and the application services are designed, respectively. In the novel fuzzy evaluation method, the interval values can be aggregated by the Dempster-Shafer Theory and be transformed into the certain value by linguistic discount factor. The consistency intensity is proposed to determine the value of the linguistic discount factor, which can reflect the mainstream opinions in the assessment.

The proposed method can solve the problem on the analysis and synthesis of the fuzzy evaluation information. An instance of trust evaluation of cloud storage service is illustrated to verify that the proposed method can express the opinions of all evaluators more adequately.

A serial of experiments are carried out on NetLogo, and the results show that the proposed method is practical and efficient.

Instead of obtaining only the qualitative results by the multi-attribute decision-making method, the fuzzy evaluation method based on consistency intensity can obtain the quantitative results from the fuzzy information according to linguistic discount factor and consistency intensity.

The purpose of this paper is to use artificial intelligence to evaluate the risks of urban power network planning.

A fuzzy Bayesian least squares support vector machine (LS_SVM) model is established in this paper, which can learn the risk information of urban power network planning through artificial intelligence and acquire expert knowledge for its risk evaluation. With the advantage of possessing learning analog simulation precision and speed, the proposed model can be effectively applied in conducting a risk evaluation of an urban network planning system. First, fuzzy theory is applied to quantify qualitative risk factors of the planning to determine the fuzzy comprehensive evaluation value of the risk factors. Then, Bayesian evidence framework is utilized in LS_SVM model parameter optimization to automatically adjust the LS_SVM regularization parameters and nuclear parameters to obtain the best parameter values. Based on this, a risk comprehensive evaluation of urban network planning based on artificial intelligence is established.

The fuzzy Bayesian LS_SVM model established in this paper is an effective artificial intelligence method for risk comprehensive evaluation in urban network planning through empirical study.

The paper breaks new ground in using artificial intelligence to evaluate urban power network planning risks.

In the supply chain disruption risk, the issue of supplier evaluation and selection is solved by an extended VIKOR method based on regret theory.

Considering the influence of irrational emotions of decision makers, an evaluation model is designed by the regret theory and VIKOR method, which makes the decision-making process closer to reality.

The paper has some innovations in the evaluation index system and evaluation model construction. The method has good stability under the risk of supply chain interruption.

The mixed evaluation information is used to describe the attributes, and the evaluation index system is constructed by the combined method of the social network analysis method and the literature research method to ensure the accuracy and accuracy of the extracted attributes. The issue of supplier evaluation and selection is solved by an extended VIKOR method based on regret theory.

The Internet offers companies many opportunities to implement new business models, called e(electronic)‐business in short, and to reinforce their competitiveness in the market. It is currently suggested that business‐to‐business commerce, which is one of the dominant segments in e‐business, is going to develop around e‐marketplaces. However, the new millennium has witnessed the rise and fall of many “dot.com” firms and now both the companies and the investors are extremely cautious about the performance and effectiveness of the e‐marketplaces. To get more benefit from this digital marketplace, suggests the use of fuzzy logic based on multi‐criteria evaluation as a superior means of improving the efficiency of the decision making for the e‐marketplace selection under uncertain conditions. This evaluation approach, in which both fuzzy analytic hierarchy process and fuzzy Delphi methodologies are employed, enables one to deal with the uncertainty and vagueness from subjective perception and the experiences of humans in a group decision process and ensures a more convincing and effective decision making. Also shows the application of the approach on a real life case.

This paper aims to propose a bed and breakfast (B&B) recommendation method that takes into account review timeliness and user preferences to help consumers choose the most satisfactory B&B.

This paper proposes a B&B ranking method based on improved intuitionistic fuzzy sets. First, text mining and cluster analysis are combined to identify the concerns of consumers and construct an attribute set. Second, an attribute-level-based text sentiment analysis is established. The authors propose an improved intuitionistic fuzzy set, which is more in line with the actual situation of sentiment analysis of online reviews. Next, subjective-objective combinatorial assignments are applied, considering the consumers’ preferences. Finally, the vlsekriterijumska optimizacija i kompromisno resenje (VIKOR) algorithm, based on the improved score function, is advised to evaluate B&Bs.

A case study is presented to illustrate the use of the proposed method. Comparative analysis with other multi-attribute decision-making (MADM) methods proves the effectiveness and superiority of the VIKOR algorithm based on the improved intuitionistic fuzzy sets proposed in this paper.

Proposing a B&B recommendation method that takes into account review timeliness and user customization is the innovation of this paper. In this approach, the authors propose improved intuitionistic fuzzy sets. Compared with the traditional intuitionistic fuzzy set, the improved intuitionistic fuzzy set increases the abstention membership, which is more in line with the actual situation of attribute-level sentiment analysis of online reviews.

The purpose of this paper is to propose a method for assessing the lean construction management performance (LCMP) of engineering projects using the analytic network process-fuzzy comprehensive evaluation (ANP-FCE) model.

Literature review and questionnaire surveys were used to achieve an evaluation index system for LCMP. The data needed to calculate the weights of the indices were derived from questionnaire surveys. The ANP and the Super Decisions (SD) software were used to calculate the weights of the indices and verify the validity of the weights. The FCE was adopted to carry out a comprehensive evaluation of LCMP, and the validity of the evaluation results was verified by applying the validity degree of maximum membership principle. The applicability of the method for evaluating LCMP was validated by empirical research.

The proposed method for assessing the LCMP using the ANP-FCE model is validated to be feasible. Decision makers can easily identify the strengths and weaknesses of lean construction (LC) management of the evaluated project from the evaluation results.

Some subjectivity may exist in the assessment process due to questionnaire surveys, and only one case has been studied to validate the applicability of the method. Therefore, researchers are encouraged to increase the number of LC experts and cases over the world to make the proposed method more standard.

The research is expected to propose an assessment method of LCMP for construction enterprises, provide a reference to develop assessment standards for LCMP for building associations or governments and help decision makers know the improving path of LC management.

This research contributes to the literature by summarizing the commonly used lean management tools for quality, cost, time, safety and organization in practice, and proposing a novel approach for assessing the LCMP of engineering projects. The study is useful for researchers, project managers and decision makers engaged in LC.

As an important product characteristic, maintainability is recognized as a highly significant factor in the economic success of engineering systems and products. Based on virtual reality technology, this paper aims to create an intuitive, immersive and interactive environment for product maintenance. In order to analyze and evaluate the product maintainability in a virtual maintenance environment, fuzzy comprehensive evaluation is used. At the early design stage, maintainability influencing factors are analyzed and the whole maintainability evaluation of the product is given by using fuzzy comprehensive evaluation method in virtual maintenance environment.

The paper provides a fuzzy comprehensive evaluation method for product maintainability evaluation in virtual environment.

Fuzzy comprehensive evaluation is used to evaluate product maintainability in virtual environment, and the application of virtual maintainability system is expanded.

According to maintainability requirement, the maintainability index set is built in this paper, and fuzzy comprehensive evaluation method is used to evaluate product maintainability in virtual maintenance environment at stage of early design.

This paper aims to enable the analysts of reliability and safety system to assess the criticality and prioritize failure modes perfectly to prefer actions for controlling the risks of undesirable scenarios.

To resolve the challenge of uncertainty and ambiguous related to the parameters, frequency, non-detection and severity considered in the traditional approach failure mode effect and criticality analysis (FMECA) for risk evaluation, the authors used fuzzy logic where these parameters are shown as members of a fuzzy set, which fuzzified by using appropriate membership functions. The adaptive neuro-fuzzy inference system process is suggested as a dynamic, intelligently chosen model to ameliorate and validate the results obtained by the fuzzy inference system and effectively predict the criticality evaluation of failure modes. A new hybrid model is proposed that combines the grey relational approach and fuzzy analytic hierarchy process to improve the exploitation of the FMECA conventional method.

This research project aims to reflect the real case study of the gas turbine system. Using this analysis allows evaluating the criticality effectively and provides an alternate prioritizing to that obtained by the conventional method. The obtained results show that the integration of two multi-criteria decision methods and incorporating their results enable to instill confidence in decision-makers regarding the criticality prioritizations of failure modes and the shortcoming concerning the lack of established rules of inference system which necessitate a lot of experience and shows the weightage or importance to the three parameters severity, detection and frequency, which are considered to have equal importance in the traditional method.

This paper is providing encouraging results regarding the risk evaluation and prioritizing failures mode and decision-makers guidance to refine the relevance of decision-making to reduce the probability of occurrence and the severity of the undesirable scenarios with handling different forms of ambiguity, uncertainty and divergent judgments of experts.

The purpose of this paper is to combine the uncertain methods of type-2 fuzzy sets and data envelopment analysis (DEA) evaluation model together. A new type-2 fuzzy DEA efficiency assessment method is established. Then the proposed procedure is applied to the poverty alleviation problem.

The research method is the DEA model, which is an effective method for efficiency assessment of social–economic systems. Considering the existence of the same efficiency values that cannot be ranked in the proposed DEA model, the balance index is introduced to solve the ranking problem of decision-making units effectively.

The results show that the proposed method can not only measure the efficiency of the existence of uncertain information but also deal with the ranking of multiple efficient decision-making units.

This paper selects type-2 fuzzy DEA model to express a lot of uncertain information in efficiency evaluation problems. We use the parameter decomposition method of type-2 fuzzy programming or the type-2 expectation values indirectly. The balance index is proposed to further distinguish the multiple effective decision-making units. Furthermore, this paper selects rural poverty alleviation in Hainan Province as a case study to verify the feasibility of the method. The relative efficiency values in different years are calculated and analyzed.

In this study, a novel framework was proposed to assess the trust in e-government (e-Gov) services under an uncertain environment. The proposed framework was applied in Iranian municipality websites of e-Gov services to evaluate the readiness score of trust in e-Gov services.

A unique hybrid research methodology was proposed. In the first phase, a comprehensive set of indices were determined from an extensive literature review and finalized by employing the fuzzy Delphi method. In the second phase, interval-valued intuitionistic fuzzy set (IVIFS) -was utilized to model the problem's uncertainty with analytic called IVIFS- hierarchy process (AHP) to determine the importance of indices and indicators by assigning the weights. In the third phase, the fuzzy evaluation method (FEM) is followed for assessing the readiness score of indices in case studies.

The findings indicated that “Trust in government” is the most significant index affecting citizen's trust in e-Gov services while “Maintenance and support” has the least impact on user's intention to use e–Gov services.

The study contributes by introducing a unique research methodology that integrates three phases, including fuzzy Delphi, IVIFS AHP and fuzzy evaluation method. Moreover, the fuzzy sets theory helps to reach a more accurate result by modeling the inherent ambiguity of indicators and indices. Interval-valued intuitionistic fuzzy models the ambiguity of experts' judgments in an interval.

The study helps policy makers to monitor wider aspects of trust in e-Gov services as well as understanding their importance. The study enables policy makers to apply the framework to any potential case studies to evaluate the readiness score of indices and recognizing strengths and weakness of trust dimensions as well as recommending advice for improving the situation.

The study is one of the few to indicate significant indices of trust in e-Gov services in developing countries. The study shows the importance of indicators and indices by assigning a weight. Additionally, the framework can assess the readiness score of various case studies.