Probabilistic linguistic term sets(PLTSs)are an effective tool for expressing subjective human cognition that offer advantages in the field ofmulti-attribute decision-making(MADM).However,studies have found that PLTSs...Probabilistic linguistic term sets(PLTSs)are an effective tool for expressing subjective human cognition that offer advantages in the field ofmulti-attribute decision-making(MADM).However,studies have found that PLTSs have lost their ability to accurately capture the views of decision-makers(DMs)in certain circumstances,such as when the DM hesitates between multiple linguistic terms or the decision information is incomplete,thus affecting their role in the decision-making process.Belief function theory is a leading streamof thought in uncertainty processing that is suitable for dealing with the limitations of PLTS.Therefore,the purpose of this study is to extend PLTS to incorporate belief function theory.First,we provide the basic concepts of the extended PLTS(i.e.,belief-based PLTS)through case analyses.Second,the aggregation operator of belief-based PLTS is defined with the ordered weighted average(OWA)-based soft likelihood function,which is improved by considering the reliability of the information source.Third,to measure the magnitude of different belief-based PLTSs,the belief interval of singleton is calculated,and the comparison method of belief-based PLTS is constructed based on probabilities.On the basis of the preceding discussion,we further develop an emergency decision framework that includes several novel techniques,such as attribute weight determination and decision information aggregation.Finally,the usefulness of the framework is demonstrated through a case study,and its effectiveness is illustrated through a series of comparisons.展开更多
Graduate education is the main way to train high-level innovative talents,the basic layout to cope with the global talent competition,and the important cornerstone for implementing the innovation-driven development st...Graduate education is the main way to train high-level innovative talents,the basic layout to cope with the global talent competition,and the important cornerstone for implementing the innovation-driven development strategy and building an innovation-driven country.Therefore,graduate education is of great remarkably to the development of national education.As an important manifestation of graduate education,the quality of a graduate thesis should receive more attention.It is conducive to promoting the quality of graduates by supervising and examining the quality of the graduate thesis.For this purpose,this work is based on textmining,expert interviews,and questionnaire surveys to obtain the factors influencing the quality of a graduate thesis first.Then,through three rounds of expert consultation,a multidimensional evaluation indicator system for the graduate thesis quality is built.Furthermore,probabilistic linguistic termsets(PLTSs)are utilized to obtain the initial evaluation information and apply the stepwise weight assessment ratio analysis method to determine the weights of attributes.In the ensuing step,the novel multi-attribute border approximation area comparison based on the PLTS method is established.Finally,the proposed method is employed in a case study concerning the quality evaluation of a graduate thesis and the effectiveness of this approach is further illustrated.展开更多
Industrial control systems(ICSs)are widely used in various fields,and the information security problems of ICSs are increasingly serious.The existing evaluation methods fail to describe the uncertain evaluation inform...Industrial control systems(ICSs)are widely used in various fields,and the information security problems of ICSs are increasingly serious.The existing evaluation methods fail to describe the uncertain evaluation information and group evaluation information of experts.Thus,this paper introduces the probabilistic linguistic term sets(PLTSs)to model the evaluation information of experts.Meanwhile,we propose a probabilistic linguistic multi-criteria decision-making(PL-MCDM)method to solve the information security assessment problem of ICSs.Firstly,we propose a novel subscript equivalence distance measure of PLTSs to improve the existing methods.Secondly,we use the Best Worst Method(BWM)method and Criteria Importance Through Inter-criteria Correlation(CRITIC)method to obtain the subjective weights and objective weights,which are used to derive the combined weights.Thirdly,we use the subscript equivalence distance measure method and the combined weight method to improve the probabilistic linguistic Visekriterijumska Optimizacija I Kompromisno Resenje(PL-VIKOR)method.Finally,we apply the proposed method to solve the information security assessment problem of ICSs.When comparing with the existing methods such as the probabilistic linguistic Tomada deDecisão Iterativa Multicritério(PL-TODIM)method and probabilistic linguistic Technique for Order Preference by Similarity to Ideal Solution(PL-TOPSIS)method,the case example shows that the proposed method can provide more reasonable ranking results.By evaluating and ranking the information security level of different ICSs,managers can identify problems in time and guide their work better.展开更多
New opportunities and challenges for information representation and processing are brought about by the rapid development of artificial intelligence.This study offers a new decision-making method that decreases inform...New opportunities and challenges for information representation and processing are brought about by the rapid development of artificial intelligence.This study offers a new decision-making method that decreases information distortion and increases computational efficiency by fusing the nested probabilistic linguistic terms with the EDAS decision-making method approach.Firstly,we introduce the concept,property,and demonstration of the cosine similarity of nested probabilistic linguistic terms,which allows for a more precise measurement of the degree of departure between their content.Secondly,the attribute weights are generated using the normalised attribute information difference value approach,which is based on the premise that attributes with bigger differences in evaluation information are more significant.Furthermore,the proposed method is utilised to address a supplier selection issue related to the procurement of medical equipment.Ultimately,the method is proven to be feasible and superior by comparison assessments with other measuring methods and decision-making methods.展开更多
To solve the problem that the traditional cloud model can't directly process the textual review information in the recommendation algorithm,this paper combines the merits of the cloud model in transforming qualita...To solve the problem that the traditional cloud model can't directly process the textual review information in the recommendation algorithm,this paper combines the merits of the cloud model in transforming qualitative and quantitative knowledge with the multi-granularity advantages of probabilistic linguistic term sets in representing uncertain information,and proposes a recommendation algorithm based on cloud model in probabilistic language environment.Initially,this paper quantifies the attributes in the review text based on the probabilistic linguistic term set.Subsequently,the maximum deviation method is used to determine the weight of each attribute in the evaluation information of the product to be recommended,and the comprehensive evaluation number and attribute weight are converted into the digital characteristic value of the cloud model by using the backward cloud generator.Finally,the products are recommended and sorted based on the digital characteristic value of the cloud model.The algorithm is applied to the recommendation of 10 hotels,and the results show that the method is effective and practical,enriching the application of cloud models in the recommendation field.展开更多
This thesis aims to propose a novel distance operator,the probabilistic linguistic term ordered weighted distance(PLTOWD)operator,which enriches the distance theory in probabilistic linguistic term circumstances.The P...This thesis aims to propose a novel distance operator,the probabilistic linguistic term ordered weighted distance(PLTOWD)operator,which enriches the distance theory in probabilistic linguistic term circumstances.The PLTOWD operator is an efficient tool to deal with qualitative evaluation information and their corresponding probabilities or importance degrees.Moreover,some of its desired properties and different families of thePLTOWDoperator are discussed.Meanwhile,the extensions of the PLTOWD operator are also investigated.Then,a method of multiple attribute group decision making(MAGDM)in probabilistic linguistic term information is proposed on the basis of the PLTOWD operator.Finally,a numerical evaluation example in public Eco-environment satisfaction is developed to illustrate the practicability and effectiveness of the given method.Some discussions and comparisons are carried out according to the case results.展开更多
基金supported by National Social Science Foundation of China (Grant No.17ZDA030).
文摘Probabilistic linguistic term sets(PLTSs)are an effective tool for expressing subjective human cognition that offer advantages in the field ofmulti-attribute decision-making(MADM).However,studies have found that PLTSs have lost their ability to accurately capture the views of decision-makers(DMs)in certain circumstances,such as when the DM hesitates between multiple linguistic terms or the decision information is incomplete,thus affecting their role in the decision-making process.Belief function theory is a leading streamof thought in uncertainty processing that is suitable for dealing with the limitations of PLTS.Therefore,the purpose of this study is to extend PLTS to incorporate belief function theory.First,we provide the basic concepts of the extended PLTS(i.e.,belief-based PLTS)through case analyses.Second,the aggregation operator of belief-based PLTS is defined with the ordered weighted average(OWA)-based soft likelihood function,which is improved by considering the reliability of the information source.Third,to measure the magnitude of different belief-based PLTSs,the belief interval of singleton is calculated,and the comparison method of belief-based PLTS is constructed based on probabilities.On the basis of the preceding discussion,we further develop an emergency decision framework that includes several novel techniques,such as attribute weight determination and decision information aggregation.Finally,the usefulness of the framework is demonstrated through a case study,and its effectiveness is illustrated through a series of comparisons.
文摘Graduate education is the main way to train high-level innovative talents,the basic layout to cope with the global talent competition,and the important cornerstone for implementing the innovation-driven development strategy and building an innovation-driven country.Therefore,graduate education is of great remarkably to the development of national education.As an important manifestation of graduate education,the quality of a graduate thesis should receive more attention.It is conducive to promoting the quality of graduates by supervising and examining the quality of the graduate thesis.For this purpose,this work is based on textmining,expert interviews,and questionnaire surveys to obtain the factors influencing the quality of a graduate thesis first.Then,through three rounds of expert consultation,a multidimensional evaluation indicator system for the graduate thesis quality is built.Furthermore,probabilistic linguistic termsets(PLTSs)are utilized to obtain the initial evaluation information and apply the stepwise weight assessment ratio analysis method to determine the weights of attributes.In the ensuing step,the novel multi-attribute border approximation area comparison based on the PLTS method is established.Finally,the proposed method is employed in a case study concerning the quality evaluation of a graduate thesis and the effectiveness of this approach is further illustrated.
文摘Industrial control systems(ICSs)are widely used in various fields,and the information security problems of ICSs are increasingly serious.The existing evaluation methods fail to describe the uncertain evaluation information and group evaluation information of experts.Thus,this paper introduces the probabilistic linguistic term sets(PLTSs)to model the evaluation information of experts.Meanwhile,we propose a probabilistic linguistic multi-criteria decision-making(PL-MCDM)method to solve the information security assessment problem of ICSs.Firstly,we propose a novel subscript equivalence distance measure of PLTSs to improve the existing methods.Secondly,we use the Best Worst Method(BWM)method and Criteria Importance Through Inter-criteria Correlation(CRITIC)method to obtain the subjective weights and objective weights,which are used to derive the combined weights.Thirdly,we use the subscript equivalence distance measure method and the combined weight method to improve the probabilistic linguistic Visekriterijumska Optimizacija I Kompromisno Resenje(PL-VIKOR)method.Finally,we apply the proposed method to solve the information security assessment problem of ICSs.When comparing with the existing methods such as the probabilistic linguistic Tomada deDecisão Iterativa Multicritério(PL-TODIM)method and probabilistic linguistic Technique for Order Preference by Similarity to Ideal Solution(PL-TOPSIS)method,the case example shows that the proposed method can provide more reasonable ranking results.By evaluating and ranking the information security level of different ICSs,managers can identify problems in time and guide their work better.
基金supported by the National Natural Science Foundation of China under grant numbers 72101168,71571123China Postdoctoral Science Foundation under grant number 2021M692259.
文摘New opportunities and challenges for information representation and processing are brought about by the rapid development of artificial intelligence.This study offers a new decision-making method that decreases information distortion and increases computational efficiency by fusing the nested probabilistic linguistic terms with the EDAS decision-making method approach.Firstly,we introduce the concept,property,and demonstration of the cosine similarity of nested probabilistic linguistic terms,which allows for a more precise measurement of the degree of departure between their content.Secondly,the attribute weights are generated using the normalised attribute information difference value approach,which is based on the premise that attributes with bigger differences in evaluation information are more significant.Furthermore,the proposed method is utilised to address a supplier selection issue related to the procurement of medical equipment.Ultimately,the method is proven to be feasible and superior by comparison assessments with other measuring methods and decision-making methods.
基金Supported by the Humanities and Social Sciences Research Planning Fund Project of the Ministry of Education(23YJA860004)the Major Basic Research Project of Philosophy and Social Sciences in Higher Education Institutions in Henan Province(2024-JCZD-27)2021 Project of Huamao Financial Research Institute of Henan University of Economics and Law(HCHM-2021YB001)。
文摘To solve the problem that the traditional cloud model can't directly process the textual review information in the recommendation algorithm,this paper combines the merits of the cloud model in transforming qualitative and quantitative knowledge with the multi-granularity advantages of probabilistic linguistic term sets in representing uncertain information,and proposes a recommendation algorithm based on cloud model in probabilistic language environment.Initially,this paper quantifies the attributes in the review text based on the probabilistic linguistic term set.Subsequently,the maximum deviation method is used to determine the weight of each attribute in the evaluation information of the product to be recommended,and the comprehensive evaluation number and attribute weight are converted into the digital characteristic value of the cloud model by using the backward cloud generator.Finally,the products are recommended and sorted based on the digital characteristic value of the cloud model.The algorithm is applied to the recommendation of 10 hotels,and the results show that the method is effective and practical,enriching the application of cloud models in the recommendation field.
基金The study receives funding from National Natural Science Foundation of China[grant numbers 71901088,71701001,and 71901001]Natural Science Foundation of Anhui Province[grant number 1808085QG211]+4 种基金Natural Sciences Research Project of Universities in Anhui[grant number KJ2020A0120]College Excellent Youth Talent Support Program[grant number gxyq2020041]Statistical Science Research Project of China[grant number 2017LZ11]Top Talent Academic Foundation for University Discipline of Anhui Province[grant number gxbjZD2020056]Social Science Innovation andDevelopment Research Project inAnhui Province[grant number 2019CX094].
文摘This thesis aims to propose a novel distance operator,the probabilistic linguistic term ordered weighted distance(PLTOWD)operator,which enriches the distance theory in probabilistic linguistic term circumstances.The PLTOWD operator is an efficient tool to deal with qualitative evaluation information and their corresponding probabilities or importance degrees.Moreover,some of its desired properties and different families of thePLTOWDoperator are discussed.Meanwhile,the extensions of the PLTOWD operator are also investigated.Then,a method of multiple attribute group decision making(MAGDM)in probabilistic linguistic term information is proposed on the basis of the PLTOWD operator.Finally,a numerical evaluation example in public Eco-environment satisfaction is developed to illustrate the practicability and effectiveness of the given method.Some discussions and comparisons are carried out according to the case results.
