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Content analysis of search engine user queries is an important task, since successful exploitation of the content of queries can result in the design of efficient information retrieval algorithms of search engines, which can offer custom‐tailored services to the web user. Identification of topic changes within a user search session is a key issue in content analysis of search engine user queries. The purpose of this study is to address these issues.

This study applies genetic algorithms and Dempster‐Shafer theory, proposed by He et al., to automatically identify topic changes in a user session by using statistical characteristics of queries, such as time intervals and query reformulation patterns. A sample data log from the Norwegian search engine FAST (currently owned by overture) is selected to apply Dempster‐Shafer theory and genetic algorithms for identifying topic changes in the data log.

As a result, 97.7 percent of topic shifts and 87.2 percent of topic continuations were estimated correctly. The findings are consistent with the previous application of the Dempster‐Shafer theory and genetic algorithms on a different search engine data log. This finding could be implied as an indication that content‐ignorant topic identification, using query patterns and time intervals, is a promising line of research.

Studies an important dimension of user behavior in information retrieval.

In this paper we report on a theoretical model of structured document indexing and retrieval based on the Dempster‐Shafer Theory of Evidence. This includes a description of our model of structured document retrieval, the representation of structured documents, the representation of individual components, how components are combined, details of the combination process, and how relevance is captured within the model. We also present a detailed account of an implementation of the model, and an evaluation scheme designed to test the effectiveness of our model. Finally we report on the details and results of a series of experiments performed to investigate the characteristics of the model.

To outsource part of their work, organizations are looking for suppliers who also have green criteria with other criteria. Selecting suppliers begins with the definition of potential suppliers and then selects the best among them. This study aims to present a two-part approach for selecting suppliers consisting of suppliers’ prioritization.

In the first part, the criteria that influence on selecting the suppliers have been identified and extracted using the literature review and experts’ opinion which consists of 19 criteria. Then, these criteria were evaluated by the content validity ratio index and using experts’ opinions, and finally, 16 criteria were selected for selecting green suppliers in a polyethylene’s products producer company in Iran. In the next step, suppliers are selected in a green supply chain using multi-criteria decision-making methods such as Dempster–Shafer theory and grey relationship analysis, which is a strategic decision.

This study attempts to improve the level of reliance on the whole uncertain degree by combining Dempster–Shafer theory and grey relational analysis (GRA), which makes the grey analysis method more robust and its results more reliable. The findings show that Supplier 4 is ranked as first within six suppliers.

Using GRA and Dempster–Shafer theory for green supplier selection problem in polyethylene industry is the novelty of this work.

This study aims to propose a new decision-making method by integrating case-based decision theory and the Dempster–Shafer theory of evidence.

The study developed the entire computational procedures for the proposed method and used a numerical example to illustrate its method.

The results show that not only the own experiences of the decision-maker but also the opinions of other persons contribute to the selection. Case-based decision theory provides a fundamental technique for the decision-making procedure, and the Dempster–Shafer theory of evidence offers support to deal with the different sources of decision information.

In case-based decision theory, the utility is a subjective concept, which cannot be measured easily in numbers. Thus, future research should seek a new method to replace the utility. In addition, how to assess the importance of different persons’ experiences and opinions is an important component of this method.

The contributions of the paper are mainly reflected in three aspects. The first is to expand the traditional concept of “case” of case-based decision theory to multiple sources of cases, which include not only the decision-maker’s own experiences but also other persons’ opinions. The second is to provide a decision-making framework by integrating case-based decision theory and the Dempster–Shafer theory of evidence. The third is to develop the entire computational procedures for the proposed method.

The Dempster‐Shafer theory of evidence gives a solid basis for reasoning about situations characterized by uncertainty. A key feature of the theory is that propositions are represented as subsets of a set which is called a hypothesis space. This power set along with the set operations is a Boolean algebra. The theory has previously been shown to cover other Boolean algebras including collections of objects such as propositions. The practical advantages of this generalization are that increased flexibility of representation is allowed and that the performance of evidence accumulation can be enhanced. The objects of interest here are geometric forms, and we can encode rectangular and other shaped forms using hexadecimal numbers according to shapes and positions. Boolean algebra of such shapes can then be used directly in evidential reasoning exercised. Discusses how medical and other fields can gain from this approach.

The absence of the reasoning stage in the analysis of long-term investment decision creates a serious gap in this classic topic in management accounting literature. The purpose of this paper is to fill this gap. The traditional analysis focuses on the evaluation stage using capital budgeting tools to rank alternative investment proposals. It tacitly assumes that the decision is to be made, thereby bypassing the reasoning stage. However, the reasoning stage may reveal that there is no sufficient justification (reasoning) to consider searching for and evaluating alternative proposals for this decision. Focusing on the reasoning component, the paper combines Fritz's (1989, 1990) “creative tension” and Janis and Mann's (1977) “challenges” as the driving forces for the problem-finding step. To demonstrate the significance of filling the reasoning gap in the long-term investment decision, the paper selects the modular manufacturing system and the complex investment decision required for its adoption. Using hypothetical data, the paper employs the Dempster-Shafer Theory of Evidence and Omer, et al's (1995) algorithm to compute the belief and plausibility values of the three reasoned actions: (1) maintain the status quo, (2) adopt Level 2 (assembly) modularity or (3) adopt Level 2 (design) modularity.

The contributions of the paper include (1) highlighting a critical gap currently existing in one of the classical decisions in the management accounting literature; (2) developing a framework for filling this gap and (3) applying this framework to the intricate nature of the modular manufacturing system and its complex investment decision.

This study aims to develop a data fusion method for powder-bed fusion (PBF) process monitoring based on process image information. The data fusion method can help improve process condition identification performance, which can provide guidance for further PBF process monitoring and control system development.

Design of reliable process monitoring systems is an essential approach to solve PBF built quality. A data fusion framework based on support vector machine (SVM), convolutional neural network (CNN) and Dempster-Shafer (D-S) evidence theory are proposed in the study. The process images which include the information of melt pool, plume and spatters were acquired by a high-speed camera. The features were extracted based on an appropriate image processing method. The three feature vectors corresponding to the three objects, respectively, were used as the inputs of SVM classifiers for process condition identification. Moreover, raw images were also used as the input of a CNN classifier for process condition identification. Then, the information fusion of the three SVM classifiers and the CNN classifier by an improved D-S evidence theory was studied.

The results demonstrate that the sensitivity of information sources is different for different condition identification. The feature fusion based on D-S evidence theory can improve the classification performance, with feature fusion and classifier fusion, the accuracy of condition identification is improved more than 20%.

An improved D-S evidence theory is proposed for PBF process data fusion monitoring, which is promising for the development of reliable PBF process monitoring systems.

In this paper, we present a brief study on various paradigms to tackle complexity or in other words manage uncertainty in the context of understanding science, society and nature. Fuzzy real numbers, fuzzy logic, possibility theory, probability theory, Dempster‐Shafer theory, artificial neural nets, neuro‐fuzzy, fractals and multifractals, etc. are some of the paradigms to help us to understand complex systems. We present a very detailed discussion on the mathematical theory of fuzzy dynamical system (FDS), which is the most fundamental theory from the point of view of evolution of any fuzzy system. We have made considerable extension of FDS in this paper, which has great practical value in studying some of the very complex systems in society and nature. The theories of fuzzy controllers, fuzzy pattern recognition and fuzzy computer vision are but some of the most prominent subclasses of FDS. We enunciate the concept of fuzzy differential inclusion (not equation) and fuzzy attractor. We attempt to present this theoretical framework to give an interpretation of cyclogenesis in atmospheric cybernetics as a case study. We also have presented a Dempster‐Shafer's evidence theoretic analysis and a classical probability theoretic analysis (from general system theoretic outlook) of carcinogenesis as other interesting case studies of bio‐cybernetics.

It is well known that the only way of making complexity in inductive (data‐driven) systems modelling manageable is to be tolerant of predictive (or retrodictive) uncertainty in the resulting models. It is argued that two complementary principles — the principles of maximum and minimum uncertainty — are essential to using uncertainty properly to combat complexity. When uncertainty is conceptualised in terms of probability theory, these principles become the well‐established principles of maximum and minimum entropy. When a more general framework of fuzzy measures is employed, uncertainty becomes a multi‐dimensional entity and the maximum and minimum uncertainty principles lead to optimisation problems with multiple objective criteria. Four distinct types of uncertainty are now recognised and their well‐justified measures determined within fuzzy set theory and one subset of fuzzy measures — the Dempster‐Shafer theory of evidence. The uncertainty types and their measures are briefly described.

The purpose of this paper is to provide proactive supply chain performance method considering risk which can be used during the supplier selection/assessment process.

In this paper, the effort is to present a model for evaluating the supply-related risk, which is based on the analytic hierarchy process (AHP) method and the Dempster-Shafer theory (DST). The proactive risk management methods used in this research is: seeking risk sources and identifying the variables to be used in the model, preprocessing the variables data to get the directions of the variables and the risk bounds, assigning variables weights via AHP method and finally evaluating the supply risk via DST method and determine the final risk degree.

The paper contributes to research in risk assessment in the specific field of supplier performance measurement. In this paper, a hybrid model using AHP and DST for risk assessment of supplier based on performance measurement is presented. An empirical analysis is conducted to illustrate the use of the model for the risk assessment in supply chain.

This methodology can be adopted by supply chain managers to evaluate the level of risk associated with current suppliers, and to assist them in making outsourcing decisions.

The proposed method makes a contribution by including risk as a performance measure in supply chain. The generated proactive supply risk assessment process uses a hybrid model of AHP and DST providing a novel approach for performance measurement which will be valuable both to academics and practitioners in this field.