A fuzzy preference‐ranking model for a quality evaluation of hospital web sites

This article presents a quality evaluation model for measuring the performance of hospital Web sites. The model is developed on the basis of a conceptual framework, which consists of seven major e‐service quality dimensions, including tangibles, reliability, responsiveness, confidence, empathy, quality of information, and integration of communication issues of Web sites. The dimensions and their associated attributes are first obtained from published articles in the health care and information technology literature and then adapted according to the suggestions of related domain experts. Two multicriteria decision‐making methods are used in the evaluation procedure. Determined Web site evaluation dimensions and their relevant attributes are weighted using the Analytic Hierarchy Process (AHP) method. Vagueness in some stages of the evaluation required the incorporation of fuzzy numbers in the assessment process. Both fuzzy and crisp data are then synthesized using the fuzzy PROMETHEE (Preference Ranking Organization Method for Enrichment Evaluation) ranking method. The model is applied initially to measure the performance of the Web sites of Turkish hospitals. This study should be of interest to health care and technology practitioners and researchers, as the findings shed light on the further development of performance measurements for hospital Web sites. © 2006 Wiley Periodicals, Inc. Int J Int Syst 21: 1181–1197, 2006.     

Intelligent e‐government services with personalized recommendation techniques

Information overload is becoming one of the problems that hinder the effectiveness of e‐government services. Intelligent e‐government services with personalized recommendation techniques can provide a solution for this problem. Existing recommendation approaches have not entirely considered the influences of attributes of various online services and may result in no guarantee of recommendation accuracy. This study proposes a new approach to handle recommendation issues of one‐and‐only items in e‐government services. The proposed approach integrates the techniques of semantic similarity and the traditional item‐based collaborative filtering. A recommender system named Smart Trade Exhibition Finder has been developed to implement the proposed recommendation approach. The recommender system can be applied in e‐government services to improve the quality of government‐to‐business online services. © 2007 Wiley Periodicals, Inc. Int J Int Syst 22: 401–417, 2007.     

α‐Cut fuzzy control charts for linguistic data

The major contribution of fuzzy set theory is its capability of representing vague data. Fuzzy logic offers a systematic base in dealing with situations that are ambiguous or not well defined. In the literature, there exist some fuzzy control charts developed for linguistic data that are mainly based on membership and probabilistic approaches. In this article, α‐cut control charts for attributes are developed. This approach provides the ability of determining the tightness of the inspection by selecting a suitable α‐level: The higher α the tighter inspection. The article also presents a numerical example and interprets and compares other results with the approaches developed previously. © 2004 Wiley Periodicals, Inc. Int J Int Syst 19: 1173–1195, 2004.     

Evaluating e‐learning web site quality in a fuzzy environment

The proliferation of the Internet and World Wide Web applications has created new opportunities as well as new challenges for institutions and individuals who are either receiving or delivering education. Electronic (e) learning is one of the most important developments in education. It recognizes the shift from teaching to learning and puts the learner or user before the institution. The objectives and expected outcomes of e‐learning are largely dependent on the quality of the teaching processes and the effectiveness of online access. Hence, assessing methods for the effectiveness of e‐learning Web sites are a critical issue in both practice and research. However, Web site quality is a complex concept and its measurement is expected to be multidimensional in nature. Multicriteria decision‐making (MCDM) techniques are widely used for evaluating and ranking such problems containing multiple, usually conflicting criteria. For this reason, this article presents a quality evaluation model based on the MCDM to measure the e‐learning Web sites' performance. In addition, the subjectivity and vagueness in the assessment process are dealt with using fuzzy logic. The study has investigated 10 worldwide and 11 locally successful Web sites with the proposed method. By suggesting an aggregated measure based on the Web site quality criteria, it is expected that the method could be useful to the e‐learning service providers and system developers, as well as to the researchers related with Web research. © 2007 Wiley Periodicals, Inc. Int J Int Syst 22: 567–586, 2007.     

A hybrid fuzzy cognitive model based on weighted OWA operators and single‐antecedent rules

Conventional fuzzy cognitive maps (FCMs) can only represent monotonic or symmetric causal relationships and cannot simulate the AND/OR combinations of the antecedent nodes. The rule‐based fuzzy cognitive maps (RBFCMs) usually suffer from the well‐known combinatorial rule explosion problem. A hybrid fuzzy cognitive model based on weighted OWA operators and single‐antecedent rules is proposed to eliminate the drawbacks of the existing FCM models. Hybrid fuzzy cognitive maps (HFCMs) represent the causal relationships with single‐antecedent fuzzy rules and handle the various AND/OR relationships among the antecedent nodes with weighted OWA aggregation operators. Compared with conventional FCMs, HFCMs have more powerful cognitive capability. Compared with RBFCMs, HFCMs reduce the scale and complexity of the rule bases significantly and have better representation and inference performance. © 2007 Wiley Periodicals, Inc. Int J Int Syst 22: 1189–1196, 2007.     

On Typical Hesitant Fuzzy Prioritized “or” Operator in Multi‐Attribute Decision Making

Since hesitant fuzzy set was proposed, multi‐attribute decision making (MADM) with hesitant fuzzy information, which is also called hesitant fuzzy MADM, has been a hot research topic in decision theory. This paper investigates a special kind of hesitant fuzzy MADM problems in which the decision data are expressed by several possible values, and the evaluative attributes are in different priority levels. Firstly, we introduce the definitions of hesitant fuzzy t‐norm and t‐conorm by extending the notions of t‐norm and t‐conorm to the hesitant fuzzy environment and explore their constructions by means of t‐norms and t‐conorms. Then motivated by the prioritized “or” operator (R. R. Yager, Prioritized aggregation operators, International Journal of Approximate Reasoning 2008;48:263–274), we develop the typical hesitant fuzzy prioritized “or” operator based on the developed hesitant fuzzy t‐norms and t‐conorms. In this operator, the degree of satisfaction of each alternative in each priority level is derived from a hesitant fuzzy t‐conorm to preserve trade‐offs among the attributes in the same priority level, and the priority weights of attributes are induced by a hesitant fuzzy t‐norm to model the prioritization relationship among attributes. Furthermore, we apply the developed typical hesitant fuzzy prioritized “or” operator to solving the MADM problems in which the decision data are expressed by several possible values and the attributes are in different priority levels. In addition, two numerical examples are given to, respectively, illustrate the applicability and superiority of the developed aggregation operator by comparative analyses with previous research.     

On a similarity measure between LR‐type fuzzy numbers and its application to database acquisition

This article presents a new similarity measure for LR‐type fuzzy numbers. The proposed similarity measure is based on a defined metric between LR‐type fuzzy numbers. It is known that an exponential operation is highly useful in dealing with the classical Shannon entropy and cluster analysis. We adopted, therefore, the exponential operation on this metric. Furthermore, we analyze its properties and make numerical comparisons to several similarity measures. The results show that the proposed similarity measure can overcome the drawbacks of the existing similarity measures. We then apply it to compound attributes for handling null queries to database systems. These applications can also be widely used in fuzzy queries to databases. © 2005 Wiley Periodicals, Inc. Int J Int Syst 20: 1001–1016, 2005.     

Local identification of prototypes for genetic learning of accurate TSK fuzzy rule‐based systems

This work presents the use of local fuzzy prototypes as a new idea to obtain accurate local semantics‐based Takagi–Sugeno–Kang (TSK) rules. This allow us to start from prototypes considering the interaction between input and output variables and taking into account the fuzzy nature of the TSK rules. To do so, a two‐stage evolutionary algorithm based on MOGUL (a methodology to obtain Genetic Fuzzy Rule‐Based Systems under the Iterative Rule Learning approach) has been developed to consider the interaction between input and output variables. The first stage performs a local identification of prototypes to obtain a set of initial local semantics‐based TSK rules, following the Iterative Rule Learning approach and based on an evolutionary generation process within MOGUL (taking as a base some initial linguistic fuzzy partitions). Because this generation method induces competition among the fuzzy rules, a postprocessing stage to improve the global system performance is needed. Two different processes are considered at this stage, a genetic niching‐based selection process to remove redundant rules and a genetic tuning process to refine the fuzzy model parameters. The proposal has been tested with two real‐world problems, achieving good results. © 2007 Wiley Periodicals, Inc. Int J Int Syst 22: 909–941, 2007.     

A novel two‐stage model for cloud service trustworthiness evaluation

In this paper, we address the cloud service trustworthiness evaluation problem, which in essence is a multi‐attribute decision‐making problem, by proposing a novel evaluation model based on the fuzzy gap measurement and the evidential reasoning approach. There are many sources of uncertainties in the process of cloud service trustworthiness evaluation. In addition to the intrinsic uncertainties, cloud service providers face the problem of discrepant evaluation information given by different users from different perspectives. To address these problems, we develop a novel fuzzy gap evaluation approach to assess cloud service trustworthiness and to provide evaluation values from different perspectives. From the evaluation values, the perception–importance, delivery–importance, and perception–delivery gaps are generated. These three gaps reflect the discrepancy evaluation of cloud service trustworthiness in terms of perception utility, delivery utility, and importance utility, respectively. Finally, the gap measurement of each perspective is represented by a belief structure and aggregated using the evidential reasoning approach to generate final evaluation results for informative and robust decision making. From this hybrid two‐stage evaluation process, cloud service providers can get improvement suggestions from intermediate information derived from the gap measurement, which is the main advantage of this evaluation process.     

Updating extended possibility‐based fuzzy relational databases

Two kinds of fuzziness in attribute values of the fuzzy relational databases can be distinguished: one is that attribute values are possibility distributions and the other is that there are resemblance relations in attribute domains. The fuzzy relational databases containing these two kinds of fuzziness simultaneously are called extended possibility‐based fuzzy relational databases. In this article, we focus on such fuzzy relational databases and investigate three update operations for the fuzzy relational databases, which are Insertion, Deletion, and Modification, respectively. We develop the strategies and implementation algorithms of these operations. © 2007 Wiley Periodicals, Inc. Int J Int Syst 22: 237–258, 2007.     

Boosting fuzzy rules in classification problems under single‐winner inference

In previous studies, we have shown that an Adaboost‐based fitness can be successfully combined with a Genetic Algorithm to iteratively learn fuzzy rules from examples in classification problems. Unfortunately, some restrictive constraints in the implementation of the logical connectives and the inference method were assumed. Alas, the knowledge bases Adaboost produces are only compatible with an inference based on the maximum sum of votes scheme, and they can only use the t‐norm product to model the “and” operator. This design is not optimal in terms of linguistic interpretability. Using the sum to aggregate votes allows many rules to be combined, when the class of an example is being decided. Because it can be difficult to isolate the contribution of individual rules to the knowledge base, fuzzy rules produced by Adaboost may be difficult to understand linguistically. In this point of view, single‐winner inference would be a better choice, but it implies dropping some nontrivial hypotheses. In this work we introduce our first results in the search for a boosting‐based genetic method able to learn weighted fuzzy rules that are compatible with this last inference method. © 2007 Wiley Periodicals, Inc. Int J Int Syst 22: 1021–1034, 2007.     

Development of VHDL‐AMS neuro‐fuzzy behavioral models for RF/microwave passive components

The new generation of System‐on‐Chip (SoC) incorporates digital, analogue, RF/microwave and mixed‐signal components. Such circuits impose to reconsider the traditional design methods. Mixed‐signal designers need novel design methodologies which will have to include accurate behavioral libraries of devices and processes into hierarchical design flows. Thus, this paper describes a behavioral modeling approach which generates neuro‐fuzzy‐based models for RF/microwave devices. The models, so obtained, can be easily integrated into a VHDL‐AMS simulator. This modeling approach is applied to a microwave tunable phase shifter and it is illustrated by the development of a VHDL‐AMS model library for RF/microwave applications. © 2007 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2007.     

Normalization and lossless join decomposition of similarity‐based fuzzy relational databases

Fuzzy relational database models generalize the classical relational database model by allowing uncertain and imprecise information to be represented and manipulated. In this article, we introduce fuzzy extensions of the normal forms for the similarity‐based fuzzy relational database model. Within this framework of fuzzy data representation, similarity, conformance of tuples, the concept of fuzzy functional dependencies, and partial fuzzy functional dependencies are utilized to define the fuzzy key notion, transitive closures, and the fuzzy normal forms. Algorithms for dependency preserving and lossless join decompositions of fuzzy relations are also given. We include examples to show how normalization, dependency preserving, and lossless join decomposition based on the fuzzy functional dependencies of fuzzy relation are done and applied to some real‐life applications. © 2004 Wiley Periodicals, Inc. Int J Int Syst 19: 885–917, 2004.     

Relevance in systems having a fuzzy‐set‐based semantics

Future automated question answering systems will typically involve the use of local knowledge available on the users' systems as well as knowledge retrieved from the Web. The determination of what information we should seek out on the Web must be directed by its potential value or relevance to our objective in the light of what knowledge is already available. Here we begin to provide a formal quantification of the concept of relevance and related ideas for systems that use fuzzy‐set‐based representations to provide the underlying semantics. We also introduce the idea of ease of extraction to quantify the ability of extracting relevant information from complex relationships. © 2007 Wiley Periodicals, Inc. Int J Int Syst 22: 385–396, 2007.     

A multigranular hierarchical linguistic model for design evaluation based on safety and cost analysis

Before implementing a design of a large engineering system different design proposals are evaluated. The information used by experts to evaluate different options may be vague and/or incomplete. Although different probabilistic tools and techniques have been used to deal with these kinds of problems, it seems better to use the fuzzy linguistic approach to model vagueness and the Dempster‐Shafter theory of evidence for modeling incompleteness and ignorance. In the evaluation of alternative designs, different criteria can be considered. In this article an evaluation process is developed in terms of Safety and Cost analysis. Both criteria involve uncertainty, vagueness, and ignorance due to their nature. Therefore, we propose an evaluation process defined in a linguistic framework where both criteria will be conducted in different utility spaces, i.e., in a multigranular linguistic domain. Once the evaluation framework has been defined, we present an evaluation process based on a Multi‐Expert Multi‐Criteria decision model that will be able to deal with multigranular linguistic information without loss of information in order to evaluate different design options for an engineering system in a precise manner. Accordingly, we propose the use of a multigranular linguistic model based on the Linguistic Hierarchies presented by Herrera and Martínez (“A model based on linguistic 2‐tuples for dealing with multigranularity hierarchical linguistic contexts in multi‐expert decision‐making.” IEEE Trans Syst Man Cybern B 2001;31(2):227–234). © 2005 Wiley Periodicals, Inc. Int J Int Syst 20: 1161–1194, 2005.     

A C‐OWA operator‐based approach to decision making with interval fuzzy preference relation

Yager [IEEE Trans Syst Man Cybern B 2004;34:1952–1963] introduced a continuous interval argument OWA (C‐OWA) operator, which extends the ordered weighted averaging (OWA) operator, introduced by Yager [IEEE Trans Syst Man Cybern B 1988;18:183–190], to the case in which the given argument is a continuous valued interval rather than a finite set of values. In this article, we utilize the C‐OWA operator to derive the priority vector of an interval fuzzy preference relation and then develop a practical approach to solving the decision‐making problem with interval fuzzy preference relation. Finally, a numerical example is provided to demonstrate the practicability and efficiency of the developed approach. © 2006 Wiley Periodicals, Inc. Int J Int Syst 21: 1289–1298, 2006.     

A distributed adverse drug reaction detection system using intelligent agents with a fuzzy recognition‐primed decision model

Discovering unknown adverse drug reactions (ADRs) in postmarketing surveillance as early as possible is highly desirable. Nevertheless, current postmarketing surveillance methods largely rely on spontaneous reports that suffer from serious underreporting, latency, and inconsistent reporting. Thus these methods are not ideal for rapidly identifying rare ADRs. The multiagent systems paradigm is an emerging and effective approach to tackling distributed problems, especially when data sources and knowledge are geographically located in different places and coordination and collaboration are necessary for decision making. In this article, we propose an active, multiagent framework for early detection of ADRs by utilizing electronic patient data distributed across many different sources and locations. In this framework, intelligent agents assist a team of experts based on the well‐known human decision‐making model called Recognition‐Primed Decision (RPD). We generalize the RPD model to a fuzzy RPD model and utilize fuzzy logic technology to not only represent, interpret, and compute imprecise and subjective cues that are commonly encountered in the ADR problem but also to retrieve prior experiences by evaluating the extent of matching between the current situation and a past experience. We describe our preliminary multiagent system design and illustrate its potential benefits for assisting expert teams in early detection of previously unknown ADRs. © 2007 Wiley Periodicals, Inc. Int J Int Syst 22: 827–845, 2007.     

Developing and assessing an intelligent forex rolling forecasting and trading decision support system for online e‐service

An effective foreign exchange (forex) trading decision is usually dependent on effective forex forecasting. In this study, an intelligent system framework integrating forex forecasting and trading decision is first proposed. Based on this framework, an advanced intelligent decision support system (DSS) incorporating a back‐propagation neural network (BPNN)‐based forex forecasting subsystem and Web‐based forex trading decision support subsystem is developed, which has been used to predict the directional change of daily forex rates and provide intelligent online decision support for financial institutions and individual investors. This article describes the forex forecasting and trading decision method, the system architecture, main functions, and operation of the developed DSS system. A comparative study is conducted between our developed system and others commonly used in order to assess the overall performance of the developed system. The assessment results show that our developed DSS outperforms some commonly used forex forecasting and trading decision systems and can provide intelligent e‐service for forex traders to make useful trading decisions in the forex market. © 2007 Wiley Periodicals, Inc. Int J Int Syst 22: 475–499, 2007.     

A fuzzy logic approach to experience‐based reasoning

Experience‐based reasoning (EBR) is a reasoning paradigm that has been used in almost every human activity such as business, military missions, and teaching activities since early human history. However, EBR has not been seriously studied from either a logical or mathematical viewpoint, although case‐based reasoning (CBR) researchers have paid attention to EBR to some extent. This article will attempt to fill this gap by providing a unified fuzzy logic‐based treatment of EBR. More specifically, this article first reviews the logical approach to EBR, in which eight different rules of inference for EBR are discussed. Then the article proposes fuzzy logic‐based models to these eight different rules of inference that constitute the fundamentals for all EBR paradigms from a fuzzy logic viewpoint, and therefore will form a theoretical foundation for EBR. The proposed approach will facilitate research and development of EBR, fuzzy systems, intelligent systems, knowledge management, and experience management. © 2007 Wiley Periodicals, Inc. Int J Int Syst 22: 867–889, 2007.     

Hybrid flexible neural‐tree‐based intrusion detection systems

An intrusion is defined as a violation of the security policy of the system, and, hence, intrusion detection mainly refers to the mechanisms that are developed to detect violations of system security policy. Current intrusion detection systems (IDS) examine all data features to detect intrusion or misuse patterns. Some of the features may be redundant or contribute little (if anything) to the detection process. The purpose of this study is to identify important input features in building an IDS that is computationally efficient and effective. This article proposes an IDS model based on a general and enhanced flexible neural tree (FNT). Based on the predefined instruction/operator sets, a flexible neural tree model can be created and evolved. This framework allows input variables selection, overlayer connections, and different activation functions for the various nodes involved. The FNT structure is developed using an evolutionary algorithm, and the parameters are optimized by a particle swarm optimization algorithm. Empirical results indicate that the proposed method is efficient. © 2007 Wiley Periodicals, Inc. Int J Int Syst 22: 337–352, 2007.