Universal fuzzy system representation with XML

Tseng, Chris; Khamisy, Wafa; Vu, Toan H.

doi:10.1016/j.csi.2004.11.005

Cited by 21 publications

(13 citation statements)

References 5 publications

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“…The choice of using an XML formalism for both configuring a fuzzy inference system and encoding its fuzzy medical knowledge is due to the possibility of supporting the seamless collaboration between users, and granting portability, extensibility and reuse, as highlighted in several studies [1,15,16]. For more details about the framework please refer to [8].…”

Section: The Proposed Editing Framework For Encoding Guidelines Into mentioning

confidence: 98%

Encoding Clinical Recommendations into Fuzzy DSSs: An Application to COPD Guidelines

Minutolo¹,

Esposito²,

Pietro³

2016

Knowledge, Information and Creativity Support Systems: Recent Trends, Advances and Solutions

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Clinical Decision Support Systems (DSSs) have been applied to medical scenarios by computerizing a set of clinical guidelines of interest, with the final aim of simulating the process followed by the physicians. In this context, fuzzy logic has been profitably used for modeling clinical guidelines affected by uncertainty and improving the interpretability of clinical DSSs through its expressivity close to natural language. However, the task of computerizing clinical guidelines in terms of fuzzy if-then rules can be complex and, often, requires technical capabilities not owned by physicians. In order to face this issue, this paper introduces a fuzzy knowledge editing framework expressly devised and designed to simplify the procedures necessary to codify clinical guidelines in terms of fuzzy if-then rules and linguistic variables. This framework is described with respect to a specific real case regarding the formalization of clinical recommendations extracted from the GOLD guidelines, which contain the best evidence for diagnosing and managing the Chronic Obstructive Pulmonary Disease.

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Section: The Proposed Editing Framework For Encoding Guidelines Into mentioning

confidence: 98%

Encoding Clinical Recommendations into Fuzzy DSSs: An Application to COPD Guidelines

Minutolo¹,

Esposito²,

Pietro³

2016

Knowledge, Information and Creativity Support Systems: Recent Trends, Advances and Solutions

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“…They also introduce several new elements for the operators and rules. It should be pointed out that the focus of (Tseng, Khamisy and Vu, 2005) is on (fuzzy) system modeling instead of data modeling. Gaurav and Alhajj (2006) incorporate fuzziness in values and structures of XML elements.…”

Section: Fuzzy Data In Xml Documentsmentioning

confidence: 99%

“…Lee and Fanjiang (2003) actually discuss the second level of fuzziness in XML documents and the fuzzy XML data in the fuzzy XML documents include the fuzzy data representations of type-3 and type-6. Tseng, Khamisy and Vu (2005) present an XML method to represent fuzzy systems for facilitating collaborations in fuzzy applications. They consider the following main fuzzy system data types: (1) linguistic variable, which has a name attribute; (2) linguistic term, which has a membership function and an identification number attribute to identify the linguistic terms of a linguistic variable; (3) membership function, which can be either a pre-defined function or a user-defined function; (4) operator, which can be either a pre-defined operator or a user-defined operator (function); (5) rule with weight attribute, in which each antecedent and consequent contain a linguistic variable and a linguistic term, and each antecedent also contains a logical operator.…”

Section: Fuzzy Data In Xml Documentsmentioning

confidence: 99%

Modeling fuzzy data with XML: A survey

2016

Fuzzy Sets and Systems

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“…On the lower side of their methodology, (Tseng et al, 2005) proposed fuzzy system data types that consist of: linguistic variables, linguistic terms, membership functions, operators, and rules. They then proposed a DTD as a kind of schema to describe fuzzy systems in XML; one DTD is defined per component and main data type.…”

Section: Fuzzy Xml Modellingmentioning

confidence: 99%

Securing Financial XML Transactions Using Intelligent Fuzzy Classification Techniques

Ammari

2017

Ontologies and Big Data Considerations for Effective Intelligence

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Keywords: XML, financial transactions, fuzzy logic, fuzzy XML, XML security, XML encryption, financial transactions, bankingThe eXtensible Markup Language (XML) has been widely adopted in many financial institutions in their daily transactions; this adoption was due to the flexible nature of XML providing a common syntax for systems messaging in general and in financial messaging in specific. Excessive use of XML in financial transactions messaging created an aligned interest in security protocols integrated into XML solutions in order to protect exchanged XML messages in an efficient yet powerful mechanism. However, financial institutions (i.e. banks) perform large volume of transactions on daily basis which require securing XML messages on large scale. Securing large volume of messages will result performance and resource issues. Therefore, an approach is needed to secure specified portions of an XML document, syntax and processing rules for representing secured parts.In this research we have developed a smart approach for securing financial XML transactions using effective and intelligent fuzzy classification techniques. Our approach defines the process of classifying XML content using a set of fuzzy variables.Upon fuzzy classification phase, a unique value is assigned to a defined attribute named "Importance Level". Assigned value indicates the data sensitivity for each XML tag.This thesis also defines the process of securing classified financial XML message content by performing element-wise XML encryption on selected parts defined in fuzzy

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Universal fuzzy system representation with XML

Cited by 21 publications

References 5 publications

Encoding Clinical Recommendations into Fuzzy DSSs: An Application to COPD Guidelines

Encoding Clinical Recommendations into Fuzzy DSSs: An Application to COPD Guidelines

Modeling fuzzy data with XML: A survey

Securing Financial XML Transactions Using Intelligent Fuzzy Classification Techniques

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