The Experts below are selected from a list of 315 Experts worldwide ranked by ideXlab platform
Stefan Spinler - One of the best experts on this subject based on the ideXlab platform.
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Supply risk Interrelationships and the derivation of key supply risk indicators
Technological Forecasting and Social Change, 2015Co-Authors: Benjamin Guertler, Stefan SpinlerAbstract:Increasing product and service complexity, outsourcing, and globalization lead to complex and dynamic supply networks. Within supply networks, Interrelationships and mutual connections among supply risks often create additional challenges for risk monitoring. During normal operation, risk Interrelationships remain largely hidden until the occurrence of a specific risk. Understanding these Interrelationships is therefore important to increase the effectiveness of risk monitoring. In this paper, the Interrelationships between supply risks are quantified and supply risks are categorized according to their role within the system. We follow a network oriented approach as defined by system theory. Our explorative research utilizes data from expert evaluations in selected case companies and emphasizes that strong Interrelationships and mutual connections exist between supply risks. We draw on these findings to establish a small and efficient set of key supply risk indicators, making the results highly relevant for executives seeking to improve risk monitoring.
Benjamin Guertler - One of the best experts on this subject based on the ideXlab platform.
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Supply risk Interrelationships and the derivation of key supply risk indicators
Technological Forecasting and Social Change, 2015Co-Authors: Benjamin Guertler, Stefan SpinlerAbstract:Increasing product and service complexity, outsourcing, and globalization lead to complex and dynamic supply networks. Within supply networks, Interrelationships and mutual connections among supply risks often create additional challenges for risk monitoring. During normal operation, risk Interrelationships remain largely hidden until the occurrence of a specific risk. Understanding these Interrelationships is therefore important to increase the effectiveness of risk monitoring. In this paper, the Interrelationships between supply risks are quantified and supply risks are categorized according to their role within the system. We follow a network oriented approach as defined by system theory. Our explorative research utilizes data from expert evaluations in selected case companies and emphasizes that strong Interrelationships and mutual connections exist between supply risks. We draw on these findings to establish a small and efficient set of key supply risk indicators, making the results highly relevant for executives seeking to improve risk monitoring.
Tetsuya Murai - One of the best experts on this subject based on the ideXlab platform.
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Fuzzy Sets, Rough Sets, Multisets and Clustering - A Review on Rough Set-Based Interrelationship Mining
Fuzzy Sets Rough Sets Multisets and Clustering, 2017Co-Authors: Yasuo Kudo, Tetsuya MuraiAbstract:Interrelationship mining, proposed by the authors, aims at extracting characteristics of objects based on Interrelationships between attributes. Interrelationship mining is an extension of rough set-based data mining, which enables us to extract characteristics based on comparison of values of two different attributes such that “the value of attribute a is higher than the value of attribute b.” In this paper, we mainly review theoretical aspects of rough set-based Interrelationship mining.
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Rough-Set-Based Interrelationship Mining for Incomplete Decision Tables
Journal of Advanced Computational Intelligence and Intelligent Informatics, 2016Co-Authors: Yasuo Kudo, Tetsuya MuraiAbstract:This chapter discusses rough-set-based Interrelationship mining for incomplete decision tables. Rough-set-based Interrelationship mining enables to extract characteristics by comparing the values of the same object between different attributes. To apply this Interrelationship mining to incomplete decision tables with null values, in this study, we discuss the treatment of null values in Interrelationships between attributes. We introduce three types of null values for interrelated condition attributes and formulate a similarity relation by such attributes with these null values.
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GrC - Interrelationship mining from a viewpoint of rough sets on two universes
2014 IEEE International Conference on Granular Computing (GrC), 2014Co-Authors: Yasuo Kudo, Tetsuya MuraiAbstract:We discuss connections between the Interrelationship mining, proposed by the authors, and rough sets on two universes. The Interrelationship mining enable us to extract characteristics based on comparison between values of different attributes. Rough sets on two universes is an theoretical extension of the original rough sets by considering connection between two universes. In this paper, we point out that Interrelationship between different attributes in the Interrelationship mining is representable by a variant of rough sets on two universes.
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Brain and Health Informatics - On a Possibility of Applying Interrelationship Mining to Gene Expression Data Analysis
Lecture Notes in Computer Science, 2013Co-Authors: Yasuo Kudo, Yoshifumi Okada, Tetsuya MuraiAbstract:Interrelationship mining was proposed by the authors to extract characteristics of objects based on Interrelationships between attributes. Interrelationship mining is an extension of rough set-based data mining, which enables us to extract characteristics based on comparison of values of two different attributes such that "the value of attribute a is higher than the value of attribute b." In this paper, we discuss an approach of applying the Interrelationship mining to bioinformatics, in particular, gene expression data analysis.
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GrC - Decision logic for rough set-based Interrelationship mining
2013 IEEE International Conference on Granular Computing (GrC), 2013Co-Authors: Yasuo Kudo, Tetsuya MuraiAbstract:Rough Rough set-based decision rule extraction is a useful tool for data mining, however, almost approaches of decision rule extraction are based on comparison between values of the same attribute. On the other hand, the authors have proposed a concept of Interrelationship mining that enables us to extract characteristics based on comparison between values of different attributes. In this paper, we consider logical aspects of Interrelationship mining by introducing decision logic for Interrelationship mining.
Borja Martinovic - One of the best experts on this subject based on the ideXlab platform.
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social identity complexity and immigrants attitude toward the host nation the intersection of ethnic and religious group identification
Personality and Social Psychology Bulletin, 2012Co-Authors: Maykel Verkuyten, Borja MartinovicAbstract:Social identity complexity refers to individual differences in the Interrelationships among multiple ingroup identities. The present research conducted in the Netherlands examines social identity complexity in relation to Muslim immigrants' national identification and the attitude toward the host majority. Three studies are reported that focused on the Interrelationship between ethnicity and religion and examined social identity complexity in different ways. Study 1 showed that lower social identity complexity is associated with lower national identification. Studies 2 and 3 examined the interaction between ethnic and religious group identification. For Muslim identifiers, higher ethnic identification was related to lower national identification and higher ingroup bias (Studies 2) and lower endorsement of national liberal practices (Study 3). In contrast, for those who did not strongly identify with Muslims, higher ethnic identification was associated with higher national identification, stronger endorsement of Dutch liberal practices, and more positive stereotypes about the Dutch outgroup (Study 3).
Bruce P. Hermann - One of the best experts on this subject based on the ideXlab platform.
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Developmental Reorganization of the Cognitive Network in Pediatric Epilepsy.
PloS one, 2015Co-Authors: Camille Garcia-ramos, Jack J. Lin, Vivek Prabhakaran, Bruce P. HermannAbstract:Traditional approaches to understanding cognition in children with epilepsy (CWE) involve cross-sectional or prospective examination of diverse test measures, an approach that does not inform the Interrelationship between different abilities or how Interrelationships evolve prospectively. Here we utilize graph theory techniques to interrogate the development of cognitive landmarks in CWE and healthy controls (HC) using the two-year percentage change across 20 tests. Additionally, we characterize the development of cognition using traditional analyses, showing that CWE perform worse at baseline, develop in parallel with HC, statically maintaining cognitive differences two years later. Graph analyses, however, showed CWE to exhibit both lower integration and segregation in development of their cognitive networks compared to HC. In conclusion, graph analyses of neuropsychological data capture a dynamic and changing complexity in the Interrelationships among diverse cognitive skills, maturation of the cognitive network over time, and the nature of differences between normally developing children and CWE.
