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  • Han-Ying Kao
  • 2008
Influence diagrams have been widely used as knowledge bases in medical informatics and many applied domains. In conventional influence diagrams, the numerical models of uncertainty are probability distributions associated with chance nodes and value tables for value nodes. However, when incomplete knowledge or linguistic vagueness is involved in the(More)
This study proposes an optimization model for optimal treatment of bacterial infections. Using an influence diagram as the knowledge and decision model, we can conduct two kinds of reasoning simultaneously: diagnostic reasoning and treatment planning. The input information of the reasoning system are conditional probability distributions of the network(More)
Recently, cloud services and cloud computing have revolutionized both academic research and industrial practices. A corresponding focus on how to improve the performance of cloud computing is growing apace. It is a significant approach to allocate virtual machines (VMs) on a set of physical machines. Computing resources can be utilized effectively with the(More)
Bayesian networks have been widely used as the knowledge bases with uncertainty. However, in most literatures, the uncertainty measure in Bayesian networks are limited in probability distributions and crisp variables, which restricts the practical usefulness of Bayesian networks when incomplete knowledge or linguistic vagueness is involved in the reasoning(More)