Abstract Discrete Bayesian Belief Network (BBN) has become a popular method for the
analysis of complex systems in various domains of application. One of its pillar is the …

Abstract Structure learning is a very important problem in the field of Bayesian networks
(BNs). It is also an active research area for more than 2 decades; therefore, many …

This paper proposes a fault diagnosis methodology for a gear pump based on the ensemble
empirical mode decomposition (EEMD) method and the Bayesian network. Essentially, the …

The purpose of this article is to describe the development of a knowledge-based system that,
aided by rules, can support the decision-making processes of the sales department of a …

Identifying potential consumers for direct marketing to very large data is a difficult and
impossible task to do manually. Therefore, the machine learning approach needs to help …

Rapid technological development at this time is not only recognized by humans, now
sensors embedded in smartphones can also recognize human activity using an …

Finding knowledge from a database and turning it into useful information is a big challenge.
The use of machine learning helps analyze data and contribute to delivering results that can …

The integration of three different in-situ test databases of standard penetration test (SPT),
cone penetration test (CPT), and shear velocity test (V s) to build an ensemble seismic …

Consider a policymaker who wants to decide which intervention to perform in order to
change a currently undesirable situation. The policymaker has at her disposal a team of …

The article presents a novel algorithm for merging Bayesian networks generated by different
methods, such as expert knowledge and data-driven approaches, while leveraging a …