An Ontology-based Bayesian network modelling for supply chain risk propagation
Purpose Supply chain risks (SCRs) do not work in isolation and have impact both on each
member of a chain and the performance of the entire supply chain. The purpose of this …
member of a chain and the performance of the entire supply chain. The purpose of this …
Bayesian Networks for the Diagnosis and Prognosis of Diseases: A Sco** Review
K Polotskaya, CS Muñoz-Valencia, A Rabasa… - Machine Learning and …, 2024 - mdpi.com
Bayesian networks (BNs) are probabilistic graphical models that leverage Bayes' theorem to
portray dependencies and cause-and-effect relationships between variables. These …
portray dependencies and cause-and-effect relationships between variables. These …
An ontology-based approach for constructing Bayesian networks
S Fenz - Data & Knowledge Engineering, 2012 - Elsevier
Bayesian networks are commonly used for determining the probability of events that are
influenced by various variables. Bayesian probabilities encode degrees of belief about …
influenced by various variables. Bayesian probabilities encode degrees of belief about …
Bayesian network learning with the PC algorithm: an improved and correct variation
M Tsagris - Applied Artificial Intelligence, 2019 - Taylor & Francis
ABSTRACT PC is a prototypical constraint-based algorithm for learning Bayesian networks,
a special case of directed acyclic graphs. An existing variant of it, in the R package pcalg …
a special case of directed acyclic graphs. An existing variant of it, in the R package pcalg …
Mobile cloud-based depression diagnosis using an ontology and a Bayesian network
Recently, depression has becomes a widespread disease throughout the world. However,
most people are not aware of the possibility of becoming depressed during their daily lives …
most people are not aware of the possibility of becoming depressed during their daily lives …
Decision support analysis for risk identification and control of patients affected by COVID-19 based on Bayesian Networks
J Shen, F Liu, M Xu, L Fu, Z Dong, J Wu - Expert Systems with Applications, 2022 - Elsevier
In the context of the outbreak of coronavirus disease (COVID-19), this paper proposes an
innovative and systematic decision support model based on Bayesian networks (BNs) to …
innovative and systematic decision support model based on Bayesian networks (BNs) to …
A survey on semanticized and personalized health recommender systems
Abstract Health 3.0 is a health‐related extension of the Web 3.0 concept. It is based on the
semantic Web which provides for semantically organizing electronic health records of …
semantic Web which provides for semantically organizing electronic health records of …
Combining ontology and probabilistic models for the design of bio-based product transformation processes
This paper presents a workflow for the design of transformation processes using different
kinds of expert's knowledge. It introduces POND (Process and observation ONtology …
kinds of expert's knowledge. It introduces POND (Process and observation ONtology …
Causal bayesian networks for medical diagnosis: A case study in rheumatoid arthritis
Bayesian network (BN) models have been widely applied in medical diagnosis. These
models can be built from different sources, including both data and domain knowledge in the …
models can be built from different sources, including both data and domain knowledge in the …
Develo** bayesian networks from a dependency‐layered ontology: a proof‐of‐concept in radiation oncology
Purpose Bayesian networks (BN s) are graphical representations of probabilistic knowledge
that offer normative reasoning under uncertainty and are well suited for use in medical …
that offer normative reasoning under uncertainty and are well suited for use in medical …