Expert Systems and Probabiistic Network Models
Expert Systems and Probabiistic Network Models
Probabilistic Networks and Expert Systems
Probabilistic Networks and Expert Systems
A dynamic Bayesian network for diagnosing ventilator-associated pneumonia in ICU patients
Expert Systems with Applications: An International Journal
Large engineering project risk management using a Bayesian belief network
Expert Systems with Applications: An International Journal
Cost-benefit factor analysis in e-services using bayesian networks
Expert Systems with Applications: An International Journal
Bayesian Networks and Decision Graphs
Bayesian Networks and Decision Graphs
Classification of newborn EEG maturity with Bayesian averaging over decision trees
Expert Systems with Applications: An International Journal
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Early detection in water evaporative installations is one of the keys to fighting against the bacterium Legionella, the main cause of Legionnaire's disease. This paper discusses the general structure, elements and operation of a probabilistic expert system capable of predicting the risk of Legionella in real time from remote information relating to the quality of the water in evaporative installations. The expert system has a master-slave architecture. The slave is a control panel in the installation at risk containing multi-sensors which continuously provide measurements of chemical and physical variables continuously. The master is a net server which is responsible for communicating with the control panel and is in charge of storing the information received, processing the data through the environment R and publishing the results in a web server. The inference engine of the expert system is constructed through Bayesian networks, which are very useful and powerful models that put together probabilistic reasoning and graphical modelling. Bayesian reasoning and Markov Chain Monte Carlo algorithms are applied in order to study the relevant unknown quantities involved in the parametric learning and propagation of evidence phases.