Design and Development of a Compound DSS for Laboratory Research

Hujer, Tomáš

doi:10.5772/16720

Cited by 9 publications

(1 citation statement)

References 16 publications

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“…1a shows a simple example of a Bayesian network consisting of four variables: ‘Cloudy’, ‘Sprinkler’, ‘Rain’, And ‘Wet grass’. 7 The network system consists of nodes and edges, representing an individual variable and a relationship between two variables, respectively. The edges are shown as arrows indicating the direction of the causal relationship, where the starting and ending points of the arrows represent the cause (parent node) and the result (child node), respectively.…”

Section: Introductionmentioning

confidence: 99%

Implementation of Bayesian networks and Bayesian inference using a Cu_0.1Te_0.9/HfO₂/Pt threshold switching memristor

Baek,

Lee,

Jang

et al. 2024

Nanoscale Adv.

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Section: Introductionmentioning

confidence: 99%

Implementation of Bayesian networks and Bayesian inference using a Cu_0.1Te_0.9/HfO₂/Pt threshold switching memristor

Baek,

Lee,

Jang

et al. 2024

Nanoscale Adv.

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A decision support framework for prediction of avian influenza

Yousefinaghani

Dara

Poljak

et al. 2020

Sci Rep

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For years, avian influenza has influenced economies and human health around the world. The emergence and spread of avian influenza virus have been uncertain and sudden. The virus is likely to spread through several pathways such as poultry transportation and wild bird migration. The complicated and global spread of avian influenza calls for surveillance tools for timely and reliable prediction of disease events. These tools can increase situational awareness and lead to faster reaction to events. Here, we aimed to design and evaluate a decision support framework that aids decision makers by answering their questions regarding the future risk of events at various geographical scales. Risk patterns were driven from pre-built components and combined in a knowledge base. Subsequently, questions were answered by direct queries on the knowledge base or through a built-in algorithm. The evaluation of the system in detecting events resulted in average sensitivity and specificity of 69.70% and 85.50%, respectively. The presented framework here can support health care authorities by providing them with an opportunity for early control of emergency situations.

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Management of information security indicators under a cognitive security model

Andrade

Torres

Flores

2018

2018 IEEE 8th Annual Computing and Communication Workshop and Conference (CCWC)

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Design and Development of a Compound DSS for Laboratory Research

Cited by 9 publications

References 16 publications

Implementation of Bayesian networks and Bayesian inference using a Cu_0.1Te_0.9/HfO₂/Pt threshold switching memristor

Implementation of Bayesian networks and Bayesian inference using a Cu_0.1Te_0.9/HfO₂/Pt threshold switching memristor

A decision support framework for prediction of avian influenza

Management of information security indicators under a cognitive security model

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Design and Development of a Compound DSS for Laboratory Research

Cited by 9 publications

References 16 publications

Implementation of Bayesian networks and Bayesian inference using a Cu0.1Te0.9/HfO2/Pt threshold switching memristor

Implementation of Bayesian networks and Bayesian inference using a Cu0.1Te0.9/HfO2/Pt threshold switching memristor

A decision support framework for prediction of avian influenza

Management of information security indicators under a cognitive security model

Contact Info

Product

Resources

About

Implementation of Bayesian networks and Bayesian inference using a Cu_0.1Te_0.9/HfO₂/Pt threshold switching memristor

Implementation of Bayesian networks and Bayesian inference using a Cu_0.1Te_0.9/HfO₂/Pt threshold switching memristor