Bayesian network

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Bayesian network

A form of artificial intelligence—named for Bayes’ theorem—which calculates probability based on a group of related or influential signs. Once a Bayesian network AI is taught the symptoms and probable indicators of a particular disease, it can assess the probability of that disease based on the frequency or number of signs in a patient.
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[4] introduced the Deep Belief Network, with multiple layers of Restricted Boltzmann Machines, greedily training one layer at a time in an unsupervised way.
proposed a Deep Belief Network and Dempster-Shafer- (DBN-DS-) based multiclassifier for the pathologic prediction of prostate cancer, tested with data from thousands of patients, and obtained high accuracy (81.27%) against the Partin table (64.14%).
Firstly, a large number of unlabeled images are selected as training samples, and a 2D deep belief network (2D-DBN) is used as a classifier structure.
Deep Belief Network is a type of deep neural network with multiple hidden layers, introduced by Hinton et al.
Third, our study indicates that physical activity alone cannot reduce diabetes disparities between Blacks and Whites based on observational inferences, an inferential approach in Bayesian Belief Network machine learning modeling.
In our investigations we used a special computer tool called BeliefSEEKER (a belief network system) that was developed at the University of Information Technology and Management in Rzeszow, Poland, in cooperation with the University of Kansas.
In this section, a novel algorithm based on deep belief network (DBN) is proposed.
A sampling of topics: supply chain performance measurement using logical aggregation, a Bayesian belief network modeling of customer behavior on apparel coordination for fashion retailing business, consensus measures for symbolic data, cigarette sensory evaluation classifier predictive control algorithm, uncertainty aversion under distorted probability, and an agent-based approach to modeling small satellite constellations.
The values of some of the belief network variables are displayed in Table 1.
Deep learning architectures overcome the learning difficulty through stratified training, such as deep belief network [30], which pre-train multiple layers from bottom to up to construct the classification model.
This paper takes deep learning as the point of penetration and uses multilayer network architecture to abstract the characteristics of layers and establish a cardiovascular disease prediction model based on deep belief network. At the same time, the prediction model based on deep trust network is improved by using reconstruction error to achieve better prediction.
Some scholars [24-27] introduce deep autoencoder network and deep belief network to solve the problem of transformer fault diagnosis.