Bayesian network is a strong tool for uncertain knowledge representation and inference. This paper mainly introduces some technologies and methods about Bayesian network based on intelligent system. In the construction of Bayesian network, divorcing technology and noisy-or technology are used. In the inference of Bayesian network, VE algorithm and sampling algorithm are introduced. Finally, Bay...

background: high-risk unsafe behaviors (hrubs) have been known as the main cause of occupational accidents. considering the financial and societal costs of accidents and the limitations of available resources, there is an urgent need for managing unsafe behaviors at workplaces. the aim of the present study was to find strategies for decreasing the rate of hrubs using an integrated approach of s...

This paper introduces a new Bayesian network structure, named a Partial Bayesian Network (PBN), and describes an algorithm for constructing it. The PBN is designed to be used for classification tasks, and accordingly the algorithm constructs an approximate Markov blanket around a classification node. Initial experiments have compared the performance of the PBN algorithm with Naïve Bayes, Tree-A...

We present a novel algorithm for estimating genome-wide gene networks using nonparametric Bayesian network models [3]. The algorithm, which is called the Neighbor Node Sampling & Repeat (NNSR) algorithm, is capable of searching a Bayesian network structure consisting of more than 20 000 nodes, which is fitted to given gene expression data. To realize the large scale Bayesian network structure s...

We present an algorithm for inducing Bayesian networks using feature selection. The algorithm selects a subset of attributes that maximizes predictive accuracy prior to the network learning phase, thereby incorporating a bias for small networks that retain high predictive accuracy. We compare the behavior of this selective Bayesian network classiier with that of (a) Bayesian network classiiers ...

A bayesian network is an appropriate tool for working with uncertainty and probability, that are typical of real-life applications. In literature we find different approaches for bayesian network learning. Some of them are based on search and score methodology and the others follow an information theory based approach. One of the most known algorithm for learning bayesian network is the SLA alg...

This paper presents a new hybrid classifier that combines the probability based Bayesian Network paradigm with the Nearest Neighbor distance based algorithm. The Bayesian Network structure is obtained from the data by using the K2 structural learning algorithm. The Nearest Neighbor algorithm is used in combination with the Bayesian Network in the deduction phase. For those data bases in which s...

In this research, the amount of Iron removal by bioleaching of a kaolin sample with high iron impurity with Aspergillus niger was optimized. In order to study the effect of initial pH, sucrose and spore concentration on iron, oxalic acid and citric acid concentration, more than twenty experiments were performed. The resulted data were utilized to train, validate and test the two layer artificia...

Bayesian network structure learning is an NP-hard problem that has been faced by a number of traditional approaches in recent decades. Currently, quantum technologies offer wide range advantages can be exploited to solve optimization tasks cannot addressed efficient way when utilizing classic computing approaches. In this work, specific type variational algorithm, the approximate was used probl...