scholarly journals Dynamic Knowledge Inference Based on Bayesian Network Learning

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Deyan Wang ◽  
Adam AmrilJaharadak ◽  
Ying Xiao
Keyword(s):  
Bayesian Network ◽  
Message Passing ◽  
Expert Knowledge ◽  
Posteriori Probability ◽  
Inference Process ◽  
Network Nodes ◽  
A Posteriori Probability ◽  
Network Learning ◽  
Bayesian Network Structure ◽  
Message Passing Algorithm

On the basis of studying datasets of students' course scores, we constructed a Bayesian network and undertook probabilistic inference analysis. We selected six requisite courses in computer science as Bayesian network nodes. We determined the order of the nodes based on expert knowledge. Using 356 datasets, the K2 algorithm learned the Bayesian network structure. Then, we used maximum a posteriori probability estimation to learn the parameters. After constructing the Bayesian network, we used the message-passing algorithm to predict and infer the results. Finally, the results of dynamic knowledge inference were presented through a detailed inference process. In the absence of any evidence node information, the probability of passing other courses was calculated. A mathematics course (a basic professional course) was chosen as the evidence node to dynamically infer the probability of passing other courses. Over time, the probability of passing other courses greatly improved, and the inference results were consistent with the actual values and can thus be visualized and applied to an actual school management system.

Efficient Risk Profiling Using Bayesian Networks and Particle Swarm Optimization Algorithm

2016 ◽  
pp. 1580-1612
Author(s):  
Goran Klepac ◽  
Leo Mrsic ◽  
Robert Kopal
Keyword(s):  
Particle Swarm Optimization ◽  
Bayesian Network ◽  
Network Structure ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Expert Knowledge ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Bayesian Network Structure ◽  
Risk Profiling

Chapter introduce usage of particle swarm optimization algorithm and explained methodology, as a tool for discovering customer profiles based on previously developed Bayesian network (BN). Bayesian network usage is common known method for risk modelling although BN's are not pure statistical predictive models (like neural networks or logistic regression, for example) because their structure could also depend on expert knowledge. Bayesian network structure could be trained using algorithm but, from perspective of businesses requirements model efficiency and overall performance, it is recommended that domain expert modify Bayesian network structure using expert knowledge and experience. Chapter will also explain methodology of using particle swarm optimization algorithm as a tool for finding most riskiness profiles based on previously developed Bayesian network. Presented methodology has significant practical value in all phases of decision support in business environment (especially for complex environments).

SPEAKER-INDEPENDENT RECOGNITION OF ISOLATED WORDS USING CONCATENATED NEURAL NETWORKS

1991 ◽  
Vol 05 (05) ◽  
pp. 693-714 ◽  
Author(s):  
CHUNG-HSIEN WU ◽  
JHING-FA WANG ◽  
CHAUG-CHING HUANG ◽  
JAU-YIEN LEE
Keyword(s):  
Bayesian Network ◽  
Clustering Algorithm ◽  
Hopfield Network ◽  
Posteriori Probability ◽  
Bayesian Neural Network ◽  
A Posteriori Probability ◽  
Time Alignment ◽  
Speaker Independent ◽  
Isolated Word ◽  
Determination Of Parameters

A speaker-independent isolated word recognizer is proposed. It is obtained by concatenating a Bayesian neural network and a Hopfield time-alignment network. In this system, the Bayesian network outputs the a posteriori probability for each speech frame, and the Hopfield network is then concatenated for time warping. A proposed splitting Learning Vector Quantization (LVQ) algorithm derived from the LBG clustering algorithm and the Kohonen LVQ algorithm is first used to train the Bayesian network. The LVQ2 algorithm is subsequently adopted as a final refinement step. A continuous mixture of Gaussian densities for each frame and multi-templates for each word are employed to characterize each word pattern. Experimental evaluation of this system with four templates/word and five mixtures/frame, using 53 speakers (28 males, 25 females) and isolated words (10 digits and 30 city names) databases, gave average recognition accuracies of 97.3%, for the speaker-trained mode and 95.7% for the speaker-independent mode, respectively. Comparisons with K-means and DTW algorithms show that the integration of the splitting LVQ and LVQ2 algorithms makes this system well suited to speaker-independent isolated word recognition. A cookbook approach for the determination of parameters in the Hopfield time-alignment network is also described.

scholarly journals Analysis Factors That Influence Escalator-Related Injuries in Metro Stations Based on Bayesian Networks: A Case Study in China

2020 ◽  
Vol 17 (2) ◽  
pp. 481 ◽  
Author(s):  
Yingying Xing ◽  
Shengdi Chen ◽  
Shengxue Zhu ◽  
Jian Lu
Keyword(s):  
Bayesian Network ◽  
Expectation Maximization ◽  
Expert Knowledge ◽  
Evidence Theory ◽  
Influential Factor ◽  
Prevention Measures ◽  
Independence Test ◽  
Severe Injuries ◽  
Bayesian Network Structure

Escalator-related injuries have become an important issue in daily metro operation. To reduce the probability and severity of escalator-related injuries, this study conducted a probability and severity analysis of escalator-related injuries by using a Bayesian network to identify the risk factors that affect the escalator safety in metro stations. The Bayesian network structure was constructed based on expert knowledge and Dempster–Shafer evidence theory, and further modified based on conditional-independence test. Then, 950 escalator-related injuries were used to estimate the posterior probabilities of the Bayesian network with expectation–maximization (EM) algorithm. The results of probability analysis indicate that the most influential factor in four passenger behaviors is failing to stand firm (p = 0.48), followed by carrying out other tasks (p = 0.32), not holding the handrail (p = 0.23), and another passenger’s movement (p = 0.20). Women (p = 0.64) and elderly people (aged 66 years and above, p = 0.48) are more likely to be involved in escalator-related injuries. Riding an escalator with company (p = 0.63) has a relatively high likelihood of resulting in escalator-related injuries. The results from the severity analysis show that head and neck injuries seem to be more serious and are more likely to require an ambulance for treatment. Passengers who suffer from entrapment injury tend to claim for compensation. Severe injuries, as expected, significantly increase the probability of a claim for compensation. These findings could provide valuable references for metro operation corporations to understand the characteristics of escalator-related injuries and develop effective injury prevention measures.

scholarly journals Hybrid Optimization Algorithm for Bayesian Network Structure Learning

Information ◽  
2019 ◽  
Vol 10 (10) ◽  
pp. 294 ◽  
Author(s):  
Xingping Sun ◽  
Chang Chen ◽  
Lu Wang ◽  
Hongwei Kang ◽  
Yong Shen ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Bayesian Networks ◽  
Bayesian Network ◽  
Network Structure ◽  
Structure Learning ◽  
Expert Knowledge ◽  
Hybrid Optimization ◽  
Experimental Simulation ◽  
Bayesian Network Structure ◽  
Bayesian Network Structure Learning

Since the beginning of the 21st century, research on artificial intelligence has made great progress. Bayesian networks have gradually become one of the hotspots and important achievements in artificial intelligence research. Establishing an effective Bayesian network structure is the foundation and core of the learning and application of Bayesian networks. In Bayesian network structure learning, the traditional method of utilizing expert knowledge to construct the network structure is gradually replaced by the data learning structure method. However, as a result of the large amount of possible network structures, the search space is too large. The method of Bayesian network learning through training data usually has the problems of low precision or high complexity, which make the structure of learning differ greatly from that of reality, which has a great influence on the reasoning and practical application of Bayesian networks. In order to solve this problem, a hybrid optimization artificial bee colony algorithm is discretized and applied to structure learning. A hybrid optimization technique for the Bayesian network structure learning method is proposed. Experimental simulation results show that the proposed hybrid optimization structure learning algorithm has better structure and better convergence.

A Bayesian Information System for Predicting Stator Faults in Induction Machines

2018 ◽  
Vol 10 (1) ◽  
pp. 67-76
Author(s):  
Ahmed Ramdane ◽  
Abdelaziz Lakehal ◽  
Ridha Kelaiaia ◽  
Salah Saad
Keyword(s):  
Bayesian Network ◽  
Graphical Model ◽  
Induction Machines ◽  
Hierarchical Level ◽  
Posteriori Probability ◽  
Industrial Case Study ◽  
A Posteriori Probability ◽  
Asynchronous Machines ◽  
Definition Of

Abstract The approach adopted in this paper focuses on the faults prediction in asynchronous machines. The main goal is to explore interesting information regarding the diagnosis and prediction of electrical machines failures by the use of a Bayesian graphical model. The Bayesian forecasting model developed in this paper provides a posteriori probability for faults in each hierarchical level related to the breakdowns process. It has the advantage that it can give needed information’s for maintenance planning. A real industrial case study is presented in which the maintenance staff expertise has been used to identify the structure of the Bayesian network and completed by the parameters definition of the Bayesian network using historical file data of an induction motor. The robustness of the proposed methodology has also been tested. The results showed that the Bayesian network can be used for safety, reliability and planning applications.

Efficient Risk Profiling Using Bayesian Networks and Particle Swarm Optimization Algorithm

2016 ◽  
pp. 91-124 ◽  
Author(s):  
Goran Klepac ◽  
Leo Mrsic ◽  
Robert Kopal
Keyword(s):  
Particle Swarm Optimization ◽  
Bayesian Network ◽  
Network Structure ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Expert Knowledge ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Bayesian Network Structure ◽  
Risk Profiling

Chapter introduce usage of particle swarm optimization algorithm and explained methodology, as a tool for discovering customer profiles based on previously developed Bayesian network (BN). Bayesian network usage is common known method for risk modelling although BN's are not pure statistical predictive models (like neural networks or logistic regression, for example) because their structure could also depend on expert knowledge. Bayesian network structure could be trained using algorithm but, from perspective of businesses requirements model efficiency and overall performance, it is recommended that domain expert modify Bayesian network structure using expert knowledge and experience. Chapter will also explain methodology of using particle swarm optimization algorithm as a tool for finding most riskiness profiles based on previously developed Bayesian network. Presented methodology has significant practical value in all phases of decision support in business environment (especially for complex environments).

scholarly journals Verification of Three-Phase Dependency Analysis Bayesian Network Learning Method for Maize Carotenoid Gene Mining

2017 ◽  
Vol 2017 ◽  
pp. 1-10
Author(s):  
Jianxiao Liu ◽  
Zonglin Tian
Keyword(s):  
Bayesian Network ◽  
Network Structure ◽  
Structure Learning ◽  
Learning Method ◽  
Dependency Analysis ◽  
Learning Methods ◽  
Network Learning ◽  
Bayesian Network Structure ◽  
Bayesian Network Structure Learning ◽  
Three Phase

Background and Objective. Mining the genes related to maize carotenoid components is important to improve the carotenoid content and the quality of maize. Methods. On the basis of using the entropy estimation method with Gaussian kernel probability density estimator, we use the three-phase dependency analysis (TPDA) Bayesian network structure learning method to construct the network of maize gene and carotenoid components traits. Results. In the case of using two discretization methods and setting different discretization values, we compare the learning effect and efficiency of 10 kinds of Bayesian network structure learning methods. The method is verified and analyzed on the maize dataset of global germplasm collection with 527 elite inbred lines. Conclusions. The result confirmed the effectiveness of the TPDA method, which outperforms significantly another 9 kinds of Bayesian network learning methods. It is an efficient method of mining genes for maize carotenoid components traits. The parameters obtained by experiments will help carry out practical gene mining effectively in the future.

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