Bayesian network equipped workflow engine to coordinate Artificial Intelligence for automating network operation

2021 ◽  
Author(s):  
Ryosuke SATO ◽  
Mizuto NAKAMURA ◽  
Atsushi TAKADA ◽  
Kyoko YAMAGOE
Keyword(s):  
Artificial Intelligence ◽  
Bayesian Network ◽  
Workflow Engine ◽  
Network Operation

The Realization of the Data Mining Technology and its Application

2014 ◽  
Vol 971-973 ◽  
pp. 1820-1823
Author(s):  
Xi Long Ding
Keyword(s):  
Artificial Intelligence ◽  
Data Mining ◽  
Bayesian Network ◽  
Network Modeling ◽  
Present Situation ◽  
Network Technology ◽  
Mining Method ◽  
Bayesian Network Modeling ◽  
Database Technology ◽  
The Relationship

data mining using the database, a variety of technologies such as artificial intelligence and mathematical statistics. This paper introduces the present situation of database technology, according to the mining method and its application in how to build a Bayesian network technology, through the scattered according to the mining to solve concrete problems encountered in the process of Bayesian network modeling, namely how to from scale effect according to the library to find the relationship between each variable and how to determine the conditional probability problem.

scholarly journals ARTIFICIAL INTELLIGENCE MODEL AS PREDICTOR FOR DENGUE OUTBREAKS

2019 ◽  
Vol 19 (2) ◽  
pp. 103-108 ◽  
Author(s):  
Dhesi Baha Raja ◽  
Rainier Mallol ◽  
Choo Yee Ting ◽  
Fadzilah Kamaludin ◽  
Rohani Ahmad ◽  
...  
Keyword(s):  
Public Health ◽  
Artificial Intelligence ◽  
Bayesian Network ◽  
Predictive Accuracy ◽  
Predictor Variables ◽  
Server Side ◽  
Public Data ◽  
Larval Count ◽  
Dengue Outbreaks ◽  
Client Side

Dengue is an increasing threat in Malaysia, particularly in the more densely populated regions of the country. We present an Artificial Intelligence driven model in predicting Aedes outbreak, using predictors of weather variables and vector indices sourced from the Ministry of Health. Analysis and predictions to estimate Aedes populations were conducted, with its results being used to infer the possibility of dengue outbreaks at pre-determined localities around the Klang Valley, Malaysia. A Bayesian Network machine learning technique was employed, with the model being trained using predictor variables such as temperature, rainfall, date of onset and notification, and vector indices such as the Ae. albopictus count, Ae. aegypti count and larval count. The interfaces of the system were developed using the C# language for Server-side configuration and programming, and HTML, CSS and JavaScript for the Client Side programming. The model was then used to predict the population of Aedes at periods of 7, 14, and 30 days. Using the Bayesian Network technique utilising the above predictor variables we proposed a finalised model with predictive accuracy ranging from 79%-84%. This model was developed into a Graphical User Interface, which was purposed to assist and educate the general public of regions at risk of developing dengue outbreak. This remains a valuable case-study on the importance of public data in the context of combating a public health risk via the development of models for predicting outbreaks of dengue which will hopefully spur further sharing of data by all parties in combating public health threats.

scholarly journals Patent Keyword Analysis of Disaster Artificial Intelligence Using Bayesian Network Modeling and Factor Analysis

2020 ◽  
Vol 12 (2) ◽  
pp. 505
Author(s):  
Sangsung Park ◽  
Sunghae Jun
Keyword(s):  
Artificial Intelligence ◽  
Factor Analysis ◽  
Bayesian Network ◽  
Network Modeling ◽  
Real Problem ◽  
Patent Data ◽  
Disaster Area ◽  
Proposed Model ◽  
Bayesian Network Modeling

At present, artificial intelligence (AI) contributes to most technological fields. AI has also been introduced in the disaster area to replace humans and contribute to the prevention of disasters and the minimization of damages. So, it is necessary to analyze disaster AI in order to effectively make use of it. In this paper, we analyze the patent documents related to disaster AI technology. We propose Bayesian network modeling and factor analysis for the technology analysis of disaster AI. This is based on probability distribution and graph theory. It is also a statistical model that depends on multivariate data analysis. In order to show how the proposed model can be applied to a real problem, we carried out a case study to collect and analyze the patent data related to disaster AI.

scholarly journals Uniqueness of the Level Two Bayesian Network Representing a Probability Distribution

2011 ◽  
Vol 2011 ◽  
pp. 1-13 ◽  
Author(s):  
Linda Smail
Keyword(s):  
Artificial Intelligence ◽  
Probability Distribution ◽  
Bayesian Networks ◽  
Bayesian Network ◽  
Probabilistic Models ◽  
Probability Distributions ◽  
Random Variables ◽  
Inference Problem ◽  
Computation Algorithm

Bayesian Networks are graphic probabilistic models through which we can acquire, capitalize on, and exploit knowledge. they are becoming an important tool for research and applications in artificial intelligence and many other fields in the last decade. This paper presents Bayesian networks and discusses the inference problem in such models. It proposes a statement of the problem and the proposed method to compute probability distributions. It also uses D-separation for simplifying the computation of probabilities in Bayesian networks. Given a Bayesian network over a family of random variables, this paper presents a result on the computation of the probability distribution of a subset of using separately a computation algorithm and D-separation properties. It also shows the uniqueness of the obtained result.

scholarly journals Criticality Analysis and Maintenance of Solar Tower Power Plants by Integrating the Artificial Intelligence Approach

Energies ◽  
2021 ◽  
Vol 14 (18) ◽  
pp. 5861
Author(s):  
Samir Benammar ◽  
Kong Fah Tee
Keyword(s):  
Artificial Intelligence ◽  
Bayesian Network ◽  
Power Plants ◽  
Expert Advice ◽  
Intervention Cost ◽  
Optimal Maintenance ◽  
Network Simulations ◽  
Index Value ◽  
Criticality Index ◽  
Criticality Analysis

Maintenance of solar tower power plants (STPP) is very important to ensure production continuity. However, random and non-optimal maintenance can increase the intervention cost. In this paper, a new procedure, based on the criticality analysis, was proposed to improve the maintenance of the STPP. This procedure is the combination of three methods, which are failure mode effects and criticality analysis (FMECA), Bayesian network and artificial intelligence. The FMECA is used to estimate the criticality index of the different elements of STPP. Moreover, corrections and improvements were introduced on the criticality index values based on the expert advice method. The modeling and the simulation of the FMECA estimations incorporating the expert advice method corrections were performed using the Bayesian network. The artificial neural network is used to predicate the criticality index of the STPP exploiting the database obtained from the Bayesian network simulations. The results showed a good agreement comparing predicted and actual criticality index values. In order to reduce the criticality index value of the critical elements of STPP, some maintenance recommendations were suggested.

The Bayesian Network Story

2017 ◽  
Author(s):  
Richard Neapolitan ◽  
Xia Jiang
Keyword(s):  
Artificial Intelligence ◽  
Probability Distribution ◽  
Bayesian Networks ◽  
Bayesian Network ◽  
Bayes Theorem ◽  
Standard Application

Bayesian networks are now among the leading architectures for reasoning with uncertainty in artificial intelligence. This chapter concerns their story, namely what they are, how and why they came into being, how we obtain them, and what they actually represent. First, it is shown that a standard application of Bayes’ Theorem constitutes inference in a two-node Bayesian network. Then more complex Bayesian networks are presented. Next the genesis of Bayesian networks and their relationship to causality is presented. A technique for learning Bayesian networks from data follows. Finally, a discussion of the philosophy of the probability distribution represented by a Bayesian network is provided.

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