Bayesian Network for Development of Expert System in Pest Management

2019 ◽  
pp. 45-65
Author(s):  
Niranjan Singh ◽  
Neha Gupta
Keyword(s):  
Expert System ◽  
Bayesian Network ◽  
Pest Management

SOYBUG: An expert system for soybean insect pest management

1989 ◽  
Vol 30 (3) ◽  
pp. 269-286 ◽  
Author(s):  
Howard W. Beck ◽  
Pierce Jones ◽  
J.W. Jones
Keyword(s):  
Expert System ◽  
Pest Management ◽  
Insect Pest ◽  
Insect Pest Management

scholarly journals An expert system based on a Bayesian network for fire safety analysis in nuclear area

2019 ◽  
Vol 105 ◽  
pp. 28-40 ◽  
Author(s):  
E. Chojnacki ◽  
W. Plumecocq ◽  
L. Audouin
Keyword(s):  
Expert System ◽  
Bayesian Network ◽  
Fire Safety ◽  
Safety Analysis ◽  
Nuclear Area

A HAZELNUT PEST MANAGEMENT EXPERT SYSTEM

1990 ◽  
pp. 21-26 ◽  
Author(s):  
R.J. Drapek ◽  
J.A. Calkin ◽  
G.C. Fisher
Keyword(s):  
Expert System ◽  
Pest Management ◽  
Management Expert

Bayesian Diagnostic Network Model for Sliding Plug Door

2011 ◽  
Vol 219-220 ◽  
pp. 1496-1499 ◽  
Author(s):  
Hui Chao Shi ◽  
Long Tian ◽  
Liang Wang
Keyword(s):  
Complex System ◽  
Fault Diagnosis ◽  
Expert System ◽  
Bayesian Network ◽  
Knowledge Base ◽  
Network Model ◽  
Model Building ◽  
Bayesian Network Model ◽  
Structure Tree ◽  
System Knowledge

For constructing Bayesian diagnostic network model of complex system is a difficult course, we propose a Bayesian network model auto-construction method based on expert system knowledge base. Bayesian diagnostic network model was built by using the CM structure, and the diagnostic knowledge was organized by product structure tree. We have applied this method to fault diagnosis for sliding plug door, and tested our methodology on many examples of diagnostic problems of sliding plug door, which prove the efficiency of the Bayesian diagnostic network model and model-building method．

scholarly journals Sistem Pakar Diagnosa Awal Penyakit Mata dengan Metode Bayesian Network

2020 ◽  
Vol 4 (2) ◽  
pp. 139-148
Author(s):  
Novanita Laylatul Husna ◽  
Fitri Bimantoro
Keyword(s):  
Expert System ◽  
Bayesian Network ◽  
Rapid Assessment ◽  
Eye Disease ◽  
Accuracy Rate ◽  
The Public ◽  
System Diagnosis ◽  
Network Method ◽  
Community Facilities ◽  
Clear Information

Almost every human activity needs an eye to support these activities, therefore it will have a bad effect if the eyes experience interference. Disorders that can be experienced in the eye can occur from minor disorders to disorders that cause loss of vision and even death. Based on the results of the Rapid Assessment Assessment of Avoidable Blindness (RAAB), the blindness rate in districts/cities in NTB was 4%. This is not only due to medical-related problems but also due to social problems related to knowledge from the community, facilities, and resources. To make it easier for the public to make an initial diagnosis of eye disease, one thing that can be done is to use an expert system. Various methods can be implemented for expert systems, one of which is the Bayesian Network method. Based on the results of accuracy testing that has been done, this application provides an accuracy rate of 84.99%. Whereas, if the system diagnosis results are a subset of expert diagnostic results, the accuracy rate is 89.99% and based on the results of the use made by the general public, this application has been running well and has provided clear information related to the diagnosis of eye disease.

Medicine expert system dynamic Bayesian Network and ontology based

2011 ◽  
Vol 38 (12) ◽  
pp. 15253-15261 ◽  
Author(s):  
Octavian Arsene ◽  
Ioan Dumitrache ◽  
Ioana Mihu
Keyword(s):  
Expert System ◽  
Bayesian Network ◽  
Dynamic Bayesian Network ◽  
System Dynamic
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