scholarly journals Dhempster-Shafer Implementation in Overcoming Uncertainty in the Inference Engine for Diagnosing Oral Cavity Cancer

2021 ◽  
Vol 13 (1) ◽  
pp. 45
Author(s):  
Riduwan Napianto ◽  
Yuri Rahmanto ◽  
Rohmat Indra Borman ◽  
Ova Lestari ◽  
Nurhasan Nugroho
Keyword(s):  
Expert System ◽  
Oral Cancer ◽  
Oral Cavity Cancer ◽  
Inference Engine ◽  
Test Results ◽  
Reliability Of Results ◽  
Sources Of Uncertainty ◽  
Dempster Shafer Theory ◽  
Inaccurate Data ◽  
Shafer Theory

<em>The word uncertainty in an expert system is related to working with wrong data, wrong information, handling identical situations, the reliability of results, etc. Sources of uncertainty can come from unreliable information. This is usually caused by unclear domain concepts or for inaccurate data. One method for overcoming uncertainty is Dhempster-Shafer's theory. Dempster-shafers come up with approaches to calculate probabilities to look for evidence based on trust functions. In general the Dempster-Shafer theory is written at an interval [Confidence, Reasonable]. Belief (Bel) is a measure of the strength of evidence in support of a series of propositions. In this study an expert system will be developed to diagnose oral cancer that can recognize oral cancer based on the symptoms felt by the user. The results showed the Dempster-shafer was able to overcome the uncertainties in the construction of the inference engine, this is because the accuracy of the test results showed an accuracy of 86.6% Dempster-shafer</em>.

Computer-aided design/engineering of bearing systems using the Dempster-Shafer theory

1995 ◽  
Vol 9 (1) ◽  
pp. 1-11 ◽  
Author(s):  
A.C. Butler ◽  
F. Sadeghi ◽  
S.S. Rao ◽  
S.R. LeClair
Keyword(s):  
Expert System ◽  
Computer Aided Design ◽  
Professional Judgment ◽  
Design Engineering ◽  
Design Environment ◽  
Dempster Shafer Theory ◽  
Computer Aided ◽  
Shafer Theory ◽  
Aided Design ◽  
Symbolic Approach

AbstractResearch in computer-aided design/engineering (CAD/E) has focused on enhancing the capability of computer systems in a design environment, and this work has continued in this trend by illustrating the use of the Dempster-Shafer theory to expand the computer’s role in a CAD/E environment. An expert system was created using Dempster-Shafer methods that effectively modeled the professional judgment of a skilled tribologist in the selection of rolling element bearings. A qualitative and symbolic approach was used, but access to simple quantitative models was provided to the expert system shell. Although there has been significant discussion in the literature regarding modification/improvement of the Dempster-Shafer theory, Shafer’s theories were found adequate in all respects for replicating the expert’s judgment. However, an understanding of the basic theory is required for interpreting the results.

scholarly journals Design of Expert System for Early Identification for Suspect Bullying On Vocational Students by Using Dempster Shafer Theory

2019 ◽  
Vol 3 (1) ◽  
pp. 48
Author(s):  
Finanta Okmayura ◽  
Noverta Effendi
Keyword(s):  
Expert System ◽  
Early Identification ◽  
Black Box ◽  
Basic Knowledge ◽  
Vocational Students ◽  
Dempster Shafer Theory ◽  
Sql Database ◽  
System Use ◽  
Negative Effect ◽  
Shafer Theory

Bullying is negatif agresif character to cover and hurt someone physically or physicology continoustly to act hard to other people who is lower than him. Some parents ignore this problem because of unknowing about the result of this problem that give negative effect to their children and other people. This system will identificate early the begining bullying character for teenager by knowing the kinds of the bullying character base on rate of presentation of higest quartel who has bullying character. This system designed by using  Dempster-hafer teory to know the begining of bullying character by using basic knowledge and forward chaining technic to know more about basic knowledge. Counting of this metodh by combining some symptoms that happen on children by calculating the possibillity disturbing by rating the symptoms from 0 to 1. Implementation of this system use PHP program and My SQL database. The black box trial on the consultation modul is done by using 12 instruments of tria  and found error value 0,88 % on the system and result of the trial expert system have suitable accuration 84 % ,  so we can conclude that this expert system  for early  identification  for suspect  bullying  on teenager good for use.

Designing an expert system for diagnosis of multiple myeloma by using rough set theory

2019 ◽  
Vol 13 ◽  
Author(s):  
Tooraj Karimi ◽  
Arvin Hojati ◽  
S. Reza Razavi
Keyword(s):  
Multiple Myeloma ◽  
Information System ◽  
Decision Support ◽  
User Interface ◽  
Expert System ◽  
Decision Support Systems ◽  
Rough Set ◽  
Support Systems ◽  
Inference Engine ◽  
Test Results

Background: One of the most interesting and important topics in the field of information systems and knowledge management is the concept of eliciting rules and collecting the knowledge of human experts in various subjects to be used in expert systems. Many scientists have used the decision support systems to support businesses or organizational decision-making activities including clinical decision support systems for medical diagnosis. Objective: In this study, a rough set based expert system is designed for diagnosis one type of blood cancer called multiple myeloma. In order to improve the validity of generated models, three condition attributes that define the shape of “Total protein”, “Beta2%” and “Gamma%” are added to the models to improve the decision attribute value domain. Method: In this study, 1100 serum protein electrophoresis tests are investigated and based on these test results, 15 condition attributes are defined. Four different rule models are obtained through extracting rules from reducts. Janson and Genetic Algorithm with "Full" and "ORR" approaches have been used to generate reducts. Results: The GA/ORR of the information system with 87% accuracy is used as an inference engine of expert system and a unique user interface is designed to automatically analyze test results based on these generated models. Gamma% is detected as a core attribute of the information system. Conclusion: Based on the results of generating reducts, the Gamma% attribute is detected as a core of the information system. This means that information, which is resulted from this conditional attribute, has the greatest impact on the diagnosis of multiple myeloma. The GA/ORR model with 87% accuracy is selected as the inference engine of the expert system and finally, a unique user interface is created to help specialists diagnose multiple myeloma.

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