scholarly journals Expert Systems in Geographical Informatics

Geografie ◽  
1992 ◽  
Vol 97 (4) ◽  
pp. 253-260
Author(s):  
Jaromír Kolejka
Keyword(s):  
Decision Making ◽  
Data Analysis ◽  
Expert System ◽  
Expert Systems ◽  
Data Management ◽  
Knowledge Base ◽  
Data Interpretation ◽  
Natural Phenomena ◽  
Remotely Sensed Data ◽  
Artifical Intelligence

The advanced GIS are equipped both by a database and a knowledge base. The knowledge base contains a system of rules for the purpose oriented data management and processing, which simulate the process of decision-making carried out by an expert. The principles of and experience with expert system creation are described. The expert system applications were tested in the territorial data analysis, the natural phenomena modelling, the remotely sensed data interpretation, the cartographic processes, the artifical intelligence experiments, etc.

scholarly journals Diagnose Expert System Computer Malfunction Certainty Factor Method

2020 ◽  
Vol 2 (1) ◽  
pp. 63-71
Author(s):  
Djouking Kiray ◽  
Fricles Ariwisanto Sianturi
Keyword(s):  
Decision Making ◽  
Expert System ◽  
Expert Systems ◽  
Knowledge Base ◽  
Base System ◽  
Certainty Factor ◽  
Factor Method ◽  
Knowledge Base System ◽  
Two Factors ◽  
Computer Based

An expert system is a knowledge base system that solves problems using an expert's knowledge that is entered into a computer, thereby increasing productivity, Because an expert can work faster than a human lay works like an expert. Expert systems Also solve problems by imitating the ways in the which an expert expert offer section with problems in his field, one of the which is in the field of computer repair, the problem of computer damage Becomes a fairly complicated problem, this problem is Generally experienced by individuals and institutions. One of them is in school institutions that have computer laboratories. to diagnose computer use can damage the certainty factor method that helps identify damage to the computer and find the cause of damage to the computer based on the symptoms that occur and the solution to repair it. Certainty Factor is one of the techniques used to deal with uncertainty in decision making. In dealing with a problem, answers are Often found that do not have full certainty. This uncertainty is influenced by two factors items, namely the uncertain rules and user uncertain answers. Uncertain rules are rules of symptoms that are determined for a damage.

Dempster Shafer Untuk Sistem Diagnosa Gejala Penyakit Kulit Pada Kucing

2017 ◽  
Vol 7 (1) ◽  
pp. 81
Author(s):  
Maura Widyaningsih ◽  
Rio Gunadi
Keyword(s):  
Decision Making ◽  
Expert System ◽  
Data Management ◽  
Skin Disease ◽  
Health Problems ◽  
Application Development ◽  
Computing Systems ◽  
Disease Symptoms ◽  
Diagnosis System ◽  
Artifical Intelligence

Expert System which is a branch of Artifical Intelligence, who learned about the estimation or decision-making ability of an expert. Methods and concepts are still needed in solving the problem of diagnosis, with engineering calculations involve computing systems., given the level of need for information and resolving cases. The application development is aimed at implementing the knowledge of an expert into a program that can help in diagnosing the symptoms of skin health problems in cats. Dempster Shafer (DS) is a method that is non monotonous in solving the problem of uncertainty due to the addition or subtraction of new facts.The system is made to diagnose the type of skin disease in cats after applying the method of DS. The system can also perform data management if there is a data change disease, symptoms, treatment solutions, as well as the rules of the disease. The diagnosis system with DS according to analysis from experts.

Diabetes Mellitus - Evaluating the Diagnostic Probabilities

2011 ◽  
pp. 169-177
Author(s):  
Adi Armoni
Keyword(s):  
Diabetes Mellitus ◽  
Decision Making ◽  
Expert System ◽  
Expert Systems ◽  
Knowledge Base ◽  
Decision Maker ◽  
Knowledge Bases ◽  
Bayesian Theory ◽  
Logical Conclusion ◽  
Human Decision

The article examines the behavior of the human decision-maker. It surveys research in which about 90 physicians specializing in various fields and with different degrees of seniority participated. It tackles the question of whether it is possible to found the majority of the knowledge bases of the expert systems on the Bayesian theory. We will discuss the way of decision making conforming to the probabilities evaluated according to the Bayesian theory. The logical conclusion, therefore, is that the development of a knowledge base for an expert system founded on probabilities calculated in accordance with the Bayesian theory must be carried out in a controlled manner and depend on the parameters mentioned above.

Artificial Intelligence in Medical Science

2014 ◽  
pp. 11-23 ◽  
Keyword(s):  
Artificial Intelligence ◽  
Image Processing ◽  
Decision Making ◽  
Signal Processing ◽  
Expert System ◽  
Knowledge Base ◽  
Medical Diagnosis ◽  
Medical Science ◽  
Inference Engine ◽  
Medical Decision

Applying Artificial Intelligence (AI) for increasing the reliability of medical decision making has been studied for some years, and many researchers have studied in this area. In this chapter, AI is defined and the reason of its importance in medical diagnosis is explained. Various applications of AI in medical diagnosis such as signal processing and image processing are provided. Expert system is defined and it is mentioned that the basic components of an expert system are a “knowledge base” or KB and an “inference engine”. The information in the KB is obtained by interviewing people who are experts in the area in question.

Differential Learning Expert System in Data Management

2009 ◽  
pp. 597-604
Author(s):  
R. Manjunath
Keyword(s):  
Expert System ◽  
Expert Systems ◽  
Data Management ◽  
Knowledge Acquisition ◽  
User Interfaces ◽  
High Performance ◽  
Evolutionary Model ◽  
Human Expert ◽  
Incremental Development ◽  
Differential Learning

Expert systems have been applied to many areas of research to handle problems effectively. Designing and implementing an expert system is a difficult job, and it usually takes experimentation and experience to achieve high performance. The important feature of an expert system is that it should be easy to modify. They evolve gradually. This evolutionary or incremental development technique has to be noticed as the dominant methodology in the expert-system area. The simple evolutionary model of an expert system is provided in B. Tomic, J. Jovanovic, & V. Devedzic, 2006. Knowledge acquisition for expert systems poses many problems. Expert systems depend on a human expert to formulate knowledge in symbolic rules. The user can handle the expert systems by updating the rules through user interfaces (J. Jovanovic, D. Gasevic, V. Devedzic, 2004). However, it is almost impossible for an expert to describe knowledge entirely in the form of rules. An expert system may therefore not be able to diagnose a case that the expert is able to. The question is how to extract experience from a set of examples for the use of expert systems.

Knowledge-acquisition tools with explicit problem-solving models

1993 ◽  
Vol 8 (1) ◽  
pp. 5-25 ◽  
Author(s):  
William Birmingham ◽  
Georg Klinker
Keyword(s):  
Expert System ◽  
Problem Solving ◽  
Expert Systems ◽  
Knowledge Acquisition ◽  
Knowledge Base ◽  
Code Generation ◽  
System Development ◽  
Generalization Error ◽  
Domain Experts ◽  
The Past

AbstractIn the past decade, expert systems have been applied to a wide variety of application tasks. A central problem of expert system development and maintenance is the demand placed on knowledge engineers and domain experts. A commonly proposed solution is knowledge-acquisition tools. This paper reviews a class of knowledge-acquisition tools that presuppose the problem-solving method, as well as the structure of the knowledge base. These explicit problem-solving models are exploited by the tools during knowledge-acquisition, knowledge generalization, error checking and code generation.

scholarly journals INFORMATION SYSTEM FOR EVALUATION OF THE STATE OF INTOXICATION OF THE ORGANISM BASED ON THE BAYES NETWORK

2021 ◽  
Vol 3 (163) ◽  
pp. 144-151
Author(s):  
O. Moyseenko
Keyword(s):  
Decision Making ◽  
Information System ◽  
Expert System ◽  
Knowledge Base ◽  
Expert Knowledge ◽  
Experimental Testing ◽  
Postoperative Treatment ◽  
Formal Representation ◽  
Medical Systems ◽  
Electrode Potentials

An expert system is a computer program that simulates the judgment and behavior of a human or an organization that has expert knowledge and experience in a particular field. It is a program that emulates the interaction a user might have with a human expert to solve a problem. The end user provides input by selecting one or more answers from a list or by entering data. An Expert System is a problem solving and decision making system based on knowledge of its task and logical rules or procedures for using knowledge. Both the knowledge and the logic are obtained from the experience of a specialist in the area. This paper considers approaches to building a knowledge base for medical systems. In developing the knowledge base of the information system, Bayesian networks were chosen as the basis for the decision-making model by type of patient pathology. This choice was due to the availability of these networks the ability to work with uncertain knowledge used in the diagnosis of diseases, in choosing the optimal course of treatment and subsequent prediction of patients. In addition, they offer the most adequate formal representation of inaccurate knowledge, as they are the result of a synthesis of statistical methods of data analysis and artificial intelligence. The presence of hydrosulfide ion intoxication (HS-intoxication), divalent iron ion intoxication (Fe-intoxication), the patient's absence of pathology and the value of Ag2S and Pt electrode potentials were selected as nodes of this network. Based on the accumulated experience of monitoring the condition of patients during their postoperative treatment (data obtained in collaboration with Ivano-Frankivsk National Medical University), as well as experimental data, conditional probabilities of values that can take the readings of the electrodes were established. Experimental testing of the adequacy of the proposed and implemented model was performed on an array of data from potentiometric measurements of patients' biomaterial. The prediction made by the network was taken as the node that had the highest probability of being in a state that indicates the presence of a pathology. Comparison of the results of the network with data obtained by other methods showed their convergence in 85% of cases. Thus, the developed network can be used to facilitate the process of diagnosing the presence and type of intoxication of the patient and is included in the information system for monitoring the patient's condition.

The application of expert systems in the clinical laboratory.

1989 ◽  
Vol 35 (8) ◽  
pp. 1595-1600 ◽  
Author(s):  
P Winkel
Keyword(s):  
Expert System ◽  
Expert Systems ◽  
Knowledge Base ◽  
Clinical Laboratory ◽  
Laboratory Medicine ◽  
General Nature ◽  
Inference System

Abstract An "expert system" consists of a knowledge base containing information of a general nature and an inference system that receives data from the user and applies the knowledge base to produce advice and explanations. An expert system stripped of its knowledge base (a tool) may be used to build new expert systems. Existing systems relevant for laboratory medicine are reviewed. The role in the laboratory of expert systems and their integration and evaluation are discussed.

scholarly journals Method for Attack Tree Data Transformation and Import Into IT Risk Analysis Expert Systems

2020 ◽  
Vol 10 (23) ◽  
pp. 8423
Author(s):  
Donatas Vitkus ◽  
Jonathan Salter ◽  
Nikolaj Goranin ◽  
Dainius Čeponis
Keyword(s):  
Expert System ◽  
Risk Analysis ◽  
Expert Systems ◽  
Knowledge Base ◽  
Automated Analysis ◽  
Knowledge Bases ◽  
It Security ◽  
Security Risk ◽  
Attack Tree ◽  
Attack Trees

Information technology (IT) security risk analysis preventatively helps organizations in identifying their vulnerable systems or internal controls. Some researchers propose expert systems (ES) as the solution for risk analysis automation since risk analysis by human experts is expensive and timely. By design, ES need a knowledge base, which must be up to date and of high quality. Manual creation of databases is also expensive and cannot ensure stable information renewal. These facts make the knowledge base automation process very important. This paper proposes a novel method of converting attack trees to a format usable by expert systems for utilizing the existing attack tree repositories in facilitating information and IT security risk analysis. The method performs attack tree translation into the Java Expert System Shell (JESS) format, by consistently applying ATTop, a software bridging tool that enables automated analysis of attack trees using a model-driven engineering approach, translating attack trees into the eXtensible Markup Language (XML) format, and using the newly developed ATES (attack trees to expert system) program, performing further XML conversion into JESS compatible format. The detailed method description, along with samples of attack tree conversion and results of conversion experiments on a significant number of attack trees, are presented and discussed. The results demonstrate the high method reliability rate and viability of attack trees as a source for the knowledge bases of expert systems used in the IT security risk analysis process.

Pest Plant Disease Prevention and Identification System Using Certainty Factor Method

2018 ◽  
Vol 7 (3.2) ◽  
pp. 763 ◽  
Author(s):  
Robbi Rahim ◽  
Nuning Nurna Dewi S ◽  
M Zamroni ◽  
Lilla Puji Lestari ◽  
Muh Barid Nizarudin Wajdi ◽  
...  
Keyword(s):  
Expert System ◽  
Disease Prevention ◽  
Expert Systems ◽  
Knowledge Base ◽  
Plant Disease ◽  
Expert Knowledge ◽  
Identification System ◽  
Certainty Factor ◽  
Factor Method

Diseases in plants are something that can happen to many plants either caused by pests or other factors, the disease in plants can be detected based on the symptoms that appear on the plant before spreading to all plants, to recognize the symptoms and types of diseases contained in plants require plant experts or also by applying expert systems with expert knowledge base applied to the system by using certain methods such as certainty factor method. Expected results with the availability of this expert system to the user can help many users to detect diseases in plants.  

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