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.

EXPERT MANAGEMENT SYSTEMS OF AGROTECHNOLOGIES IN CLOUD INFORMATION TECHNOLOGY

1970 ◽  
pp. 12-17
Author(s):  
I. М. Mikhaylenko ◽  
V. N. Timoshin
Keyword(s):  
Decision Making ◽  
Pattern Recognition ◽  
Information System ◽  
Knowledge Base ◽  
Information Technologies ◽  
Agricultural Sector ◽  
Perennial Grasses ◽  
Management Decision ◽  
Agricultural Technologies ◽  
Automation Systems

The transition to "intellectual" agriculture is the main vector of modernization of the agricultural sector of the economy. It is based on integrated automation and robotization of production, the use of automated decision-making systems. This is inevitably accompanied by a significant increase in data flow from sensors, monitoring systems, meteorological stations, drones, satellites and other external systems. Farm management has the opportunity to use various online applications for accurate recommendations and making various kinds of management decisions. In this regard, the most effective use of cloud information technologies, allowing implementing the most complex information and technical level of automation systems for management of agricultural technologies. The purpose of this work is to test the approach to creating expert management decision support systems (DSS) through the knowledge base (KB), formed in the cloud information system. For this, we consider an example of constructing a DSS for choosing the optimal date for preparing forage from perennial grasses. A complete theoretical and algorithmic database of the analytical DSS implemented in the data processing center of the cloud information system is given. On its basis, a KB is formed for a variety of different decision-making conditions. This knowledge base is transmitted to the local DSS. To make decisions about the optimal dates for the preparation of the local DSS, two variants of algorithms are used. The first option is based on management models, and the second uses the pattern recognition method. The approbation of the algorithms was carried out according to the BZ from 50 cases. According to the results of testing, the method of pattern recognition proved to be more accurate, which provides a more flexible adjustment of the situation on the local DSS to a similar situation in the KB. The considered technique can be extended to other crops.

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.

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.

scholarly journals Selecting a Strategy for Determining the Combine Harvester Parameter Settings

2020 ◽  
Vol 30 (1) ◽  
pp. 60-75
Author(s):  
Lyudmila V. Borisova ◽  
Inna N. Nurutdinova ◽  
Valeriy P. Dimitrov ◽  
Andrey K. Tugengold
Keyword(s):  
Decision Making ◽  
Knowledge Base ◽  
Performance Indicators ◽  
Intelligent System ◽  
Expert Knowledge ◽  
External Environment ◽  
Suggested Approach ◽  
Expert Information ◽  
The Matrix ◽  
Combine Harvester

Introduction.The article deals with adjusting the parameter settings of a combine harvester working bodies. For adjustment of complex hierarchical multilevel systems, the intellectual methods based on fuzzy expert information are used. The incoming quantitative, qualitative and evaluation information is analyzed when adjusting the combine harvester. The different types of uncertainty in considering semantic spaces of external environment factors and regulated parameters of the machine cause the application of logical and linguistic approach and mathematical apparatus of fuzzy logic for determining the optimal initial settings. The complex system of interrelations between parameters, indicators of quality of harvest, and factors of external environment causes the necessity to adjust the parameters of combine working elements in the process of harvesting. This function is performed by the correction unit in the intelligent decision support system. In the present article, the questions of creating a knowledge base for correcting adjustment parameters in cases when there are deviations of values of harvesting quality indicators from normative values are considered in detail. Materials and Methods. Interrelations between performance indicators and regulated parameters are established by empirical rules obtained through the collection and analysis of expert information. To optimize the mechanism of intellectual information system output and reduce the time of decision making, there is a necessity to establish the relevance of used knowledge base rules. To solve this problem, theoretical and game approaches are used, concepts of the matrix of performance indicators and the matrix of risks of making an inefficient decision are used. Results. An example of choosing a strategy of searching for an adequate response to the fault of the harvesting indices in the form of “losses of feeble grain with chaff” has been given. The choice of fault response strategies on the basis of Laplace criterion, expectedvalue criterion, and Savage test used for decision-making in “games with nature” has been considered. The method of the decision-making process in the problem under consideration with the application of the mentioned criteria were illustrated, the analysis of the obtained results was carried out. Discussion and Conclusion. The suggested approach substantially increases performance of the unit of intelligent system updating. It allows structuring the expert knowledge base and establishing an optimal sequence of application of production rules; this provides efficiency of the updating process of the adjustable harvester parameters and also reduces the time for decision-making. This approach can be used while solving the problems of updating technological adjustments in different technical systems and devices.

Pest Activity Prognosis in the Rice Field

2007 ◽  
pp. 763-768 ◽  
Author(s):  
N. Arbaiy
Keyword(s):  
Expert System ◽  
Knowledge Base ◽  
Rice Field ◽  
Expert Knowledge ◽  
Life Stage ◽  
Control Measures ◽  
Specific Knowledge ◽  
Main Components ◽  
Activity Prognosis ◽  
Consultation Session

In crops management, it is important to estimate the damage effected by pests since the degree of damage will determine the level of pest activity. Pest activity usually involves their life stage and its presence in the field. In addition, pest management in crops is a crucial problem and may yield losses if it is not handled properly. Consequently a forecasting tool is needed to predict the level of pest activity. This is important so that an early treatment or action can be applied before more damage to the plant occurs. Accordingly, the fuzzy expert system may facilitate the user through a consultation session in order to forecast the pest activity in the rice field. A set of questions will be asked to help users diagnose their given symptom in order to infer such a conclusion. Figure 1 shows the main components of an expert system including inference engine, expert, knowledge base, working memory, and user interface. The consultation performed by the expert system also involves fuzzy logic to deal with the natural and uncertainty data. Besides, all the information and knowledge about the pests, treatment control measures and prevention steps are managed in the specific knowledge base created in the system. This system is able to educate and inform the farmers and smallholders about pests and their activities in the rice field.

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.  

Research and Design of Data-Interpreting Expert System for Geographical Prospecting of Dike Hazard

2011 ◽  
Vol 323 ◽  
pp. 172-175
Author(s):  
Lei Yang ◽  
Yang Zhou
Keyword(s):  
Expert System ◽  
Knowledge Base ◽  
Expert Knowledge ◽  
Inference Process ◽  
User Research ◽  
Inference Mechanism ◽  
Data Resource ◽  
Construction Of Knowledge ◽  
Complete Knowledge ◽  
Research And Design

Based on the characteristics of geographical prospecting data of dike hazard, this paper discusses the design of the data-interpreting expert system for dike hazard, researches the calculation procedure of inference mechanism and the construction of knowledge base. In the process of design, constructing complete knowledge base and based on the production rule, vogue inference is realized. A combination is also made between inference process and interpretive mechanism, to enhance the operation efficiency of the system, putting the system in better communication and interaction with the user. Research results show that this system can effectively integrate expert knowledge, reasonably configure data resource and enhance the reliability of interpretive achievement of hazard data.

scholarly journals Automated Expert System Knowledge Base Development Method for Information Security Risk Analysis

2019 ◽  
Vol 14 (6) ◽  
pp. 743-758 ◽  
Author(s):  
Donatas Vitkus ◽  
Žilvinas Steckevičius ◽  
Nikolaj Goranin ◽  
Diana Kalibatienė ◽  
Antanas Čenys
Keyword(s):  
Expert System ◽  
Information Security ◽  
Risk Analysis ◽  
Knowledge Base ◽  
Expert Knowledge ◽  
Management Decision ◽  
Problem Solution ◽  
Security Risk ◽  
Development Method ◽  
Information Security Risk

Information security risk analysis is a compulsory requirement both from the side of regulating documents and information security management decision making process. Some researchers propose using expert systems (ES) for process automation, but this approach requires the creation of a high-quality knowledge base. A knowledge base can be formed both from expert knowledge or information collected from other sources of information. The problem of such approach is that experts or good quality knowledge sources are expensive. In this paper we propose the problem solution by providing an automated ES knowledge base development method. The method proposed is novel since unlike other methods it does not integrate ontology directly but utilizes automated transformation of existing information security ontology elements into ES rules: The Web Ontology Rule Language (OWL RL) subset of ontology is segregated into Resource Description Framework (RDF) triplets, that are transformed into Rule Interchange Format (RIF); RIF rules are converted into Java Expert System Shell (JESS) knowledge base rules. The experiments performed have shown the principal method applicability. The created knowledge base was later verified by performing comparative risk analysis in a sample company.

scholarly journals An expert system for underground coal mine planning

2017 ◽  
Vol 33 (2) ◽  
pp. 113-127
Author(s):  
Edyta Brzychczy ◽  
Marek Kęsek ◽  
Aneta Napieraj ◽  
Roman Magda
Keyword(s):  
Expert System ◽  
Knowledge Base ◽  
Planning Process ◽  
Expert Knowledge ◽  
Coal Mines ◽  
Mine Planning ◽  
Hard Coal ◽  
Mining Operations ◽  
Mining Works ◽  
Main Components

Abstract In the current market situation, mining companies are faced with the necessity to take actions to improve the efficiency of the mining process. Some of these actions enforce a centralization of activities in the field of deposit economy and planning of mining operations in these companies. In the planning process with such scope the large knowledge of designers is required, which could be additionally supported by a knowledge base, supplied by information and data obtained during the completion of mining works, which also allows for use of the expert knowledge of other organizational units of the mine or the company. The paper presents an original expert system for mining works planning in the underground hard coal mines (MinePlanEx). The aim of the developed system is to support the designers of production planning in hard coal mines within the scope of: equipment selection, mining machinery combining into equipment sets and determining characteristic curves regarding the production results in the planned excavations. Knowledge of the system is represented by the rules selected with the chosen data mining techniques (association rules and classification trees) and obtained from experts. The first part of the paper presents a knowledge base, knowledge acquisition module and inference module which are the main components of the system. The second part contains an example of system operation.

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.

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