scholarly journals Rule Based Expert System for Error Log Analysis

2020 ◽  
Vol 9 (10) ◽  
pp. 188-192
Keyword(s):  
Decision Making ◽  
Expert System ◽  
Expert Knowledge ◽  
Log Analysis ◽  
Domain Expert ◽  
Rule Based ◽  
Domain Experts ◽  
Log Files ◽  
Domain Expertise ◽  
The Given

Humans have been using their domain expertise intelligently and skillfully for making decisions in solving a problem. These decisions are made based on the knowledge that they have acquired through experience and practice over a course of time, which will be lost after the expert’s life ends. Hence, this expert knowledge is required to be stored to a database and a machine could be intelligently programmed which could use this knowledge to make decisions, known as an Expert System (ES). This system tries to emulate the decision-making skills of a domain expert by gathering knowledge of the domain experts, storing it to a knowledge base in rule format, and then using those rules to analyze the given data and provides solutions to the problems. These Expert Systems can be utilized to analyze the system log files, find issues logged into those log statements and provide solutions to the errors that are found in those logs.

Fault Diagnosis Expert System of Turbine Generator Sets Based on Rule Reasoning and Case Reasoning

2014 ◽  
Vol 513-517 ◽  
pp. 4443-4448 ◽  
Author(s):  
Chang Feng Yan ◽  
Hui Bin Wang ◽  
Li Long Zhou ◽  
Zhi Xin Li
Keyword(s):  
Fault Diagnosis ◽  
Expert System ◽  
Expert Knowledge ◽  
Rule Base ◽  
Domain Expert ◽  
Retrieval Strategy ◽  
Turbine Generator ◽  
Rule Based ◽  
Generator Sets ◽  
Case Reasoning

A synthetic fault diagnosis expert system for turbine generator sets based on rule based reasoning and cases based reasoning is built in this paper. The structure of synthetic fault diagnosis expert system is discussed. The rule base and case base for the fault diagnosis of expert system is established based on the domain expert knowledge and relevant fault cases of turbine generator sets. Both the inference flow and case retrieval strategy of diagnosis system are discussed in detail. Finally the expert system is verified by a given application example.

Mixed qualitative–quantitative approach for bidding decisions in construction

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Emad Mohamed ◽  
Parinaz Jafari ◽  
Ahmed Hammad
Keyword(s):  
Expert System ◽  
Expert Knowledge ◽  
The Body ◽  
Decision Makers ◽  
Quantitative Approach ◽  
Rule Based ◽  
Content Type ◽  
Factors Affecting ◽  
Qualitative And Quantitative ◽  
Mixed Approach

PurposeThe bid/no-bid decision is critical to the success of construction contractors. The factors affecting the bid/no-bid decision are either qualitative or quantitative. Previous studies on modeling the bidding decision have not extensively focused on distinguishing qualitative and quantitative factors. Thus, the purpose of this paper is to improve the bidding decision in construction projects by developing tools that consider both qualitative and quantitative factors affecting the bidding decision.Design/methodology/approachThis study proposes a mixed qualitative-quantitative approach to deal with both qualitative and quantitative factors. The mixed qualitative-quantitative approach is developed by combining a rule-based expert system and fuzzy-based expert system. The rule-based expert system is used to evaluate the project based on qualitative factors and the fuzzy expert system is used to evaluate the project based on the quantitative factors in order to reach the comprehensive bid/no-bid decision.FindingsThree real bidding projects are used to investigate the applicability and functionality of the proposed mixed approach and are tested with experts of a construction company in Alberta, Canada. The results demonstrate that the mixed approach provides a more reliable, accurate and practical tool that can assist decision-makers involved in the bid/no-bid decision.Originality/valueThis study contributes theoretically to the body of knowledge by (1) proposing a novel approach capable of modeling all types of factors (either qualitative or quantitative) affecting the bidding decision, and (2) providing means to acquire, store and reuse expert knowledge. Practical contribution of this paper is to provide decision-makers with a comprehensive model that mimics the decision-making process and stores experts' knowledge in the form of rules. Therefore, the model reduces the administrative burden on the decision-makers, saves time and effort and reduces bias and human errors during the bidding process.

Log analysis systems incorporating artificial intelligence techniques ? some examples

1989 ◽  
Vol 20 (2) ◽  
pp. 331 ◽  
Author(s):  
P.L. Baker
Keyword(s):  
Artificial Intelligence ◽  
Expert System ◽  
Log Analysis ◽  
Rule Based ◽  
Artificial Intelligence Techniques ◽  
Lithology Identification ◽  
Well Correlation ◽  
Log Interpretation ◽  
Rule Based Approach ◽  
Mineral Identification

Artificial Intelligence (Al) systems have been used with some success in the areas of dipmeter interpretation, quantitative log interpretation and well-to-well correlation. A prototype expert system has been developed using a rule-based approach to lithology identification. Extensions of the system are now being considered to do mineral identification for the problem of mineral model construction for multi-mineral log interpretation algorithms.

scholarly journals Optimization Model of Fuzzy Rule Based Expert System Using Max-Min Composition and Schema Mapping Translation

INSIST ◽  
2017 ◽  
Vol 2 (1) ◽  
pp. 30 ◽  
Author(s):  
Hartono Hartono ◽  
Tiarma Simanihuruk
Keyword(s):  
Decision Making ◽  
Fuzzy Logic ◽  
Expert System ◽  
Membership Function ◽  
Fuzzy Rule ◽  
Fuzzy Decision Making ◽  
Schema Mapping ◽  
Fuzzy Decision ◽  
Rule Based ◽  
Knowledge Based

Abstract— Fuzzy Decision Making involves a process of selecting one or more alternatives or solutions from a finite set of alternatives which suits a set of constraints. In the rule-based expert system, the terms following in the decision making is using knowledge based and the IF Statements of the rule are called the premises, while the THEN part of the rule is called conclusion. Membership function and knowledge based determines the performance of fuzzy rule based expert system. Membership function determines the performance of fuzzy logic as it relates to represent fuzzy set in a computer. Knowledge Based in the other side relates to capturing human cognitive and judgemental processes, such as thinking and reasoning. In this paper, we have proposed a method by using Max-Min Composition combined with Genetic Algorithm for determining membership function of Fuzzy Logic and Schema Mapping Translation for the rules assignment.Keywords— Fuzzy Decision Making, Rule-Based Expert System, Membership Function, Knowledge Based, Max-Min Composition, Schema Mapping Translation

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.

scholarly journals Amplifying Domain Expertise in Clinical Data Pipelines

10.2196/19612 ◽  
2020 ◽  
Vol 8 (11) ◽  
pp. e19612
Author(s):  
Protiva Rahman ◽  
Arnab Nandi ◽  
Courtney Hebert
Keyword(s):  
Decision Support ◽  
Review Literature ◽  
Task Completion ◽  
Domain Expert ◽  
Domain Experts ◽  
Multiple Data ◽  
Data Pipeline ◽  
Domain Expertise ◽  
Active Research ◽  
Impact Data

Digitization of health records has allowed the health care domain to adopt data-driven algorithms for decision support. There are multiple people involved in this process: a data engineer who processes and restructures the data, a data scientist who develops statistical models, and a domain expert who informs the design of the data pipeline and consumes its results for decision support. Although there are multiple data interaction tools for data scientists, few exist to allow domain experts to interact with data meaningfully. Designing systems for domain experts requires careful thought because they have different needs and characteristics from other end users. There should be an increased emphasis on the system to optimize the experts’ interaction by directing them to high-impact data tasks and reducing the total task completion time. We refer to this optimization as amplifying domain expertise. Although there is active research in making machine learning models more explainable and usable, it focuses on the final outputs of the model. However, in the clinical domain, expert involvement is needed at every pipeline step: curation, cleaning, and analysis. To this end, we review literature from the database, human-computer information, and visualization communities to demonstrate the challenges and solutions at each of the data pipeline stages. Next, we present a taxonomy of expertise amplification, which can be applied when building systems for domain experts. This includes summarization, guidance, interaction, and acceleration. Finally, we demonstrate the use of our taxonomy with a case study.

Web Mining to Create a Domain Specific Web Portal Database

2003 ◽  
pp. 36-53 ◽  
Author(s):  
Anthony Scime
Keyword(s):  
Web Mining ◽  
World Wide ◽  
Expert Knowledge ◽  
Information Access ◽  
Domain Expert ◽  
Web Portal ◽  
Web Based ◽  
Domain Specific ◽  
Domain Expertise ◽  
Access Information

The dynamic nature of the World Wide Web is causing an evolution of both information access and format. The use of a Web portal to access information about a domain relieves the searcher of the responsibility to know about, access and retrieve domain documents. In a properly constructed portal, a Web mining process has already sifted through pages found on the Web to select domain facts. This Web-generated knowledge is added to domain expert knowledge in an organized database. This chapter details the design and construction of a domain specific Web portal through the combination of domain expertise and Web-based domain facts.

Rule-Based Expert System versus Novices' Heuristics: A Matching Identification Problem

1998 ◽  
Vol 82 (3_suppl) ◽  
pp. 1423-1431
Author(s):  
A. Mehrez ◽  
G. Steinberg
Keyword(s):  
Decision Making ◽  
Expert System ◽  
Expert Systems ◽  
Identification Problem ◽  
Search Strategies ◽  
Problem Size ◽  
Rule Based ◽  
System Versus ◽  
Encoding And Retrieval

The present study focused on encoding and retrieval of knowledge as aids to decision-making by comparing the performance of rule-based expert systems and novices' heuristics within the framework of a matching identification problem. A rule-based expert system is developed with a computerized controlled procedure to evaluate and compare its performance with search strategies employed by novices. Analysis indicated that, as problem size increased, the system's outcome compared to novices' heuristics is improved.

Prioritization of congenital cardiac surgical patients using fuzzy reasoning – a solution to the problem of the waiting list?

2006 ◽  
Vol 16 (3) ◽  
pp. 289-299 ◽  
Author(s):  
Ralf Holzer ◽  
Ed Ladusans ◽  
Denise Kitchiner ◽  
Ian Peart ◽  
Gordon Gladman ◽  
...  
Keyword(s):  
Decision Making ◽  
Expert System ◽  
Production System ◽  
Medical Decision Making ◽  
Waiting List ◽  
Surgical Patients ◽  
Medical Decision ◽  
Impact Factors ◽  
Decision Making Process ◽  
Rule Based

Surgical waiting lists are of high importance in countries, where the national health system is unable to deliver surgical services at a rate that would allow patients to avoid unnecessary periods of waiting. Prioritization of these lists, however, is frequently arbitrary and inconsistent.The objective of our research was to analyze the medical decision-making process when prioritizing patients with congenital cardiac malformations for cardiac surgical procedures, identifying an appropriate representation of knowledge, and transferring this knowledge onto the design and implementation of an expert system (“PrioHeart”).The medical decision-making process was stratified into three stages. The first was to analyze the details of the procedure and patient to define important impact factors on clinical priority, such as the risk of adverse events. The second step was to evaluate these impact factors to define an appropriate “timing category” within which a procedure should be performed. The third, and final, step was to re-evaluate the characteristics of individual patients to differentiate between those in the same timing category.We implemented this decision-making process using a rule-based production system with support for fuzzy sets, using the FuzzyCLIPS inference engine and expert system shell as a suitable development environment for the knowledge base.The “PrioHeart” expert system was developed to give paediatric cardiologists a tool to allow and facilitate the prioritization of patients on the cardiosurgical waiting list. Evaluation of “PrioHeart” on limited sets of patients documented appropriate results of prioritization, with a significant correlation between the prioritization made using “PrioHeart” and those results obtained by the individual consultant specialist.We conclude that our study has demonstrated the feasibility of using an expert system approach with a fuzzy, rule-based production system to implement the prioritization of cardiac surgical patients. The approach may potentially be transferable to other medical subspecialities.

scholarly journals A Rule-Based System for Human Performance Evaluation: A Case Study

2021 ◽  
Vol 11 (7) ◽  
pp. 2904
Author(s):  
Iva Mikulić ◽  
Dragutin Lisjak ◽  
Nedeljko Štefanić
Keyword(s):  
Decision Making ◽  
Performance Evaluation ◽  
Expert System ◽  
System Application ◽  
Human Performance ◽  
Inspection System ◽  
Decision Making Process ◽  
Rule Based ◽  
Technical Inspection

The issues of many organizations are related to the proper evaluation of human performance and efficient decision-making. The expert system application within the decision-making process is not a novelty, but the widespread of its implementation regarding performance evaluation has not been recognized yet. To overcome this problem, a case study of rule-based expert system application in the decision-making process regarding human performance in periodical technical inspection stations in Croatia is presented. The rule-based expert system improves the quality of traditional decision-making as designed rules provide a visual, transparent, and accurate comparison of observed values with the expected values. Moreover, it provides easy problem identification. Therefore, rules regarding periodical technical inspection inspectors’ performance are designed and embedded in the expert system architecture. However, more effort should be made into data analysis to define parameters and their relations for the purpose of designing rules. Thus, the binary logistic regression and an ANOVA statistical test were conducted to identify which parameters can be assumed as relevant indicators regarding the performance of periodical technical inspection inspectors. In this study, the expert system application has resulted in faster response, greater efficiency, and increased objectivity. That is of utmost importance for providing an efficient and transparent periodical technical inspection system.

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