KNOWLEDGE EXTRACTION FROM RISE-TIME AUTO-CORRELATED PATTERNS

In this paper, the author presents an approach for automated knowledge extraction from rise time auto-correlated patterns by using self-organizing maps and k-means clustering. The extracted knowledge in terms of rules will be used as knowledge base for an expert system. Rise-time auto-correlated data patterns are used as a learning data set. The produced knowledge based was verified by using a conventional expert system.

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KNOWLEDGE EXTRACTION FROM FALL-TIME AUTO-CORRELATED PATTERNS BY USING NEURAL RULE BASED EXPERT SYSTEM

International Journal of Information Acquisition ◽

10.1142/s0219878906000782 ◽

2006 ◽

Vol 03 (01) ◽

pp. 15-24 ◽

Cited By ~ 1

Author(s):

NAZAR ELFADIL ◽

INTISAR IBRAHIM

Keyword(s):

Expert System ◽

Correlated Data ◽

Self Organizing Maps ◽

Data Set ◽

Rule Based ◽

Fall Time ◽

Knowledge Based ◽

Correlated Patterns ◽

Automated Knowledge ◽

Learning Data

In this paper, the author presents an approach for automated knowledge acquisition system using Kohonen self-organizing maps and k-means clustering. The extracted knowledge in terms of rules are used as knowledge base for a rule based expert system. For the sake of illustrating and validating the system overall architecture, a fall-time auto-correlated data patterns has been used as a learning data set. The verification of the produced knowledge based was conducted by conventional expert system.

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Machine Learning: Automated Knowledge Acquisition Based on Unsupervised Neural Network and Expert System Paradigms

Journal of Advanced Computational Intelligence and Intelligent Informatics ◽

10.20965/jaciii.2005.p0693 ◽

2005 ◽

Vol 9 (6) ◽

pp. 693-697 ◽

Cited By ~ 2

Author(s):

Nazar Elfadil ◽

Keyword(s):

Neural Network ◽

Expert System ◽

Knowledge Acquisition ◽

Training Data ◽

Neural Network Models ◽

Self Organizing Maps ◽

Data Set ◽

Unsupervised Neural Network ◽

Automated Knowledge ◽

Self Organizing

Self-organizing maps are unsupervised neural network models that lend themselves to the cluster analysis of high-dimensional input data. Interpreting a trained map is difficult because features responsible for specific cluster assignment are not evident from resulting map representation. This paper presents an approach to automated knowledge acquisition using Kohonen's self-organizing maps and k-means clustering. To demonstrate the architecture and validation, a data set representing animal world has been used as the training data set. The verification of the produced knowledge base is done by using conventional expert system.

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Computer-Aided Diagnosis in Jaundice: Comparison of Knowledge-based and Probabilistic Approaches

Methods of Information in Medicine ◽

10.1055/s-0038-1634634 ◽

1996 ◽

Vol 35 (01) ◽

pp. 41-51 ◽

Cited By ~ 3

Author(s):

F. Molino ◽

D. Furia ◽

F. Bar ◽

S. Battista ◽

N. Cappello ◽

...

Keyword(s):

Clinical Presentation ◽

Clinical Information ◽

Diagnostic Value ◽

Clinical Findings ◽

Clinical Documentation ◽

Data Set ◽

Knowledge Based ◽

Number Of Patients ◽

Support Tools ◽

Aided Diagnosis

AbstractThe study reported in this paper is aimed at evaluating the effectiveness of a knowledge-based expert system (ICTERUS) in diagnosing jaundiced patients, compared with a statistical system based on probabilistic concepts (TRIAL). The performances of both systems have been evaluated using the same set of data in the same number of patients. Both systems are spin-off products of the European project Euricterus, an EC-COMACBME Project designed to document the occurrence and diagnostic value of clinical findings in the clinical presentation of jaundice in Europe, and have been developed as decision-making tools for the identification of the cause of jaundice based only on clinical information and routine investigations. Two groups of jaundiced patients were studied, including 500 (retrospective sample) and 100 (prospective sample) subjects, respectively. All patients were independently submitted to both decision-support tools. The input of both systems was the data set agreed within the Euricterus Project. The performances of both systems were evaluated with respect to the reference diagnoses provided by experts on the basis of the full clinical documentation. Results indicate that both systems are clinically reliable, although the diagnostic prediction provided by the knowledge-based approach is slightly better.

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