scholarly journals Automated Prediction of Behaviours and Trends in Data Mining

2019 ◽  
Vol 9 (2) ◽  
pp. 5188-5190
Keyword(s):  
Data Mining ◽  
Information Retrieval ◽  
Expert System ◽  
High Performance ◽  
Financial Institution ◽  
Semantic Networks ◽  
Visual Images ◽  
Knowledge Based ◽  
Automated Prediction

Data mining is the process of discovering likely useful, appealing, as well as previously not known patterns coming from an extensive compilation of data. Data mining is a multidisciplinary field, enticing projects coming from places consisting of data financial institution advancement, expert system, stats, style understanding, information retrieval, semantic networks, knowledge-based units, expert system, high-performance processing, as well as files visual images. This paper delivers a quick concerning architecture, benefits and automated prediction of trends as well as behaviors in Data Mining

scholarly journals Knowledge-Based Expert System Development and Validation with Petri Nets

2008 ◽  
Vol 07 (01) ◽  
pp. 37-46 ◽  
Author(s):  
Madjid Tavana
Keyword(s):  
Expert System ◽  
Knowledge Representation ◽  
Petri Nets ◽  
Decision Trees ◽  
System Development ◽  
Semantic Networks ◽  
Knowledge Based ◽  
Medical Diagnostic ◽  
High Level ◽  
Decision Tables

Expert systems (ESs) are complex information systems that are expensive to build and difficult to validate. Numerous knowledge representation strategies such as rules, semantic networks, frames, objects and logical expressions are developed to provide high-level abstraction of a system. Rules are the most commonly used form of knowledge representation and they are derived from popular techniques such as decision trees and decision tables. Despite their huge popularity, decision trees and decision tables are static and cannot model the dynamic requirements of a system. In this study, we propose Petri Nets (PNs) for dynamic system representation and rule derivation. PNs with their graphical and precise nature and their firm mathematical foundation are especially useful for building ESs that exhibit a variety of situations, including: sequential execution, conflict, concurrency, synchronisation, merging, confusion, or prioritisation. We demonstrate the application of our methodology in the design and development of a medical diagnostic expert system.

Knowledge-Based Mechanical Spring Design System

1995 ◽  
Author(s):  
K. K. Tai ◽  
Yuyi Lin ◽  
L. X. Wang
Keyword(s):  
Expert System ◽  
Design Automation ◽  
High Performance ◽  
Product Information ◽  
Building Blocks ◽  
Design System ◽  
Knowledge Based ◽  
Expert System Shell ◽  
Mechanical Spring ◽  
Mechanical Components

Abstract Expert systems are best known for qualitative or heuristic reasoning capability. However, the design of high performance and critical mechanical components, such as automotive valve springs, requires that precise and quantitative issues be resolved. This paper discusses the extended use of an expert system shell for mechanical spring design automation. An expert system shell is utilized as a user friendly front end and a binding agent among system components. Building blocks of the complete system include a product information data base which is provided by commercial manufacturers, a dynamic modeling and simulation module which includes typical-applications models, an optimization module which uses simulated annealing algorithms, and a design specification generation module which produces production drawings and a design report. The basic methodology discussed in this paper can be applied to the design automation of other mechanical components.

scholarly journals Tuning Expert Systems for Cost-Sensitive Decisions

2011 ◽  
Vol 2011 ◽  
pp. 1-12 ◽  
Author(s):  
Atish P. Sinha ◽  
Huimin Zhao
Keyword(s):  
Data Mining ◽  
Expert System ◽  
Expert Systems ◽  
Domain Knowledge ◽  
Training Data ◽  
Business Decisions ◽  
Misclassification Cost ◽  
Knowledge Based ◽  
The Impact ◽  
And Training

There is currently a growing body of research examining the effects of the fusion of domain knowledge and data mining. This paper examines the impact of such fusion in a novel way by applying validation techniques and training data to enhance the performance of knowledge-based expert systems. We present an algorithm for tuning an expert system to minimize the expected misclassification cost. The algorithm employs data reserved for training data mining models to determine the decision cutoff of the expert system, in terms of the certainty factor of a prediction, for optimal performance. We evaluate the proposed algorithm and find that tuning the expert system results in significantly lower costs. Our approach could be extended to enhance the performance of any intelligent or knowledge system that makes cost-sensitive business decisions.

Research on Database and Information Technology in the Decision Tree of Communication Construction Scheme

2014 ◽  
Vol 1046 ◽  
pp. 461-464
Author(s):  
Yong Jun Peng ◽  
Yang Peng ◽  
Ci Fang Liu
Keyword(s):  
Data Mining ◽  
Information Technology ◽  
Decision Tree ◽  
High Performance ◽  
Database Management System ◽  
Widespread Application ◽  
Knowledge Based ◽  
Construction Scheme ◽  
Database Technology ◽  
Automated Tools

With the development of database technology as well as the widespread application of Database management system, our capabilities of both generating and collecting data have been increasing rapidly. In addition, popular use of the World Wide Web as a global information system has flooded us with a tremendous amount of data and information. This explosive growth in stored or transient data has generated an urgent need for new techniques and automated tools that can intelligently assist us in transforming the vast amounts of data into useful information and knowledge. The Data Mining technology brought forward. Data mining, also popularly referred to knowledge discovery from data (KDD), is the automated or convenient extraction of patterns representing knowledge implicitly stored or captured in large databases, data warehouses, the Web, other massive information repositories, or data streams. Data mining is a multidisciplinary field, drawing work from areas including database technology, machine learning, statistics, pattern recognition, information retrieval, neural networks, knowledge-based systems, artificial intelligence, high-performance computing, and data visualization. Database and Information Technology in the Decision Tree of is very important for the military. The Data Mining had been applied and studied in these years, and it has been applied in many domains successfully, such as business, finance and medical treatment. However, little is applied in communication construction scheme.

A Survey on Information Retrieval Models, Techniques and Applications

2017 ◽  
Vol 7 (7) ◽  
pp. 16 ◽  
Author(s):  
Ndengabaganizi Tonny James ◽  
Rajkumar Kannan
Keyword(s):  
Information Retrieval ◽  
Retrieval Models ◽  
Knowledge Based ◽  
Long Time

It has been long time many people have realized the importance of archiving and finding information. With the advent of computers, it became possible to store large amounts of information; and finding useful information from such collections became a necessity. Over the last forty years, Information Retrieval (IR) has matured considerably. Several IR systems are used on an everyday basis by a wide variety of users. Information retrieval (IR) is generally concerned with the searching and retrieving of knowledge-based information from database. In this paper, we will discuss about the various models and techniques and for information retrieval. We are also providing the overview of traditional IR models.

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