Computational Intelligence Techniques Driven Intelligent Agents for Web Data Mining and Information Retrieval

2004 ◽  
pp. 15-30 ◽  
Author(s):  
Masoud Mohammadian ◽  
Ric Jentzsch
Keyword(s):  
Fuzzy Logic ◽  
Computational Intelligence ◽  
Search Engines ◽  
Intelligent Agents ◽  
Intelligent System ◽  
Information Filtering ◽  
Evolutionary Computing ◽  
Building Blocks ◽  
Improved Performance ◽  
Search And Retrieval

The World Wide Web has added an abundance of data and information to the complexity of information for disseminators and users alike. With this complexity has come the problem of finding useful and relevant information. There is a need for improved and intelligent search and retrieval engines. Current search engines are primarily passive tools. To improve the results returned by searches, intelligent agents and other technology have the potential, when used with existing search and retrieval engines, to provide a more comprehensive search with an improved performance. This research provides the building blocks for integrating intelligent agents with current search engines. It shows how an intelligent system can be constructed to assist in better information filtering, gathering and retrieval. The research is unique in the way the intelligent agents are directed and in how computational intelligence techniques (such as evolutionary computing and fuzzy logic) and intelligent agents are combined to improve information filtering and retrieval. Fuzzy logic is used to access the performance of the system and provide evolutionary computing with the necessary information to carry out its search.

Computational Intelligence Techniques Driven Intelligent Agents for Web Data Mining and Information Retrieval

2008 ◽  
pp. 1435-1445
Author(s):  
Masoud Mohammadian ◽  
Ric Jentzsch
Keyword(s):  
Fuzzy Logic ◽  
Computational Intelligence ◽  
Search Engines ◽  
Intelligent Agents ◽  
Intelligent System ◽  
Information Filtering ◽  
Evolutionary Computing ◽  
Building Blocks ◽  
Improved Performance ◽  
Search And Retrieval

The World Wide Web has added an abundance of data and information to the complexity of information for disseminators and users alike. With this complexity has come the problem of finding useful and relevant information. There is a need for improved and intelligent search and retrieval engines. Current search engines are primarily passive tools. To improve the results returned by searches, intelligent agents and other technology have the potential, when used with existing search and retrieval engines, to provide a more comprehensive search with an improved performance. This research provides the building blocks for integrating intelligent agents with current search engines. It shows how an intelligent system can be constructed to assist in better information filtering, gathering and retrieval. The research is unique in the way the intelligent agents are directed and in how computational intelligence techniques (such as evolutionary computing and fuzzy logic) and intelligent agents are combined to improve information filtering and retrieval. Fuzzy logic is used to access the performance of the system and provide evolutionary computing with the necessary information to carry out its search.

A Gentle Introduction to Fuzzy Logic and Its Applications to Intelligent Agents Design

2011 ◽  
pp. 61-93
Author(s):  
Andre de Korvin
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Sets ◽  
Intelligent Agents ◽  
Fuzzy Systems ◽  
Intelligent Systems ◽  
Intelligent System ◽  
Membership Functions ◽  
Neuro Fuzzy ◽  
Sugeno Model ◽  
Using Data

The purpose of this chapter is to present the key properties of fuzzy logic and adaptive nets and demonstrate how to use these, separately and in combination, to design intelligent systems. The first section introduces the concept of fuzzy sets and their basic operations. The t and s norms are used to define a variety of possible intersections and unions. The next section shows two ways to estimate membership functions, polling experts, and using data to optimize parameters. Section three shows how any function can be extended to arguments that are fuzzy sets. Section four introduces fuzzy relations, fuzzy reasoning, and shows the first steps to be taken to design an intelligent system. The Mamdami model is defined in this section. Reinforcement-driven agents are discussed in section five. Sections six and seven establish the basic properties of adaptive nets and use these to define the Sugeno model. Finally, the last section discusses neuro-fuzzy systems in general. The solution to the inverted pendulum problem is given by use of fuzzy systems and also by the use of adaptive nets. The ANFIS and CANFIS architectures are defined.

scholarly journals Supervised committee machine with artificial intelligence for prediction of fluoride concentration

2013 ◽  
Vol 15 (4) ◽  
pp. 1474-1490 ◽  
Author(s):  
Ata Allah Nadiri ◽  
Elham Fijani ◽  
Frank T.-C. Tsai ◽  
Asghar Asghari Moghaddam
Keyword(s):  
Artificial Intelligence ◽  
Fuzzy Logic ◽  
Ground Water ◽  
Intelligent System ◽  
Fluoride Concentration ◽  
Mixing Zone ◽  
Ann Model ◽  
Committee Machine ◽  
Hydrogeological Characteristics ◽  
Model Predictions

The study introduces a supervised committee machine with artificial intelligence (SCMAI) method to predict fluoride in ground water of Maku, Iran. Ground water is the main source of drinking water for the area. Management of fluoride anomaly needs better prediction of fluoride concentration. However, the complex hydrogeological characteristics cause difficulties to accurately predict fluoride concentration in basaltic formation, non-basaltic formation, and mixing zone. SCMAI predicts fluoride by a nonlinear combination of individual AI models through an artificial intelligent system. Factor analysis is used to identify effective fluoride-correlated hydrochemical parameters as input to AI models. Four AI models, Sugeno fuzzy logic, Mamdani fuzzy logic, artificial neural network (ANN), and neuro-fuzzy are employed to predict fluoride concentration. The results show that all of these models have similar fitting to the fluoride data in the Maku area, and do not predict well for samples in the mixing zone. The SCMAI employs an ANN model to re-predict the fluoride concentration based on the four AI model predictions. The result shows improvement to the CMAI method, a committee machine with the linear combination of AI model predictions. The results also show significant fitting improvement to individual AI models, especially for fluoride prediction in the mixing zone.

Introduction

2013 ◽  
pp. 1-5
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Neural Networks ◽  
Computational Intelligence ◽  
Evolutionary Computing ◽  
Historical Background ◽  
Intelligent Computing ◽  
Biometric Security ◽  
Introductory Overview ◽  
Over Time

This chapter presents an introductory overview of the application of computational intelligence in biometrics. Starting with the historical background on artificial intelligence, the chapter proceeds to the evolutionary computing and neural networks. Evolutionary computing is an ability of a computer system to learn and evolve over time in a manner similar to humans. The chapter discusses swarm intelligence, which is an example of evolutionary computing, as well as chaotic neural network, which is another aspect of intelligent computing. At the end, special concentration is given to a particular application of computational intelligence—biometric security.

Computational Intelligence Foundations and Principles

2017 ◽  
pp. 62-85
Author(s):  
Murugan Sethuraman Sethuraman
Keyword(s):  
Computer Science ◽  
Computational Intelligence ◽  
Intelligent Agents ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Intelligent Agent ◽  
Rational Agents ◽  
Abstract Thought ◽  
Optical Character ◽  
The Brain

AI has been defined in different ways, including the abilities for abstract thought, understanding, communication, reasoning, learning, retaining, planning, and solving. Intelligence is most widely studied in humans, but has also been observed in animals and plants. AI is the intelligence of machines or the simulation of intelligence in machines. AI is both the intelligence of machines and the branch of Computer Science which aims to create it, through the study and design of intelligent agents or rational agents, where an intelligent agent is a system that perceives its environment and takes actions which maximize its chances of success. Achievements include constrained and well-defined problems such as games, crossword-solving and optical character recognition. Among the traits that researchers hope machines will exhibit are reasoning, knowledge, planning, learning, communication, perception, and the ability to move and manipulate objects. In the field of AI there is no consensus on how closely the brain should be simulated.

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