Neural Appl

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    CHAPTER 16

    Neural Computing Applications,

    and Advanced Artificial IntelligentSystems and Applications

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    Neural Computing Applications,and Advanced Artificial Intelligent

    Systems and ApplicationsSeveral Real-World Applications of ANN TechnologyAdvanced AI Systems

    Genetic Algorithms Fuzzy Logic Qualitative Reasoning

    Integration (Hybrids)

    Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

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    Areas of ANN Applications:

    An OverviewRepresentative Business ANN Applications

    AccountingFinance

    Human ResourcesManagementMarketing

    Operations

    Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

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    Accounting

    Identify tax fraudEnhance auditing by finding irregularities

    Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

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    FinanceSignatures and bank note verificationsMortgage underwritingForeign exchange rate forecastingCountry risk rating

    Bankruptcy predictionCustomer credit scoringCredit card approval and fraud detection

    Stock and commodity selection and trading

    Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

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    Finance 2

    Credit card profitabilityForecasting economic turning pointsBond rating and trading

    Pricing initial public offeringsLoan approvalsEconomic and financial forecastingRisk management

    Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

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    Human Resources

    Predicting employees performance and behaviorDetermining personnel resource requirements

    Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

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    Management

    Corporate merger predictionCountry risk rating

    Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

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    MarketingConsumer spending pattern classification

    Customers characteristicsSales forecastsData miningAirline fare managementDirect mail optimizationTargeted marketing

    Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

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    OperationsAirline crew schedulingPredicting airline seat demandVehicle routingAssembly and packaged goods inspectionQuality controlMatching jobs to candidatesProduction/job scheduling

    Factory process control

    Many More

    Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

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    Credit Approval

    with Neural Networks

    Increases loan processor productivity by 25 to35% over other computerized tools

    Also detects credit card fraud

    Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

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    The ANN MethodData from the application and into a database

    Preprocess applications manually

    Neural network trained in advance with manygood and bad risk cases

    Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

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    Neural Network Credit Authorizer

    Construction ProcessStep 1: Collect data

    Step 2: Separate data into training and test sets

    Step 3: Transform data into network inputs

    Step 4: Select, train, and test network

    Step 5: Deploy developed network application

    Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

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    Bankruptcy Prediction

    with Neural NetworksConcept Phase

    Paradigm: Three-layer network, back-propagation

    Training data: Small set of well-known financial ratios

    Data available on bankruptcy outcomes

    Supervised network

    Training time not to be a problemDecision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson

    6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

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    Application Design

    Five Input NodesX1: Working capital/total assets

    X2: Retained earnings/total assetsX3: Earnings before interest and taxes/total assetsX4: Market value of equity/total debt

    X5: Sales/total assets

    Single Output Node: Final classification for each firm Bankruptcy or Nonbankruptcy

    Development Tool: NeuroShellDecision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson

    6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

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    Development Three-layer network with backpropagation (Figure 16.3) Continuous valued input Single output node: 0 = bankrupt, 1 = not bankrupt

    Training Data Set: 129 firms Training Set: 74 firms; 38 bankrupt, 36 not Ratios computed and stored in input files for:

    The neural network A conventional discriminant analysis program

    Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

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    Architecture of the Bankruptcy Prediction

    Neural Network(Figure 16.3)

    X1

    Bankrupt 0

    Not bankrupt 1

    X2

    X3

    X4

    X5

    Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

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    Parameters Learning threshold Learning rate Momentum

    Testing Two Ways

    Test data set: 27 bankrupt firms, 28 nonbankrupt firms Comparison with discriminant analysis

    The neural network correctly predicted:

    81.5 percent bankrupt cases 82.1 percent nonbankrupt cases

    Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

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    ANN did better predicting 22 out of the 27 actual cases

    Discriminant analysis predicted only 16 correctly

    Error Analysis Five bankrupt firms misclassified by both methods Similar for nonbankrupt firms

    Neural network at least as good as conventional

    Accuracy of about 80 percent is usually acceptable forneural network applications

    Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

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    Stock Market Prediction System with

    Modular Neural Networks

    Accurate Stock Market Prediction - Complex Problem

    Several Mathematical Models - Disappointing Results

    Fujitsu and Nikko Securities: TOPIX Buying and Selling

    Prediction System

    Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

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    Input: Several technical and economic indexes

    Several modular neural networks relate past

    indexes, and buy/sell timing

    Prediction system Modular neural networks Very accurate

    Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

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    Network Architecture

    (Figure 16.4)

    Network Model: 3 layers, standard sigmoid function,

    continuous output [0, 1]

    High-speed Supplementary Learning Algorithm

    Training Data Data Selection Training Data

    Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

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    Preprocessing: Input Indexes - Converted into spatialpatterns, preprocessed to regularize them

    Moving Simulation Prediction Method (Figure 16.5)

    Result of Simulations Simulation for Buying and Selling Stocks Example (Figure 16.6) Excellent Profit

    Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

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    Integrated ANNs and

    Expert Systems1. Resource Requirements Advisor

    Advises users on database systems resource requirements

    Predicts the time and effort to finish a database project

    ES shell AUBREY and neural network tool NeuroShell

    ES supported data collection

    ANN used for data evaluation

    ES final analysis

    Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

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    2. Personnel Resource Requirements Advisor

    Project personnel resource requirements for maintainingnetworks or workstations at NASA

    Rule-based ES determines the final resource projections

    ANN provides project completion times for services requested(Figure 16.7)

    Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

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    3. Diagnostic System for an Airline

    Singapore Airlines

    Assists technicians in diagnosing avionics equipment

    INSIDE (Inertial Navigation System Interactive Diagnostic Expert)

    Designed to reduce the diagnostic time(Figure 16.8)

    Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

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    4. Manufacturing Product Liability

    United Technologies Carrier Two ES + ANN Patterns fed into multilayer feedforward ANN Integrated with a database into an Automatic Early Warning

    System (AEWS)

    Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

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    5. Oil Refinery Production Scheduling andEnvironmental Control

    Citgo Petroleum Corporation Lower costs Improved safety Higher product quality Higher yields

    Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

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    Genetic Algorithms

    Goal (evolutionary algorithms): Demonstrate self-organization and adaptation by exposure to theenvironmentSystem learns to adapt to changes .Example 1: Vector Game

    Random trial and error Genetic algorithm solution

    Process (Figure 16.9)Example: the game of MasterMind

    Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

    Genetic Algorithm

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    Genetic Algorithm

    Definition and ProcessGenetic algorithm : "an iterative procedure maintaining a

    population of structures that are candidate solutions to

    specific domain challenges (Grefenstette [1982])

    Each candidate solution is called a chromosome

    Chromosomes can copy themselves, mate, and mutate

    Use specific genetic operators - reproduction, crossoverand mutation

    Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

    f

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    Primary Operators of

    Most Genetic Algorithms

    Reproduction

    Crossover

    Mutation

    Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

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    Genetic Algorithm Operators

    1 0 1 0 1 1 1

    1 1 0 0 0 1 1

    Parent 1

    Parent 2

    1 0 1 0 0 1 1

    1 1 0 0 1 1 0 Mutation

    Child 1

    Child 2

    Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

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    GA Example: The Knapsack Problem

    Item: 1 2 3 4 5 6 7Benefit: 5 8 3 2 7 9 4Weight: 7 8 4 10 4 6 4Knapsack holds a maximum of 22 poundsFill it to get the maximum benefitSolutions take the form of a string of 1s

    Solution: 1 1 0 0 1 0 0Means choose items 1, 2, 5. Weight = 21, Benefit = 20Evolver solution in Figure 16.10

    Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

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    Genetic Algorithms

    Applications and Software

    Type of machine learning

    Set of efficient, domain-independent searchheuristics for a broad spectrum of applications

    Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

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    Genetic Algorithm Application Areas

    Dynamic process controlInduction of rule optimization

    Discovering new connectivity topologiesSimulating biological models of behavior and evolutionComplex design of engineering structuresPattern recognitionSchedulingTransportationLayout and circuit designTelecommunicationGraph-based problems

    Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

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    Business Applications

    Channel 4 Television (England) to schedule commercialsDriver scheduling in a public transportation systemJobshop schedulingAssignment of destinations to sourcesTrading stocksProductivity in whisky-making is increased

    Often genetic algorithm hybrids with other AI methods

    Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

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    Representative Commercial Packages

    Evolver (Excel spreadsheet add-in)Genetic Algorithm User Interface (GAUI)OOGA (Object-Oriented GA for industrial use)

    XperRule Genasys (ES shell with an embedded geneticalgorithm)Sugal Genetic Algorithm Simulator

    Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

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    Optimization Algorithms

    Via neural computing sometimes

    Genetic algorithms and their derivatives can optimize(or nearly optimize) complex problems

    Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

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    Fuzzy Logic

    Fuzzy logic deals with uncertainty

    Uses the mathematical theory of fuzzy sets

    Simulates the process of normal human reasoning

    Allows the computer to behave less precisely and

    logically

    Decision making involves gray areas and the term maybe

    Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

    d

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    Fuzzy Logic Advantages

    Provides flexibilityProvides options

    Frees the imaginationMore forgivingAllows for observationShortens system development timeIncreases the system's maintainabilityUses less expensive hardwareHandles control or decision-making problems noteasily defined by mathematical models

    Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

    Fuzzy Logic Example:

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    y g pWhat is Tall?

    In-Class ExerciseProportion

    Height Voted for510 0.05511 0.10

    6 0.6061 0.1562 0.10

    Jack is 6 feet tall Probability theory - cumulative probability There is a 75 percent chance that Jack is tall

    Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

    Fuzzy logic Jack's degree of membership within the

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    Fuzzy logic - Jack s degree of membership within theset of tall people is 0.75We are not completely sure whether he is tall or not

    Fuzzy logic - We agree that Jack is more or less tall

    Membership Function< Jack, 0.75 Tall >

    Knowledge-based system approach: Jack is tall(CF = .75)Belief functions

    Can use fuzzy logic in rule-based systems

    Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

    Membership Functions in Fuzzy Sets

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    Membership Functions in Fuzzy Sets(Figure 16.11)

    Short Medium Tall

    Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

    Membership

    Height in inches (1 inch = 2.54 cm)

    0.5

    1.0

    64 7469

    Fuzzy Logic Applications and

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    y g pp

    SoftwareDifficult to apply when people provide evidence

    Used in consumer products that have sensors Air conditioners Cameras Dishwashers Microwaves Toasters

    Special software packages

    Controls applicationsDecision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson

    6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

    E l f F L i

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    Examples of Fuzzy Logic

    Example 1: Strategic planning STRATASSIST - fuzzy expert system that helps small- to

    medium-sized firms plan strategically for a single product

    Example 2: Fuzziness in real estate

    Example 3: A fuzzy bond evaluation system

    Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

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    Fuzzy Logic Software

    Fuzzy Inference Development Environment(FIDE)Z Search

    HyperLogic Corporation demosOthers

    Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

    Q lit ti R i g (QR)

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    Qualitative Reasoning (QR)

    Means of representing and making inferences usinggeneral, physical knowledge about the world

    QR is a model-based procedure that consequentlyincorporates deep knowledge about a problemdomain

    Typical QR Logic If you touch a kettle full of boiling water on a stove, you

    will burn yourself If you throw an object off a building, it will go down

    Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

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    But

    No specific knowledge about boiling temperature, justthat it is really hot!

    No specific information about the building or object,unless you are the object, or you are trying to catch it

    Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

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    Main goal of QR: To represent common senseknowledge about the physical world, and the underlyingabstractions used in quantitative models (objects fall)

    Given such knowledge and appropriate reasoningmethods, an ES could make predictions and diagnoses,and explain the behavior of physical systems

    qualitatively, even when exact quantitative descriptionsare unavailable or intractable

    Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

    Qualitative Reasoning

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    Q g

    Relevant behavior is modeled

    Temporal and spatial qualities in decision making arerepresented effectively

    Applies common sense mathematical rules to variablesand functions

    There are structure rules and behavior rules

    Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

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    Some Real-World QR Applications

    Nuclear plant fault diagnoses

    Business processes

    Financial markets

    Economic systems

    Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

    Intelligent S stems Integr tion

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    Intelligent Systems Integration

    Combine Neural Computing

    Expert Systems Genetic Algorithms Fuzzy Logic

    Example: International investment management--stock selectionFuzzy Logic and ANN (FuzzyNet) to forecast theexpected returns from stocks, cash, bonds, and otherassets to determine the optimal allocation of assets

    Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

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    Global marketsIntegrated network architecture of the system

    (Figure 16.12)

    TechnologiesExpert system (rule-based) for country and stock selectionNeural network for forecastingFuzzy logic for assessing factors without reliable data

    Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

    FuzzyNet Architecture

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    FuzzyNet Architecture(Figure 16.12)

    Membership Function Generator (MFG)

    Fuzzy Information Processor (FIP)

    Back-propagation Neural Network (BPN)

    Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

    Data Mining and Knowledge

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    Data Mining and Knowledge

    Discovery in Databases (KDD)Hidden value in data

    Knowledge Discovery in Databases (KDD)

    Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

    The KDD Process

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    The KDD Process

    Start with Raw Data and Do

    1. Selection to produce target the appropriate datawhich undergoes

    2. Preprocessing to filter the data in preparation for

    3. Transformation so that4. Data Mining can identify patterns that go through5. Interpretation and Evaluation resulting in

    knowledge

    Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

    D Mi i

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    Data Mining

    Find kernels of value in raw data ore

    Theoretical advances

    Knowledge discovery in textual databases

    Methods based on statistics, cluster analysis, discriminant

    analysis, fuzzy logic, genetic algorithms, and neural networks

    Ideal for data mining

    Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

    AI Methods and Data Mining

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    g

    for SearchNeural Networks

    Expert Systems

    Rule Induction

    Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

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    Data Mining Applications Areas

    Marketing

    Investment

    Fraud detection

    Manufacturing

    Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

    Information Overload

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    Information Overload

    Data mining methods can sift through softinformation to identify relationships automatically

    Intelligent agents

    Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

    Important KDD andData Mining Challenges

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    Data Mining Challenges

    Dealing with larger databases

    Working with higher dimensionalities of data

    Overfitting--modeling noise rather than data patterns

    Assessing statistical significance of results

    Working with constantly changing data and knowledge

    Continue

    Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

    Working thro gh missing and nois data

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    Working through missing and noisy data

    Determining complex relationships between fields

    Making patterns more understandable to humans

    Providing better user interaction and prior knowledgeabout the data

    Providing integration with other systems

    Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ