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![Page 1: P-1 © 2005 NeuralWare. All rights reserved. Using Neural Networks in Decision Support Systems Introduction Core Technology Building and Deploying Neural.](https://reader033.fdocuments.in/reader033/viewer/2022051018/56649ee05503460f94bf0a91/html5/thumbnails/1.jpg)
P-1© 2005 NeuralWare. All rights reserved.
Using Neural Networks in Decision Support SystemsUsing Neural Networks in Decision Support SystemsUsing Neural Networks in Decision Support SystemsUsing Neural Networks in Decision Support Systems
IntroductionIntroduction
Core Core TechnologyTechnology
Building and Building and Deploying Deploying Neural Neural NetworksNetworks
Medical Medical Procedure Procedure CertificationCertification
Crime Crime ForecastingForecasting
Grain Quality Grain Quality Assessment Assessment
Jack Copper
NeuralWare
Hiroshi Maruyama
SET Software Co. Ltd.
April 2005
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P-2© 2005 NeuralWare. All rights reserved.
NeuralWareNeuralWareNeuralWareNeuralWare
Since 1987, NeuralWare has created and marketed neural network based Artificial Intelligence (AI) software for –
Data Mining (clustering) Classification Forecasting
NeuralWare collaborates with Customers and Partners to Embed Intelligent Neural Network Engines into Next-Generation Products and Systems
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P-3© 2005 NeuralWare. All rights reserved.
IntroductionIntroductionIntroductionIntroduction
Characteristics of Neural Network Decision Support Systems
Integrate Data and Analytics
Adapt to Changing Conditions
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P-4© 2005 NeuralWare. All rights reserved.
IntroductionIntroductionIntroductionIntroduction
Benefits of Neural Network Decision Support Systems
Consistent Decisions
Rapid Decisions
Reproducible Decisions
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P-5© 2005 NeuralWare. All rights reserved.
IntroductionIntroductionIntroductionIntroduction
Examples of Neural Network Decision Support Systems
Medical Procedure Certification
Crime Forecasting
Grain Quality Assessments
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P-6© 2005 NeuralWare. All rights reserved.
Core Technology - Neural NetworksCore Technology - Neural NetworksCore Technology - Neural NetworksCore Technology - Neural Networks
Historic Data
Target
Model
Target
Model
Input LayerInput Layer
Hidden LayerHidden Layer
Output LayerOutput Layer Decisions Based on Model OutputDecisions Based on Model Output
New DataNew Data
Artificial Neural Networks are connected hierarchies Artificial Neural Networks are connected hierarchies of Artificial Neurons (also called Processing Elements)of Artificial Neurons (also called Processing Elements)
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P-7© 2005 NeuralWare. All rights reserved.
Building Neural NetworksBuilding Neural NetworksBuilding Neural NetworksBuilding Neural Networks
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P-8© 2005 NeuralWare. All rights reserved.
Evaluating Neural Network PerformanceEvaluating Neural Network PerformanceEvaluating Neural Network PerformanceEvaluating Neural Network Performance
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P-9© 2005 NeuralWare. All rights reserved.
Evaluating Neural Network PerformanceEvaluating Neural Network PerformanceEvaluating Neural Network PerformanceEvaluating Neural Network Performance
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P-10© 2005 NeuralWare. All rights reserved.
Deploying Neural NetworksDeploying Neural NetworksDeploying Neural NetworksDeploying Neural Networks
NeuralWare Technology (Run-Time Engine/Models/FlashCode) embedded in Server
Browser-based wired or wireless remote PC clients do not employ NeuralWare technology
Server Contains Development and Run-Time Engine
Application Server Architecture
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P-11© 2005 NeuralWare. All rights reserved.
Deploying Neural NetworksDeploying Neural NetworksDeploying Neural NetworksDeploying Neural Networks
Wired or wireless remote PC clients employ embedded NeuralWare technology (Run-Time Engine/Models/FlashCode)
Server Contains Development Engine
Distributed Intelligence Architecture
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P-12© 2005 NeuralWare. All rights reserved.
Case Study – Medical Procedure CertificationCase Study – Medical Procedure CertificationCase Study – Medical Procedure CertificationCase Study – Medical Procedure Certification
Objectives
Reduce Workload on Doctors and Registered Nurses
Improve Responsiveness to Customers (faster decisions)
Challenges
No “Gold Standard” for decisions – even Doctors sometimes disagree
Inconsistent data formats and labeling
Process
Used NeuralSight to build and evaluate ~ 30,000 Models in 3 weeks
Developed prototype software to permit altering Model decision threshold
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P-13© 2005 NeuralWare. All rights reserved.
Case Study – Medical Procedure CertificationCase Study – Medical Procedure CertificationCase Study – Medical Procedure CertificationCase Study – Medical Procedure Certification
Performance of best models (ranked by Average Classification Rate) for the Global model and CT and MRI Modality models
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P-14© 2005 NeuralWare. All rights reserved.
Case Study – Medical Procedure CertificationCase Study – Medical Procedure CertificationCase Study – Medical Procedure CertificationCase Study – Medical Procedure Certification
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P-15© 2005 NeuralWare. All rights reserved.
Case Study – Medical Procedure CertificationCase Study – Medical Procedure CertificationCase Study – Medical Procedure CertificationCase Study – Medical Procedure Certification
Acquire/Validate Case Input
Retrieve Metrics
Select/Execute Model
Apply Thresholds
Approve Procedure?
Process ManuallyUpdate Metrics
Selected for Audit?
NO
YES
YES
DONE
Metric Database
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P-16© 2005 NeuralWare. All rights reserved.
Case Study – Crime ForecastingCase Study – Crime ForecastingCase Study – Crime ForecastingCase Study – Crime Forecasting
Objectives
Identify Patterns in Criminal Activity that indicate Potential Future Trouble Spots
Redirect Police Resources to Focus on Areas where Serious Crime is expected to Increase
Challenges
Defining Crime Categories and Severity Levels
Inconsistent data formats and labeling; missing or non-existent data
Process
Used NeuralSight to Build and Evaluate ~ 10,000 Models in 1 week
On-going evaluation by researchers at Carnegie Mellon University
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P-17© 2005 NeuralWare. All rights reserved.
Case Study – Crime ForecastingCase Study – Crime ForecastingCase Study – Crime ForecastingCase Study – Crime Forecasting
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P-18© 2005 NeuralWare. All rights reserved.
Case Study – Crime ForecastingCase Study – Crime ForecastingCase Study – Crime ForecastingCase Study – Crime Forecasting
How to Forecast Change in Crime
Police know current crime levels Have allocated resources to respond to existing crimes
Most valuable information for tactical level planning: Where is crime likely to have large increases next month? Forecast crime by area and calculate:
Forecasted Change (t+1) = Forecast (t+1) – Actual (t)
The Benefit – Better Allocation of Scarce Resources
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P-19© 2005 NeuralWare. All rights reserved.
Case Study – Crime ForecastingCase Study – Crime ForecastingCase Study – Crime ForecastingCase Study – Crime Forecasting
Forecasted Change for July
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P-20© 2005 NeuralWare. All rights reserved.
Case Study – Grain Quality AssessmentCase Study – Grain Quality AssessmentCase Study – Grain Quality AssessmentCase Study – Grain Quality Assessment
Objectives
Provide a Platform for rapidly and consistently assessing the quality of grain
Maintain detailed records of tests and build foundation for data mining
Challenges
No “Gold Standard” for decisions – even experienced human inspectors are inconsistent
Requires tedious work to identify wide variety of training data samples
Process
Used Predict and NeuralSight to Build and Evaluate many thousands of Models
Now developing image database to support agriculture research
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P-21© 2005 NeuralWare. All rights reserved.
Case Study – Grain Quality AssessmentCase Study – Grain Quality AssessmentCase Study – Grain Quality AssessmentCase Study – Grain Quality Assessment
An Instrument – and examples of seed images
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P-22© 2005 NeuralWare. All rights reserved.
Case Study – Grain Quality AssessmentCase Study – Grain Quality AssessmentCase Study – Grain Quality AssessmentCase Study – Grain Quality Assessment
Many (more than 300) initial features per seed Predict Variable Selection found a much smaller set of
features to use in building models
The characteristics of grain that are important are difficult even for human inspectors to identify Multiple neural networks are used to make the hard decisions
The value of wheat and other commodities depends on its quality – millions of dollars are at risk if quality decisions are incorrect!
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P-23© 2005 NeuralWare. All rights reserved.
What Have you Learned?What Have you Learned?What Have you Learned?What Have you Learned?
Neural Networks make Powerful Decision Support Systems Human Judgment Determines the Cost/Benefit Tradeoff for Accuracy
Know your Problem !
Neural Network Decisions are based on Learning Patterns Relationships in Historical Data are the basis for Current Action
Know your Data !
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P-24© 2005 NeuralWare. All rights reserved.
Thank You !Thank You !Thank You !Thank You !
Jack Copper
NeuralWare
Hiroshi Maruyama
SET Software Co. Ltd.