P-1 © 2005 NeuralWare. All rights reserved. Using Neural Networks in Decision Support Systems Introduction Core Technology Building and Deploying Neural.

P-1 © 2005 NeuralWare. All rights reserved. Using Neural Networks in Decision Support Systems Introduction Core Technology Building and Deploying Neural Networks Medical Procedure Certification Crime Forecasting Grain Quality Assessment Jack Copper NeuralWare jack.copper@neuralware.com Hiroshi Maruyama SET Software Co. Ltd. mal@setsw.co.jp April 2005

P-2 © 2005 NeuralWare. All rights reserved. NeuralWareNeuralWare 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

P-5 © 2005 NeuralWare. All rights reserved. IntroductionIntroduction Examples of Neural Network Decision Support Systems  Medical Procedure Certification  Crime Forecasting  Grain Quality Assessments

P-6 © 2005 NeuralWare. All rights reserved. Core Technology - Neural Networks Historic Data Target Model Target Model Input Layer Hidden Layer Output Layer Decisions Based on Model Output New Data Artificial Neural Networks are connected hierarchies of Artificial Neurons (also called Processing Elements)

P-10 © 2005 NeuralWare. All rights reserved. Deploying 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

P-11 © 2005 NeuralWare. All rights reserved. Deploying Neural Networks Wired or wireless remote PC clients employ embedded NeuralWare technology (Run- Time Engine/Models/FlashCode) Server Contains Development Engine Distributed Intelligence Architecture

P-12 © 2005 NeuralWare. All rights reserved. Case 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

P-13 © 2005 NeuralWare. All rights reserved. Case Study – Medical Procedure Certification Performance of best models (ranked by Average Classification Rate) for the Global model and CT and MRI Modality models

P-15 © 2005 NeuralWare. All rights reserved. Case Study – Medical Procedure Certification Acquire/Validate Case InputRetrieve MetricsSelect/Execute ModelApply Thresholds Approve Procedure? Process Manually Update Metrics Selected for Audit? NO YES DONE Metric Database

P-16 © 2005 NeuralWare. All rights reserved. Case 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

P-18 © 2005 NeuralWare. All rights reserved. Case 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

P-20 © 2005 NeuralWare. All rights reserved. Case 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

P-22 © 2005 NeuralWare. All rights reserved. Case 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!

P-23 © 2005 NeuralWare. All rights reserved. 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 !

P-1 © 2005 NeuralWare. All rights reserved. Using Neural Networks in Decision Support Systems Introduction Core Technology Building and Deploying Neural.

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P-1 © 2005 NeuralWare. All rights reserved. Using Neural Networks in Decision Support Systems Introduction Core Technology Building and Deploying Neural.

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