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Sunday, May 26, 2013

Where are artificial neural networks applied?

The artificial neural networks have been applied to a number of problems in diverse fields such as engineering, finance, medical, physics, medicine, and biology and so on. 
- All these applications are based on the fact that these neural networks can simulate the human brain capabilities. 
- They have found a potential use in classification and prediction problems. 
These networks can be classified under the non-linear data driven self adaptive approaches. 
They come handy as a powerful tool when the underlying data relationship is not known. 
- They find it easy to recognize and learn the patterns and can correlate between the input sets and the result values.
- Once the artificial neural networks have been trained, they can be used in the prediction of the outcomes of the data. 
- They can even work when the data is not clear i.e., when it is noisy and imprecise. 
- This is the reason why they prove to be an ideal tool for modeling the agricultural data which is often very complex. 
- Their adaptive nature is their most important feature.
- It is because of this feature that the models developed using ANN is quite appealing when the data is available but there is a lack of understanding of the problem.
- These networks are particularly useful in those areas where the statistical methods can be employed. 
- They have uses in various fields:

    1. Classification Problems:
a)   Identification of underwater sonar currents.
b)   Speech recognition
c)   Prediction of the secondary structure of proteins.
d)   Remote sensing
e)   Image classification
f)    Speech synthesis
g)   ECG/ EMG/ EEG classification
h)   Data mining
i)     Information retrieval
j)    Credit card application screening

  1. Time series applications:
a)   Prediction of stock market performance
b)   ARIMA time – series models
c)   Machine robot/ control manipulation
d)   Financial, engineering and scientific time series forecasting
e)   Inverse modeling of vocal tract

  1. Statistical Applications:
a)   Discriminant analysis
b)   Logistic regression
c)   Bayes analysis
d)   Multiple regression

  1. Optimization:
a)   Multiprocessor scheduling
b)   Task assignment
c)   VLSI routing

  1. Real world Applications:
a)   Credit scoring
b)   Precision direct mailing

  1. Business Applications:
a)   Real estate appraisal
b)   Credit scoring: It is used for determining the approval of a load as per the applicant’s information.
c)   Inputs
d)   Outputs

  1. Mining Applications
a)   Geo-chemical modeling using neural pattern recognition technology.

  1. Medical Applications:
a) Hospital patient stay length prediction system: the CRTS/ QURI system was developed using a neural network for predicting the number of days a patient has to stay in hospital. The major benefit of this system was that money was saved and better patient care. This system required the following 7 inputs:
Ø  Diagnosis
Ø  Complications and comorbidity
Ø  Body systems involved
Ø  Procedure codes and relationships
Ø  General health indicators
Ø  Patient demographics
Ø  Admission category

  1. Management Applications: Jury summoning prediction: a system was developed that could predict the number of jurors that were actually required. Two inputs were supplied: the type of case and judge number. The system is known to have saved around 70 million.
  2. Marketing Application: A neural network was developed for improving the direct mailing response rate. This network selected those individuals who were likely to respond to the 2nd mailing. 9 variables were given as the input. It saved around 35 % of the total mailing cost.
  3. Energy cost prediction: A neural network was developed that could predict the price of natural gas for the next month. It achieved an accuracy of 97%. 

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