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
- 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
- Statistical Applications:
a) Discriminant analysis
b) Logistic regression
c) Bayes analysis
d) Multiple regression
- Optimization:
a) Multiprocessor scheduling
b) Task assignment
c) VLSI routing
- Real world Applications:
a) Credit scoring
b) Precision direct mailing
- 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
- Mining Applications
a) Geo-chemical modeling
using neural pattern recognition technology.
- 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
- 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.
- 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.
- 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%.
