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

2. 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

3. Statistical Applications:

a)   Discriminant analysis

b)   Logistic regression

c)   Bayes analysis

d)   Multiple regression

4. Optimization:

a)   Multiprocessor scheduling

b)   Task assignment

c)   VLSI routing

5. Real world Applications:

a)   Credit scoring

b)   Precision direct mailing

6. 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

7. Mining Applications

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

8. 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

9. 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.
10. 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 2^nd mailing. 9 variables were given as the input. It saved around 35 % of the total mailing cost.
11. 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%. 

What is an Artificial Neural Network (ANN)?

- The artificial neural network or ANN (sometimes also called as just neural network) is a mathematical model that has got its inspiration from the biological neural networks. 

- This network is supposed to consist of several artificial neurons that are interconnected. 

- This model works with a connectionist approach for computing and thus processes information based up on this only. 

- In a number of cases, the neural network can act as an adaptive system that has the ability of making changes in its structure while it is in some learning phase. 

- These networks are particularly used in searching patterns in data and for modeling the complex relationships that exist between the outputs and inputs. 

- An analogy to artificial neural network is the neuron network of the human brain. 

- In an ANN, the artificial nodes are termed as the neurons or sometimes as neurodes or units or the ‘processing elements’. 

- They are interconnected in such a way that they resemble a biological neural network. 

- Till now, no formal definition has been given for the artificial neural networks. - These processing elements or the neurons show a complex global behavior. 

- The connections between the neurons and their parameters is what that determines this behavior.

- There are certain algorithms that are designed for altering the strength of these connections in order to produce the desired flow of the signal. 

- The ANN operates up on these algorithms. 

- As in biological neural networks, in ANN also functions are performed in parallel and collectively by the processing units.

- Here, there is no delineation of the tasks that might be assigned to different units. 

- These neural networks are employed in various fields such as:

1. Statistics
2. Cognitive psychology
3. Artificial intelligence

- There are other neural network models that emulate biological CNS and are part of the following:

1. Computational neuroscience
2. Theoretical neuroscience

- The modern software implementation of the ANNs prefers a more practical approach than biologically inspired approach. 

- This practical approach is based up on the signal processing and statistics. The former approach has been largely abandoned. 

- Many times parts of these neural networks serve as components for the other larger systems that are a combination of non – adaptive and adaptive elements.

- Even though a more practical approach for solving the real world problems is the latter one, the former has more to do with the connectionist models of the traditional artificial intelligence. 

- Well the common thing between them is the principle of distributed, non – linear, local and parallel processing and adaptation. 

- A paradigm shift was marked by the use of neural networks during the late eighties. 

- This shift was from the high level artificial intelligence (expert systems) to low level machine learning (dynamical system). 

- These models are very simple and define functions such as:

f: X à Y

- Three types of parameters are used for defining an artificial neural network:

a)   The interconnection pattern between neuron layers

b)   The learning process

c)   The activation function

- The second parameter updates the weights of the connections and the third one converts the weighted input in to output. 

- Learning is the thing that has attracted many towards it. 

- There are 3 major learning paradigms that are offered by ANN:

1. Supervised learning
2. Un – supervised learning
3. Reinforcement learning

- Training a network requires selecting from a set of models that would best minimize the cost.

- A number of algorithms are available for training purpose where gradient descent is employed by most of the algorithms.

- Other methods available are simulated annealing, evolutionary methods and so on.