International Journal of Advance Research and Innovation

Neural Network in Bioinformatics: With the advent of the 
human genome project, the area of Bioinformatics, 
especially protein sequencing, has become a major target for 
neural networks. Protein folding refers to the problem of 
predicting a protein‟s three-dimensional structure from a 
one-dimensional amino-acid sequence. So far, neural 
network have shown a lot of promise and initial 
experimental success towards the protein folding problem. 
Neural Network in Forecasting: The purpose of using 
neural networks is to be able to forecast data patterns that 
are too complex for the traditional statistical models. The 
learning ability of neural networks allows them to adjust to 
dynamic and changing market environments and is a much 
more flexible forecasting tool than traditional statistical 
models. An example of this level of flexibility is in area of 
forecasting net asset values of mutual funds. Many areas of 
business, especially finance, utilize neural networks to 
improve forecasting of their business applications and to 
create new methods of evaluating financial data and 
investment decisions. Neural networks are being used 
specially 
by 
companies 
for 
improved 
forecasting 
capabilities in analysis of the stock market. Neural network 
systems are being used to predict short-term stock 
performance. Neural networks have also been used in 
determining bond ratings. Bank loan decisions are another 
area in which neural networks are proving useful. Because 
the decision to make or deny a loan is very subjective or 
non-linear in nature, the use of neural networks resulted in a 
significant improvement in this decision making process. 
The ability to forecast server downtime has been 
advantageous to companies such as Computer Associates 
because such predictions make it possible for the company 
to fix any potential network problem prior to complete 
computer network failure. Forecasting (GDP) with neural 
networks was proven to provide more accurate predictions 
when compared to traditional statistical forecasting 
techniques. Neural network systems are being used by 
manufacturers to better determine adequate raw material 
levels and credit card companies are utilizing the 
technology for discovering and monitoring fraudulent 
activities. Sales forecasts are also being improved through 
neural network technology at both the wholesale and retail 
levels.