Artificial Intelligence

 Association Rules |Clustering |Decision Trees| Naive Bayes| Regression Analysis| Neural Networks| Time Series |Text Mining |

Genetic Programming

Neural network Algorithms find applications in

• Neural networks have been used to recognize this predictive pattern so that the appropriate treatment can be prescribed. A variety of health-related indices (heart rate, levels of various substances in the blood, respiration rate) can be monitored. The onset of a particular medical condition could be associated with a very complex (nonlinear and interactive) combination of changes on a subset of the variables being monitored.

• Neural networks are being used by many technical analysts to make predictions about stock prices based upon a large number of factors such as past performance of other stocks and various economic indicators. Fluctuations of stock prices and stock indices are another example of a complex, multidimensional, but in some circumstances at least partially-deterministic phenomenon.

•  After training a neural network on historical data, neural network analysis can identify the most relevant characteristics and use those to classify applicants as good or bad credit risks. A variety of pieces of information are usually known about an applicant for a loan. For instance, the applicant's age, education, occupation, and many other facts may be available.

•  A neural network can be trained to distinguish between the sounds a machine makes when it is running normally ("false alarms") versus when it is on the verge of a problem. After this training period, the expertise of the network can be used to warn a technician of an upcoming breakdown, before it occurs and causes costly unforeseen "downtime."  Neural networks can be instrumental in cutting costs by bringing additional expertise to scheduling the preventive maintenance of machines.

• The neural network controls the various parameters within which the engine functions, in order to achieve a particular goal, such as minimizing fuel consumption. Neural networks have been used to analyze the input of sensors from an engine.

Neural Network is a Algorithm based on biological neural networks. It consists of an interconnected group of artificial neurons and processes information using a connectionist approach to computation. Neural Network can be an adaptive system that changes its structure based on external or internal information that flows through the network during the learning phase.

Neural Network provider processes the entire set of cases , iteratively comparing the predicted classification of the cases with the known actual classification of the cases. The errors from the initial classification of the first iteration of the entire set of cases is fed back into the network, and used to modify the network's performance for the next iteration, and so on. You can later use these probabilities to predict an outcome of the predicted attribute, based on the input attributes. One of the primary differences between Neural Networks and Decision Trees algorithm, however, is that its learning process is to optimize network parameters toward minimizing the error while  Decision Trees algorithm splits rules in order to maximize information gain. The algorithm supports the prediction of both discrete and continuous attributes.

Emulative Neural Networks (ENNs) are members of a family of statistical techniques, as are flexible nonlinear regression models, discriminating models, data reduction models and nonlinear dynamic systems. These are trainable analytic tools that attempt to mimic information processing patterns in the brain. ENN's do not require assumptions about population distribution for data analysis. ENNs have been applied in modeling market response , collective behavior, telecommunication flows , real estate valuation ,and even the determinants of military expenditure . ENN's are also used for analyzing relations among economic and financial phenomena, forecasting, data filtration, generating time-series and optimization. Artificial Neural Networks (ANN's) make no assumptions about the nature of the distribution of the data. They are not biased in their analysis. Instead of making assumptions about the underlying population, ENNs with at least one middle layer use the data to develop an internal representation of the relationship between the variables.

Neural Networks for Financial Markets learn from carefully crafted training data containing such variables as interest rates, currency prices, commodity prices, the slope of the yield curve, the movement in major commodity prices, the movement in major financial averages, internal market data and technical indicators.