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What are the applications for Neural Networks?
A neural network is an array of algorithms that endeavors to identify fundamental relationships in a set of data through a process that mimics the techniques the human brain operates. In this sense, neural networks refer to systems of neurons, either organic or artificial.
Neural networks are applicable in virtually every situation in which a relationship between the predictor variables (independents, inputs) and predicted variables (dependents, outputs) exists, even when that relationship is very complex and not easy to articulate in the usual terms of “correlations” or “differences between groups.”
There are various applications of Neural Networks which are as follows −
Detection of medical phenomena − An array of health-related indices (e.g., a combination of heart rate, levels of several substances in the blood, respiration rate) can be checked. The onset of a specific medical condition can be related to a very complex (e.g., nonlinear and interactive) combination of changes on a subset of the variables being checked. Neural networks have been used to identify this predictive design so that the appropriate treatment can be recommended.
Stock market prediction − Fluctuations of stock prices and stock indices are another example of a complex, multidimensional, but in some circumstances at least partially deterministic phenomenon. Neural networks are being used by many technical analysts to make predictions about stock prices based upon a large number of factors such as the past performance of other stocks and various economic indicators.
Credit assignment − A variety of pieces of data are generally known about an applicant for a loan. For example, the applicant’s age, education, occupation, and some other facts can be accessible. 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.
Monitoring the condition of machinery − Neural networks can be instrumental in cutting values by carrying additional expertise to scheduling the preventive preservation of machines.
A neural network can be trained to categorize between the sounds a machine creates when it is running generally (“false alarms”) versus when it is on the verge of a issue. After this training period, the proficiency of the network can be used to address a technician of an upcoming breakdown, before it appears and generates costly unforeseen “downtime.”
Engine management − Neural networks have been used to evaluate the input of sensors from an engine. The neural network controls the various parameters within which the engine functions, to achieve a particular goal, such as minimizing fuel consumption.
- What are Neural Networks?
- What are the advantages and disadvantages of Artificial Neural Networks?
- What is Feed-Forward Neural Networks?
- Why are Neural Networks needed in Machine Learning?
- What are ultra-dense networks and new services and applications in 5G networks?
- What are the Design Issues for the Layers of Computer Networks?
- What are wireless networks?
- What are the applications of DBMS?
- What are the applications of clustering?
- What are the Applications of Electrolysis?
- What are the applications of OLAP?
- What are the applications of autoencoders?
- What are the methods in Multilayer Artificial Neural Network?
- What is TensorFlow and how Keras work with TensorFlow to create Neural Networks?
- What are the RF (Radio Frequency) Networks?