What Are the Uses of Neural Networks for Pattern Recognition?
There are quite a few different uses of neural networks for pattern recognition involving in a fairly wide range of industries. Much of this type of work is done in recognition in communications, such as voice recognition and handwriting recognition, though it can be used to recognize faces as well. Recognizing patterns can also be important for medical and business fields, in which such software can be used to diagnose illnesses and analyze financial statistics. There are also applications for the use of neural networks for pattern recognition in the fields of electronic entertainment and law enforcement.
The use of neural networks for pattern recognition has become quite popular over the past few decades, as computer technology has advanced sufficiently to accommodate such networks. Artificial or synthetic neural networks are a type of artificial intelligence program designed to mimic or replicate the way in which biological neurons in the human central nervous systems receive and process information. One of the best applications that many researchers have found for this technology is the use of neural networks for pattern recognition.
Some of the most common uses of neural networks for pattern recognition are in voice and handwriting recognition. A number of different software programs use voice recognition, such as talk-to-text programs and software used for automated telephone systems. There are also handwriting programs that can analyze written content and then turn that handwriting into typed text. Such programs can also be used to analyze and recognize vocal patterns, handwriting, and faces from images and sound files loaded into a computer, which can be used to identify people based on such samples.
There are also noteworthy applications in medicine and business. Neural networks can be used to “learn” common symptoms of various illnesses and then recognize those symptoms as patterns for diagnosis of patients. Similarly, patterns in business and financial trends can be analyzed to make predictions on stock performances or future earnings potential. Some financial uses of neural networks for pattern recognition can also include analysis of information on applications for loans or credit extensions.
Electronic entertainment, such as game software, can also utilize neural networks for pattern recognition. This is often done to create more realistic or intelligent opponents in video games, and is especially useful for games such as chess where pattern recognition can often indicate the best move for a computer opponent to make. Pattern recognition can also be important for use in law enforcement, such as the development of electronic “noses” that can “smell” explosives. These devices “learn” to identify trace particles associated with different types of explosives, and can then recognize their presence in a package or luggage.
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