User:RoeJogan712/Neural network

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Neural networks can be used in different fields. The tasks to which artificial neural networks are applied tend to fall within the following broad categories:
 * Function approximation, or regression analysis, including time series prediction and modeling.
 * Classification, including pattern and sequence recognition, novelty detection and sequential decision making.
 * Data processing, including filtering, clustering, blind signal separation and compression.

Application areas of ANNs include nonlinear system identification and control (vehicle control, process control), game-playing and decision making (backgammon, chess, racing), pattern recognition (radar systems, face identification, object recognition), sequence recognition (gesture, speech, handwritten text recognition), medical diagnosis, financial applications, data mining (or knowledge discovery in databases, "KDD"), visualization and e-mail spam filtering. For example, it is possible to create a semantic profile of user's interests emerging from pictures trained for object recognition.

One such field that ANN's could prove useful that is technical communication. Formulating a concrete and consistent way to analyze effective communication of complex technical language requires lots of personal bias in addition to many other In a study regarding grading technical communication in the class room, the authors lay out criteria for solely grading a student’s ability to communicate technical information effectively. They lay out a clear set of philosophies that ultimate lead to a single number called the communication coefficient. The coefficient is meant to represent how effectively one achieved proper technical communication. These philosophies could be treated as an ANN’s  parameters which would be far more consistent and concrete within a neural network as it would not be subject to human bias. Additionally, the single number output makes this process very function-like which fits the model that an ANN follows. Under the same field, a study was done on visualizing privacy on web based services. Instead of listing agreements specifically, websites were given a privacy category rating. The study showed that “privacy rating” was a more effective way to demonstrate the security of a website rather than lengthy privacy agreements. However, if there is no standard for such a rating users are likely not to trust a rating given to them. An ANN could be commissioned to scan a company’s existing privacy agreements and give scores as a percentage output. Such a process would follow the same function-based structure and could allow for more precise technical communication and effective privacy.