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The disciplines of science and engineering rely heavily on the
forecasting of prospective constraints for concepts that have not
yet been proven to exist, especially in areas such as artificial
intelligence. Obtaining quality solutions to the problems presented
becomes increasingly difficult due to the number of steps required
to sift through the possible solutions, and the ability to solve
such problems relies on the recognition of patterns and the
categorization of data into specific sets. Predictive modeling and
optimization methods allow unknown events to be categorized based
on statistics and classifiers input by researchers. The Handbook of
Research on Predictive Modeling and Optimization Methods in Science
and Engineering is a critical reference source that provides
comprehensive information on the use of optimization techniques and
predictive models to solve real-life engineering and science
problems. Through discussions on techniques such as robust design
optimization, water level prediction, and the prediction of human
actions, this publication identifies solutions to developing
problems and new solutions for existing problems, making this
publication a valuable resource for engineers, researchers,
graduate students, and other professionals.
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