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Artificial neural networks (ANN) are flexible modeling method which shown excellent performance for modeling different problems.
Game theory has been corroborated as an effective tool for modeling different problems in traditional distributed wireless communication networks [28].
Although ANNs are accurate systems for modeling different problems, but they have a disadvantage about their mathematical structure (Atashrouz et al. 2014a, b).
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Our novel approach of populating the shadow model with actually observed shadow pixel values solves the difficult problem of modeling different types of shadows.
QSAR modeling workflow completely automates the laborious and iterative process of modeling to tackle different problems.
Many of the deep learning models investigated for materials degradation have incorporated traditional hard-coded algorithms to filter and transform input data this approach does not fully leverage the power of deep learning to learn its own representations, and limits the ability to deploy models to different problems.
It can be presumed that this is because there are many different types of models and different problems that require different approaches.
The chapter introduces the principles of selecting different models to address different problems involving the deformation and fluid flow in fractured rock masses based on these simple models.
We design a set of experiments where we validate the MIP model on different problem sizes.
Information fusion technology has been applied to fault diagnosis by many scholars, who put forward some diagnostic methods and corresponding diagnostic models according to different problems arising in application.
Performances of other NN models varied across different problems from as good as 1-NN to several times less accurate.
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