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We validate our method on artificial and real world scenarios.
We investigate the behavior of the method on artificial data and provide experimental results for two real problems, handwritten character recognition and financial chart pattern recognition.
We also present efficient and naturally parallelizable discretizations of the aforementioned nonlinear PDEs and discuss properties and results of our new tracking method on artificial and real 4D data.
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In the previous analyses, we applied the methods on artificial genomes that were developed by Azad and Lawrence [42].
We show that ENIGMA outperforms other methods on artificial datasets, using a quality criterion that, unlike other criteria, can be used for algorithms that generate overlapping clusters and that can be modified to take redundancy between clusters into account.
Instead, we use the F-measure and introduce a derivative, the F'-measure (also used in the ENIGMA clustering optimization procedure described above), to compare the performance of different clustering methods on artificial datasets.
Broadly, these methods can be divided into four categories: (1) methods based on support vector machine (SVM), (2) methods based on Random Tree, (3) methods based on artificial neural network (ANN), and (4) other methods.
An implicit method based on artificial compressibility and dual-time stepping is used for time advancement.
In this case, a novel method based on artificial neural network is introduced.
The 3D optimization design method based on artificial neural network and genetic algorithm is adopted to construct the blade shape.
This paper presents an identification method based on artificial neural networks, which can be used for the robust fault detection.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com