Sentence examples for methods on artificial from inspiring English sources

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.

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.

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.

Novel methods based on Artificial Neural Networks have been widely investigated.

To overcome this situation, modern methods based on artificial intelligence techniques have been developed for sizing PV systems.

In this paper, different prediction methods based on Artificial Neural Networks (ANNs) have been investigated and compared.

The second group includes the methods based on artificial intelligence techniques such as: Artificial Neural Networks (ANN) [19 21], fuzzy logic [22, 23], Support Vector Machine (SVM) [24 26], and decision-tree [27].