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These artificial neural networks (ANNs) with different architectures are trained using the Levenberg Marquardt (LM) back-propagation algorithm and a cross validation is also performed to ensure that the results generalise to other unseen datasets.
The experimental responses and the predictions of the various models for the unseen datasets are shown in Table 5.
These statistical parameters are reported in Table 6 both for the training datasets and the unseen datasets.
Performances of the prediction models on unseen datasets were analyzed with fivefold cross-validation.
To train our final hierarchical classification model, which we employed to give predictions on further unseen datasets, we used the complete set of 3705 genes without removing duplicates.
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After the network size is further reduced to 40%, we found that the network trained with sign-based propagation tuning shows a better performance than that trained by ELM framework for the unseen dataset.
Table 5 shows an unseen dataset of twenty one jobs with their respective correlation values.
Table 3 shows an unseen dataset of sixteen jobs with their respective correlation values.
The unseen dataset consisted of the results of 8 new experiments that were not used in any model development.
The unseen dataset of twelve jobs with their respective correlation values (as per Equation (6)) are shown in Table 1.
A simulation of the scheduler with an unseen dataset of sixteen jobs on four identical machines (M1, M2, M3 and M4) is illustrated.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com