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After model training, the AI models are verified by the second NO3-N concentration dataset.

Therefore, for each time interval of model training, the dataset will be different.

During model training, the output of KF is delivered to FOS to build the nonlinear model.

In the general case, prior to model training, the dataset is divided into a training set, comprising e.g. 70% of the data, and a test set, which comprises the remaining data.

However, if the coloring of a compound changes drastically after inclusion in the model training, the descriptive model might not be sensible or structural aspects of a certain scaffold were not included.

We defined four feature sets, one consists of sequence motifs combined with k-mers (284 features) of which 100 features with highest information gain were used during model training, the second is a combination of IT features with k-mers (175 features), the third comprises of IT features (91 features), and the last considered only k-mers (84 features).

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The Scalextric Arc One car racing set has also been very popular, and of its model trains the Pendolino, Flying Scotsman and Eurostar locomotives have done well.

Variants included in the model training sets for the three syndromes studied.

The correlation coefficients and coefficients of variation of the RMSE corresponding to the model trained on the linear ramps and the model trained on all ramps differ by less than 1%and9%9%, respectively.

The predictions of the model trained on the linear ramps alone (dashed line) show nearly no difference with the output of the model trained on the entire set of ramps (plain line).

The output of the model trained on the entire set of ramps (plain line, parameter set listed in Table 1 ) shows nearly no improvement compared to the model trained on the subset of linear ramps.

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