Algorithm 2 Deep model algorithm.

Input: Parsed input data for training.

Output: Prediction results.

1:  $nRepeatModel\leftarrow 10$ {e.g., Repeat each model 10 times}

2:  $nDataTypes\leftarrow 1$ {If using multiple datasets}

3:  $nDataCount\leftarrow 1$

4:  $batchArray\leftarrow [100,200,500]$ {Different batch sizes}

5:  $epochsArray\leftarrow [100,200]$ {Set number of epochs}

6:  while $nDataCount\le nDataTypes$ do

7:  $\begin{array}{c}\hfill trainX,trainY,testX,testY,predX,predY\leftarrow loadInputData\left(\right)\end{array}$

8:  $count\leftarrow 1$

9:  for all$nEpoch$ in $epochsArray$ do

10:    for all$nBatch$ in $batchArray$ do

11:      while $count\le nRepeatModel$ do

12:      $defineDeepModel\left(\right)$

13:      $compileDeepModel\left(\right)$

14:      $fitDeepModel\left(\right)$

15:      $evaluateDeepModel\left(\right)$

16:      $saveDeepModel\left(\right)$

17:      $makePredictions\left(\right)$

18:      $savePredictedValues\left(\right)$

19:      $saveAccuracyLoss\left(\right)$

20:      $count\leftarrow count+1$

21:      end while

22:      $count\leftarrow 1$ {Reset repeat counter}

23:    end for

24:  end for

25:  $nDataCount\leftarrow nDataCount+1$

26:  end while