## 4 Experiment and Analysis
### Comparison and Analysis of the Three Kinds of Algorithms  
The proposed algorithm runs 10 times for each test set. The recognition rates of the algorithms are shown in Table 2 and Figure 10. WCNN and MLP-CNN have no test results for the HCL2000 dataset. Thus, their data are not reflected in the figure. Table 3 is the time overhead of each algorithm on the test set. It is worth noting that because the algorithm WCNN does not give the result of the time overhead test, their data are not reflected in Table 3.  
Figure 9: Boxplot of the recognition rate obtained by MCNN-DS under different SGD learning rates. (a) MNIST dataset; (b) EnglishHand dataset; (c) HCL2000 dataset. For the MNIST test set, the averages of WCNN, MLP-CNN, SVM-ELM, and the 10 operations of the proposed algorithm under the lowest recognition rate are 95.11%, 97.82%, 89.5%, and 97.36%, respectively (Table 2). In this study, the performance of the proposed algorithm ranks first, MLP-CNN ranks second, and SVM-ELM exhibits the worst performance. Under the highest and average recognition rates, the performance of the proposed algorithm ranks first, that of WCNN ranks second, and that of MLP-CNN shows the worst performance. According to the WCNN and MLP-CNN models and the performance of the proposed algorithm in feature recognition, our model is better than SVM-ELM in feature extraction and fusion even when it is the worst. Table 2 shows that the recognition rate of the proposed algorithm is the best, reaching a maximum of 99.97%. Furthermore, the average recognition rate of the MCNN-DS algorithm in the three databases is 98.4, 90.98, and 89.77, with a standard deviation of 0.0084, 0.0396, and 0.0280, respectively. The standard deviation is small and average recognition accuracy is high, which indicates that the MCNN-DS algorithm can stably extract abundant feature information. The algorithm also has the same advantage in terms of time to unlock precisely because of the implementation of the algorithm parallelization. Furthermore, the time-cost of this algorithm is merely 21.95% of the MLP-CNN algorithm, and 10.02% of the SVM-ELM algorithm. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **Metric** & **WCNN [11]** & **MLP-CNN [6]** & **SVM-ELM [27]** & **MCNN-DS** \\ \hline \multirow{4}{*}{MNIST} & The lowest recognition rate (\%) & 95.11 & 97.82 & 89.5 & 97.36 \\  & The highest recognition rate (\%) & 95.71 & 98.96 & 91.35 & 99.97 \\  & The average recognition rate (\%) & 95.36 & 96.32 & 90.26 & 98.43 \\  & Standard deviation of recognition rate & – & – & – & 0.0084 \\ \hline \multirow{4}{*}{HCL2000} & The lowest recognition rate (\%) & – & – & 83.60 & 85.42 \\  & The highest recognition rate (\%) & – & – & 90.00 & 93.99 \\  & The average recognition rate (\%) & – & – & 88.63 & 90.98 \\  & Standard deviation of recognition rate & – & – & – & 0.0396 \\ \hline \multirow{4}{*}{EnglishHand} & The lowest recognition rate (\%) & – & – & – & 84.93 \\  & The highest recognition rate (\%) & – & – & – & 95.29 \\ \cline{1-1}  & The average recognition rate (\%) & – & – & – & 89.77 \\ \cline{1-1}  & Standard deviation of recognition rate & – & – & – & 0.0280 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Algorithm recognition performance comparison. Figure 10: Boxplot of the recognition rate obtained by MCNN-DS. Compared with the SVM-ELM algorithms, this algorithm has an advantage over the HCL2000 test set. The lowest recognition rate of the proposed model is 1.82% higher than that of SVM-ELM. The highest recognition rate of the algorithm is 3.99% higher than that of SVM-ELM. The average recognition rate of the algorithm is 2.35% higher than that of SVM-ELM. The time-cost of this algorithm is merely 15.41% of the SVM-ELM.