# Sequence-Information Recognition Method Based on Integrated mDTW
## 5 Conclusions  
The purpose of the current study was to develop a sequence algorithm for temporal data modeling applications. To achieve this, we proposed a simplified hierarchical cluster model and designed a mDTW method to value the similarities between sequences. By taking HCCR as a specific research area, the proposed method has achieved a big improvement in recognizing accuracy (Top-3 and Top-10). This work contributes great confidence that reforming, improving, and appropriately using some simple methods could defeat deep learning in large-scale data-sequence processing tasks. We can outperform deep-learning approaches in the realm of intelligent computing for large-scale data-sequence processing  
\begin{table}
\begin{tabular}{l l l l} \hline \hline \(\mathbf{\alpha}\) & **Simple** & **Normal** & **Complex** \\ \hline \(\{0,1,0\}\) & 89.5\% & 92.2\% & 89.3\% \\ \(\{0.1,0.8,0.1\}\) & 93.1\% & 94.2\% & 95.7\% \\ \(\{0.45,0.45,0.1\}\) & 66.3\% & 42.2\% & 32.7\% \\ \(\{0.8,0.1,0.1\}\) & 50.3\% & 62.2\% & 48.6\% \\ \(\{0.1,0.45,0.45\}\) & 43.6\% & 38.1\% & 30.2\% \\ \(\{0.45,0.1,0.45\}\) & 56.7\% & 18.8\% & 21.9\% \\ \(\{0.1,0.1,0.8\}\) & 68.0\% & 18.2\% & 15.3\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results of the effect of different model hyperparameters \(\mathbf{\alpha}\) on different complexity of characters.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & **HCCR** & **UCI-HAR** & **WISDM** & **PAMAP2** & **UniMib** & **OPPOR** \\ \hline cGRU & 95.6\% & 84.1\% & 94.8\% & 92.9\% & 75.3\% & 88.1\% \\ LSTM & 95.1\% & 86.3\% & 94.5\% & 93.7\% & 75.8\% & 89.6\% \\ Transformer & 96.6\% & 86.2\% & 95.5\% & 93.2\% & 76.4\% & 90.4\% \\ mDTW\(\leftrightarrow\)GRU & 97.4\% & 87.6\% & 97.1\% & 94.1\% & 77.1\% & 92.1\% \\ mDTW\(+\)LSTM & 97.2\% & 88.1\% & 97.9\% & 94.1\% & 77.7\% & 92.7\% \\ mDTW\(+\)Transformer & 98.5\% & 88.7\% & 98.5\% & 94.2\% & 78.5\% & 93.5\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Ablation of mDTW Joint modeling methods.  
tasks. Moreover, we envision significant potential for the expansion of our method into other research domains focused on sequence analysis within the purview of intelligent computation. Additionally, the methodology presented in this article seamlessly lends itself to joint modeling with various deep neural network models, further enhancing its adaptability and effectiveness in intelligent computing applications. Deep models include main sequence-oriented methods, including RNN, Tf, and other models. Experimental results show that our method can improve the modeling performance of the model to a certain extent when combined with a deep model. It has achieved good performance in human action recognition and handwriting recognition tasks, and some results exceed SOTA.  
Through joint modeling, we can effectively combine the flexibility of mDTW with the powerful representation capabilities of deep-learning models. This method has significant advantages when dealing with complex time-series analysis tasks, especially when the shape and global structure of the time series need to be considered simultaneously. Future work will include validating the effectiveness of this joint modeling approach in a wider range of application scenarios and exploring further optimization strategies to improve model performance and efficiency.  
Conceptualization, B.S. and C.C.; methodology, B.S.; software, B.S.; validation, C.C.; formal analysis, B.S.; investigation, B.S.; resources, C.C.; data curation, B.S.; writing--original draft preparation, B.S.; writing--review and editing, C.C.; visualization, B.S.; supervision, C.C.; project administration, C.C.; funding acquisition, C.C. All authors have read and agreed to the published version of the manuscript.  
This paper is sponsored by the General Program of the National Natural Science Foundation of China (No. 62376279).  
Not applicable.  
The authors declare no conflict of interest.