## 4 Proposed Model Design
### Performance Evaluation Using Replicated Dataset  
We used replicated sequences in this part to analyze the proposed model performance. It's worth noting that the proposed model feature formulation module (Section 4.2) converts peptide sequences into a feature vector using a sequential technique. A considerable data situation results from continually repeating the constructed sequences (i.e., feature vector) to create a higher-dimensional feature vector. Accuracy-based measures cannot assess how well the suggested model performs since the dataset contains redundant and identical sequences. Therefore, we merely employ computational-based metrics to analyze the performance of the proposed model in this evaluation.  
Several experiments were performed to evaluate the performance of the proposed model in terms of computation times on different numbers of sequences, and the results are displayed in Figure 4. Figure 4 shows that the proposed model performed better than the sequential implementation of a deep neural network in computation time using four physical processing nodes. The execution times of the sequential deep neural networks are highly increased with the increasing number of sequences. In contrast, the execution times of the parallel proposed model are slightly increased, as shown in the figure. The proposed model achieved better performance in computation efficiency due to parallel processing.  
The proposed model scalability analysis is shown in Figure 5, which includes a variety of processing nodes and sequences. The proposed model execution time is significantly minimized as the number of processing nodes increases, as indicated in the figure. For example, when the proposed model is run on a single processing node, it takes 1380 s (i.e., execution time) to classify 12.32 million sequences, while when it is run on four processing nodes, it takes 410 s to classify the equal number of sequences. According to these findings, the execution time of the proposed model is lowered by 70.29% on a large number compared to a single machine. The proposed model is then tested for speed utilizing a range of processing nodes and sequences.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **SN (\%)** & **SP (\%)** & **MCC** & **ACC (\%)** \\ \hline AIPpred & 75.80 & 71.10 & 0.460 & 73.00 \\ PreAIP & 63.20 & 90.30 & 0.566 & 79.50 \\ Proposed Model & 89.01 & 82.35 & 0.711 & 83.40 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Comparison of proposed model performance to existing models.  
Finally, Figure 6 depicts the proposed model speedup using a replicated benchmark dataset. We assess the speed of the suggested model under various conditions. In the first scenario, the presented model achieved the maximum speed of 2.71 when processing 3.43 million sequences on four processing nodes. The proposed model achieved a speedup of 3.37 times while processing 12.32 million sequences on four processing nodes using the replicated benchmark dataset. These findings show that the suggested strategy is exceptionally scalable in terms of the number of processing nodes and the dataset size.  
Figure 4: Proposed model efficiency analysis in terms of execution times utilizing a replicate dataset.  
Figure 5: Proposed model Scalability analysis.  
Figure 6: Speedup analysis of the proposed model.  
Moreover, these results demonstrate that the proposed model outperforms others while processing many sequences (most significant speedup). This evaluation shows that the suggested strategy can handle many sequences. On the other hand, given a specific number of processing nodes, the suggested model never achieved the speedup predicted by [PERSON]'s law [71]. Several factors bring this on, including task launch overhead, network bandwidth, and cluster communication overhead. There has been a lot of discussion about this phenomenon [72].