# Metaheuristics Optimization with Deep Learning Enabled Automated Image Captioning System
## 4 Performance Validation
### Result Analysis  
Table 2 and Figure 3 inspect a detailed result analysis of the MODLE-AICT model on the test Flickr8K dataset [23; 24; 25; 26; 27; 28]. The results implied that the MODLE-AICT model has gained effectual outcomes over other models. For instance, based on BLEU-1, the MODLE-AICT model obtained a higher BLEU-1 of 69.06, whereas the M-RNN, G-NICG, \begin{table}
\begin{tabular}{l l} \hline \hline
**Sample Image** & **Different Captions** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Sample Images and its captions. L-Bilinear, DVS, ResNet50, VGA-16, and HPTDL models attained a lower BLEU-1 of 59.18, 64.13, 65.96, 58.35, 62.65, 67.69, and 68.26, respectively. At the same time, based on BLEU-4, the MODLE-AICT technique has attained a higher BLEU-4 of 27.80, whereas the M-RNN, G-NICG, L-Bilinear, DVS, ResNet50, VGA-16, and HPTDL methods have acquired a lower BLEU-4 of 14.19, 16.12, 18.49, 17.08, 26.16, 23.25, and 26.71, correspondingly. Table 3 and Figure 4 examine a detailed classification analysis of the MODLE-AICT system on the test Flickr8K dataset. The results implied that the MODLE-AICT technique has attained effectual outcomes over other models. For example, based on METEOR, the MODLE-AICT approach has gained higher METEOR of 30, whereas the SCST-IN, SCST-ALL, G-NIC, A-NIC, DenseNet, and HPTDL methodologies have obtained a lower METEOR of 20, 23, 19, 21, 25, and 28, correspondingly. Meanwhile, based on Rouge-L, the MODLE-AICT approach has received a higher Rouge-L of 53, whereas the SCST-IN, SCST-ALL, G-NIC, A-NIC, DenseNet, and HPTDL algorithms have attained a lower Rouge-L of 49, 42, 43, 48, 43, and 46, correspondingly. A comparison study of the MODLE-AICT model with recent models on the Flickr8K dataset is shown in Figure 5. The figure implied that the SCST-IN and SCST-ALL models have obtained lower performance than other models. This was followed by the G-NIC, A-NIC, and DenseNet models, which attained moderately closer results. Along with that, the HPTDL model accomplished a reasonable performance. However, the MODLE-AICT model has shown enhanced performance over other models on the test Flickr8K dataset. The training accuracy (TA) and validation accuracy (VA) attained by the MODLE-AICT approach on the Flickr8K dataset is demonstrated in Figure 6. The experimental outcome implied that the MODLE-AICT technique has gained maximum values of TA and VA. Specifically, the VA seemed to be higher than TA. The training loss (TL) and validation loss (VL) achieved by the MODLE-AICT methodology on the Flickr8K dataset are established in Figure 7. The experimental outcome inferred that the MODLE-AICT system accomplished the lowest values of TL and VL. Specifically, the VL seemed to be lower than TL. Table 4 and Figure 8 depict the detailed results of the analysis of the MODLE-AICT system on the test MS-COCO 2014 dataset. The results implied that the MODLE-AICT approach obtained effectual outcomes over other models. For instance, based on BLEU-1, \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Methods** & **BLEU-1** & **BLEU-2** & **BLEU-3** & **BLEU-4** \\ \hline M-RNN Model [24] & 59.18 & 29.09 & 24.17 & 14.19 \\ G-NICG Model [26] & 64.13 & 42.64 & 27.11 & 16.12 \\ L-Bilinear Model [27] & 65.96 & 43.29 & 28.63 & 18.49 \\ DVS Model [28] & 58.35 & 37.98 & 25.54 & 17.08 \\ ResNet50 Model [23] & 62.65 & 46.28 & 37.26 & 26.16 \\ VGA-16 Model [23] & 67.69 & 44.34 & 33.99 & 23.25 \\ HPTDL Model [25] & 68.26 & 46.16 & 37.81 & 26.71 \\ MODLE-AICT & 69.06 & 47.26 & 38.78 & 27.80 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Result analysis of MODLE-AICT algorithm with approaches on Flickr8K dataset.