# Metaheuristics Optimization with Deep Learning Enabled Automated Image Captioning System
## 4 Performance Validation
### Result Analysis
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Methods** & **METEOR** & **CIDEr** & **Rouge-L** \\ \hline SCST-IN Model [29] & 20.00 & 161.00 & 49.00 \\ SCST-ALL Model [29] & 23.00 & 154.00 & 42.00 \\ G-NIC Model [26] & 19.00 & 153.00 & 43.00 \\ A-NIC Model [26] & 21.00 & 160.00 & 48.00 \\ DenseNet Model [24] & 25.00 & 173.00 & 43.00 \\ HPTDL Model [25] & 28.00 & 175.00 & 46.00 \\ MODLE-AICT & 30.00 & 179.00 & 53.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Classification analysis of MODLE-AICT algorithm with approaches on Flickr8K dataset. the MODLE-AICT method gained a higher BLEU-1 of 75.12, whereas the M-RNN, G-NICG, L-Bilinear, DVS, ResNet50, VGA-16, and HPTDL methodologies received a lower BLEU-1 of 49.60, 67.92, 71.75, 63.86, 73.57, 70.30, and 74.28, correspondingly. Nonetheless, based on BLEU-4, the MODLE-AICT technique has gained a higher BLEU-4 of 34.75, whereas the M-RNN, G-NICG, L-Bilinear, DVS, ResNet50, VGA-16, and HPTDL methodologies acquired a lower BLEU-4 of 10.95, 24.94, 24.35, 23.29, 32.52, 30.06, and 33.96, correspondingly. Figure 5: CDF analysis of MODLE-AICT technique with existing approaches on Flickr8K dataset. Figure 6: TA and VA analysis of MODLE-AICT technique on Flickr8K dataset. Table 5 and Figure 9 review a detailed classification analysis of the MODLE-AICT system on the test MS-COCO 2014 dataset. The results implied that the MODLE-AICT methodology acquired effectual outcomes over other models. For instance, based on METEOR, the MODLE-AICT methods have obtained a higher METEOR score of 37, whereas the SCST-IN, SCST-ALL, G-NIC, A-NIC, DenseNet, and HPTDL approaches have attained a lower METEOR score of 22, 25, 21, 24, 24, and 34, correspondingly. Meanwhile, based on Rouge-L, the MODLE-AICT technique acquired a higher Rouge-L of 63, whereas the SCST-IN, SCST-ALL, G-NIC, A-NIC, DenseNet, and HPTDL algorithms acquired a lower Rouge-L of 51, 59, 51, 58, 57, and 60, correspondingly. A comparison study of the MODLE-AICT technique with recent models on the MS-COCO 2014 dataset is shown in Figure 10. The figure implied that the SCST-IN and SCST-ALL methodologies acquired a lower performance than the other models. Then, the G-NIC, A-NIC, and DenseNet approaches have gained moderately closer results. Moreover, the HPTDL approach has tried to accomplish reasonable performance. However, the MODLE-AICT system has shown an enhanced performance over other models on the test MS-COCO 2014 dataset. \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Methods** & **BLEU-1** & **BLEU-2** & **BLEU-3** & **BLEU-4** \\ \hline KNN Model [25] & 49.60 & 28.60 & 17.00 & 10.95 \\ G-NICG Model [26] & 67.92 & 46.23 & 34.03 & 24.94 \\ L-Bilinear Model [27] & 71.75 & 49.22 & 34.65 & 24.35 \\ DVS Model [28] & 63.86 & 44.98 & 32.58 & 23.29 \\ ResNet50 Model [23] & 73.57 & 57.21 & 42.05 & 32.52 \\ VGA16 Model & 70.30 & 53.72 & 40.45 & 30.06 \\ VGA-16 Model [23] & 74.28 & 59.39 & 43.33 & 33.96 \\ HPTDL Model [25] & 75.12 & 60.21 & 44.22 & 34.75 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Result analysis of MODLE-AICT algorithm with approaches on MS-COCO 2014 dataset. Figure 7: TL and VL analysis of MODLE-AICT technique on Flickr8K dataset. The TA and VA attained by the MODLE-AICT technique on the MS-COCO 2014 dataset are demonstrated in Figure 11. The experimental outcome implied that the MODLE-AICT method has gained maximum values of TA and VA. Specifically, the VA seemed to be higher than TA. The TL and VL achieved by the MODLE-AICT approach on the MS-COCO 2014 dataset are established in Figure 12. The experimental outcome inferred that the MODLE-AICT methodology has accomplished least values of TL and VL. Specifically, the VL seemed to be lower than TL. From the detailed results and discussion, it is assured that the proposed model has shown effective outcomes on the image captioning process. Figure 8: Result analysis of the MODLE-AICT algorithm on the MS-COCO 2014 dataset: (**a**) BLEU-1, (**b**) BLEU-2, (**c**) BLEU-3, and (**d**) BLEU-4.