## 5 Conclusions  
In this study, a new MDTL-ICDDC method was established for effectual identification and classification of crowd density on video surveillance systems. The MDTL-ICDDC model initially presented an SSA with NASNetLarge model as a feature extraction model in which the hyperparameter tuning process is performed by the SSA. This was followed by the WELM model, which is employed for crowd density and classification processes. At last,  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{4}{c}{**F-Score (\%)**} \\ \hline
**Methods** & **Class-0** & **Class-1** & **Class-2** & **Class-3** & **Average** \\ \hline Gabor & 54.70 & 47.60 & 57.60 & 88.00 & 61.98 \\ \hline BoW-SRP & 69.10 & 49.60 & 62.80 & 90.00 & 67.88 \\ \hline Bow-LBP & 74.90 & 58.60 & 69.50 & 94.40 & 74.35 \\ \hline GLCM-SVM & 89.44 & 85.78 & 91.64 & 85.09 & 87.99 \\ \hline GoogleNet & 83.54 & 78.67 & 75.90 & 85.89 & 81.00 \\ \hline VGGNet & 82.37 & 87.98 & 90.28 & 79.17 & 84.95 \\ \hline MDTL-ICDDC & 91.77 & 89.74 & 93.31 & 96.66 & 92.87 \\ \hline \hline \end{tabular}
\end{table}
Table 6: F-score analysis of MDTL-ICDDC technique with existing algorithms under various classes.  
Figure 14: F-score analysis of MDTL-ICDDC technique under various classes.  
the KSA is applied for the effectual parameter optimization process and thereby improves the classification results. The experimental validation of the MDTL-ICDDC system was carried out with a benchmark dataset and the outcomes are examined under several aspects. The experimental values indicated that the MDTL-ICDDC approach has accomplished enhanced performance over other models. The proposed model can be extended to crowd density estimation in public places such as railway stations, airports, sports stadiums, shopping malls, etc. In the future, the crowd density classification performance can be further boosted by the design of a hybrid metaheuristics algorithm.  
Conceptualization, F.A.; Data curation, F.A. and S.S.A.; Formal analysis, S.S.A.; Investigation, F.N.A.-W. and N.N.; Methodology, N.N. and R.A.; Project administration, R.A.; Software, R.M.; Supervision, R.M.; Validation, A.S.M. and M.A.D.; Visualization, A.S.M. and M.A.D.; Writing--original draft, F.A.; Writing--review & editing, F.N.A.-W. All authors have read and agreed to the published version of the manuscript.  
The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work through Large Groups Project under grant number (42/43). Princess Nourah Bint Abdulrahman University Researchers Supporting Project number (PNURSP2022R77), Princess Nourah Bint Abdulrahman University, Riyadh, Saudi Arabia. The authors would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code: (22 UQU4210118 DSR22). The authors would like to thank the Deanship of Scientific Research at Majmaah University for supporting this work under Project No. R-2022-xxx.  
This article does not contain any studies with human participants performed by any of the authors.  
Not Applicable.  
Data Availability StatementData sharing was not applicable to this article as no datasets were generated during the current study.  
The authors declare that they have no conflict of interest. The manuscript contains contributions from all authors. All authors have approved the final version of the manuscript.