## 6 Conclusions  
Our proposed scene recognition model integrates a CNN with _MI_-based feature selection, representing a significant advancement in scene recognition. By leveraging the power of multimodal deep learning and incorporating novel feature selection techniques, our method achieves impressive accuracies of 100% and 98.83% on the scene classification dataset and AID dataset, respectively. This surpasses the performance of several existing methods and demonstrates our model's robustness and precision in classifying scenes accurately. Moreover, our approach offers the advantage of reducing complexity and resource requirements compared to some high-performing alternatives. By combining the strengths of CNN- and _MI_-based feature selection, our method not only enhances scene recognition accuracy but also provides a foundation for further advancements in scene understanding and interpretation. This work contributes to the growing body of research aimed at enabling machines to comprehend and navigate complex visual environments, with potential applications in autonomous vehicles, surveillance systems, augmented reality, and more. Conceptualization, S.A.C. and W.A. ; methodology, M.H. and A.A.A.E.-L.; software, M.H. ; validation, M.H., S.A.C., and A.A.A.E.-L.; formal analysis, W.A. and A.A.A.E.-L.; investigation, M.H. ; resources, S.A.C. and W.A. ; data curation, M.H. and A.A.A.E.-L.; writing--original draft preparation, M.H., S.A.C., and A.A.A.E.-L.; writing--review and editing, M.H. and A.A.A.E.-L.; visualization, M.H. and W.A. ; supervision, A.A.A.E.-L.; project administration, S.A.C. and W.A. ; funding acquisition, S.A.C. and W.A. All authors have read and agreed to the published version of the manuscript. The authors extend their appreciation to the Deputyship for Research & Innovation, Ministry of Education, in Saudi Arabia for funding this research work through the project number RI-44-0490. Not applicable. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Ref./Author** & **Methods** & **Dataset** & **Performance** \\ \hline [PERSON] et al. [10] & \begin{tabular}{c} Multi-scene \\ recognition network \\ \end{tabular} & AID & \begin{tabular}{c} Precision = 64.03\% \\ Recall = 52.30\% \\ F1 Score = 52.39\% \\ \end{tabular} \\ \hline [PERSON] et al. [11] & \begin{tabular}{c} Pre-trained deep \\ models + SVM \\ \end{tabular} & AID & Overall accuracy = 93.58\% \\ \hline [PERSON] et al. [12] & \
\begin{tabular}{c} CSDS model \\ \end{tabular} & AID & Overall accuracy = 94.29\% \\ \hline [PERSON] et al. [13] &
\begin{tabular}{c} Residual dense \\ network \\ \end{tabular} & AID & Accuracy = 99\% \\ \hline [PERSON] et al. [14] & Vision Transformers & AID & Best Accuracy = 95.51\% \\ \hline [PERSON] and [PERSON] [15] & Deep learning & AID & Mean Accuracy = 93.70\% \\ \hline [PERSON] et al. [16] & SDM + MAT & AID & Accuracy = 90.90\% \\ \hline [PERSON] and [PERSON] [17] & CNN & AID & Accuracy = 94.10\% \\ \hline Our & CNN + _MI_ & AID & Accuracy = 98.83\% \\  & & & Precision = 98.83\% \\  & & & Recall = 98.83\% \\  & & & F1 Score = 98.83\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of our model with other previous models. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** Data are available at [[https://www.kaggle.com/datasets/nitishabharathi/scene-classification](https://www.kaggle.com/datasets/nitishabharathi/scene-classification)]([https://www.kaggle.com/datasets/nitishabharathi/scene-classification](https://www.kaggle.com/datasets/nitishabharathi/scene-classification)) (accessed on 6 October 2023) and [[https://www.kaggle.com/datasets/jayuanchengala/aid-scene-classification-datasets](https://www.kaggle.com/datasets/jayuanchengala/aid-scene-classification-datasets)]([https://www.kaggle.com/datasets/jayuanchengala/aid-scene-classification-datasets](https://www.kaggle.com/datasets/jayuanchengala/aid-scene-classification-datasets)) (accessed on 6 October 2023).