## 6 Conclusions  
This study addressed the challenge of multi-class anomaly identification using low-quality surveillance videos within 5G and IoT environments. By conducting experiments on the trimmed UCF-crime dataset, the videos were downscaled to 1/8 resolution and then  
Figure 8: Comparison of bicubic interpolation and super-resolution approaches.  
upscaled using bicubic interpolation and super-resolution techniques. The TempoFuseNet framework employed a two-stream architecture that was followed by GRU for long-term temporal modeling. The experimental results showcased remarkable performance, with a classification accuracy of 92.28%, F1-score of 69.29%, and false positive rate of 4.41%. Moreover, the integration of super resolution in the anomaly classifier yielded substantial enhancements over the videos upscaled using bicubic interpolation. Specifically, the super-resolution-based approach achieved a 3.7% improvement in accuracy, a significant 14.34% boost in the F1-score, and a commendable 2.11% reduction in the false positive rate. Hence, TempoFuseNet outperforms existing state-of-the-art methods in multiclass classification performance and effectively addresses the technical limitations caused by low-quality videos, making it a robust solution for real-world surveillance scenarios, particularly in 5G and IoT environments.  
Future WorkThis study makes significant progress in improving video quality and anomaly detection in surveillance scenarios. However, future research could focus on integrating real-time processing capabilities and on investigating methods for automatically fine-tuning the model in response to changes to the lighting, spatial characteristics, or other dynamic factors. Moreover, integrating multi-modal data sources, such as contextual information, could improve anomaly detection accuracy and broaden the system's applicability in a variety of surveillance scenarios.  
Conceptualization, G.S. and U.I.B.; methodology, G.S.; software, G.S.; validation, G.S., U.I.B., R.H.R. and F.Z.; formal analysis, G.S. and U.I.B.; investigation, G.S., U.I.B., R.H.R. and F.Z.; resources, G.S.; data curation, G.S., U.I.B. and R.H.R.; writing--original draft preparation, G.S., U.I.B., R.H.R. and F.Z.; writing--review and editing, G.S., U.I.B., R.H.R. and F.Z.; visualization, G.S.; supervision, U.I.B. and R.H.R.; project administration, G.S., U.I.B. and R.H.R.; funding acquisition, F.Z. All authors have read and agreed to the published version of the manuscript. This research was supported in part by the National Natural Science Foundation of China with award number 62372409, and by the Ministry of Science and Technology with award number DL2023147001L. The dataset used in this study for conducting experiments for Anomaly Recognition is publicly available at: UCF-CRIME. Retrieved September 2023, [[https://www.crcv.ucf.edu/projects/real-world/](https://www.crcv.ucf.edu/projects/real-world/)]([https://www.crcv.ucf.edu/projects/real-world/](https://www.crcv.ucf.edu/projects/real-world/)) (accessed on 8 January 2024). This study acknowledges partial support from the National Center of Big Data and Cloud Computing (NCBC) and HEC of Pakistan. The authors declare that they have no known competing financial interests or personal relationships that could appear to have influence the work reported in this paper.