Multimedia Network Data Fusion System Integrating SSA and Reinforcement Learning

To improve the performance and efficiency of multimedia network data fusion system, this study proposes an improved sparrow search algorithm on the ground of reinforcement learning algorithm and sparrow search algorithm, and improves the multimedia network data fusion model on the ground of this algorithm. A performance comparison experiment was conducted on the improved sparrow search algorithm, and it was found that the algorithm entered a convergence state after 380 iterations in a unimodal function. Its time consumption is lower than other comparison algorithms, and it has not fallen into the local optimal situation after 500 iterations in the multimodal benchmark function. Its performance is significantly superior to other comparison algorithms. Moreover, the study conducted relevant experiments on the multimedia network data fusion model and found that the F1 value output by the model was 0.37, with an accuracy of 92.4%, which is higher than other data fusion models. And the mean square error of this model reaches 0.52, and the processing time is 0.1 seconds, which is lower than other comparative data fusion models. The quality of output data and data processing efficiency of this model are better. The relevant outcomes demonstrate that the improved sparrow search algorithm possesses good global search and convergence performance. And the improved multimedia network data fusion model has better accuracy and efficiency, and has good practical application value. This study can provide reference and reference for multimedia network data fusion systems.

@article{Li2024,
  title     = {Multimedia Network Data Fusion System Integrating SSA and Reinforcement Learning},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {9},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Fangrui Li},
  doi       = {10.14569/IJACSA.2024.0150966},
  url       = {https://doi.org/10.14569/IJACSA.2024.0150966}
}