| Title |
Optimization Research of University Graduate Talent Resource Management Model Integrating AVI Factor and GA Algorithm Under the View of Digital Intelligence |
| DOI |
https://doi.org/10.5573/IEIESPC.2025.14.3.407 |
| Keywords |
Digital intelligence; Adaptive variation improvement factor; Genetic algorithm; College graduate talent resource management; Model optimization |
| Abstract |
The design of B&B space presents challenges in extracting and matching Mazu pattern elements. This study introduces an efficient approach based on the fast robust feature algorithm to address this issue. Initially, an image-aware hash model is created, incorporating the fast robust feature algorithm. Subsequently, an enhanced Siamese network model is established, integrating the fast robust feature algorithm. Results indicate that the improved fast robust feature algorithm exhibits superior robustness compared to the traditional approach, achieving a matching ratio of 0.4 to 0.6. The proposed algorithm attains 93.53% and 93.91% image retrieval accuracy on self-built and Mnist datasets, surpassing other comparison algorithms. Through grayscale histogram and perceptual hash algorithm integration, the method enhances recognition accuracy during image deformation, especially under rotation and scale changes. Although encoding times are longer at 8.12 and 5.25 seconds, respectively, the proficient handling of rotation and scale invariance remains unaffected. This study offers an effective solution for precise feature extraction in intricate patterns within B&B space design, particularly in managing image rotation and scale alterations, presenting robust technical support for image processing and pattern recognition. |