For improving the insufficient feature extraction and recognition capability of the traditional VGG16 network in the human facial expression recognition task, the study proposes a VGG16 network recognition model based on an SGE (Spatial Group Enhance) attention and feature fusion improvement, which firstly extracts the last three Block extracted from the VGG16 network and then introduces the SGE attention module to enhance the attention mechanism by spatial grouping. features are fused, and then the SGE attention module is introduced to enhance the attention mechanism by spatial grouping. Finally, the recognition effect of the improved model is analyzed, and the results show that the improved VGG16 network model improves the human facial expression recognition accuracy by 6.9%, 7.8%, and 7.4% on Cohn-Kanade, Multi-Media Interface, and AffectNet datasets, respectively, and the recognition accuracy is about 90% or more for eight common human facial expressions. The recognition accuracy for eight common human facial expressions is about 90% or more. The above data show that the research model can recognize human facial expressions more accurately.
Wireless Sensor Network (WSN) plays a vital role in the field of information technology. Sensor nodes, as the core components of the network, are responsible for sensing and collecting data in the environment and transmitting them to other nodes or a central server through the network. To increase the coverage effect and connectivity of network nodes, the study combines the Coot Bird Swarm Optimization Algorithm (COOT) with the Multi-Objective Artificial Hummingbird Algorithm (MOAHA) were combined and improved using a multi-strategy approach. The results show that the average coverage of the improved White Bone Top Bird Flock Optimization Algorithm is 97.48%, and the difference between the improved Multi-Objective Artificial Hummingbird Algorithm on the functions F1 and F2, respectively, is 0.8916 and 0.0092. Therefore, the research of sensor nodes oriented to the performance of the network effectively improves the performance of optimal coverage of the sensor nodes of the WSNs and provides the multi-objective node deployment scheme problem.
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