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- W4384038158 abstract "Emotion is significant in deciding a human's ideas, behavior, and feelings. Using the advantages of deep learning, an emotion detection system can be constructed and various applications such as face unlocking, feedback analysis, and so on are executed with high accuracy. Artificial intelligence's fast development has made a significant contribution to the technological world. However, it has several difficulties in achieving optimal recognition. Interpersonal differences, the intricacy of facial emotions, posture, and lighting, among other factors, provide significant obstacles. To resolve these issues, a novel Hybrid Deep Convolutional based Golden Eagle Network (HDC-GEN) model algorithm is proposed for the effective recognition of human emotions. The main goal of this research is to create hybrid optimal strategies that classify five diverse human facial reactions. The feature extraction of this research is carried out using Heap Coupled Bat Optimization (HBO) method. The execution of this research is performed by MATLAB software. The simulation outcomes are compared with the conventional methods in terms of accuracy, recall, precision, and F-measure and the comparison shows the effective performance of proposed approaches in facial emotion recognition." @default.
- W4384038158 created "2023-07-13" @default.
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- W4384038158 date "2023-07-11" @default.
- W4384038158 modified "2023-09-25" @default.
- W4384038158 title "Novel Hybrid Optimal Deep Network and Optimization Approach for Human Face Emotion Recognition" @default.
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- W4384038158 doi "https://doi.org/10.1002/9781119896715.ch5" @default.
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