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Stress emotion recognition with discrepancy reduction using transfer learning

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Abstract

Stress management is critical for forecasting stress levels and identifying the impact of stress on a person’s socioeconomic life. For effective stress prediction, the Deep Belief Network and Transfer Learning (DBNTL) approach extracts information on the top layers but degrades performance on various levels. As a result, this study offers a unique stress emotion identification technique with discrepancy reduction that enables stress emotion recognition-based classifiers on small-scale emotion and stress data domains. The marginal distribution difference at comparable layers and the combined distribution discrepancy of diverse levels aid stress emotion identification in learning better quality features at the top layers; even distinct emotion and stress domains have feature-level similarities. Because all of the stress emotion detection layers are similarly trained, it evaluates both marginal and joint distribution discrepancies across several layers. Furthermore, a careful balance of these two disparities can improve transferability between emotion and stress domains. Finally, when compared to convolutional neural networks and DBNTL-based stress emotion classification techniques, the experimental results show that the stress emotion identification approach is more efficient.

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  1. Department of Computer Science and Engineering, GITAM School of Technology, GITAM University Bengaluru, Bengaluru, India

    Prasannavenkatesan Theerthagiri

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  1. Prasannavenkatesan Theerthagiri
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Correspondence to Prasannavenkatesan Theerthagiri.

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Theerthagiri, P. Stress emotion recognition with discrepancy reduction using transfer learning. Multimed Tools Appl 82, 5949–5963 (2023). https://doi.org/10.1007/s11042-022-13593-6

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