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Zenodo
2026
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| Accesso online: | https://doi.org/10.5281/zenodo.19249868 |
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| _version_ | 1866901111746592768 |
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| author | Pratiksha Deshmukh Harshali Patil |
| author_facet | Pratiksha Deshmukh Harshali Patil |
| contents | <p>This section presents the proposed emotion recognition method by analysing facial expression using the deep learning CNN algorithm. The main novelty of the presented method is that it classifies the seven emotions. Furthermore, in this research, we enhanced the recognition accuracy by hyper-tuning the learning parameters of the CNN algorithm. Figure 3 shows the block<br>diagram of the proposed emotion recognition method.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_19249868 |
| institution | Zenodo |
| language | |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Emotion Recognition Method based on Convolutional Neural Network and Black Widow Optimisation Algorithm - Figure 3 Pratiksha Deshmukh Harshali Patil <p>This section presents the proposed emotion recognition method by analysing facial expression using the deep learning CNN algorithm. The main novelty of the presented method is that it classifies the seven emotions. Furthermore, in this research, we enhanced the recognition accuracy by hyper-tuning the learning parameters of the CNN algorithm. Figure 3 shows the block<br>diagram of the proposed emotion recognition method.</p> |
| title | Emotion Recognition Method based on Convolutional Neural Network and Black Widow Optimisation Algorithm - Figure 3 |
| url | https://doi.org/10.5281/zenodo.19249868 |