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2025
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| Online-Zugang: | https://doi.org/10.5281/zenodo.14845111 |
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| _version_ | 1866902072245354496 |
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| author | Katikapalli Lokesh |
| author_facet | Katikapalli Lokesh |
| contents | <h1>Ensemble-weighted Deep CNN for Visual Sentiment Analysis - v1.0.0</h1> <h2>New Features</h2> <ul> <li>Implemented Deep Convolutional Neural Network (DCNN) for automatic feature extraction</li> <li>Added ensemble learning with majority, average, and weighted voting for improved accuracy</li> <li>Achieved 70% accuracy on the AffectNet dataset</li> </ul> <h2>Enhancements</h2> <ul> <li>Optimized training pipeline with config-based model selection</li> <li>Improved training scripts for easy model training and evaluation</li> <li>Added support for ensemble-based sentiment analysis</li> </ul> <h2>Installation & Usage</h2> <ol> <li>Clone the repository:<pre><code>git clone https://github.com/katikapalli/ensemble-vsa.git cd ensemble-vsa </code></pre> </li> </ol> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_14845111 |
| institution | Zenodo |
| language | |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | katikapalli/ensemble-vsa: Initial Version Katikapalli Lokesh <h1>Ensemble-weighted Deep CNN for Visual Sentiment Analysis - v1.0.0</h1> <h2>New Features</h2> <ul> <li>Implemented Deep Convolutional Neural Network (DCNN) for automatic feature extraction</li> <li>Added ensemble learning with majority, average, and weighted voting for improved accuracy</li> <li>Achieved 70% accuracy on the AffectNet dataset</li> </ul> <h2>Enhancements</h2> <ul> <li>Optimized training pipeline with config-based model selection</li> <li>Improved training scripts for easy model training and evaluation</li> <li>Added support for ensemble-based sentiment analysis</li> </ul> <h2>Installation & Usage</h2> <ol> <li>Clone the repository:<pre><code>git clone https://github.com/katikapalli/ensemble-vsa.git cd ensemble-vsa </code></pre> </li> </ol> |
| title | katikapalli/ensemble-vsa: Initial Version |
| url | https://doi.org/10.5281/zenodo.14845111 |