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<p>We propose that employing an ensemble of deep learning models can enhance the recognition and adaptive response to human emotions, outperforming the use of single model. Our study introduces a multimodal emotional intelligence system that blends CNNs for facial emotion detection, BERT for text mood analysis, RNNs for tracking emotions over time, and GANs for creating emotion-specific content. We built these models with TensorFlow, Keras, and PyTorch, and trained them on Kaggle datasets, including FER-2013 for facial expressions and labeled text data for sentiment tasks. Our experiments show strong results: CNNs reach about 80% accuracy in recognizing facial emotions, BERT achieves about 92% accuracy in text sentiment, RNNs reach around 89% for sequential emotion tracking, and GANs produce personalized, age-related content that is judged contextually appropriate in over 90% of test cases. These findings support the idea that a combined model architecture can yield more accurate and adaptable emotional responses than simpler approaches. The framework could be useful in areas such as healthcare, customer service, education, and digital well-being, helping to create AI systems that are more empathetic and user-focused.</p>

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Empowering emotional intelligence through deep learning techniques

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月3日

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  • B. V. Gokulnath,
  • Pampana Charmitha,
  • Pampana Chathurya,
  • D. Lavanya Satya Sri,
  • Baratam Vennela,
  • S. P. Siddique Ibrahim,
  • S. Selva Kumar

摘要

We propose that employing an ensemble of deep learning models can enhance the recognition and adaptive response to human emotions, outperforming the use of single model. Our study introduces a multimodal emotional intelligence system that blends CNNs for facial emotion detection, BERT for text mood analysis, RNNs for tracking emotions over time, and GANs for creating emotion-specific content. We built these models with TensorFlow, Keras, and PyTorch, and trained them on Kaggle datasets, including FER-2013 for facial expressions and labeled text data for sentiment tasks. Our experiments show strong results: CNNs reach about 80% accuracy in recognizing facial emotions, BERT achieves about 92% accuracy in text sentiment, RNNs reach around 89% for sequential emotion tracking, and GANs produce personalized, age-related content that is judged contextually appropriate in over 90% of test cases. These findings support the idea that a combined model architecture can yield more accurate and adaptable emotional responses than simpler approaches. The framework could be useful in areas such as healthcare, customer service, education, and digital well-being, helping to create AI systems that are more empathetic and user-focused.