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AI Emotion Insights for Billboard Advertising Success

Developed an AI-enhanced emotion recognition system using a pre-trained VGG16 model on the FER-2013 dataset, achieving 80% accuracy with advanced data augmentation and hyperparameter tuning. Designed interactive visualizations and a scalable, cloud-integrated real-time pipeline to drive data-driven insights for enhanced audience engagement.

  • Project Masters Dissertation
  • YEARJan 2025
  • CATEGORY AI System
  • STATUS Completed

RESEARCH

In this project, I delved into advanced deep learning techniques for emotion recognition while critically analyzing the FER-2013 dataset. I reviewed cutting-edge CNN architectures and effective preprocessing methods to address inherent dataset biases. This research laid the foundation for a 15% improvement in classification accuracy through targeted data augmentation and normalization.

DESIGN

I crafted a robust system architecture using a pre-trained VGG16 model enhanced with dropout layers and adaptive learning rates. The design process focused on optimizing hyperparameters to maximize model performance. Additionally, I developed interactive visualizations with Matplotlib and Seaborn to clearly communicate data distributions and performance trends.

DEVELOPMENT

The system was implemented using Python and TensorFlow, integrating comprehensive data augmentation and normalization techniques. I built a scalable, cloud-based pipeline that supports real-time emotion detection. Continuous cross-validation and performance monitoring ensured the system maintained an 80% accuracy rate.

Concept

The core concept was to decode human emotions accurately using state-of-the-art deep learning methodologies. I aimed to transform raw emotional data into actionable insights for enhanced audience engagement. By merging rigorous research, innovative design, and robust development, the project set a new standard for real-time emotion analytics.

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