The Eco-Mode AI-Based Mood Music Matching system is a modern approach that adds the emotion recognition and energy optimization to deliver a personalized and sustainable music experience. As per need for intelligent and context-aware technologies increases, this system addresses two key aspects: user emotional engagement and energy efficiency. Utilizing artificial intelligence methods such as facial expression recognition, speech emotion analysis, or sentiment interpretation from text, the system identifies the user's current emotional state in real-time. Based on the detected mood—such as happy, sad, calm, or angry—the system intelligently detect and plays a music playlist that resonates with the user’s emotional needs. To optimize minimal energy use, especially in mobile and smart home environments, the system simultaneously activates an eco-mode. This eco-mode feature automatically adjusts the proper device settings such as screen brightness, audio output levels, background application use, and connectivity settings, reducing overall electricity consumption without affecting the user experience. The integration of mood-based music recommendation with eco-conscious device management creates a perfect-balanced solution to enhance emotional well-being while promoting environmental sustainability. This project prove the potential of AI to not only personalize digital experiences but also contribute to energy conservation in everyday technology use.
Artificial Intelligence, Machine Learning, CNN, Deep learning, Python.
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