Interpretable AI for Food Spoilage Prediction with IoT & Hardware Validation

This research explores a novel approach to predict food spoilage using a hybrid Deep Q-Learning framework, enhanced with synthetic data generation and hardware validation for real-world applicability. The focus on interpretability and hardware validation are notable strengths, potentially addressing key challenges in practical IoT deployments.

• •Focuses on interpretable AI for a practical IoT application.
• •Combines Deep Q-Learning with synthetic data and hardware validation.
• •Addresses the challenge of food spoilage prediction.