Opposition Learning-based Red Panda Optimization for Feature Selection in a Deep Learning Framework for Menstrual Nutrition Recommendation

The study introduces an innovative deep learning approach that combines Opposition Learning-Based Red Panda Optimisation (OL_RPO) for generating personalized recommendations on menstrual nutrition. The OL_RPO framework implements opposition-based learning together with red panda optimization strategies to identify pertinent factors that arise from high-dimensional feeding and physiological measurements. The framework predicts menstrual symptoms before creating individualized dietary plans to address mood swings together with cramps and fatigue. The research studied the Food Nutrient Dataset from Kaggle and verified that the proposed methodology achieved better prediction accuracy relative to traditional methods at a 10% improvement level. The OL_RPO method maintains a high degree of accuracy but brings down processing complexity substantially. The research outcomes suggest combining deep learning with optimized algorithms to generate personalized food plans for menstrual cycle management in women.