Enhanced Virtual First Aid Box to Prevent and Rescue in Body Health Using Deep Extreme Genetic Model-Agnostic Meta-Learning (EG-MAML) Algorithm - Cholissodin, Imam; Shampton, Achmad; Soebroto, Arief Andy (Proceedings of the International Conference on Green Technology, 2024)
This study is focus to Enhanced Virtual First Aid Box (EVFAB) leverages advanced technologies to provide health monitoring and preventive measures, integrating the Deep Extreme Genetic Model-Agnostic Meta-Learning (EG-MAML) algorithm for robust and adaptive solutions. EG-MAML enables the system to rapidly learn from minimal data, optimizing medical decision-making and preventive actions in dynamic health scenarios. The model integrates genetic algorithms with deep learning to enhance the adaptability of first aid protocols, tailoring responses to individual health conditions and real-time physiological data. This innovation not only aids in timely interventions during emergencies but also proactively identifies potential health risks, ensuring early detection and personalized prevention strategies. By utilizing an intelligent system like EG-MAML, the virtual first aid box becomes a versatile tool, contributing to public health resilience and improving individual well-being through cutting-edge machine learning techniques.