Nanorobot-Based Intelligent Symptoms Analysis and Recommendation Framework in Edge Networks

Area of Science:

• Nanotechnology
• Artificial Intelligence
• Biomedical Engineering

Background:

• Nanorobots offer precision in medicine but require instant data analysis and recommendations.
• Current nanorobot systems face challenges in real-time edge processing for immediate insights.
• Effective data analytics frameworks are crucial for leveraging nanorobot capabilities.

Purpose of the Study:

• To present an edge-enabled intelligent data analytics framework for nanorobots.
• To develop a system for predicting glucose levels and associated symptoms using wearable devices.
• To address the need for on-time, near-edge processing in nanorobot applications.

Main Methods:

• Introduction of the Transfer Learning Population Neural Network (TLPNN) framework.
• Implementation of edge-enabled intelligent data analytics for wearable sensor data.
• Validation using two publicly available glucose datasets and performance metrics.

Main Results:

• The TLPNN framework demonstrates effective prediction of glucose levels and symptoms.
• The proposed method shows superior performance compared to existing approaches.
• Simulation results confirm the efficacy of the TLPNN for real-time analytics.

Conclusions:

• The TLPNN framework successfully enables instant, edge-processed data analysis for nanorobots.
• This approach enhances the practical application of nanorobots in fields like healthcare.
• The study highlights the potential of intelligent data analytics in advancing nanorobot technology.