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

Abstract

Nanorobots are microscopic robots that operate at the molecular and cellular level and can potentially revolutionize fields such as medicine, manufacturing, and environmental monitoring due to their precision. However, the challenge for researchers is to analyze the data and provide a constructive recommendation framework instantly, as most nanorobots demand on-time and near-edge processing. To tackle this challenge, this research presents a novel edge-enabled intelligent data analytics framework called Transfer Learning Population Neural Network (TLPNN) to predict glucose levels and associated symptoms from invasive and non-invasive wearable devices. The TLPNN is designed to be unbiased in predicting symptoms during the initial phase but later modified based on the best-performing neural networks during the learning phase. The effectiveness of the proposed method is validated using two publicly available glucose datasets with various performance metrics. The simulation results demonstrate the effectiveness of the proposed TLPNN method over existing ones.