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

Sudarshan Nandy, Abhishek Hazra, Mainak Adhikari, Deepak Puthal

    Research output: Contribution to journalArticlepeer-review

    3 Scopus citations

    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.

    Original languageBritish English
    Pages (from-to)1-8
    Number of pages8
    JournalIEEE Journal of Biomedical and Health Informatics
    DOIs
    StateAccepted/In press - 2023

    Keywords

    • Artificial neural networks
    • Bioinformatics
    • Data analysis
    • Edge Computing
    • Glucose
    • Glucose-level Prediction
    • Internet of Medical Things
    • Medical services
    • Monitoring
    • Nanorobots
    • Sensors
    • Transfer Learning

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