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

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.

Deep Transfer Learning for Communicable Disease Detection and Recommendation in Edge Networks.

Considering the increasing number of communicable disease cases such as COVID-19 worldwide, the early detection of the disease can prevent and limit the outbreak. Besides that, the PCR test kits are not available in most parts of the world, and there is genuine concern about their performance and reliability. To overcome this, in this paper, we develop a novel edge-centric healthcare framework integrating with wearable sensors and advanced machine learning (ML) model for timely decisions with minimum delay.

An Intrusion Detection Mechanism for Secured IoMT Framework Based on Swarm-Neural Network.

The seamless integration of medical sensors and the Internet of Things (IoT) in smart healthcare has leveraged an intelligent Internet of Medical Things (IoMT) framework to detect the criticality of the patients. However, due to the limited storage capacity and computation power of the local IoT devices, patient's health data needs to transfer to remote computing devices for analysis, which can easily result in privacy leakage due to lack of control over the patient's health data and the vulnerability of the network for various types of attacks. Motivated by this, in this paper, an Empirical Intelligent Agent (EIA) based on a unique Swarm-Neural Network (Swarm-NN) method is proposed to identify attackers in the edge-centric IoMT framework.