Machine Learning with IoMT: Opportunities and Research Challenges

Bharathi, M.; Amsaveni, A

doi:10.1007/978-3-030-63937-2_13

Cited by 5 publications

(3 citation statements)

References 30 publications

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“…Therefore, someone cannot listen in on a conversation, obtain one-half of a photon, and then use that photon to figure out how much it is worth before letting it keep going. The two people in QKD who are honest with each other devise a plan to throw off an eavesdropper by making mistakes in their conversation, thought to have started the field or created it, was the first person to suggest and show that quantum mechanical properties could be used in communication if information bits could be physically specified [29]. Information can be encoded and sent using the spin of an electron, the manner in which a photon scatters, or a combination of these and other quantum properties.…”

Section: Quantum Computingmentioning

confidence: 99%

Quantum Machine Learning for Security Assessment in the Internet of Medical Things (IoMT)

et al. 2023

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Internet of Medical Things (IoMT) is an ecosystem composed of connected electronic items such as small sensors/actuators and other cyber-physical devices (CPDs) in medical services. When these devices are linked together, they can support patients through medical monitoring, analysis, and reporting in more autonomous and intelligent ways. The IoMT devices; however, often do not have sufficient computing resources onboard for service and security assurance while the medical services handle large quantities of sensitive and private health-related data. This leads to several research problems on how to improve security in IoMT systems. This paper focuses on quantum machine learning to assess security vulnerabilities in IoMT systems. This paper provides a comprehensive review of both traditional and quantum machine learning techniques in IoMT vulnerability assessment. This paper also proposes an innovative fused semi-supervised learning model, which is compared to the state-of-the-art traditional and quantum machine learning in an extensive experiment. The experiment shows the competitive performance of the proposed model against the state-of-the-art models and also highlights the usefulness of quantum machine learning in IoMT security assessments and its future applications.

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Section: Quantum Computingmentioning

confidence: 99%

Quantum Machine Learning for Security Assessment in the Internet of Medical Things (IoMT)

et al. 2023

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“…IoMT technology has been continuously developed, and its connection with deep learning has become stronger and stronger. In [22], the feasibility of combining machine learning with IoMT networks was proposed, pointing out that some networks are not accurate and robust enough for data prediction. In [23], it was proposed to build a set of IoMT platforms using CNN networks for the diagnosis of lung cancer combined with pulmonary embolism, but with low robustness.…”

Section: Introductionmentioning

confidence: 99%

Digital-Twin-Enabled IoMT System for Surgical Simulation Using rAC-GAN

Tai

Zhang

et al. 2022

IEEE Internet Things J.

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A digital twin-enabled Internet of Medical Things (IoMT) system for telemedical simulation is developed, systematically integrated with mixed reality (MR), 5G cloud computing, and a generative adversarial network (GAN) to achieve remote lung cancer implementation. Patient-specific data from 90 lung cancer with pulmonary embolism (PE)-positive patients, with 1372 lung cancer control groups, were gathered from Qujing and Dehong, and then transmitted and preprocessed using 5G. A novel robust auxiliary classifier generative adversarial network (rAC-GAN)-based intelligent network is employed to facilitate lung cancer with the PE prediction model. To improve the accuracy and immersion during remote surgical implementation, a real-time operating room perspective from the perception layer with a surgical navigation image is projected to the surgeon's helmet in the application layer using the digital twin-based MR guide clue with 5G. The accuracies of the area under the curve (AUC) of our new intelligent IoMT system were 0.92, and 0.93. Furthermore, the pathogenic features learned from our rAC-GAN model are highly consistent with the statistical epidemiological results. The proposed intelligent IoMT system generates significant performance improvement to process substantial clinical data at cloud centers and shows a novel framework for remote medical data transfer and deep learning analytics for digital twin-based surgical implementation.

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“…So, to model the THz channel many parameters must be taking into account in order to drop a valuable model for this environment. In alternative transmission systems with orthogonal frequency division multiplexing (OFDM) [8][9][10][11], equalizers are used in the frequency domain based on the use of the fast Fourier transform (FFT), which, with the same error probability, have less complexity. But this method has two significant drawbacks: high signal crest factor and high sensitivity to carrier frequency deviation.…”

Section: Introductionmentioning

confidence: 99%

Investigation of Radio Channel Characterization in Terahertz Range

Elaage¹,

Ghzaoui²,

Mrani³

et al. 2022

J. Nano- Electron. Phys.

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To determine the performance of a wireless system, channel characterization must be carried out. In fact, in a wireless system, the propagation of an electromagnetic wave in space is of particular importance. For this reason, it is necessary to study the correctness of this channel before starting to work on it and implementing it in a communication system. It is therefore essential to have knowledge of the mechanisms involved in the propagation channel and of its interactions with the environment in order to be able to predict the chances and conditions for establishing a radio link between a transmitter and a receiver. In this work, we propose some strategies to model the transmission channel in the THz band. Indeed, before we can evaluate a transition system over wireless system, we have to model the propagation channel. First, we present the main tools allowing the modelling of the channel in the THz frequency range, which is considered a key new technology to meet the growing demand for higher speed wireless communications, as well as its impact on radio systems. In this paper, we propose a channel model for a wireless system working in the THz band. Besides, we propose some strategies to model the entire system as well as the propagation channel. We also simulate the proposed channel model. The simulation results show that the proposed system can be considered as an example for evaluating the performance of a communication chain based on a wireless system in the THz bands.

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Machine Learning with IoMT: Opportunities and Research Challenges

Cited by 5 publications

References 30 publications

Quantum Machine Learning for Security Assessment in the Internet of Medical Things (IoMT)

Quantum Machine Learning for Security Assessment in the Internet of Medical Things (IoMT)

Digital-Twin-Enabled IoMT System for Surgical Simulation Using rAC-GAN

Investigation of Radio Channel Characterization in Terahertz Range

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