Advances in Quantum Medical Image Analysis Using Machine Learning: Current Status and Future Directions

Matondo-Mvula, Nadine; Elleithy, Khaled

doi:10.1109/qce57702.2023.00049

2023 IEEE International Conference on Quantum Computing and Engineering (QCE) 2023

DOI: 10.1109/qce57702.2023.00049

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Advances in Quantum Medical Image Analysis Using Machine Learning: Current Status and Future Directions

Nadine Matondo-Mvula,

Khaled Elleithy

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2024

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A Distributed Mobile Edge Computing Based Dynamic Resource Allocation in 5G Network Using Green Anaconda Optimization Based Deep Learning Network

Govinda Rajulu,

Padmanaban

et al. 2024

Int J Communication

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Mobile edge computing (MEC) facilitates storage, cloud computing, and analysis capabilities near to the users in 5G communication systems. MEC and deep learning (DL) are combined in 5G networks to enable automated network management that provides resource allocation (RA), energy efficiency (EE), and adaptive security, thereby reducing computational costs and enhancing user services. A hybrid quantum‐classical convolutional neural network (HQCCNN) with simplicial attention network (SAN) is presented in the study that allocates appropriate resources for various users in the network. First, the green anaconda optimization (GAO) algorithm is used to optimize the objective function for effective RA. Consequently, the neural network receives the optimized objective functions to allocate resources. In the study, the suggested HQCCNN‐GAO model assesses the degree of need for every user and, based on those needs, allots resources to every user in the 5G network while preserving higher throughput and EE. Throughput, latency, mean square errors, processing time, bit error rates, and EE are used to measure the proposed model's efficiency. A few of the RA models that are now in use are contrasted with the outcomes of the suggested method. From the obtained outcomes, it is noticed that the suggested model provides a low latency of 0.08 s and a high throughput of 790 kbps for a range of network users.

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A Distributed Mobile Edge Computing Based Dynamic Resource Allocation in 5G Network Using Green Anaconda Optimization Based Deep Learning Network

Govinda Rajulu,

Padmanaban

et al. 2024

Int J Communication

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show abstract

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Advances in Quantum Medical Image Analysis Using Machine Learning: Current Status and Future Directions

Cited by 1 publication

References 76 publications

A Distributed Mobile Edge Computing Based Dynamic Resource Allocation in 5G Network Using Green Anaconda Optimization Based Deep Learning Network

A Distributed Mobile Edge Computing Based Dynamic Resource Allocation in 5G Network Using Green Anaconda Optimization Based Deep Learning Network

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