A Group Teaching Optimization Algorithm for Priority-Based Resource Allocation in Wireless Networks

Sreethar, S.; Nandhagopal, N.; Karuppusamy, S. Anbu; Dharmalingam, Mala

doi:10.1007/s11277-021-09249-7

Cited by 11 publications

(3 citation statements)

References 24 publications

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“…Based on JavaEE technology and Ajax front-end development technology, Sreethar et al have implemented a music instructional resource management system in institution of higher learning. Based on the requirements document, a series of functional test cases are designed; in terms of system performance, two stress test schemes for read and write performance are designed [ 17 ].…”

Section: Related Workmentioning

confidence: 99%

[Retracted] Reset and Integration of Music Instructional Resources Using Deep Convolutional Neural Networks

Lei

2022

Journal of Environmental and Public Health

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In order to overcome the problem that learners and teachers cannot find instructional resources to meet their needs and information overload in the massive resources, this article proposes and designs a music instructional resource management platform based on DCNN. This article expounds the overall goal, design principle, overall structure, and interface design of the system. At the same time, the whole construction process of a music instructional resources integration system based on DCNN is discussed in detail from the aspects of configuration of development environment, localization of platform interface, and realization of main functions of the system. In addition, through the demand analysis tool, the demand of college music instructional resources management is analyzed in detail and deeply, and the demand document is formed. This article makes an in-depth study on the categories of music instructional resources and summarizes the resource classification methods that are in line with the actual instructional activities. The experiments show that the accuracy of the proposed algorithm is improved by about 6% compared with the fuzzy clustering algorithm. At the same time, the stability of this system can reach 96.14%. This system is rich in functions and easy to use and can provide a feasible scheme for the management of instructional resources in various disciplines.

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Section: Related Workmentioning

confidence: 99%

[Retracted] Reset and Integration of Music Instructional Resources Using Deep Convolutional Neural Networks

Lei

2022

Journal of Environmental and Public Health

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“…However, such An important problem is the policy's inability to respond to changes in network needs [12]. Wireless networks often introduce new systems with the same goals as current ones [13,14]. It is therefore possible to apply policies to newly arrived systems with no additional learning if they are network adaptable [15,16].…”

Section: Introductionmentioning

confidence: 99%

Fedresource

G. Satheesh,

Sasikala

2023

Int. j. electr. comput. eng. syst. (Online)

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Deep reinforcement learning can effectively deal with resource allocation (RA) in wireless networks. However, more complex networks can have slower learning speeds, and a lack of network adaptability requires new policies to be learned for newly introduced systems. To address these issues, a novel federated learning-based resource allocation (FEDRESOURCE) has been proposed in this paper which efficiently performs RA in wireless networks. The proposed FEDRESOURCE technique uses federated learning (FL) which is a ML technique that shares the DRL-based RA model between distributed systems and a cloud server to describe a policy. The regularized local loss that occurs in the network will be reduced by using a butterfly optimization technique, which increases the convergence of the FL algorithm. The suggested FL framework speeds up policy learning and allows for adoption by employing deep learning and the optimization technique. Experiments were conducted using a Python-based simulator and detailed numerical results for the wireless RA sub-problems. The theoretical results of the novel FEDRESOURCE algorithm have been validated in terms of transmission power, convergence of algorithm, throughput, and cost. The proposed FEDRESOURCE technique achieves maximum transmit power up to 27%, 55%, and 68% energy efficiency compared to Scheduling policy, Asynchronous FL framework, and Heterogeneous computation schemes respectively. The proposed FEDRESOURCE technique can increase discrimination accuracy by 1.7%, 1.2%, and 0.78% compared to the scheduling policy framework, Asynchronous FL framework, and Heterogeneous computation schemes respectively.

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“…Urban rail transit has the characteristics of large volume, fast speed, safety and convenience, and high reliability and has become the main mode of transportation to relieve urban traffic pressure [ 1 ]. Under normal conditions, the train runs in strict accordance with the operation diagram.…”

Section: Introductionmentioning

confidence: 99%

Optimization and Application of Communication Resource Allocation Algorithm for Urban Rail Transit Planning

Fang

Zhang

2022

Computational Intelligence and Neuroscience

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The construction and operation of China’s rail transit system have entered a high-speed development stage, and the rapid increase of train speed and mileage has brought greater challenges to the safety and reliability of the rail transit system. Network planning evaluation is the key to the early decision-making of urban rail transit project, which directly determines the success or failure of the whole project. How to scientifically and reasonably evaluate the urban rail transit information resource network planning has become a difficult problem for many urban planners to solve. Therefore, this paper studies the optimization of the communication resource allocation algorithm and the comprehensive evaluation of its application for urban rail transit planning. In this paper, based on CVNN structure, the network prototype is an extension of RVNN structure. In the abstract, its processing unit is composed of a pair of real-number processors that can realize certain operations. HNN is a fully connected recurrent neural network based on the idea of the energy function, which is helpful to understand the calculation mode of HNN, and the research shows that HNN can solve many combinatorial optimization problems. In addition, the combination of neural network and genetic algorithm with simulated annealing mechanism can also bring new directions for research. On the basis of experimental analysis, it can be concluded that in general, the error reduction rate of the optimization scheme designed in this paper can reach 58.6% on average. In practical application, the accuracy of the optimal bit error rate is 52.4%.

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A Group Teaching Optimization Algorithm for Priority-Based Resource Allocation in Wireless Networks

Cited by 11 publications

References 24 publications

[Retracted] Reset and Integration of Music Instructional Resources Using Deep Convolutional Neural Networks

[Retracted] Reset and Integration of Music Instructional Resources Using Deep Convolutional Neural Networks

Fedresource

Optimization and Application of Communication Resource Allocation Algorithm for Urban Rail Transit Planning

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