Journal of Machine and Computing


Performance of Neural Computing Techniques in Communication Networks



Journal of Machine and Computing

Received On : 25 August 2022

Revised On : 18 December 2022

Accepted On : 30 December 2022

Published On : 05 April 2023

Volume 03, Issue 02

Pages : 092-102


Abstract


This research investigates the use of neural computing techniques in communication networks and evaluates their performance based on error rate, delay, and throughput. The results indicate that different neural computing techniques, such as Artificial Neural Networks (ANNs), Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) and Generative Adversarial Networks (GANs) have different trade-offs in terms of their effectiveness in improving performance. The selection of technique will base on the particular requirements of the application. The research also evaluates the relative performance of different communication network architectures and identified the trade-offs and limitations associated with the application of different techniques in communication networks. The research suggests that further research is needed to explore the use of techniques, such as deep reinforcement learning; in communication networks and to investigate how the employment of techniques can be used to improve the security and robustness of communication networks.


Keywords


Artificial Neural Networks (ANNs), Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM), Generative Adversarial Networks (GANs).


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Acknowledgements


The author(s) received no financial support for the research, authorship, and/or publication of this article.


Funding


This work was supported by the National Research Foundation of Korea (NRF) and Korea government (MSIT) (No. 2021R1F1A1061514)


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Cite this article


Junho Jeong, “Performance of Neural Computing Techniques in Communication Networks”, Journal of Machine and Computing, pp. 092-102, April 2023. doi: 10.53759/7669/jmc202303010.


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© 2023 Junho Jeong. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.