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  Published Paper Details:

  Paper Title

MACHINE LEARNING TECHNIQUES FOR 5G AND BEYOND

  Authors

  Lalith Kumar R,  MohamedFaheem S,  Srithar S V,  Madhorubagan E

  Keywords

Network Embedding , Beyond 5G Internet of Things , Machine learning , 5G

  Abstract


Network embedding successfully maintains the network structure by assigning network nodes to low dimensional representations. A considerable amount of progress has recently been achieved in the direction of this new paradigm for network research. In this study, we concentrate on classifying, analyzing, and pointing out the future directions for network embedding techniques research. We begin by summarizing the purpose of network embedding. We talk about network embedding and how it relates to traditional graph embedding methods in a cognitive radio context. Following that, we give a thorough overview of a variety of network embedding techniques in a methodical way, including advanced information preserving network embedding techniques, network embedding techniques with side information, and approaches that preserve structure and properties. Additionally, many methods of network embedding assessment as well as certain practical online tools, such as network data sets and software, are explored. In our last section, we cover the foundation for utilizing these network embedding techniques to create a successful system and identify some possible future paths.

  IJCRT's Publication Details

  Unique Identification Number - IJCRT24A4561

  Paper ID - 257929

  Page Number(s) - n511-n518

  Pubished in - Volume 12 | Issue 4 | April 2024

  DOI (Digital Object Identifier) -   

  Publisher Name - IJCRT | www.ijcrt.org | ISSN : 2320-2882

  E-ISSN Number - 2320-2882

  Cite this article

  Lalith Kumar R,  MohamedFaheem S,  Srithar S V,  Madhorubagan E,   "MACHINE LEARNING TECHNIQUES FOR 5G AND BEYOND", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.12, Issue 4, pp.n511-n518, April 2024, Available at :http://www.ijcrt.org/papers/IJCRT24A4561.pdf

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ISSN: 2320-2882
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Journal Starting Year (ESTD) : 2013
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ISSN and 7.97 Impact Factor Details


ISSN
ISSN
ISSN: 2320-2882
Impact Factor: 7.97 and ISSN APPROVED
Journal Starting Year (ESTD) : 2013
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