Throughput prediction of fifth-generation cellular system using hybrid feature selection and enhanced sequential decision tree machine learning algorithm

This paper proposes enhanced sequential decision tree (ESDT) for the prediction of fifth-generation (5G) cellular network throughput. The dataset which is used as input for machine learning (ML) model without preprocessing steps is called as dataset 1 and contains 49,706 no. of records. Missing valu...

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Vydané v:	Wireless networks Ročník 31; číslo 3; s. 3025 - 3042
Hlavní autori:	Sharma, Abhilasha, Pandit, Shweta, Talluri, Salman Raju
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York Springer US 01.03.2025 Springer Nature B.V
Predmet:	Algorithms Communications Engineering Computer Communication Networks Datasets Decision trees Electrical Engineering Engineering Feature selection IT in Business Machine learning Networks Original Paper Performance enhancement Preprocessing Support vector machines Throughput prediction Random forest Support vector machine Decision tree Enhanced sequential decision tree K-nearest neighbor
ISSN:	1022-0038, 1572-8196
On-line prístup:	Získať plný text
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