Comparative Analyses of Machine Learning Paradigms for Operators’ Voice Call Quality of Service

Author:

Mebawondu Jacob O.ORCID

Publisher

Springer International Publishing

Reference24 articles.

1. Lulu, D.: Data mining approach to analyze mobile telecommunications network quality of service: the case of ethiotelecom. Msc thesis, School of Graduate Studies School of Information Science. Addis Ababa University (2014)

2. Mebawondu, J., Dahunsi, F., Adewale, O.: Hybrid intelligent model for real time assessment of voice quality of service. Sci. Afr. J. www.elsevier.com. Accessed 7 Feb 2020

3. Mebawondu, J., Dahunsi, F., Adewale, O., Alese, B.: Radio access evaluation of cellular network in Akure Metropolis, Nigeria. Niger. J. Technol. (NIJOTECH) Faculty Eng. Univ. Nigeria Nsukka 37(3) (2018). Print ISSN 0331-8443, Electronic ISSN 2467-8821

4. Mebawondu, J., Dahunsi, F., Adewale, O., Alese. B., Momoh A.: Performance Evaluation of Global System of Mobile communications Quality of Service on crowd sourced data in a developing country. Int. J. Intell. Comput. Emerg. Technol. (IJICET) 1(1), 28–34 (2017)

5. Adekitan, R.: Performance evaluation of global system for mobile telecommunication networks in Nigeria. SCSR J. Bus. Entrepreneurship (SCSR-JBE) 1(1), 09–21 (2014)

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1. Application of Machine Learning in Predicting Call Quality of Telecom Service Providers;2022 2nd Asian Conference on Innovation in Technology (ASIANCON);2022-08-26

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