3G and 4G paging success rate based mobile network anomaly detection using supervised and unsupervised learning

This thesis is submitted in partial fulfillment of the requirements for the degree of Master of Science in Computer Science and Engineering, 2022.

Detalhes bibliográficos
Autor principal:	Ahasan, Md Rakibul
Outros Autores:	Alam, Md. Golam Robiul
Formato:	Thesis
Idioma:	English
Publicado em:	Brac University 2022
Assuntos:	Anomaly detection Supervised learning KPI Mobile Networks SMOTE Unsupervised learning K-Means DBSCAN HDBSCAN Autoencoder Algorithms Computer network architectures
Acesso em linha:	http://hdl.handle.net/10361/17164

http://hdl.handle.net/10361/17164

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