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

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.

Detalles Bibliográficos
Autor Principal: Ahasan, Md Rakibul
Outros autores: Alam, Md. Golam Robiul
Formato: Thesis
Idioma:English
Publicado: Brac University 2022
Subjects:
Anomaly detection
Supervised learning
KPI
Mobile Networks
SMOTE
Unsupervised learning
K-Means
DBSCAN
HDBSCAN
Autoencoder
Algorithms
Computer network architectures
Acceso en liña:http://hdl.handle.net/10361/17164

Internet

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

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