[HTML][HTML] A comprehensive survey on machine learning for networking: evolution, applications and research opportunities
Abstract Machine Learning (ML) has been enjoying an unprecedented surge in applications
that solve problems and enable automation in diverse domains. Primarily, this is due to the …
that solve problems and enable automation in diverse domains. Primarily, this is due to the …
Unsupervised machine learning for networking: Techniques, applications and research challenges
While machine learning and artificial intelligence have long been applied in networking
research, the bulk of such works has focused on supervised learning. Recently, there has …
research, the bulk of such works has focused on supervised learning. Recently, there has …
A survey of 5G network systems: challenges and machine learning approaches
Abstract 5G cellular networks are expected to be the key infrastructure to deliver the
emerging services. These services bring new requirements and challenges that obstruct the …
emerging services. These services bring new requirements and challenges that obstruct the …
[HTML][HTML] Artificial intelligence applications and self-learning 6G networks for smart cities digital ecosystems: Taxonomy, challenges, and future directions
The recent upsurge of smart cities' applications and their building blocks in terms of the
Internet of Things (IoT), Artificial Intelligence (AI), federated and distributed learning, big data …
Internet of Things (IoT), Artificial Intelligence (AI), federated and distributed learning, big data …
Self-healing in emerging cellular networks: Review, challenges, and research directions
Mobile cellular network operators spend nearly a quarter of their revenue on network
management and maintenance. Incidentally, a significant proportion of that budget is spent …
management and maintenance. Incidentally, a significant proportion of that budget is spent …
In-network machine learning using programmable network devices: A survey
Machine learning is widely used to solve networking challenges, ranging from traffic
classification and anomaly detection to network configuration. However, machine learning …
classification and anomaly detection to network configuration. However, machine learning …
Semi-supervised learning based big data-driven anomaly detection in mobile wireless networks
With rising capacity demand in mobile networks, the infrastructure is also becoming
increasingly denser and complex. This results in collection of larger amount of raw data (big …
increasingly denser and complex. This results in collection of larger amount of raw data (big …
Cause-aware failure detection using an interpretable XGBoost for optical networks
Failure detection is an important part of failure management, and network operators
encounter serious consequences when operating under failure conditions. Machine …
encounter serious consequences when operating under failure conditions. Machine …
Intelligent wireless networks: challenges and future research topics
M Abusubaih - Journal of Network and Systems Management, 2022 - Springer
Recently, artificial intelligence (AI) has become a primary tool of serving science and
humanity in all fields. This is due to the significant development in computing. The use of AI …
humanity in all fields. This is due to the significant development in computing. The use of AI …
Fault prediction for heterogeneous telecommunication networks using machine learning: a survey
K Murphy, A Lavignotte, C Lepers - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Recently Network Fault Prediction (NFP) has arisen new scientific interest. The ability to
predict network equipment failure is increasingly identified as an effective tool to improve …
predict network equipment failure is increasingly identified as an effective tool to improve …