… to the application of machine learning to fault management in cellular networks from … fault
management. We describe the relevant machine learning techniques through to deep learning, …

… is to develop machine learning and data mining-… fault management process. This chapter
provides a survey of data mining and machine learning-based techniques for fault management…

… In this paper, basic ML concepts and its integration into existing network control and
management planes are reviewed. Case studies covering … In this paper, we review …

… characterize the complex fault patterns therein. This article discusses the application of
machine learning (ML) in actuating an automated optical network fault management architecture. …

… and diagnose the state or failure using machine learning. The diagnosis model is based on
a support vector machine (SVM)-based diagnosis model and a self-learning one-class SVM-…

… insights, and move from largely reactive to proactive fault management frameworks. In this
context, advanced machine learning based analytics solutions have the potential to exploit …

… machine learning classifiers with each other. This comparison is conducted to detect the
network fault … works related to network fault management. Section III presents an overview of five …

… According to the results on the topic of machine fault diagnosis by using machine learning,
the number of publications has rapidly increased since the 2010s. Therefore, we roughly …

… [31] presented machine learning methods for anomalous detection with special focus on
cybersecurity for 5G networks. Amid other works, these ones aim at introducing the use of deep …

… steps and performance of fault management frameworks, we … the center: database-based,
machine learning-based, and time-… fault management frameworks distribute fault management …