@article {Kamri2020298,
	title = {Machine Learning Approach for Smart Self-diagnosing Autonomic Computing Systems},
	journal = {Advances in Intelligent Systems and Computing},
	volume = {1105 AISC},
	year = {2020},
	note = {cited By 0},
	pages = {298-307},
	abstract = {While modern systems and networks are continuously growing in size, complexity and diversity, the monitoring and diagnosing of such systems is becoming a real challenge. Technically and economically, more automation of the classical diagnosing tasks is needed. This has triggered a considerable research initiative, grouped under the terms self-management and Autonomic Computing. In this paper we propose a new model for smart self-diagnosing systems based on Autonomic Computing principles and Machine Learning techniques. {\textcopyright} 2020, Springer Nature Switzerland AG.},
	keywords = {Autonomic Computing, Autonomic computing system, Diagnosing system, Intelligent systems, Learning systems, Machine learning, Machine learning approaches, Machine learning techniques, planning, Research initiatives, Self management, Self-diagnosing, Sustainable development, wireless networks},
	doi = {10.1007/978-3-030-36674-2_31},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85080896883\&doi=10.1007\%2f978-3-030-36674-2_31\&partnerID=40\&md5=3dce8bc657338047166fb3ded16c10c8},
	author = {Kamri, H. and Bounabat, B.}
}
