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prediction of resource utilization is not only based upon current usage patterns but also past system behavior may be considered. In this review, it was found that machine learning (ML) models are suitable to predict resource utilization using historical data

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. Hence some machine learning algorithms are used to predict CCI in mid and long-term operations namely K-nearest neighbour (NN) and perfect random tree ensembles (PERT) in USA [ 9 ] Neural Network, Time Series analysis and Regression methods It is

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Scientia et Securitas
Authors: László Vidács, Márk Jelasity, László Tóth, Péter Hegedűs, and Rudolf Ferenc

) 02 2020 10.1109/ibf50092.2020.9034714 Databricks Inc. Mlflow, an open source platform for the machine learning lifecycle 2020 https

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. Using machine learning to support debugging with tarantula Proceedings of the 18th IEEE International Symposium on Software Reliability 2007 Washington DC, USA

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Machine Learning for Networking: Workflow, Advances and Opportunities IEEE Network 32 2 2018 03 92-99 10.1109/mnet.2017

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been successfully applied in neural networks, machine learning, and text categorization technology [ 32, 33 ]. It operates according to the objectives sought, whether the reduction in dimensionality or the reduction of noise, the elimination of

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offers a number of enhancements that can be used to extract even more data from the measurement data set: Application of Deep/machine learning algorithms to classify multiple measurements Creating a correlation matrix for the measurement parameters

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References [1] Bansal N. , Blum A. , Chawla S. Correlation clustering , Machine Learning , Vol. 56 , No. 1-3 , 2004

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Pollack Periodica
Authors: Mykola Sysyn, Vitalii Kovalchuk, Ulf Gerber, Olga Nabochenko, and Andriy Pentsak

] Sysyn M. , Gruen , D. , Gerber , U. , Nabochenko , O. , Kovalchuk V. Turnout monitoring with vehicle based inertial measurements of operational trains: a machine learning approach , Communications

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Pollack Periodica
Authors: Dávid Nagy, Tamás Mihálydeák, and László Aszalós

. [3] Pawlak Z. , Skowron A. Rudiments of rough sets , Information Sciences , Vol. 177 , No. 1 , 2007 , pp. 3 – 27 . 10.1016/j.ins.2006.06.003 [4] Bansal N. , Blum A. , Chawla S. Correlation clustering , Machine Learning , Vol

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