He, X.,
Wang, Z.,
Li, Y.,
Khazhina, S.,
Du, W.,
Wang, J.,
Wang, W. (2022) at the
parallel machine scheduling problem. Based on this, a corresponding mixed integer programming model
the technical aspects of solving the problem of malignant tumors diagnostic using
machine learning
This article discusses the use of
machine learning methods to predict the degree of threat
-platform. For
machine learning algorithms, PyTorch 1.9.0 packages were used. An analysis of the effectiveness
Lopukhova, Ekaterina A.,
Ibragimova, Rada R.,
Idrisova, Gulnaz M.,
Lakman, Irina A.,
Mukhamadeev, Timur R.,
Grakhova, Elizaveta P.,
Bilyalov, Azat R.,
Kutluyarov, Ruslan V. (2023) is expressed in the application of various
machine learning algorithms, metrics for evaluating