Flexible Evolutionary Model of Machine Learning of Organizational Capital Development Strategies with Optimization of Spent Resources

Vasyl Porokhnya, Vladyslav Penev, Roman Ivanov, Volodymyr Kravchenko

2022

Abstract

As part of the follow-up, the conceptual pipeline was developed to the stage of machine learning Q-leaning with the method of eliminating the most effective strategy for the development of organizational capital in the structure of intellectual capital and increasing the reliability of taking away the results. In the final work, the modeling of alternative strategies for the development of organizational capital with the alternatives of machine learning was modeled. This simulation made it possible to simplify the search and development of options for strategies for the development of organizational capital, real alternative ways, and to simplify management decisions. For a more correct operation of machine learning, coefficients were introduced that affect the decision-making by machine learning. Results indicate that the capital of the strategy is the acquisition of innovative information potential and the capital of alternatives without intermediary victorious main functions of formation and the establishment of mechanisms for managing intellectual capital in the aggregate with other types of capital in them.

Paper Citation

in Harvard Style

Porokhnya V., Penev V., Ivanov R. and Kravchenko V. (2022). Flexible Evolutionary Model of Machine Learning of Organizational Capital Development Strategies with Optimization of Spent Resources. In Proceedings of 10th International Conference on Monitoring, Modeling & Management of Emergent Economy - Volume 1: M3E2; ISBN 978-989-758-640-8, SciTePress, pages 71-79. DOI: 10.5220/0011931400003432

in Bibtex Style

@conference{m3e222,
author={Vasyl Porokhnya and Vladyslav Penev and Roman Ivanov and Volodymyr Kravchenko},
title={Flexible Evolutionary Model of Machine Learning of Organizational Capital Development Strategies with Optimization of Spent Resources},
booktitle={Proceedings of 10th International Conference on Monitoring, Modeling & Management of Emergent Economy - Volume 1: M3E2},
year={2022},
pages={71-79},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011931400003432},
isbn={978-989-758-640-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of 10th International Conference on Monitoring, Modeling & Management of Emergent Economy - Volume 1: M3E2
TI - Flexible Evolutionary Model of Machine Learning of Organizational Capital Development Strategies with Optimization of Spent Resources
SN - 978-989-758-640-8
AU - Porokhnya V.
AU - Penev V.
AU - Ivanov R.
AU - Kravchenko V.
PY - 2022
SP - 71
EP - 79
DO - 10.5220/0011931400003432
PB - SciTePress