Abstract
Design principles and the data-driven system to assess and to predict an operator readiness-to-perform are discussed in the article. Principles of construction and performance of the system are formulated. The main focus is on data organization (time line, date set for the model construction) and adaptive algorithm construction. High level of the prediction accuracy for an operator readiness-to-perform (85–90%) was achieved because of use data stored (parameters of time and cognitive tasks performance by user) the system to control its performance, as well as its self-adjusted algorithm of functioning.
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The work was supported by the grant of the National Academy of Educational Sciences of Ukraine # 0118U003160 “System of computer modeling of cognitive tasks for the formation of competencies of students in natural and mathematical subjects”.
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Burov, O. et al. (2020). Self-adjusted Data-Driven System for Prediction of Human Performance. In: Ahram, T., Karwowski, W., Vergnano, A., Leali, F., Taiar, R. (eds) Intelligent Human Systems Integration 2020. IHSI 2020. Advances in Intelligent Systems and Computing, vol 1131. Springer, Cham. https://doi.org/10.1007/978-3-030-39512-4_45
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DOI: https://doi.org/10.1007/978-3-030-39512-4_45
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