prikaz prve stranice dokumenta Prediction of human performance based on psychophysiological features of resilience and machine learning
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doctoral thesis
Prediction of human performance based on psychophysiological features of resilience and machine learning
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2021. urn:nbn:hr:168:767064

University of Zagreb
Faculty of Electrical Engineering and Computing
Department of Electric Machines, Drives and Automation

Cite this document

Šarlija, M. (2021). Prediction of human performance based on psychophysiological features of resilience and machine learning (Doctoral thesis). Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:767064

Šarlija, Marko. "Prediction of human performance based on psychophysiological features of resilience and machine learning." Doctoral thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2021. https://urn.nsk.hr/urn:nbn:hr:168:767064

Šarlija, Marko. "Prediction of human performance based on psychophysiological features of resilience and machine learning." Doctoral thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2021. https://urn.nsk.hr/urn:nbn:hr:168:767064

Šarlija, M. (2021). 'Prediction of human performance based on psychophysiological features of resilience and machine learning', Doctoral thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 31 January 2023, https://urn.nsk.hr/urn:nbn:hr:168:767064

Šarlija M. Prediction of human performance based on psychophysiological features of resilience and machine learning [Doctoral thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2021 [cited 2023 January 31] Available at: https://urn.nsk.hr/urn:nbn:hr:168:767064

M. Šarlija, "Prediction of human performance based on psychophysiological features of resilience and machine learning", Doctoral thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2021. Available at: https://urn.nsk.hr/urn:nbn:hr:168:767064

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