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Title:Napovedovanje značilnosti posameznikov z uporabo dinamike tipkanja
Authors:ID Petek, David (Author)
ID Musil, Bojan (Mentor) More about this mentor... New window
ID Čuš Babić, Nenad (Comentor)
Files:.pdf MAG_Petek_David_2017.pdf (465,40 KB)
MD5: FCB6BB9831DEFC18DBEDB98E3E663E47
PID: 20.500.12556/dkum/d7446077-6fc9-430e-939e-6d209773d156
 
Language:Slovenian
Work type:Master's thesis/paper
Typology:2.09 - Master's Thesis
Organization:FF - Faculty of Arts
Abstract:Cilj raziskave je bil na podlagi dinamike tipkanja napovedati velikih pet osebnostnih lastnosti in nekatere druge osebne značilnosti. Dinamika tipkanja je proučevanje natančnih časovnih podatkov o pritiskih in izpustih tipk ob tipkanju na računalniški tipkovnici. V raziskavi je sodelovalo 60 udeležencev, ki so pretipkali vnaprej pripravljeno besedilo ter izpolnili samoporočani osebnostni vprašalnik. Podatki o tipkanju so bili združeni v spremenljivke, ki so bile uporabljene kot vhodni podatki za nadzorovano strojno učenje. Dinamika tipkanja je bila uporabljena za klasificiranje udeležencev v zgornjo ali spodnjo skupino glede na povprečje vzorca za posamezno merjeno lastnost. Z uporabo metode umetnih nevronskih mrež smo uspešno napovedali vestnost za 62 % (p = 0,046) in višino za 63 % (p = 0,026) udeležencev.
Keywords:dinamika tipkanja, biometrika, osebnostne lastnosti, spol, ročnost
Place of publishing:Maribor
Publisher:[D. Petek]
Year of publishing:2017
PID:20.500.12556/DKUM-68061 New window
UDC:159.923:57.087.1(043.2)
COBISS.SI-ID:23416328 New window
NUK URN:URN:SI:UM:DK:YUXQZSWG
Publication date in DKUM:01.02.2021
Views:809
Downloads:29
Metadata:XML DC-XML DC-RDF
Categories:FF
:
PETEK, David, 2017, Napovedovanje značilnosti posameznikov z uporabo dinamike tipkanja [online]. Master’s thesis. Maribor : D. Petek. [Accessed 21 January 2025]. Retrieved from: https://dk.um.si/IzpisGradiva.php?lang=eng&id=68061
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Licences

License:CC BY 4.0, Creative Commons Attribution 4.0 International
Link:http://creativecommons.org/licenses/by/4.0/
Description:This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.
Licensing start date:07.09.2017

Secondary language

Language:English
Title:Predicting characteristics of individuals using keystroke dynamics
Abstract:Goal of present research was to predict big five personality traits and some other personal characteristics based on keystroke dynamics. Keystroke dynamics is the study of detailed timing information about key presses and releases while typing at a computer keyboard. Sixty participants typed predetermined text and solved a self-report personality questionnaire. Keystroke data was merged into variables which were used as input for supervised machine learning. Keystroke dynamics was used to classify participants into upper or lower group based on sample average for individual characteristic. Artificial neural network method was used to correctly predict conscientiousness for 62 % of participants (p = 0,046) and height for 63 % of participants (p = 0,026).
Keywords:keystroke dynamics, bimetrics, personality traits, gender, handedness


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