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- W2955786985 abstract "Industry 4.0 leaders solve problems all of the time. Successful problem-solving behavioral pattern choice determines organizational and personal success, therefore a proper understanding of the problem-solving-related neurological dynamics is sure to help increase business performance. The purpose of this paper is two-fold: first, to discover relevant neurological characteristics of problem-solving behavioral patterns, and second, to conduct a characterization of two problem-solving behavioral patterns with the aid of deep-learning architectures. This is done by combining electroencephalographic non-invasive sensors that capture process owners’ brain activity signals and a deep-learning soft sensor that performs an accurate characterization of such signals with an accuracy rate of over 99% in the presented case-study dataset. As a result, the deep-learning characterization of lean management (LM) problem-solving behavioral patterns is expected to help Industry 4.0 leaders in their choice of adequate manufacturing systems and their related problem-solving methods in their future pursuit of strategic organizational goals." @default.
- W2955786985 created "2019-07-12" @default.
- W2955786985 creator A5018203989 @default.
- W2955786985 creator A5022600554 @default.
- W2955786985 creator A5048585660 @default.
- W2955786985 creator A5069951807 @default.
- W2955786985 date "2019-06-26" @default.
- W2955786985 modified "2023-10-14" @default.
- W2955786985 title "Characterization of Industry 4.0 Lean Management Problem-Solving Behavioral Patterns Using EEG Sensors and Deep Learning" @default.
- W2955786985 cites W1567329668 @default.
- W2955786985 cites W1585013154 @default.
- W2955786985 cites W1719320297 @default.
- W2955786985 cites W1947251450 @default.
- W2955786985 cites W1964867524 @default.
- W2955786985 cites W1974849835 @default.
- W2955786985 cites W1974893143 @default.
- W2955786985 cites W1984103729 @default.
- W2955786985 cites W1987605932 @default.
- W2955786985 cites W2001327829 @default.
- W2955786985 cites W2004141237 @default.
- W2955786985 cites W2005791255 @default.
- W2955786985 cites W2009517857 @default.
- W2955786985 cites W2023591193 @default.
- W2955786985 cites W2024385144 @default.
- W2955786985 cites W2040858483 @default.
- W2955786985 cites W2045445742 @default.
- W2955786985 cites W2045867144 @default.
- W2955786985 cites W2052610531 @default.
- W2955786985 cites W2057808459 @default.
- W2955786985 cites W2061778814 @default.
- W2955786985 cites W2071256705 @default.
- W2955786985 cites W2080146971 @default.
- W2955786985 cites W2092274588 @default.
- W2955786985 cites W2093533434 @default.
- W2955786985 cites W2103340114 @default.
- W2955786985 cites W2103655839 @default.
- W2955786985 cites W2105501219 @default.
- W2955786985 cites W2115495003 @default.
- W2955786985 cites W2124517301 @default.
- W2955786985 cites W2127160769 @default.
- W2955786985 cites W2136022845 @default.
- W2955786985 cites W2141951329 @default.
- W2955786985 cites W2143663807 @default.
- W2955786985 cites W2155942638 @default.
- W2955786985 cites W2157216840 @default.
- W2955786985 cites W2162616775 @default.
- W2955786985 cites W2166073443 @default.
- W2955786985 cites W2249386455 @default.
- W2955786985 cites W2278108273 @default.
- W2955786985 cites W2293051574 @default.
- W2955786985 cites W2296103515 @default.
- W2955786985 cites W2304102200 @default.
- W2955786985 cites W2401511547 @default.
- W2955786985 cites W2411454842 @default.
- W2955786985 cites W2530399318 @default.
- W2955786985 cites W2536342836 @default.
- W2955786985 cites W2539552309 @default.
- W2955786985 cites W2548188407 @default.
- W2955786985 cites W2554610044 @default.
- W2955786985 cites W2563686712 @default.
- W2955786985 cites W2591713472 @default.
- W2955786985 cites W2594079185 @default.
- W2955786985 cites W2605246139 @default.
- W2955786985 cites W2605663286 @default.
- W2955786985 cites W2618015635 @default.
- W2955786985 cites W2735150756 @default.
- W2955786985 cites W2735917556 @default.
- W2955786985 cites W2738505717 @default.
- W2955786985 cites W2741907166 @default.
- W2955786985 cites W2744802016 @default.
- W2955786985 cites W2775385909 @default.
- W2955786985 cites W2796257259 @default.
- W2955786985 cites W2801749081 @default.
- W2955786985 cites W2811146364 @default.
- W2955786985 cites W2883280128 @default.
- W2955786985 cites W2896255093 @default.
- W2955786985 cites W2899574002 @default.
- W2955786985 cites W2899902988 @default.
- W2955786985 cites W2909905004 @default.
- W2955786985 cites W2911969890 @default.
- W2955786985 cites W2912733907 @default.
- W2955786985 cites W2913154159 @default.
- W2955786985 cites W2914154894 @default.
- W2955786985 cites W2914294201 @default.
- W2955786985 cites W2919115771 @default.
- W2955786985 cites W2921860266 @default.
- W2955786985 cites W2932154953 @default.
- W2955786985 cites W2939344111 @default.
- W2955786985 cites W2941146140 @default.
- W2955786985 cites W2949076297 @default.
- W2955786985 cites W2952116256 @default.
- W2955786985 cites W4231109964 @default.
- W2955786985 cites W4290699462 @default.
- W2955786985 doi "https://doi.org/10.3390/s19132841" @default.
- W2955786985 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/6651207" @default.
- W2955786985 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/31247966" @default.
- W2955786985 hasPublicationYear "2019" @default.
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