Andro Matković, Thomas Ferenc, Lucija Dimjašević, Martin Drinković, Bruno Lovreković, Jelena Popić, Rhea M Mužar, Vinko Vidjak
Patient's knowledge regarding radiation exposure from various imaging modalities: a pilot study
Radiation protection dosimetry
driver fatigue represents significant peril global traffic safety necessitating advancement potent fatigue monitoring methodologies bolster road safety research introduces conditional generative adversarial network classification head integrates convolutional attention mechanisms caacgan designed precise identification fatigue driving states analysis electroencephalography eeg signals first study constructed 4d feature data model capable mirroring drivers fatigue state meticulously analyzing eeg signals frequency spatial temporal dimensions following present caacgan framework novel integration attention schemes bottleneck residual block transformer element integration designed refine processing eeg signals significantly utilizing conditional generative adversarial network equipped classification header framework aims distinguish fatigue states effectively moreover addresses scarcity authentic data generation superiorquality synthetic data empirical outcomes illustrate caacgan model surpasses various extant methods fatigue detection endeavor seedvig public dataset moreover juxtaposed leadingedge gan models model exhibits efficacy producing highquality data clearly superior investigation confirms caacgan models utility fatigue driving identification suggests fresh perspectives deep learning applications time series data generation processing