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SleepExpertNet: high-performance and class-balanced deep learning approach inspired from the expert neurologists for sleep stage classification., , , , , and . J. Ambient Intell. Humaniz. Comput., 14 (6): 8067-8083 (2023)A novel method to monitoring changes in cognitive load in video-based learning., , and . J. Comput. Assist. Learn., 35 (6): 721-730 (2019)Non-invasive optical methods for brain-machine interfacing and imaging., , , , , , , and . SMC, page 1072-1075. IEEE, (2014)Moving-object segmentation with adaptive sprite for DCT-based video coder., and . ICIP (3), page 566-569. IEEE, (2001)Classification of the Motion Artifacts in Near-infrared Spectroscopy Based on Wavelet Statistical Feature., , , , , and . SMC, page 2144-2148. IEEE, (2019)Evaluation of outlier prevalence of density distribution in brain computed tomography: Comparison of kurtosis and quartile statistics., , , , and . BCI, page 1-2. IEEE, (2018)Correction to: SleepExpertNet: high‑performance and class‑balanced deep learning approach inspired from the expert neurologists for sleep stage classification., , , , , and . J. Ambient Intell. Humaniz. Comput., 14 (6): 8085 (2023)Pathway-specific protein domains are predictive for human diseases., , , , and . PLoS Comput. Biol., (2019)Design and implementation of a standards-based interoperable clinical decision support architecture in the context of the Korean EHR., , , , and . Int. J. Medical Informatics, 79 (9): 611-622 (2010)Estimating classification error rate: Repeated cross-validation, repeated hold-out and bootstrap.. Comput. Stat. Data Anal., 53 (11): 3735-3745 (2009)