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Palabras clave – provistas por la editorial

sEMG; deep learning; neural networks; gait phase; classification; everyday walking; convolutional neural network; CRISPR; leukemia nucleus image; segmentation; soft covering rough set; clustering; machine learning algorithm; soft computing; multistage support vector machine model; multiple imputation by chained equations; SVM-based recursive feature elimination; unipolar depression; diabetic retinopathy (DR); pre-trained deep ConvNet; uni-modal deep features; multi-modal deep features; transfer learning; 1D pooling; cross pooling; IMU; gait analysis; long-term monitoring; multi-unit; multi-sensor; time synchronization; Internet of Medical Things; body area network; MIMU; early detection; sepsis; evaluation metrics; machine learning; medical informatics; feature extraction; physionet challenge; electrocardiogram; Premature ventricular contraction; sparse autoencoder; unsupervised learning; Softmax regression; medical diagnosis; artificial neural network; e-health; Tri-Fog Health System; fault data elimination; health status prediction; health status detection; health off; diffusion tensor imaging; ensemble learning; decision support systems; healthcare; computational intelligence; Alzheimer’s disease; fuzzy inference systems; genetic algorithms; next-generation sequencing; ovarian cancer; interpretable models; n/a