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Intelligent classification of antepartum cardiotocography model based on deep forest., , , , , , , , и . Biomed. Signal Process. Control., (2021)Deriving mutual modes from HPLC fingerprints of traditional Chinese medicine with non-negative matrix factorization., , , , , и . BIBM, стр. 265-270. IEEE Computer Society, (2013)Research on multimodal deep learning based on CNN and ViT for intrapartum fetal monitoring., , , , , и . BIBM, стр. 4459-4464. IEEE, (2023)A Real Adjacency Matrix-Coded Differential Evolution Algorithm for Traveling Salesman Problems., , , , и . BIC-TA (2), том 682 из Communications in Computer and Information Science, стр. 135-140. Springer, (2016)Automatic Classification of Antepartum Cardiotocography Using Fuzzy Clustering and Adaptive Neuro -Fuzzy Inference System., , , , , , , и . BIBM, стр. 1938-1942. IEEE, (2020)Application of patient-reported outcomes in clinical evaluation of acupuncture for cervical spondylosis with artificial neural network., , , , , и . BIBM, стр. 271-276. IEEE Computer Society, (2013)Research on the Design of Active Learning Algorithm based on Query-by-Committee for Intelligent Fetal Monitoring., , , , , , , , и . BIBM, стр. 515-521. IEEE, (2021)Automatic identification peaks in chromatographic fingerprints based on fuzzy matching., , , и . FSKD, стр. 292-296. IEEE, (2012)Investigating the interpretability of fetal status assessment using antepartum cardiotocographic records., , , , , , , и . BMC Medical Informatics Decis. Mak., 21 (1): 355 (2021)Imbalanced Cardiotocography Multi-classification for Antenatal Fetal Monitoring Using Weighted Random Forest., , , , , , и . ICSH, том 11924 из Lecture Notes in Computer Science, стр. 75-85. Springer, (2019)