Page 342 - SDMD CNKT va CNTT trong tien trinh CNH_HDH DBSCL
P. 342

Gulordava,  K.,  Bojanowski,  P.,  Grave,  E.,  Linzen,  T.,  &  Baroni,  M.  (2018).
              Colorless green recurrent networks dream hierarchically.  Proceedings of the
              2018  Conference  of  the  North  American  Chapter  of  the  Association  for
              Computational  Linguistics:  Human  Language  Technologies,  NAACL-HLT
              2018.  1  (pp.  1195–1205).  Association  for  Computational  Linguistics.
              https://doi.org/10.18653/v1/N18-1108
          Guo,  D.,  Kim,  Y.,  &  Rush,  A.  M.  (2020).  Sequence-level  mixed  sample  data
              augmentation. Proceedings of the 2020 Conference on Empirical Methods in
              Natural Language Processing, EMNLP 2020 (pp. 5547–5552). Association for
              Computational Linguistics. https://doi.org/10.18653/v1/2020.emnlp-main.447
          Guo,  H.  (2020).  Nonlinear  mixup:  Out-of-manifold  data  augmentation  for  text
              classification. Proceedings of the AAAI Conference on Artificial Intelligence,
              34(4), 4044–4051. https://doi.org/10.1609/aaai.v34i04.5822
          Guo, H., Mao, Y., & Zhang, R. (2019, July). Mixup as locally linear out-of-manifold
              regularization.  In  Proceedings  of  the  AAAI  Conference  on  Artificial
              Intelligence, 33(01), 3714–3722. https://doi.org/10.1609/aaai.v33i01.33013714
          Hussain, Z., Gimenez, F., Yi, D., & Rubin, D. (2017). Differential data augmentation
              techniques for medical imaging classification tasks. AMIA annual symposium
              proceedings (p. 979). American Medical Informatics Association.
          Kaur, P., Kumar, R. & Kumar, M. (2019). A healthcare monitoring system using
              random forest and internet of things (IoT). Multimedia Tools and Applications,
              78, 19905–19916. https://doi.org/10.1007/s11042-019-7327-8
          Khalilia, M., Chakraborty, S., & Popescu, M. (2011). Predicting disease risks from
              highly imbalanced data using random forest. BMC Med Inform Decis Mak, 11,
              1-13. https://doi.org/10.1186/1472-6947-11-51
          Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with
              deep  convolutional  neural  networks.  Advances  in  Neural  Information
              Processing Systems, 25.
          Linh,  P.  (2023).  Quá  tải  bệnh  nhân,  y  bác  sĩ  Cần  Thơ  chia  nhau  gồng  gánh.
              https://laodong.vn/y-te/qua-tai-benh-nhan-y-bac-si-can-tho-chia-nhau-gong-
              ganh-1269830.ldo
          Lundberg,  S.  M., &  Lee, S.  I.  (2017).  A  unified  approach to interpreting model
              predictions.  Advances  in  Neural  Information  Processing  Systems  30  (NIPS
              2017). https://doi.org/10.48550/arXiv.1705.07874
          Marling, C., & Bunescu, R. (2018). The ohiot1dm dataset for blood glucose level
                              rd
              prediction.  The  3   International  Workshop  on  Knowledge  Discovery  in
              Healthcare Data. Stockholm, Sweden.






          328
   337   338   339   340   341   342   343   344   345   346   347