[
Bergs, J., Heerinckx, P. and Verelst, S. (2014). Knowing what to expect, forecasting monthly emergency department visits: A time-series analysis, International Emergency Nursing 22(2): 112–115.
]Search in Google Scholar
[
Bingi, K. and Prusty, B.R. (2021). Forecasting models for chaotic fractional-order oscillators using neural networks, International Journal of Applied Mathematics and Computer Science 31(3): 387–398, DOI: 10.34768/amcs-2021-0026.
]Otwórz DOISearch in Google Scholar
[
Box, G.E., Jenkins, G.M., Reinsel, G.C. and Ljung, G.M. (2015). Time Series Analysis: Forecasting and Control, Wiley, Hoboken.
]Search in Google Scholar
[
Chatfield, C. and Yar, M. (1988). Holt–Winters forecasting: Some practical issues, Journal of the Royal Statistical Society: Series D (The Statistician) 37(2): 129–140.
]Search in Google Scholar
[
Chen, T. and Guestrin, C. (2016). XGBoost: A scalable tree boosting system, International Conference on Knowledge Discovery and Data Mining, San Francisco, USA, pp. 785–794.
]Search in Google Scholar
[
Chen, Z.-M., Wei, X.-S., Wang, P. and Guo, Y. (2019). Multi-label image recognition with graph convolutional networks, Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, USA, pp. 5177–5186.
]Search in Google Scholar
[
Cheng, Y. (2019). Joint Training for Neural Machine Translation, Springer theses, Springer, Singapore, chapter “Semi-supervised learning for neural machine translation”, pp. 25–40.
]Search in Google Scholar
[
Cross, S.S., Harrison, R.F. and Kennedy, R.L. (1995). Introduction to neural networks, The Lancet 346(8982): 1075–1079.
]Search in Google Scholar
[
Dai, J., He, K. and Sun, J. (2016). Instance-aware semantic segmentation via multi-task network cascades, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, USA, pp. 3150–3158.
]Search in Google Scholar
[
Dong, E., Du, H. and Gardner, L. (2020). An interactive web-based dashboard to track COVID-19 in real time, The Lancet Infectious Diseases 20(5): 533–534.
]Search in Google Scholar
[
Drucker, H., Burges, C.J., Kaufman, L., Smola, A., Vapnik, V. (1997). Support vector regression machines, Advances in Neural Information Processing Systems 9: 155–161.
]Search in Google Scholar
[
Greenwood, G.W. (1997). Training multiple-layer perceptrons to recognize attractors, IEEE Transactions on Evolutionary Computation 1(4): 244–248.
]Search in Google Scholar
[
Hadavandi, E., Shavandi, H., Ghanbari, A. and Abbasian-Naghneh, S. (2012). Developing a hybrid artificial intelligence model for outpatient visits forecasting in hospitals, Applied Soft Computing 12(2): 700–711.
]Search in Google Scholar
[
Hou, C., Wu, J., Cao, B. and Fan, J. (2021). A deep-learning prediction model for imbalanced time series data forecasting, Big Data Mining and Analytics 4(4): 266–278.
]Search in Google Scholar
[
Jiang, S., Chin, K.-S. and Tsui, K.L. (2018). A universal deep learning approach for modeling the flow of patients under different severities, Computer Methods and Programs in Biomedicine 154: 191–203.
]Search in Google Scholar
[
Kingma, D.P. and Ba, J. (2014). Adam: A method for stochastic optimization, arXiv: 1412.6980.
]Search in Google Scholar
[
Kowal, M., Skobel, M., Gramacki, A. and Korbicz, J. (2021). Breast cancer nuclei segmentation and classification based on a deep learning approach, International Journal of Applied Mathematics and Computer Science 31(1): 85–106, DOI: 10.34768/amcs-2021-0007.
]Otwórz DOISearch in Google Scholar
[
Ledersnaider, D.L. and Channon, B.S. (1998). Sdm95—Reducing aggregate care team costs through optimal patient placement, JONA: The Journal of Nursing Administration 28(10): 48–54.
]Search in Google Scholar
[
Li, Y., Ki, Y., Sun, N., Wu, F., Zheng, C., Sun, N. (2015). Predictive analysis of outpatient visits to a grade 3, class A hospital using ARIMA model, Proceedings of the 2014 International Symposium on Information Technology (ISIT 2014), Dalian, China, pp. 301–304.
]Search in Google Scholar
[
Liaw, A. and Wiener, M. (2002). Classification and regression by randomForest, R News 2(3): 18–22.
]Search in Google Scholar
[
Luo, L., Luo, L., Zhang, X. and He, X. (2017). Hospital daily outpatient visits forecasting using a combinatorial model based on ARIMA and SES models, BMC Health Services Research 17(1): 1–13.
]Search in Google Scholar
[
Mackay, M. (2001). Practical experience with bed occupancy management and planning systems: An Australian view, Health Care Management Science 4(1): 47–56.
]Search in Google Scholar
[
Makridakis, S., Spiliotis, E. and Assimakopoulos, V. (2018). Statistical and machine learning forecasting methods: Concerns and ways forward, PloS One 13(3): 1–26.
]Search in Google Scholar
[
Menke, N.B., Caputo, N., Fraser, R., Haber, J., Shields, C. and Menke, M.N. (2014). A retrospective analysis of the utility of an artificial neural network to predicted volume, The American Journal of Emergency Medicine 32(6): 614–617.
]Search in Google Scholar
[
Nair, V. and Hinton, G.E. (2010). Rectified linear units improve restricted Boltzmann machines, Proceedings of the 27th International Conference on Machine Learning, Haifa, Israel, pp. 807–814.
]Search in Google Scholar
[
Nassif, A.B., Shahin, I., Attili, I., Azzeh, M. and Shaalan, K. (2019). Speech recognition using deep neural networks: A systematic review, IEEE Access 7: 19143–19165.
]Search in Google Scholar
[
Neil, D., Pfeiffer, M. and Liu, S.-C. (2016). Phased LSTM: Accelerating recurrent network training for long or event-based sequences, International Conference on Neural Information Processing Systems, Barcelona, Spain, pp. 3889–3897.
]Search in Google Scholar
[
Oreshkin, B.N., Carpov, D., Chapados, N. and Bengio, Y. (2019). N-BEATS: Neural basis expansion analysis for interpretable time series forecasting, arXiv: 1905.10437.
]Search in Google Scholar
[
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J. and Chintala, S. (2019). PyTOrch: An imperative style, high-performance deep learning library, Advances in Neural Information Processing Systems 32: 8026–8037.
]Search in Google Scholar
[
Sanh, V., Wolf, T. and Ruder, S. (2019). A hierarchical multi-task approach for learning embeddings from semantic tasks, Proceedings of the AAAI Conference on Artificial Intelligence, Honolulu, USA, pp. 6949–6956.
]Search in Google Scholar
[
Sun, Y., Heng, B.H., Seow, Y.T. and Seow, E. (2009). Forecasting daily attendances at an emergency department to aid resource planning, BMC Emergency Medicine 9(1): 1–9.
]Search in Google Scholar
[
Wang, Y. and Gu, J. (2014). Hybridization of support vector regression and firefly algorithm for diarrhoeal outpatient visits forecasting, IEEE International Conference on Tools with Artificial Intelligence, Limassol, Cyprus, pp. 70–74.
]Search in Google Scholar
[
Zhang, J., Zheng, Y., Sun, J. and Qi, D. (2019). Flow prediction in spatio-temporal networks based on multitask deep learning, IEEE Transactions on Knowledge and Data Engineering 32(3): 468–478.
]Search in Google Scholar
[
Zhang, Y. and Yang, Q. (2021). A survey on multi-task learning, IEEE Transactions on Knowledge & Data Engineering (034(12): 5586–5609.
]Search in Google Scholar
