Agrawal, A., & Mittal, N. (2020). Using CNN for facial expression recognition: a study of the effects of kernel size and number of filters on accuracy. Visual Computer, 36(2), 405–412. https://doi.org/10.1007/s00371-019-01630-9
Bagherzadeh, J., & Asil, H. (2019). A review of various semi-supervised learning models with a deep learning and memory approach. Iran Journal of Computer Science, 2(2), 65–80. https://doi.org/10.1007/s42044-018-00027-6
Bekkar, A., Hssina, B., Douzi, S., & Douzi, K. (2021). Air-pollution prediction in smart city, deep learning approach. Journal of Big Data, 8(1), 1–21. https://doi.org/10.1186/s40537-021-00548-1
Castiñeira, D., Schlosser, K. R., Geva, A., Rahmani, A. R., Fiore, G., Walsh, B. K., Smallwood, C. D., Arnold, J. H., & Santillana, M. (2020). Adding continuous vital sign information to static clinical data improves the prediction of length of stay after intubation: A data-driven machine learning approach. Respiratory Care, 65(9), 1367–1377. https://doi.org/10.4187/respcare.07561
Dargazany, A. R., Stegagno, P., & Mankodiya, K. (2018). WearableDL: Wearable Internet-of-Things and Deep Learning for Big Data Analytics - Concept, Literature, and Future. Mobile Information Systems, 2018(Dl). https://doi.org/10.1155/2018/8125126
Davies, E. R. (2018). Face detection and recognition. In Computer Vision (Issue June). https://doi.org/10.1016/b978-0-12-809284-2.00021-6
Emmert-Streib, F., Yang, Z., Feng, H., Tripathi, S., & Dehmer, M. (2020). An Introductory Review of Deep Learning for Prediction Models With Big Data. Frontiers in Artificial Intelligence, 3(February), 1–23. https://doi.org/10.3389/frai.2020.00004
Ferreira, P., Le, D. C., & Zincir-Heywood, N. (2019). Exploring Feature Normalization and Temporal Information for Machine Learning Based Insider Threat Detection. 2019 15th International Conference on Network and Service Management (CNSM), (pp. 1-7). IEEE.
Hayou, S., Doucet, A., & Rousseau, J. (2019). On the impact of the activation function on deep neural networks training. 36th International Conference on Machine Learning, ICML 2019, 2019-June, 4746–4754.
Heydari, A., Majidi Nezhad, M., Astiaso Garcia, D., Keynia, F., & De Santoli, L. (2022). Air pollution forecasting application based on deep learning model and optimization algorithm. Clean Technologies and Environmental Policy, 24(2), 607–621. https://doi.org/10.1007/s10098-021-02080-5
Isam Drewil, G., & Jabbar Al-Bahadili, R. (2021). Multicultural Education Forecast Air Pollution in Smart City Using Deep Learning Techniques: A Review. Multicultural Education, 7(5), 38–47. https://doi.org/10.5281/zenodo.4737746
Khan, Z. Y., Niu, Z., Sandiwarno, S., & Prince, R. (2021). Deep learning techniques for rating prediction: a survey of the state-of-the-art. In Artificial Intelligence Review (Vol. 54, Issue 1). Springer Netherlands. https://doi.org/10.1007/s10462-020-09892-9
Li, X., Peng, L., Yao, X., Cui, S., Hu, Y., You, C., & Chi, T. (2017). Long short-term memory neural network for air pollutant concentration predictions: Method development and evaluation. Environmental Pollution, 231, 997–1004. https://doi.org/10.1016/j.envpol.2017.08.114
Ma, M., Dong, Z., Jang, C., Zhu, Y., & Zheng, H. (2020). Deep learning for prediction of the air quality response to emission changes. Environmental Science & Technology, 54(14), 8589–8600. https://doi.org/10.1021/acs.est.0c02923.Deep
Mairal, J. (2016). End-to-end kernel learning with supervised convolutional kernel networks. Advances in Neural Information Processing Systems, Nips, 1407–1415.
Mao, Y., & Lee, S. (2019). Deep Convolutional Neural Network for Air Quality Prediction. Journal of Physics: Conference Series, 1302(3), 032046. https://doi.org/10.1088/1742-6596/1302/3/032046
Mohsen, H., El-Dahshan, E.-S. A., El-Horbaty, E.-S. M., & Salem, A.-B. M. (2018). Classification using deep learning neural networks for brain tumors. Future Computing and Informatics Journal, 3(1), 68–71. https://doi.org/10.1016/j.fcij.2017.12.001
Mureşan, H., & Oltean, M. (2018). Fruit recognition from images using deep learning. Acta Universitatis Sapientiae, Informatica, 10(1), 26–42. https://doi.org/10.2478/ausi-2018-0002
Muthukumar, P., Cocom, E., Nagrecha, K., Comer, D., Burga, I., Taub, J., Calvert, C. F., Holm, J., & Pourhomayoun, M. (2022). Predicting PM2.5 atmospheric air pollution using deep learning with meteorological data and ground-based observations and remote-sensing satellite big data. Air Quality, Atmosphere and Health, 15(7), 1221–1234. https://doi.org/10.1007/s11869-021-01126-3
Oprea, M., Popescu, M., Mihalache, S. F., & Dragomir, E. G. (2017). Data mining and ANFIS application to particulate matter air pollutant prediction. A comparative study. ICAART 2017 - Proceedings of the 9th International Conference on Agents and Artificial Intelligence, 2(Icaart), 551–558. https://doi.org/10.5220/0006196405510558
Pires, I. M., Hussain, F., Garcia, N. M., Lameski, P., & Zdravevski, E. (2020). Homogeneous data normalization and deep learning: A case study in human activity classification. Future Internet, 12(11), 1–14. https://doi.org/10.3390/fi12110194
Schürholz, D., Kubler, S., & Zaslavsky, A. (2020). Artificial intelligence-enabled context-aware air quality prediction for smart cities. Journal of Cleaner Production, 271, 121941. https://doi.org/10.1016/j.jclepro.2020.121941
Towfek El-Kenawy, E.-S. M. (2019). A Machine Learning Model for Hemoglobin Estimation and Anemia Classification. International Journal of Computer Science and Information Security (IJCSIS), 17(2). https://sites.google.com/site/ijcsis/
Weiss, S., Xu, Z. Z., Peddada, S., Amir, A., Bittinger, K., Gonzalez, A., Lozupone, C., Zaneveld, J. R., Vázquez-Baeza, Y., Birmingham, A., Hyde, E. R., & Knight, R. (2017). Normalization and microbial differential abundance strategies depend upon data characteristics. Microbiome, 5(1), 1–18. https://doi.org/10.1186/s40168-017-0237-y
Wu, W., May, R. J., Maier, H. R., & Dandy, G. C. (2013). A benchmarking approach for comparing data splitting methods for modeling water resources parameters using artificial neural networks. Water Resources Research, 49(11), 7598–7614. https://doi.org/10.1002/2012WR012713
Zhang, J., & Ding, W. (2017). Prediction of air pollutants concentration based on an extreme learning machine: The case of Hong Kong. International Journal of Environmental Research and Public Health, 14(2), 1–19. https://doi.org/10.3390/ijerph14020114
Zhang, Q., Lam, J. C., Li, V. O., & Han, Y. (2020). Deep-AIR: A Hybrid CNN-LSTM Framework forFine-Grained Air Pollution Forecast. ArXiv Preprint ArXiv:2001.11957, 1–7. http://arxiv.org/abs/2001.11957
Zhong, Y., & Zhao, M. (2020). Research on deep learning in apple leaf disease recognition. Computers and Electronics in Agriculture, 168(August 2019), 105146. https://doi.org/10.1016/j.compag.2019.105146
Zhu, D., Cai, C., Yang, T., & Zhou, X. (2018). A machine learning approach for air quality prediction: Model regularization and optimization. Big Data and Cognitive Computing, 2(1), 1–15. https://doi.org/10.3390/bdcc2010005