Predicting diarrhoea outbreaks with climate change

27. Nannen V, Eiben A. Efficient Relevance Estimation and Value Calibration of Evolutionary Algorithm Parameters. In: 2007 IEEE congress on evolutionary computation. IEEE; 2007. p. 103–110. 28. Smit S, Eiben A. Comparing Parameter Tuning Methods for Evolutionary Algorithms. In: 2009 IEEE Congress on Evolutionary Computation. IEEE; 2009. p. 399–406. 29. Smit S, Eiben A. Beating the ‘World Champion’Evolutionary Algorithm via REVAC Tuning. In: IEEE Congress on Evolutionary Computation. IEEE; 2010. p. 1–8. 30. Jorge S, Sevilla J. Importance of Input Data Normalization for the Application of Neural Networks to Complex Industrial Problems. IEEE Transactions on Nuclear Science. 1997; 44(3):1464–1468. https:// doi.org/10.1109/23.589532 31. Borer S, Graf A. Normalization in Support Vector Machines. In: Joint Pattern Recognition Symposium. Munich, Germany: Springer; 2001. p. 277–282. 32. Pelanek R. Metrics for Evaluation of Student Models. Journal of Educational Data Mining. 2015; 7(2):1– 19. 33. Lara-Benı´tez P, Carranza-Garcı´a M, Luna-Romera JM, Riquelme JC. Temporal convolutional networks applied to energy-related time series forecasting. applied sciences. 2020; 10(7):2322. https://doi.org/ 10.3390/app10072322 34. Safavian SR, Landgrebe D. A survey of decision tree classifier methodology. IEEE transactions on sys- tems, man, and cybernetics. 1991; 21(3):660–674. https://doi.org/10.1109/21.97458 35. Ding C, Liao S, Wang Y, Li Z, Liu N, Zhuo Y, et al. Circnn: accelerating and compressing deep neural

networks using block-circulant weight matrices. In: Proceedings of the 50th Annual IEEE/ACM Interna- tional Symposium on Microarchitecture; 2017. p. 395–408. 36. Sak H, Senior A, Beaufays F. Long short-term memory based recurrent neural network architectures for large vocabulary speech recognition. arXiv preprint arXiv:14021128. 2014;. 37. Abdiansah A, Wardoyo R. Time complexity analysis of support vector machines (SVM) in LibSVM. International journal computer and application. 2015; 128(3):28–34. https://doi.org/10.5120/ ijca2015906480 38. Gevrey M, Dimopoulos I, Lek S. Review and Comparison of Methods to Study the Contribution of Vari- ables in Artificial Neural Network Models. Ecological Modelling. 2003; 160(3):249–264. https://doi.org/ 10.1016/S0304-3800(02)00257-0 39. Yang S, Yu X, Zhou Y. LSTM and GRU Neural Network Performance Comparison Study: Taking Yelp Review Dataset as an Example. In: 2020 International Workshop on Electronic Communication and Artificial Intelligence (IWECAI). IEEE; 2020. p. 98–101. 40. Nguyen V, Gupta S, Rane S, Li C, Venkatesh S. Bayesian Optimization in Weakly Specified Search Space. In: 2017 IEEE International Conference on Data Mining (ICDM). IEEE; 2017. p. 347–356. 41. Fang X, Liu W, Ai J, He M, Wu Y, Shi Y, et al. Forecasting incidence of infectious diarrhea using random forest in Jiangsu Province, China. BMC infectious diseases. 2020; 20(1):1–8. https://doi.org/10.1186/ s12879-020-4930-2 PMID: 32171261 42. Kamai T, Weisbrod N, Dragila M. Impact of Ambient Temperature on Evaporation from Surface-

exposed Fractures. Water Resources Research. 2009; 45(2). https://doi.org/10.1029/2008WR007354 PLOS ONE Predicting diarrhoea outbreaks with climate change PLOS ONE | https://doi.org/10.1371/journal.pone.0262008 April 19, 2022 18 / 18