Author:
Palbar Tenzin,Kesavulu Manoj,Gurrin Cathal,Verbruggen Renaat
Publisher
Springer International Publishing
Reference26 articles.
1. U.S. Department of Agriculture, A.R.S.: Food and nutrient database for dietary studies (fndds). In: FoodData Central. Food Surveys Research Group, Beltsville Human Nutrition Research Center (2017). http://www.ars.usda.gov/nea/bhnrc/fsrg
2. Alfian, G., et al.: Blood glucose prediction model for type 1 diabetes based on artificial neural network with time-domain features. Biocybernetics Biomed. Eng. 40(4), 1586–1599 (2020). https://doi.org/10.1016/j.bbe.2020.10.004, https://www.sciencedirect.com/science/article/pii/S0208521620301248
3. Alfian, G., Syafrudin, M., Rhee, J., Anshari, M., Mustakim, M., Fahrurrozi, I.: Blood glucose prediction model for type 1 diabetes based on extreme gradient boosting. In: IOP Conference Series: Materials Science and Engineering, vol. 803, p. 012012, May 2020. https://doi.org/10.1088/1757-899x/803/1/012012
4. Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001). https://doi.org/10.1023/A:1010933404324, http://dx.doi.org/10.1023/A%3A1010933404324
5. Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2016, pp. 785–794. Association for Computing Machinery, New York (2016). https://doi.org/10.1145/2939672.2939785