Continuous noninvasive blood glucose monitoring and estimation management by using photoplethysmography (PPG) technology always have a series of problems, such as substantial time variability, inaccuracy, and complex nonlinearity. This paper proposes a blood glucose (BG) prediction model for more precise prediction based on BG series decomposition by complete aggregation empirical mode decomposition based on adaptive white noise (CEEMDAN) and the gated recurrent unit (GRU) that is optimized by improved bacterial foraging optimization (IBFO). Hierarchical clustering technology recombines the decomposed BG series according to their sample entropy and the correlations with the original BG trends. Dynamic BG trends are regressed separately for each recombined BG series by the GRU model to realize the more precise estimations, which are optimized by IBFO for its structure and superparameters. Through experiments, the optimized and basic LSTM, RNN, and support vector regression (SVR) are compared to evaluate the performance of the proposed model. The experimental results indicate that the root mean square error (RMSE) and mean absolute percentage error (MAPE) of the 15-min IBFO-GRU prediction is improved on average by about 13.1% and 18.4%, respectively, compared with those of the RNN and LSTM optimized by IBFO. Meanwhile, the proposed model improved the Clarke error grid results by about 2.6% and 5.0% compared with those of the IBFO-LSTM and IBFO-RNN in 30-min prediction and by 4.1% and 6.6% in 15-min ahead forecast, respectively. The evaluation outcomes of our proposed CEEMDAN-IBFO-GRU model have high accuracy and adaptability and can effectively provide early intervention control of the occurrence of hyperglycemic complications.

With the advent of the mass tourism era, innovative tourism services have attracted increasing attention from numerous tourism consumers. The wide application of big data and cloud computing technology makes it possible to build an intelligent tourism service platform to improve the experience of tourists. This research proposes a series of building technical proposals for a smart tourism service platform based on big data technology. The platform mainly focuses on massive data-based tourism administration, such as emotion analysis, tourist route recommendation, tourist portrait, passenger flow trend prediction, using big data analysis, mining, and processing. It also produces a series of relevant services and constructs special applications with various functions to realize tourism data acquisitions, research, and manipulations. Finally, the intelligent tourism platform's massive data analysis and processing results provide feasible and optimized decision-making references for the government, enterprises, tourists, and other stakeholders. It puts forward countermeasures for the innovative tourism service platform's implementation and application to promote the sustainable development of creative tourism.