Blood pressure diseases have increasingly been identified as among the main factors threatening human health. How to accurately and conveniently measure blood pressure is the key to the implementation of effective prevention and control measures for blood pressure diseases. Traditional blood pressure measurement methods exhibit many inherent disadvantages, for example, the time needed for each measurement is difficult to determine, continuous measurement causes discomfort, and the measurement process is relatively cumbersome. Wearable devices that enable continuous measurement of blood pressure provide new opportunities and hopes. Although machine learning methods for blood pressure prediction have been studied, the accuracy of the results does not satisfy the needs of practical applications. This paper proposes an efficient blood pressure prediction method based on the support vector machine regression (SVR) algorithm to solve the key gap between the need for continuous measurement for prophylaxis and the lack of an effective method for continuous measurement. The results of the algorithm were compared with those obtained from two classical machine learning algorithms, i.e., linear regression (LinearR), back propagation neural network (BP), with respect to six evaluation indexes (accuracy, pass rate, mean absolute percentage error (MAPE), mean absolute error (MAE), R-squared coefficient of determination (R²) and Spearman’s rank correlation coefficient). The experimental results showed that the SVR model can accurately and effectively predict blood pressure. The multi-feature joint training and predicting techniques in machine learning can potentially complement and greatly improve the accuracy of traditional blood pressure measurement, resulting in better disease classification and more accurate clinical judgements.

Air pollution is one of the most serious environmental problems in the world. At present, air quality models are focused on pollutant prediction and source discovery, lacking the analysis of transmission paths and independent monitoring station characteristics. In this paper, complex network based on statistics is applied in air quality field and a new model is proposed based on it. Path existential network and pollutant transmission path set are generated integrating the data of air quality and meteorological monitoring stations. Mapping relation is established among them to generate real-time air quality network, then a complex network in a whole cycle is obtained through statistics. Experiments based on PM2.5 pollution data in Jing-Jin-Ji region demonstrate the rationality of the proposed model. Three characteristics of complex network: scale-free, small-world and community aggregation are verified. Characteristic detection and key station mining provide guidance for air protection in reality. In the network, we can reduce pollution effect by blocking a few important transmission paths between communities. The results provide reference for site selection of new monitoring station, dynamic evolution and pollution degradation.

Roll-bending technology has a high degree of flexibility and does not require special molds. However, based on the existing plastic mechanics theory and finite element simulation, it is difficult to accurately analyze the complex spatial relationship of profile roll forming. Therefore, a fixed-curvature prediction model is constructed based on XGBoost (extreme gradient boosting), and the coupling effect of the process parameters and material performance parameters on the roll-forming process is explored. Combined with a Bayesian optimization algorithm, the hyperparameters of the fixed-curvature prediction model are optimized. In addition, based on the prediction result of the fixed curvature, a variable-curvature prediction model is established using the conditional random field (CRF). To further improve the prediction accuracy, an error compensation network is added after the result of the CRF in order to map the discrete sequence to the continuous sequence. The experimental results show that the mean square error (MSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) predicted by the models above are much smaller than other methods, which verifies the superiority of the prediction models.

Denial of service (DoS) attack is a typical and extremely destructive attack, which poses a serious threat to the Internet security and is highly concealed, making it difficult to detect. In response to this problem, the paper proposes an efficient DoS attack traffic detection method, Random Forest and Multilayer Perceptron hybrid network attack detection algorithm (RF-MLP). At first, it is adopted that the random forest algorithm can be used for feature selection and the optimal threshold can be determined by drawing a learning curve; therefore the optimal feature subset is determined. Then the optimal feature subset is used as the input of the multilayer perceptron for training. We will analyze the experimental results obtained using different configurations by varying the number of training neurons and the number of hidden layers of the multilayer perceptron network in order to improve the accuracy and reduce the number of false results. Using the real network traffic CICIDS2017 dataset and UNSW-NB15 dataset to evaluate the method in this paper, the results show that the model can effectively detect and classify DoS attacks, the accuracy rate can reach 99.83% and 93.51%, and there is also a significant reduction in the false alarm rate, verifying the effectiveness of the method and its ease of use.

Artificial intelligence can play an important role in manufacture for reducing costs and the enhancing production efficiency and product quality. In order to assist designers in the early stages of a product development, this paper develops an intelligent methodology for integration of design and assembly planning processes, including product design assembly evaluation and redesign, assembly process planning etc. A new unified class of object-oriented knowledge based Petri nets, incorporating knowledge based expert systems and fuzzy logic into ordinary place-transition Petri nets, is defined and used for the representation and modeling of the distributed design processes. Petri net system is capability of problem solving, learning and conflict resolution, using Petri net system, product design and assembly planning can be carried out simultaneously and intelligently in an entirely computer-aided concurrent design