Diseases related to issues with blood pressure are becoming a major threat to human health. With the development of telemedicine monitoring applications, a growing number of corresponding devices are being marketed, such as the usage of remote monitoring for the purposes of increasing the autonomy of the elderly and thus encouraging a healthier and longer health span. Using the machine learning algorithms to measure blood pressure at a continuous rate is a feasible way to provide models and analysis for telemedicine monitoring data and predicting blood pressure. For this paper, we applied the gradient boosting decision tree (GBDT) whilst predicting blood pressure rates based on the human physiological data collected by the EIMO device. EIMO equipment specific signal acquisition includes: ECG, PPG. In order to avoid over-fitting, the optimal parameters are selected via the cross-validation method. Consequently, our method has displayed a higher accuracy rate and better performance in calculating the mean absolute error evaluation index than methods such as: the traditional Least squares method, Ridge regression, Lasso regression, ElasticNet, SVR and KNN algorithm. When predicting the blood pressure of a single individual, calculating the systolic pressure displays an accuracy rate of above 70% and above 64% for calculating diastolic pressure with GBDT, with the prediction time being less than 0.1s. In conclusion, applying the GBDT is the best method for predicting the blood pressure of multiple individuals: with the inclusion of data such as age, body fat, ratio and height, algorithm accuracy improves, which in turn indicates that the inclusion of new features aids prediction performance.

Biofilm formation enhances bacterial survival and antibiotic tolerance, but the underlying mechanisms are incompletely understood. Here, we show that biofilm growth is accompanied by a reduction in bacterial energy metabolism and membrane potential, together with metabolic exchanges between the inner and outer regions in biofilms. More specifically, nutrient-starved cells in the interior supply amino acids to cells in the periphery, while peripheral cells experience a decrease in membrane potential and provide fatty acids to interior cells. Fatty acids facilitate the repair of starvation-induced membrane damage in inner cells and enhance their survival in the presence of antibiotics. Thus, metabolic exchanges between inner and outer cells contribute to survival of the nutrient-starved inner cells and contribute to antibiotic tolerance within the biofilm.

Blood pressure diseases have become one of the major threats to human health. Continuous measurement of blood pressure has proven to be a prerequisite for effective incident prevention. In contrast with the traditional prediction models with low measurement accuracy or long training time, non-invasive blood pressure measurement is a promising use for continuous measurement. Thus in this paper, classification and regression trees (CART) are proposed and applied to tackle the problem. Firstly, according to the characteristics of different information, different CART models are constructed. Secondly, in order to avoid the over-fitting problem of these models, the cross-validation method is used for selecting the optimum parameters so as to achieve the best generalization of these models. Based on the biological data collected from CM400 monitor, this approach has achieved better performance than the common existing models such as linear regression, ridge regression, the support vector machine and neural network in terms of accuracy rate, root mean square error, deviation rate, Theil IC, and the required training time is also comparatively less. With increasing data, the accuracy rate of predicting systolic blood pressure and diastolic blood pressure by CART exceeds 90%, and the training time is less than 0.5s.

Intrusion detection refers to monitoring network data information, quickly detecting intrusion behavior, can avoid the harm caused by intrusion to a certain extent. Traditional intrusion detection methods are mainly focused on rule files and data mining. They have the disadvantage of not being able to detect new types of attacks and have the slow detection speed. To address these issues, an intrusion detection method based on improved PCA combined with Gaussian Naive Bayes was proposed. By weighting the first few feature vectors of the traditional PCA, data pollution can be reduced. The number of final weighted principal components is 2 through sequential selection. The dimensionality reduction of the data is achieved through improved PCA. Finally, the intrusion behaviors were detected by using the Gaussian Naive Bayes classifier. The indexes of detection accuracy, detection time, precision rate, and recall rate were applied to evaluate the results. The experimental results show that, comparing with the traditional Bayes method, the method proposed in this article can reduce the detection time by 60%, shorten it to 0.5s, and increase the detection rate to 91.06%. The mean value of detection accuracy is about 86% by cross-validation.

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.