Short-term prediction of wave height is paramount in oceanic operation-related activities. Statistical models have advantages in short-term wave prediction as complex physical process is substantially simplified. However, previous statistical models have no consideration in selection of predictive variables and dealing with prediction uncertainty. This paper develops a machine learning model by combining the dynamic Bayesian network (DBN) with the information flow (IF) designated as DBN-IF. IF is focused on selecting the best predictive variables for DBN by causal analysis instead of correlation analysis. DBN for probabilistic prediction is constructed by structure learning and parameter learning with data mining. Based on causal theory, graph theory, and probability theory, the proposed DBN-IF model could deal with the uncertainty and shows great performance in significant wave height prediction compared with the artificial neural network (ANN), random forest (RF) and support vector machine (SVM) for all lead times. The interpretable DBN-IF is proven as a promising tool for nonlinear and uncertain wave height prediction.
The Rural Cooperative Medical Scheme (RCMS) had played an important role in guaranteeing the acquisition of basic medical healthcare of China's rural populations, being an innovative model of the medical insurance system for so many years here in China. Following the boom and bust of RCMS, the central government rebuilt the New Rural Cooperative Medical Scheme (NRCMS) in 2003 across the whole country. Shanghai, one of the developed cities in China, has developed its RCMS and NRCMS as an advanced and exemplary representative of Chinese rural health insurance. But in the past 10 years, its NRCMS has encountered such challenges as a spiral of medical expenditures and a decrease of insurance participants. Previous investigations showed that the capitation and general practitioner (GP) system had great effect on medical cost containment. Thus, the capitation reform combined with GP system reform of NRCMS, based on a system design, was implemented in Pudong New Area of Shanghai as of 1 August 2012. The aim of the current investigation was to present how the reform was designed and implemented, evaluating its effect by analyzing the data acquired from 12 months before and after the reform. This was an empirical study; we made a conceptual design of the reform to be implemented in Pudong New Area. Most data were derived from the institution-based surveys and supplemented by a questionnaire survey, qualitative interviews and policy document analysis. We found that most respondents held an optimistic attitude towards the reform. We employed a structure-process-outcome evaluation index system to evaluate the effect of the reform, finding that the growth rate of the insured population's total medical costs and NRCMS funds slowed down significantly after the reform; that the total medical expenditure of the insured rural population decreased by 3.60%; and that the total expenditure of NRCMS decreased by 3.99%. The capitation was found to help the medical staff build active cost control consciousness. Approximately 2.3% of the outpatients flowed to the primary hospitals from the secondary hospitals; and farmers' annual medical burden was relieved to a certain degree. Meanwhile, it did not affect farmers' utilization and benefits of healthcare. However, further reform still faces new challenges: The capitation reform should be well combined with the primary healthcare system to realize the "dual gatekeeper" of GPs; a variety of payment methods should be mixed on the basis of capitation to avoid possible mistakes by one single approach; and the supervision of medical institutions should be strengthened. A long-term follow-up study need to be carried out to evaluate the effects of the capitation reform so as to improve the design of the program. Copyright © 2015 John Wiley & Sons, Ltd.
With the rapid development and widespread application of cloud computing, cloud computing open networks and service sharing scenarios have become more complex and changeable, causing security challenges to become more severe. As an effective means of network protection, anomaly network traffic detection can detect various known attacks. However, there are also some shortcomings. Deep learning brings a new opportunity for the further development of anomaly network traffic detection. So far, the existing deep learning models cannot fully learn the temporal and spatial features of network traffic and their classification accuracy needs to be improved. To fill this gap, this paper proposes an anomaly network traffic detection model integrating temporal and spatial features (ITSN) using a three-layer parallel network structure. ITSN learns the temporal and spatial features of the traffic and fully fuses these two features through feature fusion technology to improve the accuracy of network traffic classification. On this basis, an improved method of raw traffic feature extraction is proposed, which can reduce redundant features, speed up the convergence of the network, and ease the imbalance of the datasets. The experimental results on the ISCX-IDS 2012 and CICIDS 2017 datasets show that the ITSN can improve the accuracy of anomaly network traffic detection while enhancing the robustness of the detection system and has a higher recognition rate for positive samples.
Network intrusion detection, which takes the extraction and analysis of network traffic features as the main method, plays a vital role in network security protection. The current network traffic feature extraction and analysis for network intrusion detection mostly uses deep learning algorithms. Currently, deep learning requires a lot of training resources and has weak processing capabilities for imbalanced datasets. In this paper, a deep learning model (MFVT) based on feature fusion network and vision transformer architecture is proposed, which improves the processing ability of imbalanced datasets and reduces the sample data resources needed for training. Besides, to improve the traditional raw traffic features extraction methods, a new raw traffic features extraction method (CRP) is proposed, and the CPR uses PCA algorithm to reduce all the processed digital traffic features to the specified dimension. On the IDS 2017 dataset and the IDS 2012 dataset, the ablation experiments show that the performance of the proposed MFVT model is significantly better than other network intrusion detection models, and the detection accuracy can reach the state-of-the-art level. And, when MFVT model is combined with CRP algorithm, the detection accuracy is further improved to 99.99%.
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