Experts' Knowledge Fusion in Model-Based Diagnosis Based on Bayes Networks

In previous researches on a model-based diagnostic system, the components are assumed mutually independent. Howerver , the assumption is not always the case because the information about whether a component is faulty or not usually influences our knowledge about other components. Some experts may draw such a conclusion that 'if component m 1 is faulty, then component m 2 may be faulty too'. How can we use this experts' knowledge to aid the diagnosis? Based on Kohlas's probabilistic assumption-based reasoning method, we use Bayes networks to solve this problem. We calculate the posterior fault probability of the components in the observation state. The result is reasonable and reflects the effectiveness of the experts' knowledge.

Activities of daily living and lesion position among multiple sclerosis patients by Bayes network

Magnetic resonance imaging is a highly sensitive approach for diagnosis of multiple sclerosis, and T2-weighted images can reveal lesions in the cerebral white matter, gray matter, and spinal cord. However, the lesions have a poor correlation with measurable clinical disability. In this study, we performed a large-scale epidemiological survey of 238 patients with multiple sclerosis in eleven districts by network member hospitals in Shanghai, China within 1 year. The involved patients were scanned for position and size of lesions by MRI. Results showed that lesions in the cerebrum, spina cord, or supratentorial position had an impact on the activities of daily living in multiple sclerosis patients, as assessed by the Bayes network. On the other hand, brainstem lesions were very unlikely to influence the activities of daily living, and were not associated with the position of lesion, patient＇s gender, and patient＇s living place.

Mono-isotope Prediction for Mass Spectra Using Bayes Network

Mass spectrometry is one of the widely utilized important methods to study protein functions and components. The challenge of mono-isotope pattern recognition from large scale protein mass spectral data needs computational algorithms and tools to speed up the analysis and improve the analytic results. We utilized na¨？ve Bayes network as the classifier with the assumption that the selected features are independent to predict monoisotope pattern from mass spectrometry. Mono-isotopes detected from validated theoretical spectra were used as prior information in the Bayes method. Three main features extracted from the dataset were employed as independent variables in our model. The application of the proposed algorithm to public Mo dataset demonstrates that our na¨？ve Bayes classifier is advantageous over existing methods in both accuracy and sensitivity.

基于贝叶斯网的离港航班滑行时间动态估计

为提升离港航班运行效率,根据机场协同决策规范(A-CDM)中关于离港航班可变滑行时间(EXOT)的有关规定,分析了相关影响因素。根据数据分析处理和民航专家知识建立了一种基于贝叶斯网的离港航班滑行时间动态估计模型。贝叶斯网是一种将概率统计应用于复杂领域、进行不确定性推理和数据分析的工具。应用其增量学习特点对模型进行动态调整,实现了对场面实时变化的把控。以国内某大型枢纽机场为例,使用期望优化(EM)算法实现了对随机缺失数据的处理,并验证了模型的有效性。对实验结果与该机场实际运行数据对比表明,所建模型能有效地估计离港航班滑行时间且具有较高的置信度。

Network-based naive Bayes model for social network

Naive Bayes(NB) is one of the most popular classification methods. It is particularly useful when the dimension of the predictor is high and data are generated independently. In the meanwhile, social network data are becoming increasingly accessible, due to the fast development of various social network services and websites. By contrast, data generated by a social network are most likely to be dependent. The dependency is mainly determined by their social network relationships. Then, how to extend the classical NB method to social network data becomes a problem of great interest. To this end, we propose here a network-based naive Bayes(NNB) method, which generalizes the classical NB model to social network data. The key advantage of the NNB method is that it takes the network relationships into consideration. The computational efficiency makes the NNB method even feasible in large scale social networks. The statistical properties of the NNB model are theoretically investigated. Simulation studies have been conducted to demonstrate its finite sample performance.A real data example is also analyzed for illustration purpose.

Network Security Situation Evaluation Based on Modified D-S Evidence Theory

With the rapid development of global information and the increasing dependence on network for people, network security problems are becoming more and more serious. By analyzing the existing security assessment methods, we propose a network security situation evaluation system based on modified D-S evidence theory is proposed. Firstly, we give a modified D-S evidence theory to improve the reliability and rationality of the fusion result and apply the theory to correlation analysis. Secondly, the attack successful support is accurately calculated by matching internal factors with external threats. Multi-module evaluation is established to comprehensively evaluate the situation of network security. Finally we use an example of actual network datasets to validate the network security situation evaluation system. The simulation result shows that the system can not only reduce the rate of false positives and false alarms, but also effectively help analysts comprehensively to understand the situation of network security.

Bayes-Based ARP Attack Detection Algorithm for Cloud Centers

To address the issue of internal network security, Software-Defined Network（SDN） technology has been introduced to large-scale cloud centers because it not only improves network performance but also deals with network attacks. To prevent man-in-the-middle and denial of service attacks caused by an address resolution protocol bug in an SDN-based cloud center, this study proposed a Bayes-based algorithm to calculate the probability of a host being an attacker and further presented a detection model based on the algorithm. Experiments were conducted to validate this method.