Bayesian networks have become a widely used method in the modelling of uncertain
knowledge. Owing to the difficulty domain experts have in specifying them, techniques that …

This paper presents a novel time varying dynamic Bayesian network (TVDBN) model for the
analysis of nonstationary sequences which are of interest in many fields. The changing …

Background and objective Atrial fibrillation (AF) is the most common cardiac arrhythmia in
clinical practice, having been recognized as a true cardiovascular epidemic. In this paper, a …

In this paper, we propose a security framework based on the semi-network form game in
unison with a robust and attack resilient scheduling mechanism for a cloud integrated Cyber …

An innovative taxonomy for the classification of classifiers is presented. This new family of
meta-classifiers called Meta-Net, having its foundation in the theory of independent judges …

Activity modeling and unusual event detection in a network of cameras is challenging,
particularly when the camera views are not overlapped. We show that it is possible to detect …

Background Treatment as usual (TAU) for autism spectrum disorders (ASDs) includes
eclectic treatments usually available in the community and school inclusion with an …

摘要概率图模型能有效处理不确定性推理, 从样本数据中准确高效地学**概率图模型是其在实际
应用中的关键问题. 概率图模型的表示由参数和结构两部分组成, 其学**算法也相应分为参数 …

In this article we introduce a new stability-based dynamic Bayesian network method for
dynamic systems represented by their time series. Based on the Grow Shrink algorithm and …

Bayesian network (BN) is the well-accepted framework for representing and inferring
uncertain knowledge. To learn the BN-based uncertain knowledge incrementally in …