Predictive complex event processing based on evolving Bayesian networks

TL;DR: Results show that the RECEP consumes computational resources with a growth trend, represented by a logarithmic regression model (r-squared > 0.9); that is, the more events are processed, there is a minimum consumption of resources.

TL;DR: A novel approach for building an Artificial Neural Network to reconstruct experimental design data using Levenberg-Marquardt optimizer and validating obtained model based on K-fold cross-validation is implemented and shows the much-improved capability to predict optimal results.

TL;DR: In this article, a framework that exploits ML and CEP's synergy at the edge in distributed sensor networks is proposed, where the computation from the cloud to the power-constrained IIoT devices and allow users to adapt the on-device ML model and the CEP reasoning logic flexibly on the fly without requiring to reupload the whole program.

TL;DR: This paper proposes Complex Patterns of Failure (CPoF), an approach to providing reactive and proactive FT using Complex Event Processing (CEP) and Machine Learning (ML), and trained predictive models to learn from reactive-FT support and provide preemptive error recovery.

TL;DR: Probability distributions of linear models for regression and classification are given in this article, along with a discussion of combining models and combining models in the context of machine learning and classification.

TL;DR: In this article, a Bayesian approach for learning Bayesian networks from a combination of prior knowledge and statistical data is presented, which is derived from a set of assumptions made previously as well as the assumption of likelihood equivalence, which says that data should not help to discriminate network structures that represent the same assertions of conditional independence.

TL;DR: A novel deep-learning-based traffic flow prediction method is proposed, which considers the spatial and temporal correlations inherently and is applied for the first time that a deep architecture model is applied using autoencoders as building blocks to represent traffic flow features for prediction.

TL;DR: The first empirical results simultaneously comparing most of the major Bayesian network algorithms against each other are presented, namely the PC, Sparse Candidate, Three Phase Dependency Analysis, Optimal Reinsertion, Greedy Equivalence Search, and Greedy Search.