Accurate fault diagnosis is critical to the safe and reliable operation of the drilling pump. The
challenge in fault diagnosis of the fluid end of the drilling pump is to extract the fault features …

Probabilistic graphical models (PGMs) have attracted much attention due to their firm
theoretical foundation and inherent interpretability. However, existing PGM inference …

Abstract Hyperparameter Optimization and Neural Architecture Search are powerful in
attaining state-of-the-art machine learning models with Bayesian Optimization (BO) standing …

Abstract This article introduces Alternated Sorting Method Genetic Algorithm (ASMGA), a
simultaneous feature selection and model selection algorithm for Support Vector Machine …

Existing device authentication techniques may suffer from heavy communication,
computation, and storage overhead for identifying a growing number of devices in …

Bayesian networks (BNs) are a widely used graphical model in machine learning for
representing knowledge with uncertainty. The mainstream BN structure learning methods …

Bayesian networks (BNs) are graphical models representing uncertainty in causal discovery,
and have been widely used in medical diagnosis and gene analysis due to their …

Federated Learning (FL) is a distributed machine learning framework in parallel and
distributed systems. However, the systems' Non-Independent and Identically Distributed …

Explaining Deep Neural Networks to Establish Trust Page 1 Explaining Deep Neural Networks
to Establish Trust Peiyu Yang This thesis is presented for the degree of Doctor of Philosophy of …

Probabilistic graphical models (PGMs) serve as a powerful framework for modeling complex
systemsunder uncertainty, finding widespread application across domains such as …