With the rise of AI, algorithms have become better at learning underlying patterns from the
training data including ingrained social biases based on gender, race, etc. Deployment of …

Vector control plays a critical role in a permanent magnet synchronous motor (PMSM) drive
to deliver the desired torque in electrified vehicle applications. Motor speed and stator …

The discovery of causal relationships from observational data is an omnipresent task in data
science. In real-world scenarios, observational data is often high-dimensional, and …

Conventional neural network (CNN) has been extensively applied in the field of fault
diagnosis for multilevel inverter. However, most CNN based diagnostic strategies are …

Bayesian networks (BNs) are a widely used graphical model in machine learning. As
learning the structure of BNs is NP-hard, high-performance computing methods are …

Child mortality from preventable diseases such as pneumonia and diarrhoea in low and
middle-income countries remains a serious global challenge. We combine knowledge with …

Learning the causal structures from high-dimensional observational data is an omnipresent
challenge in data science. State-of-the-art methods for constraint-based Causal Structure …

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

We develop a novel hybrid method for Bayesian network structure learning called
partitioned hybrid greedy search (pHGS), composed of three distinct yet compatible new …

The examination of causal structures is crucial for data scientists in a variety of machine
learning application scenarios. In recent years, the corresponding interest in methods of …