OBJECTIVE To apply and compare common machine learning techniques with an expert-built Bayesian Network to determine eligibility for asthma guidelines in pediatric emergency department patients. POPULATION All patients 2-18 years of age presenting to a pediatric emergency department during a 2-month study period. METHODS We created an artificial neural network, a support vector machine, a G...

This article presents a sliding mode control (SMC) with artificial neural network (ANN) regulator for the doubly fed induction generator (DFIG) using two-level neural pulse width modulation (NPWM) technique. The proposed control scheme of the DFIG-based wind turbine system (WTS) combines the advantages of SMC control and ANN regulator. The reaching conditions, robustness and stability of the sy...

Objective: Breast cancer is one of the most common cancers affecting women. Both physicians and patients have concerned about breast cancer survivability. Many researchers have studied the breast cancer survivability applying artificial nerural network model (ANN). Usually ANN model outperformed in classification of breast cancer survivability than other models such as logistic regression, Baye...

Quantum metrology promises unprecedented measurement precision but suffers in practice from the limited availability of resources such as number probes, their coherence time, or nonclassical quantum states. The adaptive Bayesian approach to parameter estimation allows an efficient use because experiment design. For its practical success, fast numerical solutions for update and design are crucia...

Bayesian Network has provided a convenient frame structure to express causal relationship, by regarding influence diagram as a special Bayesian Network, then the value of each decision variable is imposed externally to meet the goals, rather than derive from determination probability of father node. Using influence diagram to describe the node types of directed acyclic graph has been studied, w...

We present a tool to estimate the performance of the neural network in a neural network based adaptive controller. Using a Bayesian approach, this tool supports verification and validation of the adaptive controller as well as on-line monitoring. In this paper, we discuss our approach and present simulation results using the adaptive controller developed for NASA’s IFCS (Intelligent Flight Cont...