Mathematical programming, and above all, the multi-objective scheduling problems stand as remarkably versatile tools, highly useful for optimizing health care services. In this context, present work is designed to put forward two-fold mixed integer linear programs, simultaneously integrating objectives of minimizing patients? total waiting flow time, while doctors' work-load variations. For pur...

This paper presents a decision tool for the optimisation of wind turbine technical parameters, using multiphysical model power system. includes modelling aerodynamical, mechanical and electromechanical system behaviours. The aerodynamics is based on blade element momentum model. A mass turbines components also used in this paper. performed with NSGA-II algorithm which may choose parameters (bla...

In this thesis, we propose the use of Gaussian mixture models (GMMs) as simple, yet effective predictors of perceived speech quality. A large pool of perceptual distortion features is extracted from speech files. Initially, statistical data mining algorithms are used to sift out the most relevant variables from the pool. We show that the five most salient feature variables are sufficient to con...

PURPOSE To develop and evaluate the clinical applicability of advanced machine learning models that simultaneously predict multiple optimization objective function weights from patient geometry for intensity-modulated radiation therapy of prostate cancer. METHODS A previously developed inverse optimization method was applied retrospectively to determine optimal objective function weights for ...

We develop objective Bayesian analysis for the linear regression model with random errors distributed according to the exponential power distribution. More specifically, we derive explicit expressions for three different Jeffreys priors for the model parameters. We show that only one of these Jeffreys priors leads to a proper posterior distribution. In addition, we develop fast posterior analys...