Response surface methodology (RSM) relies on the design of experiments and empirical modelling techniques to find the optimum of a process when the underlying fundamental mechanism of the process is largely unknown. This paper proposes an iterative RSM framework, where Gaussian process (GP) regression models are applied for the approximation of the response surface. GP regression is flexible an...

Kafirin microparticles have potential as colon-targeted delivery systems because of their ability to protect encapsulated material from digestive processes of the upper gastrointestinal tract (GIT). The aim was to optimize prednisolone loading into kafirin microparticles, and investigate their potential as an oral delivery system. Response surface methodology (RSM) was used to predict the optim...

The Bulk-Synchronous Parallel Model, BSPM, was proposed as a bridging model for parallel computation by Valiant. By using Randomised Shared Memory, RSM, this model o ers an asymptotically optimal emulation of the PRAM[1]. By using the BSPM with RSM, we show how a gracefully degrading massively parallel system can be obtained through: memory duplication to ensure global memory integrity, and to ...

The removal of Reactive Black 5 dye in an aqueous solution by electrocoagulation (EC) as well as addition of flocculant was investigated. The effect of operational parameters, i.e. current density, treatment time, solution conductivity and polymer dosage, was investigated. Two models, namely the artificial neural network (ANN) and the response surface method (RSM), were used to model the effect...

Generalized additive models (GAMs) are a generalization of generalized linear models (GLMs) and constitute a powerful technique which has successfully proven its ability to capture nonlinear relationships between explanatory variables and a response variable in many domains. In this paper, GAMs are proposed as base classifiers for ensemble learning. Three alternative ensemble strategies for bin...

Biofiltration is emerging as a promising cost effective technique for the Volatile Organic Compounds (VOCs) removal from industrial waste gases. In the present investigation a comparative modeling study has been carried out using Radial Basis Function Neural Network (RBFN) and Response Surface Methodology (RSM) to predict and optimize the performance of a biofilter system treating toluene (a mo...

The response surface methodology (RSM), which typically uses quadratic polynomials, is predominantly used for metamodeling in crashworthiness optimization because of the high computational cost of vehicle crash simulations. Research shows, however, that RSM may not be suitable for modeling highly nonlinear responses that can often be found in impact related problems, especially when using limit...

Raw goat milk pricing is based on the milk quality especially on fat, solid not fat (SNF) and density. Therefore, there is a need of approach for composition quantization. This study applied radial basis function network (RBFN) to calibrate fat, SNF, and density with visible and near infrared spectra (400~2500 nm). To find the optimal parameters of goal error and spread used in RBFN, a response...

Machine Learning can be divided into two schools of thought: generative model learning and discriminative model learning. While the MCS community has been focused mainly on the latter, our paper is concerned with questions that arise from ensembles of generative models. Generative models provide us with neat ways of thinking about two interesting learning issues: model selection and semi-superv...