Integration of Graph Theory and Matrix Approach with Fuzzy AHP for Equipment Selection

Purpose: The purpose of this paper is applying a new integrated method to equipment selection. Design/methodology/approach: In this paper, we proposed the new integrated approach. Proposed approach is based on fuzzy Analytic Hierarchy Process (FAHP) and GTMA (graph theory and matrix approach) methods. FAHP method is used in determining the weights of the criteria by decision makers and then rankings of equipments are determined by GTMA method. Proposed approach is applied to a problem of selecting CNC machines to be purchased in a company. Findings and Originality/value: The outcome of this research is ranking and selecting equipment using of Fuzzy AHP and GTMA techniques. Originality/value: This paper offers a new integrated method for equipment selection.Peer Reviewe

A comparison of the CAR and DAGAR spatial random effects models with an application to diabetics rate estimation in Belgium

When hierarchically modelling an epidemiological phenomenon on a finite collection of sites in space, one must always take a latent spatial effect into account in order to capture the correlation structure that links the phenomenon to the territory. In this work, we compare two autoregressive spatial models that can be used for this purpose: the classical CAR model and the more recent DAGAR model. Differently from the former, the latter has a desirable property: its ρ parameter can be naturally interpreted as the average neighbor pair correlation and, in addition, this parameter can be directly estimated when the effect is modelled using a DAGAR rather than a CAR structure. As an application, we model the diabetics rate in Belgium in 2014 and show the adequacy of these models in predicting the response variable when no covariates are available

A Statistical Approach to the Alignment of fMRI Data

Multi-subject functional Magnetic Resonance Image studies are critical. The anatomical and functional structure varies across subjects, so the image alignment is necessary. We define a probabilistic model to describe functional alignment. Imposing a prior distribution, as the matrix Fisher Von Mises distribution, of the orthogonal transformation parameter, the anatomical information is embedded in the estimation of the parameters, i.e., penalizing the combination of spatially distant voxels. Real applications show an improvement in the classification and interpretability of the results compared to various functional alignment methods