An identification problem with no a priori separation of the variables into inputs and outputs and representation invariant approximation criterion is considered. The model class consists of linear time-invariant systems of bounded complexity and the approximation criterion is the minimum of a weighted 2-norm distance between the given time series and a time series that is consistent with the m...

The slack-based measure (SBM) DEA model is a non-radial used to calculate the relative efficiency, input, and output targets of different decision-making units (DMUs) based on their best peers or efficient frontier. conventional SBM crisp inputs outputs. But, it can be observed in real-life problems that sometimes available data linguistic forms such as “few”, “many”, “small”, missing data. tec...

AbstractImage inpainting or image retouching is the method of filling in or repairing the missing area of an image from the nearby pixels information by some algorithm. Its goal is to recover images with limited data loss and tries to obtain outputs of damaged area of an image in such a way that the recovered images look identical to original image. In our proposed method we will divide the dam...

A computer program was developed to identify anaerobic bacteria by using simultaneous pattern recognition via a Bayesian probabilistic model. The system is intended for use as a rapid, precise, and reproducible aid in the identification of unknown isolates. The program operates on a data base of 28 genera comprising 238 species of anaerobic bacteria that can be separated by the program. Input t...

This work presents the results from dynamic modeling and control of an azeotropic distillation system. The model was validated with experimental data from a packed distillation unit. The physically-based process dynamic model, developed in HYSYS, was linked online with the control software used in the process. Model parameters were modified online using a feedback configuration to eliminate the...

Abstract: Convolved Gaussian Process (CGP) is able to capture the correlations not only between inputs and outputs but also among the outputs. This allows a superior performance of using CGP than standard Gaussian Process (GP) in the modelling of Multiple-Input Multiple-Output (MIMO) systems when observations are missing for some of outputs. Similar to standard GP, a key issue of CGP is the lea...

Background Policy makers need models to be able to detect groups at high risk of HIV infection. Incomplete records and dirty data are frequently seen in national data sets. Presence of missing data challenges the practice of model development. Several studies suggested that performance of imputation methods is acceptable when missing rate is moderate. One of the issues which was of less concern...

background: the aim of the article is demonstrating an application of multiple imputation (mi) for handling missing clinical data in the setting of rheumatologic surveys using data derived from 10291 people participating in the first phase of the community oriented program for control of rheumatic disorders (copcord) in iran . methods: five data subsets were produced from the original data set....

In this paper, we study the problem of semi-supervised structured output prediction, which aims to learn predictors for structured outputs, such as sequences, tree nodes, vectors, etc., from a set of data points of both inputoutput pairs and single inputs without outputs. The traditional methods to solve this problem usually learns one single predictor for all the data points, and ignores the v...