Search results for "SELECTION"
showing 10 items of 1940 documents
Cement selection criteria for full coverage restorations: A comprehensive review of literature
2021
Background Proper cement selection in fixed prosthesis plays a determinative role in providing long-term serviceability, retention, caries prevention, and patient satisfaction. This study, reviews different luting agent characteristics and their application based on different clinical situations and different types of full coverage restorations. Material and methods An electronic search was conducted through PubMed, Medline, and Google scholar using following keywords or combinations: restoration, full coverage, PFM, porcelain fused to metal, all ceramic, zirconia, ceramic, casting, fixed partial denture, cement*, dental cement, cement selection, and retention. The most related articles wer…
Effectiveness of local feature selection in ensemble learning for prediction of antimicrobial resistance
2008
In the real world concepts are often not stable but change over time. A typical example of this in the biomedical context is antibiotic resistance, where pathogen sensitivity may change over time as pathogen strains develop resistance to antibiotics that were previously effective. This problem, known as concept drift (CD), complicates the task of learning a robust model. Different ensemble learning (EL) approaches (that instead of learning a single classifier try to learn and maintain a set of classifiers over time) have been shown to perform reasonably well in the presence of concept drift. In this paper we study how much local feature selection (FS) can improve ensemble performance for da…
Artificial intelligence for affective computing : an emotion recognition case study.
2020
This chapter provides an introduction on the benefits of artificial intelligence (Al) techniques for the field of affective computing, through a case study about emotion recognition via brain (electroencephalography EEG) signals. Readers are first pro-vided with a general description of the field, followed by the main models of human affect, with special emphasis to Russell's circumplex model and the pleasur-arousal-dominance (PAD) model. Finally, an AI-based method for the detection of affect elicited via multimedia stimuli is presented. The method combines both connectivity-and channel-based EEG features with a selection method that considerably reduces the dimensionality of the data and …
A computer program suitable for analysis of choice of categories in biomedical data recognition problems.
1980
The optimum choice of categories in problems of medical data recognition is governed by the choice of categories, the selection of appropriate features, and by the choice of a loss function. Under these circumstances it is often difficult to find out the suitable classification scheme. The computer program described here serves for the design of the optimum recognition procedure. The Bayes rule is used as decision rule. A criterion for the comparison of different choice of categories is given. The program can be performed after estimation of the underlying prior probabilities and the conditional densities obtained from a training set, and before testing the decision rule with real data.
Solvent selection in liquid chromatography
2017
Many solvents and additives are used to prepare mobile phases in liquid chromatography (LC). Also, mixtures of solvents at different ratios are used to modify the mobile-phase properties. This can make solvent selection for method development a puzzling task, unless suitable guidelines are followed. This chapter summarizes the most common strategies used by skilled chromatographers in reversed-phase, normal-phase, and hydrophilic interaction LC. These are based on considerations about the global polarity of solutes, stationary phase, and mobile phase, which determine the elution strength, and on the particular profile of the contributions of intermolecular interactions to the global polarit…
Identification of Key Gin Aroma Compounds
2014
Potential impact aroma compounds of gin have been identified using Gas Chromatogry Olfactometry Mass-Spectrometry (GC-O-MS). In order to select some of them for a recombination study, we developed a specific procedure. Instead of only choosing the compounds on criteria such as their odor quality or their odor activity values, we also used physico-chemical parameters and information on their botanical origin. Data were organized in blocks homogeneous in terms of parameter type. Different statistical treatments were used in order to classify the compounds either by analyzing the parameters altogether or separately block by block.
The DrosDel Deletion Collection: A Drosophila Genomewide Chromosomal Deficiency Resource
2007
AbstractWe describe a second-generation deficiency kit for Drosophila melanogaster composed of molecularly mapped deletions on an isogenic background, covering ∼77% of the Release 5.1 genome. Using a previously reported collection of FRT-bearing P-element insertions, we have generated 655 new deletions and verified a set of 209 deletion-bearing fly stocks. In addition to deletions, we demonstrate how the P elements may also be used to generate a set of custom inversions and duplications, particularly useful for balancing difficult regions of the genome carrying haplo-insufficient loci. We describe a simple computational resource that facilitates selection of appropriate elements for generat…
Stochastic models for wind speed forecasting
2011
Abstract This paper is concerned with the problem of developing a general class of stochastic models for hourly average wind speed time series. The proposed approach has been applied to the time series recorded during 4 years in two sites of Sicily, a region of Italy, and it has attained valuable results in terms both of modelling and forecasting. Moreover, the 24 h predictions obtained employing only 1-month time series are quite similar to those provided by a feed-forward artificial neural network trained on 2 years data.
Stability-Based Model Selection for High Throughput Genomic Data: An Algorithmic Paradigm
2012
Clustering is one of the most well known activities in scien- tific investigation and the object of research in many disciplines, ranging from Statistics to Computer Science. In this beautiful area, one of the most difficult challenges is the model selection problem, i.e., the identifi- cation of the correct number of clusters in a dataset. In the last decade, a few novel techniques for model selection, representing a sharp departure from previous ones in statistics, have been proposed and gained promi- nence for microarray data analysis. Among those, the stability-based methods are the most robust and best performing in terms of predic- tion, but the slowest in terms of time. Unfortunately…
One-Sided Prototype Selection on Class Imbalanced Dissimilarity Matrices
2012
In the dissimilarity representation paradigm, several prototype selection methods have been used to cope with the topic of how to select a small representation set for generating a low-dimensional dissimilarity space. In addition, these methods have also been used to reduce the size of the dissimilarity matrix. However, these approaches assume a relatively balanced class distribution, which is grossly violated in many real-life problems. Often, the ratios of prior probabilities between classes are extremely skewed. In this paper, we study the use of renowned prototype selection methods adapted to the case of learning from an imbalanced dissimilarity matrix. More specifically, we propose the…