Search results for "computer.software_genre"
showing 10 items of 3858 documents
Automated quality control protocol for MR spectra of brain tumors.
2008
Item does not contain fulltext eTUMOUR (http://www.etumour.net/) is acquiring a large database of brain tumor (1)H MR spectra to develop automated pattern recognition methods and decision support system (DSS) for tumor diagnosis. Development of accurate pattern-recognition algorithms requires spectra undistorted by artifacts, low signal-to-noise, or broad lines. eTUMOUR currently uses panels of expert spectroscopists to subjectively grade spectra as being acceptable or unacceptable. Automated quality control (QC) would be more satisfactory for several reasons: 1) to provide a reproducible objective classification of spectrum quality; 2) for use within the future DSS to prevent misdiagnosis …
Optimization of fluorescence enhancement for silicon-based microarrays
2008
An optical technique for the enhancement of fluorescence detection sensitivity on planar samples is presented. Such a technique is based on the simultaneous optimization of excitation and light collection by properly combining interference and reflectance from the sample holder. Comparative tests have been performed in microarray applications, by evaluating the proposed solution against commercial glass-based devices, using popular labeling dyes, such as Cy3 and Cy5. The proposed technique is implemented on a substrate built with standard silicon technology and is therefore well suited for integrated micro total analysis systems (microTAS) applications.
Application of machine-vision techniques to fish-quality assessment
2012
Abstract Machine vision is a non-destructive, rapid, economic, consistent and objective inspection tool and is also an evaluation technique based on image analysis and processing with a variety of applications. We review the use of machine vision and imaging technologies for fish-quality assessment. This review updates and condenses a representative selection of recent research and industrial solutions proposed in order to evaluate the general trends of machine vision and image processing in the visible range applied for inspection of fish and fish products. In order to determine freshness and composition, it is necessary to measure and to evaluate size and volume, to estimate weight, to me…
Quality System for Production Software as Tool for Monitoring and Improving Organization KPIs
2013
In this paper we propose a solution as support for quality systems for production software. The motivation behind this study was to reduce that cost in the production area caused by gaps in the quality of the production software. Our proposal: QSPS (Quality System for Production Software) is offering support in the "vulnerable points" of these quality systems which usually generate nonconformities and have proved to be difficult or impossible to control. QSPS is a method in seven steps or modules that integrates also software tools, templates, checklists, evaluating tools elaborated complying to products, process and system quality standards. If other analyzed methods like: Scrum, XP, Fuzzy…
Sensory evaluation based on verbal judgments
1999
Studies of the repeatability and the homogeneity of expert panel scores in sensory profiling show that lasting and reliable evaluations of food products are difficult to obtain: strong inter- and intra-individual differences are commonly observed. Our hypothesis is that this variability is due to quantification methods that consist of asking panelists to furnish quantitative values (by attributing a numerical point to perceived intensity) and that using natural language in the form of verbal judgements in a hierarchical tree would allow improving the reliability of sensory evaluations. This hypothesis was tested by comparing a numerical value scale and a specific hierarchical semantic scale…
On using novel “Anti-Bayesian” techniques for the classification of dynamical data streams
2017
The classification of dynamical data streams is among the most complex problems encountered in classification. This is, firstly, because the distribution of the data streams is non-stationary, and it changes without any prior “warning”. Secondly, the manner in which it changes is also unknown. Thirdly, and more interestingly, the model operates with the assumption that the correct classes of previously-classified patterns become available at a juncture after their appearance. This paper pioneers the use of unreported novel schemes that can classify such dynamical data streams by invoking the recently-introduced “Anti-Bayesian” (AB) techniques. Contrary to the Bayesian paradigm, that compare…
Unsupervised quantitative methods to analyze student reasoning lines: Theoretical aspects and examples
2019
[This paper is part of the Focused Collection on Quantitative Methods in PER: A Critical Examination.] A relevant aim of research in education is to find and study the reasoning lines that students deploy when dealing with problematic situations. This can be done through an analysis of the answers students give to a questionnaire. In this paper, we discuss some methodological aspects involved in the quantitative analysis of a questionnaire by means of two different clustering methods, a hierarchical one and a nonhierarchical one. We start from the coding procedures needed to obtain analyzable data from the questionnaire and from a definition of a correlation coefficient suitable for measuri…
Machine learning-based models to predict modes of toxic action of phenols to Tetrahymena pyriformis.
2017
The phenols are structurally heterogeneous pollutants and they present a variety of modes of toxic action (MOA), including polar narcotics, weak acid respiratory uncouplers, pro-electrophiles, and soft electrophiles. Because it is often difficult to determine correctly the mechanism of action of a compound, quantitative structure-activity relationship (QSAR) methods, which have proved their interest in toxicity prediction, can be used. In this work, several QSAR models for the prediction of MOA of 221 phenols to the ciliated protozoan Tetrahymena pyriformis, using Chemistry Development Kit descriptors, are reported. Four machine learning techniques (ML), k-nearest neighbours, support vector…
<strong>Machine Learning and Atom-Based Quadratic Indices for Proteasome Inhibition Prediction </strong>
2015
The atom-based quadratic indices are used in this work together with some machine learning techniques that includes: support vector machine, artificial neural network, random forest and k-nearest neighbor. This methodology is used for the development of two quantitative structure-activity relationship (QSAR) studies for the prediction of proteasome inhibition. A first set consisting of active and non-active classes was predicted with model performances above 85% and 80% in training and validation series, respectively. These results provided new approaches on proteasome inhibitor identification encouraged by virtual screenings procedures. .
Application of the modelling power approach to variable subset selection for GA-PLS QSAR models
2007
A previously developed function, the Modelling Power Plot, has been applied to QSARs developed using partial least squares (PLS) following variable selection from a genetic algorithm (GA). Modelling power (Mp) integrates the predictive and descriptive capabilities of a QSAR. With regard to QSARs for narcotic toxic potency, Mp was able to guide the optimal selection of variables using a GA. The results emphasise the importance of Mp to assess the success of the variable selection and that techniques such as PLS are more robust following variable selection.