5 resultados para CLASSIFICATIONS
em Universidad de Alicante
Resumo:
Background: It has been shown that gender equity has a positive impact on the everyday activities of people (decision making, income allocation, application and observance of norms/rules) which affect their health. Gender equity is also a crucial determinant of health inequalities at national level; thus, monitoring is important for surveillance of women’s and men’s health as well as for future health policy initiatives. The Gender Equity Index (GEI) was designed to show inequity solely towards women. Given that the value under scrutiny is equity, in this paper a modified version of the GEI is proposed, the MGEI, which highlights the inequities affecting both sexes. Methods: Rather than calculating gender gaps by means of a quotient of proportions, gaps in the MGEI are expressed in absolute terms (differences in proportions). The Spearman’s rank coefficient, calculated from country rankings obtained according to both indexes, was used to evaluate the level of concordance between both classifications. To compare the degree of sensitivity and obtain the inequity by the two methods, the variation coefficient of the GEI and MGEI values was calculated. Results: Country rankings according to GEI and MGEI values showed a high correlation (rank coef. = 0.95). The MGEI presented greater dispersion (43.8%) than the GEI (19.27%). Inequity towards men was identified in the education gap (rank coef. = 0.36) when using the MGEI. According to this method, many countries shared the same absolute value for education but with opposite signs, for example Azerbaijan (−0.022) and Belgium (0.022), reflecting inequity towards women and men, respectively. This also occurred in the empowerment gap with the technical and professional job component (Brunei:-0.120 vs. Australia, Canada Iceland and the U.S.A.: 0.120). Conclusion: The MGEI identifies and highlights the different areas of inequities between gender groups. It thus overcomes the shortcomings of the GEI related to the aim for which this latter was created, namely measuring gender equity, and is therefore of great use to policy makers who wish to understand and monitor the results of specific equity policies and to determine the length of time for which these policies should be maintained in order to correct long-standing structural discrimination against women.
Resumo:
Purpose. To assess in a sample of normal, keratoconic, and keratoconus (KC) suspect eyes the performance of a set of new topographic indices computed directly from the digitized images of the Placido rings. Methods. This comparative study was composed of a total of 124 eyes of 106 patients from the ophthalmic clinics Vissum Alicante and Vissum Almería (Spain) divided into three groups: control group (50 eyes), KC group (50 eyes), and KC suspect group (24 eyes). In all cases, a comprehensive examination was performed, including the corneal topography with a Placidobased CSO topography system. Clinical outcomes were compared among groups, along with the discriminating performance of the proposed irregularity indices. Results. Significant differences at level 0.05 were found on the values of the indices among groups by means of Mann-Whitney-Wilcoxon nonparametric test and Fisher exact test. Additional statistical methods, such as receiver operating characteristic analysis and K-fold cross validation, confirmed the capability of the indices to discriminate between the three groups. Conclusions. Direct analysis of the digitized images of the Placido mires projected on the cornea is a valid and effective tool for detection of corneal irregularities. Although based only on the data from the anterior surface of the cornea, the new indices performed well even when applied to the KC suspect eyes. They have the advantage of simplicity of calculation combined with high sensitivity in corneal irregularity detection and thus can be used as supplementary criteria for diagnosing and grading KC that can be added to the current keratometric classifications.
Resumo:
Prototype Selection (PS) algorithms allow a faster Nearest Neighbor classification by keeping only the most profitable prototypes of the training set. In turn, these schemes typically lower the performance accuracy. In this work a new strategy for multi-label classifications tasks is proposed to solve this accuracy drop without the need of using all the training set. For that, given a new instance, the PS algorithm is used as a fast recommender system which retrieves the most likely classes. Then, the actual classification is performed only considering the prototypes from the initial training set belonging to the suggested classes. Results show that this strategy provides a large set of trade-off solutions which fills the gap between PS-based classification efficiency and conventional kNN accuracy. Furthermore, this scheme is not only able to, at best, reach the performance of conventional kNN with barely a third of distances computed, but it does also outperform the latter in noisy scenarios, proving to be a much more robust approach.
Resumo:
Históricamente, las taxonomías elaboradas por los especialistas sobre los nombres de marca utilizaban diferentes categorías. Tomemos, a título de ejemplo, nombres patronímicos y siglas o nombres patronímicos y acrónimos, considerados siempre como categorías exclusivas y, lo que es más importante, excluyentes: un nombre patronímico o una sigla, o un nombre patronímico o un acrónimo, etc. ¿Pero qué sucede, por ejemplo, cuando o un nombre patronímico está implícito en las siglas o cuando un nombre patronímico también está implícito en los acrónimos? ¿Será que el nombre de una empresa construido a partir de las iniciales de un nombre y de apellido dejaría de ser patronímico? Este artículo analiza las categorías tradicionalmente establecidas para clasificar los nombres de marca. Además de llevar a cabo una revisión crítica de la literatura existente, en él proponemos una nueva taxonomía que amplía y oxigena las susodichas clasificaciones al uso. Se ofrece una nueva taxonomía nunca antes usada que se extiende, complementa y ofrece aire fresco a las referidas clasificaciones tradicionales, con los consiguientes beneficios derivados de ello, entre otros, empoderamiento, creatividad y vigor para ampliar las posibilidades de viabilidad del registro de marcas corporativas.
Resumo:
Rock mass classification systems are widely used tools for assessing the stability of rock slopes. Their calculation requires the prior quantification of several parameters during conventional fieldwork campaigns, such as the orientation of the discontinuity sets, the main properties of the existing discontinuities and the geo-mechanical characterization of the intact rock mass, which can be time-consuming and an often risky task. Conversely, the use of relatively new remote sensing data for modelling the rock mass surface by means of 3D point clouds is changing the current investigation strategies in different rock slope engineering applications. In this paper, the main practical issues affecting the application of Slope Mass Rating (SMR) for the characterization of rock slopes from 3D point clouds are reviewed, using three case studies from an end-user point of view. To this end, the SMR adjustment factors, which were calculated from different sources of information and processes, using the different softwares, are compared with those calculated using conventional fieldwork data. In the presented analysis, special attention is paid to the differences between the SMR indexes derived from the 3D point cloud and conventional field work approaches, the main factors that determine the quality of the data and some recognized practical issues. Finally, the reliability of Slope Mass Rating for the characterization of rocky slopes is highlighted.