Memetic Pareto Evolutionary Artificial Neural Networks for the determination of growth limits of Listeria Monocytogenes

The main objective of this work is to automatically design neural network models with sigmoidal basis units for classification tasks, so that classifiers are obtained in the most balanced way possible in terms of CCR and Sensitivity (given by the lowest percentage of examples correctly predicted to belong to each class). We present a Memetic Pareto Evolutionary NSGA2 (MPENSGA2) approach based on the Pareto-NSGAII evolution (PNSGAII) algorithm. We propose to augmente it with a local search using the improved Rprop—IRprop algorithm for the prediction of growth/no growth of L. monocytogenes as a function of the storage temperature, pH, citric (CA) and ascorbic acid (AA). The results obtained show that the generalization ability can be more efficiently improved within a framework that is multi-objective instead of a within a single-objective one

La pedida y casamiento en las comunidades de Totonicapán

Se hace un estudio sobre las manifestaciones culturales sobre la “pedida” y “casamiento” que se practican en las comunidades de Chuatroj, Chiyax, Poxlajuj, Chuculjuyup y Cásquez del Municipio y Departamento de Totonicapán”; se describen los elementos que se utilizan para su realización. Se presentan algunos de los beneficios que se obtienen por la realización del proceso. Todo esto, está constituido en el testimonio que proporcionaron los abuelos y expertos o “Samajeles”

A guided data projection technique for classi cation of sovereign ratings: the case of European Union 27

Sovereign rating has had an increasing importance since the beginning of the nancial crisis. However, credit rating agencies opacity has been criticised by several authors highlighting the suitability of designing more objective alternative methods. This paper tackles the sovereign credit rating classi cation problem within an ordinal classi cation perspective by employing a pairwise class distances projection to build a classi cation model based on standard regression techniques. In this work the -SVR is selected as the regressor tool. The quality of the projection is validated through the classi cation results obtained for four performance metrics when applied to Standard & Poors, Moody's and Fitch sovereign rating data of U27 countries during the period 2007-2010. This validated projection is later used for ranking visualization which might be suitable to build a decision support syste

Modeling and behavior of the simulation of electric propagation during deep brain stimulation

Deep brain stimulation (DBS) is an effective treatment for Parkinson's disease. In the literature, there are a wide variety of mathematical and computational models to describe electric propagation during DBS; however unfortunately, there is no clarity about the reasons that justify the use of a specific model. In this work, we present a detailed mathematical formulation of the DBS electric propagation that supports the use of a model based on the Laplace Equation. Moreover, we performed DBS simulations for several geometrical models of the brain in order to determine whether geometry size, shape and ground location influence electric stimulation prediction by using the Finite Element Method (FEM). Theoretical and experimental analysis show, firstly, that under the correct assumptions, the Laplace equation is a suitable alternative to describe the electric propagation, and secondly, that geometrical structure, size and grounding of the head volume affect the magnitude of the electric potential, particularly for monopolar stimulation. Results show that, for monopolar stimulation, basic and more realistic models can differ more than 2900%

A weed monitoring system using UAV-imagery and the Hough transform

Usually, crops require the use of herbicides as a useful manner of controlling the quality and quantity of crop production. Although there are weed-free areas, the most common approach is to broadcast herbicides entirely over crop fields, resulting in a reduction of profits and increase in environmental risks. Recently, patch spraying has allowed the use of site-specific weed management, allowing precise and timely weed maps at very early phenological stage, either by ground sampling or remote analysis. Remote imagery from piloted planes and satellites are not suitable for this purpose given their low spatial and temporal resolutions, however, unmanned aerial vehicles (UAV) represent an excellent alternative. This paper presents a new classification framework for weed monitoring via UAV showing promising results and accurate generalisation in different scenariosLos cultivos precisan del uso de herbicidas para controlar la calidad y cantidad de producción. A pesar de que las malas hierbas se distribuyen en rodales, la práctica más extendida es la fumigación de herbicidas en todo el cultivo, resultando en un aumento del coste y de riesgos mediambientales. La pulvericación por parches ha dado lugar al auge de otras técnicas de manejo de malas hierbas, permitiendo su tratamiento en un estado fenológico temprano. Las imágenes remotas de aviones pilotados o satélites no son útiles en este caso debido a su baja resolución espacial y temporal. Sin embargo, este no es el caso de los vehículos aéreos no tripulados. Este artículo presenta un nuevo método para monitorización de malas hierbas usando este tipo de vehículos, mostrando resultados prometedore

Ordinal and nominal classication of wind speed from synoptic pressure patterns

Wind speed reconstruction is a challenging problem in areas (mainly wind farms) where there are not direct wind measures available. Di erent approaches have been applied to this reconstruction, such as measure-correlatepredict algorithms, approaches based on physical models such as reanalysis methods, or more recently, indirect measures such as pressure, and its relation to wind speed. This paper adopts the latter method, and deals with wind speed estimation in wind farms from pressure measures, but including different novelties in the problem treatment. Existing synoptic pressure-based indirect approaches for wind speed estimation are based on considering the wind speed as a continuous target variable, estimating then the corresponding wind series of continuous values. However, the exact wind speed is not always needed by wind farms managers, and a general idea of the level of speed is, in the majority of cases, enough to set functional operations for the farm (such as wind turbines stop, for example). Moreover, the accuracy of the models obtained is usually improved for the classi cation task, given that the problem is simpli ed. Thus, this paper tackles the problem of wind speed prediction from synoptic pressure patterns by considering wind speed as a discrete variable and, consequently, wind speed prediction as a classi cation problem, with four wind level categories: low, moderate, high or very high. Moreover, taking into account that these four di erent classes are associated to four values in an ordinal scale, the problem can be considered as an ordinal regression problem. The performance of several ordinal and nominal classi- ers and the improvement achieved by considering the ordering information are evaluated. The results obtained in this paper present the Support Vector Machine as the best tested classi er for this task. In addition, the use of the intrinsic ordering information of the problem is shown to signi cantly improve ranks with respect to nominal classi cation, although di erences in accuracy are smal

Semi-supervised Learning for Ordinal Kernel Discriminant Analysis

Ordinal classication considers those classication problems where the labels of the variable to predict follow a given order. Naturally, labelled data is scarce or di_cult to obtain in this type of problems because, in many cases, ordinal labels are given by an user or expert (e.g. in recommendation systems). Firstly, this paper develops a new strategy for ordinal classi_cation where both labelled and unlabelled data are used in the model construction step (a scheme which is referred to as semi-supervised learning). More specically, the ordinal version of kernel discriminant learning is extended for this setting considering the neighbourhood information of unlabelled data, which is proposed to be computed in the feature space induced by the kernel function. Secondly, a new method for semi-supervised kernel learning is devised in the context of ordinal classi_cation, which is combined with our developed classi_cation strategy to optimise the kernel parameters. The experiments conducted compare 6 different approaches for semi-supervised learning in the context of ordinal classication in a battery of 30 datasets, showing 1) the good synergy of the ordinal version of discriminant analysis and the use of unlabelled data and 2) the advantage of computing distances in the feature space induced by the kernel function

Metrics to guide a multi-objective evolutionary algorithm for ordinal classification

Ordinal classification or ordinal regression is a classification problem in which the labels have an ordered arrangement between them. Due to this order, alternative performance evaluation metrics are need to be used in order to consider the magnitude of errors. This paper presents a study of the use of a multi-objective optimization approach in the context of ordinal classification. We contribute a study of ordinal classification performance metrics, and propose a new performance metric, the maximum mean absolute error (MMAE). MMAE considers per-class distribution of patterns and the magnitude of the errors, both issues being crucial for ordinal regression problems. In addition, we empirically show that some of the performance metrics are competitive objectives, which justify the use of multi-objective optimization strategies. In our case, a multi-objective evolutionary algorithm optimizes an artificial neural network ordinal model with different pairs of metric combinations, and we conclude that the pair of the mean absolute error (MAE) and the proposed MMAE is the most favourable. A study of the relationship between the metrics of this proposal is performed, and the graphical representation in the two-dimensional space where the search of the evolutionary algorithm takes place is analysed. The results obtained show a good classification performance, opening new lines of research in the evaluation and model selection of ordinal classifiers