Classification of Domestic Water Consumption Using an ANFIS Model

This work presents classification results of different water outputs in a house. Input variables are time and flow measurements in a point of the network distribution, and the identified classes are relevant consumptions as sink consumption, shower consumption, etc. Due to human influence on consumption data, we selected a classifier based on an interpretable model; that allows the incorporation of knowledge provided by users or experts. Thus, this study is based on the well known Anfis model and AGUA (real data taken for a project being developed in Guadalajara, Mexico) the data set corresponding to a supervised case. The result shows that the proposed algorithm works well, with recognition above 91%, and it could be used for a better profit of domestic water management

Microcalcification Detection Applying Artificial Neural Networks and Mathematical Morphology in Digital Mammograms

Breast cancer is one of the leading causes to women mortality in the world and early detection is an important means to reduce the mortality rate. The presence of microcalcifications clusters has been considered as a very important indicator of malignant types of breast cancer and its detection is important to prevent and treat the disease. This paper presents an alternative and effective approach in order to detect microcalcifications clusters in digitized mammograms based on the synergy of the image processing, pattern recognition and artificial intelligence. The mathematical morphology is an image processing technique used for the purpose of image enhancement. A k-means algorithm is used to cluster the data based on the features vectors and finally an artificial neural network-based classifier is applied and the classification performance is evaluated by a ROC curve. Experimental results indicate that the percentage of correct classification was 99.72%, obtaining 100% true positive (sensitivity) and 99.67% false positive (specificity), with the best classifier proposed. In case of the best classifier, we obtained a performance evaluation of classification of Az = 0.987

Image sub-segmentation by PFCM and Artificial Neural Networks to detect pore space in 2D and 3D CT soil images

The image by Computed Tomography is a non-invasive alternative for observing soil structures, mainly pore space. The pore space correspond in soil data to empty or free space in the sense that no material is present there but only fluids, the fluid transport depend of pore spaces in soil, for this reason is important identify the regions that correspond to pore zones. In this paper we present a methodology in order to detect pore space and solid soil based on the synergy of the image processing, pattern recognition and artificial intelligence. The mathematical morphology is an image processing technique used for the purpose of image enhancement. In order to find pixels groups with a similar gray level intensity, or more or less homogeneous groups, a novel image sub-segmentation based on a Possibilistic Fuzzy c-Means (PFCM) clustering algorithm was used. The Artificial Neural Networks (ANNs) are very efficient for demanding large scale and generic pattern recognition applications for this reason finally a classifier based on artificial neural network is applied in order to classify soil images in two classes, pore space and solid soil respectively

Identification of pore spaces in 3D CT soil images using a PFCM partitional clustering

Recent advances in non-destructive imaging techniques, such as X-ray computed tomography (CT), make it possible to analyse pore space features from the direct visualisation from soil structures. A quantitative characterisation of the three-dimensional solid-pore architecture is important to understand soil mechanics, as they relate to the control of biological, chemical, and physical processes across scales. This analysis technique therefore offers an opportunity to better interpret soil strata, as new and relevant information can be obtained. In this work, we propose an approach to automatically identify the pore structure of a set of 200-2D images that represent slices of an original 3D CT image of a soil sample, which can be accomplished through non-linear enhancement of the pixel grey levels and an image segmentation based on a PFCM (Possibilistic Fuzzy C-Means) algorithm. Once the solids and pore spaces have been identified, the set of 200-2D images is then used to reconstruct an approximation of the soil sample by projecting only the pore spaces. This reconstruction shows the structure of the soil and its pores, which become more bounded, less bounded, or unbounded with changes in depth. If the soil sample image quality is sufficiently favourable in terms of contrast, noise and sharpness, the pore identification is less complicated, and the PFCM clustering algorithm can be used without additional processing; otherwise, images require pre-processing before using this algorithm. Promising results were obtained with four soil samples, the first of which was used to show the algorithm validity and the additional three were used to demonstrate the robustness of our proposal. The methodology we present here can better detect the solid soil and pore spaces on CT images, enabling the generation of better 2D?3D representations of pore structures from segmented 2D images

Air pollution Analysis with a PFCM Clustering Algorithm Applied in a Real Database of Salamanca (Mexico)

Over the last ten years, Salamanca has been considered among the most polluted cities in México. Nowadays, there is an Automatic Environmental Monitoring Network (AEMN) which measures air pollutants (Sulphur Dioxide (SO2), Particular Matter (PM10), Ozone (O3), etc.), as well as environmental variables (wind speed, wind direction, temperature, and relative humidity), and it takes a sample of the variables every minute. The AEM Network is mainly based on three monitoring stations located at Cruz Roja, DIF, and Nativitas. In this work, we use the PFCM (Possibilistic Fuzzy c Means) clustering algorithm as a mean to get a combined measure, from the three stations, looking to provide a tool for better management of contingencies in the city, such that local or general action can be taken in the city according to the pollution level given by each station and the combined measure. Besides, we also performed an analysis of correlation between pollution and environmental variables. The results show a significative correlation between pollutant concentrations and some environmental variables. So, the combined measure and the correlations can be used for the establishment of general contingency thresholds

Aportación a la extracción de conocimiento aplicada a datos mediante agrupamientos y sistemas difusos

Resumen Los avances en la tecnología en los últimos años han propiciado que se generen y se recolecten grandes cantidades de datos principalmente numéricos, y hay un gran interés en procesarlos para extraer información y conocimiento de ellos, con el principal objetivo de hacer más eficientes los sistemas de donde se han obtenido estos datos. La información en una base de datos se encuentra implícita en los valores que representan los diferentes estados de los sistemas, mientras que el conocimiento está implícito en las relaciones entre los valores de los diferentes atributos o características presentes en las bases de datos. Dichas relaciones se identifican mediante grupos (estructura interna) que hay que descubrir y que describen las relaciones entre los estados de entrada y de salida. Para ello se han desarrollado diferentes técnicas, una de las cuales es mediante los algoritmos de agrupamiento particionales. En esta Tesis se propone una aportación a la extracción de información y de conocimiento a partir de bases de datos numéricas, usando para ello algoritmos de agrupamiento particionales híbridos difusos. La información se extrae mediante la agrupación y la caracterización de datos en típicos, atípicos y ruido, así como en la aplicación a la sub-segmentación de imágenes, donde se propone un nuevo enfoque con características interesantes para la detección de píxeles atípicos, que pueden ser relacionados a microcalcificaciones para la detección de cáncer de mama, o a los nudos en la madera para evaluar su calidad, ambos casos tratados en esta tesis, o en cualquier otra aplicación de salud o industrial por ejemplo, en donde no importa si los píxeles a encontrar están presentes en muy pequeñas cantidades. El conocimiento se extrae mediante el establecimiento de dos modelos difusos de tipo Takagi-Sugeno que permiten la clasificación y caracterización automática de datos nuevos. Con ello se tiene un sistema capaz de producir información acerca de los datos numéricos procesados con estos modelos. En este trabajo hemos utilizado principalmente el algoritmo de agrupamiento híbrido PFCM (Possibilistic Fuzzy c- Means) al que hemos incorporado una mejora, cuyo algoritmo hemos denominado GKPFCM (Gustafson-Kessel Possibilistic Fuzzy c-Means), y que permite encontrar grupos con formas más aproximadas a las distribuciones naturales de los grupos de datos. Esto queda de manifiesto en un aprendizaje no supervisado para la identificación de plátanos y tomates maduros y verdes que se presentan también en este documento. Entre los principales resultados obtenidos en el desarrollo de esta tesis podemos citar: Se propone un nuevo enfoque para la sub-segmentación de imágenes digitales, aquí basado en el algoritmo de agrupamiento PFCM. El propósito es poder determinar subgrupos de datos (píxeles) de interés que pueden ser los datos típicos o los atípicos, aunque en muchas aplicaciones, particularmente en diagnóstico, son estos últimos los de más interés. En esta tesis mostramos dos aplicaciones a casos reales. Se mejora el algoritmo PFCM (GKPFCM) al incorporar la distancia de Mahalanobis ya que los grupos encontrados tienen una mejor aproximación a la distribución natural de los datos. Asimismo, se propone la construcción de un clasificador que permite obtener automáticamente información de datos nuevos al clasificarlos y caracterizarlos como típicos, atípicos o ruido. El clasificador está basado en dos modelos difusos de tipo Takagi-Sugeno, el cual obtiene sus parámetros a partir de los resultados generados por el algoritmo GKPFCM Abstract In recent years technological advances have led to the generation and collection of large amount of mainly numerical data, and there is a great interest on processing them for extract knowledge and information with the main objective of making systems more efficient where these data were obtained from. Information in a database is found implicit in the values that represent the system different states while knowledge is implicit in relations between the different attribute values or features of the data base. Those relations are identified by groups (internal structure) that must be discovered and that describe relations between input and output states. For this purpose different techniques have been developed, one of which is through partitional clustering algorithms. In this thesis a contribution to knowledge is proposed and information extraction from numerical databases through fuzzy hybrid partitional clustering algorithms. Information is extracted by grouping and characterizing data in typical, atypical and noise, as well as application to image sub-segmentation where a new approach is proposed with interesting characteristics for detecting atypical pixels that could be linked to microcalcifications in order to detect breast cancer, or wood knots for assess its quality, both cases treated on this thesis, or in any other application for industry or health, in example, where it does not matter if pixels to find are in very small quantities. Knowledge is extracted through setting up two fuzzy models of type Takagi-Sugeno that allows automatic characterization and classification of new data. This will gives a system able to produce information about the processed numerical data with these models. On this job we have mainly used the hybrid clustering algorithm PFCM (Possibilistic Fuzzy c-Means) where which we have added an improvement whose algorithm were called GKPFCM (Gustafson-Kessel Possibilistic Fuzzy c-Means) and that allows to find groups with patterns more approximated to natural distributions of the data groups. This is reflected in an unsupervised learning for identification of bananas, ripe and unripe tomatoes also presented in this document. Within major achievements of this thesis development we can cite: Is proposed a new approach for sub-segmentation of digital images based on the clustering algorithm PFCM. The purpose is to identify data sub-groups of interest that could be atypical or typical data while in many applications, particularly in diagnosis, these last are the more interesting ones. In this thesis we show up two applications for real cases. Is improved the PFCM (GKPFCM) algorithm by embodying the Mahalanobis distance because the found groups have a better approximation to the data distribution. Also is proposed a construction of a classifier that makes possible to obtain information automatically from new data by classifying and characterising them as typical, atypical or noise. Classifier is based on two fuzzy models of type Takagi-Sugeno which obtains its parameters from results generated by the GKPFCM algorithm. v

Pore detection in 3-D CT soil samples through an improved sub-segmentation method

X-ray computer tomography (CT) is a non-invasive technique for image acquisition. Recent technologicaladvances have enabled reliable and high-resolution images to be obtained. In soil samples, for example, thissubserves the identication of pores and their structure and the analysis of their geometric characteristics.However, the lack of contrast between pores and solids in soil samples makes it difcult to identify the pores, andit poses problems for their connectivity when a three-dimensional (3-D) reconstruction is made from a group ofconsecutive 2-D images obtained with a scanner. To solve this problem, an improved sub-segmentation method,which had been developed and tested previously, was applied in this research to achieve a better identication ofthe pore space and consequently the solid space in the 2-D slices of the image, followed by a 3-D reconstructionof the soil sample. In this study, two soil samples were used, one real soil sample with 255 2-D CT consecutiveimages and a synthetic image with 215 2-D images. The latter sample was used only to evaluate the robustnessof the improved sub-segmentation method and the results from analysis of the pore connectivity in a knownstructure. The results obtained with the improved sub-segmentation were compared with those of traditionalclustering algorithms for image segmentation by k-means, fuzzy c-means and Otsu’s methods. The results werepromising, and the 3-D reconstruction presents a realistic structure for the continuity and coincidence of theshapes of the pores in the consecutive images. In addition, the pore regions detected have a small non-uniformity(NU) value, which indicates both good pore detection and homogeneity, which facilitates pore connectivitybetween the different 2-D images.Sin financiación3.742 JCR (2019) Q1, 8/38 Soil Science1.267 SJR (2019) Q1, 16/145 Soil ScienceNo data IDR 2019UE

Correlations between physical and chemical defences in plants: tradeoffs, syndromes, or just many different ways to skin a herbivorous cat?

� Most plant species have a range of traits that deter herbivores. However, understanding of how different defences are related to one another is surprisingly weak. Many authors argue that defence traits trade off against one another, while others argue that they form coordinated defence syndromes. � We collected a dataset of unprecedented taxonomic and geographic scope (261 species spanning 80 families, from 75 sites across the globe) to investigate relationships among four chemical and six physical defences. � Five of the 45 pairwise correlations between defence traits were significant and three of these were tradeoffs. The relationship between species’ overall chemical and physical defence levels was marginally nonsignificant (P = 0.08), and remained nonsignificant after accounting for phylogeny, growth form and abundance. Neither categorical principal component analysis (PCA) nor hierarchical cluster analysis supported the idea that species displayed defence syndromes. � Our results do not support arguments for tradeoffs or for coordinated defence syndromes. Rather, plants display a range of combinations of defence traits. We suggest this lack of consistent defence syndromes may be adaptive, resulting from selective pressure to deploy a different combination of defences to coexisting species