Deep Learning Techniques to Improve the Performance of Olive Oil Classification

The olive oil assessment involves the use of a standardized sensory analysis according to the “panel test” method. However, there is an important interest to design novel strategies based on the use of Gas Chromatography (GC) coupled to mass spectrometry (MS), or ion mobility spectrometry (IMS) together with a chemometric data treatment for olive oil classification. It is an essential task in an attempt to get the most robust model over time and, both to avoid fraud in the price and to know whether it is suitable for consumption or not. The aim of this paper is to combine chemical techniques and Deep Learning approaches to automatically classify olive oil samples from two different harvests in their three corresponding classes: extra virgin olive oil (EVOO), virgin olive oil (VOO), and lampante olive oil (LOO). Our Deep Learning model is built with 701 samples, which were obtained from two olive oil campaigns (2014–2015 and 2015–2016). The data from the two harvests are built from the selection of specific olive oil markers from the whole spectral fingerprint obtained with GC-IMS method. In order to obtain the best results we have configured the parameters of our model according to the nature of the data. The results obtained show that a deep learning approach applied to data obtained from chemical instrumental techniques is a good method when classifying oil samples in their corresponding categories, with higher success rates than those obtained in previous works.Ministerio de Economía y Competitividad TIN2017-88209-C2-2-

Use of Deep Learning Architectures for Day-Ahead Electricity Price Forecasting over Different Time Periods in the Spanish Electricity Market

The importance of electricity in people’s daily lives has made it an indispensable commodity in society. In electricity market, the price of electricity is the most important factor for each of those involved in it, therefore, the prediction of the electricity price has been an essential and very important task for all the agents involved in the purchase and sale of this good. The main problem within the electricity market is that prediction is an arduous and difficult task, due to the large number of factors involved, the non-linearity, non-seasonality and volatility of the price over time. Data Science methods have proven to be a great tool to capture these difficulties and to be able to give a reliable prediction using only price data, i.e., taking the problem from an univariate point of view in order to help market agents. In this work, we have made a comparison among known models in the literature, focusing on Deep Learning architectures by making an extensive tuning of parameters using data from the Spanish electricity market. Three different time periods have been used in order to carry out an extensive comparison among them. The results obtained have shown, on the one hand, that Deep Learning models are quite effective in predicting the price of electricity and, on the other hand, that the different time periods and their particular characteristics directly influence the final results of the modelMinisterio de Ciencia, Innovación y Universidades TIN2017-88209-C2Junta de Andalucía US-1263341Junta de Andalucía P18-RT-277

OCEAn: Ordinal classification with an ensemble approach

Generally, classification problems catalog instances according to their target variable with out considering the relation among the different labels. However, there are real problems in which the different values of the class are related to each other. Because of interest in this type of problem, several solutions have been proposed, such as cost-sensitive classi fiers. Ensembles have proven to be very effective for classification tasks; however, as far as we know, there are no proposals that use a genetic-based methodology as the meta heuristic to create the models. In this paper, we present OCEAn, an ordinal classification algorithm based on an ensemble approach, which makes a final prediction according to a weighted vote system. This weighted voting takes into account weights obtained by a genetic algorithm that tries to minimize the cost of classification. To test the performance of this approach, we compared our proposal with ordinal classification algorithms in the literature and demonstrated that, indeed, our approach improves on previous resultsMinisterio de Ciencia, Innovación y Universidades TIN2017-88209-C2Junta de Andalucía US-126334

Mucosal Immune Defence Gene Polymorphisms as Relevant Players in the Pathogenesis of IgA Vasculitis?

ITGAM–ITGAX (rs11150612, rs11574637), VAV3 rs17019602, CARD9 rs4077515, DEFA (rs2738048, rs10086568), and HORMAD2 rs2412971 are mucosal immune defence polymorphisms, that have an impact on IgA production, described as risk loci for IgA nephropathy (IgAN). Since IgAN and Immunoglobulin-A vasculitis (IgAV) share molecular mechanisms, with the aberrant deposit of IgA1 being the main pathophysiologic feature of both entities, we assessed the potential influence of the seven abovementioned polymorphisms on IgAV pathogenesis. These seven variants were genotyped in 381 Caucasian IgAV patients and 997 matched healthy controls. No statistically significant differences were observed in the genotype and allele frequencies of these seven polymorphisms when the whole cohort of IgAV patients and those with nephritis were compared to controls. Similar genotype and allele frequencies of all polymorphisms were disclosed when IgAV patients were stratified according to the age at disease onset or the presence/absence of gastrointestinal or renal manifestations. Likewise, no ITGAM–ITGAX and DEFA haplotype differences were observed when the whole cohort of IgAV patients, along with those with nephritis and controls, as well as IgAV patients, stratified according to the abovementioned clinical characteristics, were compared. Our results suggest that mucosal immune defence polymorphisms do not represent novel genetic risk factors for IgAV pathogenesis

5to. Congreso Internacional de Ciencia, Tecnología e Innovación para la Sociedad. Memoria académica

El V Congreso Internacional de Ciencia, Tecnología e Innovación para la Sociedad, CITIS 2019, realizado del 6 al 8 de febrero de 2019 y organizado por la Universidad Politécnica Salesiana, ofreció a la comunidad académica nacional e internacional una plataforma de comunicación unificada, dirigida a cubrir los problemas teóricos y prácticos de mayor impacto en la sociedad moderna desde la ingeniería. En esta edición, dedicada a los 25 años de vida de la UPS, los ejes temáticos estuvieron relacionados con la aplicación de la ciencia, el desarrollo tecnológico y la innovación en cinco pilares fundamentales de nuestra sociedad: la industria, la movilidad, la sostenibilidad ambiental, la información y las telecomunicaciones. El comité científico estuvo conformado formado por 48 investigadores procedentes de diez países: España, Reino Unido, Italia, Bélgica, México, Venezuela, Colombia, Brasil, Estados Unidos y Ecuador. Fueron recibidas un centenar de contribuciones, de las cuales 39 fueron aprobadas en forma de ponencias y 15 en formato poster. Estas contribuciones fueron presentadas de forma oral ante toda la comunidad académica que se dio cita en el Congreso, quienes desde el aula magna, el auditorio y la sala de usos múltiples de la Universidad Politécnica Salesiana, cumplieron respetuosamente la responsabilidad de representar a toda la sociedad en la revisión, aceptación y validación del conocimiento nuevo que fue presentado en cada exposición por los investigadores. Paralelo a las sesiones técnicas, el Congreso contó con espacios de presentación de posters científicos y cinco workshops en temáticas de vanguardia que cautivaron la atención de nuestros docentes y estudiantes. También en el marco del evento se impartieron un total de ocho conferencias magistrales en temas tan actuales como la gestión del conocimiento en la universidad-ecosistema, los retos y oportunidades de la industria 4.0, los avances de la investigación básica y aplicada en mecatrónica para el estudio de robots de nueva generación, la optimización en ingeniería con técnicas multi-objetivo, el desarrollo de las redes avanzadas en Latinoamérica y los mundos, la contaminación del aire debido al tránsito vehicular, el radón y los riesgos que representa este gas radiactivo para la salud humana, entre otros

Role of age and comorbidities in mortality of patients with infective endocarditis

[Purpose]: The aim of this study was to analyse the characteristics of patients with IE in three groups of age and to assess the ability of age and the Charlson Comorbidity Index (CCI) to predict mortality. [Methods]: Prospective cohort study of all patients with IE included in the GAMES Spanish database between 2008 and 2015.Patients were stratified into three age groups:<65 years,65 to 80 years,and ≥ 80 years.The area under the receiver-operating characteristic (AUROC) curve was calculated to quantify the diagnostic accuracy of the CCI to predict mortality risk. [Results]: A total of 3120 patients with IE (1327 < 65 years;1291 65-80 years;502 ≥ 80 years) were enrolled.Fever and heart failure were the most common presentations of IE, with no differences among age groups.Patients ≥80 years who underwent surgery were significantly lower compared with other age groups (14.3%,65 years; 20.5%,65-79 years; 31.3%,≥80 years). In-hospital mortality was lower in the <65-year group (20.3%,<65 years;30.1%,65-79 years;34.7%,≥80 years;p < 0.001) as well as 1-year mortality (3.2%, <65 years; 5.5%, 65-80 years;7.6%,≥80 years; p = 0.003).Independent predictors of mortality were age ≥ 80 years (hazard ratio [HR]:2.78;95% confidence interval [CI]:2.32–3.34), CCI ≥ 3 (HR:1.62; 95% CI:1.39–1.88),and non-performed surgery (HR:1.64;95% CI:11.16–1.58).When the three age groups were compared,the AUROC curve for CCI was significantly larger for patients aged <65 years(p < 0.001) for both in-hospital and 1-year mortality. [Conclusion]: There were no differences in the clinical presentation of IE between the groups. Age ≥ 80 years, high comorbidity (measured by CCI),and non-performance of surgery were independent predictors of mortality in patients with IE.CCI could help to identify those patients with IE and surgical indication who present a lower risk of in-hospital and 1-year mortality after surgery, especially in the <65-year group

Metodología ensemble para clasificación ordinal y aplicación en el control de calidad del aceite de oliva

A día de hoy nos encontramos ante un nuevo paradigma que está revolucionando completamente la forma en la que vemos las cosas. Este nuevo paradigma surge ante el gran volumen de datos que se generan mediante multitud de dispositivos conectados entre sí y la necesidad de obtener conocimiento a partir de toda esta información. El auge de las tecnologías y la gran capacidad de computación de la que disponemos a día de hoy ha hecho posible que términos como ciencia de datos, minería de datos, inteligencia artificial o aprendizaje automático hayan cobrado mucha importancia en la sociedad actual. Todos estos términos tienen en común un factor, y es el de extraer conocimiento a partir de los datos. El aprendizaje automático es un tipo de inteligencia artificial que proporciona a las máquinas de computación la capacidad de aprender sin ser programadas explícitamente para llegar a ese conocimiento. Este aprendizaje puede verse desde 2 puntos de vista diferentes según si los datos están etiquetados previamente o no. Si los datos de partida están etiquetados se denomina aprendizaje supervisado, y si no están etiquetados se pasa a llamar aprendizaje no supervisado. El aprendizaje supervisado trata de encontrar una función capaz de explicar los datos de entrenamiento. Esta función trata de buscar relaciones que asocie entradas con salidas a partir de un conjunto de datos etiquetados, es decir, cuyas entradas y salidas son conocidas. Dependiendo del objetivo a predecir, hablamos de clasificación, si el atributo es categórico o de regresión, si el atributo es numérico. En el caso de los problemas de clasificación, la mayoría de algoritmos y modelos desarrollados hasta el momento no tienen en cuenta una posible relación de orden entre las distintas etiquetas cuando realmente sí lo hay. Aquellos modelos que sí tienen en cuenta este factor se denominan modelos de clasificación ordinal y han demostrado obtener resultados muy prometedores cuando la etiqueta a predecir tiene valores que guardan relación entre sí. Esta tesis doctoral analiza y desarrolla una nueva metodología de aprendizaje supervisado para realizar clasificación ordinal. Esta nueva propuesta consiste en un algoritmo ensemble que combina la salida de clasificadores individuales mediante un sistema de votación por pesos, dichos pesos son calculados tras un proceso de optimización llevado a cabo mediante un algoritmo genético. Esta tesis se presenta como compendio de artículos de investigación con un total de 5 publicaciones, 3 de ellas publicadas en revistas con alto índice de impacto en el Journal Citation Reports y 2 de ellas como aportaciones científicas en congresos internacionales. La necesidad de desarrollar este algoritmo surge ante el análisis de la calidad del aceite de oliva. La calidad del aceite de oliva viene determinada por factores físico-químicos que son traducidos en etiquetas que tienen un orden de relación entre ellas dependiendo del grado de calidad de las muestras. Los resultados obtenidos fueron muy prometedores, demostrando que esta metodología es una muy buena alternativa para este problema concreto. La importancia de una correcta clasificación de un producto tan importante en la economía española, como es el aceite de oliva y teniendo en cuenta la riqueza y variedad de los datos con los que contábamos, decidimos también explorar otras técnicas de inteligencia artificial, como son las redes neuronales artificiales, para tratar el mismo problema desde distintos puntos de vista, con el objetivo de obtener los mejores resultados posibles. Por último, dada la delicadeza y privacidad de los datos con los que trabajábamos, se hizo un estudio de técnicas de inteligencia artificial para la generación de datos sintéticos, con el objetivo de poder compartir datos con otros grupos de investigación sin poner en compromiso los datos originales. La técnica utilizada fue las redes neuronales generativas adversariales (GANs) que demostraron tener mucho éxito en la generación de datos sintéticos

Use of Deep Learning Architectures for Day-Ahead Electricity Price Forecasting over Different Time Periods in the Spanish Electricity Market

The importance of electricity in people’s daily lives has made it an indispensable commodity in society. In electricity market, the price of electricity is the most important factor for each of those involved in it, therefore, the prediction of the electricity price has been an essential and very important task for all the agents involved in the purchase and sale of this good. The main problem within the electricity market is that prediction is an arduous and difficult task, due to the large number of factors involved, the non-linearity, non-seasonality and volatility of the price over time. Data Science methods have proven to be a great tool to capture these difficulties and to be able to give a reliable prediction using only price data, i.e., taking the problem from an univariate point of view in order to help market agents. In this work, we have made a comparison among known models in the literature, focusing on Deep Learning architectures by making an extensive tuning of parameters using data from the Spanish electricity market. Three different time periods have been used in order to carry out an extensive comparison among them. The results obtained have shown, on the one hand, that Deep Learning models are quite effective in predicting the price of electricity and, on the other hand, that the different time periods and their particular characteristics directly influence the final results of the models

Generation of synthetic data with Conditional Generative Adversarial Networks

The generation of synthetic data is becoming a fundamental task in the daily life of any organization due to the new protection data laws that are emerging. Because of the rise in the use of Artificial Intelligence, one of the most recent proposals to address this problem is the use of Generative Adversarial Networks (GANs). These types of networks have demonstrated a great capacity to create synthetic data with very good performance. The goal of synthetic data generation is to create data that will perform similarly to the original dataset for many analysis tasks, such as classification. The problem of GANs is that in a classification problem, GANs do not take class labels into account when generating new data, it is treated as any other attribute. This research work has focused on the creation of new synthetic data from datasets with different characteristics with a Conditional Generative Adversarial Network (CGAN). CGANs are an extension of GANs where the class label is taken into account when the new data is generated. The performance of our results has been measured in two different ways: firstly, by comparing the results obtained with classification algorithms, both in the original datasets and in the data generated; secondly, by checking that the correlation between the original data and those generated is minimal.Ministerio de Ciencia e Innovación TIN2017-88209-C2-2-RJunta de Andalucía US-126334