Genetic learning of accurate and compact fuzzy rule based systems based on the 2-tuples linguistic representation

One of the problems that focus the research in the linguistic fuzzy modeling area is the trade-off between interpretability and accuracy. To deal with this problem, different approaches can be found in the literature. Recently, a new linguistic rule representation model was presented to perform a genetic lateral tuning of membership functions. It is based on the linguistic 2-tuples representation that allows the lateral displacement of a label considering an unique parameter. This way to work involves a reduction of the search space that eases the derivation of optimal models and therefore, improves the mentioned trade-off. Based on the 2-tuples rule representation, this work proposes a new method to obtain linguistic fuzzy systems by means of an evolutionary learning of the data base a priori (number of labels and lateral displacements) and a simple rule generation method to quickly learn the associated rule base. Since this rule generation method is run from each data base definition generated by the evolutionary algorithm, its selection is an important aspect. In this work, we also propose two new ad hoc data-driven rule generation methods, analyzing the influence of them and other rule generation methods in the proposed learning approach. The developed algorithms will be tested considering two different real-world problems.Spanish Ministry of Science and Technology under Projects TIC-2002-04036-C05-01 and TIN-2005-08386-C05-0

Automatic Laser Pointer Detection Algorithm for Environment Control Device Systems Based on Template Matching and Genetic Tuning of Fuzzy Rule-Based Systems

In this paper we propose a new approach for laser-based environment device control systems based on the automatic design of a Fuzzy Rule-Based System for laser pointer detection. The idea is to improve the success rate of the previous approaches decreasing as much as possible the false offs and increasing the success rate in images with laser spot, i.e., the detection of a false laser spot (since this could lead to dangerous situations). To this end, we propose to analyze both, the morphology and color of a laser spot image together, thus developing a new robust algorithm. Genetic Fuzzy Systems have also been employed to improve the laser spot system detection by means of a fine tuning of the involved membership functions thus reducing the system false offs, which is the main objective in this problem. The system presented in this paper, makes use of a Fuzzy Rule-Based System adjusted by a Genetic Algorithm, which, based on laser morphology and color analysis, shows a better success rate than previous approaches.Spanish Government TIN2008-06681-C06-01 TIN2007-68083-C02-01University of Extremadura regional government Junta de ExtremaduraConsejeria de Economia-Comercio e Innovacion European Commission GRU0910

Enhancing soft computing techniques to actively address imbalanced regression problems

This paper has been supported in part by the ERDF A way of making Europe/Health Institute Carlos III/Spanish Ministry of Science, Innovation and Universities (grant number PI20/00711), by the ERDF A way of making Europe/Regional Government of Andalusia/Ministry of Economic Transformation, Industry, Knowledge and Universities (grant numbers P18-RT-2248 and B-CTS-536-UGR20) and by the MCIN/AEI/10.13039/50110001103 (grant numbers PID2019-107793GB-I00 and PID2020-119478GB-I00). Funding for open access charge: Universidad de Granada / CBUA

Experimental Study on 164 Algorithms Available in Software Tools for Solving Standard Non-Linear Regression Problems

In the specialized literature, researchers can find a large number of proposals for solving regression problems that come from different research areas. However, researchers tend to use only proposals from the area in which they are experts. This paper analyses the performance of a large number of the available regression algorithms from some of the most known and widely used software tools in order to help non-expert users from other areas to properly solve their own regression problems and to help specialized researchers developing well-founded future proposals by properly comparing and identifying algorithms that will enable them to focus on significant further developments. To sum up, we have analyzed 164 algorithms that come from 14 main different families available in 6 software tools (Neural Networks, Support Vector Machines, Regression Trees, Rule-Based Methods, Stacking, Random Forests, Model trees, Generalized Linear Models, Nearest Neighbor methods, Partial Least Squares and Principal Component Regression, Multivariate Adaptive Regression Splines, Bagging, Boosting, and other methods) over 52 datasets. A new measure has also been proposed to show the goodness of each algorithm with respect to the others. Finally, a statistical analysis by non-parametric tests has been carried out over all the algorithms and on the best 30 algorithms, both with and without bagging. Results show that the algorithms from Random Forest, Model Tree and Support Vector Machine families get the best positions in the rankings obtained by the statistical tests when bagging is not considered. In addition, the use of bagging techniques significantly improves the performance of the algorithms without excessive increase in computational times.This work was supported in part by the University of Córdoba under the project PPG2019-UCOSOCIAL-03, and in part by the Spanish Ministry of Science, Innovation and Universities under Grant TIN2015- 68454-R and Grant TIN2017-89517-P

Transparent but Accurate Evolutionary Regression Combining New Linguistic Fuzzy Grammar and a Novel Interpretable Linear Extension

Scientists must understand what machines do (systems should not behave like a black box), because in many cases how they predict is more important than what they predict. In this work, we propose a new extension of the fuzzy linguistic grammar and a mainly novel interpretable linear extension for regression problems, together with an enhanced new linguistic tree-based evolutionary multiobjective learning approach. This allows the general behavior of the data covered, as well as their specific variability, to be expressed as a single rule. In order to ensure the highest transparency and accuracy values, this learning process maximizes two widely accepted semantic metrics and also minimizes both the number of rules and the model mean squared error. The results obtained in 23 regression datasets show the effectiveness of the proposed method by applying statistical tests to the said metrics, which cover the different aspects of the interpretability of linguistic fuzzy models. This learning process has obtained the preservation of high-level semantics and less than 5 rules on average, while it still clearly outperforms some of the previous state-of-the-art linguistic fuzzy regression methods for learning interpretable regression linguistic fuzzy systems, and even to a competitive, pure accuracyoriented linguistic learning approach. Finally, we analyze a case study in a real problem related to childhood obesity, and a real expert carries out the analysis shown.Andalusian Government P18-RT-2248Health Institute Carlos III/Spanish Ministry of Science, Innovation and Universities PI20/00711Spanish Government PID2019-107793GB-I00 PID2020-119478GB-I0

Water management assessment in a historic garden: the case study of the Real Alcazar (Seville, Spain)

Irrigation plays a very important role in a Mediterranean garden. In spite of this, there are not many studies assessing irrigation water management of landscapes. Moreover, historic gardens represent a special challenge due to their unique characteristics. The aim of this work is the characterization and evaluation of water management in a historic garden. For that, the gardens of The Real Alcazar of Seville were used as a case study. They comprise a total of 20 gardens of different styles with a total area of nearly 7 ha. Landscape water requirements and irrigation volume applied were estimated and used in conjunction with other descriptive and financial variables to calculate 6 performance indicators. Only 20% of gardens showed adequate irrigation in the spring-autumn period, being 10% during summer. However, the two well-watered gardens represent 30% of the total irrigated area. Management, operation and maintenance costs are 0.63 €·m−2 representing 0.58 € per volume of irrigation water used (m−3). Results obtained support the need of improving irrigation management. For that, simple solutions such as installing metering devices, calculating actual water requirements or optimizing irrigation schedules can be implemented. Other more complex actions such as modifying the irrigation network or creating hydrozones might also be explored

Analyzing gender disparities in STEAM: A Case Study from Bioinformatics Workshops in the University of Granada

La bioinformática es un área interdisciplinaria que ha despertado un gran interés tanto para el mundo académico como para las corporaciones en los últimos años. Esta área creciente combina conocimientos y habilidades de las áreas de biología y ciencia, tecnología, ingeniería, artes y matemáticas (STEM). Una de las ventajas de la sinergia entre estas dos áreas de trabajo es que ofrece una oportunidad para cerrar la brecha de género de STEM tradicional. A pesar de esta oportunidad y la importancia y amplia aplicación del campo de la bioinformática, este tema aún no ha ganado suficiente visibilidad en los programas de posgrado para los títulos de bachillerato en la Universidad de Granada. Esto ha motivado la organización de un "Taller educativo sobre bioinformática" anual en la Universidad de Granada por el Departamento de Ciencias de la Computación e Inteligencia Artificial. Los resultados del análisis de las dos primeras ediciones de este taller muestran un gran interés en el tema por la comunidad universitaria en todos los niveles (por ejemplo, estudiantes de pregrado y posgrado, docentes e investigadores) sin distinción significativa entre los géneros a nivel global. Al analizar el grupo de estudiantes, las mujeres mostraron un mayor interés en el tema. Sin embargo, este interés no se reflejó en los estratos universitarios superiores (docentes e investigadores), que representan un vistazo de la situación actual general española en el área.Bioinformatics is an interdisciplinary area that has raised a high interest for both academia and corporations in recent years. This rising area combines knowledge and skills from Bio and Science, Technology, Engineering, Arts and Mathematics (STEM) areas. One of the advantages of the synergy between these two work areas is that it offers an opportunity for closing the traditional STEM's gender gap. Despite this opportunity and the signi cance and wide application of bioinformatics eld, this topic has still not gained enough visibility in the graduate programs for the Bio Bachelor Degrees at the University of Granada. This has motivated the organization of an annual \Educational Workshop on Bioinformatics" at the University of Granada by the Department of Computer Science and Arti cial Intelligence. Results of the analysis of the rst two editions of this workshop show a great interest on the topic by the university community at all levels (e.g. undergraduate and graduate students, teachers and researchers) without signi cant distinction among genders at global level. When analyzing student group, women did show a higher interest on the subject. However, this interest was not reflected in the higher university strata (teachers and researchers), which represents a glimpse of the spanish general current situation on the area.Universidad de Granada: Departamento de Arquitectura y Tecnología de Computadore

Learning positive-negative rule-based fuzzy associative classifiers with a good trade-off between complexity and accuracy

Nowadays, the call for transparency in Artificial Intelligence models is growing due to the need to understand how decisions derived from the methods are made when they ultimately affect human life and health. Fuzzy Rule-Based Classification Systems have been used successfully as they are models that are easily understood by models themselves. However, complex search spaces hinder the learning process, and in most cases, lead to problems of complexity (coverage and specificity). This problem directly affects the intention to use them to enable the user to analyze and understand the model. Because of this, we propose a fuzzy associative classification method to learn classifiers with an improved trade-off between accuracy and complexity. This method learns the most appropriate granularity of each variable to generate a set of simple fuzzy association rules with a reduced number of associations that consider positive and negative dependencies to be able to classify an instance depending on the presence or absence of certain items. The proposal also chooses the most interesting rules based on several interesting measures and finally performs a genetic rule selection and adjustment to reach the most suitable context of the selected rule set. The quality of our proposal has been analyzed using 23 real-world datasets, comparing them with other proposals by applying statistical analysis. Moreover, the study carried out on a real biomedical research problem of childhood obesity shows the improved trade-off between the accuracy and complexity of the models generated by our proposal.Funding for open access charge: Universidad de Granada / CBUA.ERDF and the Regional Government of Andalusia/Ministry of Economic Transformation, Industry, Knowledge and Universities (grant numbers P18-RT-2248 and B-CTS-536-UGR20)ERDF and Health Institute Carlos III/Spanish Ministry of Science, Innovation and Universities (grant number PI20/00711)Spanish Ministry of Science and Innovation (grant number PID2019-107793GB-I00

Morphological taphonomic transformations of fossil bones in continental environments, and repercussions of their chemical composition

The aim of this paper is to provide a summary of structural or surface modifications of vertebrate bone remains in continental palaeoenvironments that may have repercussions on their chemical composition. Both before and after burial, a different set of physical, chemical and biological agencies may produce modifications of the bone morphological structure and/or bone chemical composition. Several of these morphological modifications are diagnostic of particular agents, which otherwise may not be noticed or identified in a fossil association. In order to understand diagenesis, those events that occurred before final burial have to be considered, as they may strongly modify the bone morphological structure and influence post-burial changes

Chemical and mechanical stability of air annealed cathodic arc evaporated CrAlON coatings

This study reports the synthesis and characterization of ternary Cr-Al-O and quaternary Cr-Al-O-N coatings deposited by cathodic arc physical vapour deposition, for various nitrogen and oxygen mass flow ratios during the growth process. The composition, microstructure, indentation hardness and modulus of the films have been characterized by scanning electron microscopy, electron probe micro-analysis, X-ray diffraction, and nanoindentation techniques. The evolution of the microstructure and mechanical properties of the coatings after ambient air annealing from 800 °C up to 1100 °C have been investigated. As the oxygen to nitrogen mass flow increases, the as-deposited coatings exhibit lower hardness, higher roughness, lower crystallinity and a more marked columnar structure. At oxygen to nitrogen mass flow ratios bigger than 10/90, the coatings exhibit a stoichiometry of the type (CrAl)2+εO3−ε. Only the coatings with an oxygen to nitrogen mass flow ratio smaller than 10/90 retained nitrogen in their compositions. In all cases, the coatings developed a cubic fcc lattice structure. After annealing at 1100 °C the resulting microstructure showed a clear dependency upon the initial composition of the films. The evolution of the microstructure during the high temperature tests, as well as the analysis of the nanoindentation hardness, composition and thickness also provided valuable information about the combined effects of the thermal stability and the oxidation of the deposited coatings