Special Issue on Applied Artificial Neural Networks

[Abstract]: Over the years there have been many attempts to understand, and subsequently imitate, the way that humans try to solve problems, so it can help to artificially achieve the same kind of intelligent behavior. Among these attempts, one of them has been especially successful: the artificial neural networks (ANNs), which simplify the functioning of one of the most complex organs in nature: the brain. From its earliest approaches, these networks have provided excellent solutions in the most diverse fields of research. After overcoming a small hurdle in the last stage of their use, they have revived in recent years under the nomenclature of deep neural networks, which are based on the same bases of those of ANNs and take advantage of the emergence of new learning algorithms and the greater computational capabilities that exist nowadays. This Special Issue is aimed to accommodate, on one hand, the latest theoretical advances in this field, such as new learning paradigms or new architectures, and on the other hand, those more recent works in the scientific field where the authors have used any of the many types of available neural networks or those new theorical proposals to reach the best results in their areas. Eleven manuscripts were accepted in this Special Issue, most of them emphasizing the highly successful applicability of ANNs in a great variety of fields

Computación evolutiva para el proceso de selección de variables en espacios de búsqueda multimodales

[Resumen] Esta tesis se enmarca dentro de la problemática de la selección de variables en espacios de búsqueda multimodal, Esta tarea puede ser abordada mediante diferentes técnicas, analizadas en profundidad e indicando los puntos débiles de cada una de ellas. Entre ellas, las técnicas basadas en Computación Evolutiva apartan buena soluciones cuando se trata de explorar espacios de búsqueda complejos (como los resultantes en un proceso de selección de variables). No obstante, presentan limitaciones cuando dicho espacio de búsqueda presenta múltiples soluciones globales o una única solución global pero múltiples soluciones subóptimas (mínimos locales). En estos casos, la búsqueda puede verse atrapada en un mínimo local, o bien focalizarse en el entorno de una única solución cuando lo verdaderamente interesante sería obtener el mayor número posible de soluciones válidas. Con el objetivo de paliar estas deficiencias se proponen dos técnicas basadas en Algoritmos Genéticos que permiten explorar de manera homogénea el espacio de búsqueda y localizar un mayor número de soluciones. Los resultados obtenidos tras las pruebas realizadas muestran el buen comportamiento de ambas, así como una serie de ventajas tales como la generalización del proceso de evaluación de las soluciones, puesto que se aporta una técnica de evaluación basada en el empleo de Redes de Neuronas Artificiales

Ternary derivations of finite-dimensional real division algebras

AbstractWe present for each n∈{1,2,4,8} a group acting on the set of all division algebra structures on Rn, and an invariant, the Lie algebra of ternary derivations, for this action. An exploration of these structures is conducted in terms of this new invariant obtaining simple descriptions of the division algebras involved. In the course of the investigation another family of algebras is considered, among them the algebra sl(4,F) of 4×4 traceless matrices with the symmetric product xy+yx-12t(xy)I shows an exceptional behavior

Computational health engineering applied to model infectious diseases and antimicrobial resistance spread

Infectious diseases are the primary cause of mortality worldwide. The dangers of infectious disease are compounded with antimicrobial resistance, which remains the greatest concern for human health. Although novel approaches are under investigation, the World Health Organization predicts that by 2050, septicaemia caused by antimicrobial resistant bacteria could result in 10 million deaths per year. One of the main challenges in medical microbiology is to develop novel experimental approaches, which enable a better understanding of bacterial infections and antimicrobial resistance. After the introduction of whole genome sequencing, there was a great improvement in bacterial detection and identification, which also enabled the characterization of virulence factors and antimicrobial resistance genes. Today, the use of in silico experiments jointly with computational and machine learning offer an in depth understanding of systems biology, allowing us to use this knowledge for the prevention, prediction, and control of infectious disease. Herein, the aim of this review is to discuss the latest advances in human health engineering and their applicability in the control of infectious diseases. An in-depth knowledge of host?pathogen?protein interactions, combined with a better understanding of a host?s immune response and bacterial fitness, are key determinants for halting infectious diseases and antimicrobial resistance dissemination

Implementing a Web Application for W3C WebAuthn Protocol Testing

[Abstract] During the last few years, the FIDO Alliance and the W3C have been working on a new standard called WebAuthn that aims to substitute the obsolete password as an authentication method by using physical security keys instead. Due to its recent design, the standard is still changing and so are the needs for protocol testing. This research has driven the development of a web application that supports the standard and gives extensive information to the user. This tool can be used by WebAuthn developers and researchers, helping them to debug concrete use cases with no need for an ad hoc implementation.Xunta de Galicia; ED431C 2018/4

Classification of Signals by Means of Genetic Programming

[Abstract] This paper describes a new technique for signal classification by means of Genetic Programming (GP). The novelty of this technique is that no prior knowledge of the signals is needed to extract the features. Instead of it, GP is able to extract the most relevant features needed for classification. This technique has been applied for the solution of a well-known problem: the classification of EEG signals in epileptic and healthy patients. In this problem, signals obtained from EEG recordings must be correctly classified into their corresponding class. The aim is to show that the technique described here, with the automatic extraction of features, can return better results than the classical techniques based on manual extraction of features. For this purpose, a final comparison between the results obtained with this technique and other results found in the literature with the same database can be found. This comparison shows how this technique can improve the ones found.Instituto de Salud Carlos III; RD07/0067/0005Xunta de Galicia; 10SIN105004P

Developing a Secure Low-Cost Radon Monitoring System

[Abstract] Radon gas has been declared a human carcinogen by the United States Environmental Protection Agency (USEPA) and the International Agency for Research on Cancer (IARC). Several studies carried out in Spain highlighted the high radon concentrations in several regions, with Galicia (northwestern Spain) being one of the regions with the highest radon concentrations. The objective of this work was to create a safe and low-cost radon monitoring and alert system, based on open source technologies. To achieve this objective, the system uses devices, a collection of sensors with a processing unit and a communication module, and a backend, responsible for managing all the information, predicting radon levels and issuing alerts using open source technologies. Security is one of the largest challenges for the internet of things, and it is utterly important in the current scenario, given that high radon concentrations pose a health risk. For this reason, this work focuses on securing the entire end-to-end communication path to avoid data forging. The results of this work indicate that the development of a low-cost, yet secured, radon monitoring system is feasible, allowing one to create a network of sensors that can help mitigate the health hazards that high radon concentrations pose.This project was funded by the Consolidation and Structuring of Competitive Research Units-Competitive Reference Groups (ED431C 2018/49) and Accreditation, Structuring, and Improvement of Consolidated Research Units and Singular Centers (ED431G/01), funded by the Ministry of Education, University and Vocational Training of the Xunta de Galicia endowed with EU FEDER funds. This research was supported by the Spanish Ministry of Economy, Industry and Competitiveness, R & D National Plan, via the project BIA2017-86738-R. This research and the APC were also supported by Instituto de Salud Carlos III, grant number PI17/01826 (Collaborative Project in Genomic Data Integration (CICLOGEN) funded by the Instituto de Salud Carlos III from the Spanish National plan for Scientific and Technical Research and Innovation 2013–2016 and the European Regional Development Funds (FEDER)—“A way to build Europe.”. This project was also supported by the General Directorate of Culture, Education and University Management of Xunta de Galicia ED431D 2017/16 and “Drug Discovery Galician Network” Ref. ED431G/01 and the “Galician Network for Colorectal Cancer Research” (Ref. ED431D 2017/23), and finally by the Spanish Ministry of Economy and Competitiveness through the funding of the unique installation BIOCAI (UNLC08-1E-002, UNLC13-13-3503) and the European Regional Development Funds (FEDER) by the European UnionXunta de Galicia; ED431G/01Xunta de Galicia; ED431C 2018/49Xunta de Galicia; ED431D 2017/16Xunta de Galicia; ED431D 2017/2

Approach of Genetic Algorithms With Grouping Into Species Optimized With Predator-Prey Method for Solving Multimodal Problems

[Abstract] Over recent years, Genetic Algorithms have proven to be an appropriate tool for solving certain problems. However, it does not matter if the search space has several valid solutions, as their classic approach is insufficient. To this end, the idea of dividing the individuals into species has been successfully raised. However, this solution is not free of drawbacks, such as the emergence of redundant species, overlapping or performance degradation by significantly increasing the number of individuals to be evaluated. This paper presents the implementation of a method based on the predator-prey technique, with the aim of providing a solution to the problem, as well as a number of examples to prove its effectiveness

Exploring Patterns of Epigenetic Information With Data Mining Techniques

[Abstract] Data mining, a part of the Knowledge Discovery in Databases process (KDD), is the process of extracting patterns from large data sets by combining methods from statistics and artificial intelligence with database management. Analyses of epigenetic data have evolved towards genome-wide and high-throughput approaches, thus generating great amounts of data for which data mining is essential. Part of these data may contain patterns of epigenetic information which are mitotically and/or meiotically heritable determining gene expression and cellular differentiation, as well as cellular fate. Epigenetic lesions and genetic mutations are acquired by individuals during their life and accumulate with ageing. Both defects, either together or individually, can result in losing control over cell growth and, thus, causing cancer development. Data mining techniques could be then used to extract the previous patterns. This work reviews some of the most important applications of data mining to epigenetics.Programa Iberoamericano de Ciencia y Tecnología para el Desarrollo; 209RT-0366Galicia. Consellería de Economía e Industria; 10SIN105004PRInstituto de Salud Carlos III; RD07/0067/000