Fernando Rojas Ruiz

• PhD
• Professor (Associate) at University of Granada

The ITISE 2023 (9th International Conference on Time Series and Forecasting) sought to provide a discussion forum for scientists, engineers, educators and students about the latest ideas and realizations in the foundations, theory, models and applications for inter- disciplinary and multidisciplinary research encompassing the disciplines of compute...

In this paper, several deep learning models are analyzed for the construction of the automated helping system to ECG classification. The methodology presented in this article begins with a study of the different alternatives for performing the discrete wavelet transform-based scalogram for an ECG. Then, several Deep Learning architectures are analy...

One of the most important situations in recent years has been originated by the 2019 Coronavirus disease (COVID-19). Nowadays this disease continues to cause a large number of deaths and remains one of the main diseases in the world. In this disease is very important the early detection to avoid the spread, as well as to monitor the progress of the...

The coronavirus disease 2019 (COVID-19) has caused millions of deaths and one of the greatest health crises of all time. In this disease, one of the most important aspects is the early detection of the infection to avoid the spread. In addition to this, it is essential to know how the disease progresses in patients, to improve patient care. This co...

In this contribution, a novel methodology for multi-class classification in the field of Parkinson’s disease is proposed. The methodology is structured in two phases. In a first phase, the most relevant volumes of interest (VOI) of the brain are selected by means of an evolutionary multi-objective optimization (MOE) algorithm. Each of these VOIs ar...

Intrusion detection is the act of detecting unwanted traffic on a network or a device. Several types of Intrusion Detection Systems (IDS) technologies exist due to the variance of network configurations. Each type has advantages and disadvantage in detection, configuration, and cost. In general, the traditional IDS relies on the extensive knowledge...

Neurodegenerative diseases represent a growing healthcare problem, mainly related to an aging population worldwide and thus their increasing prevalence. In particular, Alzheimer’s disease (AD) and Parkinson’s disease (PD) are leading neurodegenerative diseases. To aid their diagnosis and optimize treatment, we have developed a classification algori...

The glissadic overshoot is characterized by an unwanted type of movement known as glissades. The glissades are a short ocular movement that describe the failure of the neural programming of saccades to move the eyes in order to reach a specific target. In this paper we develop a procedure to determine if a specific saccade have a glissade appended...

Estimation of COVID-19 dynamics and its evolution is a multidisciplinary effort, which requires the unification of heterogeneous disciplines (scientific, mathematics, epidemiological, biological/bio-chemical, virologists and health disciplines to mention the most relevant) to work together in a better understanding of this pandemic. Time series ana...

In the current supplement, we are proud to present seventeen relevant contributions from the 6th International Work-Conference on Bioinformatics and Biomedical Engineering (IWBBIO 2018), which was held during April 25-27, 2018 in Granada (Spain). These contributions have been chosen because of their quality and the importance of their findings.

The aim of this work was to compare the behavior of mutual information and Chi-square as metrics in the evaluation of the relevance of the terms extracted from documents related to “software design” retrieved from PubMed database tested in two contexts: using a set of terms retrieved from the vectorization of the corpus of abstracts and using only...

This book presents a selection of peer-reviewed contributions on the latest advances in time series analysis, presented at the International Conference on Time Series and Forecasting (ITISE 2019), held in Granada, Spain, on September 25-27, 2019. The first two parts of the book present theoretical contributions on statistical and advanced mathemati...

In more recent years, a significant increase in the number of available biological experiments has taken place due to the widespread use of massive sequencing data. Furthermore, the continuous developments in the machine learning and in the high performance computing areas, are allowing a faster and more efficient analysis and processing of this ty...

This book presents selected peer-reviewed contributions from the International Conference on Time Series and Forecasting, ITISE 2018, held in Granada, Spain, on September 19-21, 2018. The first three parts of the book focus on the theory of time series analysis and forecasting, and discuss statistical methods, modern computational intelligence meth...

The two-volume set LNBI 11465 and LNBI 11466 constitutes the proceedings of the 7th International Work-Conference on Bioinformatics and Biomedical Engineering, IWBBIO 2019, held in Granada, Spain, in May 2019. The total of 97 papers presented in the proceedings, was carefully reviewed and selected from 301 submissions. The papers are organized in t...

The two-volume set LNBI 11465 and LNBI 11466 constitutes the proceedings of the 7th International Work-Conference on Bioinformatics and Biomedical Engineering, IWBBIO 2019, held in Granada, Spain, in May 2019. The total of 97 papers presented in the proceedings, was carefully reviewed and selected from 301 submissions. The papers are organized in t...

Background Nowadays, many public repositories containing large microarray gene expression datasets are available. However, the problem lies in the fact that microarray technology are less powerful and accurate than more recent Next Generation Sequencing technologies, such as RNA-Seq. In any case, information from microarrays is truthful and robust,...

The glissadic overshoot is characterized by an unwanted type of movement known as glissades. The glissades are a short ocular movement that describe the failure of the neural programming of saccades to move the eyes in order to reach a specific target. In this paper we develop a procedure to determine if a specific saccade have a glissade appended...

Auscultation is the primary tool for detection and diagnosis of cardiovascular diseases in hospitals and home visits. This fact has led in the recent years to the development of automatic methods for heart sound classification, thus allowing for detecting cardiovascular pathologies in an effective way. The aim of this paper is to review recent meth...

The differentiation of signals in presence of noise results complicated, due to the amplification effect of the traditional methods. In this work we do a preliminary evaluation of the use of the wavelet transform to obtain the first derivative in electrooculographic records generated by means of simulation and strongly contaminated by white and bio...

Los movimientos oculares tienen una funcion muy útil en la identificación de las disfunciones en un amplio rango de condiciones neurológicas. La aplicación de ICA a los registros electrocardiografos permite la obtención de dos componentes responsables de la generación del movimiento ocular sacádico. En este trabajo se realiza un análisis independie...

A saccade is an ocular movement that is characterized by speed and precision. The velocity profile of this movement is used to extract the maximum speed value, that is one of the most important features of the saccade. A gamma function was used by other authors to describe the waveform shape of the velocity profile. However, this function does not...

An accepted model for the saccade signal of ocular motor neurons comprises two components in the form of a pulse and a step. In this contribution, an assessment of two fitting functions for the saccadic pulse component is made, in order to obtain a reduced set of descriptors that could be used for the early diagnosis of ataxia. Results show that bo...

In this paper, a temperature control in real time control process was presented using several control algorithms. A quantitative comparison based on the real power consumption and (the precision and the robustness) of these controllers during the same control process and under the same conditions will be done. The proposed Adaptive and Self Organiz...

Abstract Anomalies in the oculomotor system are well known symptoms in different neurodegenerative diseases. It has been found that patients suffering from severe spino cerebellar ataxia type 2 show deterioration in the main parameters used to describe saccadic movements, specifically the slowing of horizontal saccadic eye movements. Besides, a com...

The electrocardiogram (ECG) is a noninvasive technique used to reflect underlying heart conditions by measuring the electrical activity of the heart, and nowadays it is possible with just a few derivation (with just only two), obtain important information in order than an expert can recognize abnormal heart rhythms (the heart rate is very fast, ver...

This paper is focused on the development of intelligent classifiers in the area of biomedicine, focusing on the problem of diagnosing cardiac diseases based on the electrocardiogram (ECG), or more precisely, on the differentiation of several arrhythmia using a large data set, by an autonomous intelligent system which can be used as an expert system...

Multiple sequence alignments (MSAs) are widely used approaches in bioinformatics to carry out other tasks such as structure predictions, biological function analyses or phylogenetic modeling. However, current tools usually provide partially optimal alignments, as each one is focused on specific biological features. Thus, the same set of sequences c...

Ataxia SCA2 is a neurological disorder among a group of inherited diseases of the central nervous system. In SCA2, genetic defects lead to impairment of specific nerve fibers, resulting in degeneration of the cerebellum and its afferent connections. As anomalies in the oculomotor system are well known symptoms in SCA2, electro-oculographic records...

Multiple sequence alignments (MSAs) have become one of the most studied approaches in bioinformatics to perform other outstanding tasks such as structure prediction, biological function analysis or next-generation sequencing. However, current MSA algorithms do not always provide consistent solutions, since alignments become increasingly difficult w...

The analysis of daily living human behavior has proven to be of key importance to prevent unhealthy habits. The diversity of activities and the individuals’ particular execution style determine that several sources of information are normally required. One of the main issues is to optimally combine them to guarantee performance, scalability and rob...

Analysis of saccadic eye movements is a fundamental task for the study of different neurological disorders. The Center of Research and Rehabilitation of Hereditary Ataxias (CIRAH) located in Holguín, Cuba; uses this technique in order to study the evolution of many different ataxias. Nevertheless, current available software applications do not fill...

Analysis of saccadic eye movements is a fundamental task for the study of different neurological disorders. The Center of Research and Rehabilitation of Hereditary Ataxias (CIRAH) located in Holguín, Cuba; uses this technique in order to study the evolution of many different ataxias. Nevertheless, current available software applications do not fill...

Anomalies in the oculomotor system are well known symptoms in patients with severe Spino Cerebellar Ataxia 2 form of autosomal dominant cerebellar ataxias (ADCA), including the main parameters used to describe saccadic movements. Also a combination of a pulse and step components constitutes an accepted model of the saccadic generation system, In th...

This article presents a comparative study of various control algorithms. An adaptive fuzzy logic controller is set to prove its effectiveness against other conventional controllers in a simulated control process as well as in a real environment. Through a training board that allows us to control the temperature, we can compare the behavior of each...

En este trabajo, se presenta una visión general de la nueva asignatura Ingeniería de Servidores, del nuevo plan de estudios del Grado en Ingeniería Informática de la Universidad de Granada, así como una nueva metodología interactiva para que el alumno aprenda a montar un servidor de gama baja. A través de este aprendizaje práctico, que el Espacio E...

Spino Cerebellar Ataxia type 2 is an autosomal dominant cerebellar hereditary ataxia with the highest prevalence in Cuba. Typical symptoms in patients of SCA2 ataxia include modifications in latency, peak velocity, and deviation in visual saccadic movements. After applying some electro-oculography based tests to both healthy and SCA2 afflicted indi...

This paper reports the investigations and experimental procedures conducted for designing an automatic sleep classification tool basedconly in the features extracted with wavelets from EEG, EMG and EOG (electro encephalo-mio- and oculo-gram) signals, without any visual aid or context-based evaluation. Real data collected from infants was processed...

This work discusses a new approach for ataxia SCA-2 diagnosis based on the application of independent component analysis to the data obtained by electro-oculography in several experiments carried out over healthy and sick subjects. Abnormalities in the oculomotor system are well-known clinical symptoms in patients of several neurodegenerative disea...

Precedent studies have found abnormalities in the oculomotor system in patients with severe SCA2 form of autosomal dominant cerebellar ataxias (ADCA), including the latency, peak velocity, and deviation in saccadic movements, and causing changes in the morphology of the patient response waveform. This different response suggests a higher degree of...

This work discusses a new approach for ataxia SCA-2 diagnosis based in the application of independent component analysis to the data obtained by electro-oculography in several experiments carried out over healthy and sick subjects. Abnormalities in the oculomotor system are well known clinical symptoms in patients of several neurodegenerative disea...

Traditionally, the autoregressive moving average (ARMA) model has been one of the most widely used linear models in time series prediction. Recent research activities in forecasting with artificial neural networks (ANNs) suggest that ANNs can be a promising alternative to the traditional ARMA structure. These linear models and ANNs are often compar...

The challenge of predicting future values of a time series covers a variety of disciplines. The fundamental problem of selecting the order and identifying the time varying parameters of an autoregressive moving average model (ARMA) concerns many important fields of interest such as linear prediction, system identification and spectral analysis. Rec...

The function approximation problem has been tackled many times in the literature by using radial basis function neural networks (RBFNNs). In the design of these neural networks there are several stages where, the most critical stage is the initialization of the centers of each RBF since the rest of the steps to design the RBFNN strongly depend on i...

The problem of blind inversion of Wiener systems can be considered as a special case of blind separation of post-nonlinear instantaneous mixtures. In this paper, we present an approach for nonlinear deconvolution of one signal using a genetic algorithm. The recovering of the original signal is achieved by trying to maximize an estimation of mutual...

The use of Radial Basis Function Neural Networks (RBFNNs) to solve functional approximation problems has been addressed many times in the literature. When designing an RBFNN to approximate a function, the first step consists of the initialization of the centers of the RBFs. This initialization task is very important because the rest of the steps ar...

The identification of a model is one of the key issues in the field of fuzzy system modeling and function approximation theory. An important characteristic that distinguishes fuzzy systems from other techniques in this area is their transparency and interpretability. Especially in the construction of a fuzzy system from a set of given training exam...

The new advance in computer science is computerized aided diagnosis, a digital interpretation in real-time using automated classifier. In this paper, we present a newly methodology for analyzing the region of interest in early detection of skin cancer based on the used of Image Segmentation. This method is based on the use of the Jensen-Shannon div...

This paper presents a direct adaptive fuzzy controller for unknown monotonic nonlinear systems, thus not requiring the system model, but only a little information about it: the plant monotonicity and its delay. Without any off-line pre-training, the algorithm achieves very high control performance through a three-stage algorithm: (1) output scale f...

This paper proposes a novel method for blindly separating unobservable independent component (IC) signals based on the use of a genetic algorithm. It is intended for its application to the problem of blind source separation (BSS) on post-nonlinear mixtures. The paper also includes a formal proof on the convergence of the proposed algorithm using gu...

Fuzzy systems comprise one of the models best suited to function approximation problems, but due to the non linear dependencies between the parameters that define the system rules, the solution search space for this type of problems contains many local optima. Another important issue is the identification of the optimum structure for the fuzzy syst...

In this paper we design an on-line controller which is able to modify and adapt the rule base of the system with just only qualitative knowledge about the plant to be controlled. Since flying a helicopter is an extremely difficult task, the fuzzy logic controller was necessarily quite complex. In fact, the control tasks were distributed over four i...

It is well known the relationship between source separation and blind deconvolution: If a filtered version of an unknown i.i.d. signal is observed, temporal independence between samples can be used to retrieve the original signal, in the same manner as spatial independence is used for source separation. In this paper we propose the use of a Genetic...

The main architectures, learning abilities and applications of fuzzy systems are well documented. However, to the best of our knowledge, no in-depth analyses have been carried out into the influence on the behaviour of the fuzzy system arising from the use of different alternatives for the design of the fuzzy inference process (mainly, different im...

Reducing the dimensionality of the raw input variable space is an important step in pattern recognition and functional approximation tasks often determined by practical feasibility. The purpose of this study was to investigate an information theoretic approach to feature selection. We will use mutual information (MI) as a pre-processing step for ar...

Autoregressive moving average (ARMA) has been widely used to model processes that generate linear time-series. Recent research activities in forecasting with artificial neural networks (ANNs) suggest that ANNs can be a promising alternative to the traditional ARMA structure. These linear models and ANNs are often compared with mixed conclusions in...

This paper proposes a novel method for Blindly Separating unobservable independent component (IC) Signals (BSS) based on the use of a maximum entropy guide (MEG). The paper also includes a formal proof on the convergence of the proposed algorithm using the guiding operator, a new concept in the genetic algorithm (GA) scenario. The Guiding GA (GGA)...

In this contribution, we propose and analyze three evaluation functions (contrast functions in Independent Component Analysis terminology) for the use in a genetic algorithm (PNL-GABSS, Post-NonLinear Genetic Algorithm for Blind Source Separation) which solves source separation in nonlinear mixtures, assuming the post-nonlinear mixture model. Blin...

In this work we consider the extension of Genetic-Independent Component Analysis Algorithms (GA-ICA) with guiding operators and prove their convergence to the optimum. This novel method for Blindly Separating unobservable independent component Sources (BSS) consists of novel guiding genetic operators (GGA) and finds the separation matrix which mini...

In this paper, we deal with the problem of function approximation from a given set of input/output data. This problem consists of analyzing these training examples so that we can predict the output of the model given new inputs. We present a new method for function approximation of the I/O data using radial basis functions (RBFs). This approach is...

The task of recovering a set of unknown sources from another set of mixtures directly observable and little more information about the way they were mixed is called the blind source separation problem. If the assumption in order to obtain the original sources is their statistical independence, then ICA (Independent Component Analysis) maybe the tec...

Recent advances in multimedia and image processing techniques can be utilized to assist pathologists in this respect. In fact, many investigators believe that automation of prostate cancer analysis increases the rate of early detection. In this paper, we will propose an automatic procedure for prostate cancer light micrograph based on soft-computin...

This paper presents a new adaptive procedure for the linear and nonlinear separation of signals with nonuniform, symmetrical probability distributions, based on both simulated annealing and competitive learning methods by means of a neural network, considering the properties of the vectorial spaces of sources and mixtures, and using a multiple line...

In this paper we present a novel method for blindly separating unobservable independent component signals from their linear mixtures, using genetic algorithms (GA) to minimize the nonconvex and nonlinear cost functions. This approach is very useful in many fields such as forecasting indexes in financial stock markets where the search for independen...

This paper proposes a novel Independent Component Analysis algorithm based on the use of a genetic algorithm intended for its application to the problem of blind source separation on post-nonlinear mixtures. We present a simple though effective contrast function which evaluates individuals of each population (candidate solutions) based on estimatin...