This work explores the integration of millimetre-wave (mmWave) radar and a minimal configuration of ultra-wideband (UWB) devices for enhanced multi-occupant tracking in real domestic environments. Using a low-cost, non-intrusive, and rapidly deployable device setup, our approach addresses key challenges in multi-occupant tracking, including individual identification and ease of installation. While mmWave radar precisely detects occupant presence, it lacks individual recognition and exhibits limited sensitivity. This limitations are addressed by incorporating a minimal configuration of UWB (wearable tags and ambient anchors), enabling individual identification through signal strength measurements. Several data autoencoder models, such as long short-term memories (LSTMs), Convolutional Neural Networks (CNN) or Transformers, were evaluated. Experiments conducted in two real-world domestic settings, each with up to three inhabitants, demonstrate the effectiveness of combining mmWave and UWB technologies for indoor multi-occupant tracking. Our results show that ConvLSTM achieves the best performance with a mean squared error (MSE) between 0,0142 and 0,0433 in single and multi-occupation, respectively. These findings suggest promising applications for accurate inhabitant tracking in ambient assisted living and other smart environment contexts.

This Supplement issue, presents five research articles which are distributed, mainly due to the subject they address, from the 8th International Work-Conference on Bioinformatics and Biomedical Engineering (IWBBIO 2020), which was held on line, during September, 30th-2nd October, 2020. These contributions have been chosen because of their quality and the importance of their findings. Those contributions were then invited to participate in this supplement for the following journals of BMC: BMC Bioinformatics and BMC Genomics. In the present Editorial in BMC journal, we summarize the contributions that provide a clear overview of the thematic areas covered by the IWBBIO conference, ranging from theoretical/review aspects to real-world applications of bioinformatic and biomedical engineering.

BACKGROUND: Adenocarcinoma and squamous cell carcinoma are the two most prevalent lung cancer types, and their distinction requires different screenings, such as the visual inspection of histology slides by an expert pathologist, the analysis of gene expression or computer tomography scans, among others. In recent years, there has been an increasing gathering of biological data for decision support systems in the diagnosis (e.g. histology imaging, next-generation sequencing technologies data, clinical information, etc.). Using all these sources to design integrative classification approaches may improve the final diagnosis of a patient, in the same way that doctors can use multiple types of screenings to reach a final decision on the diagnosis. In this work, we present a late fusion classification model using histology and RNA-Seq data for adenocarcinoma, squamous-cell carcinoma and healthy lung tissue. RESULTS: The classification model improves results over using each source of information separately, being able to reduce the diagnosis error rate up to a 64% over the isolate histology classifier and a 24% over the isolate gene expression classifier, reaching a mean F1-Score of 95.19% and a mean AUC of 0.991. CONCLUSIONS: These findings suggest that a classification model using a late fusion methodology can considerably help clinicians in the diagnosis between the aforementioned lung cancer cancer subtypes over using each source of information separately. This approach can also be applied to any cancer type or disease with heterogeneous sources of information.

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.

Many clinical studies have revealed the high biological similarities existing among different skin pathological states. These similarities create difficulties in the efficient diagnosis of skin cancer, and encourage to study and design new intelligent clinical decision support systems. In this sense, gene expression analysis can help find differentially expressed genes (DEGs) simultaneously discerning multiple skin pathological states in a single test. The integration of multiple heterogeneous transcriptomic datasets requires different pipeline stages to be properly designed: from suitable batch merging and efficient biomarker selection to automated classification assessment. This article presents a novel approach addressing all these technical issues, with the intention of providing new sights about skin cancer diagnosis. Although new future efforts will have to be made in the search for better biomarkers recognizing specific skin pathological states, our study found a panel of 8 highly relevant multiclass DEGs for discerning up to 10 skin pathological states: 2 healthy skin conditions a priori, 2 cataloged precancerous skin diseases and 6 cancerous skin states. Their power of diagnosis over new samples was widely tested by previously well-trained classification models. Robust performance metrics such as overall and mean multiclass F1-score outperformed recognition rates of 94% and 80%, respectively. Clinicians should give special attention to highlighted multiclass DEGs that have high gene expression changes present among them, and understand their biological relationship to different skin pathological states.

Recent years have witnessed a widespread in the use of interval type-2 fuzzy logic systems (IT2 FLSs) in real-world applications. It has been shown recently that interval type-2 fuzzy sets (IT2 FSs) are more general than interval-valued fuzzy sets (IV FSs) [1]. Hence, there is a need to explore the capabilities of the more general forms of IT2 FSs (beyond IV FSs) and the applications areas they will be more suitable for. In addition, there is a need to develop the theory of the general forms of IT2 FLSs (gfIT2 FLSs), which employ IT2 FSs that are not equivalent to IV FSs and can have nonconvex secondary membership functions (MFs). Although these systems could be considered within the scope of general type-2 FLSs (GT2 FLSs), the practical implementation of GT2 FLSs has traditionally required the secondary MFs to be convex and normal type-1 fuzzy sets (T1 FSs). In addition, the type-reduction operation still presents a challenge for GT2 FLSs because of its computational complexity. In this paper, we present a complete framework for a type-2 FLS that uses the most recent perception of IT2 FSs (the so called general forms of interval type-2 fuzzy sets, gfIT2 FSs), whose secondary grades can be nonconvex T1 FSs. This framework includes new equations for the meet and join operations of gfIT2 FSs, as well as a new type reduction procedure for the type-2 FLS involving gfIT2 FSs. In addition, we present the type-2 FLS operation for singleton and nonsingleton fuzzification. We will introduce the various operations employed within a gfIT2 FLSs, from fuzzification (including singleton and nonsingleton) to inference, type-reduction, and defuzzification. We will also present two examples in which these gfIT2 FSs arise naturally when modeling sonar sensors input noise and the antecedents/consequents from a survey including different users.

Computer-Aided Diagnosis (CAD) represents a relevant instrument to automatically classify between patients with and without Alzheimer's Disease (AD) using several actual imaging techniques. This study analyzes the optimization of volumes of interest (VOIs) to extract three-dimensional (3D) textures from Magnetic Resonance Image (MRI) in order to diagnose AD, Mild Cognitive Impairment converter (MCIc), Mild Cognitive Impairment nonconverter (MCInc) and Normal subjects. A relevant feature of the proposed approach is the use of 3D features instead of traditional two-dimensional (2D) features, by using 3D discrete wavelet transform (3D-DWT) approach for performing feature extraction from T-1 weighted MRI. Due to the high number of coefficients when applying 3D-DWT to each of the VOIs, a feature selection algorithm based on mutual information is used, as is the minimum Redundancy Maximum Relevance (mRMR) algorithm. Region optimization has been performed in order to discover the most relevant regions (VOIs) in the brain with the use of Multi-Objective Genetic Algorithms, being one of the objectives to be optimize the accuracy of the system. The error index of the system is computed by the confusion matrix obtained by the multi-class support vector machine (SVM) classifier. Principal Component Analysis (PCA) is used with the purpose of reducing the number of features to the classifier. The cohort of subjects used in the study consisted of 296 different patients. A first group of 206 patients was used to optimize VOI selection and another group of 90 independent subjects (that did not belong to the first group) was used to test the solutions yielded by the genetic algorithm. The proposed methodology obtains excellent results in multi-class classification achieving accuracies of 94.4% and also extracting significant information on the location of the most relevant points of the brain. This suggests that the proposed method could aid in the research of other neurodegenerative diseases, improving the accuracy of the diagnosis and finding the most relevant regions of the brain associated with them.

Recently, the theory regarding interval type-2 fuzzy sets and fuzzy logic systems has been further developed. In the first instance, the concepts of interval type-2 fuzzy sets were broadened, proving that this class of sets include some which are different from interval-valued sets. In a later work, the set theoretic operations of union and intersection on these sets were studied and presented under the framework of general type-2 fuzzy sets. This stimulated and motivated the further study of fuzzy logic systems using these new sets, which have been referred to as the general forms of interval type-2 fuzzy sets. However, as usual when a new fuzzy logic framework is presented, only the singleton version of these systems was introduced. In this work we aim to generalise that work, introducing the non-singleton general forms of interval type-2 fuzzy logic systems. We will present a real example on how to use these non-singleton fuzzy logic systems in real world applications.

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, thus it can be exploited through the integration of microarray data with RNA-Seq data. Additionally, information extraction and acquisition of large number of samples in RNA-Seq still entails very high costs in terms of time and computational resources.This paper proposes a new model to find the gene signature of breast cancer cell lines through the integration of heterogeneous data from different breast cancer datasets, obtained from microarray and RNA-Seq technologies. Consequently, data integration is expected to provide a more robust statistical significance to the results obtained. Finally, a classification method is proposed in order to test the robustness of the Differentially Expressed Genes when unseen data is presented for diagnosis. RESULTS: The proposed data integration allows analyzing gene expression samples coming from different technologies. The most significant genes of the whole integrated data were obtained through the intersection of the three gene sets, corresponding to the identified expressed genes within the microarray data itself, within the RNA-Seq data itself, and within the integrated data from both technologies. This intersection reveals 98 possible technology-independent biomarkers. Two different heterogeneous datasets were distinguished for the classification tasks: a training dataset for gene expression identification and classifier validation, and a test dataset with unseen data for testing the classifier. Both of them achieved great classification accuracies, therefore confirming the validity of the obtained set of genes as possible biomarkers for breast cancer. Through a feature selection process, a final small subset made up by six genes was considered for breast cancer diagnosis. CONCLUSIONS: This work proposes a novel data integration stage in the traditional gene expression analysis pipeline through the combination of heterogeneous data from microarrays and RNA-Seq technologies. Available samples have been successfully classified using a subset of six genes obtained by a feature selection method. Consequently, a new classification and diagnosis tool was built and its performance was validated using previously unseen samples.

Recently, it has been shown that interval type-2 fuzzy sets (IT2FSs) are more general than interval-valued fuzzy sets (IVFSs), and some of these IT2FSs can actually be non-convex. Although these IT2FSs could be considered within the general type-2 fuzzy sets' (GT2FSs) scope, this latter have always been studied and developed under certain conditions considering the convexity and normality of their secondary grades. In recent works the operations of intersection and union for GT2FSs have been extended to include non-convex and non-normal secondary grades. Hence, there is a need to develop the theory for those general forms of interval type-2 fuzzy logic systems (gfIT2FLSs) which use IT2FSs that are not equivalent to IVFSs and can have non-convex secondary grades. Furthermore, we will present the mathematical tools to define the inference engine. This work aims to introduce the basic structure of such gfIT2FLSs, paying special attention to those blocks presenting significant differences with the already well known type-2 FLSs which employ IT2FSs which are equivalent to IVFSs (we will term IVFLSs).

The rapid growth of social networks has led many companies to use mobile payment systems as business sales tools. As these platforms have an increasing acceptance among the consumers, the main goal of this research is to analyze the individuals’ use intention of these systems in a social network environment. The data used were collected after performing an experiment using the Zong Payment tool. The paper includes a comparative analysis of several machine learning methods applied to the variable selection problem considering strategic, sociodemographic, and behavioral variables. The methods were applied isolatedly and, in a novel approach, using multiobjective optimization criteria. The results obtained by these algorithms were validated by an expert committee, who ranked them on the basis of their quality. The proposed methodology obtained the best results in terms of optimization as well as the highest scores from the experts.

In this paper we will present two theorems for the join and meet operations for general type-2 fuzzy sets with arbitrary secondary memberships, which can be non-convex and/or non-normal type-1 fuzzy sets. These results will be used to derive the join and meet operations of the more general descriptions of interval type-2 fuzzy sets presented in [1], where the secondary grades can be non-convex. Hence, this work will help to explore the potential of type-2 fuzzy logic systems which use the general forms of interval type-2 fuzzy sets which are not equivalent to interval valued fuzzy sets. Several examples for both general type-2 and the more general forms of interval type-2 fuzzy sets are presented.

There are currently several biological databases related to proteins depending on the information we are interested to extract. Therefore, since a heterogeneous set of features will be needed (domains, structures, ontology terms, etc), several quite different databases should be consulted. The handling of different databases can become difficult, as each database could provide a different nomenclature for the same proteins. Anyway, these databases usually provides some mapping tools in order to determine the correspondence among the different nomenclatures used for the same protein. The main goal of this paper is to provide a biological database including a huge amount of protein information in order to be used for different purposes in bioinformatics. This database will be designed as a flexible and extensible tool and it could be extended and updated when new proteins are annotated or the included ones are modified.

Smoothing b-splines constitute a powerful and popular methodology for performing nonparametric regression with high accuracy. It is well known that the placement of the knots in spline smoothing approximation has an important and considerable effect on the behavior of the final approximation. For this purpose, in this paper a novel methodology is presented for optimal placement and selections of knots, in order to approximate or fit curves to data, using smoothing splines. A new method based on improved clustering algorithm is used to optimally select a reduced number of knots for constructing the base of the b-spline, while ensuring the best accuracy.

This work presents a Color Fuzzy Set Design procedure, or similarly a color naming procedure, based on the VITA Classical dental color shade guide, for composite color matching applications in direct restorations in aesthetic dentistry. For that purpose, a psychophysical experience based on a set of visual assessments by a panel of observers was performed, with the objective of identifying the areas of the CIELAB color space which can be associated to each shade of the VITA guide. A number of composite sets from four different manufacturing companies were used in the experiment. This information was processed and a set of nonparametric Fuzzy Sets was obtained for the VITA shades, with the aid of an acceptability dental color threshold taken from previous studies. The designed Fuzzy Set System was applied for the identification of the VITA shade of different dentine and enamel composite samples (bilaminar technique) from two different manufacturing companies, with the subsequent clinical applicability. Results show a high level of accuracy in VITA shade identification of the operated dental samples, when compared with visual assessment performed by experts.

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, very slow, or irregular) or a heart attack (myocardial infarction), and if it was recent or some time ago. In this paper, several intelligent classifiers are developed, 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. We will study and imitate the ECG treatment methodologies and the features extracted from the electrocardiograms used by the researchers, which obtained the best results in the PhysioNet Challenge (www.physionet.org/). We will extract a great amount of features, partly those used by these researchers and some additional others we considered to be important for the distinction previously mentioned. A new method based on different paradigms of intelligent computation (such as extreme learning machine, support vector machine, decision trees, genetic algorithms and feature selection) will be used to select the most relevant characteristics and to obtain a classifier capable of autonomously distinguishing the different types arrhythmia from the ECG signal.

MOTIVATION: 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 can produce different alignments, above all when sequences are less similar. Consequently, researchers and biologists do not agree about which is the most suitable way to evaluate MSAs. Recent evaluations tend to use more complex scores including further biological features. Among them, 3D structures are increasingly being used to evaluate alignments. Because structures are more conserved in proteins than sequences, scores with structural information are better suited to evaluate more distant relationships between sequences. RESULTS: The proposed multiobjective algorithm, based on the non-dominated sorting genetic algorithm, aims to jointly optimize three objectives: STRIKE score, non-gaps percentage and totally conserved columns. It was significantly assessed on the BAliBASE benchmark according to the Kruskal-Wallis test (P < 0.01). This algorithm also outperforms other aligners, such as ClustalW, Multiple Sequence Alignment Genetic Algorithm (MSA-GA), PRRP, DIALIGN, Hidden Markov Model Training (HMMT), Pattern-Induced Multi-sequence Alignment (PIMA), MULTIALIGN, Sequence Alignment Genetic Algorithm (SAGA), PILEUP, Rubber Band Technique Genetic Algorithm (RBT-GA) and Vertical Decomposition Genetic Algorithm (VDGA), according to the Wilcoxon signed-rank test (P < 0.05), whereas it shows results not significantly different to 3D-COFFEE (P > 0.05) with the advantage of being able to use less structures. Structural information is included within the objective function to evaluate more accurately the obtained alignments. AVAILABILITY: The source code is available at http://www.ugr.es/~fortuno/MOSAStrE/MO-SAStrE.zip.

BACKGROUND: A popular query from scientists reading a biomedical abstract is to search for topic-related documents in bibliographic databases. Such a query is challenging because the amount of information attached to a single abstract is little, whereas classification-based retrieval algorithms are optimally trained with large sets of relevant documents. As a solution to this problem, we propose a query expansion method that extends the information related to a manuscript using its cited references. RESULTS: Data on cited references and text sections in 249,108 full-text biomedical articles was extracted from the Open Access subset of the PubMed Central® database (PMC-OA). Of the five standard sections of a scientific article, the Introduction and Discussion sections contained most of the citations (mean = 10.2 and 9.9 citations, respectively). A large proportion of articles (98.4%) and their cited references (79.5%) were indexed in the PubMed® database. Using the MedlineRanker abstract classification tool, cited references allowed accurate retrieval of the citing document in a test set of 10,000 documents and also of documents related to six biomedical topics defined by particular MeSH® terms from the entire PMC-OA (p-value<0.01). Classification performance was sensitive to the topic and also to the text sections from which the references were selected. Classifiers trained on the baseline (i.e., only text from the query document and not from the references) were outperformed in almost all the cases. Best performance was often obtained when using all cited references, though using the references from Introduction and Discussion sections led to similarly good results. This query expansion method performed significantly better than pseudo relevance feedback in 4 out of 6 topics. CONCLUSIONS: The retrieval of documents related to a single document can be significantly improved by using the references cited by this document (p-value<0.01). Using references from Introduction and Discussion performs almost as well as using all references, which might be useful for methods that require reduced datasets due to computational limitations. Cited references from particular sections might not be appropriate for all topics. Our method could be a better alternative to pseudo relevance feedback though it is limited by full text availability.

Singleton interval type-2 fuzzy logic systems (FLSs) have been widely applied in several real-world applications, where it was shown that the singleton interval type-2 FLSs outperform their singleton type-1 counterparts in applications with high uncertainty levels. However, one of the main criticisms of singleton interval type-2 FLSs is the fact that they outperform singleton type-1 FLSs solely based on their use of extra degrees of freedom (extra parameters) and that type-1 FLSs with a sufficiently large number of parameters may provide the same performance as interval type-2 FLSs. In addition, most works on type-2 FLSs only compare their results with singleton type-1 FLSs but fail to consider nonsingleton type-1 systems. In this paper, we aim to directly address and investigate this criticism. In order to do so, we will perform a comparative study between optimized singleton type-1, nonsingleton type-1, and singleton interval type-2 FLSs under the presence of noise. We will also present a multiobjective evolutionary algorithm (MOEA) for the optimization of singleton type-1, nonsingleton type-1, and singleton interval type-2 fuzzy systems for function approximation problems. The MOEA will aim to satisfy two objectives to maximize the accuracy of the FLS and minimize the number of rules in the FLS, thus improving its interpretability. Furthermore, we will present a methodology to obtain “optimal” consequents for the FLSs. Hence, this paper has two main contributions: First, it provides a common methodology to learn the three types of FLSs (i.e., singleton type-1, nonsingleton type-1, and singleton interval type-2 FLSs) from data samples. The second contribution is the creation of a common framework for the comparison of type-1 and type-2 FLSs that allows us to address the aforementioned criticism. We provide details of a series of experiments and include statistical analysis showing that the type-2 FLS is able to handle higher levels of noise than its nonsingleton and singleton type-1 counterparts.

Multiple sequence alignment (MSA) is one of the most studied approach in Bioinformatics to carry out other outstanding tasks like structural predictions, biological function analysis or next-generation sequencing. However, MSA algorithms do not achieve consistent results in all cases, as alignments become difficult when sequences have low similarity. In other words, each algorithm is focused in specific features of sequences and their results depend on them. For this reason, each approach could align better those sections of sequences that include such features, obtaining partially optimal solutions. In this work, a multiobjective evolutionary algorithm based on NSGA-II will be implemented in order to assemble previously aligned sequences, trying to avoid suboptimal alignments.

In this paper, a new approach for Self-evolving PArameter-free fuzzy Rule-based Controller (SPARC) is proposed. Two illustrative examples are provided aiming a proof of concept. The proposed controller can start with no pre-defined fuzzy rules, and does not need to pre-define the range of the output or control variables. This SPARC learns autonomously from its own actions while performing the control of the plant. It does not use any parameters, explicit membership functions, any off-line pre-training nor the explicit model (e.g. in a form of differential equations) of the plant. It combines the relative older concept of indirect adaptive control with the newer concepts of (self-)evolving fuzzy rule-based systems (and controllers, in particular) and with the very recent concept of parameter-free, data cloud and data density based fuzzy rule based systems (and controllers in particular). It has been demonstrated that a fully autonomously and in an unsupervised manner (based only on the data density and selecting representative prototypes/focal points from the control hyper-surface acting as a data space) it is possible generate a parameter-free control structure and evolve it in on-line mode. Moreover, the results demonstrate that this autonomous controller is effective (has comparative error and performance characteristics) to other known controllers, including self-learning ones, but surpasses them with its flexibility and extremely lean structure (small number of prototypes/focal points which serve as seeds to form parameter-free and membership function-free fuzzy rules based on them). The illustrative examples aim primarily proof of concept.

This work introduces an open benchmark dataset to investigate inertial sensor displacement effects in activity recognition. While sensor position displacements such as rotations and translations have been recognised as a key limitation for the deployment of wearable systems, a realistic dataset is lacking. We introduce a concept of gradual sensor displacement conditions, including ideal, self-placement of a user, and mutual displacement deployments. These conditions were analysed in the dataset considering 33 fitness activities, recorded using 9 inertial sensor units from 17 participants. Our statistical analysis of acceleration features quantified relative effects of the displacement conditions. We expect that the dataset can be used to benchmark and compare recognition algorithms in the future.

There are several dementias but Alzheimer's Disease (AD) is leading cause of dementia in the world. In this paper, a new methodology for classification of MR images is proposed, using a large data base (more than one thousand patient). We have two main objectives in this paper: a first one where a classification method is developed to classify MR images as either normal or with the Alzheimer's disease and a second which is the ambitious goal of this study, the identification and classification between normal subjects, MCI patients and AD patients. It is noteworthy that with this last study we could offer a tool to assist the early diagnosis of dementia.

The automatic recognition of postures, movements and physical exercises has being recently applied to several healthcare related fields, with a special interest in chronic disease management and prevention. In this work we describe a complete method to define an accurate activity recognition system, stressing on the classification stage. As binary classifiers can be, in general, considered more efficient than direct multiclass classifiers, and looking for an appropriate multiclass extension schema, a hierarchical weighted classification model with a special application for multi-sensed problems is presented. Remarkable accuracy results are obtained for a particular activity recognition problem in contrast to a traditional multiclass majority voting algorithm.

In function approximation problems, one of the most common ways to evaluate a learning algorithm consists in partitioning the original data set (input/output data) into two sets: learning, used for building models, and test, applied for genuine out-of-sample evaluation. When the partition into learning and test sets does not take into account the variability and geometry of the original data, it might lead to non-balanced and unrepresentative learning and test sets and, thus, to wrong conclusions in the accuracy of the learning algorithm. How the partitioning is made is therefore a key issue and becomes more important when the data set is small due to the need of reducing the pessimistic effects caused by the removal of instances from the original data set. Thus, in this work, we propose a deterministic data mining approach for a distribution of a data set (input/output data) into two representative and balanced sets of roughly equal size taking the variability of the data set into consideration with the purpose of allowing both a fair evaluation of learning's accuracy and to make reproducible machine learning experiments usually based on random distributions. The sets are generated using a combination of a clustering procedure, especially suited for function approximation problems, and a distribution algorithm which distributes the data set into two sets within each cluster based on a nearest-neighbor approach. In the experiments section, the performance of the proposed methodology is reported in a variety of situations through an ANOVA-based statistical study of the results.

In this study, a novel fuzzy controller, which is able to self-design from scratch, while working online, is proposed. The controller does not use the information regarding the differential equations that govern the plant's behavior or any of their bounds. The algorithm presented is able to determine the most-adequate topology for the fuzzy controller based on the data obtained during the system's normal operation. Therefore, the controller can start operating with an empty set of fuzzy rules and needs no offline training. The proposed methodology comprises two phases: adaptation of the consequents for every selected topology and online addition of new membership functions (MFs). Some of the main advantages of this method are its robustness under changes on the plant's dynamics, good performance in noisy situations, and the ability to perform variable selection among a group of candidate variables. Unlike other online methods, the modification of the topology is based on the analysis of the whole operating region of the plant, thus providing higher robustness. Several simulation examples are used to show these features.

This paper presents a self-organizing adaptive fuzzy controller that works online. No prior knowledge about the differential equations governing the plant nor offline training is needed. Starting from very simple topologies, the algorithm uses the data obtained online during the normal operation of the system to modify the structure of the fuzzy controller. This is achieved in two phases: first, the consequents of the current fuzzy rules are adapted; in the second phase, new membership functions are added online. To show its capabilities, a real experiment with a nonlinear servo system has been carried out with satisfactory results.

E-health and e-monitoring have become an increasingly important area during recent years, being the recognition of motion, postures and physical exercises one of the main topics. In this kind of problem is common to work with a huge training data set in a multidimensional space, so feature selection is absolutely necessary. Most works are based on knowledge extraction using features which permit to make decisions about the activity realized, being feature selection the most critical stage. Conventional feature selection procedures based on wrapper methods or `branch and bound' are highly computationally expensive. In this work, we propose an alternative filter method using a feature-set ranking via a couple of two statistical criteria, which achieves remarkable accuracy rates in the classification process. We demonstrate the usefulness of our method on both laboratory and seminaturalistic activity ambient living datasets for real problems.

Affymetrix High Oligonucleotide expression arrays are widely used for the high-throughput assessment of gene expression of thousands of genes simultaneously. Although disputed by several authors, there are non-biological variations and systematic biases that must be removed as much as possible through the pre-processing step before an absolute expression level for every gene is assessed. It is important to evaluate microarray pre-processing procedures not only to the detection of differentially expressed genes, but also to classification, since a major use of microarrays is the expression-based phenotype classification. Thus, in this paper, we use several cancer microarray datasets to assess the influence of five different pre-processing methods in Support Vector Machine-based classification methodologies with different kernels: linear, Radial Basis Functions (RBFs) and polynomial.

Marital dissolutions, that include divorces and separations, are considered one of the most adverse events that can influence the health, life quality and welfare of the adults and infants implied. There have been several researches in the sanitary and sociological field that show how these processes of breaking a life in common can deteriorate the health in both, the physic and the psychological, aspect. This paper presents the application of machine learning methods to be able to predict if a marriage, that has started a dissolution process, will end up in a friendly agreement or it will be taken to the court. In order to accomplish this task, Radial Basis Function Neural Networks in combination with Mutual Information will be able to determine which elements should be considered to make a prediction. As the experiments will show, the methodology applied is able to classify with a high accuracy and robustness a real data base. The results could be applied in order to prevent some traumas to the people involved in the dissolution in the medical aspect and to perform a better management in the legal aspects.

This work presents a feasible solution to the problem of book losses prediction from financial and general data in companies. The specific problem tackled in this work corresponds to a real dataset of Spanish companies. A Mutual Information-based criterion has been applied in order to reduce the initial set of variables, and a Support Vector Machine classifier has been designed to perform the prediction. The results show that the proposed approach obtains an important reduction of the number of variables needed to perform the prediction, improving the generalization capabilities of the model. The accuracy rates were above the 84% in the test set, much better than those obtained by other soft-computing algorithms (such as Genetic Programming, Self-Organizing Maps or Artificial Neural Networks) working with the same dataset and presented in previous works. The proposed approach shows to be promising and could be determinant in providing the experts with the right tools for the selection of the relevant factors and for the prediction in this difficult problem.

This paper presents a first approach to try to determine if a newborn will be macrosomic before the labor, using a set of data taken from the mother. The problem of determining if a newborn is going to be macrosomic is important in order to plan cesarean section and other problems during the labor. The proposed model to classify the weight is a neural network whose design is based recent algorithms that will allow the networks to focus on a concrete class. Before proceeding with the design methodology to obtain the models, a previous step of variable selection is performed in order to indentify the risk factors and to avoid the curse of dimensionality. Another study is made regarding the missing values in the database since the data were not complete for all the patients. The results will show how useful the addition of the missing values into the original data set can be in order to identify new risk factors.

Tooth bleaching is receiving an increasing interest by patients and dentists since it is a relatively non-invasive approach for whitening and lightening teeth. Instrument designed for tooth color measurements and visual assessment with commercial shade guides are nowadays used to evaluate the tooth color. However, the degree of color change after tooth bleaching varied substantially among studies and currently, there are no objective guidelines to predict the effectiveness of a tooth bleaching treatment. Fuzzy Logic is a well known paradigm for data modelling; their main advantage is their ability to provide an interpretable set of rules that can be later used by the scientists. However these models have the problem that the global approximation optimization can lead to a deficient rule local modelling. This work proposes a modified fuzzy model that performs a simultaneous global and local modelling. This property is reached thanks to a special partitioning of the input space in the fuzzy system. The proposed approach is used to approximate a set of color measurements taken after a bleaching treatment using the pre-bleaching measurements. The system uses as rule antecedents the colorimetric values of the VITA commercial shade guide. The expected post-bleaching colorimetric values are immediately obtained from the local models (rules) of the system thanks to the proposed modified fuzzy model. Additionally, these post-bleaching CIELAB coordinate values have been associated with VITA shades through the evaluation of their respective membership functions, approximating which VITA shades are expected after the treatment for each pre-bleaching VITA shade.

In this paper, we propose a network interface that takes advantage of the parallelism available in network processors to improve the communication performance. Morespecifically, our proposal is based on the Intel IXP28xx network processor, as it includes sixteen processing cores with multithreading and an optimized design for packet processing. Thus, the proposed interface facilitates the exploitation of different options to optimize the communication path in the host by using network interface offloading and/or onloading strategies that try to take advantage of multi-core processors, which are available even in the network cards. The experimental results obtained for TCP/IP and raw Ethernet packets show that multithreading, implemented through the processing cores of the network processor, improves communication performance in both latency of the packets and bandwidth, thus allowing the applications that present high communication requirements to fulfill them through the use of multithreading.

Radial basis function neural networks (RBFNNs) are well known because, among other applications, they present a good performance when approximating functions although their design still remains as a difficult task. The function approximation problem arises in the construction of a control system to optimize the process of the mineral reduction. In order to regulate the temperature of the ovens and other parameters, a module to predict the final concentration of mineral that will be obtained from the source materials is necessary. In a previous work, this problem was successfully solved by designing an RBFNN using a MultiObjective genetic algorithm (MOGA). However, the more samples are obtained from the system, the more difficult it becomes to design the RBFNN due to the high dimensionality of the problem. Therefore, a new algorithm that addresses the dimensionality reduction has been developed, allowing to obtain more accurate RBFNNs, deciding which input parameters must be considered. Another important element incorporated in the algorithm is the concept of fuzzy dominance, the algorithm, when performing the sorting of the population dividing it in subsets of non-dominated individuals, uses a fuzzy criteria to decide if an individual dominates another. As the experimental results will show, the new version of the algorithm generates RBFNNs with smaller approximation errors and less complexity due to the reduction in the number of input variables and neurons.

The four papers in this special section address distinct subjects focusing on new, significant novel lines of development on genetic fuzzy systems (GFSs).

Radial basis function neural networks (RBFNNs) have been applied to solve problems of classification, function approximation and time series prediction. In the design of an RBFNN it is necessary to set the values for the positions of the centers and the radii for each RBF. In the literature it is usually performed an initialization step to set the positions of the centers and, once they are placed, the radii are calculated using a heuristic. In this paper, a new algorithm to set the value of those two parameters is presented. This new algorithm uses a supervised learning in such a way that the position of the centers will be constrained by the output of the function to be approximated. Since each center represents a neuron that is activated by the input vectors, the radii are initialized using the center's positions and their activation grades. In this way, the calculation of the radii is also influenced by the output of the target function, not like in other heuristics where only the positions of the centers or the input vectors are considered. As the experiments show, the new algorithm outperforms other algorithms previously used for this problem.

The goal of this paper is to achieve real time control of a monotonic system which, in general, may be non-linear and whose differential equations are unknown. We assume that there is no model of the plant available so there cannot be any off-line pre-training of the main controller parameters. We propose a both adaptive and self-learning algorithm capable of starting from a "void" fuzzy controller and, in real time, optimizing the fuzzy controller's rules (both antecedents and consequents) in order to translate the state of the plant to the desired value in the shortest possible time.

In this paper, the multigrid-based fuzzy system (MGFS) approach is applied for the CATS time series prediction benchmark. The MGFS architecture overcomes the problem inherent to all grid-based fuzzy systems when dealing with high dimensional input data, thus keeping low computational cost and high performance. A greedy algorithm for MGFS structure identification allows to perform the input variable selection for the time series prediction problem, while identifying the pseudo-optimal architecture according to the provided dataset.

A novel approach to achieve real-time global learning in fuzzy controllers is proposed. Both the rule consequents and the membership functions defined in the premises of the fuzzy rules are tuned using a one-step algorithm, which is capable of controlling nonlinear plants with no prior offline training. Direct control is achieved by means of two auxiliary systems: The first one is responsible for adapting the consequents of the main controller's rules to minimize the error arising at the plant output, while the second auxiliary system compiles real input-output data obtained from the plant. The system then learns in real time from these data taking into account, not the current state of the plant but rather the global identification performed. Simulation results show that this approach leads to an enhanced control policy thanks to the global learning performed, avoiding overfitting.

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 linearization in the mixture space. Moreover, the paper proposes the fusion of two important paradigms-genetic algorithms and the blind separation of sources in nonlinear mixtures. In nonlinear mixtures, optimization of the system parameters and, especially, the search for invertible functions is very difficult due to the existence of many local minima. The main characteristics of the methods are their simplicity and the rapid convergence experimentally validated by the separation of many kinds of signals, such as speech or biomedical data.

This paper presents a multiobjective evolutionary algorithm to optimize radial basis function neural networks (RBFNNs) in order to approach target functions from a set of input-output pairs. The procedure allows the application of heuristics to improve the solution of the problem at hand by including some new genetic operators in the evolutionary process. These new operators are based on two well-known matrix transformations: singular value decomposition (SVD) and orthogonal least squares (OLS), which have been used to define new mutation operators that produce local or global modifications in the radial basis functions (RBFs) of the networks (the individuals in the population in the evolutionary procedure). After analyzing the efficiency of the different operators, we have shown that the global mutation operators yield an improved procedure to adjust the parameters of the RBFNNs.

This paper proposes the fusion of two important paradigms, Genetic Algorithms and the Blind Separation of Sources in Nonlinear Mixtures (GABSS). Although the topic of BSS, by means of various techniques, including ICA, PCA, and neural networks, has been amply discussed in the literature, the possibility of using genetic algorithms has not been explored thus far. However, in Nonlinear Mixtures, optimization of the system parameters and, especially, the search for invertible functions is very difficult due to the existence of many local minima. From experimental results, this paper demonstrates the possible benefits offered by GAs in combination with BSS, such as robustness against local minima, the parallel search for various solutions, and a high degree of flexibility in the evaluation function.

This paper presents an innovative approach to self-adaptation of the structure of a fuzzy controller in real time. Without any off-line pretraining, the algorithm achieves very high control performance through the iteration of a three-stage algorithm. In the first stage, coarse tuning of the fuzzy rules (both rule consequents and membership functions of the premises) is accomplished using the sign of the dependency of the plant output with respect to the control signal and an overall analysis of the main operating regions. In stage two, fine tuning of the fuzzy rules is achieved based on the controller output error using a gradient-based method. Finally, the third stage is responsible of modifying the structure of the fuzzy controller, proposing that input variable which should get a new membership function in order to improve the control policy in an optimum way.

The identification of a model is one of the key issues in the field of fuzzy system modeling and function approximation theory. There are numerous approaches to the issue of parameter optimization within a fixed fuzzy system structure but no reliable method to obtain the optimal topology of the fuzzy system from a set of input-output data. This paper presents a reliable method to obtain the structure of a complete rule-based fuzzy system for a specific approximation accuracy of the training data, i.e., it can decide which input variables must be taken into account in the fuzzy system and how many membership functions (MFs) are needed in every selected input variable in order to reach the approximation target with the minimum number of parameters.

To date, clustering techniques have always been oriented to solve classification and pattern recognition problems. However, some authors have applied them unchanged to construct initial models for function approximators. Nevertheless, classification and function approximation problems present quite different objectives. Therefore it is necessary to design new clustering algorithms specialized in the problem of function approximation. This paper presents a new clustering technique, specially designed for function. approximation problems, which improves the performance of the approximator system obtained, compared with other models derived from traditional classification oriented clustering algorithms and input-output clustering techniques.

Interest in hybrid methods that combine artificial neural networks and evolutionary algorithms has grown in the last few years, due to their robustness and ability to design networks by setting initial weight values, by searching the architecture and the learning rule and parameters. This paper presents an exhaustive analysis of the G-Prop method, and the different parameters the method requires (population size, selection rate, initial weight range, number of training epochs, etc.) are determined. The paper also the discusses the influence of the application of genetic operators on the precision (classification ability or error) and network size in classification problems. The significance and relative importance of the parameters with respect to the results obtained, as well as suitable values for each, were obtained using the ANOVA (analysis of the variance). Experiments show the significance of parameters concerning the neural network and learning in the hybrid methods. The parameters found using this method were used to compare the G-Prop method both to itself with other parameter settings, and to other published methods.

The Web newspaper pagination problem consists of optimizing the layout of a set of articles extracted from several Web newspapers and sending it to the user as the result of a previous query. This layout should be organized in columns, as in real newspapers, and should be adapted to the client Web browser configuration in real time. This paper presents an approach to the problem based on simulated annealing (SA) that solves the problem on-line, adapts itself to the client's computer configuration, and supports articles with different widths.

Most genetic algorithm (GA) users adjust the main parameters of the design of a GA (crossover and mutation probability, population size, number of generations, crossover, mutation, and selection operators) manually. Nevertheless, when GA applications are being developed it is very important to know which parameters have the greatest influence on the behavior and performance of a GA. The purpose of this study was to analyze the dynamics of GAs when confronted with modifications to the principal parameters that define them, taking into account the two main characteristics of GAs; their capacity for exploration and exploitation. Therefore, the dynamics of GAs have been analyzed from two viewpoints. The first is to study the best solution found by the system, i.e., to observe its capacity to obtain a local or global optimum. The second viewpoint is the diversity within the population of GAs; to examine this, the average fitness was calculated. The relevancy and relative importance of the parameters involved in GA design are investigated by using a powerful statistical tool, the analysis of the variance (ANOVA).

An enhanced self-organizing controller (E-SOC) is presented to achieve real time global learning in fuzzy controllers. Direct control is achieved by means of two auxiliary systems: the first one is responsible for adapting the consequents of the main controller's rules to minimize the error arising at the plant output, while the second auxiliary system compiles real input/output data obtained from the plant. The system then learns in real time from these data taking into account, not the current state of the plant but rather the global identification performed. Simulation results show that this approach leads to an enhanced control policy thanks to the global learning performed.

A new approach is presented to fine tune a fuzzy controller based on very limited information on the nonlinear plant to be controlled. Without any off-line pre-training, the algorithm achieves very high control performance through a two-stage algorithm. In the first stage, coarse tuning of the fuzzy rules (both rule consequents and membership functions of the premises) is accomplished in real time using a small amount of information about the plant and an overall analysis of the main operating regions. In stage two, fine tuning of the fuzzy rules is achieved based on the controller output error using a gradient-based method.

This paper presents a reliable method to obtain the structure of a complete rule-based fuzzy system for a specific approximation accuracy of the training data, i.e. it can decide which input variables must be taken into account in the fuzzy system and how many membership functions are needed in every selected input variable in order to reach the approximation target with the minimum number of parameters.

This paper proposes a framework for constructing and training a radial basis function (RBF) neural network, which is an example of fuzzy system. For this purpose, a sequential learning algorithm is presented to adapt the structure of the network, in which it is possible to create a new hidden unit (rule) and also to detect and remove inactive units. The structure of the gaussian functions (membership functions) is modified using a pseudo-Gaussian function (PG) in which two sealing parameters /spl sigma/ are introduced, which eliminates the symmetry restriction and provides the neurons in the hidden layer with greater flexibility with respect to function approximation. Other important characteristics of the proposed neural system is that instead of using a single parameter for the output weights, these are functions of the input variables which leads to a significant reduction in the number of hidden units compared with the classical RBF network Finally, we examine the result of applying the proposed algorithm to time series prediction.

In this paper we propose a genetic algorithm (GA) that is capable of simultaneously optimizing the structure of the system and tuning the parameters that define the fuzzy system. For this purpose, we use the concept of multiple-deme GAs, in which several populations with different structures (number of input variables) evolve and compete with each other. In each of these populations, the element also has different numbers of membership functions in the input spaces and different numbers of rules. Instead of the normal coding system used to represent a fuzzy system, in which all the parameters are represented in vector form, we performed coding by means of multidimensional matrices, in which the elements are real-valued numbers, rather than the traditional binary or gray coding.

An analysis of the influence of weight and input perturbations in a multilayer perceptron (MLP) is made in this article. Quantitative measurements of fault tolerance, noise immunity, and generalization ability are provided. From the expressions obtained, it is possible to justify some previously reported conjectures and experimentally obtained results (e.g., the influence of weight magnitudes, the relation between training with noise and the generalization ability, the relation between fault tolerance and the generalization ability). The measurements introduced here are explicitly related to the mean squared error degradation in the presence of perturbations, thus constituting a selection criterion between different alternatives of weight configurations. Moreover, they allow us to predict the degradation of the learning performance of an MLP when its weights or inputs are deviated from their nominal values and thus, the behavior of a physical implementation can be evaluated before the weights are mapped on it according to its accuracy.

Because there are many possibilities for the set of basic functions, parameters and operators used in the design of an adaptive-network-based fuzzy inference system (ANFTS), the search for the most suitable operators and functional blocks, together with their characterization and evaluation, is also an important topic in the field of neuro-fuzzy design. As shown in papers dealing with real applications, the designer has to select the operator to be used in each phase in the design of a neuro-fuzzy system, and this decision is usually taken in terms of the most common operations performed. Nevertheless, it is very important to determine which factors have the greatest influence on the behaviour and performance of the neuro-fuzzy system. Therefore, the designer should pay close attention to the phase in which the selection of the operator is most statistically significant. In this way, it is possible to obviate a detailed analysis of different configurations that lead to systems with very similar performance. In order to perform this analysis, an appropriate statistical tool has been used: the multifactorial analysis of the variance (ANOVA) which consists of a set of statistical techniques that enable the analysis and comparison of experiments, by describing the interactions and interrelations between either the quantitative or the qualitative variables of the neural network system. By applying this methodology to a great variety of neuro-fuzzy systems, it is possible to obtain general results about the most relevant factors defining the neural network design.

Describes a structure to create a RBF neural network. This structure has 4 main characteristics. The first one is that the special RBF network architecture uses regression weights to replace the constant weights normally used. These regression weights are assumed to be functions of input variables. The second characteristic is the normalization of the activation of the hidden neurons (weighted average) before aggregating the activations, which, as observed by various authors, produces better results than the classical weighted sum architecture. The third aspect is that a new type of nonlinear function is proposed, the pseudo-gaussian function (PGBF). With this, the neural system gains flexibility, as the neurons possess an activation field that does not necessarily have to be symmetric with respect to the centre or to the location of the neuron in the input space. In addition to this new structure, we propose, as the fourth and final feature, a sequential learning algorithm, which is able to adapt the structure of the network, with this, it is possible to create new hidden units and also to detect and remove inactive units.

We propose a framework for constructing and training a radial basis function (RBF) neural network. The structure of the gaussian functions is modified using a pseudo-gaussian function (PG) in which two scaling parameters sigma are introduced, which eliminates the symmetry restriction and provides the neurons in the hidden layer with greater flexibility with respect to function approximation. We propose a modified PG-BF (pseudo-gaussian basis function) network in which the regression weights are used to replace the constant weights in the output layer. For this purpose, a sequential learning algorithm is presented to adapt the structure of the network, in which it is possible to create a new hidden unit and also to detect and remove inactive units. A salient feature of the network systems is that the method used for calculating the overall output is the weighted average of the output associated with each receptive field. The superior performance of the proposed PG-BF system over the standard RBF are illustrated using the problem of short-term prediction of chaotic time series.

In the synthesis of a fuzzy system two steps are generally employed: the identification of a structure and the optimization of the parameters defining it. The paper presents a methodology to automatically perform these two steps in conjunction using a three-phase approach to construct a fuzzy system from numerical data. Phase 1 outlines the membership functions and system rules for a specific structure, starting from a very simple initial topology. Phase 2 decides a new and more suitable topology with the information received from the previous step; it determines for which variable the number of fuzzy sets used to discretize the domain must be increased and where these new fuzzy sets should be located. This, in turn, decides in a dynamic way in which part of the input space the number of fuzzy rules should be increased. Phase 3 selects from the different structures obtained to construct a fuzzy system the one providing the best compromise between the accuracy of the approximation and the complexity of the rule set. The accuracy and complexity of the fuzzy system derived by the proposed self-organized fuzzy rule generation procedure (SOFRG) are studied for the problem of function approximation. Simulation results are compared with other methodologies such as artificial neural networks, neuro-fuzzy systems, and genetic algorithms.

In this paper, a systematic design is proposed to determine fuzzy system structure and learning its parameters, from a set of given training examples. In particular, two fundamental problems concerning fuzzy system modeling are addressed: 1) fuzzy rule parameter optimization and 2) the identification of system structure (i.e., the number of membership functions and fuzzy rules). A four-step approach to build a fuzzy system automatically is presented: Step 1 directly obtains the optimum fuzzy rules for a given membership function configuration. Step 2 optimizes the allocation of the membership functions and the conclusion of the rules, in order to achieve a better approximation. Step 3 determines a new and more suitable topology with the information derived from the approximation error distribution; it decides which variables should increase the number of membership functions. Finally, Step 4 determines which structure should be selected to approximate the function, from the possible configurations provided by the algorithm in the three previous steps. The results of applying this method to the problem of function approximation are presented and then compared with other methodologies proposed in the bibliography.

An evolutionary computation approach is used to learn online the rules that allow the processors in a parallel platform to cooperate by interchanging the local optima that they find while they concurrently explore different zones of the solution space. The cooperation of processors can greatly benefit the resolution of combinatorial optimization problems by decreasing their runtimes, by increasing the quality of the solutions obtained, or both. Moreover, as parallel computers are more and more accessible, the application of parallel processing to solve these problems becomes a practical and interesting alternative. As an example, a parallel optimization algorithm based on Boltzmann Machine has been used for a detailed description and evaluation of the proposed cooperation approach.

This paper shows a quantitative relation between the regularization techniques, the generalization ability, and the sensitivity of the Multilayer Perceptron (MLP) to input noise. Although many studies about these topics have been presented, in most cases only one of the problems is addressed, and only experimentally obtained evidence is provided to illustrate some kind of correlation between generalization, noise immunity and the use of regularization techniques to obtain a set of weights after training that provides the corresponding MLP with generalization ability and noise immunity. Here, a new measurement of noise immunity for a MLP is presented. This measurement, which is termed Mean Squared Sensitivity (MSS), explicitly evaluates the Mean Squared Error (MSE) degradation of a MLP when it is perturbed by input noise, and can be computed from the statistical sensitivities (previously proposed) of the output neurons. The MSS provides an accurate evaluation of the MLP performance loss when its inputs are perturbed by noise and can also be considered a measurement of the smoothness of the error surface with respect to the inputs. Thus, as the MSS can be used to evaluate the noise immunity or the generalization ability, it gives a criterion to select among different weight configurations that present a similar MSE after training.

Stimulus correlation and adaptive movement detection, among other tasks can be performed with VLSI general-purpose neurons that have controllable steady and transient responses. This paper presents experimental results of simple neural primitives based on the CMOS neuron approach described in [11]. Stimulus correlation experiments illustrate the well defined behavior of the CMOS approach. This basic primitive is used to implement motion detectors with adaptive capabilities that enable it to work efficiently in a wide velocity range.