L'objectiu principal del grup és avançar en les metodologies que conformen l'àrea de la infor-màtica tova (Soft Computing), així com investigar possibles hibridacions d'aquestes amb l'ob-jectiu de millorar-ne el rendiment i la fiabilitat. També és un objectiu important aplicar les metodologies desenvolupades a problemes reals en àrees com la medicina, ecologia, farmacoprote-òmica, e-learning, etc. El grup ha publicat més de 150 articles de revista, més de 300 articles en congressos, 50 capítols de llibres i 60 reports de recerca.

The term "soft computing" was coined in the nineties, and it describes the combined use of a variety of computational approaches that have been developed over the last few decades, which include but are not limited to fuzzy systems, neural networks and evolutionary algorithms.

Despite their obvious differences, a common trait of these fields is the abandonment of binary logic, static analytical models, rigid classifications and deterministic searches. In an ideal problem description, the systems that require modelling and/or control would be defined completely and precisely. In this case, formal reasoning systems can be used to associate Boolean values to state descriptions or physical systems' behaviour. Nevertheless, when tackling real-world problems, it is not unusual to find them incompletely or badly defined, which makes it difficult to model them and requires large search spaces. As a result, precise models, should they exist, might turn out to be impractical and/or costly. Usually, the relevant information that is available is presented either as empirical, a priori knowledge or as input-output instance descriptions of the systems' behaviour. This makes the use of approximate reasoning systems necessary, as they can flexibly cope with such far-from-perfect information.

The main goal of the SOCO group is to make progress in the state-of-the-art development of soft-computing methodologies, as well as to research their possible hybridisation in order to improve their robustness and efficacy. The SOCO group is currently working on the following research lines:

* Feature selection and dimensionality reduction

* Fuzzy systems (Fuzzy Inductive Reasoning, FIR)

* Artificial neural networks (feed-forward, recurrent, heterogeneous)

* Unsupervised probabilistic models

* Genetic algorithms and evolutionary strategies

* Pattern recognition and computer vision

* Hybrid soft-computing methods, including the following:

- Neural networks and support vector machines

- Fuzzy Inductive Reasoning and simulated annealing

- Fuzzy Inductive Reasoning and genetic algorithms

- Frequency selection for neural networks

- Cooperation of local experts for inductive reasoning

- Incremental construction of hybrid recurrent neural networks

The application of these methodologies to real-world problems is also one of the group's goals. The group has carried out research in the following areas of application:

* Medical (human central nervous system, cancer prediction, diagnosis, cognitive neuroscience, etc.)

* Biological (growth of white shrimp)

* Ecological (analysis of pollutant concentration in urban areas and ecological status modelling of streams)

The term "soft computing" was coined in the nineties, and it describes the combined use of a variety of computational approaches that have been developed over the last few decades, which include but are not limited to fuzzy systems, neural networks and evolutionary algorithms.

Despite their obvious differences, a common trait of these fields is the abandonment of binary logic, static analytical models, rigid classifications and deterministic searches. In an ideal problem description, the systems that require modelling and/or control would be defined completely and precisely. In this case, formal reasoning systems can be used to associate Boolean values to state descriptions or physical systems' behaviour. Nevertheless, when tackling real-world problems, it is not unusual to find them incompletely or badly defined, which makes it difficult to model them and requires large search spaces. As a result, precise models, should they exist, might turn out to be impractical and/or costly. Usually, the relevant information that is available is presented either as empirical, a priori knowledge or as input-output instance descriptions of the systems' behaviour. This makes the use of approximate reasoning systems necessary, as they can flexibly cope with such far-from-perfect information.

The main goal of the SOCO group is to make progress in the state-of-the-art development of soft-computing methodologies, as well as to research their possible hybridisation in order to improve their robustness and efficacy. The SOCO group is currently working on the following research lines:

* Feature selection and dimensionality reduction

* Fuzzy systems (Fuzzy Inductive Reasoning, FIR)

* Artificial neural networks (feed-forward, recurrent, heterogeneous)

* Unsupervised probabilistic models

* Genetic algorithms and evolutionary strategies

* Pattern recognition and computer vision

* Hybrid soft-computing methods, including the following:

- Neural networks and support vector machines

- Fuzzy Inductive Reasoning and simulated annealing

- Fuzzy Inductive Reasoning and genetic algorithms

- Frequency selection for neural networks

- Cooperation of local experts for inductive reasoning

- Incremental construction of hybrid recurrent neural networks

The application of these methodologies to real-world problems is also one of the group's goals. The group has carried out research in the following areas of application:

* Medical (human central nervous system, cancer prediction, diagnosis, cognitive neuroscience, etc.)

* Biological (growth of white shrimp)

* Ecological (analysis of pollutant concentration in urban areas and ecological status modelling of streams)

Recent Submissions

  • Forest fire forecasting using fuzzy logic models 

    Nebot Castells, M. Àngela; Múgica Álvarez, Francisco (2021-07-29)
    Article
    Open Access
    In this study, we explored hybrid fuzzy logic modelling techniques to predict the burned area of forest fires. Fast detection is crucial for successful firefighting, and a model with an accurate prediction ability is ...
  • Off-the-grid: Fast and effective hyperparameter search for kernel clustering 

    Ordozgoiti Rubio, Bruno; Belanche Muñoz, Luis Antonio (Springer, 2020)
    Conference report
    Open Access
    Kernel functions are a powerful tool to enhance the k-means clustering algorithm via the kernel trick. It is known that the parameters of the chosen kernel function can have a dramatic impact on the result. In supervised ...
  • Predicciones financieras basadas en análisis de sentimiento de textos y minería de opiniones 

    Arratia Quesada, Argimiro Alejandro (FUNCAS, 2021-04-01)
    Part of book or chapter of book
    Restricted access - publisher's policy
    En este capítulo se describe la mecánica básica para construir un modelo de predicción que utiliza indicadores de sentimiento derivados de datos textuales. Enfocamos nuestro objetivo de predicciones en series de ...
  • Cumulated burden of Covid-19 in Spain from a Bayesian perspective 

    Moriña, David; Fernandez Fontelo, Amanda; Cabaña Nigro, Ana Alejandra; Arratia Quesada, Argimiro Alejandro; Ávalos Villaseñor, Gustavo Eduardo; Puig, Pedro (2021-06-28)
    Article
    Restricted access - publisher's policy
    Background The main goal of this work is to estimate the actual number of cases of Covid-19 in Spain in the period 01-31-2020/06-01-2020 by Autonomous Communities. Based on these estimates, this work allows us to accurately ...
  • kernInt: A kernel framework for integrating supervised and unsupervised analyses in spatio-temporal metagenomic datasets 

    Ramon Gurrea, Elies; Belanche Muñoz, Luis Antonio; Molist Gasa, Francesc; Quintanilla Aguado, Raquel; Pérez Enciso, Miguel; Ramayo Caldas, Yuliaxis (2021-01-28)
    Article
    Open Access
    The advent of next-generation sequencing technologies allowed relative quantification of microbiome communities and their spatial and temporal variation. In recent years, supervised learning (i.e., prediction of a phenotype ...
  • Sentiment analysis of financial news: mechanics and statistics 

    Arratia Quesada, Argimiro Alejandro; Ávalos Villaseñor, Gustavo Eduardo; Cabaña Nigro, Ana Alejandra; Duarte López, Ariel; Renedo Mirambell, Martí (Springer, 2021-06-11)
    Part of book or chapter of book
    Open Access
    This chapter describes the basic mechanics for building a forecasting model that uses as input sentiment indicators derived from textual data. In addition, as we focus our target of predictions on financial time series, ...
  • Clustering assessment in weighted networks 

    Arratia Quesada, Argimiro Alejandro; Renedo Mirambell, Martí (2021-06-18)
    Article
    Open Access
    We provide a systematic approach to validate the results of clustering methods on weighted networks, in particular for the cases where the existence of a community structure is unknown. Our validation of clustering comprises ...
  • Una aplicación para despertar recuerdos y cuidar la salud mental de los mayores 

    Nebot Castells, M. Àngela; Benali, Anass; Múgica Álvarez, Francisco; Albino Pires, Natália; Domenech Pou, Sara (2021-04-15)
    Article
    Open Access
    El incremento de la longevidad de la población, derivado del descenso continuado de las tasas de natalidad y el aumento de la esperanza de vida, está transformando la forma de la pirámide de edad de la Unión Europea. Esta ...
  • Fault detection and identification in a fuel cell system 

    Escobet Canal, Antoni; Nebot Castells, M. Àngela (IOS Press, 2009)
    Conference report
    Restricted access - publisher's policy
    In this work a fault diagnosis system for non-linear plants based on fuzzy logic, called VisualBlock-FIR, is presented and applied to an energy generation system based on fuel cells. VisualBlock-FIR runs under the Simulink ...
  • Estimating the real burden of disease under a pandemic situation: the SARS-CoV2 case 

    Fernandez Fontelo, Amanda; Moriña, David; Cabaña Nigro, Ana Alejandra; Arratia Quesada, Argimiro Alejandro; Puig, Pedro (Public Library of Science (PLOS), 2020-12-03)
    Article
    Open Access
    The present paper introduces a new model used to study and analyse the severe acute respiratory syndrome coronavirus 2 (SARS-CoV2) epidemic-reported-data from Spain. This is a Hidden Markov Model whose hidden layer is a ...
  • Leveraging data science for a personalized haemodialysis 

    Hueso, Miguel; Haro Martín, Luis de; Calabria, Jordi; Dal-Re, R; Tebe, C; Gibert, Karina; Cruzado, Josep M; Vellido Alcacena, Alfredo (Karger, 2020-11)
    Article
    Open Access
    The 2019 Science for Dialysis Meeting at Bellvitge University Hospital was devoted to the challenges and opportunities posed by the use of data science to facilitate precision and personalized medicine in nephrology, and ...
  • Helping decision-makers manage resilience under different climate change scenarios: global vs local 

    Nebot Castells, M. Àngela (2021-02-18)
    Article
    Open Access
    The Intergovernmental Panel on Climate Change (IPCC) fifth assessment report states that warming of the climate system is unequivocal and notes that each of the last three decades has been successively warmer at the Earth’s ...

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