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

  • 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 ...
  • Unraveling response to temozolomide in preclinical GL261 glioblastoma with MRI/MRSI using radiomics and signal source extraction 

    Nuñez Vivero, Luis Miguel; Romero Merino, Enrique; Julia Sape, Margarida; Ledesma Carballo, María Jesús; Santos, Andrés; Arus Caraltó, Carles; Candiota Silveira, Ana Paula; Vellido Alcacena, Alfredo (Nature, 2020-11-12)
    Article
    Open Access
    Glioblastoma is the most frequent aggressive primary brain tumor amongst human adults. Its standard treatment involves chemotherapy, for which the drug temozolomide is a common choice. These are heterogeneous and variable ...
  • Interpreting response to TMZ therapy in murine GL261 glioblastoma by combining Radiomics, Convex-NMF and feature selection in MRI/MRSI data analysis 

    Nuñez Vivero, Luis Miguel; Julia Sape, Margarida; Romero Merino, Enrique; Arus Caraltó, Carles; Vellido Alcacena, Alfredo; Candiota Silveira, Ana Paula (Institute of Electrical and Electronics Engineers (IEEE), 2020)
    Conference report
    Open Access
    Machine learning (ML) methods have shown great potential for the analysis of data involved in medical decisions. However, for these methods to be incorpored in the medical pipeline, they must be made interpretable not only ...
  • An e-Learning toolbox based on rule-based fuzzy approaches 

    Nebot Castells, M. Àngela; Múgica Álvarez, Francisco; Castro Espinoza, Félix Agustín (Multidisciplinary Digital Publishing Institute, 2020-09-28)
    Article
    Open Access
    In this paper, an e-Learning toolbox based on a set of fuzzy logic data mining techniques is presented. The toolbox is mainly based on the fuzzy inductive reasoning (FIR) methodology and two of its key extensions: (i) the ...
  • On-the-fly syntheziser programming with fuzzy rule learning 

    Paz Ortiz, Alejandro Iván; Nebot Castells, M. Àngela; Múgica Álvarez, Francisco; Romero Merino, Enrique (2020-08-31)
    Article
    Open Access
    This manuscript explores fuzzy rule learning for sound synthesizer programming within the performative practice known as live coding. In this practice, sound synthesis algorithms are programmed in real time by means of ...
  • On the use of pairwise distance learning for brain signal classification with limited observations 

    Calhas, David; Romero Merino, Enrique; Henriques, Rui (2020-05)
    Article
    Open Access
    The increasing access to brain signal data using electroencephalography creates new opportunities to study electrophysiological brain activity and perform ambulatory diagnoses of neurological disorders. This work proposes ...
  • To be or nought to be: una qüestió irrellevant? 

    Belanche Muñoz, Luis Antonio (1991-10)
    External research report
    Open Access
  • About the attribute relevance's nature 

    Núñez Esquer, Gustavo; Cortés García, Claudio Ulises; Belanche Muñoz, Luis Antonio; Alvarado Mentado, Matías (1991-03)
    External research report
    Open Access
    The notion of relevance of an attribute in machine learning is of common use in the construction of classfication rules in inductive learning processes. In this work a formal definition of the relevance concept for a given ...

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