The Experts below are selected from a list of 49086 Experts worldwide ranked by ideXlab platform

Gracián Triviño - One of the best experts on this subject based on the ideXlab platform.

  • Linguistic Description of complex phenomena with the rldcp r package
    International Conference on Natural Language Generation, 2017
    Co-Authors: Jose M Alonso, Patricia Condeclemente, Gracián Triviño
    Abstract:

    Monitoring and analysis of complex phenomena attract the attention of both academy and industry. Dealing with data produced by complex phenomena requires the use of advance computational intelligence techniques. Namely, Linguistic Description of complex phenomena constitutes a mature research line. It is supported by the Computational Theory of Perceptions grounded on the Fuzzy Sets Theory. Its aim is the development of computational systems with the ability to generate vague Descriptions of the world in a similar way how humans do. This is a human-centric and multi-disciplinary research work. Moreover, its success is a matter of careful design; thus, developers play a key role. The rLDCP R package was designed to facilitate the development of new applications. This demo introduces the use of rLDCP, for both beginners and advance developers, in practical use cases.

  • fuzzy knowledge representation for Linguistic Description of time series
    Conference of International Fuzzy Systems Association and European Society for Fuzzy Logic and Technology, 2015
    Co-Authors: Alberto Bugarin, Nicolas Marin, Daniel Sanchez, Gracián Triviño
    Abstract:

    The Linguistic Description of data intends to provide texts that convey the most important information contained in the data. One of the main tasks to be carried out in order to build a Linguistic Description is the extraction and representation of the knowledge to be transmitted. To perform this task, adequate mechanisms for knowledge representation are needed. In this paper we focus on time series data and analyze three knowledge representation languages that arise in the field of Fuzzy Sets Theory, particularly Computing with Words and Perceptions: the use of Protoforms, the Granular Linguistic Model of a Phenomenon and, specially, the use of Fuzzy Temporal Knowledge Representation Models.

  • Linguistic Description about circular structures of the Mars' surface
    Applied Soft Computing, 2013
    Co-Authors: Daniel Sanchez-valdes, Alberto Alvarez-alvarez, Gracián Triviño
    Abstract:

    Satellites situated in the orbit of Mars have provided and continue providing thousand of images of the planet surface. Nevertheless the number of expert geologists analyzing these images is limited. Typically, these experts provide Linguistic Descriptions of their observations remarking the relevant features in the image and ignoring the irrelevant details for a given goal. In this paper, we apply our research in the field of Computational Theory of Perceptions to the challenge of developing computational systems able to generate Linguistic reports comparable with the ones provided by human experts. We present a Description of our contribution to solve this problem including last results of our research in this field. For example, we explore how to represent the multidimensional domain of computational perception values. We develop up the use of the relevance as an attribute of perceptions that allows us to generate reports that are automatically suited according to the user goals. We provide an application example as a demonstration of concept.

  • Three main components of experience base in Linguistic Description of data
    2013 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2013
    Co-Authors: Carmen Martinez-cruz, Daniel Sanchez, Gracián Triviño
    Abstract:

    In this paper, we present a contribution to solve the problem of organizing the representation of experience in computational systems which are able to generate relevant Linguistic Descriptions of data for specific users and contexts. We claim, that, typically, the expert knowledge modeled in these systems is limited to one of the dimensions of the meaning of natural language. Here, we model the experience base distinguishing among three types of meaning, namely, Ideational meaning concerning with the technical, impersonal Description of the specific phenomenon; Interpersonal meaning concerning with the role of the partners involved in the communication process and Textual meaning concerning with the contribution to the meaning of the specific realization with natural language of both previous types of meaning. In order to organize these types of meaning (also called components of experience base) in a practical computational representation, we have built an ontology that will help designers to model their experience in the application domain. Using this ontology, the computational system is able of identifying the most suitable Linguistic Descriptions for describing the input data. Our approach is presented with the support of a practical example in the domain of the maintenance of comfort in a room.

  • Linguistic Description of the human gait quality
    Engineering Applications of Artificial Intelligence, 2013
    Co-Authors: Alberto Alvarezalvarez, Gracián Triviño
    Abstract:

    The human gait is a complex phenomenon that is repeated in time following an approximated pattern. Using a three-axial accelerometer fixed in the waist, we can obtain a temporal series of measures that contains a numerical Description of this phenomenon. Nevertheless, even when we represent graphically these data, it is difficult to interpret them due to the complexity of the phenomenon and the huge amount of available data. This paper describes our research on designing a computational system able to generate Linguistic Descriptions of this type of quasi-periodic complex phenomena. We used our previous work on both, Granular Linguistic Models of Phenomena and Fuzzy Finite State Machines, to create a basic Linguistic model of the human gait. We have used this model to generate a human friendly Linguistic Description of this phenomenon focused on the assessment of the gait quality. We include a practical application where we analyze the gait quality of healthy individuals and people with lesions in their limbs.

Francisco Herrera - One of the best experts on this subject based on the ideXlab platform.

  • the 2 tuple Linguistic computational model advantages of its Linguistic Description accuracy and consistency
    International Journal of Uncertainty Fuzziness and Knowledge-Based Systems, 2001
    Co-Authors: Francisco Herrera, Luis Martinez
    Abstract:

    The Fuzzy Linguistic Approach has been applied successfully to different areas. The use of Linguistic information for modelling expert preferences implies the use of processes of Computing with Words. To accomplish these processes different approaches has been proposed in the literature: (i) Computational model based on the Extension Principle, (ii) the symbolic one(also called ordinal approach), and (iii) the 2-tuple Linguistic computational model. The main problem of the classical approaches, (i) and (ii), is the loss of information and lack of precision during the computational processes. In this paper, we want to compare the Linguistic Description, accuracy and consistency of the results obtained using each model over the rest ones. To do so, we shall solve a Multiexpert Multicriteria Decision-Making problem defined in a multigranularity Linguistic context using the different computational approaches. This comparison helps us to decide what model is more adequated for computing with words.

  • the 2 tuple Linguistic computational model advantages of its Linguistic Description accuracy and consistency
    International Journal of Uncertainty Fuzziness and Knowledge-Based Systems, 2001
    Co-Authors: Francisco Herrera, Luis Martinez
    Abstract:

    The Fuzzy Linguistic Approach has been applied successfully to different areas. The use of Linguistic information for modelling expert preferences implies the use of processes of Computing with Words. To accomplish these processes different approaches has been proposed in the literature: (i) Computational model based on the Extension Principle, (ii) the symbolic one(also called ordinal approach), and (iii) the 2-tuple Linguistic computational model. The main problem of the classical approaches, (i) and (ii), is the loss of information and lack of precision during the computational processes. In this paper, we want to compare the Linguistic Description, accuracy and consistency of the results obtained using each model over the rest ones. To do so, we shall solve a Multiexpert Multicriteria Decision-Making problem defined in a multigranularity Linguistic context using the different computational approaches. This comparison helps us to decide what model is more adequated for computing with words.

  • genetic learning of fuzzy rule based classification systems cooperating with fuzzy reasoning methods
    International Journal of Intelligent Systems, 1998
    Co-Authors: Oscar Cordon, Maria Jose Del Jesus, Francisco Herrera
    Abstract:

    In this paper, we present a multistage genetic learning process for obtaining Linguistic fuzzy rule-based classification systems that integrates fuzzy reasoning methods cooperating with the fuzzy rule base and learns the best set of Linguistic hedges for the Linguistic variable terms. We show the application of the genetic learning process to two well known sample bases, and compare the results with those obtained from different learning algorithms. The results show the good behavior of the proposed method, which maintains the Linguistic Description of the fuzzy rules. © 1998 John Wiley & Sons, Inc.

Krzysztof Wiaderek - One of the best experts on this subject based on the ideXlab platform.

  • parallel processing of images represented by Linguistic Description in databases
    International Conference on Parallel Processing, 2019
    Co-Authors: Danuta Rutkowska, Krzysztof Wiaderek
    Abstract:

    This paper concerns an application of parallel processing to color digital images characterized by Linguistic Description. Attributes of the images are considered with regard to fuzzy and rough set theories. Inference is based on the CIE chromaticity color model and granulation approach. By use of the Linguistic Description represented in databases, and the rough granulation, the problem of image retrieval and classification is presented.

  • image retrieval by use of Linguistic Description in databases
    International Conference on Artificial Intelligence and Soft Computing, 2018
    Co-Authors: Krzysztof Wiaderek, Danuta Rutkowska, Elisabeth Rakusandersson
    Abstract:

    In this paper, a new method of image retrieval is proposed. This concerns retrieving color digital images from a database that contains a specific Linguistic Description considered within the theory of fuzzy granulation and computing with words. The Linguistic Description is generated by use of the CIE chromaticity color model. The image retrieval is performed in different way depending on users’ knowledge about the color image. Specific database queries can be formulated for the image retrieval.

  • Parallel Processing of Color Digital Images for Linguistic Description of Their Content
    Parallel Processing and Applied Mathematics, 2018
    Co-Authors: Krzysztof Wiaderek, Danuta Rutkowska, Elisabeth Rakus-andersson
    Abstract:

    This paper presents different aspects of parallelization of a problem of processing color digital images in order to generate Linguistic Description of their content. A parallel architecture of an intelligent image recognition system is proposed. Fuzzy classification and inference is performed in parallel, based on the CIE chromaticity color model and granulation approach. In addition, the parallelization concerns e.g. processing a large collection of images or parts of a single image.

  • Linguistic Description of images based on fuzzy histograms
    International Conference on Image Processing, 2017
    Co-Authors: Krzysztof Wiaderek, Danuta Rutkowska
    Abstract:

    The paper presents a new approach for generating Linguistic Description of color digital images based on fuzzy histograms. The CIE chromaticity diagram is classified into fuzzy color granules and employed in order to recognize color clusters in an image or collection of images. A fuzzy inference process uses the histograms and fuzzy IF-THEN rules concerning color, location, size, and shape of the color clusters. The fuzzy histograms illustrate participation rate of pixels in the fuzzy color granules, and also within the so-called macropixels of different sizes of the same color. The histograms are derived from the matrix that expresses membership grades of pixels to the fuzzy color granules of the CIE diagram. The Linguistic Description is applied in image recognition and image retrieval tasks.

  • Linguistic Description of color images generated by a granular recognition system
    International Conference on Artificial Intelligence and Soft Computing, 2017
    Co-Authors: Krzysztof Wiaderek, Danuta Rutkowska, Elisabeth Rakusandersson
    Abstract:

    The paper proposes a new method employed in an intelligent pattern recognition system that generates Linguistic Description of color digital images. The Linguistic Description is produced based on fuzzy rules and information granules concerning colors as most important among image attributes. With regard to the color, the CIE chromaticity color model is applied, with the concept of fuzzy color areas. The Linguistic Description uses information about location of color granules in input images.

Danuta Rutkowska - One of the best experts on this subject based on the ideXlab platform.

  • face recognition with explanation by fuzzy rules and Linguistic Description
    International Conference on Artificial Intelligence and Soft Computing, 2020
    Co-Authors: Danuta Rutkowska, Damian Kurach, Elisabeth Rakusandersson
    Abstract:

    In this paper, a new approach to face recognition is proposed. The knowledge represented by fuzzy IF-THEN rules, with type-1 and type-2 fuzzy sets, are employed in order to generate the Linguistic Description of human faces in digital pictures. Then, an image recognition system can recognize and retrieve a picture (image of a face) or classify face images based on the Linguistic Description. Such a system is explainable – it can explain its decision based on the fuzzy rules.

  • parallel processing of images represented by Linguistic Description in databases
    International Conference on Parallel Processing, 2019
    Co-Authors: Danuta Rutkowska, Krzysztof Wiaderek
    Abstract:

    This paper concerns an application of parallel processing to color digital images characterized by Linguistic Description. Attributes of the images are considered with regard to fuzzy and rough set theories. Inference is based on the CIE chromaticity color model and granulation approach. By use of the Linguistic Description represented in databases, and the rough granulation, the problem of image retrieval and classification is presented.

  • image retrieval by use of Linguistic Description in databases
    International Conference on Artificial Intelligence and Soft Computing, 2018
    Co-Authors: Krzysztof Wiaderek, Danuta Rutkowska, Elisabeth Rakusandersson
    Abstract:

    In this paper, a new method of image retrieval is proposed. This concerns retrieving color digital images from a database that contains a specific Linguistic Description considered within the theory of fuzzy granulation and computing with words. The Linguistic Description is generated by use of the CIE chromaticity color model. The image retrieval is performed in different way depending on users’ knowledge about the color image. Specific database queries can be formulated for the image retrieval.

  • Parallel Processing of Color Digital Images for Linguistic Description of Their Content
    Parallel Processing and Applied Mathematics, 2018
    Co-Authors: Krzysztof Wiaderek, Danuta Rutkowska, Elisabeth Rakus-andersson
    Abstract:

    This paper presents different aspects of parallelization of a problem of processing color digital images in order to generate Linguistic Description of their content. A parallel architecture of an intelligent image recognition system is proposed. Fuzzy classification and inference is performed in parallel, based on the CIE chromaticity color model and granulation approach. In addition, the parallelization concerns e.g. processing a large collection of images or parts of a single image.

  • Linguistic Description of images based on fuzzy histograms
    International Conference on Image Processing, 2017
    Co-Authors: Krzysztof Wiaderek, Danuta Rutkowska
    Abstract:

    The paper presents a new approach for generating Linguistic Description of color digital images based on fuzzy histograms. The CIE chromaticity diagram is classified into fuzzy color granules and employed in order to recognize color clusters in an image or collection of images. A fuzzy inference process uses the histograms and fuzzy IF-THEN rules concerning color, location, size, and shape of the color clusters. The fuzzy histograms illustrate participation rate of pixels in the fuzzy color granules, and also within the so-called macropixels of different sizes of the same color. The histograms are derived from the matrix that expresses membership grades of pixels to the fuzzy color granules of the CIE diagram. The Linguistic Description is applied in image recognition and image retrieval tasks.

Daniel Sanchez - One of the best experts on this subject based on the ideXlab platform.

  • fuzzy knowledge representation for Linguistic Description of time series
    Conference of International Fuzzy Systems Association and European Society for Fuzzy Logic and Technology, 2015
    Co-Authors: Alberto Bugarin, Nicolas Marin, Daniel Sanchez, Gracián Triviño
    Abstract:

    The Linguistic Description of data intends to provide texts that convey the most important information contained in the data. One of the main tasks to be carried out in order to build a Linguistic Description is the extraction and representation of the knowledge to be transmitted. To perform this task, adequate mechanisms for knowledge representation are needed. In this paper we focus on time series data and analyze three knowledge representation languages that arise in the field of Fuzzy Sets Theory, particularly Computing with Words and Perceptions: the use of Protoforms, the Granular Linguistic Model of a Phenomenon and, specially, the use of Fuzzy Temporal Knowledge Representation Models.

  • a proposal for the hierarchical segmentation of time series application to trend based Linguistic Description
    IEEE International Conference on Fuzzy Systems, 2014
    Co-Authors: Rita Castilloortega, Nicolas Marin, Carmen Martinezcruz, Daniel Sanchez
    Abstract:

    In this paper we propose methods for obtaining hierarchical segmentations of time series on the basis of the Iterative End-Point Fit Algorithm. We discuss on the utility of the methods for different cases. We illustrate the usefulness of the hierarchical segmentations with an application in Linguistic Description of trends in time series. A Linguistic Description based on a segmentation of the time series that do not necessarily corresponds to a level of the hierarchy is obtained by describing segments in different levels that form a segmentation satisfying a quality model.

  • Three main components of experience base in Linguistic Description of data
    2013 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2013
    Co-Authors: Carmen Martinez-cruz, Daniel Sanchez, Gracián Triviño
    Abstract:

    In this paper, we present a contribution to solve the problem of organizing the representation of experience in computational systems which are able to generate relevant Linguistic Descriptions of data for specific users and contexts. We claim, that, typically, the expert knowledge modeled in these systems is limited to one of the dimensions of the meaning of natural language. Here, we model the experience base distinguishing among three types of meaning, namely, Ideational meaning concerning with the technical, impersonal Description of the specific phenomenon; Interpersonal meaning concerning with the role of the partners involved in the communication process and Textual meaning concerning with the contribution to the meaning of the specific realization with natural language of both previous types of meaning. In order to organize these types of meaning (also called components of experience base) in a practical computational representation, we have built an ontology that will help designers to model their experience in the application domain. Using this ontology, the computational system is able of identifying the most suitable Linguistic Descriptions for describing the input data. Our approach is presented with the support of a practical example in the domain of the maintenance of comfort in a room.

  • using fordbms for the Linguistic Description of images
    IEEE International Conference on Fuzzy Systems, 2010
    Co-Authors: Sergio Jaimecastillo, Juan Miguel Medina, Daniel Sanchez
    Abstract:

    In this paper we explain how a Fuzzy Object-Relational Database Management System (FORDMS) can be employed for implementing and integrating the different elements needed for the Linguistic Description of images, briefly ontology, concept representation and language generation. The approach is illustrated by its application in the Description of X-Ray images of patients suffering from scoliosis.