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    Wyświetlanie 1-3 z 3
    Tytuł:
    Handwrittenword recognition using fuzzy matching degrees
    Autorzy:
    Wróbel, Michał
    Starczewski, Janusz T.
    Fijałkowska, Justyna
    Siwocha, Agnieszka
    Napoli, Christian
    Powiązania:
    https://bibliotekanauki.pl/articles/2031113.pdf
    Data publikacji:
    2021
    Wydawca:
    Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
    Tematy:
    offline handwriting recognition
    handwritten strokes
    fuzzy matching degrees
    interval type-2 fuzzy sets
    decision trees
    bigram frequency
    Opis:
    Handwritten text recognition systems interpret the scanned script images as text composed of letters. In this paper, efficient offline methods using fuzzy degrees, as well as interval fuzzy degrees of type-2, are proposed to recognize letters beforehand decomposed into strokes. For such strokes, the first stage methods are used to create a set of hypotheses as to whether a group of strokes matches letter or digit patterns. Subsequently, the second-stage methods are employed to select the most promising set of hypotheses with the use of fuzzy degrees. In a primary version of the second-stage system, standard fuzzy memberships are used to measure compatibility between strokes and character patterns. As an extension of the system thus created, interval type-2 fuzzy degrees are employed to perform a selection of hypotheses that fit multiple handwriting typefaces.
    Źródło:
    Journal of Artificial Intelligence and Soft Computing Research; 2021, 11, 3; 229-242
    2083-2567
    2449-6499
    Pojawia się w:
    Journal of Artificial Intelligence and Soft Computing Research
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Group decision making using interval-valued intuitionistic fuzzy soft matrix and confident weight of experts
    Autorzy:
    Das, S.
    Kar, S.
    Pal, T.
    Powiązania:
    https://bibliotekanauki.pl/articles/91665.pdf
    Data publikacji:
    2014
    Wydawca:
    Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
    Tematy:
    multiple attribute group decision making
    MAGDM
    interval-valued intuitionistic fuzzy soft matrix
    IVIFSM
    interval-valued intuitionistic fuzzy soft sets
    IVIFSSs
    Opis:
    This article proposes an algorithmic approach for multiple attribute group decision making (MAGDM) problems using interval-valued intuitionistic fuzzy soft matrix (IVIFSM) and confident weight of experts. We propose a novel concept for assigning confident weights to the experts based on cardinals of interval-valued intuitionistic fuzzy soft sets (IVIFSSs). The confident weight is assigned to each of the experts based on their preferred attributes and opinions, which reduces the chances of biasness. Instead of using medical knowledgebase, the proposed algorithm mainly relies on the set of attributes preferred by the group of experts. To make the set of preferred attributes more important, we use combined choice matrix, which is combined with the individual IVIFSM to produce the corresponding product IVIFSM. This article uses IVIFSMs for representing the experts’ opinions. IVIFSM is the matrix representation of IVIFSS and IVIFSS is a natural combination of interval-valued intuitionistic fuzzy set (IVIFS) and soft set. Finally, the performance of the proposed algorithm is validated using a case study from real life.
    Źródło:
    Journal of Artificial Intelligence and Soft Computing Research; 2014, 4, 1; 57-77
    2083-2567
    2449-6499
    Pojawia się w:
    Journal of Artificial Intelligence and Soft Computing Research
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Rough support vector machine for classification with interval and incomplete data
    Autorzy:
    Nowicki, Robert K.
    Grzanek, Konrad
    Hayashi, Yoichi
    Powiązania:
    https://bibliotekanauki.pl/articles/91559.pdf
    Data publikacji:
    2020
    Wydawca:
    Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
    Tematy:
    support vector machines
    rough sets
    missing features
    interval data
    three–way decision
    maszyna wektorów nośnych
    dane interwałowe
    Opis:
    The paper presents the idea of connecting the concepts of the Vapnik’s support vector machine with Pawlak’s rough sets in one classification scheme. The hybrid system will be applied to classifying data in the form of intervals and with missing values [1]. Both situations will be treated as a cause of dividing input space into equivalence classes. Then, the SVM procedure will lead to a classification of input data into rough sets of the desired classes, i.e. to their positive, boundary or negative regions. Such a form of answer is also called a three–way decision. The proposed solution will be tested using several popular benchmarks.
    Źródło:
    Journal of Artificial Intelligence and Soft Computing Research; 2020, 10, 1; 47-56
    2083-2567
    2449-6499
    Pojawia się w:
    Journal of Artificial Intelligence and Soft Computing Research
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-3 z 3

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