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    Wyświetlanie 1-7 z 7
    Tytuł:
    Local embedding and dimensionality reduction in detection of skin tumor tissue
    Autorzy:
    Michalak, M.
    Świtoński, A.
    Powiązania:
    https://bibliotekanauki.pl/articles/333429.pdf
    Data publikacji:
    2012
    Wydawca:
    Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
    Tematy:
    rozpoznawanie wzorców
    analiza wielospektralna
    redukcja wymiarowości
    selekcja cech
    pattern recognition
    multispectral analysis
    dimensionality reduction
    feature selection
    Opis:
    This article shows the limitation of the usage of dimensionality reduction methods. For this purpose three algorithms were analyzed on the real medical data. This data are multispectral images of human skin labeled as tumor or non-tumor regions. The classification of new data required the special algorithm of new data mapping that is also described in the paper. Unfortunately, the final conclusion is that this kind of local embedding algorithms should not be recommended for this kind of analysis and prediction.
    Źródło:
    Journal of Medical Informatics & Technologies; 2012, 19; 59-65
    1642-6037
    Pojawia się w:
    Journal of Medical Informatics & Technologies
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Selection of the most important components from multispectral images for detection of tumor tissue
    Autorzy:
    Michalak, M.
    Świtoński, A.
    Stawarz, M.
    Powiązania:
    https://bibliotekanauki.pl/articles/951663.pdf
    Data publikacji:
    2011
    Wydawca:
    Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
    Tematy:
    rozpoznawanie obrazów
    analiza wielospektralna
    obniżenie wymiarowości
    wybór funkcji
    pattern recognition
    multispectral analysis
    dimensionality reduction
    feature selection
    Opis:
    The problem raised in this article is the selection of the most important components from multispectral images for the purpose of skin tumor tissue detection. It occured that 21 channel spectrum makes it possible to separate healthy and tumor regions almost perfectly. The disadvantage of this method is the duration of single picture acquisition because this process requires to keep the device very stable. In the paper two approaches to the problem are presented: hill climbing strategy and some ranking methods.
    Źródło:
    Journal of Medical Informatics & Technologies; 2011, 17; 303-308
    1642-6037
    Pojawia się w:
    Journal of Medical Informatics & Technologies
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Intelligence in manufacturing systems: the pattern recognition perspective
    Autorzy:
    Zaremba, M. B.
    Powiązania:
    https://bibliotekanauki.pl/articles/971032.pdf
    Data publikacji:
    2010
    Wydawca:
    Polska Akademia Nauk. Instytut Badań Systemowych PAN
    Tematy:
    Intelligent Manufacturing Systems
    pattern recognition
    computational intelligence
    neural networks
    distributed systems
    spatial filtering
    feature selection
    dimensionality reduction
    Opis:
    The field of Intelligent Manufacturing Systems (IMS) has been generally equated with the use of Artificial Intelligence and Computational Intelligence methods and techniques in the design and operation of manufacturing systems. Those methods and techniques are now applied in many different technological domains to deal with such pervasive problems as data imprecision and nonlinear system behavior. The focus in IMS is now shifting to a broader understanding of the intelligent behavior of manufacturing systems. The questions debated by researchers today relate more to what kind and what level of adaptability to instill in the structure and operation of a manufacturing system, with the discussions increasingly gravitating to the issue of system self-organization. This paper explores the changing face of IMS from the perspective of the pattern recognition domain. It presents design criteria for techniques that will allow us to implement manufacturing systems exhibiting adaptive and intelligent behaviour. Examples are given to show how incorporating pattern recognition capabilities can help us build more intelligence and self-organization into the manufacturing systems of the future.
    Źródło:
    Control and Cybernetics; 2010, 39, 1; 233-258
    0324-8569
    Pojawia się w:
    Control and Cybernetics
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    A pattern recognition approach to Emery-Dreifuss muscular dystrophy (EDMD) study
    Autorzy:
    Sokołowska, B.
    Jóźwik, A.
    Niebroj-Dobosz, I. M.
    Hausmanowa-Petrusewicz, I.
    Powiązania:
    https://bibliotekanauki.pl/articles/332948.pdf
    Data publikacji:
    2014
    Wydawca:
    Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
    Tematy:
    pattern recognition
    feature selection
    pair-wise linear classifier
    metalloproteinases and their tissue inhibitors
    Emery-Dreifuss muscular dystrophy
    rozpoznawanie obrazów
    wybór funkcji
    metaloproteinaza
    dystrofia mięśniowa
    Opis:
    The algorithms of pattern recognition were used for differentiation between two forms of Emery-Dreifuss muscular dystrophy (EDMD), i.e. autosomal-dominant laminopathy (AD-EDMD) and Xlinked emerynopathy (X-EDMD). A set of some matrix metalloproteinases (MMPs) and their tissue inhibitors (TIMPs) in serum of EDMD patients and healthy subjects were treated as features. In concluding MMPs and TIMPs levels are helpful to identifying the EDMD patients and the disease progress.
    Źródło:
    Journal of Medical Informatics & Technologies; 2014, 23; 165-172
    1642-6037
    Pojawia się w:
    Journal of Medical Informatics & Technologies
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Some problems with construction of the k-NN classifier for recognition of an experimental respiration pathology
    Autorzy:
    Jóźwik, A.
    Sokołowska, B.
    Powiązania:
    https://bibliotekanauki.pl/articles/332910.pdf
    Data publikacji:
    2002
    Wydawca:
    Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
    Tematy:
    rozpoznawanie wzorców
    klasyfikacja nadzorowana
    zasada k-NN
    wybór funkcji
    oddychanie
    wentylacja
    paraliż
    przepona
    pattern recognition
    supervised classification
    k-NN rule
    feature selection
    respiration
    ventilation
    paralysis
    diaphragm
    Opis:
    An objective of the work is to demonstrate some difficulties with construction of a classifier based on the k-NN rule. The standard k-NN classifier and the parallel k-NN classifier have been chosen as the two most powerful approaches. This kind of classifiers has been applied to automatic recognition of diaphragm paralysis degree. The classifier construction consists in determination of the number of nearest neighbors, selection of features and estimation of the classification quality. Three classes of muscle pathology, including the control class, and five ventilatory parameters are taken into account. The data concern a model of the diaphragm pathology in a cat. The animals were forced to breathe in three different experimental situations: air, hypercapnic and hypoxic conditions. A separate classifier is constructed for each kind of the mentioned situations. The calculation of the misclassification rate is based on the leave one out and on the testing set method. Several computational experiments are suggested for the correct feature selection, the classifier type choice and the misclassification probability estimation.
    Źródło:
    Journal of Medical Informatics & Technologies; 2002, 3; MI89-97
    1642-6037
    Pojawia się w:
    Journal of Medical Informatics & Technologies
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Nonparametric methods of supervised classification
    Autorzy:
    Jóźwik, A.
    Powiązania:
    https://bibliotekanauki.pl/articles/333226.pdf
    Data publikacji:
    2013
    Wydawca:
    Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
    Tematy:
    pattern recognition
    feature selection
    k-NN rules
    pair-wise classifier
    artificial features
    linear classifier
    reference set size reduction
    rozpoznawanie wzorca
    wybór funkcji
    reguła k-NN
    sztuczne cechy
    klasyfikator liniowy
    Opis:
    Selected nonparametric methods of statistical pattern recognition are described. A part of them form modifications of the well known k-NN rule. To this group of the presented methods belong: a fuzzy k-NN rule, a pair-wise k-NN rule and a corrected k-NN rule. They can improve classification quality as compared with the standard k-NN rule. For the cases when these modifications would offer to large error rates an approach based on class areas determination is proposed. The idea of class areas can be also used for construction of the multistage classifier. A separate feature selection can be performed in each stage. The modifications of the k-NN rule and the methods based on determination class areas can be too slow in some applications, therefore algorithms for reference set reduction and condensation, for simple NN rule, are proposed. To construct fast classifiers it is worth to consider also a pair-wise linear classifiers. The presented idea can be used as in the case when the class pairs are linearly separable as well as in the contrary case.
    Źródło:
    Journal of Medical Informatics & Technologies; 2013, 22; 21-32
    1642-6037
    Pojawia się w:
    Journal of Medical Informatics & Technologies
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    The pair-wise linear classifier and the k-NN rule in application to ALS progression differentiation
    Autorzy:
    Sokołowska, B.
    Jóźwik, A.
    Niebroj-Dobosz, I.
    Janik, P.
    Powiązania:
    https://bibliotekanauki.pl/articles/333011.pdf
    Data publikacji:
    2012
    Wydawca:
    Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
    Tematy:
    rozpoznawanie wzorców
    wybór funkcji
    klasyfikator liniowy
    zasada k-NN
    biomarkery
    stwardnienie zanikowe boczne
    pattern recognition
    feature selection
    linear classifier
    k-NN rule
    pair-wise classifier
    biomarkers
    amyotrophic lateral sclerosis
    Opis:
    The two kinds of classifier based on the k-NN rule, the standard and the parallel version, were used for recognition of severity of ALS disease. In case of the second classifier version, feature selection was done separately for each pair of classes. The error rate, estimated by the leave one out method, was used as a criterion as for determination the optimum values of k's as well as for feature selection. All features selected in this manner were used in the standard and in the parallel classifier based on k-NN rule. Furthermore, only for the verification purpose, the linear classifier was applied. For this kind of classifier the error rates were calculated by use the training set also as a testing one. The linear classifier was trained by the error correction algorithm with a modified stop condition. The data set concerned with the healthy subjects and patients with amyotrophic lateral sclerosis (ALS). The set of several biomarkers such as erythropoietin, matrix metalloproteinases and their tissue inhibitors measured in serum and cerebrospinal fluid (CSF) were treated as features. It was shown that CSF biomarkers were very sensitive for the ALS progress.
    Źródło:
    Journal of Medical Informatics & Technologies; 2012, 20; 79-83
    1642-6037
    Pojawia się w:
    Journal of Medical Informatics & Technologies
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-7 z 7

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