Informacja

Drogi użytkowniku, aplikacja do prawidłowego działania wymaga obsługi JavaScript. Proszę włącz obsługę JavaScript w Twojej przeglądarce.

English (EN)·Polski (PL)·Yкраїнський (UK)
Przejdź do strony domowej biblioteki Centralna Biblioteka Wojskowa Link otwiera się w nowym oknie Logo biblioteki Prolib Integro - strona głównaCentralna Biblioteka Wojskowa

Wyszukujesz frazę "sieci nuronowe" wg kryterium: Temat

  Lista filtrów
Wyrównaj
    Wyświetlanie 1-7 z 7
    Tytuł:
    Modelling of hemodialysis: regression versus neural model
    Autorzy:
    Szmajda, R.
    Szczepaniak, P. S.
    Powiązania:
    https://bibliotekanauki.pl/articles/333709.pdf
    Data publikacji:
    2004
    Wydawca:
    Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
    Tematy:
    hemodializy
    sieci nuronowe
    haemodialysis
    neural network
    Opis:
    In the paper, evaluation of two approaches to modelling of hemodialysis is performed. Results obtained by regression are compared to those generated by neural models. Differences in the modelling quality are small. Both models shown the same qualitative dependencies between analyzed parameters.
    Źródło:
    Journal of Medical Informatics & Technologies; 2004, 7; KB41-46
    1642-6037
    Pojawia się w:
    Journal of Medical Informatics & Technologies
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Classification of breast thermal images using artificial neural networks
    Autorzy:
    Jakubowska, T.
    Wiecek, B.
    Wysocki, M.
    Drews-Peszynski, C.
    Strzelecki, M.
    Powiązania:
    https://bibliotekanauki.pl/articles/333564.pdf
    Data publikacji:
    2004
    Wydawca:
    Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
    Tematy:
    przetwarzanie termogramów
    sieci nuronowe
    klasyfikacja
    thermal image processing
    neural network
    classification
    Opis:
    In this paper we present classification of the thermal images in order to discriminate healthy and pathological cases during breast cancer screening. Different image features and approaches for data reduction and classification have been used to distinguish healthy breast one with malignant tumour. We use image histogram and co-occurrence matrix to get thermal signatures and analyze symmetry between left and right side. The most promised method was based on wavelet transformation and nonlinear neural network classifier. The proposed approach was used in the pilot investigations in the medical centre which is permanently using thermograph for breast cancer screening, as an adjacent method for other classical diagnostic method, such as mammography.
    Źródło:
    Journal of Medical Informatics & Technologies; 2004, 7; MIP41-50
    1642-6037
    Pojawia się w:
    Journal of Medical Informatics & Technologies
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Application of neural networks in optimization of the recruitment process for sport swimming
    Autorzy:
    Ryguła, I.
    Roczniok, R.
    Powiązania:
    https://bibliotekanauki.pl/articles/333686.pdf
    Data publikacji:
    2004
    Wydawca:
    Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
    Tematy:
    proces rektutacji
    proces selekcji
    sieci nuronowe
    recruitment process
    selection process
    neural networks
    Opis:
    The essence of the recruitment and selection in sports depends on determining an aptitude vector of the candidate to sport training. For this reason, the recruitment process may be optimized by determining possibly large amount of information on the sport level of the candidate with as small as possible number of tested characteristics, using a mathematical model based on neural networks. The main aim of this work was verification of the usefulness of neural models in optimization of the process of recruitment process, both at sprint distance of 50 m and typically endurance distance of 800 m. The material for the investigation was a group of 80 young swimmers in the youngster and junior category from the Silesian macro-region. For the purpose of verification of the usefulness of neural models, statistical analysis were made of the measurement results of young swimmers and two neural models were developed - for sprint distance (50 m) and endurance distance (800 m). The developed models, based on the architecture of perception networks, has shown capability of generalization and prediction, which has enabled to reach conclusions on practical possibility of using neural networks in optimization of the recruitment and selection process for sport swimming.
    Źródło:
    Journal of Medical Informatics & Technologies; 2004, 7; KB75-82
    1642-6037
    Pojawia się w:
    Journal of Medical Informatics & Technologies
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Evolutionary algorithms and neural networks applied to the computer - aided medical diagnosis
    Autorzy:
    Zaganczyk, A.
    Powiązania:
    https://bibliotekanauki.pl/articles/333696.pdf
    Data publikacji:
    2002
    Wydawca:
    Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
    Tematy:
    algorytmy genetyczne
    sieci nuronowe
    systemy hybrydowe
    zawał mięśnia sercowego
    genetic algorithms
    neural networks
    hybrid systems
    myocardial infarction
    Opis:
    The purpose of presented work is to create a project and computer implementation of complex decision support system used in an important medical field, which is cardiology. This system is applied to support physical diagnosis concern different kinds of myocardial infraction. The system - called NEUROGEN v.01, is a kind of hybrid system, which is a combination of Genetic Algorithm (GA) and Neural Network (NN). The idea of this specific combination is that GA is used as a evolutionary method of learning of NN. In accordance with this special task, the NN is a three-layer feedforward network with eight numbers of input neurons, six numbers of hidden and five number of output neurons. The number of neurons in each layer was appointed on the base of data of the task. In this work, the purpose was to look for the optimal values of the parameters of algorithm, which are: crossover probability, mutation probability, the number of individuals in population, the number of generations of the algorithm and λ - parameter of function of activation which characterize neurons in NN. An extra task is to check if the beginning population has any influence on effectiveness of the system. In this paper there will be presented the way of rising of NEUROGEN v.01 and achieved results.
    Źródło:
    Journal of Medical Informatics & Technologies; 2002, 4; SN21-24
    1642-6037
    Pojawia się w:
    Journal of Medical Informatics & Technologies
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Speech nonfluency detection and classification based on linear prediction coefficients and neural networks
    Autorzy:
    Kobus, A.
    Kuniszyk-Jóźkowiak, W.
    Smołka, E.
    Codello, I.
    Powiązania:
    https://bibliotekanauki.pl/articles/333600.pdf
    Data publikacji:
    2010
    Wydawca:
    Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
    Tematy:
    przewidywanie liniowe
    liniowe kodowanie predykcyjne
    sieci nuronowe
    kowariancja
    brak płynności
    mowa
    wykrywanie
    perceptron
    linear prediction
    LPC
    neural networks
    Kohonen
    covariance
    nonfluency
    speech
    detection
    radial
    Opis:
    The goal of the paper is to present a speech nonfluency detection method based on linear prediction coefficients obtained by using the covariance method. The application “Dabar” was created for research. It implements three different methods of LP with the ability to send coefficients computed by them into the input of Kohonen networks. Neural networks were used to classify utterances in categories of fluent and nonfluent. The first one was Kohonen network (SOM), used to reduce LP coefficients representation of each window, which were used as input data to SOM input layer, to a vector of winning neurons of SOM output layer. Radial Basis Function (RBF) networks, linear networks and Multi-Layer Perceptrons were used as classifiers. The research was based on 55 fluent samples and 54 samples with blockades on plosives (p, b, d, t, k, g). The examination was finished with the outcome of 76% classifying.
    Źródło:
    Journal of Medical Informatics & Technologies; 2010, 15; 135-143
    1642-6037
    Pojawia się w:
    Journal of Medical Informatics & Technologies
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    The diseases classification method on gait abnormalities characteristic contributions
    Autorzy:
    Chandzlik, S.
    Piecha, J.
    Powiązania:
    https://bibliotekanauki.pl/articles/333759.pdf
    Data publikacji:
    2005
    Wydawca:
    Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
    Tematy:
    klasyfikacja chorób neurologicznych
    choroba Parkinsona
    niedowład
    udar niedokrwienny mózgu
    automatyczne zakończenie
    sieci nuronowe
    neurological disease classification
    Parkinson disease
    hemiparesis
    ischemic stroke
    automatic conclusion
    neural networks
    Opis:
    Present medicine uses computers in various applications, especially in a field of a diseases level classification and diagnosis. In many cases an automatic conclusion making units are the main goal of the computer systems usage. The software units are developed for the diseases classification or for monitoring of the disease medical treatment. An example application was described in this paper. It concerns a gait abnormalities level analysis that is described by a data records gathered by insoles of Parotec System for Windows (PSW) [17,18]. The PSW software package is used for visualisation of the gait characteristic static and dynamic characteristic features. In the authors' works many additional data components were distinguished. The field of the applications is located within the neurological gait characteristics also the source applications concern orthopaedics [16,18]. Careful analysis of the data provided the developers with new areas the PSW applications [4,11,13]. For conclusion making units the artificial networks theory was implemented [2,4,11,13]. For more effective training of the neural networks specific characteristic measures were introduced [4,5]. They allow controlling the training process more precisely, avoiding mistakes in current records classification.
    Źródło:
    Journal of Medical Informatics & Technologies; 2005, 9; 187-194
    1642-6037
    Pojawia się w:
    Journal of Medical Informatics & Technologies
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    The sigma-if neural network as a method of dynamic selection of decision subspaces for medical reasoning systems
    Autorzy:
    Huk, M.
    Powiązania:
    https://bibliotekanauki.pl/articles/951660.pdf
    Data publikacji:
    2004
    Wydawca:
    Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
    Tematy:
    sztuczna inteligencja
    wybieranie danych
    sieci nuronowe sigma-if
    wybór miejsca decyzji
    nieniszczące neuronowe przycinanie sieci
    artificial intelligence
    data mining
    sigma-if neural network
    decision space selection
    non-destructive neural network pruning
    Opis:
    To-date research in the area of applied medical artificial intelligence systems suggests that it is necessary to focus further on the characteristic requirements of this research field. One of those requirements is related to the need for effective analysis of multidimensional heterogeneous data sets, which poses particular difficulties when considering AI-suggested solutions. Recent works point to the possibility of extending the activation function of a perception to the time domain, thus significantly enhancing the capabilities of neural networks. This change results in the ability to dynamically tune the size of the decision space under consideration, which stems from continuous adaptation of the interneuron connection architecture to the data being classified. Such adaptation reflects the importance of individual decision attributes for the patterns being classified, as defined by the Sigma-if network during its training phase. These characteristics enable effective employment of such networks in solving classification problems, which emerge in medical sciences. The described approach is also a novel, interesting area of neural network research. This article discusses selected aspects of construction as well as training of Sigma-if networks, based on a sample problem of classifying Arabic numeral images.
    Źródło:
    Journal of Medical Informatics & Technologies; 2004, 7; KB65-73
    1642-6037
    Pojawia się w:
    Journal of Medical Informatics & Technologies
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-7 z 7

      Ta witryna wykorzystuje pliki cookies do przechowywania informacji na Twoim komputerze. Pliki cookies stosujemy w celu świadczenia usług na najwyższym poziomie, w tym w sposób dostosowany do indywidualnych potrzeb. Korzystanie z witryny bez zmiany ustawień dotyczących cookies oznacza, że będą one zamieszczane w Twoim komputerze. W każdym momencie możesz dokonać zmiany ustawień dotyczących cookies