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Wyszukujesz frazę "ensemble of neural networks" wg kryterium: Temat

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    Wyświetlanie 1-5 z 5
    Tytuł:
    Diagnosis of malignant melanoma by neural network ensemble-based system utilising hand-crafted skin lesion features
    Autorzy:
    Grochowski, Michał
    Mikołajczyk, Agnieszka
    Kwasigroch, Arkadiusz
    Powiązania:
    https://bibliotekanauki.pl/articles/221391.pdf
    Data publikacji:
    2019
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    decision support
    diagnostics
    image processing
    artificial neural networks
    ensemble of neural networks
    melanoma malignant
    Opis:
    Malignant melanomas are the most deadly type of skin cancer, yet detected early have high chances of successful treatment. In the last twenty years, the interest in automatic recognition and classification of melanoma dynamically increased, partly because of appearing public datasets with dermatoscopic images of skin lesions. Automated computer-aided skin cancer detection in dermatoscopic images is a very challenging task due to uneven sizes of datasets, huge intra-class variation with small interclass variation, and the existence of many artifacts in the images. One of the most recognized methods of melanoma diagnosis is the ABCD method. In the paper, we propose an extended version of this method and an intelligent decision support system based on neural networks that uses its results in the form of hand-crafted features. Automatic determination of the skin features with the ABCD method is difficult due to the large diversity of images of various quality, the existence of hair, different markers and other obstacles. Therefore, it was necessary to apply advanced methods of pre-processing the images. The proposed system is an ensemble of ten neural networks working in parallel, and one network using their results to generate a final decision. This system structure enables to increase the efficiency of its operation by several percentage points compared with asingle neural network. The proposed system is trained on over 5000 and tested afterwards on 200 skin moles. The presented system can be used as a decision support system for primary care physicians, as a system capable of self-examination of the skin with a dermatoscope and also as an important tool to improve biopsy decision making.
    Źródło:
    Metrology and Measurement Systems; 2019, 26, 1; 65-80
    0860-8229
    Pojawia się w:
    Metrology and Measurement Systems
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Development of cost estimation models based on ANN ensembles and the SVM method
    Autorzy:
    Juszczyk, Michał
    Powiązania:
    https://bibliotekanauki.pl/articles/396649.pdf
    Data publikacji:
    2020
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    construction cost estimation
    cost modelling
    ensemble of neural networks
    support vector machine
    koszty budowy
    modelowanie kosztów
    zespół sieci neuronowych
    Opis:
    Cost estimation, as one of the key processes in construction projects, provides the basis for a number of project-related decisions. This paper presents some results of studies on the application of artificial intelligence and machine learning in cost estimation. The research developed three original models based either on ensembles of neural networks or on support vector machines for the cost prediction of the floor structural frames of buildings. According to the criteria of general metrics (RMSE, MAPE), the three models demonstrate similar predictive performance. MAPE values computed for the training and testing of the three developed models range between 5% and 6%. The accuracy of cost predictions given by the three developed models is acceptable for the cost estimates of the floor structural frames of buildings in the early design stage of the construction project. Analysis of error distribution revealed a degree of superiority for the model based on support vector machines.
    Źródło:
    Civil and Environmental Engineering Reports; 2020, 30, 3; 48-67
    2080-5187
    2450-8594
    Pojawia się w:
    Civil and Environmental Engineering Reports
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Deep Classifiers and Wavelet Transformation for Fake Image Detection
    Autorzy:
    Osowski, Stanislaw
    Golgowski, Maciej
    Powiązania:
    https://bibliotekanauki.pl/articles/27312950.pdf
    Data publikacji:
    2023
    Wydawca:
    Instytut Łączności - Państwowy Instytut Badawczy
    Tematy:
    continuous wavelet transform
    convolutional neural
    networks
    deep fake
    ensemble of classifiers
    Opis:
    The paper presents a computer system for detecting deep fake images in videos. The system is based on continuous wavelet transformation combined with a set of classifiers composed of a few convolutional neural networks of diversified architectures. Three different forms of forged images taken from the FaceForensics++ database are considered in numerical experiments. The results of experiments involving the proposed system have shown good performance in comparison to other current approaches to this particular problem.
    Źródło:
    Journal of Telecommunications and Information Technology; 2023, 4; 1--8
    1509-4553
    1899-8852
    Pojawia się w:
    Journal of Telecommunications and Information Technology
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Local dynamic integration of ensemble in prediction of time series
    Autorzy:
    Osowski, S.
    Siwek, K.
    Powiązania:
    https://bibliotekanauki.pl/articles/201557.pdf
    Data publikacji:
    2019
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    neural networks
    ensemble of predictors
    dynamic integration
    time series prediction
    sieci neuronowe
    zespół predyktorów
    dynamiczna integracja
    Opis:
    The paper presents local dynamic approach to integration of an ensemble of predictors. The classical fusing of many predictor results takes into account all units and takes the weighted average of the results of all units forming the ensemble. This paper proposes different approach. The prediction of time series for the next day is done here by only one member of an ensemble, which was the best in the learning stage for the input vector, closest to the input data actually applied. Thanks to such arrangement we avoid the situation in which the worst unit reduces the accuracy of the whole ensemble. This way we obtain an increased level of statistical forecasting accuracy, since each task is performed by the best suited predictor. Moreover, such arrangement of integration allows for using units of very different quality without decreasing the quality of final prediction. The numerical experiments performed for forecasting the next input, the average PM10 pollution and forecasting the 24-element vector of hourly load of the power system have confirmed the superiority of the presented approach. All quality measures of forecast have been significantly improved.
    Źródło:
    Bulletin of the Polish Academy of Sciences. Technical Sciences; 2019, 67, 3; 517-525
    0239-7528
    Pojawia się w:
    Bulletin of the Polish Academy of Sciences. Technical Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Ensemble of classifiers based on CNN for increasing generalization ability in face image recognition
    Autorzy:
    Szmurło, Robert
    Osowski, Stanisław
    Powiązania:
    https://bibliotekanauki.pl/articles/2173680.pdf
    Data publikacji:
    2022
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    CNN
    ensemble of classifiers
    face recognition
    feature selection
    convolutional neural networks
    splotowe sieci neuronowe
    zespół klasyfikatorów
    rozpoznawanie twarzy
    wybór funkcji
    Opis:
    The paper considers the problem of increasing the generalization ability of classification systems by creating an ensemble of classifiers based on the CNN architecture. Different structures of the ensemble will be considered and compared. Deep learning fulfills an important role in the developed system. The numerical descriptors created in the last locally connected convolution layer of CNN flattened to the form of a vector, are subjected to a few different selection mechanisms. Each of them chooses the independent set of features, selected according to the applied assessment techniques. Their results are combined with three classifiers: softmax, support vector machine, and random forest of the decision tree. All of them do simultaneously the same classification task. Their results are integrated into the final verdict of the ensemble. Different forms of arrangement of the ensemble are considered and tested on the recognition of facial images. Two different databases are used in experiments. One was composed of 68 classes of greyscale images and the second of 276 classes of color images. The results of experiments have shown high improvement of class recognition resulting from the application of the properly designed ensemble.
    Źródło:
    Bulletin of the Polish Academy of Sciences. Technical Sciences; 2022, 70, 3; art. no. e141004
    0239-7528
    Pojawia się w:
    Bulletin of the Polish Academy of Sciences. Technical Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-5 z 5

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