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    Wyświetlanie 1-3 z 3
    Tytuł:
    Segmentation of Homogeneous Regions of Gravity Field Properties by Machine Learning Method in Central Area of Vietnam
    Autorzy:
    Thi, Hong Phan
    Minh, Phuong Do
    Van, Huu Tran
    Powiązania:
    https://bibliotekanauki.pl/articles/27323265.pdf
    Data publikacji:
    2023
    Wydawca:
    Polskie Towarzystwo Przeróbki Kopalin
    Tematy:
    K-means
    unsupervised learning method
    gravity field
    central area of Vietnam
    COSCAD 3D
    Wietnam
    właściwości fizyczne
    grawitacja
    Opis:
    This paper presents the results of applying the unsupervised learning method (K-means clustering) on the gravity anomaly field in the central region of Vietnam to separate the research area into different clusters, which are homologous in physical properties. In order to achieve the optimal results, the input parameter plays an important role. In this paper, we chose 04 input attributes including the gravity anomalous field attribute, the horizontal gradient attribute, the variance attribute, and the tracing coefficient of the gravity anomalous axis. The obtained results have shown that the research area could be divided into 7 clusters, 9 clusters, 11 clusters, and 13 clusters with close characteristics of the physical properties of the gravity field. The research results show that the Southwest, the Center, and the South of the study area have complex changing physical properties, this result reflects the complicated tectonic activities in these areas with the presence of crumpled and fractured rock layers in different directions and these locations are the potential places to form endogenous mineral deposits of magma origin. The Northwest, the North, and the East parts of the research area witness negligible changes in the field's physical properties, reflecting the stability of the soil and rock layers in this area, with the direction of extending structure from the Northwest to the Southeast. The clustering results according to the K-means unsupervised learning algorithm in central Vietnam initially increase the reliability of the decisions of geologists and geophysicists in interpreting the geological structure and evaluating the origin of deep-hidden mineral deposits in the area.
    Źródło:
    Inżynieria Mineralna; 2023, 2; 97--102
    1640-4920
    Pojawia się w:
    Inżynieria Mineralna
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    A novel method for automatic detection of arrhythmias using the unsupervised convolutional neural network
    Autorzy:
    Zhang, Junming
    Yao, Ruxian
    Gao, Jinfeng
    Li, Gangqiang
    Wu, Haitao
    Powiązania:
    https://bibliotekanauki.pl/articles/23944827.pdf
    Data publikacji:
    2023
    Wydawca:
    Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
    Tematy:
    convolutional neural network
    arrhythmia detection
    unsupervised learning
    ECG classification
    Opis:
    In recent years, various models based on convolutional neural networks (CNN) have been proposed to solve the cardiac arrhythmia detection problem and achieved saturated accuracy. However, these models are often viewed as “blackbox” and lack of interpretability, which hinders the understanding of cardiologists, and ultimately hinders the clinical use of intelligent terminals. At the same time, most of these approaches are supervised learning and require label data. It is a time-consuming and expensive process to obtain label data. Furthermore, in human visual cortex, the importance of lateral connection is same as feed-forward connection. Until now, CNN based on lateral connection have not been studied thus far. Consequently, in this paper, we combines CNNs, lateral connection and autoencoder (AE) to propose the building blocks of lateral connection convolutional autoencoder neural networks (LCAN) for cardiac arrhythmia detection, which learn representations in an unsupervised manner. Concretely, the LCAN contains a convolution layer, a lateral connection layer, an AE layer, and a pooling layer. The LCAN detects salient wave features through the lateral connection layer. The AE layer and competitive learning is used to update the filters of the convolution network—an unsupervised process that ensures similar weight distribution for all adjacent filters in each convolution layer and realizes the neurons’ semantic arrangement in the LCAN. To evaluate the performances of the proposed model, we have implemented the experiments on the well-known MIT–BIH Arrhythmia Database. The proposed model yields total accuracies and kappa coefficients of 98% and 0.95, respectively. The experiment results show that the LCAN is not only effective, but also a useful tool for arrhythmia detection.
    Źródło:
    Journal of Artificial Intelligence and Soft Computing Research; 2023, 13, 3; 181--196
    2083-2567
    2449-6499
    Pojawia się w:
    Journal of Artificial Intelligence and Soft Computing Research
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    A deep ensemble learning method for effort-aware just-in-time defect prediction
    Autorzy:
    Albahli, Saleh
    Powiązania:
    https://bibliotekanauki.pl/articles/117652.pdf
    Data publikacji:
    2020
    Wydawca:
    Polskie Towarzystwo Promocji Wiedzy
    Tematy:
    Deep Neural Network
    unlabeled dataset
    Just-In-Time defect prediction
    unsupervised prediction
    nieoznakowany zbiór danych
    przewidywanie defektów Just-In-Time
    przewidywanie bez nadzoru
    Opis:
    Since the introduction of Just-in-Time effort aware defect prediction, many researchers are focusing on evaluating the different learning methods for defect prediction. To predict the changes that are defect-inducing, it is im-portant for learning model to consider the nature of the dataset, its imbalance properties and the correlation between different attributes. In this paper, we evaluated the importance of dataset properties, and proposed a novel methodology for learning the effort aware just-in-time defect prediction model. We form an ensemble classifier, which consider the output of three individuals classifier i.e. Random forest, XGBoost and Deep Neural Network. Our proposed methodology shows better performance with 77% accuracy on sample dataset and 81% accuracy on different dataset.
    Źródło:
    Applied Computer Science; 2020, 16, 3; 5-15
    1895-3735
    Pojawia się w:
    Applied Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-3 z 3

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