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    Wyświetlanie 1-3 z 3
    Tytuł:
    Development of cause-effect dependence model of undesirable events using Bayes network
    Autorzy:
    Tchórzewska-Cieślak, B.
    Pietrucha-Urbanik, K.
    Szpak, D.
    Powiązania:
    https://bibliotekanauki.pl/articles/2068874.pdf
    Data publikacji:
    2017
    Wydawca:
    Uniwersytet Morski w Gdyni. Polskie Towarzystwo Bezpieczeństwa i Niezawodności
    Tematy:
    bayes network
    risk
    security
    Opis:
    In the paper the method of cause and effect analysis of undesirable events using the Bayesian networks is presented. For the analysis, due to the complexity of the calculations, it is proposed to use Java Bayes program as a free and simple tool to support Bayesian analysis. Bayesian estimation allows to identify the probability of the event occurrence. For this reason its use was proposed to determine the safety probability. Using Bayes' theorem is also possible to modify initial judgement about the situation with the use of a priori probability so that a new situation described by a posteriori probability arises. In this sense, by Bayes' theorem the data can be sequentially processed, including considerations for newer information, and thereby create a more reliable basis for decision making for the system operator. In the paper, the methodology was presented, which can be extended in order to improve the detection and monitoring of undesirable events in infrastructure.
    Źródło:
    Journal of Polish Safety and Reliability Association; 2017, 8, 1; 149--156
    2084-5316
    Pojawia się w:
    Journal of Polish Safety and Reliability Association
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Methods for identifying threats of critical infrastructure systems within Baltic Sea region
    Autorzy:
    Tchórzewska-Cieślak, Barbara
    Pietrucha-Urbanik, Katarzyna
    Szpak, Dawid
    Powiązania:
    https://bibliotekanauki.pl/articles/2068699.pdf
    Data publikacji:
    2019
    Wydawca:
    Uniwersytet Morski w Gdyni. Polskie Towarzystwo Bezpieczeństwa i Niezawodności
    Tematy:
    failure
    system safety
    safety management
    risk
    security
    threats
    FMEA
    safety
    failure analysis
    Bayes network
    Opis:
    In the analysis of the operation of critical infrastructure systems it is important to perform the analysis of the safety of the operation. The daily operation of such systems is inherently associated with the occurrence of various types of random undesirable events. Therefore, in the paper the matrix and logical trees methods used in the analysis of the risk of threats in critical infrastructure systems within the Baltic Sea, were presented. The analysis and assessment of the protection of technical system was performed using the FMEA method (Failure Mode and Effect Analysis). As to analyse the cause and effect of undesirable events the method of Bayes' theorem and Java Bayes program were implemented, which allows to identify the probability of the event occurrence.
    Źródło:
    Journal of Polish Safety and Reliability Association; 2019, 10, 1; 149--166
    2084-5316
    Pojawia się w:
    Journal of Polish Safety and Reliability Association
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    A comparative study on performance of basic and ensemble classifiers with various datasets
    Autorzy:
    Gunakala, Archana
    Shahid, Afzal Hussain
    Powiązania:
    https://bibliotekanauki.pl/articles/30148255.pdf
    Data publikacji:
    2023
    Wydawca:
    Polskie Towarzystwo Promocji Wiedzy
    Tematy:
    classification
    Naïve Bayes
    neural network
    Support Vector Machine
    Decision Tree
    ensemble learning
    Random Forest
    Opis:
    Classification plays a critical role in machine learning (ML) systems for processing images, text and high -dimensional data. Predicting class labels from training data is the primary goal of classification. An optimal model for a particular classification problem is chosen based on the model's performance and execution time. This paper compares and analyzes the performance of basic as well as ensemble classifiers utilizing 10-fold cross validation and also discusses their essential concepts, advantages, and disadvantages. In this study five basic classifiers namely Naïve Bayes (NB), Multi-layer Perceptron (MLP), Support Vector Machine (SVM), Decision Tree (DT), and Random Forest (RF) and the ensemble of all the five classifiers along with few more combinations are compared with five University of California Irvine (UCI) ML Repository datasets and a Diabetes Health Indicators dataset from Kaggle repository. To analyze and compare the performance of classifiers, evaluation metrics like Accuracy, Recall, Precision, Area Under Curve (AUC) and F-Score are used. Experimental results showed that SVM performs best on two out of the six datasets (Diabetes Health Indicators and waveform), RF performs best for Arrhythmia, Sonar, Tic-tac-toe datasets, and the best ensemble combination is found to be DT+SVM+RF on Ionosphere dataset having respective accuracies 72.58%, 90.38%, 81.63%, 73.59%, 94.78% and 94.01%. The proposed ensemble combinations outperformed the conven¬tional models for few datasets.
    Źródło:
    Applied Computer Science; 2023, 19, 1; 107-132
    1895-3735
    2353-6977
    Pojawia się w:
    Applied Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-3 z 3

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