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 "M5 model tree" wg kryterium: Temat

  Lista filtrów
Wyrównaj
    Wyświetlanie 1-4 z 4
    Tytuł:
    Soft computing based prediction of friction angle of clay
    Autorzy:
    Dutta, R. K.
    Gnananandarao, T.
    Ladol, S.
    Powiązania:
    https://bibliotekanauki.pl/articles/1818506.pdf
    Data publikacji:
    2020
    Wydawca:
    Stowarzyszenie Komputerowej Nauki o Materiałach i Inżynierii Powierzchni w Gliwicach
    Tematy:
    artificial neural network
    sensitivity analysis
    M5P model tree
    multiregression analysis
    friction angle of clay
    sztuczna sieć neuronowa
    analiza wrażliwości
    drzewo modelu M5P
    analiza wielokrotnej regresji
    Opis:
    Purpose: This article uses soft computing-based techniques to elaborate a study on the prediction of the friction angle of clay. Design/methodology/approach: A total of 30 data points were collected from the literature to predict the friction angle of the clay. To achieve the friction angle, the independent parameters sand content, silt content, plastic limit and liquid limit were used in the soft computing techniques such as artificial neural networks, M5P model tree and multi regression analysis. Findings: The major findings from this study are that the artificial neural networks are predicting the friction angle of the clay accurately than the M5P model and multi regression analysis. The sensitivity analysis reveals that the clay content is the major influencing independent parameter to predict the friction angle of the clay followed by sand content, liquid limit and plastic limit. Research limitations/implications: The proposed expressions can used to predict the friction angle of the clay accurately but can be further improved using large data for a wider range of applications. Practical implications: The proposed equations can be used to calculate the friction angle of the clay based on sand content, silt content, plastic limit and liquid limit. Originality/value: There is no such expression available in the literature based on soft computing techniques to calculate the friction angle of the clay.
    Źródło:
    Archives of Materials Science and Engineering; 2020, 104, 2; 58--68
    1897-2764
    Pojawia się w:
    Archives of Materials Science and Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    How machine learning algorithms are used in meteorological data classification: a comparative approach between DT, LMT, M5-MT, gradient boosting and GWLM-NARX models
    Autorzy:
    Fayaz, Sheikh Amir
    Zaman, Majid
    Butt, Muheet Ahmed
    Kaul, Sameer
    Powiązania:
    https://bibliotekanauki.pl/articles/38433812.pdf
    Data publikacji:
    2022
    Wydawca:
    Polskie Towarzystwo Promocji Wiedzy
    Tematy:
    meteorological data
    M5 model tree
    linear model functions
    gradient boosting
    logistic model tree
    Opis:
    Rainfall prediction is one of the most challenging task faced by researchers over the years. Many machine learning and AI based algorithms have been implemented on different datasets for better prediction purposes, but there is not a single solution which perfectly predicts the rainfall. Accurate prediction still remains a question to researchers. We offer a machine learning-based comparison evaluation of rainfall models for Kashmir province. Both local geographic features and the time horizon has influence on weather forecasting. Decision trees, Logistic Model Trees (LMT), and M5 model trees are examples of predictive models based on algorithms. GWLM-NARX, Gradient Boosting, and other techniques were investigated. Weather predictors measured from three major meteorological stations in the Kashmir area of the UT of J&K, India, were utilized in the models. We compared the proposed models based on their accuracy, kappa, interpretability, and other statistics, as well as the significance of the predictors utilized. On the original dataset, the DT model delivers an accuracy of 80.12 percent, followed by the LMT and Gradient boosting models, which produce accuracy of 87.23 percent and 87.51 percent, respectively. Furthermore, when continuous data was used in the M5-MT and GWLM-NARX models, the NARX model performed better, with mean squared error (MSE) and regression value (R) predictions of 3.12 percent and 0.9899 percent in training, 0.144 percent and 0.9936 percent in validation, and 0.311 percent and 0.9988 percent in testing.
    Źródło:
    Applied Computer Science; 2022, 18, 4; 16-27
    1895-3735
    2353-6977
    Pojawia się w:
    Applied Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Prediction of bearing capacity of H shaped skirted footings on sand using soft computing techniques
    Autorzy:
    Gnananandarao, -
    Khatri, V. N.
    Dutta, R. K.
    Powiązania:
    https://bibliotekanauki.pl/articles/1818514.pdf
    Data publikacji:
    2020
    Wydawca:
    Stowarzyszenie Komputerowej Nauki o Materiałach i Inżynierii Powierzchni w Gliwicach
    Tematy:
    bearing capacity ratio
    sand
    artificial neural networks
    M5P model tree
    regular plan shaped skirted footings
    H plan shaped skirted footings
    współczynnik nośności
    piasek
    sztuczne sieci neuronowe
    drzewo modelu M5P
    Opis:
    Purpose: The present study aims to apply soft computing techniques, Artificial Neural Network (ANN) and M5P model tree, to predict the ultimate bearing capacity of the H plan shaped skirted footing on the sand Design/methodology/approach: A total of 162 laboratory test data for the regular plan shaped (square, circular, rectangular, and strip (up to L/B = 2.5) skirted footing were collected from the literature to develop the soft computing-based models. These models were later modified for the H Plan shaped skirted footing with the introduction of the multiplication factor. The input variables chosen for the regular plan shaped footings were skirt depth to width of the footing ratio (Ds/B), friction angle of the sand (o), the ratio of the interface friction angle-to-friction angle of sand (5/o), and length-to-width (L/B) ratio of the footing. The output is the bearing capacity ratio (BCR, a ratio of the bearing capacity of the skirted footing to the bearing capacity of un-skirted footing). Findings: Sensitivity analysis was carried out to see the impact of the individual variable on the BCR). The sensitivity results reveal that the skirt depth to width of the footing ratio is the primary variable affecting the BCR. Finally, the performance of the developed soft computing models was assessed using six statistical parameters. The results from the statistical parameters reveal that model developed using ANN was performing superior to the one prepared using M5P model tree technique for the prediction of the ultimate bearing capacity of H plan shaped skirted footing on sand. Research limitations/implications: The model equations are developed with experimental laboratory data. Hence, these equations need further improvement by using field data. However, until now there no field data have been available to include in the present data set. Practical implications: These proposed model equations can be used to predict the bearing capacity of the H-shaped footing with the help of Ds/B, o, S/o and L/B without performing the laboratory experiments. Originality/value: There is no such model equation that was developed so far for the H-shaped skirted footings. Hence, an attempt was made in this article to predict the bearing capacity of the H-shaped footing by using available experimental data with the help of soft computing techniques.
    Źródło:
    Archives of Materials Science and Engineering; 2020, 103, 2; 62--74
    1897-2764
    Pojawia się w:
    Archives of Materials Science and Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Prediction of flexural strength of FRC pavements by soft computing techniques
    Autorzy:
    Kimteta, A.
    Thakur, M.S.
    Sihag, P.
    Upadhya, A.
    Sharma, N.
    Powiązania:
    https://bibliotekanauki.pl/articles/24200582.pdf
    Data publikacji:
    2022
    Wydawca:
    Stowarzyszenie Komputerowej Nauki o Materiałach i Inżynierii Powierzchni w Gliwicach
    Tematy:
    flexural strength
    fibre reinforced concrete
    artificial neural network
    random forest
    random tree
    M5P based model
    wytrzymałość na zginanie
    beton zbrojony włóknami
    sztuczna sieć neuronowa
    las losowy
    drzewo losowe
    model oparty na M5P
    Opis:
    Purpose: The mechanical characteristics of concrete used in rigid pavements can be improved by using fibre-reinforced concrete. The purpose of the study was to predict the flexural strength of the fibre-reinforced concrete for ten input variables i.e., cement, fine aggregate, coarse aggregate, water, superplasticizer/high range water reducer, glass fibre, polypropylene fibre, steel fibres, length and diameter of fibre and further to perform the sensitivity analysis to determine the most sensitive input variable which affects the flexural strength of the said fibre-reinforced concrete. Design/methodology/approach: The data used in the study was acquired from the published literature to create the soft computing modes. Four soft computing techniques i.e., Artificial neural networks (ANN), Random forests (RF), Random trees RT), and M5P, were applied to predict the flexural strength of fibre-reinforced concrete for rigid pavement using ten significant input variables as stated in the ‘purpose’. The most performing algorithm was determined after evaluating the applied models on the threshold of five statistical indices, i.e., the coefficient of correlation, mean absolute error, root mean square error, relative absolute error, and root relative squared error. The sensitivity analysis for most sensitive input variable was performed with out-performing model, i.e., ANN. Findings: The testing stage findings show that the Artificial neural networks model outperformed other applicable models, having the highest coefficient of correlation (0.9408), the lowest mean absolute error (0.8292), and the lowest root mean squared error (1.1285). Furthermore, the sensitivity analysis was performed using the artificial neural networks model. The results demonstrate that polypropylene fibre-reinforced concrete significantly influences the prediction of the flexural strength of fibre-reinforced concrete. Research limitations/implications: Large datasets may enhance machine learning technique performance. Originality/value: The article's novelty is that the most suitable model amongst the four applied techniques has been identified, which gives far better accuracy in predicting flexural strength.
    Źródło:
    Archives of Materials Science and Engineering; 2022, 117, 1; 13--24
    1897-2764
    Pojawia się w:
    Archives of Materials Science and Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-4 z 4

      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