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    Wyświetlanie 1-6 z 6
    Tytuł:
    Performance analysis of data fusion methods applied to epileptic seizure recognition
    Autorzy:
    Ludwig, Simone A.
    Powiązania:
    https://bibliotekanauki.pl/articles/2147119.pdf
    Data publikacji:
    2022
    Wydawca:
    Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
    Tematy:
    epilepsy
    ensemble method
    Choquet fuzzy integral fusion
    Opis:
    Epilepsy is a chronic neurological disorder that is caused by unprovoked recurrent seizures. The most commonly used tool for the diagnosis of epilepsy is the electroencephalogram (EEG) whereby the electrical activity of the brain is measured. In order to prevent potential risks, the patients have to be monitored as to detect an epileptic episode early on and to provide prevention measures. Many different research studies have used a combination of time and frequency features for the automatic recognition of epileptic seizures. In this paper, two fusion methods are compared. The first is based on an ensemble method and the second uses the Choquet fuzzy integral method. In particular, three different machine learning approaches namely RNN, ML and DNN are used as inputs for the ensemble method and the Choquet fuzzy integral fusion method. Evaluation measures such as confusion matrix, AUC and accuracy are compared as well as MSE and RMSE are provided. The results show that the Choquet fuzzy integral fusion method outperforms the ensemble method as well as other state-of-the-art classification methods.
    Źródło:
    Journal of Artificial Intelligence and Soft Computing Research; 2022, 12, 1; 5--17
    2083-2567
    2449-6499
    Pojawia się w:
    Journal of Artificial Intelligence and Soft Computing Research
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    An advanced ensemble modeling approach for predicting carbonate reservoir porosity from seismic attributes
    Autorzy:
    Topór, Tomasz
    Sowiżdżał, Krzysztof
    Powiązania:
    https://bibliotekanauki.pl/articles/27310145.pdf
    Data publikacji:
    2023
    Wydawca:
    Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
    Tematy:
    machine learning
    model stacking
    ensemble method
    carbonates
    seismic attributes
    porosity prediction
    Opis:
    This study uses a machine learning (ML) ensemble modeling approach to predict porosity from multiple seismic attributes in one of the most promising Main Dolomite hydrocarbon reservoirs in NW Poland. The presented workflow tests five different model types of varying complexity: K-nearest neighbors (KNN), random forests (RF), extreme gradient boosting (XGB), support vector machine (SVM), single layer neural network with multilayer perceptron (MLP). The selected models are additionally run with different configurations originating from the pre-processing stage, including Yeo–Johnson transformation (YJ) and principal component analysis (PCA). The race ANOVA method across resample data is used to tune the best hyperparameters for each model. The model candidates and the role of different pre-processors are evaluated based on standard ML metrics – coefficient of determination (R2), root mean squared error (RMSE), and mean absolute error (MAE). The model stacking is performed on five model candidates: two KNN, two XGB, and one SVM PCA with a marginal role. The results of the ensemble model showed superior accuracy over single learners, with all metrics (R2 0.890, RMSE 0.0252, MAE 0.168). It also turned out to be almost three times better than the neural net (NN) results obtained from commercial software on the same testing set (R2 0.318, RMSE 0.0628, MAE 0.0487). The spatial distribution of porosity from the ensemble model indicated areas of good reservoir properties that overlap with hydrocarbon production fields. This observation completes the evaluation of the ensemble technique results from model metrics. Overall, the proposed solution is a promising tool for better porosity prediction and understanding of heterogeneous carbonate reservoirs from multiple seismic attributes.
    Źródło:
    Geology, Geophysics and Environment; 2023, 49, 3; 245--260
    2299-8004
    2353-0790
    Pojawia się w:
    Geology, Geophysics and Environment
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Assessment measures of an ensemble classifier based on the distributivity equation to predict the presence of severe coronary artery disease
    Autorzy:
    Rak, Ewa
    Szczur, Adam
    Bazan, Jan G.
    Bazan-Socha, Stanisława
    Powiązania:
    https://bibliotekanauki.pl/articles/24200688.pdf
    Data publikacji:
    2023
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    ensemble method
    distributivity equation
    aggregation function
    CAD
    Holter ECG
    metoda zespołowa
    funkcja agregacji
    EKG Holtera
    Opis:
    The aim of this study is to apply and evaluate the usefulness of the hybrid classifier to predict the presence of serious coronary artery disease based on clinical data and 24-hour Holter ECG monitoring. Our approach relies on an ensemble classifier applying the distributivity equation aggregating base classifiers accordingly. Such a method may be helpful for physicians in the management of patients with coronary artery disease, in particular in the face of limited access to invasive diagnostic tests, i.e., coronary angiography, or in the case of contraindications to its performance. The paper includes results of experiments performed on medical data obtained from the Department of Internal Medicine, Jagiellonian University Medical College, Kraków, Poland. The data set contains clinical data, data from Holter ECG (24-hour ECG monitoring), and coronary angiography. A leave-one-out cross-validation technique is used for the performance evaluation of the classifiers on a data set using the WEKA (Waikato Environment for Knowledge Analysis) tool. We present the results of comparing our hybrid algorithm created from aggregation with the distributive equation of selected classification algorithms (multilayer perceptron network, support vector machine, k-nearest neighbors, naïve Bayes, and random forests) with themselves on raw data.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2023, 33, 3; 361--377
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Ensemble-based Method of Fraud Detection at Self-checkouts in Retail
    Autorzy:
    Vitynskyi, P.
    Tkachenko, R.
    Izonin, I.
    Powiązania:
    https://bibliotekanauki.pl/articles/410756.pdf
    Data publikacji:
    2019
    Wydawca:
    Polska Akademia Nauk. Oddział w Lublinie PAN
    Tematy:
    classification
    Ensemble-based method
    Random Forest
    fraud detection
    retail
    Ito decomposition
    imbalanced dataset
    Opis:
    The authors consider the problem of fraud detection at self-checkouts in retail in condition of unbalanced data set. A new ensemble-based method is proposed for its effective solution. The developed method involves two main steps: application of the preprocessing procedures and the Random Forest algorithm. The step-by-step implementation of the preprocessing stage involves the sequential execution of such procedures over the input data: scaling by maximal element in a column with row-wise scaling by Euclidean norm, weighting by correlation and applying polynomial extension. For polynomial extension Ito decomposition of the second degree is used. The simulation of the method was carried out on real data. Evaluating performance was based on the use of cost matrix. The experimental comparison of the effectiveness of the developed ensemble-based method with a number of existing (simples and ensembles) demonstrates the best performance of the developed method. Experimental studies of changing the parameters of the Random Forest both for the basic algorithm and for the developed method demonstrate a significant improvement of the investigated efficiency measures of the latter. It is the result of all steps of the preprocessing stage of the developed method use.
    Źródło:
    ECONTECHMOD : An International Quarterly Journal on Economics of Technology and Modelling Processes; 2019, 8, 2; 3-8
    2084-5715
    Pojawia się w:
    ECONTECHMOD : An International Quarterly Journal on Economics of Technology and Modelling Processes
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Comparison of the Accuracy of the Probabilistic Distance Clustering Method and Cluster Ensembles
    Porównanie dokładności metody odległości probabilistycznej i podejścia zagregowanego w taksonomii
    Autorzy:
    Rozmus, Dorota
    Powiązania:
    https://bibliotekanauki.pl/articles/657880.pdf
    Data publikacji:
    2016
    Wydawca:
    Uniwersytet Łódzki. Wydawnictwo Uniwersytetu Łódzkiego
    Tematy:
    grupowanie
    dokładność
    metoda odległości probabilistycznej
    podejście zagregowane w taksonomii
    clustering
    accuracy
    distance clustering method
    cluster ensemble
    Opis:
    Stosowanie metod taksonomicznych w jakimkolwiek zagadnieniu grupowania wymaga jednocześnie zapewnienia wysokiej dokładności wyników podziału. Ona bowiem warunkuje skuteczność wszelkich decyzji podjętych na podstawie uzyskanych rezultatów. Dlatego też w literaturze wciąż proponowane są nowe rozwiązania, których zadaniem jest poprawa dokładności grupowania w stosunku do tradycyjnie stosowanych metod (np. k-średnich, hierarchicznych). Przykładami mogą tu być metody polegające na zastosowaniu podejścia zagregowanego (Leisch 1999; Dudoit, Fridlyand 2003; Hornik 2006; Fred, Jain 2002), czy niedawno zaproponowana metoda odległości probabilistycznej (Ben-Israel, Iyigun 2008).Głównym celem artykułu jest porównanie dokładności omawianej metody z dokładnością podejścia zagregowanego w taksonomii.
    High accuracy of results is a very important aspect in any clustering problem t determines the effectiveness of decisions based on them. Therefore, literature proposes methods and solutions that aim to give more accurate and stable results than traditional clustering algorithms (e.g. k-means or hierarchical methods). Cluster ensembles (Leisch 1999; Dudoit, Fridlyand 2003; Hornik 2006; Fred, Jain 2002) or the distance clustering method (Ben-Israel, Iyigun 2008) are the examples of such solutions. Here, we carry out an experimental study to compare the accuracy of these two approaches.
    Źródło:
    Acta Universitatis Lodziensis. Folia Oeconomica; 2016, 3, 322
    0208-6018
    2353-7663
    Pojawia się w:
    Acta Universitatis Lodziensis. Folia Oeconomica
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Metody obliczania napięcia międzyfazowego z symulacji komputerowych
    Methods for calculation interfacial tension from computer simulations
    Autorzy:
    Chrzanowski, J.
    Lamperski, S.
    Powiązania:
    https://bibliotekanauki.pl/articles/172418.pdf
    Data publikacji:
    2014
    Wydawca:
    Polskie Towarzystwo Chemiczne
    Tematy:
    napięcie międzyfazowe
    symulacja komputerowa
    metoda Kirkwooda-Buffa
    metoda Bennetta
    metoda test-area
    układ rozszerzony
    funkcja fali kapilarnej
    metoda kwadratu gradientu
    interfacial tension
    computer simulation
    Kirkwood-Buff method
    Bennetts method
    test-area method
    expanded ensemble simulation
    capillary wave theory
    square gradient theory
    Opis:
    Interfaces have been recently a subject of profound interest for physicists, chemists and biologists because of the processes taking place in the interfacial region like adsorption, catalysis of chemical reactions etc. Computer simulations treat an interface in a full atomic level and by that they are a valuable complementary technique for experiment and theory. In this paper, different methods for the calculation of an interfacial tension by computer simulations are described and compared. The most commonly used method for the interfacial tension calculation was developed by Kirkwood-Buff. It is based on the mechanical route definition. This approach uses normal and tangential pressure components of the pressure tensor. The interfacial tension can be also evaluated through its thermodynamic definition. The method of Bennett defines the interfacial tension as the free energy difference of two (or more) systems relative to the difference in interfacial areas. The “test- -area” method is based upon the perturbation formalism. The test state is obtained from an infinitesimal change of the surface area of the reference system. The third method based on the thermodynamic route used to evaluate the interfacial tension is thought as an expanded ensemble simulation where two systems with different free energy and the interfacial area are connected by a discrete chain of intermediate subsystems. The next approach is based on the capillary wave theory formalism which provides a relationship between the surface tension and the wave width due the capillarity broadening. Interfacial tension may be also computed from the square gradient theory which is based on the expansion of the Hemholtz free energy in the Taylor series around the homogeneous state with the assumption that the molecular gradients in the interface are small compared to intermolecular distance. The theoretical basis, application and results of computer simulations of each method are presented. Aa accuracy of the methods in different simulation methodologies and systems is compared.
    Źródło:
    Wiadomości Chemiczne; 2014, 68, 3-4; 257-278
    0043-5104
    2300-0295
    Pojawia się w:
    Wiadomości Chemiczne
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-6 z 6

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