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    Wyświetlanie 1-13 z 13
    Tytuł:
    Humanoid robot path planning using rapidly explored random tree and motion primitives
    Autorzy:
    Szumowski, Maksymilian
    Zielinska, Teresa
    Powiązania:
    https://bibliotekanauki.pl/articles/1837364.pdf
    Data publikacji:
    2021
    Wydawca:
    Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
    Tematy:
    humanoid robot
    path planning
    rapidly exploring random tree
    Opis:
    Path planning is an essential function of the control sy‐ stem of any mobile robot. In this article the path planner for a humanoid robot is presented. The short description of an universal control framework and the Motion Ge‐ neration System is also presented. Described path plan‐ ner utilizes a limited number of motions called the Mo‐ tion Primitives. They are generated by Motion Generation System. Four different algorithms, namely the: Informed RRT, Informed RRT with random bias, and RRT with A* like heuristics were tested. For the last one the version with biased random function was also considered. All menti‐ oned algorithms were evaluated considering three diffe‐ rent scenarios. Obtained results are described and discus‐ sed.
    Źródło:
    Journal of Automation Mobile Robotics and Intelligent Systems; 2021, 15, 1; 24-30
    1897-8649
    2080-2145
    Pojawia się w:
    Journal of Automation Mobile Robotics and Intelligent Systems
    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
    Tytuł:
    Feature Selection and Multiple Model Approach in Discriminant Analysis
    Dobór zmiennych a podejście wielomodelowe w analizie dyskryminacyjnej
    Autorzy:
    Gatnar, Eugeniusz
    Powiązania:
    https://bibliotekanauki.pl/articles/906874.pdf
    Data publikacji:
    2007
    Wydawca:
    Uniwersytet Łódzki. Wydawnictwo Uniwersytetu Łódzkiego
    Tematy:
    tree-based models
    aggregation
    feature selection
    random subspaces
    Opis:
    Significant improvement of model stability and prediction accuracy in classification and regression can be obtained by using the multiple model approach. In classification multiple models are built on the basis of training subsets (selected from the training set) and combined into an ensemble or a committee. Then the component models (classification trees) determine the predicted class by voting. In this paper some problems of feature selection for ensembles will be discussed. We propose a new correlation-based feature selection method combined with the wrapper approach.
    Źródło:
    Acta Universitatis Lodziensis. Folia Oeconomica; 2007, 206
    0208-6018
    2353-7663
    Pojawia się w:
    Acta Universitatis Lodziensis. Folia Oeconomica
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Application of selected data mining techniques in unintentional accounting error detection
    Autorzy:
    Papík, Mário
    Papíková, Lenka
    Powiązania:
    https://bibliotekanauki.pl/articles/22444352.pdf
    Data publikacji:
    2021
    Wydawca:
    Instytut Badań Gospodarczych
    Tematy:
    financial fraud
    unintentional accounting errors
    financial restatements
    decision tree
    classification and regression tree
    random forest
    Opis:
    Research background: Even though unintentional accounting errors leading to financial restatements look like less serious distortion of publicly available information, it has been shown that financial restatements impacts on financial markets are similar to intentional fraudulent activities. Unintentional accounting errors leading to financial restatements then affect value of company shares in the short run which negatively impacts all shareholders. Purpose of the article: The aim of this manuscript is to predict unintentional accounting errors leading to financial restatements based on information from financial statements of companies. The manuscript analysis if financial statements include sufficient information which would allow detection of unintentional accounting errors. Methods: Method of classification and regression trees (decision tree) and random forest have been used in this manuscript to fulfill the aim of this manuscript. Data sample has consisted of 400 items from financial statements of 80 selected international companies. The results of developed prediction models have been compared and explained based on their accuracy, sensitivity, specificity, precision and F1 score. Statistical relationship among variables has been tested by correlation analysis. Differences between the group of companies with and without unintentional accounting error have been tested by means of Kruskal-Wallis test. Differences among the models have been tested by Levene and T-tests. Findings & value added: The results of the analysis have provided evidence that it is possible to detect unintentional accounting errors with high levels of accuracy based on financial ratios (rather than the Beneish variables) and by application of random forest method (rather than classification and regression tree method).
    Źródło:
    Equilibrium. Quarterly Journal of Economics and Economic Policy; 2021, 16, 1; 185-201
    1689-765X
    2353-3293
    Pojawia się w:
    Equilibrium. Quarterly Journal of Economics and Economic Policy
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    An assessment of machine learning and data balancing techniques for evaluating downgrade truck crash severity prediction in Wyoming
    Autorzy:
    Ampadu, Vincent-Michael Kwesi
    Haq, Muhammad Tahmidul
    Ksaibati, Khaled
    Powiązania:
    https://bibliotekanauki.pl/articles/2176018.pdf
    Data publikacji:
    2022
    Wydawca:
    Fundacja Centrum Badań Socjologicznych
    Tematy:
    crash severity
    performance
    extreme gradient boosting tree
    adaptive boosting tree
    random forest
    gradient boost decision tree
    adaptive synthetic algorithm
    Opis:
    This study involved the investigation of various machine learning methods, including four classification tree-based ML models, namely the Adaptive Boosting tree, Random Forest, Gradient Boost Decision Tree, Extreme Gradient Boosting tree, and three non-tree-based ML models, namely Support Vector Machines, Multi-layer Perceptron and k-Nearest Neighbors for predicting the level of severity of large truck crashes on Wyoming road networks. The accuracy of these seven methods was then compared. The Final ROC AUC score for the optimized random forest model is 95.296 %. The next highest performing model was the k-NN with 92.780 %, M.L.P. with 87.817 %, XGBoost with 86.542 %, Gradboost with 74.824 %, SVM with 72.648 % and AdaBoost with 67.232 %. Based on the analysis, the top 10 predictors of severity were obtained from the feature importance plot. These may be classified into whether safety equipment was used, whether airbags were deployed, the gender of the driver and whether alcohol was involved.
    Źródło:
    Journal of Sustainable Development of Transport and Logistics; 2022, 7, 2; 6--24
    2520-2979
    Pojawia się w:
    Journal of Sustainable Development of Transport and Logistics
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Comparison of tree-based methods used in survival data
    Autorzy:
    Yabaci, Aysegul
    Sigirli, Deniz
    Powiązania:
    https://bibliotekanauki.pl/articles/2034119.pdf
    Data publikacji:
    2022-03-15
    Wydawca:
    Główny Urząd Statystyczny
    Tematy:
    tree-based methods
    conditional inference trees
    conditional inference forests
    random survival forests
    Opis:
    Survival trees and forests are popular non-parametric alternatives to parametric and semiparametric survival models. Conditional inference trees (Ctree) form a non-parametric class of regression trees embedding tree-structured regression models into a well-defined theory of conditional inference procedures. The Ctree is applicable in a varietyof regression-related issues, involving nominal, ordinal, numeric, censored, as well as multivariate response variables and arbitrary measurement scales of covariates. Conditional inference forests (Cforest) consitute a survival forest method which combines a large number of Ctrees. The Cforest provides a unified and flexible framework for ensemble learning in the presence of censoring. The random survival forests (RSF) methodology extends the random forests method enabling the approximation of rich classes of functions while maintaining generalisation errors low. In the present study, the Ctree, Cforest and RSF methods are discussed in detail and the performances of the survival forest methods, namely the Cforest and RSF have been compared with a simulation study. The results of the simulation demonstrate that the RSF method with a log-rank score distinction criteria outperforms the Cforest and the RSF with log-rank distinction criteria.
    Źródło:
    Statistics in Transition new series; 2022, 23, 1; 21-38
    1234-7655
    Pojawia się w:
    Statistics in Transition new series
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Maximising accuracy and efficiency of traffic accident prediction combining information mining with computational intelligence approaches and decision trees
    Autorzy:
    Tambouratzis, T>
    Souliou, D.
    Chalikias, M.
    Gregoriades, A.
    Powiązania:
    https://bibliotekanauki.pl/articles/91652.pdf
    Data publikacji:
    2014
    Wydawca:
    Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
    Tematy:
    traffic accident
    location
    prediction
    probabilistic neural networks
    random forest
    accuracy
    efficiency
    decision tree
    Opis:
    The development of universal methodologies for the accurate, efficient, and timely prediction of traffic accident location and severity constitutes a crucial endeavour. In this piece of research, the best combinations of salient accident-related parameters and accurate accident severity prediction models are determined for the 2005 accident dataset brought together by the Republic of Cyprus Police. The optimal methodology involves: (a) information mining in the form of feature selection of the accident parameters that maximise prediction accuracy (implemented via scatter search), followed by feature extraction (implemented via principal component analysis) and selection of the minimal number of components that contain the salient information of the original parameters, which combined bring about an overall 74.42% reduction in the dataset dimensionality; (b) accident severity prediction via probabilistic neural networks and random forests, both of which independently accomplish over 96% correct prediction and a balanced proportion of under- and over-estimations of accident severity. An explanation of the superiority of the optimal combinations of parameters and models is given, as is a comparison with existing accident classification/prediction approaches.
    Źródło:
    Journal of Artificial Intelligence and Soft Computing Research; 2014, 4, 1; 31-42
    2083-2567
    2449-6499
    Pojawia się w:
    Journal of Artificial Intelligence and Soft Computing Research
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    A contribution to the systematics of the genus Tilia with respect to some hybrids by RAPD analysis
    Autorzy:
    Liesebach, H
    Sinko, Z.
    Powiązania:
    https://bibliotekanauki.pl/articles/41216.pdf
    Data publikacji:
    2008
    Wydawca:
    Polska Akademia Nauk. Instytut Dendrologii PAN
    Tematy:
    Tilia
    linden
    taxonomy
    hybrid
    DNA
    UPGMA method
    systematics
    random amplified polymorphic DNA
    Szent Istvan tree
    K3 tree
    cluster analysis
    Opis:
    The putative hybrid character of two Tilia varieties selected as avenue trees (‘Szent Istvan’ and‘K3’) should be clarified for further breeding activities. Their ancestor species should be identified by a genetic comparison with reference material (Tilia cordata, T. platyphyllos, T. dasystyla, T. × euchlora, T. × europaea and others). RAPD marker data were evaluated by UPGMA cluster and neighbour-joining analysis. The genetic comparison of the two selected clones with the collection of reference material offers insights into some systematic relationships within the genus Tilia. It was supplemented by a critical discussion of methods of RAPD data evaluation for taxonomic purposes.
    Źródło:
    Dendrobiology; 2008, 59; 13-22
    1641-1307
    Pojawia się w:
    Dendrobiology
    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
    Tytuł:
    Dimensionality Reduction for Probabilistic Neural Network in Medical Data Classification Problems
    Autorzy:
    Kusy, M.
    Powiązania:
    https://bibliotekanauki.pl/articles/226697.pdf
    Data publikacji:
    2015
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    probabilistic neural network
    dimensionality reduction
    feature selection
    feature extraction
    single decision tree
    random forest
    principal component analysis
    prediction ability
    Opis:
    This article presents the study regarding the problem of dimensionality reduction in training data sets used for classification tasks performed by the probabilistic neural network (PNN). Two methods for this purpose are proposed. The first solution is based on the feature selection approach where a single decision tree and a random forest algorithm are adopted to select data features. The second solution relies on applying the feature extraction procedure which utilizes the principal component analysis algorithm. Depending on the form of the smoothing parameter, different types of PNN models are explored. The prediction ability of PNNs trained on original and reduced data sets is determined with the use of a 10-fold cross validation procedure.
    Źródło:
    International Journal of Electronics and Telecommunications; 2015, 61, 3; 289-300
    2300-1933
    Pojawia się w:
    International Journal of Electronics and Telecommunications
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Imitation learning of car driving skills with decision trees and random forests
    Autorzy:
    Cichosz, P.
    Pawełczak, Ł.
    Powiązania:
    https://bibliotekanauki.pl/articles/329901.pdf
    Data publikacji:
    2014
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    imitation learning
    behavioral cloning
    model ensemble
    random forest
    control
    autonomous driving
    car racing
    decision tree
    drzewo decyzyjne
    lasy losowe
    sterowanie
    wyścigi samochodowe
    Opis:
    Machine learning is an appealing and useful approach to creating vehicle control algorithms, both for simulated and real vehicles. One common learning scenario that is often possible to apply is learning by imitation, in which the behavior of an exemplary driver provides training instances for a supervised learning algorithm. This article follows this approach in the domain of simulated car racing, using the TORCS simulator. In contrast to most prior work on imitation learning, a symbolic decision tree knowledge representation is adopted, which combines potentially high accuracy with human readability, an advantage that can be important in many applications. Decision trees are demonstrated to be capable of representing high quality control models, reaching the performance level of sophisticated pre-designed algorithms. This is achieved by enhancing the basic imitation learning scenario to include active retraining, automatically triggered on control failures. It is also demonstrated how better stability and generalization can be achieved by sacrificing human-readability and using decision tree model ensembles. The methodology for learning control models contributed by this article can be hopefully applied to solve real-world control tasks, as well as to develop video game bots.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2014, 24, 3; 579-597
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Nadmierne dopasowanie w drzewach decyzyjnych
    Excessive fit in decision trees
    Autorzy:
    Smaga, S.
    Powiązania:
    https://bibliotekanauki.pl/articles/91343.pdf
    Data publikacji:
    2011
    Wydawca:
    Warszawska Wyższa Szkoła Informatyki
    Tematy:
    drzewo decyzyjne
    kryterium doboru testu
    zbiór treningowy
    kryterium losowe
    kryterium entropijne
    decision tree
    criteria for the test selection
    trining collection
    random criterion
    entropy criterion
    Opis:
    W pracy staramy się sprawdzić wpływ jaki ma dobór kryterium wyboru testu na nadmierne dopasowanie w drzewach decyzyjnych. Uważamy, że losowe kryterium doboru może okazać się nie gorsze od kryterium entropijnego. Nasze przypuszczenia potwierdzają wstępne badania wykonane dla trzech (niewielkich rozmiarów) zbiorach trenujących, co w naszej opinii zasługuje na dalsze eksperymenty.
    In this paper we try to check the influence of selection criteria for the test selection for excessive fit in decision trees. We believe that a random criteria selection may not be worse than the criteria of entropy. Our supposition is confirmed by preliminary tests performed for three training sets, which in our opinion deserves further experiments.
    Źródło:
    Zeszyty Naukowe Warszawskiej Wyższej Szkoły Informatyki; 2011, 5, 5; 75-78
    1896-396X
    2082-8349
    Pojawia się w:
    Zeszyty Naukowe Warszawskiej Wyższej Szkoły Informatyki
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    A Small Wind Turbine Output Model for Spatially Constrained Remote Island Micro-Grids
    Autorzy:
    Žigman, D.
    Meštrović, K.
    Tomiša, T.
    Powiązania:
    https://bibliotekanauki.pl/articles/2172468.pdf
    Data publikacji:
    2022
    Wydawca:
    Uniwersytet Morski w Gdyni. Wydział Nawigacyjny
    Tematy:
    wind turbine
    small wind turbine
    decision tree model
    artificial neural network model
    random forest model
    micro-grids
    spatially constrained remote Island micro-grids
    remote Island micro-grid
    Opis:
    Modelling operation of the power supply system for remote island communities is essential for its operation, as well as a survival of a modern society settled in challenging conditions. Micro-grid emerges as a proper solution for a sustainable development of a spatially constrained remote island community, while at the same time reflecting the power requirements of similar maritime subjects, such as large vessels and fleets. Here we present research results in predictive modelling the output of a small wind turbine, as a component of a remote island micro-grid. Based on a month-long experimental data and the machine learning-based predictive model development approach, three candidate models of a small wind turbine output were developed, and assessed on their performance based on an independent set of experimental data. The Random Forest Model out performed competitors (Decision Tree Model and Artificial Neural Network Model), emerging as a candidate methodology for the all-year predictive model development, as a later component of the over-all remote island micro-grid model.
    Źródło:
    TransNav : International Journal on Marine Navigation and Safety of Sea Transportation; 2022, 16, 1; 143--146
    2083-6473
    2083-6481
    Pojawia się w:
    TransNav : International Journal on Marine Navigation and Safety of Sea Transportation
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-13 z 13

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