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    Wyświetlanie 1-21 z 21
    Tytuł:
    About the density of spectral measure of the two-dimensional SaS random vector
    Autorzy:
    Borowiecka-Olszewska, Marta
    Misiewicz, Jolanta
    Powiązania:
    https://bibliotekanauki.pl/articles/729784.pdf
    Data publikacji:
    2003
    Wydawca:
    Uniwersytet Zielonogórski. Wydział Matematyki, Informatyki i Ekonometrii
    Tematy:
    stable
    sub-stable
    maximal stable random vector
    spectral measure
    Opis:
    In this paper, we consider a symmetric α-stable p-sub-stable two-dimensional random vector. Our purpose is to show when the function $exp{-(|a|p + |b|p)^{α/p}}$ is a characteristic function of such a vector for some p and α. The solution of this problem we can find in [3], in the language of isometric embeddings of Banach spaces. Our proof is based on simple properties of stable distributions and some characterization given in [4].
    Źródło:
    Discussiones Mathematicae Probability and Statistics; 2003, 23, 1; 77-81
    1509-9423
    Pojawia się w:
    Discussiones Mathematicae Probability and Statistics
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Band copulas as spectral measures for two-dimensional stable random vectors
    Autorzy:
    Bojarski, Jacek
    K. Misiewicz, Jolanta
    Powiązania:
    https://bibliotekanauki.pl/articles/729780.pdf
    Data publikacji:
    2003
    Wydawca:
    Uniwersytet Zielonogórski. Wydział Matematyki, Informatyki i Ekonometrii
    Tematy:
    Symmetric stable random vector
    spectral measure
    canonical spectral measure
    copula
    James corelation for random variables
    Opis:
    In this paper, we study basic properties of symmetric stable random vectors for which the spectral measure is a copula, i.e., a distribution having uniformly distributed marginals.
    Źródło:
    Discussiones Mathematicae Probability and Statistics; 2003, 23, 1; 69-75
    1509-9423
    Pojawia się w:
    Discussiones Mathematicae Probability and Statistics
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Współczynnik ekscesu wektora losowego
    Excess Kurtosis of a Random Vector
    Autorzy:
    Budny, Katarzyna
    Powiązania:
    https://bibliotekanauki.pl/articles/587386.pdf
    Data publikacji:
    2014
    Wydawca:
    Uniwersytet Ekonomiczny w Katowicach
    Tematy:
    Analiza wielowymiarowa
    Estymacja
    Kurtoza
    Wektor losowy
    Estimation
    Kurtosis
    Multi-dimensional analysis
    Random vector
    Opis:
    Excess kurtosis of a univariate random variable is defined as its kurtosis minus 3, i.e. the kurtosis of a normal distribution. Excess kurtosis is a one of a dispersion measures. This parameter provides the information about peakedness and tail weight of a distribution compared to normal distribution. In the paper we propose a generalization of this characteristic for random vectors and analyze its basic properties. Moreover, we introduce the form of excess kurtosis for the selected multivariate distribution.
    Źródło:
    Studia Ekonomiczne; 2014, 203; 28-38
    2083-8611
    Pojawia się w:
    Studia Ekonomiczne
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Estymacja uogólnionej wariancji wybranych rozkładów wielowymiarowych
    Estimation of Generalized Variance of Chosen Multivariate Distribution
    Autorzy:
    Witaszczyk, Anna
    Powiązania:
    https://bibliotekanauki.pl/articles/1827196.pdf
    Data publikacji:
    2009-06-30
    Wydawca:
    Główny Urząd Statystyczny
    Tematy:
    uogólniona wariancja
    wektor losowy
    macierz kowariancji
    wyznacznik losowy
    generalized variance
    random vector
    covariance matrix
    random determinant
    Opis:
    Uogólniona wariancja, czyli wyznacznik macierzy kowariancji jest skalarną miarą rozrzutu rozkładów wielowymiarowych. Dokładny rozkład uogólnionej wariancji znany jest tylko dla wektorów losowych o wielowymiarowym rozkładzie normalnym. Dla wektorów losowych o dużych wymiarach przyjmuje on skomplikowaną postać, co stanowi utrudnienie w zastosowaniach praktycznych. W pracy przedstawiono tzw. G-estymator logarytmu uogólnionej wariancji otrzymany na podstawie twierdzeń granicznych dla wyznaczników losowych i jego własności na przykładach symulacyjnych dla kilku wybranych rozkładów wielowymiarowych.
    Generalized variance i.e. the determinant of the covariance matrix is a scalar measure of multivariate distribution dispersion. The exact distribution of the generalized variance is known only for multivariate normal vectors. For random vectors in high dimensional spaces it has a complicated formula very troublesome to apply. An estimator of the logarithm of generalized variance derived with the help of limit theorems for random determinants was presented as well as its properties in examples of chosen simulation multivariate distributions.
    Źródło:
    Przegląd Statystyczny; 2009, 56, 2; 135-153
    0033-2372
    Pojawia się w:
    Przegląd Statystyczny
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Continuous solutions of iterative equations of infinite order
    Autorzy:
    Kapica, R.
    Morawiec, J.
    Powiązania:
    https://bibliotekanauki.pl/articles/255177.pdf
    Data publikacji:
    2009
    Wydawca:
    Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
    Tematy:
    random-valued vector functions
    sequences of iterates
    iterative equations
    continuous solutions
    Opis:
    Given a probability space (Ω, A, P) and a complete separable metric space X, we consider R continuous and bounded solutions φ: X → R of the equations φ (x) = ∫Ω φ(ƒ(x, ω)) P(dω) and φ(x) = 1 - ∫Ωφ(ƒ(x, ω)) P(dω), assuming that the given function ƒ : X x Ω → X is controlled by a random variable L: Ω → (0, ∞) with -∞ < ∫Ω log L (ω) P (dω) < 0. An application to a refinement type equation is also presented.
    Źródło:
    Opuscula Mathematica; 2009, 29, 2; 147-155
    1232-9274
    2300-6919
    Pojawia się w:
    Opuscula Mathematica
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Method of optimal linear extrapolation of vector random sequences with full consideration of correlation connections for each component
    Metod optimal'noy linejjnojj ehstrapoljacija vektornykh sluchajjnykh posledovatel'nostejj s polnym uchetom korreljacionnykh svjazejj dlja kazhdogo komponenta
    Autorzy:
    Atamanjuk, I.
    Kondratenko, Y.
    Powiązania:
    https://bibliotekanauki.pl/articles/76650.pdf
    Data publikacji:
    2015
    Wydawca:
    Komisja Motoryzacji i Energetyki Rolnictwa
    Tematy:
    extrapolation
    vector random sequence
    algorithm
    block diagram
    canonical decomposition
    Źródło:
    Motrol. Motoryzacja i Energetyka Rolnictwa; 2015, 17, 2
    1730-8658
    Pojawia się w:
    Motrol. Motoryzacja i Energetyka Rolnictwa
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Charakterystyki wielowymiarowych wielkości finansowych oparte na definicji potęgi wektora
    Characteristics of Multivariate Financial Items Based on Definition of the Power of a Vector
    Autorzy:
    Budny, Katarzyna
    Tatar, Jan
    Powiązania:
    https://bibliotekanauki.pl/articles/591516.pdf
    Data publikacji:
    2014
    Wydawca:
    Uniwersytet Ekonomiczny w Katowicach
    Tematy:
    Analiza matematyczna
    Analiza wielowymiarowa
    Fundusze inwestycyjne
    Portfel inwestycyjny
    Wektor losowy
    Investment funds
    Investment portfolio
    Mathematical analysis
    Multi-dimensional analysis
    Random vector
    Opis:
    In the previous articles the authors proposed - different from the well-known in the probabilistic literature - definitions of such characteristics of multivariate probability distributions as the expected value, variance, standard deviation, skewness coefficient, kurtosis and excess kurtosis. The basis of these definitions is the concept of the power of the vector in an inner product space proposed by J. Tatar, among others things, in Tatar (1996b). In this paper, the formal forms of those which are mentioned above are used to describe some random vectors occurring in a typical financial market. In this case these.
    Źródło:
    Studia Ekonomiczne; 2014, 189; 27-39
    2083-8611
    Pojawia się w:
    Studia Ekonomiczne
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Acoustic intensity vector generated by vibrating set of small areas with random amplitudes
    Wektor natężenia akustycznego generowany przez układ małych płaskich elementów drgających z losowymi amplitudami
    Autorzy:
    Kozień, M. S.
    Powiązania:
    https://bibliotekanauki.pl/articles/281747.pdf
    Data publikacji:
    2009
    Wydawca:
    Polskie Towarzystwo Mechaniki Teoretycznej i Stosowanej
    Tematy:
    acoustic radiation
    random vibrations
    sound intensity vector
    structural noise
    Opis:
    The paper presents a generalisation of the hybrid method of estimation of sound radiated by vibrating surfaces, formulated previously for the deterministic case of random vibrations. The analysis is made for random amplitudes of vibrations in a narrow frequency band. The results show complexity of the analysis in comparison with the deterministic case. Therefore, the method does not seem to be efficient, like the deterministic one, in engineering applications.
    W artykule omówiono rozszerzenie metody hybrydowej oszacowania dźwięku promieniowanego przez drgające powierzchnie, sformułowanej pierwotnie dla przypadku drgań deterministycznych, na przypadek drgań losowych. Rozważono przypadek drgań z losowo zmienną amplitudą w wąskim paśmie częstotliwości. Rezultaty analiz pokazują złożoność uzyskanych formuł w stosunku do zagadnień deterministycznych. Dlatego też wydaje się, że metoda ta w prezentowanym podejściu nie jest tak użyteczna w zastosowaniach inżynierskich, jak to ma miejsce w sformułowaniu deterministycznym.
    Źródło:
    Journal of Theoretical and Applied Mechanics; 2009, 47, 2; 411-420
    1429-2955
    Pojawia się w:
    Journal of Theoretical and Applied Mechanics
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Artificial Intelligence Based Flood Forecasting for River Hunza at Danyor Station in Pakistan
    Autorzy:
    Yaseen, Muhammad Waseem
    Awais, Muhammad
    Riaz, Khuram
    Rasheed, Muhammad Babar
    Waqar, Muhammad
    Rasheed, Sajid
    Powiązania:
    https://bibliotekanauki.pl/articles/31340346.pdf
    Data publikacji:
    2022
    Wydawca:
    Polska Akademia Nauk. Instytut Budownictwa Wodnego PAN
    Tematy:
    hydrometeorology
    random forest
    support vector
    multilayer perceptron
    machine learning
    flood forecasting
    Opis:
    Floods can cause significant problems for humans and can damage the economy. Implementing a reliable flood monitoring warning system in risk areas can help to reduce the negative impacts of these natural disasters. Artificial intelligence algorithms and statistical approaches are employed by researchers to enhance flood forecasting. In this study, a dataset was created using unique features measured by sensors along the Hunza River in Pakistan over the past 31 years. The dataset was used for classification and regression problems. Two types of machine learning algorithms were tested for classification: classical algorithms (Random Forest, RF and Support Vector Classifier, SVC) and deep learning algorithms (Multi-Layer Perceptron, MLP). For the regression problem, the result of MLP and Support Vector Regression (SVR) algorithms were compared based on their mean square, root mean square and mean absolute errors. The results obtained show that the accuracy of the RF classifier is 0.99, while the accuracies of the SVC and MLP methods are 0.98; moreover, in the case of flood prediction, the SVR algorithm outperforms the MLP approach.
    Źródło:
    Archives of Hydro-Engineering and Environmental Mechanics; 2022, 69, 1; 59-77
    1231-3726
    Pojawia się w:
    Archives of Hydro-Engineering and Environmental Mechanics
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Mining Data of Noisy Signal Patterns in Recognition of Gasoline Bio-Based Additives using Electronic Nose
    Autorzy:
    Osowski, S.
    Siwek, K.
    Powiązania:
    https://bibliotekanauki.pl/articles/220792.pdf
    Data publikacji:
    2017
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    data mining
    electronic nose
    gasoline blends
    random forest
    support vector machine
    wavelet denoising
    Opis:
    The paper analyses the distorted data of an electronic nose in recognizing the gasoline bio-based additives. Different tools of data mining, such as the methods of data clustering, principal component analysis, wavelet transformation, support vector machine and random forest of decision trees are applied. A special stress is put on the robustness of signal processing systems to the noise distorting the registered sensor signals. A special denoising procedure based on application of discrete wavelet transformation has been proposed. This procedure enables to reduce the error rate of recognition in a significant way. The numerical results of experiments devoted to the recognition of different blends of gasoline have shown the superiority of support vector machine in a noisy environment of measurement.
    Źródło:
    Metrology and Measurement Systems; 2017, 24, 1; 27-44
    0860-8229
    Pojawia się w:
    Metrology and Measurement Systems
    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:
    Impact of the COVID-19 pandemic on the expression of emotions in social media
    Autorzy:
    Ghosh, Debabrata
    Powiązania:
    https://bibliotekanauki.pl/articles/2027766.pdf
    Data publikacji:
    2020
    Wydawca:
    Uniwersytet Ekonomiczny w Katowicach
    Tematy:
    Classification
    COVID-19
    Emotion
    Emotion analysis
    Naïve Bayes
    Pandemic
    Random Forest
    Support Vector Machine
    Opis:
    In the age of social media, every second thousands of messages are exchanged. Analyzing those unstructured data to find out specific emotions is a challenging task. Analysis of emotions involves evaluation and classification of text into emotion classes such as Happy, Sad, Anger, Disgust, Fear, Surprise, as defined by emotion dimensional models which are described in the theory of psychology (www 1; Russell, 2005). The main goal of this paper is to cover the COVID-19 pandemic situation in India and its impact on human emotions. As people very often express their state of the mind through social media, analyzing and tracking their emotions can be very effective for government and local authorities to take required measures. We have analyzed different machine learning classification models, such as Naïve Bayes, Support Vector Machine, Random Forest Classifier, Decision Tree and Logistic Regression with 10-fold cross validation to find out top ML models for emotion classification. After tuning the Hyperparameter, we got Logistic regression as the best suited model with accuracy 77% with the given datasets. We worked on algorithm based supervised ML technique to get the expected result. Although multiple studies were conducted earlier along the same lines, none of them performed comparative study among different ML techniques or hyperparameter tuning to optimize the results. Besides, this study has been done on the dataset of the most recent COVID-19 pandemic situation, which is itself unique. We captured Twitter data for a duration of 45 days with hashtag #COVID19India OR #COVID19 and analyzed the data using Logistic Regression to find out how the emotion changed over time based on certain social factors
    Źródło:
    Multiple Criteria Decision Making; 2020, 15; 23-35
    2084-1531
    Pojawia się w:
    Multiple Criteria Decision Making
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    System of boundary random fractional differential equations via Hadamard derivative
    Autorzy:
    Malki, Zakaria
    Berhoun, Farida
    Ouahab, Abdelghani
    Powiązania:
    https://bibliotekanauki.pl/articles/1797181.pdf
    Data publikacji:
    2021-04-14
    Wydawca:
    Uniwersytet Pedagogiczny im. Komisji Edukacji Narodowej w Krakowie
    Tematy:
    Random fractional differential equation
    Hadamard fractional differential equation
    existence
    fixed point
    vector metric space
    Opis:
    We study the existence of solutions for random system of fractional differential equations with boundary nonlocal initial conditions. Our approach is based on random fixed point principles of Schaefer and Perov, combined with a vector approach that uses matrices that converge to zero. We prove existence and uniqueness results for these systems. Some examples are presented to illustrate the theory.
    Źródło:
    Annales Universitatis Paedagogicae Cracoviensis. Studia Mathematica; 2021, 20; 17-41
    2300-133X
    Pojawia się w:
    Annales Universitatis Paedagogicae Cracoviensis. Studia Mathematica
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Assessment of Approaches for the Extraction of Building Footprints from Pléiades Images
    Autorzy:
    Taha, Lamyaa Gamal El-deen
    Ibrahim, Rania Elsayed
    Powiązania:
    https://bibliotekanauki.pl/articles/1837996.pdf
    Data publikacji:
    2021
    Wydawca:
    Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
    Tematy:
    ensemble classifiers
    machine learning
    random forest
    maximum likelihood
    support vector machines
    backpropagation
    image classification
    Opis:
    The Marina area represents an official new gateway of entry to Egypt and the development of infrastructure is proceeding rapidly in this region. The objective of this research is to obtain building data by means of automated extraction from Pléiades satellite images. This is due to the need for efficient mapping and updating of geodatabases for urban planning and touristic development. It compares the performance of random forest algorithm to other classifiers like maximum likelihood, support vector machines, and backpropagation neural networks over the well-organized buildings which appeared in the satellite images. Images were subsequently classified into two classes: buildings and non-buildings. In addition, basic morphological operations such as opening and closing were used to enhance the smoothness and connectedness of the classified imagery. The overall accuracy for random forest, maximum likelihood, support vector machines, and backpropagation were 97%, 95%, 93% and 92% respectively. It was found that random forest was the best option, followed by maximum likelihood, while the least effective was the backpropagation neural network. The completeness and correctness of the detected buildings were evaluated. Experiments confirmed that the four classification methods can effectively and accurately detect 100% of buildings from very high-resolution images. It is encouraged to use machine learning algorithms for object detection and extraction from very high-resolution images.
    Źródło:
    Geomatics and Environmental Engineering; 2021, 15, 4; 101-116
    1898-1135
    Pojawia się w:
    Geomatics and Environmental Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Prediction model of public houses’ heating systems:a comparison of support vector machine methodand random forest method
    Model prognozowania systemów grzewczych budynków użyteczności publicznej: porównanie metody support vector machine i random forest
    Autorzy:
    Perekrest, Andrii
    Chenchevoi, Vladimir
    Chencheva, Olga
    Kovalenko, Alexandr
    Kushch-Zhyrko, Mykhailo
    Kalizhanova, Aliya
    Amirgaliyev, Yedilkhan
    Powiązania:
    https://bibliotekanauki.pl/articles/2174707.pdf
    Data publikacji:
    2022
    Wydawca:
    Politechnika Lubelska. Wydawnictwo Politechniki Lubelskiej
    Tematy:
    building heat supply
    random forest
    support vector machine
    zaopatrzenie w ciepło budynku
    metoda wektorów wspierających
    Opis:
    Data analysis and predicting play an important role in managing heat-supplying systems. Applying the models of predicting the systems’ parameters is possible for qualitative management, accepting appropriate decisions relating control that will be aimed at increasing energy efficiency and decreasing the amount of the consumed power source, diagnosing and defining non-typical processes in the functioning of the systems. The article deals with comparing two methods of ma-chine learning: random forest (RF) and support vector machine (SVM) for predicting the temperature of the heat-carrying agent in the heating system based on the data of electronic weather-dependent controller. The authors use the following parameters to compare the models: accuracy, source cost and the opportunity to interpret the results and non-obvious interrelations. The time spent for defining the optimal hyperparameters and conducting the SVM model training is deter-mined to exceed significantly the data of the RF parameter despite the close meanings of the root mean square error (RMSE). The change from 15-min data to once-a-minute ones is done to improve the RF model accuracy. RMSE of the RF model on the test data equals 0.41°С. The article studies the importance of the contribution of variables to the prediction accuracy.
    Analiza danych i prognozowanie odgrywają ważną rolę w zarządzaniu systemami zaopatrzenia w ciepło. Wykorzystanie modeli do przewidywania parametrów systemu jest możliwe do zarządzania jakością, podejmowania odpowiednich decyzji sterujących, które będą miały na celu poprawę efektywności energetycznej i zmniejszenie ilości zużywanego źródła energii elektrycznej, diagnozowania i wykrywania nietypowych procesów w funkcjonowaniu systemu. W artykule porównano dwie metody uczenia maszynowego: Random Forest (RF) i Support Vector Machine (SVM) do przewidywania temperatury czynnika grzewczego w systemie grzewczym na podstawie danych elektronicznego regulatora pogodowego. Do porównania modeli autorzy wykorzystują następujące parametry: dokładność, koszt początkowy oraz możliwość interpretacji wyników i nieoczywistych zależności. Ustalono, że czas poświęcony na wyznaczenie optymalnych hiperparametrów i wytrenowanie modelu SVM znacznie przekracza dane parametru RF, pomimo zbliżonych wartości błędu średniokwadratowego (RMSE). Zmiana z danych 15-minutowych na dane raz na minutę została dokonana w celu poprawy dokładności modelu RF. RMSE modelu RF z danych testowych wynosi 0,41°C. W pracy zbadano znaczenie wkładu zmiennych w dokładność prognozy.
    Źródło:
    Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska; 2022, 12, 3; 34--39
    2083-0157
    2391-6761
    Pojawia się w:
    Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    A Machine Learning Model for Improving Building Detection in Informal Areas: A Case Study of Greater Cairo
    Autorzy:
    Taha, Lamyaa Gamal El-deen
    Ibrahim, Rania Elsayed
    Powiązania:
    https://bibliotekanauki.pl/articles/2055780.pdf
    Data publikacji:
    2022
    Wydawca:
    Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
    Tematy:
    multi-source image fusion
    random forest
    support vector machine
    DEM extraction
    unplanned unsafe areas
    remote sensing
    Opis:
    Building detection in Ashwa’iyyat is a fundamental yet challenging problem, mainly because it requires the correct recovery of building footprints from images with high-object density and scene complexity. A classification model was proposed to integrate spectral, height and textural features. It was developed for the automatic detection of the rectangular, irregular structure and quite small size buildings or buildings which are close to each other but not adjoined. It is intended to improve the precision with which buildings are classified using scikit learn Python libraries and QGIS. WorldView-2 and Spot-5 imagery were combined using three image fusion techniques. The Grey-Level Co-occurrence Matrix was applied to determine which attributes are important in detecting and extracting buildings. The Normalized Digital Surface Model was also generated with 0.5-m resolution. The results demonstrated that when textural features of colour images were introduced as classifier input, the overall accuracy was improved in most cases. The results show that the proposed model was more accurate and efficient than the state-of-the-art methods and can be used effectively to extract the boundaries of small size buildings. The use of a classifier ensample is recommended for the extraction of buildings.
    Źródło:
    Geomatics and Environmental Engineering; 2022, 16, 2; 39--58
    1898-1135
    Pojawia się w:
    Geomatics and Environmental Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Automatic speech based emotion recognition using paralinguistics features
    Autorzy:
    Hook, J.
    Noroozi, F.
    Toygar, O.
    Anbarjafari, G.
    Powiązania:
    https://bibliotekanauki.pl/articles/200261.pdf
    Data publikacji:
    2019
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    random forests
    speech emotion recognition
    machine learning
    support vector machines
    lasy
    rozpoznawanie emocji mowy
    nauczanie maszynowe
    Opis:
    Affective computing studies and develops systems capable of detecting humans affects. The search for universal well-performing features for speech-based emotion recognition is ongoing. In this paper, a?small set of features with support vector machines as the classifier is evaluated on Surrey Audio-Visual Expressed Emotion database, Berlin Database of Emotional Speech, Polish Emotional Speech database and Serbian emotional speech database. It is shown that a?set of 87 features can offer results on-par with state-of-the-art, yielding 80.21, 88.6, 75.42 and 93.41% average emotion recognition rate, respectively. In addition, an experiment is conducted to explore the significance of gender in emotion recognition using random forests. Two models, trained on the first and second database, respectively, and four speakers were used to determine the effects. It is seen that the feature set used in this work performs well for both male and female speakers, yielding approximately 27% average emotion recognition in both models. In addition, the emotions for female speakers were recognized 18% of the time in the first model and 29% in the second. A?similar effect is seen with male speakers: the first model yields 36%, the second 28% a?verage emotion recognition rate. This illustrates the relationship between the constitution of training data and emotion recognition accuracy.
    Źródło:
    Bulletin of the Polish Academy of Sciences. Technical Sciences; 2019, 67, 3; 479-488
    0239-7528
    Pojawia się w:
    Bulletin of the Polish Academy of Sciences. Technical Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Attribute selection for stroke prediction
    Autorzy:
    Zdrodowska, Małgorzata
    Powiązania:
    https://bibliotekanauki.pl/articles/386466.pdf
    Data publikacji:
    2019
    Wydawca:
    Politechnika Białostocka. Oficyna Wydawnicza Politechniki Białostockiej
    Tematy:
    data mining
    classifier
    J48 (C4.5)
    CART
    PART
    naive Bayes classifier
    random forest
    support vector machine
    multilayer perceptron
    haemorrhagic stroke
    ischemic stroke
    Opis:
    Stroke is the third most common cause of death and the most common cause of long-term disability among adults around theworld. Therefore, stroke prediction and diagnosis is a very important issue. Data mining techniques come in handy to help determine the correlations between individual patient characterisation data, that is, extract from the medical information system the knowledge necessary to predict and treat various diseases. The study analysed the data of patients with stroke using eight known classification algorithms (J48 (C4.5), CART, PART, naive Bayes classifier, Random Forest, Supporting Vector Machine and neural networks Multilayer Perceptron), which allowed to build an exploration model given with an accuracy of over 88%. The potential features of patients, which may be factors that increase the risk of stroke, were also indicated.
    Źródło:
    Acta Mechanica et Automatica; 2019, 13, 3; 200-204
    1898-4088
    2300-5319
    Pojawia się w:
    Acta Mechanica et Automatica
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Classifiers accuracy improvement based on missing data imputation
    Autorzy:
    Jordanov, I.
    Petrov, N.
    Petrozziello, A.
    Powiązania:
    https://bibliotekanauki.pl/articles/91626.pdf
    Data publikacji:
    2018
    Wydawca:
    Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
    Tematy:
    machine learning
    missing data
    model-based imputation
    neural networks
    random forests
    support vector machine
    radar signal classification
    nauczanie maszynowe
    brakujące dane
    sieci neuronowe
    maszyna wektorów nośnych
    klasyfikacja sygnałów radarowych
    Opis:
    In this paper we investigate further and extend our previous work on radar signal identification and classification based on a data set which comprises continuous, discrete and categorical data that represent radar pulse train characteristics such as signal frequencies, pulse repetition, type of modulation, intervals, scan period, scanning type, etc. As the most of the real world datasets, it also contains high percentage of missing values and to deal with this problem we investigate three imputation techniques: Multiple Imputation (MI); K-Nearest Neighbour Imputation (KNNI); and Bagged Tree Imputation (BTI). We apply these methods to data samples with up to 60% missingness, this way doubling the number of instances with complete values in the resulting dataset. The imputation models performance is assessed with Wilcoxon’s test for statistical significance and Cohen’s effect size metrics. To solve the classification task, we employ three intelligent approaches: Neural Networks (NN); Support Vector Machines (SVM); and Random Forests (RF). Subsequently, we critically analyse which imputation method influences most the classifiers’ performance, using a multiclass classification accuracy metric, based on the area under the ROC curves. We consider two superclasses (‘military’ and ‘civil’), each containing several ‘subclasses’, and introduce and propose two new metrics: inner class accuracy (IA); and outer class accuracy (OA), in addition to the overall classification accuracy (OCA) metric. We conclude that they can be used as complementary to the OCA when choosing the best classifier for the problem at hand.
    Źródło:
    Journal of Artificial Intelligence and Soft Computing Research; 2018, 8, 1; 31-48
    2083-2567
    2449-6499
    Pojawia się w:
    Journal of Artificial Intelligence and Soft Computing Research
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Queueing systems with random volume customers and a sectorized unlimited memory buffer
    Autorzy:
    Tikhonenko, Oleg
    Ziółkowski, Marcin
    Kempa, Wojciech M.
    Powiązania:
    https://bibliotekanauki.pl/articles/2055163.pdf
    Data publikacji:
    2021
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    queueing system
    random volume customers
    sectorized memory buffer
    total volume vector
    Laplace transform
    Laplace–Stieltjes transform
    multivariate L’Hospital rule
    system kolejkowania
    wektor objętości
    transformata Laplace'a
    transformata Laplace'a-Stieltjesa
    Opis:
    In the present paper, we concentrate on basic concepts connected with the theory of queueing systems with random volume customers and a sectorized unlimited memory buffer. In such systems, the arriving customers are additionally characterized by a non-negative random volume vector. The vector’s indications can be understood as the sizes of portions of information of a different type that are located in the sectors of memory space of the system during customers’ sojourn in it. This information does not change while a customer is present in the system. After service termination, information immediately leaves the buffer, releasing its resources. In analyzed models, the service time of a customer is assumed to be dependent on his volume vector characteristics, which has influence on the total volume vector distribution. We investigate three types of such queueing systems: the Erlang queueing system, the single-server queueing system with unlimited queue and the egalitarian processor sharing system. For these models, we obtain a joint distribution function of the total volume vector in terms of Laplace (or Laplace-Stieltjes) transforms and formulae for steady-state initial mixed moments of the analyzed random vector, in the case when the memory buffer is composed of two sectors. We also calculate these characteristics for some practical case in which the service time of a customer is proportional to the customer’s length (understood as the sum of the volume vector’s indications). Moreover, we present some numerical computations illustrating theoretical results.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2021, 31, 3; 471--486
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Single–server queueing system with limited queue, random volume customers and unlimited sectorized memory buffer
    Autorzy:
    Ziółkowski, Marcin
    Tikhonenko, Oleg
    Powiązania:
    https://bibliotekanauki.pl/articles/2173725.pdf
    Data publikacji:
    2022
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    single-server queueing system
    queueing systems with random volume customers
    sectorized memory buffer
    total volume vector
    Laplace-Stieltjes transform
    system kolejkowy z jednym serwerem
    system kolejkowy z losowymi klientami
    bufor pamięci sektorowany
    wektor objętości całkowity
    transformata Laplace'a-Stieltjesa
    Opis:
    In the present paper, we analyze the model of a single–server queueing system with limited number of waiting positions, random volume customers and unlimited sectorized memory buffer. In such a system, the arriving customer is additionally characterized by a non– negative random volume vector whose indications usually represent the portions of unchanged information of a different type that are located in sectors of unlimited memory space dedicated for them during customer presence in the system. When the server ends the service of a customer, information immediately leaves the buffer, releasing resources of the proper sectors. We assume that in the investigated model, the service time of a customer is dependent on his volume vector characteristics. For such defined model, we obtain a general formula for steady–state joint distribution function of the total volume vector in terms of Laplace-Stieltjes transforms. We also present practical results for some special cases of the model together with formulae for steady–state initial moments of the analyzed random vector, in cases where the memory buffer is composed of at most two sectors. Some numerical computations illustrating obtained theoretical results are attached as well.
    Źródło:
    Bulletin of the Polish Academy of Sciences. Technical Sciences; 2022, 70, 6; art. no. e143647
    0239-7528
    Pojawia się w:
    Bulletin of the Polish Academy of Sciences. Technical Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-21 z 21

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