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Tytuł:
Wykrywanie uszkodzeń węzłów w modelu ramy stalowej na podstawie analizy inertancji
Detection of defects connection between members of steel frame on the basis of FRF changes
Autorzy:
Ziaja, D.
Miller, B.
Powiązania:
https://bibliotekanauki.pl/articles/105271.pdf
Data publikacji:
2017
Wydawca:
Politechnika Rzeszowska im. Ignacego Łukasiewicza. Oficyna Wydawnicza
Tematy:
detekcja uszkodzeń
SHM
FRF
Support Vector Machines
SVM
image detection
Opis:
W artykule przedstawiono możliwość detekcji uszkodzeń węzłów na podstawie analizy proporcji pomiędzy wytypowanymi fragmentami funkcji przejścia (FRF). W ramach zadania wykonano eksperyment na modelu laboratoryjnym dwukondygnacyjnej ramy portalowej, którą poddano testom dynamicznym i dla której określono model modalny. Funkcję przejścia odpowiadającą wybranym punktom układu potraktowano jako sygnał w dziedzinie częstotliwości. Wyznaczono odcięte środków ciężkości kwadratów sygnału wybranych fragmentów funkcji, które następnie potraktowano jako dane wejściowe w metodzie wektorów nośnych. Zaproponowane podejście umożliwia skuteczną detekcję uszkodzeń węzłów badanego modelu.
The article presents the possibility of nodes failures detecting based on the analysis of the proportions between the selected intervals of FRF function. Within the scope of the task an experiment was performed on the laboratory model of a two-storey portal frame, which was subjected to dynamic tests and for which a modal model was defined. FRF function for selected system points was treated as a signal in the frequency domain. For the relevant fragments, the centers of gravity of the signal squares were determined, which were then used as input data in the Support Vector Machines (SVM) method. The proposed approach enables effective detection of connection damage in the tested structure.
Źródło:
Czasopismo Inżynierii Lądowej, Środowiska i Architektury; 2017, 64, 2/I; 247-255
2300-5130
2300-8903
Pojawia się w:
Czasopismo Inżynierii Lądowej, Środowiska i Architektury
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Using Multiclass SVM methods for classification of DNA microarray data
Autorzy:
Student, S.
Powiązania:
https://bibliotekanauki.pl/articles/333907.pdf
Data publikacji:
2007
Wydawca:
Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
Tematy:
metoda cząstkowych najmniejszych kwadratów
maszyna wektorów nośnych
Partial Least Squares PLS
dimension reductions
MMulticlass Support Vector Machines MSVM
One-Versus-One OvO
One-Versus RestOvR
Opis:
One important application of gene expression microarray data is classification of samples into categories, such as the type of tumor. A classifier using Multiclass SVM [4] (Support Vector Machines) is described in this article. Our classifier involves dimension reduction using Multivariate Partial Least Squares (MPLS) for classification more than two classes. We use also two methods based on binary classifications: One-Against-All [5] and One-Against-One [6]. These three methods have been tested on a data set involving 125 tumor/normal thyroid human DNA microarrays samples. There are 66 Papillary throid carcinoma, 32 follicular throid carcinoma and 27 normal tissues. The most important thing is to find small number of genes that discriminate between these three classes with good accuracy. The best genes can be selected for Q-PCR validation. Molecular markers differentiating between throid cancer and normal tissues can help in clinical diagnostics and therapy methods. For error estimation we are use the bootstrap .632 [8] technique. Major issue with bootstrap estimators is their high computational cost. That is why we use a OpenMosix with MPI (Message Passing Interface) cluster technology for this system for parallel computation space.
Źródło:
Journal of Medical Informatics & Technologies; 2007, 11; 197-204
1642-6037
Pojawia się w:
Journal of Medical Informatics & Technologies
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Using multi-objective affinity model for mining the rules of revisits within 72 hours for emergency department patients
Autorzy:
Chao-Wen, Chen
Yuh-Wen, Chen
Moussa, Larbani
Tzung-Hung, Li
Powiązania:
https://bibliotekanauki.pl/articles/578510.pdf
Data publikacji:
2015
Wydawca:
Uniwersytet Ekonomiczny w Katowicach
Tematy:
Data Mining
Leczenie
Macierze
Metoda wektorów nośnych (SVM)
Placówki służby zdrowia
Matrix
Medical facilities
Medical treatment
Support Vector Machines (SVM)
Opis:
When patients return to the emergency department (ED) within 72 hours after their previous ED discharge, it is generally assumed that their initial evaluation or treatment had been somehow inadequate. Mining data related to unplanned ED revisits is one method to determine whether this problem can be overcome, and to generate useful guidelines in this regard. In this study, we use the receiver operating characteristic (ROC) curve to compare the data mining model by affinity set to other well known approaches. Some scholars have validated the affinity model for its simplicity and power in handling information systems especially when showing binary consequences. In experimental results, SVM showed the best performance, with the affinity model following only slightly behind. This study demonstrated that when patients visit the ED with normotensive status or smooth breath patterns, or when the physician-patient ratio is moderate, the frequency with which patients revisit the ED is significantly higher.
Źródło:
Multiple Criteria Decision Making; 2015, 10; 5-31
2084-1531
Pojawia się w:
Multiple Criteria Decision Making
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Tenfold bootstrap procedure for support vector machines
Autorzy:
Vrigazova, Borislava
Ivanov, Ivan
Powiązania:
https://bibliotekanauki.pl/articles/1839282.pdf
Data publikacji:
2020
Wydawca:
Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
Tematy:
support vector machines
bootstrap
cross validation
Opis:
Cross validation is often used to split input data into training and test set in Support vector machines. The two most commonly used cross validation versions are the tenfold and leave-one-out cross validation. Another commonly used resampling method is the random test/train split. The advantage of these methods is that they avoid overfitting in the model and perform model selection. They, however, can increase the computational time for fitting Support vector machines with the increase of the size of the dataset. In this research, we propose an alternative for fitting SVM, which we call the tenfold bootstrap for Support vector machines. This resampling procedure can significantly reduce execution time despite the big number of observations, while preserving model’s accuracy. With this finding, we propose a solution to the problem of slow execution time when fitting support vector machines on big datasets.
Źródło:
Computer Science; 2020, 21 (2); 241-257
1508-2806
2300-7036
Pojawia się w:
Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Symulacyjna ocena jakości zagregowanych modeli zbudowanych metodą wektorów nośnych
Benchmarking Aggregated Support Vector Regression Models
Autorzy:
Trzęsiok, Michał
Powiązania:
https://bibliotekanauki.pl/articles/588038.pdf
Data publikacji:
2013
Wydawca:
Uniwersytet Ekonomiczny w Katowicach
Tematy:
Metoda wektorów nośnych (SVM)
Modele matematyczne
Symulacja
Mathematical models
Simulation
Support Vector Machines (SVM)
Opis:
Support Vector Machines (SVM) are a state-of-the-art classification method, but they are also suitable, after a special reformulation, to perform a regression task. Similarly to classification, for a nonlinear regression problem, SVMs use the kernel trick and map the input space into a high-dimensional feature space first, and then perform linear regression in the high-dimensional feature space. One can use the model ensemble approach to try to improve the prediction accuracy. The paper presents the comparison of a single SVM, aggregated SVM and other regression models (linear regression, Projection Pursuit Regression, Neural Networks, Regression Trees, Random Forest, Bagging) by the means of a mean squared test set error.
Źródło:
Studia Ekonomiczne; 2013, 132; 115-126
2083-8611
Pojawia się w:
Studia Ekonomiczne
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Solving Support Vector Machine with Many Examples
Autorzy:
Białoń, P.
Powiązania:
https://bibliotekanauki.pl/articles/308497.pdf
Data publikacji:
2010
Wydawca:
Instytut Łączności - Państwowy Instytut Badawczy
Tematy:
concept drift
convex optimization
data mining
network failure detection
stream processing
support vector machines
Opis:
Various methods of dealing with linear support vector machine (SVM) problems with a large number of examples are presented and compared. The author believes that some interesting conclusions from this critical analysis applies to many new optimization problems and indicates in which direction the science of optimization will branch in the future. This direction is driven by the automatic collection of large data to be analyzed, and is most visible in telecommunications. A stream SVM approach is proposed, in which the data substantially exceeds the available fast random access memory (RAM) due to a large number of examples. Formally, the use of RAM is constant in the number of examples (though usually it depends on the dimensionality of the examples space). It builds an inexact polynomial model of the problem. Another author's approach is exact. It also uses a constant amount of RAM but also auxiliary disk files, that can be long but are smartly accessed. This approach bases on the cutting plane method, similarly as Joachims' method (which, however, relies on early finishing the optimization).
Źródło:
Journal of Telecommunications and Information Technology; 2010, 3; 65-70
1509-4553
1899-8852
Pojawia się w:
Journal of Telecommunications and Information Technology
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Selecting Differentially Expressed Genes for Colon Tumor Classification
Autorzy:
Fujarewicz, K.
Wiench, M.
Powiązania:
https://bibliotekanauki.pl/articles/908154.pdf
Data publikacji:
2003
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
medycyna
automatyka
colon tumor
gene expression data
microarrays
support vector machines
feature selection
classification
Opis:
DNA microarrays provide a new technique of measuring gene expression, which has attracted a lot of research interest in recent years. It was suggested that gene expression data from microarrays (biochips) can be employed in many biomedical areas, e.g., in cancer classification. Although several, new and existing, methods of classification were tested, a selection of proper (optimal) set of genes, the expressions of which can serve during classification, is still an open problem. Recently we have proposed a new recursive feature replacement (RFR) algorithm for choosing a suboptimal set of genes. The algorithm uses the support vector machines (SVM) technique. In this paper we use the RFR method for finding suboptimal gene subsets for tumor/normal colon tissue classification. The obtained results are compared with the results of applying other methods recently proposed in the literature. The comparison shows that the RFR method is able to find the smallest gene subset (only six genes) that gives no misclassifications in leave-one-out cross-validation for a tumor/normal colon data set. In this sense the RFR algorithm outperforms all other investigated methods.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2003, 13, 3; 327-335
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Rough support vector machine for classification with interval and incomplete data
Autorzy:
Nowicki, Robert K.
Grzanek, Konrad
Hayashi, Yoichi
Powiązania:
https://bibliotekanauki.pl/articles/91559.pdf
Data publikacji:
2020
Wydawca:
Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:
support vector machines
rough sets
missing features
interval data
three–way decision
maszyna wektorów nośnych
dane interwałowe
Opis:
The paper presents the idea of connecting the concepts of the Vapnik’s support vector machine with Pawlak’s rough sets in one classification scheme. The hybrid system will be applied to classifying data in the form of intervals and with missing values [1]. Both situations will be treated as a cause of dividing input space into equivalence classes. Then, the SVM procedure will lead to a classification of input data into rough sets of the desired classes, i.e. to their positive, boundary or negative regions. Such a form of answer is also called a three–way decision. The proposed solution will be tested using several popular benchmarks.
Źródło:
Journal of Artificial Intelligence and Soft Computing Research; 2020, 10, 1; 47-56
2083-2567
2449-6499
Pojawia się w:
Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Robustness of Support Vector Machines in Algorithmic Trading on Cryptocurrency Market
Autorzy:
Ślepaczuk, Robert
Zenkova, Maryna
Powiązania:
https://bibliotekanauki.pl/articles/1356913.pdf
Data publikacji:
2019-08-07
Wydawca:
Uniwersytet Warszawski. Wydział Nauk Ekonomicznych
Tematy:
Machine learning
support vector machines
investment algorithm
algorithmic trading
strategy
optimization
cross-validation
overfitting
cryptocurrency market
technical analysis
meta parameters
Opis:
This study investigates the profitability of an algorithmic trading strategy based on training SVM model to identify cryptocurrencies with high or low predicted returns. A tail set is defined to be a group of coins whose volatility-adjusted returns are in the highest or the lowest quintile. Each cryptocurrency is represented by a set of six technical features. SVM is trained on historical tail sets and tested on the current data. The classifier is chosen to be a nonlinear support vector machine. The portfolio is formed by ranking coins using the SVM output. The highest ranked coins are used for long positions to be included in the portfolio for one reallocation period. The following metrics were used to estimate the portfolio profitability: %ARC (the annualized rate of change), %ASD (the annualized standard deviation of daily returns), MDD (the maximum drawdown coefficient), IR1, IR2 (the information ratio coefficients). The performance of the SVM portfolio is compared to the performance of the four benchmark strategies based on the values of the information ratio coefficient IR1, which quantifies the risk-weighted gain. The question of how sensitive the portfolio performance is to the parameters set in the SVM model is also addressed in this study.
Źródło:
Central European Economic Journal; 2018, 5, 52; 186 - 205
2543-6821
Pojawia się w:
Central European Economic Journal
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Profiling bell’s palsy based on House - Brackmann score
Autorzy:
Song, I.
Vong, J.
Yen, N. Y..
Diederich, J.
Yellowlees, P.
Powiązania:
https://bibliotekanauki.pl/articles/91551.pdf
Data publikacji:
2013
Wydawca:
Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:
facial nerve
palsy
support vector machines
SVMs
Emergent Self-Organizing Map
ESOM
House-Brackmann score
facial paralysis
facial image
Opis:
In this study, we propose to diagnose facial nerve palsy using Support Vector Machines (SVMs) and Emergent Self-Organizing Map (ESOM). This research seeks to analyze facial palsy domain using facial features and grade the degree of nerve damage based on the House-Brackmann score. Traditional diagnostic approaches involve a medical doctor recording a thorough history of a patient and determining the onset of paralysis, rate of progression and so on. The most important step is to assess the degree of voluntary movement of the facial nerves and document the grade of facial paralysis using House- Brackmann score. The significance of the work is the attempt to understand the diagnosis and grading processes using semi-supervised learning with the aim of automating the process. The value of the research is in identifying and documenting the limited literature seen in this area. The use of automated diagnosis and grading greatly reduces the duration of medical examination and increases the consistency, because many palsy images are stored to provide benchmark references for comparative purposes. The proposed automated diagnosis and grading are computationally efficient. This automated process makes it ideal for remote diagnosis and examination of facial palsy. The profiling of a large number of facial images are captured using mobile phones and digital cameras.
Źródło:
Journal of Artificial Intelligence and Soft Computing Research; 2013, 3, 1; 41-50
2083-2567
2449-6499
Pojawia się w:
Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Predicting the default risk of companies. Comparison of credit scoring models: LOGIT vs Support Vector Machines
Przewidywanie ryzyka kredytowego przedsiębiorstw niefinansowych. Porównanie modeli scoringowych: regresja logistyczna vs Support Vector Machine
Autorzy:
Nehrebecka, Natalia
Powiązania:
https://bibliotekanauki.pl/articles/425217.pdf
Data publikacji:
2018
Wydawca:
Wydawnictwo Uniwersytetu Ekonomicznego we Wrocławiu
Tematy:
Basel III
Internal Rating Based System
credit scoring
Support Vector Machines
logistic regression
Opis:
The aim of the article is to compare models on a train and validation sample, which will be created using logistic regression and Support Vector Machine (SVM) and will be used to assess the credit risk of non-financial enterprises. When creating models, the variables will be subjected to the transformation of the Weight of Evidence (WoE), the number of potential predictions will be reduced based on the Information Value (IV) statistics. The quality of the models will be assessed according to the most popular criteria such as GINI statistics, Kolmogorov-Smirnov (K-S) and Area Under Receiver Operating Characteristic (AUROC). Based on the results, it was found that there are significant differences between the logistic regression model of discriminatory character and the SVM for the model sample. In the case of a validation sample, logistic regression has the best prognostic capability. These analyses can be used to reduce the risk of negative effects on the financial sector.
Źródło:
Econometrics. Ekonometria. Advances in Applied Data Analytics; 2018, 22, 2; 54-73
1507-3866
Pojawia się w:
Econometrics. Ekonometria. Advances in Applied Data Analytics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Pattern Recognition Methods for Detecting Voltage Sag Disturbances and Electromagnetic Interference in Smart Grids
Autorzy:
Yalcin, T.
Ozdemir, M.
Powiązania:
https://bibliotekanauki.pl/articles/136160.pdf
Data publikacji:
2016
Wydawca:
EEEIC International Barbara Leonowicz Szabłowska
Tematy:
C4.5 decision trees
electromagnetic interference
feature extraction
hilbert huang transform
power quality disturbance
smart grids
support vector machines
Opis:
Identification of system disturbances, detection of them guarantees smart grids power quality (PQ) system reliability and provides long lasting life of the power system. The key goal of this study is to find the best accuracy of identification algorithm for non-stationary, non-linear power quality disturbances such as voltage sag, electromagnetic interference in smart grids. PQube, power quality and energy monitor, was used to acquire these distortions. Ensemble Empirical Mode Decomposition is used for electromagnetic interference reduction with first intrinsic mode function. Hilbert Huang Transform is used for generating instantaneous amplitude and instantaneous frequency feature of real time voltage sag power signal. Outputs of Hilbert Huang Transform is intrinsic mode functions (IMFs), instantaneous frequency (IF), and instantaneous amplitude (IA). Characteristic features are obtained from first IMFs, IF, and IA. The six features—, the mean, standard deviation,skewness, kurtosis of both IF and IA are then calculated. These features are normalized along with the inputs classifiers. The proposed power system monitoring system is able to detect power system voltage sag disturbances and capable of recognize electromagnetic interference component. In this study based on experimental studies, Hilbert Huang Transform based pattern recognition technique was used to investigate power signal to diagnose voltage sag and in power grid. Support Vector Machines and C4.5 Decision Tree were operated and their achievements were matched for precision and CPU timing. According to the analysis, decision tree algorithm without dimensionality reduction produces the best solution.
Źródło:
Transactions on Environment and Electrical Engineering; 2016, 1, 3; 86-93
2450-5730
Pojawia się w:
Transactions on Environment and Electrical Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Parameter identification of ship maneuvering models using recursive least square method based on support vector machines
Autorzy:
Zhu, M.
Hahn, A.
Wen, Y.
Bolles, A.
Powiązania:
https://bibliotekanauki.pl/articles/116455.pdf
Data publikacji:
2017
Wydawca:
Uniwersytet Morski w Gdyni. Wydział Nawigacyjny
Tematy:
ship manoeuvering
recursive least square method
ship manoeuvering model
ship maneuverability prediction
Support Vector Machines (SVM)
empirical mode decomposition (EMD)
Computational Fluid Dynamics (CFD)
Extended Kalman Filter (EKF)
Opis:
Determination of ship maneuvering models is a tough task of ship maneuverability prediction. Among several prime approaches of estimating ship maneuvering models, system identification combined with the full-scale or free- running model test is preferred. In this contribution, real-time system identification programs using recursive identification method, such as the recursive least square method (RLS), are exerted for on-line identification of ship maneuvering models. However, this method seriously depends on the objects of study and initial values of identified parameters. To overcome this, an intelligent technology, i.e., support vector machines (SVM), is firstly used to estimate initial values of the identified parameters with finite samples. As real measured motion data of the Mariner class ship always involve noise from sensors and external disturbances, the zigzag simulation test data include a substantial quantity of Gaussian white noise. Wavelet method and empirical mode decomposition (EMD) are used to filter the data corrupted by noise, respectively. The choice of the sample number for SVM to decide initial values of identified parameters is extensively discussed and analyzed. With de-noised motion data as input-output training samples, parameters of ship maneuvering models are estimated using RLS and SVM-RLS, respectively. The comparison between identification results and true values of parameters demonstrates that both the identified ship maneuvering models from RLS and SVM-RLS have reasonable agreements with simulated motions of the ship, and the increment of the sample for SVM positively affects the identification results. Furthermore, SVM-RLS using data de-noised by EMD shows the highest accuracy and best convergence.
Źródło:
TransNav : International Journal on Marine Navigation and Safety of Sea Transportation; 2017, 11, 1; 23-29
2083-6473
2083-6481
Pojawia się w:
TransNav : International Journal on Marine Navigation and Safety of Sea Transportation
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Optimal classification method for smiling vs neutral facial display recognition
Autorzy:
Nurzyńska, K.
Smołka, B.
Powiązania:
https://bibliotekanauki.pl/articles/333381.pdf
Data publikacji:
2014
Wydawca:
Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
Tematy:
local binary patterns
support vector machines
k-nearest neighbourhood
template matching
lokalne wzorce binarne
maszyna wektorów nośnych
dopasowanie wzorców
Opis:
Human face depicts what happens in the soul, therefore correct recognition of emotion on the basis of facial display is of high importance. This work concentrates on the problem of optimal classification technique selection for solving the issue of smiling versus neutral face recognition. There are compared most frequently applied classification techniques: k-nearest neighbourhood, support vector machines, and template matching. Their performance is evaluated on facial images from several image datasets, but with similar image description methods based on local binary patterns. According to the experiments results the linear support vector machine gives the most satisfactory outcomes for all conditions.
Źródło:
Journal of Medical Informatics & Technologies; 2014, 23; 87-94
1642-6037
Pojawia się w:
Journal of Medical Informatics & Technologies
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
On Some Properties of Support Vector Clustering
Analiza wybranych własności taksonomicznej metody wektorów nośnych
Autorzy:
Trzęsiok, Michał
Powiązania:
https://bibliotekanauki.pl/articles/906302.pdf
Data publikacji:
2009
Wydawca:
Uniwersytet Łódzki. Wydawnictwo Uniwersytetu Łódzkiego
Tematy:
support vector machines
clustering
admissibility conditions
Opis:
Celem referatu jest przedstawienie analizy wybranych formalnych własności taksonomicznej metody wektorów nośnych (SVC). Wyniki dotyczące nowej metody SVC zestawiono i porównano z własnościami innych znanych metod taksonomicznych. Ponieważ na ogół nie jest możliwe wskazanie, która z metod taksonomicznych daje najlepsze rezultaty, stojąc wobec konkretnego problemu, badacz musi dokonywać wyboru metody w oparciu o wiedzę dotyczącą ich własności. Zadaniem badacza jest wtedy ustalenie preferencji w zbiorze własności metod by następnie użyć ich przy doborze odpowiedniego narzędzia. Wiedza dotycząca formalnych własności metod taksonomicznych jest w referacie rozszerzona o nową- taksonomiczną metodę wektorów nośnych.
The aim o f this paper is to analyse the relatively new clustering method - Support Vector Clustering (SVC) in terms o f fulfilling admissibility conditions. The results are compared within a group o f four other clustering methods. Since it is not possible to assess which clustering method is the "best" in general, given a specific problem the user can decide which method to apply considering some properties o f clustering methods, known as admissibility conditions. This paper expands the knowledge about the properties o f clustering methods with the properties o f SVC.
Źródło:
Acta Universitatis Lodziensis. Folia Oeconomica; 2009, 228
0208-6018
2353-7663
Pojawia się w:
Acta Universitatis Lodziensis. Folia Oeconomica
Dostawca treści:
Biblioteka Nauki
Artykuł
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