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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:
Bankruptcy prediction of small- and medium-sized enterprises in Poland based on the LDA and SVM methods
Autorzy:
Ptak-Chmielewska, Aneta
Powiązania:
https://bibliotekanauki.pl/articles/1363615.pdf
Data publikacji:
2021-03-03
Wydawca:
Główny Urząd Statystyczny
Tematy:
discriminant analysis
support vector machines
bankruptcy prediction
SMEs
Opis:
The impact the last financial crisis had on the small- and medium-sized enterprises (SMEs) sector varied across countries, affecting them on different levels and to a different extent. The economic situation in Poland during and after the financial crisis was quite stable compared to other EU member states. SMEs represent one of the most important segments of the economy of every country. Therefore, it is crucial to develop a prediction model which easily adapts to the characteristics of SMEs. Since the Altman Z-Score model was devised, numerous studies on bankruptcy prediction have been written. Most of them involve the application of traditional methods, including linear discriminant analysis (LDA), logistic regression and probit analysis. However, most recent studies in the area of bankruptcy prediction focus on more advanced methods, such as case-based reasoning, genetic algorithms and neural networks. In this paper, the effectiveness of LDA and SVM predictions were compared. A sample of SMEs was used in the empirical analysis, financial ratios were utilised and non-financial factors were taken account of. The hypothesis assuming that multidimensional discrimination was more effective was verified on the basis of the obtained results.
Źródło:
Statistics in Transition new series; 2021, 22, 1; 179-195
1234-7655
Pojawia się w:
Statistics in Transition new series
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Efficient heart disease diagnosis based on twin support vector machine
Autorzy:
Brik, Youcef
Djerioui, Mohamed
Attallah, Bilal
Powiązania:
https://bibliotekanauki.pl/articles/1840868.pdf
Data publikacji:
2021
Wydawca:
Polska Akademia Nauk. Polskie Towarzystwo Diagnostyki Technicznej PAN
Tematy:
heart diseases
medical data
diagnostic
machine learning
twin support vector machines
choroba serca
diagnostyka
uczenie maszynowe
Opis:
Heart disease is the leading cause of death in the world according to the World Health Organization (WHO). Researchers are more interested in using machine learning techniques to help medical staff diagnose or detect heart disease early. In this paper, we propose an efficient medical decision support system based on twin support vector machines (Twin-SVM) for heart disease diagnosing with binary target (i.e. presence or absence of disease). Unlike conventional support vector machines (SVM) that finds only one optimal hyperplane for separating the data points of first class from those of second class, which causes inaccurate decision, Twin-SVM finds two non-parallel hyper-planes so that each one is closer to the first class and is as far from the second class as possible. Our experiments are conducted on real heart disease dataset and many evaluation metrics have been considered to evaluate the performance of the proposed method. Furthermore, a comparison between the proposed method and several well-known classifiers as well as the state-of-the-art methods has been performed. The obtained results proved that our proposed method based on Twin-SVM technique gives promising performances better than the state-of-the-art. This improvement can seriously reduce time, materials, and labor in healthcare services while increasing the final decision accuracy.
Źródło:
Diagnostyka; 2021, 22, 3; 3-11
1641-6414
2449-5220
Pojawia się w:
Diagnostyka
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Induction motor fault classification via entropy and column correlation features of 2D represented vibration data
Autorzy:
Basaran, Murat
Fidan, Mehmet
Powiązania:
https://bibliotekanauki.pl/articles/1841827.pdf
Data publikacji:
2021
Wydawca:
Polska Akademia Nauk. Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne PAN
Tematy:
entropy
fault diagnosis
support vector machines
wavelet transforms
Opis:
Due to long-term use under challenging conditions, the sub-elements of induction motors may suffer certain defects over time. Such defects impair the vibration characteristics of the motors in different ways, depending on the type of defect. Therefore, the change in vibration characteristic provides indicators about the fault type and can be used in preventive maintenance strategies to ensure safe operation of the system. In this work, discrete-time vibration data were transformed into 2-dimensional grey-level images and decomposed into individual components by the Wavelet decomposition method. Features based on entropy and column correlation were extracted from these components and used to classify motor faults by using the Support Vector Machine method implemented by using the Sequential Minimal Optimisation algorithm. When the selected classifier is compared with other popular Machine Learning algorithms, it is observed that motor faults are more successfully classified, and these observations are presented in detail with comparative classification performance results.
Źródło:
Eksploatacja i Niezawodność; 2021, 23, 1; 132-142
1507-2711
Pojawia się w:
Eksploatacja i Niezawodność
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:
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:
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:
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:
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:
A Fast Classification Method of Faults in Power Electronic Circuits Based on Support Vector Machines
Autorzy:
Cui, J.
Shi, G.
Gong, C.
Powiązania:
https://bibliotekanauki.pl/articles/220922.pdf
Data publikacji:
2017
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
power electronics
fault diagnosis
wavelet transforms
support vector machines
directed acyclic graph
nearest neighbours
Opis:
Fault detection and location are important and front-end tasks in assuring the reliability of power electronic circuits. In essence, both tasks can be considered as the classification problem. This paper presents a fast fault classification method for power electronic circuits by using the support vector machine (SVM) as a classifier and the wavelet transform as a feature extraction technique. Using one-against-rest SVM and one-against-one SVM are two general approaches to fault classification in power electronic circuits. However, these methods have a high computational complexity, therefore in this design we employ a directed acyclic graph (DAG) SVM to implement the fault classification. The DAG SVM is close to the one-against-one SVM regarding its classification performance, but it is much faster. Moreover, in the presented approach, the DAG SVM is improved by introducing the method of Knearest neighbours to reduce some computations, so that the classification time can be further reduced. A rectifier and an inverter are demonstrated to prove effectiveness of the presented design.
Źródło:
Metrology and Measurement Systems; 2017, 24, 4; 701-720
0860-8229
Pojawia się w:
Metrology and Measurement Systems
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Anomaly detection in a cutting tool by k-means clustering and support vector machines
Autorzy:
Lahrache, A.
Cocconcelli, M.
Rubini, R.
Powiązania:
https://bibliotekanauki.pl/articles/328445.pdf
Data publikacji:
2017
Wydawca:
Polska Akademia Nauk. Polskie Towarzystwo Diagnostyki Technicznej PAN
Tematy:
knife diagnostics
k-means
hierarchical clustering
support vector machines
diagnostyka
grupowanie hierarchiczne
Opis:
This paper concerns the analysis of experimental data, verifying the applicability of signal analysis techniques for condition monitoring of a packaging machine. In particular, the activity focuses on the cutting process that divides a continuous flow of packaging paper into single packages. The cutting process is made by a steel knife driven by a hydraulic system. Actually, the knives are frequently substituted, causing frequent stops of the machine and consequent lost production costs. The aim of this paper is to develop a diagnostic procedure to assess the wearing condition of blades, reducing the stops for maintenance. The packaging machine was provided with pressure sensor that monitors the hydraulic system driving the blade. Processing the pressure data comprises three main steps: the selection of scalar quantities that could be indicative of the condition of the knife. A clustering analysis was used to set up a threshold between unfaulted and faulted knives. Finally, a Support Vector Machine (SVM) model was applied to classify the technical condition of knife during its lifetime.
Źródło:
Diagnostyka; 2017, 18, 3; 21-29
1641-6414
2449-5220
Pojawia się w:
Diagnostyka
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Music Performers Classification by Using Multifractal Features : A Case Study
Autorzy:
Reljin, N.
Pokrajac, D.
Powiązania:
https://bibliotekanauki.pl/articles/177266.pdf
Data publikacji:
2017
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
music classification
multifractal analysis
support vector machines
cross-validation
mel-frequency cepstral coefficients
Opis:
In this paper, we investigated the possibility to classify different performers playing the same melodies at the same manner being subjectively quite similar and very difficult to distinguish even for musically skilled persons. For resolving this problem we propose the use of multifractal (MF) analysis, which is proven as an efficient method for describing and quantifying complex natural structures, phenomena or signals. We found experimentally that parameters associated to some characteristic points within the MF spectrum can be used as music descriptors, thus permitting accurate discrimination of music performers. Our approach is tested on the dataset containing the same songs performed by music group ABBA and by actors in the movie Mamma Mia. As a classifier we used the support vector machines and the classification performance was evaluated by using the four-fold cross-validation. The results of proposed method were compared with those obtained using mel-frequency cepstral coefficients (MFCCs) as descriptors. For the considered two-class problem, the overall accuracy and F-measure higher than 98% are obtained with the MF descriptors, which was considerably better than by using the MFCC descriptors when the best results were less than 77%.
Źródło:
Archives of Acoustics; 2017, 42, 2; 223-233
0137-5075
Pojawia się w:
Archives of Acoustics
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:
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:
A critical comparison of discriminant analysis and svm-based approaches to credit scoring
Porównanie analizy dyskryminacyjnej i maszyn wektorów podpierających w analizie ryzyka kredytowego
Autorzy:
Stąpor, Katarzyna
Powiązania:
https://bibliotekanauki.pl/articles/588064.pdf
Data publikacji:
2016
Wydawca:
Uniwersytet Ekonomiczny w Katowicach
Tematy:
Credit scoring model
Discriminant analysis
Support vector machines
Analiza dyskryminacyjna
Maszyny wektorów podpierających
Model oceny ryzyka kredytowego
Opis:
Credit scoring models are the basis for financial institutions like retail and consumer credit banks. The purpose of these models is to evaluate the likelihood of credit applicants defaulting in order to decide whether to grant them credit. The paper compares two methodologies for building credit scoring models: heteroscedastic discriminant analysis-based with the support vector machines. The real-world credit dataset is used for comparison.
Modele oceny ryzyka kredytowego stanowią podstawę działalności większości instytucji finansowych, zajmujących się udzielaniem kredytów. Celem takich modeli jest ewaluacja prawdopodobieństwa zaprzestania przez kredytobiorcę spłaty udzielonego mu kredytu. W artykule dokonano porównania dwóch modeli oceny ryzyka kredytowego, które wykorzystują nowe metody statystyczne, a także metody uczenia maszynowego do ich konstrukcji: heteroscedastyczną analizę dyskryminacyjną oraz maszyny wektorów podpierających. Dla dokonania porównania tych metod wykorzystany został ogólnie dostępny, niemiecki zbiór kredytowy.
Źródło:
Studia Ekonomiczne; 2016, 288; 59-70
2083-8611
Pojawia się w:
Studia Ekonomiczne
Dostawca treści:
Biblioteka Nauki
Artykuł
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