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Tytuł:
The size of the basic unit in geographical analysis
Autorzy:
Suchożebrski, Jarosław
Kwiatkowska, Joanna M.
Powiązania:
https://bibliotekanauki.pl/articles/2029388.pdf
Data publikacji:
2004-06-01
Wydawca:
Uniwersytet Warszawski. Wydział Geografii i Studiów Regionalnych
Tematy:
quasi-homogenous units
semivariance
nearest-neighbor method
Opis:
In geographical analysis such as mathematical classification and modeling, the study area is divided into a network of basic (quasi-homogenous) units. A technique often used in the delimitation of the basic unit to be analyzed is the division of the study area into a network of uniform geometrical figures (block-centered grid). This article presents two objective methods for dividing the surface area of the study region into a network of basic units. The geometric method makes it possible to determine the optimal size of the basic unit, relative to the surface area being analyzed. This method may be used in analysis conducted on a regional scale, in which case the analysis and the results are characterized by a greater degree of generalization. Geostatistical methods (semivariance analysis and nearest-neighbor analysis) make it possible to determine the size of the cell in the grid of quasi-homogenous units, based on the spatial variation of elements in the natural environment and on the placement of data points. These methods can be recommended for the analysis of small areas (e.g. small drainage areas), when highly detailed data and results are required.
Źródło:
Miscellanea Geographica. Regional Studies on Development; 2004, 11; 151-160
0867-6046
2084-6118
Pojawia się w:
Miscellanea Geographica. Regional Studies on Development
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Optimized jk-nearest neighbor based online signature verification and evaluation of main parameters
Autorzy:
Saleem, Muhammad
Kovari, Bence
Powiązania:
https://bibliotekanauki.pl/articles/2097967.pdf
Data publikacji:
2021
Wydawca:
Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
Tematy:
k-nearest neighbor
online signature verification
classification
Opis:
In this paper, we propose an enhanced jk-nearest neighbor (jk-NN) algorithm for online signature verification. The effect of its main parameters is evaluated and used to build an optimized system. The results show that the jk-NN classifier improves the verification accuracy by 0.73–10% as compared to a traditional one-class k-NN classifier. The algorithm achieved reasonable accuracy for different databases: a 3.93% average error rate when using the SVC2004, 2.6% for the MCYT-100, 1.75% for the SigComp’11, and 6% for the SigComp’15 databases. These results followed a state-of-the-art accuracy evaluation where both forged and genuine signatures were used in the training phase. Another scenario is also presented in this paper by using an optimized jk-NN algorithm that uses specifically chosen parameters and a procedure to pick the optimal value for k using only the signer’s reference signatures to build a practical verification system for real-life scenarios where only these signatures are available. By applying the proposed algorithm, the average error rates that were achieved were 8% for SVC2004, 3.26% for MCYT-100, 13% for SigComp’15, and 2.22% for SigComp’11.
Źródło:
Computer Science; 2021, 22 (4); 539--551
1508-2806
2300-7036
Pojawia się w:
Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Pattern recognition of sacroileitis with the use of multistage logic with a fuzzy loss function
Autorzy:
Burduk, R.
Powiązania:
https://bibliotekanauki.pl/articles/1965805.pdf
Data publikacji:
2004
Wydawca:
Politechnika Gdańska
Tematy:
multistage classifier
sacroiletis
fuzzy loss function
k-nearest neighbor method
Opis:
The article describes the problem of pattern recognition of sacroileitis. Classification is based on a scheme of multistage recognition with a fuzzy loss function dependent on the node of the decision tree. Decision rules are based on k-nearest neighbors at particular internal nodes of the decision-tree. Paper presents influence of comparison fuzzy numbers on classifications results.
Źródło:
TASK Quarterly. Scientific Bulletin of Academic Computer Centre in Gdansk; 2004, 8, 2; 217-221
1428-6394
Pojawia się w:
TASK Quarterly. Scientific Bulletin of Academic Computer Centre in Gdansk
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Non-Invasive Hemoglobin Monitoring Device Using K-Nearest Neighbor and Artificial Neural Network Back Propagation Algorithms
Autorzy:
Munadi, R.
Sussi, S.
Fitriyanti, N.
Ramadan, D. N.
Powiązania:
https://bibliotekanauki.pl/articles/2055237.pdf
Data publikacji:
2022
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
invasive
non-invasive
k-nearest neighbor
artificial neural network
back propagation
Opis:
The invasive method of medically checking hemoglobin level in human body by taking the blood sample of the patient requiring a long time and injuring the patient is seen impractical. A non-invasive method of measuring hemoglobin levels, therefore, is made by applying the K-Nearest Neighbor (KNN) algorithm and the Artificial Neural Network Back Propagation (ANN-BP) algorithm with the Internet of Things-based HTTP protocol to achieve the high accuracy and the low end-to-end delay. Based on tests conducted on a Noninvasive Hemoglobin measuring device connected to Cloud Things Speak, the prediction process using algorithm by means of Python programming based on Android application could work well. The result of this study showed that the accuracy of the K-Nearest Neighbor algorithm was 94.01%; higher than that of the Artificial Neural Network Back Propagation algorithm by 92.45%. Meanwhile, the end-to-end delay was at 6.09 seconds when using the KNN algorithm and at 6.84 seconds when using Artificial Neural Network Back Propagation Algorithm.
Źródło:
International Journal of Electronics and Telecommunications; 2022, 68, 1; 13--18
2300-1933
Pojawia się w:
International Journal of Electronics and Telecommunications
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Effect of choice of dissimilarity measure on classification efficiency with nearest neighbor method
Autorzy:
Górecki, Tomasz
Powiązania:
https://bibliotekanauki.pl/articles/729668.pdf
Data publikacji:
2005
Wydawca:
Uniwersytet Zielonogórski. Wydział Matematyki, Informatyki i Ekonometrii
Tematy:
nearest neighbor method
discriminant coordinates
dissimilarity measures
estimators of classification error
Opis:
In this paper we will precisely analyze the nearest neighbor method for different dissimilarity measures, classical and weighed, for which methods of distinguishing were worked out. We will propose looking for weights in the space of discriminant coordinates. Experimental results based on a number of real data sets are presented and analyzed to illustrate the benefits of the proposed methods. As classical dissimilarity measures we will use the Euclidean metric, Manhattan and post office metric. We gave the first two metrics weights and now these measures are not metrics because the triangle inequality does not hold. Howeover, it does not make them useless for the nearest neighbor classification method. Additionally, we will analyze different methods of tie-breaking.
Źródło:
Discussiones Mathematicae Probability and Statistics; 2005, 25, 2; 217-239
1509-9423
Pojawia się w:
Discussiones Mathematicae Probability and Statistics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Multi-view learning for software defect prediction
Autorzy:
Kiyak, Elife Ozturk
Birant, Derya
Birant, Kokten Ulas
Powiązania:
https://bibliotekanauki.pl/articles/2060905.pdf
Data publikacji:
2021
Wydawca:
Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
Tematy:
software defect prediction
multi-view learning
machine learning
k-nearest neighbor
Opis:
Background: Traditionally, machine learning algorithms have been simply applied for software defect prediction by considering single-view data, meaning the input data contains a single feature vector. Nevertheless, different software engineering data sources may include multiple and partially independent information, which makes the standard single-view approaches ineffective. Objective: In order to overcome the single-view limitation in the current studies, this article proposes the usage of a multi-view learning method for software defect classification problems. Method: The Multi-View k-Nearest Neighbors (MVKNN) method was used in the software engineering field. In this method, first, base classifiers are constructed to learn from each view, and then classifiers are combined to create a robust multi-view model. Results: In the experimental studies, our algorithm (MVKNN) is compared with the standard k-nearest neighbors (KNN) algorithm on 50 datasets obtained from different software bug repositories. The experimental results demonstrate that the MVKNN method outperformed KNN on most of the datasets in terms of accuracy. The average accuracy values of MVKNN are 86.59%, 88.09%, and 83.10% for the NASA MDP, Softlab, and OSSP datasets, respectively. Conclusion: The results show that using multiple views (MVKNN) can usually improve classification accuracy compared to a single-view strategy (KNN) for software defect prediction.
Źródło:
e-Informatica Software Engineering Journal; 2021, 15, 1; 163--184
1897-7979
Pojawia się w:
e-Informatica Software Engineering Journal
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Data-driven temporal-spatial model for the prediction of AQI in Nanjin
Autorzy:
Zhao, Xuan
Song, Meichen
Liu, Anqi
Wang, Yiming
Wang, Tong
Cao, Jinde
Powiązania:
https://bibliotekanauki.pl/articles/1837414.pdf
Data publikacji:
2020
Wydawca:
Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:
air quality prediction
k-Nearest Neighbor
BP neural network
non-monitoring stations
Opis:
Air quality data prediction in urban area is of great significance to control air pollution and protect the public health. The prediction of the air quality in the monitoring station is well studied in existing researches. However, air-quality-monitor stations are insufficient in most cities and the air quality varies from one place to another dramatically due to complex factors. A novel model is established in this paper to estimate and predict the Air Quality Index (AQI) of the areas without monitoring stations in Nanjing. The proposed model predicts AQI in a non-monitoring area both in temporal dimension and in spatial dimension respectively. The temporal dimension model is presented at first based on the enhanced k-Nearest Neighbor (KNN) algorithm to predict the AQI values among monitoring stations, the acceptability of the results achieves 92% for one-hour prediction. Meanwhile, in order to forecast the evolution of air quality in the spatial dimension, the method is utilized with the help of Back Propagation neural network (BP), which considers geographical distance. Furthermore, to improve the accuracy and adaptability of the spatial model, the similarity of topological structure is introduced. Especially, the temporal-spatial model is built and its adaptability is tested on a specific non-monitoring site, Jiulonghu Campus of Southeast University. The result demonstrates that the acceptability achieves 73.8% on average. The current paper provides strong evidence suggesting that the proposed non-parametric and data-driven approach for air quality forecasting provides promising results.
Źródło:
Journal of Artificial Intelligence and Soft Computing Research; 2020, 10, 4; 255-270
2083-2567
2449-6499
Pojawia się w:
Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Spatial structure of managed beech-dominated forest: applicability of nearest neighbors indices
Autorzy:
Szmyt, J.
Powiązania:
https://bibliotekanauki.pl/articles/41043.pdf
Data publikacji:
2012
Wydawca:
Polska Akademia Nauk. Instytut Dendrologii PAN
Tematy:
spatial structure
beech forest
forest
applicability
nearest-neighbor index
Fagus sylvatica
forest management
Opis:
High structural diversity is often attributed to old-growth forests, usually established naturally and unmanaged. Forest diversity should be considered not only in terms of species diversity and richness but also the variation in trees dimension and their spatial distribution have to be taken into consideration. The main goal of this paper was the answer if nearest neighbor indices are suitable for spatial forest structure description. To answer this question results obtained from 3 managed beech-dominated forests from natural regeneration are presented and discussed. The following indices were calculated: Clark-Evans aggregation index (R), DBH and height differentiation indices (TD and TH, respectively) and mingling index (DM) analyzing horizontal and vertical spatial structure of the forest. Results indicated that managed beech forests demonstrated rather homogenous spatial structure in both aspects. Living trees as well as future crop trees were mostly regularly distributed. Spatial variation in DBH and height between living nearest neighbors was rather low. The lowest variation in sizes was demonstrated by future crop trees. Mature beech forests revealed single species structure and other tree species – if present – were spatially segregated from beech. It can be supposed that high homogeneity structure of these forests results from biological characteristics of this species as well as thinning treatments conducted by foresters.
Źródło:
Dendrobiology; 2012, 68
1641-1307
Pojawia się w:
Dendrobiology
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Dynamics of Stochastic vs. Greedy Heuristics in Traveling Salesman Problem
Autorzy:
Białogłowski, M.
Staniaszek, M.
Laskowski, W.
Grudniak, M.
Powiązania:
https://bibliotekanauki.pl/articles/91276.pdf
Data publikacji:
2018
Wydawca:
Warszawska Wyższa Szkoła Informatyki
Tematy:
traveling salesman problem
Nearest Neighbor
Monte Carlo
Simulated Annealing
Genetic Algorithm
particle swarm optimization (PSO)
Opis:
We studied the relative performance of stochastic heuristics in order to establish the relations between the fundamental elements of their mechanisms. The insights on their dynamics, abstracted from the implementation details, may contribute to the development of an efficient framework for design of new probabilistic methods. For that, we applied four general optimization heuristics with varying number of hyperparameters to traveling salesman problem. A problem-specific greedy approach (Nearest Neighbor) served as a reference for the results of: Monte Carlo, Simulated Annealing, Genetic Algorithm, and Particle Swarm Optimization. The more robust heuristics – with higher configuration potential, i.e. with more hyperparameters – outperformed the smart ones, being surpassed only by the method specifically designed for the task.
Źródło:
Zeszyty Naukowe Warszawskiej Wyższej Szkoły Informatyki; 2018, 12, 19; 7-24
1896-396X
2082-8349
Pojawia się w:
Zeszyty Naukowe Warszawskiej Wyższej Szkoły Informatyki
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Fast attack detection method for imbalanced data in industrial cyber-physical systems
Autorzy:
Huang, Meng
Li, Tao
Li, Beibei
Zhang, Nian
Huang, Hanyuan
Powiązania:
https://bibliotekanauki.pl/articles/23944834.pdf
Data publikacji:
2023
Wydawca:
Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:
intrusion detection system
industrial cyber-physical Systems
imbalanced data
all k-nearest neighbor
LightGBM
Opis:
Integrating industrial cyber-physical systems (ICPSs) with modern information technologies (5G, artificial intelligence, and big data analytics) has led to the development of industrial intelligence. Still, it has increased the vulnerability of such systems regarding cybersecurity. Traditional network intrusion detection methods for ICPSs are limited in identifying minority attack categories and suffer from high time complexity. To address these issues, this paper proposes a network intrusion detection scheme, which includes an information-theoretic hybrid feature selection method to reduce data dimensionality and the ALLKNN-LightGBM intrusion detection framework. Experimental results on three industrial datasets demonstrate that the proposed method outperforms four mainstream machine learning methods and other advanced intrusion detection techniques regarding accuracy, F-score, and run time complexity.
Źródło:
Journal of Artificial Intelligence and Soft Computing Research; 2023, 13, 4; 229--245
2083-2567
2449-6499
Pojawia się w:
Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Categorization of Similar Objects Using Bag of Visual Words and k - Nearest Neighbour Classifier
Autorzy:
Artiemjew, P.
Górecki, P.
Sopyła, K.
Powiązania:
https://bibliotekanauki.pl/articles/298103.pdf
Data publikacji:
2012
Wydawca:
Uniwersytet Warmińsko-Mazurski w Olsztynie
Tematy:
kategoryzacja obrazu
metoda k najbliższych sąsiadów
zbiór słów wizualnych
Image categorization
k-Nearest Neighbor Classifier
Bag of Visual Words
Opis:
Image categorization is one of the fundamental tasks in computer vision, it has wide application in methods of artificial intelligence, robotic vision and many others. There are a lot of difficulties in computer vision to overcome, one of them appears during image recognition and classification. The difficulty arises from an image variance, which may be caused by scaling, rotation, changes in a perspective, illumination levels, or partial occlusions. Due to these reasons, the main task is to represent represent images in such way that would allow recognizing them even if they have been modified. Bag of Visual Words (BoVW) approach, which allows for describing local characteristic features of images, has recently gained much attention in the computer vision community. In this article we have presented the results of image classification with the use of BoVW and k - Nearest Neighbor classifier with different kinds of metrics and similarity measures. Additionally, the results of k - NN classification are compared with the ones obtained from a Support Vector Machine classifier.
Źródło:
Technical Sciences / University of Warmia and Mazury in Olsztyn; 2012, 15(2); 293-305
1505-4675
2083-4527
Pojawia się w:
Technical Sciences / University of Warmia and Mazury in Olsztyn
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Face Recognition Comparative Analysis Using Different Machine Learning Approaches
Autorzy:
Ahmed, Nisar
Khan, Farhan Ajmal
Ullah, Zain
Ahmed, Hasnain
Shahzad, Taimur
Ali, Nableela
Powiązania:
https://bibliotekanauki.pl/articles/2024199.pdf
Data publikacji:
2021
Wydawca:
Stowarzyszenie Inżynierów i Techników Mechaników Polskich
Tematy:
linear discriminant analysis
k-nearest neighbor
support vector machine
principal component analysis
liniowa analiza dyskryminacyjna
maszyna wektorów podporowych
analiza głównych składowych
Opis:
The problem of a facial biometrics system was discussed in this research, in which different classifiers were used within the framework of face recognition. Different similarity measures exist to solve the performance of facial recognition problems. Here, four machine learning approaches were considered, namely, K-nearest neighbor (KNN), Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), and Principal Component Analysis (PCA). The usefulness of multiple classification systems was also seen and evaluated in terms of their ability to correctly classify a face. A combination of multiple algorithms such as PCA+1NN, LDA+1NN, PCA+ LDA+1NN, SVM, and SVM+PCA was used. All of them performed with exceptional values of above 90% but PCA+LDA+1N scored the highest average accuracy, i.e. 98%.
Źródło:
Advances in Science and Technology. Research Journal; 2021, 15, 1; 265-272
2299-8624
Pojawia się w:
Advances in Science and Technology. Research Journal
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Zastosowanie metody mini-modeli opartej na hipersześcianie w procesie modelowania danych wielowymiarowych
Application of mini-models method based on hypercube in the modeling process of multidimensional data
Autorzy:
Pietrzykowski, Marcin
Powiązania:
https://bibliotekanauki.pl/articles/1367439.pdf
Data publikacji:
2015
Wydawca:
Uniwersytet Szczeciński. Wydawnictwo Naukowe Uniwersytetu Szczecińskiego
Tematy:
mini-model
local regression
k-nearest neighbor
mathematical modeling
instance based learning
modelowania matematyczne
algorytm najbliższych sąsiadów
lokalna regresja
metody bazujące na próbkach
Opis:
W artykule zaprezentowano metodę samo-uczenia mini-modeli (metodę MM) opartą na hiperbryłach w przestrzeni wielowymiarowej. Jest to metoda nowa i rozwojowa, będąca w trakcie intensywnych badań. Bazuje ona na próbkach pobieranych jedynie z lokalnego otoczenia punktu zapytania, a nie z obszarów odległych od tego punktu. Grupa punktów, używana w procesie uczenia mini-modelu jest ograniczona obszarem hiperbryły. Na tak zdefiniowanym lokalnym otoczeniu punktu zapytania metoda MM w procesie uczenia oraz obliczania odpowiedzi można użyć dowolnej metody aproksymacji. W artykule przedstawiono algorytm uczenia i działania metody w przestrzeni wielowymiarowej bazujący na hipersferycznym układzie współrzędnych. Metodę przebadano na zbiorach danych wielowymiarowych, a wyniki porównano z innymi metodami bazującymi na próbkach.
The article presents self-learning method of mini-models (MM-method) based on polytopes in multidimensional space. The method is new and is an object of intensive research. MM method is the instance based learning method and uses data samples only from the local neighborhood of the query point. Group of points which are used in the model-learning process is constrained by a polytope area. The MM-method can on a defined local area use any approximation algorithm to compute mini-model answer for the query point. The article describes a learning technique based on hyper-spherical coordinate system. The method was used in the modeling task with multidimensional datasets. The results of numerical experiments were compared with other instance based methods.
Źródło:
Zeszyty Naukowe. Studia Informatica; 2015, 38; 91-103
0867-1753
Pojawia się w:
Zeszyty Naukowe. Studia Informatica
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Efficient astronomical data condensation using approximate nearest neighbors
Autorzy:
Łukasik, Szymon
Lalik, Konrad
Sarna, Piotr
Kowalski, Piotr A.
Charytanowicz, Małgorzata
Kulczycki, Piotr
Powiązania:
https://bibliotekanauki.pl/articles/907932.pdf
Data publikacji:
2019
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
big data
astronomical observation
data reduction
nearest neighbor search
kd-trees
duży zbiór danych
obserwacja astronomiczna
redukcja danych
wyszukiwanie najbliższego sąsiada
drzewo kd
Opis:
Extracting useful information from astronomical observations represents one of the most challenging tasks of data exploration. This is largely due to the volume of the data acquired using advanced observational tools. While other challenges typical for the class of big data problems (like data variety) are also present, the size of datasets represents the most significant obstacle in visualization and subsequent analysis. This paper studies an efficient data condensation algorithm aimed at providing its compact representation. It is based on fast nearest neighbor calculation using tree structures and parallel processing. In addition to that, the possibility of using approximate identification of neighbors, to even further improve the algorithm time performance, is also evaluated. The properties of the proposed approach, both in terms of performance and condensation quality, are experimentally assessed on astronomical datasets related to the GAIA mission. It is concluded that the introduced technique might serve as a scalable method of alleviating the problem of the dataset size.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2019, 29, 3; 467-476
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Machine learning-based analysis of English lateral allophones
Autorzy:
Piotrowska, Magdalena
Korvel, Gražina
Kostek, Bożena
Ciszewski, Tomasz
Czyżewski, Andrzej
Powiązania:
https://bibliotekanauki.pl/articles/908115.pdf
Data publikacji:
2019
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
allophone
audio features
artificial neural network
k-nearest neighbor
self organizing map
alofon
cechy akustyczne
sztuczna sieć neuronowa
metoda najbliższych sąsiadów
mapa samoorganizująca
Opis:
Automatic classification methods, such as artificial neural networks (ANNs), the k-nearest neighbor (kNN) and self-organizing maps (SOMs), are applied to allophone analysis based on recorded speech. A list of 650 words was created for that purpose, containing positionally and/or contextually conditioned allophones. For each word, a group of 16 native and non-native speakers were audio-video recorded, from which seven native speakers’ and phonology experts’ speech was selected for analyses. For the purpose of the present study, a sub-list of 103 words containing the English alveolar lateral phoneme /l/ was compiled. The list includes ‘dark’ (velarized) allophonic realizations (which occur before a consonant or at the end of the word before silence) and 52 ‘clear’ allophonic realizations (which occur before a vowel), as well as voicing variants. The recorded signals were segmented into allophones and parametrized using a set of descriptors, originating from the MPEG 7 standard, plus dedicated time-based parameters as well as modified MFCC features proposed by the authors. Classification methods such as ANNs, the kNN and the SOM were employed to automatically detect the two types of allophones. Various sets of features were tested to achieve the best performance of the automatic methods. In the final experiment, a selected set of features was used for automatic evaluation of the pronunciation of dark /l/ by non-native speakers.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2019, 29, 2; 393-405
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
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