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    Wyświetlanie 1-11 z 11
    Tytuł:
    The Hough transform in the classification process of inland ships
    Autorzy:
    Bobkowska, Katarzyna
    Wawrzyniak, Natalia
    Powiązania:
    https://bibliotekanauki.pl/articles/135214.pdf
    Data publikacji:
    2019
    Wydawca:
    Akademia Morska w Szczecinie. Wydawnictwo AMSz
    Tematy:
    Hough transform
    k Nearest Neighbors (kNN)
    image processing
    classification
    ship recognition
    line detection
    Opis:
    This article presents an analysis of the possibilities of using image processing methods for feature extraction that allows kNN classification based on a ship’s image delivered from an on-water video surveillance system. The subject of the analysis is the Hough transform which enables the detection of straight lines in an image. The recognized straight lines and the information about them serve as features in the classification process. Above all, this approach allows ships to be recognized, which can then be characterized by a specific representation and shape. Recreational units that are often seen on inland waters were classified correctly using this method. Each analyzed camera image was previously prepared – brought to the form where the ship was visible from the side and the background removed (they were monochromatic – white). The results obtained in this work will allow for the development of the final ship classification method based on camera images. This method is a significant part of the emerging system prototype, which is implemented as part of the Automatic Ship Recognition and Identification (SHREC) project.
    Źródło:
    Zeszyty Naukowe Akademii Morskiej w Szczecinie; 2019, 58 (130); 9-15
    1733-8670
    2392-0378
    Pojawia się w:
    Zeszyty Naukowe Akademii Morskiej w Szczecinie
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Metrics and similarities in modeling dependencies between continuous and nominal data
    Autorzy:
    Grabowski, M.
    Korpusik, M.
    Powiązania:
    https://bibliotekanauki.pl/articles/91361.pdf
    Data publikacji:
    2013
    Wydawca:
    Warszawska Wyższa Szkoła Informatyki
    Tematy:
    k-nearest neighbors algorithm
    data metrics
    classification
    continuous data
    nominal data
    Opis:
    Classification theory analytical paradigm investigates continuous data only. When we deal with a mix of continuous and nominal attributes in data records, difficulties emerge. Usually, the analytical paradigm treats nominal attributes as continuous ones via numerical coding of nominal values (often a bit ad hoc). We propose a way of keeping nominal values within analytical paradigm with no pretending that nominal values are continuous. The core idea is that the information hidden in nominal values influences on metric (or on similarity function) between records of continuous and nominal data. Adaptation finds relevant parameters which influence metric between data records. Our approach works well for classifier induction algorithms where metric or similarity is generic, for instance k nearest neighbor algorithm or proposed here support of decision tree induction by similarity function between data. The k-nn algorithm working with continuous and nominal data behaves considerably better, when nominal values are processed by our approach. Algorithms of analytical paradigm using linear and probability machinery, like discriminant adaptive nearest-neighbor or Fisher’s linear discriminant analysis, cause some difficulties. We propose some possible ways to overcome these obstacles for adaptive nearest neighbor algorithm.
    Źródło:
    Zeszyty Naukowe Warszawskiej Wyższej Szkoły Informatyki; 2013, 7, 10; 25-37
    1896-396X
    2082-8349
    Pojawia się w:
    Zeszyty Naukowe Warszawskiej Wyższej Szkoły Informatyki
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    The PM-M prototype selection system
    Autorzy:
    Grudziński, K.
    Powiązania:
    https://bibliotekanauki.pl/articles/206602.pdf
    Data publikacji:
    2016
    Wydawca:
    Polska Akademia Nauk. Instytut Badań Systemowych PAN
    Tematy:
    selection of reference instances
    prototype selection
    k-Nearest Neighbors algorithm
    classification of data
    Opis:
    In this paper, the algorithm, realizing the author’s prototype selection method, called PM-M (Partial Memory - Minimization) is described in details. Computational experiments that have been carried out with the raw PM-M model and with its majority ensembles indicate that even for the system, for which the average size of the selected prototype sets constitutes only about five percent of the size of the original training datasets, the obtained results of classification are still in a good statistical agreement with the 1-Nearest Neighbor (IB1) model which has been trained on the original (i.e. unpruned) data. It has also been shown that the system under study is competitive in terms of generalization ability with respect to other well established prototype selection systems, such as, for example, CHC, SSMA and GGA. Moreover, the proposed algorithm has shown approximately one to three orders of magnitude decrement of time requirements with respect to the necessary time, needed to complete the calculations, relative to the reference prototype classifiers, taken for comparison. It has also been demonstrated that the PM-M system can be directly applied to analysis of very large data unlike most other prototype methods, which have to rely on the stratification approach.
    Źródło:
    Control and Cybernetics; 2016, 45, 4; 539-561
    0324-8569
    Pojawia się w:
    Control and Cybernetics
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Detection of distributed denial of service attacks for IoT-based healthcare systems
    Autorzy:
    Kaur, Gaganjot
    Gupta, Prinima
    Powiązania:
    https://bibliotekanauki.pl/articles/38701793.pdf
    Data publikacji:
    2023
    Wydawca:
    Instytut Podstawowych Problemów Techniki PAN
    Tematy:
    software defined networking
    k-nearest neighbors
    distributed denial of service
    DPTCM-KNN approach
    SVM
    sieci definiowane programowo
    k-najbliższy sąsiad
    rozproszona odmowa usługi
    Opis:
    One of the major common assaults in the current Internet of things (IoT) network-based healthcare infrastructures is distributed denial of service (DDoS). The most challenging task in the current environment is to manage the creation of vast multimedia data from the IoT devices, which is difficult to be handled solely through the cloud. As the software defined networking (SDN) is still in its early stages, sampling-oriented measurement techniques used today in the IoT network produce low accuracy, increased memory usage, low attack detection, higher processing and network overheads. The aim of this research is to improve attack detection accuracy by using the DPTCM-KNN approach. The DPTCMKNN technique outperforms support vector machine (SVM), yet it still has to be improved. For healthcare systems, this work develops a unique approach for detecting DDoS assaults on SDN using DPTCM-KNN.
    Źródło:
    Computer Assisted Methods in Engineering and Science; 2023, 30, 2; 167-186
    2299-3649
    Pojawia się w:
    Computer Assisted Methods in Engineering and Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Comparative Study of Supervised Learning Methods for Malware Analysis
    Autorzy:
    Kruczkowski, M.
    Niewiadomska-Szynkiewicz, E.
    Powiązania:
    https://bibliotekanauki.pl/articles/309481.pdf
    Data publikacji:
    2014
    Wydawca:
    Instytut Łączności - Państwowy Instytut Badawczy
    Tematy:
    data classification
    k-Nearest Neighbors
    malware analysis
    Naive Bayes
    support vector machine (SVM)
    Opis:
    Malware is a software designed to disrupt or even damage computer system or do other unwanted actions. Nowadays, malware is a common threat of the World Wide Web. Anti-malware protection and intrusion detection can be significantly supported by a comprehensive and extensive analysis of data on the Web. The aim of such analysis is a classification of the collected data into two sets, i.e., normal and malicious data. In this paper the authors investigate the use of three supervised learning methods for data mining to support the malware detection. The results of applications of Support Vector Machine, Naive Bayes and k-Nearest Neighbors techniques to classification of the data taken from devices located in many units, organizations and monitoring systems serviced by CERT Poland are described. The performance of all methods is compared and discussed. The results of performed experiments show that the supervised learning algorithms method can be successfully used to computer data analysis, and can support computer emergency response teams in threats detection.
    Źródło:
    Journal of Telecommunications and Information Technology; 2014, 4; 24-33
    1509-4553
    1899-8852
    Pojawia się w:
    Journal of Telecommunications and Information Technology
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Propozycja agregowanego klasyfikatora kNN z selekcją zmiennych
    The proposition of the kNN ensemble with feature selection.
    Autorzy:
    Kubus, Mariusz
    Powiązania:
    https://bibliotekanauki.pl/articles/424859.pdf
    Data publikacji:
    2016
    Wydawca:
    Wydawnictwo Uniwersytetu Ekonomicznego we Wrocławiu
    Tematy:
    k nearest neighbors
    ensemble
    feature selection
    ReliefF algorithm
    Opis:
    Aggregated classification trees have gained recognition due to improved stability, and frequently reduced bias. However, the adaptation of this approach to the k nearest neighbors method (kNN), faces some difficulties: the relatively high stability of these classifiers, and an increase of misclassifications when the variables without discrimination power are present in the training set. In this paper we propose aggregated kNN classifier with feature selection. Its classification accuracy has been verified on the real data with added irrelevant variables.
    Źródło:
    Econometrics. Ekonometria. Advances in Applied Data Analytics; 2016, 3 (53); 32-41
    1507-3866
    Pojawia się w:
    Econometrics. Ekonometria. Advances in Applied Data Analytics
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Instance based kNN modification for classification of medical data
    Autorzy:
    Orczyk, T.
    Porwik, P.
    Lewandowski, M.
    Cholewa, M.
    Powiązania:
    https://bibliotekanauki.pl/articles/333353.pdf
    Data publikacji:
    2015
    Wydawca:
    Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
    Tematy:
    k Nearest Neighbors
    kNN
    unbalanced data
    incomplete data
    algorytm najbliższych sąsiadów
    dane niesymetryczne
    dane niekompletne
    Opis:
    Paper describes a novel modification to a well known kNN algorithm, which enables using it for medical data, which often is a class-imbalanced data with randomly missing values. Paper presents the modified algorithm details, experiment setup, results obtained on a cross validated classification of a benchmark database with randomly removed values (missing data) and records (class imbalance), and their comparison with results of the state of the art classification algorithms.
    Źródło:
    Journal of Medical Informatics & Technologies; 2015, 24; 99-106
    1642-6037
    Pojawia się w:
    Journal of Medical Informatics & Technologies
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Improving prediction models applied in systems monitoring natural hazards and machinery
    Autorzy:
    Sikora, M.
    Sikora, B.
    Powiązania:
    https://bibliotekanauki.pl/articles/331302.pdf
    Data publikacji:
    2012
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    zagrożenie naturalne
    szereg czasowy
    k-najbliższy sąsiad
    natural hazards monitoring
    regression rules
    time series forecasting
    k-nearest neighbors
    Opis:
    A method of combining three analytic techniques including regression rule induction, the k-nearest neighbors method and time series forecasting by means of the ARIMA methodology is presented. A decrease in the forecasting error while solving problems that concern natural hazards and machinery monitoring in coal mines was the main objective of the combined application of these techniques. The M5 algorithm was applied as a basic method of developing prediction models. In spite of an intensive development of regression rule induction algorithms and fuzzy-neural systems, the M5 algorithm is still characterized by the generalization ability and unbeatable time of data model creation competitive with other systems. In the paper, two solutions designed to decrease the mean square error of the obtained rules are presented. One consists in introducing into a set of conditional variables the so-called meta-variable (an analogy to constructive induction) whose values are determined by an autoregressive or the ARIMA model. The other shows that limitation of a data set on which the M5 algorithm operates by the k-nearest neighbor method can also lead to error decreasing. Moreover, three application examples of the presented solutions for data collected by systems of natural hazards and machinery monitoring in coal mines are described. In Appendix, results of several benchmark data sets analyses are given as a supplement of the presented results.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2012, 22, 2; 477-491
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    A k-Nearest Neighbors Method for Classifying User Sessions in E-Commerce Scenario
    Autorzy:
    Suchacka, G.
    Skolimowska-Kulig, M.
    Potempa, A.
    Powiązania:
    https://bibliotekanauki.pl/articles/308645.pdf
    Data publikacji:
    2015
    Wydawca:
    Instytut Łączności - Państwowy Instytut Badawczy
    Tematy:
    data mining
    e-commerce
    k-Nearest Neighbors
    k-NN
    log file analysis
    online store
    R-project
    supervised classification
    web mining
    Web store
    Web traffic
    Web usage mining
    Opis:
    This paper addresses the problem of classification of user sessions in an online store into two classes: buying sessions (during which a purchase confirmation occurs) and browsing sessions. As interactions connected with a purchase confirmation are typically completed at the end of user sessions, some information describing active sessions may be observed and used to assess the probability of making a purchase. The authors formulate the problem of predicting buying sessions in a Web store as a supervised classification problem where there are two target classes, connected with the fact of finalizing a purchase transaction in session or not, and a feature vector containing some variables describing user sessions. The presented approach uses the k-Nearest Neighbors (k-NN) classification. Based on historical data obtained from online bookstore log files a k-NN classifier was built and its efficiency was verified for different neighborhood sizes. A 11-NN classifier was the most effective both in terms of buying session predictions and overall predictions, achieving sensitivity of 87.5% and accuracy of 99.85%.
    Źródło:
    Journal of Telecommunications and Information Technology; 2015, 3; 64-69
    1509-4553
    1899-8852
    Pojawia się w:
    Journal of Telecommunications and Information Technology
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Space-Time-Frequency Machine Learning for Improved 4G/5G Energy Detection
    Autorzy:
    Wasilewska, Małgorzata
    Bogucka, Hanna
    Powiązania:
    https://bibliotekanauki.pl/articles/226216.pdf
    Data publikacji:
    2020
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    spectrum sensing
    cognitive radio
    machine learning
    energy detection
    4G
    LTE
    5G
    k-nearest neighbors
    random forest
    Opis:
    In this paper, the future Fifth Generation (5G New Radio) radio communication system has been considered, coexisting and sharing the spectrum with the incumbent Fourth Generation (4G) Long-Term Evolution (LTE) system. The 4G signal presence is detected in order to allow for opportunistic and dynamic spectrum access of 5G users. This detection is based on known sensing methods, such as energy detection, however, it uses machine learning in the domains of space, time and frequency for sensing quality improvement. Simulation results for the considered methods: k-Nearest Neighbor sand Random Forest show that these methods significantly improves the detection probability.
    Źródło:
    International Journal of Electronics and Telecommunications; 2020, 66, 1; 217-223
    2300-1933
    Pojawia się w:
    International Journal of Electronics and Telecommunications
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Curve skeleton extraction via k-nearest-neighbors based contraction
    Autorzy:
    Zhou, Jianling
    Liu, Ji
    Zhang, Min
    Powiązania:
    https://bibliotekanauki.pl/articles/331332.pdf
    Data publikacji:
    2020
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    curve skeleton
    points contraction
    point cloud
    k nearest neighbors
    szkieletyzacja
    chmura punktów
    metoda k najbliższych sąsiadów
    Opis:
    We propose a skeletonization algorithm that is based on an iterative points contraction. We make an observation that the local center that is obtained via optimizing the sum of the distance to k nearest neighbors possesses good properties of robustness to noise and incomplete data. Based on such an observation, we devise a skeletonization algorithm that mainly consists of two stages: points contraction and skeleton nodes connection. Extensive experiments show that our method can work on raw scans of real-world objects and exhibits better robustness than the previous results in terms of extracting topology-preserving curve skeletons.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2020, 30, 1; 123-132
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-11 z 11

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