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    Wyświetlanie 1-5 z 5
    Tytuł:
    An Eclectic Approach to Network Service Failure Detection Based on Multicriteria Analysis with an Example of Mixing Probabilistic Context Free Grammar Models
    Autorzy:
    Białoń, P.
    Powiązania:
    https://bibliotekanauki.pl/articles/307950.pdf
    Data publikacji:
    2008
    Wydawca:
    Instytut Łączności - Państwowy Instytut Badawczy
    Tematy:
    failure detection
    linear separation
    probabilistic context free grammars
    support vector machine (SVM)
    Opis:
    A method of failure detection in telecommunication networks is presented. This is a meta-method that correlates alarms raised by failure-detection modules based on various philosophies. The correlation takes into account two main characteristics of each module and the whole metamethod: the percentage of false alarms and the percentage of omitted failures. The trade-off between them is tackled with aspiration-based multicriteria analysis. The alarms are correlated using linear classification by support vector machines. An example of the profitability of correlating alarms in such way is shown. This is an example of probabilistic context free grammars (PCFGs), used to model the proper runtime paths of network services (and thus usable for detecting an improper behavior of the services). It is shown that the linearly mixing PCFGs can add context handling to the PCFG model, thus augmenting the capabilities of the model.
    Źródło:
    Journal of Telecommunications and Information Technology; 2008, 4; 32-39
    1509-4553
    1899-8852
    Pojawia się w:
    Journal of Telecommunications and Information Technology
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Designing Smart Antennas Using Machine Learning Algorithms
    Autorzy:
    Samantaray, Barsa
    Das, Kunal Kumar
    Roy, Jibendu Sekhar
    Powiązania:
    https://bibliotekanauki.pl/articles/27312957.pdf
    Data publikacji:
    2023
    Wydawca:
    Instytut Łączności - Państwowy Instytut Badawczy
    Tematy:
    artificial neural network
    decision tree
    ensemble algorithm
    machine learning
    smart antenna
    support vector machine
    Opis:
    Smart antenna technologies improve spectral efficiency, security, energy efficiency, and overall service quality in cellular networks by utilizing signal processing algorithms that provide radiation beams to users while producing nulls for interferers. In this paper, the performance of such ML solutions as the support vector machine (SVM) algorithm, the artificial neural network (ANN), the ensemble algorithm (EA), and the decision tree (DT) algorithm used for forming the beam of smart antennas are compared. A smart antenna array made up of 10 half-wave dipoles is considered. The ANN method is better than the remaining approaches when it comes to achieving beam and null directions, whereas EA offers better performance in terms of reducing the side lobe level (SLL). The maximum SLL is achieved using EA for all the user directions. The performance of the ANN algorithm in terms of forming the beam of a smart antenna is also compared with that of the variable-step size adaptive algorithm.
    Źródło:
    Journal of Telecommunications and Information Technology; 2023, 4; 46--52
    1509-4553
    1899-8852
    Pojawia się w:
    Journal of Telecommunications and Information Technology
    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:
    The Learning System by the Least Squares Support Vector Machine Method and its Application in Medicine
    Autorzy:
    Szewczyk, P.
    Baszun, M.
    Powiązania:
    https://bibliotekanauki.pl/articles/307897.pdf
    Data publikacji:
    2011
    Wydawca:
    Instytut Łączności - Państwowy Instytut Badawczy
    Tematy:
    classification
    Grid-Search
    particle swarm optimization (PSO)
    patients diagnosis
    support vector machine (SVM)
    Opis:
    In the paper it has been presented the possibility of using the least squares support vector machine to the initial diagnosis of patients. In order to find some optimal parameters making the work of the algorithm more detailed, the following techniques have been used: K-fold Cross Validation, Grid-Search, Particle Swarm Optimization. The result of the classification has been checked by some labels assigned by an expert. The created system has been tested on the artificially made data and the data taken from the real database. The results of the computer simulations have been presented in two forms: numerical and graphic. All the algorithms have been implemented in the C# language.
    Źródło:
    Journal of Telecommunications and Information Technology; 2011, 3; 109-113
    1509-4553
    1899-8852
    Pojawia się w:
    Journal of Telecommunications and Information Technology
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Support Vector Machine based Decoding Algorithm for BCH Codes
    Autorzy:
    Sudharsan, V.
    Yamuna, B.
    Powiązania:
    https://bibliotekanauki.pl/articles/958048.pdf
    Data publikacji:
    2016
    Wydawca:
    Instytut Łączności - Państwowy Instytut Badawczy
    Tematy:
    BCH codes
    Chase-2 algorithm
    coding gain
    kernel
    multi-class classification
    Soft Decision Decoding
    Support Vector Machine
    Opis:
    Modern communication systems require robust, adaptable and high performance decoders for efficient data transmission. Support Vector Machine (SVM) is a margin based classification and regression technique. In this paper, decoding of Bose Chaudhuri Hocquenghem codes has been approached as a multi-class classification problem using SVM. In conventional decoding algorithms, the procedure for decoding is usually fixed irrespective of the SNR environment in which the transmission takes place, but SVM being a machinelearning algorithm is adaptable to the communication environment. Since the construction of SVM decoder model uses the training data set, application specific decoders can be designed by choosing the training size efficiently. With the soft margin width in SVM being controlled by an equation, which has been formulated as a quadratic programming problem, there are no local minima issues in SVM and is robust to outliers.
    Źródło:
    Journal of Telecommunications and Information Technology; 2016, 2; 108-112
    1509-4553
    1899-8852
    Pojawia się w:
    Journal of Telecommunications and Information Technology
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-5 z 5

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