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Wyszukujesz frazę "Bilski, A." wg kryterium: Autor


Wyświetlanie 1-3 z 3
Tytuł:
A Review of Artificial Intelligence Algorithms in Document Classification
Autorzy:
Bilski, A.
Powiązania:
https://bibliotekanauki.pl/articles/226245.pdf
Data publikacji:
2011
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
classifier
text classification
data mining
information retrieval
machine learning algorithms
Opis:
With the evolution of Internet, the meaning and accessibility of text documents and electronic information has increased. The automatic text categorization methods became essential in the information organization and data mining process. A proper classification of e-documents, various Internet information, blogs, emails and digital libraries requires application of data mining and machine learning algorithms to retrieve the desired data. The following paper describes the most important techniques and methodologies used for the text classification. Advantages and effectiveness of contemporary algorithms are compared and their most notable applications presented.
Źródło:
International Journal of Electronics and Telecommunications; 2011, 57, 3; 263-270
2300-1933
Pojawia się w:
International Journal of Electronics and Telecommunications
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Hysteresis Modeling Using a Preisach Operator
Autorzy:
Bilski, A.
Twardy, M.
Powiązania:
https://bibliotekanauki.pl/articles/226738.pdf
Data publikacji:
2010
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
Preisach operator
hysteresis
probability density function
Opis:
The aim of this paper is to present the analysis and modeling of the hysteresis phenomenon using a Preisach operator. The fundamentals of parameterized hysteresis modeling are introduced, by utilizing three probability density functions. Then, the Preisach operator and its characteristics are defined. Subsequently, results of simulations obtained by means of the aforementioned functions are presented and compared to the ones obtained by other authors.
Źródło:
International Journal of Electronics and Telecommunications; 2010, 56, 4; 473-478
2300-1933
Pojawia się w:
International Journal of Electronics and Telecommunications
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Detection of Wood Boring Insects’ Larvae Based on the Acoustic Signal Analysis and the Artificial Intelligence Algorithm
Autorzy:
Bilski, P.
Bobiński, P.
Krajewski, A.
Witomski, P.
Powiązania:
https://bibliotekanauki.pl/articles/177879.pdf
Data publikacji:
2017
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
wood boring insects identification
artificial intelligence classification
accelerometer
Opis:
The paper presents an application of signal processing and computational intelligence methods to detect presence of the wood boring insects larvae in the wooden constructions (such as the furniture of buildings). Such insects are one of the main sources of the degradation in such objects, therefore they should be detected as quickly as possible, before inflicting serious damage. The presented work involved the acoustic monitoring for detecting the presence of the larvae inside pieces of wood. An accelerometer was used to record the sound, further analyzed by a computer algorithm extracting features important for artificial-intelligence (AI) based classification employed to detect the old house borer’s (Hylotrupes bajulus L.) activity. The presented task is difficult, as the sounds made by the larvae are of relatively low amplitude and the background noise caused by people, electrical appliances or other sources may significantly degrade the accuracy of detection. The classification of sounds is needed to separate sources of noise which deteriorate the proper larva detection and should be suppressed if possible. The employed classification was based on features defined in the time domain followed by the support vector machine used as the binary classifier. The results allowed us to assess the effectiveness of the old house borer’s detection by the acoustic analysis enhanced with the AI algorithm.
Źródło:
Archives of Acoustics; 2017, 42, 1; 61-70
0137-5075
Pojawia się w:
Archives of Acoustics
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-3 z 3

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