Wszystkie pola: Patora-Wysocka, Zofia - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: A new approach to detection of changes in multidimensional patterns. Part 2
Autorzy:: Gałkowski, Tomasz
Krzyżak, Adam
Patora-Wysocka, Zofia
Filutowicz, Zbigniew
Wang, Lipo
Powiązania:: https://bibliotekanauki.pl/articles/2031116.pdf
Data publikacji:: 2021
Wydawca:: Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:: edge curve detection
regression function
nonparametric estimation
Opis:: In the paper we develop an algorithm based on the Parzen kernel estimate for detection of sudden changes in 3-dimensional shapes which happen along the edge curves. Such problems commonly arise in various areas of computer vision, e.g., in edge detection, bioinformatics and processing of satellite imagery. In many engineering problems abrupt change detection may help in fault protection e.g. the jump detection in functions describing the static and dynamic properties of the objects in mechanical systems. We developed an algorithm for detecting abrupt changes which is nonparametric in nature and utilizes Parzen regression estimates of multivariate functions and their derivatives. In tests we apply this method, particularly but not exclusively, to the functions of two variables.
Źródło:: Journal of Artificial Intelligence and Soft Computing Research; 2021, 11, 3; 217-227
2083-2567
2449-6499
Pojawia się w:: Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 2.

Tytuł:: Detecting anomalies in advertising web traffic with the use of the variational autoencoder
Autorzy:: Gabryel, Marcin
Lada, Dawid
Filutowicz, Zbigniew
Patora-Wysocka, Zofia
Kisiel-Dorohinicki, Marek
Chen, Guang Yi
Powiązania:: https://bibliotekanauki.pl/articles/2147149.pdf
Data publikacji:: 2022
Wydawca:: Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:: anomaly detection
web traffic
ad fraud
variational autoencoder
Opis:: This paper presents a neural network model for identifying non-human traffic to a website, which is significantly different from visits made by regular users. Such visits are undesirable from the point of view of the website owner as they are not human activity, and therefore do not bring any value, and, what is more, most often involve costs incurred in connection with the handling of advertising. They are made most often by dishonest publishers using special software (bots) to generate profits. Bots are also used in scraping, which is automatic scanning and downloading of website content, which actually is not in the interest of website authors. The model proposed in this work is learnt by data extracted directly from the web browser during website visits. This data is acquired by using a specially prepared JavaScript that monitors the behavior of the user or bot. The appearance of a bot on a website generates parameter values that are significantly different from those collected during typical visits made by human website users. It is not possible to learn more about the software controlling the bots and to know all the data generated by them. Therefore, this paper proposes a variational autoencoder (VAE) neural network model with modifications to detect the occurrence of abnormal parameter values that deviate from data obtained from human users’ Internet traffic. The algorithm works on the basis of a popular autoencoder method for detecting anomalies, however, a number of original improvements have been implemented. In the study we used authentic data extracted from several large online stores.
Źródło:: Journal of Artificial Intelligence and Soft Computing Research; 2022, 12, 4; 255--266
2083-2567
2449-6499
Pojawia się w:: Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:: Biblioteka Nauki

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