Zobrazeno 1 - 10
of 34
pro vyhledávání: '"Izabella Antoniuk"'
Publikováno v:
International Journal of Electronics and Telecommunications, Vol vol. 69, Iss No 3, Pp 461-468 (2023)
The following paper presents the players profiling methodology applied to the turn-based computer game in the audience-driven system. The general scope are mobile games where the players compete against each other and are able to tackle challenges pr
Externí odkaz:
https://doaj.org/article/c71ea7a382ab4243aa11bfe2ed9a0f68
Autor:
Agata Przybyś-Małaczek, Izabella Antoniuk, Karol Szymanowski, Michał Kruk, Alexander Sieradzki, Adam Dohojda, Przemysław Szopa, Jarosław Kurek
Publikováno v:
Applied Sciences, Vol 14, Iss 13, p 5913 (2024)
This evaluation of deep learning and traditional machine learning methods for tool state recognition in milling processes aims to automate furniture manufacturing. It compares the performance of long short-term memory (LSTM) networks, support vector
Externí odkaz:
https://doaj.org/article/a9f1abbd362d4a76ab84c501a0d95c99
Publikováno v:
Bulletin of the Polish Academy of Sciences: Technical Sciences, Vol 71, Iss 6 (2023)
In this paper, we present an improved efficient capsule network (CN) model for the classification of the Kuzushiji-MNIST and Kuzushiji-49 benchmark datasets. CNs are a promising approach in the field of deep learning, offering advantages such as robu
Externí odkaz:
https://doaj.org/article/771192afdfca49f4933a70237858d68a
Autor:
Jarosław Kurek, Gniewko Niedbała, Tomasz Wojciechowski, Bartosz Świderski, Izabella Antoniuk, Magdalena Piekutowska, Michał Kruk, Krzysztof Bobran
Publikováno v:
Agriculture, Vol 13, Iss 12, p 2259 (2023)
This research delves into the application of machine learning methods for predicting the yield of potato varieties used for French fries in Poland. By integrating a comprehensive dataset comprising agronomical, climatic, soil, and satellite-based veg
Externí odkaz:
https://doaj.org/article/1120107b6ca440cd9d26c4916d45bec7
Publikováno v:
Sensors, Vol 23, Iss 13, p 5850 (2023)
In this article, we present a novel approach to tool condition monitoring in the chipboard milling process using machine learning algorithms. The presented study aims to address the challenges of detecting tool wear and predicting tool failure in rea
Externí odkaz:
https://doaj.org/article/8e51061604a94041a2682499f568199b
Autor:
Izabella Antoniuk, Jarosław Kurek, Artur Krupa, Grzegorz Wieczorek, Michał Bukowski, Michał Kruk, Albina Jegorowa
Publikováno v:
Sensors, Vol 23, Iss 3, p 1109 (2023)
In this paper, a novel approach to evaluation of feature extraction methodologies is presented. In the case of machine learning algorithms, extracting and using the most efficient features is one of the key problems that can significantly influence o
Externí odkaz:
https://doaj.org/article/babb70c5853342f89e40b44d727493b4
Autor:
Jarosław Kurek, Artur Krupa, Izabella Antoniuk, Arlan Akhmet, Ulan Abdiomar, Michał Bukowski, Karol Szymanowski
Publikováno v:
Sensors, Vol 23, Iss 1, p 448 (2023)
In this article, an automated method for tool condition monitoring is presented. When producing items in large quantities, pointing out the exact time when the element needs to be exchanged is crucial. If performed too early, the operator gets rid of
Externí odkaz:
https://doaj.org/article/aaa4bd7eb7b742029a82618efb4d61ad
Autor:
Gniewko Niedbała, Jarosław Kurek, Bartosz Świderski, Tomasz Wojciechowski, Izabella Antoniuk, Krzysztof Bobran
Publikováno v:
Agriculture, Vol 12, Iss 12, p 2089 (2022)
In this paper, we present a high-accuracy model for blueberry yield prediction, trained using structurally innovative data sets. Blueberries are blooming plants, valued for their antioxidant and anti-inflammatory properties. Yield on the plantations
Externí odkaz:
https://doaj.org/article/7092a01fe6a9454891bb5d7e08043b43
Publikováno v:
Sensors, Vol 21, Iss 11, p 3834 (2021)
This paper presents a novel approach to the assessment of decision confidence when multi-class recognition is concerned. When many classification problems are considered, while eliminating human interaction with the system might be one goal, it is no
Externí odkaz:
https://doaj.org/article/fe93f61d0b2f403faf715ad0eb91c697
Publikováno v:
Sensors, Vol 20, Iss 23, p 6978 (2020)
In this article, a Siamese network is applied to the drill wear classification problem. For furniture companies, one of the main problems that occurs during the production process is finding the exact moment when the drill should be replaced. When th
Externí odkaz:
https://doaj.org/article/ee7b7649dc2f47779ff815015c6f408a