Zobrazeno 1 - 10
of 155
pro vyhledávání: '"Adam Wierzbicki"'
Autor:
Małgorzata Chlabicz, Aleksandra Nabożny, Jolanta Koszelew, Wojciech Łaguna, Anna Szpakowicz, Paweł Sowa, Wojciech Budny, Katarzyna Guziejko, Magdalena Róg-Makal, Sławomir Pancewicz, Maciej Kondrusik, Piotr Czupryna, Beata Cudowska, Dariusz Lebensztejn, Anna Moniuszko-Malinowska, Adam Wierzbicki, Karol A Kamiński
Publikováno v:
Journal of Medical Internet Research, Vol 26, p e48130 (2024)
BackgroundAlthough researchers extensively study the rapid generation and spread of misinformation about the novel coronavirus during the pandemic, numerous other health-related topics are contaminating the internet with misinformation that have not
Externí odkaz:
https://doaj.org/article/e3d363f1c4d94d44a084f2cd8eac8b62
Publikováno v:
PLoS ONE, Vol 19, Iss 6, p e0303806 (2024)
Over the past two decades, there has been a growing interest in research on aging and the decision-making behavior of older consumers. The subject of this article is multi-attribute decisions made using product comparison, a widely used functionality
Externí odkaz:
https://doaj.org/article/b398db1e9a5042bd80c97ab77590abce
Publikováno v:
Frontiers in Human Neuroscience, Vol 16 (2022)
Understanding how humans evaluate credibility is an important scientific question in the era of fake news. Source credibility is among the most important aspects of credibility evaluations. One of the most direct ways to understand source credibility
Externí odkaz:
https://doaj.org/article/142cb0f75b65412db5b243fa36a75e6c
Autor:
Aleksandra Nabożny, Bartłomiej Balcerzak, Adam Wierzbicki, Mikołaj Morzy, Małgorzata Chlabicz
Publikováno v:
JMIR Medical Informatics, Vol 9, Iss 11, p e26065 (2021)
BackgroundThe spread of false medical information on the web is rapidly accelerating. Establishing the credibility of web-based medical information has become a pressing necessity. Machine learning offers a solution that, when properly deployed, can
Externí odkaz:
https://doaj.org/article/c482160e1d4c46f3be5e744bce480158
Publikováno v:
Frontiers in Human Neuroscience, Vol 15 (2021)
Understanding how humans evaluate credibility is an important scientific question in the era of fake news. Message credibility is among crucial aspects of credibility evaluations. One of the most direct ways to understand message credibility is to us
Externí odkaz:
https://doaj.org/article/07c1e8457a4642bf85f82843622b4e5f
Publikováno v:
Frontiers in Neuroinformatics, Vol 14 (2020)
Understanding how humans evaluate credibility is an important scientific question in the era of fake news. Source credibility is among the most important aspects of credibility evaluations. One of the most direct ways to understand source credibility
Externí odkaz:
https://doaj.org/article/44c70d47466243d4a970dac15540409c
Publikováno v:
International Journal of Distributed Sensor Networks, Vol 11 (2015)
With a rapid progress of numerous applications in wireless sensor networks (WSNs), performance evaluation and analysis techniques face new challenges in energy efficiency area in WSN applications. One of the key issues is to perform the security trad
Externí odkaz:
https://doaj.org/article/3ac1ff936e794f3f89a330fdbbbe36f4
Publikováno v:
Journal of Telecommunications and Information Technology, Iss 4 (2006)
The dimensioning of telecommunication networks that carry elastic traffic requires the fulfillment of two conflicting goals: maximizing the total network throughput and providing fairness to all flows. Fairness in telecommunication network design is
Externí odkaz:
https://doaj.org/article/635d0f4475984b75ad750233bf2d846b
Publikováno v:
Journal of Telecommunications and Information Technology, Iss 3 (2003)
Resource allocation problems are concerned with the allocation of limited resources among competing activities so as to achieve the best overall performances of the system but providing fair treatment of all the competitors. Telecommunication network
Externí odkaz:
https://doaj.org/article/d33ce0c23e464b5394c046440cc56be5
Publikováno v:
Journal of Artificial Intelligence and Soft Computing Research. 13:73-94
Recommendation algorithms trained on a training set containing sub-optimal decisions may increase the likelihood of making more bad decisions in the future. We call this harmful effect self-induced bias, to emphasize that the bias is driven directly