Zobrazeno 1 - 10
of 10
pro vyhledávání: '"Vaidotas Drungilas"'
Autor:
Pranas Jaruševičius, Lukas Paulauskas, Vaidotas Drungilas, Mantas Jurgelaitis, Tomas Blažauskas
Publikováno v:
Applied Sciences, Vol 14, Iss 14, p 6366 (2024)
Currently, the integration of immersive virtual reality applications into learning management systems is limited, which disadvantages kinesthetic learners, as the majority of learning material is mostly written or visual in nature. This research pres
Externí odkaz:
https://doaj.org/article/d231a56750a64605858280388c6d74ae
Publikováno v:
Applied Sciences, Vol 13, Iss 12, p 7010 (2023)
This study introduces a novel performance-based weighting scheme for ensemble learning using the Shapley value. The weighting uses the reciprocal of binary cross-entropy as a base learner’s performance metric and estimates its Shapley value to meas
Externí odkaz:
https://doaj.org/article/a48081a78458412a8573ad9899531bdf
Publikováno v:
Applied Sciences, Vol 13, Iss 1, p 487 (2022)
Blockchain smart contracts can support the decentralisation of business processes, but due to smart contracts’ specifics, their development is a complicated process. Introducing model-driven development principles in smart contract development can
Externí odkaz:
https://doaj.org/article/c1b80d746c0f4931aaeb336f2e1ca32a
Publikováno v:
Applied Sciences, Vol 11, Iss 3, p 1010 (2021)
Federated learning is a branch of machine learning where a shared model is created in a decentralized and privacy-preserving fashion, but existing approaches using blockchain are limited by tailored models. We consider the possibility to extend a set
Externí odkaz:
https://doaj.org/article/69ea140e79044bdfb673cb59d7251351
Publikováno v:
Applied Sciences; Volume 13; Issue 12; Pages: 7010
This study introduces a novel performance-based weighting scheme for ensemble learning using the Shapley value. The weighting uses the reciprocal of binary cross-entropy as a base learner’s performance metric and estimates its Shapley value to meas
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=multidiscipl::cfa2478b6352358968b753f7e85e0e2c
Publikováno v:
Applied Sciences, Vol 11, Iss 1010, p 1010 (2021)
Applied Sciences
Volume 11
Issue 3
Applied Sciences
Volume 11
Issue 3
Federated learning is a branch of machine learning where a shared model is created in a decentralized and privacy-preserving fashion, but existing approaches using blockchain are limited by tailored models. We consider the possibility to extend a set
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::69d9303d2d03ebf9b3bee1347a962558
https://vb.ktu.edu/KTU:ELABAPDB85484138&prefLang=en_US
https://vb.ktu.edu/KTU:ELABAPDB85484138&prefLang=en_US
Autor:
Vaidotas Drungilas, Evaldas Vaiciukynas, Rita Butkienė, Mantas Jurgelaitis, Lina Čeponienė, Jonas Čeponis
Publikováno v:
Advances in Intelligent Systems and Computing ISBN: 9783030726539
WorldCIST (4)
WorldCIST (4)
Model Driven Architecture (MDA) together with Unified Modelling Language (UML) presents a framework which transfers the emphasis of development from source code to the higher level of abstraction i.e., models. In this paper, we demonstrate the applic
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a561ec2e3ada723d1959168fc2f82cd7
https://doi.org/10.1007/978-3-030-72654-6_7
https://doi.org/10.1007/978-3-030-72654-6_7
Publikováno v:
Computer Applications in Engineering Education. 27:332-343
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d3cbd179176e5fdb90716f600657af5c
https://doi.org/10.1002/cae.22077
https://doi.org/10.1002/cae.22077
Publikováno v:
Information Technology And Control. 47
Introducing reverse engineering of UML models can alleviate the problems with inadequately documented or undocumented websites. In this paper, we analyze possibilities of reverse engineering UML use case model composed of UML use case diagram and UML
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7387952e9289598c3d2ce34ab7e0b3f2
https://doi.org/10.5755/j01.itc.47.4.21264
https://doi.org/10.5755/j01.itc.47.4.21264