Zobrazeno 1 - 10
of 10
pro vyhledávání: '"Sándor Szénási"'
Publikováno v:
Mathematics, Vol 12, Iss 8, p 1144 (2024)
It is necessary to extensively investigate the causes of road accidents with the utmost precision to harness future technological advancements, such as autonomous driving and intelligent accident prevention systems. Nevertheless, since most accidents
Externí odkaz:
https://doaj.org/article/df3b0561b34c4c1fa9c994f70a8f6b46
Autor:
Suryakant Tyagi, Sándor Szénási
Publikováno v:
Algorithms, Vol 17, Iss 3, p 90 (2024)
Machine learning and speech emotion recognition are rapidly evolving fields, significantly impacting human-centered computing. Machine learning enables computers to learn from data and make predictions, while speech emotion recognition allows compute
Externí odkaz:
https://doaj.org/article/dc898706d74d48f5be1434627ee5545d
Publikováno v:
Algorithms, Vol 16, Iss 3, p 175 (2023)
Although current computer vision systems are closer to the human intelligence when it comes to comprehending the visible world than previously, their performance is hindered when objects are partially occluded. Since we live in a dynamic and complex
Externí odkaz:
https://doaj.org/article/d0c56b4a8677469bb33dad23e26287b0
Autor:
Sándor Szénási
Publikováno v:
PeerJ Computer Science, Vol 7, p e399 (2021)
It is expected that most accidents occurring due to human mistakes will be eliminated by autonomous vehicles. Their control is based on real-time data obtained from the various sensors, processed by sophisticated algorithms and the operation of actua
Externí odkaz:
https://doaj.org/article/30867e9a47ea449abd9ec31dfd0cd70d
Autor:
Sándor Szénási, Gábor Kertész
Publikováno v:
Algorithms, Vol 13, Iss 12, p 320 (2020)
In the field of image processing, there are several difficult issues that do not have exact solutions due to incomplete or imperfect information and limited computation capacity [...]
Externí odkaz:
https://doaj.org/article/c18f59708043474298dc35f0b08679a6
Publikováno v:
Entropy, Vol 22, Iss 2, p 245 (2020)
In this contribution, we provide a detailed analysis of the search operation for the Interval Merging Binary Tree (IMBT), an efficient data structure proposed earlier to handle typical anomalies in the transmission of data packets. A framework is pro
Externí odkaz:
https://doaj.org/article/9c53433819774ca19864b14d3512897c
Autor:
Sándor Szénási
Publikováno v:
PeerJ Computer Science, Vol 3, p e138 (2017)
The accurate knowledge of Heat Transfer Coefficients is essential for the design of precise heat transfer operations. The determination of these values requires Inverse Heat Transfer Calculations, which are usually based on heuristic optimisation tec
Externí odkaz:
https://doaj.org/article/3ebff223c39749ed87633968bfee653a
Autor:
Sándor Szénási, Imre Felde
Publikováno v:
Data, Vol 4, Iss 3, p 90 (2019)
To achieve the optimal performance of an object to be heat treated, it is necessary to know the value of the Heat Transfer Coefficient (HTC) describing the amount of heat exchange between the work piece and the cooling medium. The prediction of the H
Externí odkaz:
https://doaj.org/article/186be2259db14998a86b2c816e35799b
Autor:
Ádám Pintér, Sándor Szénási
Publikováno v:
Informatica. 45
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1e8ea93bb30509f9b487c017c6bac221
https://doi.org/10.31449/inf.v45i4.3130
https://doi.org/10.31449/inf.v45i4.3130
Publikováno v:
IEEE Access, Vol 12, Pp 96017-96050 (2024)
In recent years, large language models (LLMs) have achieved remarkable success in natural language processing (NLP). LLMs require an extreme amount of parameters to attain high performance. As models grow into the trillion-parameter range, computatio
Externí odkaz:
https://doaj.org/article/10c9f434db39404ea87a85f203724bcd