Zobrazeno 1 - 10
of 4 542
pro vyhledávání: '"Learning techniques"'
Publikováno v:
BMC Women's Health, Vol 24, Iss 1, Pp 1-9 (2024)
Abstract Objective The aim of this study is to assess the use of machine learning methodologies in the diagnosis of endometriosis (EM). Methods This study included a total of 106 patients with EM and 203 patients with non-EM conditions (like simple c
Externí odkaz:
https://doaj.org/article/8dae5812f05f4b4e85d7d9b8bec00222
Publikováno v:
Advances in Sciences and Technology, Vol 18, Iss 5, Pp 1-9 (2024)
The relationship between the power consumed in the engine and the power take-off shaft of a maize silage harvester is critical to understanding the efficiency and performance of the harvester. The power consumed in the engine directly affects the pow
Externí odkaz:
https://doaj.org/article/f4f366d3fa6c4249b97d4d13d5a70a2b
Publikováno v:
China Finance Review International, 2023, Vol. 14, Issue 2, pp. 310-331.
Externí odkaz:
http://www.emeraldinsight.com/doi/10.1108/CFRI-12-2022-0250
Autor:
Jiefeng Ren, Haijun Wang, Song Lai, Yi Shao, Hebin Che, Zaiyao Xue, Xinlian Qi, Sha Zhang, Jinkun Dai, Sai Wang, Kunlian Li, Wei Gan, Quanjin Si
Publikováno v:
BMC Cardiovascular Disorders, Vol 24, Iss 1, Pp 1-9 (2024)
Abstract Objective Accurate prediction of survival prognosis is helpful to guide clinical decision-making. The aim of this study was to develop a model using machine learning techniques to predict the occurrence of composite thromboembolic events (CT
Externí odkaz:
https://doaj.org/article/a9cbfd5b640b48c586dca0423a0989bb
Publikováno v:
Misan Journal of Engineering Sciences, Vol 3, Iss 1, Pp 100-120 (2024)
The subject of study known as radio propagation prediction refers to predicting the behaviour and characteristics of radio waves as they propagate through the atmosphere. It is a basic element of all wireless communication systems, including satellit
Externí odkaz:
https://doaj.org/article/5f72e0d2068d499fbc86c198740cd845
Publikováno v:
Alexandria Engineering Journal, Vol 97, Iss , Pp 142-159 (2024)
This paper presents a novel approach for measuring multi-shapes degrees of similarities. A new shape transformation concept is suggested through mapping the closed boundary of the shape using unfolded process into one-to-one equivalent graph (or sign
Externí odkaz:
https://doaj.org/article/2defc67ab18549c9afc13cb755e4ef52
Publikováno v:
Journal of Theoretical and Applied Electronic Commerce Research, Vol 19, Iss 2, Pp 1493-1516 (2024)
Sentiment analysis is a cornerstone of natural language processing. However, it presents formidable challenges due to the intricacies of lexical diversity, complex linguistic structures, and the subtleties of context dependence. This study introduces
Externí odkaz:
https://doaj.org/article/e49fd313d6fb42f08268106becce9e35
Publikováno v:
Al-Iraqia Journal for Scientific Engineering Research, Vol 3, Iss 3 (2024)
Recent advancements in machine learning have played a crucial role in the healthcare industry, particularly in predicting heart disease with assorted datasets. Despite the registration results indicating promising accuracy and resilience in heart dis
Externí odkaz:
https://doaj.org/article/828da4c682d641d59e21136605140164
Autor:
Muhammad Saibtain Raza, Mohammad Nowsin Amin Sheikh, I-Shyan Hwang, Mohammad Syuhaimi Ab-Rahman
Publikováno v:
Telecom, Vol 5, Iss 2, Pp 333-346 (2024)
SDN has the ability to transform network design by providing increased versatility and effective regulation. Its programmable centralized controller gives network administration employees more authority, allowing for more seamless supervision. Howeve
Externí odkaz:
https://doaj.org/article/0631ee32e5d34adc864a36bd7e91c5b4
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-12 (2024)
Abstract The strength of rock under uniaxial compression, commonly known as Uniaxial Compressive Strength (UCS), plays a crucial role in various geomechanical applications such as designing foundations, mining projects, slopes in rocks, tunnel constr
Externí odkaz:
https://doaj.org/article/a391a04ba67241b2a3914bd8617fd173