Zobrazeno 1 - 10
of 342
pro vyhledávání: '"Machine learning prediction"'
Publikováno v:
Journal of Materials Research and Technology, Vol 32, Iss , Pp 2370-2385 (2024)
The relationship between the mechanical and ballistic properties of armored ceramics was studied using a numerical method that combines finite element simulation and machine learning. A dataset containing the physical properties, mechanical propertie
Externí odkaz:
https://doaj.org/article/5adce9046bd84748b5105f7d403816c3
Publikováno v:
Soils and Foundations, Vol 64, Iss 6, Pp 101517- (2024)
In this study, machine learning prediction models (MLPMs), including artificial neural network (ANN), support vector regression (SVR), K-nearest neighbors (KNN), and random forest (RF) algorithms, were developed to predict the peak shear stress value
Externí odkaz:
https://doaj.org/article/61c5226ed3bb4f77b6eeba009f969f1e
Publikováno v:
Journal of Agriculture and Food Research, Vol 18, Iss , Pp 101406- (2024)
Machine performance modeling and optimization have emerged as crucial steps for process enhancement and efficiency. This study explored machine learning to model and optimize the cassava grating chamber of cassava grater for the quality production of
Externí odkaz:
https://doaj.org/article/e72d3952929f431aba8c4ff32310bac7
Autor:
Kevin Wang Leong So, Evan Mang Ching Leung, Tommy Ng, Rachel Tsui, Jason Pui Yin Cheung, Siu‐Wai Choi
Publikováno v:
Cancer Medicine, Vol 13, Iss 22, Pp n/a-n/a (2024)
ABSTRACT Introduction The aim of this study was to find the most appropriate variables to input into machine learning algorithms to identify those patients with primary lung malignancy with high risk for metastasis to the bone. Patient Inclusion Pati
Externí odkaz:
https://doaj.org/article/0fd3311a554c488596550b7d8df446b8
Autor:
Aryan Safakish, Amir Moslemi, Daniel Moore-Palhares, Lakshmanan Sannachi, Ian Poon, Irene Karam, Andrew Bayley, Ana Pejovic-Milic, Gregory J. Czarnota
Publikováno v:
Radiation, Vol 4, Iss 2, Pp 192-212 (2024)
Background: Head and neck cancer treatment does not yield desired outcomes for all patients. This investigation aimed to explore the feasibility of predicting treatment outcomes from routine pre-treatment magnetic resonance images (MRIs). Radiomics f
Externí odkaz:
https://doaj.org/article/2cb32c2ce8634a9a94f94b8f81770cf4
Publikováno v:
BMC Medical Informatics and Decision Making, Vol 24, Iss 1, Pp 1-12 (2024)
Abstract Hematoma expansion (HE) is a high risky symptom with high rate of occurrence for patients who have undergone spontaneous intracerebral hemorrhage (ICH) after a major accident or illness. Correct prediction of the occurrence of HE in advance
Externí odkaz:
https://doaj.org/article/1492bead9a93440795db30af79681838
Publikováno v:
AIMS Mathematics, Vol 9, Iss 7, Pp 18308-18323 (2024)
Education is essential and increasingly crucial for the development of almost all countries worldwide. As educational data has become increasingly available, scholars have shown a growing interest in exploring the correlation between students' academ
Externí odkaz:
https://doaj.org/article/a33f7e866f184520a58ef63de36e8e84
Autor:
Gatheeshgar, Perampalam a, ⁎, Ranasinghe, R.S.S. b, Simwanda, Lenganji c, d, Meddage, D.P.P. e, Mohotti, Damith e, f
Publikováno v:
In Structures January 2025 71
Publikováno v:
In Structures January 2025 71
Autor:
Nan Tang, Shuang Liu, Kangming Li, Qiang Zhou, Yanan Dai, Huamei Sun, Qingdui Zhang, Ji Hao, Chunmei Qi
Publikováno v:
Frontiers in Cardiovascular Medicine, Vol 11 (2024)
IntroductionAccurate in-hospital mortality prediction following percutaneous coronary intervention (PCI) is crucial for clinical decision-making. Machine Learning (ML) and Data Mining methods have shown promise in improving medical prognosis accuracy
Externí odkaz:
https://doaj.org/article/63309cec6eac4d05baade0341a2790d3