Zobrazeno 1 - 10
of 146
pro vyhledávání: '"Automated Machine Learning (AutoML)"'
Publikováno v:
Chinese Medicine, Vol 19, Iss 1, Pp 1-14 (2024)
Abstract The aim of this study was to develop a machine learning-assisted rapid determination methodology for traditional Chinese Medicine Constitution. Based on the Constitution in Chinese Medicine Questionnaire (CCMQ), the most applied diagnostic i
Externí odkaz:
https://doaj.org/article/753116bc38ea4d239239af31ecfdf8d4
Autor:
Arina Nisanova, BA, Arefeh Yavary, MSc, Jordan Deaner, MD, Ferhina S. Ali, MD, MPH, Priyanka Gogte, MD, Richard Kaplan, MD, Kevin C. Chen, MD, Eric Nudleman, MD, PhD, Dilraj Grewal, MD, Meenakashi Gupta, MD, Jeremy Wolfe, MD, Michael Klufas, MD, Glenn Yiu, MD, PhD, Iman Soltani, PhD, Parisa Emami-Naeini, MD, MPH
Publikováno v:
Ophthalmology Science, Vol 4, Iss 5, Pp 100470- (2024)
Purpose: Automated machine learning (AutoML) has emerged as a novel tool for medical professionals lacking coding experience, enabling them to develop predictive models for treatment outcomes. This study evaluated the performance of AutoML tools in d
Externí odkaz:
https://doaj.org/article/15a7f54b929744708a86d399eab9496a
Publikováno v:
Remote Sensing, Vol 16, Iss 14, p 2561 (2024)
Hyperspectral imaging holds significant promise in remote sensing applications, particularly for land cover and land-use classification, thanks to its ability to capture rich spectral information. However, leveraging hyperspectral data for accurate s
Externí odkaz:
https://doaj.org/article/9c93ee0b9a674e129d5db61545870fc2
Autor:
Wen-Sheng Feng, Wei-Cheng Chen, Jiun-Yi Lin, How-Yang Tseng, Chieh-Lung Chen, Ching-Yao Chou, Der-Yang Cho, Yi-Bing Lin
Publikováno v:
Sensors, Vol 24, Iss 12, p 3929 (2024)
The rapid advancements in Artificial Intelligence of Things (AIoT) are pivotal for the healthcare sector, especially as the world approaches an aging society which will be reached by 2050. This paper presents an innovative AIoT-enabled data fusion sy
Externí odkaz:
https://doaj.org/article/10b0e6e2fc6646109bbf22a552ceae00
Autor:
Jacob Stake, Christine Spiekers, Burak Han Akkurt, Walter Heindel, Tobias Brix, Manoj Mannil, Manfred Musigmann
Publikováno v:
Diagnostics, Vol 14, Iss 11, p 1070 (2024)
In this study, we sought to evaluate the capabilities of radiomics and machine learning in predicting seropositivity in patients with suspected autoimmune encephalitis (AE) from MR images obtained at symptom onset. In 83 patients diagnosed with AE be
Externí odkaz:
https://doaj.org/article/e064eb1a5cef46f48e13e6b089ce7adf
Publikováno v:
Geocarto International, Vol 38, Iss 1 (2023)
Landslide susceptibility prediction (LSP) is an important step for landslide hazard and risk assessment. Automated machine learning (AutoML) has the advantages of automatically features, models, and parameters selection. In this study, we proposed an
Externí odkaz:
https://doaj.org/article/1a416076098d4e12874dc3ca255d89b8
Autor:
Rahayu Abdul Rahman, Suraya Masrom, Masurah Mohamad, Eka Nurmala Sari, Fitriani Saragih, Abdullah Sani Abd Rahman
Publikováno v:
MethodsX, Vol 11, Iss , Pp 102364- (2023)
Machine learning has been very promising in solving real problems, but the implementation involved difficulties mainly for the inexpert data scientists. Therefore, this paper presents an automated machine learning (AutoML) to simplify and accelerate
Externí odkaz:
https://doaj.org/article/20d2394bdc614c4cbd8a2a44acf7ffcc
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 16, Pp 3442-3454 (2023)
Modeling and large-scale mapping of forest aboveground biomass (AGB) is a complicated, challenging, and expensive task. There are considerable variations in forest characteristics that create functional disparity for different models and needs compre
Externí odkaz:
https://doaj.org/article/29584b27824041a796ce95117c7d0aa5
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