Results 31 to 40 of about 8,272,458 (291)

AZOrange - High performance open source machine learning for QSAR modeling in a graphical programming environment

Journal of Cheminformatics, 2011
Background Machine learning has a vast range of applications. In particular, advanced machine learning methods are routinely and increasingly used in quantitative structure activity relationship (QSAR) modeling.
Stålring Jonna C, Carlsson Lars A, Almeida Pedro, Boyer Scott   +3 more
doaj   +1 more source

Automated Machine Learning [PDF]

, 2021
Over the past few decades, machine learning has been the subject of extensive research and application.
openaire   +1 more source

Bridging expertise with machine learning and automated machine learning in clinical medicine

Annals, Academy of Medicine, Singapore
In this issue of the Annals, Thirunavukarasu et al.’s systematic review on the clinical performance of automated machine learning (autoML) highlights its extensive applicability across 22 clinical specialties, showcasing its potential to redefine ...
Chien-Chang Lee, James Yeongjun Park, Wan-Ting Hsu   +2 more
doaj   +1 more source

Screening of anxiety and depression among the seafarers using machine learning technology

Informatics in Medicine Unlocked, 2019
Background: Seafarers are vulnerable to suffer from various mental health disorders, most commonly anxiety and depression. So, periodic screening for anxiety and depression, is necessary for health and well-being.
Arkaprabha Sau, Ishita Bhakta
doaj   +1 more source

Clinical performance of automated machine learning: A systematic review

Annals, Academy of Medicine, Singapore
Introduction: Automated machine learning (autoML) removes technical and technological barriers to building artificial intelligence models. We aimed to summarise the clinical applications of autoML, assess the capabilities of utilised platforms, evaluate
Arun James Thirunavukarasu, Kabilan Elangovan, Laura Gutierrez, Refaat Hassan, Yong Li, Ting Fang Tan, Haoran Cheng, Zhen Ling Teo, Gilbert Lim, Daniel Shu Wei Ting   +9 more
doaj   +1 more source

Comparisons of automated machine learning (AutoML) in predicting whistleblowing of academic dishonesty with demographic and theory of planned behavior

MethodsX, 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 the ...
Rahayu Abdul Rahman, Suraya Masrom, Masurah Mohamad, Eka Nurmala Sari, Fitriani Saragih, Abdullah Sani Abd Rahman   +5 more
doaj   +1 more source

Use Test of Automated Machine Learning in Cancer Diagnostics

, 2023
Our aim is to investigate the added value of automated machine learning (AutoML) for potential future applications in cancer diagnostics. Using two important diagnostic questions, the non-invasive determination of IDH mutation status and ATRX status, we ...
Nabila Gala Nacul, Manoj Mannil, Dilek N. Kasap, Walter Heindel, Manfred Musigmann   +4 more
core   +1 more source

Automated Machine Learning in Insurance

Insurance: Mathematics and Economics
Machine Learning (ML) has gained popularity in actuarial research and insurance industrial applications. However, the performance of most ML tasks heavily depends on data preprocessing, model selection, and hyperparameter optimization, which are considered to be intensive in terms of domain knowledge, experience, and manual labor.
Panyi Dong, Zhiyu Quan
openaire   +3 more sources

Machine learning derived segmentation of phase velocity encoded cardiovascular magnetic resonance for fully automated aortic flow quantification

Journal of Cardiovascular Magnetic Resonance, 2019
Background Phase contrast (PC) cardiovascular magnetic resonance (CMR) is widely employed for flow quantification, but analysis typically requires time consuming manual segmentation which can require human correction.
Alex Bratt, Jiwon Kim, Meridith Pollie, Ashley N. Beecy, Nathan H. Tehrani, Noel Codella, Rocio Perez-Johnston, Maria Chiara Palumbo, Javid Alakbarli, Wayne Colizza, Ian R. Drexler, Clerio F. Azevedo, Raymond J. Kim, Richard B. Devereux, Jonathan W. Weinsaft   +14 more
doaj   +1 more source

Clinical Hematochemical Parameters in Differential Diagnosis between Pediatric SARS-CoV-2 and Influenza Virus Infection: An Automated Machine Learning Approach

Children, 2023
Background: The influenza virus and the novel beta coronavirus (SARS-CoV-2) have similar transmission characteristics, and it is very difficult to distinguish them clinically.
Dejan Dobrijević, Jelena Antić, Goran Rakić, Jasmina Katanić, Ljiljana Andrijević, Kristian Pastor   +5 more
doaj   +1 more source