Suchergebnisse - "machine learning-based algorithm"

Application of machine learning based algorithm to predict performance of turning Al–SiC-MWCNT using cryogenically treated textured insert

Autoren: Ch Saikrupa G ChandraMohan Reddy Sriram Venkatesh

Quelle: Hybrid Advances, Vol 10, Iss , Pp 100432- (2025)

Schlagwörter: Turning: MMC, Cryogenically treated tool, Surface roughness, Power consumption, prediction, Machine learning based algorithm, Technology

Dateibeschreibung: electronic resource

Relation: http://www.sciencedirect.com/science/article/pii/S2773207X25000569; https://doaj.org/toc/2773-207X

Zugangs-URL: https://doaj.org/article/d69fb2d5748b4bf6b699ad585fa9a380

Covid-19 automated diagnosis and risk assessment through Metabolomics and Machine-Learning

Autoren: Jeany Delafiori Lydia Navarro RF Siciliano et al.

Quelle: Anal Chem
Analytical Chemistry

Schlagwörter: Male, FOS: Computer and information sciences, 0301 basic medicine, Artificial intelligence, COVID -19 diagnosis, Covid -19 Automated Diagnosis, Infectious disease (medical specialty), Analytical Chemistry, Data science, Machine Learning, Automation, Computer security, COVID -19 screening, Disease, Internal medicine, Risk assessment, 0303 health sciences, risk assessment, Life Sciences, Middle Aged, Advances in Metabolomics Research, 3. Good health, method, Medicine, Female, 23 COVID -19, Brazil, Biotechnology, Biological Sciences not elsewhere classified, Adult, machine learning-based algorithm, Bioinformatics, Information Systems not elsewhere classified, Risk Assessment, 03 medical and health sciences, Biochemistry, Genetics and Molecular Biology, Virology, Machine learning, Humans, Metabolomics, Molecular Biology, Biology, Machine Learning COVID -19, Aged, SARS-CoV-2, Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), COVID-19, Outbreak, Cell Biology, Computer science, Coronavirus disease 2019 (COVID-19), 2019-20 coronavirus outbreak, Biomarkers

Zugangs-URL: https://www.medrxiv.org/content/medrxiv/early/2020/07/27/2020.07.24.20161828.full.pdf
https://pubs.acs.org/doi/pdf/10.1021/acs.analchem.0c04497
https://pubmed.ncbi.nlm.nih.gov/33471512
https://www.medrxiv.org/content/10.1101/2020.07.24.20161828v1
http://www.medrxiv.org/content/10.1101/2020.07.24.20161828v1
https://www.medrxiv.org/content/medrxiv/early/2020/07/27/2020.07.24.20161828.full.pdf

Covid-19 Automated Diagnosis and Risk Assessment through Metabolomics and Machine Learning

Autoren: Jeany Delafiori (8354643) Luiz Cláudio Navarro (10012111) Rinaldo Focaccia Siciliano (6379223) et al.

Schlagwörter: Cell Biology, Biotechnology, Biological Sciences not elsewhere classified, Information Systems not elsewhere classified, risk assessment, Machine Learning COVID -19, COVID -19 screening, 23 COVID -19, method, Covid -19 Automated Diagnosis, COVID -19 diagnosis, machine learning-based algorithm

Relation: https://figshare.com/articles/journal_contribution/Covid-19_Automated_Diagnosis_and_Risk_Assessment_through_Metabolomics_and_Machine_Learning/13619023

Verfügbarkeit: https://doi.org/10.1021/acs.analchem.0c04497.s001