Anomaly Detection in Time Series Data using Autoencoders approach.
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Updated
Apr 20, 2023
Anomaly Detection in Time Series Data using Autoencoders approach.
This repo contains the code used for the CARSS external validation project.
Implant failure rates in a knee prosthesis sub-population of the Helios Klinikum Berlin-Buch hospitals
Prognóstico de sobrevida em cativeiro de Tityus bahiensis capturados em Americana/SP
Prognóstico de componentes hematológicos após ATQ bilateral simultânea em centro cirúrgico de referência
Consultorias em Estatística Médica e Epidemiologia Clínica. CNPJ:42.154.074/0001-22
Survival analysis of events attributed to PJI in patients that undergone TJA surgeries
Reproduce results from the paper "Development, validation and clinical usefulness of a prognostic model for relapse in relapsing-remitting multiple sclerosis. K. Chalkou et. al. Diagn Progn Res . 2021 Oct 27;5(1):17. doi: 10.1186/s41512-021-00106-6."
Effect of socioeconomic status in mortality rates after brain injury: cohort study
Conceptual models of oceanic diurnal warm layer dynamics
NASA Turbofan Jet Engine Propagation modeling
Time-adjusted effect of socioeconomic status in mortality rates after brain injury: cohort study
Sensitivity of mortality rates to the imputation of missing socioeconomic data: cohort study
This is the official repository of the R package metamisc
The NASA Prognostic Python Packages is a Python framework focused on defining and building models and algorit for prognostics (computation of remaining useful life) of engineering systems, and provides a set of models and algorithms for select components developed within this framework, suitable for use in prognostic applications.
The NASA Prognostic Model Package is a Python framework focused on defining and building models for prognostics (computation of remaining useful life) of engineering systems, and provides a set of prognostics models for select components developed within this framework, suitable for use in prognostics applications for these components.
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